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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 45883

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Prof. Dr. Benedetta Nacmias

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Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Viale Pieraccini, 6 - CUBO 3, pad 27 b, 50139 Florence, Italy
Interests: neurodegenerative diseases; neurogenetics; dementia; Alzheimer’s disease; neurodegeneration; biomarkers

Special Issue Information

Dear Colleagues,

Dementia is a common public health problem. Worldwide, approximately 47 million people have dementia, and this number is expected to increase to 131 million by 2050. Alzheimer’s disease is the common cause of dementia. Recent advances in molecular genetics techniques and the role of cerebrospinal fluid biomarkers have improved diagnosis. Causes of dementia can be diagnosed via a multidisciplinary approach with neurologist psychologists, geneticists, laboratory testing, and neuroimaging. Recently enormous research efforts have been undertaken to discover, characterize, and quantify biological markers for AD, especially during the preclinical or prodromal stages of AD so that therapeutic treatment strategies may be initiated.

Through machine learning, it is possible to create a profile of the risk of conversion from mild cognitive impairment to dementia.

Management should include both pharmacologic and nonpharmacologic approaches, although the efficacy of available treatments remains limited. Precision medicine is emerging for the most accurate and definitive prediction, diagnosis, and prognosis of this insidious and lethal brain disorder. This issue will provide updates on these emerging fields.

Prof. Dr. Benedetta Nacmias
Guest Editor

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Keywords

Diagnosis
Management
Alzheimer’s disease
Biological markers
Precision medicine
Subjective cognitive decline (SCD)
Mild cognitive impairment (MCI)
Machine learning

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Published Papers (10 papers)

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Research

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11 pages, 2555 KiB

Open AccessArticle

An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease

by Xinlei Wang, Junchang Xin, Zhongyang Wang, Chuangang Li and Zhiqiong Wang

Diagnostics 2022, 12(11), 2632; https://doi.org/10.3390/diagnostics12112632 - 30 Oct 2022

Cited by 3 | Viewed by 1819

Abstract

In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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14 pages, 1462 KiB

Open AccessArticle

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

by Kwok Tai Chui, Brij B. Gupta, Wadee Alhalabi and Fatma Salih Alzahrani

Diagnostics 2022, 12(7), 1531; https://doi.org/10.3390/diagnostics12071531 - 23 Jun 2022

Cited by 51 | Viewed by 3830

Abstract

Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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11 pages, 7376 KiB

Open AccessArticle

Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases

by Angus Lau, Iman Beheshti, Mandana Modirrousta, Tiffany A. Kolesar, Andrew L. Goertzen and Ji Hyun Ko

Diagnostics 2021, 11(11), 2023; https://doi.org/10.3390/diagnostics11112023 - 1 Nov 2021

Cited by 12 | Viewed by 2838

Abstract

Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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11 pages, 6066 KiB

Open AccessArticle

The Effect of CAG Repeats within the Non-Pathological Range in the HTT Gene on Cognitive Functions in Patients with Subjective Cognitive Decline and Mild Cognitive Impairment

by Valentina Bessi, Salvatore Mazzeo, Silvia Bagnoli, Giulia Giacomucci, Assunta Ingannato, Camilla Ferrari, Sonia Padiglioni, Virginia Franchi, Sandro Sorbi and Benedetta Nacmias

Diagnostics 2021, 11(6), 1051; https://doi.org/10.3390/diagnostics11061051 - 7 Jun 2021

Cited by 7 | Viewed by 2942

Abstract

The Huntingtin gene (HTT) is within a class of genes containing a key region of CAG repeats. When expanded beyond 39 repeats, Huntington disease (HD) develops. Individuals with less than 35 repeats are not associated with HD. Increasing evidence has suggested that CAG repeats play a role in modulating brain development and brain function. However, very few studies have investigated the effect of CAG repeats in the non-pathological range on cognitive performances in non-demented individuals. In this study, we aimed to test how CAG repeats’ length influences neuropsychological scores in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). We included 75 patients (46 SCD and 29 MCI). All patients underwent an extensive neuropsychological battery and analysis of HTT alleles to quantify the number of CAG repeats. Results: CAG repeat number was positively correlated with scores of tests assessing for executive function, visual–spatial ability, and memory in SCD patients, while in MCI patients, it was inversely correlated with scores of visual–spatial ability and premorbid intelligence. When we performed a multiple regression analysis, we found that these relationships still remained, also when adjusting for possible confounding factors. Interestingly, logarithmic models better described the associations between CAG repeats and neuropsychological scores. CAG repeats in the HTT gene within the non-pathological range influenced neuropsychological performances depending on global cognitive status. The logarithmic model suggested that the positive effect of CAG repeats in SCD patients decreases as the number of repeats grows. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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10 pages, 7958 KiB

Open AccessArticle

Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis

by Giuseppe Murdaca, Sara Banchero, Alessandro Tonacci, Alessio Nencioni, Fiammetta Monacelli and Sebastiano Gangemi

Diagnostics 2021, 11(6), 940; https://doi.org/10.3390/diagnostics11060940 - 24 May 2021

Cited by 22 | Viewed by 3374

Abstract

Vitamin D (VD) and micronutrients, including folic acid, are able to modulate both the innate and the adaptive immune responses. Low VD and folic acid levels appear to promote cognitive decline as in Alzheimer’s disease (AD). A machine learning approach was applied to analyze the impact of various compounds, drawn from the blood of AD patients, including VD and folic acid levels, on the Mini-Mental State Exam (MMSE) in a cohort of 108 patients with AD. The first analysis was aimed at predicting the MMSE at recruitment, whereas a second investigation sought to predict the MMSE after a 4 year follow-up. The simultaneous presence of low levels of VD and folic acid allow to predict MMSE, suggestive of poorer cognitive function. Such results suggest that the low levels of VD and folic acid could be associated with more severe cases of cognitive impairment in AD. It could be hypothesized that simultaneous supplementation of VD and folic acid could slow down the progression of cerebral degeneration at least in a subset of AD individuals. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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15 pages, 3420 KiB

Open AccessArticle

Relationship between Brain Tissue Changes and Blood Biomarkers of Cyclophilin A, Heme Oxygenase-1, and Inositol-Requiring Enzyme 1 in Patients with Alzheimer’s Disease

by Hyon-Il Choi, Kiyoon Kim, Jiyoon Lee, Yunjung Chang, Hak Young Rhee, Soonchan Park, Woo-In Lee, Wonchae Choe, Chang-Woo Ryu and Geon-Ho Jahng

Diagnostics 2021, 11(5), 740; https://doi.org/10.3390/diagnostics11050740 - 21 Apr 2021

Cited by 5 | Viewed by 2560

Abstract

Cyclophilin A (CypA), heme oxygenase-1 (HO-1), and inositol-requiring enzyme 1 (IRE1) are believed to be associated with Alzheimer’s disease (AD). In this study, we investigated the association between gray matter volume (GMV) changes and blood levels of CypA, HO-1, and IRE1 in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI) and AD. Forty-five elderly CN, 34 aMCI, and 39 AD subjects were enrolled in this study. The results of voxel-based multiple regression analysis showed that blood levels of CypA, HO-1, and IRE1 were correlated with GMV on brain magnetic resonance imaging (MRI) in the entire population (p = 0.0005). The three serum protein levels were correlated with GMV of signature AD regions in the population as a whole. CypA values increased with increasing GMV in the occipital gyrus (r = 0.387, p < 0.0001) and posterior cingulate (r = 0.196, p = 0.034). HO-1 values increased with increasing GMV at the uncus (r = 0.307, p = 0.0008), lateral globus pallidus and putamen (r = 0.287, p = 0.002), and hippocampus (r = 0.197, p = 0.034). IRE1 values decreased with increasing GMV at the uncus (r = −0.239, p = 0.010) and lateral globus pallidus and putamen (r = −0.335, p = 0.0002). Associations between the three serum protein levels and regional GMV indicate that the blood levels of these biomarkers may reflect the pathological mechanism of AD in the brain. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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10 pages, 857 KiB

Open AccessArticle

Dual Effect of PER2 C111G Polymorphism on Cognitive Functions across Progression from Subjective Cognitive Decline to Mild Cognitive Impairment

by Salvatore Mazzeo, Valentina Bessi, Silvia Bagnoli, Giulia Giacomucci, Juri Balestrini, Sonia Padiglioni, Giulia Tomaiuolo, Assunta Ingannato, Camilla Ferrari, Laura Bracco, Sandro Sorbi and Benedetta Nacmias

Diagnostics 2021, 11(4), 718; https://doi.org/10.3390/diagnostics11040718 - 18 Apr 2021

Cited by 3 | Viewed by 2175

Abstract

Background: Periodic circadian protein homolog 2 (PER2) has a role in the intracellular signaling pathways of long-term potentiation and has implications for synaptic plasticity. We aimed to assess the association of PER2 C111G polymorphism with cognitive functions in subjective cognitive decline (SCD). Methods: Forty-five SCD patients were included in this study. All participants underwent extensive neuropsychological investigation, analysis of apolipoprotein E (APOE) and PER2 genotypes, and neuropsychological follow-up every 12 or 24 months for a mean time of 9.87 ± 4.38 years. Results: Nine out of 45 patients (20%) were heterozygous carriers of the PER2 C111G polymorphism (G carriers), while 36 patients (80%) were not carriers of the G allele (G non-carriers). At baseline, G carriers had a higher language composite score compared to G non-carriers. During follow-up, 15 (34.88%) patients progressed to mild cognitive impairment (MCI). In this group, we found a significant interaction between PER2 G allele and follow-up time, as carriers of G allele showed greater worsening of executive function, visual-spatial ability, and language composite scores compared to G non-carriers. Conclusions: PER2 C111G polymorphism is associated with better language performance in SCD patients. Nevertheless, as patients progress to MCI, G allele carriers showed a greater worsening in cognitive performance compared to G non-carriers. The effect of PER2 C111G polymorphism depends on the global cognitive status of patients. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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Review

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20 pages, 1147 KiB

Open AccessReview

Blood-Based Biomarkers of Neuroinflammation in Alzheimer’s Disease: A Central Role for Periphery?

by Federica Angiulli, Elisa Conti, Chiara Paola Zoia, Fulvio Da Re, Ildebrando Appollonio, Carlo Ferrarese and Lucio Tremolizzo

Diagnostics 2021, 11(9), 1525; https://doi.org/10.3390/diagnostics11091525 - 24 Aug 2021

Cited by 28 | Viewed by 7606

Abstract

Neuroinflammation represents a central feature in the development of Alzheimer’s disease (AD). The resident innate immune cells of the brain are the principal players in neuroinflammation, and their activation leads to a defensive response aimed at promoting β-amyloid (Aβ) clearance. However, it is now widely accepted that the peripheral immune system—by virtue of a dysfunctional blood–brain barrier (BBB)—is involved in the pathogenesis and progression of AD; microglial and astrocytic activation leads to the release of chemokines able to recruit peripheral immune cells into the central nervous system (CNS); at the same time, cytokines released by peripheral cells are able to cross the BBB and act upon glial cells, modifying their phenotype. To successfully fight this neurodegenerative disorder, accurate and sensitive biomarkers are required to be used for implementing an early diagnosis, monitoring the disease progression and treatment effectiveness. Interestingly, as a result of the bidirectional communication between the brain and the periphery, the blood compartment ends up reflecting several pathological changes occurring in the AD brain and can represent an accessible source for such biomarkers. In this review, we provide an overview on some of the most promising peripheral biomarkers of neuroinflammation, discussing their pathogenic role in AD. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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19 pages, 1645 KiB

Open AccessReview

Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?

by Carlo Fabrizio, Andrea Termine, Carlo Caltagirone and Giulia Sancesario

Diagnostics 2021, 11(8), 1473; https://doi.org/10.3390/diagnostics11081473 - 14 Aug 2021

Cited by 66 | Viewed by 9569

Abstract

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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14 pages, 4945 KiB

Open AccessReview

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

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

Diagnostics 2021, 11(5), 788; https://doi.org/10.3390/diagnostics11050788 - 27 Apr 2021

Cited by 11 | Viewed by 6923

Abstract

The most common neurodegenerative dementias include Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). The correct etiology-based diagnosis is pivotal for clinical management of these diseases as well as for the suitable timing and choosing the accurate disease-modifying therapies when these become available. Enzyme-linked immunosorbent assay (ELISA)-based methods, detecting altered levels of cerebrospinal fluid (CSF) Tau, phosphorylated Tau, and Aβ-42 in AD, allowed the wide use of this set of biomarkers in clinical practice. These analyses demonstrate a high diagnostic accuracy in AD but suffer from a relatively restricted usefulness due to invasiveness and lack of prognostic value. In recent years, the development of novel advanced techniques has offered new state-of-the-art opportunities in biomarker discovery. These include single molecule array technology (SIMOA), a tool for non-invasive analysis of ultra-low levels of central nervous system-derived molecules from biofluids, such as CSF or blood, and real-time quaking (RT-QuIC), developed to analyze misfolded proteins. In the present review, we describe the history of methods used in the fluid biomarker analyses of dementia, discuss specific emerging biomarkers with translational potential for clinical use, and suggest an algorithm for the use of new non-invasive blood biomarkers in clinical practice. Full article

(This article belongs to the Special Issue Diagnosis and Management of Alzheimer's Disease)

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