Background: In a previous study developed by our group, we identified a phase inversion in 6-sulfatoxymelatonin - melatonin metabolite in urine - daily profile in Fabry's disease patients. Since melatonin is an endogenous marker, it could also be accompanied by behavioral changes in sleep-wake cycle, which impairs the overall patient's life quality.
Objective: In this study, we evaluated sleep-wake cycle in Fabry disease patients. We hypothesized that patients would have increased daytime naps, given our previous results for urinary 6-sulfatoxymelatonin.
Patients/methods: This was a cross-sectional and case-control study, performed between October 2016 and May 2017. Volunteers recorded activity and rest rhythm by actigraphy and answered Pittsburgh Sleep Quality Index (PSQI). From actigraphy data, we calculated sleep parameters: sleep latency, wake after sleep onset, sleep (WASO) efficiency, awakenings index (PSQI), and the amount and duration of daytime naps. We included 16 Fabry disease patients with biochemical and molecular diagnosis and 10 control individuals matched by age and gender.
Results: We did not observe significant differences for any of the parameters analyzed (p > 0.05). However, evaluating the magnitude of the effect, we found that patients dozed, on average, about 42 min longer (d = 0.9 - large effect size) than control group.
Conclusions: This is a preliminary study, a proof-of-concept, and our results indicate that changes in melatonin secretion phase may have behavioral consequences in sleep-wake cycle, with longer duration of daytime naps.
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http://dx.doi.org/10.1016/j.sleep.2020.01.012 | DOI Listing |
Ann Intern Med
January 2025
Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California (A.B., K.J.C., A.A.K.).
Background: Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) differ in their effects on body weight and risk for reoperation. However, it is unclear whether long-term health expenditures differ by procedure type in patients with diabetes.
Objective: To compare health expenditures 3 years before and 5.
Ann Intern Med
January 2025
Renal-Electrolyte Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (M.C.-P., R.B.M., C.M.P.).
Background: Prior studies indicate that 1% to 4% of Epstein-Barr virus (EBV)-seronegative recipients of EBV-seropositive donor (EBV D+/R-) kidneys develop posttransplant lymphoproliferative disorder (PTLD). However, these estimates are based on limited data that lack granularity.
Objective: To determine the associations between pretransplant EBV D+/R- and recipient EBV-seropositive status (R+) and the outcomes of PTLD and graft and patient survival among adult kidney transplant recipients.
JMIR Res Protoc
January 2025
McMaster University, Hamilton, ON, Canada.
Background: Research has shown that engaging in a range of healthy lifestyles or behavioral factors can help reduce the risk of developing dementia. Improved knowledge of modifiable risk factors for dementia may help engage people to reduce their risk, with beneficial impacts on individual and public health. Moreover, many guidelines emphasize the importance of providing education and web-based resources for dementia prevention.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Design Innovation, College of Design, University of Minnesota, Twin Cities, Minneapolis, MN, United States.
Background: Congenital heart disease (CHD) is a birth defect of the heart that requires long-term care and often leads to additional health complications. Effective educational strategies are essential for improving health literacy and care outcomes. Despite affecting around 40,000 children annually in the United States, there is a gap in understanding children's health literacy, parental educational burdens, and the efficiency of health care providers in delivering education.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
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