The subgingival microbiome, systemic inflammation and insulin resistance: The Oral Infections, Glucose Intolerance and Insulin Resistance Study
Bibliographic record
Abstract
BACKGROUND: Inflammation might link microbial exposures to insulin resistance. We investigated the cross-sectional association between periodontal microbiota, inflammation and insulin resistance. 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). Three hundred and four subgingival plaque samples were analysed using the Human Oral Microbe Identification Microarray to measure the relative abundances of 379 taxa. C-reactive protein, interleukin-6, tumour necrosis factor-α and adiponectin were assessed from venous blood and their z-scores were summed to create an inflammatory score (IS). Insulin resistance was defined via the HOMA-IR. Associations between the microbiota and both inflammation and HOMA-IR were explored using multivariable linear regressions; mediation analyses assessed the proportion of the association explained by inflammation. RESULTS: The IS was inversely associated with Actinobacteria and Proteobacteria and positively associated with Firmicutes and TM7 (p-values < 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). Eighteen individual taxa were associated with inflammation (p < 0.05) and 22 with insulin resistance (p < 0.05). No findings for individual taxa met Bonferroni-adjusted statistical significance. CONCLUSION: Bacterial measures were related to inflammation and insulin resistance among 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".