PWE-004 A study of faecal volatile organic compounds metabolome in healthy population across the countries
Bibliographic record
Abstract
Introduction Faecal biomarkers are emerging non-invasive tools for diagnosing gastrointestinal disorders. Faecal volatile organic compounds (VOCs) have been studied more recently in clinical diagnosis. Pattern of faecal VOCs in healthy population may provide basis for understanding changes in disease conditions. The VOCs within the metaboloms may be different across the countries due to differences in dietary habits and environmental conditions and may have implications in developing their clinical utility. Methods We aim to study the faecal VOCs of the healthy population from three different countries that is, England, Belgium and Canada. A total of 159 health volunteers (English=109, F=69), (Belgium=20, F=14), (Canada=30, F=17) donated faecal samples. Fresh samples were aliquoted in 18 mls sealed vials. VOCs were extracted using solid phase micro extraction and were analysed using gas chromatography–mass spectrometery. VOCs were identified using NIST library search comparing their fragment pattern. Results A total of 232 VOCs were identified. Using binary data (presence or absence of VOCs), univariate analysis was used to identify those VOCs which were statistically significant (p<0.05) in discerning differences between the three population groups. Alcohols, ketones and esters were predominantly associated with English volunteers compared to both Canadian and Belgium volunteers while aldehydes and alkenes were predominantly detected VOCs in the Canadian and Belgium groups respectively. A multivariate discriminant function analysis utilising these VOCs was able to differentiate three groups with a sensitivity of 96% and specificity of 90%. Conclusion The observed differences in the faecal VOCs metabolites of the healthy population in different countries may provide important basis in the clinical utility of faecal biomarkers. It may also provide information in studying the differences in disease prevalence and behaviour in different countries. Further studies are warranted to explore this area. Competing interests None declared. References 1. Probert CS , Ahmed I, Khalid T, et al. Volatile organic compounds as diagnostic biomarkers in gastrointestinal and liver diseases. J Gastrointestin Liver Dis 2009; 18 :337–43. 2. Angriman I , Scarpa M, D'Incà R, et al. Enzymes in feces: useful markers of chronic inflammatory bowel disease. Clin Chim Acta 2007; 381 :63–8.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".