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Record W4413801721 · doi:10.1016/j.metop.2025.100389

Selective serotonin reuptake inhibitors and glucose metabolism in Alzheimer's disease and related dementias: A systematic review and meta-analysis of brain metabolic and adverse event data

2025· review· en· W4413801721 on OpenAlexaff
Faisal Alzenaidi, Osama H Aldoweesh, Abass Fadel, Razan A Lasloom, Dhay Alharbi, Faris Almalki, Atheer Ahmad Alkhairi, Maram A. Alharbi, Norah Ahmed Alhamdan, Ahmed Y. Azzam

Bibliographic record

VenueMetabolism Open · 2025
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMeta-analysisReuptakeMedicineSerotoninDiseaseAlzheimer's diseaseReuptake inhibitorNeuroscienceBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

Introduction: Selective serotonin reuptake inhibitors (SSRIs) are commonly prescribed for depression in Alzheimer's disease (AD), however their effects on glucose metabolism remain poorly understood. We conducted a systematic review and meta-analysis to evaluate SSRI effects on brain glucose metabolism and metabolic adverse events in AD patients. Methods: Following PRISMA 2020 guidelines, we searched multiple databases up to July 11, 2025 for studies investigating SSRI effects on glucose-related outcomes in AD patients. Despite significant heterogeneity in study designs and populations, we performed meta-analyses for adverse events and coordinate-based meta-analysis for neuroimaging data. We performed meta-analyses for adverse events and coordinate-based meta-analysis for neuroimaging data. Advanced Bayesian hierarchical modeling and Markov simulations projected long-term metabolic outcomes. Results: Twelve studies with total included 7143 participants met our inclusion criteria, including nine randomized controlled trials and three observational studies. Brain FDG-PET revealed SSRI use restored dorsal raphe nucleus hypometabolism (standardized mean difference 0.87, 95 % CI: 0.52-1.22, P-value = 0.001). Meta-analysis demonstrated increased gastrointestinal adverse events (risk ratio 2.15, 95 % CI: 1.68-2.76, P-value<0.001, with moderate between-study heterogeneity), with sertraline showing highest rates. Citalopram 30 mg provided significant weight loss protection (risk ratio 0.13, 95 % CI: 0.02-0.98, P-value = 0.02), though this exceeds the recommended 20 mg maximum dose for elderly patients due to cardiac safety considerations. Long-term diabetes incidence showed no increased risk (hazard ratio 0.75, 95 % CI: 0.50-1.12, P-value = 0.15). Bayesian modeling revealed 85 % probability of beneficial brain metabolic effects and 89 % probability of citalopram superiority for weight protection. Conclusions: SSRIs restore brain glucose metabolism in AD patients while causing manageable peripheral metabolic effects. Citalopram appears the best for weight-sensitive patients, while sertraline requires gastrointestinal monitoring. These findings support SSRI safety for metabolic outcomes in AD treatment, however longer-term studies with controlled metabolic outcomes are needed to confirm our findings. The observed citalopram weight protection benefit was documented at 30 mg daily, which exceeds recommended dosing limits for elderly patients due to cardiac safety concerns.

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)0.015
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.034
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.072
GPT teacher head0.402
Teacher spread0.330 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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