Host genetics and diet composition interact to modulate gut microbiota and predisposition to metabolic syndrome in spontaneously hypertensive stroke‐prone rats
Bibliographic record
Abstract
Metabolic syndrome encompasses obesity, glucose intolerance, hypertension, and dyslipidemia; however, the interactions between diet and host physiology that predispose to metabolic syndrome are incompletely understood. Here, we explored the effects of a high-fat diet (HFD) on energy balance, gut microbiota, and risk factors of metabolic syndrome in spontaneously hypertensive stroke-prone (SHRSP) and Wistar-Kyoto (WKY) rats. We found that the SHRSP rats were hypertensive, hyperphagic, less sensitive to hypophagic effects of exendin-4, and expended more energy with diminished sensitivity to sympathetic blockade compared to WKY rats. Notably, key thermogenic markers in brown and retroperitoneal adipose tissues and skeletal muscle were up-regulated in SHRSP than WKY rats. Although HFD promoted weight gain, adiposity, glucose intolerance, hypertriglyceridemia, hepatic lipidosis, and hyperleptinemia in both SHRSP and WKY rats, the SHRSP rats weighed less but had comparable percent adiposity to WKY rats, which supports the use of HFD-fed SHRSP rats as a unique model for studying the metabolically obese normal weight (MONW) phenotype in humans. Despite distinct strain differences in gut microbiota composition, diet had a preponderant impact on gut flora with some of the taxa being strongly associated with key metabolic parameters. Together, we provide evidence that interactions between host genetics and diet modulate gut microbiota and predispose SHRSP rats to develop metabolic syndrome.-Singh, A., Zapata, R. C., Pezeshki, A., Workentine, M. L., Chelikani, P. K. Host genetics and diet composition interact to modulate gut microbiota and predisposition to metabolic syndrome in spontaneously hypertensive stroke-prone rats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".