Validation of the Rowland Universal Dementia Assessment Scale (RUDAS) in a multicultural sample across five Western European countries: diagnostic accuracy and normative data.

ABSTRACTBackground:With increasing cultural diversity and growing elderly immigrant populations in Western European countries, the availability of brief cognitive screening instruments adequate for assessment of dementia in people from diverse backgrounds becomes increasingly important. The aim of the present study was to investigate diagnostic accuracy of the Rowland Universal Dementia Assessment Scale (RUDAS) in a multicultural sample and to calculate normative data as a basis for demographic adjustment of RUDAS scores. METHODS: The study was a prospective international cross-sectional multi-center study. Receiver operating characteristic curve analysis was used to examine diagnostic accuracy. Regression analysis was used to assess the impact of demographic variables. RESULTS: Data was collected from 341 cognitively intact participants and 80 people with dementia with a wide age- and educational range. Of the 421 included participants, 239 (57%) had immigrant background. The RUDAS had high diagnostic accuracy with an area under the curve (AUC) of 0.93. The optimal cut-off score was <25 (sensitivity 0.80, specificity 0.90). Regression analysis revealed that RUDAS scores were mainly affected by education and were unrelated to data collection site and immigrant status. Education-adjusted normative data was calculated as a basis for education adjustment of RUDAS scores. Applying education-adjusted RUDAS scores slightly but significantly improved diagnostic accuracy with an AUC of 0.95. CONCLUSION: We found the RUDAS to have excellent diagnostic properties in our multicultural sample. However, we suggest that RUDAS scores should be adjusted for education to increase diagnostic accuracy and that the choice of cut-off score should be considered based on the clinical context and expected base rate of dementia.

Assessment of kallikrein 6 as a cross-sectional and longitudinal biomarker for Alzheimer's disease.

BACKGROUND: Kallikrein 6 (KLK6) is known to be an age-related protease expressed at high levels in the central nervous system. It was previously shown to be involved in proteolysis of extracellular proteins implicated in neurodegenerative diseases such as Alzheimer's disease (AD), prompting validation of KLK6 as a potential biomarker of disease. However, analyses of both plasma and cerebrospinal fluid (CSF) levels of KLK6 in patients with AD have been inconclusive. We present a detailed analysis of KLK6 in plasma and CSF in two separate cohorts in a cross-sectional and a longitudinal clinical setting. METHODS: The cross-sectional cohort included control subjects without dementia and patients with AD, and the longitudinal cohort included patients with MCI and patients with AD followed over a 2-year period. Plasma and CSF levels of KLK6 were quantified by use of a previously developed and validated enzyme-linked immunosorbent assay. Statistical analyses were performed to compare KLK6 levels between diagnostic groups and to identify potential associations between KLK6 level, age, apolipoprotein E (APOE) genotype, total apoE level and the classical CSF AD biomarkers. RESULTS: In the cross-sectional setting, KLK6 levels in plasma but not in CSF were significantly higher in the AD group than in control subjects. CSF but not plasma KLK6 levels were positively correlated with age in both the cross-sectional and longitudinal settings. In both cohorts, the CSF KLK6 levels were significantly and positively correlated with the CSF levels of core AD biomarkers. Total plasma and CSF apoE levels were positively associated with KLK6 in the cross-sectional study. Finally, during the 2-year monitoring period of the longitudinal cohort, CSF KLK6 levels increased with disease progression over time in the investigated patient groups. CONCLUSIONS: In two separate cohorts we have confirmed the previously reported correlation between age and CSF levels of KLK6. Increased plasma KLK6 levels in patients with AD with a more advanced disease stage suggest KLK6 as a potential biomarker in patients with AD with more severe dementia. Significant correlations between KLK6 levels and core CSF AD biomarkers suggest molecular links between KLK6 and AD-related pathological processes.

The Montreal Cognitive Assessment: Normative Data from a Large Swedish Population-Based Cohort.

BACKGROUND: The Montreal Cognitive Assessment (MoCA) has a high sensitivity for detecting cognitive dysfunction. Swedish normative data does not exist and international norms are often derived from populations where cognitive impairment has not been screened for and not been thoroughly assessed to exclude subjects with dementia or mild cognitive impairment. OBJECTIVE: To establish norms for MoCA and develop a regression-based norm calculator based on a large, well-examined cohort. METHODS: MoCA was administered on 860 randomly selected elderly people from a population-based cohort from the EPIC study. Cognitive dysfunction was screened for and further assessed at a memory clinic. After excluding cognitively impaired participants, normative data was derived from 758 people, aged 65-85. RESULTS: MoCA cut-offs (-1 to -2 standard deviations) for cognitive impairment ranged from <25 to <21 for the lowest educated and <26 to <24 for the highest educated, depending on age group. Significant predictors for MoCA score were age, sex and level of education. CONCLUSION: We present detailed normative MoCA data and cut-offs according to the DSM-5 criteria for cognitive impairment based on a large population-based cohort of elderly individuals, screened and thoroughly investigated to rule out cognitive impairment. Level of education, sex, and age should be taken in account when evaluating MoCA score, which is facilitated by our online regression-based calculator that provide percentile and z-score for a subject's MoCA score.

Assessment of peptide chemical modifications on the development of an accurate and precise multiplex selected reaction monitoring assay for apolipoprotein e isoforms.

Apolipoprotein E (ApoE) is a polymorphic protein that plays a major role in lipid metabolism in the central nervous system and periphery. It has three common allelic isoforms, ApoE2, ApoE3, and ApoE4, that differ in only one or two amino acids. ApoE isoforms have been associated with the occurrence and progression of several pathological conditions, such as coronary atherosclerosis and Alzheimer's disease. The aim of this study was to develop a mass spectrometry (MS)-based assay for absolute quantification of ApoE isoforms in cerebrospinal fluid and plasma samples using isotope-labeled peptides. The assay included five tryptic peptides: CLAVYQAGAR (ApoE2), LGADMEDVCGR (ApoE2 and 3), LAVYQAGAR (ApoE3 and 4), LGADMEDVR (ApoE4), and LGPLVEQGR (total ApoE). Both cerebrospinal fluid and plasma samples were assayed to validate the method. The digestion yield and the extension of chemical modifications in selected amino acid residues (methionine oxidation, glutamine deamidation, and cyclization of N-terminus carbamidomethylcysteine) were also studied. The ApoE phenotype was successfully assigned to all samples analyzed in a blinded manner. The method showed good linearity (R(2) > 0.99) and reproducibility (within laboratory imprecision <13%). The comparison of the MS-based assay with an ELISA for total ApoE concentration showed a moderate correlation (R(2) = 0.59). This MS-based assay can serve as an important tool in clinical studies aiming to elucidate the association between ApoE genotype, total ApoE, and ApoE isoform concentrations in various disorders related to ApoE polymorphisms.

Automatic temporal lobe atrophy assessment in prodromal AD: Data from the DESCRIPA study.

BACKGROUND: In the framework of the clinical validation of research tools, this investigation presents a validation study of an automatic medial temporal lobe atrophy measure that is applied to a naturalistic population sampled from memory clinic patients across Europe. METHODS: The procedure was developed on 1.5-T magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative database, and it was validated on an independent data set coming from the DESCRIPA study. All images underwent an automatic processing procedure to assess tissue atrophy that was targeted at the hippocampal region. For each subject, the procedure returns a classification index. Once provided with the clinical assessment at baseline and follow-up, subjects were grouped into cohorts to assess classification performance. Each cohort was divided into converters (co) and nonconverters (nc) depending on the clinical outcome at follow-up visit. RESULTS: We found the area under the receiver operating characteristic curve (AUC) was 0.81 for all co versus nc subjects, and AUC was 0.90 for subjective memory complaint (SMCnc) versus all co subjects. Furthermore, when training on mild cognitive impairment (MCI-nc/MCI-co), the classification performance generally exceeds that found when training on controls versus Alzheimer's disease (CTRL/AD). CONCLUSIONS: Automatic magnetic resonance imaging analysis may assist clinical classification of subjects in a memory clinic setting even when images are not specifically acquired for automatic analysis.

Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.

The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid ß (Aß42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.

Disease progression and costs of care in Alzheimer's disease patients treated with donepezil: a longitudinal naturalistic cohort.

BACKGROUND/AIMS: Improved data and methods are needed for modeling disease progression in Alzheimer's disease (AD) for economic evaluation of treatments. The aim is to estimate prediction models for long-term AD progression and subsequently economic outcomes. METHODS: Three-year follow-up data on 435 patients treated with the cholinesterase inhibitor donepezil in clinical practise were analyzed. Regression models were estimated for long-term prediction of decline in cognitive function (ADAS-cog) and activities in daily living (ADL) ability, risk of institutionalization and costs of care. RESULTS: The cognitive deterioration was estimated at between 1.6 and 4 ADAS-cog points per every 6 months, increasing with disease severity. Cognitive function was an important predictor of ADL-ability, which itself was the most important predictor of the risk of institutionalization and costs of care. Combining all models in a cross-validation process generated accurate predictions of costs of care at each 6 months follow-up. CONCLUSION: The proposed methods for representing AD progression and economic outcomes can be used in micro-simulation models for the economic evaluation of new treatments.

Patients with Lewy body dementia use more resources than those with Alzheimer's disease.

OBJECTIVES: The purpose of this study was to compare resource use and costs in patients with dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) and to assess determinants of costs of care in DLB. METHOD: Thirty-four patients with DLB were included in a cross-sectional study. The patients were matched with respect to age, gender and Mini Mental State Examination (MMSE) score to 34 patients with AD. Both groups were examined using Resource Utilisation in Dementia (RUD Lite), MMSE and the Neuropsychiatric inventory (NPI). The DLB patients were additionally examined using the Disability Assessment for Dementia Scale (DAD). RESULTS: Costs of care in patients suffering from DLB was on average 348,000 SEK (37,500 euro) per year compared to 169,000 SEK (18,200 euro) in the AD group (p < 0.001). Within the DLB group, care costs correlated significantly (r(c) = 2.77, p < 0.001) with dependency in instrumental activities of daily living measured with DAD, whereas MMSE and NPI were not significantly correlated to resource use in the DLB group. CONCLUSIONS: DLB patients use more resources, and are more costly than AD patients. Dependency in instrumental activities of daily living is strongly correlated to resource use in DLB patients.