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Objectives: To evaluate the severity and evolution of patient-reported gastrointestinal tract (GIT) symptoms in systemic sclerosis (SSc) patients, assess predictive factors for progression and determine the impact of standard of care treatment.
Methods: SSc patients from the Leiden and Oslo cohorts were included. We assessed clinical data and patient-reported GIT symptoms measured by the validated University of California, Los-Angeles Gastrointestinal-tract (UCLA-GIT) score at baseline and annually. GIT severity and progression was determined. Logistic regression was applied to identify risk factors associated with baseline GIT symptom severity. Linear mixed-effect models were applied to assess progression in GIT symptom burden and to identify predictive factors. We repeated all analysis in patients with early disease (inception cohort) to exclude the effect of longstanding disease and increase insights in development of GIT symptom burden early in the disease course.
Results: We included 834 SSc patients with baseline UCLA GIT scores, 454 from Leiden and 380 from Oslo. In the total cohort, 28% reported moderate-severe GIT symptoms at baseline, with increased risk for severity conferred by ACA, smoking and corticosteroid use, while use of calcium channel blockers appeared protective. In the inception cohort, 23% reported moderate-severe GIT symptoms at baseline, with increased risk for females and with smoking. Over time, symptom burden increased mainly for reflux/bloating. Female sex and ACA predicted GIT symptom progression.
Conclusion: High GIT symptom burden is present early in SSc disease course. Both for prevalence and for progression of GIT symptom burden, female sex and smoking were identified as risk factors.
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http://dx.doi.org/10.1093/rheumatology/keac118 | DOI Listing |
Sci Rep
September 2025
Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Republic of Korea.
Worldwide, cancer is one of the leading causes of death in humans. Interobserver variability and specialized experience are key factors in diagnosing gastrointestinal tract (GIT) abnormalities using endoscopic procedures. Due to this diversity, small lesions may go unnoticed, leading to a delay in early diagnosis.
View Article and Find Full Text PDFArthritis Care Res (Hoboken)
September 2025
University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Objective: The objective of this study is to characterize gastrointestinal (GI) manifestations in juvenile-onset systemic sclerosis (jSSc) using the UCLA Scleroderma Clinical Trial Consortium Gastrointestinal Tract 2.0 (UCLA GIT 2.0) patient-reported outcome (PRO) instrument, and to evaluate its validity and responsiveness in this population.
View Article and Find Full Text PDFSci Rep
August 2025
Neurology Department, Affiliate Children's Hospital of Xi'an Jiaotong University, Xi'an, 710003, Shaanxi Province, China.
In the field of neuroscience, epilepsy is a chronic non-communicable brain disease that affects approximately 50 million people worldwide. Electroencephalography (EEG) has become a key tool in detecting and characterizing human neurological diseases such as epilepsy. This rapid and accurate diagnosis allows doctors to provide timely and effective treatment to patients, significantly reducing the frequency of future seizures and the risk of related complications, which is crucial for ensuring the long-term health and quality of life of patients.
View Article and Find Full Text PDFPhysiol Res
August 2025
3rd Department of Internal Medicine, Department of endocrinology and metabolism, General University Hospital in Prague, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Short bowel syndrome (SBS) is an intestinal disorder characterized by reduced length of the gut most due to intestinal resection, resulting in malabsorption, malnutrition, and water and electrolyte disturbances. Intestinal adaptation is a long-term process in which GIT hormones, growth peptides, cytokines etc. are involved.
View Article and Find Full Text PDFNeural Netw
August 2025
School of Computer Science and Technology, Hainan University, Haikou Hainan,570228, China.
Brain diseases significantly impact physical and mental health, making the development of models to identify biomarkers for early diagnosis essential. However, building high-quality models typically relies on large-scale datasets, while the privacy-sensitive nature of medical data often restricts its sharing and utilization. Multi-site studies provide a potential solution by integrating data from various sources, yet existing methods frequently neglect site-specific private features, such as demographic information.
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