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Background: Misalignment of the endogenous circadian system may contribute to the risk of type 2 diabetes. This systematic review and meta-analysis examined the association between clock gene polymorphisms and glycemic parameters and type 2 diabetes.
Methods: Embase, Medline, and Web of Science databases were searched from inception to August 20, 2024. Empirical studies examining the association between core clock gene polymorphisms and type 2 diabetes and glycemic parameters, and studies examining non-core clock genes with information on environmental factors were included. A multi-level meta-analytical approach was used, and a weighted odds ratio was reported (PROSPERO, CRD42022337706).
Results: In total, 37 studies comprising 535,063 participants were included. CRY2 was associated with higher fasting blood glucose (OR: 1.07, 95 % CI: 1.03-1.11) and impaired glucose tolerance (OR: 1.02, CI: 1.00-1.04). Polymorphisms in MTNR1B were associated with a greater risk of type 2 diabetes. CLOCK was associated with lower risk of type 2 diabetes (OR: 0.94, CI: 0.89-1.00), and PER3 was associated with lower fasting insulin (OR: 0.94, CI: 0.91-0.97) and lower risk of insulin resistance (OR: 0.92, CI: 0.88-0.95). These associations reflect pooled variant-level effects within genes, and the effects of certain variants were modified by diet, alcohol consumption, physical activity, sleep, and length of daylight.
Conclusions: Specific polymorphisms in circadian genes, including CRY2, MTNR1B, CLOCK, and PER3, were associated with glycemic parameters and type 2 diabetes risk. These associations may be influenced by lifestyle and environmental factors, and interventions targeting circadian alignment could potentially modify diabetes risk, although further research is needed.
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http://dx.doi.org/10.1016/j.dsx.2025.103284 | 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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