Methylation potential associated with diet, genotype, protein, and metabolite levels in the Delta Obesity Vitamin Study
2014
Monteiro, Jacqueline Pontes | Wise, Carolyn | Morine, Melissa J. | Teitel, Candee | Pence, Lisa | Williams, Anna | McCabe-Sellers, Beverly | Champagne, Catherine | Turner, Jerome | Shelby, Beatrice | Ning, Baitang | Oguntimein, Joan | Taylor, Lauren | Toennessen, Terri | Priami, Corrado | Beger, Richard D. | Bogle, Margaret | Kaput, Jim
Micronutrient research typically focuses on analyzing the effects of single or a few nutrients on health by analyzing a limited number of biomarkers. The observational study described here analyzed micronutrients, plasma proteins, dietary intakes, and genotype using a systems approach. Participants attended a community-based summer day program for 6–14 year old in 2 years. Genetic makeup, blood metabolite and protein levels, and dietary differences were measured in each individual. Twenty-four-hour dietary intakes, eight micronutrients (vitamins A, D, E, thiamin, folic acid, riboflavin, pyridoxal, and pyridoxine) and 3 one-carbon metabolites [homocysteine (Hcy), S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH)], and 1,129 plasma proteins were analyzed as a function of diet at metabolite level, plasma protein level, age, and sex. Cluster analysis identified two groups differing in SAM/SAH and differing in dietary intake patterns indicating that SAM/SAH was a potential marker of nutritional status. The approach used to analyze genetic association with the SAM/SAH metabolites is called middle-out: SNPs in 275 genes involved in the one-carbon pathway (folate, pyridoxal/pyridoxine, thiamin) or were correlated with SAM/SAH (vitamin A, E, Hcy) were analyzed instead of the entire 1M SNP data set. This procedure identified 46 SNPs in 25 genes associated with SAM/SAH demonstrating a genetic contribution to the methylation potential. Individual plasma metabolites correlated with 99 plasma proteins. Fourteen proteins correlated with body mass index, 49 with group age, and 30 with sex. The analytical strategy described here identified subgroups for targeted nutritional interventions.
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