Circulating Glycated Albumin and Glomerular Anionic Charges

Aiming to discern the mechanisms by which circulating glycated albumin alters the glomerular filtration properties that lead to glomerular dysfunction in diabetes, the authors studied the distribution and densities of anionic charges through the rat glomerular wall upon intravascular infusion of Amadori products, as well as in various conditions of increased glomerular permselectivity. Polylysine-gold was used as the probe to reveal the anionic charges. The study was carried on renal tissue sections of bovine serum albumin (BSA)- and glycated BSA–injected, normoglycemic animals. Results were generated through morphometrical evaluations of the gold labeling. Changes in glomerular anionic distribution were corroborated on renal tissue sections of short- and long-term diabetic rats and of normal newborn rats, situations known for abnormal glomerular filtration. Altered renal function in these conditions was clearly associated with changes in glomerular anionic charges. On the other hand, the infusion of glycated albumin in the circulation of normal rats, though altering glomerular filtration properties, did not modify the distribution and density of the polylysine-gold labeling through the glomerular basement membrane. Thus, anionic charges seem not to be the factor involved in the early changes of glomerular permeability induced by circulating glycated albumin

The effect of skewness and kurtosis on the Kenward-Roger approximation when group distributions differ

This study examined the independent effect of skewness and kurtosis on the robustness of the linear mixed model (LMM), with the Kenward-Roger (KR) procedure, when group distributions are different, sample sizes are small, and sphericity cannot be assumed. Methods: A Monte Carlo simulation study considering a split-plot design involving three groups and four repeated measures was performed. Results: The results showed that when group distributions are different, the effect of skewness on KR robustness is greater than that of kurtosis for the corresponding values. Furthermore, the pairings of skewness and kurtosis with group size were found to be relevant variables when applying this procedure. Conclusions: With sample sizes of 45 and 60, KR is a suitable option for analyzing data when the distributions are: (a) mesokurtic and not highly or extremely skewed, and (b) symmetric with different degrees of kurtosis. With total sample sizes of 30, it is adequate when group sizes are equal and the distributions are: (a) mesokurtic and slightly or moderately skewed, and sphericity is assumed; and (b) symmetric with a moderate or high/extreme violation of kurtosis. Alternative analyses should be considered when the distributions are highly or extremely skewed and samples sizes are small

Comparison of the procedures of Fleishman and Ramberg et al. for generating non normal data in simulation studies

Simulation techniques must be able to generate the types of distributions most commonly encountered in real data, for example, non-normal distributions. Two recognized procedures for generating non-normal data are Fleishman's linear transformation method and the method proposed by Ramberg et al. that is based on generalization of the Tukey lambda distribution. This study compares these procedures in terms of the extent to which the distributions they generate fit their respective theoretical models, and it also examines the number of simulations needed to achieve this fit. To this end, the paper considers, in addition to the normal distribution, a series of non-normal distributions that are commonly found in real data, and then analyses fit according to the extent to which normality is violated and the number of simulations performed. The results show that the two data generation procedures behave similarly. As the degree of contamination of the theoretical distribution increases, so does the number of simulations required to ensure a good fit to the generated data. The two procedures generate more accurate normal and non-normal distributions when at least 7000 simulations are performed, although when the degree of contamination is severe (with values of skewness and kurtosis of 2 and 6, respectively) it is advisable to perform 15000 simulations

Analyzing longitudinal data and use of the generalized linear model in health and social sciences

In the health and social sciences, longitudinal data have often been analyzed without taking into account the dependence between observations of the same subject. Furthermore, consideration is rarely given to the fact that longitudinal data may come from a non-normal distribution. In addition to describing the aims and types of longitudinal designs this paper presents three approaches based on generalized estimating equations that do take into account the lack of independence in data, as well as the type of distribution. These approaches are the marginal model (population-average model), the random effects model (subject-specific model), and the transition model (Markov model or auto-correlation model). Finally, these models are applied to empirical data by means of specific procedures included in SAS, namely GENMOD, MIXED, and GLIMMIX

Using the linear mixed model to analyze non-normal data distributions in longitudinal designs

Using a Monte Carlo simulation and the Kenward-Roger (KR) correction for degrees of freedom this paper analyzes the application of the linear mixed model (LMM) to a mixed repeated measures design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential and log-normal. This showed that with ho mogeneous between-groups covariance, and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. Wit h heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null the best fit was shown by the between-subjects heterogeneous unstructured population matrix, this being the case for all the distributions analyzed. By contrast, with posit ive or negative pairing the within-subject and between-subjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased, as occurs, for example, in the log-normal distribution, robustness was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis the analysis showed that the degree of robustness increased in line with the amount of kurtosis

Apolipoprotein-E (ApoE) ε4 and cognitive decline over the adult life course

We tested the association between APOE-ε4 and processing speed and memory between ages 43 and 69 in a population-based birth cohort. Analyses of processing speed (using a timed letter search task) and episodic memory (a 15-item word learning test) were conducted at ages 43, 53, 60–64 and 69 years using linear and multivariable regression, adjusting for gender and childhood cognition. Linear mixed models, with random intercepts and slopes, were conducted to test the association between APOE and the rate of decline in these cognitive scores from age 43 to 69. Model fit was assessed with the Bayesian Information Criterion. A cross-sectional association between APOE-ε4 and memory scores was detected at age 69 for both heterozygotes and homozygotes (β = −0.68 and β = −1.38, respectively, p = 0.03) with stronger associations in homozygotes; no associations were observed before this age. Homozygous carriers of APOE-ε4 had a faster rate of decline in memory between ages 43 and 69, when compared to non-carriers, after adjusting for gender and childhood cognition (β = −0.05, p = 0.04). There were no cross-sectional or longitudinal associations between APOE-ε4 and processing speed. We conclude that APOE-ε4 is associated with a subtly faster rate of memory decline from midlife to early old age; this may be due to effects of APOE-ε4 becoming manifest around the latter stage of life. Continuing follow-up will determine what proportion of this increase will become clinically significant

Dietary glycaemic index and cognitive function: prospective associations in adults of the 1946 British birth cohort

OBJECTIVE: Evidence suggests that the rate of glucose release following consumption of carbohydrate-containing foods, defined as the glycaemic index (GI), is inversely associated with cognitive function. To date, most of the evidence stems from either single-meal studies or highly heterogeneous cohort studies. We aimed to study the prospective associations of diet GI at age 53 years with outcomes of verbal memory and letter search tests at age 69 years and rate of decline between 53 and 69 years. DESING: Longitudinal population-based birth cohort study. SETTING: MRC National Survey for Health and Development. PARTICIPANTS: Cohort members (n 1252). RESULTS: Using multivariable linear and logistic regression, adjusted for potential confounders, associations of higher-GI diet with lower verbal memory, lower letter search speed and lower number of hits in a letter search test were attenuated after adjustments for cognitive ability at age 15 years, educational attainment, further training and occupational social class. No association was observed between diet GI at 53 years and letter search accuracy or speed–accuracy trade-off at 69 years, or between diet GI at 53 years and rate of decline between 53 and 69 years in any cognitive measure. CONCLUSIONS: Diet GI does not appear to predict cognitive function or decline, which was mainly explained by childhood cognitive ability, education and occupational social class. Our findings confirm the need for further research on the association between diet and cognition from a life-course perspective