Examining the Effects of Weight Loss on Energy Expenditure in Humans
Bibliographic record
Abstract
Being able to effectively match energy intake to energy expenditure (EE) is an important aspect in preventing weight re-gain in the post-obese. Although it is generally agreed upon that resting EE decreases concomitantly with weight loss, there is no set standard comparing the deviations with differing weight loss protocols and additionally, controversy remains as to whether this decrease is greater than can predicted. In order to address these issues 2977 subjects were analyzed using a systematic review and the differences of both the protocol and length of various interventions in addition to sex were compared. Next, data was selected from this systematic review and 815 subjects were analyzed for weight loss-induced changes in resting EE, FM and FFM. Another subgroup of studies (n = 1450) was analyzed and compared against the Harris-Benedict prediction equation to determine whether the changes in resting EE were greater than what was expected. Finally, in order to determine which factors may be involved in regulating changes in resting EE during weight loss, a secondary analysis was performed on 28 post-menopausal women (age= 50.4 ± 2.0 yrs; BMI= 32.4 ± 5.2 kg/m²) who were submitted to a 6-month caloric restriction. Body composition (DXA), resting EE (indirect calorimetry), physical activity EE (PAEE) and total EE (TEE) (doubly-labelled water) were measured before and after the 6 month weight loss. Blood samples were collected before and after to measure leptin and peptide YY. The results indicate that there was indeed a depression in resting EE during weight loss regardless of the type of intervention utilized. Furthermore, these findings suggest that the changes could not fully be explained by changes of FM and FFM alone and that leptin may be an important contributor to the changes of resting EE during weight loss.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".