Publisher: BioMed Central Country of Publication: England NLM ID: 101088676 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6823 (Electronic) Linking ISSN: 14726823 NLM ISO Abbreviation: BMC Endocr Disord Subsets: MEDLINE

C-Reactive Protein*/analysis , C-Reactive Protein*/metabolism , Biomarkers*/blood , Blood Glucose*/analysis , Blood Glucose*/metabolism , Triglycerides*/blood , Diabetes Mellitus*/epidemiology , Diabetes Mellitus*/blood , Diabetes Mellitus*/diagnosis , Diabetes Mellitus, Type 2*/epidemiology , Diabetes Mellitus, Type 2*/blood , Diabetes Mellitus, Type 2*/diagnosis, Humans ; Female ; Male ; Prospective Studies ; Middle Aged ; Aged ; China/epidemiology ; Risk Factors ; Longitudinal Studies ; Prognosis ; Follow-Up Studies ; Insulin Resistance ; Inflammation/blood

Competing Interests: Declarations. Ethics approval and consent to participate: This study was conducted and approved by the Biomedical Ethics Review Committee of Peking University in accordance with the principles of the Declaration of Helsinki. In addition, all participants provided written informed consent to participate in the study (IRB approval number IRB00001052-11015). This study does not disclose any personal privacy of the participants and does not violate data protection laws.The research has been performed in accordance with the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
Objective: Triglyceride-to-glucose index (TyG index) and inflammation are both independent risk factors for diabetes. However, only a few studies have combined TyG index with inflammation indices to predict diabetes risk. C-reactive protein-triglyceride-to-glucose index (CTI index), as a new type of lipid and inflammation marker, can comprehensively assess the severity of insulin resistance and inflammation. This study explores the association between CTI index and diabetes risk.
Methods: We recruited a total of 6,728 participants from the China Health and Retirement Longitudinal Study (CHARLS) who had no history of diabetes at baseline. After determining the key predictors using the least absolute shrinkage and selection operator (LASSO) technique, the relationship between the CTI index and the risk of new-onset diabetes was assessed using multivariate COX regression, the mediating effect between insulin resistance and inflammatory indicators was explored, and restricted cubic splines (RCS) were applied to explore the association between the CTI index and the risk of new-onset diabetes. In addition, we used decision tree analysis to identify people at high risk of diabetes, calculated time-dependent Harrell's C index (95% CI) to assess the predictive ability of TyG, CRP, CTI and CRP + TyG for new-onset diabetes, and further calculated IDI and NRI to assess the predictive ability of CTI and TyG. Finally, we performed subgroup analyses for different subgroups using stratified COX proportional hazard regression models; and a series of sensitivity analyses were performed to verify the robustness of our results.
Results: The incidence of diabetes was 15.9% during the 9-year follow-up. COX regression analysis showed that the risk ratio for diabetes increased gradually with an increase in the CTI index. The RCS curve confirmed the existence of a linear relationship between the CTI index and the risk of diabetes. Decision tree analysis showed that the CTI index was a key indicator of diabetes risk. In addition, the CTI index is a much better predictor of the onset of diabetes risk than the TyG index, as demonstrated by the integrated discrimination improvement (IDI) and net reclassification improvement (NRI).
Conclusion: An increase in CTI levels is closely related to diabetes risk, and the CTI index may become a unique predictor of diabetes risk.
Clinical Trial Number: Not applicable.
(© 2025. The Author(s).)