John Butsch

Background: This study aimed to further explore the application of genetic risk assessments in 24 metabolic bariatric surgery (MBS) patients to predict weight loss outcomes three years after the procedure. Methods: Participants were assessed using the Genetic Addiction Risk Severity (GARS) test, which evaluates neurogenic polymorphisms linked to addiction and reward deficiency. Genetic and psychosocial data collected prior to surgery were analyzed in relation to post-operative weight loss measures, including weight change, body mass index (BMI), percentage of total weight loss (%TWL), and percentage of expected weight loss (%EWL). The analysis examined associations between specific genetic risk alleles, weight-related outcomes at three to four years post-surgery, and psychosocial trait scores. Results: Spearman’s correlations revealed that the DRD2 risk allele is negatively correlated with 3-year BMI (r_s = −0.481, p < 0.05, 95% CI: –0.746 to –0.083). One-way ANOVA indicated that there is a significant difference in 3-year BMI (p = 0.018) between 0 and 1 DRD2 risk allele copy. There is also a significant difference in ∆weight (p = 0.022), ∆BMI (p = 0.014), and %EWL (p = 0.032) among the different SNP expression values of the MAOA risk allele. In addition, Spearman’s correlation revealed that FCQ scores are negatively correlated with ∆BMI (r_s = −0.470, p < 0.05, 95% CI: −0.767, −0.005), %TWL (r_s = −0.561, p < 0.05, 95% CI: −0.814, −0.129), and %EWL (r_s = −0.533, p < 0.05, 95% CI: −0.800, −0.090) at 3 years post-surgery and positively correlated with 3-year weight (r_s = 0.576, p < 0.05, 95% CI: 0.151, 0.821) and 3-year BMI (r_s = 0.552, p < 0.05, 95% CI: 0.117, 0.810). Lastly, GARS scores are positively correlated with 3-year ∆weight (r_s = 0.422, p < 0.05, 95% CI: 0.010, 0.712).

This study analyzed genetic risk assessments in patients undergoing bariatric surgery to serve as a predictive factor for weight loss parameters 1 year after the operation. Thirty (30) patients were assessed for Genetic Addiction Risk Severity (GARS), which analyzes neurogenetic polymorphisms involved in addiction and reward deficiency. Genetic and psychosocial data collected before the operation were correlated with weight loss data, including changes in weight, body mass index (BMI), and percent of expected weight loss (%EWL). Results examined correlations between individual gene risk alleles, 1-year body weight data, and psychosocial trait scores. Spearman’s correlations revealed that the OPRM1 (rs1799971) gene polymorphism had significant negative correlation with 1-year weight (r_s = −0.4477, p < 0.01) and BMI (r_s = −0.4477, p < 0.05). In addition, the DRD2 risk allele (rs1800497) was correlated negatively with BMI at 1 year (r_s = −0.4927, p < 0.05), indicating that one risk allele copy was associated with lower BMI. However, this allele was positively correlated with both ∆Weight (r_s = 0.4077, p < 0.05) and %EWL (r_s = 0.5521, p < 0.05) at 1 year post-surgery. Moreover, the overall GARS score was correlated with %EWL (r_s = 0.4236, p < 0.05), ∆Weight (r_s = 0.3971, p < 0.05) and ∆BMI (r_s = 0.3778, p < 0.05). Lastly, Food Cravings Questionnaire (FCQ) scores were negatively correlated with %EWL (r_s = −0.4320, p < 0.05) and ∆Weight at 1 year post-surgery (r_s = −0.4294, p < 0.05). This suggests that individuals with a higher genetic addiction risk are more responsive to weight loss treatment, especially in the case of the DRD2 polymorphism. These results should translate clinically to improve positivity and attitude related to weight management by those individuals born with the risk alleles (rs1800497; rs1799971).