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Background: Low-density lipoprotein cholesterol (LDL-C) response to statin therapy has not been fully elucidated in real-world populations. The primary objective of this study was to characterize statin LDL-C dose-response and its heritability in a large, multi-ethnic population of statin users. Methods: We determined the effect of statin dosing on lipid measures utilizing electronic health records (EHRs) in 33,139 statin users from the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. The relationship between statin defined daily dose (DDD) and lipid parameter response (percent change) was determined. Results: DDD and LDL-C response were associated in a log-linear relationship (β = −6.17, standard error [SE] = 0.09, P < 10−300) which remained significant after adjusting for pre-specified covariates (adjusted β = −5.59, SE = 0.12, P < 10−300). Statin type, sex, age, smoking status, diabetes, and East Asian race/ethnicity were significant independent predictors of statin-induced changes in LDL-C. Based on a variance-component method within the subset of statin users who had at least one first-degree relative who was also a statin user (N = 1,036), heritability of statin LDL-C response was estimated at 11.7% (SE = 8.6%, P = 0.087). Conclusions: Using EHR data, we observed a statin LDL-C dose response consistent with the “rule of 6%” from prior clinical trial data. Clinical and demographic predictors of statin LDL-C response exhibited highly significant, but modest effects. Finally, statin-induced changes in LDL-C were not found to be strongly inherited. Ultimately, these findings demonstrate (1) the utility of EHRs as a reliable source to generate robust phenotypes for pharmacogenomic research and (2) the potential role of statin precision medicine in lipid management.

Metabolism of arachidonic acid by cytochrome P450 (CYP) to biologically active eicosanoids has been recognized increasingly as an integral mediator in the pathogenesis of cardiovascular and metabolic disease. CYP epoxygenase-derived epoxyeicosatrienoic and dihydroxyeicosatrienoic acids (EET + DHET) and CYP ω-hydroxylase-derived 20-hydroxyeicosatetraenoic acid (20-HETE) exhibit divergent effects in the regulation of vascular tone and inflammation; thus, alterations in the functional balance between these parallel pathways in liver and kidney may contribute to the pathogenesis and progression of metabolic syndrome. However, the impact of metabolic dysfunction on CYP-mediated formation of endogenous eicosanoids has not been well characterized. Therefore, we evaluated CYP epoxygenase (EET + DHET) and ω-hydroxylase (20-HETE) metabolic activity in liver and kidney in apoE(-/-) and wild-type mice fed a high-fat diet, which promoted weight gain and increased plasma insulin levels significantly. Hepatic CYP epoxygenase metabolic activity was significantly suppressed, whereas renal CYP ω-hydroxylase metabolic activity was induced significantly in high-fat diet-fed mice regardless of genotype, resulting in a significantly higher 20-HETE/EET + DHET formation rate ratio in both tissues. Treatment with enalapril, but not metformin or losartan, reversed the suppression of hepatic CYP epoxygenase metabolic activity and induction of renal CYP ω-hydroxylase metabolic activity, thereby restoring the functional balance between the pathways. Collectively, these findings suggest that the kinin-kallikrein system and angiotensin II type 2 receptor are key regulators of hepatic and renal CYP-mediated eicosanoid metabolism in the presence of metabolic syndrome. Future studies delineating the underlying mechanisms and evaluating the therapeutic potential of modulating CYP-derived EETs and 20-HETE in metabolic diseases are warranted.

In pharmacogenomic studies of quantitative change, any association between genetic variants and the pretreatment (baseline) measurement can bias the estimate of effect between those variants and drug response. A putative solution is to adjust for baseline. We conducted a series of genome-wide association studies (GWASs) for low-density lipoprotein cholesterol (LDL-C) response to statin therapy in 34,874 participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as a case study to investigate the impact of baseline adjustment on results generated from pharmacogenomic studies of quantitative change. Across phenotypes of statin-induced LDL-C change, baseline adjustment identified variants from six loci meeting genomewide significance (SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB, and SMARCA4/LDLR). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance (LPA, APOE, and SLCO1B1). A genome-wide heterogeneity test of baseline versus statin on-treatment LDL-C levels was performed as the definitive test for the true effect of genetic variants on statin-induced LDL-C change. These findings were generally consistent with the models not adjusting for baseline signifying that genome-wide significant hits generated only from baseline-adjusted analyses (SORT/CELSR2/PSRC1, APOB, SMARCA4/LDLR) were likely biased. We then comprehensively reviewed published GWASs of drug-induced quantitative change and discovered that more than half (59%) inappropriately adjusted for baseline. Altogether, we demonstrate that (1) baseline adjustment introduces bias in pharmacogenomic studies of quantitative change and (2) this erroneous methodology is highly prevalent. We conclude that it is critical to avoid this common statistical approach in future pharmacogenomic studies of quantitative change.

We explored the role of genetic ancestry in shaping the genetic architecture of whole blood gene expression using whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children. We find that heritability of gene expression significantly increases with greater proportion of genome-wide African ancestry and decreases with higher levels of Indigenous American ancestry. Fine-mapping of expression quantitative trait loci (eQTLs) in individuals with predominantly African or Indigenous American ancestry revealed ancestry-specific eQTLs in over 30% of heritable genes. We leveraged our data to train genetically derived transcriptome prediction models, which identified significantly more associated genes when applied to 28 traits from a multi-ancestry population. Our findings underscore the importance of increasing representation from ancestrally diverse populations in genomic studies to enable new discoveries and ensure their equitable translation.