High‐fat diet protects BB/OK rats from developing type 1 diabetes
Bibliographic record
Abstract
BACKGROUND: It is well known that lipid metabolism plays an important role in the early stages of type 1 diabetes (T1D). For that reason, we examined factors that influence lipid metabolism of BioBreeding/Ottawa Kalsburg (BB/OK) rats that spontaneously develop an insulin-dependent T1D. METHODS: BB/OK female rats were fed a high-fat diet during pregnancy (Ssniff R-Z + 10% tallow) and their progeny were also given this diet up to an age of 30 weeks (n = 55) or 4 weeks (n = 14) to study gene expression of Pparg, Fasn, Lep, Adipoq, Repin1, Rarres 2, and Glut4 in adipose tissue. Forty-two BB/OK rats fed the normal diet (Ssniff R-Z) during pregnancy and the observation period served as controls. RESULTS: The high-fat diet significantly decreased diabetes frequency in BB/OK rats when compared with control rats (71 versus 95%, p = 0.002). Although this difference was also reflected in the male rats (68 versus 100%, p = 0.003), no significant variation was observed in female rats (73 versus 90%, p = 0.23). The high-fat diet resulted in significantly reduced mRNA expression of examined genes in subcutaneous adipose tissue, but not in visceral adipose tissue, except for Fasn and Repin1 expression. CONCLUSIONS: A high-fat diet seems to protect BB/OK rats from T1D in a sex-specific manner. The data suggest that a high-fat diet might influence fat accumulation and/or fat metabolism and prevent T1D development in male rats, which is supported by changes in adipose tissue gene expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".