Original Article
Alimentary Tract
Genetic and Inflammatory Biomarkers Classify Small Intestine Inflammation in Asymptomatic First-degree Relatives of Patients With Crohn’s Disease

https://doi.org/10.1016/j.cgh.2019.05.061Get rights and content

Background & Aims

Relatives of individuals with Crohn's disease (CD) carry CD-associated genetic variants and are often exposed to environmental factors that increase their risk for this disease. We aimed to estimate the utility of genotype, smoking status, family history, and biomarkers can calculate risk in asymptomatic first-degree relatives of patients with CD.

Methods

We recruited 480 healthy first-degree relatives (full siblings, offspring or parents) of patients with CD through the Guy’s and St Thomas’ NHS Foundation Trust and from members of Crohn’s and Colitis, United Kingdom. DNA samples were genotyped using the Immunochip. We calculated a risk score for 454 participants, based on 72 genetic variants associated with CD, family history, and smoking history. Participants were assigned to highest and lowest risk score quartiles. We assessed pre-symptomatic inflammation by capsule endoscopy and measured 22 markers of inflammation in stool and serum samples (reference standard). Two machine-learning classifiers (elastic net and random forest) were used to assess the ability of the risk factors and biomarkers to identify participants with small intestinal inflammation in the same dataset.

Results

The machine-learning classifiers identified participants with pre-symptomatic intestinal inflammation: elastic net (area under the curve, 0.80; 95% CI, 0.62–0.98) and random forest (area under the curve, 0.87; 95% CI, 0.75–1.00). The elastic net method identified 3 variables that can be used to calculate odds for intestinal inflammation: combined family history of CD (odds ratio, 1.31), genetic risk score (odds ratio, 1.14), and fecal calprotectin (odds ratio, 1.04). These same 3 variables were among the 5 factors associated with intestinal inflammation in the random forest model.

Conclusion

Using machine learning classifiers, we found that genetic variants associated with CD, family history, and fecal calprotectin together identify individuals with pre-symptomatic intestinal inflammation who are therefore at risk for CD. A tool for detecting people at risk for CD before they develop symptoms would help identify the individuals most likely to benefit from early intervention.

Section snippets

Participants

We recruited 480 healthy FDRs (full siblings, offspring, or parents) through CD patients attending the IBD service at Guy’s & St Thomas’ NHS Foundation Trust (GSTT) and from members of Crohn’s & Colitis UK (a charity supporting patients with IBD). CD diagnosis in the probands was confirmed by their gastroenterologist or general practitioner. FDRs provided information regarding family history of IBD, medical history (including gastrointestinal symptoms), smoking status, medications, allergies,

Calculating the High- and Low-Risk Quartiles

The relative risk of CD in the 454 asymptomatic FDRs generated from 72 genetic risk markers and smoking status (Figure 1) ranged from 0.03 to 38.3 and was significantly higher than expected in the general population (Supplementary Figure 1) (P = .013). Demographic information for each risk quartile is shown in Supplementary Table 2. FDRs in the highest-risk and lowest-risk quartiles (n = 228) were invited to participate in phase 2 of the study (Figure 1); of these 81 were unable to attend for

Discussion

We found that the combination of CD family history, genetic risk, and level of FC was a good predictor of SI inflammation in our cohort of FDRs. Both machine-learning classifiers performed similarly, with significant prediction of SI inflammation, and both put CD family history, genetic risk, and FC as top predictors, which increases confidence in the model’s utility. CD family history was the strongest predictor in the elastic net model and ranked fifth most important predictor in the random

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    Conflicts of interest The authors disclose no conflicts.

    Funding This study was undertaken as part of a Guy's and St Thomas' Charity funded clinical research fellowship (R080522). The authors also acknowledge support from the National Institute for Health Research, Guy's and St Thomas' Biomedical Research Centre, London, United Kingdom, and at South London and Maudsley NHS Foundation Trust, in partnership with King's College London.

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