Stephan Müller

Little is known about the dysbiosis of the gut microbiome in patients with mild cognitive impairment (MCI) potentially at risk for the development of Alzheimer’s disease (AD). So far, only cross-sectional differences and not longitudinal changes and their prognostic significance have been in the scope of research in MCI. Therefore, we investigated the ability of longitudinal taxonomic and functional gut microbiome data from 100 healthy controls (HC) to predict the progression from normal cognition to MCI over a 4-year follow-up period (4yFU). Logistic regression models were built with baseline features that best discriminated between the two groups using an ANOVA-type statistical analysis. The best model for the discrimination of MCI converters was based on functional data using Gene Ontology (GO), which included 14 features. This model achieved an area under the receiver operating characteristic curve (AUROC) of 0.84 at baseline, 0.78 at the 1-year follow-up (1yFU), and 0.75 at 4yFU. This functional model outperformed the taxonomic model, which included 38 genera features, in terms of descriptive performance and showed comparable efficacy to combined analyses integrating functional, taxonomic, and clinical characteristics. Thus, gut microbiome algorithms have the potential to predict MCI conversion in HCs over a 4-year period, offering a promising innovative supplement for early AD identification.

Alterations in the gut microbiome are associated with the pathogenesis of Alzheimer’s disease (AD) and can be used as a diagnostic measure. However, longitudinal data of the gut microbiome and knowledge about its prognostic significance for the development and progression of AD are limited. The aim of the present study was to develop a reliable predictive model based on gut microbiome data for AD development. In this longitudinal study, we investigated the intestinal microbiome in 49 mild cognitive impairment (MCI) patients over a mean (SD) follow-up of 3.7 (0.6) years, using shotgun metagenomics. At the end of the 4-year follow-up (4yFU), 27 MCI patients converted to AD dementia and 22 MCI patients remained stable. The best taxonomic model for the discrimination of AD dementia converters from stable MCI patients included 24 genera, yielding an area under the receiver operating characteristic curve (AUROC) of 0.87 at BL, 0.92 at 1yFU and 0.95 at 4yFU. The best models with functional data were obtained via analyzing 25 GO (Gene Ontology) features with an AUROC of 0.87 at BL, 0.85 at 1yFU and 0.81 at 4yFU and 33 KO [Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog] features with an AUROC of 0.79 at BL, 0.88 at 1yFU and 0.82 at 4yFU. Using ensemble learning for these three models, including a clinical model with the four parameters of age, gender, body mass index (BMI) and Apolipoprotein E (ApoE) genotype, yielded an AUROC of 0.96 at BL, 0.96 at 1yFU and 0.97 at 4yFU. In conclusion, we identified novel and timely stable gut microbiome algorithms that accurately predict progression to AD dementia in individuals with MCI over a 4yFU period.

The early detection of cognitive impairment or dementia is in the focus of current research as the amount of cognitively impaired individuals will rise intensely in the next decades due to aging population worldwide. Currently available diagnostic tools to detect mild cognitive impairment (MCI) or dementia are time-consuming, invasive or expensive and not suitable for wide application as required by the high number of people at risk. Thus, a fast, simple and sensitive test is urgently needed to enable an accurate detection of people with cognitive dysfunction and dementia in the earlier stages to initiate specific diagnostic and therapeutic interventions. We examined digital Clock Drawing Test (dCDT) kinematics for their clinical utility in differentiating patients with amnestic MCI (aMCI) or mild Alzheimer’s dementia (mAD) from healthy controls (HCs) and compared it with the diagnostic value of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) neuropsychological battery total score. Data of 381 participants (138 patients with aMCI, 106 patients with mAD and 137 HCs) was analyzed in the present study. All participants performed the clock drawing test (CDT) on a tablet computer and underwent the CERAD test battery and depression screening. CERAD total scores were calculated by subtest summation, excluding MMSE scores. All tablet variables (i.e. time in air, time on surface, total time, velocity, pressure, pressure/velocity relation, strokes per minute, time not painting, pen-up stroke length, pen-up/pen-down relation, and CDT score) during dCDT performance were entered in a forward stepwise logistic regression model to assess, which parameters best discriminated between aMCI or mAD and HC. Receiver operating characteristics (ROC) curves were constructed to visualize the specificity in relation to the sensitivity of dCDT variables against CERAD total scores in categorizing the diagnostic groups. dCDT variables provided a slightly better diagnostic accuracy of 81.5% for discrimination of aMCI from HCs than using CERAD total score (accuracy 77.5%). In aMCI patients with normal CDT scores, both dCDT (accuracy 78.0%) and CERAD total scores (accuracy 76.0%) were equally accurate in discriminating against HCs. Finally, in differentiating patients with mAD from healthy individuals, accuracy of both dCDT (93.0%) and CERAD total scores (92.3%) was excellent. Our findings suggest that dCDT is a suitable screening tool to identify early cognitive dysfunction. Its performance is comparable with the time-consuming established psychometric measure (CERAD test battery).

Introduction: Little is known about effects of physical activity (PA) in genetically driven early‐onset autosomal dominant Alzheimer's disease (AD).Methods: A total of 372 individuals participating at the Dominantly Inherited Alzheimer Network study were examined to evaluate the cross‐sectional relationship of PA with cognitive performance, functional status, cognitive decline, and AD biomarkers in cerebrospinal fluid. Mutation carriers were categorized as high or low exercisers according to WHO recommendations.Results: Mutation carriers with high PA showed significantly better cognitive and functional performance and significantly less AD‐like pathology in cerebrospinal fluid than individuals with low PA. Mutation carriers with high PA scored 3.4 points better on Mini Mental State Examination at expected symptom onset and fulfilled the diagnosis of very mild dementia 15.1 years later compared with low exercisers.Discussion: These results support a beneficial effect of PA on cognition and AD pathology even in individuals with genetically driven autosomal dominant AD.

Previous studies have demonstrated increased tau plasma levels in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) due to AD. Much less is known whether increased tau plasma levels can already be detected in the pre-MCI stage of subjective cognitive decline (SCD). In the present study we measured tau plasma levels in 111 SCD patients and 134 age- and gender-matched cognitively healthy controls participating in the DZNE (German Center for Neurodegenerative Diseases) longitudinal study on cognition and dementia (DELCODE). Tau plasma levels were measured using ultra-sensitive, single-molecule array (Simoa) technology. We found no significant different tau plasma levels in SCD (3.4 pg/ml) compared with healthy controls (3.6 pg/ml) after controlling for age, gender, and education (p = 0.137). In addition, tau plasma levels did not correlate with Aβ42 (r = 0.073; p = 0.634), tau (r = −0.179; p = 0.240), and p-tau181 (r = −0.208; p = 0.171) cerebrospinal fluid (CSF) levels in a subgroup of 45 SCD patients with available CSF. In conclusion, plasma tau is not increased in SCD patients. In addition, the lack of correlation between tau in plasma and CSF in the examined cohort suggests that tau levels are affected by different factors in both biofluids.