A Model for Identifying Obesity Status Based on Inflammatory and Metabolic Indicators: The Role of the CRP-TG-GLU Index

Aims/Background: Obesity is a major public health concern. However, conventional anthropometric measures do not adequately reflect underlying inflammation and insulin resistance. This study aimed to validate a nomogram model for identifying obesity risk in adults, centered on the C-reactive protein-Triglyceride-Glucose composite index (CTI) and incorporating routinely available clinical indicators. Methods: A single-center retrospective observational study was conducted. A total of 360 adult subjects who completed health examinations at our hospital between January 2022 and December 2024 were consecutively enrolled. Participants were categorized into obese and non-obese groups according to World Health Organization (WHO) criteria (body mass index [BMI] ≥30 kg/m2) and randomly divided into a training set (n = 252) and an internal validation set (n = 108) at a 7:3 ratio. The core predictor, CTI, was calculated as follows: CTI = 0.412 × Ln(hs-CRP, mg/L) + Ln[TG(mg/dL) × FPG(mg/dL) / 2]. In the training set, independently associated factors were identified using univariate and multivariate logistic regression to construct the final model. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC) with bootstrap correction, while calibration was evaluated using calibration curves and the Hosmer-Lemeshow test. The incremental value of CTI was assessed using the Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI), and clinical utility was evaluated using decision curve analysis (DCA). Results: Among the 360 included subjects, the prevalence of obesity was 17.22%. Baseline characteristics were comparable between the training and validation sets (p > 0.05). Multivariate analysis identified waist circumference (WC) and CTI as independent factors associated with obesity (WC: OR = 1.117, 95% CI: 1.072–1.165, p < 0.001; CTI: OR = 1.684, 95% CI: 1.262–2.246, p < 0.001). The model demonstrated good discriminative performance, with a bootstrap-corrected AUC of 0.795, and satisfactory calibration (H-L test p > 0.05). Incremental value analysis showed that adding CTI to WC significantly improved risk reclassification (NRI = 0.242, p = 0.006; IDI = 0.072, p = 0.002). Sensitivity analysis using the Chinese BMI criterion (≥28 kg/m2) yielded consistent results. Decision curve analysis demonstrated a clinical net benefit across a broad range of threshold probabilities. Conclusion: CTI, as a composite indicator reflecting both inflammation and insulin resistance, is independently associated with obesity and provides incremental value for risk stratification beyond WC alone. The proposed nomogram, based on routinely available health examination data, provides a convenient approach for obesity risk assessment in health management settings and may facilitate early risk stratification and targeted prevention. Future multicenter prospective studies are warranted to externally validate the model.