Mild cognitive impairment-to-Alzheimer's dementia progression risk: the contribution of the Interceptor project

Flavia L Lombardo1, Naike Caraglia2, Patrizia Lorenzini1

  • 1National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.

Abstract

Insights

The INTERCEPTOR nomogram aids in predicting dementia progression risk in individuals with mild cognitive impairment (MCI). This tool enhances early identification and supports public health strategies for dementia prevention.

Area of Science:

  • Neuroscience
  • Gerontology
  • Biomarkers

Background:

  • Mild cognitive impairment (MCI) signifies a transitional stage between normal aging and dementia.
  • A significant percentage of individuals with MCI (30-50%) progress to dementia within 3-5 years.
  • Early identification of high-risk individuals is critical for effective public health interventions.

Purpose of the Study:

  • To develop and validate a predictive tool for dementia progression in MCI patients.
  • To identify key clinical, neuropsychological, and biomarker factors associated with dementia risk.

Main Methods:

  • The INTERCEPTOR project enrolled 398 individuals with MCI.
  • Comprehensive baseline assessments included sociodemographics, clinical data, neuropsychological testing, genetic analysis (apolipoprotein E), cerebrospinal fluid biomarkers (amyloid beta, tau), electroencephalogram, magnetic resonance imaging, and positron emission tomography.
  • Follow-up included neuropsychological testing every six months for three years for 351 participants.

Main Results:

  • Dementia developed in 29.6% of participants, with 22.4% meeting criteria for probable or possible Alzheimer's disease dementia.
  • A Cox model using clinical and sociodemographic data achieved 72% concordance.
  • Incorporating neuropsychology and biomarkers improved the model's concordance to 82%.

Conclusions:

  • The INTERCEPTOR nomogram is a valuable tool for predicting dementia progression risk.
  • This nomogram can support public health strategies, including risk assessment and guiding treatment decisions.
  • The findings highlight the utility of integrating multimodal data for improved dementia risk prediction.

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