Best Way to Estimate Risk for Type 2 Diabetes

 What's the most practical, inexpensive, and effective way to estimate a patient's risk for type 2 diabetes (T2D)? According to a study published in PLOS One, when it came to predicting the risk of developing T2D, none of the anthropometric indices commonly used in clinical practice — body adiposity index (BAI), visceral adiposity index (VAI), and triglycerides-glucose index (TyG) — were superior to waist circumference (WC) or body mass index (BMI).

This was the finding made by researchers — from, among others, Stanford University, Stanford, California, in the United States and the Heart Institute (InCor) at the University of São Paulo Medical School Clinics Hospital (HCFMUSP), São Paulo, Brazil — based on a 5-year follow-up of a group of adults living in Baependi, a small rural town in the state of Minas Gerais, Brazil.

Medscape interviewed the following three of the study's authors: Camila Maciel de Oliveira, MD, PhD, endocrinologist at InCor, researcher at Stanford University, Stanford, California, and consultant for health-tech startups; Rafael de Oliveira Alvim, PhD, professor of physiological sciences at the Federal University of Amazonas Manaus, Brazil (UFAM),; and endocrinologist Carlos Alberto Mourão Júnior, MD, PhD, professor at the Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil (UFJF).

The cohort consisted of 1091 individuals who had been recruited in the Baependi Heart Study. The mean age of the participants was 47 ± 15 years; 57% were women. They attended two health examinations cycles: cycle one (2005-2006) and cycle two (2010-2013).

The authors evaluated the association between BMI, WC, BAI, VAI, and TyG and the incidence of T2D.

All participants were free of T2D at baseline. After a 5-year follow-up, however, 3.8% of them developed the disease. These patients had the worst metabolic profile, with higher hypertension, dyslipidemia, and obesity rates.

One unit increase in BAI was associated with an 8% increase in the risk of developing T2D, and VAI was associated with a risk increase of 11%. Moreover, a one-unit increase in TyG was associated with more than four times the risk of developing T2D. TyG had the most substantial predictive power among all three indices.

The results show that, as expected, TyG has a more significant potential than BAI and VAI as a predictor of T2D. This superiority is likely to be clinically explained by the fact that TyG is associated with insulin resistance, which is known to be a determining factor in the etiopathogenesis and pathophysiology of T2D. Conversely, TyG (as well as the other adiposity indices) loses its predictive value when the statistical models are adjusted for conventional measures of body fat (BMI and WC).

The authors mentioned that, to standardize the study participants, it's important to adjust for a covariate. "We saw that BMI and waist circumference are factors that significantly influence the prediction of risk. The reasons for that have been extensively examined in the literature. General obesity — BMI — and abdominal obesity are mandatory predictors of risk for diseases like type 2 diabetes, as well as hypertension and dyslipidemia," they pointed out.

In addition, they stated that studies published during the past few years support the observation that "BMI is still superior to all the other adiposity indices tested, both for T2D and for hypertension (at a 5-year follow-up and also at a 10-year follow-up)."

The authors said that the indices they assessed considered low-cost clinical and laboratory exams and, therefore, their study and other studies that have highlighted the relevance of these tools give the clinician an incentive to incorporate them into regular practice.

"Excel itself can be used to calculate these indices for predicting the risk for some chronic noncommunicable diseases. That is, this could be a practical and inexpensive way to help, and also to encourage, the patient to make lifestyle changes. It'd be an additional tool for promoting health, especially in the population as a whole," the three authors emphasized. They also mentioned that several health startups have been working on compiling patient data, with the support of the Research Ethics Committee, and data recorded in the Brazilian Clinical Trials Registry to develop and perfect algorithms that make sense with respect to the population of Brazil.

https://www.medscape.com/viewarticle/980111