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Record W3033997360 · doi:10.1093/ndt/gfaa142.p1144

P1144ODORIBACTER AND ANAEROTRUNCUS: GUT MICROBIOME SIGNATURE MIGHT BE RELATED TO COGNITIVE IMPAIRMENT IN PATIENTS ON PERITONEAL DIALYSIS

2020· article· en· W3033997360 on OpenAlexaboutno aff
Fabiola Martín-del-Campo, Natali Vega‐Magaña, Noé A. Salazar-Félix, Marcela Peña‐Rodríguez, María de Lourdes Romo-Flores, Laura Cortés Sanabria, Enrique Rojas Campos, Alfonso M. Cueto Manzano

Bibliographic record

VenueNephrology Dialysis Transplantation · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeritoneal dialysisInternal medicineCognitionMicrobiomeDialysisPsychiatryBioinformaticsBiology

Abstract

fetched live from OpenAlex

Abstract Background and Aims Cognitive impairment is very common in dialysis patients, with negative effects on quality of life and mortality. In neurodegenerative conditions (Alzheimer, Parkinson) a gut-brain axis has been identified; however, there is no information about the relationship of gut microbiota alterations and presence of cognitive impairment in chronic kidney disease. The aim was to associate the gut microbiota profile with the cognitive function in patients on automated peritoneal dialysis (APD). Method Cross-sectional study in 39 APD patients; those with visual or mental disabilities, psychiatric or neurodegenerative diseases, inflammatory causes of ESRD, with active infections (including peritonitis), on anti-inflammatory drugs or antibiotics, were excluded. All patients had a clinical, biochemical, nutritional and dialysis adequacy evaluation, and were classified according to the presence of cognitive impairment using the Montreal Cognitive Assessment (MoCA) test. Fecal samples were collected and immediately stored at -80 ºC. DNA extraction was performed with Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research). Subsequently, V3 and V4 regions of 16S rRNA were sequenced using illumina platform. Statistical analysis: Student t test and χ2 were used to compare quantitative and qualitative variables, respectively. Quantitative Insights Into Microbial Ecology (QIIME) pipeline and Linear Discriminant Analysis Effect Size (LEfSe) were employed for bioinformatic analysis. Results Eighty-two percent of subjects were male, mean age 47 ± 24 years, and dialysis vintage 11 (7-48) months. Sixty-four percent of patients had cognitive impairment. Patients with cognitive impairment were significantly older (53 ± 16 vs 38 ± 14, p=0.006), had higher frequency of diabetes mellitus (56% vs 21%, p=0.04), and had lower creatinine concentrations (11.3 ± 3.7 vs 14.9 ± 5.4, p=0.02) compared to patients with normal cognitive function. No differences were found in other biochemical, nutritional or dialysis adequacy variables. A total of 38,083 sequences per patient were obtained after the microbiome analysis. LEfSe analysis showed a preponderance of S24_7, Rikenellaceae, Odoribacteraceae, Odoribacter, and Anaerotruncus in patients with cognitive impairment. In contrast, patients without cognitive impairment were characterized by Dorea, Ruminococcus, Sutterella and Fusobacteria (LDA score (Log10) > 2.5; p < 0.05). Conclusion Cognitive impairment was present in two-thirds of these APD patients. Odoribacter and Anaerotruncus were significantly more abundant in patients with cognitive impairment than in those with normal function. Such gut bacteria might enhance cognitive impairment as have been described to increase the uremic toxin production and activation of inflammatory pathways in the central nervous system. This is the first study providing evidence than brain and behavior might be influenced by the gut-brain axis in dialysis 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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