The Editors of the Journal of Alzheimer’ s Disease invited Professor Patrizia Mecocci to contribute a review article focused on the importance and implications of her research on aging, brain aging, and senile dementias over the last years. This invitation was based on an assessment that she was one of the journal’s top authors and a strong supporter of the concept that oxidative stress is a major contributor to several alterations observed in age-related conditions (sarcopenia, osteoporosis) and, more significantly, in brain aging suggesting a pivotal role in the pathogenesis and progression of one of the most dramatic age-related diseases, Alzheimer’s disease (AD). Her first pioneering research was on the discovery of high level of 8-hydroxy-2’-deoxyguanosine (OH8dG), a marker of oxidation in nucleic acids, in mitochondrial DNA isolated from cerebral cortex. This molecule increases progressively with aging and more in AD brain, supporting the hypothesis that oxidative stress, a condition of unbalance between the production of reactive oxygen species and antioxidants, gives a strong contribution to the high incidence of AD in old age subjects. OH8dG also increases in peripheral lymphocyte from AD subjects, suggesting that AD is not only a cerebral but also a systemic disease. The role of antioxidants, particularly vitamin E and zinc, were also studied in longevity and in cognitive decline and dementia. This review shows the main findings from Mecocci’s laboratory related to oxidative stress in aging, brain aging, and AD and discusses the importance and implications of some of the major achievements in this field of research.

Aging is associated with a progressive loss of bone-muscle mass and strength. When the decline in mass and strength reaches critical thresholds associated with adverse health outcomes, they are operationally considered geriatric conditions and named, respectively, osteoporosis and sarcopenia. Osteoporosis and sarcopenia share many of the same risk factors and both directly or indirectly cause higher risk of mobility limitations, falls, fractures and disability in activities of daily living. This is not surprising since bones adapt their morphology and strength to the long-term loads exerted by muscle during anti-gravitational and physical activities. Non-mechanical systemic and local factors also modulate the mechanostat effect of muscle on bone by affecting the bidirectional osteocyte-muscle crosstalk, but the specific pathways that regulate these homeostatic mechanisms are not fully understood. More research is required to reach a consensus on cut points in bone and muscle parameters that identify individuals at high risk for adverse health outcomes, including falls, fractures and disability. A better understanding of the muscle-bone physiological interaction may help to develop preventive strategies that reduce the burden of musculoskeletal diseases, the consequent disability in older persons and to limit the financial burden associated with such conditions. In this review, we summarize age-related bone-muscle changes focusing on the biomechanical and homeostatic mechanisms that explain bone-muscle interaction and we speculate about possible pathological events that occur when these mechanisms become impaired. We also report some recent definitions of osteoporosis and sarcopenia that have emerged in the literature and their implications in clinical practice. Finally, we outline the current evidence for the efficacy of available anti-osteoporotic and proposed antisarcopenic interventions in older persons.

Introduction: Vitamin E family, composed by tocopherols and tocotrienols, is a group of compounds with neuroprotective properties. The exact role in the pathogenesis and the benefit of vitamin E as treatment for Alzheimer's disease (AD) are still under debate. Methods: A literature search in PubMed, Medline, and Cochrane databases has been carried out. All types of studies, from bench and animal models to clinical, were included. Results: High plasma vitamin E levels are associated with better cognitive performance, even if clear evidence of their ability to prevent or delay cognitive decline in AD is still lacking. Each vitamin E form is functionally unique and shows specific biological functions. Tocotrienols seem to have superior antioxidant and anti‐inflammatory properties compared with tocopherols. Discussion: The benefit of vitamin E as a treatment for AD is still under debate, mainly because of the inconsistent findings from observational studies and the methodological limitations of clinical trials.

Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.