Genetic and phenotypic variation in UGT2B17, a testosterone-metabolizing enzyme, is associated with BMI in males
Bibliographic record
Abstract
OBJECTIVE: A number of candidate gene and genome-wide association studies have identified significant associations between single nucleotide polymorphisms, particularly in FTO and MC4R, and body weight. However, the association between copy number variation and body weight is less understood. Anabolic androgenic steroids, such as testosterone, can regulate body weight. In humans, UDP-glucuronosyltransferase 2B17 (UGT2B17) metabolizes testosterone to a metabolite, which is readily excreted in urine. We investigate the association between genetic and phenotypic variation in UGT2B17 and body weight. MATERIALS AND METHODS: UGT2B17 deletion was genotyped and in-vivo UGT2B17 enzymatic activity (as measured by the 3-hydroxycotinine glucuronide to free 3-hydroxycotinine ratio) was measured in 400 Alaska Native individuals and 540 African Americans. RESULTS: In Alaska Native people, UGT2B17 deletion was strongly associated with lower BMI in male individuals (P<0.001), but not in female individuals, consistent with testosterone being a male dominant steroid. The sex-specific association between UGT2B17 deletion and lower BMI was also observed in African Americans (P=0.01 in male individuals). In both populations, UGT2B17 deletion was significantly associated with lower measured in-vivo UGT2B17 activity. In male individuals, lower in-vivo UGT2B17 activity was associated with lower BMI, as observed in the sex-specific genotypic association. CONCLUSION: These data suggest that UGT2B17 deletion leads to reduced UGT2B17 activity, and lower BMI in male individuals. This is consistent with the hypothesis that reduced UGT2B17-mediated testosterone excretion results in higher testosterone levels. Future studies could confirm this hypothesis by directly measuring serum testosterone levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".