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Record W4403089795 · doi:10.1101/2024.10.02.616371

Serotonin Enhances Neurogenesis Biomarkers, Hippocampal Volumes, and Cognitive Functions in Alzheimer’s Disease

2024· preprint· en· W4403089795 on OpenAlexfundno aff
Ali Azargoonjahromi

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's Association
KeywordsNeurogenesisHippocampal formationNeuroscienceCognitive reserveDiseaseHippocampusCognitionAlzheimer's diseaseDementiaSerotoninCognitive impairmentMedicinePsychologyInternal medicine

Abstract

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Abstract Research on serotonin reveals a lack of consensus regarding its role in brain volume, especially concerning biomarkers linked to neurogenesis and neuroplasticity, such as ciliary neurotrophic factor (CNTF), fibroblast growth factor 4 (FGF-4), bone morphogenetic protein 6 (BMP-6), and matrix metalloproteinase-1 (MMP-1) in Alzheimer’s disease (AD). This study aimed to investigate the influence of serotonin on brain structure and hippocampal volumes in relation to cognitive functions in AD, as well as its link with biomarkers like CNTF, FGF-4, BMP-6, and MMP-1. Data from the ADNI included 133 AD participants. Cognitive function was assessed using CDR-SB, serotonin levels were measured with the Biocrates AbsoluteIDQ p180 kit and UPLC-MS/MS, and neurotrophic factors and biomarkers were quantified using multiplex targeted proteomics. Voxel-Based Morphometry (VBM) analyzed gray matter volume changes via MRI. Statistical analyses employed Pearson correlation and Bootstrap methods, with p-values < 0.05 or 0.01 considered significant. The analysis revealed a significant positive correlation between serotonin levels and total brain volume (r = 0.179, p = 0.039) and hippocampal volumes (right: r = 0.181, p = 0.037; left: r = 0.217, p = 0.012). Besides, higher serotonin levels were associated with improved cognitive function, evidenced by a negative correlation with CDR-SB scores (r = -0.198, p = 0.023). Furthermore, total brain volume and hippocampal volumes showed significant negative correlations with CDR-SB scores, indicating that greater cognitive impairment was associated with reduced brain volume (total: r = -0.223, p = 0.010; left: r = -0.246, p = 0.004; right: r = -0.308, p < 0.001). Finally, serotonin levels were positively correlated with BMP-6 (r = 0.173, p = 0.047), CNTF (r = 0.216, p = 0.013), FGF-4 (r = 0.176, p = 0.043), and MMP-1 (r = 0.202, p = 0.019), suggesting a link between serotonin and neurogenesis and neuroplasticity. In conclusion, increased serotonin levels are associated with improved cognitive function, increased brain volume, and elevated levels of neurotrophic factors and biomarkers—specifically CNTF, FGF-4, BMP-6, and MMP-1—that are related to neurogenesis and neuroplasticity in AD. Graphical Abstract Serotonin plays a key role in cognitive function, with higher levels linked to increased brain and hippocampal volumes and better cognitive performance, as shown by lower CDR-SB scores (indicating less cognitive impairment). In addition, serotonin is positively associated with neurotrophic factors and biomarkers like ciliary neurotrophic factor (CNTF), fibroblast growth factor 4 (FGF-4), bone morphogenetic protein 6 (BMP-6), and matrix metalloproteinase-1 (MMP-1), which are involved in neurogenesis and neuroplasticity. These findings suggest that elevated serotonin levels contribute to brain health, cognitive function, and the promotion of neural growth and plasticity, particularly in Alzheimer’s disease (AD).

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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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 designBench or experimental
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicCancer-related cognitive impairment studiesFrench-language works237,207