A single nucleotide polymorphism genetic risk score to aid diagnosis of coeliac disease: a pilot study in clinical care

Applying a genetic risk score model to enhance prediction of future multiple sclerosis diagnosis at first presentation with optic neuritis

Optic neuritis (ON) is associated with numerous immune-mediated inflammatory diseases, but 50% patients are ultimately diagnosed with multiple sclerosis (MS). Differentiating MS-ON from non-MS-ON acutely is challenging but important; non-MS ON often requires urgent immunosuppression to preserve vision. Using data from the United Kingdom Biobank we showed that combining an MS-genetic risk score (GRS) with demographic risk factors (age, sex) significantly improved MS prediction in undifferentiated ON; one standard deviation of MS-GRS increased the Hazard of MS 1.3-fold (95% confidence interval 1.07-1.55, P < 0.01). Participants stratified into quartiles of predicted risk developed incident MS at rates varying from 4% (95%CI 0.5-7%, lowest risk quartile) to 41% (95%CI 33-49%, highest risk quartile). The model replicated across two cohorts (Geisinger, USA, and FinnGen, Finland). This study indicates that a combined model might enhance individual MS risk stratification, paving the way for precision-based ON treatment and earlier MS disease-modifying therapy.

Coeliac disease: what can we learn from prospective studies about disease risk?

Paediatric prospective studies of coeliac disease with longitudinal collection of biological samples and clinical data offer a unique perspective on disease risk. This Review highlights the information now available from international paediatric prospective studies on genetic and environmental risk factors for coeliac disease. In addition, recent omics studies have made it possible to study complex interactions between genetic and environmental factors and thereby further our insight into the causes of the disease. In the future, paediatric prospective studies will be able to provide more detailed risk prediction models combining genes, the environment, and biological corroboration from multiomics. Such studies could also contribute to biomarker development and an improved understanding of disease pathogenesis.

Population level screening for celiac disease: is now the time?

PURPOSE OF REVIEW

As incidence and prevalence of celiac disease is increasing, subclinical and asymptomatic presentations are more commonly identified through celiac disease screening. However, the United States Preventive Services Task Force released a statement in 2017 maintaining that there is insufficient evidence to recommend general population screening for celiac disease for asymptomatic individuals. This review summarizes the current available evidence on celiac disease screening.

RECENT FINDINGS

Literature demonstrates that by limiting screening to individuals with recognized symptoms, celiac disease diagnosis is frequently delayed or missed entirely. Most individuals with screening-identified celiac disease have previously unrecognized symptoms that improve through treatment with a gluten-free diet. Screening-identified individuals also demonstrate signs of impaired nutrition, growth, bone health, and quality of life which improve with treatment. Overall, celiac disease screening is viewed favorably by those identified through celiac disease screening programs.

SUMMARY

Individuals with screening-identified celiac disease may still incur complications from untreated disease and receive benefit from treatment with a gluten-free diet. More data is needed to determine the cost effectiveness of different mass screening approaches that incorporate the societal perspective towards screening.

Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study

BACKGROUND

Coeliac disease (CD) affects approximately 1% of the population, although only a fraction of patients are diagnosed. Our objective was to develop diagnostic prediction models to help decide who should be offered testing for CD in primary care.

METHODS

Logistic regression models were developed in Clinical Practice Research Datalink (CPRD) GOLD (between Sep 9, 1987 and Apr 4, 2021, n=107,075) and externally validated in CPRD Aurum (between Jan 1, 1995 and Jan 15, 2021, n=227,915), two UK primary care databases, using (and controlling for) 1:4 nested case-control designs. Candidate predictors included symptoms and chronic conditions identified in current guidelines and using a systematic review of the literature. We used elastic-net regression to further refine the models.

FINDINGS

The prediction model included 24, 24, and 21 predictors for children, women, and men, respectively. For children, the strongest predictors were type 1 diabetes, Turner syndrome, IgA deficiency, or first-degree relatives with CD. For women and men, these were anaemia and first-degree relatives. In the development dataset, the models showed good discrimination with a c-statistic of 0·84 (95% CI 0·83-0·84) in children, 0·77 (0·77-0·78) in women, and 0·81 (0·81-0·82) in men. External validation discrimination was lower, potentially because 'first-degree relative' was not recorded in the dataset used for validation. Model calibration was poor, tending to overestimate CD risk in all three groups in both datasets.

INTERPRETATION

These prediction models could help identify individuals with an increased risk of CD in relatively low prevalence populations such as primary care. Offering a serological test to these patients could increase case finding for CD. However, this involves offering tests to more people than is currently done. Further work is needed in prospective cohorts to refine and confirm the models and assess clinical and cost effectiveness.

FUNDING

National Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020).

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. The potential benefits of PRSs include cost-effective enhancement of primary disease prevention, more refined diagnoses and improved precision when prescribing medicines. However, these must be weighed against the potential risks, such as uncertainties and biases in PRS performance, as well as potential misunderstanding and misuse of these within medical practice and in wider society. By addressing key issues including gaps in best practices, risk communication and regulatory frameworks, PRSs can be used responsibly to improve human health. Here, the International Common Disease Alliance's PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic.

Estimating disease prevalence in large datasets using genetic risk scores

Clinical classification is essential for estimating disease prevalence but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining disease prevalence within a cohort using genetic risk scores. We compare and evaluate methods based on the means of genetic risk scores' distributions; the Earth Mover's Distance between distributions; a linear combination of kernel density estimates of distributions; and an Excess method. We demonstrate the performance of genetic stratification to produce robust prevalence estimates. Specifically, we show that robust estimates of prevalence are still possible even with rarer diseases, smaller cohort sizes and less discriminative genetic risk scores, highlighting the general utility of these approaches. Genetic stratification techniques offer exciting new research tools, enabling unbiased insights into disease prevalence and clinical characteristics unhampered by clinical classification criteria.

Presence of high-risk HLA genotype is the most important individual risk factor for coeliac disease among at-risk relatives

BACKGROUND

Family screening has been advocated as a means to reduce the major underdiagnosis of coeliac disease. However, the precise risk of the disease in relatives and the impact of patient- and relative-related individual factors remain obscure.

AIMS

To investigate the individual risk of coeliac disease among patients' relatives.

METHODS

Altogether 2943 relatives of 624 index patients were assessed for the presence of previous coeliac disease diagnosis, or were screened for the disease. Coeliac disease-associated human leucocyte antigen (HLA) genotype was determined from all participants. The association between individual factors and new screening positivity was assessed by logistic regression.

RESULTS

There were 229 previously diagnosed non-index relatives with coeliac disease and 2714 non-affected (2067 first-degree, 647 more distant) relatives. Of these 2714 relatives, 129 (4.8%) were screening-positive (first-degree 5.1%, second-degree 3.6%, more distant 3.5%). The combined prevalence of the previously diagnosed and now detected cases in relatives was 12.2% (6.3% clinically detected, 5.9% screen-detected). In univariate analysis, age <18 years at diagnosis (odds ratio 1.60, 95% CI 1.04-2.45) in index, and age 41-60 years (1.73, 1.10-2.73), being a sibling (1.65, 1.06-2.59) and having the high-risk genotype (3.22, 2.01-5.15 DQ2.5/2.5 or DQ2.5/2.2 vs other risk alleles) in relatives were associated with screening positivity. Only high-risk HLA remained significant (2.94, 1.80-4.78) in multivariable analysis.

CONCLUSIONS

Unrecognised coeliac disease was common among at-risk relatives even in a country with an active case-finding policy, and also in relatives more distant than first-degree. The presence of a high-risk genotype was the most important predictor for screening positivity. ClinicalTrials.gov identifier NCT03136731.

Can Celiac Disease Be Prevented?

Celiac disease (CD) is an autoimmune disorder triggered by gluten in genetically susceptible individuals characterized by a variable combination of gluten-dependent symptoms, presence of specific autoantibodies and enteropathy. The health burden of CD is considerable, as it reduces quality of life  and, at a societal level, has extensive negative economic consequences. Prevention strategies are based on the identification of at-risk subjects and identification and elimination of risk factors. A number of prospective observational and interventional studies conducted on the general population, and more often in subjects at-risk, have given important information on the natural history of the disease. Both genetic and environmental factors have been identified with the former, in particular histocompatibility genes, playing a major role. Environmental factors, some operating already before birth, have been identified, with feeding pattern in the first year of life (breast feeding, amount and time of introduction of gluten) and infections being the most relevant. Prospective studies have also allowed the identification of biomarkers predictive of the disease which in perspective could better define the population on which to intervene. Interventions have been so far limited to modifications of feeding patterns. However, as also learnt from diseases that share with CD genetic risk factors and mechanisms of damage, such as type 1 diabetes (T1D), future strategies may be envisaged based on protection from infections, manipulation of microbiota, intervention on T cells.

Molecular Biomarkers for Celiac Disease: Past, Present and Future

Celiac disease (CeD) is a complex immune-mediated disorder that is triggered by dietary gluten in genetically predisposed individuals. CeD is characterized by inflammation and villous atrophy of the small intestine, which can lead to gastrointestinal complaints, malnutrition, and malignancies. Currently, diagnosis of CeD relies on serology (antibodies against transglutaminase and endomysium) and small-intestinal biopsies. Since small-intestinal biopsies require invasive upper-endoscopy, and serology cannot predict CeD in an early stage or be used for monitoring disease after initiation of a gluten-free diet, the search for non-invasive biomarkers is ongoing. Here, we summarize current and up-and-coming non-invasive biomarkers that may be able to predict, diagnose, and monitor the progression of CeD. We further discuss how current and emerging techniques, such as (single-cell) transcriptomics and genomics, can be used to uncover the pathophysiology of CeD and identify non-invasive biomarkers.

Beyond the HLA Genes in Gluten-Related Disorders

Most common food grains contain gluten proteins and can cause adverse medical conditions generally known as gluten-related disorders. Celiac disease is an immune-mediated enteropathy triggered by gluten in individuals carrying a specific genetic make-up. The presence of the human leukocyte antigens (HLA)-DQ2 and HLA-DQ8 haplotypes together with gluten intake is a necessary, although not sufficient, condition, to develop celiac disease. Fine mapping of the human genome has revealed numerous genetic variants important in the development of this disease. Most of the genetic variants are small nucleotide polymorphisms located within promoters and transcriptional enhancer sequences. Their importance is underlined by an increased risk in DQ2/DQ8 carriers who also have these non-HLA alleles. In addition, several immune-mediated diseases share susceptibility loci with celiac disease, shedding light on the reasons for co-occurrence between these diseases. Finally, most of the genes potentially involved in celiac disease by fine genetic mapping of non-HLA loci were confirmed in gene expression studies. In contrast to celiac disease, very little is known about the genetic make-up of non-celiac wheat sensitivity (NCWS), a clinically defined pathology that shares symptoms and gluten dependence with the celiac disease. We recently identified differentially expressed genes and miRNAs in the intestinal mucosa of these patients. Remarkably, the differentially expressed genes were long non-coding RNAs possibly involved in the regulation of cell functions. Thus, we can speculate that important aspects of these diseases depend on alteration of regulatory genetic circuits. Furthermore, our finding suggests that innate immune response is involved in the pathogenic mechanism of NCWS. This review is intended to convey the idea that in order to fully understand celiac disease and its relationship with other gluten-related disorders, it is worth learning more about non-HLA variants.