Risk Of Cardiovascular Disease In Diabetic Patients Research Articles

Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients

The prevalence of cardiovascular disease (CVD) is a serious public health issue, and it is of particular concern for people with diabetes because of the increased risk of cardiovascular problems that these people experience. In this study, we suggest a novel method of Ontological Data Mining (ODM) for identifying the origins of CVD risk in diabetic patients. We want to improve the readability and precision of prediction models by incorporating domain knowledge and semantic linkages into the data mining process. In this work, we examine a large dataset consisting of 70,000 patient records with 11 attributes, all of which are derived through a thorough clinical history and physical examination. As part of our methodology, we use decision trees, support vector machines (SVMs), and gradient boosting (GB). The distribution patterns of critical variables with respect to CVD outcomes can be better understood through the use of visual representations such as box plots, distributional plots, and pie charts. Finding significant connections and causal relationships between risk factors and CVD outcomes is made possible by the suggested ODM method. Our research has promising implications for bettering the treatment of patients with diabetes, facilitating targeted interventions, and enhancing risk assessment and preventative methods for cardiovascular disease.

Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients

Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients.

Additive Effect of Sarcopenia and Anemia on the 10-Year Risk of Cardiovascular Disease in Patients with Type 2 Diabetes

Objective To investigate the association between sarcopenia and anemia and the 10-year cardiovascular disease risk in diabetic patients. Methods A cross-sectional study was conducted involving 4673 hospitalized patients (2271 men and 2402 women) with type 2 diabetes mellitus, with an average age of 60.66 ± 11.93 years, of whom 542 were followed up for a median follow-up period of 24 months. All participants underwent body composition measurements, and they were grouped by sex and presence of sarcopenia using the Framingham risk model to assess their 10-year cardiovascular risk. According to the changes in the cardiovascular risk during follow-up, the patients were divided into four groups: low-low, low-high, high-low, and high-high. Results The prevalence of anemia was higher in the sarcopenia group than in the nonsarcopenia group (11.5% vs. 24.1% for men, P < 0.001; 13.9% vs. 19.7% for women, P < 0.05), and the difference remained significant after adjusting for confounders. Patients with sarcopenia and without anemia had a 46.2% increased risk of high 10-year cardiovascular disease (CVD) (odds ratio (OR) = 1.462, 95% confidence interval (CI) 1.085–1.972, P = 0.013), and the risk was twofold higher in patients with sarcopenia and anemia than in those without (OR = 3.283, 95% CI 2.038–5.289, P < 0.001). In follow-up studies, sarcopenia was associated with an increased risk of CVD at 10 years, and a reduction in appendicular skeletal muscle mass index independently predicted the increased risk of CVD. Conclusion Sarcopenia is associated with an increased risk of anemia, and the presence of both has an additive effect on the 10-year CVD risk in patients with type 2 diabetes. Loss of muscle mass can independently predict an increased CVD risk in diabetic patients.

The impact of coenzyme Q10 on metabolic and cardiovascular disease profiles in diabetic patients: A systematic review and meta-analysis of randomized controlled trials.

AimsCoenzyme Q10 (CoQ10) is well known for its beneficial effects in cardiovascular disease (CVD); however, reported evidence has not been precisely synthesized to better inform on its impact in protecting against cardiovascular‐related complications in diabetic patients.Materials and MethodologyThe current meta‐analysis included randomized controlled trials published in the past 5 years reporting on the effect of CoQ10 on metabolic and CVD‐related risk profiles in individuals with diabetes or metabolic syndrome. We searched electronic databases such as MEDLINE, Cochrane Library, Scopus and EMBASE for eligible studies. In addition to assessing the risk of bias and quality of evidence, the random and fixed‐effect models were used to calculate the standardized mean difference and 95% confidence intervals for metabolic parameters and CVD outcomes.ResultsOverall, 12 studies met the inclusion criteria, enrolling a total of 650 patients. Although CoQ10 supplementation did not statistically affect all metabolic profiles measured, it significantly reduced CVD‐risk‐related indexes such as total cholesterol and low‐density lipoprotein (LDL) levels in diabetic patients when compared to those on placebo [SMD = 0.13, 95% CI (0.03; 0.23), Chi2 = 43.62 and I 2 = 29%, P = .07].ConclusionsThe overall results demonstrated that supplementation with CoQ10 shows an enhanced potential to lower CVD risk in diabetic patients by reducing total cholesterol and LDL. Moreover, the beneficial effects of CoQ10 in lowering the CVD risk are associated with its ameliorative properties against oxidative stress and improving endothelial health.

Predictors of all-cause and cardiovascular-specific mortality in type 2 diabetes: A competing risk modeling of an Iranian population.

Background:In Asian population, diabetes mellitus is increasing and has become an important health problem in recent decades. In Iran, cardiovascular disease (CVD) accounts for nearly 46% of the total costs spent for diabetes-associated diseases. Because individuals with diabetes have highly increased CVD risk compared with normal individuals, it is important to diagnosis factors that may increase CVD risk in diabetic patients. The study objective was to identify predictors associated with CVD mortality in patients with type 2 diabetes (T2D) and to develop a prediction model for cardiovascular (CV)-death using a competing risk approach.Materials and Methods:The study population consisted of 2638 T2D (male = 1110, female = 1528) patients aged ≥35 years attending from Endocrine and Metabolism Research Center in Isfahan for a mean follow-up period of 12 years; predictors for different cause of death were evaluated using cause specific Cox proportional and subdistribution hazards models.Results:Based on competing modeling, the increase in blood pressure (BP) (spontaneously hypertensive rats [SHR]: 1.64), cholesterol (SHR: 1.55), and duration of diabetes (SHR: 2.03) were associated with CVD-death. Also, the increase in BP (SHR: 1.85), fasting blood sugar (SHR: 2.94), and duration of diabetes (SHR: 1.68) were associated with other death (consist of cerebrovascular accidents, cancer, infection, and diabetic nephropathy).Conclusions:This finding suggests that more attention should be paid to the management of CV risk in type 2 diabetic patients with high cholesterol, high BP, and long diabetes duration.

Four-Year Analysis of Cardiovascular Disease Risk Factors, Depression Symptoms, and Antidepressant Medicine Use in the Look AHEAD (Action for Health in Diabetes) Clinical Trial of Weight Loss in Diabetes

OBJECTIVETo study the association of depressive symptoms or antidepressant medicine (ADM) use with subsequent cardiovascular disease (CVD) risk factor status in the Look AHEAD (Action for Health in Diabetes) trial of weight loss in type 2 diabetes.RESEARCH DESIGN AND METHODSParticipants (n = 5,145; age [mean ± SD] 58.7 ± 6.8 years; BMI 35.8 ± 5.8 kg/m2) in two study arms (intensive lifestyle [ILI], diabetes support and education [DSE]) completed the Beck Depression Inventory (BDI), reported ADM use, and were assessed for CVD risk factors at baseline and annually for 4 years. Risk factor–positive status was defined as current smoking, obesity, HbA1c >7.0% or insulin use, and blood pressure or cholesterol not at levels recommended by expert consensus panel or medicine to achieve recommended levels. Generalized estimating equations assessed within-study arm relationships of elevated BDI score (≥11) or ADM use with subsequent year CVD risk status, controlled for demographic variables, CVD history, diabetes duration, and prior CVD risk status.RESULTSPrior year elevated BDI was associated with subsequent CVD risk factor–positive status for the DSE arm (A1C [odds ratio 1.30 (95% CI 1.09–1.56)]; total cholesterol [0.80 (0.65–1.00)]; i.e., protective from high total cholesterol) and the ILI arm (HDL [1.40 (1.12–1.75)], triglyceride [1.28 (1.00–1.64)]). Prior year ADM use predicted subsequent elevated CVD risk status for the DSE arm (HDL [1.24 (1.03–1.50)], total cholesterol [1.28 (1.05–1.57)], current smoking [1.73 (1.04–2.88)]) and for the ILI arm (A1C [1.25 (1.08–1.46)], HDL [1.32 (1.11–1.58)], triglycerides [1.75 (1.43–2.14)], systolic blood pressure [1.39 (1.11–1.74)], and obesity [1.46 (1.22–2.18)]).CONCLUSIONSAggressive monitoring of CVD risk in diabetic patients with depressive symptoms or who are treated with ADM may be warranted.

Lipoprotein(a) and cardiovascular disease in diabetic patients.

Lipoprotein(a) (Lp[a]) is a LDL-like particle consisting of an ApoA moiety linked to one molecule of ApoB(100). Recent data from large-scale prospective studies and genetic association studies provide highly suggestive evidence for a potentially causal role of Lp(a) in affecting risk of cardiovascular disease (CVD) in general populations. Patients with Type 2 diabetes display clustered metabolic abnormalities and elevated risk of CVD. Lower plasma Lp(a) levels were observed in diabetic patients in several recent studies. Epidemiology studies of Lp(a) and CVD risk in diabetic patients generated inconsistent results. We recently found that Lp(a)-related genetic markers did not predict CVD in two diabetic cohorts. The current data suggest that Lp(a) may differentially affect cardiovascular risk in diabetic patients and in the general population. More prospective studies, Mendelian randomization analysis and functional studies are needed to clarify the causal relationship of Lp(a) and CVD in diabetic patients.

Lipid measures for prediction of incident cardiovascular disease in diabetic and non-diabetic adults: results of the 8.6 years follow-up of a population based cohort study

BackgroundDiabetes is a strong risk factor for cardiovascular disease (CVD).The relative role of various lipid measures in determining CVD risk in diabetic patients is still a subject of debate. We aimed to compare performance of different lipid measures as predictors of CVD using discrimination and fitting characteristics in individuals with and without diabetes mellitus from a Middle East Caucasian population.MethodsThe study population consisted of 1021 diabetic (men = 413, women = 608) and 5310 non-diabetic (men = 2317, women = 2993) subjects, aged ≥ 30 years, free of CVD at baseline. The adjusted hazard ratios (HRs) for CVD were calculated for a 1 standard deviation (SD) change in total cholesterol (TC), log-transformed triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), non-HDL-C, TC/HDL-C and log-transformed TG/HDL-C using Cox proportional regression analysis. Incident CVD was ascertained over a median of 8.6 years of follow-up.ResultsA total of 189 (men = 91, women = 98) and 263(men = 169, women = 94) CVD events occurred, in diabetic and non-diabetic population, respectively. The risk factor adjusted HRs to predict CVD, except for HDL-C, TG and TG/HDL-C, were significant for all lipid measures in diabetic males and were 1.39, 1.45, 1.36 and 1.16 for TC, LDL-C, non- HDL-C and TC/HDL-C respectively. In diabetic women, using multivariate analysis, only TC/HDL-C had significant risk [adjusted HR1.31(1.10-1.57)].Among non-diabetic men, all lipid measures, except for TG, were independent predictors for CVD however; a 1 SD increase in HDL-C significantly decreased the risk of CVD [adjusted HR 0.83(0.70-0.97)].In non-diabetic women, TC, LDL-C, non-HDL-C and TG were independent predictors.There was no difference in the discriminatory power of different lipid measures to predict incident CVD in the risk factor adjusted models, in either sex of diabetic and non-diabetic population.ConclusionOur data according to important test performance characteristics provided evidence based support for WHO recommendation that along with other CVD risk factors serum TC vs. LDL-C, non-HDL-C and TC/HDL-C is a reasonable lipid measure to predict incident CVD among diabetic men. Importantly, HDL-C did not have a protective effect for incident CVD among diabetic population; given that the HDL-C had a protective effect only among non- diabetic men.

Adiponectin Genetic Variability, Plasma Adiponectin, and Cardiovascular Risk in Patients With Type 2 Diabetes

Adiponectin is an adipocyte-derived hormone that has shown anti-inflammatory and antiatherogenic effects. We assessed the associations of variants in the adiponectin gene (ADIPOQ) with circulating adiponectin levels and cardiovascular risk among women with type 2 diabetes. Of 989 diabetic women from the Nurses' Health Study, 285 developed cardiovascular disease (CVD) during follow-up through 2000. We genotyped five ADIPOQ polymorphisms in the CVD case and control subjects. A promoter polymorphism -11365C-->G was significantly associated with lower plasma adiponectin levels (P = 0.004). The homozygotes of allele -4034C were significantly associated with approximately 60% increased cardiovascular risk (odds ratio 1.62 [95% CI 1.07-2.45]). Adjustment for age, BMI, and other covariates did not appreciably change the associations. In addition, a common haplotype possessing allele +276T (CAATT) was associated with a significantly lower CVD risk than the most common haplotype (CAATG) (0.70 [0.50-0.98]). In our meta-analysis of 827 CVD case and 1,887 CVD-free control subjects, polymorphism +276G-->T was significantly associated with approximately 45% (20-62%) decreased CVD risk under a recessive inheritance mode in diabetic patients. In conclusion, ADIPOQ promoter polymorphism -11365C-->G was associated with plasma adiponectin levels, whereas polymorphisms -4034A-->C and +276G-->T were associated with CVD risk in diabetic patients.

The influence of type 2 diabetes on fibrin structure and function

The precise mechanisms underlying the increased risk of cardiovascular disease (CVD) in type 2 diabetes are unclear. Fibrin clot structure has been related to CVD risk in the general population. We therefore assessed this in type 2 diabetic patients as a potential mechanism whereby diabetes influences CVD risk. Fibrin clots were formed from fibrinogen purified from 150 subjects with type 2 diabetes and varying degrees of glycaemic control (assessed by HbA1c), and from 50 matched control subjects. Clot structure was assessed by turbidity, permeation and confocal microscopy. The specific effect of glucose itself was assessed by analysing the structure of clots formed from purified fibrinogen in the presence of increasing concentrations of the sugar. Clots formed by fibrinogen purified from type 2 diabetic subjects had a denser, less porous structure than those from control subjects. The structural changes found were related to the individual's glycaemic control; HbA1c correlated negatively with permeation coefficient (Ks) values (indicates clot pore size) (r = -0.57, p < 0.0001) and positively with maximum absorbance (indicator of fibre size) (r = 0.33, p < 0.0001), branch point number (r = 0.78, p < 0.0001) and fibre density (r = 0.63, p < 0.0001). The ambient glucose level influenced clot structure; hypo- (< 5 mmol) and hyperglycaemia (> or = 10 mmol/l) were both associated with a reduction in Ks values and maximum absorbance, and with increased fibre density and branch point number within clots. The structural differences found to occur in type 2 diabetes and in association with hypo- and hyperglycaemia may confer increased resistance to fibrinolysis, and in consequence contribute to the increase in CVD risk in diabetic patients.

Prevention of Cardiovascular Complications of Diabetes Mellitus by Aspirin

Eighteen million Americans have type 2 Diabetes Mellitus (DM) while another 40 million have impaired glucose tolerance. Atherosclerotic heart disease is the leading cause of death in patients with diabetes mellitus. In addition to the increased risk for CardioVascular Disease (CVD), patients with diabetes have a worse prognosis than nondiabetics when they suffer an ischemic event. Insulin resistance is increasingly recognized as a chronic, low-level, inflammatory state. Hyperinsulinemia has been proposed as the forerunner of hypertension, low high-density lipoprotein cholesterolemia, hypertriglyceridemia, abdominal obesity, and altered glucose tolerance, linking all these abnormalities to the development of coronary vascular disease. Atherosclerosis and insulin resistance share similar pathophysiological mechanisms, due to the actions of proinflammatory cytokines. The dynamic inflammatory milieu found in diabetes explains the susceptibility of diabetics to CVD and the potential mechanism by which aspirin may prevent CVD in diabetics. Aspirin decreases the risk for CVD in diabetic patients by a variety of established and novel mechanisms. Therapeutic strategies that lesson the CVD risk in diabetic patients, including the use of aspirin for primary and secondary prevention, are potentially very important. This review article addresses the antiatherosclerotic effects of aspirin, the potential anti-diabetic effects of aspirin, and the clinical trial evidence for CVD prevention by aspirin in diabetics. We also present recommendations for the use of aspirin in the diabetic population and the current guidelines put forth by the American Heart Association and by the American Diabetes Association.

Vascular complications in diabetes and their prevention.

Diabetes mellitus is increasing throughout the world. Cardiovascular disease (CVD) accounts for up to 80% of excess mortality in this high-risk population. Patients with diabetes have the same CVD risk factors as those people without diabetes. However, these risk factors are much more powerful in diabetic patients. CVD risk is especially high for diabetic women, and premenopausal diabetic women lose all the protection normally afforded to them by female sex hormones. Controlled clinical trials have clearly demonstrated that rigorous treatment of blood pressure, dyslipidemia and platelet hyperaggrebility strikingly reduces CVD risk in diabetic patients. Strategies directed at interrupting the renin-angiotensin system (both tissue and systemic systems) and the use of 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) reductase inhibitors have proven to be especially beneficial for this high-risk population.