Familial hypercholesterolemia (FH) is characterized by inherited high levels of low-density lipoprotein cholesterol (LDL-C) and premature coronary heart disease (CHD). Over a thousand low-frequency ...variants in
and
have been implicated in FH but few have been examined at the population level. We aim to estimate the phenotypic effects of a subset of FH variants on LDL-C and clinical outcomes among 331,107 multi-ethnic participants.
We examined the individual and collective association between putatively pathogenic FH variants included on the MVP biobank array and the maximum LDL-C level over an interval of 15 years (maxLDL). We assessed the collective effect on clinical outcomes by leveraging data from 61.7 million clinical encounters.
We found 8 out of 16 putatively pathogenic FH variants with ≥30 observed carriers to be significantly associated with elevated maxLDL (9.4-80.2 mg/dL). Phenotypic effects were similar for European and African Americans despite substantial differences in carrier frequencies. Based on observed effects on maxLDL, we identified a total of 748 carriers (1:443) who had elevated maxLDL (36.5±1.4 mg/dL, p=1.2×10
), and higher prevalence of clinical diagnoses related to hypercholesterolemia and CHD in a phenome-wide scan. Adjusted for maxLDL, FH variants collectively associated with higher prevalence of CHD (odds ratio, 1.59 95% CI 1.36-1.86, p=1.1×10
) but not peripheral artery disease.
The distribution and phenotypic effects of putatively pathogenic FH variants were heterogeneous within and across variants. More robust evidence of genotype-phenotype associations of FH variants in multi-ethnic populations is needed to accurately infer at-risk individuals from genetic screening.
Million Veteran Program (MVP) is the largest ongoing mega-cohort biobank program in the US with 570,131 enrollees as of May 2017. The primary aim is to describe demographics, military service, and ...major diseases and comorbidities of the MVP cohort. Our secondary aim is to examine body mass index (BMI), a proxy for general health, among enrollees.
The study population consists of Veterans who actively use the Veterans Health Administration in the US. Data evaluated in this paper combine health information from multiple sources to provide the most comprehensive demographic profile and information on height and weight of MVP enrollees. A standardized cleaning algorithm was used to curate the demographic variables for each participant in MVP. For height and weight, we derived a final data point for each participant to evaluate BMI.
Multivariable logistic regression was used to compare the differences in BMI categories across enrollment years adjusting for gender, race, and age.
< 0.05 was considered statistically significant. All analyses were conducted using Statistical Analysis System 9.2.
The MVP cohort consists of 90.4% of males with an average age of 61.9 years (standard deviation SD = 13.9). MVP is the largest multiethnic biobank cohort within the Veteran population with 73.9% White, 19.0% Black, and 6.5% Hispanic. The most common self-reported disease was hypertension (62.6%) for males and depression (47.5%) for females. Mean BMI was 29.7 kg/m
(SD = 5.8) with 38.2% obese and 42.3% overweight.
Our findings suggest that demographic representation in MVP is similar to the Veterans Health Administration population and contrasts with the overall National Health and Nutrition Examination Survey US population. The prevalence of overweight and obese is high among US Veterans, and future studies will examine the role of BMI and disease risk in the Veteran population.
IntroductionThe MRFIT Screenees project, conducted before the widespread use of statins, showed that greater serum cholesterol levels were associated with an increased risk of coronary heart disease ...(CHD) death. We examined outpatient serum cholesterol levels and 6 year CHD death in male Veterans in the VA Healthcare system between 2002-2007.MethodsAnalyses were restricted to subjects free of cancer, not on statins, and without a history of a myocardial infarction at baseline. Cox models were used to estimate hazard ratios (HR) for CHD death. CHD death was defined using I20-I25 ICD-10 codes from the National Death Index.ResultsAmong 1,261,762 subjects we observed a J-shaped relationship between cholesterol categories and age-adjusted hazard ratios for White and Black males (Table 1). CHD death was more than doubled in the highest cholesterol group for both Whites (HR2.15) and Blacks (HR2.43) compared to the second group. After multivariable adjustment for age, diabetes, major mental health diagnosis, systolic blood pressure (SBP) and smoking status, we found that the HR for CHD death among the low cholesterol group was attenuated, suggesting that some of the increased mortality observed was explained by comorbidities. In the same model, the effect of SBP on CHD death was also noteworthy5% increase risk of CHD death for SBP>140 mmHg (vs ≤140 mmHg) among Whites and 37% increase risk of CHD death among Blacks. The HR for CHD death among the highest cholesterol group for Whites and Blacks remained doubled (HR2.30 and 2.16, respectively).ConclusionsIn contrast to the MRFIT Screenees project, data from a large sample of Veterans receiving medical care do not demonstrate a continuous, graded relationship between cholesterol and 6-year CHD death. Rather, they support a J-shaped relationship where cholesterol levels under 180mg/dl also confer greater risk for CHD death.
IntroductionThe Veterans Health Administration (VHA) is the largest integrated health care system with over 16 years of electronic health record (EHR) data for more than 12 million veterans. One ...challenge with EHR data is to accurately define risk factors which may not be recorded simply as structured data elements, such as smoking status. Smoking remains a major risk factor for many chronic diseases such as cardiovascular disease and lung cancer, and increases mortality risk compared to non-smokers. As such, it is important to accurately define smoking status to quantify its effect on disease and minimize its effect as a potential confounder.MethodsWe developed a probabilistic model to predict smoking status using the Million Veteran Program (MVP) self-reported smoking data as the gold standard (N=79,440). MVP is an on-going cohort study and mega-biobank that collects genetic samples and self-reported data on lifestyle factors. Participants were categorized as a never or ever smoker based on their Baseline and Lifestyle survey responses. LASSO Lease Absolute Shrinkage and Selection Operator regression with 10-fold cross validation was used to select the most important predictors of smoking status from the EHR structured data and apply a penalty to prevent overfitting. The beta coefficients were used to calculate the predicted probability of being a never or ever smoker.ResultsThere were 73% ever and 27% never smokers in the MVP cohort. Using the most probable smoking category for a subject, the algorithm sensitivity for an ever and never smoker was 86% and 87%, respectively. The specificity for an ever and never smoker was 87% and 87%, respectively. The algorithm’s positive predictive value was 95%. The final predictors in the model were 7 of the 11 smoking-related adjudicated health factors, and ICD-9 codes for tobacco dependence.ConclusionA probabilistic model using all smoking-related structured data from a large EHR database can produce a highly sensitive and specific model for identifying ever vs. never smokers. Furthermore, a probabilistic approach results in greater utility for research projects that may have different needs for a smoking status variable e.g., studies may only want smokers with a very high probability of being a smoker.
IntroductionThe 2013 ACC/AHA guidelines on the Treatment of Blood Cholesterol to Reduce Cardiovascular Risk presented a new statin algorithm to guide the selection of a statin agent and dosage based ...on desired patient low-density lipoprotein cholesterol (LDL-C) reduction. This algorithm was derived largely from randomized control trials (RCTs). The aim of our study was to evaluate the LDL-C reduction obtained from statin therapy in a real-world population setting for imputation in genetic analysis.HypothesisThe LDL-C reduction obtained from statin therapy in a real-world setting is less than that reported by RCTs.MethodsData for individuals in the VA New England Healthcare system who were on Simvastatin or Atorvastatin therapy and had at least 2 lipid measurements in blood available at least 3 months apart were analyzed. Prescription dosage and refill data were used to calculate average daily doses of statin therapy and generate a mixed model to estimate the percentage decrease in LDL-C (%LDL-C) per mg unit change in statin therapy. These estimates were used to extrapolate expected LDL-C reductions for standard doses of Simvastatin and Atorvastatin.ResultsA total of 244,244 veterans were assessed, 96% were male, 81% Caucasian, and the mean age was 62 (±16) years at baseline. Greater statin dosage (20, 40, 60, 80 mg/day) was associated with greater %LDL-C reductions with diminishing returns (-10, -17, -20, -19% decrease in LDL-C respectively, table 1).ConclusionsReal world reduction of LDL-C in a VA population following statin initiation is much less than projected by the 2013 guidelines. Potential explanations for this include reduced adherence, inclusion of subjects with greater comorbidities, and clinician treatment to an LDL goal. Our findings have important implications in the appropriate adjustment of LDL levels for genetic association studies in the Million Veteran Program.
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