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. 2022 Sep 9;12:377. doi: 10.1038/s41398-022-02142-2

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© The Author(s) 2022

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. 2 — A consensus clustering solution across 100 subsamples was determined by the algorithm HYDRA, adjusting for sex and BMI. Simulation with the Sigclust method indicated that the clusters explained the data better than a Gaussian distribution (p = 0.021). Interferon-gamma (IFN-γ), interleukin-10 (IL-10), interleukin-6 (IL-6), interleukin-8 (IL-8), tumour necrosis factor-alpha (TNF-α), C-reactive protein (CRP), soluble intracellular adhesion molecule-1s (ICAM-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), soluble urokinase plasminogen activation receptor (suPAR), alpha-2-macroglobulin (A2M), tumour necrosis factor receptor 1 (TNFR1), tumour necrosis factor receptor 2 (TNFR2), interleukin-1 receptor type 1 (IL-1RT1), interleukin-1 receptor type 2 (IL-1RT2), interleukin-2 receptor subunit alpha (IL-2RA), interleukin-6 receptor subunit alpha (IL-6RA), cluster of differentiation 93 (CD93).