Daniel D. Kinnamon

Associations between neurohormonal and inflammatory activation and heart failure in children

BACKGROUND Adult heart failure (HF) has been shown to be associated with neuroendocrine and inflammatory activation. We hypothesize that neuroendocrine and inflammatory activation also associate with symptom severity and echocardiographic measurements in pediatric HF. METHODS Nineteen children with HF were divided into 3 symptom severity groups. Measurements were made of left ventricular (LV) ejection fraction, LV shortening fraction (LVSF), LV shortening fraction Z score (LVSFz), and LV end-systolic (LVSDz) and diastolic diameter Z scores. Blood levels of N-terminal prohormone brain natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hsCRP), tumor necrosis factor alpha, and soluble tumor necrosis factor receptor II were measured. RESULTS NT-proBNP and hsCRP were significantly elevated with more severe symptoms (P < or = .003) and discriminated between clinical severity groups (volume under the receiver operating characteristic surface = 0.58 and 0.62, P = .007 and P = .002, respectively). NT-proBNP was negatively associated with LV ejection fraction, LVSF, and LVSFz (P < or = .05) and positively associated with LVSDz (P < .001). High-sensitivity C-reactive protein was negatively associated with LVSF (P = .02) and positively associated with NT-proBNP (P = .03). Tumor necrosis factor alpha was negatively associated with LVSF and LVSFz (P < or = .03) and positively associated with LVSDz and NT-proBNP (P < or = .02). Soluble tumor necrosis factor receptor II was negatively associated with LVSFz (P = .03). CONCLUSIONS Neuroendocrine and inflammatory activation are associated with more severe symptoms and worse cardiac characteristics in pediatric HF. Blood levels of these biomarkers could be used to better assess the severity of HF in children.

Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants.

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.

Common susceptibility variants examined for association with dilated cardiomyopathy.

Rare mutations in more than 20 genes have been suggested to cause dilated cardiomyopathy (DCM), but explain only a small percentage of cases, mainly in familial forms. We hypothesised that more common variants may also play a role in increasing genetic susceptibility to DCM, similar to that observed in other common complex disorders.

The effect of a structured exercise program on nutrition and fitness outcomes in human immunodeficiency virus-infected children.

The feasibility and effectiveness of a hospital-based exercise-training program followed by a home-based program for improving fitness, strength, and changes in body composition in children and adolescents with HIV were evaluated. Subjects participated in nonrandomized 24-session, hospital supervised exercise training program followed by an 314 unsupervised home-based maintenance program. Outcome measurements included muscular strength/endurance, flexibility, relative peak VO(2), body composition, and lipids. Seventeen subjects (eight females) with a median age of 15.0 years (range: 6.0-22.6) and BMI z-score of 0.61 (range: -1.70-2.57) at entry completed the intervention. After 24 training sessions, the median increases in muscular strength were between 8% and 50%, depending on muscle group. The median increases in muscle endurance, relative peak VO(2), and lean body mass were 38.7% (95% CI: 12.5-94.7; p = 0.006), 3.0 ml/kg/min (95% CI: 1.5-6.0; p < 0.001), and 4.5% (95% CI: 2.4-6.6; p < 0.001), respectively. Twelve children completed the home-based maintenance program. Median changes in these outcomes between completion of the hospital-based intervention and a follow-up after completion of the home-based program were near zero. No adverse events occurred during the intervention. A supervised hospital-based fitness program is feasible, safe, and effective for improving general fitness and strength as well as lean body mass in children with HIV.

Valid monte carlo permutation tests for genetic case-control studies with missing genotypes

Monte Carlo permutation tests can be formally constructed by choosing a set of permutations of individual indices and a real‐valued test statistic measuring the association between genotypes and affection status. In this paper, we develop a rigorous theoretical framework for verifying the validity of these tests when there are missing genotypes. We begin by specifying a nonparametric probability model for the observed genotype data in a genetic case‐control study with unrelated subjects. Under this model and some minimal assumptions about the test statistic, we establish that the resulting Monte Carlo permutation test is exact level α if (1) the chosen set of permutations of individual indices is a group under composition and (2) the distribution of the observed genotype score matrix under the null hypothesis does not change if the assignment of individuals to rows is shuffled according to an arbitrary permutation in this set. We apply these conditions to show that frequently used Monte Carlo permutation tests based on the set of all permutations of individual indices are guaranteed to be exact level α only for missing data processes satisfying a rather restrictive additional assumption. However, if the missing data process depends on covariates that are all identified and recorded, we also show that Monte Carlo permutation tests based on the set of permutations within strata of individuals with identical covariate values are exact level α. Our theoretical results are verified and supplemented by simulations for a variety of missing data processes and test statistics.

An improved estimator for the hydration of fat-free mass from in vivo measurements subject to additive technical errors

The hydration of fat-free mass, or hydration fraction (HF), is often defined as a constant body composition parameter in a two-compartment model and then estimated from in vivo measurements. We showed that the widely used estimator for the HF parameter in this model, the mean of the ratios of measured total body water (TBW) to fat-free mass (FFM) in individual subjects, can be inaccurate in the presence of additive technical errors. We then proposed a new instrumental variables estimator that accurately estimates the HF parameter in the presence of such errors. In Monte Carlo simulations, the mean of the ratios of TBW to FFM was an inaccurate estimator of the HF parameter, and inferences based on it had actual type I error rates more than 13 times the nominal 0.05 level under certain conditions. The instrumental variables estimator was accurate and maintained an actual type I error rate close to the nominal level in all simulations. When estimating and performing inference on the HF parameter, the proposed instrumental variables estimator should yield accurate estimates and correct inferences in the presence of additive technical errors, but the mean of the ratios of TBW to FFM in individual subjects may not.