Dominik Lutter1,2 ● Stephan Sachs3 ● Marc Walter2,3 ● Anna Kerege4 ● Leigh Perreault 4 ● Darcy E. Kahn4 ● Amare D. Wolide1,2,5 ● Maximilian Kleinert 2,6,7 ● Bryan C. Bergman4 ● Susanna M. Hofmann2,3,8

Received: 15 July 2022 /Accepted: 6 December 2022 / Published online: 15 February 2023 © The Author(s) 2023

Abstract Aims/hypothesis Although insulin resistance often leads to type 2 diabetes mellitus, its early stages are often unrecognised, thus reducing the probability of successful prevention and intervention. Moreover, treatment efficacy is affected by the genetics of the individual. We used gene expression profiles from a cross-sectional study to identify potential candidate genes for the prediction of diabetes risk and intervention response.

Methods Using a multivariate regression model, we linked gene expression profiles of human skeletal muscle and intermuscular adipose tissue (IMAT) to fasting glucose levels and glucose infusion rate. Based on the expression patterns of the top predictive genes, we characterised and compared individual gene expression with clinical classifications using k-nearest neighbour clustering. The predictive potential of the candidate genes identified was validated using muscle gene expression data from a longitudinal intervention study.

Results We found that genes with a strong association with clinical measures clustered into three distinct expression patterns. Their predictive values for insulin resistance varied substantially between skeletal muscle and IMAT. Moreover, we discovered that individual gene expression-based classifications may differ from classifications based predominantly on clinical variables, indicating that participant stratification may be imprecise if only clinical variables are used for classification. Of the 15 top candidate genes, ST3GAL2, AASS, ARF1 and the transcription factor SIN3A are novel candidates for predicting a refined diabetes risk and intervention response.

Conclusion/interpretation Our results confirm that disease progression and successful intervention depend on individual gene expression states. We anticipate that our findings may lead to a better understanding and prediction of individual diabetes risk and may help to develop individualised intervention strategies.

Keywords Computational health . Diabetes subtypes . Glucose intolerance . Insulin resistance . Intermuscular adipose tissue . Obesity . Personalised medicine . Response to treatment prediction . Type 2 diabetes

Dominik Lutter 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

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1 Computational Discovery Research, Institute for Diabetes and Obesity (IDO), Helmholtz Diabetes Center (HDC), Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

2 German Center for Diabetes Research (DZD), Neuherberg, Germany

3 Institute for Diabetes and Regeneration (IDR-H), Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

4 University of Colorado Anschutz Medical Campus, Aurora, CO, USA

5 Division of Metabolic Diseases, Department of Medicine, Technische Universität München (TUM), Munich, Germany

6 Drug Development Unit, Institute for Diabetes and Obesity (IDO), Helmholtz Diabetes Center (HDC), Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

7 Group of Muscle Physiology and Metabolism, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany

8 Department of Medicine IV, University Hospital, LMU Munich, Munich, Germany

Denise Türk1 iD Nina Scherer1 & Dominik Selzer1 & Christiane Dings1 & Nina Hanke1 & Robert Dallmann2 iD  Matthias Schwab3,4,5 & Peter Timmins 6 iD  Valerie Nock7 & Thorsten Lehr1 iD 

Received: 22 November 2022 /Accepted: 31 January 2023 © The Author(s) 2023

Abstract

Aims/hypothesis The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations.

Methods A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis.

Results A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy.

Conclusions/interpretation Metformin’s pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes.

Keywords Chronopharmacology . Empirical modelling . Mechanistic modelling . Metformin . Pharmacokinetics . Renal excretion . Transporter

Thorsten Lehr

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1 Clinical Pharmacy, Saarland University, Saarbrücken, Germany

2 Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK

3 Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany

4 Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany

5 Cluster of Excellence iFIT (EXC2180) ‘Image-guided and Functionally Instructed Tumor Therapies’, University of Tübingen, Tübingen, Germany

6 Department of Pharmacy, University of Huddersfield, Huddersfield, UK

7 Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany