Recognizing the Leaky Gut as a Trans-diagnostic Target for Neuroimmune Disorders Using Clinical Chemistry and Molecular Immunology Assays
Bibliographic record
Abstract
BACKGROUND: Increased intestinal permeability with heightened translocation of Gramnegative bacteria, also known as "leaky gut", is associated with the pathophysiology of neuroimmune disorders, such as Major Depressive Disorder (MDD), Chronic Fatigue Syndrome (CSF) and (deficit) schizophrenia, as well as with general medical disorders, including irritable bowel syndrome. This review aims to summarize clinical biochemistry and molecular immunology tests that may aid in the recognition of leaky gut in clinical practice. METHODS: We searched online libraries, including PubMed/MEDLINE, Google Scholar and Scopus, with the key words "diagnosis" or "biomarkers" and "leaky gut", "bacterial translocation", and "intestinal permeability" and focused on papers describing tests that may aid in the clinical recognition of leaky gut. RESULTS: To evaluate tight junction barrier integrity, serum IgG/IgA/IgM responses to occludin and zonulin and IgA responses to actomyosin should be evaluated. The presence of cytotoxic bacterial products in serum can be evaluated using IgA/IgM responses to sonicated samples of common Gram-negative gut commensal bacteria and assays of serum lipopolysaccharides (LPSs) and other bacterial toxins, including cytolethal distenting toxin, subunit B. Major factors associated with increased gut permeability, including gut dysbiosis and yeast overgrowth, use of NSAIDs and alcohol, food hypersensitivities (IgE-mediated), food intolerances (IgG-mediated), small bacterial overgrowth (SIBO), systemic inflammation, psychosocial stressors, some infections (e.g., HIV) and dietary patterns, should be assessed. Stool samples can be used to assay gut dysbiosis, gut inflammation and decreased mucosal defenses using assays of fecal growth of bacteria, yeast and fungi and stool assays of calprotectin, secretory IgA, β-defensin, α- antitrypsin, lysozyme and lactoferrin. Blood and breath tests should be used to exclude common causes of increased gut permeability, namely, food hypersensitivities and intolerances, SIBO, lactose intolerance and fructose malabsorption. DISCUSSION: Here, we propose strategies to recognize "leaky gut" in a clinical setting using the most adequate clinical chemistry and molecular immunology assays.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".