Evidence map›Paper›PMID 41790665›Full record

Observational studyMedicine2026

Cardiometabolic index and cardiovascular disease incidence in middle-aged and older Chinese: A nationwide prospective cohort study.

Yajie Wu, Chunhua Liu, Tingting Wang, Huajian Lin, Fulan Su

Abstract readObservational Study
In one paragraph

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yajie WuLishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Chinese Medicine, Lishui, Zhejiang, China.
Chunhua Liu
Tingting Wang
Huajian Lin

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cardiometabolic index (CMI), which integrates lipid metabolism and central obesity measures, has uncertain value for predicting cardiovascular disease (CVD). We evaluated the association between CMI and incident CVD in Chinese adults. Data were drawn from the China Health and Retirement Longitudinal Study. We selected 7830 adults aged ≥45 years who were free of CVD at the 2011 to 2012 baseline and followed them in 2013, 2015, and 2018. CMI was calculated as (waist circumference/height) × (triglycerides/high-density lipoprotein cholesterol). Incident CVD during follow-up was the primary outcome. Multivariable logistic regression estimated odds ratios and 95% confidence intervals, and restricted cubic spline models assessed nonlinear associations. Stratified analyses examined effect modification. In total, 54.9% of participants were female, the mean age was 58.7 years (standard deviation 8.8), and the mean CMI was 1.89 (0.23). During the 7-year follow-up, 1914 individuals (24.4%) developed CVD. Higher CMI was associated with increased CVD risk after multivariable adjustment (odds ratio = 1.39; 95% confidence interval: 1.11-1.96; P = .017). Interactions were observed for diabetes status and current alcohol consumption. Restricted cubic spline analysis showed a nonlinear increase in CVD risk with rising CMI (P for nonlinearity = .002). Higher CMI was significantly associated with incident CVD in middle-aged and older adults, with a nonlinear rise in risk as CMI increased.

Indexed as

Cardiovascular DiseasesAgedChinaCholesterol, HDLFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedProspective StudiesRisk FactorsTriglyceridesWaist CircumferenceCholesterol, HDLTriglyceridescardiometabolic indexcardiovascular diseaseCHARLSprospective cohort study

Identifiers

PMID41790665
PMCPMC12975219

What OpenQuestion 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 OpenQuestion graph.