Gut microbiota clusters are associated with metabolic health and impaired insulin sensitivity

Corinna Geisler; Kristina Schlicht; Nathalie Rohmann; Katharina Hartmann; Kathrin Türk; Ute Settgast; Tim Hollstein; Lucy Kruse; Melissa Solano Marchini; Wiebke Diederich; Dominik M. Schulte; Andre Franke; Karin Schwarz; Matthias Laudes

doi:10.1055/s-0044-1785433

Diabetologie und Stoffwechsel, Table of Contents

Diabetologie und Stoffwechsel 2024; 19(S 01): S98
DOI: 10.1055/s-0044-1785433

Abstracts | DDG 2024

Poster

Posterwalk 15 – Diabeteskomplikationen, Begleiterkrankungen

Gut microbiota clusters are associated with metabolic health and impaired insulin sensitivity

Corinna Geisler

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Kristina Schlicht

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Nathalie Rohmann

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Katharina Hartmann

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Kathrin Türk

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Ute Settgast

²Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Klinik für Innere Medizin I Endokrinologie, Diabetologie und Klinische Ernährungsmedizin, Kiel, Germany

,

Tim Hollstein

³Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung & Klinik für Innere Medizin I Endokrinologie, Diabetologie und Klinische Ernährungsmedizin , Kiel, Germany

,

Lucy Kruse

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Melissa Solano Marchini

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Wiebke Diederich

¹Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung, Kiel, Germany

,

Dominik M. Schulte

³Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung & Klinik für Innere Medizin I Endokrinologie, Diabetologie und Klinische Ernährungsmedizin , Kiel, Germany

,

Andre Franke

⁴Christian-Albrechts-University, Kiel, Germany, Institut für Klinische Molekularbiologie, Kiel, Germany

,

Karin Schwarz

⁵Christian-Albrechts-University, Kiel, Germany, Institut für Humanernährung und Lebensmittelkunde, Abteilung Lebensmitteltechnologie, Kiel, Germany

,

Matthias Laudes

³Universitätsklinikum Schleswig-Holstein (UKSH) Campus Kiel, Institut für Diabetologie und klinische Stoffwechselforschung & Klinik für Innere Medizin I Endokrinologie, Diabetologie und Klinische Ernährungsmedizin , Kiel, Germany

› Author Affiliations

Abstract

Full Text

Background and aims: Gut microbiota can influence health and degrade the host status by modulating metabolic homeostasis. Dysbiosis in microbiota has been associated with different metabolic disturbances, such as type 2 diabetes mellitus (T2DM) and obesity. Thus, the aim of this study was to characterise gut microbiota clusters associated with metabolic health.

Methods: Cluster analysis was used to evaluate the gut microbiota clusters at the genus level using 16S rRNA gene sequencing. The clusters were then integrated into univariate and multivariate logistic regression analyses to evaluate their influence on T2DM, high lipid levels, hypertension, and coronary heart disease (CHD). Data at the genus level were partial correlated (corrected for BMI, age, and sex) with anthropometric, metabolic, and inflammatory biomarkers. Data from 1,354 subjects of the FoCus cohort stratified by HOMA-IR (> /≤ 2) were used for analysis.

Results: Data from 1,354 subjects (860 females and 494 males) were available for detailed analysis. Five clusters are generated. In cluster 5, more females (71.1%) were found, followed by males (28.9%) and BMI was the highest, and subjects had a higher metabolic risk based on waist-to-hip ratio. Triglyceride and interleukin-6 levels were higher, and a proinflammatory diet was more pronounced in cluster 5. In multivariate logistic regression analysis, subjects with higher HOMA-IR showed a higher risk of developing T2DM (OR 10.90 (2.25-75.12, p= 0.006) or hypertension (OR 2.34 (0.97-5.84, p= 0.062) related to cluster 5. Clusters 1 and 2 were related to high lipid levels, but no cluster was related to CHD. In participants with low HOMA-IR, no cluster was related to metabolic health. At the genus level, Alistipes, Bacteroides, and Faecalibacterium were depleted, whereas Blautia was increased in cluster 5. Partial correlation analysis showed that Blautia was positively correlated with insulin levels (p< 0.01), and Faecalibacterium was inversely related to all metabolic and inflammatory biomarkers (p< 0.05).

Conclusion: We were able to characterise five gut microbiota clusters, from which cluster 5 was more related to subjects with higher HOMA-IR values. None of the clusters was related to metabolic health in subjects with lower HOMA-IR values. Thus, we can assume that these clusters can discriminate subjects with a higher metabolic risk. In future, metabolomics will be integrated in the cluster generation, but these are currently under analysis.

Funding: This work was supported by the Deutsche Diabetes Gesellschaft (Allgemeine Projektförderung)