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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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American Longevity:
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American Longevity: Past, Present, and Future
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Samuel Preston is Frederick J. Warren Professor of Samuel Preston is Frederick J. Warren Professor of Demography at the University of Pennsylvania and Director of its Population Studies Center. A 1968 Ph.D. in Economics from Princeton University, he has also been a faculty member at the University of California, Berkeley, and the Universi ty of Washington. He is past president of the Population Association of America and is a member of the National Academy of Sciences, where he chaired the Committee on Population.
The Policy Brief series is a collection of essays on current public policy issues in aging, health, income security, metropolitan studies and related research done by or on behalf of the Center for Policy Research at the Maxwell School of Citizenship and Public Affairs.
Single copies of this publication may be obtained at no cost from the Center for Policy Research, Maxwell School, 426 Eggers Hall, Syracuse, NY 13244-1090.
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2ba01f9f-c32a-440b-a301-074998c93fca
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lgpknhne-0430
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Analysis of trends
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Analysis of trends in human longevity by new model
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Byung Mook Weon
LG.Philips Displays, 184, Gongda Byung Mook Weon
LG.Philips Displays, 184, Gongdan1-dong, Gumi-city, GyungBuk, 730-702, South Korea
Abstract
Trends in human longevity are puzzling, especially when considering the limits of
human longevity. Partially, the conflicting assertions are based upon demographic
evidence and the interpretation of survival and mortality curves using the Gompertz
model and the Weibull model; these models are sometimes considered to be incomplete
in describing the entire curves. In this paper a new model is proposed to take the place
of the traditional models. We directly analysed the rectangularity (the parts of the curves
being shaped like a rectangle) of survival curves for 17 countries and for 1876-2001 in
Switzerland (it being one of the longest-lived countries) with a new model. This model
is derived from the Weibull survival function and is simply described by two parameters,
in which the shape parameter indicates ‘rectangularity’ and characteristic life indicates
the duration for survival to be ‘exp(-1) % 79.3 6≈ ’. The shape parameter is essentially a
function of age and it distinguishes humans from technical devices. We find that
although characteristic life has increased up to the present time, the slope of the shape
parameter for middle age has been saturated in recent decades and that the
rectangularity above characteristic life has been suppressed, suggesting there are
ultimate limits to human longevity. The new model and subsequent findings will
contribute greatly to the interpretation and comprehension of our knowledge on the
human ageing processes.
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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vtciomis-0967
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Diet-dependent entropic a
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Diet-dependent entropic assessment of athletes’
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Cennet Yildiz1, Melek Ece Öngel2 , Bayram Yilmaz3 Cennet Yildiz1, Melek Ece Öngel2 , Bayram Yilmaz3 and Mustafa Özilgen1* 1Department of Food Engineering, Yeditepe University, Kayısdagi, Atasehir, Istanbul 34755, Turkey 2Nutrition and Dietetics Department, Yeditepe University, Kayısdagi, Atasehir, Istanbul 34755, Turkey 3Faculty of Medicine, Department of Physiology, Yeditepe University, Istanbul, Turkey
(Received 29 July 2021 – Final revision received 26 August 2021 – Accepted 26 August 2021)
Journal of Nutritional Science (2021), vol. 10, e83, page 1 of 8 doi:10.1017/jns.2021.78
Abstract Life expectancies of the athletes depend on the sports they are doing. The entropic age concept, which was found successful in the previous nutrition studies, will be employed to assess the relation between the athletes’ longevity and nutrition. Depending on their caloric needs, diets are designed for each group of athletes based on the most recent guidelines while they are pursuing their careers and for the post-retirement period, and then the metabolic entropy generation was worked out for each group. Their expected lifespans, based on attaining the lifespan entropy limit, were calculated. Thermodynamic assessment appeared to be in agreement with the observations. There may be a significant improvement in the athletes’ longevity if theyshift to a retirement diet after the age of 50. The expected average longevity for male athletes was 56 years for cyclists, 66 years for weightlifters, 75 years for rugby players and 92 years for golfers. If they should start consuming the retirement diet after 50 years of age, the longevity of the cyclists may increase for 7 years, and those of weightlifters, rugby players and golfers may increase for 22, 30 and 8 years, respectively.
Key words: Athletes’ diet: Athletes’ longevity: Entropic age: Lifespan entropy
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fioqwmlo-9810
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Grandmothers
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Grandmothers and the Evolution of Human Longevity
Grandmothers and the Evolution of Human Longevity
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“Grandmothers and the Evolution of Human Longevity “Grandmothers and the Evolution of Human Longevity”**
This PDF is a scholarly research article that presents and explains the Grandmother Hypothesis—one of the most influential evolutionary theories for why humans live so long after reproduction. The paper argues that human longevity evolved largely because ancestral grandmothers played a crucial role in helping raise their grandchildren, thereby increasing family survival and passing on genes that favored longer life.
The article combines anthropology, evolutionary biology, and demographic modeling to show that grandmothering behavior dramatically enhanced reproductive success and survival in early human societies, creating evolutionary pressure for extended lifespan.
👵 1. Core Idea: The Grandmother Hypothesis
The central argument is:
Human females live long past menopause because grandmothers helped feed, protect, and support their grandchildren, allowing mothers to reproduce more frequently.
This cooperative childcare increased survival rates and promoted the evolution of long life, especially among women.
Healthy Ageing
🧬 2. Evolutionary Background
The article explains key evolutionary facts:
Humans are unique among primates because females experience decades of post-reproductive life.
In other great apes, females rarely outlive their fertility.
Human children are unusually dependent for many years; mothers benefit greatly from help.
Grandmothers filled this gap, making longevity advantageous in evolutionary terms.
Healthy Ageing
🍂 3. Why Grandmothers Increased Survival
The study shows how ancestral grandmothers:
⭐ Provided extra food
Especially gathered foods like tubers and plant resources.
⭐ Allowed mothers to wean earlier
Mothers could have more babies sooner, increasing reproductive success.
⭐ Improved child survival
Grandmother assistance reduced infant and child mortality.
⭐ Increased group resilience
More caregivers meant better protection and food access.
These survival advantages favored genes that supported prolonged life.
Healthy Ageing
📊 4. Mathematical & Demographic Modeling
The PDF includes modeling to demonstrate:
How grandmother involvement changes fertility patterns
How increased juvenile survival leads to higher population growth
How longevity becomes advantageous over generations
Models show that adding grandmother support significantly increases life expectancy in evolutionary simulations.
Healthy Ageing
👶 5. Human Childhood and Weaning
Human children:
Develop slowly
Need long-term nutritional and social support
Rely on help beyond their mother
Early weaning—made possible by grandmother help—creates shorter birth intervals, boosting the reproductive output of mothers and promoting genetic selection for long-lived helpers (grandmothers).
Healthy Ageing
🧠 6. Implications for Human Evolution
The article argues that grandmothering helped shape:
✔ Human social structure
Cooperative families and multigenerational groups.
✔ Human biology
Long lifespan, menopause, slower childhood development.
✔ Human culture
Shared caregiving, food-sharing traditions, teaching, and cooperation.
Healthy Ageing
Grandmothers became essential to early human success.
🧓 7. Menopause and Post-Reproductive Lifespan
One major question in evolution is: Why does menopause exist?
The article explains that:
Natural selection usually favors continued reproduction.
But in humans, the benefits of supporting grandchildren outweigh late-life reproduction.
This shift created evolutionary support for long post-reproductive life.
Healthy Ageing
⭐ Overall Summary
This PDF provides a clear and compelling explanation of how grandmothering behavior shaped human evolution, helping produce our unusually long life spans. It argues that grandmothers increased survival, supported early weaning, and boosted reproduction in early humans, leading natural selection to favor individuals—especially females—who lived well past their reproductive years. The article blends anthropology, biology, and mathematical modeling to show that the evolution of human longevity is inseparable from the evolutionary importance of grandmothers....
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oaedizhh-8535
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Global and National
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Global and National Declines in Life
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Period life expectancy at birth [life expecta
Period life expectancy at birth [life expectancy thereafter] is the most-frequently used indicator
of mortality conditions. More broadly, life expectancy is commonly taken as a marker of human
progress, for instance in aggregate indices such as the Human Development Index (United
Nations Development Programme 2020). The United Nations (UN) regularly updates and makes
available life expectancy estimates for every country, various country aggregates and the world
for every year since 1950 (Gerland, Raftery, Ševčíková et al. 2014), providing a 70-year
benchmark for assessing the direction and magnitude of mortality changes....
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{"input_type": "file", "source {"input_type": "file", "source": "/home/sid/tuning/finetune/backend/output/oaedizhh-8535/data/document.pdf", "num_examples": 6, "bad_lines": 0}...
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e7d237b6-d50f-4a6c-9350-eb07238f3609
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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fnakzpii-4028
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Global and National
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Global and National Declines in Life
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xevyo-base-v1
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Period life expectancy at birth [life expecta
Period life expectancy at birth [life expectancy thereafter] is the most-frequently used indicator
of mortality conditions. More broadly, life expectancy is commonly taken as a marker of human
progress, for instance in aggregate indices such as the Human Development Index (United
Nations Development Programme 2020). The United Nations (UN) regularly updates and makes
available life expectancy estimates for every country, various country aggregates and the world
for every year since 1950 (Gerland, Raftery, Ševčíková et al. 2014), providing a 70-year
benchmark for assessing the direction and magnitude of mortality changes....
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{"input_type": "file", "source {"input_type": "file", "source": "/home/sid/tuning/finetune/backend/output/fnakzpii-4028/data/document.pdf", "num_examples": 36, "bad_lines": 0}...
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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jybvxsag-3546
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xevyo
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Health Status and Empiric
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Health Status and Empirical Model of Longevity
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This research paper by Hugo Benítez-Silva and Huan This research paper by Hugo Benítez-Silva and Huan Ni develops one of the most detailed and rigorous empirical models explaining how health status and health changes shape people’s expectations of how long they will live. It uses panel data from the U.S. Health and Retirement Study (HRS), a large longitudinal survey of older adults.
🌟 Core Purpose of the Study
The paper investigates:
How do different measures of health—especially changes in health—affect people’s expected longevity (their subjective probability of living to age 75)?
It challenges the common assumption that simply using “current health status” or lagged health is enough to measure health dynamics. Instead, the authors argue that:
➡ Self-reported health changes (e.g., “much worse,” “better”)
are more accurate and meaningful than
➡ Computed health changes (differences between two reported health statuses).
📌 Key Concepts
1. Health Dynamics Matter
Health is not static—people experience:
gradual aging
chronic disease progression
sudden health shocks
effects of lifestyle and medical interventions
These dynamic elements shape how people assess their future survival.
Health Status and Empirical Mod…
2. Why Self-Reported Health Status Is Imperfect
The paper identifies three major problems with simply using self-rated health categories:
Health Status and Empirical Mod…
a. Cut-point shifts
People’s interpretation of “good” or “very good” health can change over time.
b. Gray areas
Some individuals cannot clearly categorize their health, leading to arbitrary reports.
c. Peer/reference effects
People compare themselves with different reference groups as they age.
These issues mean self-rated health alone doesn’t capture true health changes.
📌 3. Two Measures of Health Change
The authors compare:
A. Self-Reported Health Change (Preferred)
Direct question:
“Compared to last time, is your health better, same, worse?”
Advantages:
captures subtle changes
less affected by shifting cut-points
aligns more closely with subjective survival expectations
B. Computed Health Change (Problematic)
This is calculated mathematically as:
Health score (t+1) − Health score (t)
Problems:
inconsistent with self-reports in 38% of cases
loses information when health changes but does not cross a discrete category
introduces potential measurement error
Health Status and Empirical Mod…
🧠 Why This Matters
Expected longevity influences:
savings behavior
retirement timing
annuity purchases
life insurance decisions
health care usage
Health Status and Empirical Mod…
If researchers use bad measures of health, they may misinterpret how people plan for the future.
📊 Data and Methodology
Uses six waves of the HRS (1992–2003)
Sample: 9,000+ individuals, 24,000+ observations
Controls for:
chronic conditions (heart disease, cancer, diabetes)
ADLs/IADLs
socioeconomic variables
parental longevity
demographic factors
unobserved heterogeneity
Health Status and Empirical Mod…
The model is treated like a production function of longevity, following economic theories of health investment under uncertainty.
📈 Major Findings
✔ 1. Self-reported health changes strongly predict expected longevity
People who report worsening health show large drops in survival expectations.
Health Status and Empirical Mod…
✔ 2. Computed health changes frequently misrepresent true health dynamics
38% are inconsistent
15% lose meaningful health-change information
Health Status and Empirical Mod…
✔ 3. Self-reported changes have effects similar in magnitude to current health levels
This means:
Health trajectory matters as much as current health.
Health Status and Empirical Mod…
✔ 4. Health change measures are crucial for accurate modeling
Failing to include dynamic health measures causes:
biased estimates
misinterpretation of longevity expectations
🏁 Conclusion
This paper makes a major contribution by demonstrating that:
To understand how people form expectations about their own longevity, you must measure health as a dynamic process—not just a static snapshot.
The authors recommend that future empirical models, especially those using large panel surveys like the HRS, should:
✔ prioritize self-reported health changes
✔ treat computed changes with caution
✔ incorporate dynamics of health in survival models
These insights improve research in aging, retirement economics, health policy, and behavioral modeling.
Health Status and Empirical Mod…
If you want, I can also create:
📌 A diagram/flowchart of the model
📌 A one-paragraph brief summary
📌 A bullet-point version
📌 A presentation slide style explanation
Just tell me!...
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taycgghk-5680
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A mathematical model
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A mathematical model to estimate the seasonal
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Yasuhiro Yamada1,3, Toshiro Yamada 2,4 & Kazu Yasuhiro Yamada1,3, Toshiro Yamada 2,4 & Kazuko Yamada2,4
The longevity of a honeybee colony is far more significant than the lifespan of an individual honeybee, a social insect. the longevity of a honeybee colony is integral to the fate of the colony. We have proposed a new mathematical model to estimate the apparent longevity defined in the upper limit of an integral equation. the apparent longevity can be determined only from the numbers of adult bees and capped brood. By applying the mathematical model to a honeybee colony in Japan, seasonal changes in apparent longevity were estimated in three long-term field experiments. Three apparent longevities showed very similar season-changes to one another, increasing from early autumn, reaching a maximum at the end of overwintering and falling approximately plumb down after overwintering. The influence of measurement errors in the numbers of adult bees and capped brood on the apparent longevity was investigated.
A lifespan of an animal, which is the period of time while an individual is alive, is an important index to evaluate individual activities. In the colony composed of eusocial insects such as honeybees (Apis mellifera) which exhibit age-polyethism, the lifespan of each individual cannot always give an assessment as to the activities of a colony but the longevity of colony could give it more appropriately. The longevity of a colony will have greater significance than the lifespan of each individual of the colony. The life of colony diversely depends on the inborn lifespan of an individual, the labor division distribution ratio of each honeybee performing a particular duty, the natural environment such as the weather, the amount of food, pests and pathogens, the environmental pollution due to pesticides and so on. The honeybee length of life has been observed or estimated before in the four seasons, which have a distinct bimodal distribution in temperature zones. According to previous papers, honeybees live for 2–4 weeks1 and 30–40 days2 in spring, for 1–2 weeks1, 25–30 days2 and 15–38 days3 in summer, for 2–4 weeks1 and 50–60 days2 in autumn, and for 150–200 days3, 253 days2, 270 days4, 304 days5 6–8 months6 and 150–200 days3 in winter, where it has been estimated that the difference of life length among seasons may come from the brood-rearing load imposed on honeybees1 and may mainly come from foraging and brood-rearing activity2. Incidentally, the lifetime of the queen seems to be three to four years (maximum observed nine years). The average length of life of worker bees in laboratory cages was observed to range from 30.5 to 45.5 days7. The study on the influence of altitude on the lifespan of the honeybee has found that the lifespans are 138 days at an altitude of 970 m and 73 days at an altitude of 200 m, respectively8. Many papers have discussed what factors affect the length of life (lifespan, longevity, life expectancy) on a honeybee colony as follows: Proper nutrition may increase the length of life in a honeybee colony. Honeybees taking beebread or diets with date palm pollen (the best source for hypopharyngeal gland development) showed the longest fifty percent lethal time (LT50)9. The examination for the effect of various fat proteins on honeybee longevity have shown that honeybees fed diets of red gum pollen have the longest lifespan but those fed invert sugar have the shortest lifespan10. In the discussion on nutrition-related risks to honey bee colonies such as starvation, monoculture, genetically modified crops and pesticides in pollen and sugar, protein nutrient strongly affects brood production and larval starvation (alone and or in combination with other stresses) can weaken colonies11. And protein content in
1Department of Applied Physics, Graduate School of Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan. 2Graduate School of Natural Science & Technology, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan. 3Present address: Department of Physics, Osaka University, 1-1 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan. 4Present address: 2-10-15, Teraji, Kanazawa, Ishikawa, 921-8178, Japan. correspondence and requests for materials should be addressed to t.Y. (email: yamatoshikazu0501@yahoo.co.jp)
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ticcnekp-9326
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Genetics of human longevi
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Genetics of human longevity
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Abstract. Smulders L, Deelen J. Genetics of human Abstract. Smulders L, Deelen J. Genetics of human longevity: From variants to genes to pathways. J Intern Med. 2024;295:416–35.
The current increase in lifespan without an equivalent increase in healthspan poses a grave challenge to the healthcare system and a severe burden on society. However, some individuals seem to be able to live a long and healthy life without the occurrence of major debilitating chronic diseases, and part of this trait seems to be hidden in their genome. In this review, we discuss the findings from studies on the genetic component of human longevity and the main challenges accompanying these studies. We subsequently focus on results from genetic studies in model organismsandcomparativegenomicapproachesto highlight the most important conserved longevity
associated pathways. By combining the results from studies using these different approaches, we conclude that only five main pathways have been consistently linked to longevity, namely (1) insulin/insulin-like growth factor 1 signalling, (2) DNA-damage response and repair, (3) immune function, (4) cholesterol metabolism and (5) telomere maintenance. As our current approaches to study the relevance of these pathways in humans are limited, we suggest that future studies on the genetics of human longevity should focus on the identification and functional characterization of rare genetic variants in genes involved in these pathways.
Keywords: genetics, longevity, longevity-associated pathways, rare genetic variants, functional characterization...
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cd7f6ee5-ca09-4aba-bf20-bc86fe62aff8
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vwitogci-0660
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Developmental Diet Alters
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Developmental Diet Alters the Fecundity–Longevity
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Drosophila melanogaster David H. Collins, PhD,*, D Drosophila melanogaster David H. Collins, PhD,*, David C. Prince, PhD, Jenny L. Donelan, MSc, Tracey Chapman, PhD , and Andrew F. G. Bourke, PhD School of Biological Sciences, University of East Anglia, Norwich, UK. *Address correspondence to: David H. Collins, PhD. E-mail: David.Collins@uea.ac.uk Decision Editor: Gustavo Duque, MD, PhD (Biological Sciences Section)
Abstract The standard evolutionary theory of aging predicts a negative relationship (trade-off) between fecundity and longevity. However, in principle, the fecundity–longevity relationship can become positive in populations in which individuals have unequal resources. Positive fecundity–longevity relationships also occur in queens of eusocial insects such as ants and bees. Developmental diet is likely to be central to determining trade-offs as it affects key fitness traits, but its exact role remains uncertain. For example, in Drosophila melanogaster, changes in adult diet can affect fecundity, longevity, and gene expression throughout life, but it is unknown how changes in developmental (larval) diet affect fecundity–longevity relationships and gene expression in adults. Using D. melanogaster, we tested the hypothesis that varying developmental diets alters the directionality of fecundity–longevity relationships in adults, and characterized associated gene expression changes. We reared larvae on low (20%), medium (100%), and high (120%) yeast diets, and transferred adult females to a common diet. We measured fecundity and longevity of individual adult females and profiled gene expression changes with age. Adult females raised on different larval diets exhibited fecundity–longevity relationships that varied from significantly positive to significantly negative, despite minimal differences in mean lifetime fertility or longevity. Treatments also differed in age-related gene expression, including for aging-related genes. Hence, the sign of fecundity–longevity relationships in adult insects can be altered and even reversed by changes in larval diet quality. By extension, larval diet differences may represent a key mechanistic factor underpinning positive fecundity–longevity relationships observed in species such as eusocial insects. Keywords: Aging, Eusociality, Life history, mRNA-seq, Nutrition
The standard evolutionary theory of aging predicts that, as individuals grow older, selection for increased survivorship declines with age (1). Therefore, individuals experience the age-related decrease in performance and survivorship that defines aging (senescence) (2). Additionally, given finite resources, individuals should optimize relative investment between reproduction and somatic maintenance (3). This causes tradeoffs between reproduction and longevity (4,5) with elevated reproduction often incurring costs to longevity (the costs of reproduction) (6). Such trade-offs and costs are evident in the negative fecundity–longevity relationships observed in many species. Although a negative fecundity–longevity relationship is typical, fecundity and longevity can become uncoupled (7) and some species or populations may exhibit positive fecundity– longevity relationships (4). This can occur for several reasons. First, in Drosophila melanogaster, mutations can increase longevity without apparent reproductive costs (8–11), particularly mutations in the conserved insulin/insulin-like growth factor signaling and target of rapamycin network (IIS-TOR).
This network regulates nutrient sensitivity and is an important component of aging across diverse taxa (2,12). Second, fecundity and longevity can become uncoupled when there is asymmetric resourcing between individuals (13,14). Within a population, well-resourced individuals may have higher fecundity and longevity than poorly resourced individuals, reversing the usual negative fecundity–longevity relationship. However, because costs of reproduction are not abolished even in well-resourced individuals (13,14), a within-individual trade-off between fecundity and longevity remains present. Third, fecundity and longevity can become uncoupled within and between the castes of eusocial insects (15–18), that is, species such as ants, bees, wasps, and termites with a longlived reproductive caste (queens or kings) and a short-lived non- or less reproductive caste (workers) (19–21). In some species, queens appear to have escaped costs of reproduction completely (22–25). This may have been achieved through rewiring the IIS-TOR network (12,26), which forms part of the TOR/IIS-juvenile hormone-lifespan and fecundity (TI-JLiFe) network hypothesized to underpin aging and longevity in eusocial insects by Korb et al....
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dzeplixu-2464
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foot prints in the sand
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foot prints in the sand
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Stephen Treaster1,2, David Karasik3,4*† and Matthe Stephen Treaster1,2, David Karasik3,4*† and Matthew P. Harris1,2†
1 Department of Orthopaedics, Boston Children’s Hospital, Boston, MA, United States, 2 Department of Genetics, Harvard Medical School, Boston, MA, United States, 3 Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel, 4 Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, United States
With the modern quality, quantity, and availability of genomic sequencing across species, as well as across the expanse of human populations, we can screen for shared signatures underlying longevity and lifespan. Knowledge of these mechanisms would be medically invaluable in combating aging and age-related diseases. The diversity of longevities across vertebrates is an opportunity to look for patterns of genetic variation that may signal how this life history property is regulated, and ultimately how it can be modulated. Variation in human longevity provides a unique window to look for cases of extreme lifespan within a population, as well as associations across populations for factors that influence capacity to live longer. Current large cohort studies support the use of population level analyses to identify key factors associating with human lifespan. These studies are powerful in concept, but have demonstrated limited ability to resolve signals from background variation. In parallel, the expanding catalog of sequencing and annotation from diverse species, some of which have evolved longevities well past a human lifespan, provides independent cases to look at the genomic signatures of longevity. Recent comparative genomic work has shown promise in finding shared mechanisms associating with longevity among distantly related vertebrate groups. Given the genetic constraints between vertebrates, we posit that a combination of approaches, of parallel meta-analysis of human longevity along with refined analysis of other vertebrate clades having exceptional longevity, will aid in resolving key regulators
of enhanced lifespan that have proven to be elusive when analyzed in isolation....
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836c295f-0193-463c-8463-197fd7eda2e3
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tvczpisc-6894
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Happy People Live Longer
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Happy People Live Longer
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This comprehensive review demonstrates that subjec This comprehensive review demonstrates that subjective well-being (SWB)—including happiness, life satisfaction, optimism, and positive emotions—plays a causal and measurable role in promoting better health, stronger physiological functioning, and longer life. Drawing on seven converging lines of evidence from longitudinal human studies, laboratory experiments, physiological research, animal studies, natural experiments, and intervention trials, the authors present one of the most rigorous and multidimensional examinations of the happiness–health connection.
The review shows that individuals who experience higher levels of SWB not only report better health but live significantly longer, even when controlling for baseline health status, socioeconomic factors, and lifestyle. Positive emotions predict reduced mortality, lower risk of cardiovascular disease, stronger immune function, and improved resilience to stress. In contrast, chronic negative emotions—such as depression, anxiety, and hostility—are linked to inflammation, impaired immunity, hypertension, atherosclerosis, and accelerated aging.
The document organizes evidence into seven major categories:
1. Long-term Prospective Studies
Large-scale, decades-long studies consistently show that SWB predicts longevity in healthy populations and sometimes improves survival in diseased populations. Optimists and individuals with high positive affect live longer than pessimists and those with low affect.
2. Naturalistic Physiological Studies
Everyday positive emotions correlate with lower cortisol, reduced blood pressure, healthier cardiovascular responses, and lower inflammation. Negative emotions produce harmful biological patterns such as elevated cytokines and delayed wound healing.
3. Experimental Mood Induction Studies
When researchers induce positive or negative emotions in controlled settings, they observe immediate changes in cardiovascular activity, immune function, stress hormones, and healing responses—confirming direct causal pathways.
4. Animal Research
Studies on monkeys, pigs, hamsters, and rodents show that stress compromises immunity, accelerates disease processes, and shortens lifespan, while positive social environments and reward-based experiences promote health and healing.
5. Quasi-experimental Studies of Real-world Events
Major emotional events—earthquakes, wars, bereavement—produce measurable spikes in mortality and biological stress markers, revealing how emotional states influence health at the population level.
6. Interventions That Improve SWB
Meditation, relaxation training, social support enhancement, and hostility-reduction interventions lead to measurable improvements in immune function, blood pressure, wound healing, and in some cases, longer survival.
7. Studies on Quality of Life and Pain
Positive emotions reduce pain sensitivity, accelerate functional recovery, and improve daily functioning among people with chronic illnesses.
Key Conclusion
Across diverse methods and populations, the evidence forms a compelling causal model:
**Happiness is not just an outcome of good health—
it is a contributor to it.**
SWB influences the immune, cardiovascular, endocrine, and inflammatory systems, shaping vulnerability or resilience to disease. While happiness cannot cure all illnesses, especially severe or rapidly progressing diseases, it profoundly improves health trajectories in both healthy and clinical populations.
In Essence
This document is a landmark synthesis demonstrating that happy people truly live longer, and that fostering subjective well-being is not merely a psychological luxury but a powerful public health priority with far-reaching implications for prevention, aging, and holistic healthcare.
If you'd like, I can also create:
✅ A shorter description
✅ An academic abstract
✅ A graphical diagram summarizing the pathways
✅ A bullet-point executive overview
Just tell me!...
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AGEING IN ASIA
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AGEING IN ASIA AND THE PACIFIC
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as a whole. This highlights the need for countries as a whole. This highlights the need for countries with relatively low proportion of older persons to also put in place appropriate policies and interventions to address their specific rights and needs, and to prepare for ageing societies in the future.
An increase in the proportion and number of the oldest old (persons over the age of 80 years)
The oldest old person, the number of people aged 80 years or over, in the region is also showing a dramatic upward trend. The proportion of the oldest old in the region in the total population 2016 was 1.5 per cent of the population amounting to 68 million people, which is 53 per cent of the global population over 80 years old. This proportion is expected to rise to 5 per cent of the population totaling 258 million people by 2050. Asia
Pacific would have 59 per cent of the world population over 80 years of age compared to 53 per cent at present. This has serious implications for provision of appropriate health care and long term care, as well as income security.
The causes…
The drastic increase in the pace of ageing in the region can be attributed to two key factors, declining fertility rates and increasing life expectancies.
Rapidly declining fertility: The most precipitous declines in the region’s fertility have been in the South and SouthWest, and South-East Asia subregions, with the fertility rates falling by 50 per cent in a span of 40 years. ...
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Extreme Human Lifespan
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Extreme Human Lifespan
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The indexed individual, from now on termed M116, w The indexed individual, from now on termed M116, was the world's oldest verified living person from January 17th 2023 until her passing on August 19th 2024, reaching the age of 117 years and 168 days (https://www.supercentenarian.com/records.html). She was a Caucasian woman born on March 4th 1907 in San Francisco, USA, from Spanish parents and settled in Spain since she was 8. A timeline of her life events and her genealogical tree are shown in Supplementary Fig. 1a-b. Although centenarians are becoming more common in the demographics of human populations, the so-called supercentenarians (over 110 years old) are still a rarity. In Catalonia, the historic nation where M116 lived, the lifeexpectancy for women is 86 years, so she exceeded the average by more than 30 years (https://www.idescat.cat). In a similar manner to premature aging syndromes, such as Hutchinson-Gilford Progeria and Werner syndrome, which can provide relevant clues about the mechanisms of aging, the study of supercentenarians might also shed light on the pathways involved in lifespan. To unfold the biological properties exhibited by such a remarkable human being, we developed a comprehensive multiomics analysis of her genomic, transcriptomic, metabolomic, proteomic, microbiomic and epigenomic landscapes in different tissues, as depicted in Fig. 1a, comparing the results with those observed in non-supercentenarian populations. The picture that emerges from our study shows that extremely advanced age and poor health are not intrinsically linked and that both processes can be distinguished and dissected at the molecular level.
RESULTS AND DISCUSSION Samples from the subject were obtained from four different sources: total peripheral blood, saliva, urine and stool at different times. Most of the analyses were performed in the blood material at the time point of 116 years and 74 days, unless otherwise specifically indicated (Data set 1). The simple karyotype of the supercentenarian did not show any gross chromosomal alteration (Supplementary Fig. 1c). Since many reports indicate the involvement of telomeres in aging and lifespan1, we interrogated the telomere length of the M116 individual using High-Throughput Quantitative Fluorescence In Situ Hybridization (HT-Q-FISH) analysis2. Illustrative confocal images with DAPI staining and the telomeric probe (TTAGGG) for M116 and two control samples are shown in Fig. 1b. Strikingly, we observed that the supercentenarian exhibited the shortest mean telomere length among all healthy volunteers3 with a value of barely 8 kb (Fig. 1c). Even more noticeably, the M116 individual displayed a 40% of short telomeres below the 20th percentile of all the studied samples (Fig. 1c). Thus, the observed far reach longevity of our case occurred in the chromosomal context of extremely short telomeres. Interestingly, because the M116 individual presented an overall good health status, it is tempting to speculate that, in this ...
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financial impact
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financial impact of longevity and risk
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e economic and fiscal effects of an aging society e economic and fiscal effects of an aging society have been extensively studied and are generally recognized by policymakers, but the financial consequences associated with the risk that people live longer than expected—longevity risk—has received less attention.1 Unanticipated increases in the average human life span can result from misjudging the continuing upward trend in life expectancy, introducing small forecasting errors that compound over time to become potentially significant. This has happened in the past. There is also risk of a sudden large increase in longevity as a result of, for example, an unanticipated medical breakthrough. Although longevity advancements increase the productive life span and welfare of millions of individuals, they also represent potential costs when they reach retirement. More attention to this issue is warranted now from the financial viewpoint; since longevity risk exposure is large, it adds to the already massive costs of aging populations expected in the decades ahead, fiscal balance sheets of many of the affected countries are weak, and effective mitigation measures will take years to bear fruit. The large costs of aging are being recognized, including a belated catchup to the currently expected increases in average human life spans. The costs of longevity risk—unexpected increases in life spans—are not well appreciated, but are of similar magnitude. This chapter presents estimates that suggest that if everyone lives three years longer than now expected—the average underestimation of longevity in the past—the present discounted value of the additional living expenses of everyone during those additional years of life amounts to between 25 and 50 percent of 2010 GDP. On a global scale, that increase amounts to tens of trillions of U.S. dollars, boosting the already recognized costs of aging substantially. Threats to financial stability from longevity risk derive from at least two major sources. One is the
Note: This chapter was written by S. Erik Oppers (team leader), Ken Chikada, Frank Eich, Patrick Imam, John Kiff, Michael Kisser, Mauricio Soto, and Tao Sun. Research support was provided by Yoon Sook Kim. 1See, for example, IMF (2011a).
threats to fiscal sustainability as a result of large longevity exposures of governments, which, if realized, could push up debttoGDP ratios more than 50 percentage points in some countries. A second factor is possible threats to the solvency of private financial and corporate institutions exposed to longevity risk; for example, corporate pension plans in the United States could see their liabilities rise by some 9 percent, a shortfall that would require many multiples of typical yearly contributions to address. Longevity risk threatens to undermine fiscal sustainability in the coming years and decades, complicating the longerterm consolidation efforts in response to the current fiscal difficulties.2 Much of the risk borne by governments (that is, current and future taxpayers) is through public pension plans, social security schemes, and the threat that private pension plans and individuals will have insufficient resources to provide for unexpectedly lengthy retirements. Most private pension systems in the advanced economies are currently underfunded and longevity risk alongside low interest rates further threatens their financial health. A threepronged approach should be taken to address longevity risk, with measures implemented as soon as feasible to avoid a need for much larger adjustments later. Measures to be taken include: (i) acknowledging government exposure to longevity risk and implementing measures to ensure that it does not threaten medium and longterm fiscal sustainability; (ii) risk sharing between governments, private pension providers, and individuals, partly through increased individual financial buffers for retirement, pension system reform, and sustainable oldage safety nets; and (iii) transferring longevity risk in capital markets to those that can better bear it. An important part of reform will be to link retirement ages to advances in longevity. If undertaken now, these mitigation measures can be implemented in a gradual and sustainable way. Delays would increase risks to financial and fiscal stability, potentially requiring much larger and disruptive measures in the future.
...
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Live Longer
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How to live longer ?
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How to Live Longer is a comprehensive, science-bas How to Live Longer is a comprehensive, science-based lifestyle guide that translates decades of longevity research into simple daily actions that anyone can apply. Designed as a practical handbook rather than an academic review, it organizes the most powerful, evidence-supported habits into six core pillars of healthy aging:
Stay Active
Eat Wisely
Manage Stress
Sleep Well
Build Social Connection
Maintain Mental Stimulation
These pillars form a “longevity lifestyle,” emphasizing that small, consistent actions—especially in midlife—produce large benefits in later years.
The eBook integrates insights from real-world longevity hotspots such as Blue Zones (Okinawa, Sardinia, Nicoya, Ikaria, Loma Linda), modern public-health science, and behavioral psychology to show how daily routines shape health trajectories across the lifespan.
🔍 Core Pillars & Science-Backed Practices
1. Staying Active
Activity is the single strongest predictor of how well someone ages.
The guide recommends:
Strength training
Frequent walking
Active living (taking stairs, chores, gardening)
Stretching for mobility
Regular physical activity improves the heart, brain, metabolism, muscle strength, mood, and overall vitality.
2. Eating Wisely
A longevity-focused diet emphasizes:
Mostly plant-based meals
Fruits, vegetables, whole grains, legumes
Nuts and seeds daily
Healthy fats (olive oil, omega-3s)
Smaller portions and mindful eating
The guide highlights traditional dietary patterns of Blue Zones, especially Mediterranean and Okinawan models, which are strongly linked to long life and reduced chronic disease.
3. Managing Stress
Chronic stress accelerates aging, inflammation, and disease.
The eBook recommends:
Mindfulness and meditation
Breathing exercises
Yoga
Time in nature
Hobby-based relaxation
Scheduling downtime
These practices help regulate emotional well-being, improve resilience, and support healthier biological aging.
4. Good Quality Sleep
Sleep is described as a longevity multiplier, with profound effects on immune health, metabolic balance, brain function, and emotional stability.
The guide includes:
Consistent sleep schedules
Dark, cool sleeping environments
Reducing caffeine, alcohol, and screens before bed
5. Social Connection
Loneliness is a major risk factor for early mortality, comparable to smoking and inactivity.
The eBook emphasizes:
Strong family bonds
Friendships
Community involvement
Purposeful living (“ikigai”)
This reflects consistent findings from longevity populations worldwide.
6. Staying Mentally Active
Lifelong learning, mental stimulation, and cognitively engaging activities help preserve brain function.
Recommendations include:
Reading
Learning new skills
Puzzles or games
Creative pursuits
These habits strengthen cognitive reserve and support healthier aging.
💡 Overall Insight
The eBook argues that longevity is not about extreme interventions—it is about consistent, realistic, enjoyable habits grounded in strong science. It blends public-health evidence with lifestyle medicine, emphasizing that aging well is achievable for anyone, regardless of genetics.
Across all chapters, the tone remains practical: longevity is built through everyday choices, not expensive biohacking.
🧭 In Summary
How to Live Longer is a practical, evidence-driven handbook that shows how daily movement, nutritious eating, stress control, quality sleep, social belonging, and lifelong learning combine to support longer, healthier, more fulfilling lives....
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Future-Proofing the life
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Future-Proofing the Longevity
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This document is published by the World Economic F This document is published by the World Economic Forum as a contribution to a project, insight area or interaction. The findings, interpretations and conclusions expressed herein are the result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders. In this paper, many areas of innovation have been highlighted with the potential to support the longevity economy transition. The fact that a particular company or product is highlighted in this paper does not represent an endorsement or recommendation on behalf of the World
Haleh Nazeri Lead, Longevity Economy, World Economic Forum
Graham Pearce Senior Partner, Global Defined Benefit Segment Leader, Mercer
The world appears increasingly fragmented, but one universal reality connects us all – ageing. Across the world, people are living longer than past generations, in some cases by up to 20 years. This longevity shift, coupled with declining birth rates, is reshaping economies, workforces and financial systems, with profound implications for individuals, businesses and governments alike.
As countries transform, the systems that underpin them must also evolve. Today’s reality includes a widening gap between healthspan and lifespan, the emergence of a multigenerational workforce with five generations working side by side, and the need for stronger intergenerational collaboration.
One of the most important topics to consider during this demographic transition is the economic implications of longer lives. This paper highlights five key trends that will influence and shape the financial resilience of institutions, governments
and individuals in the years ahead. It also showcases innovative solutions that are already being implemented by countries, businesses and organizations to prepare for the future.
While the challenges are significant, they also present an opportunity to develop systems that are more inclusive, equitable, resilient and sustainable for the long term. This is a chance to strengthen pension systems and social protections, not only for those who have traditionally benefited, but also for those who were left out of social contracts the first time.
We are grateful to our multistake holder consortium of leaders across business, the public sector, civil society and academia for their contributions, inputs and collaboration on this report. We look forward to seeing how others will continue to build on these innovative ideas to future-proof the longevity economy for a brighter and more ...
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A New Map of Life
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A New Map of Life
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Longevity is not a synonym of old age. The increas Longevity is not a synonym of old age. The increase in life expectancy shapes lives from childhood to old age across different domains. Among those, the nature of work will undergo profound changes from skill development and the role of retirement to the intrinsic meaning of work. To put the striking potential of a 100 year life into a historical prospective it is useful to start from how technological and demographic development shaped the organization and the definition of work in the past. This longer view can more thoughtfully explore how different the nature of work has been, from working hours to the parallelism between work, employment and task-assignment.
Throughout history the role of work has been intertwined with social and technological change. Societies developed from hunter-gather to sedentary farmers, and they transitioned from the agricultural to the industrial revolution. The latter transformed a millennial long practice of self-employed farmers and artisans, working mostly for self-subsistence, without official working hours, relying on daylight and seasonality at an unchosen job from childhood until death, into employees working 10-16 hours per day for 311 days a year, mostlyindoorsfromyouthtoretirement. Thisdrastictransformationignitedfastshiftsofworkorganization not only in the pursue of higher productivity and technological advancement, but also of social wellbeing.
Among the first changes was the abandonment of unsustainable working conditions, such as day working hours, which sharply converged toward the eight hours day tendency between the 1910s and the 1940s, see Figure 1 (Huberman and Minns 2007; Feenstra, Inklaar, and Timmer 2015; Charlie Giattino and Roser 2013). Although beneficial for the workers, this reduction worried intellectuals, such as the economist John Maynard Keynes, who wrote: “How will we all keep busy when we only have to work 15 hours a week?” (Keynes 1930). Keynes predicted people’s work to become barely necessary given the level of productivity the economy would reach over the next century: “permanent problem would be how to occupy the leisure,
1
whichscienceandcompoundinterestwillhavewonforhim. [...] Afearfulproblemfortheordinaryperson” (p. 328). For a while, Keynes seemed right since the average workweek dropped from 47 hours in 1930 to slightly less than 39 by 1970. However, after declining for more than a century, the average U.S. work week has been stagnant for four decades, at approximately eight hours per day.1
Figure 1: Average working hours per worker over a full year. Before 1950 the data corresponds only to full-time production workers(non-agricultural activities). Starting 1950 estimates cover total hours worked in the economy as measured from primarily National Accounts data. Source: Charlie Giattino and Roser (2013). Data Sources: Huberman and Minns (2007) and Feenstra, Inklaar, and Timmer (2015).
Technological change did not make work obsolete, but changed the tasks and the proportion of labor force involved in a particular job. In the last seventy years, for example, the number of people employed in the agricultural sector dropped by one third (from almost 6 million to 2 million), while the productivity tripled. Feeding or delivering calves is still part of ranchers’ days, but activities like racking and analyzing genetic traits of livestock and estimating crop yields are a big part of managing and sustaining the ranch operations. In addition, the business and administration activity like bookkeeping, logistics, market pricing, employee supervision became part of the job due to the increase in average farm size from 200 to 450 acres. Another exampleistheeffectoftheautomatedtellermachine(ATM)onbanktellers, whosenumbergrewfromabout a quarter of a million to a half a million in the 45 years since the introduction of ATMs, see Figure 2 (Bessen 2016). ATM allowed banks to operate branch offices at lower cost, which prompted them to open many 1Despite the settling, differences in the number of hours worked between the low and the high skilled widened in the last fifty years. Men without a high school degree experienced an average reduction of eight working hours a week, while college graduates faced an increase of six hours a week. Similarly, female graduates work 11 hours a week more than those who did not complete high school (Dolton 2017). Overall, American full-time employees work on average 41.5 hours per week, and about 11.1% of employees work over 50 hours per week, which is much higher than countries with a comparable level of productivity like Switzerland, where 0.4% of employees work over 50 hours per week (Feenstra, Inklaar, and Timmer 2015) and part time work is commonplace...
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Promoting Active Ageing
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Promoting Active Ageing
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“Promoting Active Ageing in Southeast Asia” is a c “Promoting Active Ageing in Southeast Asia” is a comprehensive OECD/ERIA report that examines how ASEAN countries can support healthy, productive, and secure ageing as their populations grow older at unprecedented speed. The report highlights that Southeast Asia is ageing twice as fast as OECD nations, while still facing high levels of informal employment, limited social protection, and gender inequality—making ageing a major economic and social challenge.
Core Purpose
The report identifies what policies ASEAN member states must adopt to ensure:
Older people can remain healthy,
Continue to participate socially and economically, and
Avoid income insecurity in old age.
🧩 What the Report Covers
1. Demographic & Economic Realities
Fertility has dropped across all countries; life expectancy continues to rise.
The old-age to working-age ratio will surge in the next 30 years.
Working-age populations will decrease sharply in Singapore, Thailand, and Vietnam, while still growing in Cambodia, Laos, and the Philippines.
Public expenditure is low, leaving governments with limited capacity to fund pensions or healthcare.
2. Key Barriers to Active Ageing
High informality (up to 90% in some countries): keeps workers outside formal pensions, healthcare, and protections.
Gender inequalities in work, caregiving, and legal rights compound poverty risks for older women.
Low healthcare spending, shortages of medical staff, and rural access gaps.
Limited pension adequacy, low coverage, and low retirement ages.
🧭 Major Policy Recommendations
A. Reduce Labour Market Informality
Lower the cost of formalisation for low-income workers.
Strengthen labour law enforcement and improve business registration processes.
Relax overly strict product/labour market regulations.
B. Reduce Gender Inequality in Old Age
Integrate gender perspectives into all policy design.
Reform discriminatory family and inheritance laws.
Promote financial education and career equality for women.
C. Ensure Inclusive Healthcare Access
Increase public health funding.
Improve efficiency through generics, preventive care, and technology.
Expand health insurance coverage to all.
Use telemedicine and incentives to serve rural areas.
D. Strengthen Old-Age Social Protection
Increase first-tier (basic) pensions.
Raise retirement ages where needed and link them to life expectancy.
Reform PAYG pensions to ensure sustainability.
Make pension systems easier to understand and join.
E. Support Social Participation of Older Adults
Build age-friendly infrastructure (benches, safe crossings, accessible paths).
Create community programs that encourage interaction and prevent isolation.
🧠 Why This Matters
By 2050, ASEAN countries will face dramatic demographic shifts. Without rapid and coordinated policy reforms, millions of older people risk:
Poor health
Lack of income
Social isolation
Inadequate care
This report serves as a strategic blueprint for building healthy, productive, and resilient ageing societies in Southeast Asia....
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Promoting Active Ageing
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“Promoting Active Ageing in Southeast Asia” is a c “Promoting Active Ageing in Southeast Asia” is a comprehensive OECD/ERIA report that examines how ASEAN countries can support healthy, productive, and secure ageing as their populations grow older at unprecedented speed. The report highlights that Southeast Asia is ageing twice as fast as OECD nations, while still facing high levels of informal employment, limited social protection, and gender inequality—making ageing a major economic and social challenge.
Core Purpose
The report identifies what policies ASEAN member states must adopt to ensure:
Older people can remain healthy,
Continue to participate socially and economically, and
Avoid income insecurity in old age.
🧩 What the Report Covers
1. Demographic & Economic Realities
Fertility has dropped across all countries; life expectancy continues to rise.
The old-age to working-age ratio will surge in the next 30 years.
Working-age populations will decrease sharply in Singapore, Thailand, and Vietnam, while still growing in Cambodia, Laos, and the Philippines.
Public expenditure is low, leaving governments with limited capacity to fund pensions or healthcare.
2. Key Barriers to Active Ageing
High informality (up to 90% in some countries): keeps workers outside formal pensions, healthcare, and protections.
Gender inequalities in work, caregiving, and legal rights compound poverty risks for older women.
Low healthcare spending, shortages of medical staff, and rural access gaps.
Limited pension adequacy, low coverage, and low retirement ages.
🧭 Major Policy Recommendations
A. Reduce Labour Market Informality
Lower the cost of formalisation for low-income workers.
Strengthen labour law enforcement and improve business registration processes.
Relax overly strict product/labour market regulations.
B. Reduce Gender Inequality in Old Age
Integrate gender perspectives into all policy design.
Reform discriminatory family and inheritance laws.
Promote financial education and career equality for women.
C. Ensure Inclusive Healthcare Access
Increase public health funding.
Improve efficiency through generics, preventive care, and technology.
Expand health insurance coverage to all.
Use telemedicine and incentives to serve rural areas.
D. Strengthen Old-Age Social Protection
Increase first-tier (basic) pensions.
Raise retirement ages where needed and link them to life expectancy.
Reform PAYG pensions to ensure sustainability.
Make pension systems easier to understand and join.
E. Support Social Participation of Older Adults
Build age-friendly infrastructure (benches, safe crossings, accessible paths).
Create community programs that encourage interaction and prevent isolation.
🧠 Why This Matters
By 2050, ASEAN countries will face dramatic demographic shifts. Without rapid and coordinated policy reforms, millions of older people risk:
Poor health
Lack of income
Social isolation
Inadequate care
This report serves as a strategic blueprint for building healthy, productive, and resilient ageing societies in Southeast Asia....
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Leaving No One Behind
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Leaving No One Behind In An Ageing World
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“Leaving No One Behind in an Ageing World” is the “Leaving No One Behind in an Ageing World” is the United Nations World Social Report 2023, a comprehensive and authoritative analysis of global population ageing. It explores how the world is undergoing a permanent demographic shift toward older populations—and what must be done to ensure all people can age with dignity, health, and economic security.
It explains that population ageing is not a crisis, but a global success story—the result of longer lifespans, improvements in health, education, gender equality, and reduced fertility. However, it also warns that inequality, poverty, weak care systems, and inadequate policies risk leaving millions of older persons behind.
The report provides data, trends, challenges, and policy recommendations across five major chapters.
📌 Main Themes of the Report
1. A Rapidly Ageing World
By 2050, the number of people aged 65+ will more than double—from 761 million to 1.6 billion.
The population aged 80+ will almost triple to 459 million.
Ageing is happening everywhere, but fastest in:
Northern Africa & Western Asia
Sub-Saharan Africa
Eastern & South-Eastern Asia
The world’s oldest countries are shifting from Europe to Asia.
The report highlights how societies of tomorrow will be younger in fewer places, older almost everywhere.
2. Living Longer, Healthier Lives
Rising longevity is a major human achievement.
Premature deaths have fallen.
People live more years in good health.
But gaps remain:
Women live longer but often face more unhealthy years.
Poorer populations have shorter and less healthy lives.
COVID-19 disrupted progress in life expectancy.
Healthy ageing requires lifelong investment in education, nutrition, healthcare, safety, and environments.
3. What Ageing Means for Economies
The report rejects the idea that older populations are “burdens.”
Key points:
Population ageing affects labour, consumption, taxes, pensions, and long-term care.
With good policies, ageing can bring:
Increased productivity
A stronger labour force via women and older workers
Two “demographic dividends,” if countries invest early
Many older people contribute economically through:
Paid work
Volunteering
Childcare for families
Financial support to younger generations
However, ageing challenges include:
Rising pension and healthcare costs
A shrinking workforce
Inequitable labour markets
Lower savings among future generations
4. Ageing, Poverty, and Inequality
The report stresses that ageing does not create inequality—inequality throughout life creates unequal ageing.
Key findings:
Older persons are more likely to be poor than working-age people, especially in developing countries.
Inequalities accumulate across life:
Poor childhood conditions
Unequal education
Employment insecurity
Gender discrimination
Women face far greater risks due to:
Lower lifetime earnings
Informal/unpaid caregiving roles
Longer lifespans
Higher risk of widowhood
Future generations of older people may be more unequal than today, unless countries act now.
5. A Global Crisis of Care
Demand for long-term care is skyrocketing as populations age, especially above age 80.
Problems:
Most countries are not prepared.
Care systems are underfunded.
Care jobs are low-paid and mostly done by women.
Families—especially daughters—bear the unpaid burden.
COVID-19 exposed deep weaknesses in care facilities.
Solutions recommended:
Build integrated long-term care systems.
Professionalize and protect care workers.
Ensure quality standards and monitoring.
Support “ageing in place” (staying at home).
Reduce reliance on informal unpaid care.
🌍 What “Leaving No One Behind” Means
The report shows that ageing affects:
Health systems
Education
Labour markets
Taxes
Pensions
Social protection
Gender equality
Migration
Long-term care
It argues that ageing must become a central policy priority at national and global levels.
🏛️ Key Policy Recommendations
A. Start Early—Lifelong Interventions
Equal access to quality education
Lifelong learning
Healthy environments
Decent work
Fair labour markets
Support for women, caregivers, and informal workers
B. Strengthen Social Protection & Pensions
Universal pensions or tax-funded basic benefits
Avoid shifting financial risks to individuals
Expand coverage of retirees in informal economies
Use fair and progressive tax systems
C. Build Strong Long-Term Care Systems
Public funding
Trained and protected care workers
Home- and community-based care options
Better regulation, monitoring, and accountability
D. Promote Intergenerational Equity
Address income, education, and health gaps early in life
Encourage solidarity between generations
Prepare youth now to become healthy, secure older adults later
✨ Perfect Summary Statement
The PDF is a global roadmap for managing population ageing in a way that protects rights, reduces inequality, improves health, strengthens economies, and ensures that no person—young or old—is left behind in a rapidly ageing world....
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Aging and aging-related
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Aging and aging-related disease
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Aging is a gradual and irreversible pathophysiolog Aging is a gradual and irreversible pathophysiological process. It presents with declines in tissue and cell functions and significant increases in the risks of various aging-related diseases, including neurodegenerative diseases, cardiovascular diseases, metabolic diseases, musculoskeletal diseases, and immune system diseases. Although the development of modern medicine has promoted human health and greatly extended life expectancy, with the aging of society, a variety of chronic diseases have gradually become the most important causes of disability and death in elderly individuals. Current research on aging focuses on elucidating how various endogenous and exogenous stresses (such as genomic instability, telomere dysfunction, epigenetic alterations, loss of proteostasis, compromise of autophagy, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, deregulated nutrient sensing) participate in the regulation of aging. Furthermore, thorough research on the pathogenesis of aging to identify interventions that promote health and longevity (such as caloric restriction, microbiota transplantation, and nutritional intervention) and clinical treatment methods for aging-related diseases (depletion of senescent cells, stem cell therapy, antioxidative and anti-inflammatory treatments, and hormone replacement therapy) could decrease the incidence and development of aging-related diseases and in turn promote healthy aging and longevity...
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Mortality Assumptions
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Mortality Assumptions and Longevity Risk
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This report is a clear, authoritative examination This report is a clear, authoritative examination of how mortality assumptions—the predictions actuaries make about how long people will live—directly shape the financial security, pricing, risk exposure, and solvency of life insurance companies and pension plans. As life expectancy continues to rise unpredictably, the paper explains why longevity risk—the risk that people live longer than expected—is now one of the most serious and complex challenges in actuarial science.
Its central message:
Even small errors in mortality assumptions can create massive financial consequences.
When people live longer than anticipated, insurers and pension funds must pay out benefits for many more years, straining reserves, capital, and long-term sustainability.
🧩 Core Themes & Insights
1. Mortality Assumptions Are Foundational
Mortality assumptions influence:
annuity pricing
pension liabilities
life insurance reserves
regulatory capital requirements
asset–liability management
They are used to determine how much money must be set aside today to pay benefits decades into the future.
2. Longevity Risk: People Live Longer Than Expected
Longevity risk arises from:
ongoing medical advances
healthier lifestyles
improved survival at older ages
cohort effects (younger generations aging differently)
This creates systematic risk—it affects entire populations, not just individuals. Because it is long-term and highly uncertain, it is extremely difficult to hedge.
3. Why Mortality Forecasting Is Difficult
The report highlights key sources of uncertainty:
unpredictable improvements in disease treatment
variability in long-term mortality trends
differences in male vs. female mortality improvement
cohort effects (e.g., baby boom generation)
socioeconomic and geographic differences
Traditional deterministic life tables struggle to capture these dynamic changes.
4. Stochastic Mortality Models Are Essential
The paper emphasizes the growing use of:
Lee–Carter models
CBD (Cairns–Blake–Dowd) models
Multi-factor and cohort mortality models
These models incorporate randomness and allow actuaries to estimate:
future mortality paths
probability distributions
“best estimate” and adverse scenarios
This is crucial for capital planning and solvency regulation.
5. Financial Implications of Longevity Risk
When mortality improves faster than assumed:
annuity liabilities increase
pension funding gaps widen
life insurers face reduced profits
capital requirements rise
The paper explains how regulatory frameworks (e.g., Solvency II, RBC) require insurers to hold additional capital to protect against longevity shocks.
6. Tools to Manage Longevity Risk
To control exposure, companies use:
A. Longevity swaps
Transfer the risk that annuitants live longer to reinsurers or capital markets.
B. Longevity bonds and mortality-linked securities
Spread demographic risks to investors.
C. Reinsurance
Offload part of the longevity exposure.
D. Natural hedging
Balance life insurance (mortality risk) with annuities (longevity risk).
E. Scenario testing & stress testing
Evaluate the financial impact if life expectancy rises 2–5 years faster than expected.
7. Global Perspective
Countries with rapid aging—Japan, the UK, Western Europe, China—are most exposed. Regulators encourage:
more robust mortality modeling
transparent risk disclosures
dynamic assumption-setting
stronger capital buffers
The report stresses that companies must continually update assumptions as new mortality data emerge.
🧭 Overall Conclusion
The paper concludes that accurate mortality assumptions are essential for financial stability in life insurance and pensions. As longevity continues to improve unpredictably, longevity risk becomes one of the most significant threats to solvency. Insurers must adopt:
advanced mortality models
strong risk-transfer mechanisms
dynamic assumption frameworks
robust capital strategies
Longevity is a gift for individuals—but a major quantitative, financial, and strategic challenge for institutions responsible for lifetime benefits....
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wpbbjtck-1794
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Genetic Determinants
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Genetic Determinants of Human Longevity
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Thestudyof APOE anditsisoformshasspreadinallthestu Thestudyof APOE anditsisoformshasspreadinallthestudiesaboutthegeneticsofhuman longevityandthisisoneofthefirstgenesthatemergedincandidate-genestudiesandingenome-wide analysisindifferenthumanpopulations.Thepleiotropicrolesofthisgeneaswellasthepatternof variabilityacrossdifferenthumangroupsprovideaninterestingperspectiveontheanalysisofthe evolutionaryrelationshipbetweenhumangenetics,environmentalvariables,andtheattainmentof extremelongevityasahealthyphenotype.Inthepresentreview,thefollowingtopicswillbediscussed
Serena Dato obtained a Ph.D. in Molecular Bio-Pathology in 2004. Since September 2006, she has been an Assistant Professor in Genetics at the Department of Cell Biology of the University of Calabria, where she carries out research at the Genetics Laboratory. From the beginnning, her research interests have focused on the study of human longevity and in particular on the development of experimental designs and new analytical approaches for the study of the genetic component of longevity. With her group, she developed an algorithm for integrating demographic data into genetics, which enabled the application of a genetic-demographic analysis to crosssectional samples. She was involved in several recruitment campaigns for the collection of data and DNA samples from old and oldest-old people in her region, both nonagenarian and centenarian families. She has several international collaborations with groups involved in her research field in Europe and the USA. Since 2008, she has been actively collaborating with the research group of Prof. K. Christensen at the Aging Research Center of the Institute of Epidemiology of Southern Denmark University, where she spent a year as a visiting researcher in 2008. Up to now, her work has led to forty-eight scientific papers in peer reviewed journals, two book chapters and presentations at scientific conferences.
Mette Sørensen has been active within ageing research since 2006, with work ranging from functional molecular biological studies to genetic epidemiology and bioinformatics. She obtained a Ph.D. in genetic epidemiology of human longevity in 2012 and was appointed Associate Professor at the University of Southern Denmark in March 2019. Her main research interest is in the mechanisms of ageing, age-related diseases and longevity, with an emphasis on genetic and epigenetic variation. Her work is characterized by a high degree of international collaboration and interdisciplinarity. The work has, per September 2019, led to thirty-one scientific papers in peer reviewed journal, as well as popular science communications, presentations at scientific conferences, media appearances, and an independent postdoctoral grant from the Danish Research Council in 2013.
Giuseppina Rose is Associate Professor in Genetics at the University of Calabria. She graduated from the University of Calabria School of Natural Science in 1983 and served as a Research Assistant there from 1992–1999. In 1994 she achieved a Ph.D. in Biochemistry and Molecular Biology at
Contents
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oconmngi-2383
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fast living
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fast living slow aging
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“The human body is not built for an unlimited life “The human body is not built for an unlimited lifespan. Yet there are many ways in which we can improve and prolong our health. ‘Fast Living, Slow Ageing’ is all about embracing those opportunities.” Robin Holliday, author of ‘Understanding Ageing’ and ‘Ageing: The Paradox of Life’
“Today in Australia, we eat too much and move too little. But it is our future that will carry the cost. Our current ‘fast’ lifestyles will have their greatest impact on our prospects for healthy ageing. This book highlights many of the opportunities we all have to make a diference to our outlook, at a personal and social level.” Professor Stephen Leeder, AO, Director of the Menzies Centre for Health Policy, which leads policy analysis of healthcare
“Healthy ageing can’t be found in a single supplement, diet or lifestyle change. It takes an integrated approach across a number of key areas that complement to slowly build and maintain our health. ‘Fast Living, Slow Ageing’ shows how it is possible to practically develop these kind of holistic techniques and take control of our future.” Professor Marc Cohen, MBBS (Hons), PhD (TCM), PhD (Elec Eng), BMed Sci (Hons), FAMAC, FICAE, Professor, founder of www.thebigwell.com “SLOW is about discovering that everything we do has a knock-on efect, that even our smallest choices can reshape the big picture. Understanding this can help us live more healthily, more fully and maybe even longer too.” Carl Honoré, author of ‘In Praise of Slow’
“We all know about the dangers of fast food. But food is not the only fast thing that is ruining our lives. Slow ageing is about inding important connections in the diet and lifestyle choices we make every day and embracing the possibilities for making real changes - to our own lives - in our own way.” Sally Errey, best-selling author of the cookbook ‘Staying Alive!’ “Ageing is a complex process with many diferent factors combining to determine health and longevity. To slow ageing optimally, we also need to combine a range of lifestyle changes, supplements and other activities. This practical book steers us through the many opportunities we have to change our futures for the better.” Prof Brian J Morris, PhD, DSc, Professor of Molecular Medical Sciences, Basic & Clinical Genomics Laboratory, University of Sydney
‘Fast Living, Slow Ageing’ delivers a combination of well researched strategies from both Western medicine and complementary therapies to enhance your wellness.” Dr Danika Fietz, MBBS, BN (Hons), GP Registrar
“Forget the plastic surgeons, Botox and makeovers! ‘Slow ageing’ is really about the practical choices we make every day to stay healthy, it and vital, to look great and to feel great today and in the years ahead.” Dr David Tye, GP, Kingston Family Clinic, South Brighton, SA
“We all hope that growing old will be part of our lives, although we don’t really want to think about it. In fact, ‘old’ is almost a dirty word in lots of people’s minds! ‘Fast Living, Slow Ageing’ takes you down the path of doing something about how you age, while at the same time providing you with choices and igniting an awareness to start now and take control of how you can age with grace.” Ms Robyn Ewart, businesswoman, mum and household manager
TESTIMONIALS
• 4
FAST LIVING SLOW AGEING
“Ageing is a natural and beautiful process which, all too often, we accelerate through unhealt...
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How not to die ?
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How not to die?
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This PDF is a summary-style medical-nutritional gu This PDF is a summary-style medical-nutritional guide based on Dr. Michael Greger’s bestselling book How Not to Die. It presents the scientific evidence showing how specific foods and lifestyle choices can prevent, treat, and even reverse the leading causes of death. The document is structured around the idea that diet is the strongest tool humans have to improve longevity, reduce disease risk, and strengthen the body’s natural defenses.
At its core, the PDF explains:
Most premature deaths are preventable through daily nutritional and lifestyle changes—especially a whole-food, plant-based diet.
🩺 1. Focus on Preventing the Top Killers
The PDF highlights how dietary patterns influence mortality from diseases such as:
Cardiovascular disease
High blood pressure
Cancer
Diabetes
Respiratory illnesses
Kidney disease
Neurological decline
How not to die - Michael Greger
The message is consistent: nutrition is medicine.
🌱 2. The Power of Whole Plant Foods
The document promotes a diet centered on:
Vegetables
Fruits
Legumes (beans, lentils)
Whole grains
Nuts & seeds
Herbs & spices
These foods contain fiber, antioxidants, phytonutrients, and anti-inflammatory compounds that protect against disease and support longevity.
How not to die - Michael Greger
🍇 3. “Daily Dozen” Longevity Checklist
Dr. Greger’s famous Daily Dozen appears in the text—a list of 12 food groups and habits to include every day.
These typically include:
Beans
Berries
Cruciferous vegetables
Greens
Whole grains
Nuts and seeds
Fruits
Spices (especially turmeric)
Water
Exercise
How not to die - Michael Greger
The Daily Dozen provides a simple, actionable structure for eating to extend lifespan.
❤️ 4. How Diet Reverses Disease
Key mechanisms highlighted:
✔ Reducing inflammation
Plant foods contain anti-inflammatory compounds that lower chronic disease risk.
✔ Improving endothelial (blood vessel) function
Essential for reversing heart disease.
✔ Reducing oxidative stress
Antioxidants in plants help prevent cellular damage and aging.
✔ Balancing blood sugar
Whole foods stabilize insulin and reduce diabetes risk.
✔ Supporting gut microbiome health
Fiber-rich foods promote healthy bacteria that protect longevity.
How not to die - Michael Greger
🚫 5. Foods and Habits Linked to Higher Mortality
The PDF warns against:
Processed meats
Excessive salt
Refined sugar
Ultra-processed foods
Sedentary lifestyle
Smoking
High intake of animal fats
How not to die - Michael Greger
These factors contribute significantly to premature death.
🧪 6. Evidence-Based Approach
Dr. Greger’s work is built on:
Peer-reviewed medical research
Epidemiological data
Clinical trials
Meta-analyses
The PDF reflects this, presenting diet as a scientifically grounded intervention—not a fad or trend.
How not to die - Michael Greger
👨⚕️ 7. Lifestyle as Medicine
Beyond nutrition, the document includes advice on:
Regular physical activity
Stress reduction
Adequate sleep
Social connection
These lifestyle pillars combine with diet to produce a powerful longevity effect.
How not to die - Michael Greger
⭐ Overall Summary
This PDF provides a clear, impactful overview of Dr. Michael Greger’s message: Most deaths from chronic diseases are preventable, and the most effective path to long life is a whole-food, plant-based diet combined with healthy daily habits. The document explains the foods that protect against disease, the biological mechanisms involved, and the lifestyle changes proven to extend lifespan.
How not to die - Michael Greger
If you want, I can also provide:
✅ A 5-line ultra-short summary
✅ A one-paragraph version
✅ A bullet-point cheat sheet
✅ Urdu/Hindi translation
Just tell me!...
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Family matters
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Family matters in unravelling human longevity
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Human life expectancy has doubled over the past 20 Human life expectancy has doubled over the past 200 years in industrialized countries, yet the period spent in good physical and cognitive health remains relatively short. A significant proportion of elderly individuals suffer from multiple chronic diseases; for instance, 70% of 65-year-olds and 90% of 85-year-olds have at least one disease, averaging four diseases per person. In contrast, a small subset of individuals achieves exceptional longevity without typical age-related diseases such as hypertension, cancer, or type 2 diabetes. Understanding these individuals is crucial because they likely possess gene-environment interactions that promote longevity, disease resistance, and healthy aging.
Key Insights on Longevity Research
Most knowledge on aging mechanisms is derived from animal models, which identified nine hallmarks of aging and implicated glucose and fat metabolism pathways in longevity.
Human longevity is far more complex due to heterogeneity in genomes, lifestyles, environments, and social factors.
Genetic factors contribute approximately 25% to lifespan variation, with a stronger influence observed in long-lived individuals as indicated by familial clustering.
Despite extensive genetic research, only two genes—APOE and FOXO3A—have been consistently associated with longevity.
The lack of a consistent definition of heritable longevity complicates genetic studies, often mixing sporadic long-lived cases with those from long-lived families.
The increase in centenarians (e.g., from 1 in 10,000 to 2 in 10,000 in the US between 1994 and 2012) reflects the presence of sporadically long-lived individuals, which confounds genetic analyses.
Challenges in Genetic Longevity Studies
Genome Wide Association Studies (GWAS) face difficulties because controls (average-lived individuals) might later become long-lived, blurring case-control distinctions.
Recent findings emphasize the importance of rare and structural genetic variants alongside common single nucleotide polymorphisms (SNPs).
Socio-behavioral and environmental factors (lifestyle, socio-economic status, social networks, living environment) significantly influence aging but are rarely integrated into genetic studies.
There is limited knowledge about how these non-genetic factors cluster within long-lived families.
Advances Through Family-Based Research
Two recent studies using large family tree databases—the Utah Population Database (UPDB), LINKing System for historical family reconstruction (LINKS), and Historical Sample of the Netherlands Long Lives (HSN-LL)—demonstrated that:
Longevity is transmitted across generations only if ≥30% of ancestors belong to the top 10% longest-lived of their birth cohort, and the individual themselves is in the top 10% longest-lived.
Approximately 27% of individuals with at least one long-lived parent did not show exceptional survival, indicating sporadic longevity.
To address this, the Longevity Relatives Count (LRC) score was developed to identify genetically enriched long-lived individuals, improving case selection for genetic studies and reducing sporadic longevity inclusion.
Opportunities and Recommendations
Increasing availability of population-wide family tree data (e.g., Netherlands’ civil certificate linkage, Denmark’s initiatives) enables broader analysis of long-lived families rather than individuals alone.
Integrating gene-environment (G x E) interactions by combining genetic data with genealogical, socio-behavioral, and environmental information is essential to unravel mechanisms of longevity.
Epidemiological studies should:
Recruit members from heritable longevity families.
Collect comprehensive molecular, socio-behavioral, and environmental data.
Include analyses of rare and structural genetic variants in addition to common SNPs.
Cohorts like the UK Biobank can improve the distinction between cases and controls by incorporating the LRC score based on ancestral survival data.
Conclusion
The success of genetic studies on human longevity depends on:
Applying precise, consistent definitions of heritable longevity.
Utilizing family-based approaches and large-scale genealogical data.
Incorporating non-genetic covariates such as socio-behavioral and environmental factors.
Studying interactions between genes and environment to gain comprehensive mechanistic insights into healthy aging and longevity.
Quantitative Data Table
Parameter Statistic/Description
Increase in centenarians From 1 in 10,000 (1994) to 2 in 10,000 (2012)
% of 65-year-olds with ≥1 disease 70%
% of 85-year-olds with ≥1 disease 90%
Average number of diseases in elderly 4
Genetic contribution to lifespan ~25% overall, higher in long-lived families
Ancestor longevity threshold for heritability ≥30% ancestors in top 10% longest-lived cohort
Proportion with survival similar to general population despite long-lived parent 27%
Keywords
Human longevity
Healthy aging
Gene-environment interaction (G x E)
Genetic variation
Familial clustering
Longevity Relatives Count (LRC) score
Genome Wide Association Studies (GWAS)
Rare and structural variants
Socio-behavioral factors
Epidemiological studies
Population-wide family tree databases
References
References are based on the original source and include studies on aging, longevity genetics, and epidemiological family databases....
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kvtjlwpn-8118
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Extension of longevity
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Extension of longevity in Drosophila mojavensis by
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Summary
The study by Starmer, Heed, and Rockwood- Summary
The study by Starmer, Heed, and Rockwood-Slusser (1977) investigates the extension of longevity in Drosophila mojavensis when exposed to environmental ethanol and explores the genetic and ecological factors underlying this phenomenon. The authors focus on differences between subraces of D. mojavensis, emphasizing the role of alcohol dehydrogenase (ADH) isozyme polymorphisms, environmental heterogeneity of host plants, and related genetic elements.
Core Findings
Longevity Increase by Ethanol Exposure: Adult D. mojavensis flies, which breed and feed on necrotic cacti, show a significant increase in longevity when exposed to atmospheric ethanol. This longevity extension is:
Diet-independent (i.e., does not depend on yeast ingestion).
Accompanied by retention of mature ovarioles and eggs in females, indicating not just longer life but maintained reproductive potential.
Subrace Differences: Longevity increases differ among strains from different geographic regions:
Flies from Arizona and Sonora, Mexico (subrace BI) exhibit the greatest increase in longevity.
Flies from Baja California, Mexico (subrace BII) show the least increase.
Genetic Correlations:
The longevity response correlates with the frequency of alleles at the alcohol dehydrogenase locus (Adh).
Adh-S allele (slow electrophoretic form) is prevalent in Arizona and Sonora populations; its enzyme product is more heat- and pH-tolerant.
Adh-F allele (fast electrophoretic form) predominates in Baja California populations; its enzyme product is heat- and pH-sensitive but shows higher activity with isopropanol as substrate.
Modifier genes, including those associated with chromosomal inversions on the second chromosome (housing the octanol dehydrogenase locus), may also influence longevity response.
Environmental Heterogeneity: Differences in longevity and allele frequencies correspond to the distinct physical and chemical environments of the host cacti:
Arizona-Sonora flies breed on organpipe cactus (Lemaireocereus thurberi), which exhibits extreme temperature and pH variability.
Baja California flies breed on agria cactus (Machaerocereus gummosus), which shows moderate temperature and pH but contains relatively high concentrations of isopropanol.
The interaction between substrate alcohol content, temperature, and pH likely maintains the polymorphism at the ADH locus and influences evolutionary adaptations.
Experimental Design and Key Results
Experimental Setup
Flies were exposed to various concentrations of atmospheric ethanol (0.0% to 8.0% vol/vol) in sealed vials containing cotton soaked with ethanol solutions.
Longevity was measured as the lifespan of adult flies exposed to ethanol vapors, and data were log-transformed (ln[hr]) for statistical analysis.
Different strains from Baja California, Sonora, and Arizona were tested, alongside analysis of ADH allele frequencies and chromosomal inversions.
Axenic (microbe-free) strains were used to test the effect of yeast ingestion on longevity.
Summary of Key Experiments
Experiment Purpose Main Result
1 (Ethanol dose response) Test longevity response of D. mojavensis adults to ethanol vapors at different concentrations Longevity increased significantly at 1.0%, 2.0%, and 4.0% ethanol; highest female longevity observed in 4.0% ethanol group, with retention of mature eggs
2 (Yeast dependence) Assess whether longevity increase depends on live yeast ingestion Longevity increase occurred regardless of yeast treatment; live yeasts (Candida krusei or Kloeckera apiculata) not essential for enhanced longevity
3 (Subrace and sex differences) Compare longevity response among strains from different regions and sexes Females from Arizona-Sonora (subrace BI) showed significantly greater relative longevity increase than Baja California (subrace BII); males showed less pronounced differences
4 (Isozyme stability tests) Measure heat and pH stability of ADH-F and ADH-S isozymes ADH-F enzyme less stable at high temperature (45°C) and acidic pH compared to ADH-S; ADH-F activity reduced after 7-11 minutes heat exposure
Quantitative Data Highlights
Longevity Response to Ethanol Concentrations (Experiment 1)
Ethanol Concentration (%) Effect on Longevity
0.0 (Control) Baseline
0.5 No significant increase
1.0 Significant increase
2.0 Significant increase (highest relative longevity)
4.0 Significant increase
8.0 No increase (toxicity likely)
Analysis of Variance (Table 1 and Table 3)
Source of Variation Significance (p-value) Effect Description
Ethanol treatment p < 0.001 Strong effect on longevity
Yeast treatment Not significant No strong effect on longevity
Interaction (Ethanol x Yeast) p < 0.05 Minor effects, but overall yeast not required
Subrace p < 0.001 Significant effect on relative longevity
Sex Not significant Sex alone not significant, but sex x subrace interaction significant
Subrace x Sex interaction p < 0.001 Males and females respond differently across subraces
Ethanol treatment (dose) p < 0.01 Different doses produce varying longevity effects
Correlation Coefficients (Longevity Response vs. Genetic Factors)
Genetic Factor Correlation with Longevity Response at 2.0% Ethanol Correlation at 4.0% Ethanol
Frequency of Adh-F allele -0.633 (negative correlation) -0.554 (negative correlation)
Frequency of ST chromosomal arrangement (3rd chromosome) -0.131 (non-significant) 0.004 (non-significant)
Frequency of LP chromosomal arrangement (2nd chromosome) -0.694 (negative correlation) -0.713 (negative correlation)
Ecological and Genetic Interpretations
The Adh-S allele product is more heat- and pH-tolerant, which suits the variable, extreme environment of the organpipe cactus in Arizona and Sonora.
The Adh-F allele product is less stable under heat and acidic conditions but metabolizes isopropanol effectively, aligning with the chemical environment of Baja California’s agria cactus.
The distribution of Adh alleles matches the physical and chemical characteristics of the host cactus substrates, suggesting natural selection shapes the genetic polymorphism at the ADH locus.
The presence of isopropanol in agria cactus tissues may favor the Adh-F allele, as its enzyme shows higher activity with isopropanol.
The second chromosome inversion frequency correlates with longevity response, implicating the octanol dehydrogenase locus and potential modifier genes in ethanol tolerance.
Biological Significance and Implications
The study supports the hypothesis that environmental ethanol serves as a selective agent influencing longevity and allele frequencies in desert-adapted Drosophila.
The increased longevity and maintained reproductive capacity in ethanol vapor suggest a fitness advantage and physiological adaptation.
Findings align with broader research on **genetic polymorphisms in Dros
Smart Summary
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dutcyoah-2300
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xevyo
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Extreme longevity
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Extreme longevity in proteinaceous deep-sea corals
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This study investigates the extreme longevity, gro This study investigates the extreme longevity, growth rates, and ecological significance of two proteinaceous deep-sea coral species, Gerardia sp. and Leiopathes sp., found in deep waters around Hawai’i and other global locations. Using radiocarbon dating and stable isotope analyses, the research reveals that these corals exhibit remarkably slow growth and lifespans extending thousands of years, far surpassing previous estimates. These findings have profound implications for deep-sea coral ecology, conservation, and fisheries management.
Key Insights
Deep-sea corals Gerardia sp. and Leiopathes sp. grow exceptionally slowly, with radial growth rates ranging from 4 to 85 µm per year.
Individual colonies can live for hundreds to several thousand years, with the oldest Gerardia specimen aged at 2,742 years and the oldest Leiopathes specimen at 4,265 years, making Leiopathes the oldest known skeletal accreting marine organism.
The corals feed primarily on freshly exported particulate organic matter (POM) from surface waters, as indicated by stable carbon (δ13C) and nitrogen (δ15N) isotope data.
Radiocarbon analyses confirm the skeletal carbon originates from modern surface-water carbon sources, indicating minimal incorporation of old, “14C-free” carbon into the skeleton.
These slow growth rates and extreme longevities imply that deep-sea coral habitats are vulnerable to damage and slow to recover, challenging assumptions about their renewability.
Deep-sea coral communities are critical habitat hotspots for various fish and invertebrates, contributing to deep-sea biodiversity and ecosystem complexity.
Human impacts such as commercial harvesting for jewelry, deep-water fishing, and bottom trawling pose significant threats to these fragile ecosystems.
The study emphasizes the need for international, ecosystem-based conservation strategies and suggests current fisheries management frameworks may underestimate the vulnerability of these corals.
Background and Context
Deep-sea corals colonize hard substrates on seamounts and continental margins at depths of 300 to 3,000 meters worldwide. These corals form complex habitats that support high biodiversity and serve as important ecological refuges and feeding grounds for various marine species, including commercially valuable fish and endangered marine mammals like the Hawaiian monk seal.
Prior estimates of deep-sea coral longevity were inconsistent, ranging from decades (based on amino acid racemization and growth-band counts) to over a thousand years (based on radiocarbon dating). This study clarifies these discrepancies by:
Applying high-resolution radiocarbon dating to both living and subfossil coral specimens.
Using stable isotope analysis to identify coral carbon sources and trophic levels.
Comparing radiocarbon signatures in coral tissues and skeletons with surface-water carbon histories.
Methods Overview
Samples of Gerardia and Leiopathes were collected from several deep-sea coral beds around Hawai’i (Makapuu, Lanikai, Keahole Point, and Cross Seamount) using the NOAA/Hawaiian Undersea Research Laboratory’s Pisces submersibles.
Coral skeletons were sectioned radially, and microtome slicing was used to obtain thin layers (~100 µm) for precise radiocarbon analysis.
Radiocarbon (14C) ages were calibrated to calendar years using established reservoir age corrections.
Stable isotope analyses (δ13C and δ15N) were conducted on dried polyp tissues to determine trophic level and carbon sources.
Growth rates were calculated from radiocarbon profiles and bomb-pulse 14C signatures (the increase in atmospheric 14C from nuclear testing in the 1950s-60s).
Detailed Findings
Growth Rates and Longevity
Species Radial Growth Rate (µm/year) Maximum Individual Longevity (years)
Gerardia sp. Average 36 ± 20 (range 11-85) Up to 2,742
Leiopathes sp. Approximately 5 Up to 4,265
Gerardia growth rates vary widely but average around 36 µm/year.
Leiopathes grows more slowly (~5 µm/year) but lives longer.
Some Leiopathes specimens show faster initial growth (~13 µm/year) that slows with age.
Carbon Sources and Trophic Ecology
δ13C values for living polyp tissues of both species average around –19.3‰ (Gerardia) and –19.7‰ (Leiopathes), consistent with marine particulate organic carbon.
δ15N values are enriched relative to surface POM, averaging 8.3‰ (Gerardia) and 9.3‰ (Leiopathes), indicating they are low-order consumers, feeding primarily on freshly exported surface-derived POM.
Proteinaceous skeleton δ13C is slightly enriched (~3‰) compared to tissues, likely due to lipid exclusion in skeletal formation.
Radiocarbon profiles of coral skeletons closely match surface-water 14C histories, including bomb-pulse signals, confirming rapid transport of surface carbon to depth and minimal incorporation of old sedimentary carbon.
Ecological and Conservation Implications
The extreme longevity and slow growth of these corals imply that population recovery from physical disturbance (e.g., fishing gear, harvesting) takes centuries to millennia.
Deep-sea coral beds function as keystone habitats, enhancing biodiversity and providing essential fish habitat, including for endangered species.
Physical disturbances like bottom trawling, line entanglement, and coral harvesting for jewelry threaten these corals and their associated communities.
Existing fisheries management may overestimate sustainable harvest limits, especially for Gerardia, due to underestimating longevity and growth rates.
The United States Magnuson-Stevens Fishery Conservation and Management Act (MSA) recognizes deep-sea corals as “essential fish habitat,” but enforcement and protection vary.
The study advocates for international, ecosystem-based management approaches that consider both surface ocean changes (e.g., climate change, ocean acidification) and deep-sea impacts.
The longevity data suggest that damage to these corals should not be considered temporary on human timescales, underscoring the need for precautionary management.
Timeline Table: Key Chronological Events (Related to Coral Growth and Study)
Event/Measurement Description
~4,265 years ago (calibrated 14C age) Oldest Leiopathes specimen basal attachment age
~2,742 years ago (calibrated 14C age) Oldest Gerardia specimen age
1957 Reference year for bomb-pulse 14C calibration in radiocarbon dating
2004 Sample collection year from Hawai’ian deep-sea coral beds
2006/2007 Magnuson-Stevens Act reauthorization increasing protection for deep-sea coral habitats
Present (2008-2009) Publication and review of this study
Quantitative Data Summary: Isotopic Composition of Coral Tissues and POM
Parameter Gerardia sp. (n=10) Leiopathes sp. (n=2) Hawaiian POM at 150 m (Station ALOHA)
δ13C (‰) –19.3 ± 0.8 –19.7 ± 0.3 –21 ± 1
δ15N (‰) 8.3 ± 0.3 9.3 ± 0.6 2 to 4 (range)
C:N Ratio 3.3 ± 0.3 5.1 ± 0.1 Not specified
Core Concepts
Radiocarbon dating (14C) enables precise age determination of coral skeletons by comparing measured 14C levels to known atmospheric and oceanic 14C histories.
Bomb-pulse 14C is a distinct marker from nuclear testing that provides a temporal reference point for recent growth.
Stable isotope ratios (δ13C and δ15N) provide insights into trophic ecology and carbon sources.
Radial growth rates measure the increase in coral skeleton thickness per year, reflecting growth speed.
Longevity estimates derive from radiocarbon age calibrations of inner and outer skeletal layers.
Deep-sea coral beds are ecosystem engineers, forming complex habitats critical for marine biodiversity.
Conservation challenges arise due to very slow growth and extreme longevity, combined with anthropogenic threats.
Conclusions
Gerardia and Leiopathes deep-sea corals exhibit unprecedented longevity, with lifespans of up to 2,700 and 4,200 years, respectively.
Their slow radial growth rates and feeding on freshly exported surface POM indicate a close ecological coupling between surface ocean productivity and deep-sea benthic communities.
The longevity and slow recovery rates imply that damage to deep-sea coral beds is effectively irreversible on human timescales, demanding precautionary and stringent management.
These species serve as critical habitat-formers in the deep sea, supporting diverse marine life and contributing to ecosystem complexity.
There is an urgent need for international, ecosystem-based conservation strategies to protect these unique and vulnerable communities from fishing impacts, harvesting, and environmental changes.
Current fisheries management frameworks may inadequately reflect the nonrenewable nature of these coral populations and require revision based on these findings.
Keywords
Deep-sea corals
Gerardia sp.
Leiopathes sp.
Radiocarbon dating
Longevity
Radial growth rate
Stable isotopes (δ13C, δ15N)
Particulate organic matter (POM)
Deep-sea biodiversity
Conservation
Fisheries management
Magnuson-Stevens Act
Bomb-pulse 14C
Proteinaceous skeleton
References to Note (from source)
Radiocarbon dating and longevity studies (Roark et al., 2006; Druffel et al., 1995)
Stable isotope methodology and trophic level assessment (DeNiro & Epstein, 1981; Rau, 1982)
Fisheries and habitat conservation frameworks (Magnuson-Stevens Act, 2006/2007 reauthorization)
Ecological significance of deep-sea corals (Freiwald et al., 2004; Parrish et al., 2002)
This comprehensive analysis underscores the exceptional longevity and ecological importance of proteinaceous deep-sea corals, highlighting the need for improved management and protection policies given their vulnerability and slow recovery potential.
Smart Summary
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xevyo
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Exploring Human Longevity
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Exploring Human Longevity
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This research paper investigates the impact of cli This research paper investigates the impact of climate on human life expectancy and longevity, analyzing economic and mortality data from 172 countries to establish whether living in colder climates correlates with longer life spans. By controlling for factors such as income, education, sanitation, healthcare, ethnicity, and diet, the authors aimed to isolate climate as a variable influencing longevity. The study reveals that individuals residing in colder regions tend to live longer than those in warmer climates, with an average increase in life expectancy of approximately 2.22 years attributable solely to climate differences.
Key Concepts and Definitions
Term Definition Source
Life Expectancy The average number of years a newborn is expected to live, assuming current age-specific mortality rates remain constant. United Nations Population Division
Life Span / Longevity The maximum number of years a person can live, based on the longest documented individual (122 years for humans). News Medical Life Sciences
Blue Zones Five global regions where people live significantly longer than average, characterized by healthy lifestyles and warm climates. National Geographic
Free Radical Theory A theory suggesting that aging results from cellular damage caused by reactive oxidative species (ROS), potentially slowed by cold. Antioxidants & Redox Signaling (Gladyshev)
Historical and Global Trends in Life Expectancy
Neolithic and Bronze Age: Average life expectancy was approximately 36 years, with hunter-gatherers living longer than early farmers.
Late medieval English aristocrats: Life expectancy reached around 64 years, comparable to modern averages.
19th to mid-20th century: Significant increases in life expectancy due to improvements in sanitation, education, housing, antibiotics, agriculture (Green Revolution), and reductions in infectious diseases such as HIV/AIDS, TB, and malaria.
2000 to 2016: Global average life expectancy increased by 5.5 years, the fastest rise since the 1950s (WHO).
Future projections: Life expectancy will continue to rise globally but at a slower pace, with Africa seeing the most substantial increases, while Northern America, Europe, and Latin America expect more gradual improvements.
Research Objectives and Methodology
Objective: To quantify the effect of climate on life expectancy while controlling for socio-economic factors such as income, healthcare access, education, sanitation, ethnicity, and diet.
Data sources: United Nations World Economic Situation and Prospects 2019, United Nations World Mortality Report 2019.
Country classification:
Four income groups: high, upper-middle, lower-middle, and low income.
Climate groups: “mainly warm” (tropical, subtropical, Mediterranean, savanna, equatorial) and “mainly cold” (temperate, continental, oceanic, maritime, highland).
Statistical analysis: ANOVA (Analysis of Variance) was used to determine the statistical significance of climate on life expectancy across and within groups.
Climate Classification and Geographic Distribution
Warm climate regions constitute about 66.2% of the world.
Cold climate regions constitute approximately 33.8% of the world.
Some large countries with diverse climates (e.g., USA, China) were classified based on majority regional climate.
Quantitative Results
Income Group Mean Life Expectancy (Warm Climate) Mean Life Expectancy (Cold Climate) Difference (Years) SD Warm Climate SD Cold Climate
High income Not specified Not specified Not specified Not specified Not specified
Upper-middle income Not specified Not specified Not specified Not specified Not specified
Lower-middle income Almost equal Slightly higher (by 0.237 years) 0.2372 Higher Lower
Low income Not specified Higher by 5.91 years 5.9099 Higher Lower
Overall average: Living in colder climates prolongs life expectancy by approximately 2.2163 years across all income groups.
Standard deviation: Greater variability in life expectancy was observed in warmer climates, indicating uneven health outcomes.
Regional Life Expectancy Insights
Region Climate Type Mean Life Expectancy (Years)
Southern Europe Cold 82.3
Western Europe Cold 81.9
Northern Europe Cold 81.2
Western Africa Warm 57.9
Middle Africa Warm 59.9
Southern Africa Warm 63.8
Colder regions generally show higher life expectancy.
Warmer regions, especially in Africa, tend to have lower life expectancy.
Statistical Significance (ANOVA Results)
Parameter Value Interpretation
F-value 49.88 Large value indicates significant differences between groups
p-value 0.00 (less than 0.05) Strong evidence against the null hypothesis (no effect of climate)
Variance between groups More than double variance within groups Climate significantly affects life expectancy
Theoretical Perspectives on Climate and Longevity
Warm climate argument: Some studies suggest higher mortality in colder months; e.g., 13% more deaths in winter than summer in the U.S. (Professor F. Ellis, Yale).
Cold climate argument: Supported by the free radical theory, colder temperatures may slow metabolic reactions, reducing reactive oxidative species (ROS) and cellular damage, thereby slowing aging.
Experimental evidence from animals (worms, mice) shows lifespan extension under colder conditions, with genetic pathways triggered by cold exposure.
Impact of Climate Change on Longevity
Rising global temperatures pose risks to human health and longevity, including:
Increased frequency of extreme weather events (heatwaves, floods, droughts).
Increased spread of infectious diseases.
Negative impacts on agriculture reducing food security and nutritional quality.
Air pollution exacerbating respiratory diseases.
Studies show a 1°C increase in temperature raises elderly death rates by 2.8% to 4.0%.
Projected effects include malnutrition, increased disease burden, and infrastructure stress, all threatening to reduce life expectancy.
Limitations and Considerations
Genetic factors: Approximately one-third of life expectancy variation is attributed to genetics (genes like APOE, FOXO3, CETP).
Climate classification biases: Countries with multiple climate zones were classified according to majority, potentially oversimplifying climate impacts.
Lifestyle factors: Blue zones with warm climates show exceptional longevity due to diet, exercise, and stress management, illustrating that climate is not the sole determinant.
Migration and localized data: Studies on migrants support climate’s role in longevity independent of genetics and lifestyle.
Practical Implications and Recommendations
While individuals cannot relocate easily to colder climates, practices such as cold showers and cryotherapy might induce genetic responses linked to longevity.
This study emphasizes the urgent need to address climate change mitigation to prevent adverse effects on human health and lifespan.
Calls for further research into:
The genetic mechanisms influenced by climate.
The potential of cryonics and cold exposure therapies to extend longevity.
More granular studies factoring lifestyle, genetics, and microclimates.
Conclusion
Colder climates are consistently associated with longer human life expectancy, with an average increase of about 2.2 years across income levels.
Climate change and global warming threaten to reduce life expectancy globally through multiple pathways.
While genetics and lifestyle factors play critical roles, climate remains a significant environmental determinant of longevity.
The study advocates for urgent global climate action and further research into climate-genetics interactions to better understand and protect human health.
Keywords
Life expectancy
Longevity
Climate impact
Cold climate
Warm climate
Climate change
Income groups
Free radical theory
Blue zones
Public health
References
Selected key references from the original content:
United Nations Population Division (Life Expectancy definitions)
World Health Organization (Life Expectancy data, Climate Effects)
National Geographic (Blue Zones)
American Journal of Physical Anthropology (Historical life expectancy)
Studies on genetic impact of temperature on longevity (University of Michigan, Scripps Research Institute)
Stanford University and MIT migration study on location and mortality
This summary strictly reflects the content and data presented in the source document without fabrication or unsupported extrapolations.
Smart Summary...
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wtkdpdnf-7423
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xevyo
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Extreme longevity may be
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Extreme longevity may be the rule
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This study by Breed et al. (2024) investigates the This study by Breed et al. (2024) investigates the longevity of Balaenid whales, focusing on the southern right whale (SRW, Eubalaena australis) and the North Atlantic right whale (NARW, Eubalaena glacialis). By analyzing over 40 years of mark-recapture data, the authors estimate life spans and survival patterns, revealing that extreme longevity (exceeding 130 years) is likely the norm rather than the exception in Balaenid whales, challenging previously accepted maximum life spans of 70–75 years. The study also highlights the impact of anthropogenic factors, particularly industrial whaling, on the significantly reduced life span of the endangered NARW.
Key Findings
Southern right whales (SRWs) have a median life span of approximately 73.4 years, with 10% of individuals surviving beyond 131.8 years.
North Atlantic right whales (NARWs) have a median life span of only 22.3 years, with 10% living past 47.2 years—considerably shorter than SRWs.
The reduced NARW life span is attributed to anthropogenic mortality factors, including ship strikes and entanglements, not intrinsic biological differences.
The study uses survival function modeling, bypassing traditional aging methods that rely on lethal sampling and growth layer counts, which tend to underestimate longevity.
Evidence from other whales, especially bowhead whales, supports the hypothesis that extreme longevity is widespread among Balaenids and possibly other large cetaceans.
Background and Context
Early longevity estimates in whales, such as blue and fin whales, came from counting annual growth layers in ear plugs, revealing ages up to 110–114 years.
Bowhead whales have been documented to live over 150 years, with some individuals estimated at 211 years based on aspartic acid racemization (AAR) and corroborating archaeological evidence (e.g., embedded antique harpoon tips).
Longevity estimates from traditional methods are biased low due to:
Difficulty in counting growth layers in very old whales due to tissue remodeling.
Removal of older age classes from populations by industrial whaling.
The need for lethal sampling to obtain age data, which is rarely possible in protected species.
The relation between body size and longevity supports the potential for extreme longevity in large whales, although bowhead whales exceed predictions from terrestrial mammal models.
Methodology
Data Sources:
SRW mark-recapture data from South Africa (1979–2021), including 2476 unique females, of which 139 had known birth years.
NARW mark-recapture data from the North Atlantic (1974–2020), including 328 unique females, of which 205 had known birth years.
Survival Models:
Ten parametric survival models were fitted, including Gompertz, Weibull, Logistic, and Exponential mortality functions with adjustments (Makeham and bathtub).
Models were fit using Bayesian inference with the R package BaSTA, which accounts for left truncation (unknown birth years) and right censoring (individuals surviving past the study period).
Model selection was based on Deviance Information Criterion (DIC).
Validation:
Simulated datasets, generated from fitted model parameters, were used to test for bias and accuracy.
Models accurately recovered survival parameters with minimal bias.
Estimating Reproductive Output:
The total number of calves produced by females was estimated by integrating survival curves and applying calving intervals ranging from 3 to 7 years.
Results
Parameter Southern Right Whale (SRW) North Atlantic Right Whale (NARW)
Median life span (years) 73.4 (95% CI [60.0, 88.3]) 22.3 (95% CI [19.7, 25.1])
10% survive past (years) 131.8 (95% CI [110.9, 159.3]) 47.2 (95% CI [43.0, 53.3])
Annual mortality hazard (age 5) ~0.5% 2.56%
Maximum life span potential >130 years Shortened due to anthropogenic factors
**SRW survival best fits an unmodified Gompertz model; NARW fits a Gompertz model with
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Inconvenient Truths About
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Inconvenient Truths About Human Longevity
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This review article, “Inconvenient Truths About Hu This review article, “Inconvenient Truths About Human Longevity” by S. Jay Olshansky and Bruce A. Carnes, published in the Journals of Gerontology: Medical Sciences (2019), critically examines the ongoing scientific and public debate about the limits of human longevity, the feasibility of radical life extension, and the future priorities of medicine and public health regarding aging. It argues that while advances in public health and medicine have substantially increased life expectancy over the past two centuries, biological constraints impose practical limits on human longevity, and predictions of near-future radical life extension are unsupported by empirical evidence.
Key Insights and Arguments
Historical Gains in Longevity:
Initial life expectancy gains were driven by public health improvements reducing early-age mortality (infant and child deaths).
Recent gains are largely due to reductions in mortality at middle and older ages, achieved through medical technology.
The dramatic rise in life expectancy during the 20th century cannot be linearly extrapolated into the future due to shifting mortality dynamics.
Debate on Limits to Longevity:
Two opposing views dominate the debate:
Unlimited longevity potential based on mathematical extrapolations of declining death rates.
Biologically based limits to lifespan, currently being approached.
Proponents of unlimited longevity often rely on purely mathematical models that ignore biological realities, leading to unrealistic predictions akin to Zeno’s Paradox (infinite division without reaching zero).
Critique of Mathematical Extrapolations:
Analogies such as world record running times illustrate the fallacy of linear extrapolation: records improved steadily until plateauing, indicating biological limits on human performance.
Similarly, mortality improvements have decelerated and are unlikely to continue improving at historic rates indefinitely.
Three Independent Lines of Evidence Supporting Longevity Limits:
Entropy in the Life Table: As life expectancy rises, it becomes mathematically harder to increase further because most deaths occur within a narrow old age window with high mortality rates.
Comparative Mortality Studies: Scaling mortality schedules of humans against other mammals (mice, dogs) suggests a natural lifespan limit around 85 years for humans.
Evolutionary Biology: Biological “warranty periods” related to reproduction and survival support a median lifespan limit in the mid to upper 80s.
Empirical Data on Life Expectancy Trends:
Life expectancy gains in developed nations have decelerated or plateaued near 85 years, consistent with theoretical limits.
Table below summarizes U.S. life expectancy improvements by decade:
Decade Life Expectancy at Birth (years) Annual Average Improvement (years)
1990 75.40 —
2000 76.84 0.142
2010 78.81 0.197
2016 78.91 0.017
The data show that the predicted 0.2 years per annum improvement has not been consistently met, with recent years showing a sharp slowdown.
Problems with Radical Life Extension Claims:
Predictions of cohort life expectancy at birth reaching or exceeding 100 years for babies born since 2000 are unsupported by observed mortality trends.
Claims of “actuarial escape velocity” (mortality rates falling faster than aging progresses) lack empirical or biological evidence.
These exaggerated forecasts divert resources and funding away from realistic aging research.
Biological Mechanisms and Aging:
Aging is an unintended consequence of accumulated damage and imperfect repair mechanisms driven by genetic programs optimized for reproduction, not longevity.
Humans cannot biologically exceed certain limits because of genetic and physiological constraints.
Unlike lifespan or physical performance (e.g., running speed), aging is a complex biological process that limits survival and function.
The Future Focus: Health Span over Life Span
Rather than pursuing life extension as the primary goal, public health and medicine should prioritize extending the health span—the period of life spent in good health.
This approach aims to compress morbidity, reducing the time individuals spend suffering from age-related diseases and disabilities.
Advances in aging biology (geroscience) hold promise for improving health span even if life expectancy gains are modest.
Risks of Disease-Focused Treatment Alone:
Treating individual aging-related diseases separately may increase survival but also leads to greater prevalence and severity of chronic illnesses in very
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xevyo
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The Multiomics Blueprint
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The Multiomics Blueprint of Extreme Human Lifespan
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This study presents a comprehensive multiomics ana This study presents a comprehensive multiomics analysis of an extraordinary human subject, M116, the world’s oldest verified living person from January 2023 until her death in August 2024 at the age of 117 years and 168 days. Born in 1907 in San Francisco to Spanish parents, M116 spent most of her life in Spain. Despite surpassing the average female life expectancy in Catalonia by over 30 years, she maintained an overall good health profile until her final months. The research aimed to dissect the molecular and cellular factors contributing to her extreme longevity by integrating genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiomic data derived primarily from blood, saliva, urine, and stool samples.
Key Insights and Findings
Longevity is multifactorial, with no single genetic or molecular determinant but rather a complex interplay of rare genetic variants, preserved molecular functions, and adaptive physiological traits.
Extreme age and poor health are decoupled; M116 exhibited biological markers of advanced age alongside molecular features indicative of healthy aging.
Molecular assessments reveal preserved and robust biological functions that likely contributed to her extended lifespan.
Genomic Landscape
Telomere Length:
M116 exhibited extremely short telomeres (~8 kb), shorter than all healthy volunteers studied, with 40% of her telomeres below the 20th percentile.
This suggests telomere attrition acts more as a biological aging clock rather than a predictor of age-associated diseases in this context.
The short telomeres may have contributed to cancer resistance by limiting malignant cell replication.
Structural Variants (SVs):
Ten rare SVs identified via Optical Genome Mapping, including a large 3.3 Mb deletion on chromosome 4 and a 93.5 kb deletion on chromosome 17.
These SVs may play unknown roles but were not associated with detrimental gross chromosomal alterations.
Rare Genetic Variants:
Whole Genome Sequencing identified ~3.8 million SNVs; after filtering, 91,666 variants of interest (VOI) affecting 25,146 genes were analyzed.
Seven homozygous rare variants unique to M116 were found in genes linked to immune function, cognitive retention, longevity, pulmonary function, neuroprotection, and DNA repair (e.g., DSCAML1, MAP4K3, TSPYL4, NT5DC1, PCDHA cluster, TIMELESS).
Functional enrichment highlighted pathways involving:
Immune system regulation (e.g., T cell differentiation, response to pathogens, antigen receptor signaling)
Neuroprotection and brain health
Cardioprotection and heart development
Cholesterol metabolism and insulin signaling
Mitochondrial function and oxidative phosphorylation
Mitochondrial function assays showed robust mitochondrial membrane potential and superoxide ion levels in M116’s PBMCs, surpassing those in younger controls, indicating preserved mitochondrial health.
Burden Tests:
Identified genes with significantly higher rare variant load related to neuroprotection and longevity (e.g., EPHA2, MAL, CLU, HAPLN4).
No single gene or pathway explained longevity; rather, multiple pathways acted synergistically.
Blood Cellular and Molecular Characteristics
Clonal Hematopoiesis of Indeterminate Potential (CHIP):
M116 harbored CHIP-associated mutations: one in SF3B1 (RNA splicing factor) and two in TET2 (DNA demethylase) with variant allele frequency >2%.
Despite this, she did not develop malignancies or cardiovascular disease, suggesting CHIP presence does not necessarily translate to disease.
Single-cell RNA Sequencing (scRNA-seq) of PBMCs:
Identified a diverse immune cell repertoire including naive and memory B cells, NK cells, monocytes, and T cell subpopulations.
Notably, M116 exhibited an expanded population of age-associated B cells (ABCs), expressing markers SOX5 and FCRL2, a feature unique compared to other supercentenarians.
The T cell compartment was dominated by effector and memory cytotoxic T cells, consistent with prior observations in supercentenarians.
Metabolomic and Proteomic Profiles
Metabolomics (1H-NMR Analysis):
Compared with 6,022 Spanish individuals, M116’s plasma showed:
Extremely efficient lipid metabolism:
Very low VLDL-cholesterol and triglycerides
Very high HDL-cholesterol (“good cholesterol”)
High numbers of medium and large HDL and LDL particles, indicating effective lipoprotein maturation.
Low levels of lipid biomarkers associated with poor health (saturated fatty acids, esterified cholesterol, linoleic acid, acetone).
High free cholesterol levels linked to good health and survival.
Low glycoproteins A and B, suggesting a low systemic inflammatory state (“anti-inflammaging”).
Cardiovascular risk-associated metabolites supported excellent cardiovascular health.
Some amino acid levels (glycine, histidine, valine, leucine) were low, and lactate and creatinine were high, consistent with very advanced chronological age and imminent mortality.
Proteomics of Extracellular Vesicles (ECVs):
Compared to younger post-menopausal women, 231 proteins were differentially expressed.
GO enrichment revealed eight functional clusters: coagulation, immune system, lipid metabolism, apoptosis, protein processing, detoxification, cellular adhesion, and mRNA regulation.
Proteomic signatures indicated:
Increased complement activation and B cell immunity
Enhanced lipid/cholesterol transport and lipoprotein remodeling
Elevated oxidative stress response and detoxification mechanisms
The most elevated protein was serum amyloid A-1 (SAA1), linked to Alzheimer’s disease, yet M116 showed no neurodegeneration.
Gut Microbiome Composition
16S rDNA sequencing compared M116’s stool microbiome to 445 healthy controls (61-91 years old).
M116’s microbiome showed:
Higher alpha diversity (Shannon index 6.78 vs. 3.05 controls), indicating richer microbial diversity.
Distinct beta diversity, clearly separating her microbiome from controls.
Markedly elevated Actinobacteriota phylum, primarily due to Bifidobacteriaceae family and Bifidobacterium genus, which typically decline with age but are elevated in centenarians.
Bifidobacterium is associated with anti-inflammatory effects, production of short-chain fatty acids, and conjugated linoleic acid, linking to her efficient lipid metabolism.
Lower relative abundance of pro-inflammatory genera such as Clostridium and phyla Proteobacteria and Verrucomicrobiota, associated with frailty and inflammation in older adults.
Diet likely influenced microbiome composition; M116 consumed a Mediterranean diet and daily yogurts containing Streptococcus thermophilus and Lactobacillus delbrueckii, which promote Bifidobacterium growth.
Epigenetic and Biological Age Analysis
DNA Methylation Profiling (Infinium MethylationEPIC BeadChip):
Identified 69 CpG sites with differential methylation (β-value difference >50%) compared to controls aged 21-78 years.
Majority (68%) showed hypomethylation, consistent with known aging-associated DNA methylation changes.
Differential CpGs were more often outside CpG islands and enriched in gene bodies or regulatory regions.
Hypomethylation correlated with altered expression of genes involved in:
Vascular stemness (EGFL7)
Body mass index regulation (ADCY3)
Macular degeneration (PLEKHA1)
Bone turnover (VASN)
Repetitive DNA Elements:
Unlike typical age-associated global hypomethylation, M116 retained hypermethylation in repetitive elements (LINE-1, ALU, ERV), suggesting preserved genomic stability.
Epigenetic Clocks:
Six different DNA methylation-based epigenetic clocks and an independent rDNA methylation clock (using Whole Genome Bisulfite Sequencing) consistently estimated M116’s biological age to be significantly younger than her chronological age (~117 years).
This indicates a decelerated epigenetic aging process in M116’s cells, which may contribute to her longevity.
Integration and Conclusions
Coexistence of Advanced Age Biomarkers and Healthy Aging Traits:
M116 simultaneously exhibited biological signatures indicative of very old age (short telomeres, CHIP mutations, aged B cell populations) and preserved healthy molecular and functional profiles (genetic variants protective against diseases, efficient lipid metabolism, anti-inflammatory gut microbiome, epigenome stability, robust mitochondrial function).
Decoupling of Aging and Disease:
These findings challenge the assumption that aging and disease are inseparably linked, showing that extreme longevity can occur with a healthy functional tissue environment despite advanced biological age markers.
Multidimensional and Multifactorial Basis of Longevity:
The supercentenarian’s extended lifespan likely resulted from the synergistic effects of rare genetic variants, favorable epigenetic patterns, preserved mitochondrial and immune function, healthy metabolism, and a beneficial microbiome, rather than any single factor.
Potential Implications:
Understanding the interplay of these factors could open avenues for promoting healthy aging and preventing age-related diseases in the general population.
Timeline and Demographics of M116
Event Date / Age Notes
Birth March 4, 1907 San Francisco, USA
Moved to Spain 1915 (age 8) Following father’s death
Lived in elderly residence 2001 - 2024 Olot, Catalonia, Spain
COVID-19 Infection Not specified Survived
Death August 19, 2024 (age 117y, 168d) While sleeping, no major neurodegeneration or cancer recorded
Summary Table of Key Molecular Features in M116
Feature Status in M116 Interpretation/Significance
Telomere length Extremely short (~8 kb) Aging clock marker; may limit cancer risk
Structural variants 10 rare SVs, including large deletions Unknown effect; no gross chromosomal abnormalities
Rare homozygous variants 7 unique variants in longevity/immune-related genes Suggest combined genetic contribution to longevity
CHIP mutations Present (SF3B1, TET2 mutations) No malignancy or cardiovascular disease
Mitochondrial function Robust membrane potential & superoxide levels Preserved energy metabolism
Immune cell composition Expanded ABCs, enriched cytotoxic T cells Unique immune profile linked to longevity
Lipid metabolism Very efficient (high HDL, low VLDL) Cardiovascular protection
Inflammation Low glycoproteins A & B levels Reduced inflammaging
Gut microbiome High Bifidobacterium abundance Anti-inflammatory, supports metabolism
DNA methylation Predominantly hypomethylated CpGs with preserved methylation in repeats Epigenetic stability and decelerated aging
Biological age (epigenetic clocks) Significantly younger than chronological age Indicative of healthy aging
Proteomic profile Upregulated immune and lipid metabolism proteins; elevated SAA1 Protective mechanisms with unexplained elevated SAA1
Keywords
Supercentenarian, Extreme Longevity, Multiomics, Telomere Attrition, Rare Genetic Variants, Clonal Hematopoiesis (CHIP), Immune Cell Profiling, Mitochondrial Function, Metabolomics, Proteomics, Gut Microbiome, DNA Methylation, Epigenetic Clock, Biological Age, Inflammaging, Lipid Metabolism
Conclusion
This landmark study of M116 provides the first extensive multiomics blueprint of extreme human lifespan, revealing that exceptional longevity arises from a balance of advanced biological aging markers coupled with preserved and enhanced molecular functions across multiple systems. The results underscore the importance of immune competence, metabolic health, epigenetic stability, and microbiome composition in sustaining health during extreme aging, offering valuable insights into the biological underpinnings of healthy human longevity.
Smart Summary
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nyuieybh-2436
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xevyo
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ESSENTIAL STEPS TO HEALTH
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ESSENTIAL STEPS TO HEALTHY AGING
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Kansas State University Agricultural Experiment St Kansas State University Agricultural Experiment Station and Cooperative Extension Service
Author: Erin Yelland, Ph.D., Extension Specialist, Adult Development and Aging
Program Overview
The Essential Steps to Healthy Aging is a structured educational program designed to motivate and empower participants to adopt healthy lifestyle behaviors that foster optimal aging. Developed by Kansas State University’s Cooperative Extension Service, this program highlights that aging is inevitable, but how individuals care for themselves physically, mentally, and emotionally throughout life significantly influences the quality of their later years. The program promotes the idea that healthy lifestyle changes can positively impact well-being at any age.
Core Concept
Aging well is a lifelong process influenced by daily choices. Research on centenarians (people aged 100 and over) shows that adopting certain healthy behaviors contributes to longevity and improved quality of life. The program introduces 12 essential steps to maintain health and enhance successful aging.
The 12 Essential Steps to Healthy Aging
Step Number Essential Healthy Behavior
1 Maintain a positive attitude
2 Eat healthfully
3 Engage in regular physical activity
4 Exercise your brain
5 Engage in social activity
6 Practice lifelong learning
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Evidence for a limit
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Evidence for a limit to human lifespan
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This study, published in Nature in 2016 by Xiao Do This study, published in Nature in 2016 by Xiao Dong, Brandon Milholland, and Jan Vijg, investigates whether there is a natural upper limit to the human lifespan. Despite significant increases in average human life expectancy over the past century, the authors provide strong demographic evidence suggesting that maximum human lifespan is fixed and subject to natural constraints, with limited improvement beyond a certain age threshold.
Background and Context
Life expectancy vs. maximum lifespan: Life expectancy has increased substantially since the 19th century, largely due to reduced early-life mortality and improved healthcare. However, maximum lifespan, defined as the age of the longest-lived individuals within a species, is generally considered a stable biological characteristic.
The oldest verified human was Jeanne Calment, who lived to 122 years, setting the recognized upper bound.
While animal studies show lifespan can be extended via genetics or pharmaceuticals, evidence on human maximum lifespan flexibility has been inconclusive.
Some previous research, such as studies from Sweden, suggested maximum lifespan was increasing during the 19th and early 20th centuries, challenging the notion of a fixed limit.
Key Findings
Trends in Life Expectancy and Late-Life Survival
Average life expectancy at birth has continually increased globally, especially in developed nations (e.g., France).
Gains in survival have shifted from early-life mortality reductions to improvements in late-life mortality, with more individuals reaching very old ages (70+).
However, the rate of improvement in survival declines sharply after around 100 years of age.
The age showing the greatest gains in survival over time increased during the 20th century but appears to have plateaued since around 1980.
This plateau is seen in 88% of 41 countries studied, indicating a potential biological constraint on lifespan extension beyond a certain point.
Maximum Reported Age at Death (MRAD) Analysis
Using data from the International Database on Longevity (IDL) and the Gerontological Research Group (GRG), the authors analyzed the maximum ages of supercentenarians (110+ years old) in countries with the largest datasets (France, Japan, UK, US).
The maximum reported age at death increased steadily between the 1970s and early 1990s but plateaued around the mid-1990s, near the time Jeanne Calment died (1997).
Linear regression divided into two periods (1968–1994 and 1995 onward) showed:
Pre-1995: MRAD increased by approximately 0.12–0.15 years per year.
Post-1995: No significant increase; a slight, non-significant decline occurred.
The MRAD has stabilized around 114.9 years (95% CI: 113.1–116.7).
The probability of exceeding 125 years in any given year is less than 1 in 10,000, according to a Poisson distribution model.
Additional Statistical Evidence
Analysis of the top five highest reported ages at death per year (not just the maximum) shows similar plateauing trends.
The annual average age at death among supercentenarians has not increased since 1968.
These consistent patterns across multiple metrics and datasets strengthen the evidence for a natural ceiling on human lifespan.
Biological Interpretation and Implications
The idea that aging is a programmed biological event evolved to cause death has been widely discredited.
Instead, limits to lifespan are likely an inadvertent consequence of genetic programs optimized for early life functions (development, growth, reproduction).
Species-specific longevity assurance systems encoded in the genome counteract genetic and cellular imperfections, maintaining lifespan within limits.
Extending human lifespan beyond these natural limits would likely require interventions beyond improving healthspan, potentially involving genetic or pharmacological modifications.
While current research explores such possibilities, the complexity of genetic determinants of lifespan suggests substantial biological constraints.
Timeline Table: Key Chronological Events and Findings
Period Event/Observation
1860s–1990s Maximum reported age at death in Sweden rose from ~101 to ~108 years, suggesting possible increase
1900 onwards Life expectancy at birth increased markedly globally, especially in developed countries
1970s–early 1990s Maximum reported age at death (MRAD) increased steadily in France, Japan, UK, and US
Mid-1990s (around 1995) MRAD plateaued at ~114.9 years; no further significant increase observed
1997 Death of Jeanne Calment, oldest verified human at 122 years
1980s onwards Age with greatest gains in survival plateaued, indicating diminishing improvements at oldest ages
Quantitative Data Summary
Metric Value/Trend Source/Data
Jeanne Calment’s age at death 122 years Oldest verified human
Maximum reported age at death (MRAD) plateau ~114.9 years (95% CI: 113.1–116.7) IDL, GRG databases
MRAD increase rate (pre-1995) +0.12 to +0.15 years/year Linear regression
MRAD increase rate (post-1995) Slight, non-significant decrease Linear regression
Probability of exceeding 125 years in a year <1 in 10,000 Poisson distribution model
Percentage of countries showing plateau in survival gains at oldest ages 88% 41 countries analyzed
Key Insights
Human maximum lifespan appears to be fixed and constrained, despite past increases in average lifespan.
Improvements in survival rates slow and plateau beyond approximately 100 years of age.
The world record for age at death has not significantly increased since the late 1990s.
The phenomenon is consistent across multiple countries and independent datasets.
Biological aging limits are likely an outcome of genetic programming optimized for early life, with longevity assured by species-specific genomic systems.
Substantial extension of maximum human lifespan would require overcoming complex genetic and biological constraints.
Conclusions
This comprehensive demographic analysis provides strong evidence for a natural limit to human lifespan, with little increase in maximum age at death over recent decades despite ongoing increases in average life expectancy. The data challenge optimistic views that human longevity can be indefinitely extended by current health improvements alone. Instead, future lifespan extension may depend on breakthroughs that directly target the underlying biological and genetic determinants of aging.
References to Core Concepts and Methods
Use of Human Mortality Database for survival and life expectancy trends.
Analysis of supercentenarian data from the International Database on Longevity (IDL) and Gerontological Research Group (GRG).
Application of linear regression and Poisson distribution modeling to maximum age at death data.
Consideration of species-specific genetic longevity assurance systems and aging biology literature.
Comparison to historical theories of lifespan limits (Fries 1980; Olshansky et al. 1990).
Keywords
Maximum lifespan
Life expectancy
Supercentenarians
Late-life mortality
Longevity limit
Jeanne Calment
Genetic constraints
Aging biology
Mortality trends
Demographic analysis
FAQ
Q: Has maximum human lifespan increased in recent decades?
A: No. Analysis shows the maximum reported age at death plateaued in the mid-1990s around 115 years.
Q: How does life expectancy differ from maximum lifespan?
A: Life expectancy is the average age people live to in a population, which has increased due to reduced early mortality. Maximum lifespan is the oldest age reached by individuals, which appears fixed.
Q: Is there evidence for biological constraints on human lifespan?
A: Yes. Data suggest species-specific genetic programs and longevity assurance systems impose natural upper limits.
Q: Could future interventions extend maximum lifespan?
A: Potentially, but such extensions require overcoming complex genetic and biological factors beyond current health improvements.
This summary synthesizes the core findings and implications of the study, strictly based on the provided content, reflecting a nuanced understanding of the limits to human lifespan suggested by recent demographic evidence.
Smart Summary
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ouycguat-1834
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xevyo
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Evolution of the Value
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Evolution of the Value of Longevity in China
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This study investigates the welfare effects of mor This study investigates the welfare effects of mortality decline and longevity improvement in China over six decades (1952-2012), focusing on the monetary valuation of gains in life expectancy and their role relative to economic growth. Utilizing valuation formulae from the Global Health 2035 report, the authors estimate the value of a statistical life (VSL) and analyze how longevity gains have offset poor economic performance in early periods and contributed to reducing regional welfare disparities more recently.
Key Research Objectives
To quantify the value of mortality decline in China from 1952 to 2012.
To evaluate the welfare impact of longevity improvements relative to GDP per capita growth.
To analyze regional differences in health gains and their implications for welfare inequality.
To provide a methodological framework to calculate the value of mortality decline using age-specific mortality rates and GDP data.
Institutional and Historical Context
Life expectancy at birth in China increased from ~45 years in the early 1950s to over 70 years by 2012, with a particularly rapid rise prior to economic reforms in the late 1970s.
This improvement occurred despite stagnant GDP per capita during the pre-reform period (1950-1980).
Key drivers of longevity gain included:
The establishment of grassroots primary healthcare clinics staffed by “barefoot doctors.”
The Patriot Hygiene Campaign (PHC) in the 1950s, which improved sanitation, vaccination, and eradicated infectious diseases.
A basic health system providing employer-based insurance in urban areas and cooperative medical schemes in rural areas.
Increases in primary and secondary education, which indirectly contributed to mortality reduction.
Methodology
The study uses age-specific mortality rates as a proxy for overall health status, leveraging retrospective mortality data available since the 1950s.
The Value of a Statistical Life (VSL) is monetized using a formula linking VSL to GDP per capita and age-specific life expectancy:
The VSL for a 35-year-old is set at 1.8% of GDP per capita.
The value of a small mortality risk reduction (Standardized Mortality Unit, SMU) varies with age proportional to the years of life lost relative to age 35.
The value of mortality decline between two time points is computed as the integral over age of population density multiplied by age-specific changes in mortality risk and weighted by the value of a SMU.
This approach accounts for population age structure and income levels to estimate monetary benefits of longevity improvements.
Data sources include:
United Nations World Population Prospects for mortality rates and life expectancy.
Official Chinese statistical yearbooks for GDP, health expenditures, and census data.
Provincial data analysis focuses on the period 1981 to 2010, coinciding with China’s market reforms.
Main Findings
Time Series Analysis (1952-2012)
Period GDP per capita Change (RMB, 2012 prices) Life Expectancy Gain (years) Value of Mortality Decline (RMB per capita) Ratio of Mortality Value to GDP Change (excl. health exp.)
1957-1962 -152 -0.29 -126 0.84
1962-1967 3897 12.3 2162 5.72
1972-1977 2813 1.74 344 1.28
1982-1987 18041 1.24 338 0.19
1992-1997 40507 7.39 1360 0.32
2002-2007 102971 1.35 1045 0.11
Longevity gains (value of mortality decline) were especially large during the 1960s, partly compensating for poor or negative GDP growth.
The value of mortality decline relative to GDP per capita growth was much higher before 1978, indicating health improvements contributed significantly to welfare despite stagnant incomes.
Post-1978, rapid economic growth outpaced the value of longevity gains, but the latter remained positive and substantial.
Health expenditure is subtracted from GDP to avoid double counting in welfare calculations.
Regional (Provincial) Analysis (1981-2010)
Province GDP per Capita Change (RMB, 2012 prices) Life Expectancy Gain (years) Value of Mortality Decline (RMB per capita) Ratio of Mortality Value to GDP Change (excl. health exp.)
Xinjiang 22738 17.3 2407 0.58
Yunnan 14449 13.15 1857 0.39
Gansu 14945 9.47 264 0.19
Guizhou 12095 9.19 214 0.20
Hebei 27024 5.72 873 0.11
Guangdong 43086 12.05 358 0.13
Jiangsu 50884 12.04 705 0.14
Inland provinces generally experienced larger longevity gains than coastal provinces, despite coastal regions having significantly higher GDP per capita.
The value of mortality decline relative to income growth was higher in less-developed inland provinces, suggesting health improvements partially mitigate regional welfare inequality.
Contrasting trends:
Coastal provinces: faster economic growth but smaller longevity gains.
Inland provinces: slower income growth but larger health gains.
The diminishing returns to longevity gains at higher life expectancy levels explain part of this pattern.
Economic growth can have negative health externalities (pollution, lifestyle changes), which may counteract potential longevity improvements.
Health Transition and Future Challenges
China’s epidemiological transition is characterized by a shift from infectious diseases to non-communicable diseases (NCDs) such as malignant tumors, cerebrovascular disease, heart disease, and respiratory diseases.
Mortality rates for these major NCDs show a rising trend from 1982 to 2012.
The increasing prevalence of chronic diseases imposes a rising medical cost burden, particularly due to advanced medical technologies and health system limitations.
The Chinese government initiated a major health care reform in 2009 aimed at expanding affordable and equitable coverage.
Although health spending has increased, it remains less than one-third of the U.S. level (as % of GDP), indicating room for further investment and improvement.
Conclusions and Implications
The study finds that sustained longevity improvements have played a crucial role in improving welfare in China, especially before economic reforms.
Health gains have partially compensated for weak economic performance prior to market liberalization.
In the reform era, longevity improvements have contributed to narrowing interregional welfare disparities, benefiting poorer inland provinces more.
The value of mortality decline is a meaningful supplement to GDP per capita as an indicator of welfare.
The authors caution that future longevity gains may face challenges due to rising chronic diseases and escalating medical costs.
The methodology and findings are relevant for other low- and middle-income countries undergoing similar demographic and epidemiological transitions.
Core Concepts and Definitions
Term Definition
Life Expectancy Average number of years a newborn is expected to live under current mortality conditions.
Value of a Statistical Life (VSL) Monetary value individuals place on marginal reductions in mortality risk.
Standardized Mortality Unit (SMU) A change in mortality risk of 1 in 10,000 (10^-4).
Value of a SMU (VSMU) Monetary value of reducing mortality risk by one SMU at a given age.
Full Income GDP per capita adjusted for health improvements, including the value of mortality decline.
Highlights
China’s life expectancy rose dramatically from 45 to over 70 years between 1952 and 2012, despite slow GDP growth before reforms.
The monetary value of mortality decline was often larger than GDP growth prior to 1978, showing health’s central role in welfare.
Inland provinces experienced larger longevity gains than coastal provinces, though coastal areas had higher income growth.
Health improvements have helped reduce interregional welfare inequality in China.
The shift from communicable to non-communicable diseases poses new health and economic challenges.
China’s health system reform in 2009 aims to address rising medical costs and expand coverage.
Limitations and Uncertainties
The study assumes a monotonically declining VSL with age, which simplifies but does not capture the full complexity of age-dependent valuations.
Pre-1978 health expenditure data were back-projected, introducing some uncertainty.
Provincial mortality data are only available for census years, limiting longitudinal granularity.
The analysis does not fully incorporate morbidity or quality-of-life changes beyond mortality.
Future extrapolations are uncertain due to evolving epidemiological and demographic dynamics.
References to Key Literature
Jamison et al. (2013) Global Health 2035 report for VSL valuation framework.
Murphy and Topel (2003, 2006) on economic value of health and longevity.
Nordhaus (2003) on full income including health gains.
Becker et al. (2005) on global inequality incorporating longevity.
Aldy and Viscusi (2007, 2008) on age-specific VSL valuation.
Babiarz et al. (2015) on China’s mortality decline under Mao.
Implications for Policy and Future Research
Policymakers should recognize the economic value of health improvements beyond GDP growth.
Investments in basic healthcare, sanitation, and education were critical for China’s longevity transition and remain relevant for other developing countries.
Addressing the burden of chronic diseases and medical costs requires sustained health system reforms.
Future work should explore full income accounting including quality of life, and analyze health and longevity valuation in other low-income and middle-income countries.
More granular data collection and longitudinal studies would improve understanding of regional and cohort-specific health value dynamics.
This comprehensive study demonstrates how longevity gains represent a critical dimension of welfare, particularly in the context of China’s unique historical, demographic, and economic trajectory. It provides a robust analytical framework integrating epidemiological and economic data to quantify health’s contribution to human welfare.
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xevyo
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Effects of food
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Effects of food restriction on aging
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This study, published in Proceedings of the Nation This study, published in Proceedings of the National Academy of Sciences (1984), investigates the effects of food restriction on aging, specifically aiming to disentangle the roles of reduced food intake and reduced adiposity on longevity and physiological aging markers in mice. The research focuses on genetically obese (ob/ob) and normal (C57BL/6J, or B6 +/+) female mice, examining how lifelong food restriction influences longevity, collagen aging, renal function, and immune responses. The key finding is that reduced food intake, rather than reduced adiposity, is the critical factor in extending lifespan and retarding certain aging processes.
Background and Objective
Food restriction (caloric restriction) is known to increase longevity in rodents, but the underlying mechanism remains unclear.
Previous studies suggested that reduced adiposity (body fat) might mediate the longevity effects. However, human epidemiological data show conflicting evidence: moderate obesity correlates with lower mortality, challenging the assumption that less fat is always beneficial.
Genetically obese ob/ob mice provide a model to separate effects because they maintain high adiposity even when food restricted.
The study aims to clarify whether reduced food intake or reduced adiposity is the primary driver of delayed aging and increased longevity.
Experimental Design
Subjects: Female mice of the C57BL/6J strain, both normal (+/+) and genetically obese (ob/ob).
Feeding Regimens:
Fed ad libitum (free access to food).
Restricted feeding: fixed ration daily, adjusted so restricted ob/ob mice weigh similarly to fed +/+ mice.
Food restriction started at weaning (4 weeks old) and continued lifelong.
Parameters measured:
Longevity (mean and maximum lifespan).
Body weight, adiposity (fat percentage), and food intake.
Collagen aging assessed by denaturation time of tail tendon collagen.
Renal function measured via urine-concentrating ability after dehydration.
Immune function evaluated by thymus-dependent responses: proliferative response to phytohemagglutinin (PHA) and plaque-forming cells in response to sheep erythrocytes (SRBC).
Key Quantitative Data
Group Food Intake (g/day) Body Weight (g) Body Fat (% of wt) Mean Longevity (days) Max Longevity (days) Immune Response to SRBC (% Young Control) Immune Response to PHA (% Young Control)
Fed ob/ob 4.2 ± 0.5 67 ± 5 ~66% 755 893 7 ± 7 13 ± 7
Fed +/+ 3.0* 30 ± 1* 22 ± 6 971 954 22 ± 11 49 ± 12
Restricted ob/ob 2.0* 28 ± 2 48 ± 1 823 1307 11 ± 7 8 ± 6
Restricted +/+ 2.0* 20 ± 2* 13 ± 3 810 1287 59 ± 30 50 ± 11
Note: Means not significantly different from each other are marked with an asterisk (*).
Detailed Findings
1. Body Weight, Food Intake, and Adiposity
Fed ob/ob mice consume the most food and have the highest body fat (~66% of body weight).
When food restricted, ob/ob mice consume about half as much food as when fed ad libitum but maintain a very high adiposity (~48%), nearly twice that of fed normal mice.
Restricted normal mice have the lowest fat percentage (~13%) despite eating the same amount of food as restricted ob/ob mice.
This demonstrates that food intake and adiposity can be experimentally dissociated in these genotypes.
2. Longevity
Food restriction increased mean lifespan of ob/ob mice by 56% and maximum lifespan by 46%.
In normal mice, food restriction had little effect on mean longevity but increased maximum lifespan by 32%.
Food-restricted ob/ob mice lived longer than fed normal mice, despite their greater adiposity.
These results strongly suggest that reduced food intake, not reduced adiposity, extends lifespan, even with high body fat levels.
3. Collagen Aging
Collagen denaturation time is a biomarker of aging, with shorter times indicating more advanced aging.
Collagen aging is accelerated in fed ob/ob mice compared to normal mice.
Food restriction greatly retards collagen aging in both genotypes.
Importantly, collagen aging rates were similar in restricted ob/ob and restricted +/+ mice, despite widely different body fat percentages.
Conclusion: Collagen aging correlates with food intake but not with adiposity.
4. Renal Function (Urine-Concentrating Ability)
Urine-concentrating ability declines with age in normal rodents.
Surprisingly, fed ob/ob mice did not show an age-related decline; their concentrating ability remained high into old age.
Restricted mice (both genotypes) showed a slower decline than fed normal mice.
This suggests obesity does not necessarily impair this aspect of renal function, and food restriction preserves it.
5. Immune Function
Immune responses (to PHA and SRBC) decline with age, more severely in fed ob/ob mice (only ~10% of young normal levels at old age).
Food restriction did not improve immune responses in ob/ob mice, even though their lifespans were extended.
In restricted normal mice, immune responses showed slight improvement compared to fed normal mice.
The spleens of restricted ob/ob mice were smaller, which might contribute to low immune responses measured per spleen.
These results suggest immune aging may be independent from longevity effects of food restriction, especially in genetically obese mice.
The more rapid decline in immune function with higher adiposity aligns with previous reports that increased dietary fat accelerates autoimmunity and immune decline.
Interpretation and Conclusions
The study disentangles two factors often conflated in aging research: food intake and adiposity.
Reduced food intake is the primary factor in extending lifespan and slowing collagen aging, not the reduction of body fat.
Genetically obese mice restricted in food intake live longer than normal mice allowed to eat freely, despite retaining high body fat levels.
Aging appears to involve multiple independent processes (collagen aging, immune decline, renal function), each affected differently by genetic obesity and food restriction.
The study also highlights that immune function decline is not necessarily mitigated by food restriction in obese mice, suggesting complexities in how different physiological systems age.
Findings challenge the assumption that less fat is always beneficial, offering a potential explanation for human studies showing moderate obesity correlates with lower mortality.
The results support the idea that reducing food consumption can be beneficial even in individuals with high adiposity, with implications for aging and metabolic disease research.
Implications for Human Aging and Obesity
The study cautions against equating adiposity directly with aging rate or mortality risk without considering food intake.
It suggests that caloric restriction may improve longevity even when body fat remains high, which may help reconcile conflicting human epidemiological data.
The authors note that micronutrient supplementation along with food restriction could further optimize longevity outcomes, based on related studies.
Core Concepts
Food Restriction (Caloric Restriction): Limiting food intake without malnutrition.
Adiposity: The proportion of body weight composed of fat.
ob/ob Mice: Genetically obese mice with a mutation causing defective leptin production, leading to obesity.
Longevity: Length of lifespan.
Collagen Aging: Changes in collagen denaturation time indicating tissue aging.
Immune Senescence: Decline in immune function with age.
Renal Function: Kidney’s ability to concentrate urine, an indicator of aging-related physiological decline.
References to Experimental Methods
Collagen aging measured by denaturation times of tail tendon collagen in urea.
Urine osmolality measured by vapor pressure osmometer after dehydration.
Immune function assessed by PHA-induced splenic lymphocyte proliferation in vitro and plaque-forming cell responses to SRBC in vivo.
Body fat measured chemically via solvent extraction of dehydrated tissue samples.
Summary Table of Aging Markers by Group
Marker Fed ob/ob Fed +/+ Restricted ob/ob Restricted +/+ Interpretation
Body Fat (%) ~66 22 ~48 13 Ob/ob mice retain high fat even restricted
Mean Lifespan (days) 755 971 823 810 Food restriction increases lifespan in ob/ob mice
Max Lifespan (days) 893 954 1307 1287 Max lifespan improved by restriction
Collagen Aging Rate Fast (accelerated) Normal Slow (retarded) Slow (retarded) Related to food intake, not adiposity
Urine Concentrating Ability High, no decline with age Declines with age Declines slowly Declines slowly Obesity does not impair this function
Immune Response Severely reduced (~10%) Moderately reduced Severely reduced (~10%) Slightly improved Immune aging not improved by restriction in obese mice
Key Insights
Longevity extension by food restriction is independent of adiposity levels.
Collagen aging is directly related to food consumption, not fat content.
Obesity does not necessarily impair certain renal functions during aging.
Immune function decline with age is exacerbated by obesity but is not rescued by food restriction in obese mice.
Aging is a multifactorial process with independent physiological components.
Final Remarks
This comprehensive study provides compelling evidence that lifespan extension by food restriction is primarily driven by the reduction in caloric intake rather than by decreased fat mass. It highlights the complexity of aging, showing that different physiological systems age at different rates and respond differently to genetic and environmental factors. The findings have significant implications for understanding obesity, aging, and dietary interventions in mammals, including humans.
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mooaapbz-1416
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xevyo
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The effect of drinking
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The effect of drinking water quality on the health
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This study investigates the relationship between d This study investigates the relationship between drinking water quality and human health and longevity in Mayang County, a recognized longevity region in Hunan Province, China. The research focuses on the chemical composition of local drinking water and the trace element content in the hair of local centenarians. It examines how waterborne trace elements correlate with longevity indices and health outcomes, drawing on chemical analyses, statistical correlations, and comparisons with national and international standards.
Study Context and Background
Drinking water is a crucial source of trace elements essential for human physiological functions since the human body cannot synthesize these elements.
The quality and composition of drinking water significantly influence human health and the prevalence of certain diseases.
Previous studies have linked variations in trace elements in water with incidences of gastric cancer, colon and rectal cancer, thyroid diseases, neurological disorders, esophageal cancer, and Kashin-Beck disease.
China has identified 13 longevity counties based on:
Number of centenarians per 100,000 population (≥7),
Average life expectancy at least 3 years above the national average,
Proportion of people over 80 years old accounting for ≥1.4% of the total population.
Mayang County meets these criteria and was officially designated a longevity county in 2007.
Study Area: Mayang County, Hunan Province
Located between the Wuling and Xuefeng Mountains, covering
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xevyo
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Evaluation of gender
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Evaluation of gender differences on mitochondrial
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This study investigates gender differences in mito This study investigates gender differences in mitochondrial bioenergetics, oxidative stress, and apoptosis in the C57Bl/6J (B6) mouse strain, a commonly used laboratory rodent model that shows no significant differences in longevity between males and females. The research explores whether the previously observed gender-based differences in longevity and oxidative stress in other species, often attributed to higher estrogen levels in females, are reflected in mitochondrial function and apoptotic markers in this mouse strain.
Background and Rationale
It is widely observed that in many species, females tend to live longer than males, often explained by higher estrogen levels in females potentially reducing oxidative damage.
However, this trend is not universal: in some species including certain mouse strains (C57Bl/6J), longevity does not differ between sexes, and in others (e.g., Syrian hamsters, nematodes), males may live longer.
Previous studies in rat strains (Wistar, Fischer 344) with female longevity advantage showed lower mitochondrial reactive oxygen species (ROS) production and higher antioxidant defenses in females.
The Mitochondrial Free Radical Theory of Aging suggests that aging rate is related to mitochondrial ROS production, which causes oxidative damage.
This study aims to test if gender differences in mitochondrial bioenergetics, ROS production, oxidative stress, and apoptosis exist in B6 mice, which do not show sex differences in lifespan.
Experimental Design and Methods
Animals: 10-month-old male (n=11) and female (n=12) C57Bl/6J mice were used.
Tissues studied: Heart, skeletal muscle (gastrocnemius + quadriceps), and liver.
Mitochondrial isolation: Tissue-specific protocols were used to isolate mitochondria immediately post-sacrifice.
Measurements performed:
Mitochondrial oxygen consumption: State 3 (active) and State 4 (resting) respiration measured polarographically.
ATP content: Determined via luciferin-luciferase assay in freshly isolated mitochondria.
ROS production: H2O2 generation from mitochondrial complexes I and III measured fluorometrically with specific substrates and inhibitors.
Oxidative stress markers:
Protein carbonyls in cytosolic fractions (ELISA).
8-hydroxy-2′-deoxyguanosine (8-oxodG) levels in mitochondrial DNA (HPLC-EC-UV).
Apoptosis markers:
Caspase-3 and caspase-9 activity (fluorometric assays).
Cleaved caspase-3 protein (Western blot).
Mono- and oligonucleosomes (DNA fragmentation, ELISA).
Key Quantitative Results
Parameter Tissue Male (Mean ± SEM) Female (Mean ± SEM) Statistical Difference
Body weight (g) Whole body 30.1 ± 0.55 24.1 ± 1.04 Male > Female (p<0.001)
Heart weight (mg) Heart 171 ± 0.01 135 ± 0.01 Male > Female (p<0.001)
Liver weight (g) Liver 1.52 ± 0.09 1.15 ± 0.09 Male > Female (p<0.01)
Skeletal muscle weight (mg) Quadriceps + gastrocnemius ~403 (sum) ~318 (sum) Male > Female (p<0.001)
Oxygen Consumption (nmol O2/min/mg protein) Heart, State 3 77.8 ± 7.5 65.0 ± 7.3 No significant difference
Skeletal Muscle, State 3 61.4 ± 4.9 64.8 ± 5.5 No significant difference
Liver, State 3 36.1 ± 4.5 34.9 ± 2.5 No significant difference
ATP content (nmol ATP/mg protein) Heart 3.7 ± 0.5 2.8 ± 0.4 No significant difference
Skeletal Muscle 0.12 ± 0.05 0.28 ± 0.06 No significant difference
ROS production (nmol H2O2/min/mg protein) Heart (complex I substrate) 0.7 ± 0.1 0.7 ± 0.05 No difference
Skeletal muscle (succinate) 5.9 ± 0.6 7.5 ± 0.5 Female > Male (p<0.05)
Liver (complex I substrate) 0.13 ± 0.05 0.13 ± 0.05 No difference
Protein carbonyls (oxidative damage marker) Heart, muscle, liver No difference No difference No significant difference
8-oxodG in mtDNA (oxidative DNA damage) Skeletal muscle, liver No difference No difference No significant difference
Caspase-3 and Caspase-9 activity (apoptosis markers) Heart, muscle, liver No difference No difference No significant difference
Cleaved caspase-3 (Western blot) Heart, muscle, liver No difference No difference No significant difference
Mono- and oligonucleosomes (DNA fragmentation) Heart, muscle, liver No difference No difference No significant difference
Core Findings and Interpretations
No significant sex differences were found in mitochondrial oxygen consumption or ATP content in heart, skeletal muscle, or liver mitochondria.
Mitochondrial ROS production rates were similar between sexes in heart and liver; only female skeletal muscle showed slightly higher ROS production with succinate substrate, an isolated finding.
Measures of oxidative damage to proteins and mitochondrial DNA did not differ between males and females.
Markers of apoptosis (caspase activities, cleaved caspase-3, DNA fragmentation) were not different between sexes in any tissue examined.
Despite females having higher estrogen levels, no associated protective effect on mitochondrial bioenergetics, oxidative stress, or apoptosis was observed in this mouse strain.
The lack of differences in mitochondrial function and oxidative damage correlates with the absence of sex differences in lifespan in the C57Bl/6J strain.
These data support the Mitochondrial Free Radical Theory of Aging, emphasizing the role of mitochondrial ROS production in aging rate, independent of estrogen-mediated effects.
The study suggests that body size differences might explain sex differences in longevity and oxidative stress observed in other species (e.g., rats), as mice exhibit smaller body weight differences between sexes.
The estrogen-related increase in antioxidant defenses or mitochondrial function is not universal, and estrogen’s protective role may vary by species and strain.
Apoptosis rates do not differ between sexes in middle-aged mice, but differences could potentially emerge at older ages (not specified).
Timeline Table: Key Experimental Procedures
Step Description
Animal age at study 10 months old male and female C57Bl/6J mice
Tissue collection and mitochondrial isolation Heart, skeletal muscle, liver isolated post-sacrifice
Measurements Oxygen consumption, ATP content, ROS production, oxidative damage, apoptosis markers
Data analysis Statistical comparison of males vs females
Keywords
Mitochondria
Reactive Oxygen Species (ROS)
Oxidative Stress
Apoptosis
Mitochondrial DNA (mtDNA)
Estrogen
Longevity
C57Bl/6J Mice
Mitochondrial Free Radical Theory of Aging
Conclusions
In the C57Bl/6J mouse strain, gender does not influence mitochondrial bioenergetics, oxidative stress levels, or apoptosis markers, consistent with the lack of sex differences in longevity in this strain.
Higher estrogen levels in females do not confer measurable mitochondrial protection or reduced oxidative stress in this model.
The results suggest that oxidative stress generation, rather than estrogen levels, determines aging rate in this species.
Body size and species-specific factors may underlie observed sex differences in longevity and oxidative stress in other animals.
Further research is needed in models where males live longer than females (e.g., Syrian hamsters) and in older animals to clarify the influence of sex on apoptosis and aging.
Key Insights
Gender differences in mitochondrial ROS production and apoptosis are not universal across species or strains.
Estrogen’s role in modulating mitochondrial function and oxidative stress is complex and strain-dependent.
Mitochondrial ROS production remains a central factor in aging independent of sex hormones in the studied mouse strain.
Additional Notes
The study used well-controlled, comprehensive biochemical and molecular assays to evaluate mitochondrial function and apoptosis.
The findings challenge the assumption that female longevity advantage is directly mediated by estrogen effects on mitochondria.
The lack of sex differences in this mouse strain provides a useful baseline for comparative aging studies.
This summary reflects the study’s content strictly as presented, without introducing unsupported interpretations or data.
Smart Summary...
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xevyo
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Eating for Health
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Eating for Health and Longevity
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Summary: Eating for Health and Longevity – A Pract Summary: Eating for Health and Longevity – A Practical Guide to Whole-Food, Plant-Based Diets
This guide, produced by SUNY Downstate Health Sciences University, provides a comprehensive, evidence-based overview of adopting a whole-food, plant-based (WFPB) diet to promote health, prevent chronic disease, and improve longevity. It offers practical advice for transitioning to plant-based eating, highlights nutritional benefits, and addresses common concerns and misconceptions.
Core Concepts of a Whole-Food, Plant-Based Diet
Definition: A WFPB diet emphasizes eating whole, minimally processed plant foods such as vegetables, fruits, whole grains, legumes, nuts, and seeds.
Exclusions: It minimizes or avoids meat, poultry, fish/seafood, eggs, dairy, refined carbohydrates (e.g., white bread, white rice), refined sugars, extracted oils, and highly processed foods.
Difference from Vegan Diet: Unlike some vegan diets, which may include refined grains, sweeteners, and oils, the WFPB diet focuses on whole foods for optimal health.
Health Benefits
Chronic Disease Prevention and Reversal: WFPB diets can prevent, manage, and sometimes reverse diseases such as diabetes, heart disease, obesity, and hypertension.
Weight Management: Effective for losing excess weight and maintaining a healthy weight.
Longevity and Vitality: Promotes vibrant health and potentially longer life by reducing lifestyle-related risk factors.
Foods to Include and Avoid
Foods to Eat and Enjoy Foods to Avoid or Minimize
Fresh and frozen vegetables Meats (red, processed, poultry, fish/seafood)
Fresh fruits Refined grains (white rice, white pasta, white bread)
Whole grains (oats, quinoa, barley) Products with refined sugars or sweeteners (sodas, candy)
Legumes (peas, lentils, beans) Highly processed or convenience foods with added salt
Unsalted nuts and seeds Eggs and dairy products
Dried fruits without additives Processed plant-based meat, cheese, or butter alternatives
Unsweetened non-dairy milks Refined, extracted oils (olive oil, canola, vegetable)
Alcoholic beverages
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Effect of Exceptional
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Effect of Exceptional Parental Longevity
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Summary
This study investigates the relationship Summary
This study investigates the relationship between exceptional parental longevity and the prevalence of cardiovascular disease (CVD) in their offspring, with a focus on whether lifestyle, socioeconomic status, and dietary factors influence this association. Conducted on a cohort of Ashkenazi Jewish adults aged 65-94, the research compares two groups: offspring of parents with exceptional longevity (OPEL), defined as having at least one parent living beyond 95 years, and offspring of parents with usual survival (OPUS), whose parents did not survive past 95 years. The study finds that OPEL exhibit significantly lower prevalence of hypertension, stroke, and overall cardiovascular disease compared to OPUS, independent of lifestyle, socioeconomic, and nutritional differences, thus highlighting a probable genetic influence on disease-free survival and longevity.
Background and Rationale
Individuals with exceptional longevity often experience a delay or absence of age-related diseases, making them models for studying healthy aging.
Longevity has a heritable component, with genetic markers linked to extended lifespan and resistance to diseases like CVD.
Previous studies have shown that offspring of exceptionally long-lived parents have lower incidence of CVD and other age-related illnesses.
Lifestyle factors such as physical activity, diet, smoking status, and socioeconomic status are known to influence cardiovascular health in the general population.
Prior to this study, no research compared lifestyle factors between offspring of exceptionally long-lived parents and those of usual longevity to isolate genetic effects from environmental factors.
Study Design and Methods
Population: 845 Ashkenazi Jewish adults aged 65-94 years; 395 OPEL and 450 OPUS.
Definition:
OPEL: At least one parent lived past 95 years.
OPUS: Both parents died before 95 years.
Recruitment: Systematic searches via voter registration, synagogues, community groups, and advertisements.
Exclusion Criteria: Baseline dementia, severe sensory impairments, or sibling already enrolled.
Data Collection:
Medical history including hypertension (HTN), diabetes mellitus (DM), myocardial infarction (MI), congestive heart failure (CHF), coronary interventions, and stroke.
Lifestyle factors: smoking history, alcohol use, physical activity level.
Socioeconomic factors: education and social strata score.
Dietary intake assessed in a subgroup (n=234) using the Block Brief Food Frequency Questionnaire (FFQ 2000).
Physical measures: height, weight, waist circumference; BMI calculated.
Analysis:
Comparison of prevalence of diseases and lifestyle variables between OPEL and OPUS.
Statistical adjustments for age, sex, BMI, tobacco use, social strata, and physical activity.
Stratified analyses by cardiovascular risk status (high vs. low).
Interaction testing between group status and lifestyle/socioeconomic factors.
Key Findings
Demographics and Lifestyle Factors
Characteristic OPEL (n=395) OPUS (n=450) p-value
Female (%) 59 50 <0.01
Age (years, mean ± SD) 75 ± 6 76 ± 7 <0.01
Education (years) 17 ± 3 17 ± 3 0.55
Social strata score (median, IQR) 56 (28-66) 56 (28-66) 0.76
Ever smokers (%) 55 54 0.80
Current smokers (%) 3 3 0.94
Alcohol use past year (%) 90 88 0.32
Strenuous physical activity (times/week, median) 3 (0-4) 3 (0-4) 0.71
Walking endurance >30 minutes (%) 77 70 0.05
No significant differences in lifestyle factors (smoking, alcohol, physical activity) or socioeconomic status between OPEL and OPUS.
OPEL reported greater walking endurance despite similar physical activity frequency.
Physical Characteristics and Disease Prevalence
Condition / Measure OPEL OPUS p-value OR (95% CI)a
BMI (mean ± SD) 27.5 ± 4.9 27.8 ± 4.7 0.34 Not specified
Obesity (%) (BMI≥30) 26 27 0.84 Not specified
Abdominal obesity (%) 48 48 0.95 Not specified
Systolic BP (mmHg) 129 ± 17 129 ± 17 0.78 Not specified
Diastolic BP (mmHg) 74 ± 9 74 ± 10 0.92 Not specified
Antihypertensive medication use (%) 39 49 <0.01 Not specified
Hypertension (%) 42 51 <0.01 0.71 (0.53–0.95)
Diabetes mellitus (%) 7 11 0.10 0.70 (0.43–1.15) NS
Myocardial infarction (%) 5 7 0.12 0.77 (0.42–1.42) NS
Stroke (%) 2 5 <0.01 0.35 (0.14–0.88)
Cardiovascular disease (composite) (%) 12 20 <0.01 0.65 (0.43–0.98)
OPEL had significantly lower odds of hypertension, stroke, and overall CVD compared to OPUS after adjusting for age and sex.
No significant differences observed for diabetes, MI, CHF, or coronary interventions after adjustment.
OPUS more frequently used antihypertensive medications despite similar blood pressure readings.
Stratified Cardiovascular Risk Analysis
Among high-risk individuals (defined by diabetes or ≥2 risk factors: obesity, hypertension, smoking), OPEL had a significantly lower prevalence of CVD compared to OPUS (OR 0.45; p=0.01).
Among low-risk individuals, no significant difference in CVD prevalence was observed between groups.
Significant interaction found between group status and tobacco use:
Tobacco use was not significantly associated with increased CVD odds in OPEL.
Tobacco use was nearly significantly associated with increased CVD odds in OPUS (p=0.07).
Dietary Intake (Subgroup, n=234)
Dietary Component OPEL OPUS p-value Adjusted p-valuea
Total daily calories (kcal) 1119 (906–1520) 1218 (940–1553)
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Exceptional Human
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Exceptional Human Longevity
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Exceptional human longevity represents an extreme Exceptional human longevity represents an extreme phenotype characterized by individuals who survive to very old ages, such as centenarians (100+ years) or supercentenarians (110+ years), often with delayed onset of age-related diseases or resistance to lethal illnesses. This review synthesizes evidence on the multifactorial nature of longevity, integrating genetic, environmental, cultural, and geographical influences, and discusses health, demographic trends, biological mechanisms, biomarkers, and strategies that promote extended health span and life span.
Key Insights and Core Concepts
Exceptional longevity is defined by both chronological and biological age, emphasizing delayed functional decline and preservation of physiological function.
The biology of aging is heterogeneous, even among the oldest individuals, and no single biomarker reliably predicts longevity.
Longevity is influenced by disparate combinations of genes, environment, resiliency, and chance, shaped by culture and geography.
Compression of morbidity—delaying the onset of disability and chronic diseases—is a critical concept in successful aging.
Empirical strategies supporting longevity involve dietary moderation, regular physical activity, purposeful living, and strong social networks.
Genetic factors contribute to longevity but explain only about 25% of life span variance; environmental and behavioral factors play a dominant role.
Sex differences are notable: women generally live longer than men, with possible links to reproductive biology and hormonal factors.
Resiliency, the ability to respond to stressors and maintain homeostasis, is emerging as a key determinant of successful aging and extended longevity.
Timeline and Demographic Trends
Period/Year Event/Trend
Pre-20th century Probability of living to 100 was approximately 1 in 20 million at birth.
1995 Probability of living to 100 increased to about 1 in 50 for females in low mortality nations.
2009 Probability further increased to approximately 1 in 2.
2015 (Global data) Countries with oldest populations: Japan, Germany, Italy, Greece, Finland, Sweden.
2015 (Life expectancy at age 65) Japan, Macau, Singapore, Australia, Switzerland lead with 20-25 additional years expected.
2013 Last supercentenarian of note: Jiroemon Kimura died at age 116.
Ongoing Maximum human lifespan (~122 years) remains largely unchanged despite increasing average life expectancy.
Characteristics of Centenarians and Supercentenarians
Disease Onset and Morbidity:
Onset of common age-related diseases varies considerably; 24% of males and 43% of females centenarians diagnosed with one or more diseases before age 80.
15% of females and 30% of males remain disease-free at age 100.
Cognitive impairment is often delayed; about 25% of centenarians remain cognitively intact.
Cancer and vascular diseases often develop much later or not at all in supercentenarians.
Functional Status:
Many supercentenarians remain functionally independent or require minimal assistance.
Geographic Clustering of Longevity
Certain regions globally show high concentrations of exceptionally long-lived individuals, highlighting environmental and cultural influences:
Region Notable Longevity Factors
Okinawa, Japan Caloric restriction via “hara hachi bu” (eat until 80% full), plant-based “rainbow diet,” low BMI (~20 kg/m²), slower decline of DHEA hormone.
Sardinia, Italy Genetic lineage from isolated settlers, particularly among men, with unknown genetic traits contributing to longevity.
Loma Linda, California (Seventh Day Adventists) Abstinence from alcohol and tobacco, vegetarian diet, spirituality, lower stress hormone levels.
Nicoya Peninsula, Costa Rica; Ikaria, Greece Commonalities include plant-based diets, moderate eating, purposeful living, social support, exercise, naps, and possibly sunlight exposure.
Table 1 summarizes common longevity factors in clustered populations.
Table 1: Longevity Factors Associated With Geographic Clustering
Longevity Factors
Eating in moderation (small/moderate portions) and mostly plant-based diets, with lighter meals at the end of the day
Purposeful living (life philosophy, volunteerism, work ethic)
Social support systems (family/friends interaction, humor)
Exercise incorporated into daily life (walking, gardening)
Other nutritional factors (e.g., goat’s milk, red wine, herbal teas)
Spirituality
Maintenance of a healthy BMI
Other possible factors: sunshine, hydration, naps
Trends in Longevity and Morbidity
Life expectancy has increased mainly due to reductions in premature deaths (e.g., infant mortality, infectious diseases).
Maximum lifespan (~122 years) remains stable over the past two decades.
Healthy life years vary widely (25%-75% of life expectancy at age 65), with Nordic countries showing the highest expected healthy years.
Compression of morbidity models propose:
No delay in morbidity onset, increased morbidity duration.
Delay in morbidity onset with proportional increase in life expectancy.
Delay in morbidity onset with compression (shorter duration) of morbidity.
Evidence supports some compression of morbidity, but among those aged 85+, morbidity delay may be less pronounced.
Functional disability rates declined in the late 20th century but may be plateauing in the 21st century.
Mechanisms of Longevity
Genetic Influences
Genetic contribution to longevity is supported by:
Conservation of maximum lifespan across species.
Similar longevity in monozygotic twins.
Familial clustering of exceptional longevity.
Genetic diseases of premature aging.
Candidate genes and pathways associated with longevity include:
APOE gene variants (e.g., lower ε4 allele frequency in centenarians).
Insulin/IGF-1 signaling pathways.
Cholesteryl ester transfer protein.
Anti-inflammatory cytokines (e.g., IL-10).
Stress response genes (e.g., heat shock protein 70).
GH receptor exon 3 deletion linked to longer lifespan and enhanced GH sensitivity, especially in males.
Despite these, only ~25% of lifespan variance is genetic, emphasizing the larger role of environment and behavior.
Sex Differences
Women universally live longer than men, with better female survival starting early in life.
Female longevity may relate to reproductive history; older maternal age at last childbirth correlates with longer life.
The “grandmother hypothesis” proposes post-reproductive lifespan enhances offspring and grandchild survival.
Male longevity predictors include occupation and familial relatedness to male centenarians.
Lower growth hormone secretion may explain shorter stature and longer life in women.
Despite longer life, men often show better functional status at older ages.
Resiliency
Defined as the capacity to respond to or resist stressors that cause physiological decline.
Resiliency operates across psychological, physical, and physiological domains.
Examples involve resistance to frailty, cognitive impairment, muscle loss, sleep disorders, and multimorbidity.
Exercise may promote resiliency more effectively than caloric restriction.
Psychological resilience, including reduction of depression, correlates with successful aging.
Resiliency may explain why some centenarians survive despite earlier chronic diseases.
Strategies to Achieve Exceptional Longevity
Dietary Modification:
Moderate caloric restriction (CR) shown to extend lifespan in multiple species.
Human studies (e.g., CALERIE trial) show CR improves metabolic markers and slows biological aging, though sustainability and effects on maximum lifespan remain uncertain.
Benefits of CR in humans are linked to improved cardiovascular risk factors.
Antioxidant supplementation does not convincingly extend lifespan.
Physical Activity:
Regular moderate to vigorous exercise correlates with increased life expectancy and reduced mortality.
Physical activity benefits hold across BMI categories and are especially impactful in older adults.
Body Weight:
Optimal BMI range for longevity is 20.0–24.9 kg/m²; overweight and obesity increase mortality risk.
Social Engagement and Purposeful Living:
Strong social relationships reduce mortality risk comparable to quitting smoking.
Purpose in life associates with less cognitive decline and disability.
Productive engagement improves memory and overall well-being.
Measuring Successful Aging and Biomarkers of Longevity
Biomarkers of aging are sought to quantify biological age, improving prognosis and guiding interventions.
Ideal biomarkers should correlate quantitatively with age, be independent of disease processes, and respond to aging rate modifiers.
Challenges include separating primary aging from disease effects and confounding by nutrition or interventions.
Commonly studied biomarkers include:
Biomarker Category Examples and Notes
Functional Measures Gait speed, grip strength, daily/instrumental activities of daily living (ADLs), cognitive tests
Physiological Parameters Blood glucose, hemoglobin A1c, lipids, inflammatory markers (IL-6), IGF-1, immune cell profiles
Sensory Functions Hearing thresholds, cataract presence, taste and smell tests
Physical Attributes Height (especially in men), muscle mass, body composition
Genetic and Epigenetic Markers DNA methylation patterns, senescent cell burden
Family History Longevity in parents or close relatives
Biomarkers may help distinguish between biological and chronological age, aiding individualized health screening.
Studies in younger cohorts show biological aging varies widely even among same-aged individuals.
Inclusion of centenarians in biomarker research may reveal mechanisms linking health status to exceptional longevity.
Implications for Clinical Practice and Public Health
Increased life expectancy does not necessarily mean longer periods of disability.
Understanding biological age can improve screening guidelines and preventive care by tailoring interventions to individual risk.
Current screening often ignores differences between biological and chronological age, possibly leading to over- or under-screening.
Life expectancy calculators incorporating biological and clinical markers can inform decision-making.
Anticipatory health discussions should integrate biological aging measures for better patient guidance.
Conclusion
Exceptional human longevity results from complex, multifactorial interactions among genetics, environment, culture, lifestyle, resiliency, and chance.
Aging characteristics vary widely even among long-lived individuals.
No single biomarker currently predicts longevity; a combination of clinical, genetic, and functional markers holds promise.
Observations from the oldest old support empirical lifestyle strategies—moderate eating, regular exercise, social engagement, and purposeful living—that promote health span and potentially extend life span.
Advancing biomarker research and personalized health assessments will improve screening, clinical decision-making, and promote successful aging.
Keywords
Exceptional longevity, centenarians, supercentenarians, aging, biomarkers, compression of morbidity, genetic factors, caloric restriction, physical activity, resiliency, biological age, social engagement, sex differences, life expectancy, health span.
References
References are comprehensive and include epidemiological, genetic, physiological, and clinical studies spanning decades, with key contributions from population cohorts, animal models, and intervention trials.
This summary strictly reflects the source content, synthesizing key findings, concepts, and data related to exceptional human longevity without extrapolation beyond the original text.
Smart Summary...
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Effect of eliminating
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Summary
This study, published in Revista de Saúde Summary
This study, published in Revista de Saúde Pública (2013), investigates whether the elimination of certain chronic diseases can lead to a compression of morbidity among elderly individuals in São Paulo, Brazil. It uses population-based data from the 2000 SABE (Health, Wellbeing and Ageing) study and official mortality records to evaluate changes in disability-free life expectancy (DFLE) resulting from the hypothetical removal of specific chronic conditions.
Background and Objectives
Chronic non-communicable diseases (NCDs) such as cardiovascular diseases, diabetes, and chronic pulmonary conditions account for approximately 50% of diseases in developing countries and are major contributors to morbidity and mortality.
In Brazil, these diseases represent the main health burden and priority for healthcare systems.
The compression of morbidity theory posits that delaying the onset of debilitating diseases compresses the period of morbidity into a shorter segment at the end of life, thus increasing healthy life expectancy.
Other theories include:
Expansion of morbidity: Mortality declines due to reduced lethality but incidence remains or increases, leading to longer periods of morbidity.
Dynamic equilibrium: Both mortality and morbidity decline, keeping years lived with severe disability relatively constant.
The study aims to analyze whether eliminating certain chronic diseases would compress morbidity among elderly individuals, improving overall health expectancy.
Methodology
Design: Analytical, population-based, cross-sectional study.
Population: 2,143 elderly individuals (aged 60+) from São Paulo, Brazil, sampled probabilistically in 2000 as part of the SABE study.
Data collection:
Structured questionnaire covering sociodemographics, health status, functional capacity, and chronic diseases.
Self-reported presence of 9 chronic diseases based on ICD-10: systemic arterial hypertension, diabetes mellitus, heart disease, lung disease, cancer, joint disease, cerebrovascular disease, falls in previous year, and nervous/psychiatric problems.
Functional disability defined by difficulties in activities of daily living (dressing, eating, bathing, toileting, ambulation, fecal and urinary incontinence).
Statistical analysis:
Sullivan’s method used to compute life expectancy (LE) and disability-free life expectancy (DFLE).
Cause-deleted life tables estimated probabilities of death with elimination of specific diseases.
Multiple logistic regression (controlling for age) assessed disability prevalence changes with disease elimination.
Assumption: independence between causes of death and disability.
Sampling weights and corrections for design effects were applied to represent the São Paulo elderly population.
Key Findings
Sample Characteristics
Females represented 58.6% of the sample.
Higher proportion of women aged 75+ (24.2%) than men (19.2%).
Women more frequently widowed or single; men had higher employment rates.
Women more likely to live alone.
Smart Summary
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rpqusbca-8795
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xevyo
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/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf...
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Energy Poverty and Life
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Energy Poverty and Life Expectancy in Nigeria
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xevyo-base-v1
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This study investigates the impact of energy pover This study investigates the impact of energy poverty on life expectancy in Nigeria over the period from 1981 to 2023. Utilizing time series data and the Autoregressive Distributed Lag (ARDL) model, the research examines both short-run and long-run effects, revealing a statistically significant negative relationship between energy poverty and life expectancy. The study emphasizes the critical role of energy access as a determinant of public health and longevity, urging policy reforms to improve energy infrastructure and accessibility in Nigeria to enhance health outcomes and sustainable development.
Key Concepts
Term Definition/Explanation
Life Expectancy Average number of years a newborn is expected to live, given current sex- and age-specific mortality rates.
Energy Poverty Lack of access to affordable, reliable, and clean energy services, including electricity and clean cooking fuels.
ARDL Model An econometric technique used to estimate both short-run and long-run relationships in time series data.
Sustainable Development Goals (SDGs) United Nations goals, including Goal 3 (Health and Well-being) and Goal 7 (Affordable and Clean Energy).
Background and Context
Nigeria faces a persistent energy crisis, with about 43% of the population (86 million people) lacking access to reliable and modern energy.
Life expectancy in Nigeria is significantly lower than the global average, estimated at 54.9 years for women and 54.3 years for men, compared to global averages of 76 and 70.7 years respectively.
Energy poverty in Nigeria manifests through:
Limited electricity access.
Dependence on biomass and kerosene for cooking.
Frequent power outages affecting households, hospitals, and public infrastructure.
Existing government policies (e.g., National Health Policy, Renewable Energy Master Plan) have not sufficiently improved energy access or life expectancy.
Life expectancy is a key indicator of national development and is strongly influenced by socioeconomic and infrastructural factors.
Theoretical Framework
The study is grounded in Human Capital Theory (Schultz, Becker), which posits that investments in health, education, and other social services enhance individual productivity and contribute to overall economic growth and well-being.
Access to modern energy is viewed as a critical enabler of:
Health services.
Clean environments.
Improved living standards.
Energy poverty undermines health by increasing exposure to harmful fuels and limiting access to healthcare, thereby shortening life expectancy.
Empirical Literature Highlights
Roy (2025): Clean energy access significantly increases life expectancy globally.
Olise (2025): Kerosene positively affects quality of life in Nigeria in the short and long run; premium motor spirit negatively affects life expectancy; electricity consumption had no significant impact.
Onisanwa et al. (2024): Socioeconomic factors including income, education, urbanization, and environmental degradation determine life expectancy in Nigeria.
Fan et al. (2024): Energy poverty adversely affects public health, especially in developed regions.
Abu & Orisa-Couple (2022): Unsafe energy sources (kerosene, generators) cause burns and mortality in Port Harcourt.
Okorie & Lin (2022): Energy poverty increases risk of catastrophic health expenditure among Nigerian households.
Onwube et al. (2021): Real GDP per capita, household consumption, and exchange rates positively influence life expectancy; inflation and imports have negative effects.
Data and Methodology
Data: Annual time series data (1981-2023) from World Bank’s World Development Indicators and Global Database of Inflation.
Variables:
Variable Description Expected Sign
LFE Life expectancy at birth Dependent
EPOV Energy poverty (access to electricity and clean cooking fuels) Negative (β1 < 0)
GDPK GDP per capita (constant 2015 US$) Positive (β2 > 0)
GHEX Government health expenditure per capita Positive (β3 > 0)
PVL Prevalence of undernourishment (%) Negative (β4 < 0)
LTR Literacy rate (secondary school enrollment %) Positive (β5 > 0)
Econometric Approach:
Stationarity tested using Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests.
Cointegration tested via ARDL Bounds testing.
Short-run and long-run relationships estimated using ARDL and Error Correction Model (ECM).
Descriptive Statistics
Variable Mean Min Max Std. Dev Notes
Life Expectancy (LFE) 48.78 yrs 45.49 yrs 54.59 yrs 2.87 Moderate variability over time
Energy Poverty (EPOV) 52.59% 28.20% 86.10% 13.60 Volatile energy poverty environment
GDP per capita (GDPK) $1922.55 $1408.21 $2679.56 466.60 Modest economic growth
Govt. Health Expenditure (GHEX) $6.73 $0.30 $15.84 5.62 Low health spending
Prevalence of Undernourishment (PVL) 10.61% 6.50% 19.00% 2.68 Moderate food insecurity
Literacy Rate (LTR) 33.31% 17.41% 54.88% 9.79 Low to moderate literacy
Correlation Matrix Summary
Positive moderate correlation with life expectancy: GDP per capita (0.651), government health expenditure (0.598), literacy rate (0.434).
Negative correlation: Energy poverty (-0.450).
Low correlation: Prevalence of undernourishment (0.333).
Unit Root and Cointegration Tests
Energy poverty (EPOV) stationary at level (I(0)).
Life expectancy (LFE), GDP per capita (GDPK), government health expenditure (GHEX), prevalence of undernourishment (PVL), and literacy rate (LTR) stationary at first difference (I(1)).
ARDL Bounds test confirmed cointegration, indicating a stable long-run relationship between energy poverty and life expectancy.
Regression Results
Variable Short-Run Coefficient Significance Long-Run Coefficient Significance Interpretation
Energy Poverty (EPOV) -0.299 Significant -0.699 Highly significant Energy poverty reduces life expectancy both short and long term; effect stronger over time.
GDP per capita (GDPK) 0.026 Insignificant 0.332 Significant Economic growth positively affects life expectancy, especially in the long run.
Govt. Health Expenditure (GHEX) 0.071 Significant -0.054 Insignificant Short-run benefits of health spending on life expectancy, but no significant long-run effect.
Prevalence of Undernourishment (PVL) -0.377 Significant -0.225 Significant Food insecurity negatively impacts life expectancy both short and long term.
Literacy Rate (LTR) 0.003 Insignificant 0.044 Marginal Positive but insignificant effect on life expectancy.
Error Correction Term -0.077 Highly significant Not specified Not specified Adjusts 77% of deviation from equilibrium each year, confirming model stability.
Diagnostic and Stability Tests
Breusch-Godfrey Serial Correlation LM test, Breusch-Pagan-Godfrey Heteroskedasticity test, and Ramsey RESET test showed no serial correlation, heteroskedasticity, or misspecification—indicating a robust model.
CUSUM and CUSUMSQ tests confirmed no structural breaks or parameter instability in the model over the study period.
Timeline of Key Trends (1981–2023)
Period Life Expectancy Trend Energy Poverty Trend Key Events/Context
1981–1995 Below 46.7 years, stagnant Increasing energy poverty Structural Adjustment era, economic challenges
1999–2003 Slight increase to ~47.2 years Fluctuations in energy poverty Transition to civilian rule, policy shifts
2003–2023 Gradual sustained increase to 54.6 years Sharp surge in energy poverty from 2010 onward Population growth, poor infrastructure, subsidy removal
Policy Recommendations
Prioritize Energy Sector Reforms:
Expand on-grid power generation and improve transmission and distribution infrastructure.
Promote affordable off-grid renewable energy solutions and clean cooking technologies.
Stabilize energy prices and enhance reliability of energy supply.
Increase and Improve Public Health Expenditure:
Boost healthcare infrastructure and access.
Implement institutional reforms to reduce corruption and improve resource allocation.
Address Food Insecurity:
Develop coordinated agricultural, nutritional, and welfare policies to reduce undernourishment.
Focus on Rural and Underserved Communities:
Target energy access expansion to marginalized populations to improve health and longevity.
Integrate Energy Policy with Health and Development Goals:
Align energy access initiatives with Sustainable Development Goals (SDG 3 and SDG 7).
Core Insights
Energy poverty significantly undermines life expectancy in Nigeria, with stronger effects observed over the long term.
Economic growth has a positive but delayed impact on life expectancy.
Public health expenditure improves life expectancy in the short run but shows diminished long-run effectiveness, likely due to governance challenges.
Food insecurity consistently reduces life expectancy.
Literacy improvements have a positive but statistically insignificant influence on longevity.
The relationship between energy poverty and life expectancy in Nigeria has remained stable over four decades despite policy efforts.
Keywords
Energy Poverty, Life Expectancy, Nigeria, ARDL Model, Sustainable Development Goals, Public Health, Economic Growth, Food Insecurity, Human Capital Theory.
Conclusion
This comprehensive empirical analysis confirms that energy poverty is a critical and persistent barrier to improving life expectancy in Nigeria. The negative impact of inadequate access to modern energy services on health outcomes necessitates urgent policy attention. Sustainable improvements in longevity will require integrated strategies that combine energy reforms, enhanced public health spending, food security measures, and economic growth, underpinned by strong institutional governance. Addressing energy poverty is not only vital for health but also essential for Nigeria’s broader development and achievement of international sustainability targets.
Smart Summary
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xxsdsakk-4069
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xevyo
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Estimates of the Heritabi
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Estimates of the Heritability of Human Longevity
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This investigation critically examines the heritab This investigation critically examines the heritability of human longevity, challenging prior estimates that have ranged between 15–30% by demonstrating that these figures are substantially inflated due to assortative mating—the nonrandom pairing of mates with respect to longevity-associated traits. Using an unprecedentedly large dataset derived from Ancestry public family trees, encompassing hundreds of millions of historical individuals primarily of European descent living in North America and Europe during the 19th and early 20th centuries, the authors applied advanced structural equation modeling to disentangle genetic, sociocultural, and assortative mating effects on lifespan correlations.
The study concludes that the true transferable variance (t²)—an upper bound on heritability (h²) that includes both genetic and sociocultural inherited factors—is well below 10% for birth cohorts across the 1800s and early 1900s. This suggests that earlier heritability estimates of longevity have been substantially overestimated because they did not adequately correct for assortative mating effects.
Key Concepts and Definitions
Term Definition
Heritability (h²) The fraction of phenotypic variance attributable to genetic variance.
Transferable variance (t²) Phenotypic variance due to all inherited factors, encompassing both genetic (h²) and sociocultural (b²) components, plus their covariance.
Sociocultural inheritance (b²) Non-genetic factors that influence phenotype and are transmitted through families (e.g., socioeconomic status).
Assortative mating (a) The correlation between latent genetic and sociocultural states of spouses that influences phenotypic correlations beyond genetic inheritance.
Nominal heritability Heritability estimated without correction for assortative mating or shared environment, typically based on correlation and additive relatedness.
Methodology Overview
Data Source: Aggregated and anonymized pedigrees (SAP) were created by collapsing 54 million publicly available Ancestry subscriber-generated family trees, resulting in over 831 million unique historical individuals linked by parent–child and spousal edges.
Data Quality Controls:
Removed self-edges and gender-incongruent parent-child edges.
Added missing spousal edges between parents.
Focused on individuals with known birth and death years who had offspring, limiting analysis primarily to birth cohorts from the early 1800s to 1920.
Addressed data artifacts such as birth year rounding.
Analysis Approach:
Estimated phenotypic correlations of lifespan between various relatives (siblings, cousins, spouses, in-laws).
Calculated nominal heritability using standard regression methods correcting for variance differences.
Developed and applied a structural equation model incorporating three key parameters:
Transferable variance (t²),
Inheritance coefficient (b),
Assortative mating coefficient (a).
Utilized correlations among siblings-in-law and cosiblings-in-law to solve for these parameters.
Applied an assortment-correction method using remote relative pairs and their in-law equivalents to validate estimates.
Timeline Table: Analytical Focus and Data Coverage
Period Data Characteristics and Focus
Pre-1700 Mostly European births; sparse data quality Not specified
1700–1800 Increasing data quality; European and North American births
1800–1920 Primary focus; high data quality; large sample sizes in millions
Post-1920 Decline in death-year data; excluded from lifespan analysis
Major Findings
1. Nominal Heritability Estimates Confirm Prior Literature but Are Inflated
Nominal heritability estimates for lifespan correlated with previous findings (15–30%).
Lifespan correlations among blood relatives were similar to past studies.
However, spouses and in-law relatives also showed substantial lifespan correlations, sometimes comparable to or exceeding those of blood relatives.
This indicated that shared environments and assortative mating inflate these estimates.
2. Assortative Mating Significantly Inflates Heritability Estimates
Assortative mating coefficient (a) was consistently high across all analyses, often exceeding 0.8, indicating strong nonrandom mating based on lifespan-influencing factors.
The presence of assortative mating causes phenotypic correlations between relatives to deviate from the linear relationship expected under pure additive genetics.
Correlations between in-law relatives (who do not share genetics) were substantial, confirming the importance of assortative mating rather than shared genetics alone.
3. Structural Equation Modeling Reveals True Transferable Variance (t²) Is <10%
Using sibling-in-law and cosibling-in-law correlations, the model estimated transferable variance (t²) consistently below 7% for all gender combinations and birth cohorts.
This t² value represents an upper bound on heritability (h²) because it includes both genetic and sociocultural transmitted factors.
The inheritance coefficient (b) was estimated between 0.40–0.45, slightly less than the genetic expectation of 0.5, reflecting combined genetic and sociocultural inheritance.
Shared household environmental effects were also quantified and found to be substantial but separate from transferable variance.
4. Independent Validation Using Remote Relatives Supports Low Heritability
Assortment-correction method applied to remote relatives (piblings, first cousins, first cousins once removed) and their in-law equivalents consistently estimated assortative mating coefficients (a) close to or above 0.5.
Transferable variance estimates from these analyses also remained below 10%, validating the sibling-in-law modeling approach.
5. Transferable Variance Decreases with Increasing Birth-Cohort Disparity Among Relatives
Lifespan correlation and transferable variance (t²) were higher when relatives were born closer in time; as the birth-year gap increased, t² declined significantly.
Assortative mating coefficient (a) remained stable across birth-year offsets, suggesting that the decline in transferable variance was not due to mating patterns.
This suggests that genetic and sociocultural factors affecting lifespan vary with historical context, likely reflecting changing environmental hazards and causes of death over time.
Quantitative Summary Table: Structural Equation Model Estimates by Birth Cohort
Birth Cohort Period Transferable Variance (t²) Assortative Mating Coefficient (a) Inheritance Coefficient (b) Shared Childhood Environment (csib) Shared Adult Environment (csp)
1800s–1830s ~5.9–6.5% (across relatives) ~0.68–0.88 ~0.40–0.44 ~4.3% (siblings) ~6.6% (spouses)
1840s–1870s ~4.0–5.5% ~0.53–0.88 ~0.40 ~5.1% ~5.0%
1880s–1910s ~4.0–7.2% ~0.43–0.89 ~0.40 ~6.0% ~4.4%
Values represent means across gender pairs with standard deviations; b fixed at 0.5 for some estimates; all data derived from sibling-in-law and remote relative analyses.
Core Insights
Previous heritability estimates of human longevity (~15–30%) are substantially inflated due to assortative mating.
True heritability (h²) is likely below 10%, and possibly considerably lower after accounting for sociocultural inheritance.
Assortative mating for lifespan-related factors is strong, with a coefficient often >0.8, indicating mates tend to share longevity-related traits, both genetic and environmental.
Sociocultural factors (e.g., socioeconomic status) are a significant inherited component influencing longevity, evidenced by lifespan correlations among in-law relatives and supported by sociological literature.
Transferable variance (t²) decreases as birth cohorts diverge, implying that historical environmental changes modulate the impact of inherited factors on longevity.
Fundamental biological aging processes (e.g., rate of hazard doubling) appear consistent historically, but lifespan-affecting factors mostly modify susceptibility to historically transient environmental hazards, not aging rate itself.
Implications
Genetic studies of longevity should account for assortative mating and sociocultural inheritance to avoid overestimating genetic contributions.
Interventions targeting environmental and sociocultural factors could have a larger impact on lifespan extension than currently assumed genetic predispositions.
Historical and birth cohort context is critical when interpreting heritability and lifespan data.
The biological basis of aging remains consistent, but its interaction with environment and social factors is dynamic and complex.
References to Relevant Literature Mentioned
Smart Summary
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Evolution of the Human
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Evolution of the Human Lifespan
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This comprehensive essay by Caleb E. Finch explore This comprehensive essay by Caleb E. Finch explores the evolution of human lifespan (life expectancy, LE) over hundreds of thousands of generations, emphasizing the interplay between genetics, environment, lifestyle, inflammation, infection, and diet. The work integrates paleontological, archaeological, epidemiological, and molecular data to elucidate how human longevity has changed from pre-industrial times to the present and projects challenges for the future.
Key Themes and Insights
Human life expectancy (LE) is uniquely long among primates:
Pre-industrial human LE at birth (~30–40 years) was about twice that of great apes (~15 years at puberty for chimpanzees). This extended lifespan arises from slower postnatal maturation and lower adult mortality rates, rooted in both genetics and environmental factors.
Rapid increases in LE during industrialization:
Since 1800, improvements in nutrition, hygiene, and medicine have nearly doubled human LE again, reaching 70–85 years in developed populations. Mortality improvements were not limited to early life but included significant gains in survival at older ages (e.g., after age 70).
Environmental and epigenetic factors dominate recent LE trends:
Human lifespan heritability is limited (~25%), highlighting the importance of environmental and epigenetic influences on aging and mortality.
Infection and chronic inflammation shape mortality and aging:
The essay emphasizes the “inflammatory load”—chronic exposure to infection and inflammation—as a critical factor affecting mortality trajectories both historically and evolutionarily.
Mortality Phase Framework and Historical Cohort Analysis
Finch and collaborators define four mortality phases to analyze lifespan changes using historical European data (notably Sweden since 1750):
Mortality Phase Age Range (years) Description Mortality Pattern
Phase 1 0–9 Early age mortality (mainly infec-tions) Decreasing mortality from birth to puberty
Phase 2 10–40 Basal mortality (lowest mortality) Lowest mortality across lifespan
Phase 3 40–80 Exponentially accelerating mortality Gompertz model exponential increase
Phase 4 >80 Mortality plateau (approaching max) Mortality rate approaches ~0.5/year
Key insight: Reductions in early-life mortality (Phase 1) strongly predict lower mortality at older ages (Phase 3), demonstrating persistent impacts of early infection/inflammation on aging-related deaths.
J-shaped mortality curve: Mortality rates are high in infancy, drop to a minimum around puberty, then accelerate exponentially in adulthood.
Gompertz model explains adult mortality acceleration:
[ m(x) = A e^{Gx} ]
where ( m(x) ) is mortality rate at age ( x ), ( A ) is initial mortality rate, and ( G ) is the Gompertz coefficient (rate of acceleration).
Despite improvements in LE, the rate of mortality acceleration (G) has increased, meaning aging processes remain or have intensified, but reduced background mortality (A) has driven LE gains.
Links Between Early Life Conditions and Later Health
Early life infections and inflammation leave a lifelong “cohort morbidity” imprint, influencing adult mortality and chronic disease risk (e.g., cardiovascular disease).
Studies of historical cohorts show strong correlations between neonatal mortality and mortality at age 70 across multiple European countries.
Adult height, a marker of growth and nutrition, reflects childhood infection burden and correlates inversely with early mortality.
The 1918 influenza pandemic provides a notable example: prenatal exposure led to reduced growth, lower education, and a 25% increase in adult heart disease risk for those born during or shortly after the pandemic.
Chronic Diseases, Inflammation, and Infection
Chronic infections and inflammation contribute to major aging diseases such as atherosclerosis, cancer, and vascular diseases.
The essay highlights the role of Helicobacter pylori (gastric cancer risk) and tobacco smoke (vascular inflammation and cancer) as examples linking infection/inflammation to chronic disease.
Contemporary infectious diseases like HIV/AIDS, despite improved treatment, increase the risk of vascular disease and non-AIDS cancers, illustrating ongoing infection-inflammation interactions in aging.
Insights from Hunter-Gatherer Populations: The Tsimane Case Study
The Tsimane, a Bolivian forager-horticulturalist population, have a life expectancy (~42 years) comparable to pre-industrial Europe, with high infectious and inflammatory loads (e.g., 60% parasite prevalence, elevated CRP levels).
Despite high inflammation, they have low blood pressure, low blood cholesterol, low body mass index (~23), and low incidence of ischemic heart disease, likely due to diet low in saturated fats and physical activity.
This population provides a unique natural experiment to study the relationships among infection, inflammation, diet, and aging in the absence of modern medical interventions.
Evidence of Chronic Disease in Ancient Populations
Radiological studies of Egyptian mummies (Old and New Kingdoms) reveal advanced atherosclerosis in approximately half of adult specimens, despite their infectious disease burden and diet rich in saturated fats.
Similarly, the “Tyrolean iceman” (~3300 BCE) exhibits arterial calcifications.
These findings, though limited in sample size and representativeness, suggest vascular diseases accompanied infections and inflammation in ancient humans.
Evolutionary Perspectives on Diet, Inflammation, and Lifespan
Finch proposes a framework of ecological stages in human evolution focusing on inflammatory exposures and diet, hypothesizing how humans evolved longer lifespans despite pro-inflammatory environments.
Stage Approximate Period Ecology & Group Size Diet Characteristics Infection/Inflammation Exposure
1 4–6 MYA Forest-savannah, small groups Low saturated fat intake Low exposure to excreta
2 4–0.5 MYA Forest-savannah, small groups Increasing infections from excreta & carrion; increased pollen & dust exposure Increased infection and inflammation exposure
3 0.5 MYA–15,000 YBP Varied, temperate zone, larger groups Increased meat consumption; use of domestic fire and smoke Increased exposure to smoke and inflammation
4 12,000–150 YBP Permanent settlements, larger groups Cereals and milk from domestic crops and animals Intense exposure to human/domestic animal excreta & parasites
5 1800–1950 Industrial age, high-density homes Improved nutrition year-round Improving sanitation, reduced infections
6 1950–2010 Increasing urbanization High fat and sugar consumption; rising obesity Public health measures, vaccination, antibiotics
7 21st century >90% urban, very high density Continued high fat/sugar intake Increasing ozone, air pollution, water shortages
Humans evolved longer lifespans despite increased exposure to pro-inflammatory factors such as:
Higher dietary fat (10x that of great apes), particularly saturated fats.
Exposure to infections through scavenging, carrion consumption, and communal living.
Increased inhalation of dust, pollen, and volcanic aerosols due to expanded savannah habitats.
Chronic smoke inhalation from controlled use of fire and indoor biomass fuel combustion.
Exposure to excreta in denser human settlements, contrasting with great apes’ hygienic behaviors (e.g., nest abandonment).
Introduction of dietary inflammatory agents including cooked food derivatives (advanced glycation end products, AGEs) and gluten from cereal grains.
Counterbalancing factors included antioxidants and anti-inflammatory dietary components (e.g., polyphenols, omega-3 fatty acids, salicylates).
Skeletal evidence shows a progressive decrease in adult body mass over 60,000 years prior to the Neolithic, possibly reflecting increased inflammatory burden and nutritional stress.
The Role of Apolipoprotein E (apoE) in Evolution and Aging
The apoE gene, critical for lipid transport, brain function, and immune responses, has three main human alleles: E2, E3, and E4.
ApoE4, the ancestral allele, is linked to:
Enhanced inflammatory responses.
Efficient fat storage (a “thrifty gene” hypothesis).
Increased risk of Alzheimer’s disease, cardiovascular disease, and shorter lifespan.
Possible protection against infections and better cognitive development in high-infection environments.
ApoE3, unique to humans and evolved ~0.23 MYA, is associated with reduced inflammatory responses and is predominant today.
The chimpanzee apoE resembles human apoE3 functionally, which may relate to their lower incidence of Alzheimer-like pathology and vascular disease.
This allelic variation reflects evolutionary trade-offs between infection resistance, metabolism, and longevity.
Future Challenges to Human Lifespan Gains
Current maximum human lifespan may be approaching biological limits:
Using Gompertz mortality modeling, Finch and colleagues estimate maximum survival ages of around 113 for men and 120 for women under current mortality patterns, matching current longevity records.
Further increases in lifespan require slowing or delaying mortality acceleration, which remains challenging given biological constraints and limited human evidence for such changes.
Emerging global threats may reverse recent lifespan gains:
Climate change and environmental deterioration, including increasing heat waves, urban heat islands, and air pollution (notably ozone), which disproportionately affect the elderly.
Air pollution, especially from vehicular emissions and biomass fuel smoke, exacerbates cardiovascular and pulmonary diseases and may accelerate brain aging.
Water shortages and warming expand the range and incidence of infectious diseases, including malaria, dengue, and cholera, posing risks to immunosenescent elderly.
Protecting aging populations from these risks will require:
Enhanced public health measures.
Research on dietary and pharmacological interventions (e.g., antioxidants like vitamin E).
Improved urban planning and pollution control.
Core Concepts
Life expectancy (LE): Average expected lifespan at birth or other ages.
Gompertz model: Mathematical model describing exponential increase in mortality with age.
Cohort morbidity: The lasting health impact of early life infections and inflammation on aging and mortality.
Inflammaging: Chronic, low-grade inflammation that contributes to aging and age-related diseases.
Apolipoprotein E (apoE): A protein with genetic polymorphisms influencing lipid metabolism, inflammation, infection resistance, and neurodegeneration.
Advanced glycation end products (AGEs): Pro-inflammatory compounds formed during cooking and metabolism, implicated in aging and chronic disease.
Compression of morbidity: The hypothesis that morbidity is concentrated into a shorter period before death as lifespan increases.
Quantitative and Comparative Data Tables
Table 1: Ecological Stages of Human Evolution by Diet and Infection Exposure
Stage Time Period Ecology & Group Size Diet Characteristics Infection & Inflammation Exposure
1 4–6 MYA Forest-savannah, small groups Low saturated fat intake Low exposure to excreta
2 4–0.5 MYA Forest-savannah, small groups Increasing exposure to infections Exposure to excreta, carrion, pollen, dust
3 0.5 MYA–15,000 YBP Varied, temperate zones, larger groups Increased meat consumption, use of fire Increased smoke exposure, infections
4 12,000–150 YBP Permanent settlements Cereals and milk from domesticated crops High exposure to human and animal excreta and parasites
5 1800–1950 Industrial age, high-density homes Improved nutrition Reduced infections and improved hygiene
6 1950–2010 Increasing urbanization High fat and sugar intake; rising obesity Vaccination, antibiotics, pollution control
7 21st century Highly urbanized, dense populations Continued poor diet trends Increased air pollution, ozone, climate change
Table 2: apoE Allele Differences between Humans and Chimpanzees
Residue Position Chimpanzee apoE Human apoE4 Human apoE3
61 Threonine (T) Arginine ® Arginine ®
112 Arginine ® Arginine ® Cysteine ©
158 Arginine ® Arginine ® Arginine ®
The chimpanzee apoE protein functions more like human apoE3 due to residue 61, associated with lower inflammation and different lipid binding.
Timeline of Human Lifespan Evolution and Key Events
Period Event/Characteristic
~4–6 million years ago Shared great ape ancestor; low-fat diet, low infection exposure
~4–0.5 million years ago Early Homo; increased exposure to infections, pollen, dust
~0.5 million years ago Use of fire; increased meat consumption; smoke exposure
12,000–150 years ago Neolithic settlements; cereal and milk consumption; high parasite loads
1800 Industrial revolution; sanitation, nutrition improvements lead to doubling LE
1918 Influenza pandemic; prenatal infection impacts long-term health
1950 onward Vaccines, antibiotics reduce infections; obesity rises
21st century Climate change, air pollution threaten gains in lifespan
Conclusions
Human lifespan extension is a product of complex interactions between genetics, environment, infection, inflammation, and diet.
Historical and contemporary data demonstrate that early-life infection and inflammation have lifelong impacts on mortality and aging trajectories.
The evolution of increased lifespan in Homo sapiens occurred despite increased exposure to various pro-inflammatory environmental factors, including diet, smoke, and pathogens.
Genetic adaptations, such as changes in the apoE gene, reflect trade-offs balancing inflammation, metabolism, and longevity.
While remarkable lifespan gains have been achieved, biological limits and emerging global environmental challenges (climate change, pollution, infectious disease risks) threaten to stall or reverse these advances.
Addressing these challenges requires integrated public health strategies, environmental protections, and further research into the mechanisms linking inflammation, infection, and aging.
Keywords
Human lifespan evolution
Life expectancy
Infection
Inflammation
Mortality phases
Gompertz model
Apolipoprotein E (apoE)
Hunter-gatherers (Tsimane)
Chronic diseases of aging
Environmental exposures
Climate change
Air pollution
Evolutionary medicine
Early life programming
Aging biology
FAQ
Q1: What causes the increase in human life expectancy after 1800?
A1: Improvements in hygiene, nutrition, and medicine reduced infectious disease mortality, especially in early life, enabling longer survival into old age.
Q2: How does early-life infection affect aging?
A2: Early infections induce chronic inflammation (“cohort morbidity”) that persists and accelerates aging-related mortality and diseases such as cardiovascular conditions.
Q3: Why do humans live longer than great apes despite higher inflammatory exposures?
A3: Humans evolved genetic adaptations, such as apoE variants, and lifestyle changes that mitigate some inflammatory damage, enabling longer lifespan despite greater pro-inflammatory environmental exposures.
Q4: What are the future risks to human longevity gains?
A4: Environmental degradation including air pollution, ozone increase, heat waves, water shortages, and emerging infectious diseases linked to climate change threaten to reverse recent lifespan gains, especially in elderly populations.
Q5: Can lifespan increases continue indefinitely?
A5: Modeling suggests biological and mortality limits near current record lifespans; further gains require slowing or delaying aging processes, which remain challenging.
This summary is grounded entirely in Caleb E. Finch’s original essay and faithfully reflects the detailed scientific content, key findings, and hypotheses presented therein.
Smart Summary...
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Effects of desiccation
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Effects of desiccation stress
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This study presents a systematic review and pooled This study presents a systematic review and pooled survival analysis quantifying the effects of desiccation stress (humidity) and temperature on the adult female longevity of Aedes aegypti and Aedes albopictus, the primary mosquito vectors of arboviral diseases such as dengue, Zika, chikungunya, and yellow fever. The research addresses a critical gap in vector ecology and epidemiology by providing a comprehensive, quantitative model of how humidity influences adult mosquito survival, alongside temperature effects, to improve understanding of transmission dynamics and enhance predictive models of disease risk.
Background
Aedes aegypti and Ae. albopictus are globally invasive mosquito species that transmit several major arboviruses.
Adult female mosquito longevity strongly impacts transmission dynamics because mosquitoes must survive the extrinsic incubation period (EIP) to become infectious.
While temperature effects on mosquito survival have been widely studied and incorporated into models, the role of humidity remains poorly quantified despite being ecologically significant.
Humidity influences mosquito survival via desiccation stress, affecting water loss and physiological function.
Environmental moisture also indirectly affects mosquito populations by altering evaporation rates in larval habitats, impacting larval development and adult body size, which affects vectorial capacity.
Understanding the temperature-dependent and non-linear effects of humidity can improve ecological and epidemiological models, especially in arid, semi-arid, and seasonally dry regions, which are understudied.
Objectives
Systematically review experimental studies on temperature, humidity, and adult female survival in Ae. aegypti and Ae. albopictus.
Quantify the relationship between humidity and adult survival while accounting for temperature’s modifying effect.
Provide improved parameterization for models of mosquito populations and arboviral transmission.
Methods
Systematic Literature Search: 1517 unique articles screened; 17 studies (16 laboratory, 1 semi-field) met inclusion criteria, comprising 192 survival experiments with ~15,547 adult females (8749 Ae. aegypti, 6798 Ae. albopictus).
Inclusion Criteria: Studies must report survival data for adult females under at least two temperature-humidity regimens, with sufficient methodological detail on nutrition and hydration.
Data Extraction: Variables included species, survival times, mean temperature, relative humidity (RH), and provisioning of water, sugar, and blood meals. Saturation vapor pressure deficit (SVPD) was calculated from temperature and RH to represent desiccation stress.
Survival Time Simulation: To harmonize disparate survival data formats (survival curves, mean/median longevity, survival proportions), individual mosquito survival times were simulated via Weibull and log-logistic models.
Pooled Survival Analysis: Stratified and mixed-effects Cox proportional hazards regression models were used to estimate hazard ratios (mortality risks) associated with temperature, SVPD, and nutritional factors.
Model Selection: SVPD was found to fit survival data better than RH or vapor pressure.
Sensitivity Analyses: Included testing model robustness by excluding individual studies and comparing results using only Weibull simulations.
Key Quantitative Findings
Parameter Ae. aegypti Ae. albopictus Notes
Temperature optimum (lowest mortality hazard) ~27.5 °C ~21.5 °C Ae. aegypti optimum higher than Ae. albopictus
Mortality risk trend Increases non-linearly away from optimum; sharp rise at higher temps Similar trend; possibly slightly better survival at lower temps Mortality rises rapidly at high temps for both species
Effect of desiccation (SVPD) Mortality hazard rises steeply from 0 to ~1 kPa SVPD, then more gradually Mortality hazard increases with SVPD but with less clear pattern Non-linear and temperature-dependent relationship
Species comparison (stratified model) Generally lower mortality risk than Ae. albopictus across most conditions Higher mortality risk compared to Ae. aegypti Differences not significant in mixed-effects model
Nutritional provisioning effects Provision of water, sugar, blood meals significantly reduces mortality risk Same as Ae. aegypti Provisioning modeled as binary present/absent
Qualitative and Contextual Insights
Humidity is a significant and temperature-dependent factor affecting adult female survival in Ae. aegypti, with more limited but suggestive evidence for Ae. albopictus.
Mortality risk increases sharply with desiccation stress (SVPD), especially at higher temperatures.
Ae. aegypti tends to have higher survival and a higher thermal optimum than Ae. albopictus, aligning with their geographic distributions—Ae. aegypti favors warmer, drier climates while Ae. albopictus tolerates cooler temperatures.
Provisioning of water and nutrients (sugar, blood) markedly improves survival, reflecting the importance of hydration and energy intake.
The findings support that humidity effects are underrepresented in current mosquito and disease transmission models, which often rely on simplistic or threshold-based mortality assumptions.
The use of SVPD (a measure of desiccation potential) rather than relative humidity or vapor pressure is more appropriate for modeling mosquito survival related to desiccation.
There is substantial unexplained variability among studies, likely due to unmeasured factors such as mosquito genetics, experimental protocols, and microclimatic conditions.
The majority of studies used laboratory settings and tropical/subtropical strains, with very limited data from arid or semi-arid climates, a critical gap given the importance of humidity fluctuations there.
Microclimatic variability and mosquito behavior (e.g., seeking humid refugia) may mitigate desiccation effects in the field, so laboratory results may overestimate mortality under natural conditions.
The study highlights the need for more field-based and arid region studies, and for models to incorporate nonlinear and interactive effects of temperature and humidity on mosquito survival.
Timeline Table: Study Selection and Analysis Process
Step Description
Literature search (Feb 2016) 1517 unique articles screened
Full text review 378 articles assessed for eligibility
Final inclusion 17 studies selected (16 lab, 1 semi-field)
Data extraction Survival data, temperature, humidity, nutrition, species, setting
Survival time simulation Weibull and log-logistic models used to harmonize survival data
Pooled survival analysis Stratified and mixed-effects Cox regression models
Sensitivity analyses Exclusion of individual studies, Weibull-only simulations
Model selection SVPD chosen as best humidity metric
Definitions and Key Terms
Term Definition
Aedes aegypti Primary mosquito vector of dengue, Zika, chikungunya, and yellow fever viruses
Aedes albopictus Secondary vector species with broader climatic tolerance, also transmits arboviruses
Saturation Vapor Pressure Deficit (SVPD) Difference between actual vapor pressure and saturation vapor pressure; a measure of drying potential/desiccation stress
Extrinsic Incubation Period (EIP) Time required for a virus to develop within the mosquito before it can be transmitted
Desiccation stress Physiological stress from water loss due to low humidity, impacting mosquito survival
Stratified Cox regression Survival analysis method allowing baseline hazards to vary by study
Mixed-effects Cox regression Survival analysis
Smart Summary
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Filtered merged training 6-12
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Contain lots of data various category like econimi Contain lots of data various category like econimics, medical, historical...
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LONGEVITY PAY
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This document is an official University of Texas R This document is an official University of Texas Rio Grande Valley Handbook of Operating Procedures (HOP) policy outlining the rules, eligibility, and administration of Longevity Pay for full-time employees.
Purpose
To establish how longevity pay is administered for eligible UTRGV employees.
Who It Applies To
All full-time UTRGV employees working 40 hours per week.
Key Points of the Policy
Eligibility Requirements
An employee becomes eligible after two years of state service if they:
Are full-time on the first workday of the month
Are not on leave without pay
Have at least two years of lifetime service credit
Law enforcement staff with hazardous duty pay only receive longevity credit for non-hazardous duty service. Part-time, temporary, and academic employees are not eligible.
Service Credit Rules
Lifetime service credit includes:
All prior Texas state employment (full-time, part-time, temporary, academic, legislative)
Military service when returning to state employment
Faculty service (if later moving into a non-academic role)
Credit is not given for months fully on leave without pay.
Hazardous duty service is counted only if the employee is not currently receiving hazardous duty pay.
Longevity Pay Schedule
Paid in two-year increments at the following monthly rates:
Years Monthly Pay
2 $20
4 $40
6 $60
… …
42 $420
(Full table included in the policy.)
Payment Rules
Begins the first day of the month after completing each 24-month increment.
Not prorated.
Included in regular payroll (not a lump sum).
Affects taxes, retirement contributions, and overtime calculations.
Not included in payout of vacation/sick leave.
Transfers
The employer of record on the first day of the month is responsible for payment.
Return-to-Work Retirees
Special rules apply:
Those who retired before June 1, 2005, and returned before Sept 1, 2005 receive a frozen amount of longevity pay.
Those returning after Sept 1, 2005—or retiring on or after June 1, 2005—are not eligible.
Legal Authority
Texas Government Code Sections 659.041–659.047 govern longevity pay.
Revision Note
Reviewed and amended July 13, 2022 (non-substantive update)....
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Longevity, by Design
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“Longevity, by Design” is an official Apple report “Longevity, by Design” is an official Apple report (June 2024) detailing how Apple designs products to last longer through durability, repairability, software support, and environmental responsibility. It explains Apple’s philosophy, engineering practices, and policies that contribute to long product lifespans across iPhone, iPad, Mac, and Apple Watch.
Key Themes of the Report
Product Longevity:
Apple highlights the long lifespan of its devices, citing industry-leading secondhand value, declining repair rates, and ongoing OS/security updates for many years.
Durability & Reliability Testing:
Apple describes extensive durability tests (liquid exposure, UV light, chemical exposure, drop tests, vibration tests) used on thousands of prototypes to reduce failure rates before products reach customers.
Software Support:
The document details long OS support windows—often 6+ years—and security updates even for older devices that cannot run the latest OS.
Repairability Principles:
Apple outlines four guiding principles:
Environmental impact – balancing repairability with carbon efficiency.
Access to repair services – expanding authorized and independent repair networks and Self Service Repair.
Safety, security, and privacy – especially around biometric components.
Transparency in repair – via Parts and Service History on devices.
Repairability Improvements:
Apple notes enhanced repairability in iPhone 15 (including easier back-glass repair), easier battery replacement in Macs and iPads, and upcoming support for used genuine Apple parts.
Third-Party Parts:
Apple supports third-party part usage but warns about safety issues—especially with third-party batteries, citing a UL Solutions study in which 88% failed safety tests.
Parts Pairing Explained:
Apple describes pairing as necessary for:
biometrics security
device calibration
transparency
Not a mechanism to block third-party repair except for Face ID/Touch ID security reasons.
Expansion of Repair Access:
Apple documents the growth of:
Authorized Service Providers
Independent Repair Providers
Self Service Repair in many countries
FAQs Section:
Apple answers questions about planned obsolescence, right-to-repair legislation, repair options, and environmental impacts.
If you'd like, I can also provide:
📌 a short summary,
📌 a bullet-point cheat sheet,
📌 a presentation-style outline,
📌 or extract any specific section in detail.
Just tell me what you need!SourcesDo you like this personality?...
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