Integrated Assessment of Obesity Indices and Novel Inflammatory Biomarkers in Predicting the Severity of Obstructive...

Background/Objectives: Obesity is a significant risk factor for obstructive sleep apnea (OSA); however, conventional anthropometric measures, such as body mass index (BMI), may not fully reflect the physiological burden associated with adiposity. The triponderal mass index (TMI) has been proposed as an alternative anthropometric indicator, while inflammation-related biomarkers have emerged as potential complementary tools for characterizing OSA severity. This study aimed to evaluate the relationships between BMI, TMI, hypoxemia, and systemic inflammation, and to assess whether combining anthropometric indices with inflammatory biomarkers improves the identification of severe OSA. Methods: In this retrospective cross-sectional study, 238 adults undergoing full-night polysomnography were classified into four groups: non-OSA, mild OSA, moderate OSA, and severe OSA, based on the apnea-hypopnea index (AHI). Anthropometric indices, polysomnographic parameters, and a comprehensive panel of laboratory biomarkers-including C-reactive protein (CRP), neutrophil- and platelet-derived inflammatory indices, prognostic nutritional index (PNI), CRP-to-albumin ratio (CAR), and CRP-to-lymphocyte ratio (CLR)-were analyzed. Associations were evaluated using Spearman correlation analyses, and diagnostic performance for severe OSA (AHI ≥ 30 events/h) was assessed using receiver operating characteristic (ROC) analyses, DeLong tests, and multivariable models. Results: Both BMI and TMI increased progressively with OSA severity (both p < 0.001) and showed comparable correlations with AHI and nocturnal oxygenation parameters. ROC analyses demonstrated similar discriminative performance for severe OSA (BMI AUC = 0.834; TMI AUC = 0.823; p = 0.229). Among inflammatory biomarkers, CRP, multi-inflammatory index (MII), CAR, and CLR showed moderate diagnostic accuracy. Among the evaluated markers, serum albumin (AUC = 0.836) and PNI demonstrated the highest diagnostic accuracy (AUC = 0.994). A combined model integrating BMI or TMI with PNI achieved near-perfect discrimination for severe OSA (BMI-based AUC = 0.9956; TMI-based AUC = 0.9969), while the addition of CRP-based inflammatory markers did not yield meaningful incremental benefit. Conclusions: BMI and TMI exhibit comparable performance in relation to OSA severity, hypoxemia, and systemic inflammation, with no clear superiority of TMI over BMI in adult patients. Inflammation-related biomarkers-particularly PNI-provide additional discriminatory value beyond anthropometric measures alone. Integrating simple biochemical markers with anthropometric and polysomnographic parameters may enhance risk stratification and identification of severe OSA phenotypes.