Background Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma... Show moreBackground Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma biomarkers are strongly needed for specific diagnosis and potential treatment monitoring of FTD. We aimed to identify specific FTD plasma biomarker profiles discriminating FTD from AD and controls, and between FTD pathological subtypes. In addition, we compared plasma results with results in post-mortem frontal cortex of FTD cases to understand the underlying process. Methods Plasma proteins (n = 1303) from pathologically and/or genetically confirmed FTD patients (n = 56; FTLD-Tau n = 16; age = 58.2 +/- 6.2; 44% female, FTLD-TDP n = 40; age = 59.8 +/- 7.9; 45% female), AD patients (n = 57; age = 65.5 +/- 8.0; 39% female), and non-demented controls (n = 148; 61.3 +/- 7.9; 41% female) were measured using an aptamer-based proteomic technology (SomaScan). In addition, exploratory analysis in post-mortem frontal brain cortex of FTD (n = 10; FTLD-Tau n = 5; age = 56.2 +/- 6.9, 60% female, and FTLD-TDP n = 5; age = 64.0 +/- 7.7, 60% female) and non-demented controls (n = 4; age = 61.3 +/- 8.1; 75% female) were also performed. Differentially regulated plasma and tissue proteins were identified by global testing adjusting for demographic variables and multiple testing. Logistic lasso regression was used to identify plasma protein panels discriminating FTD from non-demented controls and AD, or FTLD-Tau from FTLD-TDP. Performance of the discriminatory plasma protein panels was based on predictions obtained from bootstrapping with 1000 resampled analysis. Results Overall plasma protein expression profiles differed between FTD, AD and controls (6 proteins; p = 0.005), but none of the plasma proteins was specifically associated to FTD. The overall tissue protein expression profile differed between FTD and controls (7-proteins; p = 0.003). There was no difference in overall plasma or tissue expression profile between FTD subtypes. Regression analysis revealed a panel of 12-plasma proteins discriminating FTD from AD with high accuracy (AUC: 0.99). No plasma protein panels discriminating FTD from controls or FTD pathological subtypes were identified. Conclusions We identified a promising plasma protein panel as a minimally-invasive tool to aid in the differential diagnosis of FTD from AD, which was primarily associated to AD pathophysiology. The lack of plasma profiles specifically associated to FTD or its pathological subtypes might be explained by FTD heterogeneity, calling for FTD studies using large and well-characterize cohorts. Show less
Bron, E.E.; Klein, S.; Papma, J.M.; Jiskoot, L.C.; Venkatraghavan, V.; Linders, J.; ... ; Lugt, A. van der 2021
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD... Show moreThis work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive preprocessing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924-0.955) and CNN (0.933; 95%CI: 0.918-0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p < 0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855-0.932) and CNN (0.876; 95%CI: 0.836-0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p = 0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice. Show less
Heinen, R.; Groeneveld, O.N.; Barkhof, F.; Bresser, J. de; Exalto, L.G.; Kuijf, H.J.; ... ; TRACE-VCI Study Grp 2020
IntroductionIt is unknown whether different types of small vessel disease (SVD), differentially relate to brain atrophy and if co-occurring Alzheimer's disease pathology affects this relation... Show moreIntroductionIt is unknown whether different types of small vessel disease (SVD), differentially relate to brain atrophy and if co-occurring Alzheimer's disease pathology affects this relation.MethodsIn 725 memory clinic patients with SVD (mean age 67 +/- 8 years, 48% female) we compared brain volumes of those with moderate/severe white matter hyperintensities (WMHs; n = 326), lacunes (n = 132) and cerebral microbleeds (n = 321) to a reference group with mild WMHs (n = 197), also considering cerebrospinal fluid (CSF) amyloid status in a subset of patients (n = 488).ResultsWMHs and lacunes, but not cerebral microbleeds, were associated with smaller gray matter (GM) volumes. In analyses stratified by CSF amyloid status, WMHs and lacunes were associated with smaller total brain and GM volumes only in amyloid-negative patients. SVD-related atrophy was most evident in frontal (cortical) GM, again predominantly in amyloid-negative patients.DiscussionAmyloid status modifies the differential relation between SVD lesion type and brain atrophy in memory clinic patients. Show less
Visser, L.N.C.; Pelt, S.A.R.; Kunneman, M.; Bouwman, F.H.; Claus, J.J.; Kalisvaart, K.J.; ... ; Hillen, M.A. 2019
Background The development of novel diagnostics enables increasingly earlier diagnosis of Alzheimer's disease (AD). Timely diagnosis may benefit patients by reducing their uncertainty regarding the... Show moreBackground The development of novel diagnostics enables increasingly earlier diagnosis of Alzheimer's disease (AD). Timely diagnosis may benefit patients by reducing their uncertainty regarding the cause of symptoms, yet does not always provide patients with the desired certainty. Objective To examine, using both quantitative and qualitative methods, uncertainty communicated by memory clinic clinicians in post-diagnostic testing consultations with patients and their caregivers. Methods First, we identified all uncertainty expressions of 22 clinicians in audiotaped post-diagnostic testing consultations with 78 patients. Second, we statistically explored relationships between patient/clinician characteristics and uncertainty expressions. Third, the transcribed uncertainty expressions were qualitatively analysed, determining the topic to which they pertained, their source and initiator/elicitor (clinicians/patients/caregivers). Results Within 57/78 (73%) consultations, clinicians expressed in total 115 uncertainties, of which 37% elicited by the patient or caregiver. No apparent relationships were found between patient/clinician characteristics and whether or not, and how often clinicians expressed uncertainty. Uncertainty expressions pertained to ten different topics, most frequently patient's diagnosis and symptom progression. Expressed uncertainty was mostly related to the unpredictability of the future and limits to available knowledge. Discussion and conclusions The majority of clinicians openly discussed the limits of scientific knowledge and diagnostic testing with patients and caregivers in the dementia context. Noticeably, clinicians did not discuss uncertainty in about one quarter of consultations. More evidence is needed on the beneficial and/or harmful effects on patients of discussing uncertainty with them. This knowledge can be used to support clinicians to optimally convey uncertainty and facilitate patients' uncertainty management. Show less
Lee, S.J. van der; Conway, O.J.; Jansen, I.; Carrasquillo, M.M.; Kleineidam, L.; Akker, E. van den; ... ; GIFT Genetic Invest 2019
Objectives: The predictive value of frailty and comorbidity, in addition to more readily available information, is not widely studied. We determined the incremental predictive value of frailty and... Show moreObjectives: The predictive value of frailty and comorbidity, in addition to more readily available information, is not widely studied. We determined the incremental predictive value of frailty and comorbidity for mortality and institutionalization across both short and long prediction periods in persons with dementia.Design: Longitudinal clinical cohort study with a follow-up of institutionalization and mortality occurrence across 7 years after baseline.Setting and Participants: 331 newly diagnosed dementia patients, originating from 3 Alzheimer centers (Amsterdam, Maastricht, and Nijmegen) in the Netherlands, contributed to the Clinical Course of Cognition and Comorbidity (4C) Study.Measures: We measured comorbidity burden using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and constructed a Frailty Index (FI) based on 35 items. Time-to-death and time-to-institutionalization from dementia diagnosis onward were verified through linkage to the Dutch population registry.Results: After 7 years, 131 patients were institutionalized and 160 patients had died. Compared with a previously developed prediction model for survival in dementia, our Cox regression model showed a significant improvement in model concordance (U) after the addition of baseline CIRS-G or FI when examining mortality across 3 years (FI: U = 0.178, P = .005, CIRS-G: U = 0.180, P = .012), but not for mortality across 6 years (FI: U = 0.068, P = .176, CIRS-G: U = 0.084, P = .119). In a competing risk regression model for time-to-institutionalization, baseline CIRS-G and FI did not improve the prediction across any of the periods.Conclusions: Characteristics such as frailty and comorbidity change over time and therefore their predictive value is likely maximized in the short term. These results call for a shift in our approach to prognostic modeling for chronic diseases, focusing on yearly predictions rather than a single prediction across multiple years. Our findings underline the importance of considering possible fluctuations in predictors over time by performing regular longitudinal assessments in future studies as well as in clinical practice. (C) 2018 AMDA - The Society for Post-Acute and Long-Term Care Medicine. Show less
Background: Quality of Life (QoL) is an important outcome measure in dementia, particularly in the context of interventions. Research investigating longitudinal QoL in dementia with Lewy bodies ... Show moreBackground: Quality of Life (QoL) is an important outcome measure in dementia, particularly in the context of interventions. Research investigating longitudinal QoL in dementia with Lewy bodies (DLB) is currently lacking.Objective: To investigate determinants and trajectories of QoL in DLB compared to Alzheimer's disease (AD) and controls.Methods: QoL was assessed annually in 138 individuals, using the EQ5D-utility-score (0-100) and the health-related Visual Analogue Scale (VAS, 0-100). Twenty-nine DLB patients (age 69 +/- 6), 68 AD patients (age 70 +/- 6), and 41 controls (age 70 +/- 5) were selected from the Dutch Parelsnoer Institute-Neurodegenerative diseases and Amsterdam Dementia Cohort. We examined clinical work-up over time as determinants of QoL, including cognitive tests, neuropsychiatric inventory, Geriatric Depression Scale (GDS), and disability assessment of dementia (DAD).Results: Mixed models showed lower baseline VAS-scores in DLB compared to AD and controls (AD: beta +/- SE = - 7.6 +/- 2.8, controls: beta +/- SE = -7.9 +/- 3.0, p < 0.05). An interaction between diagnosis and time since diagnosis indicated steeper decline on VAS-scores for AD patients compared to DLB patients (beta +/- SE = 2.9 +/- 1.5, p < 0.1). EQ5D-utility-scores over time did not differ between groups. Higher GDS and lower DAD-scores were independently associated with lower QoL in dementia patients (GDS: VAS beta +/- SE = -1.8 +/- 0.3, EQ5D-utility beta +/- SE = -3.7 +/- 0.4; DAD: VAS = 0.1 +/- 0.0, EQ5D-utility beta +/- SE = 0.1 +/- 0.1, p < 0.05). No associations between cognitive tests and QoL remained in the multivariate model.Conclusion: QoL is lower in DLB, while in AD QoL shows steeper decline as the disease advances. Our results indicate that non-cognitive symptoms, more than cognitive symptoms, are highly relevant as they impact QoL. Show less
