Biological Variability of Small Dense LDL, HDL3, and Triglyceride-Rich Lipoprotein Cholesterol

The guideline-recommended lipid panel for cardiovascular disease (CVD) risk assessment measures total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and calculated low-density lipoprotein (LDL) cholesterol. Measured cholesterol in subfractions of HDL and LDL purportedly improve CVD risk prediction. Homogenous enzymatic methods are now available for measurement of the cholesterol within small dense LDL (sLDL), small dense HDL (HDL3), and triglyceride-rich lipoproteins (TRL). For meaningful interpretation of these measurements, an understanding of the potential sources and extent of result variability is needed. The smallest difference between serial measurements within a patient that likely reflects a change in clinical status is called the reference change value (RCV). Biological variability and reference change values (RCV) are well-characterized for basic lipids but there is limited information for sLDL, HDL3 or TRL. The objective of this study was to determine intra- and inter-individual variability for sLDL, HDL3, and TRL in a healthy reference population. Serum samples were collected from 24 healthy subjects (n=14 female/10 male) daily for three days (non-fasting), daily for five days (fasting), weekly for four weeks (fasting), and monthly for 7 months (fasting). sLDL, HDL3, and TRL cholesterol were measured in duplicate by enzymatic colorimetric assays (Denka, Japan) on a Roche Cobas c501. Each source of variability (between subject, within subject, and analytical) was calculated using random-effects regression models to estimate each variance component including the overall variation, standard deviation (SD), coefficient of variation (CV), and proportion of total variance (between-subject, within-subject, and analytical). Using these analytical and biological variances, the reference change value (RCV), index of individuality (IoI), and intraclass correlation coefficient (ICC) were determined. Analytic variability (CVa) from monthly testing was 1.2%, 1.1%, and 1.5% for sLDL, HDL3, and TRL, respectively. Monthly within-subject variability (CVw) was 17.1% for sLDL, 7.4% for HDL3 and 25.7% for TRL. Monthly between-subject variability (CVb) was 32.2%, 13.93%, and 33.4% for sLDL, HDL3, and TRL, respectively. Most of the monthly variation was attributed to between-subject variation for all three tests. Within-subject variation accounted for 37% of TRL variation and 22% for both sLDL and HLD3. Within-subject RCVs for monthly measurements were 16.9mg/dL for sLDL, 5.3mg/dL for HDL3, and 15.1mg/dL for TRL. IoIs for monthly testing were 0.81 for TRL, 0.57 for sLDL, and 0.61 for HDL3. Our data demonstrate that sLDL, HDL3, and TRL show low analytical variability, moderate within-subject variability, but high between-subject variability when measured by homogenous assays in a healthy population. The IoI value (>0.6) for TRL suggests use of a reference interval is appropriate for result interpretation. Conversely, clinical cut-points may be more useful than reference intervals for sLDL and HDL3 which had IoIs ~0.6. These findings may be useful for clinical interpretation, particularly when comparing successive measurements of these analytes.

FMRI multi-scale cortical spontaneous activity: 7T vs. 3T

This paper describes the use of the Human Connectome Project (HCP) data for mapping the distribution of spontaneous activity in the human brain across different spatial scales, magnets and individuals. Specifically, the resting-state functional MRI signals acquired under the HCP 3 tesla (T) and 7T magnet protocols were measured by computational methods at multiple spatial scales across the cerebral cortex using: 1) an amplitude metric on a single measuring unit (ALFF), 2) a functional homogeneity metric on a set of neighboring measuring units (ReHo) and 3) a homotopic functional connectivity metric on pairs of symmetric measuring units between the two hemispheres (VMHC). Statistical assessments on these measurements revealed that all the raw metrics were enhanced by the higher magnetic field, highlighting their dependence on magnet field strength. Measurement reliability of these global measurements were moderate to high and comparable between between 3T and 7T magnets. The differences in these measurements introduced by the higher magnetic field were spatially dependent and varied according to specific cortical regions. Specifically, the spatial contrasts of ALFF were enhanced by the 7T magnet within the anterior cortex while weakened in the posterior cortex. This is opposite for ReHo and VMHC. This scale-dependent phenomena also held true for measurement reliabilities, which were enhanced by the 7T magnet for ReHo and VMHC and weakened for ALFF. These reliability differences were primarily located in high-order associate cortex, reflecting the corresponding changes of individual differences: higher between-subject variability and lower within-subject variability for ReHo and VMHC, lower between-subject variability and higher within-subject variability for ReHo and VMHC with respect to higher magnetic field strength. Our work, for the first time, demonstrates the spatial-scale dependence of spontaneous cortical activity measurements in the human brain and their test-retest reliability across different magnet strengths, and discussed about the statistical implications for experimental design using resting-state fMRI.

Changes in quantifiable breathing pattern components predict asthma control: an observational cross-sectional study

Background Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components may provide additional useful information about asthma control. This study examined whether respiratory timing parameters and thoracoabdominal (TA) motion measures could predict and classify levels of asthma control. Methods One hundred twenty-two asthma patients at STEP 2- STEP 5 GINA asthma medication were enrolled. Asthma control was determined by the Asthma Control Questionnaire (ACQ7-item) and patients divided into ‘well controlled’ or ‘uncontrolled’ groups. Breathing pattern components (respiratory rate (RR), ratio of inspiration duration to expiration duration (Ti/Te), ratio of ribcage amplitude over abdominal amplitude during expiration phase (RCampe/ABampe), were measured using Structured Light Plethysmography (SLP) in a sitting position for 5-min. Breath-by-breath analysis was performed to extract mean values and within-subject variability (measured by the Coefficient of Variance (CoV%). Binary multiple logistic regression was used to test whether breathing pattern components are predictive of asthma control. A post-hoc analysis determined the discriminant accuracy of any statistically significant predictive model. Results Fifty-nine out of 122 asthma patients had an ACQ7-item < 0.75 (well-controlled asthma) with the rest being uncontrolled (n = 63). The absolute mean values of breathing pattern components did not predict asthma control (R2 = 0.09) with only mean RR being a significant predictor (p < 0.01). The CoV% of the examined breathing components did predict asthma control (R2 = 0.45) with all predictors having significant odds ratios (p < 0.01). The ROC curve showed that cut-off points > 7.40% for the COV% of the RR, > 21.66% for the CoV% of Ti/Te and > 18.78% for the CoV% of RCampe/ABampe indicated uncontrolled asthma. Conclusion The within-subject variability of timing parameters and TA motion can be used to predict asthma control. Higher breathing pattern variability was associated with uncontrolled asthma suggesting that irregular resting breathing can be an indicator of poor asthma control.

Importance of between and within Subject Variability in Extracellular Vesicle Abundance and Cargo when Performing Biomarker Analyses

Small extracellular vesicles (sEV) have emerged as a potential rich source of biomarkers in human blood and present the intriguing potential for a ‘liquid biopsy’ to track disease and the effectiveness of interventions. Recently, we have further demonstrated the potential for EV derived biomarkers to account for variability in drug exposure. This study sought to evaluate the variability in abundance and cargo of global and liver-specific circulating sEV, within (diurnal) and between individuals in a cohort of healthy subjects (n = 10). We present normal ranges for EV concentration and size and expression of generic EV protein markers and the liver-specific asialoglycoprotein receptor 1 (ASGR1) in samples collected in the morning and afternoon. EV abundance and cargo was generally not affected by fasting, except CD9 which exhibited a statistically significant increase (p = 0.018). Diurnal variability was observed in the expression of CD81 and ASGR1, which significantly decreased (p = 0.011) and increased (p = 0.009), respectively. These results have potential implications for study sampling protocols and normalisation of biomarker data when considering the expression of sEV derived cargo as a biomarker strategy. Specifically, the novel finding that liver-specific EVs exhibit diurnal variability in healthy subjects should have broad implications in the study of drug metabolism and development of minimally invasive biomarkers for liver disease.

Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.

Quantifiable Breathing Pattern Components Can Predict Asthma Control: an Observational Cross-sectional Study

Background: Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components can provide additional useful information about asthma control. This study examined whether respiratory timing parameters and thoracoabdominal (TA) motion measures could predict and classify levels of asthma control. Methods: 122 asthma patients at STEP 2- STEP 5 GINA asthma medication were enrolled. Asthma control was determined by the Asthma Control Questionnaire (ACQ7-item) and patients divided into ‘well controlled’ or ‘uncontrolled’ groups. Breathing pattern components (respiratory rate (RR), ratio of inspiration duration to expiration duration (Ti/Te), ratio of ribcage amplitude over abdominal amplitude during expiration phase (RCampe/ABampe), were measured using Structured Light Plethysmography (SLP) in a sitting position for 5-minutes. Breath-by-breath analysis was performed to extract mean values and within-subject variability (measured by the Coefficient of Variance (CoV%). Binary multiple logistic regression was used to test whether breathing pattern components are predictive of asthma control. A post-hoc analysis determined the discriminant accuracy of any statistically significant predictive model. Results: Fifty-nine out of 122 asthma patients had an ACQ7-item < 0.75 (well-controlled asthma) with the rest being uncontrolled (n= 63). The absolute mean values of breathing pattern components did not predict asthma control (R2 = 0.09) with only mean RR being a significant predictor (p < 0.01). The CoV% of the examined breathing components did predict asthma control (R2 = 0.45) with all predictors having significant odds ratios (p < 0.01). The ROC curve showed that cut-off points > 7.40% for the COV% of the RR, > 21.66% for the CoV% of Ti/Te and > 18.78% for the CoV% of RCampe/ABampe indicated uncontrolled asthma. Conclusion: The within-subject variability of timing parameters and TA motion can be used to predict asthma control. Higher breathing pattern variability was associated with uncontrolled asthma suggesting that irregular resting breathing is an indicator of poor asthma control.

Improved numerical stability for the bounded integer model

This article highlights some numerical challenges when implementing the bounded integer model for composite score modeling and suggests an improved implementation. The improvement is based on an approximation of the logarithm of the error function. After presenting the derivation of the improved implementation, the article compares the performance of the algorithm to a naive implementation of the log-likelihood using both simulations and a real data example. In the simulation setting, the improved algorithm yielded more precise and less biased parameter estimates when the within-subject variability was small and estimation was performed using the Laplace algorithm. The estimation results did not differ between implementations when the SAEM algorithm was used. For the real data example, bootstrap results differed between implementations with the improved implementation producing identical or better objective function values. Based on the findings in this article, the improved implementation is suggested as the new default log-likelihood implementation for the bounded integer model.

Dark Triad in the context of career choice

The research study deals with Dark Triad traits (Machiavellianism, narcissism, psychopathy) in the context of the vocational interests in high school students. We assumed that students tending towards the helping professions might score differently in Dark Triad traits compared to students oriented towards professions such as manufacturing, business, or law (Jonason et al., 2014; Kowalski et al., 2017). The main goal of the study was to examine the differences in Dark Triad traits with respect to students´ professional intentions. The research was conducted on a sample of 181 students of grammar schools and secondary vocational schools in Slovakia (Mage = 18.3 years; SD = .77); 57% were boys, 43% were girls. The Slovak version of the Short Dark Triad (Čopková & Šafár, 2020; Jones & Paulhus, 2014) and the Questionnaire of Professional Intentions (Džuka, 2006) were used. Testing the significance of differences between students with different professional intentions pointed to significant differences in subclinical psychopathy. Significantly higher psychopathy was shown by students oriented to the sphere of production compared to students oriented to art, science and education. Also in psychopathy, students oriented towards the sphere of business scored higher than students oriented towards the sphere of art and education. Testing of within-subject variability showed Machiavellianism as the most significant feature of the Dark Triad in all professional spheres.