Biomedical subjects

Josephine H Li

Publications and source records attributed to Josephine H Li.

3 recordsLinked to original sources

PubMed2026

Disentangling Sex Differences in Sulfonylurea Drug Response With Genome-Wide Association Studies in Individuals With Type 2 Diabetes.

Sulfonylureas are a cornerstone of type 2 diabetes therapy despite interindividual variability in response. Despite well-documented sex-based differences, pharmacogenomic and genome-wide association studies (GWAS) have largely overlooked sex as a biological variable. We conducted the first sex-stratified GWAS of hemoglobin A1c (HbA1c) response to sulfonylureas in Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial participants (N = 871). Variants meeting genome-wide (P < 5.0 × 10-8) and suggestive (P < 5.0 × 10-6) significance were assessed for replication in the Pharmacogenomics of Metformin (PMET1) cohort. Replicated variants were further analyzed in the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) cohort to assess acute insulin and glucose responses to a single glipizide dose. Genome-wide significant loci with sex-specific effects were identified: KAZN, KIF2B, SLC39A10, and SPINK5 (combined-sex); CRACR2A, KCNK2, and TENM2 (male-only); and NACPH2 (female-only). Two suggestive variants in the TMEM64/NECAB1 locus, associated with reduced HbA1c response to sulfonylureas in the male-only ACCORD analysis, were directly replicated in the PMET1 male-only cohort. In SUGAR-MGH, one replicated variant (rs6471250-C) was significantly associated with reduced peak insulin in males (P = 0.035) but not females (P = 0.40), demonstrating sex-specific functional effects. This study identified statistically supported and biologically plausible loci with prior evidence linking nearby genes to pathways relevant to sulfonylurea action, including insulin secretion, insulin regulation/sensitivity, calcium signaling, potassium-channel biology, and glucose transport. The findings highlight sex-specific differences in sulfonylurea response, providing mechanistic insights and underscoring the importance of sex-specific precision medicine. Identification of genetic variants influencing sex-specific response could inform dosing to optimize sulfonylureas.

Humans↗

PubMed2024

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Humans↗

PubMed2024

Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes.

Type 2 diabetes (T2D) genome-wide association studies (GWASs) often overlook rare variants as a result of previous imputation panels' limitations and scarce whole-genome sequencing (WGS) data. We used TOPMed imputation and WGS to conduct the largest T2D GWAS meta-analysis involving 51,256 cases of T2D and 370,487 controls, targeting variants with a minor allele frequency as low as 5 × 10-5. We identified 12 new variants, including a rare African/African American-enriched enhancer variant near the LEP gene (rs147287548), associated with fourfold increased T2D risk. We also identified a rare missense variant in HNF4A (p.Arg114Trp), associated with eightfold increased T2D risk, previously reported in maturity-onset diabetes of the young with reduced penetrance, but observed here in a T2D GWAS. We further leveraged these data to analyze 1,634 ClinVar variants in 22 genes related to monogenic diabetes, identifying two additional rare variants in HNF1A and GCK associated with fivefold and eightfold increased T2D risk, respectively, the effects of which were modified by the individual's polygenic risk score. For 21% of the variants with conflicting interpretations or uncertain significance in ClinVar, we provided support of being benign based on their lack of association with T2D. Our work provides a framework for using rare variant GWASs to identify large-effect variants and assess variant pathogenicity in monogenic diabetes genes.

Diabetes Mellitus, Type 2↗