Background: Thyroid eye disease (TED) is a potentially disfiguring and sight-threatening autoimmune (AI) orbitopathy, affecting up to 400,000 people in the UK. There are no accurate early predictors of TED severity. Although polyautoimmunity has been shown to affect AI disease severity, its influence on TED severity has never been investigated. The prevalence of polyautoimmunity among TED patients is also unclear, with discordant results reported in the literature. This study evaluates the prevalence of non-thyroid/"other" AI (OAI) conditions in an ethnically diverse TED cohort and assesses how polyautoimmunity affects TED severity and activity.
Methods: A retrospective study of patients presenting to multidisciplinary TED clinics across three North-West London hospitals between 2011 and 2019. Data collected included: 1) demographics; 2) OAI conditions and management; 3) endocrine management of thyroid dysfunction; 4) details of TED and clinical activity score at presentation.
Results: Two hundred and sixty-seven patients with a median age of 46 (35-54) years were included, 79.4% were female and 55% were Black, Asian and minority ethnic (BAME). Thirty-seven patients (13.9%) had OAI conditions, with rheumatoid arthritis (3.7%), vitiligo (3.0%) and psoriasis (3.0%) among the most prevalent. Of patients with OAI conditions, 43.2% (16/37) required immunosuppression prior to TED onset. Non-immunosuppressed patients with OAI conditions had a significantly higher clinical activity score at presentation than TED-only and previously immunosuppressed patients (p=0.02). No significant differences were observed in thyroid receptor antibody titers between these groups.
Conclusions: This study finds a 13.9% prevalence of OAI conditions among TED patients. Patients with OAI conditions overall have a tendency for more severe and significantly more clinically active TED than those without OAI conditions. Larger, prospective studies are warranted to further evaluate polyautoimmunity as an early predictor of TED severity.
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http://dx.doi.org/10.3389/fendo.2021.644200 | DOI Listing |
Calcif Tissue Int
December 2024
Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
To explore the hypothesis that knee osteoarthritis patients with osteoporosis represent a sub-cohort with different disease characteristics and origin of symptoms. Men and women in the Osteoarthritis Initiative (OAI) at visit 5 (36 months) were examined for osteoporosis (N = 1483) using DXA (T-score at femoral neck ≤ -2.5), use of bisphosphonates, or having experienced a fracture.
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November 2024
The University of Western Australia, School of Human Sciences, Perth, WA, Australia.
Prior meta-analyses have suggested a protective link between smoking and knee osteoarthritis (KOA), but they relied on aggregate data, potentially obscuring the true relationship. To address this limitation, we conducted an Individual Participant Data (IPD) meta-analysis using data from three large cohorts: the Osteoarthritis Initiative (OAI), the Multicenter Osteoarthritis Study (MOST), and the Cohort Hip and Cohort Knee (CHECK) study. Participants from 16 centers in the USA and Netherlands were categorized as current, former, or never smokers.
View Article and Find Full Text PDFSci Rep
November 2024
Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB, T2N 1N4, Canada.
Diagnostics (Basel)
November 2024
Deepnoid Inc., Seoul 08376, Republic of Korea.
Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs.
View Article and Find Full Text PDFNat Sci Sleep
November 2024
Respiratory and Critical Care Medicine Department, Tianjin Chest Hospital, Tianjin, People's Republic of China.
Objective: To explore the characteristics of elderly patients with central sleep apnea (CSA).
Methods: This retrospective study divided 123 patients with CSA into elderly and non-elderly groups, and compared them in terms of demographic characteristics (age, BMI, etc), underlying diseases (hypertension, coronary heart disease, and cardiac arrhythmias, etc). and polysomnography parameters.
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