Assessing erectile neurogenic dysfunction from heart rate variability through a Generalized Linear Mixed Model framework.

BACKGROUND: The low (LF) vs. high (HF) frequency energy ratio, computed from the spectral decomposition of heart beat intervals, has become a major tool in cardiac autonomic system control and sympatho-vagal balance studies. The (statistical) distributions of response variables designed from ratios of two quantities, such as the LF/HF ratio, are likely to non-normal, hence preventing e.g., from a relevant use of the t-test. Even using a non-parametric formulation, the solution may be not appropriate as the test statistics do not account for correlation and heteroskedasticity, such as those that can be observed when several measures are taken from the same patient. OBJECTIVES: The analyses for such type of data require the application of statistical models which do not assume a priori independence. In this spirit, the present contribution proposes the use of the Generalized Linear Mixed Models (GLMMs) framework to assess differences between groups of measures performed over classes of patients. METHODS: Statistical linear mixed models allow the inclusion of at least one random effect, besides the error term, which induces correlation between observations from the same subject. Moreover, by using GLMM, practitioners could assume any probability distribution, within the exponential family, for the data, and naturally model heteroskedasticity. Here, the sympatho-vagal balance expressed as LF/HF ratio of patients suffering neurogenic erectile dysfunction under three different body positions was analyzed in a case-control protocol by means of a GLMM under gamma and Gaussian distributed responses assumptions. RESULTS: The gamma GLMM model was compared with the normal linear mixed model (LMM) approach conducted using raw and log transformed data. Both raw GLMM gamma and log transformed LMM allow better inference for factor effects, including correlations between observations from the same patient under different body position compared to the raw LMM. The gamma GLMM provides a more natural distribution assumption of a response expressed as a ratio. CONCLUSIONS: A gamma distribution assumption intrinsically models quadratic relationships between the expected value and the variance of the data avoiding prior data transformation. SAS and R source code are available on request.

Non-parametric smoothing of multivariate genetic distances in the analysis of spatial population structure at fine scale.

Species dispersal studies provide valuable information in biological research. Restricted dispersal may give rise to a non-random distribution of genotypes in space. Detection of spatial genetic structure may therefore provide valuable insight into dispersal. Spatial structure has been treated via autocorrelation analysis with several univariate statistics for which results could dependent on sampling designs. New geostatistical approaches (variogram-based analysis) have been proposed to overcome this problem. However, modelling parametric variograms could be difficult in practice. We introduce a non-parametric variogram-based method for autocorrelation analysis between DNA samples that have been genotyped by means of multilocus-multiallele molecular markers. The method addresses two important aspects of fine-scale spatial genetic analyses: the identification of a non-random distribution of genotypes in space, and the estimation of the magnitude of any non-random structure. The method uses a plot of the squared Euclidean genetic distances vs. spatial distances between pairs of DNA-samples as empirical variogram. The underlying spatial trend in the plot is fitted by a non-parametric smoothing (LOESS, Local Regression). Finally, the predicted LOESS values are explained by segmented regressions (SR) to obtain classical spatial values such as the extent of autocorrelation. For illustration we use multivariate and single-locus genetic distances calculated from a microsatellite data set for which autocorrelation was previously reported. The LOESS/SR method produced a good fit providing similar value of published autocorrelation for this data. The fit by LOESS/SR was simpler to obtain than the parametric analysis since initial parameter values are not required during the trend estimation process. The LOESS/SR method offers a new alternative for spatial analysis.

A paradigm of experimentally induced mild hyperthyroidism: effects on nitrogen balance, body composition, and energy expenditure in healthy young men.

Although T3 exerts major regulatory actions in both animals and humans, most clinical studies of T3 administration have been relatively short term. The present study examined the effects of more than 2 months (63 days) of low dose T3 treatment on nitrogen balance, body composition, 24-h energy expenditure (EE), and protein turnover in seven healthy men studied at an in-patient metabolic unit. Subjects were also randomly assigned to either high or low fat diets to determine the effects of diet composition. T3 treatment produced significant losses in both lean mass (1.5 +/- 0.3 kg) and fat mass (2.7 +/- 0.4 kg) by 6 weeks, with similar reductions in both at 9 weeks. The high fat diet somewhat attenuated the loss of body fat. Nitrogen balance was significantly negative for the first 3 weeks of T3 treatment, but tended to return to baseline thereafter. There were no significant effects of treatment on protein turnover at 9 weeks, although there was a slight increase in leucine oxidation (P = 0.07). Despite the apparent adaptation in nitrogen balance, total 24-h EE and sleeping EE were significantly increased at 9 weeks. We conclude that although healthy men are able to adapt to mild hyperthyroidism in terms of nitrogen balance, they exhibit significant and persistent changes in fat and fat-free mass as well as energy balance.

Exogenous androgens influence body composition and regional body fat distribution in obese postmenopausal women--a clinical research center study.

Abdominal fat distribution is influenced by androgen levels in both men and women. The purpose of this study was to assess the effects on fat distribution of administering nandrolone decanoate (ND; an anabolic steroid with weak androgenic activity) or spironolactone (SP; an antiandrogen) in obese postmenopausal women. The design was a randomized, placebo-controlled, 9-month trial with simultaneous calorie restriction for weight loss. Women in all three groups lost comparable amounts of weight, but the ND-treated women gained lean mass relative to the other two groups (P < 0.0005) and lost more body fat than women in the SP group (P < 0.01). The resting metabolic rate also increased slightly in the ND group. ND treatment produced a gain in visceral fat, as determined by computed tomography scan, and a relatively greater loss of sc abdominal fat. SP-treated women lost significantly less sc fat than the other two groups. Serum cholesterol decreased in the placebo group, but increased slightly in the other two groups (significant for SP vs. placebo, P < 0.05). High density lipoprotein cholesterol decreased significantly in the ND-treated women. There were no significant changes in fasting glucose or insulin sensitivity. We conclude that administration of exogenous androgens modulates body composition in obese postmenopausal women and independently affects visceral and sc abdominal fat.