Education, Smoking and CRP Genetics in Relation to C-Reactive Protein Concentrations in Black South Africans
Bibliographic record
Abstract
Because elevated circulating C-reactive protein (CRP) and low socio-economic status (SES), have both been implicated in cardiovascular disease development, we investigated whether SES factors associate with and interact with CRP polymorphisms in relation to the phenotype. Included in the study were 1569 black South Africans for whom CRP concentrations, 12 CRP single nucleotide polymorphisms (SNPs), cardiovascular health markers, and SES factors were known. None of the investigated SES aspects was found to associate with CRP concentrations when measured individually; however, in adjusted analyses, attaining twelve or more years of formal education resulted in a hypothetically predicted 18.9% lower CRP concentration. We also present the first evidence that active smokers with a C-allele at rs3093068 are at an increased risk of presenting with elevated CRP concentrations. Apart from education level, most SES factors on their own are not associated with the elevated CRP phenotype observed in black South Africans. However, these factors may collectively with other environmental, genetic, and behavioral aspects such as smoking, contribute to the elevated inflammation levels observed in this population. The gene-smoking status interaction in relation to inflammation observed here is of interest and if replicated could be used in at-risk individuals to serve as an additional motivation to quit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".