Background The prevalence of neurodegenerative diseases increases significantly with increasing age. Neurodegeneration is the progressive loss of function of neurons that eventually leads to cell... Show moreBackground The prevalence of neurodegenerative diseases increases significantly with increasing age. Neurodegeneration is the progressive loss of function of neurons that eventually leads to cell death, which in turn leads to cognitive disfunction. Cognitive performance can therefore also be considered age dependent. The current study investigated if the NeuroCart can detect age related decline on drug-sensitive CNS-tests in healthy volunteers (HV), and whether there are interactions between the rates of decline and sex. This study also investigated if the NeuroCart was able to differentiate disease profiles of neurodegenerative diseases, compared to age-matched HV and if there is age related decline in patient groups. Methods This retrospective study encompassed 93 studies, performed at CHDR between 2005 and 2020 that included NeuroCart measurements, which resulted in data from 2729 subjects. Five NeuroCart tests were included in this analysis: smooth and saccadic eye movements, body sway, adaptive tracking, VVLT and N-back. Data from 84 healthy male and female volunteer studies, aged 16-90, were included. Nine studies were performed in patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD) or vascular dementia (VaD). The data were analyzed with regression analyses on age by group, sex, sex by age, group by sex and group by sex by age. Least square means (LSMs) and 95% confidence intervals (CIs) were calculated for each group at the average age of the group, and at the average age of each of the other groups, and per sex. Results Mean age and standard deviation (SD) for all groups was: HV 36.2 years (19.3), AD 68.3 years (8), PD 62.7 years (8.5), HD 51.4 years (9.8) and VaD 66.9 years (8.1). Performance on all NeuroCart tests decreased significantly each year in HV. Saccadic peak velocity (SPV) was increased in AD compared to age-matched HV (+26.28 degrees/s, p =0.007), while SPV was decreased for PD and HD compared to age-matched HV (PD: -15.87 degrees/s, p=0.038, HD: -22.52 degrees/s, p=0.018). In HD patients SPV decreased faster with age compared to HV. On saccadic peak velocity the slopes between HD vs HV were significantly different, indicating a faster decline in performance on this task for HD patients compared to HV per age year. Smooth pursuit showed an overall significant difference between subject groups (p=0.037. Significantly worse performance was found for AD (-12.87%, p=<0.001), PD (-4.45%, p=<0.001) and VaD (-5.69%, p=0.005) compared to age-matched HV. Body sway significantly increased with age (p=0.021). Postural stability was decreased for both PD and HD compared to age-matched HV (PD: +38.8%, p=<0.001, HD: 154.9%, p=<0.001). The adaptive tracking was significantly decreased with age (p=<0.001). Adaptive tracking performance by AD (-7.54%, p=<0.001), PD (-8.09%, p=<0.001), HD (-5.19%, p=<0.001) and VaD (-5.80%, p=<0.001) was decreased compared to age-matched HV. Adaptive tracking in PD patients vs HV and in PD vs HD patients was significantly different, indicating a faster decline on this task per age year for PD patients compared to HV and HD. The VVLT delayed word recall showed an overall significant effect of subject group (p=0.006. Correct delayed word recall was decreased for AD (-5.83 words, p=<0.001), HD (-3.40 words, p=<0.001) and VaD (-5.51 words, p=<0.001) compared to age-matched HV. Conclusion This study showed that the NeuroCart can detect age-related decreases in performance in HV, which were not affected by sex. The NeuroCart was able to detect significant differences in performance between AD, PD, HD, VaD and age-matched HV. Disease durations were unknown, therefore this cross-sectional study was not able to show age-related decline after disease onset. This article shows the importance of investigating age-related decline on digitalized neurocognitive test batteries. Performance declines with age, which emphasizes the need to correct for age when including HV in clinical trials. Patients with different neurogenerative diseases have distinct performance patterns on the NeuroCart , which this should be considered when performing NeuroCart tasks in patients with AD, PD, HD and VaD. Show less
Background: This study investigated plasma biomarkers for neuroinflammation associated with Alzheimer's disease (AD) in subjects with preclinical AD compared to healthy elderly. How these... Show moreBackground: This study investigated plasma biomarkers for neuroinflammation associated with Alzheimer's disease (AD) in subjects with preclinical AD compared to healthy elderly. How these biomarkers behave in patients with AD, compared to healthy elderly is well known, but determining these in subjects with preclinical AD is not and will add information related to the onset of AD. When found to be different in preclinical AD, these inflammatory biomarkers may be used to select preclinical AD subjects who are most likely to develop AD, to participate in clinical trials with new disease-modifying drugs. Methods: Healthy elderly (n= 50; age 71.9; MMSE >24) and subjects with preclinical AD (n=50; age 73.4; MMSE >24) defined by CSF A beta 1-42 levels < 1000 pg/mL were included. Four neuroinflammatory biomarkers were determined in plasma, GFAP, YKL-40, MCP-1, and eotaxin-1. Differences in biomarker outcomes were compared using ANCOVA. Subject characteristics age, gender, and APOE epsilon 4 status were reported per group and were covariates in the ANCOVA. Least square means were calculated for all 4 inflammatory biomarkers using both the A beta+/A beta- cutoff and Ptau/A beta 1-42 ratio. Results: The mean (standard deviation, SD) age of the subjects (n=100) was 72.6 (4.6) years old with 62 male and 38 female subjects. Mean (SD) overall MMSE score was 28.7 (0.49) and 32 subjects were APOE epsilon 4 carriers. The number of subjects in the different APOE epsilon 4 status categories differed significantly between the A beta+ and A beta- groups. Plasma GFAP concentration was significantly higher in the A beta+ group compared to the A beta- group with significant covariates age and sex, variables that also correlated significantly with GFAP. Conclusion: GFAP was significantly higher in subjects with preclinical AD compared to healthy elderly which agrees with previous studies. When defining preclinical AD based on the Ptau181/A beta 1-42 ratio, YKL-40 was also significantly different between groups. This could indicate that GFAP and YKL-40 are more sensitive markers of the inflammatory process in response to the A beta misfolding and aggregation that is ongoing as indicated by the lowered A beta 1-42 levels in the CSF. Characterizing subjects with preclinical AD using neuroinflammatory biomarkers is important for subject selection in new disease-modifying clinical trials. Show less
