Log In Sign up
The largest database of trusted experimental protocols
> Disorders > Pathologic Function > Atherogenesis

Atherogenesis

Atherogenesis is the complex process of plaque formation within the arterial walls, leading to the development of atherosclerosis.
This process involves the accumulation of lipids, inflammation, and the buildup of fibrous elements, ultimately narrowing the blood vessels and restricting blood flow.
Understanding the mechanisms of atherogenesis is crucial for advancinig research and developing effective therapies to prevent and treat cardiovascular diseases.
PubCompare.ai's AI-driven platform can help optimize your atherogenesis research by locating the best protocols and products, comparing data from literature, preprints, and patents to streamline your workflow and accelerate your discoverie with powerful compariosn tools.

Most cited protocols related to «Atherogenesis»

1
Multivariable Mendelian Randomization: Dissecting Lipid Traits
We first conducted univariable MR analyses for each lipid-related trait. For this, we harmonised SNPs identified from our GWASs of lipoprotein lipid traits in the UKBB to those SNPs available in CARDIoGRAMplusC4D by either matching the SNP directly or by selecting proxy SNPs in high LD (r2 > 0.8). This led to a small drop in the number of SNPs being available for MR, with a median of 93% SNPs identified in GWASs available for MR (the numbers available for each trait are provided in Table 1). We used the inverse variance weighted approach, which, in brief, takes the form of a linear regression of the SNP–outcome association regressed on the SNP–exposure association weighted by the inverse of the square of the standard error of the SNP–outcome association, with the intercept constrained at the origin.
We next conducted multivariable MR, which is a statistical approach that allows for the association of SNPs with multiple phenotypes to be incorporated into the analysis, permitting an estimation of the direct effect of each phenotype on the outcome (i.e., an effect that is not mediated by any other factor in the model [28 (link)]); see S1 Fig for further details. In this manuscript, we use the term ‘adjusted’ in the context of multivariable MR to mean ‘direct’ effects, i.e., the effect of a lipid trait on CHD whilst accounting for either mediation or confounding by another trait included in the model. For the multivariable MR analyses, we fitted a model with apolipoprotein B, LDL cholesterol, and triglycerides to identify which one or more traits appeared to be responsible for the effect of ‘atherogenic’ lipid-related traits on risk of CHD. We then took the atherogenic trait(s) that retained an effect on CHD in the multivariable MR model forward and further adjusted for apolipoprotein A-I and HDL cholesterol to assess the potential causal roles of HDL-related phenotypes in the development of CHD. In the setting of multivariable MR, we included all GWAS-associated SNPs for all traits in the model. This meant that there were differing numbers of SNPs in the 2 multivariable models tested.
We characterised instrument strengths in both the univariable and multivariable MR settings as follows: for the univariable estimates, we generated the mean F-statistic, using the approximation described by Bowden and colleagues [44 (link)]. For the multivariable estimate, we generated the conditional F-statistic [28 (link),45 ]. Further details are provided in S1 Text.
Richardson T.G., Sanderson E., Palmer T.M., Ala-Korpela M., Ference B.A., Davey Smith G, & Holmes M.V. (2020). Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Medicine, 17(3), e1003062.
Full text: Click here
Publication 2020
Apolipoprotein A-I Apolipoproteins B Atherogenesis Cholesterol, beta-Lipoprotein Genome-Wide Association Study High Density Lipoprotein Cholesterol Lipid A Lipids Lipoproteins Phenotype Single Nucleotide Polymorphism Triglycerides
2
Comparing Dietary Models for Cardiovascular Events

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2016
Angina, Unstable Atherogenesis Blood Pressure Carbohydrates Cardiac Arrest Cardiac Arrhythmia Cardiac Death Cardiac Events Cardiovascular Diseases Cardiovascular System Cerebrovascular Accident Cholesterol, beta-Lipoprotein Diabetes Mellitus, Non-Insulin-Dependent Diet Diet, Mediterranean Disease Progression Endothelium Epigenetic Process Fat-Restricted Diet Glucose Heart Failure Heart Transplantation High Density Lipoprotein Cholesterol Immune Tolerance Inflammation Insulin Intermittent Claudication Light Lipids Malignant Neoplasms Mental Deterioration Metabolic Syndrome X Microbial Community Myocardial Infarction Oil, Olive Peripheral Arterial Diseases Prognostic Factors Secondary Prevention Stroke, Ischemic
3
Mouse Aortic Smooth Muscle Cell Studies
Primary mouse aortic smooth muscle cells (VSMC, passages 3–5) and atherogenic ApoE−/− mice were used for in vitro and in vivo studies. In vitro co-culture was performed with VSMC and bone marrow macrophages from wild type C57BL6 mice. Details of materials and experimental procedures are in the Methods section in the Online Data Supplement.
Byon C.H., Sun Y., Chen J., Yuan K., Mao X., Heath J.M., Anderson P.G., Tintut Y., Demer L.L., Wang D, & Chen Y. (2011). RUNX2-UPREGULATED RANKL IN CALCIFYING SMOOTH MUSCLE CELLS PROMOTES MIGRATION AND OSTEOCLASTIC DIFFERENTIATION OF MACROPHAGES. Arteriosclerosis, thrombosis, and vascular biology, 31(6), 1387-1396.
Publication 2011
Aorta Apolipoproteins E Atherogenesis Bone Marrow Coculture Techniques Dietary Supplements Macrophage Mus Myocytes, Smooth Muscle
4
Cardiometabolic Risk Factors in Iranian Adults
A total of 5207 subjects (2499 men and 2708 women), aged between 15 and 70 years, were asked to complete a checklist of study variables and patients’ blood sample results including sex (male, female), age (<30, 31-40, 41-50, >50), waist circumference (obese >90 cm for both sexes), fasting blood glucose (mg/dL), cholesterol (mg/dL), TG (mg/dL), HDL (mg/dL), and LDL (mg/dL) after obtaining informed consent. The protocol for the study was approved by the Research and Ethics Committee of Birjand University of Medical Sciences. The anthropometric measurements were determined by weighting scale and measuring tape. Based on the values of BMI, participants were classified as underweight (BMI < 18.5 kg/m2), normal (BMI: 18.5-24.9 kg/m2), overweight (BMI: 25-29.9 kg/m2), and obese (BMI ≥ 30 kg/m2). After completing the checklist, fasting blood samples were collected and sent to the lab. Serum was separated and stored in a freezer at −20°C until testing.7 Atherogenic index (AI = LDL-C/HDL-C) and CRI (CRI = TC/HDL-C) were calculated for all subjects. Plasma lipid abnormality was based on the expert panel of the National Cholesterol Education Program (NCEP) cutoff values.8 (link) This study was approved by the research ethics committee of the university (Ethics Code 1392-09-22) and adhered to the Declaration of Helsinki.
The test results and medical recommendations were given to the patients and after surveying the information and ensuring that they were correctly entered into the IBM SPSS software (version 22), descriptive information was presented using mean and standard error and then data were analyzed by independent sample t test, 1-way analysis of variance method and Pearson correlation. A P value of less than .05 was considered significant.
Kazemi T., Hajihosseini M., Moossavi M., Hemmati M, & Ziaee M. (2018). Cardiovascular Risk Factors and Atherogenic Indices in an Iranian Population: Birjand East of Iran. Clinical Medicine Insights. Cardiology, 12, 1179546818759286.
Publication 2018
Atherogenesis BLOOD Blood Glucose Cholesterol Ethics Committees Ethics Committees, Research Females Gender Lipids Males Obesity Patients Plasma Programmed Learning Serum Waist Circumference Woman
5
Lipid Abnormality Definitions and Cardiovascular Risk Indices
Lipid abnormality was defined as raised when TG level ≥1.7 mmol/L, reduced HDL-C - <1.03 mmol/L in males and <1.30 mmol/L in females, and TC level ≥5.2 mmol/L (200 mg/dl).[20 (link)]
The atherogenic index and lipid ratios were calculated using the following established formulas:[10 (link)21 (link)]
5. CHOLIndex = LDL-C − HDL-C (TG <400) = LDL-C – HDL-C + 1/5 TG (TG >400).
The following are the abnormal values of AIP, lipid ratios, and CHOLIndex for cardiovascular risk: AIP >0.1, CRI-I >3.5 in males and >3.0 in females, CRI-II >3.3, AC >3.0, and CHOLIndex >2.07.[10 (link)11 22 (link)23 (link)]
Olamoyegun M.A., Oluyombo R, & Asaolu S.O. (2016). Evaluation of dyslipidemia, lipid ratios, and atherogenic index as cardiovascular risk factors among semi-urban dwellers in Nigeria. Annals of African Medicine, 15(4), 194-199.
Publication 2016
Atherogenesis Diet, Formula Females LDL-1 Lipids Males

Most recents protocols related to «Atherogenesis»

1
Murine NAFLD and Atherosclerosis Protocol
All animal procedures were performed under a project licence (PPL P94B395E0) approved by the U.K. Home Office under the Animals (Scientific Procedures) Act 1986 and the University of Aberdeen ethics review board. Studies were performed following the recommendations in the ARRIVE guidelines under guidance by the Veterinary Surgeon and Animal Care and Welfare Officers of the institutional animal research facility. Thus, all methods were performed in accordance with the relevant guidelines and regulations. Male LDLR−/− mice, aged 4–6 weeks, were purchased from The Jackson Laboratory (supplied by Charles River UK Ltd), male and female ApoE−/− mice were bred in-house (University of Aberdeen). All mice were fed chow diet until 12 weeks of age then placed into three groups and fed the following diets (all Research Diets Inc.) to induce atherogenesis and NAFLD for 14 weeks: control (10% kCal fat D14121001) or high-fat/high-cholesterol diet (HFD, 40% kCal fat from cocoa butter and soybean oil, 34.5% kcal and 5.5% kcal respectively, plus 1.25% cholesterol, Clinton/Cybulsky D12108C) + /- 0.04% Fenretinide (FEN-HFD, D18061502,16 (link),27 (link)–29 (link)). Mice were maintained at 22–24 °C on 12-h light/dark cycle with free access to food/water. At week 14, mice were fasted for 5 h and injected intraperitoneally with either saline or insulin (10 mU/g body weight) for 10 min prior to CO2-induced anaesthesia followed by cervical dislocation. Heart and aortic tissues were collected for histological analysis. Peripheral metabolic tissues (liver, muscle and white adipose tissue (WAT)) were frozen in liquid nitrogen and stored at − 80 °C until subsequent analysis.
Thompson D., Mahmood S., Morrice N., Kamli-Salino S., Dekeryte R., Hoffmann P.A., Doherty M.K., Whitfield P.D., Delibegović M, & Mody N. (2023). Fenretinide inhibits obesity and fatty liver disease but induces Smpd3 to increase serum ceramides and worsen atherosclerosis in LDLR−/− mice. Scientific Reports, 13, 3937.
Full text: Click here
Publication 2023
Anesthesia Animals Aorta Apolipoproteins E Atherogenesis Body Weight Cholesterol cocoa butter Diet Diet, High-Fat Females Fenretinide Food Freezing Heart Insulin Joint Dislocations LDLR protein, human Liver Males Mice, House Muscle Tissue Neck Nitrogen Non-alcoholic Fatty Liver Disease Rivers Saline Solution Soybean oil Surgeons Therapy, Diet Tissues White Adipose Tissue
2
Cardiac Risk Ratios and Atherogenic Indices
The Castelli risk index-I (CRI-I), also known as cardiac risk ratio (CRR), suggests the development of coronary plaques with a diagnostic value as good as the determination of CHOL (27 (link)). The CRI-I was calculated by the standardised formula: CRI-I = CHOL/HDL-c. The scientific literature report that in an effort to enhance the predictive capacity of lipid-related indices, several lipoprotein ratios or “atherogenic indices” have been defined. The atherogenic coefficient (AC) is a significant index that can be used as a stand-alone index for CVD risk estimation (28 (link)). The AC was calculated by the standardised formula: AC = (CHOL–HDL-c)/HDL-c.
Sukhram S.D., Zarini G.G., Shaban L.H., Vaccaro J.A., Sukhram A.R, & Huffman F.G. (2023). Serum cotinine as a predictor of lipid-related indices in Turkish immigrants with type 2 diabetes: A clinic-based cross-sectional study. Frontiers in Medicine, 10, 1011045.
Full text: Click here
Publication 2023
Atherogenesis Diagnosis Heart Lipids Lipoproteins Senile Plaques
3
Atherosclerosis Progression in Baboons
In previous work, 112 adult baboons (47 females and 65 males) were challenged with HCHF diet for 2 years and tissues were collected at the end of the study. Blood samples were collected at the beginning (baseline) and end of the study. Mean age of the females was 12.6 years (range = 8.7–17.0 years) and that of the males was 11.4 years (range = 8.1–14.1 years). Study details and diet composition have been described19 (link).
CIA were collected and preserved in 10% formalin. The CIA were defatted and dissected longitudinally and stained as described19 (link),21 (link). The lesion type and the extent (proportion of tissue surface area covered by lesions) of atherogenic lesions was evaluated as described19 (link). The lesion types were validated by immunohistochemistry20 (link).
For the current study, a subset of CIA was selected from the 112 samples that represented the two categories of lesion type: fatty streaks (FS) and fibrous plaques (FP). The characteristics of the samples included 3 males and 5 females for FS, and 4 males and 4 females for FP. Mean age of the females was 12.0 years (range = 11.1–12.5 years) and that of the males was 11.2 years (range = 11.6–10.9 years).
Karere G.M., Glenn J.P., Li G., Konar A., VandeBerg J.L, & Cox L.A. (2023). Potential miRNA biomarkers and therapeutic targets for early atherosclerotic lesions. Scientific Reports, 13, 3467.
Full text: Click here
Publication 2023
Adult Atherogenesis BLOOD Diet Females Fibrosis Formalin Males Papio Senile Plaques Tissues
4
Metabolic Markers and Insulin Resistance
Blood samples were drawn via a venipuncture without any anticoagulant tubes for serum and EDTA—anticoagulant tubes for plasma. All blood samples were centrifuged at 1800 g for 10 min. After separation of serum and plasma samples, they were frozen at −80 °C until analysis. Glucose, lipid (TC and TG), and lipoprotein (HDL-C and LDL-C) levels were determined using the AU5800 auto analyzer (Beckman Coulter, Shizuoka, Japan). Insulin level was measured using the IMMULITE 2000 XPi analyzer (Siemens, Munich, Germany). To evaluate insulin resistance, the homeostasis model assessment of insulin resistance (HOMA-IR) was used. The formula used for calculating the index was as follows: fasting serum insulin (μU/mL) × fasting plasma glucose (mmol/L)/22.5 [28 (link)]. Fasting remnant lipoprotein cholesterol (RLP-C) was calculated using the formula TC˗(HDL-C + LDL-C) [29 (link)], and the atherogenic index of plasma (AIP) was calculated as log (TG/HDL-C) [30 (link)].
Ozer Yaman S., Balaban Yucesan F., Orem C., Vanizor Kural B, & Orem A. (2023). Elevated Circulating Endocan Levels Are Associated with Increased Levels of Endothelial and Inflammation Factors in Postprandial Lipemia. Journal of Clinical Medicine, 12(4), 1267.
Full text: Click here
Publication 2023
Anticoagulants Atherogenesis BLOOD Edetic Acid Freezing Glucose Homeostasis Insulin Insulin Resistance Lipids lipoprotein cholesterol Lipoproteins Plasma Serum Venipuncture
5
Insulin Resistance Indices and Anthropometric Markers
The study was conducted from 2015 to 2018 in the Department of Endocrinology, Piekary Medical Centre, St. Luke’s Local Hospital in Piekary Śląskie, Poland.
Blood samples were taken from patients in the morning, before breakfast, and 1 mL of blood collected as part of routine testing was preserved for further analysis, which after centrifugation was frozen and stored at −70 degrees Celsius.
The value of the HOMA-IR (Homeostatic model assessment) index was calculated using the following formula:
HOMA-IR = fasting insulinemia (mU/mL) × fasting glycemia (mmol/L)/22.5 [19 (link)].
The value of the METS-IR (The metabolic score for insulin resistance) was calculates as (In ((2 × fasting glucose) (mg/dL) + TG (mg/dL) × BMI)/(Ln (HDL-C) (mg/dL)) [14 (link)].
TyG index was computed using the formula following: In [fasting glucose (mg/dL) × TG (mg/dL)/2] [19 (link)].
TyG-BMI index was defined as: [fasting plasma glucose (mg/dL) × fasting triglicerydes (mg/dL)/2] × BMI [20 (link)].
TyG-WC was defined as: [fasting glucose (mg/dL) × TG (mg/dL)/2 × WC] [20 (link)].
LCI was calculated using the following formula: ((TC (mmol/L) × TG (mmol/L) × LDL-C (mmol/L))/HDL-C (mmol/L)) [15 (link)].
Castelli’s risk index-I was calculated according to the following formula [9 (link)] = (TC/HDL-C).
Castelli’s risk index-II was calculated according to the following formula [9 (link)] = (LDL-C/HDL-C).
Atherogenic coefficient (AC) was calculated according to the following formula [9 (link)] = (TC-HDL-C/HDL-C).
Atherogenic index of plasma (AIP) was calculated according to the following formula [9 (link)] = (log(TG/HDL-C)).
Trigliceryde to HDL-cholesterol was calculated according to the following formula [9 (link)] = (TG/HDL-C).
Anthropometric parameters were measured with the use of standard methods in the morning. These measurements included body weight [kg], height [cm], waist circumference [cm], and hip circumference [cm].
BMI was calculated according to the following formula [21 ]:
BMI = body weight [kg]/height [m]2;
WHR was calculated according to the following formula [22 ]:
WHR = waist circumference [cm]/hip circumference [cm];
WhtR was calculated according to the following formula [22 ]:
WHtR = waist circumference [cm]/height [cm];
BAI was calculated according to the following formula [23 (link)]:
BAI = (hip circumference [cm]/height [m]1.5) [18 (link)];
VAI was calculated according to the following formula [19 (link)]:
VAI = [waist circumference [cm]/(36.58 + (1.89 × BMI))] × (triglyceride concentration [mmol/L]/0.81) × (1.52/HDL concentration [mmol/L]);
LAP was calculated according to the following formula [19 (link)]:
LAP = (waist circumference [cm] − 58) × (triglyceride concentration [mmol/L]);
BRI was calculated according to the following formula [24 (link)]:
BRI = 365.2 − 365.5 × √(1 − (((WC/2π)2)/[(0.5 × height)]2);
ABSI was calculated according to the following formula [24 (link)]:
ABSI = WC[m]/[(BMI)2/3) × (height [m])1/2)].
Jabczyk M., Nowak J., Jagielski P., Hudzik B., Kulik-Kupka K., Włodarczyk A., Lar K, & Zubelewicz-Szkodzińska B. (2023). Metabolic Deregulations in Patients with Polycystic Ovary Syndrome. Metabolites, 13(2), 302.
Full text: Click here
Publication 2023
Atherogenesis BLOOD Body Weight Centrifugation Freezing Glucose High Density Lipoprotein Cholesterol Homeostasis Insulin Resistance Patients Plasma System, Endocrine Triglycerides Waist Circumference

Top products related to «Atherogenesis»

1
Zebron zb 88 by Phenomenex
Sourced in United States
Zebron ZB-88 is a gas chromatography column designed for the separation and analysis of a wide range of analytes. It features a 100% dimethylpolysiloxane stationary phase, which provides excellent thermal stability and inertness. The column is suitable for a variety of applications, including the analysis of volatile organic compounds, hydrocarbons, and other small molecules.
2
Elisa kit by Abcam
Sourced in United Kingdom, United States, China, France, Canada, Hong Kong, Spain, Australia, Italy, Germany
ELISA (Enzyme-Linked Immunosorbent Assay) kits are laboratory tools used to detect and quantify specific proteins, hormones, antibodies, or other analytes in biological samples. These kits employ the principle of antigen-antibody interactions and an enzyme-based detection system to generate colorimetric or fluorometric signals proportional to the concentration of the target analyte in the sample.
3
Spss software by IBM
Sourced in United States, Japan, United Kingdom, Germany, Austria, Belgium, China, Italy, India, Israel, France, Spain, Denmark, Canada, Hong Kong, Poland, Australia
SPSS is a software package used for statistical analysis. It provides a graphical user interface for data manipulation, statistical analysis, and visualization. SPSS offers a wide range of statistical techniques, including regression analysis, factor analysis, and time series analysis.
4
Pentra c200 by Horiba
Sourced in Japan, Spain, France, Switzerland
The Pentra C200 is a fully automated clinical chemistry analyzer designed for high-throughput laboratory testing. It offers a range of analytical capabilities for the quantitative determination of various analytes in biological samples. The core function of the Pentra C200 is to provide accurate and reliable test results for clinical diagnosis and monitoring purposes.
5
C57bl 6j by Jackson ImmunoResearch
Sourced in United States, Montenegro, Germany, United Kingdom, Japan, China, Canada, Australia, France, Colombia, Netherlands, Spain
C57BL/6J is a mouse strain commonly used in biomedical research. It is a common inbred mouse strain that has been extensively characterized.
6
Epi info version 5 by Abacus
Sourced in United States
Epi-info version 5 is a free software package designed for the purpose of epidemiological investigations and data management. It provides tools for data entry, data analysis, and the generation of reports and graphics.
7
Dri chem 3500i by Fujifilm
Sourced in Japan
The DRI-CHEM 3500i is a compact, fully automated clinical chemistry analyzer designed for rapid and accurate analysis of a wide range of biochemical parameters in clinical samples. It utilizes dry-chemistry technology to provide reliable and consistent results.
8
Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
9
37 component fame mix by Merck Group
Sourced in United States, Germany
The 37 Component FAME Mix is a laboratory standard used for the identification and quantification of fatty acid methyl esters (FAMEs) by gas chromatography. This mix contains 37 individual FAME components, allowing for the comprehensive analysis of a wide range of fatty acids in various samples.
10
Statview version 5 by Abacus
Sourced in United States
StatView version 5.0 is a data analysis software application. It provides functionality for statistical analysis, data manipulation, and graphical representation of research data.

More about "Atherogenesis"

Atherogenesis is the complex process of plaque formation within the arterial walls, leading to the development of atherosclerosis.
This process involves the accumulation of lipids, inflammation, and the buildup of fibrous elements, ultimately narrowing the blood vessels and restricting blood flow.
Understanding the mechanisms of atherogenesis is crucial for advancing research and developing effective therapies to prevent and treat cardiovascular diseases.
Atherogenesis is closely linked to the development of atherosclerosis, a condition characterized by the buildup of plaque in the arteries.
This plaque can restrict blood flow, increasing the risk of heart attacks, strokes, and other cardiovascular events.
Research into the underlying causes and mechanisms of atherogenesis is essential for developing new treatments and preventive strategies.
Key subtopics related to atherogenesis include lipid metabolism, inflammation, endothelial dysfunction, smooth muscle cell proliferation, and extracellular matrix remodeling.
Techniques such as ELISA kits, Zebron ZB-88 gas chromatography, and Pentra C200 analyzers can be used to study these processes in animal models like the C57BL/6J mouse strain.
Data analysis software like SPSS, SAS 9.4, and Statview version 5.0 can be utilized to interpret the results of atherogenesis studies.
Additionally, tools like DRI-CHEM 3500i and Epi-info version 5 can aid in the collection and management of data.
Optimizing your atherogenesis research can be achieved by leveraging the power of AI-driven platforms like PubCompare.ai.
These tools can help you locate the best protocols and products, compare data from literature, preprints, and patents, and streamline your workflow to accelerate your discoveries.