A Phenome-Wide Association Study of Drugs and Comorbidities Associated With Gastrointestinal Dysfunction in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To explore the causes of and contributors to gastrointestinal (GI) dysfunction in systemic sclerosis (SSc) in a phenome-wide association study (PheWAS), using real-world clinical records data. METHODS: Twelve thousand five hundred thirty-five documented clinical assessments of 2058 consenting individuals with SSc at the Royal Free Hospital (UK) were available for detailed phenotyping. Diagnoses and drugs were mapped to structured dictionaries of terms (Disease Ontology project and DrugBank Open Data, respectively). A PheWAS model was used to explore links between 6 important SSc-GI domains (constipation, diarrhea, dysmotility, incontinence, gastroesophageal reflux, and small intestinal bacterial overgrowth [SIBO]) and exposure to various comorbidities and drugs. "Hits" from the PheWAS model were confirmed and explored in a subcohort reporting quantitative GI symptom scores from the University of California Los Angeles Scleroderma Clinical Trials Consortium Gastrointestinal Tract Instrument 2.0 (GIT 2.0) questionnaire. RESULTS: One thousand five hundred forty-six individuals were entered into the PheWAS analysis. Six hundred seventy-three distinct diagnoses and 634 distinct drugs were identified in the dataset, as well as SSc-specific phenotypes such as antinuclear antibodies (ANA). PheWAS analysis revealed associations between drugs, diagnoses, and ANAs with 6 important SSc-GI outcomes: constipation, diarrhea, dysmotility, incontinence, reflux, and SIBO. Subsequently, using GIT 2.0 symptom scores links with SSc-GI were confirmed for 22 drugs, 4 diagnoses, and 3 ANAs. CONCLUSION: Using a hypothesis-free PheWAS approach, we replicated known, and revealed potential novel, risk factors for SSc-GI dysfunction, including drug classes such as opioid, antimuscarinic, and endothelin receptor antagonist, and ANA subgroup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".