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Record W4390192976 · doi:10.1002/alz.079161

Blood‐based biomarkers for predicting treatment response in the Apathy in Dementia Methylphenidate Trial 2 randomized clinical trial

2023· article· en· W4390192976 on OpenAlexaff
Shankar Tumati, Danielle Soares Rocha Vieira, Kritleen K. Bawa, Ana C. Andreazza, Jacobo Mintzer, Roberta W. Scherer, Paul B. Rosenberg, Christopher H. van Dyck, Prasad R. Padala, Olga Brawman‐Mintzer, Anton P. Porsteinsson, Alan J. Lerner, Suzanne Craft, Allan I. Levey, W. J. Burke, Jamie Perin, David Shade, Nathan Herrmann, Krista L. Lanctôt

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsApathyPlaceboInternal medicineMedicineMethylphenidateBiomarkerRandomized controlled trialConfidence intervalPsychologyAttention deficit hyperactivity disorderPsychiatryDiseasePathology

Abstract

fetched live from OpenAlex

Abstract Background The Apathy in Dementia Methylphenidate Trial 2 (ADMET 2) found that methylphenidate (MPH) was modestly efficacious is treating apathy in Alzheimer’s disease (AD) with 66% response (> = 4 point decrease on Neuropsychiatric Inventory – apathy (NPI‐A). Here, we evaluated if blood‐based biomarkers potentially associated with treatment‐limiting neuronal damage predicted the probability of treatment response. Method ADMET 2 participants with baseline and endpoint (6 month) NPI‐A and baseline concentrations of (i) neuronal damage: neurofilament light (NFL) and S‐100B, (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 included. Biomarkers were normalized by log transformation and pareto scaling. Univariate models examined whether each biomarker interacted with the treatment condition to predict a change of at least 2 points on the NPI‐A. Next, biomarkers with significant associations were included in a multivariate logistical regression model that was used to predict treatment response to MPH and placebo. The index score (difference between response probabilities for MPH and placebo) of each participant indicated their favourability for MPH treatment. Participants were grouped into quartiles by their index score with confidence intervals estimated using non‐parametric bootstrapping. Result Forty‐nine participants (placebo = 26, MPH = 23, age = 75.4 years (standard deviation [SD] = 7.6), 57% males, Mini Mental State Examination = 19.7 [SD = 4.6]) were included. MPH was more efficacious than placebo in those with higher baseline NFL (‐5.8, t = ‐3.1, p = 0.003), and less efficacious in those with higher LPH (2.0, t = 1.48, p = 0.15). Apathy worsened in participants with high TNFα (2.71, t = 1.94, p = 0.06) in both MPH and placebo groups. Participants in the top quartile of the biomarker index score (66.7% on MPH) were more likely to respond to MPH than placebo (index score = 0.58, 95%CI: 0.48, 0.69). Conclusion Individuals with greater neuronal injury were more likely to respond to MPH, suggesting that MPH supplementation provides more benefit when neuronal injury is more severe. Combining blood biomarkers yielded a prediction score where participants with high NFL and low LPH levels were more likely to respond to MPH. This approach utilizes biomarkers to address heterogeneity in treatment response to MPH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.107
GPT teacher head0.421
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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