Development of a Novel Homogeneous Assay for Remnant Lipoprotein Particle Cholesterol

2018 ◽  
Vol 3 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Yuhko Hirao ◽  
Katsuyuki Nakajima ◽  
Tetsuo Machida ◽  
Masami Murakami ◽  
Yasuki Ito
Keyword(s):  
Disease Risk ◽  
Cholesterol Oxidase ◽  
Cholesterol Esterase ◽  
Lipoprotein Particle ◽  
Type Iii Hyperlipoproteinemia ◽  
Automated Assay ◽  
Clinical Laboratories ◽  
Remnant Lipoprotein ◽  
Homogeneous Assay ◽  
Artery Disease

Abstract Background Quantification of remnant lipoprotein particle cholesterol (RLP-C) by automated assay is useful in routine clinical laboratories to assess coronary artery disease risk and diagnose type III hyperlipoproteinemia. Methods Enzymes and surfactants were screened to establish a homogeneous RLP-C assay using the chylomicron-VLDL, LDL, and HDL fractions isolated by ultracentrifugation, along with the RLP fraction isolated by immunoaffinity gel. All data were generated using a Hitachi analyzer. Results A specific cholesterol esterase with a polyoxyethelene styrenated phenyl ether derivative (surfactant) was used for the establishment of a homogeneous RLP-C assay. This cholesterol esterase with subunits of >40 kDa (H-CE) was found to react with lipoproteins other than RLP, whereas this enzyme with subunits of <40 kDa (L-CE) reacted with RLP. H-CE was applied for the first reaction step with the specific surfactant to decompose non-RLP lipoproteins, degrading non-RLP cholesterol into water and oxygen in the presence of cholesterol oxidase and catalase. For the second step, L-CE was applied to release cholesterol from RLP, and then the released RLP-C was determined in a standard cholesterol oxidase and peroxidase system. This new homogeneous assay exhibited good correlation with the RLP-C immunoseparation method. Conclusions We established a simple, rapid, automated homogeneous assay for RLP-C. The assay can determine RLP-C levels in 10 min in a fully automated manner, processing a large number of samples in routine clinical laboratories.

Family Environment and Its Correlation with Anxiety and Depression: A Study on Heart Patients

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Dr. Meena Jain ◽  
Saloni Chandalia
Keyword(s):  
Heart Disease ◽  
Family Environment ◽  
Heart Attack ◽  
Psychiatric Symptoms ◽  
Disease Risk ◽  
Positive Association ◽  
Anxiety And Depression ◽  
Depression And Anxiety ◽  
Increased Risk ◽  
Artery Disease

This research paper deals with the Family Environment and its Correlation with Anxiety and Depression level among persons with Heart Disease. There had been a number of researches that investigated that ischemic heart disease patients who suffer significant anxiety have close to a 5-fold increased risk of experiencing frequent angina and those with depression have more than a 3-fold increased risk for these episodes. This observed link between psychiatric symptoms and angina underlines the importance of treating anxiety and depression in cardiac patients, according to study co author Dr Mark D Sullivan (University of Washington School of Medicine, Seattle). To gather the needed data, Hamilton Anxiety Scale and Becks Depression Inventory were used. As stated from literatures, for people with heart dysfunction, depression and anxiety can increase the risk of an adverse cardiac event such as a heart attack or blood clots. For people who do not have heart disease, depression and anxiety can also increase the risk of a heart attack and development of coronary artery disease. Researchers have also emphasized on the role of family psychosocial environment and its positive association with the Coronary Heart Disease risk.

scholarly journals Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 551
Author(s):  
Chris Boyd ◽  
Greg Brown ◽  
Timothy Kleinig ◽  
Joseph Dawson ◽  
Mark D. McDonnell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Model Comparison ◽  
Disease Risk ◽  
Imaging Modalities ◽  
Current Status ◽  
Advantages And Disadvantages ◽  
Body Regions ◽  
Artery Disease

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

scholarly journals Physical activity, sedentary behavior and risk of coronary artery disease, myocardial infarction and ischemic stroke: a two-sample Mendelian randomization study

2021 ◽  
Author(s):  
Martin Bahls ◽  
Michael F. Leitzmann ◽  
André Karch ◽  
Alexander Teumer ◽  
Marcus Dörr ◽  
...  
Keyword(s):  
Physical Activity ◽  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Ischemic Stroke ◽  
Coronary Artery ◽  
Sedentary Behavior ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Artery Disease

Abstract Aims Observational evidence suggests that physical activity (PA) is inversely and sedentarism positively related with cardiovascular disease risk. We performed a two-sample Mendelian randomization (MR) analysis to examine whether genetically predicted PA and sedentary behavior are related to coronary artery disease, myocardial infarction, and ischemic stroke. Methods and results We used single nucleotide polymorphisms (SNPs) associated with self-reported moderate to vigorous PA (n = 17), accelerometer based PA (n = 7) and accelerometer fraction of accelerations > 425 milli-gravities (n = 7) as well as sedentary behavior (n = 6) in the UK Biobank as instrumental variables in a two sample MR approach to assess whether these exposures are related to coronary artery disease and myocardial infarction in the CARDIoGRAMplusC4D genome-wide association study (GWAS) or ischemic stroke in the MEGASTROKE GWAS. The study population included 42,096 cases of coronary artery disease (99,121 controls), 27,509 cases of myocardial infarction (99,121 controls), and 34,217 cases of ischemic stroke (404,630 controls). We found no associations between genetically predicted self-reported moderate to vigorous PA, accelerometer-based PA or accelerometer fraction of accelerations > 425 milli-gravities as well as sedentary behavior with coronary artery disease, myocardial infarction, and ischemic stroke. Conclusions These results do not support a causal relationship between PA and sedentary behavior with risk of coronary artery disease, myocardial infarction, and ischemic stroke. Hence, previous observational studies may have been biased. Graphic abstract

Genetic insight into the role of MRAS in coronary artery disease risk

Gene ◽  
2015 ◽  
Vol 564 (1) ◽  
pp. 63-66 ◽  
Author(s):  
Lei Liu ◽  
Ling You ◽  
Lun Tan ◽  
Dao Wen Wang ◽  
Wei Cui
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Disease Risk ◽  
Coronary Artery Disease Risk ◽  
Artery Disease ◽  
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