There is a need for increased nosological knowledge to enable rational trials in Alzheimer's disease (AD) and related disorders. The ongoing Gothenburg mild cognitive impairment (MCI) study is an attempt to conduct longitudinal indepth phenotyping of patients with different forms and degrees of cognitive impairment using neuropsychological, neuroimaging, and neurochemical tools. Particular attention is paid to the interplay between AD and subcortical vascular disease, the latter representing a disease entity that may cause or contribute to cognitive impairment with an effect size that may be comparable to AD. Of 664 patients enrolled between 1999 and 2013, 195 were diagnosed with subjective cognitive impairment (SCI), 274 with mild cognitive impairment (MCI), and 195 with dementia, at baseline. Of the 195 (29%) patients with dementia at baseline, 81 (42%) had AD, 27 (14%) SVD, 41 (21%) mixed type dementia (¼AD þ SVD ¼ MixD), and 46 (23%) other etiologies. After 6 years, 292 SCI/MCI patients were eligible for followup. Of these 292, 69 (24%) had converted to dementia (29 (42%) AD, 16 (23%) SVD, 15 (22%) MixD, 9 (13%) other etiologies). The study has shown that it is possible to identify not only AD but also incipient and manifest MixD/SVD in a memory clinic setting. These conditions should be taken into account in clinical trials.

BACKGROUNDCerebral ischemia promotes morphological reactions of the neurons, astrocytes, oligodendrocytes, and microglia in experimental studies. Our aim was to examine the profile of CSF (cerebrospinal fluid) biomarkers and their relation to stroke severity and degree of white matter lesions (WML).METHODSA total of 20 patients (mean age 76 years) were included within 5–10 days after acute ischemic stroke (AIS) onset. Stroke severity was assessed using NIHSS (National Institute of Health stroke scale). The age-related white matter changes (ARWMC) scale was used to evaluate the extent of WML on CT-scans. The concentrations of specific CSF biomarkers were analyzed.RESULTSPatients with AIS had significantly higher levels of NFL (neurofilament, light), T-tau, myelin basic protein (MBP), YKL-40, and glial fibrillary acidic protein (GFAP) compared with controls; T-Tau, MBP, GFAP, and YKL-40 correlated with clinical stroke severity, whereas NFL correlated with severity of WML (tested by Mann–Whitney test).CONCLUSIONSSeveral CSF biomarkers increase in AIS, and they correlate to clinical stroke severity. However, only NFL was found to be a marker of degree of WML.

This study examines the intra-individual stability of cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease (AD) over 2 years in 83 patients with mild cognitive impairment (MCI) and 17 cognitively healthy control individuals. All participants underwent clinical and neuropsychological evaluation and lumbar puncture at baseline and after 2 years at a university hospital memory clinic. CSF was analyzed for total tau (T-tau), phospho-tau181 (P-tau181) and amyloid-β1−42 (Aβ1−42). During the 2-year observational time, 12 MCI patients progressed to AD and 3 progressed to vascular dementia, while 68 remained stable. Baseline T-tau and P-tau181 levels were elevated in the MCI-AD group as compared to the stable MCI patients and the control group (p < 0.01), while baseline Aβ1−42 levels were lower (p < 0.001). Stable MCI patients were biochemically indistinguishable from controls. The biomarker levels at baseline and after 2 years showed Pearson R values between 0.81 and 0.91 (p < 0.001) and coefficients of variation of 7.2 to 8.7%. In conclusion, intra-individual biomarker levels are remarkably stable over 2 years. Thus, even minor biochemical changes induced by treatment against AD should be detectable using these biomarkers, which bodes well for their usefulness as surrogate markers for drug efficacy in clinical trials.

BackgroundWith the shift of research focus towards the pre-dementia stage of Alzheimer’s disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification.MethodsWe examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects.ResultsIn univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures.ConclusionsAmyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.Electronic supplementary materialThe online version of this article (10.1186/s13195-018-0428-1) contains supplementary material, which is available to authorized users.

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