State-of-the-Art Methods and Emerging Fluid Biomarkers in the Diagnostics of Dementia—A Short Review and Diagnostic Algorithm

Solje, Eino; Benussi, Alberto; Buratti, Emanuele; Remes, Anne M.; Haapasalo, Annakaisa; Borroni, Barbara

doi:10.3390/diagnostics11050788

Cited by 13 publications

(10 citation statements)

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“…Serum NfL levels have already been shown to be a consistent and reliable marker of disease severity in both genetic and sporadic FTD, 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 with levels increasing already in the prodromal phases of disease, 47 and correlating with disease survival. 43 Also in this study, serum NfL levels were the most significant predictors of survival.…”

Section: Discussionmentioning

confidence: 96%

“…Moreover, f-FTD showed a shorter survival rate than s-FTD, and this finding is supported by several studies showing that disease survival is generally shorter in the genetic forms of FTD. 16,[36][37][38] Serum NfL levels have already been shown to be a consistent and reliable marker of disease severity in both genetic and sporadic FTD, [39][40][41][42][43][44][45][46] with levels increasing already in the prodromal phases of disease, 47 and correlating with disease survival. 43 Also in this study, serum NfL levels were the most significant predictors of survival.…”

Section: Discussionmentioning

confidence: 99%

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Differences and similarities between familial and sporadic frontotemporal dementia: An Italian single‐center cohort study

Benussi

Libri

Premi

et al. 2022

A&D Transl Res & Clin Interv

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Introduction The possibility to generalize our understandings on treatments and assessments to both familial frontotemporal dementia (f‐FTD) and sporadic FTD (s‐FTD) is a fundamental perspective for the near future, considering the constant advancement in potential disease‐modifying therapies that target particular genetic forms of FTD. We aimed to investigate differences in clinical features, cerebrospinal fluid (CSF), and blood‐based biomarkers between f‐FTD and s‐FTD. Methods In this longitudinal cohort study, we evaluated a consecutive sample of symptomatic FTD patients, classified as f‐FTD and s‐FTD according to Goldman scores (GS). All patients underwent clinical, behavioral, and neuropsychiatric symptom assessment, CSF biomarkers and serum neurofilament light (NfL) analysis, and brain atrophy evaluation with magnetic resonance imaging. Results Of 570 patients with FTD, 123 were classified as f‐FTD, and 447 as s‐FTD. In the f‐FTD group, 95 had a pathogenic FTD mutation while 28 were classified as GS = 1 or 2; of the s‐FTD group, 133 were classified as GS = 3 and 314 with GS = 4. f‐FTD and s‐FTD cases showed comparable demographic features, except for younger age at disease onset, age at diagnosis, and higher years of education in the f‐FTD group (all P < .05). f‐FTD showed worse behavioral disturbances as measured with Frontal Behavioral Inventory (FBI) negative behaviors (14.0 ± 7.6 vs. 11.6 ± 7.4, P = .002), and positive behaviors (20.0 ± 11.0 vs. 17.4 ± 11.8, P = .031). Serum NfL concentrations were higher in patients with f‐FTD (70.9 ± 37.9 pg/mL) compared to s‐FTD patients (37.3 ± 24.2 pg/mL, P < .001), and f‐FTD showed greater brain atrophy in the frontal and temporal regions and basal ganglia. Patients with f‐FTD had significantly shorter survival than those with s‐FTD ( P = .004). Discussion f‐FTD and s‐FTD are very similar clinical entities, but with different biological mechanisms, and different rates of progression. The parallel characterization of both f‐FTD and s‐FTD will improve our understanding of the disease, and aid in designing future clinical trials for both genetic and sporadic forms of FTD. Highlights Do clinical features and biomarkers differ between patients with familial frontotemporal dementia (f‐FTD) and sporadic FTD (s‐FTD)? In this cohort study of 570 patients with FTD, f‐FTD and s‐FTD share similar demographic features, but with younger age at disease onset and diagnosis in the f‐FTD group. f‐FTD showed higher serum neurofilament light concentrations, greater brain damage, and shorter survival, compared to s‐FTD. f‐FTD and s‐FTD are very similar clinical entities, but with different cognitive reserve mechanisms and different ra...

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Differences and similarities between familial and sporadic frontotemporal dementia: An Italian single‐center cohort study

Benussi

Libri

Premi

et al. 2022

A&D Transl Res & Clin Interv

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“…There are diagnostic methods based on biomarker detection; cerebrospinal fluid (CSF) biomarkers Aβ 1-42 , T-tau, and P-tau are well-validated, and are being increasingly used in clinical practice as tools for the affirmative diagnosis of AD [ 41 ]. The long-term stability of core CSF biomarkers in patients with AD provides further support for their use in clinical studies and treatment monitoring in clinical trials [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun

Wang

Liu

2021

Sensors

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The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

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“…In cognitively asymptomatic individuals with positive biomarkers for AD, the lifetime dementia risk is estimated to be from 5% to 42% [ 7 ]. Blood neurofilament light chain (NfL) is suggested to be a biomarker for neurodegenerative disorders, but it is not disease-specific and rather reflects neuronal damage in general [ 8 ]. Hence, there is an urgent need for new diagnostic, prognostic and monitoring biomarker innovations.…”

Section: Introductionmentioning

confidence: 99%

Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia

et al. 2023

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Millions of people suffer with dementia worldwide. However, early diagnosis of neurodegenerative diseases/dementia (NDD) is difficult, and no specific biomarkers have been found. This study aims to review the applications of salivary metabolomics in diagnostics and the treatment monitoring of NDD A literature search of suitable studies was executed so that a total of 29 original research articles were included in the present review. Spectroscopic methods, mainly nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, give us a broad view of changes in salivary metabolites in neurodegenerative diseases. The role of different salivary metabolites in brain function is discussed. Further studies with larger patient cohorts should be carried out to investigate the association between salivary metabolites and brain function and thus learn more about the complicated pathways in the human body.

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State-of-the-Art Methods and Emerging Fluid Biomarkers in the Diagnostics of Dementia—A Short Review and Diagnostic Algorithm

Cited by 13 publications

References 114 publications

Differences and similarities between familial and sporadic frontotemporal dementia: An Italian single‐center cohort study

Differences and similarities between familial and sporadic frontotemporal dementia: An Italian single‐center cohort study

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia

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