Diet Composition and Host Genetics Interact to Modulate Gut Microbiota and Predisposition to Metabolic Syndrome in Spontaneously Hypertensive Stroke-Prone Rats (P08-023-19)
Bibliographic record
Abstract
Metabolic syndrome encompasses obesity, glucose intolerance, hypertension and dyslipidemia, and is a global health concern; however, the interactions between diet and host physiology that predispose to metabolic syndrome are incompletely understood. Our Objectives were to determine the effects of high-fat diet (HFD) on energy balance, gut microbiota and key risk factors of metabolic syndrome in spontaneously hypertensive stroke-prone (SHRSP) and Wistar-Kyoto (WKY) rats. Male rats (SHRSP and WKY, 5 weeks old) were randomized to either chow or HFD diets (33% fat n = 7/group) and followed for 12 weeks. Exendin-4 (GLP-1 receptor blocker), Propranolol (beta-adrenergic blocker) or vehicle were administered IP acutely during the study. Measurements included blood pressure, food intake and energy expenditure (CLAMS®), body composition (Minispec LF110 NMR), glucose and meal tolerance, gut hormones, and gut microbiota (16S sequencing). We found that SHRSP rats were hypertensive, hyperphagic, less sensitive to the 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 adipose and skeletal muscle tissues were upregulated in SHRSP than WKY rats. Further, HFD promoted weight gain, adiposity, glucose intolerance, hypertriglyceridemia, hepatic lipidosis and increased plasma leptin in both SHRSP and WKY. Importantly, despite distinct differences in gut microbiota composition between the strains, diet had a preponderant impact on gut flora with some of the taxa being strongly associated with key metabolic parameters. High fat feeding comparably increased indices of adiposity and hypertriglyceridemia in both SHRSP and WKY rats, but unlike WKY, the SHRSP have unique characteristics including hyperphagia, diminished sensitivity to hypophagic effects of gut satiety signals, limited capacity to clear glucose after a meal, dysbiotic gut flora, and sustained hypertension. Thus, the SHRSP rat model has considerable potential to dissect the complex interplay of genetics, diet and gut microbiota that occur with metabolic syndrome. Heart and Stroke Foundation of Canada.
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
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".