Our study suggests that CMV infection, total plasma cholesterol, decreased Qac, and nPCR are important risk factors of AVF failure in HD patients.

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.

The definition of a precise and consistent aging phenotype that allows to measure the physical and cognitive decline, as well as the increase of mortality hazard late in life, is a major problem for studies aimed at finding the genetic factors modulating rate and quality of human aging. In this frame, it seems promising the concept of frailty which tends to figure out the subjects who are more vulnerable and more prone to negative outcomes, such as death or hospitalization. Cognitive, functional and psychological measures turned out to be the most effective measures to define frailty, as they condense most of the frailty cycle that occurs in the elderly and is probably responsible of the aging related physical decline. We used MMSE, Hand Grip strength, and GDS as variable parameters in a hierarchical Cluster Analysis (CA) in order to recognise aging phenotypes. By using a sample of 65-85 years old subjects we identified three frailty phenotypes that were consistent from both geriatric and genetic perspectives. Therefore, the method we propose may provide unbiased phenotypes suitable for the identification of genetic variants affecting the quality of aging in this age range. The CA method was less effective in ultranonagenarians, probably due to the high prevalence of frail subjects in this age group that makes difficult to distinguish discrete phenotypes.

No abstract

This article introduces a five-parameter Beta-Dagum distribution from which moments, hazard and entropy, and reliability measures are then derived. These properties show the high flexibility of the said distribution. The maximum likelihood estimators of the Beta-Dagum parameters are examined and the expected Fisher information matrix provided. Next, a simulation study is carried out which shows the good performance of maximum likelihood estimators for finite samples. Finally, the usefulness of the new distribution is illustrated through real data sets.