Lipidomic Analysis Reveals Sphingomyelin and Phosphatidylcholine Species Associated with Renal Impairment and All-cause Mortality in Type 1 Diabetes

Overview

Journal Sci Rep

Specialty Science

Date 2019 Nov 10

PMID 31705008

Citations 43

Authors

Nete Tofte

Tommi Suvitaival

Linda Ahonen

Signe A Winther

Simone Theilade

Marie Frimodt-Moller

Tarunveer S Ahluwalia

Peter Rossing

Affiliations

Soon will be listed here.

Abstract

There is an urgent need for a better molecular understanding of the pathophysiology underlying development and progression of diabetic nephropathy. The aim of the current study was to identify novel associations between serum lipidomics and diabetic nephropathy. Non-targeted serum lipidomic analyses were performed with mass spectrometry in 669 individuals with type 1 diabetes. Cross-sectional associations of lipid species with estimated glomerular filtration rate (eGFR) and urinary albumin excretion were assessed. Moreover, associations with register-based longitudinal follow-up for progression to a combined renal endpoint including ≥30% decline in eGFR, ESRD and all-cause mortality were evaluated. Median follow-up time was 5.0-6.4 years. Adjustments included traditional risk factors and multiple testing correction. In total, 106 lipid species were identified. Primarily, alkyl-acyl phosphatidylcholines, triglycerides and sphingomyelins demonstrated cross-sectional associations with eGFR and macroalbuminuria. In longitudinal analyses, thirteen lipid species were associated with the slope of eGFR or albuminuria. Of these lipids, phosphatidylcholine and sphingomyelin species, PC(O-34:2), PC(O-34:3), SM(d18:1/24:0), SM(d40:1) and SM(d41:1), were associated with lower risk of the combined renal endpoint. PC(O-34:3), SM(d40:1) and SM(d41:1) were associated with lower risk of all-cause mortality while an SM(d18:1/24:0) was associated with lower risk of albuminuria group progression. We report distinct associations between lipid species and risk of renal outcomes in type 1 diabetes, independent of traditional markers of kidney function.

Citing Articles

Metabolomic and Lipidomic Profiling for Pre-Transplant Assessment of Delayed Graft Function Risk Using Chemical Biopsy with Microextraction Probes.

Warmuzinska N, Luczykowski K, Stryjak I, Wojtal E, Woderska-Jasinska A, Masztalerz M Int J Mol Sci. 2025; 25(24.

PMID: 39769265 PMC: 11728147. DOI: 10.3390/ijms252413502.

Metabolite signatures of chronological age, aging, survival, and longevity.

Sebastiani P, Monti S, Lustgarten M, Song Z, Ellis D, Tian Q Cell Rep. 2024; 43(11):114913.

PMID: 39504246 PMC: 11656345. DOI: 10.1016/j.celrep.2024.114913.

Insulin Resistance, Obesity, and Lipotoxicity.

Yazici D, Demir S, Sezer H Adv Exp Med Biol. 2024; 1460:391-430.

PMID: 39287860 DOI: 10.1007/978-3-031-63657-8_14.

Sphingolipids and Chronic Kidney Disease.

Sakic Z, Atic A, Potocki S, Basic-Jukic N J Clin Med. 2024; 13(17).

PMID: 39274263 PMC: 11396415. DOI: 10.3390/jcm13175050.

Research progress on in the treatment of diabetic nephropathy.

Wang J, Wang X, Ma T, Xie Y Front Pharmacol. 2024; 15:1390672.

PMID: 38948461 PMC: 11211572. DOI: 10.3389/fphar.2024.1390672.

References

Bowden J, Heckert A, Ulmer C, Jones C, Koelmel J, Abdullah L . Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma. J Lipid Res. 2017; 58(12):2275-2288. PMC: 5711491. DOI: 10.1194/jlr.M079012. View

Han L, Xia J, Liang Q, Wang Y, Wang Y, Hu P . Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography-mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy. Anal Chim Acta. 2011; 689(1):85-91. DOI: 10.1016/j.aca.2011.01.034. View

Fornoni A, Merscher S, Kopp J . Lipid biology of the podocyte--new perspectives offer new opportunities. Nat Rev Nephrol. 2014; 10(7):379-88. PMC: 4386893. DOI: 10.1038/nrneph.2014.87. View

Liu J, Ghosh S, Kovalik J, Ching J, Choi H, Tavintharan S . Profiling of Plasma Metabolites Suggests Altered Mitochondrial Fuel Usage and Remodeling of Sphingolipid Metabolism in Individuals With Type 2 Diabetes and Kidney Disease. Kidney Int Rep. 2017; 2(3):470-480. PMC: 5678636. DOI: 10.1016/j.ekir.2016.12.003. View

Folch J, Lees M, SLOANE STANLEY G . A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem. 1957; 226(1):497-509. View

Hirayama A, Nakashima E, Sugimoto M, Akiyama S, Sato W, Maruyama S . Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem. 2012; 404(10):3101-9. DOI: 10.1007/s00216-012-6412-x. View

Makinen V, Tynkkynen T, Soininen P, Peltola T, Kangas A, Forsblom C . Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane Study). J Proteome Res. 2011; 11(3):1782-90. DOI: 10.1021/pr201036j. View

Groop P, Thomas M, Moran J, Waden J, Thorn L, Makinen V . The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes. 2009; 58(7):1651-8. PMC: 2699848. DOI: 10.2337/db08-1543. View

Helweg-Larsen K . The Danish Register of Causes of Death. Scand J Public Health. 2011; 39(7 Suppl):26-9. DOI: 10.1177/1403494811399958. View

10.

Miyamoto S, Hsu C, Hamm G, Darshi M, Diamond-Stanic M, Decleves A . Mass Spectrometry Imaging Reveals Elevated Glomerular ATP/AMP in Diabetes/obesity and Identifies Sphingomyelin as a Possible Mediator. EBioMedicine. 2016; 7:121-34. PMC: 4909366. DOI: 10.1016/j.ebiom.2016.03.033. View

11.

Sas K, Karnovsky A, Michailidis G, Pennathur S . Metabolomics and diabetes: analytical and computational approaches. Diabetes. 2015; 64(3):718-32. PMC: 4338589. DOI: 10.2337/db14-0509. View

12.

Klein R, Hammad S, Baker N, Hunt K, Al Gadban M, Cleary P . Decreased plasma levels of select very long chain ceramide species are associated with the development of nephropathy in type 1 diabetes. Metabolism. 2014; 63(10):1287-95. PMC: 5894336. DOI: 10.1016/j.metabol.2014.07.001. View

13.

Zeni L, Norden A, Cancarini G, Unwin R . A more tubulocentric view of diabetic kidney disease. J Nephrol. 2017; 30(6):701-717. PMC: 5698396. DOI: 10.1007/s40620-017-0423-9. View

14.

Thurberg B, Rennke H, Colvin R, Dikman S, Gordon R, Collins A . Globotriaosylceramide accumulation in the Fabry kidney is cleared from multiple cell types after enzyme replacement therapy. Kidney Int. 2002; 62(6):1933-46. DOI: 10.1046/j.1523-1755.2002.00675.x. View

15.

Merscher S, Fornoni A . Podocyte pathology and nephropathy - sphingolipids in glomerular diseases. Front Endocrinol (Lausanne). 2014; 5:127. PMC: 4115628. DOI: 10.3389/fendo.2014.00127. View

16.

Kang H, Ahn S, Choi P, Ko Y, Han S, Chinga F . Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat Med. 2014; 21(1):37-46. PMC: 4444078. DOI: 10.1038/nm.3762. View

17.

Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C . Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2012; 14(4):469-90. DOI: 10.1093/bib/bbs037. View

18.

OGorman A, Suvitaival T, Ahonen L, Cannon M, Zammit S, Lewis G . Identification of a plasma signature of psychotic disorder in children and adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Transl Psychiatry. 2017; 7(9):e1240. PMC: 5639252. DOI: 10.1038/tp.2017.211. View

19.

Pang L, Liang Q, Wang Y, Ping L, Luo G . Simultaneous determination and quantification of seven major phospholipid classes in human blood using normal-phase liquid chromatography coupled with electrospray mass spectrometry and the application in diabetes nephropathy. J Chromatogr B Analyt Technol Biomed Life Sci. 2008; 869(1-2):118-25. DOI: 10.1016/j.jchromb.2008.05.027. View

20.

Levey A, Stevens L, Schmid C, Zhang Y, Castro 3rd A, Feldman H . A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150(9):604-12. PMC: 2763564. DOI: 10.7326/0003-4819-150-9-200905050-00006. View