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

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.

A 3D Monte Carlo analysis of the role of dyadic space geometry in spark generation.

In multiple biological systems, vital intracellular signaling processes occur locally in minute periplasmic subspaces often referred to as signaling microdomains. The number of signaling molecules in these microdomains is small enough to render the notion of continuous concentration changes invalid, such that signaling events are better described using stochastic rather than deterministic methods. Of particular interest is the dyadic cleft in the cardiac myocyte, where short-lived, local increases in intracellular Ca2+ known as Ca2+ sparks regulate excitation-contraction coupling. The geometry of dyadic spaces can alter in disease and development and display significant interspecies variability. We created and studied a 3D Monte Carlo model of the dyadic cleft, specifying the spatial localization of L-type Ca2+ channels and ryanodine receptors. Our analysis revealed how reaction specificity and efficiency are regulated by microdomain geometry as well as the physical separation of signaling molecules into functional complexes. The spark amplitude and rise time were found to be highly dependent on the concentration of activated channels per dyadic cleft and on the intermembrane separation, but not very sensitive to other cleft dimensions. The role of L-type Ca2+ channel and ryanodine receptor phosphorylation was also examined. We anticipate that this modeling approach may be applied to other systems (e.g., neuronal growth cones and chemotactic cells) to create a general description of stochastic events in Ca2+ signaling.