Evidence map›Paper›PMID 40736194›Full record

ArticleChinese medical journal2025

Association between cardiovascular-kidney-metabolic health metrics and long-term cardiovascular risk: Findings from the Chinese Multi-provincial Cohort Study.

Ziyu Wang, Xuan Deng, Zhao Yang, Jiangtao Li, Pan Zhou, Wenlang Zhao, Yongchen Hao, Qiuju Deng, Na Yang, Lizhen Han and 2 more

Abstract read
In one paragraph

Article in Chinese medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Lifestyle factors and cardiometabolic risk.Chinese medical journal · 2026
    Review
  2. Article
  3. Review
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

12 authors.

Ziyu WangEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Xuan DengCenter for Clinical and Epidemiologic Research, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing 100029, China.
Zhao YangEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Jiangtao LiEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Pan ZhouEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Wenlang ZhaoEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Yongchen HaoEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Qiuju DengEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Na YangEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Lizhen HanEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Yue QiEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.
Jing LiuEpidemiology Branch, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing 100029, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) health and stage, reflecting the interaction among metabolism, chronic kidney disease (CKD), and the cardiovascular system. However, the association between CKM stage and the long-term risk of cardiovascular disease (CVD) has not been validated. This study aimed to evaluate the long-term CVD risk associated with CKM health metrics and CKM stage using data from a population-based cohort study.

methodsIn total, 5293 CVD-free participants were followed up to around 13 years in the Chinese Multi-provincial Cohort Study (CMCS). Considering the pathophysiologic progression of CKM health metrics abnormalities (comprising obesity, central adiposity, prediabetes, diabetes, hypertriglyceridemia, CKD, and metabolic syndrome), participants were divided into CKM stages 0, 1, and 2. The time-dependent Cox regression models were used to estimate the cardiovascular risk associated with CKM health metrics and stage. Additionally, broader CVD outcomes were examined, with a specific assessment of the impact of stage 3 in 2581 participants from the CMCS-Beijing subcohort.

resultsAmong participants, 91.2% (4825/5293) had at least one abnormal CKM health metric, 8.8% (468/5293), 13.3% (704/5293), and 77.9% (4121/5293) were in CKM stages 0, 1, and 2, respectively; and 710 incident CVD cases occurred during a median follow-up time of 13.3 years (interquartile range: 12.1 to 13.6 years). Participants with each poor CKM health metric exhibited significantly higher CVD risk. Compared with stage 0, the hazard ratio (HR) (95% confidence interval [CI]) for CVD incidence was 1.31 (0.84-2.04) in stage 1 and 2.27 (1.57-3.28) in stage 2. Significant interactive impacts existed between CKM stage and age or sex, with higher CVD risk related to increased CKM stages in participants aged <60 years or females.

conclusionThese findings highlight the contribution of CKM health metrics and CKM stage to the long-term risk of CVD, suggesting the importance of multi-component recognition and management of poor CKM health in CVD prevention.

Indexed as

Cardiovascular DiseasesAdultAgedChinaCohort StudiesEast Asian PeopleFemaleHumansMaleMetabolic SyndromeMiddle AgedRenal Insufficiency, ChronicRisk FactorsCardiovascular diseaseCardiovascular-kidney-metabolic healthChronic kidney diseaseCohort studyLong-term CVD riskMetabolic disorder

Identifiers

PMID40736194
PMCPMC12407164

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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 OpenQuestion graph.