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. 2024 Jul 3;16:146. doi: 10.1186/s13195-024-01502-y

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© The Author(s) 2024

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 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 — Plasma biomarkers performance to identify DLB. ROC analysis: a, to compare single biomarkers performance to discriminate between DLB patients and NC; b, to compare biomarkers combination to discriminate between DLB and NC; c, to compare single biomarkers performance to discriminate between DLB and AD patients; d, to compare biomarkers combination to discriminate between DLB and AD patients. ROC analysis results are presented as AUC (95% CI). Combinations of biomarkers were selected through binary logistic regression with backward stepwise elimination, including age and sex as constant variables. ^a model including p-tau181 outperformed all other models (∂AIC > 4), ^b no significant difference in the model’s fit with the All markers model (∂AIC < 4).