Photo Credit: Microgen

The following is a summary of “Changes in Isoleucine, Sarcosine, and Dimethylglycine During OGTT as Risk Factors for Diabetes,” published in the July 2024 issue of Endocrinology by Liu, et al.

Most current metabolomics research in diabetes has concentrated on fasting states, with limited studies exploring the effects of glucose intake in the satiated state. 

For a study, researchers sought to integrate the oral glucose tolerance test (OGTT) with metabolomics to investigate metabolite-level variations across different glucose tolerance statuses and to assess how these changes might indicate diabetes risk.

Participants were classified into 3 groups, normal glucose tolerance (NGT), impaired glucose regulation (IGR), and newly diagnosed type 2 diabetes (NDM). During the OGTT, serum samples were collected at 0, 30, 60, 120, and 180 minutes. Metabolite level changes were tracked throughout the OGTT and compared among the 3 groups. A generalized estimating equation (GEE) analysis was conducted to evaluate the association between metabolite levels during the OGTT and the risk of diabetes and prediabetes, adjusting for variables such as sex, body mass index, fasting insulin levels, heart rate, smoking status, and blood pressure (BP).

Glucose intake led to significant metabolic changes, including increased levels of glycolytic intermediates and decreased levels of amino acids, glycerol, ketone bodies, and triglycerides. Notable differences in isoleucine levels were observed between the NGT and NDM groups, also between NGT and IGR groups. Sarcosine levels changed in the opposite direction to glycine levels in diabetes groups. The GEE analysis identified that during the OGTT, elevated levels of isoleucine, sarcosine, and acetic acid were associated with an increased risk of NDM, while isoleucine and acetate levels were linked to IGR risk.

Metabolic profiles exhibited distinct patterns following glucose intake based on glucose tolerance status. Variations in metabolite levels during the OGTT could serve as potential biomarkers for predicting diabetes risk.

Source: academic.oup.com/jcem/article-abstract/109/7/1793/7517349