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Observational Study
. 2024 Mar 18;14(1):6490.
doi: 10.1038/s41598-024-56933-2.

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

Monik Chaudhry  1 , Mohit Kumar #  1 , Vatsal Singhal #  1 , Bhuvan Srinivasan  2
Affiliations
Observational Study

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

Monik Chaudhry et al. Sci Rep. .

Abstract

Continuous glucose monitoring (CGM) device adoption in non- and pre-diabetics for preventive healthcare has uncovered a paucity of benchmarking data on glycemic control and insulin resistance for the high-risk Indian/South Asian demographic. Furthermore, the correlational efficacy between digital applications-derived health scores and glycemic indices lacks clear supportive evidence. In this study, we acquired glycemic variability (GV) using the Ultrahuman (UH) M1 CGM, and activity metrics via the Fitbit wearable for Indians/South Asians with normal glucose control (non-diabetics) and those with pre-diabetes (N = 53 non-diabetics, 52 pre-diabetics) for 14 days. We examined whether CGM metrics could differentiate between the two groups, assessed the relationship of the UH metabolic score (MetSc) with clinical biomarkers of dysglycemia (OGTT, HbA1c) and insulin resistance (HOMA-IR); and tested which GV metrics maximally correlated with inflammation (Hs-CRP), stress (cortisol), sleep, step count and heart rate. We found significant inter-group differences for mean glucose levels, restricted time in range (70-110 mg/dL), and GV-by-SD, all of which improved across days. Inflammation was strongly linked with specific GV metrics in pre-diabetics, while sleep and activity correlated modestly in non-diabetics. Finally, MetSc displayed strong inverse relationships with insulin resistance and dysglycemia markers. These findings present initial guidance GV data of non- and pre-diabetic Indians and indicate that digitally-derived metabolic scores can positively influence glucose management.

Keywords: Continuous glucose monitoring; Digital health; Glycemic control; Inflammation; Insulin resistance; Metabolic Score; Metabolism; Non-diabetics; Prediabetes; Wearables.

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Conflict of interest statement

B.S, V.S., and M.K. declare that they are stakeholders in Ultrahuman Healthcare Private Limited. M.C. was a full-time employee of Ultrahuman during the study and analysis period. M.K. and V.S. declare no other conflict of interest. B.S. is a stakeholder of Triomics Healthcare.

Figures

Figure 1
Figure 1
Participant disposition.
Figure 2
Figure 2
Primary outcome measures in healthy vs. pre-diabetic within the study period. Graphical representation of designated primary outcomes of this study relating to (A) mean glucose level, (B) time in range, (C) time above range, (D) time below range, (E) glycemic variability calculated by standard deviation, (F) glycemic variability calculated by coefficient of variation (CV), (G) mean amplitude glucose excursion and (H) averaged before and after measure of venous fasting blood glucose. CV: Coefficient of variation; MAGE: Mean amplitude of glycemic excursion; error bars denote: standard deviation. *, **, *** denotes p < 0.05, 0.01 and 0.001, by Two-way ANOVA (see text for details).
Figure 3
Figure 3
Correlation of CGM-derived glycemic metrics with biomarkers associated with metabolic syndrome. Graphical representation of selected correlation analyses between inflammation, sleep duration, step count and heart rate with glycemic variability indices. R—correlation coefficient. Linked to Supplementary Tables S2–S5.
See this image and copyright information in PMC

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