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. 2022 Mar 31;65(7):1145–1156. doi: 10.1007/s00125-022-05687-5

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© The Author(s) 2022, corrected publication 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 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/.

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Fig. 3 — Association of HMI with incident type 2 diabetes and modulation by dietary and lifestyle factors. (a) HMI and type 2 diabetes incidence (n = 1829). Poisson regression was used to examine the association of baseline HMI (per SD unit) with incident type 2 diabetes, adjusted for demographic, anthropometric, dietary and lifestyle factors. Subgroup analyses stratified by geographic region, age group, sex, BMI level and urbanisation level (city or rural) were performed to test the robustness of the model. (b) Association of dietary and lifestyle factors with gut microbiota (n = 2772). Linear regression was used to estimate the difference in glycaemic trait-related gut microbiota or HMI (in SD units) per SD change for continuous dietary or lifestyle factors (per-category change for categorical dietary or lifestyle factors), with adjustment for the confounders and mutually adjusted for the other tested dietary or lifestyle factors. Red arrows indicate gut microbiota that were positively associated with glycaemic traits; green arrows indicate gut microbiota that were inversely associated with glycaemic traits. The Benjamini–Hochberg method was used to control the FDR. An FDR value <0.05 was considered statistically significant