Coarctation Duration and Severity Predict Risk of Hypertension Precursors in a Preclinical Model and Hypertensive Status Among Patients.

BACKGROUND: Coarctation of the aorta (CoA) often leads to hypertension posttreatment. Evidence is lacking for the current >20 mm Hg peak-to-peak blood pressure (BP) gradient (BPGpp) guideline, which can cause aortic thickening, stiffening, and dysfunction. This study sought to find the BPGpp severity and duration that avoid persistent dysfunction in a preclinical model and test if predictors translate to hypertension status in patients with CoA. METHODS: Rabbits (n=75; 5-12/group) were exposed to mild, intermediate, or severe CoA (≤12, 13-19, ≥20 mm Hg BPGpp) for ≈1, 3, or 22 weeks using dissolvable and permanent sutures with thickening, stiffening, contraction, and endothelial function evaluated via multivariate regression. Relevance to patients with CoA (n=239; age, 0.01-46 years; median 3.7 months) was tested by retrospective review of predictors (preoperative BPGpp, surgical age, etc.) versus follow-up hypertension status. RESULTS: CoA duration and severity were predictive of aortic remodeling and active dysfunction in rabbits, and hypertension in patients with CoA. Interaction between patient age and BPGpp at surgery contributed significantly to hypertension, similar to rabbits, suggesting preclinical findings translate to patients. Machine learning decision tree analysis uncovered that preoperative BPGpp and surgical age predict risk of hypertension along with residual postoperative BPGpp. CONCLUSIONS: These findings suggest the current BPGpp threshold determined decades ago is likely too high to prevent adverse coarctation-induced aortic remodeling. The results and decision tree analysis provide a foundation for revising CoA treatment guidelines considering the interaction between CoA severity and duration to limit the risk of hypertension.

Coarctation duration and severity predict risk of hypertension precursors in a preclinical model and hypertensive status among patients.

Background: Coarctation of the aorta (CoA) often leads to hypertension (HTN) post-treatment. Evidence is lacking for the current >20 mmHg peak-to-peak blood pressure gradient (BPGpp) guideline, which can cause aortic thickening, stiffening and dysfunction. This study sought to find the BPGpp severity and duration that avoid persistent dysfunction in a preclinical model, and test if predictors translate to HTN status in CoA patients. Methods: Rabbits (N=75; 5-12/group) were exposed to mild, intermediate or severe CoA (≤12, 13-19, ≥20 mmHg BPGpp) for ~1, 3 or 22 weeks using dissolvable and permanent sutures with thickening, stiffening, contraction and endothelial function evaluated via multivariate regression. Relevance to CoA patients (N=239; age=0.01-46 years; median 3.7 months) was tested by retrospective review of predictors (preoperative BPGpp, surgical age, etc.) vs follow-up HTN status. Results: CoA duration and severity were predictive of aortic remodeling and active dysfunction in rabbits, and HTN in CoA patients. Interaction between patient age and BPGpp at surgery contributed significantly to HTN, similar to rabbits, suggesting preclinical findings translate to patients. Machine learning decision tree analysis uncovered that pre-operative BPGpp and surgical age predict risk of HTN along with residual post-operative BPGpp. Conclusions: These findings suggest the current BPGpp threshold determined decades ago is likely too high to prevent adverse coarctation-induced aortic remodeling. The results and decision tree analysis provide a foundation for revising CoA treatment guidelines considering the interaction between CoA severity and duration to limit the risk of HTN.

Consistency of the continuous flow pressure gradient despite aortic arch anomalies co-existing with coarctation.

Aims: Severity assessment for coarctation of the aorta (CoA) is challenging due to concomitant morphological anomalies (complex CoA) and inaccurate Doppler-based indices. Promising diagnostic performance has been reported for the continuous flow pressure gradient (CFPG), but it has not been studied in complex CoA. Our objective was to characterize the effect of complex CoA and associated hemodynamics on CFPG in a clinical cohort. Methods and Results: Retrospective analysis identified discrete juxtaductal (n=25) and complex CoA (n=43; transverse arch and/or isthmus hypoplasia) patients with arm-leg systolic blood pressure gradients (BPG) within 24 hours of echocardiography for comparison to BPG by conventional Doppler indices (simplified Bernoulli equation and modified forms correcting for proximal kinetic energy and/or recovered pressure). Results were interpreted using the current CoA guideline (BPG ≥20 mmHg) to compare diagnostic performance indicators including receiver operating characteristic curves, sensitivity, specificity, and diagnostic accuracy, among others. Echocardiography Z-scored aortic diameters were applied with computational stimulations from a preclinical CoA model to understand aspects of the CFPG driving performance differences.Diagnostic performance was substantially reduced from discrete to complex CoA for conventional Doppler indices calculated from patient data, and by hypoplasia and/or long segment stenosis in simulations. In contrast, diagnostic indicators for the CFPG only modestly dropped for complex vs discrete CoA. Simulations revealed differences in performance due to inclusion of the Doppler velocity index and diastolic pressure half-time in the CFPG calculation. Conclusion: CFPG is less affected by aortic arch anomalies co-existing with CoA when compared to conventional Doppler indices.

Aortic Remodeling Kinetics in Response to Coarctation-Induced Mechanical Perturbations.

Background: Coarctation of the aorta (CoA; constriction of the proximal descending thoracic aorta) is among the most common congenital cardiovascular defects. Coarctation-induced mechanical perturbations trigger a cycle of mechano-transduction events leading to irreversible precursors of hypertension including arterial thickening, stiffening, and vasoactive dysfunction in proximal conduit arteries. This study sought to identify kinetics of the stress-mediated compensatory response leading to these alterations using a preclinical rabbit model of CoA. Methods: A prior growth and remodeling (G&R) framework was reformulated and fit to empirical measurements from CoA rabbits classified into one control and nine CoA groups of various severities and durations (n = 63, 5-11/group). Empirical measurements included Doppler ultrasound imaging, uniaxial extension testing, catheter-based blood pressure, and wire myography, yielding the time evolution of arterial thickening, stiffening, and vasoactive dysfunction required to fit G&R constitutive parameters. Results: Excellent agreement was observed between model predictions and observed patterns of arterial thickening, stiffening, and dysfunction among all CoA groups. For example, predicted vascular impairment was not significantly different from empirical observations via wire myography (p-value > 0.13). Specifically, 48% and 45% impairment was observed in smooth muscle contraction and endothelial-dependent relaxation, respectively, which were accurately predicted using the G&R model. Conclusions: The resulting G&R model, for the first time, allows for prediction of hypertension precursors at neonatal ages that is currently challenging to examine in preclinical models. These findings provide a validated computational tool for prediction of persistent arterial dysfunction and identification of revised severity-duration thresholds that may ultimately avoid hypertension from CoA.

Clinical, Experimental, and Computational Validation of a New Doppler-Based Index for Coarctation Severity Assessment.

BACKGROUND: Long-term morbidity including hypertension often persists in coarctation patients despite current guidelines. Coarctation severity can be invasively assessed via peak-to-peak catheter pressure gradient (PPCG), which is estimated noninvasively via simplified Bernoulli equation and conventionally reported as peak instantaneous Doppler gradient (PIDG). However, underlying simplifications of the equation limit diagnostic accuracy. We studied the diagnostic performance of a new Doppler-based diastolic index called the continuous flow pressure gradient (CFPG) versus conventional indices in assessing coarctation severity. METHODS: In a rabbit model mimicking human aortic coarctation, temporal blood pressure waveforms revealed the diastolic instantaneous pressure gradients and spectral Doppler features impacted by coarctation severity. We therefore hypothesized that CFPG provides superior correlation with coarctation gradients measured invasively. PIDG and CFPG were quantified using color flow echocardiography in humans and rabbits with discrete coarctations. Results were compared with PPCG in rabbits (n = 34) and arm-leg systolic gradients (n = 25) in humans via 1-way analysis of variance, Pearson's correlation, linear regression, and Bland-Altman analysis. RESULTS: A threshold of CFPG ≥ 4.6 mm Hg was identified via the Youden index as representative of PPCG ≥ 20 mm Hg (the current guideline value for coarctation intervention) in rabbits, while a CFPG ≥1.0 mm Hg represented an arm-leg systolic gradient ≥20 mm Hg in humans. Accuracy measures revealed superior correlation of CFPG (R2 > 0.80) and mild receiver operating characteristic improvement (area under the receiver operating characteristic curve, 0.94-0.95) compared with PIDG (R2 < 0.63; area under the receiver operating characteristic curve, 0.89-0.95). Inter-/intraobserver variability tested by intraclass correlation coefficient revealed measurement reliability with differences ≤8.2% and 10.7%, respectively. Computational simulations of anesthetized versus conscious hemodynamics showed parameters were minimally impacted by isoflurane inherent in the data used to derive CFPG. These results confirm the potential diagnostic accuracy of CFPG in echocardiography-based coarctation severity assessment. We are optimistic that CFPG will be useful for translation of results from preclinical studies that revisit current guidelines to limit morbidity in humans with aortic coarctation.

A gene-based recessive diplotype exome scan discovers FGF6, a novel hepcidin-regulating iron-metabolism gene.

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor-encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

Nonparametric collective spectral density estimation with an application to clustering the brain signals.

In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a prespecified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Moreover, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at "https://ncsde.shinyapps.io/NCSDE" is developed for visualization, training, and learning the SDFs collectively using the proposed technique. Finally, we apply our method to cluster similar brain signals recorded by the for identifying synchronized brain regions according to their spectral densities.

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions.

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

Preparing Family Caregivers to Recognize Delirium Symptoms in Older Adults After Elective Hip or Knee Arthroplasty.

OBJECTIVES: To test the feasibility of a telephone-based intervention that prepares family caregivers to recognize delirium symptoms and how to communicate their observations to healthcare providers. DESIGN: Mixed-method, pre-post quasi-experimental design. SETTING: A Midwest Veterans Affairs Medical Center and a nonprofit health system. PARTICIPANTS: Forty-one family caregiver-older adult dyads provided consent; 34 completed the intervention. INTERVENTION: Four telephone-based education modules using vignettes were completed during the 3 weeks before the older adult's hospital admission for elective hip or knee replacement. Each module required 20 to 30 minutes. MEASUREMENTS: Interviews were conducted before the intervention and 2 weeks and 2 months after the older adult's hospitalization. A researcher completed the Confusion Assessment Method (CAM) and a family caregiver completed the Family Version of the Confusion Assessment Method (FAM-CAM) 2 days after surgery to assess the older adults for delirium symptoms. RESULTS: Family caregivers' knowledge of delirium symptoms improved significantly from before the intervention to 2 weeks after the intervention and was maintained after the older adult's hospitalization. They also were able to recognize the presence and absence of delirium symptoms in the vignettes included in the intervention and in the older adult after surgery. In 94% of the cases, the family caregiver rating on the FAM-CAM approximately 2 days after the older adult's surgery agreed with the researcher rating on the CAM. Family caregivers expressed satisfaction with the intervention and stated that the information was helpful. CONCLUSION: Delivery of a telephone-based intervention appears feasible. All family caregivers who began the program completed the four education modules. Future studies evaluating the effectiveness of the educational program should include a control group.

The Decay of Disease Association with Declining Linkage Disequilibrium: A Fine Mapping Theorem.

Several important and fundamental aspects of disease genetics models have yet to be described. One such property is the relationship of disease association statistics at a marker site closely linked to a disease causing site. A complete description of this two-locus system is of particular importance to experimental efforts to fine map association signals for complex diseases. Here, we present a simple relationship between disease association statistics and the decline of linkage disequilibrium from a causal site. Specifically, the ratio of Chi-square disease association statistics at a marker site and causal site is equivalent to the standard measure of pairwise linkage disequilibrium, r2. A complete derivation of this relationship from a general disease model is shown. Quite interestingly, this relationship holds across all modes of inheritance. Extensive Monte Carlo simulations using a disease genetics model applied to chromosomes subjected to a standard model of recombination are employed to better understand the variation around this fine mapping theorem due to sampling effects. We also use this relationship to provide a framework for estimating properties of a non-interrogated causal site using data at closely linked markers. Lastly, we apply this way of examining association data from high-density genotyping in a large, publicly-available data set investigating extreme BMI. We anticipate that understanding the patterns of disease association decay with declining linkage disequilibrium from a causal site will enable more powerful fine mapping methods and provide new avenues for identifying causal sites/genes from fine-mapping studies.

Testing multiple hypotheses with skewed alternatives.

In many practical cases of multiple hypothesis problems, it can be expected that the alternatives are not symmetrically distributed. If it is known a priori that the distributions of the alternatives are skewed, we show that this information yields high power procedures as compared to the procedures based on symmetric alternatives when testing multiple hypotheses. We propose a Bayesian decision theoretic rule for multiple directional hypothesis testing, when the alternatives are distributed as skewed, under a constraint on a mixed directional false discovery rate. We compare the proposed rule with a frequentist's rule of Benjamini and Yekutieli (2005) using simulations. We apply our method to a well-studied HIV dataset.

Integrating Data Transformation in Principal Components Analysis.

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.

Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles.

Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence-structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations.

Analyzing multiple-probe microarray: estimation and application of gene expression indexes.

Gene expression index estimation is an essential step in analyzing multiple probe microarray data. Various modeling methods have been proposed in this area. Amidst all, a popular method proposed in Li and Wong (2001) is based on a multiplicative model, which is similar to the additive model discussed in Irizarry et al. (2003a) at the logarithm scale. Along this line, Hu et al. (2006) proposed data transformation to improve expression index estimation based on an ad hoc entropy criteria and naive grid search approach. In this work, we re-examined this problem using a new profile likelihood-based transformation estimation approach that is more statistically elegant and computationally efficient. We demonstrate the applicability of the proposed method using a benchmark Affymetrix U95A spiked-in experiment. Moreover, We introduced a new multivariate expression index and used the empirical study to shows its promise in terms of improving model fitting and power of detecting differential expression over the commonly used univariate expression index. As the other important content of the work, we discussed two generally encountered practical issues in application of gene expression index: normalization and summary statistic used for detecting differential expression. Our empirical study shows somewhat different findings from the MAQC project (MAQC, 2006).