Predicting onset of secondary-progressive multiple sclerosis using genetic and non-genetic factors
Predicting onset of secondary-progressive multiple sclerosis using genetic and non-genetic factors
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DOI：
10.1007/s00415-020-09850-z
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发表时间：
2020-04-24
影响因子：
6
通讯作者：
Briggs, Farren B. S.
中科院分区：
文献类型：
--
作者：
Misicka, Elina;Sept, Corriene;Briggs, Farren B. S.
背景在病程早期预测多发性硬化（MS）从复发缓解型（RR）向继发进展型（SP）的转变具有挑战性。目的利用MS发病时或接近MS发病时可用的社会人口统计学和自我报告的临床指标构建SPMS的预测模型，并特别考虑MS遗传危险因素。方法我们进行了一项回顾性横断面研究的基础上，1295白色，非西班牙裔个体。使用社会人口统计学、共病健康信息、乳腺病学和其他早期疾病活动指标，为三种删失SPMS结局（曾经过渡、10年内过渡和20年内过渡）生成考克斯比例风险预测模型。在每个模型中反复考虑HLADRB 1 *15：01和HLA-A*02：01以及遗传风险评分。我们还探讨了位于主要组织相容性复合体外的所有200种MS风险变异的关系。为最终预测模型生成列线图。结果MS发病年龄越大、男性患者SPMS潜伏期越短，两次复发间隔时间越长，SPMS潜伏期越长。对于每个删失结果，合并症和发病率可预测转换为SPMS的风险。最值得注意的观察结果是HLA-A*02：01，它降低了MS的风险，也有助于降低SPMS的危害。结论：这些结果有可能利用发病时或接近发病时可用的信息来促进MS患者的诊断，从而有可能改善MS患者的护理和生活质量。
Background Predicting the transition from relapsing-remitting (RR) to secondary-progressive (SP) multiple sclerosis (MS) from early in the disease course is challenging. Objective To construct prediction models for SPMS using sociodemographic and self-reported clinical measures that would be available at/near MS onset, with specific considerations for MS genetic risk factors. Methods We conducted a retrospective cross-sectional study based on 1295 white, non-Hispanic individuals. Cox proportional hazard prediction models were generated for three censored SPMS outcomes (ever transitioning, transitioning within 10 years, and transitioning within 20 years) using sociodemographic, comorbid health information, symptomatology, and other measures of early disease activity. HLADRB1*15:01 and HLA-A*02:01, as well as a genetic risk score, were iteratively considered in each model. We also explored the relationships for all 200 MS risk variants located outside the major histocompatibility complex. Nomograms were generated for the final prediction models. Results An older age of MS onset and being male predicted a short latency to SPMS, while a longer interval between the first two relapses predicted a much longer latency. Comorbid conditions and onset symptomatology variably predicted the risk for transitioning to SPMS for each censored outcome. The most notable observation was that HLA-A*02:01, which confers decreased risk for MS, also contributed to decreased hazards for SPMS. Conclusions These results have the potential to advance prognostication for a person with MS using information available at or near onset, potentially improving care and quality of life for those who live with MS.