Association between lean muscle mass and treatment-resistant late-life depression in the IRL-GRey randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between lean muscle mass and treatment response in treatment-resistant late-life depression (TR-LLD). We hypothesized that lower lean muscle mass would be associated with older age, higher physical comorbidities, higher depressive symptom severity, and poorer treatment response. DESIGN: Secondary analysis of a randomized, placebo-controlled trial. SETTING: Three academic hospitals in the United States and Canada. PARTICIPANTS: Adults aged 60+ years with major depressive disorder who did not remit following open treatment with venlafaxine extended-release (XR) (n = 178). MEASUREMENTS: We estimated lean muscle mass using dual-energy X-ray absorptiometry (DEXA) scans prior to and following randomized treatment with aripiprazole or placebo added to venlafaxine XR. Multivariate regressions estimated influence of demographic and clinical factors on baseline lean muscle mass, and whether baseline lean muscle mass was associated with treatment response, adjusted for treatment arm. RESULTS: Low lean muscle mass was present in 22 (12.4%) participants. Older age and female sex, but not depressive symptom severity, were independently associated with lower lean muscle mass at baseline. Marital status, baseline depressive symptom severity, and treatment group were associated with improvement of depressive symptoms in the randomized treatment phase. Baseline lean muscle mass was not associated with improvement, regardless of treatment group. CONCLUSION: As expected, older age and female sex were associated with lower lean muscle mass in TR-LLD. However, contrary to prior results in LLD, lean muscle mass was not associated with depression severity or outcome. This suggests that aripiprazole augmentation may be useful for TR-LLD, even in the presence of anomalous body composition.clinicaltrials.gov Identifier: NCT00892047.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".