A Computational Monte Carlo Simulation Strategy to Determine the Temporal Ordering of Abnormal Age Onset Among Biomarkers of Alzheimer's Disease

Xiaojuan Guo, Kewei Chen, Yinghua Chen, Chengjie Xiong, Yi Su, Li Yao, Eric M. Reiman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.

Original languageEnglish
Pages (from-to)2613-2622
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume19
Issue number5
DOIs
StatePublished - 2022

Keywords

  • Abnormal age onset
  • Alzheimer's disease
  • Monte Carlo simulation
  • biomarker
  • linear mixed effects
  • temporal ordering

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