Interactions between C-Reactive Protein Genotypes with Markers of Nutritional Status in Relation to Inflammation
Bibliographic record
Abstract
Inflammation, as indicated by C-reactive protein concentrations (CRP), is a risk factor for chronic diseases. Both genetic and environmental factors affect susceptibility to inflammation. As dietary interventions can influence inflammatory status, we hypothesized that dietary effects could be influenced by interactions with single nucleotide polymorphisms (SNPs) in the CRP gene. We determined 12 CRP SNPs, as well as various nutrition status markers in 2010 black South Africans and analyzed their effect on CRP. Interactions were observed for several genotypes with obesity in determining CRP. Lipid intake modulated the pro-inflammatory effects of some SNPs, i.e., an increase in both saturated fatty acid and monounsaturated fatty acid intake in those homozygous for the polymorphic allele at rs2808630 was associated with a larger increase in CRP. Those harboring the minor alleles at rs3093058 and rs3093062 presented with significantly higher CRP in the presence of increased triglyceride or cholesterol intake. When harboring the minor allele of these SNPs, a high omega-6 to -3 ratio was, however, found to be anti-inflammatory. Carbohydrate intake also modulated CRP SNPs, as HbA1C and fasting glucose levels interacted with some SNPs to influence the CRP. This investigation highlights the impact that nutritional status can have on reducing the inherent genetic susceptibility to a heightened systemic inflammatory state.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".