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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Alzheimer's Imaging Consortium.

Jonathan Gallego Rudolf1,2, Alex I Wiesman2, Sylvain Baillet2,3

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Summary
This summary is machine-generated.

Biomarker accuracy for predicting mild cognitive impairment (MCI) progression varies with time. Amyloid-beta (Aβ) PET and plasma biomarkers maintain accuracy longer than tau PET and neurophysiology for MCI risk assessment.

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Area of Science:

  • Neuroscience
  • Biomarker Research
  • Cognitive Aging

Background:

  • Predicting progression from asymptomatic to mild cognitive impairment (MCI) is crucial.
  • Existing biomarkers need dynamic accuracy assessment over time.
  • Neurophysiological activity's role in MCI risk prediction is underexplored.

Purpose of the Study:

  • To characterize the temporal accuracy of various biomarkers for predicting MCI.
  • To investigate the contribution of neurophysiological activity to MCI risk prediction.

Main Methods:

  • Assessed spectral power from magnetoencephalographic (MEG) recordings, MRI hippocampal volumes, plasma biomarkers, and PET for amyloid-beta (Aβ) and tau.
  • Evaluated biomarker accuracy over time using logistic regression in 102 cognitively unimpaired older adults with family history of Alzheimer's disease (AD).
  • 31 participants progressed to MCI, with a mean 4-year interval between biomarker collection and diagnosis.

Main Results:

  • Neurophysiological activity and tau PET were informative up to ~4 years before MCI diagnosis.
  • Aβ PET and plasma biomarkers retained accuracy up to 6 years prior to MCI diagnosis.
  • Age, sex, plasma p-tau217, Aβ PET, tau PET, and neurophysiological activity were significant predictors at varying time intervals.

Conclusions:

  • Biomarker accuracy for predicting MCI progression is time-dependent.
  • Different biomarkers exhibit dynamic sensitivity relative to the time of diagnosis.
  • Findings are critical for optimizing biomarker use in clinical trials for MCI screening.