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.

中文翻译：

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计算蒙特卡罗模拟策略确定阿尔茨海默病生物标志物中异常年龄起始的时间顺序


为了定量确定阿尔茨海默病 (AD) 各种生物标志物中异常年龄开始 (AAO) 的时间顺序，我们引入了计算蒙特卡罗模拟 (CMCS) 来统计检查 AAO 对或所有 AAO 的这种顺序。 CMCS 1) 模拟纵向数据，估计每次迭代的 AAO，并最终评估 AAO 对或所有 AAO 排序的 I 类误差。使用海马体积（VHC）、脑葡萄糖低代谢收敛指数（HCI）、血浆神经丝光（NfL）、迷你精神状态检查（MMSE）、听觉语言学习测试-长期记忆（AVLT-LTM）、短期记忆(AVLT-STM) 和临床痴呆评分盒量表总和 (CDR-SOB) 来自 382 名轻度认知障碍转化者和非转化者，CMCS 估计 VHC、AVLT_STM 和 AVLT_LTM 的早期 AAO 均比 MMSE 的 I 型错误显着 (p<0.002)。 VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL 的总体 AAO 时间排序的 I 类错误为 p = 0.012。这些发现表明，我们的 CMCS 能够为量化 AAO 排序提供统计推断，这对于增进我们对 AD 的理解具有重要意义。