Objective: Glycemic regression is common in real-world settings, but the contribution of regression to the mean (RTM) has been little investigated. We aimed to estimate glycemic regression before and after adjusting for RTM in a free-living cohort of adults with newly ascertained diabetes and intermediate hyperglycemia (IH).
Research Design And Methods: The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a cohort study of 15,105 adults screened between 2008 and 2010 with standardized oral glucose tolerance test and HbA, repeated after 3.84 ± 0.42 years. After excluding those receiving medical treatment for diabetes, we calculated partial or complete regression before and after adjusting baseline values for RTM.
Results: Regarding newly ascertained diabetes, partial or complete regression was seen in 49.4% (95% CI 45.2-53.7); after adjustment for RTM, in 20.2% (95% CI 12.1-28.3). Regarding IH, regression to normal levels was seen in 39.5% (95% CI 37.9-41.3) or in 23.7% (95% CI 22.6-24.3), depending on use of the World Health Organization (WHO) or the American Diabetes Association (ADA) definition, respectively; after adjustment, corresponding frequencies were 26.1% (95% CI 22.4-28.1) and 19.4% (95% CI 18.4-20.5). Adjustment for RTM reduced the number of cases detected at screening: 526 to 94 cases of diabetes, 3,118 to 1,986 cases of WHO-defined IH, and 6,182 to 5,711 cases of ADA-defined IH. Weight loss ≥2.6% was associated with greater regression from diabetes (relative risk 1.52, 95% CI 1.26-1.84) and IH (relative risk 1.30, 95% CI 1.17-1.45).
Conclusions: In this quasi-real-world setting, regression from diabetes at ∼4 years was common, less so for IH. Regression was frequently explained by RTM but, in part, also related to improved weight loss and homeostasis over the follow-up.
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http://dx.doi.org/10.2337/dc20-2030 | DOI Listing |
Biomed Phys Eng Express
January 2025
Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
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January 2025
Faculty Xavier Institute of Engineering, Mahim, India.
In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040.
View Article and Find Full Text PDFBrain
January 2025
Translational Neuroimaging Laboratory, Montreal Neurological Institute, H3A 2B4, Montreal, Canada.
Plasma phosphorylated tau biomarkers open unprecedented opportunities for identifying carriers of Alzheimer's disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment.
View Article and Find Full Text PDFJMIR Form Res
January 2025
School of Psychology, Ulster University, Coleraine, United Kingdom.
Background: Psychologists have developed frameworks to understand many constructs, which have subsequently informed the design of digital mental health interventions (DMHIs) aimed at improving mental health outcomes. The science of happiness is one such domain that holds significant applied importance due to its links to well-being and evidence that happiness can be cultivated through interventions. However, as with many constructs, the unique ways in which individuals experience happiness present major challenges for designing personalized DMHIs.
View Article and Find Full Text PDFNeurology
February 2025
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Background And Objectives: Lipid metabolism in older adults is affected by various factors including biological aging, functional decline, reduced physiologic reserve, and nutrient intake. The dysregulation of lipid metabolism could adversely affect brain health. This study investigated the association between year-to-year intraindividual lipid variability and subsequent risk of cognitive decline and dementia in community-dwelling older adults.
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