Platero Carlos, Pineda-Pardo José Ángel
Health Science Technology Group, Technical University of Madrid, 28012, Madrid, Spain.
HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain; Instituto de Investigación Sanitaria HM Hospitales, Spain.
Parkinsonism Relat Disord. 2024 Dec;129:107184. doi: 10.1016/j.parkreldis.2024.107184. Epub 2024 Oct 21.
Identifying Parkinson's disease (PD) patients at risk of cognitive decline is crucial for enhancing clinical interventions. While several models predicting cognitive decline in PD exist, a new machine learning framework called disease progression models (DPMs) offers a data-driven approach to understand disease evolution.
We enrolled 423 PD patients and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI). Our study encompassed a range of biomarkers, including motor, neurocognitive, and neuroimaging evaluations at baseline and annually. A methodology was employed to select optimal combinations of biomarkers for constructing DPMs with superior predictive capabilities for both diagnosing and estimating conversion times toward cognitive decline.
At baseline, the approach showed excellent performance in identifying individuals at high risk of cognitive decline within the first five years. Furthermore, the proposed timeline from cognitive impairment to dementia was also used to explore clinical events such as the onset of cognitive impairment, the development of dementia or amyloid pathology. The presence of amyloid pathology did not alter the progression of cognitive impairment among PD patients.
Neuropsychological measures and certain biomarkers, including cerebrospinal fluid (CSF) amyloid beta 42 (Aβ) and dopamine transporter deficits, can be used to predict cognitive decline and estimate a timeline from cognitive impairment to dementia, with amyloid pathology preceding the onset of dementia in many cases. Our DPMs suggested that the profiles of CSF Aβ and phosphorylated tau in PD patients may differ from those in aging patients and those with Alzheimer's disease.
识别有认知能力下降风险的帕金森病（PD）患者对于加强临床干预至关重要。虽然存在几种预测PD认知能力下降的模型，但一种名为疾病进展模型（DPM）的新机器学习框架提供了一种数据驱动的方法来理解疾病演变。
我们从帕金森病进展标志物计划（PPMI）中招募了423名PD患者和196名健康对照者。我们的研究涵盖了一系列生物标志物，包括基线时以及每年的运动、神经认知和神经影像学评估。采用了一种方法来选择生物标志物的最佳组合，以构建对诊断和估计向认知能力下降转变的时间具有卓越预测能力的DPM。
在基线时，该方法在识别头五年内有高认知能力下降风险的个体方面表现出色。此外，从认知障碍到痴呆的提议时间线也被用于探索诸如认知障碍的发作、痴呆或淀粉样蛋白病理的发展等临床事件。淀粉样蛋白病理的存在并未改变PD患者认知障碍的进展。
神经心理学测量和某些生物标志物，包括脑脊液（CSF）淀粉样蛋白β42（Aβ）和多巴胺转运体缺陷，可用于预测认知能力下降并估计从认知障碍到痴呆的时间线，在许多情况下，淀粉样蛋白病理先于痴呆发作。我们的DPM表明，PD患者脑脊液Aβ和磷酸化tau的特征可能与老年患者和阿尔茨海默病患者不同。
