Yuk-Lam Ho

Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program

The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).Analysis of genetic data and blood lipid measurements from over 300,000 participants in the Million Veteran Program identifies new associations for blood lipid traits.

Yield and bias in defining a cohort study baseline from electronic health record data

AIMS Despite growing interest in using electronic health records (EHR) to create longitudinal cohort studies, the distribution and missingness of EHR data might introduce selection bias and information bias to such analyses. We aimed to examine the yield and potential for these healthcare process biases in defining a study baseline using EHR data, using the example of cholesterol and blood pressure (BP) measurements. METHODS We created a virtual cohort study of cardiovascular disease (CVD) from patients with eligible cholesterol profiles in the New England (NE) and Southeast (SE) networks of the Veterans Health Administration in the United States. Using clinical data from the EHR, we plotted the yield of patients with BP measurements within an expanding timeframe around an index date of cholesterol testing. We compared three groups: (1) patients with BP from the exact index date; (2) patients with BP not on the index date but within the network-specific 90th percentile around the index date; and (3) patients with no BP within the network-specific 90th percentile. RESULTS Among 589,361 total patients in the two networks, 146,636 (61.0%) of 240,479 patients from NE and 289,906 (83.1%) of 348,882 patients from SE had BP measurements on the index date. Ninety percent had BP measured within 11 days of the index date in NE and within 5 days of the index date in SE. Group 3 in both networks had fewer available race data, fewer comorbidities and CVD medications, and fewer health system encounters. CONCLUSIONS Requiring same-day risk factor measurement in the creation of a virtual CVD cohort study from EHR data might exclude 40% of eligible patients, but including patients with infrequent visits might introduce bias. Data visualization can inform study-specific strategies to address these challenges for the research use of EHR data.

Alcohol Consumption and Risk of Coronary Artery Disease (from the Million Veteran Program)

Moderate alcohol consumption has been associated with a lower risk of coronary artery disease (CAD) in the general population but has not been well studied in US veterans. We obtained self-reported alcohol consumption from Million Veteran Program participants. Using electronic health records, CAD events were defined as 1 inpatient or 2 outpatient diagnosis codes for CAD, or 1 code for a coronary procedure. We excluded participants with prevalent CAD (n = 69,995) or incomplete alcohol information (n = 8,449). We used a Cox proportional hazards model to estimate hazard ratios and 95% confidence intervals for CAD, adjusting for age, gender, body mass index, race, smoking, education, and exercise. Among 156,728 participants, the mean age was 65.3 years (standard deviation = 12.1) and 91% were men. There were 6,153 CAD events during a mean follow-up of 2.9 years. Adjusted hazard ratios (95% confidence intervals) for CAD were 1.00 (reference), 1.02 (0.92 to 1.13), 0.83 (0.74 to 0.93), 0.77 (0.67 to 0.87), 0.71 (0.62 to 0.81), 0.62 (0.51 to 0.76), 0.58 (0.46 to 0.74), and 0.95 (0.85 to 1.06) for categories of never drinker; former drinker; current drinkers of ≤0.5 drink/day, >0.5 to 1 drink/day, >1 to 2 drinks/day, >2 to 3 drinks/day, and >3 to 4 drinks/day; and heavy drinkers (>4 drinks/day) or alcohol use disorder, respectively. For a fixed amount of ethanol, intake at ≥3 days/week was associated with lower CAD risk compared with ≤1 day/week. Beverage preference (beer, wine, or liquor) did not influence the alcohol-CAD relation. Our data show a lower risk of CAD with light-to-moderate alcohol consumption among US veterans, and drinking frequency may provide a further reduction in risk.

DASH Score and Subsequent Risk of Coronary Artery Disease: The Findings From Million Veteran Program

Background While adherence to healthful dietary patterns has been associated with a lower risk of coronary artery disease (CAD) in the general population, limited data are available among US veterans. We tested the hypothesis that adherence to Dietary Approach to Stop Hypertension (DASH) food pattern is associated with a lower risk of developing CAD among veterans. Methods and Results We analyzed data on 153 802 participants of the Million Veteran Program enrolled between 2011 and 2016. Information on dietary habits was obtained using a food frequency questionnaire at enrollment. We used electronic health records to assess the development of CAD during follow‐up. Of the 153 802 veterans who provided information on diet and were free of CAD at baseline, the mean age was 64.0 (SD=11.8) years and 90.4% were men. During a mean follow‐up of 2.8 years, 5451 CAD cases occurred. The crude incidence rate of CAD was 14.0, 13.1, 12.6, 12.3, and 11.1 cases per 1000 person‐years across consecutive quintiles of Dietary Approach to Stop Hypertension score. Hazard ratios (95% confidence interval) for CAD were 1.0 (ref), 0.91 (0.84–0.99), 0.87 (0.80–0.95), 0.86 (0.79–0.94), and 0.80 (0.73–0.87) from the lowest to highest quintile of Dietary Approach to Stop Hypertension score controlling for age, sex, body mass index, race, smoking, exercise, alcohol intake, and statin use (P linear trend, <0.0001). Conclusions Our data are consistent with an inverse association between Dietary Approach to Stop Hypertension diet score and incidence of CAD among US veterans.

Prognostic Significance of Baseline Serum Sodium in Heart Failure With Preserved Ejection Fraction

Background The purpose of this study was to evaluate the relationship between serum sodium at the time of diagnosis and long term clinical outcomes in a large national cohort of patients with heart failure with preserved ejection fraction. Methods and Results We studied 25 440 patients with heart failure with preserved ejection fraction treated at Veterans Affairs medical centers across the United States between 2002 and 2012. Serum sodium at the time of heart failure diagnosis was analyzed as a continuous variable and in categories as follows: low (115.00–134.99 mmol/L), low‐normal (135.00–137.99 mmol/L), referent group (138.00–140.99 mmol/L), high normal (141.00–143.99 mmol/L), and high (144.00–160.00 mmol/L). Multivariable Cox regression and negative binomial regression were performed to estimate hazard ratios (95% confidence interval [CI]) and incidence density ratios (95% CI) for the associations of serum sodium with mortality and hospitalizations (heart failure and all‐cause), respectively. The average age of patients was 70.8 years, 96.2% were male, and 14% were black. Compared with the referent group, low, low‐normal, and high sodium values were associated with 36% (95% CI, 28%–44%), 6% (95% CI, 1%–12%), and 9% (95% CI, 1%–17%) higher risk of all‐cause mortality, respectively. Low and low‐normal serum sodium were associated with 48% (95% CI, 10%–100%) and 38% (95% CI, 8%–77%) higher risk of number of days of heart failure hospitalizations per year, and with 44% (95% CI, 32%–56%) and 18% (95% CI, 10%–27%) higher risk of number of days of all‐cause hospitalizations per year, respectively. Conclusions Both elevated and reduced serum sodium, including values currently considered within normal range, are associated with adverse outcomes in patients with heart failure with preserved ejection fraction.

Association of Interleukin 6 Receptor Variant With Cardiovascular Disease Effects of Interleukin 6 Receptor Blocking Therapy: A Phenome-Wide Association Study

Importance Electronic health record (EHR) biobanks containing clinical and genomic data on large numbers of individuals have great potential to inform drug discovery. Individuals with interleukin 6 receptor (IL6R) single-nucleotide polymorphisms (SNPs) who are not receiving IL6R blocking therapy have biomarker profiles similar to those treated with IL6R blockers. This gene–drug pair provides an example to test whether associations of IL6R SNPs with a broad range of phenotypes can inform which diseases may benefit from treatment with IL6R blockade. Objective To determine whether screening for clinical associations with the IL6R SNP in a phenome-wide association study (PheWAS) using EHR biobank data can identify drug effects from IL6R clinical trials. Design, Setting, and Participants Diagnosis codes and routine laboratory measurements were extracted from the VA Million Veteran Program (MVP); diagnosis codes were mapped to phenotype groups using published PheWAS methods. A PheWAS was performed by fitting logistic regression models for testing associations of the IL6R SNPs with 1342 phenotype groups and by fitting linear regression models for testing associations of the IL6R SNP with 26 routine laboratory measurements. Significance was reported using a false discovery rate of 0.05 or less. Findings were replicated in 2 independent cohorts using UK Biobank and Vanderbilt University Biobank data. The Million Veteran Program included 332 799 US veterans; the UK Biobank, 408 455 individuals from the general population of the United Kingdom; and the Vanderbilt University Biobank, 13 835 patients from a tertiary care center. Exposures IL6R SNPs (rs2228145; rs4129267). Main Outcomes and Measures Phenotypes defined by International Classification of Diseases, Ninth Revision codes. Results Of the 332 799 veterans included in the main cohort, 305 228 (91.7%) were men, and the mean (SD) age was 66.1 (13.6) years. The IL6R SNP was most strongly associated with a reduced risk of aortic aneurysm phenotypes (odds ratio, 0.87-0.90; 95% CI, 0.84-0.93) in the MVP. We observed known off-target effects of IL6R blockade from clinical trials (eg, higher hemoglobin level). The reduced risk for aortic aneurysms among those with the IL6R SNP in the MVP was replicated in the Vanderbilt University Biobank, and the reduced risk for coronary heart disease was replicated in the UK Biobank. Conclusions and Relevance In this proof-of-concept study, we demonstrated application of the PheWAS using large EHR biobanks to inform drug effects. The findings of an association of the IL6R SNP with reduced risk for aortic aneurysms correspond with the newest indication for IL6R blockade, giant cell arteritis, of which a major complication is aortic aneurysm.

A phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: the Million Veteran Program

Background Large databases provide an efficient way to analyze patient data. A challenge with these databases is the inconsistency of ICD codes and a potential for inaccurate ascertainment of cases. The purpose of this study was to develop and validate a reliable protocol to identify cases of acute ischemic stroke (AIS) from a large national database. Methods Using the national Veterans Affairs electronic health-record system, Center for Medicare and Medicaid Services, and National Death Index data, we developed an algorithm to identify cases of AIS. Using a combination of inpatient and outpatient ICD9 codes, we selected cases of AIS and controls from 1992 to 2014. Diagnoses determined after medical-chart review were considered the gold standard. We used a machine-learning algorithm and a neural network approach to identify AIS from ICD9 codes and electronic health-record information and compared it with a previous rule-based stroke-classification algorithm. Results We reviewed administrative hospital data, ICD9 codes, and medical records of 268 patients in detail. Compared with the gold standard, this AIS algorithm had a sensitivity of 91%, specificity of 95%, and positive predictive value of 88%. A total of 80,508 highly likely cases of AIS were identified using the algorithm in the Veterans Affairs national cardiovascular disease-risk cohort (n=2,114,458). Conclusion Our algorithm had high specificity for identifying AIS in a nationwide electronic health-record system. This approach may be utilized in other electronic health databases to accurately identify patients with AIS.