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. 2023 Jul 25;13:268. doi: 10.1038/s41398-023-02558-4

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© The Author(s) 2023

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — The combination of biomarkers obtained by LASSO regression first, and plasma GFAP as a single biomarker second, resulted in the two largest AUCs for predicting Aβ positivity in the whole dataset (A) and in the prior to PET MCI group (B). C Plasma NfL was the best predictor for conversion to AD in the MCI Aβ+ group. D, E, F Visualisation of the pooled variable obtained via LASSO regression for the prior to PET MCI group that resulted the best AUC (and specificity and negative predictive values of 100%) from panel (B). D No false positives were identified by the pooled variable. E, F Plasma GFAP and pTau231 values are visualised according to point size, and the colour of the balloons is determined by a threshold for maximisation of the negative predictive value (NPV) (having a test with no false negatives); 143 (pg/ml) and 19.3 (pg/ml) for GFAP and pTau231, respectively.