Western diet changes cardiac acyl-CoA composition in obese rats: a potential role for hepatic lipogenesis.

The "lipotoxic footprint" of cardiac maladaptation in diet-induced obesity is poorly defined. We investigated how manipulation of dietary lipid and carbohydrate influenced potential lipotoxic species in the failing heart. In Wistar rats, contractile dysfunction develops at 48 weeks on a high-fat/high-carbohydrate "Western" diet, but not on low-fat/high-carbohydrate or high-fat diets. Cardiac content of the lipotoxic candidates--diacylglycerol, ceramide, lipid peroxide, and long-chain acyl-CoA species--was measured at different time points by high-performance liquid chromatography and biochemical assays, as was lipogenic capacity in the heart and liver by qRT-PCR and radiometric assays. Changes in membranes fluidity were also monitored using fluorescence polarization. We report that Western feeding induced a 40% decrease in myocardial palmitoleoyl-CoA content and a similar decrease in the unsaturated-to-saturated fatty acid ratio. These changes were associated with impaired cardiac mitochondrial membrane fluidity. At the same time, hepatic lipogenic capacity was increased in animals fed Western diet (+270% fatty acid elongase activity compared with high-fat diet), while fatty acid desaturase activity decreased over time. Our findings suggest that dysregulation of lipogenesis is a significant component of heart failure in diet-induced obesity.

ANKRD1, the gene encoding cardiac ankyrin repeat protein, is a novel dilated cardiomyopathy gene.

OBJECTIVES: We evaluated ankyrin repeat domain 1 (ANKRD1), the gene encoding cardiac ankyrin repeat protein (CARP), as a novel candidate gene for dilated cardiomyopathy (DCM) through mutation analysis of a cohort of familial or idiopathic DCM patients, based on the hypothesis that inherited dysfunction of mechanical stretch-based signaling is present in a subset of DCM patients. BACKGROUND: CARP, a transcription coinhibitor, is a member of the titin-N2A mechanosensory complex and translocates to the nucleus in response to stretch. It is up-regulated in cardiac failure and hypertrophy and represses expression of sarcomeric proteins. Its overexpression results in contractile dysfunction. METHODS: In all, 208 DCM patients were screened for mutations/variants in the coding region of ANKRD1 using polymerase chain reaction, denaturing high-performance liquid chromatography, and direct deoxyribonucleic acid sequencing. In vitro functional analyses of the mutation were performed using yeast 2-hybrid assays and investigating the effect on stretch-mediated gene expression in myoblastoid cell lines using quantitative real-time reverse transcription-polymerase chain reaction. RESULTS: Three missense heterozygous ANKRD1 mutations (P105S, V107L, and M184I) were identified in 4 DCM patients. The M184I mutation results in loss of CARP binding with Talin 1 and FHL2, and the P105S mutation in loss of Talin 1 binding. Intracellular localization of mutant CARP proteins is not altered. The mutations result in differential stretch-induced gene expression compared with wild-type CARP. CONCLUSIONS: ANKRD1 is a novel DCM gene, with mutations present in 1.9% of DCM patients. The ANKRD1 mutations may cause DCM as a result of disruption of the normal cardiac stretch-based signaling.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.