Objective: We aimed to investigate the influence of the genetic variability of candidate genes on survival at old age in good health. Methods: First, on the basis of a synthetic survival curve constructed using historic mortality data taken from the Italian population from 1890 onward, we defined three age classes ranging from 18 to 106 years. Second, we assembled a multinomial logistic regression model to evaluate the effect of dichotomous variables (genotypes) on the probability to be assigned to a specific category (age class). Third, we applied the regression model to a cross-sectional dataset (10 genes; 972 subjects selected for healthy status) categorized according to age and sex. Results: We found that genetic factors influence survival at advanced age in good health in a sex- and age-specific way. Furthermore, we found that genetic variability plays a stronger role in males than in females and that, in both genders, its impact is especially important at very old ages. Conclusions: The analyses presented here underline the age-specific effect of the gene network in modulating survival at advanced age in good health.

In financial analysis it is useful to study the dependence between two or more time series as well as the temporal dependence in a univariate time series. This article is concerned with the statistical modeling of the dependence structure in a univariate financial time series using the concept of copula. We treat the series of financial returns as a first order Markov process. The Archimedean two-parameter BB7 copula is adopted to describe the underlying dependence structure between two consecutive returns, while the log-Dagum distribution is employed to model the margins marked by skewness and kurtosis. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. Furthermore, we apply the model to the daily returns of four stocks and, finally, we illustrate how its fitting to data can be improved when the dependence between consecutive returns is described through a copula function.

Introduction: The histology of neointimal hyperplasia, the primary cause of arteriovenous fistula (AVF) stenosis, resembles the histology of atherosclerosis. We evaluated classic atherogenic risk factors such as hypertension, smoking, diabetes, cholesterol, and evaluated the role of expanded risk factors such as: cytomegalovirus (CMV), Helicobacter pylori (H. pylori), Chlamydia pneumoniae (C. pneumoniae), infection, and malnutrition, as possible causes of AVF failure in hemodialysis (HD) patients. Methods: AVF of 91 HD patients were monitored by on-line blood flow measurement (Qac); levels of albumin, fibrinogen, C-reactive protein and plasma cholesterol were recorded. Nutrition was evaluated via the Malnutrition Inflammation Score and the normalized protein intake (nPCR). Seropositivity to CMV, C. pneumoniae and H. Pylori were assessed. Results: Twenty-one patients had at least one episode of vascular access thrombosis; 17 patients had stenotic lesions. Analysis of survival tables revealed that patients who had high IgG CMV antibody levels had a higher probability of AVF failure than patients with lower CMV antibody levels. The difference in the empirical survival functions was statistically significant when we stratified by CMV antibody levels, unlike H. pylori or C. pneumoniae. In a logistic regression model, CMV, increased cholesterol, and decreases in nPCR and Qac significantly increased the risk of AVF failure. Conclusion: Our study suggests that CMV infection, total plasma cholesterol, decreased Qac, and nPCR are important risk factors of AVF failure in HD patients.