Effects of Gluten on the Plasma Proteome in Humans
Bibliographic record
Abstract
Gluten‐free foods have increased in popularity over the past decade and are now being consumed by individuals without celiac disease; however, the physiological effects of gluten intake in individuals without celiac disease remain unknown. High‐abundance plasma proteins involved in inflammation, endothelial function and other physiological pathways may represent potential biomarkers of biological effects of gluten intake. The objective was to examine the association between gluten intake and plasma proteomic biomarkers in a population of young adults without celiac disease (n=1,095). Dietary gluten intake was estimated using a one month 196‐item semi‐quantitative food frequency questionnaire. The concentrations of 54 plasma proteins were measured using a multiple reaction monitoring HPLC‐MS/MS assay. The association between gluten intake and each proteomic biomarker was examined using general linear models. Increased gluten intake was associated with increased concentrations of plasma α 2 ‐macroglobulin (p=0.01), a marker of inflammation and cytokine release. The association remained after adjusting for age, sex, BMI, ethnicity, physical activity, energy intake, fiber intake, and hormonal contraceptive use among women. This relationship was not modified by human leukocyte antigen ( HLA )‐DQ2 or ‐DQ8 risk variants required for the development of celiac disease. These results suggest that gluten consumption may have effects on inflammation independent of celiac disease. Research support from the Advanced Foods and Materials Network.
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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 | 0.001 |
| 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) | 0.001 | 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".