Bivariate correlation coefficients in family-type clustered studies

Gut Microbiota Richness and Diversity Track With T Cell Aging in Healthy Adults

BACKGROUND

This study examined how gut microbiota diversity and richness relate to T cell aging among 96 healthy adults of all ages. It also explored whether these links differed throughout the lifespan.

METHODS

Peripheral blood was obtained from 96 study participants (N = 96, aged 21-72) to assess mRNA markers of T cell aging (p16ink4a, p14ARF, B3gat1, Klrg1) and DNA methylation. T cell aging mRNA markers were combined into an aging index, and the Horvath epigenetic clock algorithm was used to calculate epigenetic age based on DNA methylation status of over 500 loci. Participants also collected a stool sample from which the V4 region of the 16S rRNA gene was sequenced to derive the Shannon and Simpson diversity indices, and the total count of observed operational taxonomic units (richness). Models controlled for BMI, comorbidities, sex, dietary quality, smoking status, physical activity, and sleep quality.

RESULTS

Lower microbiota richness was associated with higher T cell age based on mRNA markers, but when probing the region of significance, this relationship was only significant among adults 45 years and older (p = .03). Lower Shannon diversity (p = .05) and richness (p = .07) marginally correlated with higher epigenetic age (ie, greater T cell DNA methylation).

CONCLUSIONS

Gut microbiota complexity may correspond with the rate of T cell aging, especially in mid-to-late life. These results suggest an interplay between the gut microbiome and immunological aging that warrants further experimental work.

Statistical estimation and comparison of group-specific bivariate correlation coefficients in family-type clustered studies

Bivariate correlation coefficients (BCCs) are often calculated to gauge the relationship between two variables in medical research. In a family-type clustered design where multiple participants from same units/families are enrolled, BCCs can be defined and estimated at various hierarchical levels (subject level, family level and marginal BCC). Heterogeneity usually exists between subject groups and, as a result, subject level BCCs may differ between subject groups. In the framework of bivariate linear mixed effects modeling, we define and estimate BCCs at various hierarchical levels in a family-type clustered design, accommodating subject group heterogeneity. Simplified and modified asymptotic confidence intervals are constructed to the BCC differences and Wald type tests are conducted. A real-world family-type clustered study of Alzheimer disease (AD) is analyzed to estimate and compare BCCs among well-established AD biomarkers between mutation carriers and non-carriers in autosomal dominant AD asymptomatic individuals. Extensive simulation studies are conducted across a wide range of scenarios to evaluate the performance of the proposed estimators and the type-I error rate and power of the proposed statistical tests. Abbreviations: BCC: bivariate correlation coefficient; BLM: bivariate linear mixed effects model; CI: confidence interval; AD: Alzheimer's disease; DIAN: The Dominantly Inherited Alzheimer Network; SA: simple asymptotic; MA: modified asymptotic.

A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer's disease

Development of tau-based therapies for Alzheimer's disease requires an understanding of the timing of disease-related changes in tau. We quantified the phosphorylation state at multiple sites of the tau protein in cerebrospinal fluid markers across four decades of disease progression in dominantly inherited Alzheimer's disease. We identified a pattern of tau staging where site-specific phosphorylation changes occur at different periods of disease progression and follow distinct trajectories over time. These tau phosphorylation state changes are uniquely associated with structural, metabolic, neurodegenerative and clinical markers of disease, and some (p-tau217 and p-tau181) begin with the initial increases in aggregate amyloid-β as early as two decades before the development of aggregated tau pathology. Others (p-tau205 and t-tau) increase with atrophy and hypometabolism closer to symptom onset. These findings provide insights into the pathways linking tau, amyloid-β and neurodegeneration, and may facilitate clinical trials of tau-based treatments.

Association of Longitudinal Changes in Cerebrospinal Fluid Total Tau and Phosphorylated Tau 181 and Brain Atrophy With Disease Progression in Patients With Alzheimer Disease

IMPORTANCE

The amyloid/tau/neurodegeneration (A/T/N) framework uses cerebrospinal fluid (CSF) levels of total tau (tTau) as a marker of neurodegeneration and CSF levels of phosphorylated tau 181 (pTau181) as a marker of tau tangles. However, it is unclear whether CSF levels of tTau and pTau181 have similar or different trajectories over the course of Alzheimer disease.

OBJECTIVES

To examine the rates of change in CSF levels of tTau and pTau181 across the Alzheimer disease course and how the rates of change are associated with brain atrophy as measured by magnetic resonance imaging.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was set in tertiary research clinics. Each participant was a member of a pedigree with a known mutation for dominantly inherited Alzheimer disease. Participants were divided into 3 groups on the basis of the presence of a mutation and their Clinical Dementia Rating score. Data analysis was performed in June 2019.

MAIN OUTCOMES AND MEASURES

Rates of change of CSF tTau and pTau181 levels and their association with the rate of change of brain volume.

RESULTS

Data from 465 participants (283 mutation carriers and 182 noncarriers) were analyzed. The mean (SD) age of the cohort was 37.8 (11.3) years, and 262 (56.3%) were women. The mean (SD) follow-up duration was 2.7 (1.5) years. Two or more longitudinal CSF and magnetic resonance imaging assessments were available for 160 and 247 participants, respectively. Sixty-five percent of mutation carriers (183) did not have symptoms at baseline (Clinical Dementia Rating score, 0). For mutation carriers, the annual rates of change for CSF tTau and pTau181 became significantly different from 0 approximately 10 years before the estimated year of onset (mean [SE] rates of change, 5.5 [2.8] for tTau [P = .05] and 0.7 [0.3] for pTau 181 [P = .04]) and 15 years before onset (mean [SE] rates of change, 5.4 [3.9] for tTau [P = .17] and 1.1 [0.5] for pTau181 [P = .03]), respectively. The rate of change of pTau181 was positive and increased at the early stages of the disease, showing a positive rate of change starting at 15 estimated years before onset until 5 estimated years before onset (mean [SE], 0.4 [0.3]), followed by a positive but decreasing rate of change at year 0 (mean [SE], 0.1 [0.3]) and then negative rates of change at 5 years (mean [SE], -0.3 [0.4]) and 10 years (mean [SE], -0.6 [0.6]) after symptom onset. In individuals without symptoms (Clinical Dementia Rating score, 0), the rates of change of CSF tTau and pTau181 were negatively associated with brain atrophy (high rates of change in CSF measures were associated with low rates of change in brain volume in asymptomatic stages). After symptom onset (Clinical Dementia Rating score, >0), an increased rate of brain atrophy was not associated with rates of change of levels of both CSF tTau and pTau181.

CONCLUSIONS AND RELEVANCE

These findings suggest that CSF tTau and pTau181 may have different associations with brain atrophy across the disease time course. These results have implications for understanding the dynamics of disease pathobiology and interpreting neuronal injury biomarker concentrations in response to Alzheimer disease progression and disease-modifying therapies.

Emerging cerebrospinal fluid biomarkers in autosomal dominant Alzheimer's disease

INTRODUCTION

Four less well-studied but promising "emerging" cerebrospinal fluid (CSF) biomarkers are elevated in late-onset Alzheimer disease (AD): neurogranin, synaptosomal-associated protein-25 (SNAP-25), visinin-like protein 1 (VILIP-1), and chitinase-3-like protein 1 (YKL-40).

METHODS

CSF neurogranin, SNAP-25, VILIP-1, and YKL-40 were measured in families carrying autosomal-dominant AD mutations.

RESULTS

The four emerging CSF biomarkers were significantly elevated in the mutation carriers (n = 235) versus noncarriers (n = 145). CSF SNAP-25, VILIP-1, and YKL-40 were altered very early in the AD time course, approximately 15-19 years before estimated symptom onset. All CSF biomarkers predicted important AD-related outcomes including performance on a cognitive composite, brain amyloid burden as measured by amyloid positron emission tomography, and the estimated years from symptom onset.

DISCUSSION

Early abnormalities in CSF tTau, pTau, SNAP-25, VILIP-1, and YKL-40 suggest that synaptic damage, neuronal injury, and neuroinflammation begin shortly after the commencement of brain amyloid accumulation.

Use of a Portable Functional Near-Infrared Spectroscopy (fNIRS) System to Examine Team Experience During Crisis Event Management in Clinical Simulations

Objective: The aim of this study was to investigate the utilization of a portable functional near-infrared spectroscopy (fNIRS) system, the fNIRS Pioneer^TM, to examine team experience in high-fidelity simulation-based crisis event management (CEM) training for anesthesiologists in operating rooms.

Background: Effective evaluation of team performance and experience in CEM simulations is essential for healthcare training and research. Neurophysiological measures with wearable devices can provide useful indicators of team experience to compliment traditional self-report, observer ratings, and behavioral performance measures. fNIRS measured brain blood oxygenation levels and neural synchrony can be used as indicators of workload and team engagement, which is vital for optimal team performance.

Methods: Thirty-three anesthesiologists, who were attending CEM training in two-person teams, participated in this study. The participants varied in their expertise level and the simulation scenarios varied in difficulty level. The oxygenated and de-oxygenated hemoglobin (HbO and HbR) levels in the participants’ prefrontal cortex were derived from data recorded by a portable one-channel fNIRS system worn by all participants throughout CEM training. Team neural synchrony was measured by HbO/HbR wavelet transformation coherence (WTC). Observer-rated workload and self-reported workload and mood were also collected.

Results: At the individual level, the pattern of HbR level corresponded to changes of workload for the individuals in different roles during different phases of a scenario; but this was not the case for HbO level. Thus, HbR level may be a better indicator for individual workload in the studied setting. However, HbR level was insensitive to differences in scenario difficulty and did not correlate with observer-rated or self-reported workload. At the team level, high levels of HbO and HbR WTC were observed during active teamwork. Furthermore, HbO WTC was sensitive to levels of scenario difficulty.

Conclusion: This study showed that it was feasible to use a portable fNIRS system to study workload and team engagement in high-fidelity clinical simulations. However, more work is needed to establish the sensitivity, reliability, and validity of fNIRS measures as indicators of team experience.

Meta-analytic framework for modeling genetic coexpression dynamics

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third “coordinator” gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

Estimating diagnostic accuracy for clustered ordinal diagnostic groups in the three-class case-Application to the early diagnosis of Alzheimer disease

Many medical diagnostic studies involve three ordinal diagnostic populations in which the diagnostic accuracy can be summarized by the volume or partial volume under the receiver operating characteristic surface for a diagnostic marker. When the diagnostic populations are clustered, e.g. by families, we propose to model the diagnostic marker by a general linear mixed model that takes into account of the correlation on the diagnostic marker from members of the same clusters. This model then facilitates the maximum likelihood estimation and statistical inferences of the diagnostic accuracy for the diagnostic marker. This approach naturally allows the incorporation of covariates as well as missing data when some clusters do not have subjects on all diagnostic groups in the estimation of, and the subsequent inferences on the diagnostic accuracy. We further study the performance of the proposed methods in a large simulation study with clustered data. Finally, we apply the proposed methodology to the data of several biomarkers collected by the Dominantly Inherited Alzheimer Network, an international family-clustered registry to study autosomal dominant Alzheimer disease which is a rare form of Alzheimer disease caused by mutations in any of the three genes including the amyloid precursor protein, presenilin 1 and presenilin 2. We estimate the accuracy of several cerebrospinal fluid and neuroimaging biomarkers in differentiating three diagnostic and genetic populations: normal non-mutation carriers, asymptomatic mutation carriers, and symptomatic mutation carriers.

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes predictors from a MGLMM are always preferable to scatterplots of empirical Bayes predictors generated by separate models, unless the true association between outcomes is zero.

Serum thymidine kinase 1 activity as a pharmacodynamic marker of cyclin-dependent kinase 4/6 inhibition in patients with early-stage breast cancer receiving neoadjuvant palbociclib

BACKGROUND

Thymidine kinase 1 (TK1) is a cell cycle-regulated enzyme with peak expression in the S phase during DNA synthesis, and it is an attractive biomarker of cell proliferation. Serum TK1 activity has demonstrated prognostic value in patients with early-stage breast cancer. Because cyclin-dependent kinase 4/6 (CDK4/6) inhibitors prevent G₁/S transition, we hypothesized that serum TK1 could be a biomarker for CDK4/6 inhibitors. We examined the drug-induced change in serum TK1 as well as its correlation with change in tumor Ki-67 levels in patients enrolled in the NeoPalAna trial (ClinicalTrials.gov identifier NCT01723774).

METHODS

Patients with clinical stage II/III estrogen receptor-positive (ER+)/HER2-negative breast cancer enrolled in the NeoPalAna trial received an initial 4 weeks of anastrozole, followed by palbociclib on cycle 1, day 1 (C1D1) for four 28-day cycles, unless C1D15 tumor Ki-67 was > 10%, in which case patients went off study owing to inadequate response. Surgery occurred following 3-5 weeks of washout from the last dose of palbociclib, except in eight patients who received palbociclib (cycle 5) continuously until surgery. Serum TK1 activity was determined at baseline, C1D1, C1D15, and time of surgery, and we found that it was correlated with tumor Ki-67 and TK1 messenger RNA (mRNA) levels.

RESULTS

Despite a significant drop in tumor Ki-67 with anastrozole monotherapy, there was no statistically significant change in TK1 activity. However, a striking reduction in TK1 activity was observed 2 weeks after initiation of palbociclib (C1D15), which then rose significantly with palbociclib washout. At C1D15, TK1 activity was below the detection limit (<20 DiviTum units per liter Du/L) in 92% of patients, indicating a profound effect of palbociclib. There was high concordance, at 89.8% (95% CI: 79.2% - 96.2%), between changes in serum TK1 and tumor Ki-67 in the same direction from C1D1 to C1D15 and from C1D15 to surgery time points. The sensitivity and specificity for the tumor Ki-67-based response by palbociclib-induced decrease in serum TK1 were 94.1% (95% CI 86.2% - 100%) and 84% (95% CI 69.6% -98.4%), respectively. The κ-statistic was 0.76 (p < 0.001) between TK1 and Ki-67, indicating substantial agreement.

CONCLUSIONS

Serum TK1 activity is a promising pharmacodynamic marker of palbociclib in ER+ breast cancer, and its value in predicting response to CDK4/6 inhibitors warrants further investigation.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01723774. Registered on 6 November 2012.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity

Background

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average.

Methods

Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data.

Results

There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121–0.420) and random slopes (ρ = 0.579, 95% CI: 0.349–0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples.

Conclusion

Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).