Artificial Intelligence and Leukocyte Epigenomics: Evaluation and Prediction of late-onset Alzheimer's disease

PONE-D-21-04089

Dear Dr. Uppala,

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Bahado-Singh et al., have explored the increasing interest in the potential of epigenetic biomarkers to differentiate individuals with late onset Alzheimer's disease (LAOD) to those without. Some of the gene regions, and identified pathways appear biologically relevant, suggesting that these may not only serve as biomarkers but potentially involved in the disease pathogenesis. The main strength of this study is the robust systematic data driven analysis framework with extensive comparison of the methods, performance measures, and validation.

There still remains a gap between methylation biomarker studies and the potential to be used in a clinical setting. To date, almost all such studies have been case-control approach, and indicates the lack of temporal information to track disease incidence, and follow large groups of over time. As such it remains to be elucidated whether these methylation markers mediate detectable transcriptomic changes which will fully emphasize their efficacy.

This study indicates the potential for a future study, and validation of the findings in a well-defined longitudinal cohort consisting of LOAD cases, presymptomatic, and healthy controls.

Reviewer #2: This is a well written manuscript with important conclusions. The sample size is not extensive but still the analysis took into consideration this caveat. The use of AI provides high resolution analysis of the data.

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Reviewer #1: No

Reviewer #2: No