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Associations of inflammation-related hematological profile with the early-stages of cardiovascular-kidney-metabolic syndrome and the mediating role of body composition: evidence from the China National Health Survey
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DOI：10.1186/s13098-025-02062-3.png)
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Abstract
En 中文
Chronic low-grade inflammation is increasingly recognized as a pivotal driver in the development of early-stage cardiovascular-kidney-metabolic (CKM) syndrome. Nonetheless, the complex interplay among inflammation-associated hematological indices, body composition, and CKM risk remains inadequately understood. In this cross-sectional study, data from 5,692 participants of the China National Health Survey (CNHS) were analyzed. We employed advanced machine learning techniques—including Random Forest, Least Absolute Shrinkage and Selection Operator(LASSO) regression, and eXtreme Gradient Boosting (XGBoost)—in conjunction with traditional epidemiological methods to assess the predictive value of an inflammation-related hematological profile for early-stage CKM. Restricted cubic spline regression was used to explore nonlinear dose–response relationships, and generalized structural equation modeling investigated the mediating role of body composition in the inflammation–CKM pathway. Six biomarkers—Neutrophil-to-HDL ratio (NHR), Monocyte-to-HDL ratio (MHR), High-sensitivity C-reactive protein/albumin ratio (CAR), High-fluorescence reticulocyte fraction (HFR), Reticulocyte production index (RPI), and Reticulocyte count (RET#)—were consistently prioritized across models. Participants in the highest quartiles of NHR, MHR, and RET# exhibited markedly elevated odds of early-stages of CKM (OR = 10.4, 7.75, and 6.99, respectively; all P for trend < 0.0001). Nonlinear analyses revealed critical thresholds—specifically, NHR > 7.05 and MHR > 0.66—beyond which early-stages of CKM risk escalated steeply. Mediation analyses indicated that imbalances in body composition, particularly increased adiposity and reduced muscle mass, accounted for 20–57% of the association between systemic inflammation and early-stage of CKM syndrome. Subgroup analyses further underscored that the predictive impact of reticulocyte parameters was amplified in smokers and individuals aged < 60 years. This study validates NHR and MHR as robust, clinically actionable biomarkers for early CKM screening. The delineated nonlinear thresholds and the mediating effects of body composition provide a strategic framework for targeted interventions—prioritizing anti-inflammatory treatments in high-risk groups (e.g., smokers with RET# >143.03 × 10³/µL) and muscle-preserving therapies to mitigate sarcopenic adiposity.
Keywords:
Inflammation-related hematological profile
Cardiovascular-kidney-metabolic (CKM) syndrome
Machine learning
Body composition mediation
Threshold effects
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