Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study

Background: The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions. Objective: This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone. Methods: A cohort of 393 participants enrolled in Digbi Health’s personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data. Results: Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R 2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks. Conclusions: Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants’ genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficac

Preliminary evidence supports circulating microRNAs as prognostic biomarkers for type 2 diabetes.

Background:Circulating microRNAs are emerging as potential prognostic biomarkers for the development of type 2 diabetes. However, microRNAs are also associated with complications from impaired glucose metabolism (e.g. endothelial cell function). Prior studies have not evaluated for associations between trajectories of circulating microRNAs with trajectories of fasting blood glucose over time and the responses to behavioral interventions to reduce risk. This study performed longitudinal assessment of microRNAs and fasting blood glucose and identified relationships between microRNAs and behavioral risk reduction interventions. Methods:MicroRNAs (n = 353) were measured in subsets (n = 10, n = 8) of participants from previously completed clinical trials that studied behavioral risk reduction interventions. Fasting blood glucose trajectories were associated with changes in 45 microRNAs over 12 months. Results:Following a 3-month physical activity and dietary intervention compared with baseline, 13 microRNAs were differentially expressed. Seven microRNAs (i.e. miR-106b, miR-20b, miR-363, miR-486, miR-532, miR-92a and miR-93) were commonly identified between the two analyses. Conclusions:Further studies are needed to determine which microRNAs are prognostic biomarkers of risk for type 2 diabetes versus consequences of impaired glucose metabolism. Additional future directions of this research are to differentiate whether microRNAs are prognostic and/or diagnostic biomarkers for risk for type 2 diabetes and predictive biomarkers of responses to risk reduction interventions

A Different Weight Loss Experience: A Qualitative Study Exploring the Behavioral, Physical, and Psychosocial Changes Associated with Yoga That Promote Weight Loss

Yoga interventions improve obesity-related outcomes including body mass index (BMI), body weight, body fat, and waist circumference, yet it is unclear whether these improvements are due to increased physical activity, increased lean muscle mass, and/or changes in eating behaviors. The purpose of this study is to expand our understanding of the experience of losing weight through yoga. Methods. Semistructured interviews were qualitatively analyzed using a descriptive phenomenological approach. Results. Two distinct groups who had lost weight through yoga responded: those who were overweight and had repeatedly struggled in their attempts to lose weight (55%, n=11) and those who were of normal weight and had lost weight unintentionally (45%, n=9). Five themes emerged that differed slightly by group: shift toward healthy eating, impact of the yoga community/yoga culture, physical changes, psychological changes, and the belief that the yoga weight loss experience was different than past weight loss experiences. Conclusions. These findings imply that yoga could offer diverse behavioral, physical, and psychosocial effects that may make it a useful tool for weight loss. Role modeling and social support provided by the yoga community may contribute to weight loss, particularly for individuals struggling to lose weight

Behavioral failure in the process of weight regain: a data driven protocol

Background: The prevalence of obesity is still an issue of high public health significance. Dietary self-monitoring (DSM) has been identified as the key component in standard behavioral treatment (SBT) for obesity that supports weight loss maintenance. However, little is known about the process of the weight regain in interventions using SBT. Previous research showed the temporal trend of adherence to DSM which preceded weight regain. We hypothesized that participants experienced the failure in adherence to DSM before the onset of weight regain. We then hypothesized that the adherence to daily time-contingent surveys in ecological momentary assessments (EMA) would protect people from behavioral failure and weight regain. Methods: In this study, we provided a data-driven protocol to define and analyze the weight regain and behavioral failure. With the self-weighing data, we used piecewise linear model to detect the onset of weight regain and classified participants as maintainers and regainers. We used Bai-Perron’s test to detect the failure in DSM adherence before weight regain and classified participants as collapsers and sustainers. Group-based trajectory modeling was used to cluster the longitudinal patterns of adherence to the time-contingent EMA surveys into two groups (the consistent group and the decline group). We constructed a three-state Markov transition model for the process of weight regain via a behavioral failure and used Cox models to explore the group effect on the transition intensities among states. Results: According to the self-weighing trajectories, 148 participants were classified as regainers (66.89%) and maintainers (25.68%). Among the regainers and maintainers (N=137), 62.04% was classified as collapsers versus sustainers (37.96%) for adherence to DSM. of the participants were organized as the consistent group (73.8%) versus the decline group for adherence to EMA surveys. Being consistently adherent to EMA surveys significantly was related to: (1) greater amount of percent weight loss before weight regain; (2) longer duration of weight loss and maintenance before weight regain; (3) longer duration without behavioral failure and weight regain; and (4) lower hazard of behavioral failure in DSM adherence. Conclusions: Failure in the adherence of DSM was a more hazardous state for weight regain. Consistent adherence to time-contingent EMA surveys was associated with lower hazard of failure in the adherence to DSM