A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts.

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

The polygenic architecture of left ventricular mass mirrors the clinical epidemiology.

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.

Clinical Pharmacogenetics Implementation Consortium Guideline for Thiopurine Dosing Based on TPMT and NUDT15 Genotypes: 2018 Update.

Thiopurine methyltransferase (TPMT) activity exhibits a monogenic codominant inheritance and catabolizes thiopurines. TPMT variant alleles are associated with low enzyme activity and pronounced pharmacologic effects of thiopurines. Loss-of-function alleles in the NUDT15 gene are common in Asians and Hispanics and reduce the degradation of active thiopurine nucleotide metabolites, also predisposing to myelosuppression. We provide recommendations for adjusting starting doses of azathioprine, mercaptopurine, and thioguanine based on TPMT and NUDT15 genotypes (updates on www.cpicpgx.org).

Rooted in risk: genetic predisposition for low-density lipoprotein cholesterol level associates with diminished low-density lipoprotein cholesterol response to statin treatment.

AIMS: To utilize previously reported lead SNPs for low-density lipoprotein cholesterol (LDL-c) levels to find additional loci of importance to statin response, and examine whether genetic predisposition to LDL-c levels associates with differential statin response. METHODS: We investigated effects on statin response of 59 LDL-c SNPs, by combining summary level statistics from the Global Lipids Genetics and Genomic Investigation of Statin Therapy consortia. RESULTS: Lead SNPs for APOE, SORT1 and NPC1L1 were associated with a decreased LDL-c response to statin treatment, as was overall genetic predisposition for increased LDL-c levels as quantified with 59 SNPs, with a 5.4% smaller statin response per standard deviation increase in genetically raised LDL-c levels. CONCLUSION: Genetic predisposition for increased LDL-c level may decrease efficacy of statin therapy.

Creation and Validation of an EMR-based Algorithm for Identifying Major Adverse Cardiac Events while on Statins.

Statin medications are often prescribed to ameliorate a patient's risk of cardiovascular events due in part to cholesterol reduction. We developed and evaluated an algorithm that can accurately identify subjects with major adverse cardiac events (MACE) while on statins using electronic medical record (EMR) data. The algorithm also identifies subjects experiencing their first MACE while on statins for primary prevention. The algorithm achieved 90% to 97% PPVs in identification of MACE cases as compared against physician review. By applying the algorithm to EMR data in BioVU, cases and controls were identified and used subsequently to replicate known associations with eight genetic variants. We replicated 6/8 previously reported genetic associations with cardiovascular diseases or lipid metabolism disorders. Our results demonstrated that the algorithm can be used to accurately identify subjects with MACE and MACE while on statins. Consequently, future e studies can be conducted to investigate and validate the relationship between statins and MACE using real-world clinical data.

Modeling drug exposure data in electronic medical records: an application to warfarin.

Identification of patients' drug exposure information is critical to drug-related research that is based on electronic medical records (EMRs). Drug information is often embedded in clinical narratives and drug regimens change frequently because of various reasons like intolerance or insurance issues, making accurate modeling challenging. Here, we developed an informatics framework to determine patient drug exposure histories from EMRs by combining natural language processing (NLP) and machine learning (ML) technologies. Our framework consists of three phases: 1) drug entity recognition - identifying drug mentions; 2) drug event detection - labeling drug mentions with a status (e.g., "on" or "stop"); and 3) drug exposure modeling - predicting if a patient is taking a drug at a given time using the status and temporal information associated with the mentions. We applied the framework to determine patient warfarin exposure at hospital admissions and achieved 87% precision, 79% recall, and an area under the receiver-operator characteristic curve of 0.93.

Assessment of adherence to and persistence on disease-modifying antirheumatic drugs (DMARDs) in patients with rheumatoid arthritis.

OBJECTIVE: Biologic disease-modifying antirheumatic drugs (DMARDs) are efficacious for treating rheumatoid arthritis (RA). However, measurements of relative effectiveness, including treatment adherence and persistence, are lacking. We evaluated adherence and persistence during new episodes of use of traditional and biologic DMARDs. METHODS: Using Tennessee Medicaid databases (1995-2004), we assembled a retrospective cohort of patients diagnosed with RA, and identified new episodes of use for 12 DMARD regimens. We evaluated persistence through survival analyses, and adherence within episodes through the medication possession ratio. A risk score was included in the analyses to account for measured confounders. RESULTS: We identified 14,932 patients with RA; 6018 patients had 10,547 episodes of new use of DMARDs. Considering methotrexate as the reference and after adjustment for measured confounders, episodes of new use of sulfasalazine [adjusted hazard ratio (aHR) = 1.59; 95% confidence interval (CI) = 1.47-1.72] and infliximab alone (aHR = 1.37, 95% CI = 1.09-1.73) were more likely to be discontinued; and new episodes of etanercept (aHR = 0.82, 95% CI = 0.73-0.92) and methotrexate + adalimumab (aHR = 0.63, 95% CI = 0.48-0.84) were less likely to be discontinued. Compared with methotrexate, adherence was higher for leflunomide, infliximab, etanercept, and adalimumab and lower for sulfasalazine and all combined therapies. CONCLUSIONS: We developed an approach to assess persistence on and adherence to the most common DMARD therapies. In this large cohort, persistence and adherence to leflunomide and most biologic DMARD therapies were at least comparable to methotrexate. Adherence was lower for sulfasalazine and all combined therapies.

Immune dysregulation accelerates atherosclerosis and modulates plaque composition in systemic lupus erythematosus.

Patients with systemic lupus erythematosus (SLE) have accelerated atherosclerosis. The underlying mechanisms are poorly understood, and investigations have been hampered by the absence of animal models that reflect the human condition of generalized atherosclerosis and lupus. We addressed this problem by transferring lupus susceptibility to low-density lipoprotein (LDL) receptor-deficient (LDLr-/-) mice, an established model of atherosclerosis, creating radiation chimeras with NZM2410-derived, lupus-susceptible, B6.Sle1.2.3 congenic or C57BL/6 control donors (LDLr.Sle and LDLr.B6, respectively). LDLr.Sle mice developed a lupus-like disease characterized by production of double-stranded DNA autoantibodies and renal disease. When fed a Western-type diet, LDLr.Sle chimeras had increased mortality and atherosclerotic lesions. The plaques of LDLr.Sle mice were highly inflammatory and contained more CD3+ T cells than controls. LDLr.Sle mice also had increased activation of CD4+ T and B cells and significantly higher antibody to oxidized LDL and cardiolipin. Collectively, these studies demonstrate that the lupus-susceptible immune system enhances atherogenesis and modulates plaque composition.