Frailty and cytokines in preclinical models: Comparisons with humans
Bibliographic record
Abstract
Chronic low-grade elevations of blood-borne cytokines/chemokines in older age tend to associate with frailty in humans. This persistent inflammation is often called "inflammageing" and likely contributes to frailty progression. Preclinical models such as ageing and/or genetically modified mice offer a unique opportunity to mechanistically study how these inflammatory mediators affect frailty. In this review, we summarize and contrast evidence relating cytokines/chemokines to frailty in humans and in mouse models of frailty. In humans and mice, higher levels of the pro-inflammatory cytokine interleukin-6 regularly increased in proportion to the degree of frailty. Evidence linking other cytokines/chemokines to frailty in humans and mice is less certain. The chemokines CXCL-10 and monocyte chemoattractant protein-1 related to frailty across both species, but evidence is limited and inconsistent. Several other cytokines/chemokines, including tumour necrosis factor-α relate to frailty in humans or in mice, but evidence to date is species- and tissue-dependent. It is important for future studies to validate common mechanistic inflammatory biomarkers of frailty between humans and mice. Achieving this goal will accelerate the search for drugs to treat frailty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".