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Pace of Aging analysis of healthspan and lifespan in older adults in the US and UK

Nature Aging volume 5, pages 1132–1142 (2025)Cite this article

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Abstract

As societies age, policy makers need tools to understand how demographic aging will affect population health and to develop programs to increase healthspan. The current metrics used for policy do not distinguish differences caused by early-life factors, like prenatal care and nutrition, from those caused by ongoing changes in people’s bodies that are due to aging and that may be modifiable. Here we introduce an adapted Pace of Aging method designed to quantify differences between individuals and populations in the speed of aging-related health declines. The adapted Pace of Aging method, implemented in parallel in data from the US Health and Retirement Study and in the English Longitudinal Study of Aging (combined n = 19,045), integrates longitudinal data on blood biomarkers, physical measurements and functional tests. It reveals stark differences in rates of aging between population subgroups and demonstrates strong and consistent prospective associations with incident morbidity, disability and mortality. This adapted and generalizable method to measure Pace of Aging can advance the population science of healthy longevity.

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Fig. 1: Study Design for Pace of Aging analysis in the US Health and Retirement Study and the English Longitudinal Study of Aging.
Fig. 2: Pace of Aging in the US Health and Retirement Study and the English Longitudinal Study of Aging.
Fig. 3: Association of Pace of Aging with mortality, morbidity and disability.
Fig. 4: Comparison of Pace of Aging with blood-chemistry measures of biological age.
Fig. 5: Comparison of Pace of Aging with epigenetic clocks.

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Data availability

Data for the US HRS were obtained from the Institute for Social Research at the University of Michigan (https://hrs.isr.umich.edu/data-products). Access to blood biomarker and genomic data require investigators to commit not to attempt to identify participants.

Data for the ELSA were obtained from the UK Data Service (accession GN 33368; https://doi.org/10.5255/UKDA-Series-200011).

Code availability

All analysis code available at https://github.com/Columbia-Aging-Center-GeroScience-Core/Balachandran_et_al_Nature_Aging_2025.

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Acknowledgements

This research was supported by National Institutes of Health grant R01AG061378, Russel Sage Foundation BioSS grant 1810-08987 and the Robert N Butler Columbia Aging Center. A.F. is supported by T32AI114398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. D.W.B. is a fellow of the CIFAR CBD Network. The HRS is sponsored by the National Institute on Aging (grant no. U01AG009740) and is conducted by the University of Michigan.

Author information

Authors and Affiliations

  1. Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA

    Arun Balachandran, Heming Pei, Yifan Shi, John R. Beard, Alan A. Cohen, Claire Eckstein Indik, Alex Furuya, Meeraj Kothari, Calen P. Ryan, Yuan S. Zhang & Daniel W. Belsky

  2. Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA

    Heming Pei, Yifan Shi, John R. Beard, Alex Furuya & Daniel W. Belsky

  3. Department of Health Policy & Management, Columbia University Mailman School of Public Health, New York, NY, USA

    John R. Beard

  4. Department of Psychology & Neuroscience, Duke University, Durham, NC, USA

    Avshalom Caspi & Terrie E. Moffitt

  5. Social, Genetic, and Developmental Psychiatry Unit, Institute of Psychiatry, Kings College, University of London, London, UK

    Avshalom Caspi & Terrie E. Moffitt

  6. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA

    Alan A. Cohen

  7. Graduate School of Education, Stanford University, Palo Alto, CA, USA

    Benjamin W. Domingue

  8. National Institute on Aging, Bethesda, MD, USA

    Luigi Ferrucci

  9. Norwegian Institute for Public Health, Oslo, Norway

    Vegard Skirbekk

  10. Department of Sociomedical Sciences, Columbia University Mailman School of Public Health, New York, NY, USA

    Yuan S. Zhang

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Contributions

D.W.B. conceived the study; A.B., M.K., H.P., Y.S. and D.W.B. designed the study and analyzed the data. A.B., M.K. and D.W.B. wrote the manuscript. All authors contributed to data interpretation and critical review of the manuscript. A.B., H.P. and D.W.B. had complete access to the data.

Corresponding author

Correspondence to Daniel W. Belsky.

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Competing interests

D.W.B., A.C. and T.E.M. are inventors of DunedinPACE, a Duke University and University of Otago invention licensed to TruDiagnostic. The other authors declare no competing interests.

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Nature Aging thanks Joris Deelen, David Rehkopf, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Biomarker Adjustment.

The figure shows original and period-effect-adjusted levels of biomarkers included in Pace of Aging analysis. Panel A shows data for the US Health and Retirement Study. Panel B shows data for the English Longitudinal Study of Aging. We conducted analysis to adjust for period effects as follows: We fitted regression models to each biomarker including covariates for participant age (3rd-degree b-spline), sex, race/ethnicity, their interactions, and a series of indicator variables encoding year of data collection. Models included random intercepts to account for repeated observations of individuals. We used coefficient estimates for year-of-data-collection indicator variables to adjust biomarker values for period differences in biomarker distributions relative to the baseline reference year. Year-to-year variation in biomarker distributions was modest, but statistically different from zero in nearly all cases. Following adjustment, conditional distributions did not differ in their means. The figure graphs distributions of the original biomarker data in green and distributions of the period-effect-adjusted biomarker data in pink. Where distributions overlap, they appear as gray.

Extended Data Fig. 2 Changes in biomarker values over follow-up time.

Panel A in shows changes in biomarker values across baseline and four-and eight-year follow-up assessments among participants in the US Health and Retirement Study (HRS, N = 13,358). The HRS collected biomarker data from participants beginning either in 2006 or 2008. The figure combines data from these groups of participants; for example, the baseline (time-0) data include observations made in 2006 of one group and in 2008 of the other group. Y axis values are z-scores (M = 0, SD = 1) based on sex-specific distributions of values in participants aged <65. For gait speed, which was measured only in participants aged 65 and older, distributions were formed from participants aged 65–75. Biomarkers known to decline with aging were reverse coded so that higher values on the Y axis corresponds to ‘older’ levels of the biomarkers. Therefore, positive values for slopes of change indicate aging-related change in the direction expected. Panel B shows the parallel data for ELSA. Panel C shows comparison of biomarker slopes of change in HRS and ELSA. For this plot, biomarker slopes have not been reverse coded; biomarkers that decline with aging are shown as having negative slopes. The figure plots HRS slopes on the X axis and ELSA slopes on the Y axis. Slopes are highly correlated between datasets with the exception that Cystatin-C and hemoglobin have opposite directions of change with aging.

Extended Data Fig. 3 Correlations among slopes of biomarker change over time.

The figure shows correlations among slopes of biomarker change estimated from mixed-effects growth models in the US Health and Retirement Study (HRS) (Panel A) and the English Longitudinal Study of Aging (ELSA) (Panel B).

Extended Data Fig. 4 Correlations among baseline chronological age and biomarker slopes of change averaged within biomarker groups.

Panel A shows data for the US Health and Retirement Study. Panel B shows data for the English Longitudinal Study of Aging. The figure shows matrices of correlations and association plots among Pace of Aging and slopes of change averaged across blood biomarkers, physical assessments, and functional tests. The diagonal cells of the matrix list the measures. The half of the matrix below the diagonal shows scatter plots of associations. For each scatter-plot cell, the y axis corresponds to the variable named along the matrix diagonal to the right of the plot and the x axis corresponds to the variable named along the matrix diagonal above the plot. The half of the matrix above the diagonal lists Pearson correlations. For each correlation cell, the value reflects the correlation of the variables named along the matrix diagonal to the left of the cell and below the cell.

Extended Data Fig. 5 Differences in Pace of Aging among US Older Adults by Race and Ethnicity.

Figure shows data from non-Hispanic White (N = 9,150), Black (N = 2,204), and Hispanic (N = 1,630) -identifying older adults in the US Health and Retirement Study. Panel A shows the differences in distribution of Pace of Aging between White, Black, and Hispanic participants. Densities reflect distributions of Pace of Aging after adjustment for chronological age. White lines show group means. Panel B shows effect-size estimates for the differences in Pace of Aging relative to White participants. The figure illustrates overall faster pace of aging in Hispanic- and Black-identifying older adults as compared with White-identifying older adults.

Extended Data Fig. 6 Associations of Pace of Aging with mortality and incident chronic disease, disability, and cognitive impairment within demographic subgroups of HRS participants.

The figure graphs associations of Pace of Aging with mortality and incident chronic disease and disability estimated within demographic subgroups of HRS participants. Y axes show predicted mortality scores, counts of incident chronic diseases and ADL and IADL disabilities, and cognitive test scores. The X axis shows Pace of Aging. Slopes of predicted values are graphed separately for men and women (Panel A), White, Black, and Hispanic participants (Panel B), and older (aged >=65) and younger (aged <65) participants (Panel C).

Extended Data Fig. 7 Leave-one-out analysis comparing versions of the Pace of Aging measure excluding each biomarker in turn.

The figure shows comparisons of versions of the Pace of Aging measurement composed of different subsets of the biomarkers. Panel A shows correlations among different versions of the measure. Comparisons to the version of the measure including all biomarkers are shown in the first column/first row of the matrix. Panel B shows effect sizes for different versions of the measure. The red bars show the effect sizes for the original Pace of Aging (9 biomarkers). The other bars show the effect sizes for versions of the Pace of Aging composed of all possible subsets of 8 biomarkers. Panels C and D report parallel information for ELSA.

Extended Data Fig. 8 Receiver Operating Characteristic (ROC) Curve analysis.

Panel A shows data for the sample with data on Pace of Aging and blood-chemistry measures of biological age (n = 7,537). Panel B shows data for the sample with data on Pace of Aging and epigenetic clocks (n = 2,848). ROC curves are generated by graphing sensitivity against 1-specificity for each value of a prediction metric. A predictor that generates no improvement in classification relative to random chance generates a diagonal line (sensitivity=1-specificty). ROC curves for predictors can be summarized by the area between the ROC curve and the diagonal, referred to as area under the curve (AUC). ROC Curves are drawn for model-based predictions of the outcomes including the aging measure indicated in the legend and a set of covariates (age, sex, race, education level, and smoking history). For reference, predictions from a model including only the covariates is also shown (‘base model’).

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Balachandran, A., Pei, H., Shi, Y. et al. Pace of Aging analysis of healthspan and lifespan in older adults in the US and UK. Nat Aging 5, 1132–1142 (2025). https://doi.org/10.1038/s43587-025-00866-6

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