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

OBJECTIVES

The 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).

DESIGN

Population-based prospective cohort study.

SETTING

The 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.

RESULTS

During 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.

CONCLUSIONS

The 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.