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Record W4404773699 · doi:10.3389/fnagi.2024.1478557

Gut microbiota dysbiosis in patients with Alzheimer’s disease and correlation with multiple cognitive domains

2024· article· en· W4404773699 on OpenAlexaboutno aff
Qionglei Chen, Jiayu Shi, Gaojie Yu, Huijia Xie, Shicheng Yu, Jin Xu, Jiaming Liu, Jing Sun

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsDysbiosisDiseaseCognitionGut floraCorrelationGut–brain axisMedicineNeurosciencePsychologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Background Accumulating evidence suggested that Alzheimer’s disease (AD) was associated with altered gut microbiota. However, the relationships between gut microbiota and specific cognitive domains of AD patients have yet been fully elucidated. The aim of this study was to explore microbial signatures associated with global cognition and specific cognitive domains in AD patients and to determine their predictive value as biomarkers. Methods A total of 64 subjects (18 mild AD, 23 severe AD and 23 healthy control) were recruited in the study. 16 s rDNA sequencing was performed for the gut bacteria composition, followed by liquid chromatography electrospray ionization tandem mass spectrometry (LC/MS/MS) analysis of short-chain fatty acids (SCFAs). The global cognition, specific cognitive domains (abstraction, orientation, attention, language, etc.) and severity of cognitive impairment, were evaluated by Montreal Cognitive Assessment (MoCA) scores. We further identified characteristic bacteria and SCFAs, and receiver operating characteristic (ROC) curve was used to determine the predictive value. Results Our results showed that the microbiota dysbiosis index was significantly higher in the severe and mild AD patients compared to the healthy control (HC). Linear discriminant analysis (LDA) showed that 12 families and 17 genera were identified as key microbiota among three groups. The abundance of Butyricicoccus was positively associated with abstraction, and the abundance of Lachnospiraceae_UCG-004 was positively associated with attention, language, orientation in AD patients. Moreover, the levels of isobutyric acid and isovaleric acid were both significantly negatively correlated with abstraction, and level of propanoic acid was significantly positively associated with the attention. In addition, ROC models based on the characteristic bacteria Lactobacillus , Butyricicoccus and Lachnospiraceae_UCG-004 could effectively distinguished between low and high orientation in AD patients (area under curve is 0.891), and Butyricicoccus and Agathobacter or the combination of SCFAs could distinguish abstraction in AD patients (area under curve is 0.797 and 0.839 respectively). Conclusion These findings revealed the signatures gut bacteria and metabolite SCFAs of AD patients and demonstrated the correlations between theses characteristic bacteria and SCFAs and specific cognitive domains, highlighting their potential value in early detection, monitoring, and intervention strategies for AD patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.229
Teacher spread0.223 · 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 teacher head, 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

Citations23
Published2024
Admission routes1
Has abstractyes

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