Influence of ADRB2 Gln27Glu and ADRB3 Trp64Arg polymorphisms on body weight and body composition changes after a controlled weight-loss intervention
Bibliographic record
Abstract
The β-2 and β-3 adrenergic receptors (ADRB2 and ADRB3) are thought to play a role in energy expenditure and lipolysis. However, the effects of the ADRB2 glutamine (Gln) 27 glutamic acid (glutamate) (Glu) and ADRB3 tryptophan (Trp) 64 arginine (Arg) polymorphisms on weight loss remain controversial. The aim of this study was to investigate the effect of these polymorphisms on changes in weight and body composition during a controlled weight-loss program. One hundred seventy-three healthy overweight and obese participants (91 women, 82 men) aged 18-50 years participated in a 22-week-long intervention based on a hypocaloric diet and exercise. They were randomly assigned to 1 of 4 groups: strength, endurance, strength and endurance combined, and physical activity recommendations only. Body weight, body mass index (BMI), and body composition variables were assessed before and after the intervention. Genetic analysis was carried out according to standard protocols. No effect of the ADRB2 gene was shown on final weight, BMI, or body composition, although in the supervised male group, Glu27 carriers tended to have greater weight (p = 0.019, 2.5 kg) and BMI (p = 0.019, 0.88 kg/m(2)) reductions than did noncarriers. There seems to be an individual effect of the ADRB3 polymorphism on fat mass (p = 0.004) and fat percentage (p = 0.036), in addition to an interaction with exercise for fat mass (p = 0.038). After the intervention, carriers of the Arg64 allele had a greater fat mass and fat percentage than did noncarriers (p = 0.004, 2.8 kg). In conclusion, the ADRB2 Gln27Glu and ADRB3 Trp64Arg polymorphisms may influence weight loss and body composition, although the current evidence is weak; however, further studies are necessary to clarify their roles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".