Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA.
Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, MI, 48109, USA.
J Neurol. 2024 Oct;271(10):6923-6934. doi: 10.1007/s00415-024-12644-2. Epub 2024 Sep 9.
Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function, and a cure for this devastating disease remains elusive. This study aimed to identify pre-disposing genetic, phenotypic, and exposure-related factors for amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential.
Utilizing data from the UK (United Kingdom) Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates.
Both PRSs modestly predicted ALS diagnosis but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved the prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a fourfold higher ALS risk (95% CI [2.04, 7.73]) versus those in the 40%-60% range.
By leveraging UK Biobank data, our study uncovers pre-disposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.
肌萎缩侧索硬化症（ALS）导致严重的神经功能损伤，而这种毁灭性疾病的治愈方法仍然难以捉摸。本研究旨在利用多模态数据确定肌萎缩侧索硬化症的易患遗传、表型和暴露相关因素，并评估它们的联合预测潜力。
利用英国生物库（UK Biobank）的数据，我们分析了一组 292 例肌萎缩侧索硬化症病例和 408831 例欧洲血统对照。构建了两个多基因风险评分（PRS）：“GWAS Hits PRS”和“PRS-CS”，分别反映了寡基因和多基因肌萎缩侧索硬化症的风险特征。进行时间受限的表型全基因组关联研究（PheWAS）以确定增加 ALS 风险的预先存在的情况，并将其整合到表型风险评分（PheRS）中。多暴露评分（“PXS”）捕获通过问卷调查测量的环境暴露的影响。我们评估了这些评分在预测 ALS 发病率和分层风险方面的表现，同时调整了基线人口统计学协变量。
两个 PRS 适度预测了 ALS 的诊断，但当它们结合使用时，预测能力有所提高（调整后的基线协变量的接收者操作特征曲线 [AAUC] = 0.584 [0.525，0.639]）。PheRS 纳入了 ALS 发病前 1 年的诊断（PheRS1）适度区分了病例和对照（AAUC = 0.515 [0.472，0.564]）。“PXS”不能显著预测 ALS。然而，一个包含 PRS 和 PheRS1 的模型改善了 ALS 的预测（AAUC = 0.604 [0.547，0.667]），优于一个结合所有风险评分的模型。该联合风险评分确定了风险评分分布的前 10%，其 ALS 风险高四倍（95%置信区间 [2.04，7.73]），而分布在 40%-60%范围内的风险则较低。
通过利用英国生物库的数据，我们的研究揭示了易患 ALS 的因素，突出了多因素预测模型在识别 ALS 风险最高的个体方面的有效性。
