Gene Expression Changes in Adipose Tissue with Diet- and/or Exercise-Induced Weight Loss
Bibliographic record
Abstract
Adipose tissue plays a role in obesity-related cancers via increased production of inflammatory factors, steroid hormones, and altered adipokines. The impact of weight loss on adipose tissue gene expression may provide insights into pathways linking obesity with cancer risk. We conducted an ancillary study within a randomized trial of diet, exercise, or combined diet + exercise versus control among overweight/obese postmenopausal women. In 45 women, subcutaneous adipose tissue biopsies were conducted at baseline and after 6 months, and changes in adipose tissue gene expression were determined by microarray with an emphasis on prespecified candidate pathways as well as by unsupervised clustering of more than 37,000 transcripts (Illumina). Analyses were conducted first by randomization group and then by degree of weight change at 6-months in all women combined. At 6 months, diet, exercise, and diet + exercise participants lost a mean of 8.8, 2.5, and 7.9 kg (all P < 0.05 vs. no change in controls). There was no significant change in candidate gene expression by intervention group. In analysis by weight change category, greater weight loss was associated a decrease in 17β-hydroxysteroid dehydrogenase-1 (HSD17B1, Ptrend < 0.01) and leptin (LEP, Ptrend < 0.01) expression, and marginally significant increased expression of estrogen receptor-1 (ESR1, Ptrend = 0.08) and insulin-like growth factor-binding protein-3 (IGFBP3, Ptrend = 0.08). Unsupervised clustering revealed 83 transcripts with statistically significant changes. Multiple gene expression changes correlated with changes in associated serum biomarkers. Weight loss was associated with changes in adipose tissue gene expression after 6 months, particularly in two pathways postulated to link obesity and cancer, that is, steroid hormone metabolism and IGF signaling. Cancer Prev Res; 6(3); 217-31. ©2013 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 | 0.000 | 0.000 |
| 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".