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Bilirubin has antioxidant and anti-inflammatory properties in vitro and in animal studies and protects against inflammatory, cardiovascular, and other diseases in observational studies; therefore, bilirubin has potential as a therapeutic agent. However, observational studies could be confounded by many factors. We used a genetic (n = 61,281) and clinical (n = 234,670) approach to define the association between bilirubin and 19 conditions with a putative protective signal in observational studies. We also tested if individuals with genetically higher bilirubin levels underwent more diagnostic tests. We used a common variant in UGT1A1 (rs6742078) associated with an 26% increase in bilirubin levels in the genetic studies. Carriers of the variant had higher bilirubin levels (P = 2.2 × 10-16 ) but there was no significant association with any of the 19 conditions. In a phenome-wide association study (pheWAS) to seek undiscovered genetic associations, the only significant finding was increased risk of "jaundice-not of newborn." Carriers of the variant allele were more likely to undergo an abdominal ultrasound (odds ratio = 1.04, [1.00-1.08], P = 0.03). In contrast, clinically measured bilirubin levels were significantly associated with 15 of the 19 conditions (P

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

This study was aimed at analysing the mechanical characteristics of different mandibular extraction modes using a clear aligner. Three experimental schemes of different extraction patterns were designed to treat mandibular crowding, including extraction of one mandibular central incisor, bilateral first premolars, and bilateral second premolars. The stress distribution during the space closing was analysed using the finite element method. When a central incisor was extracted, a significant retraction force was found in the anterior region, in line with the design expectation. The posterior teeth, which were designed to move mesially, acted as anchorage for anterior retraction, and were subjected to a mesial force. The anterior teeth were retracted when the bilateral first premolars were extracted. The lateral incisors and canines were subjected to a significant distal force and moment, while the central incisors and canines were subjected to lingual forces and moments. Additionally, the canines were subjected to a non-designated intruding force. The molars were designed to move mesially when the bilateral second premolars were extracted. All molars were subjected to a significant mesial force, while the lingual force on the front teeth was slight. The bilateral second molars were subjected to non-design mesial moment and extrusive force. The bilateral first molars were subjected to a non-designated mesial moment, and the bilateral first premolars on both sides were subjected to non-designated intrusive force and distal moment. When one incisor was extracted, attachments on the anterior teeth had a controlling effect on the tooth axis, but the anterior teeth still tended to tilt. When the bilateral first premolars were extracted, the anterior teeth showed a tendency for lingual inclination. The risk of distal inclination of the canines and lingual inclination of the central incisor increased. When the bilateral second premolars were extracted and the posterior teeth were designed to move mesially, the teeth on both sides of the extraction sites showed an obvious bowing effect.

Objective We developed and evaluated Drug-Drug Interaction Wide Association Study (DDIWAS). This novel method detects potential drug-drug interactions (DDIs) by leveraging data from the electronic health record (EHR) allergy list. Materials and Methods To identify potential DDIs, DDIWAS scans for drug pairs that are frequently documented together on the allergy list. Using deidentified medical records, we tested 616 drugs for potential DDIs with simvastatin (a common lipid-lowering drug) and amlodipine (a common blood-pressure lowering drug). We evaluated the performance to rediscover known DDIs using existing knowledge bases and domain expert review. To validate potential novel DDIs, we manually reviewed patient charts and searched the literature. Results DDIWAS replicated 34 known DDIs. The positive predictive value to detect known DDIs was 0.85 and 0.86 for simvastatin and amlodipine, respectively. DDIWAS also discovered potential novel interactions between simvastatin-hydrochlorothiazide, amlodipine-omeprazole, and amlodipine-valacyclovir. A software package to conduct DDIWAS is publicly available. Conclusions In this proof-of-concept study, we demonstrate the value of incorporating information mined from existing allergy lists to detect DDIs in a real-world clinical setting. Since allergy lists are routinely collected in EHRs, DDIWAS has the potential to detect and validate DDI signals across institutions.

Objective Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs. Materials and Methods PheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. Results In this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online. Conclusions PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.

Carotid artery atherosclerotic disease (CAAD) is a risk factor for stroke. We used a genome‐wide association (GWAS) approach to discover genetic variants associated with CAAD in participants in the electronic Medical Records and Genomics (eMERGE) Network. We identified adult CAAD cases with unilateral or bilateral carotid artery stenosis and controls without evidence of stenosis from electronic health records at eight eMERGE sites. We performed GWAS with a model adjusting for age, sex, study site, and genetic principal components of ancestry. In eMERGE we found 1793 CAAD cases and 17,958 controls. Two loci reached genome‐wide significance, on chr6 in LPA (rs10455872, odds ratio [OR] (95% confidence interval [CI]) = 1.50 (1.30–1.73), p = 2.1 × 10−8) and on chr7, an intergenic single nucleotide variant (SNV; rs6952610, OR (95% CI) = 1.25 (1.16–1.36), p = 4.3 × 10−8). The chr7 association remained significant in the presence of the LPA SNV as a covariate. The LPA SNV was also associated with coronary heart disease (CHD; 4199 cases and 11,679 controls) in this study (OR (95% CI) = 1.27 (1.13–1.43), p = 5 × 10−5) but the chr7 SNV was not (OR (95% CI) = 1.03 (0.97–1.09), p = .37). Both variants replicated in UK Biobank. Elevated lipoprotein(a) concentrations ([Lp(a)]) and LPA variants associated with elevated [Lp(a)] have previously been associated with CAAD and CHD, including rs10455872. With electronic health record phenotypes in eMERGE and UKB, we replicated a previously known association and identified a novel locus associated with CAAD.

Objective This study was undertaken to investigate the hypothesis that a genetic predisposition toward rheumatoid arthritis (RA) increases the risk of 10 cardiometabolic and autoimmune disorders previously associated with RA in epidemiologic studies, and to define new genetic pleiotropy present in RA. Methods Two approaches were used to test our hypothesis. First, we constructed a weighted genetic risk score (wGRS) and then examined its association with 10 prespecified disorders. Additionally, a phenome-wide association study (PheWAS) was carried out to identify potential new associations. Second, inverse variance-weighted regression (IVWR) meta-analysis was used to characterize the association between genetic susceptibility to RA and the prespecified disorders, with the results expressed as odds ratios (ORs) and 95% confidence intervals (95% CIs). Results The wGRS for RA was significantly associated with type 1 diabetes mellitus (DM) (OR 1.10 [95% CI 1.04-1.16]; P = 9.82 × 10-4 ) and multiple sclerosis (OR 0.82 [95% CI 0.77-0.88]; P = 1.73 × 10-8 ), but not with other cardiometabolic phenotypes. In the PheWAS, wGRS was also associated with an increased risk of several autoimmune phenotypes including RA, thyroiditis, and systemic sclerosis, and with a decreased risk of demyelinating disorders. In the IVWR meta-analyses, RA was significantly associated with an increased risk of type 1 DM (P = 1.15 × 10-14 ), with evidence of horizontal pleiotropy (Mendelian Randomization-Egger intercept estimate P = 0.001) likely driven by rs2476601, a PTPN22 variant. The association between type 1 DM and RA remained significant (P = 9.53 × 10-9 ) after excluding rs2476601, with no evidence of horizontal pleiotropy (intercept estimate P = 0.939). RA was also significantly associated with type 2 DM and C-reactive protein levels. These associations were driven by variation in the major histocompatibility complex region. Conclusion This study presents evidence of pleiotropy between the genetic predisposition to RA and associated phenotypes found in other autoimmune and cardiometabolic disorders, including type 1 DM.

Azathioprine is used frequently to treat several inflammatory conditions. However, treatment is limited by adverse events-in particular, myelotoxicity. Thiopurine-S-methyltransferase (TPMT) and nudix hydrolase-15 (NUDT15) are enzymes involved in azathioprine metabolism; variants in the genes encoding these enzymes increase the risk for azathioprine myelotoxicity. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has recommended dose adjustments based on the results of TPMT and NUDT15 genotyping. However, little is known about the importance of this genetic information in routine clinical care. We hypothesized that in patients with inflammatory diseases, TPMT and NUDT15 genotype data predict the risk of discontinuing azathioprine due to myelotoxicity. This was a retrospective cohort study in 1,403 new adult azathioprine users for the management of inflammatory conditions for whom we had genetic information and clinical data. Among patients who discontinued azathioprine, we adjudicated the reason(s). Genotyping was performed using the Illumina Infinium Expanded Multi-Ethnic Genotyping Array plus custom content. We used CPIC guidelines to determine TPMT and NUDT15 metabolizer status; patients were grouped as either: (i) poor/intermediate, or (ii) normal/indeterminate metabolizers. We classified 110 patients as poor/intermediate, and 1,293 patients as normal/indeterminate metabolizers. Poor/intermediate status was associated with a higher risk for azathioprine discontinuation due to myelotoxicity compared to normal/indeterminate metabolizers (hazard ratio (HR) = 2.90, 95% confidence interval (CI): 1.58-5.31, P = 0.001). This association remained significant after adjustment for race, age at initiation, sex, primary indication, and initial daily dose of azathioprine (adjusted HR (aHR) = 2.67, 95% CI: 1.44-4.94, P = 0.002). In conclusion, TPMT and NUDT15 metabolizer status predicts discontinuation due to myelotoxicity for patients taking azathioprine for inflammatory conditions.

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Objectives To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. Methods Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. Results The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10−5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. Conclusion A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

Graphical abstract, Highlights • There may be long-term consequences of delaying procedures in response to COVID-19. • Electronic health record data can help us understand the impact of procedure delay. • Our high-throughput approach identifies procedures impacted by COVID-19. • Impact of procedure delay on patient outcomes are evaluated with retrospective data. • Our results can help hospital systems minimize adverse patient outcomes from delays., Objective During the COVID-19 pandemic, health systems postponed non-essential medical procedures to accommodate surge of critically-ill patients. The long-term consequences of delaying procedures in response to COVID-19 remains unknown. We developed a high-throughput approach to understand the impact of delaying procedures on patient health outcomes using electronic health record (EHR) data. Materials and Methods We used EHR data from Vanderbilt University Medical Center’s (VUMC) Research and Synthetic Derivatives. Elective procedures and non-urgent visits were suspended at VUMC between March 18, 2020 and April 24, 2020. Surgical procedure data from this period were compared to a similar timeframe in 2019. Potential adverse impact of delay in cardiovascular and cancer-related procedures was evaluated using EHR data collected from January 1, 1993 to March 17, 2020. For surgical procedure delay, outcomes included length of hospitalization (days), mortality during hospitalization, and readmission within six months. For screening procedure delay, outcomes included 5-year survival and cancer stage at diagnosis. Results We identified 416 surgical procedures that were negatively impacted during the COVID-19 pandemic compared to the same timeframe in 2019. Using retrospective data, we found 27 significant associations between procedure delay and adverse patient outcomes. Clinician review indicated that 88.9% of the significant associations were plausible and potentially clinically significant. Analytic pipelines for this study are available online. Conclusion Our approach enables health systems to identify medical procedures affected by the COVID-19 pandemic and evaluate the effect of delay, enabling them to communicate effectively with patients and prioritize rescheduling to minimize adverse patient outcomes.

Background: The pathophysiology of lipoprotein (a) [Lp(a)] remains obscure. We conducted a phenome wide analysis study (PheWAS) to identify pleiotropic effects of LPA variants. Methods: Among 51,843 European-ancestry participants (age 58±16 years, 54% female) of the electronic MEdical Records and Genomics (eMERGE) network we assessed whether LPA variants exhibited pleiotropic affects through an electronic health record-based PheWAS. We adjusted significant associations by presence of atherosclerotic cardiovascular disease (ASCVD; defined as a composite of coronary heart disease, cerebrovascular disease, and peripheral artery disease) to determine whether those associations were independent of ASCVD. Results: In PheWAS analysis, we tested 864 phecodes with 36 independent LPA variants. We identified 21 significant associations with non-ASCVD phenotypes including heart failure, aortic valve stenosis, and chronic kidney disease of which 14 were replicated across independent cohorts. The strength of associations between the LPA variant rs10455872 and both heart failure and aortic valve stenosis related phecodes were significantly attenuated after adjustment for ASCVD. However, the association with chronic kidney disease phecode remained independent following adjustment for ASCVD without any attenuation in the estimated odds ratio (Table). Conclusions: LPA genetic variants were associated with aortic valve stenosis, heart failure and chronic kidney disease. The observed association of LPA variant rs10455872 with aortic stenosis and heart failure appear to be partially mediated by ASCVD, while the association with chronic kidney disease appears to be independent of ASCVD. Additional studies are needed to elucidate potential mechanisms by which Lp(a) may contribute to the pathogenesis of non-ASCVD disease phenotypes.

ObjectiveIdentifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) that systematically scans a disease’s clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.MethodsUsing the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms.ResultsWe processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including “absent sense of smell” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “with cough fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC).ConclusionConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.

Tizanidine, a widely used muscle relaxant that can lower blood pressure, is metabolized by the cytochrome P450 1A2 (CYP1A2). We studied 1,626 patients prescribed tizanidine and 5,012 prescribed cyclobenzaprine concurrently with a strong CYP1A2 inhibitor. The primary outcome was severe hypotension, defined as systolic blood pressure (SBP) ≤ 70 mmHg during periods of drug co-exposure. Severe hypotension occurred more often in the tizanidine group (2.03%; n = 33) than the cyclobenzaprine group (1.28%; n = 64); odds ratio (OR) = 1.60; P = 0.029. This difference remained statistically significant after adjustment for a log-transformed propensity score that included age, sex, race, Charlson's comorbidity index, and concurrent use of antihypertensive medications (OR = 1.57; P = 0.049). A sensitivity analysis that defined hypotension as SBP < 90 mmHg also yielded higher rates of hypotension among patients prescribed tizanidine. In conclusion, CYP1A2 inhibition increases the risk of hypotensive episodes associated with the use of tizanidine in routine clinical practice.

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations., Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.

Because polygenic risk scores (PRSs) for coronary heart disease (CHD) are derived from mainly European ancestry (EA) cohorts, their validity in African ancestry (AA) and Hispanic ethnicity (HE) individuals is unclear. We investigated associations of “restricted” and genome-wide PRSs with CHD in three major racial and ethnic groups in the U.S. The eMERGE cohort (mean age 48 ± 14 years, 58% female) included 45,645 EA, 7,597 AA, and 2,493 HE individuals. We assessed two restricted PRSs (PRS(Tikkanen) and PRS(Tada); 28 and 50 variants, respectively) and two genome-wide PRSs (PRS(metaGRS) and PRS(LDPred); 1.7 M and 6.6 M variants, respectively) derived from EA cohorts. Over a median follow-up of 11.1 years, 2,652 incident CHD events occurred. Hazard and odds ratios for the association of PRSs with CHD were similar in EA and HE cohorts but lower in AA cohorts. Genome-wide PRSs were more strongly associated with CHD than restricted PRSs were. PRS(metaGRS), the best performing PRS, was associated with CHD in all three cohorts; hazard ratios (95% CI) per 1 SD increase were 1.53 (1.46–1.60), 1.53 (1.23–1.90), and 1.27 (1.13–1.43) for incident CHD in EA, HE, and AA individuals, respectively. The hazard ratios were comparable in the EA and HE cohorts (p(interaction) = 0.77) but were significantly attenuated in AA individuals (p(interaction)= 2.9 × 10(−3)). These results highlight the potential clinical utility of PRSs for CHD as well as the need to assemble diverse cohorts to generate ancestry- and ethnicity PRSs.

Leukopenia is a serious, frequent side effect associated with azathioprine use. Currently, we use thiopurine methyltransferase (TPMT) testing to predict leukopenia in patients taking azathioprine. We hypothesized that a risk score incorporating additional clinical and genetic variables would improve the prediction of azathioprine-associated leukopenia. In the discovery phase, we developed four risk score models: (1) age, sex, and TPMT metabolizer status; (2) model 1 plus additional clinical variables; (3) sixty candidate single nucleotide polymorphisms; and (4) model 2 plus model 3. The area under the receiver-operating-characteristic curve (AUC) of the risk scores was 0.59 (95%CI: 0.54-0.64), 0.75 (0.71-0.80), 0.66 (0.61-0.71), and 0.78 (0.74-0.82) for models one, two, three and four, respectively. During the replication phase, models two and four (AUC=0.64, 95%CI: 0.59-0.70 and AUC=0.63, 95%CI: 0.58-0.69, respectively) were significant in an independent group. Compared to TPMT testing alone, additional genetic and clinical variables improve the prediction of azathioprine-associated leukopenia.

Key Points Question Are there associations between candidate noncardiovascular diseases and statin treatment using genetic variants in the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) gene that decrease low-density lipoprotein cholesterol levels as an instrumental variable? Findings In a mendelian randomization approach cohort study including 53 385 individuals, using variants in the HMGCR gene affecting low-density lipoprotein cholesterol levels replicated the association between statin use and increased type 2 diabetes risk. However, no strong associations between 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibition by statins and other diseases were noted. Meaning In this cohort study, statin treatment was associated with an increased risk of type 2 diabetes, but there appeared to be no strong indication of other pleiotropic outcomes of the low-density lipoprotein cholesterol level–decreasing outcomes with this drug class on the clinical phenotypes studied., Importance Observational studies suggest that statins, which inhibit 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, may be associated with beneficial effects in many noncardiovascular diseases. Objective To construct a weighted HMG-CoA reductase (HMGCR) gene genetic risk score (GRS) using variants in the HMGCR gene affecting low-density lipoprotein cholesterol as an instrumental variable for mendelian randomization analyses to test associations with candidate noncardiovascular phenotypes previously associated with statin use in observational studies. Design, Setting, and Participants This cohort study included 53 385 unrelated adults of European ancestry with genome-wide genotypes available from BioVU (a practice-based biobank, used for discovery) and 30 444 unrelated adults with European ancestry available in the Electronic Medical Records and Genomics (eMERGE; a research consortium that conducts genetic research using electronic medical records, used for replication). The study was conducted from February 6, 2015, through April 31, 2019; data analysis was performed from August 26, 2019, through December 22, 2020. Interventions An HMGCR GRS was calculated. Main Outcomes and Measures The association between the HMGCR GRS and the presence or absence of 22 noncardiovascular phenotypes previously associated with statin use in clinical studies. Results Of the 53 385 individuals in BioVU, 29 958 (56.1%) were women; mean (SD) age was 59.9 (15.6) years. The finding between the HMGCR GRS and the noncardiovascular phenotypes of interest in this cohort was significant only for type 2 diabetes. An HMGCR GRS equivalent to a 10-mg/dL decrease in the low-density lipoprotein cholesterol level was associated with an increased risk of type 2 diabetes (odds ratio [OR], 1.09; 95% CI, 1.04-1.15; P = 5.58 × 10−4). The HMGCR GRS was not associated with other phenotypes; the closest were increased risk of Parkinson disease (OR, 1.30; 95% CI, 1.07-1.58; P = .007) and kidney failure (OR, 1.18; 95% CI, 1.05-1.34; P = .008). Of the 30 444 individuals in eMERGE, 16 736 (55.0%) were women; mean (SD) age was 68.7 (15.4) years. The association between the HMGCR GRS and type 2 diabetes was replicated in this cohort (OR, 1.09; 95% CI, 1.01-1.17; P = .02); however, the HMGCR GRS was not associated with Parkinson disease (OR, 0.93; 95% CI, 0.75-1.16; P = .53) and kidney failure (OR, 1.18; 95% CI, 0.98-1.41; P = .08) in the eMERGE cohort. Conclusions and Relevance A mendelian randomization approach using variants in the HMGCR gene replicated the association between statin use and increased type 2 diabetes risk but provided no strong evidence for pleiotropic effects of statin-induced decrease of the low-density lipoprotein cholesterol level on other diseases., This cohort study uses a mendelian randomization approach with variants in the HMGCR gene to evaluate associations between statin therapy and noncardiovascular diseases.

Objective Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) – that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. Methods We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. Results We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including “anosmia” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “cough with fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). Conclusion ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.

BACKGROUND: In contrast to most other common diseases, few genetic variants have been identified that impact risk of cirrhosis. We aimed to identify new genetic variants that predispose to cirrhosis, to test whether such variants, aggregated into a polygenic score, enable genomic risk stratification and to test whether alcohol intake or body mass index interact with polygenic predisposition. METHODS: A multi-trait genome-wide association study (GWAS) combining cirrhosis and alanine aminotransferase (ALT) levels, performed in five discovery studies (UK Biobank, Vanderbilt BioVU, the Atherosclerosis Risk in Communities study, and two case-control studies containing 4,829 individuals with cirrhosis and 72,705 controls and 362,539 individuals with ALT levels). Identified variants were replicated in three studies (Partners HealthCare Biobank, FinnGen and Biobank Japan containing 3,554 individuals with cirrhosis and 343,826 controls). A polygenic score was tested in Partners HealthCare Biobank RESULTS: Five previously-reported and seven newly-identified genetic variants were associated with cirrhosis in both the discovery studies multi-trait GWAS (p< 5×10(−8)) and the replication studies (p< 0.05), including a missense variant in the APOE gene and a noncoding variant near EFN1A. These 12 variants were used to generate a polygenic score. Among Partners HealthCare Biobank participants, high polygenic score -- defined as the top quintile of the distribution -- was associated with significantly increased risk of cirrhosis (OR 2.26, p

BackgroundDrug effects can be investigated through natural variation in the genes for their protein targets. We aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are amongst the most commonly used medications worldwide.MethodsWe identified genetic instruments for antihypertensive drug classes as variants in the gene for the corresponding target that associated with systolic blood pressure at genome-wide significance. To validate the instruments, we compared Mendelian randomisation (MR) estimates for drug effects on coronary heart disease (CHD) and stroke risk to randomised controlled trial (RCT) results. Phenome-wide association study (PheWAS) in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University Biobank (BioVU) and in observational analysis of the UK Biobank.FindingsWe identified suitable genetic instruments for beta-blockers (BBs) and calcium channel blockers (CCBs). MR estimates for their effect on CHD and stroke risk respectively were comparable to results from RCTs against placebo. PheWAS in the UK Biobank identified an association of the CCB genetic risk score (scaled to drug effect) with increased risk of diverticulosis (odds ratio [OR] 1.23, 95%CI 1.10-1.38), with a consistent estimate found in BioVU (OR 1.16, 95%CI 0.94-1.44). Association with diverticulosis was further supported in observational analysis of CCB use in the UK Biobank (OR 1.08, 95%CI 1.02-1.15).InterpretationWe identified valid genetic instruments for BBs and CCBs. Using genetic and observational approaches, we highlighted a previously unreported potential detrimental effect of CCBs on risk of diverticulosis. This work serves as a proof of concept that investigation of genetic variants can offer a complementary approach to exploring the efficacy and side effects of anti-hypertensive medications.FundingWellcome Trust.

Background: Coronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown. Methods: We performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus. Results: The meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35–1.86; P =2.6×10 − 10 ). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14–2.57; P =0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17–2.24; P =0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18–4.75; P =0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes. Conclusions: Genetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.

Background Observations from statin clinical trials and from Mendelian randomization studies suggest that low low-density lipoprotein cholesterol (LDL-C) concentrations may be associated with increased risk of type 2 diabetes mellitus (T2DM). Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations in increased risk of T2DM. Methods and findings We used de-identified electronic health records (EHRs) at Vanderbilt University Medical Center to compare the risk of T2DM in a cross-sectional study among individuals with very low (≤60 mg/dl, N = 8,943) and normal (90–130 mg/dl, N = 71,343) LDL-C levels calculated using the Friedewald formula. LDL-C levels associated with statin use, hospitalization, or a serum albumin level < 3 g/dl were excluded. We used a 2-phase approach: in 1/3 of the sample (discovery) we used T2DM phenome-wide association study codes (phecodes) to identify cases and controls, and in the remaining 2/3 (validation) we identified T2DM cases and controls using a validated algorithm. The analysis plan for the validation phase was constructed at the time of the design of that component of the study. The prevalence of T2DM in the very low and normal LDL-C groups was compared using logistic regression with adjustment for age, race, sex, body mass index (BMI), high-density lipoprotein cholesterol, triglycerides, and duration of care. Secondary analyses included prespecified stratification by sex, race, BMI, and LDL-C level. In the discovery cohort, phecodes related to T2DM were significantly more frequent in the very low LDL-C group. In the validation cohort (N = 33,039 after applying the T2DM algorithm to identify cases and controls), the risk of T2DM was increased in the very low compared to normal LDL-C group (odds ratio [OR] 2.06, 95% CI 1.80–2.37; P < 2 × 10−16). The findings remained significant in sensitivity analyses. The association between low LDL-C levels and T2DM was significant in males (OR 2.43, 95% CI 2.00–2.95; P < 2 × 10−16) and females (OR 1.74, 95% CI 1.42–2.12; P = 6.88 × 10−8); in normal weight (OR 2.18, 95% CI 1.59–2.98; P = 1.1× 10−6), overweight (OR 2.17, 95% CI 1.65–2.83; P = 1.73× 10−8), and obese (OR 2.00, 95% CI 1.65–2.41; P = 8 × 10−13) categories; and in individuals with LDL-C < 40 mg/dl (OR 2.31, 95% CI 1.71–3.10; P = 3.01× 10−8) and LDL-C 40–60 mg/dl (OR 1.99, 95% CI 1.71–2.32; P < 2.0× 10−16). The association was significant in individuals of European ancestry (OR 2.67, 95% CI 2.25–3.17; P < 2 × 10−16) but not in those of African ancestry (OR 1.09, 95% CI 0.81–1.46; P = 0.56). A limitation was that we only compared groups with very low and normal LDL-C levels; also, since this was not an inception cohort, we cannot exclude the possibility of reverse causation. Conclusions Very low LDL-C concentrations occurring in the absence of statin treatment were significantly associated with T2DM risk in a large EHR population; this increased risk was present in both sexes and all BMI categories, and in individuals of European ancestry but not of African ancestry. Longitudinal cohort studies to assess the relationship between very low LDL-C levels not associated with lipid-lowering therapy and risk of developing T2DM will be important.

BackgroundCurrent approaches to predicting Cardiovascular disease rely on conventional risk factors and cross-sectional data. In this study, we asked whether: i) machine learning and deep learning models with longitudinal EHR information can improve the prediction of 10-year CVD risk, and ii) incorporating genetic data can add values to predictability.MethodsWe conducted two experiments. In the first experiment, we modeled longitudinal EHR data with aggregated features and temporal features. We applied logistic regression (LR), random forests (RF) and gradient boosting trees (GBT) and Convolutional Neural Networks (CNN) and Recurrent Neural Networks, using Long Short-Term Memory (LSTM) units. In the second experiment, we proposed a late-fusion framework to incorporate genetic features.ResultsOur study cohort included 109, 490 individuals (9,824 were cases and 99, 666 were controls) from Vanderbilt University Medical Center’s (VUMC) de-identified EHRs. American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equations had areas under receiver operating characteristic curves (AUROC) of 0.732 and areas under receiver under precision and recall curves (AUPRC) of 0.187. LSTM, CNN and GBT with temporal features achieved best results, which had AUROC of 0.789, 0.790, and 0.791, and AUPRC of 0.282, 0.280 and 0.285, respectively. The late fusion approach achieved a significant improvement for the prediction performance.ConclusionsMachine learning and deep learning with longitudinal features improved the 10-year CVD risk prediction. Incorporating genetic features further enhanced 10-year CVD prediction performance, underscoring the importance of integrating relevant genetic data whenever available in the context of routine care.

OBJECTIVE: Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes – how they emerge, evolve and contribute to health outcomes – is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery. METHODS: In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10 years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis. RESULTS: From a cohort of 12,380 adult CVD individuals with 1,068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (p < 0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD. CONCLUSION: This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.

Key Points Question What is the association between low levels of low-density lipoprotein cholesterol and risk of sepsis in patients admitted to the hospital with serious infection? Findings In this cohort study of deidentified electronic medical records of patients admitted to the hospital with infection, measured levels of low-density lipoprotein cholesterol in 3961 patients and a low-density lipoprotein cholesterol genetic risk score in 7804 patients were not associated with increased risk of sepsis after adjusting for comorbidities. Meaning Levels of low-density lipoprotein cholesterol are not directly associated with the risk of sepsis or poor outcomes in patients hospitalized with infection., This cohort study of medical records assesses whether low-density lipoprotein cholesterol (LDL-C) levels are associated with an increased risk of sepsis among patients admitted to the hospital with infection., Importance Whether low levels of low-density lipoprotein cholesterol (LDL-C) are associated with increased risk of sepsis and poorer outcomes is unknown. Objective To examine the association between LDL-C levels and risk of sepsis among patients admitted to the hospital with infection. Design, Setting, and Participants Cohort study in which deidentified electronic health records were used to define a cohort of patients admitted to Vanderbilt University Medical Center, Nashville, Tennessee, with infection. Patients were white adults, had a code indicating infection from the International Classification of Diseases, Ninth Revision, Clinical Modification, and received an antibiotic within 1 day of hospital admission (N = 61 502). Data were collected from January 1, 1993, through December 31, 2017, and analyzed from January 24 through October 31, 2018. Interventions Clinically measured LDL-C levels (excluding measurements .05 for all). The LDL-C GRS correlated with measured LDL-C levels (r = 0.24; P

Aim: Simvastatin is a lactone prodrug that exists in equilibrium with its active hydroxyacid through a process mediated by UGT1A enzymes. The UGT1A locus has been associated with simvastatin response and disposition in humans. Therefore, we fine-mapped the UGT1A locus to identify genetic variations contributing to simvastatin disposition and response variability. Methods: Using de-identified electronic medical records linked to a DNA biobank, we extracted information about dose and low-density lipo­protein cholesterol (LDL-C) concentrations for patients who received more than two different doses of simvastatin. Pharmacodynamic measures of simvastatin potency and efficacy were calculated from dose–response curves (E0 = baseline LDL-C, ED50 = dose yielding 50% maximum response, and Emax = maximum decrease in LDL-C) in 1100 patients. We selected 153 polymorphisms in UGT1A1 and UGT1A3 for genotyping and conducted genotype–phenotype associations using a prespecified additive model. Results: Two variants in UGT1A1 (rs2003569 and rs12052787) were associated with Emax (p = 0.0059 and 0.031, respectively; for rs2003569 the mean Emax was 59.3 ± 23.0, 62.0 ± 22.4, and 69.7 ± 24.8 mg/dl, for patients with 0, 1 or 2 copies of the minor A allele, respectively). When stratified by race, the difference in response was greater in African–Americans than in European Americans. Rs2003569 was also negatively associated with total serum bilirubin levels (p = 7.85 × 10-5). Four rare SNPs were nominally associated with E0 and ED50. Conclusion: We identified a UGT1A1 promoter variant (rs2003569) associated with simvastatin efficacy. Original submitted 26 March 2014; Revision submitted 26 August 2014

Simvastatin is among the most commonly used prescription medications for cholesterol reduction. A single coding single-nucleotide polymorphism, rs4149056T>C, in SLCO1B1 increases systemic exposure to simvastatin and the risk of muscle toxicity. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for simvastatin based on SLCO1B1 genotype. This article is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium guideline for SLCO1B1 and simvastatin-induced myopathy.

HMG CoA reductase inhibitors (statins) are among the most commonly prescribed medications in the industrialized world. They are generally regarded as safe. Mild myalgias can occur in up to 10 % of patients exposed to statins, but skeletal muscle damage (accompanied by an increase in circulating creatine kinase levels) occurs much less frequently. Clinical predictors of statin-induced rhabdomyolysis (severe muscle damage with end organ failure) include female gender, advanced age, and concomitant medications known to interact with critical pharmacokinetic and pharmacodynamic processes. The influence of genetic variations has been investigated by candidate gene association studies, genome-wide association studies, and whole-genome sequencing. This chapter summarizes current available approaches to clinical and genetic characterization of statin-related adverse effect.

Cholesterol reduction from statin therapy has been one of the greatest public health successes in modern medicine. Simvastatin is among the most commonly used prescription medications. A non-synonymous coding single-nucleotide polymorphism (SNP), rs4149056, in SLCO1B1 markedly increases systemic exposure to simvastatin and the risk of muscle toxicity. This guideline explores the relationship between rs4149056 (c.521T>C, p.V174A) and clinical outcome for all statins. The strength of the evidence is high for myopathy with simvastatin. We limit our recommendations accordingly.

HMG CoA reductase inhibitors (statins) are among the most commonly prescribed medications in the industrialized world. They are generally regarded as safe. Mild myalgias can occur in up to 10% of patients exposed to statins, but skeletal muscle damage (accompanied by an increase in circulating creatine kinase levels) occurs much less frequently. Clinical predictors of statin-induced rhabdomyolysis (severe muscle damage with end organ failure) include female gender, advanced age, and concomitant medications known to interact with critical pharmacokinetic and pharmacodynamic processes. Although the underlying pathophysiologic mechanism remains somewhat poorly characterized, growing evidence supports the claim that statin-related changes in protein prenylation disrupt mitochondrial function, as well as altering the balance between cell cycling and apoptosis in skeletal myocytes. This chapter surveys current knowledge regarding mechanism, and it explores novel genomic approaches being leveraged to provide deeper insight.

Background: Taxane is one of the first line treatments of lung cancer. In order to identify novel single nucleotide polymorphisms (SNPs) that might contribute to taxane response, we performed a genome-wide association study (GWAS) for two taxanes, paclitaxel and docetaxel, using 276 lymphoblastoid cell lines (LCLs), followed by genotyping of top candidate SNPs in 874 lung cancer patient samples treated with paclitaxel. Methods: GWAS was performed using 1.3 million SNPs and taxane cytotoxicity IC50 values for 276 LCLs. The association of selected SNPs with overall survival in 76 small or 798 non-small cell lung cancer (SCLC, NSCLC) patients were analyzed by Cox regression model, followed by integrated SNP-microRNA-expression association analysis in LCLs and siRNA screening of candidate genes in SCLC (H196) and NSCLC (A549) cell lines. Results: 147 and 180 SNPs were associated with paclitaxel or docetaxel IC50s with p-values, National Institutes of Health (U.S.) ( NIH grant K22 CA130828), National Institutes of Health (U.S.) (NIH grant R01 CA138461), National Institutes of Health (U.S.) (NIH grant U19 GM61388), National Institutes of Health (U.S.) (Pharmacogenomics Research Network (R01 CA80127), National Institutes of Health (U.S.) (Pharmacogenomics Research Network (R01 CA84354)), National Institutes of Health (U.S.) (Pharmacogenomics Research Network (R01 CA105857))