Key result
Pre-diabetic individuals exhibited significantly higher mean glucose levels (112.2 mg/dL) compared to non-diabetics (102.4 mg/dL) over a 14-day period of continuous glucose monitoring.
Why the study?
Data are limited on short-term outcomes for glycemic control, insulin resistance, and algorithm-derived scores in non-diabetic populations, particularly in high-risk Indian/South Asian demographics.
Does the Ultrahuman M1 continuous glucose monitoring platform and fitness tracker improve glycemic variability and correlate with metabolic biomarkers in non- and pre-diabetic Indians?
Observational (n=105)
Yes
Does the Ultrahuman M1 continuous glucose monitoring platform and fitness tracker improve glycemic variability and correlate with metabolic biomarkers in non- and pre-diabetic Indians?
Short-term use of a continuous glucose monitor and fitness tracker was associated with improved glycemic indices in both non-diabetic and pre-diabetic Indian adults.
Answers are grounded in this paper and cite their sources.
Should not yet change practice for wellness CGM; leaves open whether MetSc-guided tracking causally improves insulin resistance in high-risk South Asians.
Background CGM-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. Objectives To 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. Methods Participants (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. Results Mean 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. Conclusions The 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.
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Chaudhry et al. (2023) conducted an observational in Pre-diabetes and Normoglycemia (n=105). Pre-diabetes vs. Non-diabetic was evaluated on Mean glucose levels over 14 days (p=<0.0001). Pre-diabetic individuals exhibited significantly higher mean glucose levels (112.2 mg/dL) compared to non-diabetics (102.4 mg/dL) over a 14-day period of continuous glucose monitoring.
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