Chapman Caron, Lucas Robyn M, Ponsonby Anne-Louise, Taylor Bruce
Barwon Health, PO Box 281, Geelong, VIC 3220, Australia.
National Centre for Epidemiology and Population Health, The Australian National University, Cnr Mills and Eggleston Roads, Canberra 2601, Australia.
Brain Commun. 2022 Jul 9;4(4):fcac181. doi: 10.1093/braincomms/fcac181. eCollection 2022.
Understanding the predictors of progression from a first to a second demyelinating event (and formerly, a diagnosis of clinically definite multiple sclerosis) is important clinically. Previous studies have focused on predictors within a single domain, e.g. radiological, lacking prospective data across multiple domains. We tested a comprehensive set of personal, environmental, neurological, MRI and genetic characteristics, considered together, as predictors of progression from a first demyelinating event to clinically definite multiple sclerosis. Participants were aged 18-59 years and had a first demyelinating event during the study recruitment period (1 November 2003-31 December 2006) for the Ausimmune Study ( = 216) and had follow-up data to 2-3 years post-initial interview. Detailed baseline data were available on a broad range of demographic and environmental factors, MRI, and genetic and viral studies. Follow-up data included confirmation of clinically definite multiple sclerosis (or not) and changes in environmental exposures during the follow-up period. We used multivariable logistic regression and Cox proportional hazards regression modelling to test predictors of, and time to, conversion to clinically definite multiple sclerosis. On review, one participant had an undiagnosed event prior to study recruitment and was excluded ( = 215). Data on progression to clinically definite multiple sclerosis were available for 91.2% ( = 196); 77% were diagnosed as clinically definite multiple sclerosis at follow-up. Mean (standard deviation) duration of follow-up was 2.7 (0.7) years. The set of predictors retained in the best predictive model for progression from a first demyelinating event to clinically definite multiple sclerosis were as follows: younger age at first demyelinating event [adjusted odds ratio (aOR) = 0.92, 95% confidence interval (CI) = 0.87-0.97, per additional year of age); being a smoker at baseline (versus not) (aOR = 2.55, 95% CI 0.85-7.69); lower sun exposure at age 6-18 years (aOR = 0.86, 95% CI 0.74-1.00, per 100 kJ/m increment in ultraviolet radiation dose), presence (versus absence) of infratentorial lesions on baseline magnetic resonance imaging (aOR = 7.41, 95% CI 2.08-26.41); and single nucleotide polymorphisms in human leukocyte antigen () (rs2523393, aOR = 0.25, 95% CI 0.09-0.68, for any G versus A:A), (rs1800693, aOR = 5.82, 95% CI 2.10-16.12, for any C versus T:T), and a vitamin D-binding protein gene (rs7041, aOR = 3.76, 95% CI 1.41-9.99, for any A versus C:C). The final model explained 36% of the variance. Predictors of more rapid progression to clinically definite multiple sclerosis (Cox proportional hazards regression) were similar. Genetic and magnetic resonance imaging characteristics as well as demographic and environmental factors predicted progression, and more rapid progression, from a first demyelinating event to a second event and clinically definite multiple sclerosis.
了解首次脱髓鞘事件进展为第二次脱髓鞘事件(以及之前的临床确诊多发性硬化症诊断)的预测因素在临床上具有重要意义。以往的研究集中在单一领域的预测因素,如放射学方面,缺乏跨多个领域的前瞻性数据。我们测试了一组综合的个人、环境、神经学、MRI和基因特征,综合考虑这些因素作为首次脱髓鞘事件进展为临床确诊多发性硬化症的预测因素。参与者年龄在18 - 59岁之间,在澳大利亚免疫研究(n = 216)的研究招募期间(2003年11月1日 - 2006年12月31日)经历了首次脱髓鞘事件,并在初次访谈后有2 - 3年的随访数据。详细的基线数据涵盖了广泛的人口统计学和环境因素、MRI以及基因和病毒研究。随访数据包括临床确诊多发性硬化症的确认情况(或未确诊情况)以及随访期间环境暴露的变化。我们使用多变量逻辑回归和Cox比例风险回归模型来测试进展为临床确诊多发性硬化症的预测因素及所需时间。经审查,一名参与者在研究招募前有未确诊的事件,被排除(n = 215)。有91.2%(n = 196)的参与者有进展为临床确诊多发性硬化症的数据;77%在随访时被诊断为临床确诊多发性硬化症。随访的平均(标准差)时长为2.7(0.7)年。在从首次脱髓鞘事件进展为临床确诊多发性硬化症的最佳预测模型中保留的预测因素如下:首次脱髓鞘事件时年龄较小[调整后的优势比(aOR)= 0.92,95%置信区间(CI)= 0.87 - 0.97,每增加一岁];基线时为吸烟者(与非吸烟者相比)(aOR = 2.55,95% CI 0.85 - 7.69);6 - 18岁时阳光暴露较少(aOR = 0.86,95% CI 0.74 - 1.00,紫外线辐射剂量每增加100 kJ/m),基线磁共振成像上存在(与不存在相比)幕下病变(aOR = 7.41,95% CI 2.08 - 26.41);以及人类白细胞抗原(HLA)中的单核苷酸多态性(rs2523393,对于任何G与A:A相比,aOR = 0.25,95% CI 0.09 - 0.68),(rs1800693,对于任何C与T:T相比,aOR = 5.82,95% CI 2.10 - 16.12),以及维生素D结合蛋白基因(rs7041,对于任何A与C:C相比,aOR = 3.76,95% CI 1.41 - 9.99)。最终模型解释了36%的方差。进展为临床确诊多发性硬化症更快的预测因素(Cox比例风险回归)相似。基因和磁共振成像特征以及人口统计学和环境因素预测了从首次脱髓鞘事件到第二次事件以及临床确诊多发性硬化症的进展,以及更快的进展。