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. 2023 Jun 12;21:384. doi: 10.1186/s12967-023-04244-x

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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 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. 3 — Distribution of metabolomic BMI, actual BMI, and metabolites used to predict BMI in the applicative set. A subgrouping for all individuals in application dataset, the correlation r² value is 0.381; B distribution of actBMI across groups; C refers to the top 30 metabolites with high correlation ratio selected by ridge regression model. NW: normal weight of metabolic BMI; OW: overweight of metabolic BMI; OB: obesity of metabolic BMI; OE: metabolome-defined BMI over than actual BMI; UE: metabolome-defined BMI under than actual BMI