A Data‐Driven Examination of Apathy and Depression in Cognitively Normal Older Adults
Bibliographic record
Abstract
BACKGROUND: Apathy and mood symptoms are increasingly recognised as clinical important aspects of prodromal dementia; both are associated with increased risk of dementia even in cognitively normal people. The clinical overlap between apathy and low mood poses a challenge in distinguishing between the two conditions. It is crucial to differentiate between depression and apathy, along with any underlying syndromes, to facilitate the development of targeted treatments. Using a data-driven approach, we recently reported the existence of distinct apathy and depression clusters in dementia, confirming observations from the clinic and epidemiology. In this study we sought to establish whether similar patterns of symptoms were present in cognitively normal older adults METHOD: We analysed data from 21,925 community dwelling older adults. Latent class analysis (LCA) was applied to self-reported and proxy ratings (obtained using the Mild Behavioral Impairment Checklist) of apathy and mood. Polygenic Risk Scores for Alzheimer's disease (AD) and Major Depression (MDD) were tested for associated with class membership. RESULT: The LCA analysis using proxy data showed a 4-class group which was considered the best model: No symptoms, Depression, Apathy/depression, and an Apathy group. The LCA using self-reported data reveals the 4-class group without a as a clear apathy class as the proxy data (see Figures 1 and 2). PRS for AD and MDD were only associated with depression and apathy/depression classes in self-reported data (not in proxy data). CONCLUSION: This analysis highlights the apathy phenotype as a unique and separate condition, underscoring the imperative for additional research in this area. This emphasizes the potential for innovative approaches to delve deeper into the exploration and comprehension of apathy. The differences between the self and proxy reported data highlights the possibility of under reporting of apathy by patients.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".