Faculty Opinions recommendation of Human metabolic individuality in biomedical and pharmaceutical research.
Bibliographic record
Abstract
Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research. PMID: 21886157 Funding information This work was supported by: NHLBI NIH HHS, United States Grant ID: N01‐HC‐55015 NHLBI NIH HHS, United States Grant ID: N01‐HC‐55020 NHLBI NIH HHS, United States Grant ID: HL087647 NHLBI NIH HHS, United States Grant ID: P01HL076491‐06 NHLBI NIH HHS, United States Grant ID: P01 HL098055 NHLBI NIH HHS, United States Grant ID: N01‐HC‐55021 Intramural NIH HHS, United States CIHR, Canada Grant ID: MOP‐82810 Wellcome Trust, United Kingdom Grant ID: 091746 NIDDK NIH HHS, United States Grant ID: R01DK080732 NIA NIH HHS, United States Grant ID: N01‐AG‐12100 CIHR, Canada Grant ID: MOP172605 NHLBI NIH HHS, United States Grant ID: 1R01HL103931‐01 Cancer Research UK, United Kingdom Biotechnology and Biological Sciences Research Council, United Kingdom British Heart Foundation, United Kingdom NHLBI NIH HHS, United States Grant ID: N01‐HC‐55019 Wellcome Trust, United Kingdom Grant ID: 091746/Z/10/Z NHLBI NIH HHS, United States Grant ID: N01‐HC‐55016 NHLBI NIH HHS, United States Grant ID: N01‐HC‐55022 NHLBI NIH HHS, United States Grant ID: R01 HL087676 NHLBI NIH HHS, United States Grant ID: P01HL087018 NHLBI NIH HHS, United States Grant ID: R01HL089650‐02 NHLBI NIH HHS, United States Grant ID: N01‐HC‐55018 CIHR, Canada Grant ID: MOP77682 Medical Research Council, United Kingdom More Less keyboard_arrow_down
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.657 | 0.580 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".