Longevity studies can estimate how long populations or groups may live, and they can identify factors associated with higher or lower mortality risk. They cannot tell a particular person the date or age at which they will die. Even models built with extensive personal data leave most individual lifespan variation unexplained, and their performance is not the same for every demographic group.
The most useful way to read a longevity finding is to ask: who was studied, what outcome was measured, and is the result a population average, a risk association, or a prediction about an individual? Those distinctions determine what the number can—and cannot—mean for you.
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Why a life-expectancy figure is not a personal forecast
Life expectancy is calculated from mortality patterns in a defined population or cohort. A life table summarizes the rates at which people of different ages die in that population and uses them to estimate how long a hypothetical person would live if those patterns applied throughout their life. The World Health Organization’s technical explanation of life-expectancy methods makes clear why the estimate depends on population mortality data and the method used.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThat figure describes a group-level expectation, not a promise about any member of the group. Some people will die younger than the average; others will live longer. A national life expectancy, for example, does not account in detail for an individual’s health history, living conditions, future medical care, or the unpredictable events that may affect their life. Even a figure calculated for people of a particular age or sex remains an average across a population, not a personalized countdown.
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There is also a difference between life expectancy at birth and remaining life expectancy at a later age. The first is calculated for a newborn under a specified set of mortality assumptions. The second starts with people who have already reached a given age. Neither, by itself, answers exactly how long a named individual will live.
What an individual prediction model can do—and where it falls short
A model can use a person’s characteristics to estimate relative risk or the probability of surviving to a particular age. That is more individualized than a population average, but it is not necessarily an accurate estimate of a person’s exact age at death. The distinction between ranking risk and predicting a precise lifespan matters: a model can often distinguish people at higher risk from those at lower risk without explaining how long any one person will live.
The 2026 Health and Retirement Study analysis
A 2026 study by Badolato and colleagues compared 12 statistical and machine-learning models using more than 150 predictors from the U.S. Health and Retirement Study. The authors reported comparable accuracy and relatively high discrimination across models, yet found that the models failed to account for most lifespan heterogeneity at the individual level. In their words, “Statistical and machine learning models report comparable accuracy and relatively high discriminative performance, but they fail to account for most lifespan heterogeneity at the individual level.” (Demography study abstract.)
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The study also found lower predictive accuracy for men, non-Hispanic Black participants, and people with lower educational attainment. That result is a warning against treating an overall performance measure as equally reliable for everyone. It describes this study’s models and data; it does not establish how every longevity tool performs for those groups or in other populations.
Why group differences do not make individual lifespans predictable
Life expectancy can differ substantially between social or demographic groups while remaining difficult to predict for any one person within those groups. A group average describes a pattern across many people. Individual prediction asks a harder question: which person will live longer, and by how much?
Breen and Seltzer studied linked U.S. Census and death-record data for 121,000 men born in 1910. In that historical, male-only cohort, early-adulthood social, economic, and demographic characteristics explained less than 2% of overall individual lifespan variation. The finding does not mean social conditions have no effect on health or mortality. It means that the particular measured characteristics, in that cohort and analysis, explained little of the variation in lifespan between individuals. The authors’ study is available through Population and Development Review.
It would be a mistake to generalize that percentage to people alive today, to women, or to populations in other countries. It is also not evidence that inequality is unimportant: sizable group disparities can coexist with substantial uncertainty about which individuals within a group will die earlier or later.
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What biological-age tests and biomarkers reveal
Biological-age measures use biomarkers to characterize aspects of aging and compare them with chronological age. A result that says someone appears biologically older than their calendar age may be associated with elevated mortality risk in a study population. That association is not the same as a verified prediction of the person’s death date, nor does it show that changing the test result would change lifespan by a particular amount.
The Lifelines cohort
Akeju and colleagues analyzed 52,418 participants in the Lifelines cohort. In their 2024 study, each one-year increase in biological age was associated with an 11% higher overall death risk at a given chronological age and sex (hazard ratio 1.11; 95% confidence interval 1.09–1.13). The association remained after adjustment for genetic factors. This is a cohort-level association, not an 11% reduction in an individual’s lifespan, and it does not validate any consumer biological-age reading as a personal forecast. See the study in The Journals of Gerontology: Series A.
The EPIC-Heidelberg analysis
A 2022 EPIC-Heidelberg case-cohort analysis followed 5,789 middle-aged and older adults for a median of 18 years. Five serum markers added predictive information to models that included lifestyle factors. Among participants aged 60 or older, the study’s modeled life-expectancy difference between the highest- and lowest-risk quintiles was 16.8 years for men and 9.87 years for women. With aging markers included, the modeled differences were 22.7 years for men and 14.00 years for women. These are comparisons between risk groups in that analysis—not an uncertainty range, an individual forecast, or a promise that a particular person can gain or lose those years. The study is indexed at PubMed.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThese results show how biomarkers can help researchers separate groups with different observed or modeled outcomes. They do not establish that the same markers produce a precise, transferable prediction for a person from another population, or that a test marketed as a biological-age score can estimate that person’s lifespan.
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How much do genes affect longevity?
Heritability is a population statistic. It estimates how much of the variation in a trait within a studied population is associated with genetic differences under particular assumptions and conditions. It does not say what percentage of one person’s lifespan is fixed by genes, and it is not a measure of how accurately a genetic test can predict an individual’s age at death.
A 2026 paper by Shenhar and colleagues estimated intrinsic lifespan heritability above 50% after modeling the influence of extrinsic mortality, such as deaths from accidents or infections. The estimate concerns variation in intrinsic lifespan in the authors’ model; it should not be read as genetic destiny for any individual. The paper is available through Science.
That finding and the Lifelines biological-age study address different questions. One estimates how genetic factors contribute to population variation in lifespan after accounting for specified causes of death; the other reports an association between a biological-age measure and mortality in a cohort, including after genetic adjustment. Neither supplies a countdown clock for a reader.
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Uncertainty is not limited to scientific models. A 2018 French survey asked 3,331 people to report several survival probabilities and used those responses to measure subjective uncertainty about longevity. Under the study’s measure, uncertainty exceeded 10 years for both men and women. This figure reflects what respondents reported in that survey; it is not a universal estimate of the scientifically correct uncertainty interval for every person. The study is indexed at PubMed.
Subjective expectations and statistical estimates are different kinds of evidence. A person’s estimate may reflect personal experience, family history, or beliefs about the future. A model’s estimate depends on the variables, population, outcome, and assumptions used to build it. Neither should be mistaken for certainty simply because it produces a number.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What cohort forecasts say about future generations
A cohort forecast projects mortality patterns for people born in specified years. It addresses how life expectancy may change across birth cohorts if modeled trends continue; it does not estimate the remaining lifespan of a particular person who is alive today.
A 2025 analysis covering 23 high-income countries forecast signs of deceleration in life-expectancy gains for cohorts born from 1939 through 2000. That is a forecast contingent on the study’s methods and the mortality trends it modeled, not a conclusion that life expectancy must stop increasing or that a reader’s own lifespan will follow a particular path. The study is indexed at PubMed.
How to evaluate a longevity claim or tool
Before treating a longevity number as relevant to you, identify what it actually estimates. A population life expectancy, a cohort forecast, a relative mortality-risk score, and a predicted age at death are not interchangeable outputs. Ask the following questions:
- Who was studied? Check the country, birth cohort, age range, sex, demographic composition, and whether participants were recruited from a clinical or community setting.
- What outcome was measured? A study may report all-cause mortality risk, survival probability to an age, modeled life expectancy, or observed lifespan. Those outcomes answer different questions.
- What went into the estimate? Inputs may include social and economic characteristics, health history and habits, genetic information, biomarkers, or some combination. More inputs do not automatically make an estimate accurate for an individual.
- Is the result an association or a causal effect? An association means a factor and an outcome occurred together in the analyzed data. It does not prove that changing the factor will change someone’s lifespan by the reported amount.
- How was performance assessed? Look for external validation, calibration, discrimination, and uncertainty intervals. A strong discrimination result alone does not establish accurate age-at-death predictions.
- Does performance differ between groups? A model’s overall result may conceal lower accuracy for some populations. Check whether the study reports subgroup performance and whether the validation population resembles you.
- What is the time horizon? A short follow-up, a long-term cohort observation, and a forecast across future birth cohorts each have different limits and should not be treated as the same kind of prediction.
If a consumer service gives a single age or a precise-looking number without explaining its population, outcome, validation, and uncertainty, the number should not be treated as a reliable personal lifespan forecast. The studies summarized here support population research and risk stratification; they do not establish that a consumer test can tell an individual how long they will live.
How to read the main types of longevity evidence
| Evidence type | What it answers | Example in the studies | What it does not establish |
|---|---|---|---|
| Life table | Average lifespan under mortality patterns for a defined population or cohort. | WHO describes life-expectancy methods based on population mortality data: technical document. | The age at death of a particular person. |
| Individual prediction model | How well measured characteristics predict or rank lifespan-related outcomes for people in the model’s data. | Badolato and colleagues compared 12 models and more than 150 predictors in U.S. Health and Retirement Study data; most individual lifespan heterogeneity remained unexplained: 2026 study. | A universally accurate personal death-age estimate. |
| Historical cohort analysis | How selected characteristics relate to lifespan variation in a particular cohort. | Early-adulthood characteristics explained less than 2% of individual lifespan variation among 121,000 U.S. men born in 1910: 2026 study. | A timeless result for all people or proof that group inequality has no effect. |
| Biomarker association or risk stratification | Whether measured markers are associated with mortality or help distinguish risk groups in a study population. | Lifelines included 52,418 participants; EPIC-Heidelberg included 5,789 participants with median 18-year follow-up: Lifelines and EPIC-Heidelberg. | That a consumer reading predicts an individual’s death date or that changing a marker causes a specific lifespan change. |
| Heritability estimate | How genetic variation relates to variation in a trait within a defined population and model. | Shenhar and colleagues estimated intrinsic lifespan heritability above 50% after modeling extrinsic mortality: 2026 paper. | That genes determine a fixed percentage of one person’s lifespan. |
| Cohort forecast | How mortality and life expectancy may develop for specified birth cohorts if modeled trends apply. | A 2025 analysis covered 23 high-income countries and cohorts born 1939–2000: forecast study. | The remaining lifespan of a named person alive today. |
What the evidence means if you are asking, “How long will I live?”
The scientifically responsible answer is that these studies cannot tell you exactly. Population statistics can put mortality patterns in context, and risk factors or biomarkers can help describe differences between groups. Individual prediction remains uncertain, and an estimate’s usefulness depends on how closely its study population and outcome match the person and question at hand.
For personal health decisions, a risk association or population average is not a substitute for medical advice about your own circumstances. A longevity estimate should be understood as a model-based description with limits, not as a verdict about how many years you have.
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