A study on the correlation between the mean platelet volume to monocyte count ratio and long-term prognosis in patients with newly diagnosed coronary artery disease

TL;DR

This study finds that a blood test measuring platelet and monocyte levels can predict heart disease outcomes better than standard methods.

Contribution

The study introduces the mean platelet volume-to-monocyte count ratio (MMR) as a novel biomarker for predicting cardiovascular risk in CAD patients.

Findings

MMR shows a nonlinear, inverted U-shaped relationship with major adverse cardiovascular events (MACE) risk.

Adding MMR to standard risk models improves predictive accuracy, with a higher AUC (0.718 vs. 0.673).

Patients in the middle MMR quintile (L3) had significantly worse outcomes compared to other groups.

Abstract

Coronary atherosclerotic heart disease (CAD) remains a major global health burden and a leading cause of mortality. Its pathogenesis is closely linked to multiple risk factors, among which inflammation plays a central role. While inflammatory biomarkers such as platelet and monocyte counts have been incorporated into prognostic assessments, their predictive accuracy remains limited. Further investigation of novel inflammatory indices is needed to refine risk stratification and guide clinical management.

This study aimed to evaluate the prognostic value of the mean platelet volume-to-monocyte count ratio (MMR) for predicting major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.

A total of 652 treatment-naïve CAD patients were enrolled. Kaplan–Meier survival analysis and univariate Cox proportional hazards models were applied to assess the association between MMR levels and MACE. Subgroup analyses were performed to test for effect modification. Restricted cubic spline (RCS) models were used to explore the dose–response relationship. The incremental predictive value of MMR beyond conventional risk factors was examined using changes in the concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Patients were stratified into quintiles based on MMR values (L1: 7.89–14.43; L2: 14.50–17.96; L3: 18.00–22.16; L4: 22.25–28.53; L5: 28.67–60.67). Kaplan–Meier analysis revealed significantly poorer outcomes in the L3 group compared with other quintiles (log-rank P = 0.0014). RCS analysis demonstrated a significant nonlinear association between MMR levels and MACE risk (P = 0.001), characterized by an inverted U-shaped relationship. Incorporating MMR into conventional risk models significantly improved predictive performance (AUC 0.718 vs. 0.673; P = 0.018).

In newly diagnosed CAD patients, MMR shows a nonlinear, inverted U-shaped association with MACE risk. The addition of MMR to standard risk models enhances prognostic accuracy. Further multicenter prospective studies and mechanistic trials are needed to verify the prognostic value of MMR and to elucidate its mechanism of action.

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