ArticleInternational journal of molecular sciences2025
Cardiometabolic Biomarkers and Cardiovascular Risk Stratification in Polish Military Personnel: A Chemometric Approach.
Article in International journal of molecular sciences, 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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Abstract
Recent progress in laboratory medicine provides powerful tools for the detailed evaluation of cardiovascular risk in military populations. This study aimed to characterize cardiometabolic biomarker profiles across four Polish military groups through chemometric analysis. The study included 392 participants (336 men, 56 women, aged 19-56 years). In total, 23 serum biomarkers from lipid, metabolic, hepatic, hormonal, and bone axes, and lactate dehydrogenase (LDH) were analyzed. Random forest (RF) modeling and effect-size profiling identified group-specific signatures. Group 4 (exposed to extreme acceleration forces and ionizing radiation) exhibited a systemic stress and metabolic-load profile with higher N-terminal pro-B-type natriuretic peptide (NT-proBNP, 36.7 ± 48.2 pg/mL) and calcium (Ca, 10.4 ± 0.88 mg/dL), and lower parathyroid hormone (PTH, 15.4 ± 10.1 pg/mL) and C-terminal telopeptide of type I collagen (β-CTX, 0.22 ± 0.19 ng/mL). Group 2 (exposed to fuels and exhaust gases) and group 3 (exposed to vibration, noise, ionizing radiation) showed an atherogenic-hepatometabolic axis with elevated apolipoprotein B (apoB, 1.04 ± 0.31; 0.97 ± 0.29 g/L), non-high-density lipoprotein cholesterol (N-HDL, 151.0 ± 46.7; 147.0 ± 41.4 mg/dL), and alanine aminotransferase (ALT). Group 1 (exposed to a biological hazard) displayed higher glucose (Glu, 96.0 ± 25.6 mg/dL) and triglycerides (TG, 151.0 ± 113.0 mg/dL) with lower magnesium (Mg, 2.03 ± 0.27 mg/dL). RF modeling confirmed these constellations. This study was exploratory in nature, providing a foundation for future longitudinal research. These findings provide a rationale for tailored cardiovascular surveillance, although causal inference is limited by the cross-sectional design.
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