Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches.

Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept.

INTRODUCTION: Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics. RESEARCH DESIGN AND METHODS: We recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8-10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2-6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models. RESULTS: A total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor's importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%). CONCLUSIONS: This study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

Enhanced Glucose Control Following Vertical Sleeve Gastrectomy Does Not Require a ß-Cell Glucagon-Like Peptide 1 Receptor.

Bariatric surgeries, including vertical sleeve gastrectomy (VSG), resolve diabetes in 40-50% of patients. Studies examining the molecular mechanisms underlying this effect have centered on the role of the insulinotropic glucagon-like peptide 1 (GLP-1), in great part because of the ∼10-fold rise in its circulating levels after surgery. However, there is currently debate over the role of direct ß-cell signaling by GLP-1 to mediate improved glucose tolerance following surgery. In order to assess the importance of ß-cell GLP-1 receptor (GLP-1R) for improving glucose control after VSG, a mouse model of this procedure was developed and combined with a genetically modified mouse line allowing an inducible, ß-cell-specific Glp1r knockdown (Glp1rß-cell-ko). Mice with VSG lost ∼20% of body weight over 30 days compared with sham-operated controls and had a ∼60% improvement in glucose tolerance. Isolated islets from VSG mice had significantly greater insulin responses to glucose than controls. Glp1r knockdown in ß-cells caused glucose intolerance in diet-induced obese mice compared with obese controls, but VSG improved glycemic profiles to similar levels during oral and intraperitoneal glucose challenges in Glp1rß-cell-ko and Glp1rWT mice. Therefore, even though the ß-cell GLP-1R seems to be important for maintaining glucose tolerance in obese mice, in these experiments it is dispensable for the improvement in glucose tolerance after VSG. Moreover, the metabolic physiology activated by VSG can overcome the deficits in glucose regulation caused by lack of ß-cell GLP-1 signaling in obesity.

Carnitine Acetyltransferase Mitigates Metabolic Inertia and Muscle Fatigue during Exercise.

Acylcarnitine metabolites have gained attention as biomarkers of nutrient stress, but their physiological relevance and metabolic purpose remain poorly understood. Short-chain carnitine conjugates, including acetylcarnitine, derive from their corresponding acyl-CoA precursors via the action of carnitine acetyltransferase (CrAT), a bidirectional mitochondrial matrix enzyme. We show here that contractile activity reverses acetylcarnitine flux in muscle, from net production and efflux at rest to net uptake and consumption during exercise. Disruption of this switch in mice with muscle-specific CrAT deficiency resulted in acetyl-CoA deficit, perturbed energy charge, and diminished exercise tolerance, whereas acetylcarnitine supplementation produced opposite outcomes in a CrAT-dependent manner. Likewise, in exercise-trained compared to untrained humans, post-exercise phosphocreatine recovery rates were positively associated with CrAT activity and coincided with dramatic shifts in muscle acetylcarnitine dynamics. These findings show acetylcarnitine serves as a critical acetyl buffer for working muscles and provide insight into potential therapeutic strategies for combatting exercise intolerance.

Targeted metabolomics connects thioredoxin-interacting protein (TXNIP) to mitochondrial fuel selection and regulation of specific oxidoreductase enzymes in skeletal muscle.

Thioredoxin-interacting protein (TXNIP) is an α-arrestin family member involved in redox sensing and metabolic control. Growing evidence links TXNIP to mitochondrial function, but the molecular nature of this relationship has remained poorly defined. Herein, we employed targeted metabolomics and comprehensive bioenergetic analyses to evaluate oxidative metabolism and respiratory kinetics in mouse models of total body (TKO) and skeletal muscle-specific (TXNIP(SKM-/-)) Txnip deficiency. Compared with littermate controls, both TKO and TXNIP(SKM-/-) mice had reduced exercise tolerance in association with muscle-specific impairments in substrate oxidation. Oxidative insufficiencies in TXNIP null muscles were not due to perturbations in mitochondrial mass, the electron transport chain, or emission of reactive oxygen species. Instead, metabolic profiling analyses led to the discovery that TXNIP deficiency causes marked deficits in enzymes required for catabolism of branched chain amino acids, ketones, and lactate, along with more modest reductions in enzymes of ß-oxidation and the tricarboxylic acid cycle. The decrements in enzyme activity were accompanied by comparable deficits in protein abundance without changes in mRNA expression, implying dysregulation of protein synthesis or stability. Considering that TXNIP expression increases in response to starvation, diabetes, and exercise, these findings point to a novel role for TXNIP in coordinating mitochondrial fuel switching in response to nutrient availability.