Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction.

Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFpEF study consumed three times a week and 8 mmol nitrate for the HTN study consumed daily) while the other half consumed a beet juice beverage with the nitrate removed (placebo). The main result was that there was no added benefit observed for any outcomes when comparing BRJ to placebo in either HTN or HFpEF patients undergoing exercise training (p ≥ 0.14). There were within-group benefits. In the pilot study in patients with HFpEF, aerobic endurance (primary outcome), defined as the exercise time to volitional exhaustion during submaximal cycling at 75% of maximal power output, improved during exercise training within each group from baseline to end of study, 369 ± 149 s vs 520 ± 257 s (p = 0.04) for the placebo group and 384 ± 129 s vs 483 ± 258 s for the BRJ group (p = 0.15). Resting systolic blood pressure in patients with HFpEF also improved during exercise training in both groups, 136 ± 16 mm Hg vs 122 ± 3 mm Hg for the placebo group (p < 0.05) and 132 ± 12 mm Hg vs 119 ± 9 mm Hg for the BRJ group (p < 0.05). In the HTN pilot study, during a treadmill graded exercise test, peak oxygen consumption (primary outcome) did not change significantly, but time to exhaustion (also a primary outcome) improved in both groups, 504 ± 32 s vs 601 ± 38 s (p < 0.05) for the placebo group and 690 ± 38 s vs 772 ± 95 s for the BRJ group (p < 0.05) which was associated with a reduction in supine resting systolic blood pressure in BRJ group. Arterial compliance also improved during aerobic exercise training in both the HFpEF and the HTN patients for both BRJ and placebo groups. Future work is needed to determine if larger nitrate doses would provide an added benefit to supervised aerobic exercise in HTN and HFpEF patients.

Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain.

BACKGROUND: Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. METHODS: Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. RESULTS: Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). Differences in second-order connections between the somatomotor cortex and insular cortex were also significant; the exercise + BRJ group (M = 3.28, SE = 0.167) had a significantly lower number of connections than exercise + placebo (M = 3.91, SE = 0.18, p = .017) following the intervention. Evaluation of peak MET capacity revealed a trend for the exercise + BRJ group to have higher MET capacity following the intervention. CONCLUSIONS: Older adults who exercised and consumed BRJ demonstrated greater consistency within the motor community and fewer secondary connections with the insular cortex compared with those who exercised without BRJ. The exercise + BRJ group had brain networks that more closely resembled those of younger adults, showing the potential enhanced neuroplasticity conferred by combining exercise and BRJ consumption.

Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates.

Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature.

Bayesian probabilistic network modeling from multiple independent replicates.

Often protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edge's partial correlation between two proteins is at least moderate, then the composite's posterior probability is large.

Influence of weight loss, body composition, and lifestyle behaviors on plasma adipokines: a randomized weight loss trial in older men and women with symptomatic knee osteoarthritis.

OBJECTIVE: To investigate effects of weight loss on adipokines and health measures in obese older adults with symptomatic knee osteoarthritis. METHODS: Participants were randomly assigned to either weight loss (WL) (men: 12, women: 14) or weight stable (WS) group (men: 12, women: 13). WL intervention included meal replacements and structured exercise training. Measurements of leptin, adiponectin, soluble leptin receptor, lifestyle behaviors, and body composition were collected at baseline and 6 months. Univariate analysis of covariance was performed on 6 month variables, and Spearman and partial correlations were made between variables. RESULTS: Weight loss was 13.0% and 6.7% in WL for men and women, respectively. Women in WL had lower whole body and trunk fat than WS. The leptin : adiponectin ratio was lower for women in WL than WS at 6 months, with no group differences in adipokines for men. Leptin and free leptin index correlated with body fat in both genders at baseline. Interestingly, only women showed reductions in leptin (P < 0.100) and correlations between the percentage change leptin and trunk fat and the percentage changes in free leptin index with total fat and trunk fat. Partial correlations between 6 month adipokines after adjustments for covariates and group/time period show potential multivariate influences. CONCLUSIONS: In the presence of an effective weight loss intervention in older obese adults, there are significant relationships between weight and fat loss and leptin in women, but not men, suggesting gender-specific features of adipokine metabolism in this age group.

Continuous cotemporal probabilistic modeling of systems biology networks from sparse data.

Modeling of biological networks is a difficult endeavor, but exploration of this problem is essential for understanding the systems behavior of biological processes. In this contribution, developed for sparse data, we present a new continuous Bayesian graphical learning algorithm to cotemporally model proteins in signaling networks and genes in transcriptional regulatory networks. In this continuous Bayesian algorithm, the correlation matrix is singular because the number of time points is less than the number of biological entities (genes or proteins). A suitable restriction on the degree of the graph's vertices is applied and a Metropolis-Hastings algorithm is guided by a BIC-based posterior probability score. Ten independent and diverse runs of the algorithm are conducted, so that the probability space is properly well-explored. Diagnostics to test the applicability of the algorithm to the specific data sets are developed; this is a major benefit of the methodology. This novel algorithm is applied to two time course experimental data sets: 1) protein modification data identifying a potential signaling network in chondrocytes, and 2) gene expression data identifying the transcriptional regulatory network underlying dendritic cell maturation. This method gives high estimated posterior probabilities to many of the proteins' directed edges that are predicted by the literature; for the gene study, the method gives high posterior probabilities to many of the literature-predicted sibling edges. In simulations, the method gives substantially higher estimated posterior probabilities for true edges and true subnetworks than for their false counterparts.

Open capture-recapture models with heterogeneity: II. Jolly-Seber model.

Estimation of abundance is important in both open and closed population capture-recapture analysis, but unmodeled heterogeneity of capture probability leads to negative bias in abundance estimates. This article defines and develops a suite of open population capture-recapture models using finite mixtures to model heterogeneity of capture and survival probabilities. Model comparisons and parameter estimation use likelihood-based methods. A real example is analyzed, and simulations are used to check the main features of the heterogeneous models, especially the quality of estimation of abundance, survival, recruitment, and turnover. The two major advances in this article are the provision of realistic abundance estimates that take account of heterogenetiy of capture, and an appraisal of the amount of overestimation of survival arising from conditioning on the first capture when heterogeneity of survival is present.

Comparison of Co-Temporal Modeling Algorithms on Sparse Experimental Time Series Data Sets.

Multiple approaches for reverse-engineering biological networks from time-series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co-temporal modeling. The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co-temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1) identify two types of entity relationships, parent and sibling, between biological entities, 2) deal with experimental sparse time course data, and 3) handle experimental noise seen in replicate data sets. These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co-temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.

Open capture-recapture models with heterogeneity: I. Cormack-Jolly-Seber model.

In open population capture-recapture studies, it is usually assumed that similar animals (e.g., of the same sex and age group) have similar survival rates and capture probabilities. These assumptions are generally perceived to be an oversimplification, and they can lead to incorrect model selection and biased parameter estimates. Allowing for individual variability in survival and capture probabilities among apparently similar animals is now becoming possible, due to advances in closed population models and improved computing power. This article presents a flexible framework of likelihood-based models which allow for individual heterogeneity in survival and capture rates. Heterogeneity is modeled using finite mixtures, which have enough flexibility of distribution shape to accommodate a wide variety of different patterns of individual variation. The models condition on the first capture of each animal, and include as a special case the Cormack-Jolly-Seber model. Model selection is done either using Akaike's information criterion or by likelihood ratio tests, making available checks of different influences on survival rates. Bias in parameter estimates is reduced by including individual heterogeneity. Model selection and bias reduction are important in population studies and for making informed management decisions.

Older adults in cardiac rehabilitation: a new strategy for enhancing physical function.

PURPOSE: This study contrasted the effect of a group-mediated cognitive-behavioral intervention (GMCB) versus traditional cardiac rehabilitation (CRP) upon changes in objective and self-reported physical function of older adults [mean (SD) age of 64.7 (7.5) yr] after 3 months of exercise therapy. METHODS: This randomized clinical trial enrolled 147 participants who were eligible for inclusion into cardiac rehabilitation. Baseline to 3-month changes in self-reported and performance related measures of physical function were assessed using a physical functioning questionnaire, a 6-min walk test, and measured MET levels. RESULTS: Paired t-tests revealed that participants made improvements in all measures across the first 3 months of the study, irrespective of group treatment (P < 0.001). General linear models including effects for baseline levels of physical function, treatment, and gender revealed that lower functioning men in the GMCB treatment made greater improvements than any other subgroup on the two performance outcomes: 6-min walk and measured MET levels (P < 0.01). Gender did not moderate change in self-reported level of physical function (P > 0.05); however, the lower functioning participants in the GMCB intervention experienced greater improvements in self-reported physical function than those in CRP (P < 0.05). CONCLUSIONS: Exercise therapy is a valuable intervention for improving physical function of older adults with cardiovascular disease (CVD) and those at increased risk for CVD. Baseline level of physical function and gender are important variables to consider when studying the relationship between exercise therapy and improvements in physical function.