Unconventional microarray design reveals the response to obesity is largely tissue specific: analysis of common and divergent responses to diet-induced obesity in insulin-sensitive tissues
Bibliographic record
Abstract
Obesity is a chronic condition involving the excessive accumulation of adipose tissue that adversely affects all systems in the body. The aim of the present study was to employ an unbiased, genome-wide assessment of transcript abundance in order to identify common gene expression pathways within insulin-sensitive tissues in response to dietary-induced diabetes. Following 20 weeks of chow or high-fat feeding (60% kcal), age-matched mice underwent a euglycemic-hyperinsulinemic clamp to assess insulin sensitivity. High-fat-fed animals were obese and highly insulin resistant, disposing of ∼75% less glucose compared with their chow-fed counterparts. Tissues were collected, and gene expression was examined by microarray in 4 tissues known to exhibit obesity-related metabolic disturbances: white adipose tissue, skeletal muscle, liver, and heart. A total of 463 genes were differentially expressed between diets. Analysis of individual tissues showed skeletal muscle to exhibit the largest number of differentially expressed genes (191) in response to high-fat feeding, followed by adipose tissue (169), liver (115), and heart (65). Analyses revealed that the response of individual genes to obesity is distinct and largely tissue specific, with less than 10% of transcripts being shared among tissues. Although transcripts are largely tissue specific, a systems approach shows numerous commonly activated pathways, including those involved in signal transduction, inflammation, oxidative stress, substrate transport, and metabolism. This suggests a coordinated attempt by tissues to limit metabolic perturbations occurring in early-stage obesity. Many identified genes were associated with a variety of disorders, thereby serving as potential links between obesity and its related health risks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".