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Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease.
He, Bing; Wu, Ruiming; Sangani, Neel; Pugalenthi, Pradeep Varathan; Patania, Alice; Risacher, Shannon L; Nho, Kwangsik; Apostolova, Liana G; Shen, Li; Saykin, Andrew J; Yan, Jingwen.
Affiliation
  • He B; Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA.
  • Wu R; Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Sangani N; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Pugalenthi PV; Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA.
  • Patania A; Department of Mathematics Statistics, University of Vermont, Burlington, Vermont, USA.
  • Risacher SL; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Nho K; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Apostolova LG; Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Shen L; Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Saykin AJ; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Yan J; Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA.
Alzheimers Dement ; 2024 Sep 17.
Article in En | MEDLINE | ID: mdl-39285750
ABSTRACT

INTRODUCTION:

Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment.

METHODS:

Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI).

RESULTS:

Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI.

DISCUSSION:

Our risk score holds great potential to improve the precision of early risk assessment. HIGHLIGHTS Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.
Key words

Full text: 1 Database: MEDLINE Language: En Journal: Alzheimers Dement Year: 2024 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Language: En Journal: Alzheimers Dement Year: 2024 Type: Article Affiliation country: United States