ArticleAging clinical and experimental research2023
Who live longer than their age peers: individual predictors of longevity among older individuals.
Article in Aging clinical and experimental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Application of GUHA data mining method in cohort data to explore paths associated with premature death: a 29-year follow-up study.BMC medical research methodology · 2025Article
- Tree-based analysis of longevity predictors and their ten-year changes: a 35-Year mortality follow-up.BMC geriatrics · 2024Article
- The Quality of Life of Former Portuguese Football Players.Sports (Basel, Switzerland) · 2024Article
- Gut microbiota in centenarians: A potential metabolic and aging regulator in the study of extreme longevity.Aging medicine (Milton (N.S.W)) · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThere are a very few studies focusing on the individual-based survival with a long follow-up time.
aimTo identify predictors and determine their joint predictive value for longevity using individual-based outcome measures.
methodsData were drawn from Tampere Longitudinal Study on Aging (TamELSA), a study of individuals' age 60-89 years (N = 1450) with a mortality follow-up of up to 35 years. Two measures of longevity were used: the longevity difference (LD) and realized probability of dying (RPD), both of which compare each individual's longevity with their life expectancy as derived from population life tables. Independent variables were categorized into five domains: sociodemographic, health and functioning, subjective experiences, social activities, and living conditions. Linear regression models were used in three steps: bivariate analysis for each variable, multivariate analysis based on backward elimination for each domain, and one final model.
resultsThe most important predictors of both outcomes were marital status, years smoked regularly, mobility, self-rated health, endocrine and metabolic diseases, respiratory diseases, and unwillingness to do things or lack of energy. The explained variance in longevity was 13.8% for LD and 14.1% for RPD. This demonstrated a large proportion of unexplained error margins for the prediction of individual longevity, even though many known predictors were used. DISCUSSION AND
conclusionsSeveral predictors associated with longer life were found. Yet, on an individual level, it remains difficult to predict who will live longer than their age peers. The stochastic element in the process of aging and in death may affect this prediction.
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