High Olfactory Receptor-Rich 11q11 Copy Number in Girls and African American Children.

Copy number variants (CNVs) provide numerous genetic differences between individuals, and they have been linked with multiple human diseases. Obesity is one of the highly heritable complex disorders, which is associated with copy number variance (CNV). A recent report shows that the 11q11 gene, a novel olfactory receptor, and its copy number variants are involved in the early onset of obesity. In the current study, we analyzed the 11q11 gene copy number variance (CNV) based on gender in White/European American (EA) and African American (AA) normal weight and overweight/obese children. Sixty-nine boys and fifty-eight girls between the ages of 6 and 10 years belonging to either EA or AA ethnicity were involved in this study. As per World Health Organization (WHO) guidelines, each participant's body weight and height were recorded. DNA was extracted from saliva, and the copy number variants for the 11q11 gene were measured using digital PCR. The descriptive analysis of the 11q11 copy number showed significantly more copies in girls compared to boys; similarly, AA participants had significantly increased CNV compared to EA. The normal weight (NW) and overweight/obese (OW/OB) girls were significantly less likely to belong to the low copy number variant (LCNV) group of 11q11 compared to boys; similarly, NW and OW/OB AA children were significantly less likely to belong to the LCNV group. The AA girls in LCNV had significantly higher BMI z-scores. Our findings suggest that the 11q11 copy number in children is race and gender-specific.

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 R2 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 efficacy.