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60766956-e0ac-4992-84c4-aa05c296bbd9
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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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Credible Power-Sharing
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Credible Power-Sharing and the Longevity
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“Credible Power-Sharing: Evidence From Cogovernanc “Credible Power-Sharing: Evidence From Cogovernance in Colombia” is a research study examining whether power-sharing institutions can help reduce violence and build political stability in regions historically affected by armed conflict. Focusing on a cogovernance reform in Colombia, the paper evaluates whether granting communities a formal role in local decision-making can create credible commitments between the state and citizens, thereby reducing conflict-related violence.
The reform introduced a municipal cogovernance mechanism that gave civilians shared authority over public resource allocation. The authors combine administrative data, qualitative fieldwork, and quantitative causal-inference methods to measure the reform’s effect on governance outcomes and security conditions.
The findings show that cogovernance significantly increased civilian participation, improved transparency in local government, and reduced opportunities for corruption. Most importantly, the study documents a substantial decline in violence, especially in areas with a strong presence of armed groups. The mechanism worked by enhancing the credibility of state commitments: when citizens gained real influence in local policy, trust increased, and armed groups had fewer incentives to interfere.
The paper concludes that credible power-sharing arrangements can meaningfully reduce violence when they provide communities with real authority and when institutions are robust enough to enforce shared decision-making. The Colombian case offers broader insights for countries attempting to transition out of conflict through participatory governance.
If you want, I can also provide:
✅ A short 3–4 line summary
✅ A student-friendly simple version
✅ MCQs or quiz questions from this file
Just tell me!...
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zouruihl-4573
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xevyo
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Social support and Life
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Social support and Longevity
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This document is a comprehensive scientific review This document is a comprehensive scientific review published in Frontiers in Psychology in 2021, authored by Jaime Vila, examining how social support—our relationships, connections, and sense of belonging—profoundly influences health, disease, and lifespan.
It integrates findings from 23 meta-analyses (covering 1,187 studies and more than 1.45 billion participants) to provide the strongest, most complete evidence to date that supportive social relationships significantly reduce disease risk and extend longevity.
What the Paper Does
1. Summarizes 60 years of scientific evidence
The author reviews decades of research showing that people with strong social support:
live longer,
have lower disease risk,
and experience better mental and physical health.
The paper shows that the effect of social support on mortality is as strong as major health factors like smoking or obesity.
Main Findings
A. Meta-analysis Evidence: Social Support Predicts Longevity
Across 23 large meta-analyses, the paper reports:
Complex social integration (being part of diverse, frequent social ties) is the strongest predictor of lower mortality.
Perceived social support—believing that one is loved, valued, and cared for—is also highly predictive.
Loneliness is a powerful risk factor, increasing mortality and disease risk.
People with low social support show:
23% to over 600% higher risk of adverse health outcomes depending on the condition
Social support and Longevity
.
Meta-analyses reveal consistent findings across:
diseases (heart disease, cancer, dementia, mental health)
age groups
cultures and countries
types of social support (structural and functional)
Importantly, these relationships hold even after controlling for confounders such as age, socioeconomic status, and baseline health
Social support and Longevity
.
B. The Multidimensional Nature of Social Support
The paper explains that "social support" is not a single thing—it has many components:
Structural support: marriage, social network size, frequency of contact, community involvement.
Functional support: emotional, instrumental, informational, financial, perceived vs. received support.
Different types predict disease and longevity in different ways, highlighting the complexity of studying social relationships
Social support and Longevity
.
C. Psychobiological Mechanisms
The paper examines how social support improves longevity through three biological systems:
1. Autonomic Nervous System
Supportive social cues reduce cardiovascular stress and increase heart-rate variability, a marker of health.
2. Neuroendocrine System (HPA axis & oxytocin)
Social connection dampens cortisol (stress hormone).
Love, attachment, and bonding trigger oxytocin release, reducing threat responses.
3. Immune System
Strong support reduces inflammation, a major risk factor for chronic diseases.
Social isolation increases inflammation and lowers immune resilience.
This supports the Stress-Buffering Hypothesis:
being with trusted social partners reduces activation of stress systems, thereby protecting long-term health
Social support and Longevity
.
D. Evolutionary, Lifespan, and Systemic Perspectives
The paper extends the discussion into three broader research domains:
1. Evolutionary Evidence
Social mammals (primates, rodents, ungulates, whales) show the same relationship:
animals with richer social connections live longer and are healthier
Social support and Longevity
.
2. Lifespan Development
Social support shapes health from childhood to old age.
Early adversity shortens lifespan; nurturing social environments protect it across the lifespan
Social support and Longevity
.
3. Systemic Level
Social support works at four levels:
individual
family/close relationships
community
society
Societal norms, cultural behaviors, and social policy also influence longevity through social connection
Social support and Longevity
.
Conclusion of the Paper
The evidence is clear:
Social support is a fundamental determinant of human health and longevity.
Supportive social relationships:
reduce stress responses,
regulate biological systems,
and significantly decrease the risk of disease and death.
The author concludes that promoting a global culture of social support—beyond individuals, stretching to communities and societies—is essential for public health and for addressing growing global issues like loneliness and social fragmentation
Social support and Longevity
....
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European Longevity Record
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European Longevity Records
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European Longevity Records is a visually rich, dat European Longevity Records is a visually rich, data-driven document presenting verified supercentenarian records across Europe, organized by country. Using flags, icons, portrait photos, and highlighted record boxes, the document showcases the oldest known individuals from dozens of European nations, including their names, ages, birth/death years, and longevity rankings.
The booklet serves as a continental longevity atlas, featuring entries such as:
UK (England) – Charlotte Hughes
UK (Scotland) – Annie Knight
Spain – María Branyas Morera
Italy – Emma Morano
France – Jeanne Calment (the world’s oldest verified person)
Belgium – Joanna Distelmans Van Geystelen
Netherlands – Hendrikje van Andel-Schipper
Germany – Auguste Steinmann
Iceland – Jón Daníelsson (earliest entry in the list)
Each country has a dedicated “longevity card” containing:
A flag symbol
A portrait of the recordholder
Gender icon
Their maximum verified age (e.g., 122 years, 5 months, 14 days)
Birth and death dates
A ranking indicator (e.g., “1st,” “3rd,” “7th”)
The layout intentionally highlights the extraordinary lifespan of each individual, often showing bold age numbers (e.g., 122, 119, 116), making cross-country comparison simple and intuitive.
The publication also includes:
A brief methodological note (“Supercentenarian = age ≥ 110”)
Highlighting that the list is maintained by the GRG European Supercentenarian Database (ESD) and identifies the oldest documented person ever from each country
A disclaimer that validation standards follow international demographic verification protocols
The document functions as both:
A historical archive of Europe’s longest-lived individuals, and
A demographic reference illustrating extreme longevity patterns across nations.
Overall, European Longevity Records is a concise, authoritative, beautifully designed compilation of Europe’s verified supercentenarians—effectively a “who’s who” of exceptional human longevity across the continent.
If you’d like, I can also create:
📌 a condensed one-page summary
📌 a country-by-country breakdown
📌 an infographic-style list
📌 or a comparison across all your longevity documents
Just tell me!...
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Longevity Asia-Pacific
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Longevity in Asia-Pacific population
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Longevity in Asia-Pacific Populations” is a compre Longevity in Asia-Pacific Populations” is a comprehensive analytical presentation examining how mortality patterns, demographic shifts, and socio-economic changes across Asia-Pacific countries compare to Europe and North America. Using Human Mortality Database data, global socio-economic indicators, and three major industry mortality models (CMI, AG, and MIM), the study evaluates both historical trends and future mortality projections for key APAC populations.
Mark Woods (Canada Life Re) shows that Asia-Pacific mortality improvements have been among the strongest in the world, with Japan, Hong Kong, South Korea, and Taiwan now competing with or surpassing Western nations in life expectancy—especially for women. The analysis highlights how demographic aging, economic transitions, healthcare reforms, and cohort-specific phenomena (such as the “golden cohort”) shape longevity outcomes across the region.
The document reveals that although APAC populations share some global drivers of mortality improvement, each country’s trajectory is unique, influenced by distinct socio-economic history, health systems, and risk exposures. The COVID-19 period introduced additional complexity: some APAC countries showed little early excess mortality, while others experienced delayed effects compared with Western regions.
Finally, the study demonstrates that mortality model selection strongly affects future projections and the valuation of pensions and annuities, producing significant differences in expected mortality improvements across APAC countries through 2030.
🔍 Key Insights
1. Asia-Pacific vs Europe/North America
APAC countries such as Japan, Hong Kong, and South Korea display exceptionally light mortality, especially among females.
Longevity in asia pacific popul…
New Zealand has rapidly improved from high-mortality levels to among the lightest in the dataset.
The U.S. now has heavier mortality than most APAC peers.
2. Demographic Dynamics
All APAC nations are aging, but Japan and South Korea are experiencing the fastest demographic aging in the world.
Longevity in asia pacific popul…
Hong Kong and Taiwan saw rapid earlier growth in younger populations.
Average age differences across countries have narrowed dramatically over recent decades.
3. Socio-Economic Drivers
HDI (Human Development Index), education levels, and income growth correlate strongly with mortality improvements.
Longevity in asia pacific popul…
Korea and Hong Kong have shown extraordinary upward socio-economic mobility.
Japan has experienced plateauing trends due to long-run economic stagnation.
4. Mortality Trends & Heatmaps
Heatmaps show consistent cohort effects, including:
the Golden Cohort (1930s births) with exceptional survivorship
country-specific shocks: Japan’s economic crisis, suicide rates, and “karoshi”; the U.S. opioid crisis.
Longevity in asia pacific popul…
Asian female mortality improvements have been steadier than Western countries.
5. Model Comparisons (CMI, AG, MIM)
Mortality projections differ substantially depending on the model:
CMI uses population-specific smoothing with long-term convergence.
AG uses a multi-population structure linking APAC to European baselines.
MIM relies on Whittaker–Henderson smoothing without cohort effects.
Longevity in asia pacific popul…
These methodological differences produce wide variation in future mortality levels.
6. Projected Mortality by 2030
Expected mortality improvement from 2020–2030 ranges widely across APAC countries:
Japan and Hong Kong: modest further improvements
Taiwan, New Zealand, Korea: substantial projected gains
Female gains generally exceed male gains
Longevity in asia pacific popul…
7. Impact on Pensions & Annuities
Valuation results differ materially by model:
Annuity present values can vary ±5% or more depending solely on projection methodology.
Longevity in asia pacific popul…
This sensitivity underscores the financial significance of model selection for insurers and pension schemes.
8. Post-2019 Experience
APAC showed:
Little or no excess mortality early in the pandemic (e.g., Australia, New Zealand)
Later and milder mortality excesses than Europe/US
Some evidence of recovery toward expected trends
Longevity in asia pacific popul…
🧭 Overall Essence
This is one of the most detailed comparative explorations of APAC longevity trends to date. It demonstrates that Asia-Pacific populations have rapidly converged toward or surpassed Western longevity levels, but future outcomes remain highly sensitive to model choice, demographic pressure, and evolving health dynamics. For actuaries and insurers, these findings carry major implications for pricing, reserving, and long-term risk management....
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ziloctab-0107
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Mortality Assumptions
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Mortality Assumptions and Longevity Risk
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This report is a clear, authoritative examination This report is a clear, authoritative examination of how mortality assumptions—the predictions actuaries make about how long people will live—directly shape the financial security, pricing, risk exposure, and solvency of life insurance companies and pension plans. As life expectancy continues to rise unpredictably, the paper explains why longevity risk—the risk that people live longer than expected—is now one of the most serious and complex challenges in actuarial science.
Its central message:
Even small errors in mortality assumptions can create massive financial consequences.
When people live longer than anticipated, insurers and pension funds must pay out benefits for many more years, straining reserves, capital, and long-term sustainability.
🧩 Core Themes & Insights
1. Mortality Assumptions Are Foundational
Mortality assumptions influence:
annuity pricing
pension liabilities
life insurance reserves
regulatory capital requirements
asset–liability management
They are used to determine how much money must be set aside today to pay benefits decades into the future.
2. Longevity Risk: People Live Longer Than Expected
Longevity risk arises from:
ongoing medical advances
healthier lifestyles
improved survival at older ages
cohort effects (younger generations aging differently)
This creates systematic risk—it affects entire populations, not just individuals. Because it is long-term and highly uncertain, it is extremely difficult to hedge.
3. Why Mortality Forecasting Is Difficult
The report highlights key sources of uncertainty:
unpredictable improvements in disease treatment
variability in long-term mortality trends
differences in male vs. female mortality improvement
cohort effects (e.g., baby boom generation)
socioeconomic and geographic differences
Traditional deterministic life tables struggle to capture these dynamic changes.
4. Stochastic Mortality Models Are Essential
The paper emphasizes the growing use of:
Lee–Carter models
CBD (Cairns–Blake–Dowd) models
Multi-factor and cohort mortality models
These models incorporate randomness and allow actuaries to estimate:
future mortality paths
probability distributions
“best estimate” and adverse scenarios
This is crucial for capital planning and solvency regulation.
5. Financial Implications of Longevity Risk
When mortality improves faster than assumed:
annuity liabilities increase
pension funding gaps widen
life insurers face reduced profits
capital requirements rise
The paper explains how regulatory frameworks (e.g., Solvency II, RBC) require insurers to hold additional capital to protect against longevity shocks.
6. Tools to Manage Longevity Risk
To control exposure, companies use:
A. Longevity swaps
Transfer the risk that annuitants live longer to reinsurers or capital markets.
B. Longevity bonds and mortality-linked securities
Spread demographic risks to investors.
C. Reinsurance
Offload part of the longevity exposure.
D. Natural hedging
Balance life insurance (mortality risk) with annuities (longevity risk).
E. Scenario testing & stress testing
Evaluate the financial impact if life expectancy rises 2–5 years faster than expected.
7. Global Perspective
Countries with rapid aging—Japan, the UK, Western Europe, China—are most exposed. Regulators encourage:
more robust mortality modeling
transparent risk disclosures
dynamic assumption-setting
stronger capital buffers
The report stresses that companies must continually update assumptions as new mortality data emerge.
🧭 Overall Conclusion
The paper concludes that accurate mortality assumptions are essential for financial stability in life insurance and pensions. As longevity continues to improve unpredictably, longevity risk becomes one of the most significant threats to solvency. Insurers must adopt:
advanced mortality models
strong risk-transfer mechanisms
dynamic assumption frameworks
robust capital strategies
Longevity is a gift for individuals—but a major quantitative, financial, and strategic challenge for institutions responsible for lifetime benefits....
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Optimal Dose of Running
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Optimal Dose of Running for Longevity
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This editorial evaluates one of the most debated q This editorial evaluates one of the most debated questions in exercise science: Is there an optimal dose of running for longevity—and can too much running actually reduce the benefits? Using findings from the Copenhagen City Heart Study and several large-scale running cohorts, the commentary examines whether the relationship between running and mortality is linear (“more is better”) or U-shaped (“too much may be harmful”).
It concludes that light to moderate running produces substantial longevity benefits, while very high doses show no clear additional advantage—but the evidence is still incomplete, and higher volumes might still be beneficial with better data. The article urges caution in making extreme claims and highlights the need for better-designed studies.
🧩 What the Study Found — and How the Editorial Interprets It
1. Even small amounts of jogging reduce mortality significantly
Jogging less than 1 hour per week or once per week meaningfully lowers all-cause mortality compared with sedentary adults.
Optimal_dose_of_running_for_lon…
This is encouraging for people with limited time.
2. The “optimal” zone appears to be:
1–2.4 hours per week
2–3 jogging sessions per week
slow or average pace
Optimal_dose_of_running_for_lon…
Joggers in this range lived the longest in the dataset.
3. Higher doses of running showed no better survival
In the Copenhagen study:
Running >2.5 hours/week
Running >3 times/week
Running at fast pace
…did not show better survival than sedentary non-joggers.
Optimal_dose_of_running_for_lon…
This suggested a U-shaped curve, where both very low and very high doses show reduced benefit.
🛑 BUT — the Editorial Identifies Major Limitations
The authors argue these “U-shaped” findings may be misleading because of methodological weaknesses:
1. Poor comparison group
Only 413 sedentary non-joggers were used as the reference group. They were:
older
more obese
much sicker (5–6× higher hypertension and diabetes)
Optimal_dose_of_running_for_lon…
This inflates the benefits of jogging.
2. Very small numbers of high-volume runners
Only:
47 joggers ran >4 hours/week
80 jogged >3 times/week
And there were almost no deaths in these groups (only 1–5 deaths).
Optimal_dose_of_running_for_lon…
Small samples make it impossible to determine the real risk.
3. Running dose categories were arbitrary
The grouping may have distorted the dose–response shape.
4. Other studies contradict the “too much running is harmful” idea
Large cohorts (55,000+ runners) show:
Significant mortality benefits even at the highest running volumes
High doses still outperform non-running
Optimal_dose_of_running_for_lon…
Thus, high-volume running may still be beneficial.
❤️ Possible Risks of Excessive Endurance Training (Still Uncertain)
The editorial reviews evidence suggesting that extreme endurance exercise might increase:
arrhythmia risk (e.g., atrial fibrillation in long-distance skiers)
temporary myocardial injury in marathon runners
Optimal_dose_of_running_for_lon…
But evidence is mixed and not conclusive.
🧭 Overall Conclusion
The commentary emphasizes three key messages:
1. Small amounts of running produce large longevity benefits.
Even <1 hour/week is protective.
2. Moderate running appears to be the “sweet spot” for most people.
3. The claim that “too much running is harmful” is not scientifically proven
— existing data are inconsistent, underpowered, or confounded.
4. More research is needed with:
better measurement
larger high-volume runner samples
objective fitness tracking
cause-specific mortality analysis
For now, the safe, evidence-backed conclusion is:
“More is not always better — but more may not be worse.”...
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Effects of longevity
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Effects of longevity and mortality
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Mugi: Effects of Mortality and Longevity Risk in R Mugi: Effects of Mortality and Longevity Risk in Risk Management in Life Insurance Companies is a clear and rigorous exploration of how mortality risk (people dying earlier than expected) and longevity risk (people living longer than expected) affect the financial stability, pricing, reserving, and strategic management of life insurance companies. The report explains why longevity—usually celebrated from a public health perspective—creates serious financial challenges for insurers, pension funds, and annuity providers.
The central message:
As people live longer, life insurance companies face rising liabilities, growing uncertainty, and the need for advanced risk-management tools to remain solvent and competitive.
🧩 Core Themes & Insights
1. Mortality vs. Longevity Risk
The paper distinguishes two opposing risks:
Mortality Risk (Life insurance)
People die earlier than expected → insurers pay out death benefits sooner → financial losses.
Longevity Risk (Annuities & Pensions)
People live longer than expected → insurers must keep paying benefits for more years → liabilities increase.
Longevity risk is now the dominant threat as global life expectancy rises.
2. Why Longevity Risk Is Growing
The study highlights several forces:
Continuous declines in mortality
Medical advances extending life
Rising survival at older ages
Uncertainty in future mortality trends
Rapid global population aging
For insurers offering annuities, pension guarantees, or long-term products, this creates a systemic, long-horizon risk that is difficult to hedge.
3. Impact on Life Insurance Companies
Longevity risk affects insurers in multiple ways:
A. Pricing & Product Design
Annuities become more expensive to offer
Guarantees become riskier
Traditional actuarial assumptions become outdated faster
B. Reserving & Capital Requirements
Companies must hold larger technical reserves
Regulators impose stricter solvency requirements
Balance sheets become more volatile
C. Profitability & Shareholder Value
Longer lifespans → higher liabilities → reduced profit margins unless risks are hedged.
4. Tools to Manage Longevity Risk
The paper reviews modern strategies used globally:
A. Longevity Swaps
Transfer longevity exposure to reinsurers or investors.
B. Longevity Bonds / Mortality-Linked Securities
Payments tied to survival rates; spreads risk to capital markets.
C. Reinsurance
Traditional method for offloading part of the risk.
D. Hedging Through Natural Offsets
Balancing life insurance (benefits paid when people die early) with annuities (benefits paid when people live long).
E. Improving Mortality Modeling
Using:
Lee–Carter models
Stochastic mortality models
Scenario stress testing
Cohort analysis
Accurate forecasting is critical—even small misestimates of future mortality can cost insurers billions.
5. Risk Management Framework
A strong longevity risk program includes:
identifying exposures
assessing potential solvency impacts
using internal models
scenario analysis (e.g., “life expectancy improves by +3 years”)
hedging and reinsurance
regulatory capital alignment
The goal is maintaining solvency under a variety of demographic futures.
6. Global Context
Countries with rapidly aging populations (Japan, Western Europe, China) face the strongest longevity pressures.
Regulators worldwide are:
requiring better capital buffers
encouraging transparency
exploring longevity-linked capital market instruments
🧭 Overall Conclusion
Longevity, though positive for individuals and society, represents a major financial uncertainty for life insurers. Rising life expectancy increases long-term liabilities and challenges traditional actuarial models. To remain stable, life insurance companies must adopt modern risk-transfer tools, advanced mortality modeling, diversified product portfolios, and robust solvency management.
The paper positions longevity risk as one of the most critical issues for the future of global insurance and pension systems....
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zfpbspro-9748
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Inconvenient Truths About
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Inconvenient Truths About Human Longevity
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This review article, “Inconvenient Truths About Hu This review article, “Inconvenient Truths About Human Longevity” by S. Jay Olshansky and Bruce A. Carnes, published in the Journals of Gerontology: Medical Sciences (2019), critically examines the ongoing scientific and public debate about the limits of human longevity, the feasibility of radical life extension, and the future priorities of medicine and public health regarding aging. It argues that while advances in public health and medicine have substantially increased life expectancy over the past two centuries, biological constraints impose practical limits on human longevity, and predictions of near-future radical life extension are unsupported by empirical evidence.
Key Insights and Arguments
Historical Gains in Longevity:
Initial life expectancy gains were driven by public health improvements reducing early-age mortality (infant and child deaths).
Recent gains are largely due to reductions in mortality at middle and older ages, achieved through medical technology.
The dramatic rise in life expectancy during the 20th century cannot be linearly extrapolated into the future due to shifting mortality dynamics.
Debate on Limits to Longevity:
Two opposing views dominate the debate:
Unlimited longevity potential based on mathematical extrapolations of declining death rates.
Biologically based limits to lifespan, currently being approached.
Proponents of unlimited longevity often rely on purely mathematical models that ignore biological realities, leading to unrealistic predictions akin to Zeno’s Paradox (infinite division without reaching zero).
Critique of Mathematical Extrapolations:
Analogies such as world record running times illustrate the fallacy of linear extrapolation: records improved steadily until plateauing, indicating biological limits on human performance.
Similarly, mortality improvements have decelerated and are unlikely to continue improving at historic rates indefinitely.
Three Independent Lines of Evidence Supporting Longevity Limits:
Entropy in the Life Table: As life expectancy rises, it becomes mathematically harder to increase further because most deaths occur within a narrow old age window with high mortality rates.
Comparative Mortality Studies: Scaling mortality schedules of humans against other mammals (mice, dogs) suggests a natural lifespan limit around 85 years for humans.
Evolutionary Biology: Biological “warranty periods” related to reproduction and survival support a median lifespan limit in the mid to upper 80s.
Empirical Data on Life Expectancy Trends:
Life expectancy gains in developed nations have decelerated or plateaued near 85 years, consistent with theoretical limits.
Table below summarizes U.S. life expectancy improvements by decade:
Decade Life Expectancy at Birth (years) Annual Average Improvement (years)
1990 75.40 —
2000 76.84 0.142
2010 78.81 0.197
2016 78.91 0.017
The data show that the predicted 0.2 years per annum improvement has not been consistently met, with recent years showing a sharp slowdown.
Problems with Radical Life Extension Claims:
Predictions of cohort life expectancy at birth reaching or exceeding 100 years for babies born since 2000 are unsupported by observed mortality trends.
Claims of “actuarial escape velocity” (mortality rates falling faster than aging progresses) lack empirical or biological evidence.
These exaggerated forecasts divert resources and funding away from realistic aging research.
Biological Mechanisms and Aging:
Aging is an unintended consequence of accumulated damage and imperfect repair mechanisms driven by genetic programs optimized for reproduction, not longevity.
Humans cannot biologically exceed certain limits because of genetic and physiological constraints.
Unlike lifespan or physical performance (e.g., running speed), aging is a complex biological process that limits survival and function.
The Future Focus: Health Span over Life Span
Rather than pursuing life extension as the primary goal, public health and medicine should prioritize extending the health span—the period of life spent in good health.
This approach aims to compress morbidity, reducing the time individuals spend suffering from age-related diseases and disabilities.
Advances in aging biology (geroscience) hold promise for improving health span even if life expectancy gains are modest.
Risks of Disease-Focused Treatment Alone:
Treating individual aging-related diseases separately may increase survival but also leads to greater prevalence and severity of chronic illnesses in very
Smart Summary
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xevyo
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LONGEVITY AND REGENERATIV
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LONGEVITY AND REGENERATIVE THERAPIES BIL
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Four keys of longevity
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The Longevity and Regenerative Therapies Bill, 202 The Longevity and Regenerative Therapies Bill, 2024 establishes a comprehensive legal framework in The Bahamas to regulate, approve, and oversee all therapies related to longevity, stem cells, gene therapy, immunotherapy, and regenerative medicine. Its purpose is to ensure that advanced medical treatments are developed and administered safely, ethically, and in alignment with global scientific standards, while promoting innovation and positioning The Bahamas as a leader in medical and wellness tourism.
The Act creates several governing bodies, including the National Longevity and Regenerative Therapy Board, responsible for fostering innovation, developing standards, monitoring compliance, and reporting to the Minister. It also establishes an independent Ethics Review Committee, which evaluates and approves applications for new therapies or research based on safety, efficacy, and ethical considerations.
The Bill outlines clear application and approval procedures for individuals or institutions seeking to administer or research therapies. Approvals may be full, provisional, or research-based, and no therapy can begin without written authorization. It further grants the Board powers to request information, inspect facilities, and maintain a national registry of approved therapies.
Strict prohibitions are included, such as bans on human embryo genetic modification intended for birth, unauthorized gene therapy testing, germline editing, and other unsafe or unethical practices. A Monitoring Body is created to ensure ongoing compliance with standards, inspect premises, and review marketing practices.
The Act also imposes licensing requirements for health facilities, gives the Minister authority to suspend unsafe operations, and sets out stringent penalties for violations, including fines and imprisonment. Finally, it repeals the previous Stem Cell Research and Therapy Act and preserves valid approvals issued under that legislation.
If you want, I can also provide:
✅ A short summary (3–4 lines)
✅ A one-page explanation
✅ A quiz or MCQs
✅ A simplified student-friendly version...
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457eaf9a-5e3b-41ef-9772-b592b0631bbb
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yyhpvmic-0921
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xevyo
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THE RISE IN LIFE
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THE RISE IN LIFE EXPECTANCY
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Expansion of Morbidity – People live longer but sp Expansion of Morbidity – People live longer but spend more years in poor health.
Compression of Morbidity – People live longer and healthier; disability occurs later.
Dynamic Equilibrium – Chronic diseases become more common but less severe due to medical progress.
📌 Main Purpose of the Study
The paper reviews evidence on:
Whether elderly health is improving or worsening over time
How chronic diseases, disability, and functional ability have changed
How these trends affect future healthcare and elderly-care needs
How medical technology, obesity, and lifestyle changes influence health
How future spending on health and social care may evolve
It draws from dozens of empirical studies across the USA, Sweden, the Netherlands, Canada, and other OECD countries.
📚 Key Findings
1. Chronic diseases are increasing
More elderly people are living with chronic conditions (e.g., diabetes, heart disease, hypertension).
People spend a larger share of life with diagnosed illness than earlier generations.
2. BUT: Disabilities and functional limitations are decreasing
Thanks to medical progress, assistive devices, better buildings, and rehabilitation.
People maintain mobility and independence for more years.
3. Elderly are living longer with milder, better-managed diseases
This matches the Dynamic Equilibrium theory:
Greater life expectancy
More years with disease
But less severe disease, better quality of life
Less need for nursing-home care than expected
4. Medical advances, not aging alone, push costs upward
New technologies extend life and treat disease, but also increase costs.
5. Obesity is a major future threat
Rising obesity may reverse some health gains
Increases diabetes, disability, and medical spending
Could slow improvements in life expectancy
6. Predictions about future healthcare
Models show:
Health-care spending will rise, not because the elderly are sicker, but because they live longer and use care for more years.
Elderly-care (nursing home) use may decrease or be delayed.
Technology and lifestyle changes strongly influence future cost projections.
🏥 Implications
Elderly will need health care for longer periods.
But may need elderly/social care for shorter periods due to better functional health.
Governments need better forecasting tools, not simple age-based cost prediction.
Preventive care, obesity control, and innovation are key factors.
🎯 Final Overall Summary
The PDF concludes that aging populations are living longer with chronic diseases that are less severe. Functionality is improving, disability is decreasing, and medical advances are the main driver of cost growth. The overall trend supports the Dynamic Equilibrium scenario rather than pure expansion or compression of morbidity....
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ysercdhs-0147
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xevyo
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Longevity Increment
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Longevity Increment
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The Longevity Increment document is an official Ci The Longevity Increment document is an official City policy statement (dated 12/15/1988) that explains how longevity-based salary increases are awarded to eligible municipal employees. It defines what a longevity increment is, who qualifies for it, how it is calculated, and how it should be processed administratively.
Its core purpose is to ensure that employees with many years of continuous City service receive periodic, structured pay increases beyond their normal step progression, as recognition for long-term loyalty and experience.
🧩 Key Elements Explained
1. Definition of Longevity Increment
A longevity increment is a salary increase granted after an employee completes a specified number of years of City service, based on their representative organization (such as C.M.E.A, C.U.B, or M.A.P.S.).
Longevity Increment
It is processed using a signed CHANGE NOTICE (28-1618-5143) once the employee meets all criteria (years of service, time in grade).
2. How the Increase Is Calculated
The increment amount is:
A fixed percentage of the maximum step in the employee’s salary grade
or
A flat salary amount, depending on the employee’s representative organization.
Longevity Increment
To determine the exact value, staff must consult the specific Salary Schedule associated with the employee group.
3. Eligible Service Milestones
Longevity increments are awarded at 10, 15, 20, 25, and 30 years of service.
Longevity Increment
Special rule:
M.A.P.S. employees are not eligible for the 30-year increment.
Their eligibility is also tied to how long they have served beyond the maximum merit step of their salary grade.
4. Effective Date Rules
The effective date for longevity increments follows the same rules and procedures used for other salary changes in City employment.
Longevity Increment
5. Related Policy References
The document links to governing policies:
AM-205-1 – SALARY
AM-290 – SALARY SCHEDULES
Longevity Increment
These provide the broader framework controlling pay structures and increments.
🧭 Summary in One Sentence
The Longevity Increment policy ensures that long-serving City employees receive structured, milestone-based salary increases—based on years of service, salary schedules, and union/organization rules—with standardized administrative procedures for awarding them....
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Psychological stress
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Psychological stress declines rapidly from age 50
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“Psychological Stress Declines Rapidly from Age 50 “Psychological Stress Declines Rapidly from Age 50 in the United States: Yet Another Well-Being Paradox” is a large-scale, multi-dataset study revealing a striking and counterintuitive pattern: psychological stress remains high from ages 20 to 50, then drops steeply and continuously from the mid-50s through the late 70s. Using over 1.5 million participants from the Gallup-Healthways survey—supported by two additional national studies (ATUS and HRS)—the paper demonstrates that this decline is real, robust, and cannot be explained by conventional demographic, social, or health variables.
The central paradox: even though physical health worsens with age, emotional stress dramatically decreases, contradicting what many might expect.
Core Insights & Major Findings
1. A Massive Dataset Shows a Clear Decline After 50
Across the Gallup-Healthways sample:
~45% of younger adults (20s–30s) report high stress.
After age 50, stress drops sharply.
By age 70–80, fewer than 25% report high stress.
Psychological stress declines r…
The turning point in all datasets occurs between age 50–57, followed by a steady decline.
2. Replication Across Three Independent National Studies
The authors validated the finding using:
• Gallup-Healthways (1.5M respondents)
Daily “stress yesterday” measure → strong age-related drop.
• American Time Use Survey (ATUS)
Moment-to-moment stress ratings across daily activities → same downward curve after mid-50s.
• Health and Retirement Study (HRS)
30-day distress measure → again confirms lower distress in older age groups.
All three converge on the same pattern: stress declines reliably with age.
Psychological stress declines r…
3. No Social, Demographic, or Health Factor Can Explain the Pattern
The researchers tested a wide range of variables, including:
Employment
Marital status
Income
Social support
Health problems, health insurance
Neighborhood safety
Children at home
Religious attendance
Diagnosed conditions (blood pressure, diabetes, depression, cancer, etc.)
None of these variables flattened or explained the steep stress decline:
Some acted as mild confounders, others as suppressors,
But none eliminated the age effect.
Psychological stress declines r…
This indicates the decline is not caused by fewer responsibilities, improved finances, reduced childcare, better health, or increased religiosity.
4. The “Stress Paradox”
Despite:
increased health problems
reduced mobility
greater disability risk
shrinking social networks
older adults experience significantly less psychological stress.
The authors label this phenomenon a new well-being paradox, parallel to the known “U-shaped” pattern of life satisfaction.
5. Possible Explanations (Not Tested Directly)
The paper suggests psychological theories that may offer answers:
• Socioemotional Selectivity Theory (Carstensen)
Older adults prioritize emotional regulation and meaningful activities, reducing exposure to stressors.
• Wisdom & Emotional Intelligence Models (Baltes)
Aging brings improved emotional regulation, perspective, and coping.
These theories imply that psychological maturation, rather than social or health variables, may drive the decline.
6. Measurement Biases Are Considered
The authors acknowledge possible age-related reporting differences:
memory changes
interpretation of stress questions
social desirability
But these cannot fully explain the sharp, consistent decline across datasets.
Overall Conclusion
The study offers powerful evidence that perceived daily stress in the US drops dramatically starting around age 50, continuing into the 70s and 80s. This decline is:
Large in magnitude
Replicated across multiple massive datasets
Unaffected by demographic or health adjustments
The result challenges assumptions about aging and emotional well-being, suggesting that older adulthood brings a psychological transformation that protects against everyday stress—despite rising physical health challenges....
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Longevity and mortality
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This PDF is a short scientific communication publi This PDF is a short scientific communication published in the Journal of Mental Health & Aging (2023). It provides a concise, structured overview of the major biological, environmental, socioeconomic, and lifestyle factors that influence how long people live (longevity) and why people die at different rates (mortality). The paper’s goal is to summarize the multidimensional causes of lifespan variation in global populations.
The article emphasizes that longevity is shaped by a complex interaction of genetics, environment, healthcare access, social conditions, education, medical advancements, and lifestyle choices. It also highlights how these factors differ across populations, contributing to unequal health outcomes.
🔶 1. Purpose of the Article
The paper aims to:
Clarify the major determinants of human longevity
Summarize scientific evidence on mortality risk factors
Highlight how biological and environmental factors interact
Emphasize that many determinants are modifiable (e.g., lifestyle, environment, healthcare access)
longevity-and-mortality-underst…
It serves as an accessible summary for researchers, students, and health professionals.
🔶 2. Key Determinants of Longevity and Mortality
The pdf identifies several core categories that influence life expectancy:
✔ A) Genetic Factors
Genetics contributes significantly to individual longevity:
Some genetic variants support long life
Others predispose individuals to chronic diseases
longevity-and-mortality-underst…
Thus, inherited biology sets a baseline for lifespan potential.
✔ B) Lifestyle Factors
These are among the strongest and most modifiable influences:
Diet quality
Physical activity
Smoking and alcohol use
Substance abuse
longevity-and-mortality-underst…
Healthy lifestyles reduce chronic disease risk and boost life expectancy.
✔ C) Environmental Factors
Environment plays a major role in mortality risk:
Air pollution
Exposure to toxins
Access to clean water and sanitation
Availability of healthy food
longevity-and-mortality-underst…
Living in hazardous or polluted settings increases cardiovascular, respiratory, and other disease risks.
✔ D) Socioeconomic Status (SES)
The paper stresses that income and education have profound impacts on health:
Higher-income individuals typically have:
better access to healthcare
safer living conditions
healthier diets
Lower SES is linked to higher mortality and lower life expectancy
longevity-and-mortality-underst…
✔ E) Healthcare Access and Quality
Regular medical care is critical:
Preventive screenings
Early diagnosis
Effective treatment
Management of chronic conditions
longevity-and-mortality-underst…
Disparities in healthcare access create significant differences in mortality rates between populations.
✔ F) Education
Education improves lifespan by:
increasing health literacy
encouraging healthy behaviors
improving access to resources
longevity-and-mortality-underst…
Education is presented as a key structural determinant of longevity.
✔ G) Social Connections
Strong social support improves both mental and physical health, increasing lifespan.
Loneliness and social isolation, by contrast, elevate mortality risk.
longevity-and-mortality-underst…
✔ H) Gender Differences
Women live longer than men due to:
biological advantages
hormonal differences
differing sociocultural behaviors
longevity-and-mortality-underst…
Although the gap is narrowing, gender continues to be a strong predictor of longevity.
✔ I) Medical Advances
Modern medicine plays a major role in rising life expectancy:
surgery
pharmaceuticals
new treatments
technological improvements
longevity-and-mortality-underst…
These innovations prevent and manage diseases that previously caused early mortality.
🔶 3. Major Conclusion
The article concludes that:
Longevity and mortality are shaped by a wide network of interacting factors
Many influences (lifestyle, environment, healthcare access) are modifiable
Improving these areas can significantly raise life expectancy
Despite progress, many aspects of longevity remain incompletely understood
longevity-and-mortality-underst…
⭐ Perfect One-Sentence Summary
This article summarizes how longevity and mortality are shaped by genetics, lifestyle, environment, socioeconomic status, healthcare access, education, social support, gender, and medical advances, emphasizing that these interconnected factors create significant differences in lifespan across populations...
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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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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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Insurance and the Life
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Insurance and the Longevity Economy
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The report “Insurance and the Longevity Economy” e The report “Insurance and the Longevity Economy” explores how rising global life expectancy and demographic shifts are transforming economic behavior, health systems, and financial security. It introduces the concept of a longevity economy, where longer life spans reshape savings, work patterns, healthcare needs, and public policy. Using a global survey of 15,000 people across 12 countries, the report uncovers a longevity paradox: while individuals worry about healthcare access, financial preparedness, retirement adequacy, and long-term independence, they often overestimate their actual readiness.
The report evaluates how insurance can evolve to meet the needs of 100-year lives by aligning life span, health span, and wealth span. It highlights opportunities for insurers to innovate through integrated solutions that combine mortality, longevity, and health risks; flexible and personalised savings products; dynamic underwriting supported by data and technology; and reimagined long-term care models. It also stresses the importance of insurer collaboration with policymakers to strengthen social safety nets, manage systemic risks, and ensure sustainable protection for aging populations. Overall, the document provides a strategic roadmap for insurers to lead and support a resilient longevity economy.
If you want, I can also create short, extra-short, detailed, or bullet-point versions....
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Innovative Approaches
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Innovative Approaches to Managing Longevity Risk
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This PDF is a professional research presentation t This PDF is a professional research presentation that examines how Asia’s rapidly aging population is reshaping financial markets, pension systems, and risk management frameworks across the region. Its central theme is that longevity risk—the possibility that people live longer than expected—is rising sharply in Asia and requires innovative, multi-sector solutions involving governments, insurers, asset managers, and international risk-transfer markets.
The report emphasizes that population aging in Asia is occurring faster than anywhere else worldwide, creating urgent challenges for sustainability of pensions, healthcare financing, and long-term care systems. It also highlights how insurers and governments can prepare through better risk modeling, capital frameworks, and risk-transfer tools (like reinsurance and capital markets solutions).
🔶 1. The Growing Scale of Longevity Risk in Asia
✔ Asia is the fastest-aging region in the world
Life expectancy across Asia has increased dramatically in the last 50 years due to:
improvements in nutrition
medical advances
declining fertility
improved public health
But this demographic shift widens the gap between expected life-years and actual longevity, directly increasing longevity risk.
Managing Longevity risk in asia
✔ The financial implications are enormous
As people live longer, long-term financial obligations grow:
pension payouts increase
annuity liabilities grow
healthcare costs rise
long-term care burdens escalate
These combined pressures threaten the stability of retirement systems and can strain public finances and insurers’ balance sheets.
Managing Longevity risk in asia
🔶 2. Why Longevity Risk Is Harder to Manage in Asia
The document highlights several structural challenges:
✔ Limited historical data
Many Asian countries have shorter records of mortality data, making it harder to build reliable longevity models.
✔ Rapid pace of demographic transition
Asia is aging much faster than Europe or North America did, reducing the time available to prepare.
✔ Limited annuitization
Most retirement income systems in Asia rely on lump-sum payouts, not lifelong annuities—shifting longevity risk back to individuals.
✔ Cultural and socioeconomic diversity
Asia includes both advanced economies and emerging markets, creating highly varied risk profiles within the region.
✔ Underdeveloped risk-transfer markets
Longevity swaps, reinsurance treaties, and capital-market hedges are still emerging.
Managing Longevity risk in asia
🔶 3. Pension Systems Under Pressure
The report notes that many Asian pension systems:
face solvency and sustainability challenges
lack mandatory annuitization
have insufficient contribution rates
rely heavily on government funding
As life expectancy increases, the mismatch between contributions and payouts becomes unsustainable.
Managing Longevity risk in asia
This creates opportunities for:
pension reform
greater use of annuities
development of longevity-linked financial instruments
🔶 4. Solutions for Managing Longevity Risk
The PDF outlines several strategies for Asian markets:
✔ A) Strengthening national pension frameworks
Key steps include:
raising retirement ages
implementing longevity-risk sharing
incentivizing longer working lives
transitioning toward funded pension schemes
Managing Longevity risk in asia
✔ B) Development of insurance & annuity markets
Insurers should expand:
guaranteed lifetime annuities
deferred annuities
long-term care insurance
hybrid retirement products
These products help spread longevity risk across large populations.
✔ C) Use of reinsurance and capital market solutions
Global reinsurers can help Asian insurers hedge tail risks through:
longevity swaps
reinsurance treaties
capital markets transactions (e.g., longevity bonds)
This is essential because longevity risk can accumulate quickly on insurer balance sheets.
Managing Longevity risk in asia
✔ D) Improving risk modeling and data quality
The presentation recommends:
better mortality data collection
locally calibrated longevity models
advanced stochastic modeling
incorporating medical breakthroughs into forecasting
Managing Longevity risk in asia
🔶 5. Case Examples & Regional Insights
The report references how different Asian countries are responding to longevity risk:
Japan: mature annuity and long-term care markets; advanced reforms
Singapore & Hong Kong: early adoption of longevity solutions
China, Malaysia, Thailand: rapid aging but underdeveloped annuity markets
Emerging Asia: huge exposure to demographic change with limited preparation
Each region faces unique pressures due to demographic speed, cultural practices, and policy frameworks.
Managing Longevity risk in asia
🔶 6. The Report’s Core Message
The PDF argues that Asia cannot rely on traditional pension or insurance structures to manage longevity risk. Instead, it needs a whole-ecosystem approach combining:
regulation
pension reform
insurance innovation
reinsurance support
capital market development
better data and modeling
long-term planning
This collaboration is essential to create sustainable retirement systems for an aging Asian population.
⭐ Perfect One-Sentence Summary
This PDF explains how Asia’s unprecedented aging trend is creating major longevity risks for pension systems and insurers, and outlines a coordinated strategy—spanning policy reform, insurance innovation, reinsurance, and improved modeling—to ensure financial stability as people live longer....
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PVC Pipe longevity
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PVC Pipe Longevity Report
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The PVC Pipe Longevity Report, prepared through ex The PVC Pipe Longevity Report, prepared through extensive research at Utah State University’s Buried Structures Laboratory, is a comprehensive technical analysis evaluating the performance, durability, failure rates, and long-term service life of PVC (polyvinyl chloride) pipes used in water and sewer infrastructure across the United States, Canada, Europe, and Australia.
⭐ Purpose of the Report
The study investigates how PVC pipe performs over decades of real-world usage, using dig-up examinations, mechanical testing, accelerated aging studies, and global water main break surveys. It combines engineering, field data, and financial analysis to determine whether PVC is a sustainable, long-lived, and cost-effective pipe replacement option for modern utility systems.
🧪 Key Findings on PVC Longevity & Performance
1. PVC pipes reliably last 100+ years
Global dig-up studies show PVC pipes removed after 20–50 years show no measurable degradation, retaining ductility, strength, and pressure resistance. Many tested pipes are expected to last well beyond 100 years under normal operating conditions.
49 pvc-pipe-longevity-report
2. PVC has the lowest water main break rate
Across U.S. and Canadian utilities, PVC consistently outperforms cast iron, ductile iron, asbestos cement, steel, and concrete pipes.
Corrosion—responsible for most breaks—does not affect PVC.
49 pvc-pipe-longevity-report
3. Excavated pipe testing confirms excellent condition
PVC pipes exhumed after 25–49 years passed all quality control tests, including:
Burst pressure
Hydrostatic integrity
Flattening and impact resistance
Tensile strength and fracture toughness
49 pvc-pipe-longevity-report
4. International studies match U.S. findings
Research in Australia, the U.K., Germany, Sweden, and the Netherlands all conclude:
No chemical or physical degradation
No embrittlement
Stable modulus and yield strength
Expected lifetimes > 100 years
49 pvc-pipe-longevity-report
5. Installation quality is the biggest factor in early failures
Short-term PVC failures almost always stem from poor installation or improper bedding—not from pipe material defects.
49 pvc-pipe-longevity-report
💧 Global Water Main Break Data
Studies across North America and Europe reveal:
The average water main fails at 47 years, usually due to corrosion of iron pipes.
PVC avoids corrosion altogether, significantly reducing breaks.
Cities switching to PVC (e.g., Edmonton) saw dramatic improvements in reliability—even under freezing conditions.
49 pvc-pipe-longevity-report
📉 Life Cycle Cost Analysis (LCCA)
The report stresses that affordability must be evaluated through long-term costs, not just the initial pipe price. LCCA includes:
Installation
Maintenance and repair
Corrosion control (significant for iron pipes)
Replacement cycles
49 pvc-pipe-longevity-report
PVC consistently delivers the lowest life-cycle cost because of its long service life, low break rate, and lack of corrosion.
🛠 Major Conclusions
✔ PVC is confirmed to be a 100+ year pipe material
✔ It has the lowest break rate of all common pipe types
✔ It shows no degradation even after decades of service
✔ Installation quality is key to maximizing longevity
✔ PVC dramatically improves long-term affordability and sustainability
✔ PVC is a reliable solution to the aging North American water infrastructure crisis
The report ultimately concludes that PVC’s durability, resistance to corrosion, and cost-effectiveness make it one of the most sustainable long-term choices for water and sewer networks.
If you want, I can also provide:
✅ A short summary
✅ A student-friendly simple version
✅ MCQs or quiz questions from this file
Just tell me!...
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Estimates of the Heritabi
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Estimates of the Heritability of Human Longevity
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This investigation critically examines the heritab This investigation critically examines the heritability of human longevity, challenging prior estimates that have ranged between 15–30% by demonstrating that these figures are substantially inflated due to assortative mating—the nonrandom pairing of mates with respect to longevity-associated traits. Using an unprecedentedly large dataset derived from Ancestry public family trees, encompassing hundreds of millions of historical individuals primarily of European descent living in North America and Europe during the 19th and early 20th centuries, the authors applied advanced structural equation modeling to disentangle genetic, sociocultural, and assortative mating effects on lifespan correlations.
The study concludes that the true transferable variance (t²)—an upper bound on heritability (h²) that includes both genetic and sociocultural inherited factors—is well below 10% for birth cohorts across the 1800s and early 1900s. This suggests that earlier heritability estimates of longevity have been substantially overestimated because they did not adequately correct for assortative mating effects.
Key Concepts and Definitions
Term Definition
Heritability (h²) The fraction of phenotypic variance attributable to genetic variance.
Transferable variance (t²) Phenotypic variance due to all inherited factors, encompassing both genetic (h²) and sociocultural (b²) components, plus their covariance.
Sociocultural inheritance (b²) Non-genetic factors that influence phenotype and are transmitted through families (e.g., socioeconomic status).
Assortative mating (a) The correlation between latent genetic and sociocultural states of spouses that influences phenotypic correlations beyond genetic inheritance.
Nominal heritability Heritability estimated without correction for assortative mating or shared environment, typically based on correlation and additive relatedness.
Methodology Overview
Data Source: Aggregated and anonymized pedigrees (SAP) were created by collapsing 54 million publicly available Ancestry subscriber-generated family trees, resulting in over 831 million unique historical individuals linked by parent–child and spousal edges.
Data Quality Controls:
Removed self-edges and gender-incongruent parent-child edges.
Added missing spousal edges between parents.
Focused on individuals with known birth and death years who had offspring, limiting analysis primarily to birth cohorts from the early 1800s to 1920.
Addressed data artifacts such as birth year rounding.
Analysis Approach:
Estimated phenotypic correlations of lifespan between various relatives (siblings, cousins, spouses, in-laws).
Calculated nominal heritability using standard regression methods correcting for variance differences.
Developed and applied a structural equation model incorporating three key parameters:
Transferable variance (t²),
Inheritance coefficient (b),
Assortative mating coefficient (a).
Utilized correlations among siblings-in-law and cosiblings-in-law to solve for these parameters.
Applied an assortment-correction method using remote relative pairs and their in-law equivalents to validate estimates.
Timeline Table: Analytical Focus and Data Coverage
Period Data Characteristics and Focus
Pre-1700 Mostly European births; sparse data quality Not specified
1700–1800 Increasing data quality; European and North American births
1800–1920 Primary focus; high data quality; large sample sizes in millions
Post-1920 Decline in death-year data; excluded from lifespan analysis
Major Findings
1. Nominal Heritability Estimates Confirm Prior Literature but Are Inflated
Nominal heritability estimates for lifespan correlated with previous findings (15–30%).
Lifespan correlations among blood relatives were similar to past studies.
However, spouses and in-law relatives also showed substantial lifespan correlations, sometimes comparable to or exceeding those of blood relatives.
This indicated that shared environments and assortative mating inflate these estimates.
2. Assortative Mating Significantly Inflates Heritability Estimates
Assortative mating coefficient (a) was consistently high across all analyses, often exceeding 0.8, indicating strong nonrandom mating based on lifespan-influencing factors.
The presence of assortative mating causes phenotypic correlations between relatives to deviate from the linear relationship expected under pure additive genetics.
Correlations between in-law relatives (who do not share genetics) were substantial, confirming the importance of assortative mating rather than shared genetics alone.
3. Structural Equation Modeling Reveals True Transferable Variance (t²) Is <10%
Using sibling-in-law and cosibling-in-law correlations, the model estimated transferable variance (t²) consistently below 7% for all gender combinations and birth cohorts.
This t² value represents an upper bound on heritability (h²) because it includes both genetic and sociocultural transmitted factors.
The inheritance coefficient (b) was estimated between 0.40–0.45, slightly less than the genetic expectation of 0.5, reflecting combined genetic and sociocultural inheritance.
Shared household environmental effects were also quantified and found to be substantial but separate from transferable variance.
4. Independent Validation Using Remote Relatives Supports Low Heritability
Assortment-correction method applied to remote relatives (piblings, first cousins, first cousins once removed) and their in-law equivalents consistently estimated assortative mating coefficients (a) close to or above 0.5.
Transferable variance estimates from these analyses also remained below 10%, validating the sibling-in-law modeling approach.
5. Transferable Variance Decreases with Increasing Birth-Cohort Disparity Among Relatives
Lifespan correlation and transferable variance (t²) were higher when relatives were born closer in time; as the birth-year gap increased, t² declined significantly.
Assortative mating coefficient (a) remained stable across birth-year offsets, suggesting that the decline in transferable variance was not due to mating patterns.
This suggests that genetic and sociocultural factors affecting lifespan vary with historical context, likely reflecting changing environmental hazards and causes of death over time.
Quantitative Summary Table: Structural Equation Model Estimates by Birth Cohort
Birth Cohort Period Transferable Variance (t²) Assortative Mating Coefficient (a) Inheritance Coefficient (b) Shared Childhood Environment (csib) Shared Adult Environment (csp)
1800s–1830s ~5.9–6.5% (across relatives) ~0.68–0.88 ~0.40–0.44 ~4.3% (siblings) ~6.6% (spouses)
1840s–1870s ~4.0–5.5% ~0.53–0.88 ~0.40 ~5.1% ~5.0%
1880s–1910s ~4.0–7.2% ~0.43–0.89 ~0.40 ~6.0% ~4.4%
Values represent means across gender pairs with standard deviations; b fixed at 0.5 for some estimates; all data derived from sibling-in-law and remote relative analyses.
Core Insights
Previous heritability estimates of human longevity (~15–30%) are substantially inflated due to assortative mating.
True heritability (h²) is likely below 10%, and possibly considerably lower after accounting for sociocultural inheritance.
Assortative mating for lifespan-related factors is strong, with a coefficient often >0.8, indicating mates tend to share longevity-related traits, both genetic and environmental.
Sociocultural factors (e.g., socioeconomic status) are a significant inherited component influencing longevity, evidenced by lifespan correlations among in-law relatives and supported by sociological literature.
Transferable variance (t²) decreases as birth cohorts diverge, implying that historical environmental changes modulate the impact of inherited factors on longevity.
Fundamental biological aging processes (e.g., rate of hazard doubling) appear consistent historically, but lifespan-affecting factors mostly modify susceptibility to historically transient environmental hazards, not aging rate itself.
Implications
Genetic studies of longevity should account for assortative mating and sociocultural inheritance to avoid overestimating genetic contributions.
Interventions targeting environmental and sociocultural factors could have a larger impact on lifespan extension than currently assumed genetic predispositions.
Historical and birth cohort context is critical when interpreting heritability and lifespan data.
The biological basis of aging remains consistent, but its interaction with environment and social factors is dynamic and complex.
References to Relevant Literature Mentioned
Smart Summary
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Seed Longevity Chart
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Seed Longevity Chart
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The “Seed Longevity Chart” is a comprehensive refe The “Seed Longevity Chart” is a comprehensive reference guide from the joegardener® Online Gardening Academy that outlines how long different types of vegetable, fruit, herb, and flower seeds remain viable when stored under ideal conditions. The chart emphasizes that seed longevity depends on three major factors: initial seed moisture content, seed variety, and the storage environment. Proper storage requires keeping seeds in a cool, dark, low-humidity location, with the recommended method being a sealed glass jar in the refrigerator accompanied by a desiccant pack.
The chart organizes longevity estimates by category—Vegetables & Fruits, Herbs, and Flowers—and provides a year-range for each seed type. For example, beans last 2–4 years, kale 3–5 years, lettuce 1–6 years, peppers 2–5 years, basil 3–5 years, and zinnias 1–5 years. Flower seed longevity varies widely, with some species like calendula lasting 4–6 years, while more delicate seeds like lupine remain viable for only 1 year.
Overall, the document serves as an easy, practical guide for gardeners to determine how long their stored seeds are likely to remain viable and helps them plan planting, storage, and seed rotation more effectively.
If you want, I can also provide:
✅ A short 3–4 line summary
✅ A simplified beginner-friendly version
✅ A table or quiz based on this chart
Just tell me!...
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xofkgdzk-4012
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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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identification of
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This study presents a rigorous demographic investi This study presents a rigorous demographic investigation that identifies and validates a unique region of exceptional human longevity on the island of Sardinia—known today as one of the world’s first confirmed Blue Zones. Using verified birth, marriage, and death records from 377 municipalities, the researchers introduce the Extreme Longevity Index (ELI) to measure the probability that individuals born between 1880 and 1900 reached age 100.
The analysis reveals a distinct cluster in the mountainous central-eastern region of Sardinia where the likelihood of becoming a centenarian is dramatically higher than the island average. This “Blue Zone” displays not only elevated longevity but also an extraordinary male-to-female centenarian ratio, including areas where men outnumber female centenarians—an unprecedented finding in global longevity research.
Through Gaussian spatial smoothing and chi-square testing, the authors demonstrate that this longevity pattern is statistically significant, geographically coherent, and unlikely to be due to random variation or data error. The study discusses potential explanations: long-term geographic isolation, low immigration, high rates of endogamy, a culturally preserved lifestyle, traditional diet, and genetic homogeneity that may confer protection against age-related diseases.
The paper concludes that the Sardinian Blue Zone is a scientifically validated longevity hotspot and calls for further genetic, cultural, and environmental studies to uncover the mechanisms that support such exceptional survival patterns.
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Leaving No One Behind
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Leaving No One Behind In An Ageing World
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“Leaving No One Behind in an Ageing World” is the “Leaving No One Behind in an Ageing World” is the United Nations World Social Report 2023, a comprehensive and authoritative analysis of global population ageing. It explores how the world is undergoing a permanent demographic shift toward older populations—and what must be done to ensure all people can age with dignity, health, and economic security.
It explains that population ageing is not a crisis, but a global success story—the result of longer lifespans, improvements in health, education, gender equality, and reduced fertility. However, it also warns that inequality, poverty, weak care systems, and inadequate policies risk leaving millions of older persons behind.
The report provides data, trends, challenges, and policy recommendations across five major chapters.
📌 Main Themes of the Report
1. A Rapidly Ageing World
By 2050, the number of people aged 65+ will more than double—from 761 million to 1.6 billion.
The population aged 80+ will almost triple to 459 million.
Ageing is happening everywhere, but fastest in:
Northern Africa & Western Asia
Sub-Saharan Africa
Eastern & South-Eastern Asia
The world’s oldest countries are shifting from Europe to Asia.
The report highlights how societies of tomorrow will be younger in fewer places, older almost everywhere.
2. Living Longer, Healthier Lives
Rising longevity is a major human achievement.
Premature deaths have fallen.
People live more years in good health.
But gaps remain:
Women live longer but often face more unhealthy years.
Poorer populations have shorter and less healthy lives.
COVID-19 disrupted progress in life expectancy.
Healthy ageing requires lifelong investment in education, nutrition, healthcare, safety, and environments.
3. What Ageing Means for Economies
The report rejects the idea that older populations are “burdens.”
Key points:
Population ageing affects labour, consumption, taxes, pensions, and long-term care.
With good policies, ageing can bring:
Increased productivity
A stronger labour force via women and older workers
Two “demographic dividends,” if countries invest early
Many older people contribute economically through:
Paid work
Volunteering
Childcare for families
Financial support to younger generations
However, ageing challenges include:
Rising pension and healthcare costs
A shrinking workforce
Inequitable labour markets
Lower savings among future generations
4. Ageing, Poverty, and Inequality
The report stresses that ageing does not create inequality—inequality throughout life creates unequal ageing.
Key findings:
Older persons are more likely to be poor than working-age people, especially in developing countries.
Inequalities accumulate across life:
Poor childhood conditions
Unequal education
Employment insecurity
Gender discrimination
Women face far greater risks due to:
Lower lifetime earnings
Informal/unpaid caregiving roles
Longer lifespans
Higher risk of widowhood
Future generations of older people may be more unequal than today, unless countries act now.
5. A Global Crisis of Care
Demand for long-term care is skyrocketing as populations age, especially above age 80.
Problems:
Most countries are not prepared.
Care systems are underfunded.
Care jobs are low-paid and mostly done by women.
Families—especially daughters—bear the unpaid burden.
COVID-19 exposed deep weaknesses in care facilities.
Solutions recommended:
Build integrated long-term care systems.
Professionalize and protect care workers.
Ensure quality standards and monitoring.
Support “ageing in place” (staying at home).
Reduce reliance on informal unpaid care.
🌍 What “Leaving No One Behind” Means
The report shows that ageing affects:
Health systems
Education
Labour markets
Taxes
Pensions
Social protection
Gender equality
Migration
Long-term care
It argues that ageing must become a central policy priority at national and global levels.
🏛️ Key Policy Recommendations
A. Start Early—Lifelong Interventions
Equal access to quality education
Lifelong learning
Healthy environments
Decent work
Fair labour markets
Support for women, caregivers, and informal workers
B. Strengthen Social Protection & Pensions
Universal pensions or tax-funded basic benefits
Avoid shifting financial risks to individuals
Expand coverage of retirees in informal economies
Use fair and progressive tax systems
C. Build Strong Long-Term Care Systems
Public funding
Trained and protected care workers
Home- and community-based care options
Better regulation, monitoring, and accountability
D. Promote Intergenerational Equity
Address income, education, and health gaps early in life
Encourage solidarity between generations
Prepare youth now to become healthy, secure older adults later
✨ Perfect Summary Statement
The PDF is a global roadmap for managing population ageing in a way that protects rights, reduces inequality, improves health, strengthens economies, and ensures that no person—young or old—is left behind in a rapidly ageing world....
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Investigating causal
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This research article presents one of the largest This research article presents one of the largest and most comprehensive Mendelian Randomization (MR) analyses ever conducted to uncover which environmental exposures (the exposome) have a causal impact on human longevity. Using 461,000+ UK Biobank participants and genetic instruments from 4,587 environmental exposures, the study integrates exposome science with MR methods to identify which factors genuinely cause longer or shorter lifespans, instead of merely being associated.
The study uses genetic variants as unbiased proxies for exposures, allowing the researchers to overcome typical problems in observational studies such as confounding and reverse causation. Longevity is defined by survival to the 90th or 99th percentile of lifespan in large European-ancestry cohorts.
🔶 1. Purpose of the Study
The article aims to:
Identify which components of the exposome causally affect longevity.
Distinguish between real causes of longer life and simple correlations.
Highlight actionable targets for public health and aging research.
It is the first study to systematically test thousands of environmental exposures for causal effects on human lifespan.
🔶 2. Methods
A. Exposures
4,587 environmental exposures were initially screened.
704 exposures met strict quality criteria for MR.
Exposures were grouped into:
Endogenous factors (internal biology)
Exogenous individual-level factors (behaviors, lifestyle)
Exogenous macro-level factors (socioeconomic, environmental)
B. Outcomes
Longevity was defined as survival to:
90th percentile age (≈97 years)
99th percentile age (≈101 years)
C. Analysis
Two-sample Mendelian Randomization
Sensitivity analyses: MR-Egger, weighted median, MR-PRESSO
False discovery rate (FDR) correction applied
Investigating causal relationsh…
🔶 3. Key Results
After rigorous analysis, 53 exposures showed evidence of causal relationships with longevity. These fall into several categories:
⭐ A. Diseases That Causally Reduce Longevity
Several age-related medical conditions strongly decreased the odds of surviving to very old age:
Coronary atherosclerosis
Ischemic heart disease
Angina (diagnosed or self-reported)
Hypertension
Type 2 diabetes
High cholesterol
Alzheimer’s disease
Venous thromboembolism (VTE)
For example:
Ischemic heart disease → 34% lower odds of longevity
Hypertension → 30–32% lower odds of longevity
Investigating causal relationsh…
These findings confirm cardiovascular and metabolic conditions as major causal barriers to long life.
⭐ B. Body Fat and Anthropometric Traits
Higher body fat mass, especially centralized fat, had significant causal negative effects on longevity:
Trunk fat mass
Whole-body fat mass
Arm fat mass
Leg fat mass
Higher BMI
Lean mass, height, and fat-free mass did not causally influence longevity.
Investigating causal relationsh…
This underscores fat accumulation—particularly visceral fat—as a biologically damaging factor for lifespan.
⭐ C. Diet-Related Findings
Unexpectedly, the trait “never eating sugar or sugary foods/drinks” was linked to lower odds of longevity.
This does not mean sugar prolongs life; instead, it likely reflects:
Illness-driven dietary restriction
Reverse causation captured genetically
Investigating causal relationsh…
This finding needs further investigation.
⭐ D. Socioeconomic and Behavioral Factors
One of the strongest protective factors was:
Higher educational attainment
College/university degree → causally increased longevity
Investigating causal relationsh…
This supports the idea that education improves health literacy, income, lifestyle choices, and access to medical care, all contributing to longer life.
⭐ E. Early-Life Factors
Greater height at age 10 was causally associated with lower longevity.
High childhood growth velocity has been linked to metabolic stress later in life.
⭐ F. Family History & Medications
Genetically proxied traits like:
Having parents with heart disease or Alzheimer’s disease
Use of medications like blood pressure drugs, metformin, statins, aspirin
showed causal relationships that mostly mirror their disease categories.
Medication use was negatively associated with longevity, likely reflecting underlying disease burden rather than drug harm.
🔶 4. Validation
Independent datasets confirmed causal effects for:
Myocardial infarction
Coronary artery disease
VTE
Alzheimer’s disease
Body fat mass
Education
Lipids (LDL, HDL, triglycerides)
Type 2 diabetes
Investigating causal relationsh…
This strengthens the reliability of the findings.
🌟 5. Core Conclusions
✔️ Some age-related diseases are true causal reducers of lifespan, especially:
Cardiovascular disease, diabetes, Alzheimer’s, hypertension, and lipid disorders.
✔️ Higher body fat is a causal risk factor for reduced longevity, especially central fat.
✔️ Education causally increases lifespan, pointing to the importance of socioeconomic factors.
✔️ New potential targets for improving longevity include:
Managing VTE
Childhood growth patterns
Healthy body fat control
Optimal sugar intake
Investigating causal relationsh…
⭐ Perfect One-Sentence Summary
This paper uses Mendelian Randomization on thousands of environmental exposures to identify which factors truly cause longer or shorter human lifespans, revealing that cardiovascular and metabolic diseases, high body fat, and low education are major causal reducers of longevity...
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Navigating Longevity Risk
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Navigating Longevity Risk in Asia
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This PDF is a professional presentation that analy This PDF is a professional presentation that analyzes how Asia’s unprecedented demographic aging is transforming financial systems, insurance markets, and public policy across the region. Created for industry, policy, and actuarial audiences, the report outlines the scale of longevity risk, the pressures aging places on pension and healthcare systems, and the new solutions required to manage these challenges in diverse Asian markets.
The presentation draws on UN and OECD datasets, global pension indices, and cross-country case studies to give a comprehensive, data-driven overview of aging in Asia.
🔶 Core Themes of the PDF
1. Asia Is Aging Faster Than Any Other Region
The report highlights the speed and intensity of demographic aging:
By 2054, 1 in 5 people in Asia-Pacific will be over age 65, reaching 1.1 billion older adults
Many Asian countries become “aged” (14% elderly) and “super-aged” (21% elderly) in as little as 8–16 years, far faster than Western countries
Navigating-longevity-risk-in-As…
This rapid shift is driven by rising life expectancy and declining fertility.
2. Growing Burden on Public Pension and Health Systems
a) Burden of longevity risk
Countries across Asia face:
Increasing old-age dependency ratios
Lower birth rates
Rising long-term care needs
Higher public spending pressure
The presentation shows how old-age–to–working-age ratios will worsen dramatically by 2054.
Navigating-longevity-risk-in-As…
b) Governments Respond With Structural Reform
Many governments are redesigning pension landscapes:
Transition to fully funded national pension systems
Mandatory annuitization within workplace pension schemes
Expansion of private annuity products
Navigating-longevity-risk-in-As…
Countries like Denmark, Singapore, and the Netherlands rank highest in pension system sustainability, serving as models for reform.
🔶 3. Changing Demographics Require New Insurance & Financial Solutions
Asia’s demographic transformation creates gaps in current insurance offerings, including:
Key challenges:
Declining birth rates and shrinking households
Rising age-related diseases (e.g., dementia)
Longer lifespans outlasting traditional pension models
Limited specialized products for older customers
Navigating-longevity-risk-in-As…
Japan as a Case Study
Japan—already a super-aged society—shows how insurers are adapting:
Dementia insurance (standalone or rider)
Prevention and after-diagnosis care services
Advanced medical coverage
Foreign-currency annuities with LTC benefits
Financial literacy programs
Navigating-longevity-risk-in-As…
Housing as a Retirement Asset
Asian households hold 60–80% of their wealth in property—much higher than Europe (40–60%).
This makes housing liquidation an essential part of retirement planning.
Navigating-longevity-risk-in-As…
Korea’s “Home Pension” and annuitization riders illustrate innovative ways to convert illiquid assets into stable retirement income.
🔶 4. Complexities in Managing Longevity Risk in Asia
The report explains why Asia is uniquely difficult for risk managers:
a) Enormous diversity
Asia varies widely by:
Religion
Ethnicity
Culture
Economic development
Urban-rural divides
Policy environments
Navigating-longevity-risk-in-As…
This diversity weakens universal risk assumptions.
b) Wide differences in mortality trends
Examples include:
A persistent rural–urban mortality disadvantage
Highly variable longevity improvements among countries
Different levels of female longevity advantage (pLE65)
Navigating-longevity-risk-in-As…
These patterns make long-term forecasting challenging.
c) External shocks can rapidly change life expectancy
Events like pandemics, environmental hazards, or economic crises can dramatically shift mortality trends.
5. Asia Leads in AI Adoption for Longevity Business
The report highlights Asia’s rapid use of AI for:
Enhanced sales and customer experience
Advanced analytics and risk insights
Automated longevity risk modeling
AI-driven product design
Modernized existence-check procedures
Navigating-longevity-risk-in-As…
🔶 6. Building Longevity Expertise: The Development Cycle
The presentation outlines a maturity cycle for insurers:
Launch longevity-focused solutions
Accumulate data and experience
Strengthen risk management capability
Develop more sophisticated retirement products
Navigating-longevity-risk-in-As…
This iterative cycle improves long-term resilience.
⭐ Perfect One-Sentence Summary
This PDF provides a comprehensive analysis of Asia’s rapidly aging demographics and the escalating longevity risks they create, showing how governments, insurers, and financial systems must adopt tailored, innovative, and data-driven solutions to ensure sustainable retirement and healthcare systems across the region....
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Life Expectancy Table
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Life Expectancy Table
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The Life Expectancy Table is a straightforward act The Life Expectancy Table is a straightforward actuarial reference chart presenting remaining years of life expectancy for males and females at every age from 0 to 119. It reflects standard mortality assumptions used in insurance, pensions, demographic forecasting, and public planning.
The table shows how life expectancy declines with age, while consistently demonstrating the well-established pattern that females live longer than males at every age. For example:
At birth: Male 74.14 years, Female 79.45 years
At age 50: Male 27.85 years, Female 31.75 years
At age 80: Male 7.31 years, Female 8.95 years
As age increases, the remaining life expectancy declines progressively but never reaches zero — even at age 119, there is still a small remaining expectancy (0.56 years), showing that actuarial models always assign a non-zero survival probability at extreme ages.
The table is formatted into two continuous sections, covering:
Ages 0–59, with life expectancy decreasing gradually from childhood into midlife
Ages 60–119, where mortality accelerates and expectancy declines more sharply
This tool allows actuaries, policymakers, and planners to:
Estimate longevity for retirement planning
Assess future benefit payments in pensions and insurance
Model population aging
Compare male–female longevity differences across the lifespan
Its purpose is purely quantitative: to provide a standardized, age-specific benchmark of expected remaining years of life for both sexes based on current mortality patterns....
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Institutional Change
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Institutional Change and the Longevity
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“Institutional Change and the Longevity of the Chi “Institutional Change and the Longevity of the Chinese Empire” is a historical–institutional analysis that explains how the Chinese empire survived for over two millennia through deliberate and adaptive institutional reforms. The study argues that the empire’s longevity cannot be understood simply through military power or cultural unity; instead, it was the result of continuous reinvention of political institutions, especially in response to crises such as population growth, territorial expansion, administrative overload, and fiscal stress.
The paper highlights several transformative reforms across dynasties:
1. Establishment of a Centralized Bureaucracy
Early imperial rulers replaced hereditary aristocracies with a merit-based civil service, enabling the state to govern vast territories through professional administrators rather than powerful families.
2. Evolution of the Examination System
The civil service exam system matured over centuries, creating one of the most stable and sophisticated systems of bureaucratic recruitment in world history. This system helped prevent elite capture and ensured a constant supply of educated officials.
3. Fiscal and Land Reforms
Successive dynasties introduced new taxation methods, land redistribution policies, and state granaries to stabilize rural society and prevent unrest—key ingredients of regime durability.
4. Military Institutional Adjustments
From the Tang to the Ming dynasties, China shifted between militia systems, hereditary military households, and standing armies to manage internal and external security pressures.
5. Governance Adaptability
The empire demonstrated an exceptional ability to learn from failures, absorb local customs, integrate diverse populations, and decentralize or recentralize authority when necessary.
The paper concludes that the Chinese empire endured because of its capacity for long-term institutional adaptation. Rather than rigid tradition, it was institutional flexibility, combined with bureaucratic professionalism and continuous reform, that supported one of the longest-lasting political systems in human history.
If you want, I can also provide:
✅ A short 3–4 line summary
✅ A simple student-friendly version
✅ Quiz / MCQs from this file
Just tell me!...
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Maximising the longevity
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Maximising the longevity dividend
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The document “Maximising the Longevity Dividend” e The document “Maximising the Longevity Dividend” explains how an ageing population should not be viewed as an economic burden but as a major opportunity. It shows that people aged 50 and over are becoming increasingly important to the economy through their growing spending power, rising workforce participation, and substantial earned income.
The report highlights that:
Older consumers already account for over half of all UK spending, and by 2040 this will rise to 63%.
Older workers are staying in employment longer, contributing more earnings and forming a larger share of the workforce.
If barriers to spending and working are removed, the UK could unlock a powerful longevity dividend, adding 2% to 8% to GDP through higher consumption and 1.3% to 2% through extended employment.
However, these benefits depend on major actions, including:
Supporting healthy ageing
Reducing age discrimination
Making workplaces flexible and age-inclusive
Improving accessibility of goods, services, and high streets
Encouraging businesses to innovate for older consumers
The central message: ageing is not a crisis but a huge economic opportunity — if society takes proactive steps to support older people as both consumers and workers.
If you want, I can also create:
📌 a summary
📌 quiz questions
📌 exam answers
📌 short notes
📌 or explanations of specific parts of the document....
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Exceptional Human
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Exceptional Human Longevity
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Exceptional human longevity represents an extreme Exceptional human longevity represents an extreme phenotype characterized by individuals who survive to very old ages, such as centenarians (100+ years) or supercentenarians (110+ years), often with delayed onset of age-related diseases or resistance to lethal illnesses. This review synthesizes evidence on the multifactorial nature of longevity, integrating genetic, environmental, cultural, and geographical influences, and discusses health, demographic trends, biological mechanisms, biomarkers, and strategies that promote extended health span and life span.
Key Insights and Core Concepts
Exceptional longevity is defined by both chronological and biological age, emphasizing delayed functional decline and preservation of physiological function.
The biology of aging is heterogeneous, even among the oldest individuals, and no single biomarker reliably predicts longevity.
Longevity is influenced by disparate combinations of genes, environment, resiliency, and chance, shaped by culture and geography.
Compression of morbidity—delaying the onset of disability and chronic diseases—is a critical concept in successful aging.
Empirical strategies supporting longevity involve dietary moderation, regular physical activity, purposeful living, and strong social networks.
Genetic factors contribute to longevity but explain only about 25% of life span variance; environmental and behavioral factors play a dominant role.
Sex differences are notable: women generally live longer than men, with possible links to reproductive biology and hormonal factors.
Resiliency, the ability to respond to stressors and maintain homeostasis, is emerging as a key determinant of successful aging and extended longevity.
Timeline and Demographic Trends
Period/Year Event/Trend
Pre-20th century Probability of living to 100 was approximately 1 in 20 million at birth.
1995 Probability of living to 100 increased to about 1 in 50 for females in low mortality nations.
2009 Probability further increased to approximately 1 in 2.
2015 (Global data) Countries with oldest populations: Japan, Germany, Italy, Greece, Finland, Sweden.
2015 (Life expectancy at age 65) Japan, Macau, Singapore, Australia, Switzerland lead with 20-25 additional years expected.
2013 Last supercentenarian of note: Jiroemon Kimura died at age 116.
Ongoing Maximum human lifespan (~122 years) remains largely unchanged despite increasing average life expectancy.
Characteristics of Centenarians and Supercentenarians
Disease Onset and Morbidity:
Onset of common age-related diseases varies considerably; 24% of males and 43% of females centenarians diagnosed with one or more diseases before age 80.
15% of females and 30% of males remain disease-free at age 100.
Cognitive impairment is often delayed; about 25% of centenarians remain cognitively intact.
Cancer and vascular diseases often develop much later or not at all in supercentenarians.
Functional Status:
Many supercentenarians remain functionally independent or require minimal assistance.
Geographic Clustering of Longevity
Certain regions globally show high concentrations of exceptionally long-lived individuals, highlighting environmental and cultural influences:
Region Notable Longevity Factors
Okinawa, Japan Caloric restriction via “hara hachi bu” (eat until 80% full), plant-based “rainbow diet,” low BMI (~20 kg/m²), slower decline of DHEA hormone.
Sardinia, Italy Genetic lineage from isolated settlers, particularly among men, with unknown genetic traits contributing to longevity.
Loma Linda, California (Seventh Day Adventists) Abstinence from alcohol and tobacco, vegetarian diet, spirituality, lower stress hormone levels.
Nicoya Peninsula, Costa Rica; Ikaria, Greece Commonalities include plant-based diets, moderate eating, purposeful living, social support, exercise, naps, and possibly sunlight exposure.
Table 1 summarizes common longevity factors in clustered populations.
Table 1: Longevity Factors Associated With Geographic Clustering
Longevity Factors
Eating in moderation (small/moderate portions) and mostly plant-based diets, with lighter meals at the end of the day
Purposeful living (life philosophy, volunteerism, work ethic)
Social support systems (family/friends interaction, humor)
Exercise incorporated into daily life (walking, gardening)
Other nutritional factors (e.g., goat’s milk, red wine, herbal teas)
Spirituality
Maintenance of a healthy BMI
Other possible factors: sunshine, hydration, naps
Trends in Longevity and Morbidity
Life expectancy has increased mainly due to reductions in premature deaths (e.g., infant mortality, infectious diseases).
Maximum lifespan (~122 years) remains stable over the past two decades.
Healthy life years vary widely (25%-75% of life expectancy at age 65), with Nordic countries showing the highest expected healthy years.
Compression of morbidity models propose:
No delay in morbidity onset, increased morbidity duration.
Delay in morbidity onset with proportional increase in life expectancy.
Delay in morbidity onset with compression (shorter duration) of morbidity.
Evidence supports some compression of morbidity, but among those aged 85+, morbidity delay may be less pronounced.
Functional disability rates declined in the late 20th century but may be plateauing in the 21st century.
Mechanisms of Longevity
Genetic Influences
Genetic contribution to longevity is supported by:
Conservation of maximum lifespan across species.
Similar longevity in monozygotic twins.
Familial clustering of exceptional longevity.
Genetic diseases of premature aging.
Candidate genes and pathways associated with longevity include:
APOE gene variants (e.g., lower ε4 allele frequency in centenarians).
Insulin/IGF-1 signaling pathways.
Cholesteryl ester transfer protein.
Anti-inflammatory cytokines (e.g., IL-10).
Stress response genes (e.g., heat shock protein 70).
GH receptor exon 3 deletion linked to longer lifespan and enhanced GH sensitivity, especially in males.
Despite these, only ~25% of lifespan variance is genetic, emphasizing the larger role of environment and behavior.
Sex Differences
Women universally live longer than men, with better female survival starting early in life.
Female longevity may relate to reproductive history; older maternal age at last childbirth correlates with longer life.
The “grandmother hypothesis” proposes post-reproductive lifespan enhances offspring and grandchild survival.
Male longevity predictors include occupation and familial relatedness to male centenarians.
Lower growth hormone secretion may explain shorter stature and longer life in women.
Despite longer life, men often show better functional status at older ages.
Resiliency
Defined as the capacity to respond to or resist stressors that cause physiological decline.
Resiliency operates across psychological, physical, and physiological domains.
Examples involve resistance to frailty, cognitive impairment, muscle loss, sleep disorders, and multimorbidity.
Exercise may promote resiliency more effectively than caloric restriction.
Psychological resilience, including reduction of depression, correlates with successful aging.
Resiliency may explain why some centenarians survive despite earlier chronic diseases.
Strategies to Achieve Exceptional Longevity
Dietary Modification:
Moderate caloric restriction (CR) shown to extend lifespan in multiple species.
Human studies (e.g., CALERIE trial) show CR improves metabolic markers and slows biological aging, though sustainability and effects on maximum lifespan remain uncertain.
Benefits of CR in humans are linked to improved cardiovascular risk factors.
Antioxidant supplementation does not convincingly extend lifespan.
Physical Activity:
Regular moderate to vigorous exercise correlates with increased life expectancy and reduced mortality.
Physical activity benefits hold across BMI categories and are especially impactful in older adults.
Body Weight:
Optimal BMI range for longevity is 20.0–24.9 kg/m²; overweight and obesity increase mortality risk.
Social Engagement and Purposeful Living:
Strong social relationships reduce mortality risk comparable to quitting smoking.
Purpose in life associates with less cognitive decline and disability.
Productive engagement improves memory and overall well-being.
Measuring Successful Aging and Biomarkers of Longevity
Biomarkers of aging are sought to quantify biological age, improving prognosis and guiding interventions.
Ideal biomarkers should correlate quantitatively with age, be independent of disease processes, and respond to aging rate modifiers.
Challenges include separating primary aging from disease effects and confounding by nutrition or interventions.
Commonly studied biomarkers include:
Biomarker Category Examples and Notes
Functional Measures Gait speed, grip strength, daily/instrumental activities of daily living (ADLs), cognitive tests
Physiological Parameters Blood glucose, hemoglobin A1c, lipids, inflammatory markers (IL-6), IGF-1, immune cell profiles
Sensory Functions Hearing thresholds, cataract presence, taste and smell tests
Physical Attributes Height (especially in men), muscle mass, body composition
Genetic and Epigenetic Markers DNA methylation patterns, senescent cell burden
Family History Longevity in parents or close relatives
Biomarkers may help distinguish between biological and chronological age, aiding individualized health screening.
Studies in younger cohorts show biological aging varies widely even among same-aged individuals.
Inclusion of centenarians in biomarker research may reveal mechanisms linking health status to exceptional longevity.
Implications for Clinical Practice and Public Health
Increased life expectancy does not necessarily mean longer periods of disability.
Understanding biological age can improve screening guidelines and preventive care by tailoring interventions to individual risk.
Current screening often ignores differences between biological and chronological age, possibly leading to over- or under-screening.
Life expectancy calculators incorporating biological and clinical markers can inform decision-making.
Anticipatory health discussions should integrate biological aging measures for better patient guidance.
Conclusion
Exceptional human longevity results from complex, multifactorial interactions among genetics, environment, culture, lifestyle, resiliency, and chance.
Aging characteristics vary widely even among long-lived individuals.
No single biomarker currently predicts longevity; a combination of clinical, genetic, and functional markers holds promise.
Observations from the oldest old support empirical lifestyle strategies—moderate eating, regular exercise, social engagement, and purposeful living—that promote health span and potentially extend life span.
Advancing biomarker research and personalized health assessments will improve screening, clinical decision-making, and promote successful aging.
Keywords
Exceptional longevity, centenarians, supercentenarians, aging, biomarkers, compression of morbidity, genetic factors, caloric restriction, physical activity, resiliency, biological age, social engagement, sex differences, life expectancy, health span.
References
References are comprehensive and include epidemiological, genetic, physiological, and clinical studies spanning decades, with key contributions from population cohorts, animal models, and intervention trials.
This summary strictly reflects the source content, synthesizing key findings, concepts, and data related to exceptional human longevity without extrapolation beyond the original text.
Smart Summary...
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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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How Long is Longevity
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How Long is Long in Longevity
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This PDF is a research paper by Jesús-Adrián Álvar This PDF is a research paper by Jesús-Adrián Álvarez, published by the Society of Actuaries Research Institute (2023). It deeply examines a fundamental and surprisingly unresolved question:
**What does it actually mean for a life to be “long”?
Where does longevity begin?**
The paper argues that traditional definitions—“old age starts at 60 or 70”—are arbitrary, outdated, and disconnected from modern demographic reality. Instead, Álvarez proposes a rigorous, mathematical, population-based definition of when a life becomes “long,” using survivorship ages (s-ages) and concepts from demography, evolutionary biology, and reliability theory.
🧠 1. Purpose of the Paper
The main goal is to develop a formal, scientifically grounded definition of the onset of longevity. The author:
Reviews historical and modern definitions of old age
Shows how chronological-age thresholds fail
Introduces s-ages as a more accurate way to measure longevity
Demonstrates how survival patterns reveal a natural “start” to longevity
Uses mortality mathematics to locate that threshold
Longevity 2023
📜 2. Historical Background: Why Age 60 or 70?
The paper explains how the idea that old age starts at 60–70 came from:
Ancient Greece (age 60 military cut-off)
Medieval Europe (age 70 tax exemption)
Early pension systems (Bismarck’s Germany, Denmark, UK, Australia)
These were social or political definitions—not scientific ones.
Today, many 70-year-olds live healthy, active lives, making old thresholds meaningless.
Longevity 2023
📊 3. The Problem With Traditional Measures of Longevity
Common demographic indicators are examined:
✔ Life Expectancy
Mean lifespan, but ignores lifespan variation.
✔ Modal Age at Death
Most common age at death, but problematic in populations with high infant mortality.
✔ Entropy Threshold
Measures sensitivity of life expectancy to mortality improvements.
All these measures describe aspects of population longevity—but none cleanly answer:
When does a long life begin?
Longevity 2023
🔍 4. The New Solution: Survivorship Ages (s-Ages)
Álvarez and Vaupel propose defining longevity using:
s-age = the age at which a proportion s of the population is still alive.
For example:
x(0.5) = the median age
x(0.1) = age when 10% survive
x(0.37) = the threshold of longevity proposed in this paper
This transforms mortality analysis into a population-relative scale, rather than a fixed chronological one.
Longevity 2023
🚨 5. Breakthrough Finding: Longevity Begins at s = 0.37
Using hazard theory and survival mathematics, the paper shows:
Longevity begins when 37% of the population is still alive.
Mathematically:
Longevity onset occurs at the s-age x(0.37)
This is where cumulative hazard equals 1, meaning:
The population has experienced enough mortality to kill the “average” individual.
This is a universal, population-based threshold, not a fixed age like 60 or 70.
Longevity 2023
🧬 6. Biological Interpretation
From evolutionary biology:
Natural selection pressures drop sharply after reproductive years
After this point, life is governed by “force of failure” (aging processes)
Álvarez connects this transition to the mathematical threshold H = 1, aligning biology with demography
Thus, x(0.37) represents the beginning of “post-Darwinian longevity.”
Longevity 2023
📈 7. Empirical Findings (Denmark, France, USA)
Using mortality data (1950–2020), the paper shows:
🔹 Major longevity indicators (life expectancy, modal age, entropy threshold, s-age 0.37):
All rise dramatically over time
All exceed age 70
All cluster closely around each other
🔹 Key insight:
Longevity begins well after the traditional retirement ages of 60–70.
Longevity 2023
⭐ 8. Main Conclusions
Old age cannot be defined by fixed ages like 60 or 70.
Longevity is population-relative, not chronological.
The onset of longevity should be defined as x(0.37)—the age when 37% of a population remains alive.
This threshold is biologically meaningful, mathematically grounded, and consistent across countries.
Modern populations experience much later onset of old age than historical definitions suggest.
Longevity 2023
🌟 One-Sentence Summary
Longevity begins not at a fixed age like 60 or 70, but at the survivorship age x(0.37), the age at which only 37% of the population remains alive—a dynamic, scientifically derived threshold....
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Lifespan PDF
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Lifespan PDF
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This PDF is a comprehensive, scientifically ground This PDF is a comprehensive, scientifically grounded introduction to human aging biology, explaining why humans age, why we die, and how modern geroscience is beginning to intervene in the aging process. It presents aging as a biological mechanism, not an inevitable fate, and explores how genetics, lifestyle, environmental exposures, and cellular processes determine how long we live.
The document synthesizes decades of aging research into a clear framework covering the biological, environmental, and technological factors that influence human lifespan. It emphasizes the importance of slowing aging—not just treating age-related diseases—to extend healthy life.
🔶 1. Purpose of the PDF
The document aims to:
Explain why aging happens
Describe the biological mechanisms behind aging
Summarize the key factors that influence lifespan
Present modern scientific strategies that may extend life
Show how lifestyle and environment shape longevity
Lifespan PDF
It serves as a foundational educational piece for students, researchers, and anyone interested in longevity science.
🔶 2. Aging and Lifespan — The Core Concepts
The PDF defines aging as:
The gradual decline of physiological function
Resulting from cellular and molecular damage
Leading to increased risk of disease and death
Lifespan is influenced by:
Genetics
Environment
Lifestyle choices
Access to healthcare
Biological aging rate
Lifespan PDF
It distinguishes chronological age (years lived) from biological age (actual cellular condition), arguing that biological age is the true determinant of health.
🔶 3. The Biological Mechanisms of Aging
The document highlights the major theories and hallmarks of aging:
⭐ Genetic Factors
Genes and inherited variants contribute to disease risk and lifespan potential.
⭐ Cellular Senescence
Aging cells stop dividing and release harmful inflammatory factors.
⭐ Oxidative Stress
Accumulation of reactive oxygen species damages DNA, proteins, and lipids.
⭐ Telomere Shortening
Protective chromosome ends shorten with each division, leading to cellular dysfunction.
⭐ Mitochondrial Decline
Energy production decreases, contributing to fatigue, metabolic slowing, and organ deterioration.
⭐ DNA Damage
Mutations and molecular errors accumulate over time.
Lifespan PDF
These mechanisms together drive the biological aging process.
🔶 4. Lifestyle Factors That Affect Longevity
The PDF discusses modifiable contributors to aging:
Nutrition (balanced diet, caloric moderation)
Physical exercise
Sleep quality
Stress management
Avoiding toxins (smoking, pollution, alcohol misuse)
Lifespan PDF
Healthy habits slow the biological aging rate and prevent chronic disease.
🔶 5. Medical Advances and Scientific Strategies to Extend Life
The document reviews current scientific approaches such as:
Early detection and preventive care
Drugs that target aging pathways (e.g., metformin, rapalogs)
Regenerative medicine
Gene therapy
Senolytics (removal of senescent cells)
Lifespan PDF
It also highlights the potential of emerging technologies to slow or reverse aspects of aging.
🔶 6. Environmental and Social Influences
Longevity is strongly shaped by:
socioeconomic status
access to healthcare
quality of living conditions
education
social support
Lifespan PDF
The PDF emphasizes that aging is not only biological, but also social and environmental.
🔶 7. Key Message of the Document
Aging is modifiable, not fixed.
By understanding the mechanisms that drive aging and adopting better lifestyle and medical strategies, humans can:
delay disease
improve healthspan
potentially extend lifespan
This aligns with modern geroscience, which aims not to achieve immortality but to give people more healthy years.
⭐ Perfect One-Sentence Summary
This PDF provides a clear, science-based overview of how aging works, what determines human lifespan, and how genetics, lifestyle, environment, and emerging biomedical technologies can slow the aging process and extend healthy life....
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wvptnahr-9268
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xevyo
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longevity of C. elegans m
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longevity of C. elegans mutants
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This study delivers a deep, mechanistic explanatio This study delivers a deep, mechanistic explanation of how changes in lipid biosynthesis—specifically in fatty-acid chain length and saturation—contribute directly to the extraordinary longevity of certain C. elegans mutants, especially those with disrupted insulin/IGF-1 signaling (IIS). By comparing ten nearly genetically identical worm strains that span a tenfold range of lifespans, the authors identify precise lipid signatures that track strongly with lifespan and experimentally confirm that altering these lipid pathways causally extends or reduces lifespan.
Its central insight:
Long-lived worms reprogram lipid metabolism to make their cell membranes more resistant to oxidative damage, particularly by reducing peroxidation-prone polyunsaturated fatty acids (PUFAs) and shifting toward shorter and more saturated lipid chains.
This metabolic remodeling lowers the substrate available for destructive free-radical chain reactions, boosting both stress resistance and lifespan.
🧬 Core Findings, Explained Perfectly
1. Strong biochemical patterns link lipid structure to lifespan
Across all strains, two lipid features were the strongest predictors of longevity:
A. Shorter fatty-acid chain length
Long-lived worms had:
more short-chain fats (C14:0, C16:0)
fewer long-chain fats (C18:0, C20:0, C22:0)
Average chain length decreased almost perfectly in proportion to lifespan.
B. Fewer polyunsaturated fatty acids (PUFAs)
Long-lived mutants had:
sharply reduced PUFAs (EPA, arachidonic acid, etc.)
dramatically lower peroxidation index (PI)
fewer double bonds (lower DBI)
These changes make membranes much less susceptible to lipid peroxidation damage.
2. Changes in enzyme activity explain the lipid shifts
By measuring mRNA levels and inferred enzymatic activity, the study shows:
Downregulated in long-lived mutants
Elongases (elo-1, elo-2, elo-5) → shorter chains
Δ5 desaturase (fat-4) → fewer PUFAs
Upregulated
Δ9 desaturases (fat-6, fat-7) → more monounsaturated, oxidation-resistant MUFAs
This combination produces membranes that are:
just fluid enough (thanks to MUFAs)
much harder to oxidize (thanks to less PUFA content)
This is a perfect, balanced redesign of the membrane.
3. RNAi experiments prove these lipid changes CAUSE longevity
Knocking down specific genes in normal worms produced dramatic effects:
Increasing lifespan
fat-4 (Δ5 desaturase) RNAi → +25% lifespan
elo-1 or elo-2 (elongases) RNAi → ~10–15% lifespan increase
Combined elo-1 + elo-2 knockdown → even larger increase
Reducing lifespan
Knockdown of Δ9 desaturases (fat-6, fat-7) slightly shortened lifespan
Stress resistance matched the lifespan effects
The same interventions boosted survival under hydrogen peroxide oxidative stress, confirming that resistance to lipid peroxidation is a key mechanism of longevity.
4. Dietary experiments confirm the same mechanism
When worms were fed extra PUFAs like EPA or DHA:
lifespan dropped by 16–24%
Even though these fatty acids are often considered “healthy” in humans, in worms they create more oxidative vulnerability, validating the model.
5. Insulin/IGF-1 longevity mutants remodel lipids as part of their longevity program
The longest-lived mutants—especially age-1(mg44), which can live nearly 10× longer—show the greatest lipid remodeling:
lowest elongase expression
lowest PUFA levels
highest MUFA-producing Δ9 desaturases
This suggests that IIS mutants extend lifespan partly through targeted remodeling of membrane lipid composition, not just through metabolic slowdown or stress-response pathways.
💡 What This Means
The core conclusion
Longevity in C. elegans is intimately connected to reducing lipid peroxidation, a major source of cellular damage.
Worms extend their lifespan by:
shortening lipid chains
reducing PUFA content
elevating MUFAs
suppressing enzymes that create vulnerable lipid species
enhancing enzymes that create stable ones
These changes:
harden membranes against oxidation
reduce chain-reaction damage
increase survival under stress
extend lifespan significantly
**This is one of the clearest demonstrations that lipid composition is not just correlated with longevity—
it helps cause longevity.**...
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Evaluating the Effect o
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Evaluating the Effect of Project Longevity
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This report evaluates the impact of Project Longev This report evaluates the impact of Project Longevity, a focused-deterrence violence-reduction initiative implemented in New Haven, Connecticut, on reducing group-involved shootings and homicides. The program targets violent street groups, delivering a coordinated message that violence will bring swift sanctions while offering social services, support, and incentives for individuals who choose to disengage from violent activity.
The study uses detailed group-level data and statistical modeling to assess changes in violent incidents following the program’s launch. The analysis reveals that Project Longevity significantly reduced group-related shootings and homicides, with estimates indicating reductions of approximately 25–30% after implementation. The results are robust across multiple models and remain consistent after adjusting for group characteristics, prior levels of violence, and time trends.
The report explains that Project Longevity works by mobilizing three key components:
Law enforcement partners, who coordinate enforcement responses to group violence;
Social service providers, who offer job training, counseling, and other support;
Community moral voices, who communicate collective intolerance for violence.
Together, these elements reinforce the central message: violence will no longer be tolerated, but help is available for those willing to change.
The authors conclude that Project Longevity is an effective violence-prevention strategy, demonstrating clear reductions in serious violent crime among the most at-risk populations. The findings support the broader evidence base for focused deterrence strategies and suggest that continued implementation could sustain long-term reductions in group-involved violence.
If you want, I can also provide:
✅ A short 3–4 line summary
✅ A simple student-friendly version
✅ MCQs or quiz questions from this file...
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Extreme longevity may be
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Extreme longevity may be the rule
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This study by Breed et al. (2024) investigates the This study by Breed et al. (2024) investigates the longevity of Balaenid whales, focusing on the southern right whale (SRW, Eubalaena australis) and the North Atlantic right whale (NARW, Eubalaena glacialis). By analyzing over 40 years of mark-recapture data, the authors estimate life spans and survival patterns, revealing that extreme longevity (exceeding 130 years) is likely the norm rather than the exception in Balaenid whales, challenging previously accepted maximum life spans of 70–75 years. The study also highlights the impact of anthropogenic factors, particularly industrial whaling, on the significantly reduced life span of the endangered NARW.
Key Findings
Southern right whales (SRWs) have a median life span of approximately 73.4 years, with 10% of individuals surviving beyond 131.8 years.
North Atlantic right whales (NARWs) have a median life span of only 22.3 years, with 10% living past 47.2 years—considerably shorter than SRWs.
The reduced NARW life span is attributed to anthropogenic mortality factors, including ship strikes and entanglements, not intrinsic biological differences.
The study uses survival function modeling, bypassing traditional aging methods that rely on lethal sampling and growth layer counts, which tend to underestimate longevity.
Evidence from other whales, especially bowhead whales, supports the hypothesis that extreme longevity is widespread among Balaenids and possibly other large cetaceans.
Background and Context
Early longevity estimates in whales, such as blue and fin whales, came from counting annual growth layers in ear plugs, revealing ages up to 110–114 years.
Bowhead whales have been documented to live over 150 years, with some individuals estimated at 211 years based on aspartic acid racemization (AAR) and corroborating archaeological evidence (e.g., embedded antique harpoon tips).
Longevity estimates from traditional methods are biased low due to:
Difficulty in counting growth layers in very old whales due to tissue remodeling.
Removal of older age classes from populations by industrial whaling.
The need for lethal sampling to obtain age data, which is rarely possible in protected species.
The relation between body size and longevity supports the potential for extreme longevity in large whales, although bowhead whales exceed predictions from terrestrial mammal models.
Methodology
Data Sources:
SRW mark-recapture data from South Africa (1979–2021), including 2476 unique females, of which 139 had known birth years.
NARW mark-recapture data from the North Atlantic (1974–2020), including 328 unique females, of which 205 had known birth years.
Survival Models:
Ten parametric survival models were fitted, including Gompertz, Weibull, Logistic, and Exponential mortality functions with adjustments (Makeham and bathtub).
Models were fit using Bayesian inference with the R package BaSTA, which accounts for left truncation (unknown birth years) and right censoring (individuals surviving past the study period).
Model selection was based on Deviance Information Criterion (DIC).
Validation:
Simulated datasets, generated from fitted model parameters, were used to test for bias and accuracy.
Models accurately recovered survival parameters with minimal bias.
Estimating Reproductive Output:
The total number of calves produced by females was estimated by integrating survival curves and applying calving intervals ranging from 3 to 7 years.
Results
Parameter Southern Right Whale (SRW) North Atlantic Right Whale (NARW)
Median life span (years) 73.4 (95% CI [60.0, 88.3]) 22.3 (95% CI [19.7, 25.1])
10% survive past (years) 131.8 (95% CI [110.9, 159.3]) 47.2 (95% CI [43.0, 53.3])
Annual mortality hazard (age 5) ~0.5% 2.56%
Maximum life span potential >130 years Shortened due to anthropogenic factors
**SRW survival best fits an unmodified Gompertz model; NARW fits a Gompertz model with
Smart Summary
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THE NMDOT LONGEVITY PAY P
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THE NMDOT LONGEVITY PAY PROGRAM
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The NMDOT Longevity Pay Program is an employee-rec The NMDOT Longevity Pay Program is an employee-recognition initiative launched by the New Mexico Department of Transportation (NMDOT) to reward staff for their continuous years of service. Effective December 2023, the program provides structured, one-time annual longevity payments to eligible classified employees based on their accumulated uninterrupted service with the department.
The program outlines a tiered payment system, beginning at $250 for employees with 2–4 years of service and increasing progressively up to $3,000 for employees who have completed 50 or more years of service. Payments are issued once per year, included in an employee’s regular paycheck following the first pay-period ending in December. These payments are taxable, are not part of base salary, and do not count toward pension calculations.
Eligibility requires that employees:
Are active NMDOT staff at the time of payment, and
Have not received a Notice of Final Action of Dismissal or Separation prior to the payment date.
The document defines “continuous service” as unbroken employment from the latest hire date, including probationary and temporary service if no break occurs. A break in employment is defined as at least one workday not in classified service, though transitions from temporary to permanent roles without gaps do not count as breaks.
Starting in 2024 and future years, payments will continue annually using a simplified table: employees receive longevity pay at the completion of each 2-, 5-, 10-, 15-, 20-, 25-year milestone, and so on, with $3,000 awarded at 50 years and every five years thereafter.
The program reflects NMDOT’s commitment to appreciating long-serving employees and will continue as long as organizational resources allow.
If you want, I can also provide:
✅ A short summary
✅ A simple student-friendly version
✅ MCQs or quiz questions from this file...
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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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Lifetime Stress
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Lifetime Stress Exposure and Health
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This PDF is a scholarly, psychological–biomedical This PDF is a scholarly, psychological–biomedical review that examines how stress experienced across a person’s entire life—childhood, adolescence, and adulthood—shapes physical and mental health outcomes. It presents a comprehensive model of lifetime stress exposure, explains the biological systems affected, and shows how early-life adversity has long-lasting effects, often predicting disease decades later. The paper emphasizes that stress is not a single event but a cumulative life-course experience with deep consequences for aging, longevity, and chronic illness.
The core message:
Stress exposure across the lifespan—its timing, severity, duration, and pattern—has profound and measurable impacts on long-term health, from cellular aging to immune function to chronic disease risk.
🧠 1. What the Paper Seeks to Explain
The article answers key questions:
How does stress accumulate over a lifetime?
Why do early childhood stressors have especially strong effects?
What biological systems encode the “memory” of stress?
How does lifetime stress exposure increase disease risk and accelerate aging?
It integrates psychology, neuroscience, immunology, and epidemiology into one life-course model.
Lifetime Stress Exposure and He…
⏳ 2. Types and Patterns of Lifetime Stress
The paper presents a multidimensional perspective on stress exposure:
⭐ A. Chronic Stress
Ongoing stressors such as poverty, family conflict, caregiving duties
→ strongest predictor of long-term health problems.
⭐ B. Acute Stressful Events
Traumas, accidents, sudden losses; impact depends on timing and recovery.
⭐ C. Early-Life Stress (ELS)
Abuse, neglect, household dysfunction
→ disproportionately powerful effects on adult health.
⭐ D. Cumulative Stress
The sum of stressors across life, building “allostatic load.”
Lifetime Stress Exposure and He…
🧬 3. Biological Pathways Linking Stress to Disease
The paper identifies the core physiological systems affected by lifetime stress:
✔️ The HPA Axis (Cortisol System)
Chronic activation leads to hormonal imbalance and impaired stress recovery.
✔️ Autonomic Nervous System
Sympathetic overactivation increases cardiovascular strain.
✔️ Immune System
Chronic stress provokes inflammation and suppresses immune defense.
✔️ Gene Expression & Epigenetics
Stress alters DNA methylation and regulates genes related to aging and inflammation.
✔️ Accelerated Cellular Aging
Stress is linked to shorter telomeres, impaired repair processes, and faster biological aging.
Lifetime Stress Exposure and He…
Together, these systems create a “biological embedding” of stress.
👶 4. Why Early-Life Stress Has Powerful Long-Term Effects
Childhood is a period of rapid brain, immune, and endocrine development.
Stress during this period:
Permanently alters stress regulation systems
Creates long-term vulnerability to anxiety, depression, and disease
Shapes lifelong patterns of coping and resilience
Increases risk for cardiovascular disease, metabolic dysfunction, and mental disorders
Lifetime Stress Exposure and He…
ELS is one of the strongest predictors of adult morbidity and mortality.
🪫 5. Cumulative Stress and Allostatic Load
The paper uses the concept of allostatic load, the “wear and tear” on the body from chronic stress.
High allostatic load results in:
Chronic inflammation
Weakened immunity
Hypertension
Metabolic disorders
Reduced cognitive function
Shortened lifespan
Lifetime Stress Exposure and He…
This cumulative burden explains why stress accelerates biological aging.
🧩 6. The Lifetime Stress Exposure Model
The PDF proposes a comprehensive framework combining:
⭐ Exposure Dimensions
Severity
Frequency
Duration
Timing
Accumulation
Perceived vs. objective stress
⭐ Contextual Factors
Socioeconomic status
Social support
Environment
Early-life caregiving
Coping styles
⭐ Health Outcomes
Cardiometabolic disease
Immune dysfunction
Psychiatric conditions
Shortened life expectancy
Lifetime Stress Exposure and He…
This model captures the complexity of how stress interacts with biology over decades.
🌿 7. Resilience and Protective Factors
The paper also highlights buffers against stress:
Strong social support
Positive relationships
Effective coping strategies
Healthy behaviors (sleep, exercise, diet)
Access to mental health care
Secure early-life environments
Lifetime Stress Exposure and He…
These reduce the health impact of stress exposure.
⭐ Overall Summary
This PDF provides a detailed scientific analysis of how stress across the entire lifespan shapes physical and mental health. It shows that the timing, intensity, and accumulation of stress profoundly influence biological systems, especially when stress occurs early in life. Chronic and cumulative stress accelerate aging, increase disease risk, and shorten lifespan through hormonal, immune, neural, and epigenetic pathways. At the same time, resilience factors can buffer these effects....
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Eating for Health
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Eating for Health and Longevity
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Summary: Eating for Health and Longevity – A Pract Summary: Eating for Health and Longevity – A Practical Guide to Whole-Food, Plant-Based Diets
This guide, produced by SUNY Downstate Health Sciences University, provides a comprehensive, evidence-based overview of adopting a whole-food, plant-based (WFPB) diet to promote health, prevent chronic disease, and improve longevity. It offers practical advice for transitioning to plant-based eating, highlights nutritional benefits, and addresses common concerns and misconceptions.
Core Concepts of a Whole-Food, Plant-Based Diet
Definition: A WFPB diet emphasizes eating whole, minimally processed plant foods such as vegetables, fruits, whole grains, legumes, nuts, and seeds.
Exclusions: It minimizes or avoids meat, poultry, fish/seafood, eggs, dairy, refined carbohydrates (e.g., white bread, white rice), refined sugars, extracted oils, and highly processed foods.
Difference from Vegan Diet: Unlike some vegan diets, which may include refined grains, sweeteners, and oils, the WFPB diet focuses on whole foods for optimal health.
Health Benefits
Chronic Disease Prevention and Reversal: WFPB diets can prevent, manage, and sometimes reverse diseases such as diabetes, heart disease, obesity, and hypertension.
Weight Management: Effective for losing excess weight and maintaining a healthy weight.
Longevity and Vitality: Promotes vibrant health and potentially longer life by reducing lifestyle-related risk factors.
Foods to Include and Avoid
Foods to Eat and Enjoy Foods to Avoid or Minimize
Fresh and frozen vegetables Meats (red, processed, poultry, fish/seafood)
Fresh fruits Refined grains (white rice, white pasta, white bread)
Whole grains (oats, quinoa, barley) Products with refined sugars or sweeteners (sodas, candy)
Legumes (peas, lentils, beans) Highly processed or convenience foods with added salt
Unsalted nuts and seeds Eggs and dairy products
Dried fruits without additives Processed plant-based meat, cheese, or butter alternatives
Unsweetened non-dairy milks Refined, extracted oils (olive oil, canola, vegetable)
Alcoholic beverages
Smart Summary
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Promoting product life
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Promoting product longevity
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The document explains why products today do not la The document explains why products today do not last as long as they could and proposes policies, standards, and market solutions to encourage long-lasting, durable, repairable, and reusable products across Europe.
It emphasizes:
Reducing premature obsolescence
Improving repairability
Designing for durability
Supporting sustainable business models
Empowering consumers
Promoting product Longevity
🔍 Key Themes in the PDF
1. The Problem: Products Don’t Last Long Enough
The report shows that modern products—especially electronics, appliances, and textiles—often have short lifespans, causing:
Environmental harm
Increased waste volumes
Higher resource demand
Consumer frustration
Promoting product Longevity
Manufacturers may design products that are:
Hard to repair
Built with cheap materials
Quickly outdated by new models
Non-upgradeable
Promoting product Longevity
2. Why Product Longevity Matters
Extending product lifetimes creates:
Lower environmental impact (less extraction of raw materials)
Lower waste generation
Better household affordability
More sustainable production cycles
Promoting product Longevity
3. Consumer Perspective
The PDF highlights strong evidence that consumers want longer-lasting products:
People value durability and repairability
Many experience products failing too soon
Repair options are often too expensive or unavailable
Promoting product Longevity
Consumers need:
Reliable durability labels
Better warranties
Affordable repair services
Promoting product Longevity
4. Business & Industry Perspective
The report analyzes how businesses can:
Reduce lifecycle impact
Offer repair services
Adopt circular business models (leasing, refurbishing, remanufacturing)
Promoting product Longevity
It also addresses barriers, such as:
High upfront durability costs
Lack of incentives
Competitive pressure to release new models frequently
5. Policy Solutions for Long-Lasting Products
The final section proposes policy actions to promote durability and repairability:
A. Ecodesign & Durability Standards
Require manufacturers to design stronger, long-lasting products
Set minimum durability and repairability criteria
Promoting product Longevity
B. Right-to-Repair Regulations
Ensure spare parts availability
Ensure repair information is accessible
Support independent repair shops
C. Consumer Information Tools
Durability labels
Repairability scores
Standardized warranties
D. Economic Incentives
VAT reduction on repairs
Financial support for circular business models
E. Market & Innovation Support
Encourage remanufacturing industries
Support longer-use business models
🧩 Overall Message
The PDF concludes that product longevity is essential for achieving Europe’s environmental targets, reducing waste, empowering consumers, and supporting sustainable economic growth. It calls for coordinated action across:
Government
Industry
Consumers
Researchers
to create a market where long-lasting, repairable, durable products become the norm, not the exception....
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longevity by preventing
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longevity by preventing the age
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This scientific paper, published in PLOS Biology ( This scientific paper, published in PLOS Biology (2025), investigates how removing the protein Maf1—a natural repressor of RNA Polymerase III—in neurons can significantly extend lifespan and improve age-related health in Drosophila melanogaster (fruit flies). The study focuses on how aging reduces the ability of neurons to perform protein synthesis, and how reversing this decline affects longevity.
Core Scientific Insight
Maf1 normally suppresses the production of small, essential RNA molecules (like 5S rRNA and tRNAs) needed for building ribosomes and synthesizing proteins. Aging decreases protein synthesis in many tissues including the brain. This study shows that removing Maf1 specifically from adult neurons increases Pol III activity, boosts production of 5S rRNA, maintains protein synthesis, and ultimately promotes healthier aging and longer life.
Major Findings
Knocking down Maf1 in adult neurons extends lifespan, in both female and male flies, with larger effects in females.
Longevity effects are cell-type specific: extending lifespan works via neurons, not gut or fat tissues.
Neuronal Maf1 removal:
Delays age-related decline in motor function
Improves sleep quality in aged flies
Protects the gut barrier from age-related failure
Aging naturally causes a sharp decline in 5S rRNA levels in the brain. Maf1 knockdown prevents this decline.
Maf1 depletion maintains protein synthesis rates in old age, which normally fall significantly.
Longevity requires Pol III initiation on 5S rRNA—genetically blocking this eliminates the life-extending effect.
The intervention also reduces toxicity in a fruit-fly model of C9orf72 neurodegenerative disease (linked to ALS and FTD), highlighting potential therapeutic importance.
Biological Mechanism
Removing Maf1 → increased Pol III activity → restored 5S rRNA levels → increased ribosome functioning → maintained protein synthesis → improved neuronal and systemic health → extended lifespan.
Broader Implications
The study challenges the long-standing assumption that reducing translation always extends lifespan. Instead, it reveals a cell-type–specific benefit: neurons, unlike other tissues, require sustained translation for healthy aging. The findings suggest similar mechanisms may exist in mammals, potentially offering insights into combatting neurodegeneration and age-related cognitive decline....
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Pandemics and the Economi
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Pandemics and the Economics of Aging and Longevity
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This PDF is an academic chapter examining how pand This PDF is an academic chapter examining how pandemics—especially COVID-19—interact with aging populations, longevity trends, and the economics of health and survival. It combines insights from demography, economics, health policy, and epidemiology to show how pandemics reshape mortality patterns, longevity gains, public spending, and the wellbeing of older adults.
The central message:
Pandemics do not just affect death rates—they transform long-term economic and demographic patterns, especially in aging societies.
📘 Purpose of the Chapter
The document explores:
How pandemics alter survival rates by age
Why older adults experience the highest mortality burden
Economic trade-offs between longevity investments and pandemic preparedness
How societies should rethink health systems in the context of demographic aging
How pandemics interact with inequality, economic resilience, and the value of life
It positions pandemics as a major factor influencing the economics of longevity, aging, and intergenerational welfare.
🧠 Core Themes and Arguments
1. Pandemics Hit Aging Societies Much Harder
The chapter explains that COVID-19 caused:
Extremely high mortality among older adults
Severe pressure on health systems
Significant declines in life expectancy
Long-term economic losses concentrated among the elderly
It highlights that the demographic structure of a society strongly determines the overall mortality impact of a pandemic.
2. Pandemics Reduce Longevity Gains
For decades, life expectancy had been rising. Pandemics can:
Reverse these gains
Increase mortality rates for older cohorts
Create “scarring effects” in population health
It notes that longevity is not guaranteed—health shocks can disrupt historical progress.
3. Economic Value of Life and Risk
The text examines how societies evaluate:
The value of preventing deaths
The cost of lockdowns
The economic returns of reducing mortality risks
How much governments should invest in protecting older adults
Pandemics raise complicated questions about resource allocation, equity, and the economic value of extended life.
4. Intergenerational Impacts
The pandemic created tensions between:
Younger people (job losses, school closures)
Older adults (higher mortality risk)
The chapter discusses the economics of fairness:
Who bears the cost of pandemic control?
Who benefits most from saved lives?
How generational burden-sharing should be designed?
5. Longevity, Health Systems, and Preparedness
The document explains that aging societies must:
Strengthen chronic disease management
Build resilient health systems
Improve long-term care
Prepare for repeated pandemics
It argues that the rising share of elderly people requires rethinking pandemic preparedness—because older adults are both more vulnerable and more expensive to protect.
6. COVID-19 as an Economic and Demographic Shock
The chapter uses COVID-19 as a case study to show:
Economic shutdowns
Health system overload
Labor market disruptions
Inequality between rich and poor older adults
Disproportionate mortality among low-income, marginalized, and unhealthy aging populations
It highlights that pandemics expose and magnify pre-existing inequalities, especially in health.
7. Lessons for the Future
The text concludes that societies should invest in:
Disease prevention
Universal health coverage
Vaccination systems
Social protection
Healthy aging policies
Cross-border pandemic collaboration
It stresses that pandemics will become more common, and their impact will grow as populations age.
⭐ Overall Summary
This PDF provides a comprehensive, multidisciplinary examination of how pandemics fundamentally reshape the dynamics of aging, longevity, mortality, and the economics of health. It argues that aging societies must rethink how they value life, prepare for pandemics, and build resilient, equitable health systems capable of protecting older generations....
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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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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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xevyo
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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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a899b0b5-d187-4a93-8cea-938ff817f30a
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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vmsdiqjm-7013
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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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Effects of desiccation
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Effects of desiccation stress
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This study presents a systematic review and pooled This study presents a systematic review and pooled survival analysis quantifying the effects of desiccation stress (humidity) and temperature on the adult female longevity of Aedes aegypti and Aedes albopictus, the primary mosquito vectors of arboviral diseases such as dengue, Zika, chikungunya, and yellow fever. The research addresses a critical gap in vector ecology and epidemiology by providing a comprehensive, quantitative model of how humidity influences adult mosquito survival, alongside temperature effects, to improve understanding of transmission dynamics and enhance predictive models of disease risk.
Background
Aedes aegypti and Ae. albopictus are globally invasive mosquito species that transmit several major arboviruses.
Adult female mosquito longevity strongly impacts transmission dynamics because mosquitoes must survive the extrinsic incubation period (EIP) to become infectious.
While temperature effects on mosquito survival have been widely studied and incorporated into models, the role of humidity remains poorly quantified despite being ecologically significant.
Humidity influences mosquito survival via desiccation stress, affecting water loss and physiological function.
Environmental moisture also indirectly affects mosquito populations by altering evaporation rates in larval habitats, impacting larval development and adult body size, which affects vectorial capacity.
Understanding the temperature-dependent and non-linear effects of humidity can improve ecological and epidemiological models, especially in arid, semi-arid, and seasonally dry regions, which are understudied.
Objectives
Systematically review experimental studies on temperature, humidity, and adult female survival in Ae. aegypti and Ae. albopictus.
Quantify the relationship between humidity and adult survival while accounting for temperature’s modifying effect.
Provide improved parameterization for models of mosquito populations and arboviral transmission.
Methods
Systematic Literature Search: 1517 unique articles screened; 17 studies (16 laboratory, 1 semi-field) met inclusion criteria, comprising 192 survival experiments with ~15,547 adult females (8749 Ae. aegypti, 6798 Ae. albopictus).
Inclusion Criteria: Studies must report survival data for adult females under at least two temperature-humidity regimens, with sufficient methodological detail on nutrition and hydration.
Data Extraction: Variables included species, survival times, mean temperature, relative humidity (RH), and provisioning of water, sugar, and blood meals. Saturation vapor pressure deficit (SVPD) was calculated from temperature and RH to represent desiccation stress.
Survival Time Simulation: To harmonize disparate survival data formats (survival curves, mean/median longevity, survival proportions), individual mosquito survival times were simulated via Weibull and log-logistic models.
Pooled Survival Analysis: Stratified and mixed-effects Cox proportional hazards regression models were used to estimate hazard ratios (mortality risks) associated with temperature, SVPD, and nutritional factors.
Model Selection: SVPD was found to fit survival data better than RH or vapor pressure.
Sensitivity Analyses: Included testing model robustness by excluding individual studies and comparing results using only Weibull simulations.
Key Quantitative Findings
Parameter Ae. aegypti Ae. albopictus Notes
Temperature optimum (lowest mortality hazard) ~27.5 °C ~21.5 °C Ae. aegypti optimum higher than Ae. albopictus
Mortality risk trend Increases non-linearly away from optimum; sharp rise at higher temps Similar trend; possibly slightly better survival at lower temps Mortality rises rapidly at high temps for both species
Effect of desiccation (SVPD) Mortality hazard rises steeply from 0 to ~1 kPa SVPD, then more gradually Mortality hazard increases with SVPD but with less clear pattern Non-linear and temperature-dependent relationship
Species comparison (stratified model) Generally lower mortality risk than Ae. albopictus across most conditions Higher mortality risk compared to Ae. aegypti Differences not significant in mixed-effects model
Nutritional provisioning effects Provision of water, sugar, blood meals significantly reduces mortality risk Same as Ae. aegypti Provisioning modeled as binary present/absent
Qualitative and Contextual Insights
Humidity is a significant and temperature-dependent factor affecting adult female survival in Ae. aegypti, with more limited but suggestive evidence for Ae. albopictus.
Mortality risk increases sharply with desiccation stress (SVPD), especially at higher temperatures.
Ae. aegypti tends to have higher survival and a higher thermal optimum than Ae. albopictus, aligning with their geographic distributions—Ae. aegypti favors warmer, drier climates while Ae. albopictus tolerates cooler temperatures.
Provisioning of water and nutrients (sugar, blood) markedly improves survival, reflecting the importance of hydration and energy intake.
The findings support that humidity effects are underrepresented in current mosquito and disease transmission models, which often rely on simplistic or threshold-based mortality assumptions.
The use of SVPD (a measure of desiccation potential) rather than relative humidity or vapor pressure is more appropriate for modeling mosquito survival related to desiccation.
There is substantial unexplained variability among studies, likely due to unmeasured factors such as mosquito genetics, experimental protocols, and microclimatic conditions.
The majority of studies used laboratory settings and tropical/subtropical strains, with very limited data from arid or semi-arid climates, a critical gap given the importance of humidity fluctuations there.
Microclimatic variability and mosquito behavior (e.g., seeking humid refugia) may mitigate desiccation effects in the field, so laboratory results may overestimate mortality under natural conditions.
The study highlights the need for more field-based and arid region studies, and for models to incorporate nonlinear and interactive effects of temperature and humidity on mosquito survival.
Timeline Table: Study Selection and Analysis Process
Step Description
Literature search (Feb 2016) 1517 unique articles screened
Full text review 378 articles assessed for eligibility
Final inclusion 17 studies selected (16 lab, 1 semi-field)
Data extraction Survival data, temperature, humidity, nutrition, species, setting
Survival time simulation Weibull and log-logistic models used to harmonize survival data
Pooled survival analysis Stratified and mixed-effects Cox regression models
Sensitivity analyses Exclusion of individual studies, Weibull-only simulations
Model selection SVPD chosen as best humidity metric
Definitions and Key Terms
Term Definition
Aedes aegypti Primary mosquito vector of dengue, Zika, chikungunya, and yellow fever viruses
Aedes albopictus Secondary vector species with broader climatic tolerance, also transmits arboviruses
Saturation Vapor Pressure Deficit (SVPD) Difference between actual vapor pressure and saturation vapor pressure; a measure of drying potential/desiccation stress
Extrinsic Incubation Period (EIP) Time required for a virus to develop within the mosquito before it can be transmitted
Desiccation stress Physiological stress from water loss due to low humidity, impacting mosquito survival
Stratified Cox regression Survival analysis method allowing baseline hazards to vary by study
Mixed-effects Cox regression Survival analysis
Smart Summary
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61cf2f07-0031-4731-8c55-3c893a185702
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8684964a-bab1-4235-93a8-5fd5e24a1d0a
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vleedipm-6476
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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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LONGEVITY PAY Program
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LONGEVITY PAY Program Guide
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The Longevity Pay Program Guide is an official 18- The Longevity Pay Program Guide is an official 18-page policy and administration manual issued by the Oklahoma Office of Management and Enterprise Services (OMES) – Human Capital Management, revised in November 2024. It serves as the definitive statewide reference for how longevity pay is calculated, awarded, managed, and governed for Oklahoma state employees. It explains eligibility rules, creditable service, payout provisions, statutory authority, and administrative procedures in clear detail.
The guide begins with the historical foundation of the program, established in 1982 to help agencies attract and retain skilled employees. It then provides a structured breakdown of who is entitled to longevity pay and which types of employment count toward creditable service. These include most state employees, certain educational institutions under the State Regents for Higher Education, employees in the judicial branch, legislative session employees with at least two years’ part-time service, and contract employees paid with state fiscal resources. It also lists non-eligible groups such as members of boards and commissions, elected officials, city/county employees, and workers in private or proprietary universities.
The document defines eligibility status, emphasizing rules around continuous service, breaks in service, temporary employment conversion, legislative service provisions, and different categories of leave without pay (LWOP) such as workers’ compensation leave, active military duty, and other unpaid leave. Each type of LWOP impacts the longevity anniversary date differently.
A major section describes creditable service, outlining conditions for counting part-time or temp-to-permanent employment, rules regarding dual employment, and special provisions for employees affected by reduction-in-force. It explains how all prior qualifying service is totaled, rounded down to whole years, and certified using official OMES longevity forms.
The guide then details payout provisions, including the full statutory longevity payment schedule, which awards annual lump-sum payments ranging from $250 (2–4 years) up to $2,000 (20 years), with an additional $200 added every two years beyond 20 years. Full-time and qualifying part-time employees receive the entire amount, while other part-time or LWOP-affected employees receive prorated payments. It also explains special payout rules for employees separating due to reduction-in-force, voluntary buyout, retirement, or death.
A built-in longevity calculator is referenced for agencies to compute payments accurately, and a robust FAQ section addresses real-world scenarios such as temporary service conversion, workers’ compensation periods, fragmented prior service, retirement timing, and special cases like CompSource Oklahoma or Pathfinder retirement eligibility.
The appendices provide important supporting materials:
Appendix A – the official OMES HCM-52 Longevity Certification Form.
Appendix B – a complete list of eligible institutions under the State Regents for Higher Education.
Appendix C – a list of independent/private universities that are not eligible.
Appendix D – institutions under the Department of Career and Technology Education.
Appendix E – the full statutory text of 74 O.S. § 840-2.18, which legally governs Oklahoma’s longevity pay system.
Overall, the guide is the authoritative source for ensuring accurate, consistent, statewide administration of longevity pay, combining legislative requirements, policy clarification, and practical, step-by-step administrative guidance.
If you'd like, I can prepare:
📌 a simplified one-page summary
📌 a comparison with your other longevity documents
📌 a training guide or slide deck version
📌 or a cross-document integrated briefing
Just tell me!...
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vgsshyvs-3844
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longevity in mammals
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longevity in mammals
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This PDF is a high-level evolutionary biology rese This PDF is a high-level evolutionary biology research article published in PNAS that investigates why some mammals live longer than others. It tests a powerful hypothesis:
Mammals that live in trees (arboreal species) evolve longer lifespans because tree-living reduces external sources of death such as predators, disease, and environmental hazards.
Using a massive dataset of 776 mammalian species, the study compares lifespan, body size, and habitat across nearly all mammalian clades. It provides one of the strongest empirical tests of evolutionary ageing theory in mammals.
The core message:
Arboreal mammals live significantly longer than terrestrial mammals, even after accounting for body size and evolutionary history — supporting the evolutionary theory of ageing and clarifying why primates (including humans) evolved long lifespans.
🌳 1. Why Arboreality Should Increase Longevity
Evolutionary ageing theory predicts:
High extrinsic mortality (predators, disease, accidents) → earlier ageing, shorter lifespan
Low extrinsic mortality → slower ageing, longer lifespan
Tree living offers protection:
Harder for predators to attack
Less exposure to ground hazards
Improved escape options
Therefore, species that spend more time in trees should evolve greater lifespan and delayed senescence.
Longevity in mammals
📊 2. Dataset and Methodology
The paper analyzes:
776 species of non-flying, non-aquatic mammals
Lifespan records (mostly from captive data for accurate maxima)
Species classified into:
Arboreal
Semiarboreal
Terrestrial
Body mass as a key covariate
Phylogenetically independent contrasts (PIC) to remove evolutionary bias
This allows a robust test of whether habitat causes differences in longevity.
Longevity in mammals
🕒 3. Main Findings
⭐ A. Arboreal mammals live longer
Across mammals, tree-living species have significantly longer maximum lifespans than terrestrial ones when body size is held constant.
Longevity in mammals
⭐ B. The pattern holds in most mammalian groups
In 8 out of 10 subclades, arboreal species live longer than terrestrial relatives.
⭐ C. Exceptions reveal evolutionary history
Two groups do not show this pattern:
Primates & Their Close Relatives (Euarchonta)
Arboreal and terrestrial species do not differ significantly
Likely because primates evolved from highly arboreal ancestors
Their long lifespan may have been established early and retained
Even terrestrial primates inherit long-living traits
Longevity in mammals
Marsupials (Metatheria)
No longevity advantage for arboreal vs. terrestrial species
Marsupials in general are not long-lived, regardless of habitat
Longevity in mammals
⭐ D. Squirrels provide a clear example
Within Sciuroidea:
Arboreal squirrels live longer than terrestrial squirrels
Semiarboreal species fall in between
Longevity in mammals
🔎 4. Why Primates Are a Special Case
The article provides an important evolutionary insight:
Primates did not gain longevity from becoming arboreal — they were already arboreal.
Arboreality is the ancestral primate condition
Long lifespan likely evolved early as primates adapted to tree life
Later terrestrial primates (baboons, humans) retained this long-lived biology
Additional survival strategies (large body size, social structures, intelligence) further reduce predation
Longevity in mammals
This helps explain why humans—the most terrestrial primate—still have extremely long lifespans.
🧬 5. Evolutionary Significance
The study strongly supports evolutionary ageing theory:
Low extrinsic mortality → slower ageing
Arboreality functions like a protective “life-extending shield”
Similar patterns seen in flying mammals (bats) and gliding mammals
Reduced risk environments create selection pressure for longer lives
Longevity in mammals
🐾 6. Additional Insights
✔️ Body size explains ~60% of lifespan variation
Larger mammals generally live longer, but habitat explains additional differences.
✔️ Arboreal habitats evolve multiple times
Many mammal groups that shifted from ground to trees repeatedly evolved greater longevity — independently.
✔️ Sociality reduces predation too
Large social groups (e.g., in primates and some marsupials) reduce predator risk, altering ageing patterns.
Longevity in mammals
⭐ Overall Summary
This PDF provides a groundbreaking comparative analysis showing that arboreal mammals live longer than terrestrial mammals, validating key predictions of evolutionary ageing theory. It demonstrates that reduced exposure to predators and environmental hazards in tree habitats leads to delayed ageing and increased lifespan. While most mammals follow this pattern, primates and marsupials are exceptions due to their unique evolutionary histories — particularly primates, who long ago evolved the long-living biology that humans still carry today.
This study is one of the most compelling demonstrations of how ecology, behavior, and evolutionary history shape lifespan across mammals....
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Motivation for Longevity
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Motivation for Longevity
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This PDF is an academic manuscript analyzing why p This PDF is an academic manuscript analyzing why people want to live longer, how their motivations differ, and what psychological, social, cultural, and demographic factors shape desired longevity. It focuses on the concept of Subjective Life Expectancy (SLE)—how long individuals expect or want to live—and explores its relationship to gender, age, health, family structure, religion, and personal beliefs.
The core message is:
Longevity motivation is deeply shaped by personal meaning, gender, family responsibilities, health, and cultural context—not just by chronological age.
📘 Purpose of the Study
The document aims to understand:
What motivates people to desire longer lives
Why some people want to live to extreme ages (90, 100, 120+)
How gender roles and family expectations influence longevity desires
How health, autonomy, and independence shape longevity motivation
How cultural expectations (e.g., family caregiving) influence desired lifespan
It draws from psychological research, demographic studies, and global survey trends.
🧠 Core Themes and Key Insights
1. Longevity Desire ≠ Actual Life Expectancy
People’s desired lifespan often differs from:
Their statistical life expectancy
Their real expected survival
For example:
Women live longer but desire shorter lives than men.
Men expect shorter lives but desire longer ones.
This paradox reveals deeply gendered motivations.
2. Gender Differences in Longevity Motivation
The PDF emphasizes that:
Men generally want to live longer than women.
Women are more cautious about very old ages (85+).
Reasons for gender differences:
Women have higher rates of widowhood and late-life loneliness
Women fear dependency more
Men associate longevity with achievement and legacy
Women worry about burdening others and caregiving expectations
3. Health and Independence Are Crucial
People strongly want:
Physical function
Autonomy
Cognitive sharpness
Meaningful activity
Social connection
People do NOT want longevity if it means:
Frailty
Dementia
Chronic suffering
Being a burden on family
This creates the idea:
People desire “healthy longevity,” not just “long life.”
4. The Role of Family Structure
Family context heavily affects longevity desires:
Parents, especially mothers, want longer lives to see children succeed.
People without children often show lower longevity desire.
Caregiving responsibilities reduce desire for extreme old age.
Cultural expectations around caring for aging parents—and being cared for by children—shape people’s psychological comfort with a long life.
5. Cultural and Religious Influences
The PDF shows that:
Some religions encourage acceptance of natural lifespan.
Others view long life as a blessing or reward.
Cultures valuing elders (Asia, Africa) show higher positive longevity motivation.
Western cultures emphasize autonomy, making extreme old age less appealing.
6. Fear of Old Age and Death
People who have:
High anxiety about aging
High fear of death
tend to desire either:
Much shorter lives, or
Extremely long lives (120+)
This “U-shaped” response is driven by psychological coping mechanisms.
7. Future Orientation and Optimism
People who:
Feel in control of life
Are optimistic
Have long-term goals
Invest in health and learning
show stronger motivation for longer, meaningful life.
8. Subjective Life Expectancy (SLE) as a Predictor
SLE influences:
Retirement planning
Health behaviors
Saving and investment
Mental wellbeing
Long-term decision-making
The paper suggests using SLE as a tool for:
Public health planning
Longevity policy
Ageing research
Economic modeling
⭐ Overall Summary
“Motivation for Longevity” provides a deep psychological and sociocultural analysis of why people desire longer or shorter lives. Longevity motivation is shaped by gender, health, culture, family roles, fears, optimism, and expectations about quality of life in old age. The paper highlights that people want extended years only if they are healthy, autonomous, meaningful, and socially connected, and urges policymakers to consider human motivation when designing longevity strategies....
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Protocol for comparative
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Protocol for comparative seed longevity testing
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xevyo-base-v1
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The “Protocol for Comparative Seed Longevity Testi The “Protocol for Comparative Seed Longevity Testing” is an official technical information sheet from the Millennium Seed Bank (MSB) that describes a standardized method used to compare the seed longevity of different plant species stored in conservation collections. The goal of the protocol is to generate a seed survival curve that reveals how quickly seed viability declines under controlled ageing conditions, allowing species to be ranked into longevity categories.
The method uses controlled rehydration followed by accelerated ageing. Seeds are first equilibrated at 47% relative humidity (RH) and 20°C to stabilize moisture content. They are then transferred to an ageing environment of 60% RH and 45°C, created using non-saturated lithium chloride (LiCl) solutions inside airtight containers. These uniform conditions ensure that all seed samples experience identical ageing stress.
During the ageing process, samples of 50 seeds are removed on a scheduled series of days (1, 2, 5, 9, 20, 30, 50, 75, 100, and 125). Each sample undergoes germination testing for at least 42 days, followed by a “cut test” to assess seed viability and identify empty, infested, or abnormal seeds. The resulting data are used to plot viability decline curves, typically analyzed using probit analysis and the Ellis & Roberts viability equation. A key output is p50, the time it takes for seed viability to drop to 50%, which enables clear comparisons across species and against two known “marker species” used by MSB.
The document also includes detailed preparation steps, practical guidance for ensuring accurate humidity control, tips for handling different seed types, and recommended equipment (such as hygrometers, fan-assisted ovens, airtight containers, and statistical software). It emphasizes that although the method does not predict exact natural longevity, it reliably ranks species and helps identify factors—such as seed maturity or post-harvest handling—that influence long-term seed survival.
If you want, I can also provide:
✅ A short summary
✅ A simple student-friendly version
✅ MCQs / quiz from this file
Just tell me!...
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xevyo
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Omics of human aging
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Omics of human aging
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xevyo-base-v1
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This PDF is an editorial overview published in Fro This PDF is an editorial overview published in Frontiers in Genetics (2022) introducing a special research collection on how omics technologies—genomics, transcriptomics, proteomics, metabolomics, and exposomics—are transforming the scientific study of human aging and longevity. It highlights how aging, once studied one biomarker or one gene at a time, now requires systems-biology approaches, large datasets, multi-omics integration, and advanced computational methods to understand the full complexity of the aging process.
The editorial summarizes six scientific articles (three reviews and three original studies) that collectively explore the genetic, environmental, and molecular pathways that shape aging and age-related diseases.
🔶 Core Themes of the PDF
1. Aging Is Complex and Multifactorial
The document emphasizes that aging is influenced by:
Numerous genetic variants with small effects
Environmental exposures
Interconnected biological pathways and regulatory networks
Because of this complexity, aging cannot be understood through single markers alone; instead, researchers need holistic multi-omics strategies.
Omics of Human aging and longev…
2. The Rise of Multi-Omics and Systems Biology
High-throughput technologies have produced massive quantities of data, enabling:
Discovery of aging-related biomarkers
Integration of genetic, transcriptomic, proteomic, and metabolic signals
Network-level analysis of age-related diseases
The editorial stresses that data integration, not data quantity, is the main challenge.
Omics of Human aging and longev…
📌 Highlights of the Six Included Articles
The editorial summarizes the contributions of each article in the special issue:
A) Review: Multi-Omics Bioinformatics for Aging (Dato et al.)
This review explains powerful modern techniques such as:
Tensor decomposition for uncovering hidden relationships
Machine learning & deep neural networks
Integration of multi-omics datasets
It also provides a list of public databases useful in aging research (e.g., AgeFactDB, NeuroMuscleDB) and recommends:
Prioritizing population diversity
Improving data sharing among research groups
Omics of Human aging and longev…
B) Study: GWAS & Alzheimer’s Disease (Napolioni et al.)
Using large public genomic datasets, this study shows:
Recent consanguinity and autozygosity increase the risk of late-onset Alzheimer’s disease
This effect is independent of APOE genotypes and education
The study identifies a rare recessive variant in RPH3AL potentially linked to Alzheimer’s risk
Omics of Human aging and longev…
C) Study: Comparative Genomics of Aging (Podder et al.)
Using multi-species datasets (human, mouse, fly, worm), they identify:
Conserved aging pathways: FoxO, mTOR, autophagy
Rapamycin (an mTOR inhibitor) targets proteins conserved across species
A public interactive portal for comparative genomics results
Omics of Human aging and longev…
D) Review: Cross-Species Aging Genetics (Treaster et al.)
This article shows how comparative genomics can uncover:
Shared aging pathways across species
Gene sets under constrained evolutionary pressure
New candidate longevity genes that may apply to humans
Omics of Human aging and longev…
E) Study: Cognitive Function & Gene Regulation in Twins (Mohammadnejad et al.)
Using a large cohort of monozygotic twins, the study identifies:
Five novel cognition-related genes: APOBEC3G, H6PD, SLC45A1, GRIN3B, PDE4D
Dysregulated pathways related to neurodegeneration:
Ribosome function
Focal adhesion
Regulatory networks of activated and repressed transcription factors
Omics of Human aging and longev…
F) Review: The Chemical Exposome & Aging (Misra)
The exposome includes all environmental chemical exposures—diet, drugs, pollutants, toxins. The review shows:
Some exposures accelerate aging: pesticides, nitrosamines, heavy metals, smoking
Some exposures protect aging: selenium, crocin
Chemical exposures influence telomere length, cognitive decline, skin aging
Huge challenges remain in understanding combined effects of multiple chemicals
Omics of Human aging and longev…
🔶 Key Takeaway of the Entire PDF
The editorial concludes that:
Aging research is shifting from reductionist approaches to integrated systems biology
Multi-omics datasets and computational advances now allow the discovery of new molecular aging pathways
Data integration, diversity, and data sharing are essential for future breakthroughs
Omics of Human aging and longev…
⭐ Perfect One-Sentence Summary
This PDF provides a clear, modern overview of how multi-omics technologies and cross-disciplinary computational methods are transforming the scientific understanding of human aging and longevity, highlighting key studies that reveal genetic, environmental, and network-level mechanisms of aging....
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