Evidence map›Paper›PMID 40038610›Full record

ArticleBMC public health2025

Association of clinical biomarker-based biological age and aging trajectory with cardiovascular disease and all-cause mortality in Chinese adults: a population-based cohort study.

Qiaoyun Dai, Huayu Sun, Xueying Yang, Shuohua Chen, Xinyuan Zhang, Zhe Yin, Xiujuan Zhao, Shouling Wu, Zongfu Cao, Yuntao Wu and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
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

11 authors.

Qiaoyun Dai *National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China.
Huayu Sun *Department of Cardiology, Kailuan General Hospital, Tangshan, China.
Xueying YangNational Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China.
Shuohua ChenGraduate School, North China University of Science and Technology, Tangshan, China.
Xinyuan ZhangNational Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China.
Zhe YinNational Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China.
Xiujuan ZhaoDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Shouling WuGraduate School, North China University of Science and Technology, Tangshan, China.
Zongfu CaoNational Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China. zongfu_cao@163.com.
Yuntao WuDepartment of Cardiology, Kailuan General Hospital, Tangshan, China. wyt0086@163.com.
Xu MaNational Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China. maxubioinfo@163.com.

Funding

National Human Genetic Resources Sharing Service Platform, Ministry of Science and Technology, People's Republic of China 2005DKA21300National Key Research and Development Program, Ministry of Science and Technology, People's Republic of China 2016YFC1000307
6 · The paper itself

Abstract

backgroundEvidence on the association of clinical biomarker-based biological age (BA) with cardiovascular disease (CVD) and mortality remains insufficient, particularly concerning aging trajectories' relationship with these two outcomes.

methodsSeventy-five thousand five hundred thirty-seven Chinese adults from the Kailuan study who participated in the first checkup (2006-2007) were included. BA was predicted by 32 clinical indicators using deep neural networks models. Aging status was divided into decelerated, accelerated, and normal aging based on BA in the first checkup. Six aging trajectories were developed in the initial three checkups. CVD and mortality were followed up till December 31, 2021.

resultsAfter adjusting for chronological age, sex, education level, occupation, physical activity, smoking status, alcohol consumption, salt consumption habit, history of hypertension, diabetes, and dyslipidemia, as well as the use of antihypertensive, antidiabetic, and lipid-lowering drugs, Cox proportional hazard models showed that relative to normal aging, accelerated aging was a risk factor for CVD (adjusted hazard ratio [aHR], 1.17 [95% CI 1.11-1.23]) and mortality (aHR, 1.17 [1.12-1.22]), while participants with decelerated aging had a lower risk for CVD (aHR, 0.85 [0.80-0.90]) and mortality (aHR, 0.86 [0.82-0.90]). Relative to low-stable trajectory, other aging trajectories associated with higher risk of CVD and death, and high-stable trajectory associated with the highest risk of CVD (aHR, 1.62 [1.45-1.81]) and mortality (aHR, 1.55 [1.41-1.71]). Relative to high-stable trajectory, high-decreasing trajectory was associated with lower risk of CVD (aHR, 0.76 [0.67-0.86]) and death (aHR, 0.78 [0.70-0.87]), and decreasing-increasing trajectory was associated with lower risk of death (aHR, 0.86 [0.75-0.98]).

conclusionsAccelerated BA aging is associated with a higher risk of CVD and mortality, whereas decelerated aging is associated with a lower risk compared to normal aging. Those persistently at high aging levels are at the highest risk for both CVD and death; conversely, it is the act of lowering and continually maintaining a reduced aging state that effectively mitigates these risks.

Indexed as

AgingBiomarkersCardiovascular DiseasesMortalityAdultAgedCause of DeathChinaCohort StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedRisk FactorsBiomarkersAging trajectoryBiological ageCardiovascular diseaseMortality

Identifiers

PMID40038610
PMCPMC11881332

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.