A-206 Association of Lipids and Glucose with High-sensitivity CRP (hs-CRP) in a Community-based Adult Population
Bibliographic record
Abstract
Abstract Background Research and clinical studies have established a pivotal role of inflammation in the development and progression of both cardiac and vascular diseases, responsive to injurious factors such as smoking, hypertension, dyslipidemia, and hyperglycemia. This study analyzed the association of various lipids, HbA1c and glucose with high-sensitivity CRP (hs-CRP), a marker of low-grade chronic inflammation, in a community-based adult population. Methods Data analysis was carried out on within-collection results obtained from adults (> = 20 yrs) from 2019 to 2022. Tests included hs-CRP, with cholesterol (n = 74 560), triglycerides (n = 74 551), LDL-C (n = 73 426), HDL-C (n = 74 458), non-HDL-C (n = 69 808), ApoB (n = 3122), Lp(a) (n = 4444), fasting glucose (n = 42 954) or HbA1c (n = 62 486). All tests were performed on Roche Cobas instrumentation. Chi square tests were performed to evaluate the association between each lipid and glycemic indicator with hs-CRP. Relative risk (RR) and odds ratio (OR) were calculated to assess the strength of the association. Results Chi-square test demonstrated association of hs-CRP with HDL-C, triglycerides, HbA1c, non-HDL-C, LDL-C, fasting glucose, cholesterol and ApoB, but not Lp(a). Increased risk and odds to have elevated hs-CRP (3.0 -10.0 mg/L) were observed with abnormal glucose and lipid levels. Higher RR and OR were seen with decreased HDL-C and elevated HbA1c, fasting glucose, triglycerides, and non-HDL-C. Slightly increased RR and OR were also observed with elevated LDL-C, ApoB, but not Lp(a). Conclusion Glucose and most lipid parameters were associated with hs-CRP. Stronger association was observed with HDL-C and carbohydrates (glucose and triglycerides), followed by triglycerides-rich non-HDL-C. LDL-C, ApoB and cholesterol had weaker association, and Lp(a) had no association with hs-CRP. This observation from a large data set from a community population may provide helpful insight to prevention and clinical management of low-grade chronic inflammation.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".