Micronutrient Deficiencies Associated with a Gluten-Free Diet in Patients with Celiac Disease and Non-Celiac Gluten or Wheat Sensitivity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A gluten-free diet (GFD) has been shown to be nutritionally inadequate for those with wheat-related disorders. However, the differences in findings and the absence of quantitative analysis limits the interpretation of previous reviews. Objectives: We conducted a systematic review and meta-analysis to identify the risk of micronutrient deficiencies in patients with celiac disease (CeD) and non-celiac gluten or wheat sensitivity (NCWS). Methods: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, and Web of Science (Ovid) databases. The risk of bias was determined using the ROBINS-1, and the quality of evidence was assessed using the GRADE approach. Results We identified 7940 studies; 46 observational studies (11 cohort, 9 cross-sectional, and 26 case–control) were eligible for analysis. CeD patients had an increased risk of vitamin D and E deficiencies compared with the non-CeD controls. CeD on a GFD had a decreased risk of vitamin D, B12, E, calcium, and iron deficiencies compared with untreated CeD. NCWS had an increased risk of vitamin B12, folate, and iron deficiency compared to the controls. The overall quality of evidence was rated very low. Conclusions: The risk of various micronutrient deficiencies is increased in CeD but is decreased for some after a GFD. Adequately powered studies with a rigorous methodology are needed to inform the risk of nutrient deficiencies in patients with CeD and NCWS. Protocol registration: Prospero-CRD42022313508.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".