Abstract 291: Association of Single Nucleotide Polymorphisms in Inflammatory Candidate Genes with Circulating Inflammatory Biomarker Concentrations: The Framingham Heart Study
Bibliographic record
Abstract
Background : Chronic subclinical inflammation is a prominent feature of atherosclerotic disease. The genetic background for this pro-inflammatory state is not well-established. Circulating biomarker concentrations have become attractive candidates to measure disease activity and prognosis. Methods : We examined 2356 single-nucleotide polymorphisms (SNPs) in 235 inflammatory pathway genes in association with 11 circulating inflammatory biomarkers in about 1800 Framingham Offspring cohort participants [CD40 ligand, CRP, intercellular adhesion molecule-1, interleukin-6 (IL6), urinary isoprostanes, monocyte chemoattractant protein-1 (MCP1), myeloperoxidase, P-selectin, tumor necrosis factor alpha, tumor necrosis factor receptor-2, fibrinogen]. We created residuals of log transformed biomarker concentrations adjusting for 16 potential confounders. Only SNPs with call rate ≥0.98 and HWE p>0.01, which had at least 5 minor allele carriers entered analyses. False discovery rate (FDR) and q-value methods were applied. Results : We observed similar results with FDR and q-value methods. A total of 45 associations were significant at a cutoff q value of 0.25. The top SNPs were observed in the SELP gene in association with P-selectin concentrations (rs6136 [nonsynonymous], p= 5.17*10 −39 , rs3753305 [intronic], p= 6.17*10 −9 ) and the ICAM1 gene in relation to ICAM-1 concentrations (rs1799969 [coding-nonsynonymous], p= 1.32*10 −8 ). Lowest p-values for trans-acting SNPs were observed for APCS (rs1374486 [function unknown], p= 1.01*10 −7 , and rs6695377 [function unknown], p= 1.85*10 −7 ) with MCP-1 concentrations and for IL6R (rs8192284 [coding-nonsynonimous,intronic], p= 3.36*10 −5 ) with IL6 concentrations. In addition, we could replicate reported findings for rs1799969, and rs5498 in the ICAM-1 gene in relation to ICAM-1 concentrations as well as associations of SNPs rs2857654, rs1024611, and rs2857657 in the CCL-2 gene with MCP-1 concentrations. Conclusions : The results of this candidate gene approach support the relevance of genetic variation for circulating inflammatory biomarker traits. Some former findings were confirmed and novel potential candidates are reported. Our findings merit replication in other cohorts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".