Dietary patterns are related to cognitive functioning in elderly enriched with individuals at increased risk for Alzheimer's disease.

PURPOSE: To investigate cross-sectional associations between dietary patterns and cognitive functioning in elderly free of dementia. METHODS: Data of 389 participants from the German DELCODE study (52% female, 69 ± 6 years, mean Mini Mental State Score 29 ± 1) were included. The sample was enriched with elderly at increased risk for Alzheimer's disease (AD) by including participants with subjective cognitive decline, mild cognitive impairment (MCI) and siblings of AD patients. Mediterranean and MIND diets were derived from 148 Food Frequency Questionnaire items, and data-driven patterns by principal component analysis (PCA) of 39 food groups. Associations between dietary patterns and five cognitive domain scores were analyzed with linear regression analyses adjusted for demographics (model 1), and additionally for energy intake, BMI, other lifestyle variables and APOe4-status (model 2). For PCA-derived dietary components, final model 3 included all other dietary components. RESULTS: In fully adjusted models, adherence to Mediterranean and MIND diet was associated with better memory. The 'alcoholic beverages' PCA component was positively associated with most cognitive domains. Exclusion of MCI subjects (n = 60) revealed that Mediterranean and MIND diet were also related to language functions; associations with the alcoholic beverages component were attenuated, but most remained significant. CONCLUSION: In line with data from elderly population samples, Mediterranean and MIND diet and some data-derived dietary patterns were related to memory and language function. Longitudinal data are needed to draw conclusions on the putative effect of nutrition on the rate of cognitive decline, and on the potential of dietary interventions in groups at increased risk for AD.

[Longitudinal Predictors of the Need for Care]. / Einflussfaktoren auf die Pflegebedürftigkeit im Längsschnitt.

Background: Little is known about the longitudinal predictors of the need for care in old age. However, the knowledge of these factors is important for developing strategies for prevention or delay the need for care. Thus, we aimed at investigating the factors affecting the need for care in old age. Methods: In this population-based prospective cohort study (AgeCoDe, with n=3 217 individuals aged 75 years and above at baseline), the need for care was observed over 4.5 years. The need for care was quantified by the care level defined by the German Law (§ 15 SGB XI). Longitudinal predictors (sociodemographic variables, impairment in mobility/hearing/vision, dementia and depression) of the need for care were examined by using Random Effects Logit regressions. Results: Longitudinal regression analysis revealed that the probability of the need for care significantly increased with the occurrence of dementia (OR: 48.2), mobility impairments (aggravated walking, OR: 26.4; disability of walking, OR: 747.9) and age (e. g. 90 years and above vs.<80 years, OR: 32.3). The influence of family status, living conditions, visual impairment and depression on need for care was markedly smaller, and the effect of hearing impairments did not achieve statistical significance. Conclusion: In order to prevent or delay the need for care in old age, treatments should aim at preserving mobility and cognition. Due to demographic ageing, developing such programs is of major importance for health policy.

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation.

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aß42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.