The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions, and MCA was used to detect and explore relationships between cognitive, clinical, physical, and lifestyle variables. Two PCA dimensions were identified (general cognition/executive function and memory), and two MCA dimensions were retained. Poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators, and presence of pathology. The first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association was between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics. The weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education, and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing if a relationship exists and how variables are related, and offering statistical results that can be seen both analytically and visually.

Identification of predictors of cognitive trajectories through the establishment of composite or single-parameter dimensional categories of cognition and mood may facilitate development of strategies to improve quality of life in the elderly. Participants (n = 487, aged 50+ years) were representative of the Portuguese population in terms of age, gender, and educational status. Cognitive and mood profiles were established using a battery of neurocognitive and psychological tests. Data were subjected to principal component analysis to identify core dimensions of cognition and mood, encompassing multiple test variables. Dimensions were correlated with age and with respect to gender, education, and occupational status. Cluster analysis was applied to isolate distinct patterns of cognitive performance and binary logistic regression models to explore interrelationships between aging, cognition, mood, and socio-demographic characteristics. Four main dimensions were identified: memory, executive function, global cognitive status, and mood. Based on these, strong and weak cognitive performers were distinguishable. Cluster analysis revealed further distinction within these two main categories into very good, good, poor, and very poor performers. Mood was the principal factor contributing to the separation between very good and good, as well as poor and very poor, performers. Clustering was also influenced by gender and education, albeit to a lesser extent; notably, however, female gender × lower educational background predicted significantly poorer cognitive performance with increasing age. Mood has a significant impact on the rate of cognitive decline in the elderly. Gender and educational level are early determinants of cognitive performance in later life.

It is relevant to unravel the factors that may mediate the cognitive decline observed during aging. Previous reports indicate that education has a positive influence on cognitive performance, while age, female gender and, especially, depressed mood were associated with poorer performances across multiple cognitive dimensions (memory and general executive function). Herein, the present study aimed to characterize the cognitive performance of community-dwelling individuals within distinct educational groups categorized by the number of completed formal school years: “less than 4,” “4, completed primary education,” and “more than 4.” Participants (n = 1051) were randomly selected from local health registries and representative of the Portuguese population for age and gender. Neurocognitive and clinical assessments were conducted in local health care centers. Structural equation modeling was used to derive a cognitive score, and hierarchical linear regressions were conducted for each educational group. Education, age and depressed mood were significant variables in directly explaining the obtained cognitive score, while gender was found to be an indirect variable. In all educational groups, mood was the most significant factor with effect on cognitive performance. Specifically, a depressed mood led to lower cognitive performance. The clinical disease indices cardiac and stroke associated with a more negative mood, while moderate increases in BMI, alcohol consumption and physical activity associated positively with improved mood and thus benefitted cognitive performance. Results warrant further research on the cause-effect (longitudinal) relationship between clinical indices of disease and risk factors and mood and cognition throughout aging.

Specific clusters of metabolic syndrome (MetS) components impact differentially on arterial stiffness, indexed as pulse wave velocity (PWV). Of note, in several population-based studies participating in the MARE (Metabolic syndrome and Arteries REsearch) Consortium the occurrence of specific clusters of MetS differed markedly across Europe and the US. The aim of the present study was to investigate whether specific clusters of MetS are consistently associated with stiffer arteries in different populations.

hyperpolarising factor. EETs are synthesised from arachidonic acids by cytochrome P450 enzymes, and soluble epoxide hydrolase (SEH) inhibition may up-regulate EETs. EETs signaling may be implicated in cardiovascular risk groups. The effects of two agonists in stimulating EETs release were compared, and the best agonist was chosen to investigate this pathway in cardiovascular patient groups, and to confirm target engagement in a first in human clinical trial of a novel SEH inhibitor. Methods: Healthy volunteers (12 male, 12 female) underwent 4 forearm venous occlusion plethysmography studies to compare the effects of intraarterial bradykinin and acetylcholine co-infused with saline, fluconazole (cytochrome P450 inhibitor), L-monomethylarginine (nitric oxide synthase inhibitor) plus aspirin (cyclo-oxygenase inhibitor) (LNMMA+ASA), or with all three inhibitors (Triple). Data were analysed by repeated measures analysis of variance. MeanAESEM are presented. Results: Fluconazole had no effect on basal tone (pZ0.25). Bradykinin and acetylcholine both caused dose related vasodilatation (p<0.0001 vs. p<0.001). Fluconazole inhibited bradykinin-induced flow, but not acetylcholine (p<0.0001 vs. pZ0.86). LNMMA+ASA inhibited bradykinin and acetylcholine induced vasodilatation (p<0.0001 vs. p<0.0001). There was no additive effect with triple inhibition. At top agonist doses, fluconazole inhibited bradykinin-induced flow, but not acetylcholine (-18.84AE5.08% vs. 3.36AE9.07%; pZ0.01). LNMMA+ASA inhibited bradykinin and acetylcholine induced flow (-35.74AE7.57% vs. -32.78AE10.60% pZ0.74). There were no gender differences. Conclusions: Basal tone is not dependent on EETs signaling. Bradykinininduced flow is EETs dependent, therefore bradykinin was chosen to probe EETs in cardiovascular patient groups.

Our data indicates that children 10-12 years old have a high salt intake that is well above the proposed recommendations and that a strategy based on theoretical and practical education may achieve in some children an important reduction in daily salt intake which, if maintained over time, may assume important public health implications. These results suggest that in those children a more complete theoretical and practical intervention is more productive and efficient towards reduction of salt intake than single theoretical or no intervention.

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the “best fit” model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.

Pheochromocytomas (PHEO) and paragangliomas (PGL) are rare tumors originated in cells derived from the neural crest. The first ones are located in the adrenal medulla, and the second ones in the sympathetic and parasympathetic nervous system. These kind of tumors may secrete excess catecholamines, including epinephrine, norepinephrine, dopamine and/or their metabolite metanephrine, normetanephrine and 3-methoxytyramine, respectively. Its clinical manifestations depend on the location, the secretory profile and the malignant potential of the tumor. These tumors are frequently benign in their presentation. Some arise in the context of familiar syndromes, accounting for up to one-third of the total of diagnosis. The metastatic form is the most common presentation of the tumors with familiar origin and due to their rarity, their diagnosis and management is often difficult. Over the years, our knowledge and perception of PHEO and PGL has greatly expanded and changed. This review article aims to focus on the genetic, clinical, diagnostic, therapeutic and prognostic approaches, to give the clinician knowledge of the most recent updates regarding these themes.