Dietary Weight Loss, Exercise, and Oxidative Stress in Postmenopausal Women: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Oxidative stress, a potential mechanism linking obesity and cancer, results from an imbalance between activation/inactivation of reactive oxygen species, byproducts of cellular metabolism. In a randomized controlled trial, we investigated effects of diet and/or exercise on biomarkers of oxidative stress. A total of 439 overweight/obese [body mass index (BMI) > 25 kg/m2] postmenopausal women, ages 50 of 75 years, were randomized to 12 months of (i) reduced-calorie weight loss diet (“diet”; n = 118); (ii) moderate-to-vigorous intensity aerobic exercise (“exercise”; n = 117); (iii) combined diet and exercise intervention (“diet + exercise”; n = 117); or (iv) control (n = 87). Outcomes were circulating markers of oxidative stress, including fluorescent oxidation products (FOP), F2-isoprostanes, and oxidized low-density lipoprotein (LDL). On average, participants were 57.9 years, with a BMI of 30.9 kg/m2. F2-isprostanes were significantly reduced in the diet (−22.7%, P = 0.0002) and diet + exercise (−23.5%, P < 0.0001) arms versus controls (−2.99%) and nonsignificantly reduced in the exercise arm (−14.5%, P = 0.01). Participants randomized to the diet and diet + exercise arms had significant increases in levels of FOP [control −5.81%; diet +14.77% (P = 0.0001); diet + exercise +17.45%, (P = 0.0001)]. In secondary analyses, increasing weight loss was statistically significantly associated with linear trends of greater reductions in oxidized LDL and in F2-isoprostanes and increases in FOP. Compared with controls, exercise participants whose maximal oxygen consumption increased had significant decreases in levels of F2-isoprostanes and in oxidized LDL and increases in FOP. Dietary weight loss, with or without exercise, significantly reduced some markers of oxidative stress in postmenopausal women. Cancer Prev Res; 9(11); 835–43. ©2016 AACR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".