GFAP and NfL as fluid biomarkers for clinical disease severity and disease progression in multiple system atrophy (MSA).

BACKGROUND: Multiple system atrophy (MSA), an atypical parkinsonian syndrome, is a rapidly progressive neurodegenerative disease with currently no established fluid biomarkers available. MSA is characterized by an oligodendroglial α-synucleinopathy, progressive neuronal cell loss and concomitant astrocytosis. Here, we investigate glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) as fluid biomarkers for differential diagnosis, assessment of clinical disease severity and prediction of disease progression in MSA. METHODS: GFAP and NfL levels were analyzed in plasma and CSF samples of 47 MSA patients as well as 24 Parkinson's disease (PD) and 25 healthy controls (HC) as reference cohorts. In MSA, biomarker levels were correlated to baseline and longitudinal clinical disease severity (UMSARS scores). RESULTS: In MSA, GFAP levels in CSF and plasma predicted baseline clinical disease severity as indicated by UMSARS scores, while NfL levels predicted clinical disease progression as indicated by longitudinal changes in UMSARS scores. Cross-sectionally, NfL levels in CSF and plasma were significantly elevated in MSA compared to both PD and HC. Receiver operating curves (ROC) indicated high diagnostic accuracy of NfL for distinguishing MSA from PD (CSF: AUC = 0.97, 95% CI 0.90-1.00; plasma: AUC = 0.90, 95% CI 0.81-1.00). DISCUSSION: In MSA, GFAP shows promise as novel biomarker for assessing current clinical disease severity, while NfL might serve as biomarker for prediction of disease progression and differential diagnosis of MSA against PD.

The comorbidity profiles and medication issues of patients with multiple system atrophy: a systematic cross-sectional analysis.

BACKGROUND: Multiple system atrophy (MSA) is a complex and fatal neurodegenerative movement disorder. Understanding the comorbidities and drug therapy is crucial for MSA patients' safety and management. OBJECTIVES: To investigate the pattern of comorbidities and aspects of drug therapy in MSA patients. METHODS: Cross-sectional data of MSA patients according to Gilman et al. (2008) diagnostic criteria and control patients without neurodegenerative diseases (non-ND) were collected from German, multicenter cohorts. The prevalence of comorbidities according to WHO ICD-10 classification and drugs administered according to WHO ATC system were analyzed. Potential drug-drug interactions were identified using AiDKlinik®. RESULTS: The analysis included 254 MSA and 363 age- and sex-matched non-ND control patients. MSA patients exhibited a significantly higher burden of comorbidities, in particular diseases of the genitourinary system. Also, more medications were prescribed MSA patients, resulting in a higher prevalence of polypharmacy. Importantly, the risk of potential drug-drug interactions, including severe interactions and contraindicated combinations, was elevated in MSA patients. When comparing MSA-P and MSA-C subtypes, MSA-P patients suffered more frequently from diseases of the genitourinary system and diseases of the musculoskeletal system and connective tissue. CONCLUSIONS: MSA patients face a substantial burden of comorbidities, notably in the genitourinary system. This, coupled with increased polypharmacy and potential drug interactions, highlights the complexity of managing MSA patients. Clinicians should carefully consider these factors when devising treatment strategies for MSA patients.

The comorbidity and co-medication profile of patients with progressive supranuclear palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) is usually diagnosed in elderly. Currently, little is known about comorbidities and the co-medication in these patients. OBJECTIVES: To explore the pattern of comorbidities and co-medication in PSP patients according to the known different phenotypes and in comparison with patients without neurodegenerative disease. METHODS: Cross-sectional data of PSP and patients without neurodegenerative diseases (non-ND) were collected from three German multicenter observational studies (DescribePSP, ProPSP and DANCER). The prevalence of comorbidities according to WHO ICD-10 classification and the prevalence of drugs administered according to WHO ATC system were analyzed. Potential drug-drug interactions were evaluated using AiDKlinik®. RESULTS: In total, 335 PSP and 275 non-ND patients were included in this analysis. The prevalence of diseases of the circulatory and the nervous system was higher in PSP at first level of ICD-10. Dorsopathies, diabetes mellitus, other nutritional deficiencies and polyneuropathies were more frequent in PSP at second level of ICD-10. In particular, the summed prevalence of cardiovascular and cerebrovascular diseases was higher in PSP patients. More drugs were administered in the PSP group leading to a greater percentage of patients with polypharmacy. Accordingly, the prevalence of potential drug-drug interactions was higher in PSP patients, especially severe and moderate interactions. CONCLUSIONS: PSP patients possess a characteristic profile of comorbidities, particularly diabetes and cardiovascular diseases. The eminent burden of comorbidities and resulting polypharmacy should be carefully considered when treating PSP patients.

Cognitive Trajectories in Preclinical and Prodromal Alzheimer's Disease Related to Amyloid Status and Brain Atrophy: A Bayesian Approach.

Background: Cognitive decline is a key outcome of clinical studies in Alzheimer's disease (AD). Objective: To determine effects of global amyloid load as well as hippocampus and basal forebrain volumes on longitudinal rates and practice effects from repeated testing of domain specific cognitive change in the AD spectrum, considering non-linear effects and heterogeneity across cohorts. Methods: We included 1,514 cases from three cohorts, ADNI, AIBL, and DELCODE, spanning the range from cognitively normal people to people with subjective cognitive decline and mild cognitive impairment (MCI). We used generalized Bayesian mixed effects analysis of linear and polynomial models of amyloid and volume effects in time. Robustness of effects across cohorts was determined using Bayesian random effects meta-analysis. Results: We found a consistent effect of amyloid and hippocampus volume, but not of basal forebrain volume, on rates of memory change across the three cohorts in the meta-analysis. Effects for amyloid and volumetric markers on executive function were more heterogeneous. We found practice effects in memory and executive performance in amyloid negative cognitively normal controls and MCI cases, but only to a smaller degree in amyloid positive controls and not at all in amyloid positive MCI cases. Conclusions: We found heterogeneity between cohorts, particularly in effects on executive functions. Initial increases in cognitive performance in amyloid negative, but not in amyloid positive MCI cases and controls may reflect practice effects from repeated testing that are lost with higher levels of cerebral amyloid.

Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression.

BACKGROUND: Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. METHODS: In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aß42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). RESULTS: Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. CONCLUSION: Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.

The Multiple Object Test as a performance-based tool to assess the decline of ADL function in Parkinson's disease.

INTRODUCTION: As cognitive-driven worsening of activities of the daily living (ADL) in Parkinson's disease (PD) is the core feature of PD dementia (PDD), there is great need for sensitive quantitative assessment. Aim of our study was the evaluation of cognitive-driven worsening of ADL by the performance-based Multiple Object Test (MOT), offering an essential clinical advantage as it is quick and easy to apply in a clinical context even on severely impaired patients. METHODS: 73 PD patients were assessed longitudinally over a period of 37 (6-49) months. According to their neuropsychological profile the sample was divided into two groups: PD patients with (n = 34, PD-CI) and without cognitive impairment (n = 39, PD-noCI). The MOT comprises five routine tasks (e.g. to make coffee) quick and easy to apply. Quantitative (total error number, processing time) and qualitative parameters (error type) were analyzed using non-parametric test statistic (e.g.Wilcoxon signed-rank test, binary logistic regression). RESULTS: Median number of total errors (p = 0.001), processing time (p<0.001), perplexity (p = 0.035), and omission errors (p<0.001) increased significantly from baseline to follow-up in the total sample. Worsening of MOT performance was correlated to cognitive decline in the attention/ executive function and visuo-constructive domain. PD-CI showed an increase in omission errors (p = 0.027) compared to PD-noCI over time. This increase in omission errors between visits was further identified as a risk marker for PDD conversion. CONCLUSION: The MOT, especially frequency of omission errors, is a promising tool to rate PD patients objectively and might help to identify patients with a high risk for having mild cognitive impairment or dementia.

The Multiple Object Test as a Performance Based Tool to Assess Cognitive Driven Activity of Daily Living Function in Parkinson's Disease.

BACKGROUND: There is need for multidimensional quantitative assessment of cognitive driven activities of daily living (ADL) functions in Parkinson's disease (PD). OBJECTIVE: To determine whether there is an ADL profile related to cognitive impairment in PD assessed by the Multiple Object Test (MOT). We assumed MOT performance to be lower in PD patients versus controls and in PD patients with more severe cognitive impairment. METHODS: 50 PD patients with no cognitive impairment (PD-NC), 54 patients with PD-mild cognitive impairment (PD-MCI), 29 with Parkinson's disease dementia (PDD), and 40 healthy controls (HC) were investigated. Besides comprehensive cognitive testing, the MOT, a performance based test consisting of five routine tasks (e.g., preparing a cup of coffee), was applied. Quantitative (total errors and time) and qualitative (error type) MOT parameters were analyzed. RESULTS: Total time and number of MOT errors was increased in PD patients compared to controls (p < 0.001). These parameters also differentiated PDD patients from other cognitive groups (p < 0.05). No control subject had ≥ 4 errors in the MOT, but 30% of PD patients, especially PDD, scored above this cut-off. Omission (p < 0.001) and mislocation (p < 0.03) errors were more prominent in PDD than other cognitive groups. Perplexity errors did not differ between PD-MCI and PDD but between PD-NC and PDD (p = 0.01). MOT parameters discriminating between cognitive groups correlated mainly with lower test performance in psychomotor speed and executive function. CONCLUSION: Performance based testing is promising to identify quantitative and qualitative ADL aspects differentiating between different cognitive groups which might be helpful for an early detection of PDD.