An Investigation of Hormone and Lipid Associations after Weight Loss in Women
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to determine 1) whether the extent of weight loss is predictive of the degree of changes in hormone and lipid levels; 2) the interactions between energy regulating hormones after weight loss through an energy deficit/exercise protocol diet and exercise; 3) whether initial metabolic parameters are indicative of the extent of weight loss. METHODS: Thirty-five hyperlipidemic females (BMI 28-39 kg/m2) 35-60 years old participated in a six month weight loss trial. Weight loss resulted from a diet and exercise program that when combined produced a 30% energy deficit. Fasting plasma taken during 2 wk stabilization periods at the beginning and end of the study was analysed for lipids, hormone and glucose levels. RESULTS: Average weight loss was 11.7 +/- 2.5 kg (p < 0.0001). TC, LDL-C, and triacylglycerols decreased 9.3 +/- 9.5% (p < 0.0001), 7.4 +/- 12.2% (p < 0.001), and 26.8 +/- 19.6% (p < 0.05), respectively, while HDL-C increased (p < 0.05) by 8.2 +/- 16.3%. Leptin levels declined (p < 0.001) 48.9 +/- 16.0% and ghrelin levels rose (p < 0.001) 21.2 +/- 26.7%. While overall levels of adiponectin did not differ, individual values changed such that weight loss predicted increases in adiponectin levels. Though initial weight did not predict weight loss, baseline lipid and insulin levels positively predicted weight loss. CONCLUSION: Initial metabolic parameters may be predictors of weight loss. Beneficial effects of weight loss as achieved through diet and exercise on measured parameters indicate moderate weight loss reduces key risk factors of cardiovascular disease in overweight individuals.
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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) | 0.001 | 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".