Erythrocyte n-6 Fatty Acids and Risk for Cardiovascular Outcomes and Total Mortality in the Framingham Heart Study

Background: The prognostic value of erythrocyte levels of n-6 fatty acids (FAs) for total mortality and cardiovascular disease (CVD) outcomes remains an open question. Methods: We examined cardiovascular (CV) outcomes and death in 2500 individuals in the Framingham Heart Study Offspring cohort without prevalent CVD (mean age 66 years, 57% women) as a function of baseline levels of different length n-6 FAs (18 carbon, 20 carbon, and 22 carbon) in the erythrocyte membranes. Clinical outcomes were monitored for up to 9.5 years (median follow up, 7.26 years). Cox proportional hazards models were adjusted for a variety of demographic characteristics, clinical status, and red blood cell (RBC) n-6 and long chain n-3 FA content. Results: There were 245 CV events, 119 coronary heart disease (CHD) events, 105 ischemic strokes, 58 CVD deaths, and 350 deaths from all causes. Few associations between either mortality or CVD outcomes were observed for n-6 FAs, with those that were observed becoming non-significant after adjusting for n-3 FA levels. Conclusions: Higher circulating levels of marine n-3 FA levels are associated with reduced risk for incident CVD and ischemic stroke and for death from CHD and all-causes; however, in the same sample little evidence exists for association with n-6 FAs. Further work is needed to identify a full profile of FAs associated with cardiovascular risk and mortality

Erythrocyte Long-Chain Omega-3 Fatty Acid Levels are Inversely Associated with Mortality and with Incident Cardiovascular Disease: The Framingham Heart Study

Background: The extent to which omega-3 fatty acid status is related to risk for death from any cause and for incident cardiovascular disease (CVD) remains controversial. Objective: To examine these associations in the Framingham Heart Study. Design: Prospective and observational. Setting: Framingham Heart Study Offspring cohort. Measurements: The exposure marker was red blood cell levels of eicosapentaenoic and docosahexaenoic acids (the Omega-3 Index) measured at baseline. Outcomes included mortality (total, CVD, cancer, and other) and total CVD events in participants free of CVD at baseline. Follow-up was for a median of 7.3 years. Cox proportional hazards models were adjusted for 18 variables (demographic, clinical status, therapeutic, and CVD risk factors). Results: Among the 2500 participants (mean age 66 years, 54% women), there were 350 deaths (58 from CVD, 146 from cancer, 128 from other known causes, and 18 from unknown causes). There were 245 CVD events. In multivariable-adjusted analyses, a higher Omega-3 Index was associated with significantly lower risks (P-values for trends across quintiles) for total mortality (P = .02), for non-CVD and non-cancer mortality (P = .009), and for total CVD events (P = .008). Those in the highest (\u3e6.8%) compared to those in the lowest Omega-3 Index quintiles (\u3c4.2%) had a 34% lower risk for death from any cause and 39% lower risk for incident CVD. These associations were generally stronger for docosahexaenoic acid than for eicosapentaenoic acid. When total cholesterol was compared with the Omega-3 Index in the same models, the latter was significantly related with these outcomes, but the former was not. Limitations: Relatively short follow-up time and one-time exposure assessment. Conclusions: A higher Omega-3 Index was associated with reduced risk of both CVD and all-cause mortality

Estimation and predictors of the Omega-3 Index in the UK Biobank

Information on the Omega-3 Index (O3I) in the United Kingdom (UK) are scarce. The UK-Biobank (UKBB) contains data on total plasma omega-3 polyunsaturated fatty acids (n3-PUFA%) and DHA% measured by NMR. The aim of our study was to create an equation to estimate the O3I (eO3I) from these data. We first performed an interlaboratory experiment with 250 random blood samples in which the O3I was measured in erythrocytes by gas chromatography, and total n3% and DHA% were measured in plasma by NMR. The best predictor of eO3I included both DHA% and a derived metric, the total n3%-DHA%. Together these explained 65% of the variability (r=0.832, p<0.0001). We then estimated the O3I in 117, 108 UKBB subjects and correlated it with demographic and lifestyle variables in multivariable adjusted models. The mean (SD) eO3I was 5.58% (2.35%) this UKBB cohort. Several predictors were significantly correlated with eO3I (all p<0.0001). In general order of impact and with directionality (- = inverse, + = direct): oily-fish consumption (+), fish oil supplement use (+), female sex (+), older age (+), alcohol use (+), smoking (-), higher waist circumference and BMI (-), lower socioeconomic status and less education (-). Only 20.5% of eO3I variability could be explained by predictors investigated, and oily-fish consumption accounted for 7.0% of that. With the availability of the eO3I in the UKBB cohort we will be in a position to link risk for a variety of diseases with this commonly-used and well-documented marker of n3-PUFA biostatus

Mentoring Undergraduate Research in Statistics: Reaping the Benefits and Overcoming the Barriers

Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome barriers and thereby provide greater opportunity for undergraduate statistics students to gain research experience

Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association

Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential genotyping errors reduce power, while differential genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of genotyping errors. In this manuscript we use a comprehensive simulation study to explore the effects of numerous factors on the type I error rate of rare variant tests of association in the presence of differential genotyping error. We find that increased sample size, decreased minor allele frequency, and an increased number of single nucleotide variants (SNVs) included in the test all increase the type I error rate in the presence of differential genotyping errors. We also find that the greater the relative difference in case-control genotyping error rates the larger the type I error rate. Lastly, as is the case for single marker tests, genotyping errors classifying the common homozygote as the heterozygote inflate the type I error rate significantly more than errors classifying the heterozygote as the common homozygote. In general, our findings are in line with results from single marker tests. To ensure that type I error inflation does not occur when analyzing next-generation sequencing data careful consideration of study design (e.g. use of randomization), caution in meta-analysis and using publicly available controls, and the use of standard quality control metrics is critical

Association of Reported Fish Intake and Supplementation Status with the Omega-3 Index

Background: An Omega-3 Index (O3I; EPA+DHA as a % of erythrocyte total fatty acids) in the desirable range (8%-12%) has been associated with improved heart and brain health. Objective: To determine the combination of fish intake and supplement use that is associated with an O3I of \u3e8%. Design: Two cross-sectional studies comparing the O3I to EPA+DHA/fish intake. Participants/setting: The first study included 28 individuals and assessed their fish and EPA+DHA intake using both a validated triple-pass 24-hr recall dietary survey and a single fish-intake question. The second study used de-identified data from 3,458 adults (84% from US) who self-tested their O3I and answered questions about their fish intake and supplement use. Statistical analyses performed: Study 1, chi-squared, one-way ANOVA, and Pearson correlations were computed. In Study 2, multi-variable regression models were used to predict O3I levels from reported fish/supplement intakes. Results: The mean ± SD O3I was 4.87 ± 1.32%, and 5.99 ± 2.29% in the first and second studies, respectively. Both studies showed that for every increase in fish intake category the O3I increased by 0.50–0.65% (p \u3c 0.0001). In the second study, about half of the population was taking omega-3 supplements, 32% reported no fish intake and 17% reported eating fish \u3e2 times per week. Taking an EPA+DHA supplement increased the O3I by 2.2% (p \u3c 0.0001). The odds of having an O3I of ≥8% were 44% in the highest intake group (≥3 servings/week and supplementation) and 2% in the lowest intake group (no fish intake or supplementation); and in those consuming 2 fish meals per week but not taking supplements (as per recommendations), 10%. Conclusion: Current AHA recommendations are unlikely to produce a desirable O3I. Consuming at least 3 fish servings per week plus taking an EPA+DHA supplement markedly increases the likelihood of achieving this target level

GAW20: Methods and strategies for the new frontiers of epigenetics and pharmacogenomics

© 2018 The Author(s). GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication

General Approach for Combining Diverse Rare Variant Association Tests Provides Improved Robustness Across a Wider Range of Genetic Architectures

The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture. Recently, tests have been proposed which combine two particular individual tests (one burden and one variance components) to minimize power loss while improving robustness to a wider range of genetic architectures. In our analysis we propose an expansion of these approaches, yielding a general method that works for combining any number of individual tests. We demonstrate that running multiple different tests on the same dataset and using a Bonferroni correction for multiple testing is never better than combining tests using our general method. We also find that using a test statistic that is highly robust to the inclusion of non-causal variants (Joint-infinity) together with a previously published combined test (SKAT-O) provides improved robustness to a wide range of genetic architectures and should be considered for use in practice. Software for this approach is supplied. We support the increased use of combined tests in practice-- as well as further exploration of novel combined testing approaches using the general framework provided here--to maximize robustness of rare-variant testing strategies against a wide range of genetic architectures

A Genome-Wide Association Study of Red-Blood Cell Fatty Acids and Ratios Incorporating Dietary Covariates: Framingham Heart Study Offspring Cohort

Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis found nine previously identified loci associated with FA levels (FADS, ELOVL2, PCOLCE2, LPCAT3, AGPAT4, NTAN1/PDXDC1, PKD2L1, HBS1L/MYB and RAB3GAP1/MCM6), while identifying four novel loci. The latter include an association between variants in CALN1 (Chromosome 7) and eicosapentaenoic acid (EPA), DHRS4L2(Chromosome 14) and a FA ratio measuring delta-9-desaturase activity, as well as two loci associated with less well understood proteins. Thus, the inclusion of dietary covariates had a modest impact, helping to uncover four additional loci. While genome-wide association studies continue to uncover additional genes associated with circulating FA levels, much of the heritable risk is yet to be explained, suggesting the potential role of rare genetic variation, epistasis and gene-environment interactions on FA levels as well. Further studies are needed to continue to understand the complex genetic picture of FA metabolism and synthesis

Predicting the Effects of Supplemental EPA and DHA on the Omega-3 Index

Background: Supplemental long-chain omega-3 (n–3) fatty acids (EPA and DHA) raise erythrocyte EPA + DHA [omega-3 index (O3I)] concentrations, but the magnitude or variability of this effect is unclear. Objective: The purpose of this study was to model the effects of supplemental EPA + DHA on the O3I. Methods: Deidentified data from 1422 individuals from 14 published n–3 intervention trials were included. Variables considered included dose, baseline O3I, sex, age, weight, height, chemical form [ethyl ester (EE) compared with triglyceride (TG)], and duration of treatment. The O3I was measured by the same method in all included studies. Variables were selected by stepwise regression using the Bayesian information criterion. Results: Individuals supplemented with EPA + DHA (n = 846) took a mean ± SD of 1983 ± 1297 mg/d, and the placebo controls (n = 576) took none. The mean duration of supplementation was 13.6 ± 6.0 wk. The O3I increased from 4.9% ± 1.7% to 8.1% ± 2.7% in the supplemented individuals ( P \u3c 0.0001). The final model included dose, baseline O3I, and chemical formulation type (EE or TG), and these explained 62% of the variance in response (P \u3c 0.0001). The model predicted that the final O3I (and 95% CI) for a population like this, with a baseline concentration of 4.9%, given 850 mg/d of EPA + DHA EE would be ∼6.5% (95% CI: 6.3%, 6.7%). Gram for gram, TG-based supplements increased the O3I by about 1 percentage point more than EE products. Conclusions: Of the factors tested, only baseline O3I, dose, and chemical formulation were significant predictors of O3I response to supplementation. The model developed here can be used by researchers to help estimate the O3I response to a given EPA + DHA dose and chemical form