Leen M 't Hart1,2,3, Nicole Vogelzangs4, Dennis O Mook-Kanamori5,6, Adela Brahimaj7, Jana Nano7,8,9, Amber A W A van der Heijden10, Ko Willems van Dijk11,12,13, Roderick C Slieker1,3, Ewout W Steyerberg14, M Arfan Ikram7, Marian Beekman2, Dorret I Boomsma15, Cornelia M van Duijn7, P Eline Slagboom2, Coen D A Stehouwer16,17, Casper G Schalkwijk16,17, Ilja C W Arts4, Jacqueline M Dekker3, Abbas Dehghan7,18, Taulant Muka7, Carla J H van der Kallen16,17, Giel Nijpels10, Marleen M J van Greevenbroek16,17. 1. Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden ZA, Netherlands. 2. Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden ZA, Netherlands. 3. Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam HV, Netherlands. 4. Cardiovascular Research Institute Maastricht and Maastricht Centre for Systems Biology, Maastricht University, Maastricht LK, Netherlands. 5. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden ZA, Netherlands. 6. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden ZA, Netherlands. 7. Department of Epidemiology, Erasmus Medical Center, Rotterdam GD, Netherlands. 8. Institute of Epidemiology, German Research Center for Environment Health, Helmholtz Zentrum Munich, Munich, Germany. 9. German Center for Diabetes Research (Deutsches Zentrum für Diabetesforschung), Munich, Germany. 10. Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, Netherlands. 11. Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden ZA, Netherlands. 12. Department of Human Genetics, Leiden University Medical Center, Leiden ZA, Netherlands. 13. Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden ZA, Netherlands. 14. Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden ZA, Netherlands. 15. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam HV, Netherlands. 16. Cardiovascular Research Institute Maastricht, School for Cardiovascular Diseases, Maastricht University, Maastricht LK, Netherlands. 17. Department of Internal Medicine, Maastricht University Medical Center, Maastricht LK, Netherlands. 18. Department of Biostatistics and Epidemiology, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom.
Abstract
Objective: We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy. Methods: A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698). Results: After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6). Conclusion: Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.
Objective: We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy. Methods: A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698). Results: After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6). Conclusion: Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.
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