Epidemiology and Impact of Disorders of Gut–Brain Interaction in Multiethnic Malaysia: An In‐Depth Analysis of Local Data From the Rome Foundation Global Epidemiology Study
Bibliographic record
Abstract
INTRODUCTION: Previous epidemiology studies from Malaysia on disorders of gut-brain interaction (DGBI) have utilized variable criteria. Furthermore, the impact of DGBI on quality of life (QoL), healthcare utilization, diet, work productivity, and sleep in a multiethnic Asian setting like Malaysia remains underexplored. Here, we aimed to determine the epidemiology and impact of DGBI among multiethnic Malaysians based on the Rome IV criteria. METHODS: 2072 Malaysian participants from the Rome Foundation Global Epidemiology Study (RFGES) with complete data on DGBI were included in the in-depth analysis. We assessed the prevalence of DGBI diagnoses, psychological distress, QoL, healthcare utilization, dietary patterns, impact on sleep, work productivity, and activity impairment. RESULTS: The overall prevalence of any DGBI in Malaysia was 19.3% (95% CI 17.6%-21.0%). The top three most prevalent DGBI diagnoses were functional constipation (5.1%; 95% CI 4.2%-6.1%), functional dyspepsia (3.4%; 95% CI 2.7%-4.3%), and functional diarrhea (1.6%; 95% CI 1.1%-2.2%). Participants with DGBI reported higher levels of psychological distress (somatization, anxiety, and depression), significantly higher healthcare utilization, and dietary change (low FODMAPs but higher Mediterranean-based diet and probiotics). Furthermore, there was greater daytime sleepiness and higher proportions of presenteeism, overall work impairment, and activity impairment in individuals with DGBI. CONCLUSION: The disease burden of DGBI is significant in Malaysia, with increased psychological distress, healthcare utilization, dietary change, greater daytime sleepiness, and greater overall work and activity impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".