Biomarkers of cardiometabolic health and nutritional status in individuals with positive celiac disease serology
Bibliographic record
Abstract
BACKGROUND: Celiac disease (CD) is an autoimmune disorder characterized by damage to the intestinal mucosa and nutrient malabsorption in severe cases. However, it remains unclear whether nutrient deficiencies and other adverse health effects are prevalent in individuals with positive CD serology identified through screening studies. OBJECTIVE: The objective was to determine whether biomarkers of cardiometabolic health and nutritional status differ between those with positive and negative CD serology identified in a screening study of Canadian adults. METHODS: Participants ( n=2832) were from the Toronto Nutrigenomics and Health Study and the Toronto Healthy Diet Study. Individuals were screened for CD-specific anti-tissue transglutaminase autoantibodies. Lipid profiles as well as concentrations of six carotenoids (α-carotene, β-carotene, β-cryptoxanthin, lutein, lycopene, and zeaxanthin), three tocopherols (α-tocopherol, δ-tocopherol, and γ-tocopherol), retinol, ascorbic acid, and 25-hydroxyvitamin D were cross-sectionally compared between those with positive and negative CD serology using general linear mixed models. RESULTS: Individuals with positive CD serology ( n=23) had significantly lower levels of HDL-cholesterol ( p=0.008) and apolipoprotein-AI ( p=0.02), a higher ratio of total cholesterol to HDL-cholesterol ( p=0.006), and a higher apolipoprotein-B/AI ratio ( p=0.03) than those with negative CD serology. Positive CD serology was also associated with significantly lower concentrations of retinol ( p=0.006) in fully adjusted models. Those with positive CD serology had lower serum 25-hydroxyvitamin D in unadjusted models ( p=0.01), but not in fully adjusted models ( p=0.08). CONCLUSIONS: Individuals with undiagnosed CD may have unfavorable lipid profiles and be at elevated risk for inadequacy of certain fat-soluble vitamins, but not widespread nutrient deficiencies.
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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 | 0.001 |
| 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 | 0.001 | 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".