Age estimation from DNA methylation markers has seen an exponential growth of interest, not in the least from forensic scientists. The current published assays, however, can still be improved by lowering the number of markers in the assay and by providing more accurate models to predict chronological age. From the published literature we selected 4 age-associated genes (ASPA, PDE4C, ELOVL2, and EDARADD) and determined CpG methylation levels from 206 blood samples of both deceased and living individuals (age range: 0-91 years). This data was subsequently used to compare prediction accuracy with both linear and non-linear regression models. A quadratic regression model in which the methylation levels of ELOVL2 were squared showed the highest accuracy with a Mean Absolute Deviation (MAD) between chronological age and predicted age of 3.75 years and an adjusted R 2 of 0.95. No difference in accuracy was observed for samples obtained either from living and deceased individuals or between the 2 genders. In addition, 29 teeth from different individuals (age range: 19-70 years) were analyzed using the same set of markers resulting in a MAD of 4.86 years and an adjusted R 2 of 0.74. Cross validation of the results obtained from blood samples demonstrated the robustness and reproducibility of the assay. In conclusion, the set of 4 CpG DNA methylation markers is capable of producing highly accurate age predictions for blood samples from deceased and living individuals

Currently the human population is aging faster. This leads to higher dependency rates and the transformation of health and social care to adapt to this aged population. Among the changes developed by this population is frailty. It is defined as a clinically detectable syndrome, related to the aging of multiple physiological systems, which prompts a situation of vulnerability. The etiology of frailty seems to be multifactorial and its pathophysiology is influenced by the interaction of numerous factors. Morley et al. propose four main mechanisms triggering the frailty: atherosclerosis, sarcopenia, cognitive deterioration and malnutrition, with their respective metabolic alterations. Malnutrition is associated with cognitive impairment or functional loss, but it is also known that an inadequate nutritional status predisposes to cognitive frailty. Additionally, nutritional factors that may influence vascular risk factors will potentially have an effect on dementia decline among patients with cognitive frailty. This review aims to describe the nutritional factors that have been researched so far which may lead to the development of frailty, and especially cognitive decline.

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