The Association Between High Cholesterol Levels and Severity of Periodontitis
Bibliographic record
Abstract
Periodontitis is a common inflammatory condition affecting tooth-supporting structures, leading to tooth loss and is linked to systemic diseases, including cardiovascular disease. Objectives: To examine the association between high cholesterol levels and periodontitis severity in a sample from Lahore, Pakistan. Methods: A cross-sectional study was conducted at De' Montmorency College of Dentistry, Lahore, over six months. A total of 154 participants aged ≥40 years with periodontitis symptoms and without any systemic diseases were included. Participants were grouped based on the severity of periodontal disease status into no periodontitis group, mild disease group, moderate disease group, and severe periodontitis. Blood samples were collected and analyzed for lipid profile parameters. Multivariable regression analyses were performed, adjusting for age, gender, BMI, smoking, alcohol use, and exercise, to evaluate associations between lipid levels and periodontitis severity. Results: Severe periodontitis was found in 58 participants (37.7%). Multivariable regression indicated an inversely associated link between high-density lipoprotein cholesterol in blood and disease severity (p<0.05). Lower total cholesterol and higher triglyceride levels were associated with severe periodontitis (p<0.05). Logistic regression showed that participants with severe periodontitis had significantly higher values of the odds ratio of decreased high-density lipoprotein cholesterol (OR 1.34, 95% CI 1.05–1.72), total cholesterol (OR 1.26, 95% CI 1.02–1.55), and triglyceride levels (OR 1.48, 95% CI 1.12–1.96). Conclusions: It was concluded that severe periodontitis is greatly linked with lower high-density lipoprotein cholesterol and elevated triglyceride and total cholesterol levels, suggesting that periodontal health may influence lipid profile and increase cardiovascular disease risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".