In the last decade, the association between diet and cognitive function or dementia has been largely investigated. In the present article, we systematically reviewed observational studies published in the last three years (2014–2016) on the relationship among dietary factors and late-life cognitive disorders at different levels of investigation (i.e., dietary patterns, foods and food-groups, and dietary micro- and macronutrients), and possible underlying mechanisms of the proposed associations. From the reviewed evidence, the National Institute on Aging–Alzheimer’s Association guidelines for Alzheimer’s disease (AD) and cognitive decline due to AD pathology introduced some evidence suggesting a direct relation between diet and changes in the brain structure and activity. There was also accumulating evidence that combinations of foods and nutrients into certain patterns may act synergistically to provide stronger health effects than those conferred by their individual dietary components. In particular, higher adherence to a Mediterranean-type diet was associated with decreased cognitive decline. Moreover, also other emerging healthy dietary patterns such as the Dietary Approach to Stop Hypertension (DASH) and the Mediterranean-DASH diet Intervention for Neurodegenerative Delay (MIND) diets were associated with slower rates of cognitive decline and significant reduction of AD rate. Furthermore, some foods or food groups traditionally considered harmful such as eggs and red meat have been partially rehabilitated, while there is still a negative correlation of cognitive functions with saturated fatty acids and a protective effect against cognitive decline of elevated fish consumption, high intake of monounsaturated fatty acids and polyunsaturated fatty acids (PUFA), particularly n-3 PUFA.

Non-Alcoholic Fatty Liver Disease (NAFLD) is currently the most common form of liver disease worldwide affecting all ages and ethnic groups and it has become a consistent threat even in young people. Our aim was to estimate the effect of a Low Glycemic Index Mediterranean Diet (LGIMD) on the NAFLD score as measured by a Liver Ultrasonography (LUS). NUTRIzione in EPAtologia (NUTRIEPA) is a population-based Double-Blind RCT. Data were collected in 2011 and analyzed in 2013-14. 98 men and women coming from Putignano (Puglia, Southern Italy) were drawn from a previous randomly sampled population-based study and identified as having moderate or severe NAFLD. The intervention strategy was the assignment of a LGIMD or a control diet. The main outcome measure was NAFLD score, defined by LUS. After randomization, 50 subjects were assigned to a LGIMD and 48 to a control diet. The study lasted six months and all participants were subject to monthly controls/checks. Adherence to the LGIMD as measured by Mediterranean Adequacy Index (MAI) showed a median of 10.1. A negative interaction between time and LGIMD on the NAFLD score (−4.14, 95% CI −6.78,−1.49) was observed, and became more evident at the sixth month (−4.43, 95%CI −7.15, −1.71). A positive effect of the interaction among LGIMD, time and age (Third month: 0.07, 95% CI 0.02, 0.12; Sixth month: 0.08, 95% CI 0.03,0.13) was also observed. LGIMD was found to decrease the NAFLD score in a relatively short time. Encouraging those subjects who do not seek medical attention but still have NAFLD to follow a LGIMD and other life-style interventions, may reduce the degree of severity of the disease. Dietary intervention of this kind, could also form the cornerstone of primary prevention of Type 2 Diabetes Mellitus (T2DM) and cardiovascular disease.

Background and objective Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and its progression. Methods We conducted a systematic review and selected 55 articles on the application of MLAs in CKD. PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms. Results MLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians. Conclusions The application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.

Background: Most studies focused on only one measure of social dysfunction in older age, without proper validation and distinction across different dimensions including subjectivity, structural, and functional aspects. Objective: We sought to validat