Background: Diabetes is associated with an increased risk of several cancers; however, greater detection of cancer around the time of diabetes diagnosis may partly contribute to this relationship. The goal of the current study was to explore the temporal relationship between diabetes and cancer incidence.
Methods: The authors conducted a retrospective, population-based cohort study of >1 million adults living in Ontario, Canada to evaluate the association between diabetes diagnosis and the incidence of cancer in 3 time periods: within the 10 years before a diabetes diagnosis, within the first 3 months after a diabetes diagnosis, and from 3 months to 10 years after a diabetes diagnosis.
Results: Individuals with diabetes were significantly more likely to have been diagnosed with cancer within the 10 years before a diabetes diagnosis compared with individuals without diabetes (odds ratio, 1.23; 95% confidence interval [95% CI], 1.19-1.27). Cancer incidence also was found to be significantly higher in individuals with diabetes within the 3-month period after a diabetes diagnosis (hazard ratio, 1.62; 95% CI, 1.52-1.74), whereas the risk was not found to be elevated in the later period (hazard ratio, 0.97; 95% CI, 0.95-0.98). Similar trends were noted for individual cancers.
Conclusions: The results demonstrated that individuals with diabetes had a significantly higher risk of most cancers, which was limited to the time periods before and immediately after a diabetes diagnosis. The highest risk period was observed within the first 3 months after a diabetes diagnosis, suggesting a partial role of detection bias in the apparent relationship between diabetes and cancer. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2731-2738. © 2016 American Cancer Society.
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http://dx.doi.org/10.1002/cncr.30095 | DOI Listing |
J Pediatr Endocrinol Metab
January 2025
Division of Gastroenterology, Hepatology, & Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Objectives: The association of celiac disease (CD) in type 1 diabetes mellitus (T1DM) is well-established, yet variation exists in screening practices. This study measures the accuracy of early screening with tissue transglutaminase Immunoglobulin A (TTG-IgA) and endomysial antibody (EMA) in newly diagnosed T1DM.
Methods: This is a retrospective study of children with T1DM between 2013 and 2019 with early CD screening and follow-up.
Curr Obes Rep
January 2025
Section of Nutrition, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Purpose Of Review: To review evidence supporting human umbilical cord mesenchymal stem cells (UC-MSC) as an innovative model system advancing obesity precision medicine.
Recent Findings: Obesity prevalence is increasing rapidly and exposures during fetal development can impact individual susceptibility to obesity. UC-MSCs exhibit heterogeneous phenotypes associated with maternal exposures and predictive of child cardiometabolic outcomes.
Nat Rev Nephrol
January 2025
APHP, Reference Center for Rare Diseases of Calcium and Phosphate Metabolism, and Filière OSCAR, endo ERN and ERN BOND, Paris, France.
X-linked hypophosphataemia (XLH) is a rare metabolic bone disorder caused by pathogenic variants in the PHEX gene, which is predominantly expressed in osteoblasts, osteocytes and odontoblasts. XLH is characterized by increased synthesis of the bone-derived phosphaturic hormone fibroblast growth factor 23 (FGF23), which results in renal phosphate wasting with consecutive hypophosphataemia, rickets, osteomalacia, disproportionate short stature, oral manifestations, pseudofractures, craniosynostosis, enthesopathies and osteoarthritis. Patients with XLH should be provided with multidisciplinary care organized by a metabolic bone expert.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No.63 Wenhua Road, Shunqing District, Nanchong, 637000, China.
This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. Clinical and imaging data from 271 patients who had undergone enhanced CT scans after first-episode acute pancreatitis from March 2017-June 2023 were retrospectively analyzed. Patients were classified into PPDM-A (n = 109) and non-PPDM-A groups (n = 162), and split into training (n = 189) and testing (n = 82) cohorts at a 7:3 ratio.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China.
This study aims to develop a nomogram prediction model for assessing the cardiogenic composite endpoint, which includes intracardiac thrombosis (ICT) combined with heart failure (HF) in patients with non-compaction cardiomyopathy (NCM) patients. We retrospectively analyzed clinical data from NCM patients (January 2018 to May 2024), who were randomly assigned to training and validation cohorts. Independent predictors were identified using logistic regression, and a nomogram model was developed.
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