Exploring the interconnected between type 2 diabetes mellitus and nonalcoholic fatty liver disease: Genetic correlation and Mendelian randomization analysis

Epidemiological and clinical studies have indicated a higher risk of nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), implying a potentially shared genetic etiology, which is still less explored. Genetic links between T2DM and NAFLD were assessed using linkage disequilibrium score regression and pleiotropic analysis under composite null hypothesis. European GWAS data have identified shared genes, whereas SNP-level pleiotropic analysis under composite null hypothesis has explored pleiotropic loci. generalized gene-set analysis of GWAS data determines pleiotropic pathways and tissue enrichment using eQTL mapping to identify associated genes. Mendelian randomization analysis was used to investigate the causal relationship between NAFLD and T2DM. Linkage disequilibrium score regression analysis revealed a strong genetic correlation between T2DM and NAFLD, and identified 24 pleiotropic loci. These single-nucleotide polymorphisms are primarily involved in biosynthetic regulation, RNA biosynthesis, and pancreatic development. generalized gene-set analysis of GWAS data analysis revealed significant enrichment in multiple brain tissues. Gene mapping using these 3 methods led to the identification of numerous pleiotropic genes, with differences observed in liver and kidney tissues. These genes were mainly enriched in pancreas, brain, and liver tissues. The Mendelian randomization method indicated a significantly positive unidirectional causal relationship between T2DM and NAFLD. Our study identified a shared genetic structure between NAFLD and T2DM, providing new insights into the genetic pathogenesis and mechanisms of NAFLD and T2DM comorbidities.

Genome-wide association study of cardiometabolic multimorbidity in the UK Biobank

Considering the high prevalence and poor prognosis of cardiometabolic multimorbidity (CMM), identifying causal factors and actively implementing preventive measures is crucial. However, Mendelian randomization (MR), a key method for identifying the causal factors of CMM, requires knowledge of the effects of SNPs on CMM, which remain unknown. We first analyzed the genetic overlap of single cardiometabolic diseases (CMDs) using the latest genome-wide association study (GWAS) for evidential support and comparison. We observed strong positive genetic correlations and shared loci among all CMDs. Further, GWAS and post-GWAS analyses of CMM were performed in 407 949 European ancestry individuals from the UK Biobank. Eleven loci and 12 lead SNPs were identified. By comparison, we found these SNPs were a subset of SNPs associated with CMDs, including both shared and non-shared SNPs. Then, the polygenic risk score model predicted the risk of CMM (C-index = 0.62) and we identified candidate genes related to lipid metabolism and immune function. Finally, as an example, two-sample MR analysis based on the GWAS revealed potential causal effects of total cholesterol, serum urate, body mass index, and smoking on CMM. These results provide a basis for future MR research and inspire future studies on the mechanism and prevention of CMM.

Understanding the relationship between asthma and autism spectrum disorder: a population-based family and twin study

BACKGROUND

There is some evidence that autism spectrum disorder (ASD) frequently co-occurs with immune-mediated conditions including asthma. We aimed to explore the familial co-aggregation of ASD and asthma using different genetically informed designs.

METHODS

We first examined familial co-aggregation of asthma and ASD in individuals born in Sweden from 1992 to 2007 (n = 1 569 944), including their full- and half-siblings (n = 1 704 388 and 356 544 pairs) and full cousins (n = 3 921 890 pairs), identified using Swedish register data. We then applied quantitative genetic modeling to siblings (n = 620 994 pairs) and twins who participated in the Child and Adolescent Twin Study in Sweden (n = 15 963 pairs) to estimate the contribution of genetic and environmental factors to the co-aggregation. Finally, we estimated genetic correlations between traits using linkage disequilibrium score regression (LDSC).

RESULTS

We observed a within-individual association [adjusted odds ratio (OR) 1.33, 95% confidence interval (CI) 1.28-1.37] and familial co-aggregation between asthma and ASD, and the magnitude of the associations decreased as the degree of relatedness decreased (full-siblings: OR 1.44, 95% CI 1.38-1.50, maternal half-siblings: OR 1.28, 95% CI 1.18-1.39, paternal half-siblings: OR 1.05, 95% CI 0.96-1.15, full cousins: OR 1.06, 95% CI 1.03-1.09), suggesting shared familial liability. Quantitative genetic models estimated statistically significant genetic correlations between ASD traits and asthma. Using the LDSC approach, we did not find statistically significant genetic correlations between asthma and ASD (coefficients between -0.09 and 0.12).

CONCLUSIONS

Using different genetically informed designs, we found some evidence of familial co-aggregation between asthma and ASD, suggesting the weak association between these disorders was influenced by shared genetics.

Disentangling the common genetic architecture and causality of rheumatoid arthritis and systemic lupus erythematosus with COVID-19 outcomes: Genome-wide cross trait analysis and bidirectional Mendelian randomization study

Coronavirus Disease (COVID-19) may cause a dysregulation of the immune system and has complex relationships with multiple autoimmune diseases, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). However, little is known about their common genetic architecture. Using the latest data from COVID-19 host genetics consortium and consortia on RA and SLE, we conducted a genome-wide cross-trait analysis to examine the shared genetic etiology between COVID-19 and RA/SLE and evaluated their causal associations using bidirectional Mendelian randomization (MR). The cross-trait meta-analysis identified 23, 28, and 10 shared genetic loci for severe COVID-19, COVID-19 hospitalization, and SARS-CoV-2 infection with RA, and 14, 17, and 7 shared loci with SLE, respectively. Co-localization analysis identified five causal variants in TYK2, IKZF3, PSORS1C1, and COG6 for COVID-19 with RA, and four in CRHR1, FUT2, and NXPE3 for COVID-19 with SLE, involved in immune function, angiogenesis and coagulation. Bidirectional MR analysis suggested RA is associated with a higher risk of COVID-19 hospitalization, and COVID-19 is not related to RA or SLE. Our novel findings improved the understanding of the genetic etiology shared by COVID-19, RA and SLE, and suggested an increased risk of COVID-19 hospitalization in people with higher genetic liability to RA.

Identification of shared genetic architecture between non-alcoholic fatty liver disease and type 2 diabetes: A genome-wide analysis

BACKGROUND

The incidence of complications of non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D) has been increasing.

METHOD

In order to identify the shared genetic architecture of the two disease phenotypes of NAFLD and T2D, a European population-based GWAS summary and a cross-trait meta-analysis was used to identify significant shared genes for NAFLD and T2D. The enrichment of shared genes was then determined through the use of functional enrichment analysis to investigate the relationship between genes and phenotypes. Additionally, differential gene expression analysis was performed, significant differentially expressed genes in NAFLD and T2D were identified, genes that overlapped between those that were differentially expressed and cross-trait results were reported, and enrichment analysis was performed on the core genes that had been obtained in this way. Finally, the application of a bidirectional Mendelian randomization (MR) approach determined the causal link between NAFLD and T2D.

RESULT

A total of 115 genes were discovered to be shared between NAFLD and T2D in the GWAS analysis. The enrichment analysis of these genes showed that some were involved in the processes such as the decomposition and metabolism of lipids, phospholipids, and glycerophospholipids. Additionally, through the use of differential gene expression analysis, 15 core genes were confirmed to be linked to both T2D and NAFLD. They were correlated with carcinoma cells and inflammation. Furthermore, the bidirectional MR identified a positive causal relationship between NAFLD and T2D.

CONCLUSION

Our study determined the genetic structure shared between NAFLD and T2D, offering a new reference for the genetic pathogenesis and mechanism of NAFLD and T2D comorbidities.

Genetic Overlap Analysis Identifies a Shared Etiology between Migraine and Headache with Type 2 Diabetes

Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D (rg = 0.06, p = 1.37 × 10−5) and between headache and T2D (rg = 0.07, p = 3.0 × 10−4). Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (Pmeta < 5 × 10−8) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (Pgene-based < 3.85 × 10−6) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes (EHMT2, SLC44A4, PLEKHA1, CFDP1, TMEM170A, CHST6, and BCAR1) common between them. There was also a significant overlap of genes nominally associated (Pgene-based < 0.05) with both migraine and T2D (Pbinomial-test = 2.83 × 10−46) and headache and T2D (Pbinomial-test = 4.08 × 10−29). Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated (inverse-variance weighted, ORIVW = 0.90, Pivw = 7 × 10−3) with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D.

Shared Genetics and Causality Between Decaffeinated Coffee Consumption and Neuropsychiatric Diseases: A Large-Scale Genome-Wide Cross-Trait Analysis and Mendelian Randomization Analysis

Coffee or caffeine consumption has been associated with neuropsychiatric disorders, implying a shared etiology. However, whether these associations reflect causality remains largely unknown. To understand the genetic structure of the association between decaffeinated coffee consumption (DCC) and neuropsychiatric traits, we examined the genetic correlation, causality, and shared genetic structure between DCC and neuropsychiatric traits using linkage disequilibrium score regression, bidirectional Mendelian randomization (MR), and genome-wide cross-trait meta-analysis in large GWAS Consortia for coffee consumption (N = 329,671) and 13 neuropsychiatric traits (sample size ranges from 36,052 to 500,199). We found strong positive genetic correlations between DCC and lifetime cannabis use (LCU; Rg = 0.48, P = 8.40 × 10^-19), alcohol use disorder identification test (AUDIT) total score (AUDIT_T; Rg = 0.40, P = 4.63 × 10^-13), AUDIT_C score (alcohol consumption component of the AUDIT; Rg = 0.40, P = 5.26 × 10^-11), AUDIT_P score (dependence and hazardous-use component of the AUDIT; Rg = 0.28, P = 1.36 × 10^-05), and strong negative genetic correlations between DCC and neuroticism (Rg = -0.15, P = 7.27 × 10^-05), major depressed diseases (MDD; Rg = -0.15, P = 0.0010), and insomnia (Rg= -0.15, P = 0.0007). In the cross-trait meta-analysis, we identified 6, 5, 1, 1, 2, 31, and 27 shared loci between DCC and Insomnia, LCU, AUDIT_T, AUDIT_C, AUDIT_P, neuroticism, and MDD, respectively, which were mainly enriched in bone marrow, lymph node, cervix, uterine, lung, and thyroid gland tissues, T cell receptor signaling pathway, antigen receptor-mediated signaling pathway, and epigenetic pathways. A large of TWAS-significant associations were identified in tissues that are part of the nervous system, digestive system, and exo-/endocrine system. Our findings further indicated a causal influence of liability to DCC on LCU and low risk of MDD (odds ratio: 0.90, P = 9.06 × 10^-5 and 1.27, P = 7.63 × 10^-4 respectively). We also observed that AUDIT_T and AUDIT_C were causally related to DCC (odds ratio: 1.83 per 1-SD increase in AUDIT_T, P = 1.67 × 10^-05, 1.80 per 1-SD increase in AUDIT_C, P = 5.09 × 10^-04). Meanwhile, insomnia and MDD had a causal negative influence on DCC (OR: 0.91, 95% CI: 0.86-0.95, P = 1.51 × 10^-04 for Insomnia; OR: 0.93, 95% CI: 0.89-0.99, P = 6.02 × 10^-04 for MDD). These findings provided evidence for the shared genetic basis and causality between DCC and neuropsychiatric diseases, and advance our understanding of the shared genetic mechanisms underlying their associations, as well as assisting with making recommendations for clinical works or health education.

Approaching Shared Pathophysiology in Immune-Mediated Diseases through Functional Genomics

Immune-mediated diseases (IMDs) are complex pathologies that are strongly influenced by environmental and genetic factors. Associations between genetic loci and susceptibility to these diseases have been widely studied, and hundreds of risk variants have emerged during the last two decades, with researchers observing a shared genetic pattern among them. Nevertheless, the pathological mechanism behind these associations remains a challenge that has just started to be understood thanks to functional genomic approaches. Transcriptomics, regulatory elements, chromatin interactome, as well as the experimental characterization of genomic findings, constitute key elements in the emerging understandings of how genetics affects the etiopathogenesis of IMDs. In this review, we will focus on the latest advances in the field of functional genomics, centering our attention on systemic rheumatic IMDs.

Investigating asthma heterogeneity through shared and distinct genetics: Insights from genome-wide cross-trait analysis

Asthma is a heterogeneous respiratory disease reflecting distinct pathobiologic mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding of the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiologic associations between asthma subtypes and those between coexisting diseases and/or traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytic approach-genome-wide cross-trait analysis-to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytic aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages, and their limitations. We also make recommendations for future directions: (1) ethnicity-specific asthma GWASs and (2) application of cross-trait methods to multiomics data to dissect the heritability found in GWASs. Finally, these analytic approaches are also applicable to complex and heterogeneous traits beyond asthma.

Genome-Wide Assessment for Resting Heart Rate and Shared Genetics With Cardiometabolic Traits and Type 2 Diabetes

BACKGROUND

High resting heart rate (RHR) occurs in parallel with type 2 diabetes (T2D) and metabolic disorders, implying shared etiology between them. However, it is unknown if they are causally related, and no study has been conducted to investigate the shared mechanisms underlying these associations.

OBJECTIVES

The objective of this study was to understand the genetic basis of the association between resting heart rate and cardiometabolic disorders/T2D.

METHODS

This study examined the genetic correlation, causality, and shared genetics between RHR and T2D using LD Score regression, generalized summary data-based Mendelian randomization, and transcriptome wide association scan (TWAS) in UK Biobank data (n = 428,250) and summary-level data for T2D (74,124 cases and 824,006 control subjects) and 8 cardiometabolic traits (sample size ranges from 51,750 to 236,231).

RESULTS

Significant genetic correlation between RHR and T2D (r_g = 0.22; 95% confidence interval: 0.18 to 0.26; p = 1.99 × 10^-22), and 6 cardiometabolic traits (fasting insulin, fasting glucose, waist-hip ratio, triglycerides, high-density lipoprotein, and body mass index; r_g range -0.12 to 0.24; all p < 0.05) were observed. RHR has significant estimated causal effect on T2D (odds ratio: 1.12 per 10-beats/min increment; p = 7.79 × 10^-11) and weaker causal estimates from T2D to RHR (0.32 beats/min per doubling increment in T2D prevalence; p = 6.14 × 10^-54). Sensitivity analysis by controlling for the included cardiometabolic traits did not modify the relationship between RHR and T2D. TWAS found locus chr2q23.3 (rs1260326) was highly pleiotropic among RHR, cardiometabolic traits, and T2D, and identified 7 genes (SMARCAD1, RP11-53O19.3, CTC-498M16.4, PDE8B, AKTIP, KDM4B, and TSHZ3) that were statistically independent and shared between RHR and T2D in tissues from the nervous and cardiovascular systems. These shared genes suggested the involvement of epigenetic regulation of energy and glucose metabolism, and AKT activation-related telomere dysfunction and vascular endothelial aging in the shared etiologies between RHR and T2D. Finally, FADS1 was found to be shared among RHR, fasting glucose, high-density lipoprotein, and triglycerides.

CONCLUSIONS

These findings provide evidence of significant genetic correlations and causation between RHR and T2D/cardiometabolic traits, advance our understanding of RHR, and provide insight into shared etiology for high RHR and T2D.

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank

BACKGROUND

Clinical and epidemiologic studies have shown that obesity is associated with asthma and that these associations differ by asthma subtype. Little is known about the shared genetic components between obesity and asthma.

OBJECTIVE

We sought to identify shared genetic associations between obesity-related traits and asthma subtypes in adults.

METHODS

A cross-trait genome-wide association study (GWAS) was performed using 457,822 subjects of European ancestry from the UK Biobank. Experimental evidence to support the role of genes significantly associated with both obesity-related traits and asthma through a GWAS was sought by using results from obese versus lean mouse RNA sequencing and RT-PCR experiments.

RESULTS

We found a substantial positive genetic correlation between body mass index and later-onset asthma defined by asthma age of onset at 16 years or greater (Rg = 0.25, P = 9.56 × 10-22). Mendelian randomization analysis provided strong evidence in support of body mass index causally increasing asthma risk. Cross-trait meta-analysis identified 34 shared loci among 3 obesity-related traits and 2 asthma subtypes. GWAS functional analyses identified potential causal relationships between the shared loci and Genotype-Tissue Expression (GTEx) quantitative trait loci and shared immune- and cell differentiation-related pathways between obesity and asthma. Finally, RNA sequencing data from lungs of obese versus control mice found that 2 genes (acyl-coenzyme A oxidase-like [ACOXL] and myosin light chain 6 [MYL6]) from the cross-trait meta-analysis were differentially expressed, and these findings were validated by using RT-PCR in an independent set of mice.

CONCLUSIONS

Our work identified shared genetic components between obesity-related traits and specific asthma subtypes, reinforcing the hypothesis that obesity causally increases the risk of asthma and identifying molecular pathways that might underlie both obesity and asthma.

Genomics of body fat percentage may contribute to sex bias in anorexia nervosa

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = -0.17), suggesting that the female preponderance in AN may, in part, be explained by sex-specific anthropometric and metabolic genetic factors increasing liability to AN.

Autophagy-related gene LRRK2 is likely a susceptibility gene for systemic lupus erythematosus in northern Han Chinese

Autophagy is associated with various immune diseases, including systemic lupus erythematosus (SLE). Seven variants within autophagy-related genes previously reported to show top association signals by genome-wide association studies in immune diseases were selected for analysis. Initially, 510 SLE patients (631 controls) were enrolled in the study. An additional independent cohort of 511 SLE patients (687 controls) was included for replication. Polymorphism rs2638272 in LRRK2 gene showed significant association with susceptibility to SLE (P = 1.14 × 10⁻²) within the initial patient population. This was independently replicated (second patient cohort), and was reinforced with combination (P = 2.82 × 10⁻³). By combining multiple layers of regulatory effects, rs1491941 in high linkage disequilibrium with rs2638272 (r² = 0.99) was regarded to have the strongest function in LRRK2. The rs1491941 protective A-allele exhibited an increase of nuclear protein binding, and an increase in LRRK2 transcription compared with G-allele. Furthermore, we observed increased transcription levels of LRRK2 in peripheral blood mononuclear cells from SLE patients compared with controls. In conclusion, we have identified a novel genetic association between the autophagy-related LRRK2 gene and susceptibility to SLE. By integrating layers of functional data, we derived the beneficial effect of autophagy on the pathogenesis of SLE.

A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL

In genome-wide association studies (GWAS), "generalization" is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family-wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses. It also fails to leverage the two-stage design to increase power for detecting generalized associations. We provide a formal statistical framework for quantifying the evidence of generalization that accounts for the (in)consistency between the directions of associations in the discovery and follow-up studies. We develop the directional generalization FWER (FWER_g ) and FDR (FDR_g ) controlling r-values, which are used to declare associations as generalized. This framework extends to generalization testing when applied to a published list of Single Nucleotide Polymorphism-(SNP)-trait associations. Our methods control FWER_g or FDR_g under various SNP selection rules based on P-values in the discovery study. We find that it is often beneficial to use a more lenient P-value threshold than the genome-wide significance threshold. In a GWAS of total cholesterol in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), when testing all SNPs with P-values <5×10-8 (15 genomic regions) for generalization in a large GWAS of whites, we generalized SNPs from 15 regions. But when testing all SNPs with P-values <6.6×10-5 (89 regions), we generalized SNPs from 27 regions.

Cancer in first-degree relatives of people with celiac disease

BACKGROUND

Celiac disease (CD) has been linked to cancer, especially lymphoproliferative malignancy (LPM). Earlier research has shown that first-degree relatives (FDRs) to individuals with CD are at increased risk of autoimmunity including CD, but data on their risk of cancer are scarce and contradictory. We aimed to assess whether Swedish FDRs to individuals with CD are at increased risk of cancer.

METHODS

Individuals with CD (identified through biopsy reports equal to Marsh grade III) were matched on sex, age, county, and calendar year with up to 5 control individuals. All FDRs (father, mother, sibling, offspring) of CD individuals ("celiac FDRs": n = 109,391) and controls (n = 548,465) were identified through Swedish healthcare registries. Through Cox regression, we calculated hazard ratios (HRs) for cancer incidence (all cancer, breast cancer, gastrointestinal cancer, and LPM).

RESULTS

During follow-up, celiac FDRs experienced 10,750 unique cancers as opposed to 54,686 in-control FDRs. Celiac FDRs were at a slightly lower risk of any cancer (HR 0.97, 95% confidence interval [CI] 0.95-0.99), partially due to the lower risk of breast cancer (HR 0.92, 95% CI 0.87-0.98). The relative risks of LPM (HR 0.99, 95% CI 0.91-1.08) and gastrointestinal cancer (HR 0.98, 95%CI 0.93-1.03) were both close to 1. As opposed to earlier research, we found no excess risk of LPM in siblings to individuals with CD (HR 0.98, 95% CI 0.81-1.19).

CONCLUSION

Celiac FDRs are not at increased risk of cancer, including LPM, arguing that shared genetics is unlikely to explain previous reports of an excess risk of LPM in patients with CD.

Ischaemic heart disease in first-degree relatives to coeliac patients

OBJECTIVE

Coeliac disease (CD) has been linked to an increased risk of ischaemic heart disease (IHD). We examined the risk of IHD in first-degree relatives and spouses to coeliac patients to ascertain the genetic contribution to IHD excess risk.

STUDY DESIGN AND SETTING

Coeliac disease was defined as having a biopsy-verified villous atrophy (Marsh grade 3) in 1969-2008 (n = 29,096). Coeliac patients were matched to 144,522 controls. Through Swedish registers, we identified all first-degree relatives and spouses to coeliac patients and their controls, in total 87,622 unique coeliac relatives and 432,655 unique control relatives. Our main outcome measure was IHD defined according to relevant international classification of disease codes in the Swedish Inpatient Registry or in the Cause of Death Registry. Hazard ratios (HR) and confidence intervals (CI) were estimated through Cox regression adjusted for sex, age-group and calendar year at study entry of the relative.

RESULT

During a median follow-up of 10·8 years, 2880 coeliac relatives and 13,817 control relatives experienced IHD. First-degree relatives of coeliac patients were at increased risk of IHD (HR = 1·05; 95% CI = 1·00-1·09, P-value = 0·04), while spouses were at no increased risk (HR = 0·99; 95% CI = 0·87-1·12). The excess risk of IHD in coeliac first-degree relatives aged 40-59 years was 70/100,000 person-years.

CONCLUSION

First-degree relatives to coeliac patients seem to be at an increased risk of IHD but the excess risk is so small that it has little clinical relevance.