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BACKGROUND: There is evidence that adherence to Mediterranean-like diet reduces cognitive decline and brain atrophy in Alzheimer's disease (AD). However, lengthy dietary assessments, such as food frequency questionnaires (FFQs), discourage more frequent use. OBJECTIVE: Here we aimed to validate a 6-items short questionnaire for a Mediterranean-like diet (6-QMD) and explore its associations with memory performance and hippocampal atrophy in healthy elders and individuals at risk for AD. METHODS: We analyzed 938 participants (N = 234 healthy controls and N = 704 participants with an increased AD risk) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). The 6-QMD was validated against the Mediterranean Diet (MeDi) score and the Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) score, both derived from a detailed FFQ. Furthermore, associations between the 6-QMD and memory function as well as hippocampal atrophy were evaluated using linear regressions. RESULTS: The 6-QMD was moderately associated with the FFQ-derived MeDi adherence score (ρ = 0.25, p

We investigated whether the impact of tau-pathology on memory performance and on hippocampal/medial temporal memory function in non-demented individuals depends on the presence of amyloid pathology, irrespective of diagnostic clinical stage. We conducted a cross-sectional analysis of the observational, multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Two hundred and thirty-five participants completed task functional MRI and provided CSF (92 cognitively unimpaired, 100 experiencing subjective cognitive decline and 43 with mild cognitive impairment). Presence (A+) and absence (A−) of amyloid pathology was defined by CSF amyloid-β42 (Aβ42) levels. Free recall performance in the Free and Cued Selective Reminding Test, scene recognition memory accuracy and hippocampal/medial temporal functional MRI novelty responses to scene images were related to CSF total-tau and phospho-tau levels separately for A+ and A− individuals. We found that total-tau and phospho-tau levels were negatively associated with memory performance in both tasks and with novelty responses in the hippocampus and amygdala, in interaction with Aβ42 levels. Subgroup analyses showed that these relationships were only present in A+ and remained stable when very high levels of tau (>700 pg/ml) and phospho-tau (>100 pg/ml) were excluded. These relationships were significant with diagnosis, age, education, sex, assessment site and Aβ42 levels as covariates. They also remained significant after propensity score based matching of phospho-tau levels across A+ and A− groups. After classifying this matched sample for phospho-tau pathology (T−/T+), individuals with A+/T+ were significantly more memory-impaired than A−/T+ despite the fact that both groups had the same amount of phospho-tau pathology. ApoE status (presence of the E4 allele), a known genetic risk factor for Alzheimer’s disease, did not mediate the relationship between tau pathology and hippocampal function and memory performance. Thus, our data show that the presence of amyloid pathology is associated with a linear relationship between tau pathology, hippocampal dysfunction and memory impairment, although the actual severity of amyloid pathology is uncorrelated. Our data therefore indicate that the presence of amyloid pathology provides a permissive state for tau-related hippocampal dysfunction and hippocampus-dependent recognition and recall impairment. This raises the possibility that in the predementia stage of Alzheimer’s disease, removing the negative impact of amyloid pathology could improve memory and hippocampal function even if the amount of tau-pathology in CSF is not changed, whereas reducing increased CSF tau-pathology in amyloid-negative individuals may not proportionally improve memory function.

ObjectivesThe main goal of machine learning approaches to classify people into healthy, increased Alzheimer’s disease (AD) risk, and AD is the identification of valuable predictors for valid classification, prediction of conversion, and automatization of the process. While biomarkers from cerebrospinal fluid (CSF) are the best-established predictors for AD, other less invasive, easy-to-assess candidate predictors have been identified. Here, we evaluated the predictive value of such less invasive, predictors separately and in different combinations for classification of healthy controls (HC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and mild AD.MethodsWe evaluated the predictive value of personality scores, geriatric anxiety and depression scores, a resting-state functional magnetic resonance imaging (fMRI) marker (mPerAF), apoliprotein E (ApoE), and CSF markers (tTau, pTau181, Aβ42/40 ratio) separately and in different combinations in multi-class support vector machine classification. Participants (189 HC, 338 SCD, 132 MCI, 74 mild AD) were recruited from the multi-center DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE).ResultsHC were best predicted by a feature set comprised of personality, anxiety, and depression scores, while participants with AD were best predicted by a feature set containing CSF markers. Both feature sets had equally high overall decoding accuracy. However, all assessed feature sets performed relatively poorly in the classification of SCD and MCI.ConclusionOur results highlight that SCD and MCI are heterogeneous groups, pointing out the importance of optimizing their diagnosis criteria. Moreover, CSF biomarkers, personality, depression, and anxiety indicate complementary value for class prediction, which should be followed up on in future studies.Key PointsUsing multi-class support vector machine, we compared the predictive value of well-established versus non-invasive, easy-to-assess candidate variables for classification of participants with healthy cognition, subjective cognitive decline, mild cognitive impairment, and mild Alzheimer’s disease.Personality traits, geriatric anxiety and depression scores, resting-state mPerAF, ApoE genotype, and CSF markers were comparatively evaluated both separately and in different combinations.Predictive accuracy was similarly high for a combination of personality, anxiety and depression scores as for CSF markers.Both established as well as candidate variables performed poorly in classifying SCD and MCI, highlighting heterogenous causes of those cognitive states.CSF biomarkers and extended personality measures show complementary value for class prediction, which should be followed up on in future studies.