Evidence map›Paper›PMID 40954085›Full record

ArticleBMJ open2025

Predictive value of monocytes for coronary heart disease in Chinese adults: a population-based cohort study.

Jianfeng Pei, Maryam Zaid, Yiling Wu, Na Wang, Xuebo Liu, Qi Zhao, Yonggen Jiang, Wang Hong Xu, Genming Zhao

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Article in BMJ open, 2025. 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

9 authors.

Jianfeng PeiDepartment of Epidemiology, Fudan University School of Public Health, Shanghai, China.
Maryam ZaidDepartment of Epidemiology, Fudan University School of Public Health, Shanghai, China.
Yiling WuShanghai Songjiang District Center for Disease Control and Prevention, Shanghai, China.
Na WangDepartment of Epidemiology, Fudan University School of Public Health, Shanghai, China.
Xuebo LiuDepartment of Cardiology, Tongji Hospital Affiliated to Tongji University, Shanghai, China.
Qi ZhaoDepartment of Epidemiology, Fudan University School of Public Health, Shanghai, China.ORCID http://orcid.org/0000-0003-0140-3920
Yonggen JiangShanghai Songjiang District Center for Disease Control and Prevention, Shanghai, China.
Wang Hong XuDepartment of Epidemiology, Fudan University School of Public Health, Shanghai, China wanghong.xu@fudan.edu.cn.ORCID http://orcid.org/0000-0003-2045-2218
Genming ZhaoDepartment of Epidemiology, Fudan University School of Public Health, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe development of simple tools to identify individuals at high risk of coronary heart disease (CHD) would enable rapid implementation of preventive measures. This study was designed to construct predictive models and scoring systems for CHD using monocyte count and its ratio to high-density lipoprotein cholesterol (HDL-C) (MHR).

designPopulation-based prospective cohort study.

settingThe Shanghai Suburban Adult Cohort and Biobank (SSACB). PARTICIPANTS AND OUTCOME MEASURES: This prospective study included 44 013 CHD-free participants of the SSACB. The Songjiang subcohort served as the training set, in which three predictive models and corresponding scoring systems were developed with monocyte count or MHR using stepwise Cox regression. The models and algorithms were tested internally using 10-fold cross-validation and externally in the Jiading subcohort. Discriminations were assessed based on area under the curve (AUC) values, while calibrations were evaluated using the Hosmer-Lemeshow goodness-of-fit test.

resultsDuring a mean follow-up period of 4.8 years, 883 CHD events occurred, with an incidence of 415.7/100 000. Monocyte count and MHR were significantly associated with the risk of CHD. The constructed model incorporating monocyte count (Model 2) achieved AUC values of 0.746 (0.726, 0.766) for 4-year CHD prediction in the training set, 0.746 (0.690, 0.796) in the cross-validation, and 0.717 (0.674, 0.761) in the external validation, comparable to the models including HDL-C (model 1) or MHR (model 3). Calibration plots demonstrated good agreement between predicted and actual probabilities. Similar results were observed for the corresponding scoring algorithms.

conclusionsThe monocyte-based model is a simple, low-cost and well-calibrated risk-stratification tool for CHD. However, the declined discrimination in external validation indicates limited generalisability. Prospective multicentre validation and recalibration are therefore warranted before clinical adoption.

Indexed as

Cholesterol, HDLCoronary DiseaseMonocytesAdultAgedChinaEast Asian PeopleFemaleHumansLeukocyte CountMaleMiddle AgedPredictive Value of TestsProportional Hazards ModelsProspective StudiesRisk AssessmentCholesterol, HDLAdult cardiologyClinical Decision-MakingCoronary heart diseaseRisk Factors

Identifiers

PMID40954085
PMCPMC12439155

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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.