Scholarlink

<p>The underlying mechanisms for neurodegeneration in multiple sclerosis are complex and incompletely understood. Multivariate and multimodal investigations integrating demographic, clinical, multi-omics, and neuroimaging data provide opportunities for nuanced analyses, aimed to define disease progression markers. We used data from a 12-year longitudinal multicenter cohort of 88 people with multiple sclerosis, to test the predictive value of multi-omics, T<sub>1</sub>-weighted MRI (lesion count and volume, lesion-filled brain-predicted age), clinical examinations, self-reports on quality of life, demographics, and general health-related variables for future functional and cognitive disability. Systematic increases in Expanded Disability Status Scale (EDSS) scores were used to stratify a progressive disability group (PDG) from relatively stabile disability. A processing speed decline group (PSDG) was defined by a ≥ 20% decrease of Paced Auditory Serial Addition Test score from previous timepoints. We used a multiverse approach to identify which baseline variables were most predictive for PDG and PSDG memberships, considering multiple analysis paths. Future disability (median area under the curve: mAUC = 0.83 ± 0.04, median Brier score: mBS = 0.16 ± 0.02) and the loss of processing speed (mAUC = 0.89 ± 0.05, mBS = 0.10 ± 0.03) could be successfully classified across models. Varibles significantly (median p-values &lt; 0.05) predicting stable disability included receiving disease modifying treatment at 12-year follow-up (median Odds Ratio: mOR<sub>PDG</sub> = 7.44 ± 4.07, p<sub>median</sub> = 0.013, proportion of the OR’s directionality: PORSD = 100%), lower baseline EDSS for each 1-unit (mOR<sub>PDG</sub> = 0.25 ± 0.11, p<sub>median</sub> = 0.013, PORSD = 100%), and counter-intuitively every year increase in baseline age (mOR<sub>PDG</sub> = 1.12 ± 0.04, p<sub>median</sub> = 0.020, PORSD = 100%), and lower vitamin A per 1 umol/L (mOR<sub>PDG</sub> = 0.10 ± 0.05, p<sub>median</sub> = 0.016, PORSD = 99.7%) and D levels per 1&#xa0;nmol/L (mOR<sub>PDG</sub> = 0.95 ± 0.02, p<sub>median</sub> = 0.025, PORSD = 100%). Variables significantly predicting stable processing speed were receiving disease modifying treatment at 12-year follow-up (mOR<sub>PSDG</sub> = 0.10 ± 0.08, p<sub>median</sub> = 0.013, PORSD = 100%) and baseline PASAT score (mOR<sub>PSDG</sub> = 0.86 ± 0.03, p<sub>median</sub> = 0.005, PORSD = 99.73%). These findings were supported by an additional simulation study. Concordant with the literature, disease modifying treatments influence disability progression, as well as a higher EDSS and PASAT scores at measurement start. Experimental and counterintuitive findings on vitamin A and D levels require further validation. The large variability across models suggests a strong influence of analytic flexibility, such as the selection of covariates.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multimodal predictors of disability progression and processing speed decline in relapsing–remitting multiple sclerosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

查看全文
  • Max Korbmacher,
  • Ingrid Anne Lie,
  • Kristin Wesnes,
  • Eric Westman,
  • Thomas Espeseth,
  • Karsten Specht,
  • Ole Andreas Andreassen,
  • Lars Tjelta Westlye,
  • Stig Wergeland,
  • Kjell-Morten Myhr,
  • Øivind Torkildsen,
  • Einar August Høgestøl

摘要

The underlying mechanisms for neurodegeneration in multiple sclerosis are complex and incompletely understood. Multivariate and multimodal investigations integrating demographic, clinical, multi-omics, and neuroimaging data provide opportunities for nuanced analyses, aimed to define disease progression markers. We used data from a 12-year longitudinal multicenter cohort of 88 people with multiple sclerosis, to test the predictive value of multi-omics, T1-weighted MRI (lesion count and volume, lesion-filled brain-predicted age), clinical examinations, self-reports on quality of life, demographics, and general health-related variables for future functional and cognitive disability. Systematic increases in Expanded Disability Status Scale (EDSS) scores were used to stratify a progressive disability group (PDG) from relatively stabile disability. A processing speed decline group (PSDG) was defined by a ≥ 20% decrease of Paced Auditory Serial Addition Test score from previous timepoints. We used a multiverse approach to identify which baseline variables were most predictive for PDG and PSDG memberships, considering multiple analysis paths. Future disability (median area under the curve: mAUC = 0.83 ± 0.04, median Brier score: mBS = 0.16 ± 0.02) and the loss of processing speed (mAUC = 0.89 ± 0.05, mBS = 0.10 ± 0.03) could be successfully classified across models. Varibles significantly (median p-values < 0.05) predicting stable disability included receiving disease modifying treatment at 12-year follow-up (median Odds Ratio: mORPDG = 7.44 ± 4.07, pmedian = 0.013, proportion of the OR’s directionality: PORSD = 100%), lower baseline EDSS for each 1-unit (mORPDG = 0.25 ± 0.11, pmedian = 0.013, PORSD = 100%), and counter-intuitively every year increase in baseline age (mORPDG = 1.12 ± 0.04, pmedian = 0.020, PORSD = 100%), and lower vitamin A per 1 umol/L (mORPDG = 0.10 ± 0.05, pmedian = 0.016, PORSD = 99.7%) and D levels per 1 nmol/L (mORPDG = 0.95 ± 0.02, pmedian = 0.025, PORSD = 100%). Variables significantly predicting stable processing speed were receiving disease modifying treatment at 12-year follow-up (mORPSDG = 0.10 ± 0.08, pmedian = 0.013, PORSD = 100%) and baseline PASAT score (mORPSDG = 0.86 ± 0.03, pmedian = 0.005, PORSD = 99.73%). These findings were supported by an additional simulation study. Concordant with the literature, disease modifying treatments influence disability progression, as well as a higher EDSS and PASAT scores at measurement start. Experimental and counterintuitive findings on vitamin A and D levels require further validation. The large variability across models suggests a strong influence of analytic flexibility, such as the selection of covariates.