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Little is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P –9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.

Statins reduce cholesterol, prevent cardiovascular disease, and are among the most commonly prescribed medications in the world. Statin-associated musculoskeletal symptoms (SAMS) impact statin adherence and ultimately can impede the long-term effectiveness of statin therapy. There are several identified pharmacogenetic variants that impact statin disposition and adverse events during statin therapy. SLCO1B1 encodes a transporter (SLCO1B1; alternative names include OATP1B1 or OATP-C) that facilitates the hepatic uptake of all statins. ABCG2 encodes an efflux transporter (BCRP) that modulates the absorption and disposition of rosuvastatin. CYP2C9 encodes a phase I drug metabolizing enzyme responsible for the oxidation of some statins. Genetic variation in each of these genes alters systemic exposure to statins (i.e., simvastatin, rosuvastatin, pravastatin, pitavastatin, atorvastatin, fluvastatin, lovastatin), which can increase the risk for SAMS. We summarize the literature supporting these associations and provide therapeutic recommendations for statins based on SLCO1B1, ABCG2, and CYP2C9 genotype with the goal of improving the overall safety, adherence, and effectiveness of statin therapy. This document replaces the 2012 and 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for SLCO1B1 and simvastatin-induced myopathy.

A previous study in 356 healthy Finnish volunteers showed that glycochenodeoxycholate 3-O-glucuronide (GCDCA-3G) and glycodeoxycholate 3-O-glucuronide (GDCA-3G) are promising biomarkers of organic anion transporting polypeptide 1B1 (OATP1B1). In the same cohort, we now evaluated the performances of two other OATP1B1 biomarkers, coproporphyrin I (CPI) and III (CPIII), and compared them with GCDCA-3G and GDCA-3G. Based on decreased (*5 and *15) and increased (*14 and *20) function SLCO1B1 haplotypes, we stratified the participants to poor, decreased, normal, increased, and highly increased OATP1B1 function groups. Fasting plasma CPI concentration was 68% higher in the poor (95% confidence interval, 44%, 97%; P = 1.74 x 10(-10)), 7% higher in the decreased (0%, 15%; P = 0.0385), 10% lower in the increased (3%, 18%; P = 0.0087), and 23% lower in the highly increased (1%, 40%; P = 0.0387) function group than in the normal function group. CPIII concentration was 27% higher (7%, 51%; P = 0.0071) in the poor function group than in the normal function group. CPI and CPIII detected poor OATP1B1 function with areas under the precision-recall curve (AUPRC) of 0.388 (95% confidence interval, 0.197, 0.689) and 0.0798 (0.0485, 0.203), and receiver operating characteristic curve (AUROC) of 0.888 (0.851, 0.919) and 0.731 (0.682, 0.776). The AUPRC and AUROC of GCDCA-3G were, however, 0.389 (0.258, 0.563) and 0.100 (-0.0046, 0.204; P = 0.0610) larger than those of CPI, and 0.697 (0.555, 0.831) and 0.257 (0.141, 0.373; P < 0.0001) larger than those of CPIII. In conclusion, these data indicate that plasma CPI outperforms CPIII in detecting altered OATP1B1 function, but GCDCA-3G is an even more sensitive OATP1B1 biomarker than CPI.

Purpose Organic Anion Transporting Polypeptide 1B1 (OATP1B1) mediates hepatic influx and clearance of many drugs, including statins. The SLCO1B1 gene is highly polymorphic and its function-impairing variants can predispose patients to adverse effects. The effects of rare genetic variants of SLCO1B1 are mainly unexplored. We examined the impact of eight naturally occurring rare variants and the well-known SLCO1B1 c.521C > T (V174A) variant on in vitro transport activity, cellular localization and abundance. Methods Transport of rosuvastatin and 2,7-dichlorofluorescein (DCF) in OATP1B1 expressing HEK293 cells was measured to assess changes in activity of the variants. Immunofluorescence and confocal microscopy determined the cellular localization of OATP1B1 and LC–MS/MS based quantitative targeted absolute proteomics analysis quantified the amount of OATP1B1 in crude membrane fractions. Results All studied variants, with the exception of P336R, reduced protein abundance to varying degree. V174A reduced protein abundance the most, over 90% compared to wild type. Transport function was lost in G76E, V174A, L193R and R580Q variants. R181C decreased activity significantly, while T345M and L543W retained most of wild type OATP1B1 activity. P336R showed increased activity and H575L decreased the transport of DCF significantly, but not of rosuvastatin. Decreased activity was interrelated with lower absolute protein abundance in the studied variants. Conclusions Transmembrane helices 2, 4 and 11 appear to be crucial for proper membrane localization and function of OATP1B1. Four of the studied variants were identified as loss-of-function variants and as such could make the individual harboring these variants susceptible to altered pharmacokinetics and adverse effects of substrate drugs.

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response., Statins are effectively used to prevent and manage cardiovascular disease, but patient response to these drugs is highly variable. Here, the authors identify two new genes associated with the response of LDL cholesterol to statins and advance our understanding of the genetic basis of drug response.

Muscle wasting is associated with poor prognosis in chronic obstructive pulmonary disease (COPD). Exercise stimulates muscle recovery, but its efficacy is variable, depending on the clinical condition and medical treatment. Systemic glucocorticoids, commonly administered in high doses during acute disease exacerbations or as maintenance treatment in end-stage disease, are known to contribute to muscle wasting. As muscle mass recovery involves insulin-like growth factor (IGF)-I signaling, which can be stimulated by anabolic steroids, the impact of glucocorticoids and the effect of simultaneous IGF-I stimulation by anabolic steroids on muscle recovery and growth were investigated. The effects of, and interactions between, glucocorticoid and IGF-I signaling on skeletal muscle growth were assessed in differentiating C2C12 myocytes. As proof of principle, we performed a post hoc analysis stratifying patients by glucocorticoid use of a clinical trial investigating the efficacy of anabolic steroid supplementation on muscle recovery in muscle-wasted patients with COPD. Glucocorticoids strongly impaired protein synthesis signaling, myotube formation, and muscle-specific protein expression. In contrast, in the presence of glucocorticoids, IGF-I synergistically stimulated myotube fusion and myofibrillar protein expression, which corresponded with restored protein synthesis signaling by IGF-I and increased transcriptional activation of muscle-specific genes by glucocorticoids. In COPD patients on maintenance glucocorticoid treatment, the clinical trial also revealed an enhanced effect of anabolic steroids on muscle mass and respiratory muscle strength. In conclusion, synergistic effects of anabolic steroids and glucocorticoids on muscle recovery may be caused by relief of the glucocorticoid-imposed blockade on protein synthesis signaling, allowing effective translation of glucocorticoid-induced accumulation of muscle-specific gene transcripts.