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Objective: The American Diabetes Association (ADA) guidelines recommend A1C testing schedules for patients with type 2 diabetes; however, level of real-world guideline adherence remains unclear. The current study evaluated A1C testing frequency and its association with glycemic control and cardiovascular outcomes.
Methods: A retrospective study was conducted utilizing Aetna's Enterprise Data Warehouse. Adult patients with a medical claim for type 2 diabetes in 2017 (index date) were included. Patients had continuous enrollment through December 2019 and ≥1 reported A1C measurement from 2017 to 2019. Follow-up was up to 36 months post-index date.
Results: Of the 112,572 eligible patients, 50.0% were female and median age was 70 years; 32.9% of patients with controlled baseline A1C (<8%, 64 mmol/mol) received less than the 2 tests/year recommended by the ADA, while 60.6% of patients with uncontrolled baseline A1C received less than the quarterly testing recommended by the ADA. More frequent testing was associated with age (65-75 years), uncontrolled baseline A1C and presence of comorbidities. In separate multivariable models, 2-3 A1C tests/year were associated with greater likelihood of A1C < 8% (64 mmol/mol) vs. <2 tests/year (OR = 1.07, 95% confidence interval [CI] 1.02-1.12), while >3 tests/year was associated with a modestly increased risk of cardiovascular events vs. <2 tests/year (OR = 1.08, 95% CI 1.01-1.15).
Conclusions: A large proportion of type 2 diabetes patients were not tested per guideline recommendations. The relationship between A1C testing frequency and glycemic control was inconsistent, though there was a significant association between more frequent testing and experiencing a CV event.
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http://dx.doi.org/10.1080/03007995.2021.1965562 | DOI Listing |
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFSpiritual interventions, including meditation, prayer, mindfulness, and compassionate care, have gained increasing attention for their potential to enhance both psychological resilience and overall health. This systematic review and meta-analysis examined eight eligible studies conducted across the USA, Europe, and China to assess the impact of such interventions on key outcomes, namely anxiety reduction, quality of life, chronic disease symptom management, and patient satisfaction. Seven studies contributed quantitative data.
View Article and Find Full Text PDFBMJ Open Diabetes Res Care
September 2025
NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK.
Introduction: Frequent glycated hemoglobin A1c (HbA1c) monitoring is recommended in individuals with type 2 diabetes mellitus (T2D). We aimed to identify distinct, long-term HbA1c trajectories following a T2D diagnosis and investigate how these glycemic control trajectories were associated with health-related traits and T2D complications.
Research Design And Methods: A cohort of 12,435 unrelated individuals of European ancestry with T2D was extracted from the UK Biobank data linked to primary care records.
Cureus
July 2025
Medicine and Surgery, Omdurman Islamic University, Omdurman, SDN.
Sickle cell disease is characterized by various forms of hemoglobin that interfere with hemoglobin A1c (HbA1c) testing, which is commonly used to diagnose and monitor diabetes. This interference puts patients with sickle cell disease at risk of inaccurate monitoring and misdiagnosis due to improperly planned HbA1c testing. Despite awareness of these issues, there is still disagreement regarding the most appropriate method of measuring HbA1c in patients with sickle cell disease, along with a lack of clear guidance on using fructosamine as an alternative marker in patients with diabetes and sickle cell disease.
View Article and Find Full Text PDFJ Arthroplasty
August 2025
Department of Orthopaedic Surgery & Rehabilitation, Loyola University Health System, Maywood, IL, USA.
Background: Large national databases have enabled extensive outcomes research in arthroplasty. Their vast sample sizes, however, raise concerns regarding the identification of statistically significant, but clinically meaningless associations. We hypothesized that completely random pairings between database variables, without any clinical rationale, would still frequently yield statistically significant associations, driven purely by sample size rather than any meaningful relationship.
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