Impact of Longevity Interventions on a Validated Mouse Clinical Frailty Index
Bibliographic record
Abstract
This article investigates the effect on the mouse frailty index (FI), of factors known to influence lifespan and healthspan in mice: strain (short-lived DBA/2J mice vs long-lived C57BL/6J mice), calorie restriction (CR), and resveratrol treatment. The mouse FI, based on deficit accumulation, was recently validated in C57BL/6J mice by Whitehead JC, Hildebrand BA, Sun M, et al. (A clinical frailty index in aging mice: comparisons with frailty index data in humans. J Gerontol A Biol Sci Med Sci. 2014;69:621-632) and shares many characteristics of the human FI. FI scores were measured in male and female aged (18 months) ad-libitum fed and CR DBA/2J and C57BL/6J mice, as well as male aged (24 months) C57BL/6J mice ad-libitum fed with or without resveratrol (100 mg/kg/day) in the diet for 6 months. Mean scores of two raters were used, and the raters had excellent inter-rater reliability (ICC = 0.88, 95% CI [0.80, 0.92]). Furthermore, the interventions of CR and resveratrol were associated with a significant reduction in FI scores in C57BL/6J mice, compared to age-matched controls. The short-lived DBA/2J mice also had slightly higher FI scores than the C57BL/6J mice, for the male calorie-restricted groups (DBA/2J FI = 0.16±0.03, C57BL/6J FI = 0.11±0.03, p = .01). This study uses the mouse FI developed by Whitehead JC, Hildebrand BA, Sun M, et al. (A clinical frailty index in aging mice: comparisons with frailty index data in humans. J Gerontol A Biol Sci Med Sci. 2014;69:621-632) in a different mouse colony and shows that this tool can be applied to quantify the effect of dietary and pharmaceutical interventions on frailty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".