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Maetzler, W.

Publications and source records attributed to Maetzler, W..

3 recordsLinked to original sources

Foot placement coordination is impaired in people with Parkinson's Disease

BackgroundGait instability is a common and disabling symptom of Parkinsons disease (PD), contributing to frequent falls and reduced quality of life. While clinical balance tests and spatiotemporal gait measures can predict fall risk, they do not fully explain the underlying control mechanisms. In healthy individuals, foot placement is actively adjusted based on an internal estimate of the center of mass state to maintain gait stability, known as foot placement control. This estimation relies on the integration of multisensory information, which has been shown to be impaired in PD, potentially disrupting the control of gait stability through foot placement. ObjectiveTo investigate whether foot placement coordination during overground walking is impaired in people with PD. MethodsFifty people with PD and 51 healthy older adults walked overground for ten minutes at self-selected, comfortable walking speed. Foot placement errors were quantified as the deviation between the actual foot placement and the predicted placement derived from the center of mass kinematic state during the preceding swing phase. ResultsFoot placement errors were significantly higher in people with PD than in healthy older adults in both mediolateral (p < .05) and anteroposterior directions (p < .0001), at both mid-swing and terminal swing. Relative explained variance in mediolateral direction was significantly higher in people with PD compared to healthy older adults (p < .005). ConclusionWe provide first evidence of impaired coordination between the center of mass and foot placement in PD. Future work should investigate a causal relationship between impaired foot placement control, sensorimotor integration and gait instability.

pathology↗

Gut microbiome signatures of risk and prodromal markers of Parkinson's disease

ObjectivesAlterations of the gut microbiome in Parkinsons disease (PD) have been repeatedly demonstrated. However, little is known about whether such alterations precede disease onset and how they may be related to risk and prodromal markers of PD. We investigated associations of these features with gut microbiome composition. MethodsEstablished risk and prodromal markers of PD as well as factors related to diet/lifestyle, bowel function and medication were studied in relation to bacterial -/{beta}-diversity, enterotypes, and taxonomic composition in stool samples of 666 elderly TREND study participants. ResultsAmong risk and prodromal markers, physical inactivity, constipation and age showed associations with - and {beta}-diversity, and for both measures subthreshold parkinsonism and physical inactivity showed interaction effects. Moreover, male sex, possible REM-sleep behavior disorder (RBD), smoking as well as body-mass-index, antidiabetic and urate-lowering medication were associated with {beta}-diversity. Physical inactivity and constipation severity were increased in individuals with the Firmicutes-enriched enterotype. Subthreshold parkinsonism was least frequently observed in individuals with the Prevotella-enriched enterotype. Differentially abundant taxa were linked to constipation, physical inactivity, possible RBD, and subthreshold parkinsonism. Substantia nigra hyperechogenicity, olfactory loss, depression, orthostatic hypotension, urinary/erectile dysfunction, PD family history and the overall prodromal PD probability showed no significant microbiome associations. InterpretationSeveral risk and prodromal markers of PD are associated with changes in gut microbiome composition. However, the impact of the gut microbiome on PD risk and potential microbiome-dependent subtypes in the prodrome of PD need further investigation based on prospective clinical and (multi)omics data in incident PD cases.

neuroscience↗

Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs

BackgroundTo develop therapeutics for Alzheimers disease, early detection of patients awakes new hope. Circulating small non-coding RNAs are among the prominent candidates for a blood-based diagnosis, requiring however growing cohort sizes.\n\nMethodsWe determined abundance levels of 21 known circulating microRNAs in 465 individuals encompassing Alzheimers patients and controls recruited in US and Germany. We computed models to assess the relation between microRNA-expression and phenotypes, gender, age and disease severity (Mini-Mental State Examination MMSE).\n\nResults20 of 21 miRNAs were consistently dys-regulated in the US and Germany. 18 were significantly correlated to neurodegeneration (adjusted p<0.05) with highest significance for miR-532-5p (adjusted p=4.8x10-30). Ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted p=0.0002). Machine learning models reached an AUC value of 87.6% in differentiating AD patients from controls.\n\nConclusionsOur data provide strong evidence for the relevance of circulating non-coding RNAs to detect Alzheimers from a blood sample.

bioinformatics↗