Frontiers in GeneticsGenomicsPeer-reviewed journal article
Mendelian randomization identifies the potential causal impact of dietary patterns on circulating blood metabolites
Nele TabaiD, Hanna-Kristel Valge, Andres MetspaluiD, Tõnu EskoiD, James F. WilsoniD, Krista FischeriD, Nicola PirastuiD
Abstract
Nutrition plays an important role in the development and progress of several health conditions, but the exact mechanism is often still unclear. Blood metabolites are likely candidates to be mediating these relationships, as their levels are strongly dependent on the frequency of consumption of several foods/drinks. Understanding the causal effect of food on metabolites is thus of extreme importance. To establish these effects we utilized Two-sample Mendelian randomization using the genetic variants associated with dietary traits as instrumental variables. The estimates of single-nucleotide polymorphisms effects on exposures were obtained from a recent genome-wide association study (GWAS) of 25 individual and 15 principal-component dietary traits, whereas the ones for outcomes were obtained from a GWAS of 123 blood metabolites measured by nuclear magnetic resonance spectroscopy. We identified 417 potentially causal links between food and metabolites, replicating previous findings, such as the association between increased oily fish consumption and higher DHA, and highlighting several novel associations. Most of the associations were related to very-low-density, intermediate-density (IDL) and low-density lipoproteins (LDL). For example, we found that constituents of IDL particles and large LDL particles were raised by coffee and alcohol while lowered by an overall healthier diet and fruit consumption. Our results represent one of the first examples of the estimates of long-term causal effects of diet on metabolites and start bridging the gap in the mechanistic understanding linking food consumption to its health consequences.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 15
- FWCI
- 0.99
- Field percentile
- top 26%
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