Challenges associated with biomarker-based classification systems for Alzheimer's disease

Altres ajuts: This work was also supported by research grants from the Carlos III Institute of Health, Spain and the CIBERNED program (Program 1, Alzheimer Disease to Alberto Lleó and SIGNAL study, www.signalstudy.es), partly funded by Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, "Una manera de hacer Europa". This work has also been supported by a "Marató TV3" grant (20141210 to Juan Fortea and 044412 to Rafael Blesa) and by Generalitat de Catalunya and a grant from the Fundació Bancaria La Caixa to Rafael Blesa. I. Illán-Gala is supported by the i-PFIS grant from the FIS, Instituto de Salud Carlos III and the Rio Hortega grant (CM17/00074) from "Acción estratégica en Salud 2013-2016" and the European Social Fund. USPHS NIH grants awarded to M.J.d.L. include: AG13616, AG022374, AG12101, and AG057570.We aimed to evaluate the consistency of the A/T/N classification system. We included healthy controls, mild cognitive impairment, and dementia patients from Alzheimer's disease Neuroimaging Initiative. We assessed subject classification consistency with different biomarker combinations and the agreement and correlation between biomarkers. Subject classification discordance ranged from 12.2% to 44.5% in the whole sample; 17.3%-46.4% in healthy controls; 11.9%-46.5% in mild cognitive impairment, and 1%-35.7% in dementia patients. Amyloid, but not neurodegeneration biomarkers, showed good agreement both in the whole sample and in the clinical subgroups. Amyloid biomarkers were correlated in the whole sample, but not along the Alzheimer's disease continuum (as defined by a positive amyloid positron emission tomography). Neurodegeneration biomarkers were poorly correlated both in the whole sample and along the Alzheimer's disease continuum. The relationship between biomarkers was stage-dependent. Our findings suggest that the current A/T/N classification system does not achieve the required consistency to be used in the clinical setting

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic.

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.I recieved an honorarium from Roche Diagnostics for my participation in the advisory panel meeting leading to this pape

Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning

Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models

A precision medicine initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling

After intense scientific exploration and more than a decade of failed trials, Alzheimer’s disease (AD) remains a fatal global epidemic. A traditional research and drug development paradigm continues to target heterogeneous late-stage clinically phenotyped patients with single 'magic bullet' drugs. Here, we propose that it is time for a paradigm shift towards the implementation of precision medicine (PM) for enhanced risk screening, detection, treatment, and prevention of AD. The overarching structure of how PM for AD can be achieved will be provided through the convergence of breakthrough technological advances, including big data science, systems biology, genomic sequencing, blood-based biomarkers, integrated disease modeling and P4 medicine. It is hypothesized that deconstructing AD into multiple genetic and biological subsets existing within this heterogeneous target population will provide an effective PM strategy for treating individual patients with the specific agent(s) that are likely to work best based on the specific individual biological make-up. The Alzheimer’s Precision Medicine Initiative (APMI) is an international collaboration of leading interdisciplinary clinicians and scientists devoted towards the implementation of PM in Neurology, Psychiatry and Neuroscience. It is hypothesized that successful realization of PM in AD and other neurodegenerative diseases will result in breakthrough therapies, such as in oncology, with optimized safety profiles, better responder rates and treatment responses, particularly through biomarker-guided early preclinical disease-stage clinical trials

What Can Quantitative Gait Analysis Tell Us about Dementia and Its Subtypes? A Structured Review

Distinguishing dementia subtypes can be difficult due to similarities in clinical presentation. There is increasing interest in discrete gait characteristics as markers to aid diagnostic algorithms in dementia. This structured review explores the differences in quantitative gait characteristics between dementia and healthy controls, and between four dementia subtypes under single-task conditions: Alzheimer’s disease (AD), dementia with Lewy bodies and Parkinson’s disease dementia, and vascular dementia. Twenty-six papers out of an initial 5,211 were reviewed and interpreted using a validated model of gait. Dementia was associated with gait characteristics grouped by slower pace, impaired rhythm, and increased variability compared to normal aging. Only four studies compared two or more dementia subtypes. People with AD are less impaired in pace, rhythm, and variability domains of gait compared to non-AD dementias. Results demonstrate the potential of gait as a clinical marker to discriminate between dementia subtypes. Larger studies using a more comprehensive battery of gait characteristics and better characterized dementia sub-types are required

Blood-based high sensitivity measurements of beta-amyloid and phosphorylated tau as biomarkers of Alzheimer's disease: a focused review on recent advances

Discovery and development of clinically useful biomarkers for Alzheimer’s disease (AD) and related dementias have been the focus of recent research efforts. While cerebrospinal fluid and positron emission tomography or MRI-based neuroimaging markers have made the in vivo detection of AD pathology and its consequences possible, the high cost and invasiveness have limited their widespread use in the clinical setting. On the other hand, advances in potentially more accessible blood-based biomarkers had been impeded by lack of sensitivity in detecting changes in markers of the hallmarks of AD, including amyloid-β (Aβ) peptides and phosphorylated tau (P-tau). More recently, however, emerging technologies with superior sensitivity and specificity for measuring Aβ and P-tau have reported high concordances with AD severity. In this focused review, we describe several emerging technologies, including immunoprecipitation-mass spectrometry (IP-MS), single molecule array and Meso Scale Discovery immunoassay platforms, and appraise the current literature arising from their use to identify plaques, tangles and other AD-associated pathology. While there is potential clinical utility in adopting these technologies, we also highlight the further studies needed to establish Aβ and P-tau as blood-based biomarkers for AD, including validation with existing large sample sets, new independent cohorts from diverse backgrounds as well as population-based longitudinal studies. In conclusion, the availability of sensitive and reliable measurements of Aβ peptides and P-tau species in blood holds promise for the diagnosis, prognosis and outcome assessments in clinical trials for AD