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Predictive value of monocytes for coronary heart disease in Chinese adults: a population-based cohort study
Jianfeng Pei1, Maryam Zaid1, Yiling Wu2
1Department of Epidemiology, Fudan University School of Public Health, Shanghai, China.
Insights
A new monocyte count model effectively predicts coronary heart disease (CHD) risk. This simple, low-cost tool aids in identifying high-risk individuals for prompt preventive interventions.
Area of Science:
- Cardiovascular Disease Epidemiology
- Biomarker Discovery
- Preventive Cardiology
Background:
- Coronary heart disease (CHD) risk prediction is crucial for implementing preventive strategies.
- Simple, low-cost tools are needed for rapid identification of high-risk individuals.
- Monocyte count and its ratio to high-density lipoprotein cholesterol (MHR) are potential biomarkers.
Purpose of the Study:
- To develop and validate predictive models and scoring systems for CHD risk using monocyte count and MHR.
- To assess the performance of these models in identifying individuals at high risk of CHD.
- To evaluate the clinical utility of a monocyte-based risk stratification tool.
Main Methods:
- Population-based prospective cohort study (Shanghai Suburban Adult Cohort and Biobank, SSACB) with 44,013 CHD-free participants.
- Development of three predictive models and scoring systems using stepwise Cox regression with monocyte count or MHR.
- Internal validation via 10-fold cross-validation and external validation in a separate subcohort.
Main Results:
- Monocyte count and MHR were significantly associated with CHD risk.
- The monocyte-based model (Model 2) demonstrated good discrimination (AUC ~0.72-0.75) and calibration for 4-year CHD prediction.
- Performance was comparable to models using HDL-C or MHR, with similar results for scoring algorithms.
Conclusions:
- A monocyte-based model serves as a simple, low-cost, and well-calibrated tool for CHD risk stratification.
- External validation showed limited generalizability, suggesting a need for further validation.
- Prospective multicenter validation and recalibration are recommended before widespread clinical adoption.
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.
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