Gene-Environment Interactions in the Etiopathogenesis of Inflammatory Bowel Disease
Bibliographic record
Abstract
The etiopathogenesis of inflammatory bowel diseases (IBD) is unknown. The last few years have seen a tremendous growth in efforts to identify causes, mechanisms and potential cure for the disease. Although few successes have been achieved, major elements in the intricate pathways leading to the disease remain unclear and deciphering them presents numerous challenges. A major hurdle (and interest) has been the observed widespread heterogeneity in the risk (and risk factors) for IBD among geographically diverse populations. Both environmental and genetic factors contribute to this heterogeneity. Much of the present focus has been on delineating the genetic susceptibility for IBD with limited emphasis on potential environmental risk factors. Previous studies exploring environmental risk factors have been inconsistent with the spectrum of risk going from low to high. This is especially true for the two most commonly studied risk factors: dietary elements and infection exposures. Although some of the inconsistency in findings across studies is likely due to differences in study designs, analytic methods and degree of biases, we have hypothesized that the heterogeneity is likely due to the presence of gene-environment interactions (G x E). On the basis of the latter, we have put forward two potential interaction pathways elucidating the mechanisms leading to IBD. The first pathway (1) describes interactions between dietary substrates (fats, vegetables and fruits) and xenobiotic metabolizing enzymes (XME). XMEs are a family of enzymes that are commonly involved in the metabolism of both exogenous and endogenous substrates. They are broadly divided into two major classes: Phase 1 enzymes, such as the cytochrome P-450, metabolism by which on most occasions leads to the formation of toxic intermediates, and Phase 2 enzymes such as the gluthatione S-transferases, metabolism by which leads to detoxification and excretion of toxic intermediates. Genes coding these enzymes show variation resulting in individual differences both in the activity as well as the level of the enzymes. Our interaction pathway is based on the observations that a) many XMEs are expressed in the gastrointestinal tract; b) the encoding genes are located in chromosomal areas previously linked to IBD; and c) dietary fatty acids are common substrates for these enzymes and dietary fruits and vegetables modify their levels. Considering that genetic variants determine the ability of individuals to metabolize dietary substrates, it has been postulated that interactions between dietary substrates and DNA variants in the XME genes would modify risk for IBD, making some individuals at lower or higher risk. The second pathway (2) emphasizes the role of G x E between infections and genetic variants related to the cytokine and NOD2 genes. The role of infections in the etiopathogenesis of especially Crohn's disease (CD) has been controversial with some studies indicating protection, whereas others suggest elevated risks. These differences among populations are probably due to differential distribution of genetic factors. In this pathway we have hypothesized that potential interactions between DNA variants in cytokine and NOD2 genes and infectious exposures could modify risk for CD. Cyokines are known to modulate response to infections. Similarly, the NOD2 protein is thought to play a significant role in the pathogenesis of CD. Potential interactions between these elements could influence the development of T-cells and alter T-cell balance and predispose certain individuals to disease. IBD is a complex disease and the above described interaction pathways are unlikely to be all-encompassing. Nevertheless, they highlight the need for undertaking studies of potential G x E for theidentification of populations most susceptible to IBD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".