Background Conicity index, body-shape index, lipid accumulation product (LAP), waist circumference (WC), triglyceride, triglyceride-glucose (TyG) index, hepatic steatosis index (HSI), waist-to-height ratio (WHtR), TyG index-related parameters (TyG-WHtR, TyG-BMI, TyG-WC), body mass index (BMI), visceral adiposity index, triglyceride to high-density lipoprotein cholesterol ratio and body roundness index have been reported as reliable markers of non-alcoholic fatty liver disease (NAFLD). However, there is debate about which of the above obesity and lipid-related indices has the best predictive performance for NAFLD risk. Methods This study included 6870 female and 7411 male subjects, and 15 obesity and lipid-related indices were measured and calculated. NAFLD was diagnosed by abdominal ultrasound. The area under the curve (AUC) of 15 obesity and lipid-related indices were calculated by receiver operating characteristic (ROC) analysis. Results Among the 15 obesity and lipid-related indices, the TyG index-related parameters had the strongest association with NAFLD. ROC analysis showed that except for ABSI, the other 14 parameters had high predictive value in identifying NAFLD, especially in female and young subjects. Most notably, TyG index-related parameters performed better than other parameters in predicting NAFLD in most populations. In the female population, the AUC of TyG-WC for predicting NAFLD was 0.9045, TyG-BMI was 0.9084, and TyG-WHtR was 0.9071. In the male population, the AUC of TyG-WC was 0.8356, TyG-BMI was 0.8428, and TyG-WHtR was 0.8372. In addition, BMI showed good NAFLD prediction performance in most subgroups (AUC>0.8). Conclusions Our data suggest that TyG index-related parameters, LAP, HSI, BMI, and WC appear to be good predictors of NAFLD. Of these parameters, TyG index-related parameters showed the best predictive potential.

Background Triglyceride glucose-body mass index (TyG-BMI) has been recommended as an alternative indicator of insulin resistance. However, the association between TyG-BMI and pre-diabetes remains to be elucidated. Methods More than 100,000 subjects with normal glucose at baseline received follow-up. The main outcome event of concern was pre-diabetes defined according to the diagnostic criteria recommended by the American Diabetes Association (ADA) in 2018 and the World Health Organization (WHO) in 1999. A Cox proportional hazard regression model was used to evaluate the role of TyG-BMI in identifying people at high risk of pre-diabetes. Results At a mean observation period of 3.1 years, the incidence of pre-diabetes in the cohort was 3.70 and 12.31% according to the WHO and ADA diagnostic criteria for pre-diabetes, respectively. The multivariate Cox regression analysis demonstrated that TyG-BMI was independently positively correlated with pre-diabetes, and there was a special population dependence phenomenon. Among them, non-obese people, women and people under 50 years old had a significantly higher risk of TyG-BMI-related pre-diabetes (P-interaction< 0.05). Conclusions These findings suggest that a higher TyG-BMI significantly increases an individual’s risk of pre-diabetes, and this risk is significantly higher in women, non-obese individuals, and individuals younger than 50 years of age.

Purpose Diabetes is one of the most prevalent chronic diseases in the world, and its prevalence is expected to rise further. To help understand the utility of the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) in diabetes prevention, this large-scale longitudinal cohort study aims to explore the association of NHHR with diabetes risk and compare it as a risk predictor with conventional lipid parameters. Patients and Methods This observational study extracted data from the NAGALA longitudinal cohort study conducted in Japan between 2004 and 2015. Multivariate Cox regression analysis was used to evaluate the association between NHHR and the risk of diabetes. The dose–response relationship was analyzed by restricted cubic spline (RCS) regression and the potential risk threshold was estimated. The receiver operator characteristic curve (ROC) was used to analyze and calculate the predictive value and optimal threshold of NHHR and other conventional lipids for new-onset diabetes. Results Of the 15,464 people aged 18–79, 373 (2.41%) were diagnosed with new-onset diabetes during the study period, with a median age of 46 years. The sensitivity analysis based on multivariate adjustment showed that the independent positive correlation between diabetes and NHHR was stable in different populations. RCS and ROC analysis indicated that the association between NHHR and diabetes was non-linear, and the NHHR was a better marker for predicting diabetes risk than other conventional lipid parameters; Additionally, it is worth noting that an NHHR of approximately 2.74 may be the optimal threshold for intervention in diabetes risk. Conclusion In the general population, NHHR is a better marker for predicting diabetes risk than conventional lipid parameters, and an NHHR of about 2.74 may be the optimal threshold for assessing diabetes risk.