A Murine Frailty Index Based on Clinical and Laboratory Measurements: Links Between Frailty and Pro-inflammatory Cytokines Differ in a Sex-Specific Manner
Bibliographic record
Abstract
A frailty index (FI) based on clinical deficit accumulation (FI-Clinical) quantifies frailty in aging mice. We aimed to develop a laboratory test-based murine FI tool (FI-Lab) and to investigate the effects of age and sex on FI-Lab scores, FI-Clinical scores, and the combination (FI-Combined), as well as to explore links between frailty and inflammation. Studies used older (17 and 23 months) C57BL/6 mice of both sexes. We developed an FI-Lab (blood pressure, blood chemistry, echocardiography) based on deviation from reference values in younger adults (12 months), which showed similar characteristics to a human FI-Lab tool. Interestingly, while FI-Clinical scores were higher in females, the opposite was true for FI-Lab scores and there was no sex difference in FI-Combined scores. All three FI tools revealed a positive correlation between pro-inflammatory cytokine levels and frailty in aging mice that differed between the sexes. Elevated levels of the pro-inflammatory cytokines interleukin (IL)-6, IL-9, and interferon-γ were associated with higher FI scores in aging females, while levels of IL-12p40 rose as FI scores increased in older males. Thus, an FI tool based on common laboratory tests can quantify frailty in mice; the positive correlation between inflammation and frailty scores in naturally aging mice differs between the sexes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".