Neurobeachin (NBEA) is a novel gene associated with GLP-1 receptor agonist associated weight loss

Abstract

Aims: Nearly 42% of adults in the United States have obesity, a significant risk factor for many cardiometabolic diseases and cancers. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are promising interventions for weight loss, but their efficacy varies significantly across individuals. This study investigates the role of neurobeachin (NBEA), a gene that encodes a protein kinase A anchor protein, on weight loss response in two large, real-world cohorts.

Materials and methods: We utilised data from individuals prescribed a GLP-1RA in the NIH All of Us (N = 6556) and validated in the UK Biobank (N = 241). The NBEA genetic score for weight loss (12-18 months) was developed using the NIH All of Us cohort and independently validated in the UK Biobank. Logistic regression modelled associations between the score and outcomes, including high responsiveness (top 20th percentile for weight loss) and non-responsiveness (weight change ≥0%).

Results: Individuals meeting the responsive NBEA score threshold were 82% more likely to be highly responsive (FDR p = 1·8 × 10^-6) on liraglutide and were validated in the UK Biobank (odds ratio (OR) = 2·37; p = ·008). Individuals on semaglutide meeting this threshold for highly responsive had OR = 1·63 and OR = 2·21 in discovery and validation sets respectively (p < ·05). Individuals on liraglutide with a non-responsive NBEA score were 50% more likely to not lose weight (FDR p = 2.9 × 10^-4) and were validated in the UK Biobank (OR = 1·81; p = ·041), but the non-response score did not validate for semaglutide.

Conclusion: These findings indicate that NBEA genetic variation is predictive of GLP-1RA weight loss and may support future efforts to identify individuals likely to experience significant weight loss with GLP-1RAs, enabling personalised obesity treatment strategies.

Keywords: GLP‐1RA; NBEA; electronic health records; genetic score; weight loss.

FIGURE 1

Study workflow.

FIGURE 2

NBEA score associations with weight change percentiles in the All of Us cohort. (A) The association between response type and the NBEA score. Logistic regression models were used for estimation while adjusting for sex, genetic ancestry, prescription duration, age and BMI at the time of GLP‐1 prescription. (B) The percentages of the cohort (excluding those taking exenatide) categorised based on their NBEA score. (C) The observed proportion of individuals who were classified correctly as highly responsive (top 20% weight loss) compared to non‐responders (weight change ≥0%) according to the NBEA score.

FIGURE 3

NBEA score associations with weight change percentiles in the UK Biobank cohort and Semaglutide validation set. (A) The association between weight loss response type and the NBEA score. Liraglutide validation data were collected from the UK Biobank (UKB) and semaglutide validation data consisted of randomly selected 30% of patients from the NIH All of Us Cohort (AoU) that were not used in the discovery set. Logistic regression models were used for estimation while adjusting for sex, genetic ancestry, prescription duration, age and BMI at the time of GLP‐1RA prescription. (B) The percentages of the cohort categorised based on their NBEA score. (C) The observed proportion of individuals who were classified correctly as highly responsive (top 20% weight loss) compared to non‐responders (weight change ≥0%) according to the NBEA score.

FIGURE 4

PheWAS of NBEA SNVs in the All of Us cohort. (A) Bar plot depicting number of significant phenotypes (FDR p < 0.05) associated with a single‐nucleotide variant (SNV) for each cohort stratified by phenotype category. (B) Manhattan plot depicting PheWAS results for 403 NBEA SNVs and their association with cardiovascular, endocrine/metabolism and neurological phenotypes in the full All of Us cohort (N = 168 660). Similar phenotypes are plotted closer. The table shows significant associations.