Are obesity and rheumatoid arthritis interrelated?

OBJECTIVES: In recent years, both the prevalence of obesity and the incidence of RA have been rising. Our aim was to assess the association between overweight or obesity and rheumatoid arthritis (RA). DESIGN: Patients who were diagnosed with RA were compared with population-based controls, matched for age and sex (by a ratio of 1:5). Body measurements and smoking status were collected from medical records. Body mass index was classified in WHO categories of underweight, normal, overweight and obese (<18.5, 18.5-<25, 25-<30, ≥30 kg/m2 ). χ2 and t-tests and logistic regression models were used to compare the study groups and to assess the association between obesity and RA. SETTING: A cross-sectional analysis performed utilizing the database of Clalit Health Services, the largest healthcare provider organisation in Israel. Data were collected from the beginning of computerised database usage (around year 2000) until 2015. PARTICIPANTS: CHS covers over 4.4 million enrollees, of which all RA patients and matched controls were selected. MAIN OUTCOME MEASURES: Proportion of obesity (BMI≥30.0 kg/m2 ) among RA patients and controls. RESULTS: The study included 11 406 patients with RA and 54 701 controls. The proportion of obese subjects among RA patients was higher in comparison with controls, (33.4% vs 31.6%, respectively). In multivariate regression model, smoking and obesity were found to be associated with RA, whereas male gender was found as inversely related to RA. CONCLUSIONS: Our findings demonstrate that obesity is significantly associated with RA. This finding underlines the role that obesity plays in inflammation and autoimmune conditions.

A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding.

(1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012−2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization. We assessed the correlation between various parameters with endoscopic intervention and examined the prediction performance of the Glasgow-Blatchford score (GBS) and the pre-endoscopic Rockall score for endoscopic intervention. We also trained and tested a new machine learning-based model for the prediction of endoscopic intervention. (3) Results: A total of 883 patients were included. Risk factors for endoscopic intervention included cirrhosis (9.0% vs. 3.8%, p = 0.01), syncope at presentation (19.3% vs. 5.4%, p < 0.01), early EGD (6.8 h vs. 17.0 h, p < 0.01), pre-endoscopic administration of tranexamic acid (TXA) (43.4% vs. 31.0%, p < 0.01) and erythromycin (17.2% vs. 5.6%, p < 0.01). Higher GBS (11 vs. 9, p < 0.01) and pre-endoscopy Rockall score (4.7 vs. 4.1, p < 0.01) were significantly associated with endoscopic intervention; however, the predictive performance of the scores was low (AUC of 0.54, and 0.56, respectively). A combined machine learning-developed model demonstrated improved predictive ability (AUC 0.68) using parameters not included in standard GBS. (4) Conclusions: The GBS and pre-endoscopic Rockall score performed poorly in endoscopic intervention prediction. An improved predictive tool has been proposed here. Further studies are needed to examine if predicting this important triaging decision can be further optimized.

Deep Learning Multi-Domain Model Provides Accurate Detection and Grading of Mucosal Ulcers in Different Capsule Endoscopy Types.

BACKGROUND AND AIMS: The aim of our study was to create an accurate patient-level combined algorithm for the identification of ulcers on CE images from two different capsules. METHODS: We retrospectively collected CE images from PillCam-SB3's capsule and PillCam-Crohn's capsule. ML algorithms were trained to classify small bowel CE images into either normal or ulcerated mucosa: a separate model for each capsule type, a cross-domain model (training the model on one capsule type and testing on the other), and a combined model. RESULTS: The dataset included 33,100 CE images: 20,621 PillCam-SB3 images and 12,479 PillCam-Crohn's images, of which 3582 were colonic images. There were 15,684 normal mucosa images and 17,416 ulcerated mucosa images. While the separate model for each capsule type achieved excellent accuracy (average AUC 0.95 and 0.98, respectively), the cross-domain model achieved a wide range of accuracies (0.569-0.88) with an AUC of 0.93. The combined model achieved the best results with an average AUC of 0.99 and average mean patient accuracy of 0.974. CONCLUSIONS: A combined model for two different capsules provided high and consistent diagnostic accuracy. Creating a holistic AI model for automated capsule reading is an essential part of the refinement required in ML models on the way to adapting them to clinical practice.

The Incidence and Predictors of Solid- and Hematological Malignancies in Patients with Giant Cell Arteritis: A Large Real-World Database Study.

BACKGROUND: The association between giant cell arteritis (GCA) and malignancies had been widely investigated with studies reporting conflicting results. Therefore, in this study, we aimed to investigate this association using a large nationwide electronic database. METHODS: This study was designed as a retrospective cohort study including GCA patients first diagnosed between 2002-2017 and age, sex and enrollment time-matched controls. Follow-up began at the date of first GCA-diagnosis and continued until first diagnosis of malignancy, death or end of study follow-up. RESULTS: The study enrolled 7213 GCA patients and 32,987 age- and sex-matched controls. The mean age of GCA diagnosis was 72.3 (SD 9.9) years and 69.1% were women. During the follow-up period, 659 (9.1%) of GCA patients were diagnosed with solid malignancies and 144 (2.0%) were diagnosed with hematologic malignancies. In cox-multivariate-analysis the risk of solid- malignancies (HR = 1.12 [95%CI: 1.02-1.22]), specifically renal neoplasms (HR = 1.60 [95%CI: 1.15-2.23]) and sarcomas (HR = 2.14 [95%CI: 1.41-3.24]), and the risk of hematologic malignancies (HR = 2.02 [95%CI: 1.66-2.47]), specifically acute leukemias (HR = 1.81 [95%CI: 1.06-3.07]), chronic leukemias (HR = 1.82 [95%CI: 1.19-2.77]), Hodgkin's lymphomas (HR = 2.42 [95%CI: 1.12-5.20]), non-Hodgkin's-lymphomas (HR = 1.66: [95%CI 1.21-2.29]) and multiple myeloma(HR = 2.40 [95%CI: 1.63-3.53]) were significantly increased in GCA patients compared to controls. Older age at GCA-diagnosis (HR = 1.36 [95%CI: 1.25-1.47]), male-gender (HR = 1.46 [95%CI: 1.24-1.72]), smoking (HR = 1.25 [95%CI: 1.04-1.51]) and medium-high socioeconomic status (HR = 1.27 [95%CI: 1.07-1.50]) were independently associated with solid malignancy while age (HR = 1.47 [95%CI: 1.22-1.77]) and male-gender (HR = 1.61 [95%CI: 1.14-2.29]) alone were independently associated with hematologic- malignancies. CONCLUSION: our study demonstrated higher incidence of hematologic and solid malignancies in GCA patients. Specifically, leukemia, lymphoma, multiple myeloma, kidney malignancies, and sarcomas. Age and male gender were independent risk factors for hematological malignancies among GCA patients, while for solid malignancies, smoking and SES were risk factors as well.

No male predominance in offspring of women with rheumatoid arthritis or systemic lupus erythematosus.

To assess the proportion of male versus female offspring of women diagnosed with SLE or RA, disorders in which female predominance is well known and PsA a disease in which female dominance is less established. The study population encompassed all females aged 16-46, who were members of the Maccabi Health Services (MHS) throughout the period of 2000-2011 and had at least one pregnancy. Data were retrieved from the computerized database of MHS, a 2-million enrollee health maintenance organization operating in Israel. The database was also used to collect data on patients with RA, SLE, and PsA. A total of 182,073 women had at least one indication of pregnancy during the study period. Among them, 546, 270, and 170 were diagnosed with RA, SLE, and PsA, respectively. The proportion of live-born males in 380,472 offspring of women free of these diseases was 51.5 % (95 % CI 51.4-51.7 %). The proportion (95 % CIs) of male offspring born to mothers diagnosed with of RA, SLE, and PsA were 46.3 % (42.3-50.3 %), 51.8 % (46.6-57.0 %), and 50.6 % (42.8-58.5 %), respectively. Our findings support the primary contribution of the hormonal phenotype rather than the genetic phenotype on autoimmunity. Neither patients with SLE or RA differ from the general population by the sex of their offspring.