Stricter control of risk factors has been pursued as a compelling strategy to mitigate cardiovascular events (CVE) in type 2 diabetes (T2D) individuals. However, the achievement rate of the recommended goals has remained low in clinical practice. This study investigated the 2019 ESC guideline recommendation attainment among T2D individuals enrolled in a national cohort held in Brazil. Data from 1030 individuals (mean age: 58 years old; 54% male; mean T2D duration: 9.7 years) were analyzed. The control rates were 30.6% for SBP, 18.8% for LDL-C, and 41% for A1c, and only 3.2% of the study participants met all three targets. Statins and high-intensity lipid-lowering therapy prescription rates were 45% and 8.2%, respectively. Longer T2D duration and those at higher CV risk were less likely to be controlled. Longer diabetes duration and higher CV risk were inversely related to the chance of achieving the recommended targets. Treatment escalation using conventional therapies would be sufficient to gain optimal control in most of the study sample. In conclusion, a minimal proportion of T2D individuals comply with guidelines-oriented CV prevention targets. Given the significant burden of the disease, and the substantial effect size predicted for these therapies, bridging this gap between guidelines and clinical practice should be considered an urgent call to public health managers.
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http://dx.doi.org/10.3390/diagnostics12040814 | DOI Listing |
Physiol Rep
January 2025
Department of Kinesiology, School of Education and Human Development, University of Virginia, Charlottesville, Virginia, USA.
Type 2 diabetes (T2D) is a common metabolic disorder in which only 25% of patients meet management targets. While the primary care setting is positioned to provide lifestyle management education, studies are lacking which integrate behavior interventions in this setting utilizing clinic staff. Thus, we evaluated a 90-day lifestyle intervention for management of glycemia at a family practice clinic administered by clinic medical assistants.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Endocrinology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Hepingli, Chaoyang District, 100029, Beijing, China.
Background: The prevalence of type 2 diabetes (T2D) and asthma is rising, yet evidence regarding the relationship between T2D and asthma, particularly in the context of genetic predispositions, remains limited.
Methods: This study utilized data from the UK Biobank longitudinal cohort, involving 388,775 participants. A polygenic risk score (PRS) for asthma was derived from genome-wide association studies summary.
PLoS One
January 2025
Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Previous studies reported that focusing on healthy lifestyle, especially high diet quality is necessary for preventing type 2 diabetes (T2D). This study investigated the association between the innovative index, the Global Diet Quality Score (GDQS), and the risk of Type 2 Diabetes incidence.
Methods: In this secondary analysis, we included elective adult participants (n = 5948) from the third and fourth survey of the Tehran Lipid and Glucose Study.
Tunis Med
January 2025
Department of Endocrinology and Internal Medicine, Fattouma Bourguiba Hospital, Monastir. Tunisia.
Unlabelled: Introduction-Aim: Type 2 diabetes (T2D) is a major public health problem. To succeed its management and prevent its complications, good therapeutic adherence must be ensured. The objectives of our work were to estimate the prevalence of poor therapeutic adherence in our patients and to identify its associated factors.
View Article and Find Full Text PDFDiabetes Metab Res Rev
January 2025
Division of Research, Kaiser Permanente Northern California, Pleasanton, California, USA.
Aims: Gestational diabetes mellitus (GDM) poses a significant risk for developing type 2 diabetes mellitus (T2D) and exhibits heterogeneity. However, understanding the link between different types of post-GDM individuals without diabetes and their progression to T2D is crucial to advance personalised medicine approaches.
Materials And Methods: We employed a discovery-based unsupervised machine learning clustering method to generate clustering models for analysing metabolomics, clinical, and biochemical datasets.
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