Abstract
Background
Metabolic syndrome (MetS) is a risk factor for cardiovascular diseases and cancer, and its pre-stage is as well. The incidence of MetS is increasing annually, but currently, there is no unified diagnostic criterion, and the diagnostic conditions are complex, posing challenges for primary healthcare professionals. Insulin resistance indicators are widely used for MetS screening, but there is limited research on their discriminatory ability for preMetS.
Objective
To assess the prevalence of preMetS in adults in Southeast China and the differences among three MetS standards. Additionally, to compare the differences in the correlation and diagnostic value of six insulin resistance indicators with preMetS.
Methods
A total of 9,399 individuals participating in health examinations in five communities in Fuzhou City were selected for questionnaire surveys, physical examinations, and laboratory tests. Binary logistic regression was used to analyze the correlation between each indicator and preMetS, and a restricted cubic spline model was used to analyze the dose-response relationship between the two. The diagnostic abilities of each indicator were compared using the area under the receiver operating characteristic curve. A nomogram model combining various indicators and age was established to improve and reassess diagnostic capabilities.
Results
The overall prevalence of preMetS ranged from 10.63–49.68%. Regardless of gender, the kappa values between the revised ATP III and JCDCG ranged from 0.700 to 0.820, while those with IDF ranged from 0.316 to 0.377. In the ATP and JCDCG standards, the TyG index was the best screening indicator, with maximum AUC values of 0.731 (95% CI: 0.718–0.744) and 0.724 (95% CI: 0.712–0.737), and optimal cutoff values of 7.736 and 7.739, respectively. Additionally, WHtR showed consistent performance with TyG in the JCDCG standard, with AUC and cutoff values of (95% CI: 0.698–0.725) and 0.503. In the normal weight population, in the revised ATP III, there was no significant difference in screening abilities between TG/HDL and TyG. The nomogram model combining age with TG/HDL or TyG showed better screening abilities for preMetS compared to other indicators, but the model with age and TG/HDL had a better fit.
Conclusion
The consistency between the revised ATP III and JCDCG in MetS tri-classification is good. TyG has the best identification ability for preMetS (revised ATP III and JCDCG). Additionally, WHtR has equally good identification ability for preMetS (JCDCG). The nomogram model with TG/HDL has the best identification ability. In conclusion, the consistency of MetS tri-classification is better in the revised ATP III and JCDCG. TyG is an effective indicator for identifying preMetS in adults in Southeast China. WHtR is a non-invasive indicator for screening preMetS (JCDCG). The diagnostic capabilities are improved with the inclusion of age and TG/HDL in the nomogram model, with less error.