Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up
Open accessAbstract
Biological age measurements (BAs) assess aging-related physiological change and predict health risks among individuals of the same chronological age (CA). Multiple BAs have been proposed and are well studied individually but not jointly. We included 845 individuals and 3973 repeated measurements from a Swedish population-based cohort and examined longitudinal trajectories, correlations, and mortality associations of nine BAs across 20 years follow-up. We found the longitudinal growth of functional BAs accelerated around age 70; average levels of BA curves differed by sex across the age span (50–90 years). All BAs were correlated to varying degrees; correlations were mostly explained by CA. Individually, all BAs except for telomere length were associated with mortality risk independently of CA. The largest effects were seen for methylation age estimators (GrimAge) and the frailty index (FI). In joint models, two methylation age estimators (Horvath and GrimAge) and FI remained predictive, suggesting they are complementary in predicting mortality.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Age gaps in second-generation mortality-optimized clocks (Zhang, PhenoAge, GrimAge) also correlate only mildly with each other.
“Age gaps in second-generation mortality-optimized clocks (Zhang, PhenoAge, GrimAge) also correlate only mildly with each other69,151.”
From Measuring biological age using omics data (2022) · cites this paper as [151]
Age gaps from first-generation epigenetic clocks (Hannum, Horvath, Lin, Weidner, Vidal-Bralo) only mildly correlate with each other (r = 0.1-0.5).
“Despite all being modelled and trained on the chronological age of individuals, mostly from blood cells, age gaps from first-generation epigenetic clocks (Hannum, Horvath, Lin, Weidner, Vidal-Bralo) seem to only mildly correlate with each other (r = 0.1–0.5)69,151.”
From Measuring biological age using omics data (2022) · cites this paper as [151]
Li et al. found that the age gaps for the different clocks tested are largely uncorrelated.
“this study found that the age gaps for the different clocks tested are largely uncorrelated.”
From Measuring biological age using omics data (2022) · cites this paper as [151]
Li et al. is one of the few current papers that compares multiple omics ageing-clock measurements longitudinally.
“One of the few current papers that compares multiple omics ageing-clock measurements longitudinally”
From Measuring biological age using omics data (2022) · cites this paper as [151]
Inflection points in DNA methylation indicative of non-monotonous changes were observed in a 20-year follow-up Swedish aging cohort of 845 individuals around ages 70 and 85.
“Inflection points in DNA methylation indicative of non-monotonous changes were also observed in a 20-year follow-up Swedish aging cohort of 845 individuals around ages 70 and 85.35”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [35]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 36 · Cited by 390
- Authors
- Li, X., Ploner, A., Wang, Y., Magnusson, P. K., Reynolds, C., Finkel, D., Pedersen, N. L., Jylhävä, J., & Hägg, S.
- Affiliations
- Karolinska Institutet (SE)
- University of California, Riverside (US)
- Indiana University Southeast (US)
- University of Michigan (US)
- Corresponding author
- Hägg, Sara
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2020-02-11
- Volume
- 9
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC7012595
- OpenAlex
- W3004434362
- Cited by
- 390
- References
- 36
- Citations by year
- 2026: 48 · 2025: 74 · 2024: 72 · 2023: 62 · 2022: 53 · 2021: 56 · 2020: 25
- FWCI
- 25.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (R01 AG04563, R01 AG10175, R01 AG028555)
- MacArthur Foundation (Research Network on Successful Aging)
- Swedish Research Council for Health, Working Life and Welfare (97:0147:1B, 2009-0795, 2013-2292)
- Swedish Research Council (825-2007-7460, 2015-03255, 2017-00641, 2018-02077, 825-2009-6141, 521-2013-8689, 2019-01272)
- Karolinska Institutet (Foundation for Geriatric Diseases, Strategic Research Program in Epidemiology)
- Magnus Bergvall Foundation
- King Gustaf V and Queen Victoria's Foundation of Freemasons
- China Scholarship Council
- Topics
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- biological age, mortality risk, longitudinal trajectories, GrimAge, frailty index, telomere length, chronological age, sex differences, aging
- MeSH terms
- Frailty, Aged, Aged, 80 and over, Aging, Cognition, Female, Follow-Up Studies, Humans, Male, Middle Aged, Sweden, DNA Methylation, Telomere Homeostasis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
