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Record W7119213024 · doi:10.1002/alz70856_105185

Uncovering Lipid Biomarkers Linked to Methylphenidate Efficacy in Treating Apathy in Alzheimer's Disease: Insights from the ADMET 2 trial

2025· article· en· W7119213024 on OpenAlexaff
Myuri Ruthirakuhan, P. Rosenberg, Norman J. Haughey, Jacobo Mintzer, Nathan Herrmann, Suzanne Craft, A B Lerner, Allan I. Levey, Prasad R. Padala, Anton P. Porsteinsson, Christopher H van Dyck, David Shade, Maya Mills, Krista L. Lanctôt

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsApathyLipidomicsMethylphenidateDiseasePathway analysisRandomized controlled trialLipid profile

Abstract

fetched live from OpenAlex

Abstract Background Apathy is a prevalent neuropsychiatric symptom (NPS) in Alzheimer's disease (AD), linked to functional impairment and reduced quality of life. The Apathy in Dementia Methylphenidate Trial 2 (ADMET‐2) found modest efficacy of methylphenidate (MPH) for treating apathy, but treatment responses varied. This highlights the need for biomarkers to personalize treatments. Lipidomic profiling offers a promising approach by providing insights into the molecular basis of treatment response. Beyond their structural role in cell membranes, lipids serve as bioactive signaling molecules essential to neurotransmission, neuroinflammation, and synaptic plasticity—processes disrupted in NPS and AD. This study aimed to identify lipid species associated with MPH treatment response and explore lipid pathway disruptions in responders versus non‐responders. Method Participants randomized to MPH in ADMET‐2 were analyzed. Responders were defined by a 4‐point improvement on the Neuropsychiatric Inventory Apathy subscale (NPI‐A). Baseline plasma samples underwent lipidomic profiling. Partial Least Squares Discriminant Analysis (PLS‐DA) was used to identify lipid species distinguishing responders from non‐responders, with model performance evaluated by area under the curve (AUC). Identified lipid species were analyzed in MetaboAnalyst for pathway enrichment. Result A total of 45 participants were included, with 28 classified as responders. The PLS‐DA model achieved robust discrimination between responders and non‐responders, with an AUC of 0.81, indicating good predictive performance. Pathway analysis in MetaboAnalyst revealed disruption in lipid pathways related to ceramide, phosphospingolipid, and glycosphingolipid metabolism. Conclusion This study demonstrates the utility of lipidomic profiling in identifying biomarkers of response to MPH in AD patients with apathy. The observed disruptions in ceramide, phosphosphingolipid, and glycosphingolipid metabolism suggest a role for sphingolipid signaling which could have effects on neurotransmission, neuroinflammation, and synaptic plasticity—key processes implicated in NPS and AD. The identified lipidomic species and pathways offer insights into the molecular mechanisms underlying treatment response and could inform future biomarker‐guided interventions.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.325
Teacher spread0.294 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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