Within-subject Variability Research Articles

A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank.

This study aimed to identify the genetic architecture and potential biomarkers of BMD. Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform. A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10-126), FAM3C (P = 4.18 × 10-108), and CPED1 (P = 8.48 × 10-106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10-13) and RGS7 (P = 4.72 × 10-10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10-3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10-3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY. Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD.

Reference change values of FIB-4

When comparing two analytical results for the same analyte, the clinicians may benefit from knowing the reference change values (RCVs) of the analyte. For Fibrosis-4 Index (FIB-4), a noninvasive test used for assessing the risk of liver fibrosis, no RCVs have been published for non-cirrhotic individuals. Therefore, we estimated RCVs for adults, using retrospectively collected data from outpatients with AST, ALT, and thrombocytes within the respective reference intervals. FIB-4 was calculated as (age × AST)/(thrombocytes × ALT0.5). From two FIB-4 values in each patient we calculated the RCVs parametrically and non-parametrically. For both methods, we estimated the limits of the central 90% of the distribution of the ratio between the second and the first measurement. We obtained data on 599 outpatients with two blood tests taken 3 - 972 (median 258) days apart. The RCVs were 0.72 − 1.40 and 0.72 − 1.43, respectively, using the parametric and non-parametric methods. The 5 and 95 percentiles were not statistically significantly associated with sex, age, level of analyte, or the time between the measurements. The within-subject biological variation of FIB-4 was estimated to be 13.9%. Conclusion: In 90% of the patients the ratio between the second and the first FIB-4 result was approximately 0.7 − 1.4.

Everything is varied: The surprising impact of instantial variation on ML reliability

Instantial variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given instance or the measurement process. Although taking into account IV is critical for the proper analysis of the results, this source of uncertainty and its impact on robustness have so far been neglected in Machine Learning (ML). To fill this gap, we look at how IV affects ML performance and generalization, and how its impact can be mitigated. Specifically, we provide a methodological contribution to formalize the problem of IV in the statistical learning framework. To prove the relevance of our contribution, we focus on one of the most critical domains, healthcare, and take individual (analytical and biological) variation as a specific kind of IV; in this domain, we use one of the largest real-world laboratory medicine datasets for the task of COVID-19 detection, to show that: (1) common state-of-the-art ML models are severely impacted by the presence of IV in data; and (2) advanced learning strategies, based on data augmentation and soft computing methods (data imprecisiation), and proper study designs can be effective at improving robustness to IV. Our findings demonstrate the critical relevance of correctly accounting for IV to enable safe deployment of ML in real-world settings.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

Characterization of direct and/or indirect genetic associations for multiple traits in longitudinal studies of disease progression.

When quantitative longitudinal traits are risk factors for disease progression and subject to random biological variation, joint model analysis of time-to-event and longitudinal traits can effectively identify direct and/or indirect genetic association of single nucleotide polymorphisms (SNPs) with time-to-event. We present a joint model that integrates: (1) a multivariate linear mixed model describing trajectories of multiple longitudinal traits as a function of time, SNP effects, and subject-specific random effects and (2) a frailty Cox survival model that depends on SNPs, longitudinal trajectory effects, and subject-specific frailty accounting for dependence among multiple time-to-event traits. Motivated by complex genetic architecture of type 1 diabetes complications (T1DC) observed in the Diabetes Control and Complications Trial (DCCT), we implement a 2-stage approach to inference with bootstrap joint covariance estimation and develop a hypothesis testing procedure to classify direct and/or indirect SNP association with each time-to-event trait. By realistic simulation study, we show that joint modeling of 2 time-to-T1DC (retinopathy and nephropathy) and 2 longitudinal risk factors (HbA1c and systolic blood pressure) reduces estimation bias in genetic effects and improves classification accuracy of direct and/or indirect SNP associations, compared to methods that ignore within-subject risk factor variability and dependence among longitudinal and time-to-event traits. Through DCCT data analysis, we demonstrate feasibility for candidate SNP modeling and quantify effects of sample size and Winner's curse bias on classification for 2 SNPs identified as having indirect associations with time-to-T1DC traits. Joint analysis of multiple longitudinal and multiple time-to-event traits provides insight into complex traits architecture.

Biological variation of biochemical urine and serum analytes in healthy dogs.

Biological variation (BV) of urinary (U) biochemical analytes has not been described in absolute terms, let alone as a ratio of the U-creatinine or fractional excretion in healthy dogs. These analytes are potential diagnostic tools for different types of kidney damage and electrolyte disorders in dogs. We aimed to investigate the BV of specific gravity, osmolality, creatinine, urea, protein, glucose, chloride, sodium, potassium, calcium, and phosphate in urine from healthy pet dogs. Blood and urine samples from 13 dogs were collected once weekly for 8 weeks. Samples were analyzed in duplicate and in randomized order. For each sample, U-analyte and serum concentrations were measured, and U-analyte/U-creatinine and fractional excretion (FE) were calculated. Components of variance, estimated by restricted maximum likelihood, were used to determine within-subject variation (CVI ), between-subject variation (CVG ), and analytical variation (CVA ). Index of individuality (II) and reference change values were calculated. CVI for all urine analytes varied between 12.6% and 35.9%, except for U-sodium, U-sodium/U-Cr, and FE-sodium, which had higher CVI s (59.5%-60.7%). For U-protein, U-sodium, U-potassium, U-sodium/U-creatinine, FE-urea, FE-glucose, FE-sodium, FE-potassium, and FE-phosphate II were low, indicating that population-based RIs were appropriate. The remaining analytes had an intermediate II, suggesting that population-based RIs should be used with caution. This study presents information on the biological variation of urinary and serum biochemical analytes from healthy dogs. These data are important for an appropriate interpretation of laboratory results.

Correlations between within-subject variability of pain intensity reports and rubber hand illusion proprioceptive drift

IntroductionConsistent with the Bayesian brain hypothesis, the within-subject variability of pain intensity reports as captured with the Focused Analgesia Selection Test (FAST) might be a surrogate measure of the certainty in ascending noxious signals. The outcomes of a non-pain-related task, the rubber hand illusion, were hypothesized to reflect the same construct. This study aimed to explore whether within-subject differences in variability of pain intensity reports and the outcomes of the rubber hand illusion might be related. MethodsNonclinical participants underwent the classic rubber hand illusion under synchronous (experimental) and asynchronous (control) conditions. Two outcomes were assessed: proprioceptive drift and feeling of ownership. Thereafter, participants underwent the FAST to assess the within-subject variability of pain reports in response to heat stimuli. Intraclass correlation (ICC) and the correlation coefficient (R2) were the main outcomes. Spearman’s correlations were used to assess associations between the outcomes of the 2 tasks. ResultsThirty-six volunteers completed the study. Both FAST outcomes—ICC (Spearman’s r = 0.355, p = 0.033) and R2 (Spearman’s r = 0.349, p = 0.037)—were positively correlated with proprioceptive drift in the synchronous but not asynchronous conditions (p > 0.05). The subjective feeling of ownership and FAST outcomes did not correlate (p > 0.05). ConclusionsThe associations between the 2 tasks’ outcomes imply that both tasks at least partly assess similar constructs. Current knowledge suggests that this construct represents the person’s certainty in perceiving ascending sensory signals, or, in Bayesian terminology, the certainty of the likelihood.

0194 Sleep Tracker: How Accurate Does It Need To Be?

Abstract Introduction Commercial sleep tracker devices offer the ability to measure an individual’s sleep patterns, providing a portable and low-cost option to collect sleep-wake data to enhance fatigue management and minimize alertness impairment. While multiple studies have reported that sleep parameters estimated with these devices differ from gold-standard polysomnography (PSG), the extent to which these differences are acceptable is not known. Here, we provide a method to quantitatively assess whether sleep-duration errors generated by sleep trackers hinder our ability to assess alertness impairment. Methods To obtain representative statistics of sleep-duration errors, we performed a literature review and identified 14 studies that compared estimations of sleep parameters between 18 unique commercial sleep trackers and PSG. Then, we used the well-validated Unified Model of Performance (UMP), a mathematical model that predicts alertness impairment as a function of sleep history, to quantitatively assess the effect of sleep-duration errors on alertness predictions. For this purpose, we performed 20,000 simulations in which an individual slept 8 hour per night for 30 consecutive days. Each day, we randomly sampled sleep-duration errors using the obtained representative statistics and computed daily alertness-prediction errors. We repeated these simulations for 5 and 9 hours of sleep per night. We assumed that sleep-duration errors that lead to alertness-prediction errors of ≤30 ms (i.e., the within-subject variability under well-rested conditions) are not a concern. Results Based on the 14 studies, sleep trackers yielded sleep-duration errors with a mean of 22 minutes and a standard deviation of 44 minutes. Using these statistics to sample daily errors, we found that, at the end of the 30 days of UMP simulations, these sleep-duration errors led to a mean alertness-prediction error of ~20 ms, with ~75% of the errors ≤30 ms. We also found that, for each additional minute of sleep-duration error, the prediction error increased by ~1 ms. These results were consistent when the hours of sleep per night varied from 5 to 9 hours. Conclusion On average, sleep trackers lead to alertness-prediction errors that are consistently smaller than the within-subject variability of alertness impairment, suggesting that they are an alternative approach for measuring sleep duration. Support (if any)

0178 2B-Alert App: Personalized Caffeine Recommendations for Optimal Alertness

Abstract Introduction Sleep loss causes millions of individuals around the world to carry out daily activities with impaired alertness, affecting injury risk and productivity. If properly consumed, caffeine can safely and effectively mitigate the effects of sleep loss on alertness. However, to be effective, caffeine should be consumed at the right time and in the correct amount, depending on sleep history, work schedule and, importantly, sensitivity to sleep loss. Here, we present the 2B-Alert app, a unique smartphone application with capabilities to learn an individual’s trait-like response to sleep loss and to provide personalized caffeine recommendations to optimize alertness. Methods We conducted a prospective 62-h total sleep deprivation study to validate the capabilities of 2B-Alert. Throughout the challenge, 21 participants used the app to measure their alertness via the psychomotor vigilance test (PVT). The app used the PVT data collected during the first 36 h of wakefulness to learn each participant’s sleep-loss response and provided on-the-fly personalized caffeine recommendations (from 0 to 800 mg), so that each participant would sustain alertness at a pre-specified target level (mean PVT response time of 270 ms) during a 6-h period starting at 44 h into the challenge. To assess the effectiveness of the personalized recommendations, we computed the amount of PVT data during the 6-h period that fell within the target alertness level +/-2 times the within-subject variability of alertness impairment (i.e., +/-60 ms). Results We observed a wide range of responses to sleep loss, with some participants displaying high levels of resilience and others high levels of vulnerability. Accordingly, 2B-Alert recommended no caffeine to five participants, 100-400 mg to 11 participants, and 500-800 mg to five participants. Regardless of their sleep-loss response and the consumed amount of caffeine, participants sustained the target alertness level ~80% of the time during the 6-h period. Conclusion 2B-Alert automatically learns an individual’s trait-like response to sleep loss and provides personalized caffeine recommendations in real time so that individuals achieve a desired alertness level regardless of their susceptibility to sleep loss. Support (if any)

0498 Home automated OSA quantification of night-to-night short term variability: analysis of the associated conditions

Abstract Introduction Single night in-lab polysomnography (PSG) is still the conventional procedure for making obstructive sleep apnea (OSA) diagnosis, or alternatively a home sleep apnea test (HSAT) in patients free from comorbidity. Recent publications drew attention to considerable internight within-subject variability in OSA clinical status, notably but not only with HSAT. Taking benefit of multiple nights recording in home-based setting using measurements of mandibular jaw movement (MJM) combined with automated machine learning analysis, this study aimed to investigate the effect of change in sleep parameters on the internight variability of the apnea hypopnea index (VAHI). Methods 160 adults suspected of having OSA successfully completed 3 sleep tests at home with the Sunrise device (Sunrise, Namur, Belgium). Data analysis implied generalized estimating equation (GEE) regression. Results Even though the internight variability of AHI was not statistically significant at the population level with a mean VAHI of -1.1 ± 7.8 events/h, such variability could lead to misdiagnosis and wrong therapeutic decisions based on a single night. After adjusting for effects of gender, body mass index (BMI), age, total sleep time (TST) and time interval between sleep tests, regression models indicated that sleeping in supine position was positively and independently associated with VAHI, the AHI increased by 0.95 event/h for each 10% of increase in sleeping time in supine position. VAHI was negatively and significantly (p < 0.01) associated with sleep efficiency and deep sleep; VAHI decreased of 0.16 event/h (0.04 to 0.28) and 0.29 event/h (0.17 to 0.42) for each % of increase in sleep efficiency and proportion of deep sleep, respectively. Conclusion At the individual level, position, sleep efficiency and deep sleep are associated with short-term internight variability in AHI. Clinical decision based on a single-night testing may lead to errors in OSA diagnosis with delayed or wrong therapeutic decisions. Support (if any)

A leucine-rich repeat kinase 2 (LRRK2) pathway biomarker characterization study in patients with Parkinson's disease with and without LRRK2 mutations and healthy controls.

Increased leucine-rich repeat kinase 2 (LRRK2) kinase activity is an established risk factor for Parkinson's disease (PD), and several LRRK2 kinase inhibitors are in clinical development as potential novel disease-modifying therapeutics. This biomarker characterization study explored within- and between-subject variability of multiple LRRK2 pathway biomarkers (total LRRK2 [tLRRK2], phosphorylation of the serine 935 (Ser935) residue on LRRK2 [pS935], phosphorylation of Rab10 [pRab10], and total Rab10 [tRab10]) in different biological sources (whole blood, peripheral blood mononuclear cells [PBMCs], neutrophils) as candidate human target engagement and pharmacodynamic biomarkers for implementation in phase I/II pharmacological studies of LRRK2 inhibitors. PD patients with a LRRK2 mutation (n = 6), idiopathic PD patients (n = 6), and healthy matched control subjects (n = 10) were recruited for repeated blood and cerebrospinal fluid (CSF) sampling split over 2 days. Within-subject variability (geometric coefficient of variation [CV], %) of these biomarkers was lowest in whole blood and neutrophils (range: 12.64%-51.32%) and considerably higher in PBMCs (range: 34.81%-273.88%). Between-subject variability displayed a similar pattern, with relatively lower variability in neutrophils (range: 61.30%-66.26%) and whole blood (range: 44.94%-123.11%), and considerably higher variability in PBMCs (range: 189.60%-415.19%). Group-level differences were observed with elevated mean pRab10 levels in neutrophils and a reduced mean pS935/tLRRK2 ratio in PBMCs in PD LRRK2-mutation carriers compared to healthy controls. These findings suggest that the evaluated biomarkers and assays could be used to verify pharmacological mechanisms of action and help explore the dose-response of LRRK2 inhibitors in early-phase clinical studies. In addition, comparable α-synuclein aggregation in CSF was observed in LRRK2-mutation carriers compared to idiopathic PD patients.

Surgeon estimation of retropubic trocar position in blind 3D space.

Retropubic midurethral sling surgery involves the blind passage of trocars near vital organs. We quantified the proximity of surgeons' mental representation of trocar position relative to actual position using a pelvis simulation platform. We hypothesized that novice surgeons, compared with experts, would estimate the trocar's location to be further from the actual location. Novice and expert surgeons performed bilateral retropubic trocar passes of a Gynecare TVT trocar (#810041B-#810,051) on the simulation platform. We measured the trocar tip's position using a motion capture system, and recorded vocalizations when they perceived contacting the bone and crossing three landmark-oriented planes. We calculated differences (∆Bone, ∆Turn, ∆Top, ∆Pop) between vocalization times and when the trocar crossed the corresponding plane. We performed Mann-Whitney and Chi-squared tests to investigate differences between novices and experts and Levene's test to assess equality of variances for subject-level variation. A total of 34 trials, including 22 expert and 12 novice trials, were performed by six participants. ∆Bone was significantly smaller among novice surgeons (1.27 vs 2.81s, p=0.013). There were no significant differences in the remaining three deltas or in vocalizing early versus late. Levene's test revealed no significant differences in within-subject variability for any of the four deltas. Novices passed the trocar anterior to the pubic bone on three passes. Novices were similar to expert surgeons in their estimation of the trocar's location and may have relied more heavily on anticipatory mechanisms to compensate for lack of experience. Teaching surgeons should make sure the novice surgeon trocar pass starts posterior to the bone.

Within-Subject Variability and the Influence of Exercise Training History on the Resting Plasma Metabolome in Men

This study investigated within-subject variability in the circulating metabolome under controlled conditions, and whether divergent exercise training backgrounds were associated with alterations in the circulating metabolome assessed in resting samples. Thirty-seven men comprising of endurance athletes (END; body mass, 71.0 ± 6.8kg; fat-free mass index, 16.9 ± 1.1kg/m2), strength athletes (STR; 94.5 ± 8.8kg; 23.0 ± 1.8kg/m2), and recreationally active controls (CON; 77.6 ± 7.7kg; 18.1 ± 1.0kg/m2) provided blood samples after an overnight fast on two separate occasions controlled for time of day of sampling, recent dietary intake, time since last meal, and time since last exercise training session. A targeted profile of metabolites, performed using liquid chromatography and mass spectrometry on plasma samples, identified 166 individual metabolites and metabolite features, which were analyzed with intraclass correlation coefficients, a multilevel principal component analysis, and univariate t tests adjusted for multiple comparisons. The median intraclass correlation coefficient was .49, with 46 metabolites displaying good reliability and 31 metabolites displaying excellent reliability. No difference in the abundance of any individual metabolite was identified within groups when compared between visits, but a combined total of 44 metabolites were significantly different (false discovery rate <0.05) between groups (END vs. CON, 42 metabolites; STR vs. CON, 10 metabolites; and END vs. STR, five metabolites). Under similar measurement conditions, the reliability of resting plasma metabolite concentrations varies largely at the level of individual metabolites with ∼48% of metabolites displaying good-to-excellent reliability. However, a history of exercise training was associated with alterations in the abundance of ∼28% of metabolites in the targeted profile employed in this study.

Analytical and Non-Analytical Variation May Lead to Inappropriate Antimicrobial Dosing in Neonates: An In Silico Study.

Therapeutic drug monitoring (TDM) of aminoglycosides and vancomycin is used to prevent oto- and nephrotoxicity in neonates. Analytical and nonanalytical factors potentially influence dosing recommendations. This study aimed to determine the impact of analytical variation (imprecision and bias) and nonanalytical factors (accuracy of drug administration time, use of non-trough concentrations, biological variation, and dosing errors) on neonatal antimicrobial dosing recommendations. Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing. Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin). Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.

Functional clustering methods for binary longitudinal data with temporal heterogeneity

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.

Intra-individual stability of longitudinal Urinary Steroid Profiles in Swedish Athletes.

The steroid module of the Athlete Biological Passport (ABP) aims to detect doping with endogenous steroids by longitudinally monitoring epitestosterone (E), testosterone (T), and four metabolically related steroids and their ratios. There are large variations in the urinary levels of the androgen metabolites due to genetic polymorphisms, drug use, menstrual cycle, and other factors. In this study, we aimed to increase our understanding of the natural, within-individual variations of the established ABP markers in males and females over time, looking at samples collected both in and out of competition (IC/OOC). Urinary steroid profiles from 323 Swedish athletes, with at least five samples per athlete, were extracted from ADAMS together with information on type of sport, IC/OOC, and time of day. Data were analyzed using coefficient of variation (CV%) to examine within-subject variability, and linear mixed effects models to estimate within-subject change in the metabolites over time. The metabolites and ratios expressed higher individual CV% in females (23-56) than in males (18-39). Samples taken OOC showed larger intra-individual variations than samples collected IC for most of the ABP metabolites in both sexes. The median concentrations were higher IC for some metabolites, particularly testosterone being 52% higher among females. Time of day influenced the intra-individual variation of the urinary steroid profile with decreases in androgen metabolites over time, if measured in evening vs daytime. These findings can aid in the testing strategies and interpretation of the steroidal module of ABP.

Biological variation of CA 15-3, CA 125 and HE 4 on lithium heparinate plasma in apparently healthy Caucasian volunteers

Tumor markers are well-known for being important tools in the support of diagnosis, monitoring of treatment efficacy and follow-up of cancers. CA 125, CA 15-3 and HE 4 have demonstrated potential efficacy in other clinical indications. The main objective was to evaluate the biological variation of these glycoproteins using two different immunoassays in an apparently healthy Caucasian population. Nineteen healthy volunteers including 11 women and 8 men were sampled weekly for 5 consecutive weeks. Samples were analyzed in duplicate on Lumipulse® G600II (Fujirebio) and on the Cobas e602 (Roche Diagnostics) analyzers. After assessment of normality, exclusion of outliers and analysis of homogeneity of variance, analytical variation (CVA), within-subject biological variation (CVI) and between-subject biological variation (CVG) were determined using a nested ANOVA. CVA, CVI and CVG were determined on both analyzers and both genders. For CA 125, the CVA ranges from 1.0 to 3.4%, the CVI from 5.7 to 13.8% and the CVG from 32.2 to 42.9%. For CA 15-3, the CVA is between 1.1 and 3.4%, the CVI between 3.9 and 6.5% and the CVG between 43.7 and 196.9%. Lastly, HE 4 has CVA values between 1.4 and 2.4%, CVI between 5.1 and 10.5% and CVG between 7.1 and 12.6%. Our study provided updated data on the biological variation of CA 125, HE 4 and CA 15-3. These data allow to improve the clinical interpretation and thus the management of the patient.

On the psychometric evaluation of cognitive control tasks: An Investigation with the Dual Mechanisms of Cognitive Control (DMCC) battery

The domain of cognitive control has been a major focus of experimental, neuroscience, and individual differences research. Currently, however, no theory of cognitive control successfully unifies both experimental and individual differences findings. Some perspectives deny that there even exists a unified psychometric cognitive control construct to be measured at all. These shortcomings of the current literature may reflect the fact that current cognitive control paradigms are optimized for the detection of within-subject experimental effects rather than individual differences. In the current study, we examine the psychometric properties of the Dual Mechanisms of Cognitive Control (DMCC) task battery, which was designed in accordance with a theoretical framework that postulates common sources of within-subject and individual differences variation. We evaluated both internal consistency and test–retest reliability, and for the latter, utilized both classical test theory measures (i.e., split-half methods, intraclass correlation) and newer hierarchical Bayesian estimation of generative models. Although traditional psychometric measures suggested poor reliability, the hierarchical Bayesian models indicated a different pattern, with good to excellent test–retest reliability in almost all tasks and conditions examined. Moreover, within-task, between-condition correlations were generally increased when using the Bayesian model-derived estimates, and these higher correlations appeared to be directly linked to the higher reliability of the measures. In contrast, between-task correlations remained low regardless of theoretical manipulations or estimation approach. Together, these findings highlight the advantages of Bayesian estimation methods, while also pointing to the important role of reliability in the search for a unified theory of cognitive control.

The effect of standing posture on amplitude and variability of postural tremor in Parkinson’s disease

IntroductionThis study examined whether altering body position (i.e., sitting or standing) affected the dynamics of physiological tremor for healthy older adults and persons with Parkinson’s disease (PD). It was also of interest to determine how consistent the tremor was for both groups as determined by examining changes in within-subject variability of tremor amplitude, regularity and frequency. MethodsTen Parkinsonian participants (65.1 ± 3.2 yrs.) and twelve elderly persons (71.2 ± 2.6 yrs.) participated in this study. Tremor was collected from the index finger and hand segments using lightweight accelerometers during the performance of a bilateral pointing task. Persons performed the pointing task in a standing or sitting position. ResultsAs expected, the tremor for the PD persons was greater in magnitude (mean RMS, peak power), more regular (lower SampEn), and more inconsistent from trial-to-trial (increased intra-individual variability, IIV) than the tremor recorded for the elderly. Further, when assessed during standing, the magnitude of the tremor for all individuals (elderly and PD) was greater, more variable, and less complex compared to the tremor when assessed during the sitting posture. The only measure which did not change within each group was the frequency of the major tremor peak which remained consistent, showing no significant change between limbs or as a function of the posture adopted. ConclusionThe findings revealed that tremor increased in amplitude and decreased in regularity for all individuals was assessed when standing compared to sitting. It is likely that these increases were task-related, reflecting the increased physical demands of performing the task when standing rather than being driven by specific age- or disease-related changes in the mechanisms underlying tremorgenesis. Further, the tremor for the PD individuals tended to be more variable from trial-by-trial in terms of both amplitude and regularity as compared to the elderly persons. Interestingly, the only tremor metric which showed no change within each group was the frequency of the major tremor peak which was consistent within both groups irrespective of the posture adopted.

Inattention symptom severity and cognitive processes in children at risk of ADHD: the moderating role of separation anxiety

ABSTRACT Impairments in cognitive processes and their associations with dimensional measures of inattention, hyperactivity-impulsivity and anxiety were examined in children at risk of Attention-Deficit Hyperactivity Disorder. Children referred by teachers for exhibiting ADHD-type problems (n = 116; 43 meeting full diagnostic criteria for ADHD; 4–8 years) completed computerized tasks measuring episodic memory, response inhibition, visuomotor control and sustained attention, while parents were interviewed (DAWBA) to assess ADHD and anxiety symptoms. Of the 116 children assessed, 72% exhibited impaired cognitive processes; 47% had impaired visuomotor control, 37% impaired response inhibition, and 35% had impaired episodic memory. Correlational and hierarchical regression analyses using our final analytic sample (i.e., children who completed all cognitive tasks and a vocabulary assessment, n = 114) showed that poorer task performance and greater within-subject variability were significantly associated with more severe inattention symptoms but not with hyperactivity-impulsivity severity. Symptoms of separation anxiety, which were reported in over half of the sample, moderated associations between inattention and episodic memory, and between inattention and inhibition. Only children without separation anxiety showed significant correlations between ADHD symptoms and poor performance. However, separation anxiety had no moderating effect on associations between inattention and visuomotor control or sustaining attention. Children exhibiting signs of ADHD show impairments across a range of cognitive tasks. Further research to improve our understanding of these processes may be useful in the development of early interventions. Our results suggest that separation anxiety should be taken into account when considering interventions to address emerging neuropsychological deficits associated with this disorder.