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Background: Many common risk factors for cardiovascular disease (CVD) originate in childhood and adolescence. There is a lack of literature examining variability within study populations, as well as a shortage of simultaneous analyses of CVD risk factors operating in tandem., Methods: We used data from Project HeartBeat!-a multi-cohort longitudinal growth study of children and adolescents in the US - for assessing multiple profiles for lipids, blood pressure, and anthropometric measures. Principal component functional curve analysis methods were used to summarize trajectories of multiple measurements. Subsequently less favorable health (high risk) and more favorable (low risk) groups from both female and male cohorts were identified and compared to US national norms., Results: Compared to national norms, the high risk groups have increased waist circumference, body mass index, and percent body fat as well as higher low-density lipoprotein cholesterol and triglyceride levels, and lower high-density lipoprotein cholesterol. The risk profiles also exhibit patterns of convergence and divergence across the high and low risk groups as a function of age., Conclusions: These observations may have clinical and public health implications in identifying groups of children at high risk of CVD for earlier interventions.

Background: Dendritic cells (DC) play a central role in primary immune responses and become potent stimulators of the adaptive immune response after undergoing the critical process of maturation. Understanding the dynamics of DC maturation would provide key insights into this important process. Time course microarray experiments can provide unique insights into DC maturation dynamics. Replicate experiments are necessary to address the issues of experimental and biological variability. Statistical methods and averaging are often used to identify significant signals. Here a novel strategy for filtering of replicate time course microarray data, which identifies consistent signals between the replicates, is presented and applied to a DC time course microarray experiment., Results: The temporal dynamics of DC maturation were studied by stimulating DC with poly(I:C) and following gene expression at 5 time points from 1 to 24 hours. The novel filtering strategy uses standard statistical and fold change techniques, along with the consistency of replicate temporal profiles, to identify those differentially expressed genes that were consistent in two biological replicate experiments. To address the issue of cluster reproducibility a consensus clustering method, which identifies clusters of genes whose expression varies consistently between replicates, was also developed and applied. Analysis of the resulting clusters revealed many known and novel characteristics of DC maturation, such as the up-regulation of specific immune response pathways. Intriguingly, more genes were down-regulated than up-regulated. Results identify a more comprehensive program of down-regulation, including many genes involved in protein synthesis, metabolism, and housekeeping needed for maintenance of cellular integrity and metabolism., Conclusions: The new filtering strategy emphasizes the importance of consistent and reproducible results when analyzing microarray data and utilizes consistency between replicate experiments as a criterion in both feature selection and clustering, without averaging or otherwise combining replicate data. Observation of a significant down-regulation program during DC maturation indicates that DC are preparing for cell death and provides a path to better understand the process. This new filtering strategy can be adapted for use in analyzing other large-scale time course data sets with replicates.

Background: Older heart failure (HF) patients exhibit exercise intolerance during activities of daily living. We hypothesized that reduced lower extremity blood flow (LBF) due to reduced forward cardiac output would contribute to submaximal exercise intolerance in older HF patients., Methods and Results: Twelve HF patients both with preserved and reduced left ventricular ejection fraction (LVEF) (aged 68 +/- 10 years) without large (aorta) or medium sized (iliac or femoral artery) vessel atherosclerosis, and 13 age and gender matched healthy volunteers underwent a sophisticated battery of assessments including a) peak exercise oxygen consumption (peak VO2), b) physical function, c) cardiovascular magnetic resonance (CMR) submaximal exercise measures of aortic and femoral arterial blood flow, and d) determination of thigh muscle area. Peak VO2 was reduced in HF subjects (14 +/- 3 ml/kg/min) compared to healthy elderly subjects (20 +/- 6 ml/kg/min) (p = 0.01). Four-meter walk speed was 1.35 +/- 0.24 m/sec in healthy elderly verses 0.98 +/- 0.15 m/sec in HF subjects (p < 0.001). After submaximal exercise, the change in superficial femoral LBF was reduced in HF participants (79 +/- 92 ml/min) compared to healthy elderly (222 +/- 108 ml/min; p = 0.002). This occurred even though submaximal stress-induced measures of the flow in the descending aorta (5.0 +/- 1.2 vs. 5.1 +/- 1.3 L/min; p = 0.87), and the stress-resting baseline difference in aortic flow (1.6 +/- 0.8 vs. 1.7 +/- 0.8 L/min; p = 0.75) were similar between the 2 groups. Importantly, the difference in submaximal exercise induced superficial femoral LBF between the 2 groups persisted after accounting for age, gender, body surface area, LVEF, and thigh muscle area (p

Background: Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other., Results: We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles., Conclusion: Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.