Lack of Association of Ghrelin Precursor Gene Variants and Percentage Body Fat or Serum Lipid Profiles
Bibliographic record
Abstract
Ghrelin has been recognized for its involvement in food intake, control of energy homeostasis, and lipid metabolism. However, the roles of genetic variations in the ghrelin precursor gene (GHRL) on body compositions and serum lipids are not clear in humans. Our study investigated five single-nucleotide polymorphisms (SNPs) within GHRL to determine their relationship with body fat percentage (BF), trunk fat percentage (TF), lower body (legs) fat percentage (LF), and serum lipids in 1,464 subjects, which were recruited from the genetically homogeneous population of Newfoundland and Labrador (NL), Canada. Serum glucose, insulin, total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, and triglycerides were determined. Five SNPs are rs35684 (A/G: a transition substitution in exon 1), rs4684677 (A/T: a missense mutation), rs2075356 (C/T: intron), rs26802 (G/T: intron), and rs26311 (A/G: near the 3' untranslated region) of GHRL were genotyped using TaqMan validated or functionally tested SNP genotyping assays. Our study found no significant evidence of an allele or genotype association between any of the variant sites and body compositions or serum lipids. Furthermore, haplotype frequencies were not found to be significantly different between lean and obese subjects. In summary, the results of our study do not support a significant role for genetic variations in GHRL in the differences of body fat and serum lipid profiles in the NL population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".