Development of electronic health record based algorithms to identify individuals with diabetic retinopathy.

OBJECTIVES: To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs). MATERIALS AND METHODS: We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination. RESULTS: The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR. CONCLUSIONS/DISCUSSION: We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

Genetic predictors of blood pressure traits are associated with preeclampsia.

Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University's BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (ORDBP = 1.10, 95% CI 1.02-1.17, p = 7.68 × 10-3; ORSBP = 1.16, 95% CI 1.09-1.23, p = 2.23 × 10-6; ORPP = 1.14, 95% CI 1.07-1.27, p = 9.86 × 10-5). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Adaptive selection at G6PD and disparities in diabetes complications.

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

A new test for trait mean and variance detects unreported loci for blood-pressure variation.

Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci.

Development of Portable Electronic Health Record Based Algorithms to Identify Individuals with Diabetic Retinopathy.

Objectives: To develop, validate and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHR)s. Methods : We developed and validated EHR-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in three independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet one of three criteria: 1) two or more dates with any DR ICD-9/10 code documented in the EHR, or 2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or 3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology exam. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology exam. Results: The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.97 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV=0.94; NPV=0.86) and lower in MGB (PPV=0.84; NPV=0.76). In comparison, use of DR definition as implemented in Phenome-wide association study (PheWAS) in VUMC, yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62,000 DR cases with genetic data including 14,549 African Americans and 6,209 Hispanics with DR. Conclusions/Discussion: We demonstrate the robustness of the algorithms at three separate health-care centers, with a minimum PPV of 0.84 and substantially improved NPV than existing high-throughput methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records.

BACKGROUND: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery. METHODS: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR). FINDINGS: After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25). INTERPRETATION: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions. FUNDING: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

Variant-based heritability assessment of dexmedetomidine and fentanyl clearance in pediatric patients.

Despite complex pathways of drug disposition, clinical pharmacogenetic predictors currently rely on only a few high effect variants. Quantification of the polygenic contribution to variability in drug disposition is necessary to prioritize target drugs for pharmacogenomic approaches and guide analytic methods. Dexmedetomidine and fentanyl, often used in postoperative care of pediatric patients, have high rates of inter-individual variability in dosing requirements. Analyzing previously generated population pharmacokinetic parameters, we used Bayesian hierarchical mixed modeling to measure narrow-sense (additive) heritability ( h SNP 2 ) of dexmedetomidine and fentanyl clearance in children and identify relative contributions of small, moderate, and large effect-size variants to h SNP 2 . We used genome-wide association studies (GWAS) to identify variants contributing to variation in dexmedetomidine and fentanyl clearance, followed by functional analyses to identify associated pathways. For dexmedetomidine, median clearance was 33.0 L/h (interquartile range [IQR] 23.8-47.9 L/h) and h SNP 2 was estimated to be 0.35 (90% credible interval 0.00-0.90), with 45% of h SNP 2 attributed to large-, 32% to moderate-, and 23% to small-effect variants. The fentanyl cohort had median clearance of 8.2 L/h (IQR 4.7-16.7 L/h), with estimated h SNP 2 of 0.30 (90% credible interval 0.00-0.84). Large-effect variants accounted for 30% of h SNP 2 , whereas moderate- and small-effect variants accounted for 37% and 33%, respectively. As expected, given small sample sizes, no individual variants or pathways were significantly associated with dexmedetomidine or fentanyl clearance by GWAS. We conclude that clearance of both drugs is highly polygenic, motivating the future use of polygenic risk scores to guide appropriate dosing of dexmedetomidine and fentanyl.

Genetic Predictors of Blood Pressure Traits are Associated with Preeclampsia.

Background: Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. The mechanisms leading to preeclampsia are unclear; however, there is evidence that preeclampsia is highly heritable. We evaluated the association of polygenic risk scores (PRS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Methods: Participants were obtained from Vanderbilt University's BioVU, the Electronic Medical Records and Genomics network, and the Penn Medicine Biobank. Non-Hispanic Black and White females of reproductive age with indications of pregnancy and genotype information were included. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PRS were obtained from Giri et al 2019. Associations between preeclampsia and each PRS were evaluated separately by race and study population before evidence was meta-analyzed. Prediction models were developed and evaluated using 10-fold cross validation. Results: In the 3,504 Black and 5,009 White individuals included, the rate of preeclampsia was 15.49%. The DBP and SBP PRSs were associated with preeclampsia in Whites but not Blacks. The PP PRS was significantly associated with preeclampsia in Blacks and Whites. In trans-ancestry meta-analysis, all PRSs were associated with preeclampsia (OR DBP =1.10, 95% CI=1.02-1.17, p =7.68×10 -3 ; OR SBP =1.16, 95% CI=1.09-1.23, p =2.23×10 -6 ; OR PP =1.14, 95% CI=1.07-1.27, p =9.86×10 -5 ). However, addition of PRSs to clinical prediction models did not improve predictive performance. Conclusions: Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Association between nitric oxide synthase 3 genetic variant and acute kidney injury following pediatric cardiac surgery.

BACKGROUND: Acute kidney injury (AKI) complicates 30% to 50% of cardiac surgeries in pediatric patients. Genetic variants that affect renal blood flow and inflammation have been associated with AKI after cardiac surgery in diverse populations of adults but have not been studied in children. The objective of this study is to test the hypothesis that common candidate genetic variants are associated with AKI following pediatric cardiac surgery. METHODS: This is a retrospective cohort study at a single tertiary referral children's hospital of 2,062 individual patients undergoing surgery for congenital heart disease from September 2007 to July 2020. Pre-specified variants in candidate genes (AGTR1, APOE, IL6, NOS3, and TNF) were chosen. AKI was defined using Kidney Disease: Improving Global Outcomes serum creatinine criteria in the first week following surgery. Outcomes were analyzed by univariate and multivariable analysis of demographic, clinical, and genetic factors. RESULTS: The study population had median age of 6 (interquartile range [IQR], 1-53) months, 759 (37%) of whom met criteria for postoperative AKI. In unadjusted analyses of each genetic variant, only NOS3 (rs2070744) was associated with lower risk for AKI (OR 0.75, 95% CI 0.62-0.9, P = .002). In logistic regression analyses adjusting for body surface area, previously identified genetic syndrome, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) score, cardiopulmonary bypass time, and nephrotoxic medication exposure, the NOS3 variant remained protective against AKI (OR 0.7, 95% CI 0.58-0.85, P<.001). CONCLUSIONS: A common variant in NOS3 is associated with decreased incidence of AKI in children undergoing cardiac surgery. Further analysis of the genetic contributions to postoperative AKI may help identify individual risk in the pediatric population.

Diastolic Blood Pressure Alleles Improve Congenital Heart Defect Repair Outcomes.

BACKGROUND: Congenital heart defects (CHDs) affect 40 000 US births per year, half of which require surgical intervention. Individual differences in surgical outcomes including mortality and complications are not well understood but may be due to genetic variability. We hypothesized that polygenic risk scores (PRSs) for blood pressure in adults are associated with treatments and postsurgical outcomes in children with CHD, as CHD survivors are at higher risk of negative cardiometabolic disease. METHODS: We used imputed genotype data from pediatric participants requiring surgery for CHD (median age at surgery, 201 days; nmax=2498). Base data for the systolic and diastolic blood pressure PRSs (nmax=760 226) came from published genome-wide association study. The blood pressure PRSs were tested for association with postsurgical outcomes. All effects presented are per SD increase in PRS and adjusted for age, sex, body mass index, surgical complexity score, and first 10 principal components of ancestry. RESULTS: A higher diastolic blood pressure PRS was associated with decreased in-hospital mortality risk (odds ratio, 0.57 [0.39-0.82]; P=0.0022). Additional analyses suggest an interaction between diastolic blood pressure PRS and vasopressor dose. Those with a diastolic blood pressure PRS 1 SD above the mean, receiving a vasopressor dose in the top tertile, were estimated to have 52% (32%-66%) lower risk of in-hospital mortality compared with those with a vasopressor dose in the bottom tertile. CONCLUSIONS: These results suggest a genetically determined postsurgical survival advantage for CHD patients with blood pressure increasing alleles. Further study may reveal novel mechanisms contributing to postoperative morbidity and mortality, and this approach may assist in early identification of children at risk for adverse postoperative outcomes.

Effect of CYP3A5 and CYP3A4 Genetic Variants on Fentanyl Pharmacokinetics in a Pediatric Population.

Fentanyl is an anesthetic/analgesic commonly used in surgical and recovery settings. CYP3A4 and CYP3A5 encode enzymes, which metabolize fentanyl; genetic variants in these genes impact fentanyl pharmacokinetics in adults. Pharmacokinetic (PK) studies are difficult to replicate in children due to the burden of additional blood taken solely for research purposes. The aim of this study is to test the effect of CYP3A5 and CYP3A4 genetic variants on fentanyl PKs in children using opportunistically collected samples. Fentanyl concentrations were measured from remnant blood specimens and dosing data were extracted from electronic health records. Variant data defining CYP3A4*1G and CYP3A5*3 and *6 alleles were available from prior genotyping; alleles with no variant were defined as *1. The study cohort included 434 individuals (median age 9 months, 52% male subjects) and 1,937 fentanyl concentrations were available. A two-compartment model was selected as the base model, and the final covariate model included age, weight, and surgical severity score. Clearance was significantly associated with either CYP3A5*3 or CYP3A5*6 alleles, but not the CYP3A4*1G allele. A genotype of CYP3A5*1/*3 or CYP3A5*1/*6 (i.e., intermediate metabolizer status) was associated with a 0.84-fold (95% confidence interval (CI): 0.71-1.00) reduction in clearance vs. CYP3A5*1/*1 (i.e., normal metabolizer status). CYP3A5*3/*3, CYP3A5*3/*6, or CYP3A5*6/*6 (i.e., poor metabolizer status) was associated with a 0.76-fold (95% CI: 0.58-0.99) reduction in clearance. In the final model, expected clearance was 8.9 and 6.8 L/hour for a normal and poor metabolizer, respectively, with median population covariates (9 months old, 7.7 kg, low surgical severity).

Population pharmacokinetic analysis of dexmedetomidine in children using real-world data from electronic health records and remnant specimens.

AIMS: Our objectives were to perform a population pharmacokinetic analysis of dexmedetomidine in children using remnant specimens and electronic health records (EHRs) and explore the impact of patient's characteristics and pharmacogenetics on dexmedetomidine clearance. METHODS: Dexmedetomidine dosing and patient data were gathered from EHRs and combined with opportunistically sampled remnant specimens. Population pharmacokinetic models were developed using nonlinear mixed-effects modelling. Stage 1 developed a model without genotype variables; Stage 2 added pharmacogenetic effects. RESULTS: Our final study population included 354 post-cardiac surgery patients aged 0-22 years (median 16 mo). The data were best described with a 2-compartment model with allometric scaling for weight and Hill maturation function for age. Population parameter estimates and 95% confidence intervals were 27.3 L/h (24.0-31.1 L/h) for total clearance, 161 L (139-187 L) for central compartment volume of distribution, 26.0 L/h (22.5-30.0 L/h) for intercompartmental clearance and 7903 L (5617-11 119 L) for peripheral compartment volume of distribution. The estimate for postmenstrual age when 50% of adult clearance is achieved was 42.0 weeks (41.5-42.5 weeks) and the Hill coefficient estimate was 7.04 (6.99-7.08). Genotype was not statistically or clinically significant. CONCLUSION: Our study demonstrates the use of real-world EHR data and remnant specimens to perform a population pharmacokinetic analysis and investigate covariate effects in a large paediatric population. Weight and age were important predictors of clearance. We did not find evidence for pharmacogenetic effects of UGT1A4 or UGT2B10 genotype or CYP2A6 risk score.

Exome-wide evaluation of rare coding variants using electronic health records identifies new gene-phenotype associations.

The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease1. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P < 10-6), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into 'gene burdens' for identifying new gene-disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes.

Equity in Health: Consideration of Race and Ethnicity in Precision Medicine.

The causes for disparities in implementation of precision medicine are complex, due in part to differences in clinical care and a lack of engagement and recruitment of under-represented populations in studies. New tools and large genetic cohorts can change these circumstances and build access to personalized medicine for disadvantaged populations.

Tks5 SH3 domains exhibit differential effects on invadopodia development.

The Src substrate Tks5 helps scaffold matrix-remodeling invadopodia in invasive cancer cells. Focus was directed here on how the five SH3 domains of Tks5 impact that activity. Mutations designed to inhibit protein-protein interactions were created in the individual SH3 domains of Tks5, and the constructs were introduced into the LNCaP prostate carcinoma cell line, a model system with intrinsically low Tks5 expression and which our lab had previously showed the dependence of Src-dependent Tks5 phosphorylation on invadopodia development. In LNCaP cells, acute increases in wild-type Tks5 led to increased gelatin matrix degradation. A similar result was observed when Tks5 was mutated in its 4th or 5th SH3 domains. This was in contrast to the 1st, 2nd, and 3rd SH3 domain mutations of Tks5 where each had a remarkable accentuating effect on gelatin degradation. Conversely, in the invadopodia-competent Src-3T3 model system, mutations in any one of the first three SH3 domains had a dominant negative effect that largely eliminated the presence of invadopodia, inhibited gelatin degradation activity, and redistributed both Src, cortactin, and Tks5 to what are likely endosomal compartments. A hypothesis involving Tks5 conformational states and the regulation of endosomal trafficking is presented as an explanation for these seemingly disparate results.