The modifying effect of nutritional factors on the association between IL1-β single nucleotide polymorphism and serum CXCL10 levels in young Canadian adults
Bibliographic record
Abstract
Background: Genetic and nutritional factors play an important role in inflammatory response and diseases. CXCL10 is a critical biomarker that is involved in multiple inflammatory diseases, and elevated levels of CXCL10 have been associated with the development of several chronic and infectious diseases. In contrast, micronutrients can attenuate inflammatory responses. Single nucleotide polymorphisms in the pro-inflammatory cytokine genes such as IL-1β at rs16944 contributed to a number of inflammatory disorders and may substantiate the convergance between chronic and infectious diseases. Aim: This study aims to identify the modifying effect of nutritional factors on the association between IL-1β genotypes and CXCL10 levels. Methods: Participants ( N = 386) were healthy males and females from the Toronto Nutrigenomics and Health study recruited from the University of Toronto. Levels of micronutrients and inflammatory markers were measured in plasma. IL-1β genotypes were extracted from the Affymetrix 6.0 SNP chip. Results: CXCL10 levels were not different across different IL-1β genotypes. Among those with the GA genotype, elevated CXCL10 levels were observed with higher than median ascorbic acid (β = 0.004 ± 0.002, P = 0.047) or higher than median vitamin D status (β = 0.003 ± 0.002, P = 0.044). Among participants with the AA genotype, subjects with low α-tocopherol status had elevated levels of CXCL10 (β = −0.016 ± 0.007, P = 0.012). Conclusion: The association between IL-1β rs16944 genotype and CXCL10 levels was modified by the levels of ascorbic acid, α-tocopherol and vitamin D. These findings may aid in understanding the combined effect of genetic and dietary factors in the development of various infectious and chronic diseases in which IL-1β and CXCL10 may play an etiological role.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".