The Power of the Heterogeneous Stock Rat Founder Strains in Modeling Metabolic Disease
Bibliographic record
Abstract
Metabolic diseases are a host of complex conditions, including obesity, diabetes mellitus, and metabolic syndrome. Endocrine control systems (eg, adrenals, thyroid, gonads) are causally linked to metabolic health outcomes. N/NIH Heterogeneous Stock (HS) rats are a genetically heterogeneous outbred population developed for genetic studies of complex traits. Genetic mapping studies in adult HS rats identified loci associated with cardiometabolic risks, such as glucose intolerance, insulin resistance, and increased body mass index. This study determined underappreciated metabolic health traits and the associated endocrine glands within available substrains of the HS rat founders. We hypothesize that the genetic diversity of the HS rat founder strains causes a range of endocrine health conditions contributing to the diversity of cardiometabolic disease risks. ACI/EurMcwi, BN/NHsdMcwi, BUF/MnaMcwi, F344/StmMcwi, M520/NRrrcMcwi, and WKY/NCrl rats of both sexes were studied from birth until 13 weeks of age. Birth weight was recorded, body weight was measured weekly, metabolic characteristics were assessed, and blood and tissues were collected. Our data show wide variation in endocrine traits and metabolic health states in ACI, BN, BUF, F344, M520, and WKY rat strains. This is the first report to compare birth weight, resting metabolic rate, endocrine gland weight, hypothalamic-pituitary-thyroid axis hormones, and brown adipose tissue weight in these rat strains. Importantly, this work unveils new potential for the HS rat population to model early life adversity and adrenal and thyroid pathophysiology. The HS population likely inherited risk alleles for these strain-specific traits, making the HS rat a powerful model to investigate interventions on endocrine and metabolic health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".