LINKING THE GUT-IMMUNE PHENOTYPE TO BEHAVIOUR IN NEURODEVELOPMENTAL DISORDERS
Bibliographic record
Abstract
Diverse clinical presentation in neurodevelopmental disorders (NDDs) leads to difficulty in matching individuals with effective treatments. Autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD) are the two most prevalent neurodevelopmental disorders (NDDs), characterized by deficits in communication, social interactions, and behaviours. There is high within-diagnosis heterogeneity and striking overlap between diagnoses. The literature suggests that current diagnostic criteria do not align well with behaviour metrics. Therefore, identifying novel biomarkers underlying behaviour in NDDs may provide a reliable way to group individuals with similar behavioural phenotypes. This thesis examines how gut-immune biology is linked to clinical heterogeneity in children with NDDs. The first study used unsupervised machine learning to cluster typically developing (TD), ADHD, and ASD participants by their behaviour metrics in a diagnosis-agnostic approach. The results produced a six-cluster solution, five of which were a mix of all diagnostic categories. Further, gastrointestinal (GI) symptoms were mapped to the clusters, revealing a link between constipation, social communication deficits and restrictive-repetitive behaviours. The second study used hierarchical clustering to group TD and NDD participants based on a profile of gut and inflammatory markers. Participants clustered into two biotypes, both containing TD and NDD participants. Additionally, using regression analysis, novel markers were linked to anxiety. The third study evaluated the multisite biospecimen collection protocol of the Province of Ontario Neurodevelopmental Disorders (POND) Network. The final study used biospecimens collected from the POND network to phenotype peripheral blood mononuclear cells in TD and NDD participants. In NDD groups, monocyte and B cell activation markers were differentially expressed compared to TD. Overall, this thesis demonstrates that gut-immune mechanisms contribute to clinical heterogeneity in a subset of people and contribute to the search for biomarkers in NDDs.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".