<i>Arg64</i>β<sub>3</sub>‐Adrenoceptor Variant and the Components of Energy Expenditure
Bibliographic record
Abstract
OBJECTIVE: To determine Trp64Arg beta(3)-adrenoceptor genotype-specific differences in the components of energy expenditure. HYPOTHESIS: We hypothesized that resting metabolic rate (RMR) and physical activity levels would be lower and that thermic effect of feeding (TEF) would be higher in those with the Arg64 allele. RESEARCH METHODS AND PROCEDURES: RMR and TEF were measured by indirect calorimetry, physical activity by questionnaire, and total energy expenditure by the doubly labeled water method. Genotype-specific measures were compared using ANOVA and analysis of covariance (ANCOVA). RESULTS: RMR in Arg64 homozygotes was significantly lower than in Trp64 homozygotes [Arg64, 1373 +/- 259 kcal/d (n = 15) vs. Trp64Arg, 1538 +/- 238 kcal/d (n = 25) vs. Trp64, 1607 +/- 290 kcal/d (n = 22); p < 0.01]. TEF was significantly higher in Arg64 homozygotes compared with Trp64 homozygotes (Arg64, 359 +/- 28 kcal/d; Trp64Arg, 322 +/- 22 kcal/d; and Trp64, 279 +/- 23 kcal/d; p < 0.05). No differences were identified between genotypes in physical activity or in total energy expenditure. DISCUSSION: Our results suggest that the Arg64 beta(3)-adrenoceptor allele contributes significantly to the genetic variability in both RMR and TEF.
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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) | 0.003 | 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".