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High Density Lipoprotein Cholesterol

High Density Lipoprotien Cholesterol (HDL-C) is a type of cholestrol that plays a key role in cholestrol metabolism and transport.
HDL-C helps remove excess cholestrol from the body, reducing the risk of heart disease and other cardiovascular conditions.
Optimizing HDL-C research is critical for understanding its physiological effects and developing effective treatments.
PubCompare.ai is an AI-driven platform that can enhance the reproducibility and accuracy of HDL-C studies by helping researchers identify the best protocols from literature, preprints, and patents.
This tool can streamline the research process and improve the quality of findings, leading to more robust conclusions about the role of HDL-C in health and disease.

Most cited protocols related to «High Density Lipoprotein Cholesterol»

1
Genetic Variants Prioritization for Lipid Traits
We genotyped 196,710 genetic variants prioritized on the basis of prior GWAS for cardiovascular and metabolic phenotypes using the Illumina iSelect Metabochip8 genotyping array. To design the Metabochip, we used our previous GWAS of ~100,000 individuals4 (link) to prioritize 5,023 SNPs for HDL cholesterol, 5,055 for LDL cholesterol, 5,056 for triglycerides, and 938 for total cholesterol. These independent SNPs represent most loci with P < .005 in our original GWAS for HDL cholesterol, LDL cholesterol and triglycerides and with P < .0005 for total cholesterol. An additional 28,923 SNPs were selected for fine-mapping of 65 previously identified lipid loci. The Metabochip also included 50,459 SNPs prioritized based on GWAS of non-lipid traits and 93,308 SNPs selected for fine-mapping of loci associated with non-lipid traits (5 of these loci were associated with blood lipids by the analyses described here).
Willer C.J., Schmidt E.M., Sengupta S., Peloso G.M., Gustafsson S., Kanoni S., Ganna A., Chen J., Buchkovich M.L., Mora S., Beckmann J.S., Bragg-Gresham J.L., Chang H.Y., Demirkan A., Den Hertog H.M., Do R., Donnelly L.A., Ehret G.B., Esko T., Feitosa M.F., Ferreira T., Fischer K., Fontanillas P., Fraser R.M., Freitag D.F., Gurdasani D., Heikkilä K., Hyppönen E., Isaacs A., Jackson A.U., Johansson Å., Johnson T., Kaakinen M., Kettunen J., Kleber M.E., Li X., Luan J., Lyytikäinen L.P., Magnusson P.K., Mangino M., Mihailov E., Montasser M.E., Müller-Nurasyid M., Nolte I.M., O’Connell J.R., Palmer C.D., Perola M., Petersen A.K., Sanna S., Saxena R., Service S.K., Shah S., Shungin D., Sidore C., Song C., Strawbridge R.J., Surakka I., Tanaka T., Teslovich T.M., Thorleifsson G., Van den Herik E.G., Voight B.F., Volcik K.A., Waite L.L., Wong A., Wu Y., Zhang W., Absher D., Asiki G., Barroso I., Been L.F., Bolton J.L., Bonnycastle L.L., Brambilla P., Burnett M.S., Cesana G., Dimitriou M., Doney A.S., Döring A., Elliott P., Epstein S.E., Ingi Eyjolfsson G., Gigante B., Goodarzi M.O., Grallert H., Gravito M.L., Groves C.J., Hallmans G., Hartikainen A.L., Hayward C., Hernandez D., Hicks A.A., Holm H., Hung Y.J., Illig T., Jones M.R., Kaleebu P., Kastelein J.J., Khaw K.T., Kim E., Klopp N., Komulainen P., Kumari M., Langenberg C., Lehtimäki T., Lin S.Y., Lindström J., Loos R.J., Mach F., McArdle W.L., Meisinger C., Mitchell B.D., Müller G., Nagaraja R., Narisu N., Nieminen T.V., Nsubuga R.N., Olafsson I., Ong K.K., Palotie A., Papamarkou T., Pomilla C., Pouta A., Rader D.J., Reilly M.P., Ridker P.M., Rivadeneira F., Rudan I., Ruokonen A., Samani N., Scharnagl H., Seeley J., Silander K., Stančáková A., Stirrups K., Swift A.J., Tiret L., Uitterlinden A.G., van Pelt L.J., Vedantam S., Wainwright N., Wijmenga C., Wild S.H., Willemsen G., Wilsgaard T., Wilson J.F., Young E.H., Zhao J.H., Adair L.S., Arveiler D., Assimes T.L., Bandinelli S., Bennett F., Bochud M., Boehm B.O., Boomsma D.I., Borecki I.B., Bornstein S.R., Bovet P., Burnier M., Campbell H., Chakravarti A., Chambers J.C., Chen Y.D., Collins F.S., Cooper R.S., Danesh J., Dedoussis G., de Faire U., Feranil A.B., Ferrières J., Ferrucci L., Freimer N.B., Gieger C., Groop L.C., Gudnason V., Gyllensten U., Hamsten A., Harris T.B., Hingorani A., Hirschhorn J.N., Hofman A., Hovingh G.K., Hsiung C.A., Humphries S.E., Hunt S.C., Hveem K., Iribarren C., Järvelin M.R., Jula A., Kähönen M., Kaprio J., Kesäniemi A., Kivimaki M., Kooner J.S., Koudstaal P.J., Krauss R.M., Kuh D., Kuusisto J., Kyvik K.O., Laakso M., Lakka T.A., Lind L., Lindgren C.M., Martin N.G., März W., McCarthy M.I., McKenzie C.A., Meneton P., Metspalu A., Moilanen L., Morris A.D., Munroe P.B., Njølstad I., Pedersen N.L., Power C., Pramstaller P.P., Price J.F., Psaty B.M., Quertermous T., Rauramaa R., Saleheen D., Salomaa V., Sanghera D.K., Saramies J., Schwarz P.E., Sheu W.H., Shuldiner A.R., Siegbahn A., Spector T.D., Stefansson K., Strachan D.P., Tayo B.O., Tremoli E., Tuomilehto J., Uusitupa M., van Duijn C.M., Vollenweider P., Wallentin L., Wareham N.J., Whitfield J.B., Wolffenbuttel B.H., Ordovas J.M., Boerwinkle E., Palmer C.N., Thorsteinsdottir U., Chasman D.I., Rotter J.I., Franks P.W., Ripatti S., Cupples L.A., Sandhu M.S., Rich S.S., Boehnke M., Deloukas P., Kathiresan S., Mohlke K.L., Ingelsson E, & Abecasis G.R. (2013). Discovery and Refinement of Loci Associated with Lipid Levels. Nature genetics, 45(11), 1274-1283.
Publication 2013
BLOOD Cardiovascular System Cholesterol Cholesterol, beta-Lipoprotein Genetic Diversity Genome-Wide Association Study High Density Lipoprotein Cholesterol Lipids Phenotype Single Nucleotide Polymorphism Triglycerides
2
Causal Effects of Lipids and Urate on CHD
Do and colleagues16 (link) performed a two-sample MR analysis to evaluate the causal effect of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides on coronary heart disease (CHD) risk, using a total of 185 genetics variants. Summary association results were obtained from the Global Lipids Genetics Consortium17 (link) and the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis Consortium,18 (link) and were downloaded from Do and colleagues’ supplementary material (standard errors were estimated based on the regression coefficients and P-values). Genetic variants were classified as instruments for each lipid fraction using a statistical criterion (P < 1 × 10−8), resulting in 73 instruments for LDL-C, 85 for HDL-C and 31 for triglycerides.
White and colleagues19 (link) performed a similar analysis, but with plasma urate levels rather than lipid fractions. 31 variants associated with urate levels (P < 5 × 10−7) were used as genetic instruments, and the required summary statistics were obtained from the GWAS catalogue [https://www.ebi.ac.uk/gwas/].
Hartwig F.P., Davey Smith G, & Bowden J. (2017). Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International Journal of Epidemiology, 46(6), 1985-1998.
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Publication 2017
Cholesterol, beta-Lipoprotein Coronary Arteriosclerosis DNA Replication Genetic Diversity Genome Genome-Wide Association Study Heart Disease, Coronary High Density Lipoprotein Cholesterol Lipids Plasma Reproduction Triglycerides Urate
3
Mediterranean Diet Adherence Assessment
At baseline, registered dietitians completed a 14-item Mediterranean Diet adherence screener (Table 1) in a face-to-face interview with the participant [21] (link), [27] –[29] (link). The dietitians had been previously trained and certified to implement the PREDIMED protocol and had been hired to work full-time for the trial. The 14-item tool was developed in a Spanish case-control study of myocardial infarction [30] (link), where the best cut-off points for discriminating between cases and controls were selected for each food or food group. With this first step, 9 of the 14 items were obtained [31] (link). Five additional items that were felt to be especially relevant to assess adherence to the traditional Mediterranean diet were subsequently added. Two of these items used short questions to inquire on food habits: Do you use olive oil as the principal source of fat for cooking? and Do you prefer to eat chicken, turkey or rabbit instead of beef, pork, hamburgers or sausages? The other 3 items inquired on frequency of consumption of nuts, soda drinks and a typical Mediterranean sauce (“sofrito”): How many times do you consume nuts per week? How many carbonated and/or sugar-sweetened beverages do you consume per day? How many times per week do you consume boiled vegetables, pasta, rice, or other dishes with a sauce (“sofrito”) of tomato, garlic, onion, or leeks sauteed in olive oil?[26] (link).
The baseline 14-item questionnaire (Table 1) was the primary measure used in this study to appraise adherence of participants to the Mediterranean diet. In addition, a full-length 137-item validated FFQ [32] (link) was also administered to all participants. We obtained information about total energy intake and alcohol intake (only with descriptive purposes) from this FFQ. In the validation study, the score obtained with brief 14-item questionnaire correlated significantly with that obtained from the full-length FFQ score (Pearson correlation coefficient (r) = 0.52; intraclass correlation coefficient = 0.51). Associations in the anticipated directions for the different dietary intakes reported on the FFQ were found [26] (link). Significant inverse correlations of the 14-item tool with fasting glucose, total:HDL cholesterol ratio, triglycerides and the 10-y estimated coronary artery disease risk also supported the validity of this brief Mediterranean diet adherence screener [26] (link).
Also a general medical questionnaire, and the validated Spanish version of the Minnesota Leisure-Time Physical Activity Questionnaire [33] (link)–[34] (link) were collected by the dietitians in the personal interview with each participant [21] (link). Weight, height and WC were directly measured by registered nurses who had been previously trained and certified to implement the PREDIMED protocol and were hired to work full-time for this trial, as previously described [21] (link), [27] –[29] (link). The WHtR was calculated as WC divided by height, both in centimeters.
Martínez-González M.A., García-Arellano A., Toledo E., Salas-Salvadó J., Buil-Cosiales P., Corella D., Covas M.I., Schröder H., Arós F., Gómez-Gracia E., Fiol M., Ruiz-Gutiérrez V., Lapetra J., Lamuela-Raventos R.M., Serra-Majem L., Pintó X., Muñoz M.A., Wärnberg J., Ros E, & Estruch R. (2012). A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial. PLoS ONE, 7(8), e43134.
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Publication 2012
Allium cepa Beef Chickens Coronary Arteriosclerosis Diet, Mediterranean Dietitian Face Feelings Food Garlic Glucose High Density Lipoprotein Cholesterol Hispanic or Latino Hyperostosis, Diffuse Idiopathic Skeletal Leeks Myocardial Infarction Nuts Oil, Olive Oryza sativa Pastes Physical Examination Pork Rabbits Registered Nurse Sugar-Sweetened Beverages Tomatoes Triglycerides Vegetables
4
Design and Development of Metabochip
The Metabochip was designed by representatives of the Body Fat Percentage [9] (link), CARDIoGRAM (coronary artery disease and myocardial infarction) [10] (link), DIAGRAM (type 2 diabetes) [11] (link), GIANT (anthropometric traits) [3] (link), [12] (link), [13] (link), Global Lipids Genetics (lipids) [4] (link), HaemGen (hematological measures) [14] (link), ICBP (blood pressure) [15] (link), MAGIC (glucose and insulin) [16] (link)–[18] (link), and QT-IGC (QT interval) [19] (link), [20] (link) GWAS meta-analysis consortia. The array is comprised of SNPs selected across two tiers of traits (Table 1). Tier 1 is comprised of eleven traits deemed to be of primary interest: type 2 diabetes (T2D), fasting glucose, coronary artery disease and myocardial infarction (CAD/MI), low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, body mass index (BMI), systolic and diastolic blood pressure, QT interval, and waist-to-hip ratio adjusted for BMI (WHR). Tier 2 is comprised of twelve traits of secondary interest: fasting insulin, 2-hour glucose, glycated hemoglobin (HbA1c), T2D age of diagnosis, early onset T2D (diagnosis age<45 years), waist circumference adjusted for BMI, height, body fat percentage, total cholesterol, platelet count, mean platelet volume, and white blood cell count.
We included three design classes of SNPs on the Metabochip (Table 2):
In total, 217,695 SNPs were chosen for the array (Table 2). 20,970 SNPs (9.6%) failed during the assay manufacturing process, resulting in 196,725 SNPs available for genotyping. A summary file annotating each Metabochip SNP with ascertainment criteria, SNP assay, a list of unintended duplicate SNPs (Supplementary Table S4), and reference strand orientation for alleles is provided at http://www.sph.umich.edu/csg/kang/MetaboChip/.
Voight B.F., Kang H.M., Ding J., Palmer C.D., Sidore C., Chines P.S., Burtt N.P., Fuchsberger C., Li Y., Erdmann J., Frayling T.M., Heid I.M., Jackson A.U., Johnson T., Kilpeläinen T.O., Lindgren C.M., Morris A.P., Prokopenko I., Randall J.C., Saxena R., Soranzo N., Speliotes E.K., Teslovich T.M., Wheeler E., Maguire J., Parkin M., Potter S., Rayner N.W., Robertson N., Stirrups K., Winckler W., Sanna S., Mulas A., Nagaraja R., Cucca F., Barroso I., Deloukas P., Loos R.J., Kathiresan S., Munroe P.B., Newton-Cheh C., Pfeufer A., Samani N.J., Schunkert H., Hirschhorn J.N., Altshuler D., McCarthy M.I., Abecasis G.R, & Boehnke M. (2012). The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits. PLoS Genetics, 8(8), e1002793.
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Publication 2012
ADCAD1 Alleles Biological Assay Blood Pressure Body Fat Cholesterol Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Genome-Wide Association Study Gigantism Glucose Hemoglobin, Glycosylated High Density Lipoprotein Cholesterol Index, Body Mass Insulin Leukocyte Count Lipids Low-Density Lipoproteins Platelet Counts, Blood Pressure, Diastolic Single Nucleotide Polymorphism Systole Triglycerides Volumes, Mean Platelet Waist-Hip Ratio Waist Circumference
5
Dietary Inflammation and CRP Levels
Summary statistics were used to describe the study population at baseline separately for both 24HR and 7DDR subsets (as the numbers of participants with complete data from each were unequal; n 495 and n 559, respectively). Comparisons of baseline characteristics by sex were made using χ2 tests for categorical variables and two-sample t tests for continuous variables. DII was converted to tertiles and tests for trend across DII tertiles were carried out for age, smoking status, hs-CRP, BMI, MET/d, LDL-cholesterol and HDL-cholesterol. Generalized linear mixed models (proc GLIMMIX in SAS) were used for more complex analyses. Here, we used a compound symmetry covariance matrix to account for the dependence of observations made on the same individuals. The primary outcome variable for this analysis was hs-CRP, which was dichotomized to ≤3 mg/l and >3 mg/l, and the odds of elevated hs-CRP (>3 mg/l) was determined. Values of hs-CRP >10 mg/l were excluded from the total number of observations because this may be a result of acute inflammation; only sixty-five such values (3% of the total) were excluded from the total of 2165 available hs-CRP measures as a consequence of this(60 (link)). The primary independent variable was the score obtained from the DII and tertiles of DII. Both unadjusted and adjusted analyses were carried out. We also tested for effect modification between DII score and categories of BMI, age and infection status by including interaction terms in the model. Variables controlled in analyses were age, sex, race, BMI, smoking status, alcohol consumption status, physical activity, marital status, HDL-cholesterol, total cholesterol, anti-inflammatory medication use, light season, herbal supplement use, and a variable indicating if the participant had an infection during the study quarter. Race was dichotomized into ‘White’ and ‘Other’ because 90% of the study population was White. BMI was categorized into normal weight (18·5 to <25·0 kg/m2), overweight (25·0 to <30·0 kg/m2) and obese (≥30·0kg/m2). Participants considered underweight (<18·5 kg/m2) were excluded from analysis. Smoking status was dichotomized as yes/no. Level of education was categorized into high-school graduate or less, vocational/trade and some college, and college graduate or more. Marital status was categorized into single, married, living with a partner, separated, divorced or widowed. Total cholesterol and HDL-cholesterol were left as continuous variables. Seasons were categorized using the ‘light season’ definition centred at the equinoxes/solstices (winter: 6 November to 4 February; spring: 5 February to 6 May; summer: 7 May to 5 August; and autumn: 6 August to 5 November). Participants who reported having arthritis were excluded from analysis. Also, observations missing hs-CRP were excluded from analysis. All data analyses were performed using the SAS® statistical software package version 9·2.
Shivappa N., Steck S.E., Hurley T.G., Hussey J.R., Ma Y., Ockene I.S., Tabung F, & Hébert J.R. (2013). A population-based dietary inflammatory index predicts levels of C-reactive protein in the Seasonal Variation of Blood Cholesterol Study (SEASONS). Public Health Nutrition, 17(8), 1825-1833.
Publication 2013
Anti-Inflammatory Agents Arthritis Cholesterol Cholesterol, beta-Lipoprotein C Reactive Protein Herbal Supplements High Density Lipoprotein Cholesterol Infection Inflammation Light Obesity

Most recents protocols related to «High Density Lipoprotein Cholesterol»

1
Cardiometabolic Risk Factors in NHANES
The following covariates were considered in the study: age, sex, race/ethnicity, family poverty income ratio (PIR), education level, marital status, the complication of hypertension, and diabetes mellitus (DM), smoker, drinker, body mass index (BMI), waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean energy intake, hemoglobin (Hb), fast glucose (FBG), glycosylated hemoglobin (HbA1c), alanine transaminase (Alt), aspartate aminotransferase (Ast), albumin, total cholesterol (TC), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), uric acid (UA), blood urea nitrogen (BUN), serum creatinine (Scr), and estimated glomerular filtration rate (eGFR). Individuals who have smoked less than 100 cigarettes in their lifetime/smoked less than 100 cigarettes in their lifetime, do not smoke at all at present/smoked more than 100 cigarettes in their lifetime, and smoke some days or every day were defined as never smoke, former smokers, and now smokers, respectively. There are three categories of drinkers: current heavy alcohol consumption were defined as ≥3 drinks per day for females, ≥4 drinks per day for males, or binge drinking [≥4 drinks on same occasion for females, ≥5 drinks on same occasion for males] on 5 or more days per month; current moderate alcohol consumption were defined as ≥2 drinks per day for females, ≥3 drinks per day for males, or binge drinking ≥2 days per month. Those who did not meet the above criteria were classified as current mild alcohol user.21 (link) Hypertension was defined as an average systolic blood pressure more than 140 mmHg/diastolic blood pressure greater than 90 mmHg or self-reported use of antihypertensive medication. DM will be assessed by measures of blood glycohemoglobin, fasting plasma glucose, 2-hour glucose (Oral Glucose Tolerance Test), serum insulin in participants aged 12 years and over. Hb, FBG, HbA1c, Alt, Ast, albumin, TC, TG, HDL-C, UA, BUN, Scr, and eGFR were all determined in the laboratory. More information regarding the variables used is available at https://www.cdc.gov/nchs/nhanes/index.htm.
Xiao S., Wang Z., Zuo R., Zhou Y., Yang Y., Chen T, & Liu N. (2023). Association of Systemic Immune Inflammation Index with All-Cause, Cardiovascular Disease, and Cancer-Related Mortality in Patients with Cardiovascular Disease: A Cross-Sectional Study. Journal of Inflammation Research, 16, 941-961.
Publication 2023
Alanine Transaminase Albumins Alcohols Antihypertensive Agents BLOOD Cholesterol Creatinine Diabetes Mellitus Ethnicity Females Glomerular Filtration Rate Glucose Hemoglobin Hemoglobin, Glycosylated High Blood Pressures High Density Lipoprotein Cholesterol Index, Body Mass Insulin Males Oral Glucose Tolerance Test Plasma Pressure, Diastolic Serum Smoke Systolic Pressure Transaminase, Serum Glutamic-Oxaloacetic Triglycerides Urea Nitrogen, Blood Uric Acid Waist Circumference
2
Comprehensive Health Evaluation of Postmenopausal Women
A structured and detailed survey designed by professional physicians was used to collect the demographic and clinical parameters of the study subjects including self-reported illness and the currently used medications. The number of subjects in the smoking and alcohol consumption groups were low among early postmenopausal women, and were therefore excluded from analysis. Systolic and diastolic blood pressure was measured using an electronic brachial sphygmomanometer (T30J, OMRON, Japan). Anthropometric parameters including height, weight, waist circumference, and hip circumference were measured using standard procedures by well-trained nurses. Blood samples (8–10 mL) were collected from the antecubital vein after at least 8 h of overnight fasting and evaluated in the laboratory center within 24 h. Metabolic biomarkers and liver function parameters including fasting blood glucose (FBG), triglycerides (TGs), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum uric acid (UA), serum aspartate aminotransferase (AST), and serum alanine aminotransferase (ALT) levels were measured. Furthermore, the blood counts of white blood cells (WBC) and neutrophils (NE) were also analyzed. Abdominal ultrasonography was performed using the SIEMENS ACUSON S2000 ABVS ultrasound scanner (Siemens Healthineers, Erlangen, Germany), and was operated by experienced ultra-sonographers. The data was recorded in the electronic medical system of the Health Examination Center.
Lu Y., Ge L., Yang H., He Y, & Wang Y. (2023). Chinese Visceral Adipose Index Shows Superior Diagnostic Performance in Predicting the Risk of Metabolic Dysfunction Associated Fatty Liver Disease in Early Postmenopausal Chinese Women. Diabetes, Metabolic Syndrome and Obesity, 16, 607-617.
Publication 2023
Abdomen Alanine Transaminase Aspartate Transaminase Biological Markers BLOOD Blood Glucose Cholesterol Cholesterol, beta-Lipoprotein High Density Lipoprotein Cholesterol Leukocytes Liver Neutrophil Nurses Pharmaceutical Preparations Physicians Pressure, Diastolic Serum Sphygmomanometers Systole Triglycerides Ultrasonography Uric Acid Veins Waist Circumference Woman
3
Liver Fibrosis Assessment in Stroke Patients
Demographics, medical history, National Institutes of Health Stroke Scale (NIHSS) scores (17 (link)), and admission blood pressure were documented at baseline. Laboratory tests [including serum levels of creatinine, glucose levels, hemoglobin (Hb), platelet count (PLT), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), alanine aminotransferase (ALT), aspartate aminotransferase (AST) total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, γ-glutamyltransferase (γGT), and creatine phosphokinase (CPK)] were determined by admission blood tests.
To quantify the extent of liver fibrosis, we used the noninvasive liver fibrosis score (FIB-4) for each patient at the time of admission.
The FIB-4 score was computed for every patient as follows:
As validated in previous clinical trials, prediction of advanced liver fibrosis was indicated using a cut-off value ≥2.67, whereas a score value <1.30 was used to exclude severe liver fibrosis with high probability (18 (link), 19 (link)).
Norata D., Lattanzi S., Broggi S., Rocchi C., Bartolini M, & Silvestrini M. (2023). Liver fibrosis-4 score predicts outcome of patients with ischemic stroke undergoing intravenous thrombolysis. Frontiers in Neurology, 14, 1103063.
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Publication 2023
Aspartate Transaminase Blood Pressure Cerebrovascular Accident Cholesterol Cholesterol, beta-Lipoprotein Creatine Kinase Creatinine D-Alanine Transaminase Fibrosis, Liver Glucose Hematologic Tests Hemoglobin High Density Lipoprotein Cholesterol Lymphocyte Count Monocytes Neutrophil Patient Admission Patients Platelet Counts, Blood Serum Triglycerides
4
Comprehensive Cardiometabolic Risk Assessment
A case report form was developed to record general characteristics, clinical diagnosis, and biochemical examination. Waist circumference (WC) was measured at the middle point between the costal margin and iliac crest. BMI was calculated as body weight in kilograms divided by body height in meters squared (kg/m2). Smoking habit was categorized as current smoking, ever smoking, or no smoking. Current smoking was determined when subjects were smoking currently and more than one cigarette daily in at least one year continuously. Ever smoking was determined when subjects smoked more than one cigarette daily, but had quitted smoking at least one year before. Drinking habit was categorized as current drinking, ever drinking, or no drinking. Current drinking was determined when subjects were drinking liquor, beer or wine currently in at least one year. Ever drinking was determined when subjects drank previously, but had quitted drinking at least one year before. History of lipid disorders included that plasma total cholesterol was ≥ 5.7 mmol/l, or low-density lipoprotein cholesterol (LDL-C) was ≥ 3.6 mmol/l, or high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/l, triglyceride was ≥ 1.7 mmol/l, or treatment with antihyperlipidemic agents due to hyperlipidemia. Hypertension was diagnosed by systolic blood pressure (SBP) ≥ 140 mmHg, or diastolic blood pressure (DBP) ≥ 90 mmHg, or being actively treated with anti-hypertension drugs. Diabetes mellitus was diagnosed by a fasting plasma glucose ≥ 7.0 mmol/l, or by a random plasma glucose ≥ 11.1 mmol/l, or when they were actively receiving therapy using insulin or oral medications for diabetes. Chronic kidney disease was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2.
Wang Y., Guo X., Zhang Y., Zhang R, & Li J. (2023). Different associations of general and abdominal obesity with upper and lower extremity artery disease among a community population in China. Nutrition & Metabolism, 20, 14.
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Publication 2023
Amniotic Fluid Antihypertensive Agents Beer Body Height Body Weight Cholesterol Cholesterol, beta-Lipoprotein Chronic Kidney Diseases Costal Arch Diabetes Mellitus Glomerular Filtration Rate Glucose High Blood Pressures High Density Lipoprotein Cholesterol Hyperlipidemia Hypolipidemic Agents Iliac Crest Insulin Lipid Metabolism Disorders Pharmaceutical Preparations Plasma Pressure, Diastolic Systolic Pressure Therapeutics Triglycerides Waist Circumference Wine
5
Anthropometric Measurements and Biochemical Profiles
The measurements of anthropometric attributes and biochemical profiles have been described previously [23 (link)]. In brief, we used a digital system (BW-2200; NAGATA Scale Co. Ltd., Tainan, Taiwan) to measure the subject’s body weight and height. Waist circumference (WC) was measured at the level of mid-distance between the bottom of the rib cage and the top of the iliac crest. Hip circumference was the distance around the largest part of the subject’s hips. Blood pressure was measured three times, with an interval of 3 min, after 10 min of rest. The averages of repeated measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were used for analyses. The fasting blood levels of total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (FTG), and glucose (FPG) were determined by an autoanalyzer (Toshiba TBA c16000; Toshiba Medical System, Holliston, MA, USA) with commercial kits (Denka Seiken, Tokyo, Japan).
We also used a structured questionnaire to collect personal histories of common diseases in adults and health behaviors. In the study, hypertension was defined as subjects who had physician-diagnosed hypertension or a history of taking antihypertensive medications. Hyperlipidemia was defined as subjects having been diagnosed with high blood lipids by a physician or having a history of taking lipid-lowering medications. DM was defined as FPG ≥ 126 mg/dL or the use of insulin or other hypoglycemic agents. Cigarette smoking and alcohol drinking were defined as having smoked cigarettes or drank alcohol-containing beverages at least 4 days per week during the past month before enrollment.
Wu T.W., Chou C.L., Cheng C.F., Lu S.X., Wu Y.J, & Wang L.Y. (2023). Associations of genetic markers of diabetes mellitus with carotid atherosclerosis: a community-based case–control study. Cardiovascular Diabetology, 22, 51.
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Publication 2023
Adult Alcoholic Beverages Antihypertensive Agents BLOOD Blood Pressure Body Weight Cholesterol Cholesterol, beta-Lipoprotein Coxa Fingers Glucose High Blood Pressures High Density Lipoprotein Cholesterol Hyperlipidemia Hypoglycemic Agents Iliac Crest Insulin Lipid A Lipids Pharmaceutical Preparations Physicians Pressure, Diastolic Rib Cage Systolic Pressure Triglycerides Waist Circumference

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Variant 2 by Bio-Rad
Sourced in United States, Germany, United Kingdom
The Variant II is a laboratory equipment designed for protein analysis. It utilizes electrophoresis technology to separate and analyze proteins based on their molecular weight and charge. The core function of the Variant II is to provide consistent and reliable protein separation for various applications in the life sciences and biotechnology industries.
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Modular p800 by Roche
Sourced in Germany, Switzerland, United States, United Kingdom
The Modular P800 is a laboratory instrument designed for automated clinical chemistry analysis. It is a modular system that can be configured to meet the specific needs of a laboratory. The Modular P800 performs a range of routine clinical chemistry tests and provides accurate and reliable results.
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Cobas integra 800 by Roche
Sourced in Switzerland, Germany, United States, Japan
The Cobas Integra 800 is a fully automated, high-throughput clinical chemistry analyzer designed for use in medical laboratories. It is capable of performing a wide range of clinical chemistry tests on a variety of sample types. The Cobas Integra 800 utilizes advanced technology to provide accurate and reliable results, with the ability to handle a high volume of samples efficiently.
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Advia 2400 by Siemens
Sourced in Germany, United States, China, United Kingdom, Japan, Italy, Spain
The ADVIA 2400 is a fully automated clinical chemistry analyzer designed for high-volume laboratory testing. It features advanced technology for efficient sample processing and accurate analysis of a wide range of clinical chemistry parameters.
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Cobas c501 by Roche
Sourced in Switzerland, Germany, United States, Japan, Denmark, France, Sweden
The Cobas c501 is a clinical chemistry analyzer developed by Roche. It is designed for routine and specialized in vitro diagnostic testing in a laboratory setting. The Cobas c501 performs a wide range of biochemical and immunochemical analyses on various sample types.

More about "High Density Lipoprotein Cholesterol"

High Density Lipoprotein Cholesterol (HDL-C) is a crucial component of the cholesterol transport system in the body.
Also known as 'good cholesterol', HDL-C plays a vital role in removing excess cholesterol from the bloodstream and transporting it to the liver for excretion, thereby reducing the risk of cardiovascular diseases like heart attacks and strokes.
Optimizing HDL-C research is essential for understanding its physiological effects and developing effective treatments.
PubCompare.ai, an AI-driven platform, can enhance the reproducibility and accuracy of HDL-C studies by helping researchers identify the best protocols from literature, preprints, and patents.
This tool can streamline the research process and improve the quality of findings, leading to more robust conclusions about the role of HDL-C in health and disease.
Measuring HDL-C levels is commonly done using automated chemistry analyzers like the Cobas 8000, AU5800, Hitachi Automatic Analyzer 7600, Cobas 6000, Variant II, Modular P800, Cobas Integra 800, and ADVIA 2400.
These instruments utilize various methodologies, such as enzymatic colorimetric assays and direct homogeneous assays, to accurately quantify HDL-C concentrations.
By leveraging the insights gained from these analytical platforms and the power of PubCompare.ai, researchers can optimize their HDL-C studies, leading to more reproducible and accurate findings that contribute to our understanding of cholesterol metabolism and the development of effective therapies for cardiovascular health.