Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease

被引:3
|
作者
Platero, Carlos [1 ]
机构
[1] Tech Univ Madrid, Hlth Sci Technol Grp, Ronda Valencia 3, Madrid 28012, Spain
基金
美国国家卫生研究院;
关键词
Mild cognitive impairment; Longitudinal analysis; Predictive models; Alzheimers disease; CLINICAL-TRIALS; MRI; BIOMARKERS; MCI; ENRICHMENT; CONVERSION; COGNITION; MARKERS;
D O I
10.1016/j.jneumeth.2022.109581
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: A preclinical stage of Alzheimer's disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset. New method: We propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI. Results: A very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms. Comparison with existing methods: Unlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models. Conclusions: The estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/A beta. Furthermore, pTAU/A beta, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Rates of progression in early stage Alzheimer's disease
    Storandt, M
    Grant, E
    Miller, JP
    Morris, JC
    NEUROBIOLOGY OF AGING, 2002, 23 (01) : S28 - S28
  • [2] Predictive Factors for Disease Progression in Patients With Early-Onset Alzheimer's Disease
    Yoon, Bora
    Shim, Yong S.
    Park, Hee-Kyung
    Park, Sun Ah
    Choi, Seong Hye
    Yang, Dong Won
    JOURNAL OF ALZHEIMERS DISEASE, 2016, 49 (01) : 85 - 91
  • [3] Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks
    Tingyan Wang
    Robin G. Qiu
    Ming Yu
    Scientific Reports, 8
  • [4] Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks
    Wang, Tingyan
    Qiu, Robin G.
    Yu, Ming
    SCIENTIFIC REPORTS, 2018, 8
  • [5] Alzheimer's Disease: Lecanemab in early Stage of the Disease?
    Simon, Annika
    DEUTSCHE MEDIZINISCHE WOCHENSCHRIFT, 2023, 148 (10) : 593 - 594
  • [6] Validating MRI measures of disease stage and progression in Alzheimer's disease
    Jack, CR
    LIVING BRAIN AND ALZHEIMER'S DISEASE, 2004, : 75 - 86
  • [7] Disease Progression Modeling Shows Blood Linked Peptides to be Early Markers in Alzheimer's Disease Subtypes
    Mitchell, Cassie
    Tandon, Raghav
    Lah, James
    ANNALS OF NEUROLOGY, 2024, 96 : S121 - S121
  • [8] Disease progression modeling of Alzheimer's disease according to education level
    Kim, Ko Woon
    Woo, Sook Young
    Kim, Seonwoo
    Jang, Hyemin
    Kim, Yeshin
    Cho, Soo Hyun
    Kim, Si Eun
    Kim, Seung Joo
    Shin, Byoung-Soo
    Kim, Hee Jin
    Na, Duk L.
    Seo, Sang Won
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [9] Disease progression modeling of Alzheimer’s disease according to education level
    Ko Woon Kim
    Sook Young Woo
    Seonwoo Kim
    Hyemin Jang
    Yeshin Kim
    Soo Hyun Cho
    Si Eun Kim
    Seung Joo Kim
    Byoung-Soo Shin
    Hee Jin Kim
    Duk L. Na
    Sang Won Seo
    Scientific Reports, 10
  • [10] Predictability of dementia progression in early stage Alzheimer's disease using neuropsychological testing
    Yoshizawa, H.
    Seki, M.
    Uchiyama, Y.
    Kitagawa, K.
    JOURNAL OF THE NEUROLOGICAL SCIENCES, 2017, 381 : 1032 - 1032