Hu Yi-Han, Meirelles Osorio, Shiroma Eric J, Satizabal Claudia L, Seshadri Sudha, Tracy Russell P, Gudnason Vilmundur, Launer Lenore J
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, Maryland, USA.
Department of Population Health Sciences and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Alzheimers Dement. 2024 Dec;20(12):8556-8565. doi: 10.1002/alz.14293.
Peripheral risk factors (PRFs) may correlate with dementia plasma biomarkers, potentially reflecting peripheral rather than brain health. This study explores the associations between PRFs and plasma biomarkers glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and total-tau, and their role in predicting future dementia.
Data from the Age, Gene/Environment Susceptibility-Reykjavik Study (2002-2015) included 4353 participants mean age of 76.6 years. A subsample of 910 participants tested their association with PRFs and plasma biomarkers' predictive performance. Sociodemographic, clinical, laboratory, sensory, and lifestyle variables (n = 305) were grouped into 34 clusters.
Besides age and estimated glomerular filtration rate (eGFR), significant associations were found between plasma biomarkers and clusters related to hemoglobin, red blood cell distribution, and inflammation. Incorporating these clusters into predictive models enhanced precision and sensitivity, though overall prediction improvement was modest (area under the precision-recall curve: GFAP 0.17 to 0.34, NfL 0.20 to 0.38).
PRFs are significantly associated with dementia plasma biomarkers; Considering these factors may enhance the predictive accuracy of dementia biomarkers.
Machine learning identifies key peripheral factors influencing neurodegenerative biomarkers. Hemoglobin and red blood cell distribution cluster associates significantly with biomarker levels. Incorporating diverse peripheral factors modestly enhances incident dementia prediction accuracy in community settings.
外周风险因素(PRFs)可能与痴呆症血浆生物标志物相关,这可能反映外周而非大脑健康状况。本研究探讨了PRFs与血浆生物标志物胶质纤维酸性蛋白(GFAP)、神经丝轻链(NfL)和总tau之间的关联,以及它们在预测未来痴呆症方面的作用。
来自年龄、基因/环境易感性-雷克雅未克研究(2002 - 2015年)的数据包括4353名参与者,平均年龄为76.6岁。910名参与者的子样本测试了他们与PRFs及血浆生物标志物预测性能的关联。社会人口统计学、临床、实验室、感官和生活方式变量(n = 305)被分为34个集群。
除年龄和估计肾小球滤过率(eGFR)外,还发现血浆生物标志物与血红蛋白、红细胞分布和炎症相关的集群之间存在显著关联。将这些集群纳入预测模型提高了精度和敏感性,尽管整体预测改善幅度不大(精确召回曲线下面积:GFAP从0.17提高到0.34,NfL从0.20提高到0.38)。
PRFs与痴呆症血浆生物标志物显著相关;考虑这些因素可能提高痴呆症生物标志物的预测准确性。
机器学习识别影响神经退行性生物标志物的关键外周因素。血红蛋白和红细胞分布集群与生物标志物水平显著相关。纳入多种外周因素适度提高了社区环境中痴呆症发病预测的准确性。