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<p>DNA methylation has been shown to play a crucial role in many diseases, including Alzheimer’s disease (AD). Although many studies have correlated DNA methylation in blood samples with risk of clinical AD diagnosis, few have examined links with AD neuropathology. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we investigate the associations between peripheral blood DNA methylation and three AD-associated biomarkers in cerebrospinal fluid: amyloid-<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_22004_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\beta\)</EquationSource> </InlineEquation>, phosphorylated tau-181, and total tau using an innovative multivariate approach. In our approach, we first adjusted the methylation values for covariates that have known wide-spread effects on methylation. We then developed and implemented a multivariate penalized model to find associations, jointly, between CSF biomarkers and sets of methylation residuals defined by regions around each gene. These penalized models then selected probes showing associations with one or more CSF biomarkers. We demonstrate, using both simulations and actual data, that our proposed multivariate approach is beneficial for detecting weak signals. We also provide complementary validation using data from the Canadian Longitudinal Study on Aging. Our multivariate strategy has the potential to increase feature selection accuracy among correlated predictors in epigenetic studies.</p>

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A multivariate approach to identify association between peripheral blood DNA methylation and cerebrospinal fluid biomarkers of Alzheimer disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Bowei Xiao,
  • Yixiao Zeng,
  • Kathleen Oros Klein,
  • Bianca Granato,
  • Mathieu Blanchette,
  • Xiaojian Shao,
  • Celia M. T. Greenwood

摘要

DNA methylation has been shown to play a crucial role in many diseases, including Alzheimer’s disease (AD). Although many studies have correlated DNA methylation in blood samples with risk of clinical AD diagnosis, few have examined links with AD neuropathology. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we investigate the associations between peripheral blood DNA methylation and three AD-associated biomarkers in cerebrospinal fluid: amyloid- \(\beta\) , phosphorylated tau-181, and total tau using an innovative multivariate approach. In our approach, we first adjusted the methylation values for covariates that have known wide-spread effects on methylation. We then developed and implemented a multivariate penalized model to find associations, jointly, between CSF biomarkers and sets of methylation residuals defined by regions around each gene. These penalized models then selected probes showing associations with one or more CSF biomarkers. We demonstrate, using both simulations and actual data, that our proposed multivariate approach is beneficial for detecting weak signals. We also provide complementary validation using data from the Canadian Longitudinal Study on Aging. Our multivariate strategy has the potential to increase feature selection accuracy among correlated predictors in epigenetic studies.