Investigating the efficiency of novel indicators in predicting risk of metabolic syndrome in the Iranian adult population.

Abstract

Background: Whether new anthropometric indicators are superior to conventional anthropometric indicators and whether they can better identify MetS in apparently healthy people needs further research. Thus, this study aimed to estimate the efficiency of novel indicators in predicting the risk of metabolic syndrome (MetS) in the Iranian adult population.

Material and methods: In this cross-sectional study, 800 subjects were selected by clustered random sampling. The metabolic factors, traditional and novel anthropometric indices, the triglyceride and glucose index (TyG index) and modified TyG indices (TyG-BMI, TyG-WC, TyG-WHR, and TyG-WHtR), and metabolic score for insulin resistance (METS-IR) were evaluated. The MetS was calculated according to the IDF criteria. To investigate the risk of MetS, logistic regression was used along with modeling.

Results: In all three models, all traditional anthropometric indices were associated with MetS (P < 0.001). Regarding novel anthropometric indices, all indices (except for ABSI) significantly predicted the risk of MetS in all participants before and after adjustment (P < 0.001). WTI index presented the highest Odds ratios for MetS (29.50, 95% CI: 15.53-56.03). A positive association was found in all models between TyG and modified TyG indices and METS-IR with MetS (P for all < 0.001). TyG-WHtR index presented the highest Odds ratios for MetS (70.07, 95% CI: 32.42-151.43).

Conclusion: A combination of the TyG index and WHtR (TyG-WHtR index) was better than the TyG index alone, with a higher odds ratio in predicting MetS. Due to the simplicity of these indices, cost-effectiveness, and facility at small-scale labs and being predictive of MetS risk it is suggested to include these markers in clinical practice.