Regulation of the PBMCs gene expression profile with the Western dietary pattern in healthy men and women
Bibliographic record
Abstract
Diet regulates gene expression. The objective is to examine gene expression in relation with the Western dietary pattern. Methods 30 Caucasians (13 males; 17 females) were recruited. Dietary patterns were derived from a food frequency questionnaire using factor analysis. RNA was extracted from peripheral blood mononuclear cells (PBMCs), when subjects were in the fasting state. Expression levels of 47 231 mRNA transcripts (> 31000 genes) were assessed using the Illumina Human‐6 v3 Expression BeadChips®. Microarray data was processed with Flexarray software and pathway analysis was assessed using Ingenuity Pathway analysis (IPA). Results The Western pattern was characterised by high intakes of refined grain products and meats. In subjects with high versus low scores for the Western pattern, 193 transcripts were upregulated and 197 were downregulated. IPA reveals that networks related to inflammatory response, immunological diseases and cancer were different in subjects with high versus low scores for the Western pattern. Conclusion Studying gene expression profiles according to dietary patterns may help to understand the overall effect of nutrition in chronic diseases development. CIHR (MOP229488)
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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 | 0.000 |
| 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) | 0.002 | 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".