Cardiovascular disease prevention and therapy in women with Type 2 diabetes

2021 ◽  
Author(s):  
Camilla Cocchi ◽  
Francesca Coppi ◽  
Alberto Farinetti ◽  
Anna Vittoria Mattioli
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Cardiovascular Risk ◽  
Risk Category ◽  
Cvd Risk ◽  
Men And Women ◽  
Delay In Diagnosis

Cardiovascular disease (CVD) is the leading cause of death among men and women, although women are usually underdiagnosed and experience a delay in diagnosis. This also occurs in women with type 2 diabetes mellitus, despite the fact that diabetes is recognized as a major cardiovascular risk factor. Several factors influence the gap between diagnosis and treatment of cardiovascular disease in women: lack of perception of cardiovascular risk, effects of sex-related risk factors and the action of drugs in women. Women with Type 2 diabetes mellitus are more likely to be assigned a lower CVD risk category and to receive lifestyle counseling as well as less intensive CVD therapy compared with men. The present narrative review aims to analyze the risk of CVD in women with Type 2 diabetes mellitus and whether there is a difference between men and women in the efficacy of SGLT-2 inhibitors, new hypoglycemic drugs.

Assessing 10-Year Cardiovascular Disease Risk in Malaysians With Type 2 Diabetes Mellitus: Framingham Cardiovascular Versus United Kingdom Prospective Diabetes Study Equations

2019 ◽  
Vol 31 (7) ◽  
pp. 622-632
Author(s):  
Sheng Qian Yew ◽  
Yook Chin Chia ◽  
Michael Theodorakis
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
United Kingdom ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cvd Risk ◽  
Fatal Stroke

In this study, we evaluated the performance of the Framingham cardiovascular disease (CVD) and the United Kingdom Prospective Diabetes Study (UKPDS) risk equations to predict the 10-year CVD risk among type 2 diabetes mellitus (T2DM) patients in Malaysia. T2DM patients (n = 660) were randomly selected, and their 10-year CVD risk was calculated using both the Framingham CVD and UKPDS risk equations. The performance of both equations was analyzed using discrimination and calibration analyses. The Framingham CVD, UKPDS coronary heart disease (CHD), UKPDS Fatal CHD, and UKPDS Stroke equations have moderate discrimination (area under the receiver operating characteristic [aROC] curve = 0.594-0.709). The UKPDS Fatal Stroke demonstrated a good discrimination (aROC curve = 0.841). The Framingham CVD, UKPDS Stroke, and UKPDS Fatal Stroke equations showed good calibration ( P = .129 to .710), while the UKPDS CHD and UKPDS Fatal CHD are poorly calibrated ( P = .035; P = .036). The UKPDS is a better prediction equation of the 10-year CVD risk among T2DM patients compared with the Framingham CVD equation.

scholarly journals Knowledge and Perceptions towards Cardiovascular Disease Prevention among Patients with Type 2 Diabetes Mellitus: A Review of Current Assessments and Recommendations

2020 ◽  
Vol 16 ◽  
Author(s):  
Mohamed Hassan Elnaem ◽  
Mahmoud E Elrggal ◽  
Nabeel Syed ◽  
Atta Abbas Naqvi ◽  
Muhammad Abdul Hadi
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Original Research ◽  
Knowledge Gaps ◽  
Cvd Risk ◽  
Cvd Prevention

Introduction: Patients with type 2 diabetes mellitus (T2DM) are at significantly higher risk of developing cardiovascular disease (CVD). There is scarcity of literature reviews that describes and summarises T2DM patients' knowledge and perception about CVD prevention. Objectives: To describe and summarise the assessment of knowledge and perceptions about CVD risk and preventive approaches among patients with T2DM. Methods: A scoping review methodology was adopted, and three scientific databases, Google Scholar, Science Direct and PubMed were searched using predefined search terms. A multistage screening process that considered relevancy, publication year (2009-2019), English language, and article type (original research) was followed. We formulated research questions focused on the assessment of levels of knowledge and perceptions of the illness relevant to CVD prevention and the identification of associated patients' characteristics. Results: A total of 16 studies were included. Patients were not confident to identify CVD risk and other clinical consequences that may occur in the prognostic pathway of T2DM. Furthermore, patients were less likely to identify all CV risk factors indicating a lack of understanding of the multi-factorial contribution of CVD risk. Patients' beliefs about medications were correlated with their level of adherence to medications for CVD prevention. Many knowledge gaps were identified, including the basic disease expectations at the time of diagnosis, identification of individuals' CVD risk factors and management aspects. Knowledge and perceptions were affected by patients' demographic characteristics, e.g., educational level, race, age, and area of residence. Conclusion: There are knowledge gaps concerning the understanding of CVD risk among patients with T2DM. The findings necessitate educational initiatives to boost CVD prevention among patients with T2DM. Furthermore, these should be individualised based on patients' characteristics and knowledge gaps, disease duration and estimated CVD risk.

scholarly journals Acetylcarnitine Is Associated With Cardiovascular Disease Risk in Type 2 Diabetes Mellitus

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuo Zhao ◽  
Ming-Li Liu ◽  
Bing Huang ◽  
Fu-Rong Zhao ◽  
Ying Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Disease Risk ◽  
Tertiary Care ◽  
Short Chain ◽  
Forward Selection ◽  
Cvd Risk

ObjectiveThis study aimed to identify the association between specific short-chain acylcarnitines and cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM).MethodWe retrieved 1,032 consecutive patients with T2DM who meet the inclusion and exclusion criteria from the same tertiary care center and extracted clinical information from electronic medical records from May 2015 to August 2016. A total of 356 T2DM patients with CVD and 676 T2DM patients without CVD were recruited. Venous blood samples were collected by finger puncture after 8 h fasting and stored as dried blood spots. Restricted cubic spline (RCS) analysis nested in binary logistic regression was used to identify possible cutoff points and obtain the odds ratios (ORs) and 95% confidence intervals (CIs) of short-chain acylcarnitines for CVD risk in T2DM. The Ryan–Holm step-down Bonferroni procedure was performed to adjust p-values. Stepwise forward selection was performed to estimate the effects of acylcarnitines on CVD risk.ResultThe levels of C2, C4, and C6 were elevated and C5-OH was decreased in T2DM patients with CVD. Notably, only elevated C2 was still associated with increased CVD inT2DM after adjusting for potential confounders in the multivariable model (OR = 1.558, 95%CI = 1.124–2.159, p = 0.008). Furthermore, the association was independent of previous adjusted demographic and clinical factors after stepwise forward selection (OR = 1.562, 95%CI = 1.132–2.154, p = 0.007).ConclusionsElevated C2 was associated with increased CVD risk in T2DM.

scholarly journals Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Haiyun Chu ◽  
Lu Chen ◽  
Xiuxian Yang ◽  
Xiaohui Qiu ◽  
Zhengxue Qiao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Integrated Treatment ◽  
Health Improvement ◽  
Cvd Risk ◽  
Test Set

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.

scholarly journals A Study of QTc Prolongation as a Screening Tool for Cardiovascular Disease in Type 2 Diabetes Patients of Karad, India

2021 ◽  
Vol 10 (34) ◽  
pp. 2934-2938
Author(s):  
Sanjay Tukaram Thorat ◽  
Parikshit Gajanan Mankar ◽  
Niyati Kaila ◽  
Avanti Jaywant Damle ◽  
Radhika Ratanlal Bajaj ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Cardiovascular Risk ◽  
Qt Interval ◽  
Glycated Haemoglobin ◽  
Qtc Interval ◽  
Qtc Prolongation

BACKGROUND The occurrence of QT interval prolongation is higher in subjects with type 2 diabetes mellitus (T2DM). Duration of QT interval corrected (QTc) for heart rate is independently related with severity of cardiovascular diseases in diabetics. This study was proposed to assess the QTc prolongation as a diagnostic tool for cardiovascular disease in T2DM patients. METHODS This study included 100 diabetic patients admitted in wards of a tertiary care center. A thorough clinical examination was carried out for all the patients. Patients were investigated for the fasting blood glucose level, glycated haemoglobin (HbA1c), lipid profile & electrocardiogram (ECG). Data was represented as percentage. Mean and standard deviation (SD) of quantitative variables were tabulated, t test was used for correlation and receiver operating characteristic (ROC) curve was used for evaluating area under curve. P < 0.05 was considered statistically significant. RESULTS Male preponderance was observed. All the study subjects had a prolonged period of diabetes with various metabolic complications. The area under the curve estimation of QTc > 400 ms with respect to HbA1c and duration of diabetes showed significant correlation between longer duration of diabetes and raised HbA1c associated with raised QTc interval (P < 0.05). CONCLUSIONS Diagnosis of prolonged QTc interval could be utilized for estimating cardiovascular risk in diabetes patients. It can be easily assessed on ECG besides being a noninvasive investigation which is also affordable in evaluating the cardiovascular risk in T2DM patients. KEY WORDS Blood Glucose, Cardiovascular Diseases, Electrocardiography, Glycated Haemoglobin A, Long QT Syndrome, Type 2 Diabetes Mellitus

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