Alleles on Rat Chromosome 4 (<i>D4Got41‐Fabp1/Tacr1</i>) Regulate Subphenotypes of Obesity
Bibliographic record
Abstract
OBJECTIVE: The use of inbred animal models is an essential component of the genetic dissection of complex diseases. Because quantitative trait loci for serum triglycerides, total cholesterol, and body weight were mapped on chromosome 4 in a cross of BioBreeding/OttawaKarlsburg (BB/OK) and spontaneously hypertensive (SHR) rats, we established a congenic BB.SHR rat strain by introgressing a SHR segment of chromosome 4 (D4Got41-Tacr1) into a BB/OK background. The phenotype of these BB.SHR rats (BB.4S) confirmed the quantitative trait loci. To discover whether the phenotype of BB.4S can only be attributed to the SHR segment per se, we established an additional congenic BB.WOKW strain by introgressing a similar segment of chromosome 4 (D4Got41-Fabp1) of the Wistar Ottawa Karlsburg RT1(u) rat into a BB/OK background, termed briefly BB.4W. RESEARCH METHODS AND PROCEDURES: Male normoglycemic BB/OK (20), BB.4S (20), and BB.4W (16) rats were longitudinally studied for body weight, serum triglycerides, total and high-density lipoprotein-cholesterol, and glucose tolerance. At the end of the observation period (32 weeks), serum insulin, leptin, and adiposity index (AI) were determined. RESULTS AND DISCUSSION: Congenic BB.4S and BB.4W were significantly heavier, and AI, serum triglycerides, and total cholesterol values were significantly elevated in BB.4S and BB.4W compared with BB/OK but more pronounced in BB.4S. The highest serum insulin was found in BB.4W and highest leptin in BB.4S. Because the body weight gain and AI were comparable between BB.4S and BB.4W, the obviously higher insulin levels in BB.4W and higher leptin values in BB.4S suggest that the two congenics most probably define two subphenotypes of obesity and provide the unique opportunity to study their genetics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".