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Hypoalphalipoproteinemia, Familial

Hypoalphalipoproteinemia, Familial is a genetic condition characterized by abnormally low levels of high-density lipoprotein (HDL) cholesterol in the blood.
This can lead to an increased risk of cardiovascular disease.
Patients with this disorder may have a family history of low HDL levels.
Effective management often involves lifestyle changes and medication to target the underlying metabolic imbalance.
Reserachers can leverage PubCompare.ai's AI-driven protocol optimization to enhance the accuracy of their studies on this condition, identifying the best protocols from literature, preprints, and patents to take their work to the next level.

Most cited protocols related to «Hypoalphalipoproteinemia, Familial»

Avon Longitudinal Study of Parents and Children

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based birth cohort study that recruited 14,541 pregnant women resident in Avon, UK with expected dates of delivery 1^st April 1991 to 31^st December 1992 (http://www.alspac.bris.ac.uk.).^{13 (link)} There were 13,678 mother-offspring pairs from singleton live births who survived to at least one year of age; only singleton pregnancies are considered in this paper. We further restricted analyses in this paper to women with term deliveries (between 37-44weeks gestation): N = 12,447. Of these women 11,702 (94%) gave consent for abstraction of data from their obstetric records. 6,668 (57%) offspring of these 11,702 women attended the 9-year follow-up clinic. Of the 6,668 mother-offspring eligible pairs, complete data on GWG, offspring anthropometry, blood pressure and potential confounders were available for 5,154 (77% of attendees; 41% of 12,447 total). In addition, 3,457 (52% of attendees; 28% of total) had complete data on offspring blood assays.
Six trained research midwives abstracted data from obstetric medical records. There was no between-midwife variation in mean values of abstracted data and repeat data entry checks demonstrated error rates consistently < 1%. Obstetric data abstractions included every measurement of weight entered into the medical records and the corresponding gestational age and date. To allocate women to IOM categories (box 1) we used weight measurements from the obstetric notes and subtracted the first from the last weight measurement in pregnancy to derive absolute weight gain. Pre-pregnancy BMI was based on the predicted pre-pregnancy weight using multilevel models (see below) and maternal report of height.
Maternal age, parity, mode of delivery (caesarean section / vaginal delivery) and the child’s sex were obtained from the obstetric records. Based on questionnaire responses, the highest parental occupation was used to allocate the children to family social class groups (classes I (professional / managerial) to V (unskilled manual workers)). Maternal smoking in pregnancy, categorised as - never smoked; smoked before pregnancy or in the first trimester and then stopped; smoked throughout pregnancy – was obtained from questionnaire responses.
Offspring weight and height were measured in light clothing, without shoes. Weight was measured to the nearest 0.1kg using Tanita scales. Height was measured to the nearest 0.1cm using a Harpenden stadiometer. WC was measured to the nearest 1mm at the mid-point between the lower ribs and the pelvic bone with a flexible tape and with the child breathing normally. Fat mass was assessed using dual energy X-ray densitometry (DXA). We examined BMI, WC and fat mass as continuously measured variables. We also examined binary outcomes of overweight/obese (BMI) and abdominally obese (WC) using age- and sex-specific thresholds for both child BMI (International Obesity Task Force) ^{14 (link)} and WC (>=90^th percentile^{15 (link)} based on WC percentile curves derived for British children^{16 (link)}).
Blood pressure was measured using a Dinamap 9301 Vital Signs Monitor with the child rested and seated and their arm supported at chest level on a table. Two readings of systolic and diastolic blood pressure (SBP and DBP) were recorded and the mean of each was used. Non-fasting blood samples were taken using standard procedures with samples immediately spun and frozen at −80°C. The measurements were assayed in plasma in 2008 after a median of 7.5 years in storage with no previous freeze-thaw cycles during this period. Lipids (total cholesterol, triglycerides and HDL-C) were performed by modification of the standard Lipid Research Clinics Protocol using enzymatic reagents for lipid determinations. Apolipoprotein (apo) A1 and apoB were measured by immunoturbidimetric assays (Hitachi/Roche). Leptin was measured by an in house ELISA validated against commercial methods. Adiponectin and high sensitivity IL-6 were measured by ELISA (R&D systems) and CRP was measured by automated particle-enhanced immunoturbidimetric assay (Roche UK, Welwyn Garden City, UK). All assay coefficients of variation were <5%. Non-HDLc was calculated as total cholesterol minus HDLc.
All pregnancy weight measurements (median number of repeat measurements per woman: 10,range: 1, 17) were used to develop a linear spline multilevel model (with two levels: woman and measurement occasion) relating weight (outcome) to gestational age (exposure). Full details of this statistical modelling are provided in supplementary web-material. High levels of agreement were found between estimated and observed weights (Web-table1 and Web-figure2). We scaled maternal pre-pregnancy weight and gestational weight change to be clinically meaningful – examining the variation in offspring outcomes per additional 1kg of maternal weight at conception and per 400g gain per week of gestation for GWG.² Sensitivity analyses were conducted in which we repeated analyses including only those women who had at least 9 measurements of gestational weight.
Associations of offspring outcomes with the IOM categories and with the estimates of maternal pre-pregnancy weight and early-, mid- and late-pregnancy GWG were undertaken using linear regression. We explored the linearity of the relationships between all outcomes and the exposures using fractional polynomials. Where there was evidence of non-linearity, we used spline models to approximate the relationship. In the basic model we adjusted for offspring gender and age at the time of outcome measurement and for all models with fat mass for height and height-squared. We considered the following potential confounders: pre-pregnancy weight and GWG in the previous period (for the multilevel model exposures only), gestational age (for IOM categories only, since this is taken account of in the multilevel models), maternal age, parity, pregnancy smoking, social class, and mode of delivery. In order to examine whether effects were mediated by birthweight we adjusted for it and for non-adiposity outcomes we also examined potential mediation by adiposity. Triglycerides, leptin, CRP and IL-6 were log transformed in order to normalize their distributions. The resultant regression coefficients were exponentiated to give a ratio of geometric means per change in exposure. Results are presented jointly for mothers of female and male offspring as associations were all very similar in both genders.

Fraser A., Tilling K., Macdonald-Wallis C., Sattar N., Brion M.J., Benfield L., Ness A., Deanfield J., Hingorani A., Nelson S.M., Smith G.D, & Lawlor D.A. (2010). Association of maternal weight gain in pregnancy with offspring obesity and metabolic and vascular traits in childhood. Circulation, 121(23), 2557-2564.

Publication 2010

Adiponectin APOB protein, human Apolipoprotein A-I Biological Assay Birth Cohort Birth Weight BLOOD Blood Pressure Cesarean Section Chest Child Cholesterol Conception Densitometry, X-Ray Diastole Enzyme-Linked Immunosorbent Assay Enzymes Females Freezing Gestational Age Hip Bone Hypersensitivity Hypoalphalipoproteinemia, Familial Immunoturbidimetric Assay Leptin Light Lipids Midwife Mothers Obesity Obstetric Delivery Parent Plasma Pregnancy Pregnant Women Ribs Signs, Vital Systolic Pressure Triglycerides Vagina Woman Workers

Cardiovascular Risk Factors in Aging Men

Study participants derived from the Physicians Health Study-II (PHS-II), a nationwide cohort of US men 50 years and older free of cardiovascular disease, diabetes, and cancer with blood collection initiated in December 19952 (link). Men eligible for the current analysis were those younger than 80 at baseline who had complete ascertainment of baseline exposure variables of interest including age (years), blood pressure (mm HG), smoking status (current, not current), and parental history of myocardial infarction before age 60 years (yes/no) and who provided an adequate baseline plasma sample for analysis of total cholesterol (mg/dL), HDLC (mg/dL), and hsCRP (mg/L). In total, 10,724 men had data available on all seven covariates and were included in the primary analysis; 10,407 also had information available on treatment of hypertension or hyperlipidemia, and were included in the secondary analyses.
All men were followed up through March 2008 for a median period of 10.8 years (inter-quartile range 7.8 to 11.2) for incident myocardial infarction, stroke, coronary revascularization, or cardiovascular death; these were adjudicated by an end-points committee after medical record review using standardized criteria. All participants provided written informed consent. The study protocol was approved by the institutional review board of Brigham and Women's Hospital (Boston, Mass).

Ridker P.M., Paynter N.P., Rifai N., Gaziano J.M, & Cook N.R. (2008). C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men. Circulation, 118(22), 2243-2251.

Publication 2008

Blood Pressure Cardiovascular Diseases Cardiovascular System Cerebrovascular Accident Cholesterol C Reactive Protein Diabetes Mellitus Ethics Committees, Research Heart Hematologic Neoplasms High Blood Pressures Hyperlipidemia Hypoalphalipoproteinemia, Familial Myocardial Infarction Parent Physicians Plasma Youth

Generating Plausible Virtual Populations for Biological Models

A flow diagram of the algorithm is shown in Figure1. To implement this procedure for a given model it is necessary to define bounds for input parameters and model outputs (e.g., steady states or dynamic behavior). If bounds cannot be defined empirically, feasible ranges of parameter values can be asserted from physiological knowledge or theoretical considerations. For example, the tissue concentration of a species may not be known, but typical weight and water content of that tissue may be known, which allows us to put an upper limit on the species concentration.
We have provided a detailed description of terminology, definitions, and the derivation of this algorithm in Table1 and the Supplementary Material. Briefly, our approach is to generate a large number of “plausible patients.” We define these patients as a parameter set for which every component of the model (whether it be the parameter values themselves, computed species concentrations, or combinations of these that are experimentally measurable) falls into a biologically plausible range. From this “plausible population” we can then select the virtual population such that it matches the empirical distribution of interest. This is achieved by calculating a probability of inclusion of a plausible patient into the virtual population. This probability is computed from both the empirical distribution and the density of plausible patients (see Supplementary Materials for more details).
An important prerequisite to this approach is the ability to generate a large number of plausible patients within the region of the empirical data. To accelerate this process we take an initial parameter guess (within the predefined bounds) and optimize this choice until the required outputs are within physiologically plausible ranges. Rather than optimize to specific points, it is more efficient to be agnostic as to where in the plausible ranges the optimization routine ends. To implement this we shift the typical cost function f(p) we would use optimizing a model to a new function, g(p), where we consider both as purely dependent on the parameter set p. If we constrain parameters using a number of model outputs M_i(p), with data d_i then f (in the simplest, unweighted case) would be:

f (p) = \sum {(M_{i} (p) - d_{i})}^{2} .

To generate plausible patients, we modify this sum‐of‐squared errors expression to:

g (p) = {\sum_{i} \max [(M_{i} (p) - \frac{l_{i} + u_{i}}{2})}^{2} - {(\frac{u_{i}}{2} - \frac{l_{i}}{2})}^{2}, 0]

where u_i and l_i are the predefined plausible upper and lower bounds, respectively, for M_i(p). This expression ensures that if M_i(p) is in the plausible range then the contribution of the corresponding term in the expression is zero. The effect of replacing f(p) with g(p) is visualized in 2D in Figure2.
To test this approach we used a previously published model of cholesterol metabolism.17 We chose this model because we could use publicly available data from the NHANES database16 to establish the empirical multivariate distribution for LDL cholesterol, HDL cholesterol, and total cholesterol (LDL_c, HDL_c, and TC, respectively). Note that the distribution of these variables is well approximated by a multivariate log‐normal distribution (Supplementary Figure 1). For the remainder of the article we will describe these variables, either as model outputs or from NHANES, in log units (prior to taking the logarithm, units are mg/dL for cholesterol measures). The published version of this model does not explicitly calculate LDL_c or TC; instead, the outputs are HDL_c and non‐HDL_c. From these two quantities TC is easily calculated. For full comparison with the NHANES data we introduced a new parameter to the model, k₂₂, which is simply the ratio between LDL_c and non‐HDL_c. The supplied MATLAB (MathWorks, Natick, MA) file “input_ranges.m” gives details on parameter and output ranges for the van de Pas model. Also in the Supplementary Material is the code used in this case, which is easily modifiable for application to other models.

Allen R., Rieger T, & Musante C. (2016). Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models. CPT: Pharmacometrics & Systems Pharmacology, 5(3), 140-146.

Publication 2016

Cholesterol Cholesterol, beta-Lipoprotein High Density Lipoprotein Cholesterol Hypoalphalipoproteinemia, Familial Metabolism Patients Tissues Tissue Specificity

Stroke Risk Factors and Lipid Indices

Ethical clearance was obtained from the Institutional Human Ethics Committee. The study group consists of patients admitted to the hospital with clinically diagnosed stroke during the period January 2015-2016. The control group consisted of apparently healthy volunteers selected from the master health checkup department.
The medical records of the study participants were analyzed, and data collected include age, gender, lipid profile parameters (total cholesterol, HDL, LDL and triglyceride levels), and the number of days of hospital stay for stroke patients.
All parameters were estimated using dedicated kits and reagents in autoanalyzer. Lipid indices were calculated using following formulae:
AIP = Log (serum triglyceride/serum HDLc),
CRI-I = Serum total cholesterol/serum HDLc,
CRI-II = Serum LDL cholesterol/serum HDLc,
AC = (Serum total cholesterol−serum HDLc)/serum HDLc,
NHC = Serum total cholesterol-serum HDLc.
All statistical analysis was performed with SPSS version 19. Data were expressed as mean ± standard deviation (SD). For comparison of variables, statistical test was done using Mann-Whitney U-test for skewed distribution and chi-square test for categorical variables. Odds ratio and 95% confidence interval was calculated. Factors found to be significant in the univariate analysis were subjected to logistic regression analysis. P < 0.050 was considered as statistically significant.

Sujatha R, & Kavitha S. (2017). Atherogenic indices in stroke patients: A retrospective study. Iranian Journal of Neurology, 16(2), 78-82.

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Publication 2017

Cerebrovascular Accident Cholesterol Cholesterol, beta-Lipoprotein Gender Healthy Volunteers Homo sapiens Hypoalphalipoproteinemia, Familial i-cholesterol Institutional Ethics Committees Lipids Patients Serum Triglycerides

Mendelian Randomization Causal Effect Analysis

The Egger regression method was introduced above as a test for directional pleiotropy; this test does not make any assumption about the genetic variants. However, under an assumption that is weaker than standard instrumental variable assumptions, the slope coefficient from the Egger regression method provides an estimate of the causal effect that is consistent asymptotically even if all the genetic variants have pleiotropic effects on the outcome.46 (link) This is the assumption that pleiotropic effects of genetic variants (that is, direct effects of the genetic variants on the outcome that do not operate via the risk factor) are independent of instrument strength (known as the InSIDE assumption – Instrument Strength Independent of Direct Effect). This same assumption was considered by Kolesár et al. with individual-level data.54 (link) The motivation for the Egger regression method is that, under the InSIDE assumption, stronger genetic variants should have more reliable estimates of the causal effect than weaker variants. Once the average pleiotropic effect of variants is accounted for through the intercept term in Egger regression, any residual dose–response relationship in the genetic associations provides evidence of a causal effect. The Egger regression estimate is consistent under the InSIDE assumption as the sample size tends to infinity if the correlation between the direct effects and instrument strength is exactly zero; otherwise it is consistent as the sample size and the number of genetic variants both tend to infinity. As previously stated, Egger regression assumes linearity and homogeneity in the associations between the genetic variants, risk factor and outcome.
The InSIDE assumption may not be satisfied in practice, particularly if the pleiotropic effects of genetic variants on the outcome act via a single confounding variable. There is some evidence for the general plausibility of the InSIDE assumption, as associations of genetic variants with different phenotypic variables have been shown to be largely uncorrelated in an empirical study.55 (link) The Egger regression estimate may have much wider confidence intervals than those from other methods in practice, as it relies on variants having different strengths of association with the risk factor. A situation with many independent genetic variants having identical magnitudes of association with the risk factor and with the outcome would intuitively provide strong evidence of a causal effect; however, the Egger estimate in this case would not be identified.
The Egger regression method gives consistent estimates if all the genetic variants are invalid instruments provided that the InSIDE assumption is satisfied, whereas the penalization and median-based methods rely on over half of the genetic variants being valid instrumental variables for consistent estimation. However, the penalization and median-based methods allow more general departures from the instrumental variable assumptions for the invalid instruments. In practice, it would seem prudent to compare estimates from a range of methods. If all methods provide similar estimates, then a causal effect is more plausible. For example, using genetic variants chosen solely on the basis of their association with the risk factor, a broad range of methods affirmed that LDL-c was a causal risk factor for CAD risk. However, the causal effect of HDLc on CAD risk suggested by a liberal Mendelian randomization analysis using the inverse-variance weighted method (see also31 (link)) was not supported by robust analysis methods.53 (link) The median-based and Egger regression methods have also been shown to have lower Type 1 (false positive) error rates than the inverse-variance weighted method in simulation studies with some invalid instrumental variables for finite sample sizes,46 (link), 53 (link) although they were above the nominal level in the case of directional pleiotropy (for the median method), and when the InSIDE assumption was violated (for the Egger regression method).

Burgess S., Bowden J., Fall T., Ingelsson E, & Thompson S.G. (2016). Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology (Cambridge, Mass.), 28(1), 30-42.

Publication 2016

A-factor (Streptomyces) factor A Genetic Diversity Hypoalphalipoproteinemia, Familial Motivation Phenotype

Most recents protocols related to «Hypoalphalipoproteinemia, Familial»

Cardiovascular Disease Risk Prediction Model

All the statistical data analyses were performed in R with version 4.0.3 (2020‐10‐10). Kolmogorov–Smirnoff test was used to perform normality test for continuous variables. For the normally distributed variables, we used Student's t‐test and one‐way ANOVA to compare among different groups, otherwise nonparametric method Wilcoxon test was used for the variables that do not follow normal distribution. For categorical variables, the χ² test was used to compare the difference among groups. p Value < 0.05 indicates there were significant differences among the groups.
Sex‐specific Cox proportional hazard regression³⁵, ³⁶, ³⁷ was used to construct a risk prediction model to predict the probability of developing CVD over a 9‐year follow up. In order to maximize performance of the calibration and discrimination for the prediction models and to minimize the impact of extreme observations, natural log–transformation for all continuous covariates in the model was performed. Risk factors in our models were selected by using the traditional cardiovascular risk factors including age, current smoking state (yes/no), CHOL, HDLC, and diabetes.³⁸ In addition to these factors, we also include the SBP and physical activity in our model. Hence, the selected factors were the same as China‐PAR¹⁴ in addition to physical activity. Besides, the interaction terms between age and other risk factors were also included in the model. Participants were randomly divided into training cohort and validation cohort based on the R function “createFolds” in the R package caret.³⁹ Ten‐fold cross validation was performed for the internal validation. C statistic, calibration χ² were calculated to evaluate the performance of the equations.⁴⁰, ⁴¹, ⁴² PA equations were also compared with the China‐PAR equation¹⁴ by the ROC curves. We also calculated the AUC for quantitative comparison; the log‐rank test was used to calculate p‐value for checking whether the difference was significant.⁴³

Zhao J., Yu Y., Zhu X., Xie Y., Ai S., Lehmann H.I., Deng X., Hu F., Li G., Zhou Y, & Xiao J. (2023). Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study. MedComm, 4(2), e220.

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Publication 2023

Diabetes Mellitus Discrimination, Psychology Hypoalphalipoproteinemia, Familial neuro-oncological ventral antigen 2, human

Biomarkers Measurement Protocol

Glucose and insulin were measured in plasma on the Cobas 6000 instrument (Roche Diagnostics, USA) using an enzymatic hexokinase assay or electrochemiluminescence, respectively. HbA1c was determined using the HPLC D10 instrument (Bio-Rad, USA). High sensitivity C-reactive protein (hsCRP) was measured in plasma with the immunoturbidometric method assay (Abbott Architect, USA). Fructosamine was measured in serum via colorimetric rate reaction (Roche Diagnostics, USA). Cholesterol, triglyceride and HDL cholesterol concentrations were measured by enzymatic assays (Abbott Architect, USA). LDL was calculated with the following equation:
LDL = (1.06*Chol) – (1.03*HDLC) – (0.117*Trig) – (0.00047*(TRIG*(Chol-HDLC))) + (0.000062*(Trig*Trig)) – 9.44

Dixon S.A., Mishra S., Dietsche K.B., Jain S., Mabundo L., Stagliano M., Krenek A., Courville A., Yang S., Turner S.A., Meyers A.G., Estrada D.E., Yadav H, & Chung S.T. (2023). The effects of prebiotics on gastrointestinal side effects of metformin in youth: A pilot randomized control trial in youth-onset type 2 diabetes. Frontiers in Endocrinology, 14, 1125187.

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Publication 2023

Cholesterol Colorimetry C Reactive Protein Diagnosis Enzyme Assays Fructosamine Glucose Hexokinase High-Performance Liquid Chromatographies High Density Lipoprotein Cholesterol Hypoalphalipoproteinemia, Familial Immunoturbidimetric Assay Insulin Plasma Serum Triglycerides

Serum Lipid Profiling Protocol

All the samples from each participant were stored in the Shiraz Endocrinology Research Center after overnight fasting. The samples were collected at 8 a.m. Serum total cholesterol, HDL-C, and TG values were assessed by enzymatic reagents (Biosystems, Barcelona, Spain) with an A-25 Biosystem Autoanalyser. The Friedwald equation was used to indirectly measure LDL concentrations from calculated TG, HDLc, and TC [20 (link)]. Non-HDL-C was calculated by detracting HDL-C from total cholesterol [21 (link)]. The coefficient of variation (CV%) for TG, TC, and HDL methods was 1.7–2.6%, 1–1.9.5%, and 1.3–1.5%, respectively.

Montazeri-Najafabady N., Dabbaghmanesh M.H., Asmarian N, & Rais Pour H. (2023). Age-, Sex-, and Puberty-Associated Reference Intervals for Lipid Profile in Iranian Children and Adolescents. International Journal of Endocrinology, 2023, 9143234.

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Publication 2023

Cholesterol Enzymes Hypoalphalipoproteinemia, Familial Serum System, Endocrine

Cardiovascular Disease Risk Factors Calculation

Body mass index (BMI) was calculated as body weight (kg) divided by height squared (m²). The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease equation using baseline serum creatinine levels (11 (link)). Remnant cholesterol was calculated as TC (mmol/L) – HDLC (mmol/L) – LDLC (mmol/L) (8 (link)). Hypertension was defined as repeated blood pressure measurements ≥140/90 mmHg at least 3 times on different occasions or a self-reported diagnosis of hypertension (12 (link)). Diabetes mellitus (DM) was defined as glycated hemoglobin >6.5%, a fasting serum glucose level ≥ 7.0 mmol/L, random glucose ≥11.1 mmol/L, and/or current diabetes treatment (13 (link)). Coronary artery disease (CAD) was defined as angiography-proven coronary stenosis ≥50% of at least one coronary artery (14 (link)). HF with preserved ejection fraction (HFpEF) was defined as HF with left ventricular ejection fraction (LVEF) ≥50%. HF with mildly reduced ejection fraction (HFmrEF) was defined as HF with LVEF 41–49%. HF with reduced ejection fraction (HFrEF) was defined as HF with LVEF ≤40% (15 (link)).

Zhao L., Zhao X., Tian P., Liang L., Huang B., Huang L., Feng J., Zhang Y, & Zhang J. (2023). Predictive value of remnant cholesterol level for all-cause mortality in heart failure patients. Frontiers in Cardiovascular Medicine, 10, 1063562.

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Publication 2023

Angiography Body Weight Cholesterol Coronary Angiography Coronary Arteriosclerosis Coronary Stenosis Creatinine Determination, Blood Pressure Diabetes Mellitus Diagnosis Diet Glomerular Filtration Rate Glucose Hemoglobin, Glycosylated High Blood Pressures Hypoalphalipoproteinemia, Familial Index, Body Mass Kidney Diseases Serum Stenosis Ventricular Ejection Fraction

Fasting Blood Collection and Analysis

Fasting blood samples were collected using sterile, disposable materials by a licensed phlebotomist. Blood was drawn directly into SST vacutainers. SST tubes sat at room temperature for 30 min and were then centrifuged in a refrigerated Centra CL3R (International Equipment Co.) for 10 min at 100× g at 10 °C. Next, 50 uL of serum was inserted into a basic metabolic reagent disk and placed into a Piccolo Xpress Chemistry Analyzer (Abbott, Princeton, NJ, USA) for determination of glucose and CVD risk markers, which included the following: glucose, total cholesterol, HDL, LDL, VLDL, non-HDLc and triglycerides. Ratios of total cholesterol to HDL and LDL to HDL were calculated.
Insulin was measured in duplicate using Meso Scale Delivery (MSD) Multi-plex Assay System and were conducted according to the manufacturer’s instructions. Briefly, 150 uL of Blocker A was added to each well of the MSD plate, which was then sealed, incubated and shook (1000 rpm) for one hour at room temperature. The plate was then washed with phosphate-buffered saline plus 0.05% Tween-20 (PBS-T), and 50 uL of the sample and standard were added to each well. The plate was then sealed, incubated and shook (1000 rpm) for 2 h at room temperature. The plate was washed again with PBS-T and then 25 uL of detection antibody solution was added to each well. The plate was then sealed, incubated and shook (1000 rpm) at room temperature for one hour. The plate was washed for a final time with PBS-T and then 150 uL of Read Buffer T was added to each well. The plate was read on the MSD QuickPlex SQ 120 imager and quantified using an 8-point standard curve. Insulin concentrations were used to calculate HOMA-IR and QUICKI (Equation (3)).

Richardson C.E., Tovar A.P., Davis B.A., Van Loan M.D., Keim N.L, & Casazza G.A. (2023). An Intervention of Four Weeks of Time-Restricted Eating (16/8) in Male Long-Distance Runners Does Not Affect Cardiometabolic Risk Factors. Nutrients, 15(4), 985.

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Publication 2023

Biological Assay BLOOD Buffers Cholesterol Glucose Hypoalphalipoproteinemia, Familial Immunoglobulins Insulin Obstetric Delivery Phosphates Saline Solution Serum Sterility, Reproductive Tremor Triglycerides Tween 20

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What are some common symptoms of Familial Hypoalphalipoproteinemia?

Familial Hypoalphalipoproteinemia is often asymptomatic, meaning individuals may not experience any noticeable symptoms. However, the condition is characterized by abnormally low levels of high-density lipoprotein (HDL) cholesterol in the blood. This can increase the risk of cardiovascular disease over time, even if no immediate symptoms are present. Regular check-ups and monitoring by a healthcare provider are important for managing this genetic condition.

How is Familial Hypoalphalipoproteinemia diagnosed?

Diagnosis of Familial Hypoalphalipoproteinemia typically involves a blood test to measure HDL cholesterol levels. If an individual has persistently low HDL cholesterol, with no other underlying cause identified, a genetic test may be performed to confirm the presence of the genetic mutation associated with this condition. Family history is also an important factor, as Familial Hypoalphalipoproteinemia is an inherited disorder.

What are the treatment options for Familial Hypoalphalipoproteinemia?

Treatment for Familial Hypoalphalipoproteinemia often focuses on lifestyle changes and medication. Adopting a healthy diet, engaging in regular exercise, and maintaining a healthy weight can help manage the underlying metabolic imbalance. In addition, cholesterol-lowering medications, such as statins or fibrates, may be prescribed to help increase HDL cholesterol levels and reduce the risk of cardiovascular disease. Regular monitoring and close collaboration with a healthcare provider are essential for effective management of this condition.

How can PubCompare.ai assist researchers studying Familial Hypoalphalipoproteinemia?

PubCompare.ai's AI-driven protocol optimization can be a valuable tool for researchers studying Familial Hypoalphalipoproteinemia. The platform allows researchers to efficiently screen protocol literature, leveraging AI to pinopint critical insights. By identifying the most effective protocols related to Familial Hypoalphalipoproteinemia, researchers can choose the best options for their specific research goals, enhancing reproducibility and accuracy. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to make informed decisions and take their work to the next level.

More about "Hypoalphalipoproteinemia, Familial"

Hypoalphalipoproteinemia, Familial is a hereditary condition characterized by abnormally low levels of high-density lipoprotein (HDL) cholesterol in the blood.
This metabolic imbalance can lead to an increased risk of cardiovascular disease.
Patients with this disorder often have a family history of low HDL levels.
Effective management typically involves lifestyle changes and medication to target the underlying lipid abnormalities.
Researchers can leverage advanced tools like the Seahorse XFe24 Analyzer and Cobas 6000 analyzer to enhance the accuracy of their studies on Hypoalphalipoproteinemia, Familial.
These instruments can provide detailed insights into the metabolic processes and lipid profiles of affected individuals.
Additionally, the Cobas Integra 400 plus analyzer and Roche Modular P Chemistry Analyzer can be used to measure HDL and other cholesterol fractions with high precision.
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