Five separate metabolomics studies used mass spectrometry or NMR to measure hundreds of small molecules in blood and urine, and repeatedly converged on the same handful of metabolism genes.
Solid lines are connections this site curates. Dashed lines mean the two ends share a research paper — worth knowing, and not a claim that one explains the other.
Blood metabolite levels refers to the concentrations of small molecules — amino acids, lipids, sugars, and other metabolic byproducts — circulating in blood or excreted in urine, measured directly with mass spectrometry or NMR spectroscopy rather than the handful of standard clinical labs (like glucose or cholesterol) ordered in routine care. This page is about that broad research measurement, distinct from this site's separate Metabolic Syndrome page, which covers the specific clinical risk factors used to diagnose that syndrome.
Suhre et al. 2011 ran a landmark non-targeted metabolomics GWAS and found 37 genetic loci associated with blood metabolite concentrations, 25 of them with unusually large effect sizes — some alleles shifted a metabolite's level by 10-60% per copy, far larger than typical GWAS effects on disease risk. Sixteen of this page's variants trace directly to that paper, including rs662138 in SLC22A1, a liver drug transporter, and rs612169 in ABO, the same blood-type gene with effects across many unrelated traits on this site.
Four smaller, more targeted studies independently converged on several of the same genes. Illig et al. 2010, in 1,809 people (replicated in 422 more), named eight loci directly, including FADS1, ACADS, ACADM, and ACADL — all fatty-acid metabolism genes — matching five of this page's own variants. Hong et al. 2013, in 402 people (replicated in 489 more), separately named FADS1 and ACADL among its seven loci — the same two genes Illig's study had already implicated. Nicholson et al. 2011 found that rs9309473, in NAT8, sits in a haplotype block bearing the genetic signature of recent positive selection in Europeans. And Xie et al. 2013, in 1,004 people (replicated in 342 more), traced variants in the glycine and betaine metabolism pathway; this page's rs17823642 sits in BHMT2, in the same gene cluster as that paper's own BHMT finding, though not an exact match to its named gene.
Taken together, the repeated independent discovery of the same fatty-acid and one-carbon metabolism genes — FADS1, the ACAD family, and the betaine pathway — across five separate cohorts is a stronger form of evidence than any single study alone.
Positions joined since this page was written
What this is The text above discusses the variants this page was written around. Since then the catalogue has joined 1 more position to it, by shared trait or shared paper. They are listed here by the paper each came from; the text does not describe them, and each variant page carries that study's own record.
Chai JF et al. 2020, Human molecular genetics rs116853509 (SLCO1B1) — PMID:31628463
2026-05-02 · Population-based genome-wide association study of plasma complex lipid species. Nature Communications. 2026. DOI:10.1038/s41467-026-72542-1
Standard cholesterol/triglyceride panels measure only a handful of broad lipid categories, but the human lipidome actually contains hundreds of distinct molecular species whose individual genetic architecture was mostly unknown. This study ran GWAS on 970 individual lipid species and 267 fatty-acid composite measures using the Rhineland Study (n=6,096), validating findings in two independent cohorts (FinnGen, EPIC-Potsdam). Of 217 lead genomic loci found, 136 were novel, including FDFT1. Using Mendelian randomization and gene-expression data, the study identified 43 likely causal gene-to-lipid-species relationships, including FDFT1 driving a specific diacylglycerol species (16:0/18:0). This kind of granular lipid-species genetics matters because different lipid species within the same broad category (e.g., different triglyceride or diacylglycerol subtypes) can have very different disease relevance, information a standard lipid panel collapses into one number. This site's blood metabolite levels page carries 26 variants; FDFT1 and the newly identified loci are not currently among them.
These metabolite panels are research tools, not standard clinical tests, and none of the variants here changes how any disease is diagnosed or treated. Suhre et al. 2011 specifically noted these findings offer functional insight into previously reported disease associations — they do not replace clinical testing.
Unlike a routine glucose or cholesterol test, the metabolomics platforms behind this page's findings measure hundreds of metabolites at once using mass spectrometry or NMR — research infrastructure, not something available through ordinary clinical labs. For clinical risk factors specifically, see this site's Metabolic Syndrome page.
What a 23andMe/AncestryDNA export or raw VCF can and can't tell you about Blood Metabolite Levels comes down to these specific, well-studied positions — not a diagnosis. 27 positions are linked to this page; the ones this page's own text discusses are shown first.
The studies behind these variants recruited participants from different ancestries — a result found in one population doesn't always transfer to another. Based on 26 of 27 linked studies with a resolved discovery ancestry.
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Blood Metabolite Levels. MyGeneLog™. https://www.mygenelog.com/conditions/blood-metabolite-levels
Blood metabolite levels are the concentrations of small molecules — amino acids, lipids, sugars, and other metabolic byproducts — circulating in blood or urine, measured with mass spectrometry or NMR rather than standard clinical labs.
Five separate studies, the largest by Suhre et al. 2011 finding 37 loci, repeatedly converged on the same genes — particularly FADS1 and the ACAD family of fatty-acid metabolism genes — across independent cohorts.
No. These metabolomics platforms measure hundreds of metabolites at once using mass spectrometry or NMR — research tools, not the handful of standard tests (like glucose or cholesterol) ordered in routine clinical care.
No individual variant does. These are population-level statistical associations with metabolite concentrations, and Xie et al. 2013 specifically found no robust link between their glycine-pathway variants and diabetes itself.
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