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ARS Home » Pacific West Area » Davis, California » Western Human Nutrition Research Center » Obesity and Metabolism Research » Research » Publications at this Location » Publication #307705

Research Project: Improving Public Health by Understanding Diversity in Diet, Body, and Brain Interactions

Location: Obesity and Metabolism Research

Title: A novel approach to identify optimal metabotypes of elongase and desaturase activities in prevention of acute coronary syndrome

Author
item Tintle, Nathan - Dordt College
item Newman, John
item Shearer, Gregory - Pennsylvania State University

Submitted to: Metabolomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/10/2015
Publication Date: 2/21/2015
Publication URL: http://download.springer.com/static/pdf/778/art%253A10.1007%252Fs11306-015-0787-6.pdf?auth66=1425576528_cbffb083accca3a6cc8997688517fdad&ext=.pdf
Citation: Tintle, N.L., Newman, J.W., Shearer, G.C. 2015. A novel approach to identify optimal metabotypes of elongase and desaturase activities in prevention of acute coronary syndrome. Metabolomics. 11(1):1-11. doi: 10.1007/s11306-015-0787-6.

Interpretive Summary: Genomic and metabolomic data sets are valuable for disease risk analysis, however typically applied statistical approaches which evaluate average differences between subjects with and without disease do not model disease risk well with such data. If individuals with genetic variations that functionally impact metabolism represent distinct populations (i.e. metabotypes) which are distinct from the predominant one, then disease risk in subjects with the disease would be revealed as a changed mixing proportion of these metabotypes in the groups with and without disease (i.e. cases vs controls). Here we validate a model accounting for mixed populations using biomarkers of fatty acid metabolism derived from a case/control study of acute coronary syndrome subjects. Classical risk assessments using both metabolomic and genomic approaches have been previously reported. Using simulated data, we first showed that the mixing model approach improved power and sensitivity of risk detection compared to classic approaches. We then used metabolic biomarkers to test for evidence of distinct metabotypes and different proportions among cases and controls. In simulation, our model outperformed all other approaches including the mean comparison using either the nonparametric Mann-Whitney U-test or the parametric Students T-test, and evaluations of the proportional distribution of metabotypes by condition using the chi-square test. Using real data, we found distinct metabotypes of six of the seven fatty acid metabolism indices tested, and different mixing proportions in five of the six activity biomarkers including Delta-9-Desaturase, ELongase Of Very Long chain fatty acid 6, ELOVL5, Fatty Acid DeSaturase 1 (i.e. Delta-5-Desaturase), and Sprecher pathway chain shortening (SCS). This model suggests that high activity metabotypes of non-essential fatty acids and SCS decreased odds for acute coronary syndrome (ACS), however high activity metabotypes of FADS1, responsible for 20-carbon polyunsaturated fatty acid synthesis, increased odds for ACS. Our study validates an approach that accounts for both metabolomic and genomic theory by demonstrating improved sensitivity and specificity, better performance in real world data, which provides a more straightforward interpretation of results.

Technical Abstract: Both metabolomic and genomic approaches are valuable for risk analysis, however typical approaches evaluating differences in means do not model the changes well. Gene polymorphisms that alter function would appear as distinct populations, or metabotypes, from the predominant one, in which case risk is revealed as changed mixing proportions between control and case samples. Here we validate a model accounting for mixed populations using biomarkers of fatty acid metabolism derived from a case/control study of acute coronary syndrome subjects in which both metabolomic and genomic approaches have been used previously. We first used simulated data to show improved power and sensitivity in the approach compared to classic approaches. We then used the metabolic biomarkers to test for evidence of distinct metabotypes and different proportions among cases and controls. In simulation, our model outperformed all other approaches including Mann-Whitney, t-tests, and 'R'. Using real data, we found distinct metabotypes of six of the seven activities tested, and different mixing proportions in five of the six activity biomarkers: D9D, ELOVL6, ELOVL5, FADS1, and Sprecher pathway chain shortening (SCS). High activity metabotypes of non-essential fatty acids and SCS decreased odds for acute coronary syndrome (ACS), however high activity metabotypes of 20-carbon fatty acid synthesis increased odds. Our study validates an approach that accounts for both metabolomic and genomic theory by demonstrating improved sensitivity and specificity, better performance in real world data, and more straight forward interpretability.