NfL predicts relapse-free progression in a longitudinal multiple sclerosis cohort study.
NfL predicts relapse-free progression in a longitudinal multiple sclerosis cohort study.
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DOI：
10.1016/j.ebiom.2021.103590
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发表时间：
2021-10
期刊：
影响因子：
11.1
通讯作者：
Bittner S
中科院分区：
文献类型：
--
作者：
Uphaus T;Steffen F;Muthuraman M;Ripfel N;Fleischer V;Groppa S;Ruck T;Meuth SG;Pul R;Kleinschnitz C;Ellwardt E;Loos J;Engel S;Zipp F;Bittner S
容易获得的生物标志物能够识别那些长期积累不可逆残疾的多发性硬化症（MS）患者，这对于指导早期治疗决策至关重要。我们在此研究了血清神经丝轻链（sNfL）在预测无复发残疾进展和转化为继发性进展性多发性硬化症（SPMS）方面的应用，这是前瞻性神经丝和长期预后的多发性硬化症（NaloMS）队列。在发展队列（NaloMS, n=196例复发-缓解型多发性硬化（RRMS）或临床孤立综合征患者）中评估sNfL基线和sNfL随访(FU)/基线（BL）比率对残疾进展的预测能力，并通过外部独立队列（d<s:1> sseldorf, Essen, n=204）进行验证。研究无复发edss进展（RFP：炎症无关edss在FU前12个月增加）和spms转变（最低edss评分为3.0）。在研究期间，17% （n=34）的NaloMS患者发生RFP， 14% （n=27）在FU时转化为SPMS（验证队列RFP n=42， SPMS转化n=24）。RFP患者BL处sNfL升高（10.8 pg/ml（四分位数范围（IQR） 7.7-15.0） vs. 7.2 pg/ml (4.5-12.5), p<0.017）。在多变量logistic回归模型中，支持向量机显示，BL处sNfL水平升高（比值比（OR） 1.02, 95%置信区间（CI） 1.01-1.04, p=0.012）仍然是RFP的独立危险因素，预测个体RFP风险的准确率为82% （NaloMS）和83%（验证队列）。此外，spms -转化器的sNfL FU/BL比(1.16 （0.89-1.70)vs. 0.96 (0.75-1.23), p=0.011）也有所增加。多变量logistic回归模型证实了这一点，因为sNfL FU/BL比率仍然存在于模型中（OR 1.476, 95%CI 1.078- 2019, p=0.015），并且机器学习显示，个体sNfL FU/BL比率在NaloMS中的预测准确率为72%（验证队列中为63%）。在一项6年后的前瞻性纵向队列研究中，基线sNfL水平可预测无复发残疾进展。虽然预测在一个独立的队列中得到了证实，但sNfL在随访中进一步区分了SPMS患者，并支持早期识别有后期SPMS转化风险的患者。这项工作得到了德国研究委员会（CRC-TR-128）、Else Kröner费森尤斯基金会和Hertie-Stiftung的支持。
Easily accessible biomarkers enabling the identification of those patients with multiple sclerosis (MS) who will accumulate irreversible disability in the long term are essential to guide early therapeutic decisions. We here examine the utility of serum neurofilament light chain (sNfL) for forecasting relapse-free disability progression and conversion to secondary progressive MS (SPMS) in the prospective Neurofilamentandlongtermoutcome inMS (NaloMS) cohort. The predictive ability of sNfL at Baseline and sNfL follow-up (FU)/ Baseline (BL) ratio with regard to disability progression was assessed within a development cohort (NaloMS, n=196 patients with relapsing-remitting MS (RRMS) or clinically isolated syndrome) and validated with an external independent cohort (Düsseldorf, Essen, n=204). Both relapse-free EDSS-progression (RFP: inflammatory-independent EDSS-increase 12 months prior to FU) and SPMS-transition (minimum EDSS-score of 3.0) were investigated. During the study period, 17% (n=34) of NaloMS patients suffered from RFP and 14% (n=27) converted to SPMS at FU (validation cohort RFP n=42, SPMS-conversion n=24). sNfL at BL was increased in patients with RFP (10.8 pg/ml (interquartile range (IQR) 7.7-15.0) vs. 7.2 pg/ml (4.5-12.5), p<0.017). In a multivariable logistic regression model, increased sNfL levels at BL (Odds Ratio (OR) 1.02, 95% confidence interval (CI) 1.01-1.04, p=0.012) remained an independent risk factor for RFP and predicted individual RFP risk with an accuracy of 82% (NaloMS) and 83% (validation cohort) as revealed by support vector machine. In addition, the sNfL FU/BL ratio was increased in SPMS-converters (1.16 (0.89-1.70) vs. 0.96 (0.75-1.23), p=0.011). This was confirmed by a multivariable logistic regression model, as sNfL FU/BL ratio remained in the model (OR 1.476, 95%CI 1.078-2,019, p=0.015) and individual sNfL FU/BL ratios showed a predictive accuracy of 72% in NaloMS (63% in the validation cohort) as revealed by machine learning. sNfL levels at baseline predict relapse-free disability progression in a prospective longitudinal cohort study 6 years later. While prediction was confirmed in an independent cohort, sNfL further discriminates patients with SPMS at follow-up and supports early identification of patients at risk for later SPMS conversion. This work was supported by the German Research Council (CRC-TR-128), Else Kröner Fresenius Foundation and Hertie-Stiftung.
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影响因子：
9.9
作者：
Lublin FD;Reingold SC;Cohen JA;Cutter GR;Sørensen PS;Thompson AJ;Wolinsky JS;Balcer LJ;Banwell B;Barkhof F;Bebo B Jr;Calabresi PA;Clanet M;Comi G;Fox RJ;Freedman MS;Goodman AD;Inglese M;Kappos L;Kieseier BC;Lincoln JA;Lubetzki C;Miller AE;Montalban X;O'Connor PW;Petkau J;Pozzilli C;Rudick RA;Sormani MP;Stüve O;Waubant E;Polman CH
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影响因子：
11.2
作者：
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通讯作者：
Hauser, Stephen L.
影响因子：
5.8
作者：
Sellebjerg, Finn;Royen, Lydia;Jensen, Poul Erik Hyldgaard
通讯作者：
Jensen, Poul Erik Hyldgaard
影响因子：
29
作者：
Bjornevik, Kjetil;Munger, Kassandra L.;Ascherio, Alberto
通讯作者：
Ascherio, Alberto
影响因子：
120.7
作者：
Brown, J. William L.;Coles, Alasdair;Zwanikken, Cees
通讯作者：
Zwanikken, Cees