Confounder-adjusted MRI-based predictors of multiple sclerosis disability.
Confounder-adjusted MRI-based predictors of multiple sclerosis disability.
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DOI：
10.3389/fradi.2022.971157
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Bielekova, Bibiana
中科院分区：
文献类型：
--
作者：
Kim, Yujin;Varosanec, Mihael;Kosa, Peter;Bielekova, Bibiana
关键词：
衰老和多发性硬化症（MS）都会导致中枢神经系统（CNS）萎缩。MS中的过度脑萎缩被解释为“加速老化”。目前的论文测试了另一种假设：MS通过与生理老化不同的机制引起CNS萎缩。因此，减去生理混杂因素对CNS结构的影响将分离MS特异性效应。在ClinicalTrials.gov标识符：NCT 00794352方案中招募的646名参与者中前瞻性获得标准化脑MRI和神经系统检查。通过自动化病变-TOADS算法和脊髓造影，以盲法回顾性测量CNS体积。通过逐步多元线性回归消除80名健康志愿者中发现的生理混杂因素。在非MS队列（n = 158）中评估了混杂因素调整的MRI特征的MS特异性。MS患者被随机分为训练（n = 277）和验证（n = 131）队列。在MS训练队列中，根据四种残疾量表，从未调整和调整混杂因素的CNS体积生成梯度增强机（GBM）模型。混杂因素校正强调MS特异性CNS白色物质进行性丢失。从训练到交叉验证，再到独立验证队列，GBM模型性能大幅下降，但所有模型预测认知和身体残疾的p值和效应量都较低，优于基于最近荟萃分析的已发表文献。在验证队列中，从混杂因素调整的MRI预测因子构建的模型优于从未调整的预测因子构建的模型。来自混淆因素调整的体积MRI特征的GBM模型反映了MS特异性CNS损伤，并且由于与脑萎缩相比与临床结局的相关性更强，因此应在未来的MS临床试验中探索这些模型。
Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects. Standardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.
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影响因子：
4.3
作者：
Masvekar R;Phillips J;Komori M;Wu T;Bielekova B
通讯作者：
Bielekova B
影响因子：
3.4
作者：
Boukhvalova, Alexandra K.;Fan, Olivia;Bielekova, Bibiana
通讯作者：
Bielekova, Bibiana
影响因子：
5.3
作者：
Kosa, Peter;Barbour, Christopher;Bielekova, Bibiana
通讯作者：
Bielekova, Bibiana
影响因子：
15.8
作者：
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通讯作者：
Ioannidis, JPA
影响因子：
48
作者：
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通讯作者：
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