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New more accessible and affordable techniques for identifying people at risk of Alzheimer’s disease
In specialist clinics, cerebrospinal fluid biomarkers for Abeta1-42, Abeta1-40, tTau, and pTau or positron electron tomography imaging are used to identify individuals at risk of Alzheimer’s disease. More accessible and affordable early identification of Alzheimer’s disease is the future using machine learning, electroencephalography techniques and plasma Abeta1-42/Abeta1-40 testing, explained experts in a fascinating well-attended scientific symposium at EAN 2021.
Electroencephalography is strongly predictive of neurodegeneration
Early biomarkers will identify individuals at risk of Alzheimer’s disease and enable early intervention
Older people who believe they are losing their memory but have no objective deficits (subjective cognitive decline, SCD) are twice as likely to develop dementia as individuals who do not have SCD, said Stéphane Epelbaum, Paris, France. However, only 2.3% of people with SCD progress to dementia and 6.6% to mild cognitive impairment (MCI) each year.1
We can do better than just using the cognitive complaint to improve the identification of Alzheimer’s disease (AD), said Dr Epelbaum.
He described the use of electroencephalography (EEG) as a new affordable more effective biomarker for preclinical AD.
The EEG is a useful biomarker for preclinical Alzheimer’s disease
Dr Epelbaum explained that the most prominent effects of neurodegeneration on the EEG are in the frontocentral brain with increased high-frequency beta and gamma oscillations and decreased low-frequency delta oscillations. In addition, a non-linear relationship between amyloid burden and the oscillations in subjects with neurodegeneration subjects—a U-shape curve for delta or an inverted U-shape curve beta and gamma—suggests initial compensatory mechanisms that are later overwhelmed for the highest amyloid load.2
A machine learning approach has now been used with the EEG data, said Dr Epelbaum. This has demonstrated that the EEG is strongly predictive of neurodegeneration even when the number of electrodes is decreased from 224 to four, with a negative predictive value of 82%, a positive predictive value of 38%, 77% specificity, and 45% sensitivity.3
Plasma Abeta42/Abeta40 ratio
Plasma Abeta42/Abeta40 ratio can improve selection of patients for trials
Plasma Abeta42/Abeta40 ratio has potential as a prescreener for identifying preclinical AD in cognitively normal individuals with SCD, said Femke H. Bouwman, Amsterdam, The Netherlands.
Dr Bouwman described how the plasma Abeta42/Abeta40 ratio can improve the selection of patients for randomized controlled trials. In the past patients would be selected by identifying amyloid abnormality in the cerebrospinal fluid (CSF), but the lumbar puncture would need to be carried out in 434 individuals with SCD to identify 110 subjects with abnormal amyloid for inclusion in a trial.
Using the plasma Abeta42/Abeta40 ratio before CSF screening would mean that lumbar puncture would need to be carried out in only 220 individuals with SCD to identify 110 subjects.4
Our correspondent’s highlights from the symposium are meant as a fair representation of the scientific content presented. The views and opinions expressed on this page do not necessarily reflect those of Lundbeck.
References
- Mitchell AJ, et al. Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis. Acta Psychiatr Scand 2014;130:439–51.
- Gaubert S, et al. EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease. Brain 2019;142:2096–112.
- Gaubert S, et al. A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiology of Aging 2021;105:205–16.
- Verberk IMW, et al. Plasma amyloid as prescreener for the earliest Alzheimer pathological changes. Ann Neurol 2018;84:656–66.