Exercise mitigates the adverse effects of hyperhomocysteinemia on macrophages, MMP-9, skeletal muscle, and white adipocytes
Bibliographic record
Abstract
Regular exercise is a great medicine with its benefits encompassing everything from prevention of cardiovascular risk to alleviation of different muscular myopathies. Interestingly, elevated levels of homocysteine (Hcy), also known as hyperhomocysteinemia (HHcy), antagonizes beta-2 adrenergic receptors (β2AR), gamma amino butyric acid (GABA), and peroxisome proliferator-activated receptor-gamma (PPARγ) receptors. HHcy also stimulates an elevation of the M1/M2 macrophage ratio, resulting in a more inflammatory profile. In this review we discuss several potential targets altered by HHcy that result in myopathy and excessive fat accumulation. Several of these HHcy mediated changes can be countered by exercise and culminate into mitigation of HHcy induced myopathy and metabolic syndrome. We suggest that exercise directly impacts levels of Hcy, matrix metalloproteinase 9 (MMP-9), macrophages, and G-protein coupled receptors (GPCRs, especially Gs). While HHcy promotes the M1 macrophage phenotype, it appears that exercise may diminish the M1/M2 ratio, resulting in a less inflammatory phenotype. HHcy through its influence on GPCRs, specifically β₂AR, PPARγ and GABA receptors, promotes accumulation of white fat, whereas exercise enhances the browning of white fat and counters HHcy-mediated effects on GPCRs. Alleviation of HHcy-associated pathologies with exercise also includes reversal of excessive MMP-9 activation. Moreover, exercise, by reducing plasma Hcy levels, may prevent skeletal muscle myopathy, improve exercise capacity and rescue the obese phenotype. The purpose of this review is to summarize the pathological conditions surrounding HHcy and to clarify the importance of regular exercise as a method of disease prevention.
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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".