Evidence map›Paper›PMID 42682659›Full record

ArticleThe EPMA journal2026

Dynamic reversibility of biological aging and risk of cardiovascular disease and stroke: a longitudinal cohort study.

Binbin Wang, Quan Li, Xizhi Tang, Xiaoqi Niu, Huakang Li

Abstract read
In one paragraph

Article in The EPMA journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Binbin WangDepartment of Cardiology, The Chenggong Hospital Affiliated to Xiamen University, Xiamen, Fujian PR China.
Quan LiDepartment of Orthopedics, The Chenggong Hospital Affiliated to Xiamen University, Xiamen, Fujian PR China.
Xizhi TangDepartment of Cardiovascular Medicine, Southwest Hospital, Army Medical University, No. 30 Gaotanyan Street, Shapingba District, Chongqing, 400038 China.
Xiaoqi NiuDepartment of Cardiovascular Medicine, Southwest Hospital, Army Medical University, No. 30 Gaotanyan Street, Shapingba District, Chongqing, 400038 China.
Huakang LiDepartment of Cardiovascular Medicine, Southwest Hospital, Army Medical University, No. 30 Gaotanyan Street, Shapingba District, Chongqing, 400038 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Aging status, indexed by biological age acceleration (BAacc), is closely associated with cardiovascular disease (CVD). However, transitioning from reactive healthcare to predictive, preventive, and personalised medicine (PPPM/3PM) requires elucidating how longitudinal, dynamic transitions in aging status impact incident CVD risk. Methods: We analyzed 4,756 participants from the China Health and Retirement Longitudinal Study. Biological age was calculated using the Klemera-Doubal method (KDM). KDM-BAacc was measured at Wave 1 (baseline) and Wave 3 (follow-up) to derive multidimensional dynamic metrics: change groups, K-means clusters, aging rates, cumulative burden, and extreme phenotypes. Wave 2 was excluded from baseline calculations due to the absence of blood biomarker collection. Incident CVD (stroke and heart disease) was ascertained over a median 9.13-year follow-up. Cox models were adjusted for sociodemographic, lifestyle, and clinical risk factors. Results: Compared to sustained acceleration, reversing aging status ("accelerated to non-accelerated") significantly lowered risks of composite CVD (HR: 0.72, 95% CI: 0.59-0.89) and stroke (HR: 0.46, 95% CI: 0.33-0.64). Consistent protective effects were observed in "high-to-low" cluster trajectories. Conversely, the "Rapid aging" phenotype (sustained highest quartile) nearly doubled stroke risk (HR: 1.97) versus the "Anti-aging" group. Faster aging rates (≥ 1) were associated with a 32% increased stroke risk, demonstrating a linear dose-response relationship. Notably, associations with heart disease were attenuated after adjusting for comorbidities. Conclusions: Biological aging is dynamically reversible, and reversing KDM-BAacc significantly mitigates CVD and stroke risks. For routine medical application, we recommend integrating longitudinal KDM-BAacc profiling into annual primary care check-ups. Crucially, these findings strongly support the paradigm shift from reactive medical services to PPPM/3PM. By identifying reversible suboptimal health prior to disease onset, this predictive tool enables targeted prevention and personalised interventions based on individual aging trajectories, decisively surpassing conventional generalized approaches. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s13167-026-00460-9.

Indexed as

Biological agingCardiovascular diseaseCHARLSClinical risk factorsKDMLifestylePredictive preventive personalised medicine (PPPM/3PM)StrokeSuboptimal health

Identifiers

PMID42682659
PMCPMC13530061

What Socratic holds

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Registered trials

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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 Socratic graph.