Role of Ghrelin Polymorphisms in Obesity Based on Three Different Studies
Bibliographic record
Abstract
OBJECTIVE: Associations between preproghrelin DNA variants and obesity-related phenotypes were studied in 3004 subjects from the Québec Family Study (QFS), the HERITAGE Family Study (HERITAGE), and the Swedish Obese Subjects (SOS) Study. RESEARCH METHODS AND PROCEDURES: Body mass index (BMI), fat mass (FM) from underwater weighing, and abdominal fat from computerized tomography were measured. The ghrelin polymorphisms were identified by polymerase chain reaction. RESULTS: Arg51Gln QFS subjects (n = 6) had lower ghrelin concentrations (p = 0.007) than Arg51Arg subjects (n = 14). White preproghrelin Met72Met subjects in HERITAGE had the lowest BMI (p = 0.020), and those in the QFS cohort had the lowest FM (p < 0.001). Met72 carrier status (Met72+) was associated with lower FM (p = 0.026) and higher insulin-like growth factor-1 levels (p = 0.019) among blacks. Met72Met QFS subjects had less visceral fat (p = 0.002) and a lower fasting respiratory quotient (p = 0.037). HERITAGE Met72+ white subjects also showed lower exercise respiratory quotient (p = 0.030) and higher maximal oxygen uptake (p = 0.023). Furthermore, the prevalence of Met72+ was higher (19.2%; p < 0.05) in SOS subjects whose BMI was < or =25 kg/m(2) than in those with BMI >25 kg/m(2) (14.8%). SOS Met72+ obese women had a lower (11.4%; p = 0.032) prevalence of hypertension than noncarriers (23.9%). DISCUSSION: Arg51Gln mutation was associated with lower plasma ghrelin levels but not with obesity. The preproghrelin Met72 carrier status seems to be protective against fat accumulation and associated metabolic comorbidities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".