Drazenka Pongrac Barlovic1,2,3, Valma Harjutsalo3,4,5,6, Niina Sandholm3,4,5, Carol Forsblom3,4,5, Per-Henrik Groop7,8,9,10. 1. University Medical Center Ljubljana, Ljubljana, Slovenia. 2. Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia. 3. Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland. 4. Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 5. Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 6. National Institute for Health and Welfare, Helsinki, Finland. 7. Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland. per-henrik.groop@helsinki.fi. 8. Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. per-henrik.groop@helsinki.fi. 9. Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland. per-henrik.groop@helsinki.fi. 10. Department of Diabetes, Monash University, Melbourne, Victoria, Australia. per-henrik.groop@helsinki.fi.
Abstract
AIMS/HYPOTHESIS: Lipid abnormalities are associated with diabetic kidney disease and CHD, although their exact role has not yet been fully explained. Sphingomyelin, the predominant sphingolipid in humans, is crucial for intact glomerular and endothelial function. Therefore, the objective of our study was to investigate whether sphingomyelin impacts kidney disease and CHD progression in individuals with type 1 diabetes. METHODS: Individuals (n = 1087) from the Finnish Diabetic Nephropathy (FinnDiane) prospective cohort study with serum sphingomyelin measured using a proton NMR metabolomics platform were included. Kidney disease progression was defined as change in eGFR or albuminuria stratum. Data on incident end-stage renal disease (ESRD) and CHD were retrieved from national registries. HRs from Cox regression models and regression coefficients from the logistic or linear regression analyses were reported per 1 SD increase in sphingomyelin level. In addition, receiver operating curves were used to assess whether sphingomyelin improves eGFR decline prediction compared with albuminuria. RESULTS: During a median (IQR) 10.7 (6.4, 13.5) years of follow-up, sphingomyelin was independently associated with the fastest eGFR decline (lowest 25%; median [IQR] for eGFR change: <-4.4 [-6.8, -3.1] ml min-1 [1.73 m-2] year-1), even after adjustment for classical lipid variables such as HDL-cholesterol and triacylglycerols (OR [95% CI]: 1.36 [1.15, 1.61], p < 0.001). Similarly, sphingomyelin increased the risk of progression to ESRD (HR [95% CI]: 1.53 [1.19, 1.97], p = 0.001). Moreover, sphingomyelin increased the risk of CHD (HR [95% CI]: 1.24 [1.01, 1.52], p = 0.038). However, sphingomyelin did not perform better than albuminuria in the prediction of eGFR decline. CONCLUSIONS/ INTERPRETATION: This study demonstrates for the first time in a prospective setting that sphingomyelin is associated with the fastest eGFR decline and progression to ESRD in type 1 diabetes. In addition, sphingomyelin is a risk factor for CHD. These data suggest that high sphingomyelin level, independently of classical lipid risk factors, may contribute not only to the initiation and progression of kidney disease but also to CHD. Graphical abstract.
AIMS/HYPOTHESIS: Lipid abnormalities are associated with diabetic kidney disease and CHD, although their exact role has not yet been fully explained. Sphingomyelin, the predominant sphingolipid in humans, is crucial for intact glomerular and endothelial function. Therefore, the objective of our study was to investigate whether sphingomyelin impacts kidney disease and CHD progression in individuals with type 1 diabetes. METHODS: Individuals (n = 1087) from the Finnish Diabetic Nephropathy (FinnDiane) prospective cohort study with serum sphingomyelin measured using a proton NMR metabolomics platform were included. Kidney disease progression was defined as change in eGFR or albuminuria stratum. Data on incident end-stage renal disease (ESRD) and CHD were retrieved from national registries. HRs from Cox regression models and regression coefficients from the logistic or linear regression analyses were reported per 1 SD increase in sphingomyelin level. In addition, receiver operating curves were used to assess whether sphingomyelin improves eGFR decline prediction compared with albuminuria. RESULTS: During a median (IQR) 10.7 (6.4, 13.5) years of follow-up, sphingomyelin was independently associated with the fastest eGFR decline (lowest 25%; median [IQR] for eGFR change: <-4.4 [-6.8, -3.1] ml min-1 [1.73 m-2] year-1), even after adjustment for classical lipid variables such as HDL-cholesterol and triacylglycerols (OR [95% CI]: 1.36 [1.15, 1.61], p < 0.001). Similarly, sphingomyelin increased the risk of progression to ESRD (HR [95% CI]: 1.53 [1.19, 1.97], p = 0.001). Moreover, sphingomyelin increased the risk of CHD (HR [95% CI]: 1.24 [1.01, 1.52], p = 0.038). However, sphingomyelin did not perform better than albuminuria in the prediction of eGFR decline. CONCLUSIONS/ INTERPRETATION: This study demonstrates for the first time in a prospective setting that sphingomyelin is associated with the fastest eGFR decline and progression to ESRD in type 1 diabetes. In addition, sphingomyelin is a risk factor for CHD. These data suggest that high sphingomyelin level, independently of classical lipid risk factors, may contribute not only to the initiation and progression of kidney disease but also to CHD. Graphical abstract.
Authors: Luis F Ferreira-Divino; Tommi Suvitaival; Cristina Legido-Quigley; Peter Rossing; Viktor Rotbain Curovic; Nete Tofte; Kajetan Trošt; Ismo M Mattila; Simone Theilade; Signe A Winther; Tine W Hansen; Marie Frimodt-Møller Journal: Cardiovasc Diabetol Date: 2022-07-18 Impact factor: 8.949
Authors: Lukasz Marczak; Jakub Idkowiak; Joanna Tracz; Maciej Stobiecki; Bartłomiej Perek; Katarzyna Kostka-Jeziorny; Andrzej Tykarski; Maria Wanic-Kossowska; Marcin Borowski; Marcin Osuch; Dorota Formanowicz; Magdalena Luczak Journal: Metabolites Date: 2021-04-27
Authors: Tim Vigers; Lauren A Vanderlinden; Randi K Johnson; Patrick M Carry; Ivana Yang; Brian C DeFelice; Alexander M Kaizer; Laura Pyle; Marian Rewers; Oliver Fiehn; Jill M Norris; Katerina Kechris Journal: Metabolites Date: 2021-08-14
Authors: Huali Jiang; Li Li; Weijie Chen; Benfa Chen; Heng Li; Shanhua Wang; Min Wang; Yi Luo Journal: Front Physiol Date: 2021-11-29 Impact factor: 4.566