Abstract 644: CD36 is Essential to Increased Atherosclerosis Mediated by Oral Infection With Porphromonas gingivalis
Bibliographic record
Abstract
Epidemiological evidence strongly support a link between periodontal disease & cardiovascular disease, but the mechanism(s) remains poorly understood. Using the human periodontal disease associated bacteria, Porphyromonas gingivalis (Pg) as a model, we carried out studies in macrophages & low density lipoprotein receptor (LDLR) KO mice. Pg associated similarly with macrophages from wild type & CD36 KO mice, but there were differences in responses dependent on Toll-like receptor (TLR) 2. We observed decreased NFkB activation & IL1beta generation following Pg treatment in CD36 KO macrophages, despite similar levels of TLR2 expression. OxLDL strongly inhibited Pg mediated IL-1beta generation in a CD36 dependent manner. Macrophage foam cell formation as a result of incubation with oxLDL & PgLPS was increased in a CD36 dependent manner. LDLR KO & CD36/LDLR double KO mice were orally infected with Pg & fed a Western diet (12 weeks). There was a significant increase in the cemento-enamel junction of molars of infected compared with uninfected mice, demonstrating the validity of the model. Histological analysis showed inflammatory cell infiltrates in gums of infected mice after 12 weeks, supporting a chronic inflammatory process. Differences in plasma parameters & weight gain did not necessarily track with atherosclerosis burden, however blood neutrophils & cytokines were increased in infected LDLR KO mice compared with all other groups. Infected LDLR KO mice had significantly increased atherosclerotic lesion burden compared with uninfected LDLR KO mice, and all of the increased lesion was CD36-dependent. Our data suggest that atherosclerosis associated with periodontal disease is mediated by cellular inflammatory responses involving both CD36 & TLR2. Pg enhances oxLDL mediated foam cell formation in a CD36 dependent manner, and this may explain increased lesion burden. Generation of IL1beta, a key pro-atherogenic cytokine, is altered as a result of CD36 expression. Periodontal disease affects more than 20% of the population of the US/Canada, & is associated with increasing age, which is also a risk factor for atherosclerosis. Targeting CD36 may provide important supplemental therapy to current lipid lowering strategies to reduce atherosclerosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".