Evidence map›Paper›PMID 40598448›Full record

ArticleBMC medicine2025

Association of biological aging acceleration transitions and burdens with incident cardiovascular disease: longitudinal insights from a national cohort study.

Xin Zhang, Yu Yan, Yuxin Liu, Zixin Wang, Yuchen Jiang, Shuo Zhang, Tongda Xu, Ke Wang, Chu Zheng, Ping Zeng

Abstract read
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xin ZhangDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Yu YanDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Yuxin LiuDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Zixin WangDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Yuchen JiangDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Shuo ZhangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Tongda XuDepartment of Cardiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Ke WangDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Chu ZhengDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
Ping ZengDepartment of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China. zpstat@xzhmu.edu.cn.

Funding

Basic Science (Natural Science) Research Project of Jiangsu Provincial Colleges and Universities 23KJD310004National Natural Science Foundation of China 82173630Natural Science Foundation of Jiangsu Province of China BK20241952Open Project Fund from Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, China MEKLCEPP/SXMU-202415Open Project of Jiangsu Health Emergency Research Institute and Medical Emergency Rescue Research Center of Xuzhou Medical University JSWSYJ-20240203Postgraduate Research & Practice Innovation Program of Jiangsu Province SJCX25_1533Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province 2024SJYB0809Research Initiation Fund Project of Xuzhou Medical University D2022053Students' Research and Innovation Project of National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University 2024BMS26Training Project for Youth Teams of Science and Technology Innovation at Xuzhou Medical University TD202008
6 · The paper itself

Abstract

backgroundPrevious studies have examined the association between biological aging acceleration (BioAgeAccel) and cardiovascular disease (CVD). However, the effects of BioAgeAccel transitions and burdens on CVD risk remained unclear, and little was known about the association of BioAgeAccel with age at CVD onset.

methodsWe included 316,417 participants from the UK Biobank in the baseline analyses and reserved 7249 in the visit-to-visit analyses. BioAgeAccel was defined as the residual derived from a linear regression of biological age against chronological age, with higher values indicating accelerated aging. We defined BioAgeAccel transitions based on aging status at baseline and the first follow-up, and created three indicators to reflect BioAgeAccel burdens. Cox models were used to evaluate the associations of baseline BioAgeAccel, BioAgeAccel transitions, and BioAgeAccel burdens with incident CVD risk. Linear models were employed to investigate their impacts on age at CVD onset.

resultsCompared to individuals maintaining stable non-accelerated aging patterns, those transitioning to accelerated aging status showed a 29.8% (4.2-61.8%) increased CVD risk, while participants with sustained accelerated aging demonstrated a more pronounced 65.5% (35.9-101.5%) risk elevation. Reversal from accelerated to non-accelerated aging status was associated with a significant 25.6% (3.9-42.3%) risk reduction compared to persistent accelerated aging. Higher BioAgeAccel burdens were related to enhanced incidence and advanced onset of CVD, all of which were greater than the effect of baseline BioAgeAccel, with cumulative BioAgeAccel showing the greatest influence on CVD risk (HR = 1.26 [1.07-1.47]) and the most pronounced contribution to earlier onset of CVD (0.989 [0.558-1.420] years). BioAgeAccel burdens were associated with a higher CVD risk compared to FRS or SCORE2 burdens and could enhance the predictive capacity of the two risk scores. Drug treatments did not substantially impact these results. We further discovered socioeconomic status likely antagonized the associations of BioAgeAccel burdens with CVD.

conclusionsThis study revealed BioAgeAccel progression was associated with a higher incident CVD risk, while its reversal was linked to a lower risk. BioAgeAccel burdens were associated with increased risk and earlier onset of CVD, exceeding the effects of baseline BioAgeAccel and some well-known risk scores, and cumulative BioAgeAccel exhibited the strongest impact among them.

Indexed as

AgingCardiovascular DiseasesAdultAgedCohort StudiesFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedRisk FactorsUnited KingdomAge at onsetBioAgeAccel transitions and burdensBiological aging accelerationCardiovascular diseaseVisit-to-visit analysis

Identifiers

PMID40598448
PMCPMC12211316

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.