Chronic Polypharmacy with Increasing Drug Burden Index Exacerbates Frailty and Impairs Physical Function, with Effects Attenuated by Deprescribing, in Aged Mice
Bibliographic record
Abstract
Polypharmacy (use of ≥5 medications) and increasing Drug Burden Index (DBI) score (measure of person's total exposure to anticholinergic/sedative medications) are associated with impaired physical function in observational studies of older adults. Deprescribing, the supervised withdrawal of medications for which harms outweigh benefits for an individual, may be a useful intervention. Current knowledge is limited to clinical observational studies that are unable to determine causality. Here, we establish a preclinical model that investigates the effects of chronic polypharmacy, increasing DBI, and deprescribing on global health outcomes in aging. In a longitudinal study, middle-aged (12 months) male C57BL/6J (B6) mice were administered control feed or feed and/or water containing polypharmacy or monotherapy with different DBI scores. At 21 months, each treatment group was subdivided (stratified by frailty at 21 months) to either continue on treatment for life or to have treatment withdrawn (deprescribed). Frailty and physical function were evaluated at 12, 15, 18, and 24 months, and were analyzed using a mixed modeling approach. Polypharmacy with increasing DBI and monotherapy with citalopram caused mice to become frailer, less mobile, and impaired their strength and functional activities. Critically, deprescribing in old age reversed a number of these outcomes. This is the first preclinical study to demonstrate that chronic polypharmacy with increasing DBI augments frailty and impairs function in old age, and that drug withdrawal in old age reversed these outcomes. It was not the number of drugs (polypharmacy) but the type and dose of drugs (DBI) that caused adverse geriatric outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".