Biomedical subjects

William D Figg

Publications and source records attributed to William D Figg.

2 recordsLinked to original sources

PubMed2026

Sociodemographic trends in prostate cancer: insights from the All of Us Research Program.

BACKGROUND: Prostate cancer disproportionately affects vulnerable populations. The All of Us Research Program (AoURP) is a database that aims to encapsulate the diversity of the United States. To explore the utility of this dataset in assessing prostate cancer disparities, we investigated whether treatment usage, disease progression, and genomic research participation vary across sociodemographic factors among AoURP participants with prostate cancer. METHODS: We identified AoURP participants with prostate cancer. Genomic research participation in AoURP, treatment usage, time-to-treatment, and time-to-metastasis were assessed by demographics and distance from a National Cancer Institute-designated comprehensive cancer center. Multivariable logistic regression and Cox proportional hazards regression were performed to evaluate treatment usage and time-to-treatment and time-to-metastasis, respectively. RESULTS: We observed lower genomic data availability in Black vs White patients (P < .001). In multivariable analyses, patients residing more than 80 miles from an NCI-designated comprehensive cancer center were less likely to receive androgen receptor pathway inhibitors (odds ratio [OR] = 0.30, 95% CI = 0.14 to 0.66; P = .002) and bone targeting agents (OR = 0.46, 95% CI = 0.30 to 0.70; P < .001) but more likely to undergo prostatectomy (OR = 1.97, 95% CI = 1.43 to 2.71; P < .001) than those residing less than 40 miles away. These patients also initiated treatment faster (hazard ratio [HR] = 1.54, 95% CI = 1.27 to 1.87; P < .001) and developed metastasis slower (HR = 0.58, 95% CI = 0.40 to 0.86; P = .006). Black patients were less likely to receive radiation (OR = 0.45, 95% CI = 0.23 to 0.88; P = .020), prostatectomy (OR = 0.65, 95% CI = 0.44 to 0.96; P = .028), and bone targeting agents (OR = 0.65, 95% CI = 0.45 to 0.93; P = .018) than White patients. CONCLUSIONS: Prostate cancer treatment usage, disease progression, and genomic research participation varied between demographic populations. As AoURP matures, additional studies may leverage future data releases to confirm these findings.

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PubMed2026

OCT1 Variants Are Associated with Metformin Clearance and Gluconeogenesis: Mechanistic Insights for Youth-Onset Type 2 Diabetes in the MIGHTY Study.

AIMS/HYPOTHESIS: Behavioral and phenotypic characteristics do not fully explain variability in African Americans with youth-onset type 2 diabetes (Y-T2D) treated with metformin with or without liraglutide. We hypothesized that biological heterogeneity, including genetic variation in the metformin transporter OCT1, influences metformin pharmacokinetics and hepatic glucose flux. Therefore, we sought to characterize metformin pharmacokinetics in Y-T2D and evaluate genetic variants known to modulate metformin efficacy in adults to determine the mechanisms underlying variation in treatment response. METHODS: We evaluated genetic variants related to metformin transport and mechanisms of action in 30 Y-T2D using a candidate-gene approach to evaluate the association of pharmacogenetic variants with fasting glucose and gluconeogenesis. In a subset of Y-T2D randomized to 3 months of metformin (n=11) or metformin and liraglutide (n=8), we constructed a metformin population pharmacokinetic model and evaluated gene variant associations. RESULTS: A one-compartment first-order absorption and elimination pharmacokinetic model provided the optimal fit. Metformin pharmacokinetic parameters were similar by group and not related to glycemia. The rs628031_OCT1 A allele was associated with greater metformin clearance. The rs622342_OCT1 C allele was associated with lower post-treatment fractional gluconeogenesis (β [95% CI] = -8.8 [-14.13, -3.47] %, Adjusted R2 = 0.56, P = 0.003). The rs7903146_TCF7L2 T allele was associated with greater reductions in fasting glucose among those treated with metformin + liraglutide (β = -1.32 [-2.42, -0.22] mmol/L, Adjusted R2 = 0.8, P<0.002), but baseline glucose and gluconeogenesis (P<0.0001) were the strongest predictors of post-treatment glycemia. CONCLUSION/INTERPRETATION: In Y-T2D, OCT1 gene variants rs628031 and rs622342 were associated with metformin clearance and gluconeogenesis, respectively. TCF7L2 variant rs7903146 may contribute to differences in glycemic response in youth treated with metformin and liraglutide. These findings suggest genetic variants may be important for understanding variable metformin response in Y-T2D.

SLC22A1↗