Metabolic health tracking using Ultrahuman M1 continuous glucose monitoring platform in non- and pre-diabetic Indians: a multi-armed observational study

Abstract

AbstractBackgroundCGM-based tracking is expanding in non-diabetic groups to meet wellness and preventive care needs. However, data is limited on short-term outcomes for glycemic control, insulin resistance and correlation of algorithm-derived score to known glycemic metrics in controlled settings, making benchmarking difficult. This is especially true for the high-risk Indian/South Asian demographic.ObjectivesTo examine changes resulting from the Ultrahuman (UH) M1 CGM application-with concomitant FitBit tracker use in patterns of glucose variability (GV). Evaluate GV correlations with stress, sleep duration, inflammation, and activity. Examine correlations between UH metabolic score (UH-MS) and biomarkers of dysglycemia and insulin resistance.MethodsParticipants (N=53 non-diabetic, 52 pre-diabetic) wore the UH-M1 CGM and FitBit tracker for a 14-day period. HsCRP, cortisol, OGTT, HbA1c, HOMA-IR levels, and standard blood profile measurements were obtained.ResultsMean glucose levels, restricted time in range (70-110mg/dL), and GV metrics were significantly different between non- and pre-diabetics and displayed improvements with M1 use. Strong correlations of specific GV metrics with inflammation were found in pre-diabetics, with modest correlation between sleep and activity in non-diabetics. Elevated HOMA-IR, HbA1c, and OGTT were linked with J-index and high blood glucose index in pre-diabetics, and low blood glucose index in non-diabetics. UH-MS displayed a strong inverse relationship with insulin resistance and glucose dysregulation.ConclusionsThe study presents the first guidance values of glycemic indices of non- and pre-diabetic Indians and supports the notion that short-duration CGM use with algorithm scores can affect positive changes in glucose management.