Etiology and diagnostic biomarkers of Alzheimer's disease

SONGNan, LURui, WANGJunfeng

Chinese Journal of Alzheimer's Disease and Related Disorders ›› 2019, Vol. 2 ›› Issue (2) : 379-385.

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Chinese Journal of Alzheimer's Disease and Related Disorders

Abbreviation (ISO4): Chinese Journal of Alzheimer's Disease and Related Disorders      Editor in chief: Jun WANG

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Chinese Journal of Alzheimer's Disease and Related Disorders ›› 2019, Vol. 2 ›› Issue (2) : 379-385. DOI: 10.3969/j.issn.2096-5516.2019.02.013

Etiology and diagnostic biomarkers of Alzheimer's disease

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Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder resulted from complex interactions between genetic and environmental factors. Although there is no effective treatment so far, clinical trials suggest that early patients may be more likely to respond to treatment. Therefore, continuous research on pathogenic mechanism of AD and the AD diagnostic criteria, which has been revised in recent years, are increasingly emphasizing the use of biomarkers to achieve early diagnosis and intervention of AD. We now review the main pathogenic mechanisms and the advance s of AD biomarkers.

Key words

Alzheimer's disease / / Biomarker

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SONG Nan , LU Rui , WANG Junfeng. Etiology and diagnostic biomarkers of Alzheimer's disease[J]. Chinese Journal of Alzheimer's Disease and Related Disorders. 2019, 2(2): 379-385 https://doi.org/10.3969/j.issn.2096-5516.2019.02.013

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In vivo imaging of brain β-amyloid, a hallmark of Alzheimer disease, may assist in the clinical assessment of suspected Alzheimer disease.To determine the sensitivity and specificity of positron emission tomography imaging with flutemetamol injection labeled with radioactive fluorine 18 to detect β-amyloid in the brain using neuropathologically determined neuritic plaque levels as the standard of truth.Open-label multicenter imaging study that took place at dementia clinics, memory centers, and hospice centers in the United States and England from June 22, 2010, to November 23, 2011. Participants included terminally ill patients who were 55 years or older with a life expectancy of less than 1 year.Flutemetamol injection labeled with radioactive fluorine 18 (Vizamyl; GE Healthcare) administration followed by positron emission tomography imaging and subsequent brain donation.Sensitivity and specificity of flutemetamol injection labeled with radioactive fluorine 18 positron emission tomography imaging for brain β-amyloid. Images were reviewed without and with computed tomography scans and classified as positive or negative for β-amyloid by 5 readers who were blind to patient information. In patients who died, neuropathologically determined neuritic plaque levels were used to confirm scan interpretations and determine sensitivity and specificity.Of 176 patients with evaluable images, 68 patients (38%) died during the study, were autopsied, and had neuritic plaque levels determined; 25 brains (37%) were β-amyloid negative; and 43 brains (63%) were β-amyloid positive. Imaging was performed a mean of 3.5 months (range, 0 to 13 months) before death. Sensitivity without computed tomography was 81% to 93% (median, 88%). Median specificity was 88%, with 4 of 5 of the readers having specificity greater than 80%. When scans were interpreted with computed tomography images, sensitivity and specificity improved for most readers but the differences were not significant. The area under the receiver operating curve was 0.90. There were no clinically meaningful findings in safety parameters.This study showed that flutemetamol injection labeled with radioactive fluorine 18 was safe and had high sensitivity and specificity in an end-of-life population. In vivo detection of brain β-amyloid plaque density may increase diagnostic accuracy in cognitively impaired patients.
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Alzheimer's disease biomarkers are important for early diagnosis in routine clinical practice and research. Three core CSF biomarkers for the diagnosis of Alzheimer's disease (Aβ42, T-tau, and P-tau) have been assessed in numerous studies, and several other Alzheimer's disease markers are emerging in the literature. However, there have been no comprehensive meta-analyses of their diagnostic performance. We systematically reviewed the literature for 15 biomarkers in both CSF and blood to assess which of these were most altered in Alzheimer's disease.In this systematic review and meta-analysis, we screened PubMed and Web of Science for articles published between July 1, 1984, and June 30, 2014, about CSF and blood biomarkers reflecting neurodegeneration (T-tau, NFL, NSE, VLP-1, and HFABP), APP metabolism (Aβ42, Aβ40, Aβ38, sAPPα, and sAPPβ), tangle pathology (P-tau), blood-brain-barrier function (albumin ratio), and glial activation (YKL-40, MCP-1, and GFAP). Data were taken from cross-sectional cohort studies as well as from baseline measurements in longitudinal studies with clinical follow-up. Articles were excluded if they did not contain a cohort with Alzheimer's disease and a control cohort, or a cohort with mild cognitive impairment due to Alzheimer's disease and a stable mild cognitive impairment cohort. Data were extracted by ten authors and checked by two for accuracy. For quality assessment, modified QUADAS criteria were used. Biomarker performance was rated by random-effects meta-analysis based on the ratio between biomarker concentration in patients with Alzheimer's disease and controls (fold change) or the ratio between biomarker concentration in those with mild cognitive impariment due to Alzheimer's disease and those with stable mild cognitive impairment who had a follow-up time of at least 2 years and no further cognitive decline.Of 4521 records identified from PubMed and 624 from Web of Science, 231 articles comprising 15 699 patients with Alzheimer's disease and 13 018 controls were included in this analysis. The core biomarkers differentiated Alzheimer's disease from controls with good performance: CSF T-tau (average ratio 2·54, 95% CI 2·44-2·64, p<0·0001), P-tau (1·88, 1·79-1·97, p<0·0001), and Aβ42 (0·56, 0·55-0·58, p<0·0001). Differentiation between cohorts with mild cognitive impairment due to Alzheimer's disease and those with stable mild cognitive impairment was also strong (average ratio 0·67 for CSF Aβ42, 1·72 for P-tau, and 1·76 for T-tau). Furthermore, CSF NFL (2·35, 1·90-2·91, p<0·0001) and plasma T-tau (1·95, 1·12-3·38, p=0·02) had large effect sizes when differentiating between controls and patients with Alzheimer's disease, whereas those of CSF NSE, VLP-1, HFABP, and YKL-40 were moderate (average ratios 1·28-1·47). Other assessed biomarkers had only marginal effect sizes or did not differentiate between control and patient samples.The core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Emerging CSF biomarkers NSE, VLP-1, HFABP, and YKL-40 were moderately associated with Alzheimer's disease, whereas plasma Aβ42 and Aβ40 were not. Due to their consistency, T-tau, P-tau, Aβ42, and NFL in CSF should be used in clinical practice and clinical research.Swedish Research Council, Swedish State Support for Clinical Research, Alzheimer's Association, Knut and Alice Wallenberg Foundation, Torsten Söderberg Foundation, Alzheimer Foundation (Sweden), European Research Council, and Biomedical Research Forum.Copyright © 2016 Elsevier Ltd. All rights reserved.
[54]
Hanash SM, Baik CS, Kallioniemi O. Emerging molecular biomarkers-blood-based strategies to detect and monitor cancer[J]. Nat Rev Clin Oncol, 2011, 8(3): 142-150.
There is an urgent need for blood-based, noninvasive molecular tests to assist in the detection and diagnosis of cancers in a cost-effective manner at an early stage, when curative interventions are still possible. Additionally, blood-based diagnostics can classify tumors into distinct molecular subtypes and monitor disease relapse and response to treatment. Increasingly, biomarker strategies are becoming critical to identify a specific patient subpopulation that is likely to respond to a new therapeutic agent. The improved understanding of the underlying molecular features of common cancers and the availability of a multitude of recently developed technologies to interrogate the genome, transcriptome, proteome and metabolome of tumors and biological fluids have made it possible to develop clinically applicable and cost-effective tests for many common cancers. Overall, the paradigm shift towards personalized and individualized medicine relies heavily on the increased use of diagnostic biomarkers and classifiers to improve diagnosis, management and treatment. International collaborations, involving both the private and public sector will be required to facilitate the development of clinical applications of biomarkers, using rigorous standardized assays. Here, we review the recent technological and scientific advances in this field.
[55]
Kohane IS, Valtchinov VI. Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome[J]. Bioinformatics, 2012, 28(4): 538-545.
We investigate and quantify the generalizability of the white blood cell (WBC) transcriptome to the general, multiorgan transcriptome. We use data from the NCBI's Gene Expression Omnibus (GEO) public repository to define two datasets for comparison, WBC and OO (Other Organ) sets.Comprehensive pair-wise correlation and expression level profiles are calculated for both datasets (with sizes of 81 and 1463, respectively). We have used mapping and ranking across the Gene Ontology (GO) categories to quantify similarity between the two sets. GO mappings of the most correlated and highly expressed genes from the two datasets tightly match, with the notable exceptions of components of the ribosome, cell adhesion and immune response. That is, 10 877 or 48.8% of all measured genes do not change >10% of rank range between WBC and OO; only 878 (3.9%) change rank >50%. Two trans-tissue gene lists are defined, the most changing and the least changing genes in expression rank. We also provide a general, quantitative measure of the probability of expression rank and correlation profile in the OO system given the expression rank and correlation profile in the WBC dataset.
[56]
Peters MJ, Joehanes R, Pilling LC, et al. The transcriptional landscape of age in human peripheral blood[J]. Nat Commun, 2015, 6: 8570.
[57]
Kiddle SJ, Steves CJ, Mehta M, et al. Plasma protein biomarkers of Alzheimer’s disease endophenotypes in asymptomatic older twins: early cognitive decline and regional brain volumes[J]. Transl Psychiatry, 2015, 5 (6): e584.
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O°Bryant SE, Xiao G, Barber R, et al. A Serum Protein-Based Algorithm for the Detection of Alzheimer Disease[J]. Arch Neurol, 2010, 67(9): 1077-1081.
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Booij BB, Lindahl T, Wetterberg P, et al. A gene expression pattern in blood for the early detection of Alzheimer's disease[J]. J Alzheimers Dis, 2011; 23(1): 109-119.
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Leidinger P, Backes C, Deutscher S, et al. A blood based 12-miRNA signature of Alzheimer disease patients[J]. Genome Biol, 2013, 14(7): R78.
[61]
Mapstone M, Cheema AK, Fiandaca MS, et al. Plasma phospholipids identify antecedent memory impairment in older adults[J]. Nat Med, 2014, 20(4): 415-418.
Alzheimer's disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels, structural and functional magnetic resonance imaging and the recent use of brain amyloid imaging or inflammaging, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer's disease with the required sensitivity and specificity. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer's disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer's disease within a 2-3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer's disease.
[62]
Bjorkqvist M, Ohlsson M, Minthon L, et al. Evaluation of a previously suggested plasma biomarker panel to identify Alzheimer's disease[J]. PLoS One, 2012, 7(1): 29868.
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Li D, Misialek JR, Boerwinkle E, et al. Plasma phospholipids and prevalence of mild cognitive impairment and/or dementia in the ARIC Neurocognitive Study (ARIC-NCS)[J]. Alzheimers Dement (Amst), 2016, 3: 73-82
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Kiddle SJ, Voyle N, Dobson RJB. A blood test for Alzheimer’s disease: progress, challenges, and recommendations[J]. J Alzheimers Dis, 2018, 64(s1): S289-S297.
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