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Background: Previous research has demonstrated co-occurrence of asthma and type 1 diabetes in children, but the relationship is not as clear between allergic rhinitis or eczema and type 1 diabetes. Shared familial factors could explain a comorbidity, but the genetic overlap remains to be examined.
Objective: The aim was to further the etiologic understanding of the comorbidity between allergic disease and type 1 diabetes.
Methods: A Swedish population-based cohort of 3 million children born 1987-2017 was linked to nationwide registers. Associations between each allergic disease and type 1 diabetes were estimated within individuals and the familial coaggregation between relatives. For the genetic overlap, linkage disequilibrium score regression was applied on the basis of genome-wide association studies. In genotyped individuals from the Swedish Twin Registry, polygenic risk scores were developed to test the prediction of genetic risk of one disease on the phenotype of the other.
Results: Asthma, allergic rhinitis, and eczema were associated with type 1 diabetes (odds ratio [95% confidence interval], 1.11 [1.07-1.15] for asthma, 1.23 [1.19-1.27] for allergic rhinitis, and 1.31 [1.26-1.35] for eczema). Familial coaggregation was only detected for asthma or allergic rhinitis, not for eczema. Linkage disequilibrium score regression and polygenic risk score analysis yielded little evidence for a genetic overlap.
Conclusions: Allergic diseases and type 1 diabetes seem to co-occur in individuals. For asthma and allergic rhinitis, this association existed also between relatives indicating a shared etiology but was not evident for eczema. No strong signals of a genetic overlap using molecular genetic approaches were uncovered.
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http://dx.doi.org/10.1016/j.jacig.2025.100519 | DOI Listing |
Nephrol Dial Transplant
September 2025
Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Background: We investigated circulating protein profiles and molecular pathways among various chronic kidney disease (CKD) etiologies to study its underlying molecular heterogeneity.
Methods: We conducted a proteomic biomarker analysis in the DAPA-CKD trial recruiting adults with and without type 2 diabetes with an eGFR of 25 to 75 mL/min/1.73m2 and a UACR of 200 to 5000 mg/g.
JAMA Netw Open
September 2025
Division of Cardiology, Department of Internal Medicine, New Taipei Municipal TuCheng Hospital, New Taipei, Taiwan.
Importance: The cardiovascular benefits of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may vary by body mass index (BMI), but evidence on BMI-specific outcomes remains limited.
Objective: To investigate the associations of GLP-1 RA use with cardiovascular and kidney outcomes across BMI categories in patients with type 2 diabetes.
Design, Setting, And Participants: This retrospective cohort study used the Chang Gung Research Database, a clinical dataset covering multiple hospitals in Taiwan.
JAMA Pediatr
September 2025
Diabetes Research Envisioned and Accomplished in Manitoba (DREAM) Research Theme, Children's Hospital Research Institute of Manitoba, Winnipeg, Canada.
Importance: Youth living with type 1 diabetes (T1D) are increasingly choosing automated insulin delivery (AID) systems to manage their blood glucose. Few systematic reviews meta-analyzing results from randomized clinical trials (RCTs) are available to guide decision-making.
Objective: To study the association of prolonged AID system use in an outpatient setting with measures of glucose management and quality of life in youth with T1D.
Nutr Health
September 2025
Independent researcher, Rome, Italy.
Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.
View Article and Find Full Text PDFCell Mol Biol (Noisy-le-grand)
September 2025
M-DT1, Roquefort-les Pins, France.
To date, the closed-loop system represents the best commercialized management of type 1 diabetes. However, mealtimes still require carbohydrate estimation and are often associated with postprandial hyperglycemia which may contribute to poor metabolic control and long -term complications. A multicentre, prospective, non-interventional clinical trial was designed to determine the effectiveness of a novel algorithm to predict changes in blood glucose levels two hours after a usual meal.
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