Finding a Link between Obesity and Senescence: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Cell aging is associated with changes in telomeres due to DNA damage arising from chronic inflammation in obese patients. The aim of the systematic review and meta-analysis was to find the relationship between obesity and aging or senescence. Methods: The systematic review was conducted through PRISMA guideline, beginning with literature search within 2012-2022 in several databases (PubMed, EBSCOHost, Science Direct, Scopus, and Cochrane) followed by screening process using predetermined PICO criteria. Original studies on the topic of obesity and senescence (aging), from preclinical studies to clinical research (cohort or cross-sectional studies) that were published within the last ten years. All studies were appraised using SYRCLE risk of bias tool for preclinical studies and Newcastle-Ottawa Scale (NOS) for cross-sectional and cohort studies. The data extraction on the studies' characteristic and outcome on aging or senescence were followed by quantitative analysis using MetaXL process on prevalence ratio and hazard ratio of obesity to comorbidities and mortality. Results: Fifteen studies were enrolled. Obesity and white adipose tissue cause increased levels of pro-inflammatory and pro-senescence cytokine and macrophage whilst the aging process lowers metabolism with increased insulin resistance and linked to increased risk of obesity. Obesity occurs in 22% (95% CI 18%-26%) of elderly population with higher prevalence rate in the women population. Obesity is associated with significant increased risk of multimorbidity by 56% (OR = 1.58 [95% CI 1.48-1.96]). Conclusion: The obesity and aging or senescence has reciprocal relationship between each other.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".