The Role of Triglyceride-Glucose Index in the Prediction of the Development of Hypertension - Findings from a Community Cohort in Singapore.

Objectives: Triglyceride-glucose index (TyGI) is an emerging surrogate marker of insulin resistance. We aim to explore the role of triglyceride-glucose index in the prediction of the development of hypertension. Methodology: We conducted a retrospective cohort study that included 3,183 study participants identified from a community health screening programme who had no baseline hypertension and were then followed up after an average of 1.7 years. Cox proportional-hazard model was used to assess the association between risk of incident hypertension and TyGI in quartiles, while adjusting for demographics and clinical characteristics. Results: Hypertension occurred in 363 study participants (11.4%). Those who developed hypertension had higher TyGI [8.6 (IQR 8.2-9.0)] than those who did not [8.2 (IQR 8.0-8.7)] (p<0.001). Significant association between TyGI and hypertension was observed in both the unadjusted and proportional hazard model [Quartile (Q)2, p=0.010; Q3, p<0.001 and Q4, p<0.001] and the model that adjusted for demographics (Q2, p=0.016; Q3, p=0.003; Q4, p<0.001). In the model adjusted for clinical covariates, the hazard of developing hypertension remained higher in TyGI Q4 compared to TyGI Q1(Hazard Ratio=2.57; 95% Confidence Interval: 1.71, 3.87). Increasing triglyceride-glucose index accounted for 16.4% of the association between increasing BMI and incident hypertension, after adjusting for age, gender, ethnicity and baseline HDL cholesterol (p<0.001). Conclusion: Triglyceride-glucose index was an independent predictor of the development of hypertension. It may potentially be used as an inexpensive indicator to predict the development of hypertension and risk-stratify individuals to aid management in clinical practice.

Development of a metabolic syndrome severity score and its association with incident diabetes in an Asian population-results from a longitudinal cohort in Singapore.

PURPOSE: Metabolic syndrome (MetS) is a constellation of clinical factors that indicates elevated risk of diabetes. It is diagnosed based on three or more abnormalities in its components. This does not take into account that MetS can likely present as a continuum of risk. We aim to develop a MetS severity score and assess its association with incident diabetes. METHODS: In total, 4149 subjects without baseline diabetes participated in a community screening programme in 2013-2017. MetS was defined according to International Diabetes Federation criteria. A MetS severity z-score was derived from standardised loading coefficients of a confirmatory factor analysis for waist circumference, triglycerides, HDL-cholesterol, blood pressure and fasting plasma glucose (FPG). Multivariable cox proportional hazards regression model was used to assess the risk of diabetes by the score with adjustment for demographics and MetS components. RESULTS: Diabetes occurred in 130 subjects. Quintile 5 of the baseline MetS severity z-score was significantly associated with development of diabetes even in fully adjusted model with HR 2.63 (95% CI: 1.04-6.64; p = 0.040). The relationship between MetS and incident diabetes became attenuated and non-significant in fully adjusted model with HR 0.67 (95% CI: 0.34-1.29; p = 0.228). Mediation analysis showed that MetS severity z-score accounted 61.0% of the association between increasing body mass index and development of diabetes (p < 0.001). CONCLUSIONS: The MetS severity z-score is an inexpensive and clinically-available continuous measure of MetS to identify individuals at high risk of diabetes.

The role of triglyceride glucose index in development of Type 2 diabetes mellitus.

AIMS: Triglyceride-Glucose (TyG) is an emerging surrogate indicator of insulin resistance. We explored the role of TyG in development of Type 2 Diabetes Mellitus (T2DM) and elucidated the mechanism for the relationship. METHODS: 4109 subjects without baseline T2DM participated in a community screening programme in 2013-2016. TyG was calculated as Ln[fasting triglyceride level (mg/dl) × fasting plasma glucose (FPG) (mg/dl)/2]. Outcome was T2DM defined as FPG ≥ 7.0 mmol/l; current treatment with anti-diabetes medication; and/or self-reported diabetes on follow-up screening. We used Cox proportion-hazard model to assess risk of T2DM by TyG quartiles at baseline. Binary mediation analysis was performed to examine extent of mediation by TyG between Body Mass Index (BMI) and T2DM development. RESULTS: After 5734.23 person-years of follow-up, T2DM developed in 117 subjects with an incidence of 20.40/1000 person-years. Risk of T2DM incidence was increased with quartiles 2, 3 and 4 versus quartile 1 of TyG (adjusted HR 1.79(95%CI 0.80-3.99), 2.54 (1.18-5.49) and 4.68(2.19-10.01), Ptrend < 0.001) across TyG quartiles. TyG accounted for 35.1% of association between BMI and T2DM development, having adjusted for potential cofounders (p < 0.001). CONCLUSIONS: TyG is potentially useful for predicting T2DM in clinical practice. It is a potential mediator of association between BMI and T2DM development.