Clinical features, epidemiology, autoantibody status, HLA haplotypes and genetic mechanisms of type 1 diabetes mellitus among children in Qatar

To describe the clinical features, epidemiology, autoantibody status, HLA haplotypes and genetic mechanisms of type 1 diabetes mellitus (T1DM). Patients (0–18 years) with diabetes were recruited. Clinical data was collected, autoantibodies and c-peptide were measured. Whole Genome Sequencing was performed. Genomic data analysis was compared with the known genes linked with T1DM and HLA alleles were studied. 1096 patients had one or more antibody positivity. The incidence of T1DM in 2020 was 38.05 per 100,000 children and prevalence was 249.73. GADA was the most common autoantibody followed by IAA. Variants in GSTCD, SKAP2, SLC9B1, BANK1 were most prevalent. An association of HLA haplotypes DQA1*03:01:01G (OR = 2.46, p value = 0.011) and DQB1*03:02:01G (OR = 2.43, p value = 0.022) was identified. The incidence of T1DM in Qatar is the fourth highest in the world, IA2 autoantibody was the most specific with some patients only having ZnT8 or IA2 autoantibodies thus underlining the necessity of profiling all 4 autoantibodies. The genes associated with T1DM in the Arab population were different from those that are common in the Caucasian population. HLA-DQ was enriched in the Qatari patients suggesting that it can be considered a major risk factor at an early age.

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies

To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyositis (PM) and 19 with non-IIM were enrolled. Using 2D manual segmentation, 93 original features as well as 93 local binary pattern (LBP) features were extracted from MRI (short-tau inversion recovery [STIR] imaging) of proximal limb muscles. To construct and compare ML models that predict disease groups using each set of features, dimensional reductions were performed using a reproducibility analysis by inter-reader and intra-reader correlation coefficients, collinearity analysis, and the sequential feature selection (SFS) algorithm. Models were created using the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF) and multi-layer perceptron (MLP) classifiers, and validated using tenfold cross-validation repeated 100 times. We also investigated whether it was possible to construct models predicting autoantibody status. Our ML-based MRI radiomics models showed the potential to distinguish between PM, DM, and ADM. Models using LBP features provided better results, with macro-average AUC values of 0.767 and 0.714, accuracy of 61.2 and 61.4%, and macro-average recall of 61.9 and 59.8%, in the LDA and k-NN classifiers, respectively. In contrast, the accuracies of radiomics models distinguishing between non-IIM and IIM disease groups were low. A subgroup analysis showed that classification models for anti-Jo-1 and anti-ARS antibodies provided AUC values of 0.646–0.853 and 0.692–0.792, with accuracy of 71.5–81.0 and 65.8–78.3%, respectively. ML-based TA of muscle MRI may be used to predict disease groups or the autoantibody status in patients with IIM and is useful in non-invasive assessments of disease mechanisms.

Effectiveness of pirfenidone in idiopathic pulmonary fibrosis according to the autoantibody status: a retrospective cohort study

Background Pirfenidone is an anti-fibrotic agent shown to slow the progression of idiopathic pulmonary fibrosis (IPF). However, its effectiveness in association with serological autoimmune features in IPF remains unclear. Methods We retrospectively reviewed the medical records of patients with IPF treated at a tertiary care hospital in South Korea. The autoantibody status was defined as positive if we detected autoantibodies meeting the serological domain criteria for interstitial pneumonia with autoimmune features or anti-neutrophil cytoplasmic antibodies. Results We included 142 patients with IPF treated with pirfenidone for over six months (93 were autoantibody-positive and 49 were autoantibody-negative). The mean age was 69.5 ± 7.3 years, and 77.5% of the patients were male. The adjusted mean changes over one year were − 34.4 and − 112.2 mL (p = 0.168) in forced vital capacity (FVC), and − 0.53 and − 0.72 mL/mmHg/min (p = 0.356) in the lungs diffusion capacity for carbon monoxide (DLCO) in the autoantibody-negative and autoantibody-positive groups, respectively. Conclusions Reductions in FVC and DLCO were similar in autoantibody-positive and autoantibody-negative patients with IPF treated with pirfenidone. Pirfenidone is effective in attenuating the progression of IPF, irrespective of the autoantibody status.

Serum TSH is not a risk factor for malignancy of pediatric thyroid nodules

It is uncertain whether serum TSH concentration is an independent risk factor for the malignancy of pediatric thyroid nodules. We sought for the association of serum TSH concentration with the malignancy of pediatric thyroid nodules and with the characteristics of pediatric thyroid cancer. A total of 219 pediatric thyroid nodule patients were collected retrospectively for 5 consecutive years. The medical records collected included sex, age, serum TSH concentration, thyroid autoantibody status, thyroid ultra-sonography parameters, histological type, and pathological TNM stages. The serum TSH concentrations were compared between benign and malignant nodules or corresponding subgroups. Binary logistic regression analysis was used to evaluate the correlation of TSH concentration with the malignancy of thyroid nodules and with the characteristics of pediatric thyroid cancer. There was no significant difference in TSH concentration between benign nodule and thyroid cancer in total subjects and various subgroups. The serum TSH level not correlated with the malignancy of thyroid nodules in univariate analysis, but negatively correlated with the malignancy of thyroid nodules (odds ratio = 0.856, p = 0.013) after adjusting for the patients’ sex, age, thyroid autoantibody status, and nodule size. The serum TSH level not correlated with the tumor characteristics in pediatric thyroid cancer patients. In conclusion, the serum TSH concentration seems not to be a carcinogenic factor in pediatric thyroid nodule patients, nor to be an independent risk factor for characteristics of pre-existing pediatric thyroid cancers.

Clinical features, Epidemiology, Autoantibody status, HLA Haplotypes and Genetic Mechanisms of Type 1 diabetes mellitus among Children in Qatar

Aims - To describe the clinical features, epidemiology, autoantibody status, HLA haplotypes and genetic mechanisms of type 1 diabetes mellitus (T1DM). Methods - Patients (0–18 years) with diabetes were recruited. Clinical data was collected, autoantibodies and c-peptide were measured. Whole Genome Sequencing was performed. Genomic data analysis was compared with the known genes linked with T1DM and HLA alleles were studied. Results - 1096 patients had one or more antibody positivity. The incidence of T1DM in 2020 was 38.05 per 100,000 children and prevalence was 249.73. GAD65 was the most common autoantibody and IA2 was most specific. Variants in GSTCD, SKAP2, SLC9B1, BANK1 were most prevalent. An association of HLA haplotypes DQA1*03:01:01G (OR = 2.46, pvalue = 0.011) and DQB1*03:02:01G (OR = 2.43, p value = 0.022) was identified. Conclusions - In this first prospective study, IA2 autoantibody was the most specific, some patients only have ZnT8 or IA2 autoantibodies thus underlining the necessity of profiling all 4 antibodies. The genes associated with T1DM in the Arab population were different from those that are common in the Caucasian population. HLA-DQ was enriched in the Qatari patients suggesting that it can be considered a major risk factor at an early age.

Factors that influence biological survival in rheumatoid arthritis: results of a real-world academic cohort from the Netherlands

We aim to explore real-world biological survival stratified for discontinuation reason and determine its influenceability in rheumatoid arthritis (RA) patients. Data from the local pharmacy database and patient records of a university hospital in the Netherlands were used. RA patients who started a biological between 2000 and 2020 were included. Data on age, anti-citrullinated protein antibody (ACPA) and rheumatoid factor (RF) status, presence of erosions, gender, body mass index, time to first biological, biological survival time, use of csDMARDs, and discontinuation reasons were collected. Of the included 318 patients, 12% started their first biological within 6 months after diagnosis. The median time to first biological was 3.6 years (95% CI, 1.0–7.2). The median survival of the first- and second-line biological was respectively 1.7 years (95% CI, 1.3–2.2) and 0.8 years (95% CI, 0.5–1.0) (p = 0.0001). Discontinuation reasons for the first-line biological were ineffectiveness (47%), adverse events (17%), remission (16%), pregnancy (30%), or patient preference (10%). Multivariable Cox regression analyses for discontinuation due to inefficacy or adverse events showed that concomitant use of csDMARDs (HR = 1.32, p < 0.001) positively while RF positivity negatively (HR = 0.82, p = 0.03) influenced biological survival. ACPA positivity was associated with the inability to discontinue biologicals after achieving remission (HR = 1.43, p = 0.023). Second-line TNF inhibitor survival was similar between patients with a primary and secondary non-response on the first-line TNF inhibitor (HR = 1.28, p = 0.34). Biological survival diminishes with the number of biologicals used. Biological survival is prolonged if patients use csDMARDs. RF was negatively associated with biological survival. ACPA was negatively associated with the inability to discontinue biologicals after achieving remission. Therefore, tailoring treatment based upon autoantibody status might be the first step towards personalized medicine in RA. Key Points• Prolonged biological survival is a surrogate for treatment effectiveness; however, an increasing amount of patients will taper treatment due to remission, and factors influencing biological survival based on separate reasons for discontinuation have not been explored.• We found that combining a biological DMARD with a conventional synthetic DMARD increases biological DMARD survival. Rheumatoid factor is negatively associated with biological survival. Anti-citrullinated protein antibody is negatively associated with the inability to discontinue the biological when remission was reached.• The first step towards personalized medicine might be tailoring of treatment based upon autoantibody status.