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. 2023 Feb 23;41(11):1633–1644. doi: 10.1038/s41587-023-01688-w

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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. 5 — a, Compared to the original results from the ZOE PREDICT 1 study based on the MetaPhlAn 3 taxonomic profiles²², random forest (RF) models trained on the MetaPhlAn 4 microbiome profiles (n = 1,001 samples) substantially improve classification (circles and right-side y axis) and regression (box plots and left-side y axis) result for a panel of 19 markers representative of nutritional and cardiometabolic health (see Methods). Box plots show the median (center), 25th/75th percentile (lower/upper hinges), 1.5× interquartile range (whiskers) and outliers (points.) b, Panel of the 20 unknown microbial species (uSGBs) showing the strongest overall correlations with the positive (top-half list) and negative (bottom-half list) dietary and cardiometabolic health markers, respectively (^∗FDR < 0.2).