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6fe90131-32fe-4ceb-aabc-afa11bb7448c
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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taycgghk-5680
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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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A mathematical model
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A mathematical model to estimate the seasonal
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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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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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f458f62f-605d-4d2c-9a72-a02676873dac
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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rlitfkqf-2632
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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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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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5c3bc022-5cbf-42f3-9e07-e6a343b2ab21
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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kwzpadlx-9963
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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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The effect of water
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The effect of drinking water
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Theeffectofdrinkingwaterqualityonthehealthand long Theeffectofdrinkingwaterqualityonthehealthand longevityofpeople-AcasestudyinMayang,HunanProvince, China
JLu1,2 andFYuan1 1DepartmentofEngineeringandSafety,UiTTheArcticUniversityofNorway,N9037Tromsø,Norway
E-mail:Jinmei.lu@uit.no Abstract. Drinking water is an important source for trace elements intake into human body. Thus, the drinking water quality has a great impact on people’s health and longevity. This study aims to study the relationship between drinking water quality and human health and longevity. A longevity county Mayang in Hunan province, China was chosen as the study area. The drinking water and hair of local centenarians were collected and analyzed the chemical composition. The drinking water is weak alkalineandrichintheessentialtraceelements.ThedailyintakesofCa,Cu,Fe,Se,Sr from drinking water for residents in Mayang were much higher than the national average daily intake from beverage and water. There was a positive correlation between Ni and Pb in drinking water and Ni and Pb in hair. There were significant correlationsbetweenCu,KindrinkingwaterandBa,Ca,Mg,Srinthehairatthe0.01 level. The concentrations of Mg, Sr, Se in drinking water showed extremely significant positive relation with two centenarian index 100/80% and 100/90% correlation. Essential trace elements in drinking water can be an important factor for localhealthandlongevity.
1. Introduction Trace elements can not be manufactured by human body itself, and they must be taken from the natural environment. Water is a major source of trace elements necessary for the growth of biological organisms. The composition of trace elements in water has a significant impact on human health. Changes in drinking water and groundwater sources can lead to significant changes in health risk relatedwithtraceelements[1]. Insufficient or excessive trace elements in water can lead to the occurrence of certain diseases. Liu XJ et al. found that the concentrations of Cu, Fe, Sr, Ti and V in the water samples from area with high incidence of gastric cancer were significantly higher than those in the area with low incidence of gastric cancer [2]. Another research on the relationship between the concentration of trace elements in drinking water and gastric cancer showed that Se and Zn can significantly prevent the development of gastric cancer [3]. Kikuchi H. et al. studied the relationship between the levels of trace elements in water and age-adjusted incidence of colon and rectal cancer, and the results showed that the incidence ...
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7b2a2799-a74e-4dd4-93a8-4bbabe61ca47
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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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cd7f6ee5-ca09-4aba-bf20-bc86fe62aff8
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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vwitogci-0660
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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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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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42f0b47e-7ea7-456d-80db-d7e53fefb810
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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taqjaqel-7779
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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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Determinants of longevity
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Determinants of longevity
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K. CHRISTENSENa & J. W. VAUPELb From abOdense K. CHRISTENSENa & J. W. VAUPELb From abOdense University Medical School, Odense, Denmark; bSanford Institute, Duke University, Durham, NC, USA; and aThe Danish Epidemiology Science Centre, The Steno Institute of Public Health, Department of Epidemiology and Social Medicine, Aarhus University Hospital, Aarhus, Denmark
Abstract. Christensen K, Vaupel JW (Odense University Medical School, Odense, Denmark; Sanford Institute, Duke University, Durham, NC, USA; and The Danish Epidemiology Science Centre, The Steno Institute of Public Health, Department of Epidemiology and Social Medicine, Aarhus University Hospital, Aarhus, Denmark). Determinants of longevity: genetic, environmental and medical factors (Review). J Intern Med 1996; 240: 333–41.
This review focuses on the determinants of longevity in the industrialized world, with emphasis on results from recently established data bases. Strong evidence is now available that demonstrates that in developed
Introduction
The determinants of longevity might be expected to be well understood. The duration of life has captured the attention of many people for thousands of years; an enormous array of vital-statistics data are available for many centuries. Life-span is easily measured compared with other health phenomena, and in many countries data are available on whole populations and not just study samples. Knowledge concerning determinants of human longevity, however, is still sparse, and much of the little that is known has been learned in recent years. This review
countries the maximum lifespan as well as the mean lifespan have increased substantially over the past century. There is no evidence of a genetically determined lifespan of around 85 years. On the contrary, the biggest absolute improvement in survival in recent decades has occurred amongst 80 year-olds. Approximately one-quarter of the variation in lifespan in developed countries can be attributed to genetic factors. The influence of both genetic and environmental factors on longevity can potentially be modified by medical treatment, behavioural changes and environmental improvements.
Keywords: centenarians, life expectancy, lifespan, mortality.
focuses on genetic, environmental and medical factors as determinants of longevity in developed countries and discusses alternative paradigms concerning human longevity.
How should longevity be measured?
Longevity can be studied in numerous ways; key questions include the following. How long can a human live? What is the average length of life? Are the maximum and average lengths of life approaching limits? Why do some individuals live longer than others? In addressing these questions, it is useful to
# 1996 Blackwell Science Ltd 333
334 K. CHRISTENSEN & J. W. VAUPEL
study the maximum lifespan actually achieved in various populations, the mean lifespan, and the variation in lifespan. Estimating the maximum lifespan of human beings is simply a matter of finding a well-documented case report of a person who lived longer than other welldocumented cases. The assessment of mean lifespan in an actual population requires that the study population is followed from birth to extinction. An alternative approach is to calculate age-specific death rates at some point in time for a population, and then use these death rates to determine how long people would live on average in a hypothetical population in which these death rates prevailed over the course of the people’s lives. This second kind of mean lifespan is generally known as life expectancy. The life expectancy of the Swedish population in 1996 is the average lifespan that would be achieved by the 1996 birth cohort if Swedish mortality rates at each age remained at 1996 levels for the entire future life of this cohort. Assessment of determinants of life expectancy and variation in lifespan amongst individuals rely on demographic comparisons of different populations and on such traditional epidemiological designs as follow-up studies of exposed or treated versus nonexposed or nontreated individuals. Designs from genetic epidemiology – such as twin, adoption and other family studies – are useful in estimating the relative importance of genes and environment for the variation in longevity.
Determinants of extreme longevity
Numerous extreme long-livers have been reported in various mountainous regions, including Georgia, Kashmir, and Vilcabamba. In most Western countries, including the Scandinavian countries, exceptional lifespans have also been reported. Examples are Drachenberg, a Danish–Norwegian sailor who died in 1772 and who claimed that he was born in 1626, and Jon Anderson, from Sweden, who claimed to be 147 years old when he died in 1729. There is noconvincingdocumentationfortheseextremelonglivers. When it has been possible to evaluate such reports, they have proven to be very improbable [1, 2]. In countries, like Denmark and Sweden, with a long tradition of censuses and vital statistics, remarkable and sudden declines in the number of
extreme long-livers occur with the introduction of more rigorous checking of information on age of death, as the result of laws requiring birth certificates, the development of church registers and the establishment of statistical bureaus [3, 4]. This suggests that early extreme long-livers were probably just cases of age exaggeration. Today (March 1996), the oldest reported welldocumented maximum lifespan for females is 121 years [5] and for males 113 years [6]. Both these persons are still alive. Analyses of reliable cases of long-livers show that longevity records have been repeatedly broken over past decades [3, 6]; this suggests that even longer human lifespans may occur in the future. There has been surprisingly little success in identifying factors associated with extreme longevity. A variety of centenarian studies have been conducted during the last half century. As reviewed by Segerberg [7], most of the earlier studies were based on highly selected samples of individuals, without rigorous validation of the ages of reputed centenarians. During the last decade several more comprehensive, less selected centenarian studies have been carried out in Hungary [8], France [9], Finland [10] and Denmark [11]. A few specific genetic factors have been found to be associated with extreme longevity. Takata et al. [12] found a significantly lower frequency of HLA-DRw9 amongst centenarians than in an adult control group in Japan, as well as a significantly higher frequency of HLA-DR1. The HLA-antigens amongst the Japanese centenarians are negatively associated with the presence of autoimmune diseases in the Japanese population, which suggests that the association with these genetic markers is mediated through a lower incidence of diseases. More recently, both a French study [13] and a Finnish study [14] found a low prevalence of the e4 allele of apolipoprotein E amongst centenarians. The e4 allele has consistently been shown to be a risk factor both for coronary heart disease and for Alzheimer’s dementia. In the French study [13], it was also found that centenarians had an increased prevalence of the DDgenotype of angiotensin-converting enzyme (ACE) compared with adult controls. This result is contrary to what was expected as the DD-genotype of ACE has been reported to be associated with myocardial infarction. Only a few genetic association studies concerning extreme longevity have been published...
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fcfbd6a9-78fc-4c53-8c83-19511b4d9bd5
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azjxghdg-4763
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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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Inconvenient Truths About
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Inconvenient Truths About Human Longevity
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S. Jay Olshansky, PhD1,* and Bruce A. Carnes, PhD2 S. Jay Olshansky, PhD1,* and Bruce A. Carnes, PhD2
1University of Illinois at Chicago, Division of Epidemiology and Biostatistics. 2University of Oklahoma. *Address correspondence to: S. Jay Olshansky, PhD, University of Illinois at Chicago. E-mail: sjayo@uic.edu
Received: February 2, 2019; Editorial Decision Date: April 3, 2019
Decision Editor: Anne Newman, MD, MPH
Abstract The rise in human longevity is one of humanity’s crowning achievements. Although advances in public health beginning in the 19th century initiated the rise in life expectancy, recent gains have been achieved by reducing death rates at middle and older ages. A debate about the future course of life expectancy has been ongoing for the last quarter century. Some suggest that historical trends in longevity will continue and radical life extension is either visible on the near horizon or it has already arrived; whereas others suggest there are biologically based limits to duration of life, and those limits are being approached now. In “inconvenient truths about human longevity” we lay out the line of reasoning and evidence for why there are limits to human longevity; why predictions of radical life extension are unlikely to be forthcoming; why health extension should supplant life extension as the primary goal of medicine and public health; and why promoting advances in aging biology may allow humanity to break through biological barriers that influence both life span and health span, allowing for a welcome extension of the period of healthy life, a compression of morbidity, but only a marginal further increase in life expectancy.
Keywords: Longevity, Public Health, Life Expectancy....
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Evidence for a limit
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Evidence for a limit to human lifespan
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Driven by technological progress, human life expec Driven by technological progress, human life expectancy has increased greatly since the nineteenth century. Demographic evidence has revealed an ongoing reduction in old-age mortality and a rise of the maximum age at death, which may gradually extend human longevity1,2. Together with observations that lifespan in various animal species is flexible and can be increased by genetic or pharmaceutical intervention, these results have led to suggestions that longevity may not be subject to strict, species-specific genetic constraints. Here, by analysing global demographic data, we show that improvements in survival with age tend to decline after age 100, and that the age at death of the world’s oldest person has not increased since the 1990s. Our results strongly suggest that the maximum lifespan of humans is fixed and subject to natural constraints. Maximum lifespan is, in contrast to average lifespan, generally assumed to be a stable characteristic of a species3. For humans, the
maximum reported age at death is generally set at 122 years, the age at death of Jeanne Calment, still the oldest documented human
individual who ever lived4. However, some evidence suggests that
maximum lifespan is not fixed. Studies in model organisms have shown that maximum lifespan is flexible and can be affected by genetic and pharmacological interventions5. In Sweden, based on a long series of reliable information on the upper limits of human lifespan, the
maximum reported age at death was found to have risen from about
101 years during the 1860s to about 108 years during the 1990s6. According to the authors, this finding refutes the common assertion that human lifespan is fixed and unchanging over time6. Indeed, the most convincing argument that the maximum lifespan of humans is not fixed is the ongoing increase in life expectancy in most countries over the course of the last century1,2. Figure 1a shows this increase for France, a country with high-quality mortality data, but very similar patterns were found for most other developed nations (Extended Data Fig. 1). Hence, the possibility has been considered that mortality may decline further, breaking any pre-conceived boundaries of human lifespan1,7. As shown by data from the Human Mortality Database8, many of the historical gains in life expectancy have been attributed to a
reduction in early-life mortality. More recent data, however, show
evidence for a decline in late-life mortality, with the fraction of each birth cohort reaching old age increasing with calendar year. In France, the number of individuals per 100,000 surviving to old age (70 and up) has increased since 1900 (Fig. 1b), which points towards a continuing increase in human life expectancy. This pattern is very similar across the other 40 countries and territories included in the database (Extended Data Figs 2, 3). However, the rate of improvement in survival peaks and then declines for very old age levels (Fig. 1c), which points
1Department of Genetics, Albert Einstein College of Medicine, Bronx, New York 10461, USA. 2Department of Ophthalmology & Visual Sciences, Albert Einstein College of Medicine, Bronx, New York 10461, USA. *These authors contributed equally to this work.
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Figure 1 | Trends in life expectancy and late-life survival. a, Life expectancy at birth for the population in each given year. Life expectancy in France has increased over the course of the 20th and early 21st centuries. b, Regressions of the fraction of people surviving to old age demonstrate that survival has increased since 1900, but the rate of increase appears to be slower for ages over 100. c, Plotting the rate of
change (coefficients resulting from regression of log-transformed data) reveals that gains in survival peak around 100 years of age and then rapidly decline. d, Relationship between calendar year and the age that experiences the most rapid gains in survival over the past 100 years. The age with most rapid gains has increased over the century, but its rise has been slowing and it appears to have reached a plateau...
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hnaapmmu-5222
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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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ebb71696-6557-46e6-b524-bf6e8229c5ed
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ldrmouen-6866
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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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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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The Four Keys
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The Four Keys to Longevity
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Famous comedian George Burns was once quoted as sa Famous comedian George Burns was once quoted as saying, “If you live to be one hundred, you’ve got it made. Very few people die past that age”. By 2050, it is estimated that there will be more than one million centenarians living in the u.S.1 For most people, planning for retirement or their later years is focused mostly on finances and how they will spend their time. However, ensuring they spend those years in good health is something that many overlook. The times are certainly changing, with medical advances and technological breakthroughs, planning for retirement and living longer needs to be more holistic.
In 1970, average life expectancy at birth in the United States was 71 years. In 2014, it is 79 years; and by 2050, the U.S. Census Bureau projects that average life expectancy will be 84 years.2 Today, according to the National Institute on Aging, there are over 40 million people in the United States aged 65 or older, accounting for about 13 percent of the total population. In 1900, there were just 3.1 million older Americans, or about 4.1% of the population.3 The vast majority of baby boomers—those born between 1946 and 1964—are on a quest to improve their odds of living longer than previous generations. They not only want to live longer, they want to live healthily, happily and more financially secure than ever before. Although there is no magic potion to ensure a long and healthy life, there are some notable accounts of individuals, families, and even whole communities that have defied the aging odds.
The holy grail of longevity In one such amazing story, Stamatis Moraitis, a Greek veteran of World War II, narrates how he was diagnosed with lung cancer in the 1960s
while living in the United States.4 He decided to forgo chemotherapy, and instead returned to his birthplace, Ikaria, the island where “people forget to die”. Moraitis abandoned his western diet and lifestyle and embraced the traditional island culture. His American doctors had told Moraitis he had only nine months to live, yet after moving to Ikaria he was still living— cancer free—45 years after his original diagnosis. According to the story, he never had chemotherapy, took drugs or sought therapy of any sort. All he did was move home to Ikaria and embrace the local lifestyle. He claimed he even outlived his U.S. physicians who, decades earlier, had predicted his imminent death as the only plausible outcome of his devastating diagnosis. Moraitis is not alone when it comes to longevity on the island of Ikaria. In fact, University of Athens researchers have concluded that people on Ikaria are reaching the age of 90 at two-and-a-half times the rate of their American counterparts.5 Stark differences in their lifestyle are apparent, even to a casual observer. ...
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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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From Life Span to Health
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From Life Span to Health Span: Declaring “Victory”
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S. Jay Olshansky
School of Public Health, Univers S. Jay Olshansky
School of Public Health, University of Illinois at Chicago, Chicago, Illinois 60612, USA Correspondence: sjayo@uic.edu
Adifficultdilemmahaspresenteditselfinthecurrentera.Modernmedicineandadvancesin the medical sciences are tightly focused on a quest to find ways to extend life—without considering either the consequences of success or the best way to pursue it. From the perspectiveofphysicianstreatingtheirpatients,itmakessensetohelpthemovercomeimmediate healthchallenges,butfurtherlifeextensioninincreasinglymoreagedbodieswillexposethe savedpopulationtoanelevatedriskofevenmoredisablinghealthconditionsassociatedwith aging. Extended survival brought forth by innovations designed to treat diseases will likely push more people into a“ red zone”a later phase in life when the risk of frailty and disability risesexponentially.Theinescapableconclusionfromtheseobservationsisthatlifeextension should no longer be the primary goal of medicine when applied to long-lived populations. The principal outcome and most important metric of success should be the extension of health span, and the technological advances described herein that are most likely to make the extension of healthy life possible.
ON THE ORIGIN OF LIFE SPAN How long people live as individuals, the expected duration of life of people of any age base do current death rates in a national population, and the demographic aging of national populations (e.g., proportion of the population aged 65 and older), are simple metrics that are colloquially understood as reflective of health and longevity. Someone that lives for 100 years had a lifespan of a century ,and a life expectancy at birth of 80 years for men in the United States means that male babies born today will live to an average of 80 years if death rates at all ages today prevail throughout the life of the cohort. When life expectancy rises or declines, that is inter pretend
as an improvement or worsening of public health. These demographic and statistical metrics are reflective measurement tools only—they disclose little about why they change or vary, they reveal nothing about why they exist at all, and theyare indirect and imprecise measures of the health of a population. Understandingwhythereisaspecies-specific life span to begin with and what forces influence its presence ,level ,and the dynamics of variation and change (collectively referred to her “life span determination”) is critical to comprehending why the topic
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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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Gene expression signature
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Gene expression signatures of human cell
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Inge Seim1,2, Siming Ma1 and Vadim N Gladyshev1
D Inge Seim1,2, Siming Ma1 and Vadim N Gladyshev1
Different cell types within the body exhibit substantial variation in the average time they live, ranging from days to the lifetime of the organism. The underlying mechanisms governing the diverse lifespan of different cell types are not well understood. To examine gene expression strategies that support the lifespan of different cell types within the human body, we obtained publicly available RNA-seq data sets and interrogated transcriptomes of 21 somatic cell types and tissues with reported cellular turnover, a bona fide estimate of lifespan, ranging from 2 days (monocytes) to a lifetime (neurons). Exceptionally long-lived neurons presented a gene expression profile of reduced protein metabolism, consistent with neuronal survival and similar to expression patterns induced by longevity interventions such as dietary restriction. Across different cell lineages, we identified a gene expression signature of human cell and tissue turnover. In particular, turnover showed a negative correlation with the energetically costly cell cycle and factors supporting genome stability, concomitant risk factors for aging-associated pathologies. In addition, the expression of p53 was negatively correlated with cellular turnover, suggesting that low p53 activity supports the longevity of post-mitotic cells with inherently low risk of developing cancer. Our results demonstrate the utility of comparative approaches in unveiling gene expression differences among cell lineages with diverse cell turnover within the same organism, providing insights into mechanisms that could regulate cell longevity.
npj Aging and Mechanisms of Disease (2016) 2, 16014; doi:10.1038/npjamd.2016.14; published online 7 July 2016
INTRODUCTION Nature can achieve exceptional organismal longevity, 4100 years in the case of humans. However, there is substantial variation in ‘cellular lifespan’, which can be conceptualized as the turnover of individual cell lineages within an individual organism.1 Turnover is defined as a balance between cell proliferation and death that contributes to cell and tissue homeostasis.2 For example, the integrity of the heart and brain is largely maintained by cells with low turnover/long lifespan, while other organs and tissues, such as the outer layers of the skin and blood cells, rely on high cell turnover/short lifespan.3–5 Variation in cellular lifespan is also evident across lineages derived from the same germ layers formed during embryogenesis. For example, the ectoderm gives rise to both long-lived neurons4,6,7 and short-lived epidermal skin cells.8 Similarly, the mesoderm gives rise to long-lived skeletal muscle4 and heart muscle9 and short-lived monocytes,10,11 while the endoderm is the origin of long-lived thyrocytes (cells of the thyroid gland)12 and short-lived urinary bladder cells.13 How such diverse cell lineage lifespans are supported within a single organism is not clear, but it appears that differentiation shapes lineages through epigenetic changes to establish biological strategies that give rise to lifespans that support the best fitness for cells in their respective niche. As fitness is subject to trade-offs, different cell types will adjust their gene regulatory networks according to their lifespan. We are interested in gene expression signatures that support diverse biological strategies to achieve longevity. Prior work on species longevity can help inform strategies for tackling this research question. Species longevity is a product of evolution and is largely shaped by genetic and environmental factors.14 Comparative transcriptome
studies of long-lived and short-lived mammals, and analyses that examined the longevity trait across a large group of mammals (tissue-by-tissue surveys, focusing on brain, liver and kidney), have revealed candidate longevity-associated processes.15,16 They provide gene expression signatures of longevity across mammals and may inform on interventions that mimic these changes, thereby potentially extending lifespan. It then follows that, in principle, comparative analyses of different cell types and tissues of a single organism may similarly reveal lifespan-promoting genes and pathways. Such analyses across cell types would be conceptually similar, yet orthogonal, to the analysis across species. Publicly available transcriptome data sets (for example, RNA-seq) generated by consortia, such as the Human Protein Atlas (HPA),17 Encyclopedia of DNA Elements (ENCODE),18 Functional Annotation Of Mammalian genome (FANTOM)19 and the Genotype-Tissue Expression (GTEx) project,20 are now available. They offer an opportunity to understand how gene expression programs are related to cellular turnover, as a proxy for cellular lifespan. Here we examined transcriptomes of 21 somatic cells and tissues to assess the utility of comparative gene expression methods for the identification of longevity-associated gene signatures.
RESULTS We interrogated publicly available transcriptomes (paired-end RNA-seq reads) of 21 human cell types and tissues, comprising 153 individual samples, with a mean age of 56 years (Table 1; details in Supplementary Table S1). Their turnover rates (an estimate of cell lifespan4) varied from 2 (monocytes) to 32,850 (neurons) days, with all three germ layers giving rise to both short-lived a...
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Gene Expression Biomarker
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Gene Expression Biomarkers and Longevity
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Chronological age, a count of how many orbits of t Chronological age, a count of how many orbits of the sun an individual has made as a passenger of planet earth, is a useful but limited proxy of aging processes. Some individuals die of age related diseases in their sixties, while others live to double that age. As a result, a great deal of effort has been put into identifying biomarkers that reflect the underlying biological changes involved in aging. These markers would provide insights into what processes were involved, provide measures of how much biological aging had occurred and provide an outcome measure for monitoring the effects of interventions to slow ageing processes. Our DNA sequence is the fixed reference template from which all our proteins are produced. With the sequencing of the human genome we now have an accurate reference library of gene sequences. The recent development of a new generation of high throughput array technology makes it relatively inexpensive to simultaneously measure a large number of base sequences in DNA (or RNA, the molecule of gene expression). In the last decade, array technologies have supported great progress in identifying common DNA sequence differences (SNPs) that confer risks for age related diseases, and similar approaches are being used to identify variants associated with exceptional longevity [1]. A striking feature of the findings is that the majority of common disease-associated variants are located not in the protein coding sequences of genes, but in regions of the genome that do not produce proteins. This indicates that they may be involved in the regulation of nearby genes, or in the processing of their messages. While DNA holds the static reference sequences for life, an elaborate regulatory system influences whether and in what abundance gene transcripts and proteins are produced. The relative abundance of each tran
script is a good guide to the demand for each protein product in cells (see section 2 below). Thus, by examining gene expression patterns or signatures associated with aging or age related traits we can peer into the underlying production processes at a fundamental level. This approach has already proved successful in clinical applications, for example using gene signatures to classify cancer subtypes [2]. In aging research, recent work conducted in the InCHIANTI cohort has identified gene-expression signatures in peripheral leucocytes linked to several aging phenotypes, including low muscle strength, cognitive impairment, and chronological age itself. In the sections that follow we provide a brief introduction to the underlying processes involved in gene expression, and summarize key work in laboratory models of aging. We then provide an overview of recent work in humans, thus far mostly from studies of circulating white cells.
2 Introducing gene expression
Since the early 1900s a huge worldwide research effort has lead to the discovery and widespread use of genetic science (see the NIH website [3] for a comprehensive review of the history of the subject, and a more detailed description of the transfer of genetic information). The human genome contains the information needed to create every protein used by cells. The information in the DNA is transcribed into an intermediate molecule known as the messenger RNA (mRNA), which is then translated into the sequence of aminoacids (proteins) which ultimately determine the structural and functional characteristics of cells, tissues and organisms (see figure 1 for a summary of the process). RNA is both an intermediate to proteins and a regulatory molecule; therefore the transcriptome (the RNA ∗Address correspondence to Prof. David Melzer, Epidemiology and Public Health Group, Medical School, University of Exeter, Exeter EX1 2LU, UK. E-mail: D.Melzer@exeter.ac.uk
1
2 INTRODUCING GENE EXPRESSION
Figure 1: Representation of the transcription and translation processes from DNA to RNA to Protein — DNA makes RNA makes Protein. This is the central dogma of molecular biology, and describes the transfer of information from DNA (made of four bases; Adenine, Guanine, Cytosine and Thymine) to RNA to Protein (made of up to 20 different amino acids). Machinery known as RNA polymerase carries out transcription, where a single strand of RNA is created that is complementary to the DNA (i.e. the sequence is the same, but inverted although in RNA thymine (T) is replaced by uracil (U)). Not all RNA molecules are messenger RNA (mRNA) molecules: RNA can have regulatory functions (e.g. micro RNAs), and or can be functional themselves, for example in translation transfer RNA (tRNA) molecules have an amino acid bound to one end (the individual components of proteins) and at the other bind to a specific sequence of RNA (a codon again, this is complementary to this original sequence) for instance in the figure a tRNA carrying methionine (Met) can bind to the sequence of RNA, and the ribosome (also in part made of RNA) attaches the amino acids together to form a protein.
production of a particular cell, or sample of cells, at a given time) is of particular interest in determining the underlying molecular mechanisms behind specific traits and phenotypes. Genes are also regulated at the posttranscriptional level, by non-coding RNAs or by posttranslational modifications to the encoded proteins. Transcription is a responsive process (many factors regulate transcription and translation in response to specific intra and extra-cellular signals), and thus the amount of RNA produced varies over time and between cell types and tissues. In addition to the gene and RNA transcript sequences that will determine the final protein sequence (so called exons) there are also intervening sections (the introns) that are removed by a process known as mRNA splicing. While it was once assumed that each gene produced only one protein, it is now
clear that up to 90% of our genes can produce different versions of their protein through varying the number of exons included in the protein, a process called alternative splicing. Alteration in the functional properties of the protein can be introduced by varying which exons are included in the transcript, giving rise to different isoforms of the same gene. Many RNA regulatory factors govern this process, and variations to the DNA sequence can affect the binding of these factors (which can be thousands of base pairs from the gene itself) and alter when, where and for how long a particular transcript is produced. The amount of mRNA produced for a protein is not necessarily directly related to the amount of protein produced or present, as other regulatory processes are involved. The amount of mRNA is broadly indicative of...
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Genetic longevity
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Genetic Longevity
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Markus Valge, Richard Meitern and Peeter Hõrak*
D Markus Valge, Richard Meitern and Peeter Hõrak*
Department of Zoology, University of Tartu, Tartu, Estonia
Life-history traits (traits directly related to survival and reproduction) co-evolve and materialize through physiology and behavior. Accordingly, lifespan can be hypothesized as a potentially informative marker of life-history speed that subsumes the impact of diverse morphometric and behavioral traits. We examined associations between parental longevity and various anthropometric traits in a sample of 4,000–11,000 Estonian children in the middle of the 20th century. The offspring phenotype was used as a proxy measure of parental genotype, so that covariation between offspring traits and parental longevity (defined as belonging to the 90th percentile of lifespan) could be used to characterize the aggregation between longevity and anthropometric traits. We predicted that larger linear dimensions of offspring associate with increased parental longevity and that testosterone-dependent traits associate with reduced paternal longevity. Twelve of 16 offspring traits were associated with mothers’ longevity, while three traits (rate of sexual maturation of daughters and grip strength and lung capacity of sons) robustly predicted fathers’ longevity. Contrary to predictions, mothers of children with small bodily dimensions lived longer, and paternal longevity was not linearly associated with their children’s body size (or testosterone-related traits). Our study thus failed to find evidence that high somatic investment into brain and body growth clusters with a long lifespan across generations, and/or that such associations can be detected on the basis of inter-generational phenotypic correlations.
KEYWORDS
anthropometric traits, body size, inter-generational study, longevity, obesity, sex difference
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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
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Genetics of extreme human
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Genetics of extreme human longevity to guide drug
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Zhengdong D. Zhang 1 ✉, Sofiya Milman1,2, Jhih-R Zhengdong D. Zhang 1 ✉, Sofiya Milman1,2, Jhih-Rong Lin1, Shayne Wierbowski3, Haiyuan Yu3, Nir Barzilai1,2, Vera Gorbunova4, Warren C. Ladiges5, Laura J. Niedernhofer6, Yousin Suh 1,7, Paul D. Robbins 6 and Jan Vijg1,8
Ageing is the greatest risk factor for most common chronic human diseases, and it therefore is a logical target for developing interventions to prevent, mitigate or reverse multiple age-related morbidities. Over the past two decades, genetic and pharmacologic interventions targeting conserved pathways of growth and metabolism have consistently led to substantial extension of the lifespan and healthspan in model organisms as diverse as nematodes, flies and mice. Recent genetic analysis of long-lived individuals is revealing common and rare variants enriched in these same conserved pathways that significantly correlate with longevity. In this Perspective, we summarize recent insights into the genetics of extreme human longevity and propose the use of this rare phenotype to identify genetic variants as molecular targets for gaining insight into the physiology of healthy ageing and the development of new therapies to extend the human healthspan...
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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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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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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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Global Roadmap for Health
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Global Roadmap for Healthy Longevity
(Consensus Global Roadmap for Healthy Longevity
(Consensus Study Report, National Academy of Medicine, 2022)
This report presents a global, evidence-based strategy for transforming aging into an opportunity by promoting healthy longevity—a state where people live long lives in good health, with full physical, cognitive, and social functioning, and where societies harness the potential of older adults.
🧠 1. Why This Roadmap Matters
Across the world, populations are aging faster than ever due to:
Longer life expectancy, and
Declining birth rates
The number of people aged 65+ has been growing more rapidly than any other age group, and this trend will continue.
Global Roadmap for Healthy Long…
However, a critical problem exists:
📉 People are living longer, but not healthier.
Between 2000 and 2019, global lifespan increased, especially in low- and middle-income countries,
but years of good health stagnated, meaning more years are spent in poor health.
Global Roadmap for Healthy Long…
🌍 2. Purpose of the Roadmap
To address this challenge, the National Academy of Medicine convened a global, multidisciplinary commission to create a roadmap for achieving healthy longevity worldwide.
Global Roadmap for Healthy Long…
The aim is to help countries develop data-driven, all-of-society strategies that promote health, equity, productivity, and human flourishing across the lifespan.
❤️ 3. What Healthy Longevity Means
According to the commission, healthy longevity is:
Living long with health, function, meaning, purpose, dignity, and social well-being, where years in good health approach the biological lifespan.
Global Roadmap for Healthy Long…
This reflects the WHO definition of health as a state of complete:
physical
mental
social well-being
—not merely the absence of disease.
🎯 4. Vision for the Future
The report emphasizes that aging societies can thrive, not decline, if healthy longevity is embraced as a societal goal.
With the right policies, older adults can:
Contribute meaningfully to families and communities
Participate in the workforce or volunteer roles
Live with dignity, purpose, and independence
Support strong economies and intergenerational cohesion
Global Roadmap for Healthy Long…
⭐ The future can be optimistic—if we act now.
⚠️ 5. The Cost of Inaction
If societies fail to respond, consequences include:
More years lived in poor health
Higher suffering and dependency
Increased financial burden on families
Lost productivity and fewer opportunities for younger and older people
Lower GDP
Larger fiscal pressures on governments
Global Roadmap for Healthy Long…
In short:
Ignoring healthy longevity is expensive—socially and economically.
🧩 6. Principles for Achieving Healthy Longevity
The commission identifies five core principles:
Global Roadmap for Healthy Long…
1. People of all ages should reach their full health potential
With dignity, meaning, purpose, and functioning.
2. Societies must enable optimal health at every age
Creating conditions where individuals can flourish physically, mentally, and socially.
3. Reduce disparities and advance equity
So that people of all countries and social groups benefit.
4. Recognize older adults as valuable human, social, and financial capital
Their contributions strengthen families, communities, and economies.
5. Use data and meaningful metrics
To measure progress, guide policy, and ensure accountability.
🏛️ 7. How Countries Should Act
Every nation must create its own pathway based on its unique demographics, infrastructure, and culture.
However, the roadmap emphasizes:
✔ Government-led calls to action
✔ Evidence-based planning
✔ Multisector collaboration (healthcare, urban design, technology, finance, education)
✔ Building supportive social and community infrastructure
Global Roadmap for Healthy Long…
These are essential for transforming aging from a crisis into an opportunity.
🌟 Perfect One-Sentence Summary
The Global Roadmap for Healthy Longevity outlines how aging societies can ensure that people live longer, healthier, more meaningful lives—and emphasizes that now is the time for coordinated global action to achieve this future.
If you'd like, I can also create:
📌 A diagram / infographic
📌 A short summary
📌 A comparison with your other longevity PDFs
📌 A PowerPoint-style slide set
Just tell me!...
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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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Gut microbiota variations
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Gut microbiota variations over the lifespan and
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This study investigates how the gut microbiota (th This study investigates how the gut microbiota (the community of microorganisms living in the gut) changes throughout the reproductive lifespan of female rabbits and how these changes relate to longevity. It compares two maternal rabbit lines:
Line A – a standard commercial line selected mainly for production traits.
Line LP – a long-lived line created using longevity-based selection criteria.
🔬 What the Study Did
Researchers analyzed 319 fecal samples collected from 164 female rabbits across their reproductive lives (from first parity to death/culling). They used advanced DNA sequencing of the gut microbiome, including:
16S rRNA sequencing
Bioinformatics (DADA2, QIIME2)
Alpha diversity (richness/evenness within a sample)
Beta diversity (differences between samples)
Zero-inflated negative binomial mixed models (ZINBMM)
Animals were categorized into three longevity groups:
LL: Low longevity (died/culled before 5th parity)
ML: Medium longevity (5–10 parities)
HL: High longevity (more than 10 parities)
🧬 Key Findings
1. Aging Strongly Alters the Gut Microbiome
Age caused a consistent decline in diversity:
Lower richness
Lower evenness
Reduced Shannon index
20% of ASVs in line A and 16% in line LP were significantly associated with age.
Most age-associated taxa declined with age.
Age explained the greatest proportion of sample-to-sample microbiome variation.
2. Longevity Groups Have Distinct Microbiomes
High-longevity rabbits (HL) showed lower evenness, meaning fewer taxa dominated the community.
Differences between longevity groups were more pronounced in line A than line LP.
In line A, 15–16% of ASVs differed between HL and LL/ML.
In line LP, only 4% differed.
Suggests genetic selection for longevity stabilizes microbiome patterns.
3. Strong Genetic Line Effects
LP rabbits consistently had higher alpha diversity than A rabbits.
About 6–12% of ASVs differed between lines even when comparing animals of the same longevity, proving:
Genetics shape the microbiome independently of lifespan.
Several bacterial families were consistently different between lines, such as:
Lachnospiraceae
Oscillospiraceae
Ruminococcaceae
Akkermansiaceae
🧩 What It Means
The gut microbiota shifts dramatically with age, even under identical feeding and environmental conditions.
Specific bacteria decline as rabbits age, likely tied to immune changes, reproductive stress, or physiological aging.
Longevity is partially linked to microbiome composition—but genetics strongly determines how much the microbiome changes.
The LP line shows more microbiome stability, hinting at genetic resilience.
🌱 Why It Matters
This research helps:
Understand aging biology in mammals
Identify microbial markers of longevity
Improve breeding strategies for long-lived, healthy livestock
Explore microbiome-driven approaches for health and productivity...
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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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Has the Rate of Human Age
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Has the Rate of Human Aging Already Been Modified
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This paper investigates whether the biological rat This paper investigates whether the biological rate of human aging has changed over the past century, or whether improvements in survival and life expectancy result mostly from reducing early-life and midlife mortality rather than slowing aging itself.
The study uses historical mortality data and aging-rate models to determine if humans age more slowly today or if we simply live longer before aging starts dominating mortality.
🔍 Core Question
Has aging itself slowed down, or do we just survive long enough to reach old age more often?
📊 Methods Used
The study examines:
Mortality curves over time (e.g., 1900–present)
The Gompertz function, which mathematically describes how mortality risk doubles with age
Changes in:
Initial mortality rate (IMR)
Rate of aging (Gompertz slope)
Data comes from:
Historical life tables
Cross-country mortality records
Comparisons of birth cohorts over time
The focus is on whether the slope of mortality increase with age has changed — this slope is considered a direct indicator of the rate of aging.
🧠 Key Findings (Perfect Summary)
1. Human aging rate appears largely unchanged
The study finds no strong evidence that the rate at which mortality increases with age (the Gompertz slope) has slowed.
This means humans likely age at the same biological speed as they did 100 years ago.
2. What has changed is the starting point of aging
Early-life and midlife mortality have dropped dramatically due to sanitation, medicine, nutrition, and public health.
As a result, more people reach old age, giving the impression that aging has slowed.
But aging itself (measured by mortality acceleration) has remained stable.
3. Modern longevity gains are driven by shifting the mortality curve
Rather than flattening the curve (slower aging), society has:
Pushed the curve downward (lower mortality at all ages)
Delayed the onset of chronic disease
Improved survival after age 60
These factors extend lifespan without changing the underlying biological aging rate.
4. Even in recent decades, aging rate shows stability
Improvements after 1970 came from:
Cardiovascular improvements
Medical interventions
Smoking decline
But studies consistently show the rate of mortality acceleration remains constant.
🧬 Overall Interpretation
Human aging — measured as the exponential rise in mortality risk with age — has not slowed.
Instead, society has become better at preventing early death, allowing more people to reach advanced ages.
In short:
❗ We live longer not because we age slower, but because we avoid dying earlier.
📌 One-Sentence Perfect Summary
The paper concludes that although human life expectancy has increased dramatically, the biological rate of aging has remained essentially unchanged, and modern longevity gains are due to reduced mortality before and during old age rather than slower aging itself.
If you want, I can also provide:
A diagram or flowchart
A 5-line summary
A student-friendly explanation
A PDF or PowerPoint version
Just tell me!...
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naoffskb-1736
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xevyo
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health services
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health services use by older adults
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This PDF is a fact sheet that summarizes how older This PDF is a fact sheet that summarizes how older adults (age 65+) use health services in the United States. It presents national statistics on doctor visits, chronic diseases, hospital care, emergency care, prescription drug use, long-term services, and long-term care needs among seniors.
The focus is to show how rising longevity, chronic illness, and disability shape healthcare demands in older populations.
The document is structured with clear data points, percentages, and brief explanations—ideal for public health professionals, students, policymakers, and caregivers.
📌 Main Topics Covered
1. Use of Physician Services
Seniors account for 26% of all physician visits in the U.S.
Doctor visits increase with age due to chronic disease management.
Many older adults see multiple specialists annually.
2. Hospital Use
People aged 65+ make up a large proportion of hospital admissions.
Older adults have higher rates of:
inpatient stays
readmissions
longer lengths of stay
Hospitalization risk increases with complex chronic conditions.
3. Emergency Department (ED) Visits
Seniors frequently use emergency departments for:
falls
injuries
acute illness episodes
complications of chronic diseases
ED visits rise significantly after age 75.
4. Chronic Diseases
The PDF highlights the heavy burden of chronic illness in late life:
80% of older adults have at least one chronic condition.
Up to 50% have two or more chronic diseases.
Common conditions include:
arthritis
heart disease
diabetes
hypertension
osteoporosis
COPD
Chronic illness is the primary driver of healthcare utilization in older populations.
5. Prescription Drug Use
Older adults use a disproportionately high number of medications.
Polypharmacy (using 5+ medications at once) is common and increases risks of:
adverse drug reactions
drug–drug interactions
falls
hospitalization
6. Long-Term Services and Supports (LTSS)
The PDF includes essential data on long-term care:
Older adults are the largest users of home care, community-based services, and institutional care.
A growing population of seniors requires:
help with activities of daily living (ADLs)
nursing home services
home health care
personal care services
7. Long-Term Care Facilities
The data highlight the following:
65+ adults represent the majority of people living in:
nursing homes
assisted living facilities
Many residents have significant functional or cognitive impairment (e.g., dementia).
8. Summary of Utilization Patterns
The PDF shows a clear pattern:
Older adults are the highest users of healthcare across almost all service types.
Their needs are shaped by:
multiple chronic diseases
declining mobility
cognitive decline
functional impairments
increased vulnerability to acute health events
As longevity increases, demand for health services will continue to rise.
🧾 Overall Conclusion
The PDF provides a concise but comprehensive portrait of how much and what types of healthcare older adults use.
Key messages:
✔ Older adults use far more physician services, hospital care, and emergency care than younger groups.
✔ Chronic diseases dominate health service use.
✔ Prescription medication use is high, with major safety concerns.
✔ Long-term services and institutional care are essential for many seniors.
✔ As the population ages, the healthcare system must adapt to growing demand.
If you want, I can also prepare:
✅ a short summary
✅ a data-only summary
✅ an infographic-style description
Just tell me!...
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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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Healthy Ageing
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Healthy Ageing
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This document is an academic research article titl This document is an academic research article titled “Healthy Ageing and Mediated Health Expertise” by Christa Lykke Christensen, published in Nordicom Review (2017). It explores how older adults understand health, how they think about ageing, and most importantly, how media influence their beliefs and behaviors about healthy living.
✅ Main Purpose of the Article
The study investigates:
How older people use media to learn about health.
Whether they trust media health information.
How media messages shape their ideas of active ageing, lifestyle, and personal responsibility for health.
🧓📺 Core Focus
The article is based on 16 qualitative interviews with Danish adults aged 65–86. Through these interviews, the author analyzes how elderly people react to health information in media such as TV, magazines, and online content.
⭐ Key Insights and Themes
1️⃣ Two Different Ageing Strategies Identified
The research shows that older adults fall into two broad groups:
(A) Those who maintain a youthful lifestyle into old age
Highly active (gym, sports, diet programs).
Use media health content as guidance (exercise shows, magazines, expert advice).
Believe good lifestyle can prolong life.
Try hard to “control” ageing through diet and activity.
(B) Those who accept natural ageing
Define health as simply “not being sick.”
Value mobility, independence, social interaction.
More relaxed about diet and exercise.
Focus on quality of life, relationships, emotional well-being.
More critical and skeptical of media health claims.
2️⃣ Role of Media
The article describes a dual influence:
Positive influence
Media provide accessible knowledge.
Inspire healthy habits.
Offer motivation and new routines.
Negative influence
Information often contradicts itself.
Creates pressure to meet unrealistic standards.
Can lead to guilt, frustration, confusion.
Overemphasis of diet/exercise overshadows social and emotional health.
3️⃣ “The Will to Be Healthy”
Inspired by previous research, the article explains that modern society expects older people to:
Stay active
Eat perfectly
Avoid illness through personal discipline
Continuously self-improve
Older adults feel that being healthy becomes a moral obligation, not just a personal choice.
4️⃣ Media’s Framing of Ageing
The media often portray older adults as:
Energetic
Positive
Fit
Productive
These representations push the idea of “successful ageing,” creating pressure for older individuals to avoid looking or feeling old.
5️⃣ Tension and Dilemmas
The study reveals emotional conflicts such as:
Wanting a long life but not wanting to feel old.
Trying to follow health advice but feeling overwhelmed.
Personal health needs vs. societal expectations.
Desire for autonomy vs. media pressure.
📌 Conclusions
The article concludes that:
Health and ageing are shaped heavily by media messages.
Older people feel responsible for their own ageing process.
Media act as a “negotiating partner” — guiding, confusing, pressuring, or inspiring.
Ageing today is not passive; it requires continuous decision-making and self-management.
There is no single way to age healthily — each individual balances ideals, limitations, and life experience....
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Healthy Aging Among
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Healthy Aging Among Centenarians and Near-Centenar
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This PDF is a comprehensive academic research pape This PDF is a comprehensive academic research paper that explores what allows people to live to 100 years and beyond while still maintaining physical, psychological, and social well-being. It examines the characteristics, lifestyles, health patterns, and resilience factors of centenarians and near-centenarians, highlighting why some individuals age successfully despite extreme longevity.
The paper integrates demographic data, medical profiles, social determinants, and psychological traits to understand healthy aging in the oldest-old—a population that is rapidly increasing worldwide.
🔶 1. Purpose of the Study
The document aims to:
Identify what differentiates healthy centenarians from those with typical age-related decline
Analyze their physical health, cognitive functioning, and emotional well-being
Explore long-life determinants including lifestyle, genetics, environment, and personality
Understand how these individuals maintain independence and quality of life
Provide insights for public health and aging research
It serves as a foundational resource for gerontologists, clinicians, and policymakers.
🔶 2. Who Are the Participants?
The study focuses on:
Centenarians (100+ years)
Near-centenarians (ages 95–99)
These groups are compared across:
Health status
Cognitive functioning
Daily living ability
Social networks
Psychological resilience
🔶 3. Key Findings
⭐ A. Physical Health Patterns
The paper notes:
Many centenarians delay major diseases until very late in life (“compression of morbidity”)
Some maintain surprisingly good mobility and independence
Common chronic issues include vision, hearing, and musculoskeletal limitations
Hospitalization rates are not always higher than younger elderly groups
Despite extreme age, a proportion of centenarians preserve functional health.
⭐ B. Cognitive Functioning
The study highlights:
A meaningful number maintain intact cognitive abilities
Others show mild impairments, but dementia is not universal
Cognitive resilience is linked to higher education, mental engagement, and social activity
Longevity does not guarantee cognitive decline; variability is wide.
⭐ C. Psychological Strength & Emotional Well-Being
A central message is that many centenarians possess strong mental resilience:
High optimism
Emotional stability
Adaptive coping skills
Lower depressive symptoms than expected
Positive psychological traits strongly correlate with healthy aging.
⭐ D. Social Environment & Support
Findings show:
Strong family support is crucial
Continued social engagement boosts health and mood
Many maintain close relationships with caregivers and relatives
Successful aging is deeply connected to social connection.
⭐ E. Lifestyle Factors
Patterns common among long-lived individuals include:
Moderation in diet
Regular light physical activity
Avoidance of smoking
Effective stress management
Consistent daily routines
These habits contribute significantly to longevity quality—not just lifespan.
⭐ F. Biological & Genetic Contributions
Although lifestyle matters, the study notes:
Genetics plays a major role in reaching 100+
Longevity-associated genes influence inflammation, metabolism, and cellular repair
Family history of longevity is a strong predictor
🔶 4. Broader Implications
The paper stresses that understanding healthy aging in centenarians can:
Help identify protective factors for the general population
Guide interventions for aging societies
Improve caregiving and support systems
Challenge stereotypes about extreme old age
🔶 5. Central Conclusion
Healthy aging at 100+ is shaped by a combination of genetics, lifestyle, psychological resilience, and strong social support. Many centenarians remain physically functional, mentally active, emotionally stable, and socially connected—demonstrating that long life can also be a high-quality life.
⭐ Perfect One-Sentence Summary
This PDF provides a detailed scientific examination of how centenarians and near-centenarians achieve healthy aging, revealing that exceptional longevity is supported by resilient psychological traits, strong social networks, delayed disease onset, functional independence, and a meaningful interplay between lifestyle and genetics....
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Healthy life expectancy,
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Healthy life expectancy, mortality, and age
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This paper explains why traditional measures of He This paper explains why traditional measures of Healthy Life Expectancy (HLE) can be misleading when they rely only on age-specific morbidity (illness/disability) rates.
The authors show that many health conditions in older ages are not primarily driven by age, but by Time-To-Death (TTD)—how close someone is to dying. Because of this, the usual practice of linking health problems to chronological age produces distorted results, especially when comparing populations or tracking trends over time.
Key Insights
Morbidity often rises sharply in the final years before death, regardless of the person's age.
Therefore, when life expectancy increases, the population shifts so that more people are farther from death, leading to lower observed disability at a given age—even if the true underlying health process hasn’t changed.
This means that improvements in mortality alone can make it appear that morbidity has decreased or that people are healthier at older ages.
As a result, period HLE estimates may falsely suggest real health improvements, when the change actually comes from mortality declines—not better health.
What the Study Demonstrates
Using U.S. Health and Retirement Study data and mortality tables:
They model disability patterns based on TTD and convert them into apparent age patterns.
They show mathematically and empirically how mortality changes distort age-based morbidity curves.
They test how much bias enters standard health expectancy decompositions (e.g., Sullivan method).
They find that a 5-year increase in life expectancy after age 60 can artificially reduce disability estimates by up to 1 year, even if actual morbidity is unchanged.
Core Message
Age-based prevalence of disease/disability cannot be reliably interpreted without understanding how close individuals are to death.
Thus, comparing HLE between populations—or within a population over time—can be biased unless TTD dynamics are considered....
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healthy lifespan
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Healthy lifespan inequality
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This document provides a comprehensive global anal This document provides a comprehensive global analysis of healthy lifespan inequality (HLI)—a groundbreaking indicator that measures how much variation exists in the age at which individuals first experience morbidity. Unlike traditional health metrics that capture only averages, such as life expectancy (LE) and health-adjusted life expectancy (HALE), HLI reveals the distribution and timing of health deterioration within populations.
Using data from the Global Burden of Disease Study 2019, the authors reconstruct mortality and morbidity curves to compare lifespan inequality (LI) with healthy lifespan inequality across 204 countries and territories from 1990 to 2019. This analysis uncovers significant global patterns in how early or late people begin to experience disease, disability, or less-than-good health.
The document presents several key findings:
1. Global Decline in Healthy Lifespan Inequality
Between 1990 and 2019, global HLI decreased for both sexes, indicating progress in narrowing the spread of ages at which morbidity begins. However, high-income countries experienced stagnation, showing no further improvement despite increases in longevity.
2. Significant Regional Differences
Lowest HLI is observed in high-income regions, East Asia, and Europe.
Highest HLI is concentrated in Sub-Saharan Africa and South Asia.
Countries such as Mali, Niger, Nigeria, Pakistan, and Haiti exhibit the widest variability in morbidity onset.
3. Healthy Lifespan Inequality Is Often Greater Than Lifespan Inequality
Across most regions, HLI exceeds LI—meaning variability in health loss is greater than variability in death. This indicates populations are becoming more equal in survival but more unequal in how and when they experience disease.
4. Gender Differences
Women tend to experience higher HLI than men, reinforcing the “health–survival paradox”:
Women live longer
But spend more years in poor health
And experience more uncertainty about when morbidity begins.
5. Rising Inequality After Age 65
For older adults, HLI65 has increased globally, signaling that while people live longer, the onset of morbidity is becoming more unpredictable in later life. Longevity improvements do not necessarily compress morbidity at older ages.
6. A Shift in Global Health Inequalities
The study reveals that as mortality declines worldwide, inequalities are shifting away from death and toward disease and disability. This transition marks an important transformation in modern population health and has major implications for:
healthcare systems
pension planning
resource allocation
long-term care
public health interventions
7. Policy Implications
The findings stress that improving average lifespan is not enough. Policymakers must also address when morbidity begins and how uneven that experience is across populations. Rising heterogeneity in morbidity onset, especially among older adults, requires:
stronger preventative health strategies
lifelong health monitoring
reduction of socioeconomic and regional disparities
integration of morbidity-related indicators into national health assessments
In Short
This study reveals a crucial and previously overlooked dimension of global health: even as people live longer, the timing of health deterioration is becoming more unequal, especially in high-income and aging societies. Healthy lifespan inequality is emerging as a vital metric for understanding the true dynamics of global aging and for designing health systems that prioritize not only longer life, but fairer and healthier life.
If you want, I can also create:
✅ A shorter perfect description
✅ An executive summary
✅ A diagram for HLI vs LI
✅ A simplified student-level explanation...
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Healthy lifestyle
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Healthy lifestyle and life expectancy with
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This scientific study investigates how healthy lif This scientific study investigates how healthy lifestyle behaviors in midlife influence life expectancy, both with and without major chronic diseases, over a 20-year period. The research uses data from 57,053 Danish adults aged 50–69 years from the well-known Diet, Cancer and Health cohort.
The authors aim to understand how everyday lifestyle choices shape long-term health, disease onset, multimorbidity, and healthcare use.
🔑 Purpose of the Study
The study asks:
How does a combined healthy lifestyle score relate to:
Life expectancy free of major chronic diseases
Life expectancy with disease
Multimorbidity (2+ simultaneous chronic illnesses)
Days of hospitalization over 20 years?
It quantifies how much longer and healthier people live as their lifestyle improves.
🧪 How the Study Was Conducted
Population
57,053 men and women, ages 50–69
Denmark, followed for up to 21.5 years
Free of major disease at the start (1997)
Lifestyle Health Score (0–9 points)
Based on 5 behavioral factors:
Smoking (0–2 points)
Sport activity (0–1 point)
Alcohol intake (0–2 points)
Diet quality (0–2 points)
Waist circumference (0–2 points)
A higher score = healthier lifestyle.
Diseases included
Participants were tracked for the development of:
Cancer
Type 2 diabetes
Stroke
Heart disease
Dementia
COPD
Asthma
Follow-up outcomes
Life expectancy without disease
Life expectancy with disease
Time with one disease and multi-disease
Hospitalization days
📊 Key Findings (Perfect Summary)
🟢 1. Healthy behavior significantly extends disease-free life
For 65-year-old participants, each 1-point increase in the health score resulted in:
+0.83 years of disease-free life for men
+0.86 years for women
People with the highest score (9) lived ~7.5 more years disease-free compared to those with the lowest score (0).
🔴 2. Healthy lifestyle reduces the years lived with chronic disease
For each 1-point increase in health score:
Men: –0.18 years with disease
Women: –0.37 years with disease
Women gained the most reduction.
🔵 3. Multimorbidity drops sharply with higher health scores
Among 65-year-olds:
Men with a low score spent 16.8% of life with 2+ diseases
Men with high scores spent only 3.6%
The pattern is similar in women.
Healthy lifestyle greatly compresses time lived with multiple illnesses.
🟣 4. Healthy lifestyle dramatically cuts hospitalization days
For 65-year-old men:
Score 0 → 6.1 days/year in the hospital
Score 9 → 2.4 days/year
For women:
Score 0 → 5.5 days/year
Score 9 → 2.5 days/year
Healthier behaviors = less burden on healthcare systems.
🔥 Which behavior mattered most?
1. Smoking (largest impact)
Current smoking reduced disease-free life by:
–3.20 years in men
–3.74 years in women
And increased years with disease.
2. High waist circumference
Reduced disease-free years by:
–2.54 years (men)
–1.90 years (women)
3. Diet, exercise, & alcohol
These had moderate but meaningful positive effects.
🧠 Final Interpretation
The study clearly shows:
Healthy living in midlife extends life, delays disease, and reduces hospital use.
Even small lifestyle improvements make measurable differences.
The health score is a simple but powerful predictor of later-life health outcomes.
📌 One Perfect Sentence Summary
A healthy lifestyle combining no smoking, regular activity, optimal diet, balanced alcohol intake, and healthy waist size can extend disease-free life by more than 7 years, reduce multimorbidity, and significantly cut hospitalization over 20 years.
If you'd like, I can create:
✅ A simple student summary
✅ A diagram/flowchart
✅ A presentation (PPT)
✅ A PDF summary
✅ A visual table of results
Just tell me!...
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Healthy lifestyle
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Healthy lifestyle and life expectancy
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This PDF is a scientific study that examines how f This PDF is a scientific study that examines how four major lifestyle behaviors affect life expectancy, especially in people with and without chronic diseases. The research evaluates how combinations of healthy habits can increase lifespan, even for individuals already diagnosed with long-term medical conditions.
It provides evidence on how lifestyle choices—including smoking, alcohol consumption, physical activity, and body weight—change the number of years a person can expect to live from age 50 onward.
The paper includes summary tables, life expectancy comparisons, and detailed statistical analysis across three chronic diseases.
📌 Main Purpose of the Study
To quantify how healthy lifestyle patterns influence:
✔ Life expectancy at age 50
✔ Additional years lived with and without chronic disease
✔ Survival differences between lifestyle groups
✔ The impact of disease type on lifestyle benefits
The research aims to show that lifestyle improvement is beneficial at any health status, including for patients with:
Cancer
Cardiovascular disease
Type 2 diabetes
🧬 Key Lifestyle Behaviors Analyzed
The study focuses on four major risk factors:
Smoking status
Body Mass Index (BMI)
Physical activity levels
Alcohol intake
Participants are grouped into three lifestyle categories (as shown in the table):
Unhealthy lifestyle
Intermediate lifestyle
Healthy lifestyle
📊 Major Findings
1️⃣ Healthy lifestyle significantly increases life expectancy
For all participants, adopting a healthy lifestyle increases life expectancy at age 50 by:
5.2 additional years for men
4.9 additional years for women
Even moderate improvement (intermediate lifestyle) adds several years of life.
2️⃣ Benefits apply to people WITH chronic diseases
Individuals with existing chronic diseases also gain extra years from healthier behaviors.
Cancer patients
Healthy lifestyle adds 6.1 years
Cardiovascular disease patients
Healthy lifestyle adds 5.0 years
Patients with diabetes
Healthy lifestyle adds 3.4 years
This proves that lifestyle still matters, even after disease onset.
3️⃣ Unhealthy lifestyle causes large losses in life expectancy
For the unhealthy lifestyle group, expected life after age 50 drops below:
20.7 years for men
24.1 years for women
—significantly lower than those living healthily.
4️⃣ Healthy lifestyle increases disease-free years
The study shows that individuals with healthier habits spend:
more years without chronic disease
fewer years with disability
more years with better physical functioning
📉 Data Table Summary (from PDF)
The table in the PDF summarizes life expectancy under 4 conditions:
Without disease ("—")
Cancer
Cardiovascular disease (CVD)
Diabetes
Life expectancy from age 50 varies by lifestyle:
Healthy lifestyle (best outcomes)
≈ 29.0–31.0 additional years
Intermediate
≈ 26.0–28.0 years
Unhealthy lifestyle
≈ 20.7–24.1 years
The table clearly displays the contribution of each lifestyle category and disease state to total remaining lifespan.
🧾 Overall Conclusion
The PDF concludes that a healthy lifestyle dramatically increases life expectancy, regardless of disease status.
Key takeaways:
✔ Lifestyle improvements reduce mortality
✔ Benefits apply to both healthy individuals and those with chronic disease
✔ Smokers, inactive individuals, and those with obesity have significantly shorter lives
✔ Healthy habits add 4–7 years of life after age 50
The message is clear:
It is never too late to adopt a healthier lifestyle.
If you'd like, I can also create:
✅ a short summary
✅ a very easy explanation
✅ a comparison with other longevity papers
Just tell me!...
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Host Longevity Matters
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Host Longevity Matters
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“Host Longevity Matters” investigates how the rema “Host Longevity Matters” investigates how the remaining lifespan of a host influences the basic reproduction number (R₀) of infectious diseases. Unlike traditional epidemiological models—which often assume infinite infectious duration or ignore host lifespan—the authors show that R₀ is deeply shaped by host longevity, especially for long-lasting infections.
The study combines two powerful components:
A within-host model capturing pathogen replication, mutation, immune response, and resource dynamics.
A between-host transmission model capturing contact structure, secondary infections, and network effects.
By integrating both layers, the paper explores how pathogen evolution depends on two internal parameters:
Replication rate (ρ)
Successful mutation probability (δ)
and one external ecological parameter:
Host contact rate (α)
The goal is to determine which pathogen strategy maximizes R₀ under different host lifespans.
🔍 Core Insight
Pathogens evolve toward one of two fundamental strategies:
1. Killer-like Strategy
Fast replication
Intermediate mutation rates
High pathogen load
Short, intense infections
Favors rapid spread when:
Host lifespan is short, OR
Host contact rates are low
2. Milker-like Strategy
Slow replication
High mutation rates
Low, sustained pathogen load
Long infection duration
Favors persistence when:
Host lifespan is long, AND/OR
Contact rates are high
The study demonstrates a sharp transition between these strategies depending on the combination of:
Host longevity (Dmax)
Contact rate (α)
This yields a bifurcation line separating killer-like from milker-like evolutionary optima.
📈 Key Findings
1. Host Longevity Strongly Shapes R₀
For short-lived hosts (e.g., insects), R₀ increases roughly linearly with contact rate.
For long-lived hosts (e.g., humans), R₀ rapidly reaches a plateau even with moderate contact.
The impact of longevity is large enough to change evolutionary conclusions from previous models.
2. Strategy Switch Depends on Contact Rate
There exists a critical contact rate αₙ, where pathogens switch from:
Killer strategy (fast replication)
to Milker strategy (slow replication)
The value of αₙ shifts strongly with host lifespan.
3. Above a Certain Longevity Threshold, Only Milker Strategy Is Optimal
For very long-lived hosts:
Killer-like strategies disappear entirely.
Pathogens evolve toward mild, persistent infections.
This explains why many long-standing human diseases show long-duration, low-virulence dynamics.
4. Zoonotic Diseases Are Exceptions
Because they originate from short-lived animals, zoonoses (e.g., avian influenza, Ebola) are often:
Highly virulent
Fast-replicating
Short-lasting (killer-like)
This aligns with the model’s predictions.
🧠 Implications
For Evolutionary Epidemiology
Host longevity must be included when predicting pathogen evolution.
Long-lived species tend to select for milder, persistent pathogens.
For Public Health
Models ignoring host lifespan may misestimate epidemic thresholds.
When evaluating disease control strategies, lifespan restriction (e.g., culling, selective breeding) can alter pathogen evolution.
For Theory
This model is among the first to show that R₀ is not purely a pathogen trait, but emerges from interaction between:
Host immune dynamics
Lifespan constraints
Contact structures
Pathogen mutation and replication
🧭 In Summary
“Host Longevity Matters” shows that the lifespan of a host is a critical, previously overlooked determinant of pathogen fitness and evolution.
Long-lived hosts push pathogens toward slow, stealthy, “milker-like” behavior.
Short-lived hosts favor fast, damaging “killer-like” pathogens.
This work demonstrates that R₀, infection strategy, and pathogen evolution are inseparable from host longevity....
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How chronic disease
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How chronic disease affects ageing?
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This monographic report, How Chronic Diseases Affe This monographic report, How Chronic Diseases Affect Ageing, provides a comprehensive and multidisciplinary analysis of how the global rise in life expectancy is directly influencing the prevalence, complexity, and long-term impact of chronic diseases in ageing populations. Drawing on international health organisations, national statistics, clinical research, and current care models, the document explains how chronic diseases—such as cardiovascular conditions, diabetes, chronic respiratory illnesses, cancer, and other age-associated disorders—shape the physical, functional, cognitive, emotional, and social dimensions of older adults.
The report examines demographic trends, theoretical frameworks, and epidemiological data to explain why chronicity is becoming one of the major public health challenges of the 21st century. It details the increasing coexistence of multiple chronic conditions (multimorbidity), the clinical complexities of polypharmacy, the progressive decline in autonomy, and the emergence of frailty—both physical and social—as a defining characteristic of advanced age.
Through a structured and evidence-based approach, the document outlines:
✔ Types of chronic diseases prevalent in ageing adults
Including cardiovascular disease, COPD, cancer, diabetes, arthritis, hypertension, osteoporosis, depression, and neurodegenerative disorders such as Alzheimer’s.
✔ The chronic patient profile
Describing levels of complexity, comorbidity, frailty, care dependence, and the growing role of multidisciplinary teamwork in long-term management.
✔ Risk factors
From modifiable lifestyle behaviours (tobacco, diet, activity) to metabolic, genetic, environmental, and socio-economic determinants.
✔ Key challenges
Such as medication reconciliation, treatment non-adherence, limited access to specialised geriatric resources, fragmented care systems, psychological burden, and nutritional vulnerabilities.
✔ Solutions and innovations
Including preventive strategies (primary, secondary, tertiary, quaternary), strengthened primary care, case management models, specialised geriatric resources, PROMs and PREMs for quality-of-life measurement, and advanced technologies—AI, remote monitoring, predictive models—to anticipate complications and personalise care.
✔ Conclusions
Highlighting the need for integrated, person-centred, preventive, predictive, and technologically supported healthcare models capable of addressing the growing burden of chronic diseases in an ageing world.
This report serves as an essential resource for healthcare professionals, policymakers, researchers, and organisations seeking to better understand, manage, and innovate within the intersection of chronicity and ageing.
If you want, I can also create:
✅ A short description
✅ A meta description for SEO
✅ A 100-word executive description
✅ A title, keywords, and index for the document
Just tell me!...
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How has the variance
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How has the variance of longevity changed ?
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This document is a comprehensive research paper th This document is a comprehensive research paper that examines how the variance of longevity (variation in age at death) has changed across different population groups in the United States over the past several decades. Rather than focusing only on life expectancy, it highlights how unpredictable lifespan is, which is crucial for retirement planning and the value of lifetime income products like annuities.
🔎 Main Purpose of the Study
The core purpose is to analyze:
How lifespan variation has changed from the 1970s to 2019
How differences vary across race, gender, and socioeconomic status (education level)
How changes in lifespan variability influence the economic value of annuities
The authors focus heavily on the implications for retirement planning, longevity risk, and financial security.
🔍 Populations Analyzed
The study evaluates five major groups:
General U.S. population
Annuitants (people who purchase annuities)
White—high education
White—low education
Black—high education
Black—low education
All groups are analyzed separately for men and women, and conditional on survival to ages 50, 62, 67, and 70.
📈 Key Findings (Perfect Summary)
1. Population-level variance has remained stable since the 1970s
Even though life expectancy increased, the spread of ages at death (standard deviation) remained mostly unchanged for the general population.
2. SES and racial disparities in lifespan variation remain large
Black and lower-education individuals have consistently greater lifespan variation.
They face higher risks of both premature death and very late death.
This inequality captures an important dimension of social and economic disadvantage.
3. Different groups show different trends (2000–2019)
Variance increased for almost all groups
→ especially high-education Black and low-education White individuals.
Exception: Low-education Black males
→ They showed a substantial decrease in variability mostly due to reduced premature mortality.
4. Annuitants have less lifespan variation at age 50
Those who purchase annuities tend to be healthier, wealthier, and show less lifespan uncertainty.
However, by age 67, the difference in variation between annuitants and the general population nearly disappears.
💰 Economic Insights: Impact on Annuity Value
Using a lifecycle model, the study calculates wealth equivalence — how much additional wealth a person would need to compensate for losing access to a fair annuity.
Key insight:
Even though longevity variance increased, the value of annuities actually declined over time.
Why?
Because life expectancy increased, delaying mortality credits to older ages — lowering annuity value in economic terms.
Quantitative Findings
A one-year increase in standard deviation → raises annuity value by 6.8% of initial wealth.
A one-year increase in life expectancy → reduces annuity value by 3.1%.
From 2000–2019:
General population saw only a 1.3–2.0% increase in annuity value due to rising variance.
By group:
High-education Black males: +13.6%
Low-education Black males: –6.1%
🔬 Methodology
The study uses:
SSA cohort life tables for the general population
Mortality estimates using NVSS & ACS data for race-education groups
Annuity mortality tables (1971 IAM, 1983 IAM, 2000, 2012 IAM) for annuitants
Lifespan variation measured using standard deviation of age at death (Sx)
Wealth equivalence is computed using a CRRA utility model with full annuitization and actuarially fair payouts.
🧠 Why This Matters
Lifespan uncertainty directly affects:
✔ Retirement planning
✔ Optimal savings behavior
✔ Need for annuities or guaranteed lifetime income
✔ Social welfare policy
Groups with higher lifespan uncertainty benefit more from annuities.
The study’s results emphasize:
Persistent inequalities in mortality patterns
The importance of accessible lifetime income options
The role of policy in addressing retirement security
📌 Perfect One-Sentence Summary
The document shows that while life expectancy has risen, the variance of longevity has remained stable overall but diverged notably across racial and socioeconomic groups, significantly influencing the economic value and importance of annuities in retirement planning.
If you want:
✅ A diagram
✅ A simplified student-friendly summary
✅ A PPT, PDF, or infographic
✅ A comparison table
✅ A visual chart
Just tell me — I can generate it!...
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How Long is Longevity
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How Long is Long in Longevity
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This paper explores a deceptively simple question: This paper explores a deceptively simple question: When does longevity actually begin?
Historically, societies have defined “old age” using fixed ages such as 60, 65, or 70, but this study shows that such ages are arbitrary, outdated, and demographically meaningless. Instead, the author proposes a scientific, population-based approach to define the true onset of longevity.
🧠 1. Main Argument
Traditional age thresholds (60–70 years) are not reliable indicators of longevity because:
They were created for social or economic reasons (military service, taxes, pensions).
They ignore how populations change over time.
They do not reflect biological, demographic, or evolutionary realities.
How Long is Long in Longevity
The study’s central idea:
Longevity should not be defined by chronological age—but by how many people remain alive at a given age.
How Long is Long in Longevity
The paper therefore redefines longevity in terms of survivorship, not age.
🔍 2. Why Chronological Age Is Misleading
The author reviews commonly used demographic indicators:
A. Life expectancy
Measures the average lifespan.
Useful, but only shows the mean and not the distribution.
How Long is Long in Longevity
B. Modal age at death (M)
The most common age at death.
Meaningful, but problematic in populations with high infant mortality.
How Long is Long in Longevity
C. Lifetable entropy threshold
Measures lifespan variability and identifies where mortality improvements matter most.
How Long is Long in Longevity
Each indicator gives partial insight, but none fully captures when a life becomes “long.”
🌱 3. A New Concept: Survivorship Ages (s-ages)
The author introduces s-ages, defined as:
x(s) = the age at which a proportion s of the population remains alive.
How Long is Long in Longevity
This is the inverse of the survival function:
s = 1 → birth
s = 0.5 → median lifespan
s = 0.37 → the proposed longevity threshold
S-ages reflect how survival shifts across generations and are mathematically tied to mortality, failure rates, and evolutionary pressures.
⚡ 4. The Key Scientific Breakthrough: Longevity Begins at x(0.37)
Why 37%?
Using the cumulative hazard concept from reliability theory, the author shows:
When cumulative hazard H(x) = 1, the population has experienced enough mortality to kill the average individual.
Mathematically, H(x) = −ln(s).
Setting H(x) = 1 gives s = e⁻¹ ≈ 0.37.
How Long is Long in Longevity
Interpretation:
Longevity begins at the age when only 37% of the population remains alive—x(0.37).
This is a scientifically grounded threshold based on:
Demography
Reliability theory
Evolutionary biology
Not arbitrary retirement-age traditions.
🧬 5. Biological Meaning (Evolutionary View)
Evolutionary biologists argue:
Natural selection weakens after reproductive ages.
Early-life forces determine vitality; later life is governed by “force of failure.”
How Long is Long in Longevity
By linking these views:
The onset of longevity is the point where natural selection stops dominating and accumulated damage becomes the main driver of survival.
This aligns perfectly with the hazard threshold H(x) = 1 → s = 0.37.
📊 6. Empirical Evidence (USA, Denmark, France, 1950–2020)
The paper shows survival curves and s-ages shifting toward older ages across decades.
Key patterns:
The longevity threshold x(0.37) consistently lies well above age 70.
It increases over time along with life expectancy, the entropy threshold, and modal age at death.
All indicators move upward together—showing that longevity is dynamic, not fixed.
How Long is Long in Longevity
In all countries studied:
People in the 1950s reached the x(0.37) longevity threshold much earlier than people today.
Meaning: survival to advanced ages is improving steadily.
🔑 7. Major Conclusions
✔ Longevity cannot be defined by a fixed age like 60 or 65.
✔ Longevity is a population-relative concept—based on survival, not age.
✔ The scientifically justified threshold is:
The age at which only 37% of the population remains alive — x(0.37).
✔ All longevity indicators point to a continuously increasing threshold over time.
✔ Old age today begins much later than traditional retirement ages.
🌟 Perfect One-Sentence Summary
Longevity should be defined not by chronological age but by the survival threshold x(0.37), where only 37% of the population is still alive—marking the scientifically grounded onset of a long life.
If you want, I can also create:
📌 A diagram of the 37% longevity threshold
📌 A mind map
📌 A short summary
📌 A comparison with your other longevity PDFs
📌 A PowerPoint presentation
Just tell me!...
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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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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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Title: Human Capital and Longevity: Evidence from Title: Human Capital and Longevity: Evidence from 50,000 Twins
Authors: Petter Lundborg, Carl Hampus Lyttkens, Paul Nystedt
Published: July 2012
Dataset: Swedish Twin Registry (≈50,000 same-sex twins, 1886–1958)
🔍 What the Study Investigates
The document analyzes why well-educated people live longer, using one of the world’s largest collections of identical (MZ) and fraternal (DZ) twins. Because twins share genes and environments, this study uniquely isolates whether the connection between education and longevity is causal or simply due to shared background factors.
📊 Core Research Questions
Does education truly increase lifespan?
Or do unobserved factors—such as genetics, early-life health, birth weight, family environment, or ability—explain the link?
How much extra life expectancy is gained from higher education?
🧬 Why Twins Are Used
Twins help the researchers eliminate:
Shared genes
Shared childhood environments
Early-life conditions
Many unobserved family-level factors
This allows a much cleaner measurement of the effect of education alone.
📈 Main Findings (Clear & Strong)
1️⃣ Education strongly increases longevity.
Across all models:
Each extra year of schooling reduces mortality by about 6%.
2️⃣ Even after controlling for:
Shared genes
Shared environment
Birth weight differences
Height (proxy for IQ & early health)
Only twins who differ in schooling
➡️ The relationship remains significant and strong.
3️⃣ High education adds 2.5–3 additional years of life at age 60.
This effect is:
Consistent for men and women
Consistent across birth cohorts
Strongest in younger generations
Stronger at mid-life (age 50–60) than in old age
🧪 Key Tests & Evidence
Birth Weight Test
Birth weight differences predict schooling differences
BUT birth weight does not predict mortality
→ So omission of birth weight does not bias the education effect.
Height (Ability Proxy) Test
Taller twins achieve more schooling
But height does not predict mortality in twin comparisons
→ Ability differences cannot explain the education–longevity link.
MZ vs DZ Twins
Identical twins (MZ) share 100% genes
Fraternal twins (DZ) share ~50%
Results are extremely similar
Suggests genetics are not driving the relationship.
📉 Non-Linear Benefits
Education levels:
<10 years
10–12 years
≥13 years (university level)
Effects:
Middle group: ~13% lower mortality
University group: 35–40% lower mortality
Very strong evidence of a degree effect.
⏳ Age Patterns
The effect is strongest between ages 50–60
The benefit declines slightly at older ages
But remains significant across all age groups
📅 Cohort Patterns
The education–longevity gap has grown stronger over time
Likely due to rising skill demands and better health knowledge among educated groups
📘 Methodology
The study uses advanced statistical tools:
Cox proportional hazards models
Stratified partial likelihood (twin fixed-effects)
Gompertz survival models
Linear probability models for survival to 70 and 80
These allow precise estimation of the effect of education on mortality.
📌 Policy Implications
Education has large, long-term health returns
These returns go far beyond labor market earnings
Increasing education could significantly raise population longevity—especially in developing countries
Evidence suggests education improves:
Health behaviors
Decision-making
Access to knowledge
Use of medical information
🎯 Final Summary (Perfect One-Liner)
The study provides powerful evidence that education itself—not genes, family environment, or early-life factors—directly increases human lifespan by several years, making schooling one of the most effective longevity-enhancing investments in society....
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Implausibility of Radical Life Extension in Humans Implausibility of Radical Life Extension in Humans in the Twenty-First Century
Human in 21st century
This study, published in Nature Aging (2024), analyzes real demographic data from the world’s longest-lived populations to determine whether radical human life extension is occurring—or likely to occur—in this century. The authors conclude that radical life extension is not happening and is biologically implausible unless we discover ways to slow biological aging itself, not just treat diseases.
🧠 1. Central Argument
Over the 20th century, life expectancy grew rapidly due to public health and medical advances. But since 1990, improvements in life expectancy have slowed dramatically across all longest-lived nations.
Human in 21st century
The core message:
Unless aging can be biologically slowed, humans are already near the upper limits of natural life expectancy.
Human in 21st century
📉 2. Has Radical Life Extension Happened?
The authors define radical life extension as:
👉 A 0.3-year increase in life expectancy per year (3 years per decade) — similar to gains during the 20th-century longevity revolution.
Using mortality data from 1990–2019 (Australia, France, Italy, Japan, South Korea, Spain, Sweden, Switzerland, Hong Kong, USA):
🔴 Findings:
Only Hong Kong and South Korea briefly approached this rate (mostly in the 1990s).
Every country shows slowed growth in life expectancy since 2000.
Human in 21st century
The U.S. even experienced declines in life expectancy in recent decades due to midlife mortality.
Human in 21st century
🎯 3. Will Most People Today Reach 100?
The data say no.
Actual probabilities of reaching age 100:
Females: ~5%
Males: ~1.8%
Highest observed: Hong Kong (12.8% females, 4.4% males)
Human in 21st century
Nowhere near the 50% survival to 100 predicted by “radical life extension” futurists.
📊 4. How Hard Is It to Increase Life Expectancy Today?
To add just one year to life expectancy, countries now must reduce mortality at every age by far more than in the past.
Example: For Japanese females (2019):
To go from 88 → 89 years requires
👉 20.3% reduction in death rates at ALL ages.
Human in 21st century
These reductions are increasingly unrealistic using current medical approaches.
🧬 5. Biological & Demographic Constraints
Three demographic signals show humans are approaching biological limits:
A. Life table entropy (H*) is stabilizing
Shows mortality improvements are becoming harder.
Human in 21st century
B. Lifespan inequality (Φ*) is decreasing
Deaths are increasingly compressed into a narrow age window — meaning humans are already dying close to the biological limit.
Human in 21st century
C. Maximum lifespan has stagnated
No increase beyond Jeanne Calment’s record of 122.45 years.
Human in 21st century
Together, these metrics prove that life expectancy gains are slowing because humans are nearing biological constraints—not because progress in medicine has stopped.
🚫 6. What Would Radical Life Extension Require?
The authors create a hypothetical future where life expectancy reaches 110 years.
To achieve this:
70% of females must survive to 100
24% must survive beyond 122.5 (breaking the maximum human lifespan)
6–7% must live to 150
Human in 21st century
This would require:
88% reduction in death rates at every age up to 150
Human in 21st century
This is impossible using only disease treatment. It would require curing most causes of death.
🌍 7. Composite “Best-Case” Mortality Worldwide
The authors compile the lowest death rates ever observed in any country (2019):
Best-case female life expectancy: 88.7 years
Best-case male life expectancy: 83.2 years
Human in 21st century
Even with zero deaths from birth to age 50, life expectancy increases by only one additional year.
Human in 21st century
This shows why further increases are extremely difficult.
🧭 8. Final Conclusions
Radical life extension is not happening in today’s long-lived nations.
Biological and demographic forces limit life expectancy to about 85–90 years for populations.
Survival to 100 will remain rare (around 5–15% for females; 1–5% for males).
Treating diseases alone cannot extend lifespan dramatically.
Only slowing biological aging (geroscience) could meaningfully shift these limits.
Human in 21st century
🌟 Perfect One-Sentence Summary
Humanity is already near the biological limits of life expectancy, and radical life extension in the 21st century is implausible unless science discovers ways to slow the fundamental processes of aging....
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HUMAN LONGEVITY
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HUMAN LONGEVITY AND IMPLICATIONS FOR SOCIAL
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Title: Human Longevity and Implications for Social Title: Human Longevity and Implications for Social Security – Actuarial Status
Authors: Stephen Goss, Karen Glenn, Michael Morris, K. Mark Bye, Felicitie Bell
Published by: Social Security Administration, Office of the Chief Actuary (Actuarial Note No. 158, June 2016)
📌 Purpose of the Document
This report examines how changing human longevity (declining mortality rates) affects:
The age distribution of the U.S. population
The financial status of Social Security
Long-term cost projections for Social Security trust funds
It explains how mortality rates have changed historically, how they may change in the future, and why accurate longevity projections are essential for determining Social Security’s sustainability.
📌 Key Points and Insights
1. Demographic changes drive Social Security finances
Mortality, fertility, and immigration shape the ratio of workers to retirees, known as the aged dependency ratio.
Lower fertility since the baby boom greatly increased the proportion of older adults.
Mortality improvements (people living longer) also steadily increase Social Security costs.
2. Life expectancy improvements are slowing
The report explains that:
Increases in life expectancy historically came from reducing infant and child mortality.
Today, with child deaths already extremely low, gains must come from reducing deaths at older ages, which is harder and slower.
Recent research (Vallin, Meslé, Lee) suggests life expectancy follows an S-shaped curve, not unlimited linear growth, meaning natural limits are becoming visible.
3. Mortality improvement varies significantly with age
The report shows a clear age gradient:
Faster mortality improvement at younger ages
Slower improvement at older ages
This pattern appears consistently in the U.S., Canada, and the U.K.
Future projections must consider:
Whether this age gradient continues
How medical progress will change mortality in each age group
4. Health spending and policy historically reduced mortality
Huge declines in death rates during the 20th century were driven by:
better nutrition
expanded medical care
antibiotics
Medicare & Medicaid
However:
The same level of improvement cannot be repeated.
Health spending as % of GDP has flattened, and per-beneficiary Medicare growth is slowing.
Therefore future mortality improvement will likely decelerate.
5. Mortality reduction varies by cause of death
The report compares:
Cardiovascular disease
Respiratory disease
Cancer
Using Social Security projections and independent Johns Hopkins research, it finds:
Cardiovascular improvements are slowing
Respiratory disease has mixed trends
Cancer improvements remain steady but modest
Cause-specific analysis leads to more realistic projections.
6. Longevity differences by income levels matter
People with higher lifetime earnings:
Have lower mortality
Experience faster mortality improvement
This affects Social Security because:
Higher earners live longer
They collect benefits for more years
This increases system costs over time
7. Recent slowdown since 2009
The report highlights that:
Mortality improvements after 2009 have been much slower than expected, especially for older adults.
If this slowdown continues, Social Security’s long-term costs could be lower than projected, improving system finances.
8. Comparing projection methods
The report evaluates two approaches:
a) Social Security Trustees’ method
Includes:
age gradient
cause-specific modeling
gradual deceleration
Produces conservative and stable long-range estimates
b) Lee & Carter method
Fits age-specific mortality trends mathematically
Assumes no deceleration
Keeps the full historical age gradient
Findings:
Lee’s method produces a more favorable worker-to-retiree ratio until ~2050
After 2050, unrealistic lack of deceleration makes older survival too high
Over 75 years, both methods produce similar overall actuarial outcomes
📌 Final Conclusions
The document concludes that:
Mortality improvements will continue, but more slowly than in the past.
The Social Security Trustees’ current mortality assumptions—moderate improvement with deceleration—are reasonable and well supported by evidence.
Social Security’s financial outlook is highly sensitive to longevity patterns, especially at older ages.
Continued research and updated data (including the slowdown since 2009) are essential for accurate projections....
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Human longevity
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Human longevity at the cost of reproductive
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This scientific paper provides a comprehensive, gl This scientific paper provides a comprehensive, global-scale analysis showing that human longevity and reproductive success are biologically linked through a life-history trade-off: populations where women have more children tend to have shorter average lifespans, even after adjusting for economic, geographic, ethnic, religious, and disease-related factors.
Authored by Thomas, Teriokhin, Renaud, De Meeûs, and Guégan, the study combines evolutionary theory with large-scale demographic data from 153 countries to examine whether humans—like other organisms—experience the classic evolutionary trade-off:
More reproduction → less somatic maintenance → shorter lifespan
🔶 1. Purpose of the Study
The authors aim to determine whether humans display the fundamental evolutionary principle that reproduction is costly—and that allocating energy to childbirth reduces resources for body repair, thereby shortening lifespan.
This principle is widely documented in animals but rarely tested in humans at the global level.
🔶 2. Background Theory
The paper draws on life-history theory, explaining that aging evolves due to:
Accumulation of late-acting mutations (Medawar)
Antagonistic pleiotropy: genes improving early reproduction may harm late survival (Williams)
Allocation of limited energy between reproduction and somatic maintenance (Kirkwood’s Disposable Soma theory)
Evidence from insects, worms, and other species shows that higher reproductive effort often leads to:
Reduced survival
Faster aging
Increased physiological damage
🔶 3. What Makes This Study Unique
Unlike most previous work on humans (e.g., genealogical studies of British aristocracy), this study uses broad international datasets:
153 countries
Measures of:
Female life expectancy
Fecundity (average lifetime births per woman)
Infant mortality
Economic indicators (GNP)
Disease burden (16 infectious diseases)
Geography and population structure
Religion
Ethnic/phylogenetic groupings
This allows the authors to control for confounding factors and test whether the relationship remains after adjustment.
🔶 4. Methods Overview
⭐ Longevity calculation
Life expectancy was reconstructed using:
Infant mortality rates
Gompertz mortality function (for age-related mortality)
Environmental mortality (country-specific)
Only female life expectancy at age 1 (L1) was used in final models.
⭐ Fecundity measurement
Log-transformed average number of children per woman
Only includes women who survived to reproductive age
Not affected by childhood mortality
⭐ Control variables included
Ethnic group (8 categories)
Religion (5 categories)
16 infectious disease categories
GDP per capita (log)
Population density, size, growth
Hemisphere, island vs. continent, latitude, longitude
Country surface area
⭐ Statistical approach
General linear models (GLMs)
Backward stepwise elimination
Inclusion threshold: p < 0.05
Multicollinearity checks
Residual correlations to test trade-off
🔶 5. Key Findings
⭐ 1. A strong negative raw correlation
Across 153 countries:
More children = shorter female lifespan
r = –0.70, p < 0.001
Human longevity at the cost of …
This shows that high-fecundity populations (e.g., developing nations) tend to have lower longevity.
⭐ 2. The trade-off remains after controlling for all confounders
After removing effects of:
Economy
Disease load
Ethnicity
Religion
Geography
The relationship still exists:
Women who have more children live shorter lives on average.
(r = –0.27, p = 0.0012)
Human longevity at the cost of …
⭐ 3. Economic and disease factors matter
Higher GDP → higher longevity & lower fertility
Higher infectious disease burden → lower longevity & higher fertility
⭐ 4. Ethnic and religious groupings have significant predictive power
Human phylogeny and culture influence both fertility patterns and lifespan variability.
🔶 6. Interpretation
The results strongly support the evolutionary trade-off theory:
Investing biological resources in reproduction reduces the energy available for body repair, leading to earlier aging and death.
This parallels findings in:
Fruit flies
Nematodes
Birds
Mammals
The study suggests these trade-offs operate even at the societal and population level, not only within individuals.
🔶 7. Limitations Acknowledged
The authors caution that:
Human reproduction is strongly influenced by socio-cultural factors (e.g., education, contraception), not purely biology
Some cultural factors may confound the relationship
Genetic vs. environmental contributions are not disentangled
Country-level averages do not reflect individual variation
However, despite these limitations, the consistency of the global pattern is compelling.
🔶 8. Conclusion (Perfect Summary)
This study provides robust global evidence that human longevity and reproductive success are linked by a fundamental biological trade-off: populations with higher fertility have shorter female lifespans, even after controlling for economic, geographic, disease-related, ethnic, and cultural factors. The findings extend life-history theory to humans on a worldwide scale and support the idea that allocating energy to childbearing reduces resources for somatic maintenance, accelerating aging....
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Human longevity: Genetics
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Human longevity: Genetics or Lifestyle
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This review explains that human longevity is shape This review explains that human longevity is shaped by a dynamic interaction between genetics and lifestyle, where neither factor alone is sufficient. About 25% of lifespan variation is due to genetics, while the remainder is influenced by lifestyle, environment, medical care, and epigenetic changes across life.
The paper traces the scientific journey behind understanding longevity, beginning with early experiments in C. elegans showing that mutations in key genes can dramatically extend lifespan. These findings led to the discovery of conserved genetic pathways — such as IGF-1/insulin signaling, FOXO transcription factors, TOR, DNA repair genes, telomere maintenance, and mitochondrial function — that influence cellular maintenance, metabolism, and aging in humans.
Human studies, including twin studies, family studies, and genome-wide association research, confirm a modest but real genetic influence. Siblings of centenarians consistently show higher survival rates, especially men, indicating inherited resilience. However, no single gene determines longevity; instead, many small-effect variants combine, and their cumulative action shapes aging and survival.
The review shows that while genetics provides a foundational capacity for longer life, lifestyle and environment have historically produced the greatest gains in life expectancy. Improvements in sanitation, nutrition, public health, and medical care significantly lengthened lifespan worldwide. Yet these gains have not equally extended healthy life expectancy, prompting research into interventions that target the biological mechanisms of aging.
One key insight is that calorie restriction and nutrient-sensing pathways (IGF-1, FOXO, TOR) are strongly linked to longer life in animals. These discoveries explain why certain traditional diets — like the Mediterranean diet and the Okinawan low-calorie, nutrient-dense diet — are associated with exceptional human longevity. They also motivate the development of drugs that mimic the effects of dietary restriction without requiring major lifestyle changes.
A major emerging field discussed is epigenetics. Epigenetic modifications, such as DNA methylation, reflect both genetic background and lifestyle exposure. They change predictably with age and have become powerful biomarkers through the “epigenetic clock.” These methylation patterns can predict biological age, disease risk, and even all-cause mortality more accurately than telomere length. Epigenetic aging is accelerated in conditions like Down syndrome and slowed in long-lived individuals.
🔍 Key Takeaways
1. Genetics explains ~25% of lifespan variation
Twin and family studies show strong but limited heritability, more pronounced in men and at older ages.
2. Longevity genes maintain cellular integrity
Genes involved in:
DNA repair
Telomere protection
Stress response
Mitochondrial efficiency
Nutrient sensing (IGF-1, FOXO, TOR)
play essential roles in determining aging pace.
3. Lifestyle and environment have the largest historical impact
Modern sanitation, medical advances, nutrition, and lower infection rates dramatically increased human lifespan in the 20th century.
4. Exceptional longevity comes from a “lucky” combination
Some individuals inherit optimal metabolic and stress-response variants; others can mimic these genetic advantages through diet, exercise, and targeted interventions.
5. Epigenetics links genes and lifestyle
DNA methylation patterns:
reflect biological aging
predict mortality
respond to lifestyle factors
may soon serve as targets for anti-aging interventions
6. The future of longevity research targets interactions
Extending healthspan requires approaches that modulate both genetic pathways and lifestyle behaviors, emphasizing that genetics and lifestyle “dance together.”
🧭 Overall Conclusion
Human longevity is not simply written in DNA nor solely determined by lifestyle. Instead, it emerges from the interplay between inherited biological systems and environmental influences across the life course. Small genetic advantages make some individuals naturally more resilient, but lifestyle — particularly nutrition, activity, and stress exposure — can harness or hinder these genetic potentials. Epigenetic processes act as the bridge between the two, shaping how genes express and how fast the body ages.
Longevity, therefore, “takes two to tango”:
genes set the stage, but lifestyle leads the dance....
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Impact of Ecological
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Impact of Ecological Footprint on the Longevity of
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This study investigates how environmental degradat This study investigates how environmental degradation, ecological footprint, climate factors, and socioeconomic variables influence human life expectancy in major emerging Asian economies including Bangladesh, China, India, Malaysia, South Korea, Singapore, Thailand, and Vietnam.
1. Core Purpose
The research aims to determine whether rising ecological footprint—the pressure placed on natural ecosystems by human use of resources—reduces life expectancy, and how other factors such as globalization, GDP, carbon emissions, temperature, health expenditure, and infant mortality interact with longevity in these countries (2000–2019).
🌍 2. Key Findings
A. Negative Environmental Impacts on Life Expectancy
The study finds that:
Higher ecological footprint ↓ life expectancy
Each 1% rise in ecological footprint reduces life expectancy by 0.021%.
Carbon emissions ↓ life expectancy
A 1% rise in CO₂ emissions reduces life expectancy by 0.0098%.
Rising average temperature ↓ life expectancy
Heatwaves, diseases, respiratory problems, and infectious illnesses are intensified by climate change.
B. Positive Determinants of Longevity
Globalization ↑ life expectancy
Increased trade, technology spread, and global integration improve development and healthcare.
GDP ↑ life expectancy
Economic growth improves living standards, jobs, nutrition, and health services.
Health expenditure ↑ life expectancy
Every 1% rise in public health spending increases life expectancy by 0.089%.
C. Negative Social Determinants
Infant mortality ↓ life expectancy
A 1% rise in infant deaths decreases life expectancy by 0.061%, reflecting poor healthcare quality.
🔍 3. Data & Methods
Panel data (2000–2019) from 8 Asian economies.
Variables include ecological footprint, CO₂ emissions, temperature, GDP, globalization, health expenditure, and infant mortality.
Econometric models used:
Cross-sectional dependence tests
Second-generation unit root tests (Pesaran CADF)
KAO Cointegration
FMOLS (Fully Modified Ordinary Least Squares) for long-run estimations.
The statistical model explains 94% of life expectancy variation (R² = 0.94).
🌱 4. Major Conclusions
Environmental degradation significantly reduces human longevity in emerging Asian countries.
Ecological footprint and temperature rise are major threats to health and human welfare.
Carbon emissions drive respiratory, cardiovascular, and infectious diseases.
Globalization, GDP, and health spending improve life expectancy.
Strong environmental policies are needed to reduce ecological pressure and carbon emissions.
Health systems must be strengthened, especially in developing Asian economies.
🧭 5. Policy Recommendations
Reduce ecological footprint by improving resource efficiency.
Decarbonize industry, transport, and energy sectors.
Invest more in public health systems and medical infrastructure.
Create markets for ecosystem services.
Promote sustainable development, green energy, and trade policies.
Reduce infant mortality through prenatal, maternal, and child healthcare....
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The Impact of Longevity
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The Impact of Longevity Improvements on U.S.
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This PDF is a policy-oriented actuarial and econom This PDF is a policy-oriented actuarial and economic analysis that explains how improvements in U.S. longevity—people living longer than previous generations—affect population size, economic productivity, Social Security, Medicare, government budgets, and overall national well-being. The document uses demographic projections, mortality data, and economic modeling to show how even small improvements in life expectancy significantly change the financial and social landscape of the United States.
Its central message is clear:
Longevity improvements generate substantial economic and societal benefits, but also increase long-term public spending, especially through Social Security and Medicare. Both the benefits and costs must be understood together.
📈 1. What the Document Examines
The paper analyzes:
How rising life expectancy will reshape the U.S. population
The economic value created when people live longer
Increased tax revenues from longer working lives
Higher federal spending resulting from extended retirements
Effects on Social Security, Medicare, and fiscal sustainability
Impact of Longevity improvement…
👥 2. Population & Longevity Trends
The analysis highlights:
The U.S. population is aging as mortality declines.
Even modest improvements in longevity generate large changes in the number of older Americans.
The share of adults over age 65 will continue rising for decades.
Impact of Longevity improvement…
These demographic shifts increase both the economic potential of a healthier older population and the fiscal pressure on entitlement programs.
💵 3. Economic Benefits of Longevity Improvements
Living longer and healthier creates major economic gains:
✔ Increased Labor Supply
Many adults work longer if they remain healthy.
✔ Higher Productivity
Longer education, more experience, and healthier aging improve worker output.
✔ Greater Tax Revenues
Extended working years increase income taxes, payroll taxes, and spending.
✔ Larger Consumer Market
An aging but healthy population boosts demand for goods, services, and innovation.
Impact of Longevity improvement…
🏛 4. Fiscal Costs of Longevity Improvements
The report explains that increased longevity also increases federal spending:
✔ Higher Social Security Outlays
More retirees receiving benefits for more years.
✔ Higher Medicare & Medicaid Costs
Longer lifespans mean longer periods of medical care and long-term care use.
✔ Potential Strain on Disability & Pension Systems
If health improvements do not keep pace with lifespan gains, disability costs may rise.
Impact of Longevity improvement…
⚖️ 5. Net Impact: Benefits vs. Costs
A key conclusion:
Longevity improvements produce very large economic benefits, but public program spending rises as well, requiring policy adjustments.
The document quantifies both sides:
Benefits: trillions of dollars in increased economic value
Costs: higher federal program obligations, especially for the elderly
Impact of Longevity improvement…
The net impact depends on policy choices such as retirement age, health system investment, and how healthspan improves relative to lifespan.
🔮 6. Policy Implications
The PDF suggests that policymakers must prepare for an aging America by:
● Strengthening Social Security solvency
● Reforming Medicare to handle long-term cost growth
● Encouraging longer working lives
● Investing in preventive health and chronic disease management
● Focusing on healthspan, not just lifespan
Impact of Longevity improvement…
If reforms are implemented effectively, longevity improvements can become an economic advantage rather than a fiscal burden.
⭐ Overall Summary
This PDF provides a balanced and research-driven examination of how increasing longevity influences the U.S. economy, government programs, and national finances. It shows that longer lives bring enormous economic value—in productivity, workforce participation, and consumer activity—but also increase federal spending on Social Security and Medicare. The report emphasizes that preparing for an aging population requires proactive adjustments in retirement policy, health care, and fiscal planning....
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Impact of rapamycin on longevity
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This document is a comprehensive scientific review This document is a comprehensive scientific review exploring how rapamycin influences aging and longevity across biological systems. It explains, in clear mechanistic detail, how rapamycin inhibits the mTOR pathway, a central regulator of growth, metabolism, and cellular aging.
The paper summarizes:
1. Why Aging Happens
It describes aging as the gradual accumulation of cellular and molecular damage, leading to reduced function, increased disease risk, and ultimately death.
2. The Role of mTOR in Aging
mTOR is a nutrient-sensing pathway that controls growth, metabolism, protein synthesis, autophagy, and mitochondrial function.
Overactivation of mTOR accelerates aging.
Rapamycin inhibits mTORC1 and indirectly mTORC2, creating conditions that slow aging at the cellular, tissue, and organ level.
3. Rapamycin as a Longevity Drug
The review highlights extensive evidence from yeast, worms, flies, and mice, showing that rapamycin:
Extends lifespan
Improves healthspan
Reduces age-related diseases
4. Key Anti-Aging Mechanisms of Rapamycin
The document details multiple biological pathways influenced by rapamycin:
Protein Homeostasis
Improves fidelity of protein translation
Reduces toxic misfolded protein accumulation
Suppresses harmful senescence-associated secretory phenotype (SASP)
Autophagy Activation
Encourages the removal of damaged organelles and proteins
Protects against neurodegeneration, heart aging, liver aging, and metabolic decline
Mitochondrial Protection
Enhances function and reduces oxidative stress
Immune Rejuvenation
Balances inflammatory signaling
Reduces age-related immune dysfunction
5. Organ-Specific Benefits
The paper includes a detailed table summarizing preclinical evidence showing rapamycin’s benefits in:
Cardiovascular system
Nervous system
Liver
Kidneys
Muscles
Reproductive organs
Respiratory system
Gastrointestinal tract
These benefits involve improvements in:
Autophagy
Stem cell activity
Inflammation
Oxidative stress
Mitochondrial health
6. Limitations & Challenges
While promising, rapamycin has:
Metabolic side effects
Immune-related risks
Dose-timing challenges
Proper therapeutic regimens are required before safe widespread human use.
In Summary
This document provides an up-to-date, detailed, and scientific overview of how rapamycin may slow aging and extend lifespan by targeting mTOR signaling. It integrates molecular biology, animal research, and clinical considerations to outline rapamycin’s potential as one of the most powerful known geroprotective drugs....
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