Predictive and monitoring value of blood‐based biomarkers for apathy treatment in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Apathy in Alzheimer’s disease improves with methylphenidate (MPH) but treatment response was found to vary depending on clinical factors. Here, we explored whether underlying biological factors assessed by blood‐based biomarkers of neurodegeneration, inflammation and oxidative stress affect apathy treatment response. METHOD: A subset of participants from the Apathy in Dementia Methylphenidate Trial 2 (ADMET 2) were included in this study whose blood samples were available at baseline and at the 6‐month treatment completion. Apathy was assessed with the Neuropsychiatric Inventory apathy subscale (NPI‐A, range: 0‐12). Blood concentrations of (i) neuronal damage: neurofilament light (NFL) and S‐100B (available at baseline only), (ii) inflammation: interleukin (IL)‐6, IL‐10, Tumor Necrosis Factor‐alpha (TNFα), and (iii) oxidative stress: lipid hydroperoxide (LPH), 4‐hydroxynonenal (4‐HNE), 8‐isoprostane (8‐ISO) were obtained with ELISA assays. Biomarkers were normalized by log transformation and pareto scaling. Predictive value of biomarkers was assessed by examining differences in treatment response between each biomarker tertile level. We also assessed whether biomarkers improved predictive models for established clinical predictors. Monitoring value was assessed by linear mixed models with NPI‐A as the dependent variable and interaction between biomarker and time or biomarker and treatment as the independent variable. RESULT: Among 55 participants (MPH: 24, age: 75.4 years [standard deviation (SD): 7.6], MMSE: 19.9 [SD: 4.9]) at baseline and 49 at the 6 month end point, the change in NPI‐A showed a greater than 2‐point difference between tertiles of NFL (6.8 points), TNFα (4.2 points) and 8‐ISO (5.5 points) (Fig. 1). Treatment response prediction improved by adding NFL with cholinesterase use (likelihood ratio test[lrt]: 4.6, p: 0.03), presence of agitation (lrt: 4.4, p: 0.04) or presence of anxiety (lrt: 4.5, p: 0.04); no added value was found with TNFα and 8‐ISO. As a monitoring biomarker, TNFα (but not NFL and 8‐ISO) levels over time were associated with NPI‐A score (t: 2.69, p: 0.009). CONCLUSION: Blood‐based biomarkers of neurodegeneration, inflammation and oxidative stress are associated with apathy and affect treatment response, indicating potential predictive and monitoring value. Peripheral inflammation (TNFα) may have added value along with clinical predictors of response.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".