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1. Genome-Wide Association Study of Hypoglycemia in Adults With Diabetes in the Million Veteran Program

   https://doi.org/10.2337/db25-0304  

   Raghavan, Sridharan; Litkowski, Elizabeth; Jensen, Aubrey; Charest, Brian; Wang, Zeyuan; Hui, Qin; Chen, Hua-Chang; Rhee, Mary K; Leong, Aaron; Meigs, James B; et al (September 2025, Diabetes)

   Hypoglycemia is a preventable adverse treatment effect in diabetes patients, but genetic markers to identify those with increased susceptibility are lacking. We performed a case/control genome-wide association study (GWAS) of hypoglycemia in U.S. Million Veteran Program (MVP) participants with medication-treated diabetes. Case participants had an outpatient random serum/plasma glucose <70 mg/dL or an emergency department visit for hypoglycemia. GWAS was stratified by race/ethnicity, adjusted for age at MVP enrollment, sex, and top 10 population-specific principal components, followed by multipopulation meta-analysis. Secondary analyses examined genetic associations with hypoglycemia stratified by diabetes medication exposure as well as replication in UK Biobank and the Action to Control Cardiovascular Risk in Diabetes clinical trial. The study included 72,244 (22,045 case participants) non-Hispanic White participants, 24,162 (10,441 case participants) non-Hispanic Black participants, and 9,196 (2,800 case participants) Hispanic participants. Four loci had genome-wide significant associations with hypoglycemia in multipopulation meta-analysis: rs12712928 (chromosome 2, SIX2/SIX3 locus), rs1064173 (chromosome 6, HLA-DQB1/DQA2 locus), rs35198068 (chromosome 10, TCF7L2 locus), and rs113748381 (chromosome 17, SCL16A11 locus). All four loci replicated in at least one independent cohort, and the magnitude of associations with hypoglycemia varied by diabetes type. Genome-wide analyses may complement candidate pharmacogenetic studies to identify risk markers of adverse drug effects. Article HighlightsGenetic variants associated with hypoglycemia risk in individuals with medication-treated diabetes have not been evaluated genome-wide. The specific question we asked was whether common genetic variants are associated with hypoglycemia among individuals with diabetes treated with glucose-lowering medications. We found four genomic loci were associated with hypoglycemia in a genome-wide association study. One locus—on chromosome 6—was associated with hypoglycemia only in individuals with likely type 1 diabetes, and two loci—on chromosome 2 and chromosome 6—were associated with hypoglycemia only in the context of treatment with sulfonylureas (chromosome 2) or with insulin (chromosome 6). Genetic variants may help identify individuals with diabetes at increased hypoglycemia risk, but additional study is needed to address the clinical utility of genetic data to inform hypoglycemia risk. 

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2. PheWAS and cross-disorder analysis reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders

   https://doi.org/10.3389/fimmu.2023.1147573  

   Topaloudi, Apostolia; Jain, Pritesh; Martinez, Melanie B.; Bryant, Josephine K.; Reynolds, Grace; Zagoriti, Zoi; Lagoumintzis, George; Zamba-Papanicolaou, Eleni; Tzartos, John; Poulas, Konstantinos; et al (September 2023, Frontiers in Immunology)

   IntroductionAutoimmune disorders (ADs) are a group of about 80 disorders that occur when self-attacking autoantibodies are produced due to failure in the self-tolerance mechanisms. ADs are polygenic disorders and associations with genes both in the human leukocyte antigen (HLA) region and outside of it have been described. Previous studies have shown that they are highly comorbid with shared genetic risk factors, while epidemiological studies revealed associations between various lifestyle and health-related phenotypes and ADs. MethodsHere, for the first time, we performed a comparative polygenic risk score (PRS) - Phenome Wide Association Study (PheWAS) for 11 different ADs (Juvenile Idiopathic Arthritis, Primary Sclerosing Cholangitis, Celiac Disease, Multiple Sclerosis, Rheumatoid Arthritis, Psoriasis, Myasthenia Gravis, Type 1 Diabetes, Systemic Lupus Erythematosus, Vitiligo Late Onset, Vitiligo Early Onset) and 3,254 phenotypes available in the UK Biobank that include a wide range of socio-demographic, lifestyle and health-related outcomes. Additionally, we investigated the genetic relationships of the studied ADs, calculating their genetic correlation and conducting cross-disorder GWAS meta-analyses for the observed AD clusters. ResultsIn total, we identified 508 phenotypes significantly associated with at least one AD PRS. 272 phenotypes were significantly associated after excluding variants in the HLA region from the PRS estimation. Through genetic correlation and genetic factor analyses, we identified four genetic factors that run across studied ADs. Cross-trait meta-analyses within each factor revealed pleiotropic genome-wide significant loci. DiscussionOverall, our study confirms the association of different factors with genetic susceptibility for ADs and reveals novel observations that need to be further explored. 

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3. Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation

   https://doi.org/10.1186/s12863-023-01168-9  

   Jain, Pritesh R.; Burch, Myson; Martinez, Melanie; Mir, Pablo; Fichna, Jakub P.; Zekanowski, Cezary; Rizzo, Renata; Tümer, Zeynep; Barta, Csaba; Yannaki, Evangelia; et al (December 2023, BMC Genomic Data)

   Abstract Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases. 

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4. Interactions Between Enhanced Polygenic Risk Scores and Lifestyle for Cardiovascular Disease, Diabetes, and Lipid Levels

   https://doi.org/10.1161/CIRCGEN.120.003128  

   Ye, Yixuan; Chen, Xi; Han, James; Jiang, Wei; Natarajan, Pradeep; Zhao, Hongyu (February 2021, Circulation: Genomic and Precision Medicine)

   null (Ed.)

   Background: Both lifestyle and genetic factors confer risk for cardiovascular diseases, type 2 diabetes, and dyslipidemia. However, the interactions between these 2 groups of risk factors were not comprehensively understood due to previous poor estimation of genetic risk. Here we set out to develop enhanced polygenic risk scores (PRS) and systematically investigate multiplicative and additive interactions between PRS and lifestyle for coronary artery disease, atrial fibrillation, type 2 diabetes, total cholesterol, triglyceride, and LDL-cholesterol. Methods: Our study included 276 096 unrelated White British participants from the UK Biobank. We investigated several PRS methods (P+T, LDpred, PRS continuous shrinkage, and AnnoPred) and showed that AnnoPred achieved consistently improved prediction accuracy for all 6 diseases/traits. With enhanced PRS and combined lifestyle status categorized by smoking, body mass index, physical activity, and diet, we investigated both multiplicative and additive interactions between PRS and lifestyle using regression models. Results: We observed that healthy lifestyle reduced disease incidence by similar multiplicative magnitude across different PRS groups. The absolute risk reduction from lifestyle adherence was, however, significantly greater in individuals with higher PRS. Specifically, for type 2 diabetes, the absolute risk reduction from lifestyle adherence was 12.4% (95% CI, 10.0%–14.9%) in the top 1% PRS versus 2.8% (95% CI, 2.3%–3.3%) in the bottom PRS decile, leading to a ratio of >4.4. We also observed a significant interaction effect between PRS and lifestyle on triglyceride level. Conclusions: By leveraging functional annotations, AnnoPred outperforms state-of-the-art methods on quantifying genetic risk through PRS. Our analyses based on enhanced PRS suggest that individuals with high genetic risk may derive similar relative but greater absolute benefit from lifestyle adherence. 

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5. Sex Differences in Stress Response: Classical Mechanisms and Beyond

   https://doi.org/10.2174/1570159X22666231005090134  

   Hodes, Georgia E.; Bangasser, Debra; Sotiropoulos, Ioannis; Kokras, Nikolaos; Dalla, Christina (March 2024, Current Neuropharmacology)

   Abstract:Neuropsychiatric disorders, which are associated with stress hormone dysregulation, occurat different rates in men and women. Moreover, nowadays, preclinical and clinical evidence demonstratesthat sex and gender can lead to differences in stress responses that predispose males andfemales to different expressions of similar pathologies. In this curated review, we focus on what isknown about sex differences in classic mechanisms of stress response, such as glucocorticoid hormonesand corticotrophin-releasing factor (CRF), which are components of the hypothalamicpituitary-adrenal (HPA) axis. Then, we present sex differences in neurotransmitter levels, such as serotonin,dopamine, glutamate and GABA, as well as indices of neurodegeneration, such as amyloid βand Tau. Gonadal hormone effects, such as estrogens and testosterone, are also discussed throughoutthe review. We also review in detail preclinical data investigating sex differences caused by recentlyrecognizedregulators of stress and disease, such as the immune system, genetic and epigenetic mechanisms,as well neurosteroids. Finally, we discuss how understanding sex differences in stress responses,as well as in pharmacology, can be leveraged into novel, more efficacious therapeutics forall. Based on the supporting evidence, it is obvious that incorporating sex as a biological variable intopreclinical research is imperative for the understanding and treatment of stress-related neuropsychiatricdisorders, such as depression, anxiety and Alzheimer’s disease. 

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