CSF and Blood Biomarkers in Neuroinflammatory and Neurodegenerative Diseases: Implications for Treatment

Gaetani, Lorenzo; Paoletti, Federico Paolini; Bellomo, G.; Mancini, Andrea; Simoni, Simone; Filippo, Massimiliano Di; Parnetti, Lucilla

doi:10.1016/j.tips.2020.09.011

Cited by 68 publications

(56 citation statements)

References 130 publications

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“…Such phenotypic heterogeneity sometimes makes the differential diagnosis between AD and other similar neurological diseases problematic (Sawyer et al, 2017;de Souza et al, 2019;Villain and Dubois, 2019). Cerebrospinal fluid (CSF) core biomarkers for AD -i.e., amyloidbeta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) -are largely used in clinical settings, research, and drug trials (Paterson et al, 2018;Gaetani et al, 2020). However, their clinical utility to differentiate AD from non-AD neurodegenerative dementias, such as dementia with Lewy bodies (DLB) or frontotemporal dementia (FTD), is less established (Bartlett et al, 2012;Molinuevo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer’s Disease and Other Neurological Disorders

et al. 2021

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Amyloid-beta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) are considered core biomarkers for the diagnosis of Alzheimer’s disease (AD). The use of fully automated biomarker assays has been shown to reduce the intra- and inter-laboratory variability, which is a critical factor when defining cut-off values. The calculation of cut-off values is often influenced by the composition of AD and control groups. Indeed, the clinically defined AD group may include patients affected by other forms of dementia, while the control group is often very heterogeneous due to the inclusion of subjects diagnosed with other neurological diseases (OND). In this context, unsupervised machine learning approaches may overcome these issues providing unbiased cut-off values and data-driven patient stratification according to the sole distribution of biomarkers. In this work, we took advantage of the reproducibility of automated determination of the CSF core AD biomarkers to compare two large cohorts of patients diagnosed with different neurological disorders and enrolled in two centers with established expertise in AD biomarkers. We applied an unsupervised Gaussian mixture model clustering algorithm and found that our large series of patients could be classified in six clusters according to their CSF biomarker profile, some presenting a typical AD-like profile and some a non-AD profile. By considering the frequencies of clinically defined OND and AD subjects in clusters, we subsequently computed cluster-based cut-off values for Aβ42/Aβ40, p-tau, and t-tau. This approach promises to be useful for large-scale biomarker studies aimed at providing efficient biochemical phenotyping of neurological diseases.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer’s Disease and Other Neurological Disorders

et al. 2021

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“…Using factors in CSF as biomarkers can minimize sources of preanalytical variability. Therefore, CSF represents the gold standard fluid for biomarker identification for neurological diseases 38–40 …”

Section: Discussionmentioning

confidence: 99%

Use of a panel of four microRNAs in CSF as a predicted biomarker for postoperative neoangiogenesis in moyamoya disease

et al. 2021

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Introduction and Aims At present, the treatment for moyamoya disease (MMD) primarily consists of combined direct and indirect bypass surgery. Nevertheless, more than half of indirect bypass surgeries fail to develop good collaterals from the dura and temporal muscle. This study aimed to investigate whether microRNAs (miRNAs) in cerebrospinal fluid (CSF) could serve as biomarkers for the prediction of postoperative collateral formation. Methods Moyamoya disease patients with indirect bypass surgery were divided into angiogenesis and non‐angiogenesis groups, CSF was obtained, and miRNA sequencing was performed using the CSF. Candidate miRNAs were filtered and subsequently verified through qRT‐PCR. The diagnostic utility of these differential miRNAs was investigated by using receiver operating characteristic (ROC) curve analysis. Finally, the potential biological processes and signaling pathways associated with candidate miRNAs were analyzed using R software. Results The expression levels of four miRNAs (miR‐92a‐3p, miR‐486‐3p, miR‐25‐3p, and miR‐155‐5p) were significantly increased in the angiogenesis group. By combining these four miRNAs (area under the curve [AUC] =0.970), we established an accurate predictive model of collateral circulation after indirect bypass surgery in MMD patients. GO and KEGG analyses demonstrated a high correlation with biological processes and signaling pathways related to angiogenesis. Conclusion The 4‐miRNA signature is a good model to predict angiogenesis after indirect bypass surgery and help the surgeon to select a appreciate bypass strategy.

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“…The proximity of CSF to CNS makes this biofluid the ideal source of biomarkers of the ongoing biological processes. Though the measurement of CNS-derived biomarkers is possible also in blood, blood biomarkers cannot still completely replace CSF biomarkers, which rely on more standardized analytical procedures and on validation in larger cohorts [ 18 ].…”

Section: Body Fluids Biomarkers and Psychiatric Symptoms In Neurodegenerative And Neuroinflammatory Diseasesmentioning

confidence: 99%

“…During the last three decades, the use of diagnostic tools able to provide objective and reproducible findings supported the transition from a clinical characterization of CNS diseases to a combined framework that considers their functional, structural and biochemical correlates [ 17 , 18 ]. Particularly, body fluids biomarkers allow for the molecular characterization of CNS disorders according to their underlying neurobiological substrate, thus improving diagnostic accuracy and better addressing treatment approaches [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Insights into the Pathophysiology of Psychiatric Symptoms in Central Nervous System Disorders: Implications for Early and Differential Diagnosis

Menculini

Chipi

Paoletti

et al. 2021

IJMS

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Different psychopathological manifestations, such as affective, psychotic, obsessive-compulsive symptoms, and impulse control disturbances, may occur in most central nervous system (CNS) disorders including neurodegenerative and neuroinflammatory diseases. Psychiatric symptoms often represent the clinical onset of such disorders, thus potentially leading to misdiagnosis, delay in treatment, and a worse outcome. In this review, psychiatric symptoms observed along the course of several neurological diseases, namely Alzheimer’s disease, fronto-temporal dementia, Parkinson’s disease, Huntington’s disease, and multiple sclerosis, are discussed, as well as the involved brain circuits and molecular/synaptic alterations. Special attention has been paid to the emerging role of fluid biomarkers in early detection of these neurodegenerative diseases. The frequent occurrence of psychiatric symptoms in neurological diseases, even as the first clinical manifestations, should prompt neurologists and psychiatrists to share a common clinico-biological background and a coordinated diagnostic approach.

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CSF and Blood Biomarkers in Neuroinflammatory and Neurodegenerative Diseases: Implications for Treatment

Cited by 68 publications

References 130 publications

Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer’s Disease and Other Neurological Disorders

Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer’s Disease and Other Neurological Disorders

Use of a panel of four microRNAs in CSF as a predicted biomarker for postoperative neoangiogenesis in moyamoya disease

Insights into the Pathophysiology of Psychiatric Symptoms in Central Nervous System Disorders: Implications for Early and Differential Diagnosis

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