CGM with remote patient monitoring for pediatric patients newly diagnosed with type 1 diabetes is cost-effective compared to self-monitoring of blood glucose and to CGM alone. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-2825
Informative infographic showing hypercholesterolemia and lipid-lowering medication (LLM) in children with type 1 diabetes. It includes three sections: prevalence, treatment frequency, and target achievement. Prevalence shows 60.9% had high LDL cholesterol, with varied distribution of LDL levels. Treatment frequency indicates 3.5% received LLM. Target achievement shows 15.7% reached LDL cholesterol target with LLM, compared to 6.2% without treatment. Text notes that despite guidelines, only 7.3% received LLM, and over 90% of those untreated didn’t achieve cholesterol targets.
The prevalence of LDL-hypercholesterolemia in children with type 1 diabetes was high, but the treatment frequency remained low and treatment targets were not achieved. #DCare
Read here ➡️ doi.org/10.2337/dc25-2459
Flowchart depicting a study on weight loss paths over 24 months. It starts with randomization into two groups: Control and Caloric Restriction. Both lead to Initial Temporary Weight Loss at 12 months. Outcomes at 24 months include Weight Gain (>5%), Stable Weight, and Sustained Weight Loss, each with corresponding symbols indicating metabolic benefits: a minus (red), a plus (blue), and a double plus (green).
In CALERIE-2, weight regain after caloric restriction reversed gains in insulin sensitivity & IGF-1 bioavailability. Sustained weight reduced biological age and diabetes risk. #Aging #CR #DCare
Read here ➡️ doi.org/10.2337/dc25-1911
Global Reach🌍 . Practical Insight. Our journal portfolio delivers highly cited, mission-driven research that advances diabetes and obesity science and care worldwide. Now expanded with Diabetes, Obesity, and Cardiometabolic CARE® Explore: https://bit.ly/4lbA0QB
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This image is an infographic titled "Social Adversity Profiles are Differentially Related to Diabetes Status Over Time." It outlines the study's objective, design, and results related to Hispanic/Latino adults. Key elements include an objective to identify patterns of social adversity affecting diabetes status, and a design involving two visits and three levels of diabetes status: normoglycemia, prediabetes, and diabetes. The results section features a line chart comparing four social adversity profiles over various factors. The "SOL" logo and "Hispanic Community Health Study" are at the bottom.
Four profiles of social adversity (low adversity, social-educational strengths, acculturated and under-resourced, and high adversity) differentially predict worsening diabetes status across 12 years. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-2797
A diagram illustrates research on 5,229 participants from 16 U.S. cohorts. It shows a pregnant person over a U.S. map, with test tubes indicating serum or plasma taken during pregnancy. Several chemicals are listed: PFOA, PFOS, PFHxS, PFNA, PFDA, MeFOSAA. An arrow points to "Gestational diabetes" and "Fasting glucose" with a question mark. The Environmental influences on Child Health Outcomes (ECHO) logo appears on the right.
In a large, pooled sample of US pregnant women from the NIH ECHO Cohort Consortium, greater concentrations of PFAS were not associated with higher prevalence of GDM. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-1340
The image shows three line graphs comparing hazard ratios of Type 2 Diabetes risk to standardized mtDNA copy number across different age groups from two studies: KARE and UKB. Each panel represents a different age group: 40-55 years, 56-65 years, and 65+ years. The top graphs highlight U-shaped curves for each study, with KARE in blue and UKB in red. The bottom graph shows a shaded area with a downward trend in mtDNA-CN over age. A text box emphasizes the coincidence of the U-shaped relationship with the decline in mtDNA-CN.
Study finds a U-shaped link between blood mtDNA copy number and type 2 diabetes risk. Both low and high levels raise risk in the young, challenging prior linear views. #DCare
Read here ➡️ doi.org/10.2337/dc25-2198
@American Diabetes Association
The image is an infographic titled "Machine Learning Models to Predict Type 2 Diabetes Development Among Youth With Prediabetes." It shows a flowchart of a study by a large Midwest health system with 532 participants. The flowchart includes: predictor set 1, containing variables like age, race, BMI, and HbA1c; predictor set 2, including area deprivation index (ADI); and classification using logistic regression. A comparison shows a higher AUC (0.73) with ADI. Additional notes mention utilizing electronic medical records and predictive features.
A new study shows adding a socioeconomic maker to HbA1c improves a prediction of which youth with prediabetes are more likely to develop type 2 diabetes in the next year. #Dcare
Read here ➡️ doi.org/10.2337/dc25-3060
This flowchart depicts a study on screening for Cystic Fibrosis-Related Diabetes (CFRD) using a 50-gram Oral Glucose Challenge Test (GCT). It starts with 185 participants, of which 51% had normal glucose tolerance (NGT), and 49% had elevated GCT results (≥147 mg/dL). Of those elevated, 12 underwent an Oral Glucose Tolerance Test (OGTT), revealing 9 had CFRD. The chart includes icons representing sets of ten participants and uses arrows to illustrate the screening process. A legend clarifies the symbols used for NGT.
A random 1-hour, 50g oral glucose challenge test is an effective screening tool for cystic fibrosis-related diabetes and can reduce the need for annual OGTT by 50% in people with cystic fibrosis. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-2806
This image is a flowchart and infographic detailing a study on polygenic risk scores for diabetes. It begins with genetic risk segmentation of 24,025 participants from the Malmö Diet and Cancer Study. The flow includes the segmentation into diabetes types: SAID, MARD, MOD, and SIDD, which are associated with coronary artery disease. It depicts genetic instruments and Mendelian randomization, leading to exposure and outcomes, including any ischemic stroke, large artery stroke, coronary artery disease, myocardial infarction, and coronary revascularization. The right side displays a bar graph showing odds ratios for these conditions.
The current study demonstrates that a high genetic susceptibility for moderate obesity-related diabetes can predict the onset of both diabetes and coronary artery disease. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-1711
This image is an infographic titled "Associations of genetically predicted liability to type 2 diabetes (T2D) with circulating metabolic biomarkers." It is divided into sections. On the left, there's a description of the Mexico City Prospective Study detailing the study population of 125,257 participants and the focus on T2D genetic risk score. On the right, various biomarkers associated with T2D are listed, including ketone bodies, choline, glycolysis components, and fatty acids like VLDL and LDL particles. The infographic uses arrows and boxes to organize the information.
Genetically-predicted liability to T2D is associated with widespread changes in the circulating metabolome in Mexican adults possibly driving high T2D-associated vascular risk in this population. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-2933
The image is an infographic titled "Early and Sustained Glycemic Management After Gestational Diabetes Diagnosis May Mitigate the Risk of Childhood Obesity in the Offspring." It presents data comparing 191,594 individuals without gestational diabetes mellitus (GDM) and 14,870 individuals with GDM who followed glycemic management strategies. A line graph shows four glycemic management trajectories and their impact on offspring's BMI and obesity risk by age 10. Key findings include a decrease in childhood obesity risk with improved glycemic control during pregnancy. Text boxes highlight statistical associations and findings regarding BMI and obesity risk based on glycemic management.
Early and sustained glycemic management among mothers with gestational diabetes is associated with lower risk of childhood obesity, suggesting a potential window for prevention starting in utero. #DCare #Article
Read here ➡️ doi.org/10.2337/dc25-1643
Community Voices: Hospital noise & light create measurable stress that compromises healing. Drawing on Temple Grandin's sensory design work, this essay argues for auditing environmental stressors as patient safety. #DCare #Diabetes
Read here ➡️ doi.org/10.2337/dca25-0144
🚀 NEW: Joint ADA-EASD position statement on individualizing diabetes technology!
There's no one-size-fits-all approach. Everyone deserves access to the right tech for their needs.
Let's bridge the digital divide together. #DCare #EASD
📖 FREE to read: doi.org/10.2337/dci26-0018
Global Reach🌍 . Practical Insight. Our journal portfolio delivers highly cited, mission-driven research that advances diabetes and obesity science and care worldwide. Now expanded with Diabetes, Obesity, and Cardiometabolic CARE® Explore: https://bit.ly/4lbA0QB
#Portfolio #DOCM #Diabetes #DCare
