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Record W3034831360 · doi:10.1002/hep.31417

Microbiomics, Metabolomics, Predicted Metagenomics, and Hepatic Steatosis in a Population‐Based Study of 1,355 Adults

2020· article· en· W3034831360 on OpenAlexaff
Louise J. M. Alferink, Djawad Radjabzadeh, Nicole S. Erler, Dina Vojinović, Carolina Medina‐Gómez, André G. Uitterlinden, Robert J. de Knegt, Najaf Amin, M. Arfan Ikram, Harry L.A. Janssen, Jessica C. Kiefte–de Jong, Herold J. Metselaar, Cornelia M. van Duijn, Robert Kraaij, Sarwa Darwish Murad

Bibliographic record

VenueHepatology · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsToronto General HospitalUniversity Health Network
FundersKU LeuvenNederlandse Organisatie voor Wetenschappelijk OnderzoekErasmus Universiteit RotterdamGilead SciencesErasmus Universitair Medisch Centrum RotterdamAmerican Association for the Study of Liver DiseasesErasmus Medisch CentrumGlaxoSmithKlineEuropean CommissionBristol-Myers Squibb
KeywordsSteatosisMetagenomicsMetabolomicsLiver steatosisPopulationBiologyComputational biologyMedicineInternal medicineBioinformaticsEnvironmental healthFatty liverGeneticsDiseaseGene

Abstract

fetched live from OpenAlex

Background and Aims Previous small studies have appraised the gut microbiome (GM) in steatosis, but large‐scale studies are lacking. We studied the association of the GM diversity and composition, plasma metabolites, predicted functional metagenomics, and steatosis. Approach and Results This is a cross‐sectional analysis of the prospective population‐based Rotterdam Study. We used 16S ribosomal RNA gene sequencing and determined taxonomy using the SILVA reference database. Alpha diversity and beta diversity were calculated using the Shannon diversity index and Bray–Curtis dissimilarities. Differences were tested across steatosis using permutational multivariate analysis of variance. Hepatic steatosis was diagnosed by ultrasonography. We subsequently selected genera using regularized regression. The functional metagenome was predicted based on the GM using Kyoto Encyclopedia of Genes and Genomes pathways. Serum metabolomics were assessed using high‐throughput proton nuclear magnetic resonance. All analyses were adjusted for age, sex, body mass index, alcohol, diet, and proton‐pump inhibitors. We included 1,355 participants, of whom 472 had steatosis. Alpha diversity was lower in steatosis ( P = 1.1∙10 −9 ), and beta diversity varied across steatosis strata ( P = 0.001). Lasso selected 37 genera of which three remained significantly associated after adjustment ( Coprococcus3 : β = −65; Ruminococcus Gauvreauiigroup : β = 62; and Ruminococcus Gnavusgroup : β = 45, Q ‐value = 0.037). Predicted metagenome analyses revealed that pathways of secondary bile‐acid synthesis and biotin metabolism were present, and D‐alanine metabolism was absent in steatosis. Metabolic profiles showed positive associations for aromatic and branched chain amino acids and glycoprotein acetyls with steatosis and R. Gnavusgroup , whereas these metabolites were inversely associated with alpha diversity and Coprococcus3 . Conclusions We confirmed, on a large‐scale, the lower microbial diversity and association of Coprococcus and Ruminococcus Gnavus with steatosis. We additionally showed that steatosis and alpha diversity share opposite metabolic profiles.

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 score0.014
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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

Citations87
Published2020
Admission routes1
Has abstractyes

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