Celiac disease (CD) is a chronic immune disorder which is triggered by the ingestion of gluten. There are increased chances of having autoimmune disorders (Type 1 Diabetes Mellitus, Thyroid Disorder, and Rheumatoid Arthritis and others) in people who have CD. However, there is limited research available till date on understanding the combined effect of lifestyle factors, physical, sociodemographic characteristics and immune-hematological profiles of autoimmune disorders who also had CD. This study aims to bridge this gap on understanding of associations of these parameters and help identify immune-hematological parameters that may serve as shared indicator across autoimmune conditions. This study utilised the large scale database from UK Biobank which comprises over 502,132 participants biomedical, lifestyle, diet and sociodemographic data of aged 40–70 years. To find the statistically quantifiable associations between sociodemographic, physical, and lifestyle factors and the likelihood of CD among individuals with Type 1 Diabetes Mellitus, Thyroid Disorders, and Rheumatoid Arthritis, multivariate logistic regression models were used. Independent samples t-tests (significance level: 0.05) were conducted to compare the means of immune-hematological parameters between individuals with CD alone and those with coexisting autoimmune diagnoses. CD was consistently associated with female sex, lower BMI, and recent illness-related dietary changes across all autoimmune subgroups. Minority Ethnic Origin was inversely associated with CD diagnosis. Immune-hematological parameters revealed a high degree of similarity across the examined disease groups. Notably, the immune-hematological parameters, including monocytes, eosinophils, and basophils percentage, were found to be significantly associated between individuals with isolated CD and those with coexisting autoimmune diagnoses. The observed convergence in immune-hematological profiles across autoimmune diseases supports their potential utility as baseline indicators for early diagnosis and longitudinal monitoring. Parameters demonstrating consistent similarity across disease states—such as monocytes, eosinophils, and basophils percentage—could enhance diagnostic precision and facilitate more standardized approaches to screening in at-risk populations.
A K Chandrakar (Mon,) studied this question.