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Importance: Glucagon-like peptide 1 receptor agonists (GLP-1RAs) may have nephroprotective properties beyond those related to weight loss and glycemic control., Objective: To investigate the association of genetically proxied GLP-1RAs with kidney disease progression., Design, Setting, and Participants: This genetic association study assembled a national retrospective cohort of veterans aged 18 years or older from the US Department of Veterans Affairs Million Veteran Program between January 10, 2011, and December 31, 2021. Data were analyzed from November 2023 to February 2024., Exposures: Genetic risk score for systemic GLP1R gene expression that was calculated for each study participant based on genetic variants associated with GLP1R mRNA levels across all tissue samples within the Genotype-Tissue Expression project., Main Outcomes and Measures: The primary composite outcome was incident end-stage kidney disease or a 40% decline in estimated glomerular filtration rate. Cox proportional hazards regression survival analysis assessed the association between genetically proxied GLP-1RAs and kidney disease progression., Results: Among 353 153 individuals (92.5% men), median age was 66 years (IQR, 58.0-72.0 years) and median follow-up was 5.1 years (IQR, 3.1-7.2 years). Overall, 25.7% had diabetes, and 45.0% had obesity. A total of 4.6% experienced kidney disease progression. Overall, higher genetic GLP1R gene expression was associated with a lower risk of kidney disease progression in the unadjusted model (hazard ratio [HR], 0.96; 95% CI, 0.92-0.99; P = .02) and in the fully adjusted model accounting for baseline patient characteristics, body mass index, and the presence or absence of diabetes (HR, 0.96; 95% CI, 0.92-1.00; P = .04). The results were similar in sensitivity analyses stratified by diabetes or obesity status., Conclusions and Relevance: In this genetic association study, higher GLP1R gene expression was associated with a small reduction in risk of kidney disease progression. These findings support pleiotropic nephroprotective mechanisms of GLP-1RAs independent of their effects on body weight and glycemic control.

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)