EFFECT OF CHRONIC POLYPHARMACY AND THE DRUG BURDEN INDEX (DBI) ON MUSCLE FUNCTION AND STRUCTURE IN AGED MICE
Bibliographic record
Abstract
Ageing, polypharmacy (≥ 5 different drugs) and increasing DBI (anticholinergic/sedative medication exposure) are associated with falls and impaired physical function. Preclinical ageing models can assess underlying mechanistic changes. We investigated whether chronic therapeutic drugs (polypharmacy or monotherapy), with increasing DBI and/or ceasation (deprescribing), affected physical function and/or muscle histology in mice. 12-month-old male C57BL/6 mice received either control diet or study drug(s). Polypharmacy diets consisted of Zero DBI (metoprolol, simvastatin, omeprazole, paracetamol, irbesartan), Low DBI (metoprolol, simvastatin, omeprazole, paracetamol, citalopram) and High DBI (metoprolol, simvastatin, citalopram, oxycodone, oxybutynin). Individual drugs (High DBI regimen) were tested as monotherapy. At 21-months, animals were randomised to continue treatment or gradual withdrawal. Rotarod performance was assessed at 12-24-months, and balance beam (6mm) at 24-months. Gastrocnemius muscle samples were collected at 27-months. Rotarod results indicate significant reduced endurance for citalopram mice (n=15–36) compared to control (n=24–29; p<0.05) at all time-points, and deprescribing (n=12) improved performance. Compared to control (n=24), balance assessment showed a trend towards impaired performance in High-DBI (n=18; p=0.061) and citalopram (n=14; p=0.134). Compared to metoprolol mice (n=16), metoprolol-deprescribed mice (n=16) showed higher coordination (p=0.016). Preliminary histology results suggest a trend towards less muscle fibres per field in control (n=3), compared to High-DBI (n=3; p=0.119) and citalopram animals (n=2; p=0.049). Our preclinical results suggest High DBI, citalopram and metoprolol drug regimens impact measures of muscle function and structure. Future research will continue to characterise histological changes in muscle including fibre size and types.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".