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Record W4241464417 · doi:10.1161/circ.133.suppl_1.p046

Abstract P046: The Subgingival Microbiome, Systemic Inflammation and Insulin Resistance

2016· article· en· W4241464417 on OpenAlexaff
Ryan T. Demmer, Alexander Breskin, Michael Rosenbaum, Aleksandra M. Zuk, Charles A. LeDuc, Rudolph L. Leibel, Bruce J. Paster, Moı̈se Desvarieux, David R. Jacobs, Panos N. Papapanou

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInsulin resistanceMedicineInternal medicineDiabetes mellitusInflammationInsulinAdiponectinFirmicutesSystemic inflammationImmunologyEndocrinologyBiologyBacteriaGenetics

Abstract

fetched live from OpenAlex

Introduction: Chronic inflammation is hypothesized as a mechanism linking microbial exposures to insulin resistance. We investigated the association between periodontal microbiota, systemic inflammation and insulin resistance among diabetes-free adults. Hypotheses: We hypothesized that subgingival microbial signatures would be related to both inflammatory phenotype and measures of insulin resistance and that there would be evidence of mediation by inflammatory markers. Methods: The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 152 diabetes-free adults (77% female) aged 20-55 years (mean=34±10). 304 subgingival plaque samples (2 per participant) were analyzed using the Human Oral Microbe Identification Microarray (HOMIM) to measure the relative abundances of 379 taxa from eight different phyla. C-reactive protein, interleukin-6, tumor necrosis factor-α and adiponectin were assessed from venous blood and their z-scores were summed to create an inflammatory score (IS). Fasting glucose and insulin levels defined insulin resistance via the HOMA-IR. Associations between the subgingival microbiota and both inflammation and insulin resistance were explored using multivariable linear regression models adjusted for cardiometabolic risk factors; mediation analyses assessed the proportion of the association explained by inflammation. Results: The IS was inversely associated with the relative abundance of Actinobacteria and Proteobacteria and positively associated with Firmicutes and TM7 (all p-values<0.05). The IS was positively associated with glucose, insulin and HOMA-IR (all p<0.05). Proteobacteria levels were associated with insulin resistance (p<0.05). Inflammation explained 30%-98% of the observed associations between levels of Actinobacteria, Proteobacteria or Firmicutes and insulin resistance (p-values<0.05). 18 individual taxa were associated with inflammation (p<0.05) and 22 with insulin resistance (p<0.05). Conclusion: Subgingival microbial community structure and membership were related to systemic inflammation and insulin resistance in a sample of diabetes-free adults.

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)0.001
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 score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.266
Teacher spread0.248 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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