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. 2022 Sep 30;20:327. doi: 10.1186/s12916-022-02525-8

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© The Author(s) 2022

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 — Multivariate linear regression models were adjusted for age, sex, weight changes, and the three intervention groups. We aimed to identify the independent associations between the changes in abdominal fat depots and biomarkers in multivariate models, adjusted for age, sex, intervention group, and weight loss. While VAT loss was independently associated with an improved lipid profile, the deep SAT loss was independently and significantly associated with beneficial glycemic biomarkers during the intervention (p < 0.05 for all). The association between changes in blood biomarkers and reduction in abdominal fat subdepots is presented by the β standardized coefficient. *p < 0.05 considered statistically significant. HDL-c, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model of insulin resistance; LDL-c, low-density lipoprotein cholesterol