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The present study was conducted to test the association between adherence to specific exercise modalities and sarcopenia severity in Italian older adults. Data were collected as part of the ongoing Longevity Check-Up 7+ (Lookup 7+) project. Lookup 7+ began in June 2015 and has since been conducted in unconventional settings (e.g., exhibitions, malls, social events) throughout Italy. In the present study, we used data on adults 65 years and older. Sarcopenia was identified according to the simultaneous presence of dynapenia and low appendicular muscle mass. Muscle strength was measured by isometric handgrip and sit-to-stand (STS) testing. Sarcopenia was categorized as severe if participants reported difficulty or inability to walk 400 m. Engagement in running and/or swimming (RS) or strength training with or without stretching (SS) was used to define exercise modalities. Analyses were conducted in 3289 participants (mean age: 72.7 ± 5.7 years; 1814 women). The results of the binary regression showed negative associations between RS and the presence of STS-based sarcopenia in women, and between RS and STS-based severe sarcopenia in men. Collectively, these findings indicate that RS is negatively associated with the presence of sarcopenia in large sample of relatively unselected Italian older adults. [ABSTRACT FROM AUTHOR]

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional "one-size-fits-all" strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients' complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI), also termed mild neurocognitive disorder, includes a heterogeneous group of conditions characterized by declines in one or more cognitive domains greater than that expected during "normal" aging but not severe enough to impair functional abilities. MCI has been associated with an increased risk of developing dementia and even considered an early stage of it. Therefore, noninvasively accessible biomarkers of MCI are highly sought after for early identification of the condition. Systemic inflammation, metabolic perturbations, and declining physical performance have been described in people with MCI. However, whether biological and functional parameters differ across MCI neuropsychological subtypes is presently debated. Likewise, the predictive value of existing biomarkers toward MCI conversion into dementia is unclear. The "develOpment of metabolic and functional markers of Dementia IN Older people" (ODINO) study was conceived as a multi-dimensional investigation in which multi-marker discovery will be coupled with innovative statistical approaches to characterize patterns of systemic inflammation, metabolic perturbations, and physical performance in older adults with MCI. The ultimate aim of ODINO is to identify potential biomarkers specific for MCI subtypes and predictive of MCI conversion into Alzheimer's disease or other forms of dementia over a three-year follow-up. Here, we describe the rationale, design, and methods of ODINO. [ABSTRACT FROM AUTHOR]