ArticleCommunications medicine2025
Healthspan-lifespan gap differs in magnitude and disease contribution across world regions.
Article in Communications medicine, 2025. 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.
- Low-Dose Fisetin Supplementation and Its Association With Chronic Inflammation in Middle-Aged and Older Adults: Study Protocol for a Triple-Blind, Randomised, Placebo-Controlled Trial.Basic & clinical pharmacology & toxicology · 2026Article
- Cellular Senescence and Aging: Mechanisms, Disease Convergence, and Therapeutic Frontiers.MedComm · 2026Review
- Exploring the Therapeutic Effect ofNutrients · 2026Article
- The BHARAT study: a multi-modal, multi-omics investigation of aging signatures in the Indian population.Aging · 2026Observational
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundLongevity gains have not been matched by equivalent advances in healthy longevity, giving rise to the healthspan-lifespan gap. This study maps, by world region, the healthspan-lifespan gap; identifies gap-associated demographic, economic, and health indicators; and deciphers disease burden patterns contributing to gap profiles.
methodsWorld Health Organization (WHO) Global Health Observatory, United Nations World Population Prospects and Global Health Expenditure Database were interrogated. The healthspan-lifespan gap was quantified from estimates of life expectancy and health-adjusted life expectancy. Regression analysis evaluated healthspan-lifespan gap correlates with a spatial error model used to adjust for confounders arising from geographic proximity. Dimensionality reduction by principal component analysis and clustering by machine learning discriminated disease burden patterns linked to healthspan-lifespan gap identity. Supervised machine learning enabled validation of disease burden pattern distinctness.
resultsCharted for six WHO-designated regions, comprising 183 member states, the healthspan-lifespan gap differs in size across regions. Life expectancy, gross domestic product, and noncommunicable disease burden most consistently correlate with the healthspan-lifespan gap. Unsupervised machine learning identifies three clusters delineating global morbidity patterns. Cluster-informed stratification discerns inter- and intra-regional gap heterogeneity. Africa, although exhibiting the narrowest healthspan-lifespan gap, is overrepresented in countries with larger than predicted healthspan-lifespan gaps and shows the greatest gap expansion and disease burden pattern restructuring. In contrast, Europe is overrepresented in countries with healthspan-lifespan gaps smaller than anticipated. Projections into 2100 forecast continuous widening of the healthspan-lifespan gap across regions.
conclusionsThe healthspan-lifespan gap is universal yet differs in magnitude and disease contribution among world regions. Gap identities imposed by distinct disease burden patterns caution against global generalization, necessitating region-informed solutions to maximize equitable healthy longevity.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.