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1.
J Clin Med ; 13(8)2024 Apr 10.
Artigo em Inglês | MEDLINE | ID: mdl-38673469

RESUMO

Background: Individuals diagnosed with type 2 diabetes mellitus (T2DM) are more prone to experiencing severe cardiovascular (CV) events, often occurring at a younger age, due to a complex interplay of risk factors. T2DM diagnosis inherently classifies patients as belonging to a higher CV risk group. In light of the increased susceptibility to severe CV outcomes, our study aims to assess the distribution of CV risk categories and the attainment of therapeutic targets among Romanian patients diagnosed with T2DM. Methods: A cross-sectional analysis was performed, including 885 patients diagnosed with T2DM who were consecutively admitted to a secondary care hospital unit between January and July 2019. Data collection included demographics, lipid profile, glycated hemoglobin (HbA1c), blood pressure (BP), estimated glomerular filtration rate (eGFR), and medication specifics for T2DM and associated conditions. Patients were stratified into CV risk categories based on the ESC/EAS guidelines, encompassing moderate, high, and very high risk categories. The rationale for selecting these guidelines for CV risk categories was that they were current and provided best practice recommendations for T2DM patients during the cross-sectional evaluation. We assessed therapeutic target achievement rates for LDL-C, HbA1C, and BP for each CV risk category. Additionally, we examined utilization rates of statins and novel cardio- and reno-protective, non-insulin antidiabetic medications. Results: The group's average age was 62.9 ± 7.7 years and comprised 53.7% females. An average HbA1c level of 7.1 ± 1.3% was observed in the group. Within the cohort, 83% had hypertension, with a mean systolic BP of 132 ± 16.2 mm Hg and mean diastolic BP of 80 ± 9.6 mm Hg. Additionally, 64.6% of patients were obese, with a mean body mass index of 32.3 ± 5.3 kg/m2. Mean LDL-C levels varied across the different CV risk categories: 106.6 ± 35.6 mg/dL in the very high risk category, 113 ± 39.3 mg/dL in the high risk category, and 124.3 ± 38.3 mg/dL in the moderate risk category. Most treatment schemes included metformin (87.0%) and statins (67.0%), with variable use rates for other glucose-lowering and CV risk-modifying therapies. The percentage of patients using GLP-1 RAs was 8.1%, while 3.9% used SGLT2 inhibitors. Conclusions: Most Romanian patients with T2DM are at very high or high CV risk. Despite reaching glycemic control targets, most patients are not achieving the composite target, which includes, besides glycemic control, BP values and lipid profile. Many patients with T2DM are not benefiting from DM therapies with additional cardiorenal benefits or statins.

2.
Int J Mol Sci ; 25(7)2024 Mar 31.
Artigo em Inglês | MEDLINE | ID: mdl-38612699

RESUMO

Diabetes mellitus (DM), due to its long-term hyperglycemia, leads to the accumulation of advanced glycation end-products (AGEs), especially in the vessel walls. Skin autofluorescence (SAF) is a non-invasive tool that measures AGEs. DM patients have a rich dietary source in AGEs, associated with high oxidative stress and long-term inflammation. AGEs represent a cardiovascular (CV) risk factor, and they are linked with CV events. Our objective was to assess whether SAF predicts future CV events (CVE) by examining its association with other CV risk factors in patients with type 2 DM (T2DM). Additionally, we assessed the strengths and limitations of SAF as a predictive tool for CVE. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, we conducted a systematic review with CRD42024507397 protocol, focused on AGEs, T2DM, SAF, and CV risk. We identified seven studies from 2014 to 2024 that predominantly used the AGE Reader Diagnostic Optic tool. The collective number of patients involved is 8934, with an average age of 63. So, SAF is a valuable, non-invasive marker for evaluating CV risk in T2DM patients. It stands out as a CV risk factor associated independently with CVE. SAF levels are influenced by prolonged hyperglycemia, lifestyle, aging, and other chronic diseases such as depression, and it can be used as a predictive tool for CVE.


Assuntos
Biomarcadores , Doenças Cardiovasculares , Diabetes Mellitus Tipo 2 , Produtos Finais de Glicação Avançada , Pele , Humanos , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/complicações , Doenças Cardiovasculares/etiologia , Doenças Cardiovasculares/metabolismo , Doenças Cardiovasculares/diagnóstico , Pele/metabolismo , Produtos Finais de Glicação Avançada/metabolismo , Medição de Risco/métodos , Fatores de Risco de Doenças Cardíacas , Imagem Óptica/métodos , Fatores de Risco
3.
J Pers Med ; 12(10)2022 Oct 05.
Artigo em Inglês | MEDLINE | ID: mdl-36294795

RESUMO

With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.

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