Body composition analysis components as markers for coronary artery diseases in type 2 diabetic patients.

Objectives: The objective of the current study is to investigate the potential of body composition analysis components for predicting coronary artery disease (CAD) in the Type 2 diabetic Asian Indian population. Methods: This case-control study was performed by recruiting 50 type 2 diabetic patients with CAD along with 50 controls. The participants recruited were those between the ages of 40-70 who visited the Department of Cardiology at a tertiary care referral centre in Manipal, India. The diagnosis of CAD was confirmed by electrocardiogram tests and coronary angiogram changes. An anthropometric evaluation was conducted, and body composition analysis was conducted using Bodystat 1500MDD equipment. Results: In type 2 diabetics, for a unit increase in fat, the odds of CAD increased by 4.43 times. For a unit decrease in lean mass, the odds of CAD increased by 4.98 times. For a unit rise in body fat mass index, the odds of CAD increased by 1.75 times. Conclusion: In Type 2 diabetics, increased body fat mass with decreased lean tissue mass were valuable markers of CAD. Future studies can examine the benefits of different types of nutritional and exercise interventions targeted at improving muscle mass and reducing fat content in the body with an aim to reduce the occurrence of CAD in patients diagnosed with T2DM.

Triglyceride Glucose (TyG) Index: A surrogate biomarker of insulin resistance.

The predictability of triglyceride glucose index (TyG index) as a biomarker for identification of insulin resistance (IR) is being extensively studied in various ethnic populations. TyG index could be a beneficial tool for identification of IR and populations at high risk for developing diabetes in future. However, more studies are required to standardize optimal cut-off values in different ethnicities and populations. The present review describes existing literature, and identifies merits and demerits of TyG index as a surrogate marker for IR.

Predictive performance of traditional and novel lipid combined anthropometric indices to identify prediabetes.

BACKGROUND AND AIMS: Diabetes mellitus is one of the critical public health challenges in the Indian healthcare scenario. Novel anthropometric indices are promising surrogate markers to detect prediabetes compared to the traditional anthropometric indices that only reflect gross obesity. Thus, the authors aim to analyse the potential of three novel lipid combined anthropometric indices in predicting prediabetes in the Asian Indian population. METHODS: We conducted an age and gender-matched case-control study to identify the predictors of prediabetes. Prediabetes was diagnosed as per the American Diabetes Association (ADA) guidelines 2010. The traditional anthropometric measurements including waist circumference (WC), waist to hip ratio (WHR) and body mass index (BMI) were executed using standardised methods. Fasting lipid profile was obtained and using standardised formulas, the novel lipid combined anthropometric indices such as lipid accumulation product (LAP), visceral adiposity index (VAI) and triglyceride glucose index (TyG index) were derived. TyG related indices such as triglyceride glucose-waist circumference (TyG-WC) and triglyceride glucose-body mass index (TyG-BMI) were also calculated. RESULTS: The novel lipid combined anthropometric indices LAP, VAI, TyG index, TyG-WC and TyG-BMI were significantly higher in subjects with prediabetes of both the genders (p < 0.05). During receiver operating characteristic (ROC) curve evaluation, TyG index (AUC = 0.802) was the superior predictive measure in males, while in females, TyG-WC (AUC = 0.767) was the best among all the markers. CONCLUSION: TyG index and TyG-WC seem to be a superior indicator of prediabetes in the Asian Indian population in comparison with other anthropometric indices to screen prediabetes.