Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms.
Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms.
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DOI：
10.1038/s41467-022-35357-4
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发表时间：
2022-12-12
影响因子：
16.6
通讯作者：
Bielekova, Bibiana
中科院分区：
文献类型：
--
作者：
Kosa, Peter;Barbour, Christopher;Varosanec, Mihael;Wichman, Alison;Sandford, Mary;Greenwood, Mark;Bielekova, Bibiana
虽然尸检研究确定了死于神经系统疾病的受试者的中枢神经系统（CNS）中的许多异常，但由于没有在整个生命周期内对活体受试者进行量化，因此无法将致病过程与副现象区分开来。使用机器学习（ML），我们搜索了多发性硬化症（MS）的可能致病机制。我们将来自1305种蛋白质的脑脊液（CSF）生物标志物（在未经治疗的MS患者（N = 129）的训练数据集中盲测）聚合到模型中，预测所有MS表型的过去和未来残疾累积速度。健康志愿者（N = 24）的数据区分自然衰老和性别效应与MS相关机制。在独立纵向队列（N = 98）中验证的所得模型（Rho 0.40-0.51，p < 0.0001）揭示了个体内分子异质性。虽然候选的致病过程必须在成功的临床试验中得到验证，但在活体中测量它们将能够筛选药物以获得所需的药效学效果。这将有助于药物开发，希望它更有效和成功。多发性硬化症（MS）改变了CSF的组成。在这里，作者使用患者样本和聚合CSF生物标志物进入模型，预测所有MS表型的残疾，并确定潜在的因果机制和分子疾病异质性。
While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful. Multiple sclerosis (MS) changes the composition of the CSF. Here the authors use patient samples and aggregate CSF biomarkers into models that predict disability across all MS phenotypes, and identify potentially causal mechanisms and molecular disease heterogeneity.
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影响因子：
5.8
作者：
Gu, Zuguang;Eils, Roland;Schlesner, Matthias
通讯作者：
Schlesner, Matthias
影响因子：
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作者：
Guillaumet-Adkins A;Yañez Y;Peris-Diaz MD;Calabria I;Palanca-Ballester C;Sandoval J
通讯作者：
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影响因子：
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作者：
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通讯作者：
Calabresi, Peter A.
影响因子：
11.2
作者：
Barbour, Christopher;Kosa, Peter;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