Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Using Electronic Health Records in a Large Population-Based Cohort

Oni‐Orisan, Akinyemi; Hoffmann, Thomas J.; Ranatunga, Dilrini K.; Medina, Marisa W.; Jorgenson, Eric; Schaefer, Catherine; Krauss, Ronald M.; Iribarren, Carlos; Risch, Neil

doi:10.1161/circgen.117.002043

Cited by 31 publications

(42 citation statements)

References 35 publications

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“…We used electronic health records (EHRs) from the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as previously described. [21][22][23] Lipid panel measurements, statin dispensing records, and other medical records from individuals who initiated statin therapy between 1996 and early 2018 were extracted for the study. We used the same methods as previously described to define a new statin user and classify individuals by cardiovascular risk factor status (hypertension, diabetes, cigarette smoking status).…”

Section: Data Source and Study Populationmentioning

confidence: 99%

“…We used the same methods as previously described to define a new statin user and classify individuals by cardiovascular risk factor status (hypertension, diabetes, cigarette smoking status). 23 Briefly, participants had to have ≥2 dispensing records of a statin (to exclude potentially nonadherent participants) as well as ≥1 lipid measurement before and after statin initiation (to determine statin LDL-C response). Approval was obtained from Kaiser Permanente and University of California Institutional Review Boards.…”

Section: Data Source and Study Populationmentioning

confidence: 99%

“…All lipid measurement data collected from the EHRs were fasting levels, were obtained in the course of patient care, and were assayed in Kaiser Permanente laboratories, as previously described. 10,23 From the pair of LDL-C measurements per participant, we calculated statin LDL-C response using two formulas. The first formula was based on the definition of Postmus et al 15 as the difference between the natural logtransformed baseline LDL-C (X) and on-treatment LDL-C (Y) values adjusted for the natural log-transformed baseline value: ln(Y) − ln(X) adjusted for ln (X).…”

Section: Phenotypementioning

confidence: 99%

“…The second formula was a percent change in LDL-C from statin therapy that we used previously. 23 Briefly, response was expressed as (Y − X)/X. Both phenotypes were adjusted for the following prespecified covariates (at the time of statin initiation) within self-reported race/ethnicity groups for each participant: age, sex, body mass index (BMI), statin type, statin dose, hypertension, diabetes, and cigarette smoking (current/former).…”

Section: Phenotypementioning

confidence: 99%

“…Statin dose adjustment was based on a revised defined daily dose equivalency table as previously described. 23 Genetic ancestry eigenvectors previously generated from principal component analyses within the race/ethnicity groups were also included as covariates. 21 In particular, the first ten eigenvectors for White/Europeans and the first six eigenvectors for Black/Africans, East Asians, and Hispanic/Latinos were included as covariates.…”

Section: Phenotypementioning

confidence: 99%

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The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change

et al. 2020

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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.

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Section: Data Source and Study Populationmentioning

confidence: 99%

Section: Data Source and Study Populationmentioning

confidence: 99%

Section: Phenotypementioning

confidence: 99%

Section: Phenotypementioning

confidence: 99%

Section: Phenotypementioning

confidence: 99%

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The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change

et al. 2020

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Effect of SLCO1B1 T521C on Statin‐Related Myotoxicity With Use of Lovastatin and Atorvastatin

Sun

Seraydarian

et al. 2021

Clin Pharma and Therapeutics

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The association between the c.521T>C variant allele in SLCO1B1 (reference single nucleotide polymorphism (rs)4149056) and simvastatin‐induced myotoxicity was discovered over a decade ago; however, whether this relationship represents a class effect is still not fully known. The aim of this study was to investigate the relationship between rs4149056 genotype and statin‐induced myotoxicity in patients taking atorvastatin and lovastatin. Study participants were from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. A total of 233 statin‐induced myopathy + rhabdomyolysis cases met the criteria for inclusion and were matched to 2,342 controls. To validate the drug response phenotype, we replicated the previously established association between rs4149056 genotype and simvastatin‐induced myotoxicity. In particular, compared with homozygous T allele carriers, there was a significantly increased risk of simvastatin‐induced myopathy + rhabdomyolysis in homozygous carriers of the C allele (CC vs. TT, odds ratio [OR] 4.62, 95% confidence interval [CI] 1.58–11.90, P = 0.003). For lovastatin users, homozygous carriers of the C allele were also at increased risk of statin‐induced myopathy + rhabdomyolysis (CC vs. TT, OR 4.49, 95% CI 1.68–10.80, P = 0.001). In atorvastatin users, homozygous carriers of the C allele were twice as likely to experience statin‐induced myopathy, though this association did not achieve statistical significance (CC vs. TT, OR 2.00, 95% CI 0.44–6.59, P = 0.30). In summary, our findings suggest that the association of rs4149056 with simvastatin‐related myotoxicity may also extend to lovastatin. More data is needed to determine the extent of the association in atorvastatin users. Altogether, these data expand the evidence base for informing guidelines of pharmacogenetic‐based statin prescribing practices.

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Polygenic Risk Score and Statin Relative Risk Reduction for Primary Prevention of Myocardial Infarction in a Real‐World Population

Oni‐Orisan

Haldar

Cayabyab

et al. 2022

Clin Pharma and Therapeutics

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Genetic substudies of randomized controlled trials demonstrate that high coronary heart disease (CHD) polygenic risk score modifies statin CHD relative risk reduction; it is unknown if the association extends to statin users undergoing routine care. We sought to determine how statin effectiveness is modified by CHD polygenic risk score in a real‐world cohort of participants without previous myocardial infarction. We determined CHD polygenic risk scores in participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Covariate‐adjusted Cox regression models were used to compare the risk of cardiovascular outcomes between statin users and matched nonusers. Statin effectiveness on incident myocardial infarction showed no gradient with increasing 10‐year Pooled Cohort Equations atherosclerotic cardiovascular disease (ASCVD) risk across low, borderline, intermediate, and high ASCVD risk score groups. In contrast, statin effectiveness by polygenic risk was largest in the high polygenic risk score group (hazard ratio (HR) 0.41, 95% confidence interval (CI), 0.31–0.53; P = 1.5E‐11), intermediate in the intermediate polygenic risk score group (HR 0.56, 95% CI, 0.47–0.66; P = 8.4E‐12), and smallest in the low polygenic risk score group (HR 0.67, 95% CI, 0.47–0.97; P = 0.03; P for high vs. low = 0.01). ASCVD risk and statin low‐density lipoprotein cholesterol (LDL‐C) lowering did not differ across polygenic risk score groups. In patients undergoing routine care, CHD polygenic risk modified statin relative risk reduction of incident myocardial infarction independent of LDL‐C lowering. Our findings extend prior work by identifying a subset (i.e., self‐identified White individuals with low CHD polygenic risk scores) with attenuated clinical benefit from statins.

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Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Using Electronic Health Records in a Large Population-Based Cohort

Cited by 31 publications

References 35 publications

The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change

The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change

Effect of SLCO1B1 T521C on Statin‐Related Myotoxicity With Use of Lovastatin and Atorvastatin

Polygenic Risk Score and Statin Relative Risk Reduction for Primary Prevention of Myocardial Infarction in a Real‐World Population

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