Application of DNA Microarrays in the Study of Human Obesity and Type 2 Diabetes
Bibliographic record
Abstract
DNA microarrays have provided medical researchers with a powerful tool to study the mechanisms of complex diseases, including obesity and type 2 diabetes (T2D). The technology has been used to dissect virtually every aspect of the genetic and molecular basis of these two diseases. Gene expression profiling is the major application of DNA microarrays so far. Subcutaneous fat, visceral fat, adipocyte and preadipocyte, muscle, liver, pancreas and specific nuclei in the hypothalamus under normal and disease conditions are used in addressing the profile of gene expression in obesity and T2D. Comparisons of fat depots in humans and animal models - including ob/ob and db/db mice, diet-induced obese mice, fa/fa Zucker rats, gene knockout (plin (-/-), GLUT4 (-/-)) and transgenic mice (GLUT4-Tg) - have been employed in microarray experiments. The effects of various interventions, such as hormonal and drug treatments, exercise, and surgery, have been studied to determine the expression profile of different developmental stages in cells and the effect of treatment on the two diseases. In this review, the application of microarrays in elucidating the role of retinol binding protein 4 as a link between obesity and T2D is discussed. The possible role in obesity of a common genetic variant near the INSIG2 gene and the discovery of the BBS9 gene are also discussed. The problems and challenges are summarized under eight categories and suggestions for the future direction of research in this area are proposed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".