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Background
The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.
Methods
We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.
Results
Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.
Conclusion
A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.
Memory Concerns, Memory Performance and Risk of Dementia in Patients with Mild Cognitive Impairment
(2014)
Background:
Concerns about worsening memory (‘‘memory concerns’’; MC) and impairment in memory performance are both predictors of Alzheimer’s dementia (AD). The relationship of both in dementia prediction at the pre-dementia disease stage, however, is not well explored. Refined understanding of the contribution of both MC and memory performance in dementia prediction is crucial for defining at-risk populations. We examined the risk of incident AD by MC and memory performance in patients with mild cognitive impairment (MCI).
Methods:
We analyzed data of 417 MCI patients from a longitudinal multicenter observational study. Patients were classified based on presence (n = 305) vs. absence (n = 112) of MC. Risk of incident AD was estimated with Cox Proportional-Hazards regression models.
Results:
Risk of incident AD was increased by MC (HR = 2.55, 95%CI: 1.33–4.89), lower memory performance (HR = 0.63,95%CI: 0.56–0.71) and ApoE4-genotype (HR = 1.89, 95%CI: 1.18–3.02). An interaction effect between MC and memory
performance was observed. The predictive power of MC was greatest for patients with very mild memory impairment and decreased with increasing memory impairment.
Conclusions:
Our data suggest that the power of MC as a predictor of future dementia at the MCI stage varies with the patients’ level of cognitive impairment. While MC are predictive at early stage MCI, their predictive value at more advanced stages of MCI is reduced. This suggests that loss of insight related to AD may occur at the late stage of MCI.
Background: The German Dementia Competence Network (DCN) has established procedures for standardized multicenter acquisition of clinical, biological and imaging data, for centralized data management, and for the evaluation of new treatments. Methods: A longitudinal cohort study was set up for patients with mild cognitive impairment (MCI), patients with mild dementia and control subjects. The aims were to establish the diagnostic, differential diagnostic and prognostic power of a range of clinical, laboratory and imaging methods. Furthermore, 2 clinical trials were conducted with patients suffering from MCI and mild to moderate Alz- heimer’s Disease (AD). These trials aimed at evaluating the efficacy and safety of the combination of galantamine and memantine versus galantamine alone. Results: Here, we report on the scope and projects of the DCN, the methods that were employed, the composition and flow within the diverse groups of patients and control persons and on the clinical and neuropsychological baseline characteristics of the group of 2,113 subjects who participated in the observational and clinical trials. Conclusion: These data have an impact on the procedures for the early and differential clinical diagnosis of dementias, the current standard treatment of AD as well as on future clinical trials in AD.
Introduction
White matter (WM) magnetic resonance imaging (MRI) hyperintensities are common in Alzheimer’s disease (AD), but their pathophysiological relevance and relationship to genetic factors are unclear. In the present study, we investigated potential apolipoprotein E (APOE)-dependent effects on the extent and cognitive impact of WM hyperintensities in patients with AD.
Methods
WM hyperintensity volume on fluid-attenuated inversion recovery images of 201 patients with AD (128 carriers and 73 non-carriers of the APOE ε4 risk allele) was determined globally as well as regionally with voxel-based lesion mapping. Clinical, neuropsychological and MRI data were collected from prospective multicenter trials conducted by the German Dementia Competence Network.
Results
WM hyperintensity volume was significantly greater in non-carriers of the APOE ε4 allele. Lesion distribution was similar among ε4 carriers and non-carriers. Only ε4 non-carriers showed a correlation between lesion volume and cognitive performance.
Conclusion
The current findings indicate an increased prevalence of WM hyperintensities in non-carriers compared with carriers of the APOE ε4 allele among patients with AD. This is consistent with a possibly more pronounced contribution of heterogeneous vascular risk factors to WM damage and cognitive impairment in patients with AD without APOE ε4-mediated risk.
Information on circulating miRNAs in frontotemporal lobar degeneration is very limited and conflicting results have complicated an interpretation in Alzheimer’s disease thus far. In the present study we I) collected samples from multiple clinical centers across Germany, II) defined 3 homogenous patient groups with high sample sizes (bvFTD n = 48, AD n = 48 and cognitively healthy controls n = 44), III) compared expression levels in both CSF and serum samples and IV) detected a limited set of miRNAs by using a MIQE compliant protocol based on SYBR-green miRCURY assays that have proven reliable to generate reproducible results. We included several quality controls that identified and reduced technical variation to increase the reliability of our data. We showed that the expression levels of circulating miRNAs measured in CSF did not correlate with levels in serum. Using cluster analysis we found expression pattern in serum that, in part, reflects the genomic organization and affiliation to a specific miRNA family and that were specifically altered in bvFTD, AD, and control groups. Applying factor analysis we identified a 3-factor model characterized by a miRNA signature that explained 80% of the variance classifying healthy controls with 97%, bvFTD with 77% and AD with 72% accuracy. MANOVA confirmed signals like miR-320a and miR-26b-5p at BH corrected significance that contributed most to discriminate bvFTD cases with 96% sensitivity and 90% specificity and AD cases with 89% sensitivity and specificity compared to healthy controls, respectively. Correlation analysis revealed that miRNAs from the 3-factor model also correlated with levels of protein biomarker amyloid-beta1-42 and phosphorylated neurofilament heavy chain, indicating their potential role in the monitoring of progressive neuronal degeneration. Our data show that miRNAs can be reproducibly measured in serum and CSF without pre-amplification and that serum includes higher expressed signals that demonstrate an overall better ability to classify bvFTD, AD and healthy controls compared to signals detected in CSF.
Abstract
Introduction
The term primary progressive aphasia (PPA) sums up the non‐fluent (nfv), the semantic (sv), and the logopenic (lv) variant. Up to now, there is only limited data available concerning magnetic resonance imaging volumetry to monitor disease progression.
Methods
Structural brain imaging and an extensive assessment were applied at baseline and up to 4‐year(s) follow‐up in 269 participants. With automated atlas‐based volumetry 56 brain regions were assessed. Atrophy progression served to calculate sample sizes for therapeutic trials.
Results
At baseline highest atrophy appeared in parts of the left frontal lobe for nfvPPA (–17%) and of the left temporal lobe for svPPA (–34%) and lvPPA (–24%). Severest progression within 1‐year follow‐up occurred in the basal ganglia in nfvPPA (–7%), in the hippocampus/amygdala in svPPA (–9%), and in (medial) temporal regions in lvPPA (–6%).
Conclusion
PPA presents as a left‐dominant, mostly gray matter sensitive disease with considerable atrophy at baseline that proceeds variant‐specific.
Background
With upcoming therapeutic interventions for patients with primary progressive aphasia (PPA), instruments for the follow-up of patients are needed to describe disease progression and to evaluate potential therapeutic effects. So far, volumetric brain changes have been proposed as clinical endpoints in the literature, but cognitive scores are still lacking. This study followed disease progression predominantly in language-based performance within 1 year and defined a PPA sum score which can be used in therapeutic interventions.
Methods
We assessed 28 patients with nonfluent variant PPA, 17 with semantic variant PPA, 13 with logopenic variant PPA, and 28 healthy controls in detail for 1 year. The most informative neuropsychological assessments were combined to a sum score, and associations between brain atrophy were investigated followed by a sample size calculation for clinical trials.
Results
Significant absolute changes up to 20% in cognitive tests were found after 1 year. Semantic and phonemic word fluency, Boston Naming Test, Digit Span, Token Test, AAT Written language, and Cookie Test were identified as the best markers for disease progression. These tasks provide the basis of a new PPA sum score. Assuming a therapeutic effect of 50% reduction in cognitive decline for sample size calculations, a number of 56 cases is needed to find a significant treatment effect. Correlations between cognitive decline and atrophy showed a correlation up to r = 0.7 between the sum score and frontal structures, namely the superior and inferior frontal gyrus, as well as with left-sided subcortical structures.
Conclusion
Our findings support the high performance of the proposed sum score in the follow-up of PPA and recommend it as an outcome measure in intervention studies.
Importance
The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective
Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants
Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions
N.A.
Main outcomes and measures
Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results
Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance
Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
Abstract
Frontotemporal dementia (FTD) is a clinically and genetically heterogeneous disorder. To which extent genetic aberrations dictate clinical presentation remains elusive. We investigated the spectrum of genetic causes and assessed the genotype-driven differences in biomarker profiles, disease severity and clinical manifestation by recruiting 509 FTD patients from different centers of the German FTLD consortium where individuals were clinically assessed including biomarker analysis. Exome sequencing as well as C9orf72 repeat analysis were performed in all patients. These genetic analyses resulted in a diagnostic yield of 18.1%. Pathogenic variants in C9orf72 (n = 47), GRN (n = 26), MAPT (n = 11), TBK1 (n = 5), FUS (n = 1), TARDBP (n = 1), and CTSF (n = 1) were identified across all clinical subtypes of FTD. TBK1-associated FTD was frequent accounting for 5.4% of solved cases. Detection of a homozygous missense variant verified CTSF as an FTD gene. ABCA7 was identified as a candidate gene for monogenic FTD. The distribution of APOE alleles did not differ significantly between FTD patients and the average population. Male sex was weakly associated with clinical manifestation of the behavioral variant of FTD. Age of onset was lowest in MAPT patients. Further, high CSF neurofilament light chain levels were found to be related to GRN-associated FTD. Our study provides large-scale retrospective clinico-genetic data such as on disease manifestation and progression of FTD. These data will be relevant for counseling patients and their families.
Abstract
Background
Recent data support beta‐synuclein as a blood biomarker to study synaptic degeneration in Alzheimer's disease (AD).
Methods
We provide a detailed comparison of serum beta‐synuclein immunoprecipitation – mass spectrometry (IP‐MS) with the established blood markers phosphorylated tau 181 (p‐tau181) (Simoa) and neurofilament light (NfL) (Ella) in the German FTLD consortium cohort (n = 374) and its relation to brain atrophy (magnetic resonance imaging) and cognitive scores.
Results
Serum beta‐synuclein was increased in AD but not in frontotemporal lobar degeneration (FTLD) syndromes. Beta‐synuclein correlated with atrophy in temporal brain structures and was associated with cognitive impairment. Serum p‐tau181 showed the most specific changes in AD but the lowest correlation with structural alterations. NfL was elevated in all diseases and correlated with frontal and temporal brain atrophy.
Discussion
Serum beta‐synuclein changes differ from those of NfL and p‐tau181 and are strongly related to AD, most likely reflecting temporal synaptic degeneration. Beta‐synuclein can complement the existing panel of blood markers, thereby providing information on synaptic alterations.
Highlights
Blood beta‐synuclein is increased in Alzheimer's disease (AD) but not in frontotemporal lobar degeneration (FTLD) syndromes.
Blood beta‐synuclein correlates with temporal brain atrophy in AD.
Blood beta‐synuclein correlates with cognitive impairment in AD.
The pattern of blood beta‐synuclein changes in the investigated diseases is different to phosphorylated tau 181 (p‐tau181) and neurofilament light (NfL).
This paper presents a new approach for the determination of the wind speed distribution based on wind speed data. This approach is based on the fact that, in general, wind speed distributions restricted to seasons of year or months are different. Therefore, instead of one Weibull density function, a convex combination of Weibull density functions is considered for a calendar year. This model improves the maximum likelihood of the estimated wind speed distribution. Numerical results including a Kolmogorov–Smirnov test are given for a site at Jamaica. Numerical comparisons are carried out for different sites and various known methods for the estimation of the wind speed distribution.