Wei Zhang, Juan I. Young, Lissette Gomez, Michael A. Schmidt, David Lukacsovich, Brian Kunkle, X. Steven Chen, Eden R. Martin, Lily Wang
Alzheimer’s disease and related dementias pose a significant public health challenge, especially as the population ages. Dementia cases are often underreported, highlighting the need to identify individuals at risk early. However, distinguishing between molecular changes that precede dementia onset and those resulting from the disease is challenging with cross-sectional studies. To address this, we studied blood DNA methylation (DNAm) differences and incident dementia in two large longitudinal cohorts: the Offspring cohort of the Framingham Heart Study (FHS) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.
We analyzed blood DNAm samples from over 1,000 cognitively unimpaired subjects. FHS participants (n = 907) were followed for up to 7.72 years after blood sample collection at Exam 9; ADNI participants (n = 216) were followed for up to 11.11 years after their initial visits. Mean ages at sample collection were 72.03 years in FHS and 76.73 years in ADNI. Meta-analysis of results from cox regression models identified 44 CpGs and 44 differentially methylated regions consistently associated with time to dementia in both cohorts. Our integrative analysis identified early processes in dementia, such as immune responses and metabolic dysfunction. External validations with two independent datasets, the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study and the AddNeuroMed study, showed significant discriminatory classification of dementia samples versus controls using methylation risk scores based on 44 dementia-associated CpGs. These findings demonstrate that DNA methylation offers a promising pathway for early detection and prevention of dementia in at-risk populations.
DNA methylation samples from both FHS and ADNI were measured using the same Illumina HumanMethylation EPIC v1 bead chips. Quality control of both probes and samples were performed.
| File and folder | Description |
|---|---|
| code/ADNI/preprocessing | Code for preprocessing of ADNI DNAm blood samples |
| code/Framingham/preprocessing | Code for preprocessing of FHS9 DNAm blood samples |
To evaluate the relationship between incident dementia and DNA methylation, we conducted Cox proportional regression analyses on both FHS and ADNI datasets separately, via the coxph function in the survival R package.
| File and folder | Description |
|---|---|
| code/ADNI/cpg_test/cox_cpg_test_ADNI.Rmd | Code for individual CpGs test of ADNI (under Main analysis section) |
| code/Framingham/cpg_test/cox_cpg_test_FHS9.Rmd | Code for individual CpGs test of FHS9 (under Main analysis section) |
To meta-analyze individual CpG results across both the FHS and ADNI datasets, we used the inverse-variance weighted fixed-effects model, implemented in the meta R package.
| File and folder | Description |
|---|---|
| code/meta_analysis/meta_analysis.Rmd | Code for meta-analysis (under Main analysis section) |
To identify biological pathways enriched with significant DNA methylation differences, we used the methylRRA function in the methylGSA R package1.
| File and folder | Description |
|---|---|
| code/pathway_analysis | Code for pathway analysis |
To evaluate the effect of DNA methylation on the expression of nearby genes, we overlapped our dementia-associated CpGs, including both significant individual CpGs and those located within DMRs, with eQTm analysis results in Supplementary Tables 2 and 3 of Yao et al (2021)2.
To assess the correlation of dementia-associated CpGs and DMRs methylation levels in blood and brain samples, we used the London cohort, which consisted of 69 samples with matched PFC and blood samples.
| File and folder | Description |
|---|---|
| code/check_overlap | Code for integrative analyses |
| code/brain_blood_corr | Code for brain-to-blood correlations |
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We evaluated if dementia risk factors would likely confound the DNA methylation to dementia associations we observed. To this end, we first performed regression analysis to assess the association between dementia-associated CpGs and dementia risk factors collected by the Framingham study.
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To evaluate the impact of family structure in the discovery of significant CpGs, we compared the results a model that accounts for family relationships in the Cox regression model using a kinship matrix.
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The coMethDM3 software was used to evaluate the robustness of genomic regions defined by the 44 DMRs identified by comb-p for their association with time to incident dementia.
| File and folder | Description |
|---|---|
| code/covariates_analysis | Code for covariate analysis |
| code/Framingham/cpg_test/kinship.Rmd | Code for family structure analysis |
| code/DMR/coMethDMR.Rmd | Code for DMR analysis |
- Include smoking status as a covariate variable: The code for this analysis can be found in the Main Analysis section under the
Sensitivity analysis 1: smoking. - Exclude CN subjects with strong biomarker evidence for AD, but short follow-up durations: The code for this analysis can be found in the Main Analysis section under the
Sensitivity analysis 2: exclude high-risk individual. - AD dementia: The code for this analysis can be found in the Main Analysis section under the
Sensitivity analysis 3: AD in Dementia.
We performed an out-of-sample validation using the ADNI dataset. For each subject at baseline, the MRS was computed by summing the methylation M-values for the 151 CpGs weighted by coefficients estimated from the ridge regression model described above. We then performed Cox regression analyses on the ADNI dataset to evaluate the association between baseline MRS and disease progression. This analysis was performed using the coxph function in the survival R package.
| File and folder | Description |
|---|---|
| code/MRS_analysis | Code for MRS analysis |
To perform the analysis, begin by installing the packages found in session_info.R (Link to the script). Then, load the auxiliary functions from folder code/utility (Link to the file). Follow the sequence provided in the Description to conduct the analysis.
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Footnotes
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Ren, X. & Kuan, P.F. methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics 35, 1958-1959 (2019) ↩
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Yao, C. et al. Epigenome-wide association study of whole blood gene expression in Framingham Heart Study participants provides molecular insight into the potential role of CHRNA5 in cigarette smoking-related lung diseases. Clin Epigenetics 13, 60 (2021) ↩
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Gomez, L. et al. coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes. Nucleic Acids Res 47, e98 (2019). ↩