Changing Patterns of Relationships Between Geographic Markers and IBD: Possible Intrusion of Obesity
Bibliographic record
Abstract
Abstract Background Latitude and lactase digestion status influence incidence and prevalence rates of some noncommunicable diseases. Latitudinal correlations helped define beneficial roles of vitamin D in many diseases like inflammatory bowel disease (IBD). In view of recent global expansion of IBD and population migrations, we reexamine relations with these markers. As these changes also paralleled the pandemic of obesity, we explore possible interactions with IBD. Methods We undertook a literature review to compare rates of obesity, Crohn’s disease and ulcerative colitis with the geographic markers of lactase digestion status, average population-weighted national latitude, and national yearly sunshine exposure. Pearson correlations were used throughout to determine r correlation factors. Statistical significance was accepted at P <0.05 using 2-tailed tests. Results Forty-seven countries were matched with various data sets that could be analyzed (range of availability was 49%–85%). While global correlations of IBD with latitude and lactase status remain similar to previous analyses, in Europe and Asia, outcomes were different. Global outcome contains a statistical paradox related to combining countries from Europe and Asia. Obesity showed moderate global correlations with IBD but weak and negligible correlations in Europe and Asia. There was also a weak global correlation with latitude. Conclusions It is suggested that global correlations point to parallel geographic spread of IBD and obesity. The lack of latitudinal relations with obesity suggests reduced vitamin D effect. The paradox supports epidemiological differences in western and eastern IBD. Obesity combined with IBD may contribute to different relations, partly due to variable vitamin D effects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".