The Important Role of Intermuscular Adipose Tissue on Metabolic Changes Interconnecting Obesity, Ageing and Exercise: A Systematic Review
Bibliographic record
Abstract
As age increases, adipose tissue infiltrates muscle tissue and leads to sarcopenia. When excessive accumulation of adipose tissue accompanied progressive decrease in lean body mass especially visceral fat, termed as sarcopenic obesity (SO) and related metabolic intermuscular adipose tissue (IMAT) is an ectopic tissue found between muscle groups, and is distinct from subcutaneous adipose tissue. Until now, the association between IMAT and metabolic health was not understood. This study is the first systematic review assessing the association between IMAT and metabolic health. The PubMed, Science Direct and Cochrane databases were searched for studies reporting IMAT and metabolic risk. The descriptions of the extracted data are guided by the Preferred Reporting Items for Systematic Reviews (PRISMA) statement with a Grading of Recommendations Assessment, Development and Evaluation approach. This study is registered at PROSPERO (identifier: CRD42022337518). Six studies were pooled and reviewed using critical appraisal by the Newcastle Ottawa Scale and Centre for Evidence-Based Medicine checklist. Two clinical trials and four observational trials were included. Our results reveal that IMAT is associated with metabolic risk, especially in older adults and patients with obesity. However, in a person with abdominal obesity, VAT has a more significant role in metabolic risk than IMAT. The largest decrease in IMAT was achieved by combining aerobic with resistance training.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".