Background The lipid hypothesis postulates that lower blood cholesterol is associated with reduced coronary heart disease (CHD) risk, which has been challenged by reports of a U‐shaped relation between cholesterol and death in recent studies. We sought to examine whether the U‐shaped relationship is true and to assess the impact of age on this association. Method and Results We conducted a prospective cohort study of 4 467 942 veterans aged >18 years, with baseline outpatient visits from 2002 to 2007 and follow‐up to December 30, 2018, in the Veterans Health Administration electronic health record system. We observed a J‐shaped relation between total cholesterol (TC) and CHD mortality after a comprehensive adjustment of confounding factors: flat for TC <180 mg/dL, and greater risk was present at higher cholesterol levels. Compared with veterans with TC between 180 and 199 mg/dL, the multiadjusted hazard ratios (HRs) for CHD death were 1.03 (95% CI, 1.02–1.04), 1.07 (95% CI, 1.06–1.09), 1.15 (95% CI, 1.13–1.18), 1.25 (95% CI, 1.22–1.28), and 1.45 (95% CI, 1.42–1.49) times greater among veterans with TC (mg/dL) of 200 to 219, 220 to 239, 140 to 259, 260 to 279 and ≥280, respectively. Similar J‐shaped TC‐CHD mortality patterns were observed among veterans with and without statin use at or before baseline. Conclusions The cholesterol paradox, for example, higher CHD death in patients with a low cholesterol level, was a reflection of reverse causality, especially among older participants. Our results support the lipid hypothesis that lower blood cholesterol is associated with reduced CHD. Furthermore, the hypothesis remained true when TC was low due to use of statins or other lipid‐lowering medication.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10−9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

Successful implementation of the 2013 American College of Cardiology/American Heart Association cholesterol guidelines hinges on a clear understanding of the clinician-patient risk discussion (CPRD). This is a dialogue between the clinician and patient about potential for atherosclerotic cardiovascular disease risk reduction benefits, adverse effects, drug-drug interactions, and patient preferences. Designed especially for primary prevention patients, this process of shared decision making establishes the appropriateness of a statin for a specific patient. CPRD respects the autonomy of an individual striving to make an informedchoice aligned with personal values and preferences. Dedicating sufficient timeto high-quality CPRD offers an opportunity to strengthen clinician-patient relationships, patient engagement, and medication adherence. We review the guideline-recommended CPRD, the general concept of shared decision making and decision aids, the American College of Cardiology/American Heart Association Risk Estimator application as an implementation tool, and address potential barriers to implementation.

Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality; however, its genetic determinants remain incompletely defined. In total, 10 previously identified risk loci explain a small fraction of AAA heritability. Methods: We performed a genome-wide association study in the Million Veteran Program testing ≈18 million DNA sequence variants with AAA (7642 cases and 172 172 controls) in veterans of European ancestry with independent replication in up to 4972 cases and 99 858 controls. We then used mendelian randomization to examine the causal effects of blood pressure on AAA. We examined the association of AAA risk variants with aneurysms in the lower extremity, cerebral, and iliac arterial beds, and derived a genome-wide polygenic risk score (PRS) to identify a subset of the population at greater risk for disease. Results: Through a genome-wide association study, we identified 14 novel loci, bringing the total number of known significant AAA loci to 24. In our mendelian randomization analysis, we demonstrate that a genetic increase of 10 mm Hg in diastolic blood pressure (odds ratio, 1.43 [95% CI, 1.24-1.66]; P=1.6×10-6), as opposed to systolic blood pressure (odds ratio, 1.06 [95% CI, 0.97-1.15]; P=0.2), likely has a causal relationship with AAA development. We observed that 19 of 24 AAA risk variants associate with aneurysms in at least 1 other vascular territory. A 29-variant PRS was strongly associated with AAA (odds ratioPRS, 1.26 [95% CI, 1.18-1.36]; PPRS=2.7×10-11per SD increase in PRS), independent of family history and smoking risk factors (odds ratioPRS+family history+smoking, 1.24 [95% CI, 1.14-1.35]; PPRS=1.27×10-6). Using this PRS, we identified a subset of the population with AAA prevalence greater than that observed in screening trials informing current guidelines. Conclusions: We identify novel AAA genetic associations with therapeutic implications and identify a subset of the population at significantly increased genetic risk of AAA independent of family history. Our data suggest that extending current screening guidelines to include testing to identify those with high polygenic AAA risk, once the cost of genotyping becomes comparable with that of screening ultrasound, would significantly increase the yield of current screening at reasonable cost.

AIM: 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. MATERIALS AND METHOD: 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. STATISTICAL ANALYSIS USED: Multivariable logistic regression was used to compare the differences in BMI categories across enrollment years adjusting for gender, race, and age. P < 0.05 was considered statistically significant. All analyses were conducted using Statistical Analysis System 9.2. RESULTS: 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/m2 (SD = 5.8) with 38.2% obese and 42.3% overweight. CONCLUSIONS: 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.

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).

Introduction Although plasminogen activator inhibitor (PAI-1) plays a key regulatory role in fibrinolysis, it has not been clearly shown to independently predict cardiovascular disease (CVD) among individuals without prior CVD. We investigated, in the Framingham Heart Study offspring cohort, whether PAI-1 predicted CVD risk among individuals without prior CVD. Methods Plasma PAI-1 antigen and tissue plasminogen activator (TPA) antigen were measured in 3203 subjects without prior CVD between 1991 and 1995; average follow-up of 10 years. PAI-1 was remeasured 4 years after baseline, to determine the effect of serial change on risk. Results PAI-1 levels (mean ± SD) were 29.1 ng/ml (19.2) versus 22.1 (16.5) for those and without incident CVD; p < 0.001, and TPA levels were 12.0 ng/ml (5.7) versus 9.0 (4.7); p < 0.001. PAI-1 and TPA antigen levels had a strong unadjusted linear relation with incident CVD (p < 0.001). After adjustment for conventional risk factors, the hazard ratios (HRs) for higher quartiles of PAI-1, compared with the lowest, were 1.9, 1.9, 2.6 (linear trend p = 0.006), and 1.6, 1.6, 2.9 (p < 0.001) for TPA antigen. The adjusted HRs for increasing quartiles of serial change in PAI-1 at 4 years, compared with the lowest, were 0.9, 0.8, 1.3 (p = 0.050). C statistic assessment showed that adding PAI-1 or TPA to conventional risk factors resulted in small increases in discrimination and modest reclassification of risk, which was statistically significant for TPA (net reclassification 6.8%, p = 0.037) but not PAI-1 (4.8%, p = 0.113). Conclusion PAI-1 and TPA antigen levels are predictive of CVD events after accounting for established risk factors. A serial increase in PAI-1 is associated with a further increase in risk. These findings support the importance of fibrinolytic potential in CVD.

Background Low-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C). Objectives We derived a novel method for LDL-C estimation from the standard lipid profile using a machine learning (ML) approach utilizing random forests (the Weill Cornell model). We compared its correlation to direct LDL-C with the Friedewald and Martin-Hopkins equations for LDL-C estimation. Methods The study cohort comprised a convenience sample of standard lipid profile measurements (with the directly measured components of total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], and TG) as well as chemical-based direct LDL-C performed on the same day at the New York-Presbyterian Hospital/Weill Cornell Medicine (NYP-WCM). Subsequently, an ML algorithm was used to construct a model for LDL-C estimation. Results are reported on the held-out test set, with correlation coefficients and absolute residuals used to assess model performance. Results Between 2005 and 2019, there were 17,500 lipid profiles performed on 10,936 unique individuals (4,456 females; 40.8%) aged 1 to 103. Correlation coefficients between estimated and measured LDL-C values were 0.982 for the Weill Cornell model, compared to 0.950 for Friedewald and 0.962 for the Martin-Hopkins method. The Weill Cornell model was consistently better across subgroups stratified by LDL-C and TG values, including TG >500 and LDL-C <70. Conclusions An ML model was found to have a better correlation with direct LDL-C than either the Friedewald formula or Martin-Hopkins equation, including in the setting of elevated TG and very low LDL-C.

Background & aims: Observational and clinical trial evidence suggests an inverse association of omega-3 polyunsaturated fatty acids with coronary artery disease (CAD) mortality, although relationships with non-fatal CAD and stroke are less clear. We investigated whether omega-3 fatty acid supplement use and fish intake were associated with incident non-fatal CAD and ischemic stroke among US Veterans. Methods: The Million Veteran Program (MVP) is an ongoing nation-wide longitudinal cohort study of US Veterans with self-reported survey, biospecimen, and electronic health record data. Regular use of omega-3 supplements (yes/no) and frequency of fish intake within the past year were assessed using a food frequency questionnaire. Cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the associations of omega-3 supplement use and fish intake with incident non-fatal CAD and ischemic stroke, defined from electronic health records using validated algorithms. Multivariable models included demographics, body mass index, education, smoking status, alcohol intake, and exercise frequency. Results: Among 197,761 participants with food frequency data (mean age: 66 ± 12 years, 92% men), 21% regularly took omega-3 supplements and median fish intake was 1 (3–5 ounce) serving/week. Over a median follow-up of 2.9 years for non-fatal CAD and 3.3 years for non-fatal ischemic stroke, we observed 6265 and 4042 incident cases of non-fatal CAD and non-fatal ischemic stroke, respectively. Omega-3 fatty acid supplement use was independently associated with a lower risk of non-fatal ischemic stroke [HR (95% CI): 0.88 (0.81, 0.95)] but not non-fatal CAD [0.99 (0.93, 1.06)]. Fish intake was not independently associated with non-fatal CAD [1.01 (0.94, 1.09) for 1–3 servings/month, 1.03 (0.98, 1.11) for 1 serving/week, 1.02 (0.93, 1.11) for 2–4 servings/week, and 1.15 (0.98, 1.35) for ≥5 servings/week, reference = <1 serving/month, linear p-trend = 0.09] or non-fatal ischemic stroke [0.92 (0.84, 1.00) for 1–3 servings/month, 0.93 (0.85, 1.02) for 1 serving/week, 0.96 (0.86, 1.07) for 2–4 servings/week, and 1.13 (0.93–1.38) for ≥5 servings/week, linear p-trend = 0.16]. Conclusions: Neither omega-3 supplement use, nor fish intake, was associated with non-fatal CAD among US Veterans. While omega-3 supplement use was associated with lower risk of non-fatal ischemic stroke, fish intake was not. Randomized controlled trials are needed to confirm whether omega-3 supplementation is protective against ischemic stroke in a US population.