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Accelerated biological aging elevates the risk of cardiometabolic multimorbidity and mortality

Nature Cardiovascular Research volume 3, pages 332–342 (2024)Cite this article

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Abstract

Associations of biological aging with the development and mortality of cardiometabolic multimorbidity (CMM) remain unclear. Here we conducted a multistate analysis in 341,159 adults of the UK Biobank. CMM was defined as the coexistence of two or three cardiometabolic diseases (CMDs), including type 2 diabetes, ischemic heart disease and stroke. Biological aging was measured using the Klemera–Doubal Method Biological Age and PhenoAge algorithms. Over a median follow-up of 8.84 years, biologically older participants demonstrated robust higher risks from first CMD to CMM and then to death. In particular, adjusted hazard ratios for first CMD to CMM and for CMM to death were 1.15 (95% confidence interval (CI): 1.12, 1.19) and 1.26 (95% CI: 1.17, 1.35) per 1 s.d. increase in PhenoAge acceleration, respectively. Compared with frailty, Framingham Risk Score and Systematic Coronary Risk Evaluation 2 (SCORE2), biological aging measures yielded consistent substantial associations with CMM development. Accelerated biological aging may help identify individuals with CMM risks, potentially enabling early intervention and subclinical prevention.

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Fig. 1: Numbers (percentages) of participants in transition pattern A from baseline to FCMD, CMM and death and transition pattern B from baseline to one of IHD, T2D and stroke and then to CMM and death.
Fig. 2: Associations of the accelerated biological aging, FRS, SCORE2 and frailty with the risks of FCMD, CMM and death using the multistate model.
Fig. 3: Dose–response curves smoothed by RSC regression models of the accelerated biological aging with the transition pattern A.
Fig. 4: Transition probabilities over time from baseline to FCMD and death, from FCMD to CMM and death and from CMM to death in a high or low level of accelerated biological aging by using a multistate model.
Fig. 5: Flowchart of participant selection.

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Data availability

This research was conducted using the UK Biobank Resource under application number 44430. The UK Biobank data are available upon application to the UK Biobank (www.ukbiobank.ac.uk/).

Code availability

The codes for data analysis have been placed in a public repository (https://github.com/gearpku2020/CMMmultistate.github.io.git).

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Acknowledgements

X. Gao was supported by grants from the National Key Research and Development Program of China (2023YFC3603401) and the National Natural Science Foundation of China (82304098). We thank C. Chen from Merck for language assistance, Y. Yu from Fudan University and J. Jia from Peking University for statistical assistance, and P. Zheng from The BMJ for journal selection.

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Authors and Affiliations

  1. Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China

    Meijie Jiang, Sifan Tian, Shuzhen Liu, Yuting Wang, Xinbiao Guo & Xu Gao

  2. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China

    Tao Huang

  3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Xihong Lin

  4. Department of Epidemiology and Butler Columbia Aging Center, Columbia University, New York, NY, USA

    Daniel W. Belsky

  5. Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Andrea A. Baccarelli

  6. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China

    Xu Gao

  7. Center for Healthy Aging, Peking University Health Science Center, Beijing, China

    Xu Gao

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  1. Meijie Jiang
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Contributions

X. Gao and M.J. developed the research question. X. Gao and M.J. carried out the main data analyses and interpreted the data. S.T., S.L. and Y.W. prepared and revised the figures. T.H. conducted the data clean and applied for the data of the UK Biobank. X.L., D.W.B., X. Guo and A.A.B. reviewed and edited the paper. All authors reviewed the drafts, and critically revised and approved the final paper.

Corresponding author

Correspondence to Xu Gao.

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Nature Cardiovascular Research thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Associations of the accelerated biological aging (a) and Framingham risk score, SCORE2 (b), and frailty (c) with the risks from baseline to one of IHD, T2D, and stroke, then to cardiometabolic multimorbidity (CMM), and subsequently to death using the multi-state model of transition pattern B.

Models were adjusted for age, sex, ethnicity, BMI, years of education, smoking status, alcohol intake status, physical activity status, total household income, and employment status. Estimates were demonstrated per s.d. increase. Dots (centers of error bars): Point estimate; Error bar: 95% confidence limits; Black solid line: Reference line. The measures of the center for the error bars are point estimates of the associations. Source data are provided as a Source Data file.

Source data

Extended Data Table 1 Transition probabilities over time from baseline to FCMD and death, from FCMD to CMM and death and from CMM to death in a high or low level of accelerated biological aging by using a multistate model
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Extended Data Table 2 The Harrell’s and Uno’s C-statistics of the accelerated biological aging, FRS, SCORE2, frailty and age in the prediction of CMM for transition pattern A
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Extended Data Table 3 The Harrell’s and Uno’s C-statistics of the accelerated biological aging, FRS, SCORE2, frailty and age with the trajectories of CMM of transition pattern B
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Extended Data Table 4 The sensitivity, specificity, PPV and NPV of the accelerated biological aging, FRS, SCORE2 and frailty with the trajectories of CMM of transition pattern A using the logistic regression model
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Extended Data Table 5 The sensitivity, specificity, PPV and NPV of the accelerated biological aging, FRS, SCORE2 and frailty with the trajectories of CMM of transition pattern B using the logistic regression model
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Extended Data Table 6 ICD-10 codes of CMDs
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Extended Data Table 7 Full names and field IDs of variables for the construction of biological ages
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Extended Data Table 8 Description and field IDs of frailty
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Extended Data Table 9 Calculation of the FRS and SCORE2 algorithm in the UK Biobank
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Supplementary information

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

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Jiang, M., Tian, S., Liu, S. et al. Accelerated biological aging elevates the risk of cardiometabolic multimorbidity and mortality. Nat Cardiovasc Res 3, 332–342 (2024). https://doi.org/10.1038/s44161-024-00438-8

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