Therapeutic Potential of Cranberry Proanthocyanidins in Addressing the Pathophysiology of Metabolic Syndrome: A Scrutiny of Select Mechanisms of Action
Bibliographic record
Abstract
Metabolic syndrome (MetS) constitutes a spectrum of interconnected conditions comprising obesity, dyslipidemia, hypertension, and insulin resistance (IR). While a singular, all-encompassing treatment for MetS remains elusive, an integrative approach involving tailored lifestyle modifications and emerging functional food therapies holds promise in preventing its multifaceted manifestations. Our main objective was to scrutinize the efficacy of cranberry proanthocyanidins (PAC, 200 mg/kg/day for 12 weeks) in mitigating MetS pathophysiology in male mice subjected to standard Chow or high-fat/high-fructose (HFHF) diets while unravelling intricate mechanisms. The administration of PAC, in conjunction with an HFHF diet, significantly averted obesity, evidenced by reductions in body weight, adiposity across various fat depots, and adipocyte hypertrophy. Similarly, PAC prevented HFHF-induced hyperglycemia and hyperinsulinemia while also lessening IR. Furthermore, PAC proved effective in alleviating key risk factors associated with cardiovascular diseases by diminishing plasma saturated fatty acids, as well as levels of triglycerides, cholesterol, and non-HDL-C levels. The rise in adiponectin and drop in circulating levels of inflammatory markers showcased PAC's protective role against inflammation. To better clarify the mechanisms behind PAC actions, gut-liver axis parameters were examined, showing significant enhancements in gut microbiota composition, microbiota-derived metabolites, and marked reductions in intestinal and hepatic inflammation, liver steatosis, and key biomarkers associated with endoplasmic reticulum (ER) stress and lipid metabolism. This study enhances our understanding of the complex mechanisms underlying the development of MetS and provides valuable insights into how PAC may alleviate cardiometabolic dysfunction in HFHF mice.
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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) | 0.000 | 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 | 0.001 |
| 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".