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. 2020 Nov 10;12:145. doi: 10.1186/s13195-020-00716-0

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Kaplan-Meier curves of survival without conversion to AD produced by the prediction models. We calculated a prognostic index for each subject by applying the miR-eQTLs and clinical factors to our prognosis prediction model. a Based on the prognostic index, we divided the samples of the discovery cohort into high (red) and low (blue) risk groups. The optimal cutoff values were detected by using the minimum P value from the log-rank test and comparing the differences in survival without MCI-to-AD conversion as determined by Kaplan-Meier curves (optimal cutoff = 7.85, minimum P = 3.63 × 10⁻⁷). b The adjusted model was then evaluated on the validation cohort (log-rank test P = 3.44 × 10⁻⁴). c, d Prediction models constructed using only clinical factors (without miR-eQTLs) in the discovery cohort (c) and the validation cohort (d)