Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

Nature Reviews Nephrology volume 18, pages 38–55 (2022)Cite this article

Subjects

Abstract

Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures. CKD-associated dyslipidaemia occurs as a consequence of complex interactions between genetic, environmental and kidney-specific factors, which to understand, requires an appreciation of perturbations in the underlying network of genes, proteins and lipids. Modern lipidomic technologies attempt to systematically identify and quantify lipid species from biological systems. The rapid development of a variety of analytical platforms based on mass spectrometry has enabled the identification of complex lipids at great precision and depth. Insights from lipidomics studies to date suggest that the overall architecture of free fatty acid partitioning between fatty acid oxidation and complex lipid fatty acid composition is an important driver of CKD progression. Available evidence suggests that CKD progression is associated with metabolic inflexibility, reflecting a diminished capacity to utilize free fatty acids through β-oxidation, and resulting in the diversion of accumulating fatty acids to complex lipids such as triglycerides. This effect is reversed with interventions that improve kidney health, suggesting that targeting of lipid abnormalities could be beneficial in preventing CKD progression.

Key points

  • Lipidomic analyses remain a challenge due to the numerous species that exist within each class; techniques that reduce analytical complexity while retaining important information about lipid structure are useful for elucidating biological significance from lipidomics studies.

  • The immense structural diversity of lipids means that lipidomic analyses are guided by a choice of untargeted and targeted analysis; sample preparation, extraction and separation methods may require optimization for specific classes of interest.

  • A key challenge with profiling technologies such as lipidomics is the ability to use the data to gain insights into biological processes, including mechanisms of disease onset and progression

  • Fatty acid profiles that are enriched in shorter and more saturated species are associated with later stages of chronic kidney disease (CKD) and are predictive of CKD progression.

  • Acylcarnitine profiles provide insights into mitochondrial function; a lower ratio of long-chain to medium-chain acylcarnitines is indicative of increased mitochondrial inefficiency and is associated with worsening CKD.

  • Patients with CKD or kidney failure demonstrate enrichment of complex lipids, such as triacylglycerols and phosphatidylethanolamines, with enrichment of longer and more unsaturated fatty acyl side-chains; an inverse association between mitochondrial efficiency and these complex lipids suggests a mechanistic link between fatty acid oxidation and fatty acid profiles.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Identification of lipid classes using mass spectrometry.
Fig. 2: Structural diversity of triglycerides.
Fig. 3: Alterations in the abundance of lipid classes in patients with CKD.
Fig. 4: Differential network enrichment analysis for lipidomics data from patients with CKD.
Fig. 5: Dyslipidaemia in late-stage CKD.

Similar content being viewed by others

References

  1. Johansen, K. L. et al. US Renal Data System 2020 annual data report: epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 77, A7–A8 (2021).

    PubMed  PubMed Central  Google Scholar 

  2. Sarnak, M. J. et al. Kidney disease as a risk factor for development of cardiovascular disease. Circulation 108, 2154–2169 (2003).

    PubMed  Google Scholar 

  3. Noels, H., Lehrke, M., Vanholder, R. & Jankowski, J. Lipoproteins and fatty acids in chronic kidney disease: molecular and metabolic alterations. Nat. Rev. Nephrol. 17, 528–542 (2021).

    CAS  PubMed  Google Scholar 

  4. Thompson, S. et al. Cause of death in patients with reduced kidney function. J. Am. Soc. Nephrol. 26, 2504–2511 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. Zhao, Y. Y., Wu, S. P., Liu, S., Zhang, Y. & Lin, R. C. Ultra-performance liquid chromatography–mass spectrometry as a sensitive and powerful technology in lipidomic applications. Chem. Biol. Interact. 220, 181–192 (2014).

    CAS  PubMed  Google Scholar 

  6. Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Cotter, D., Maer, A., Guda, C., Saunders, B. & Subramaniam, S. LMPD: LIPID MAPS proteome database. Nucleic Acids Res. 34, D507–D510 (2006).

    CAS  PubMed  Google Scholar 

  8. Richmond, G. S. & Smith, T. K. Phospholipases A1. Int. J. Mol. Sci. 12, 588–612 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Cajka, T. & Fiehn, O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal. Chem. 88, 524–545 (2016).

    CAS  PubMed  Google Scholar 

  10. Züllig, T., Trötzmüller, M. & Köfeler, H. C. Lipidomics from sample preparation to data analysis: a primer. Anal. Bioanal. Chem. 412, 2191–2209 (2020).

    PubMed  Google Scholar 

  11. Afshinnia, F. et al. Lipidomics and biomarker discovery in kidney disease. Semin. Nephrol. 38, 127–141 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Rhee, E. P. et al. Variability of two metabolomic platforms in CKD. Clin. J. Am. Soc. Nephrol. 14, 40–48 (2019).

    CAS  PubMed  Google Scholar 

  13. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    CAS  PubMed  Google Scholar 

  15. Afshinnia, F. et al. Increased lipogenesis and impaired β-oxidation predict type 2 diabetic kidney disease progression in American Indians. JCI Insight 4, e130317 (2019).

    PubMed Central  Google Scholar 

  16. Afshinnia, F. et al. Impaired β-oxidation and altered complex lipid fatty acid partitioning with advancing CKD. J. Am. Soc. Nephrol. 29, 295–306 (2018).

    CAS  PubMed  Google Scholar 

  17. Basu, S. et al. Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics 33, 1545–1553 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Krumsiek, J., Bartel, J. & Theis, F. J. Computational approaches for systems metabolomics. Curr. Opin. Biotechnol. 39, 198–206 (2016).

    CAS  PubMed  Google Scholar 

  19. Ma, J. et al. Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease. Bioinformatics 35, 3441–3452 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Afshinnia, F. et al. Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort. Kidney Int. Rep. 1, 256–268 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. Karnovsky, A. et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28, 373–380 (2012).

    CAS  PubMed  Google Scholar 

  22. Subramanian, I., Verma, S., Kumar, S., Jere, A. & Anamika, K. Multi-omics data integration, interpretation, and its application. Bioinforma. Biol. Insights https://doi.org/10.1177/1177932219899051 (2020).

    Article  Google Scholar 

  23. Brinkkoetter, P. T. et al. Anaerobic glycolysis maintains the glomerular filtration barrier independent of mitochondrial metabolism and dynamics. Cell Rep. 27, 1551–1566.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Forbes, J. M. & Thorburn, D. R. Mitochondrial dysfunction in diabetic kidney disease. Nat. Rev. Nephrol. 14, 291–312 (2018).

    CAS  PubMed  Google Scholar 

  25. Mandel, L. J. Metabolic substrates, cellular energy production, and the regulation of proximal tubular transport. Annu. Rev. Physiol. 47, 85–101 (1985).

    CAS  PubMed  Google Scholar 

  26. Scerbo, D. et al. Kidney triglyceride accumulation in the fasted mouse is dependent upon serum free fatty acids. J. Lipid Res. 58, 1132–1142 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Khan, S. et al. Kidney proximal tubule lipoapoptosis is regulated by fatty acid transporter-2 (FATP2). J. Am. Soc. Nephrol. 29, 81–91 (2018).

    CAS  PubMed  Google Scholar 

  28. Kang, H. M. et al. Defective fatty acid oxidation in renal tubular epithelial cells plays a key role in kidney fibrosis development. Nat. Med. 21, 37–46 (2015).

    CAS  PubMed  Google Scholar 

  29. Kampe, K., Sieber, J., Orellana, J. M., Mundel, P. & Jehle, A. W. Susceptibility of podocytes to palmitic acid is regulated by fatty acid oxidation and inversely depends on acetyl-CoA carboxylases 1 and 2. Am. J. Physiol. Ren. Physiol. 306, F401–F409 (2014).

    CAS  Google Scholar 

  30. Yuan, Y. et al. Activation of peroxisome proliferator-activated receptor-γ coactivator 1α ameliorates mitochondrial dysfunction and protects podocytes from aldosterone-induced injury. Kidney Int. 82, 771–789 (2012).

    CAS  PubMed  Google Scholar 

  31. Tran, M. et al. PGC-1α promotes recovery after acute kidney injury during systemic inflammation in mice. J. Clin. Invest. 121, 4003–4014 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang, H. et al. Rosiglitazone reduces renal and plasma markers of oxidative injury and reverses urinary metabolite abnormalities in the amelioration of diabetic nephropathy. Am. J. Physiol. Ren. Physiol. 295, F1071–F1081 (2008).

    CAS  Google Scholar 

  33. Miguel, V. et al. Renal tubule Cpt1a overexpression protects from kidney fibrosis by restoring mitochondrial homeostasis. J. Clin. Invest. 131, e140695 (2021).

    CAS  PubMed Central  Google Scholar 

  34. Baines, R. J. et al. CD36 mediates proximal tubular binding and uptake of albumin and is upregulated in proteinuric nephropathies. Am. J. Physiol. Ren. Physiol. 303, F1006–F1014 (2012).

    CAS  Google Scholar 

  35. Herman-Edelstein, M., Scherzer, P., Tobar, A., Levi, M. & Gafter, U. Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy. J. Lipid Res. 55, 561–572 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Hua, W. et al. CD36 mediated fatty acid-induced podocyte apoptosis via oxidative stress. PLoS One 10, e0127507 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. Okamura, D. M. et al. CD36 regulates oxidative stress and inflammation in hypercholesterolemic CKD. J. Am. Soc. Nephrol. 20, 495–505 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Souza, A. C. P. et al. Antagonism of scavenger receptor CD36 by 5A peptide prevents chronic kidney disease progression in mice independent of blood pressure regulation. Kidney Int. 89, 809–822 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang, P. et al. Inflammatory stress promotes the development of obesity-related chronic kidney disease via CD36 in mice. J. Lipid Res. 58, 1417–1427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Yang, Y. L. et al. CD36 is a novel and potential anti-fibrogenic target in albumin-induced renal proximal tubule fibrosis. J. Cell. Biochem. 101, 735–744 (2007).

    CAS  PubMed  Google Scholar 

  41. Li, L. C. et al. Palmitate aggravates proteinuria-induced cell death and inflammation via CD36-inflammasome axis in the proximal tubular cells of obese mice. Am. J. Physiol. Ren. Physiol. 315, F1720–F1731 (2018).

    CAS  Google Scholar 

  42. Cui, W. et al. Interaction of thrombospondin1 and CD36 contributes to obesity-associated podocytopathy. Biochim. Biophys. Acta 1852, 1323–1333 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kim, J.-J. et al. Discoidin domain receptor 1 activation links extracellular matrix to podocyte lipotoxicity in Alport syndrome. EBioMedicine 63, 103162 (2021).

    CAS  PubMed  Google Scholar 

  44. Kennedy, D. J. et al. CD36 and Na/K-ATPase-α1 form a pro-inflammatory signaling loop in kidney. Hypertension 61, 216–224 (2013).

    CAS  PubMed  Google Scholar 

  45. Pennathur, S. et al. The macrophage phagocytic receptor CD36 promotes fibrogenic pathways on removal of apoptotic cells during chronic kidney injury. Am. J. Pathol. 185, 2232–2245 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Yang, X. et al. CD36 in chronic kidney disease: novel insights and therapeutic opportunities. Nat. Rev. Nephrol. 13, 769–781 (2017).

    CAS  PubMed  Google Scholar 

  47. Kuwahara, S. et al. Megalin-mediated tubuloglomerular alterations in high-fat diet-induced kidney disease. J. Am. Soc. Nephrol. 27, 1996–2008 (2016).

    CAS  PubMed  Google Scholar 

  48. Khan, S. et al. Fatty acid transport protein-2 regulates glycemic control and diabetic kidney disease progression. JCI Insight 5, e136845 (2020).

    PubMed Central  Google Scholar 

  49. Zager, R. A., Johnson, A. C. M. & Hanson, S. Y. Renal tubular triglyercide accumulation following endotoxic, toxic, and ischemic injury. Kidney Int. 67, 111–121 (2005).

    CAS  PubMed  Google Scholar 

  50. Johnson, A. L. I. C. M., Stahl, A. & Zager, R. A. Triglyceride accumulation in injured renal tubular cells: alterations in both synthetic and catabolic pathways. Kidney Int. 67, 2196–2209 (2005).

    CAS  PubMed  Google Scholar 

  51. Weinberg, J. M. Lipotoxicity. Kidney Int. 70, 1560–1566 (2006).

    CAS  PubMed  Google Scholar 

  52. Jiang, T. et al. Diet-induced obesity in C57BL/6J mice causes increased renal lipid accumulation and glomerulosclerosis via a sterol regulatory element-binding protein-1c-dependent pathway. J. Biol. Chem. 280, 32317–32325 (2005).

    CAS  PubMed  Google Scholar 

  53. Lin, Y. C. et al. Nifedipine exacerbates lipogenesis in the kidney via KIM-1, CD36, and SREBP upregulation: implications from an animal model for human study. Int. J. Mol. Sci. 21, 4359 (2020).

    CAS  PubMed Central  Google Scholar 

  54. Chen, H. et al. Combined clinical phenotype and lipidomic analysis reveals the impact of chronic kidney disease on lipid metabolism. J. Proteome Res. 16, 1566–1578 (2017).

    CAS  PubMed  Google Scholar 

  55. Szczuko, M. et al. Comparison of fatty acid profiles in a group of female patients with chronic kidney diseases (CKD) and metabolic syndrome (MetS)–similar trends of changes, different pathophysiology. Int. J. Mol. Sci. 20, 1719 (2019).

    CAS  PubMed Central  Google Scholar 

  56. Szczuko, M. et al. The C18:3n6/C22:4n6 ratio is a good lipid marker of chronic kidney disease (CKD) progression. Lipids Health Dis. 19, 77 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Afshinnia, F. et al. Circulating free fatty acid and phospholipid signature predicts early rapid kidney function decline in patients with type 1 diabetes. Diabetes Care 44, 2098–2106 (2021).

    CAS  PubMed  Google Scholar 

  58. Listenberger, L. L. et al. Triglyceride accumulation protects against fatty acid-induced lipotoxicity. Proc. Natl Acad. Sci. USA 100, 3077–3082 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ackerman, D. et al. Triglycerides promote lipid homeostasis during hypoxic stress by balancing fatty acid saturation. Cell Rep. 24, 2596–2605.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Czumaj, A. et al. Alterations of fatty acid profile may contribute to dyslipidemia in chronic kidney disease by influencing hepatocyte metabolism. Int. J. Mol. Sci. 20, 2470 (2019).

    PubMed Central  Google Scholar 

  61. Wang, L. et al. Plasma lipidomics investigation of hemodialysis effects by using liquid chromatography-mass spectrometry. J. Proteome Res. 15, 1986 (2016).

    CAS  PubMed  Google Scholar 

  62. Sieber, J. et al. Susceptibility of podocytes to palmitic acid is regulated by stearoyl-CoA desaturases 1 and 2. Am. J. Pathol. 183, 735–744 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Iwai, T. et al. Stearoyl-CoA desaturase-1 protects cells against lipotoxicity-mediated apoptosis in proximal tubular cells. Int. J. Mol. Sci. 17, 1868 (2016).

    PubMed Central  Google Scholar 

  64. de Luis, D. A., Izaola, O., Aller, R., de La Fuente, B. & Pacheco, D. Effects of C358A polymorphism of the endocannabinoid degrading enzyme fatty acid amide hydrolase (FAAH) on weight loss, adipocytokines levels, and insulin resistance after a high polyunsaturated fat diet in obese patients. J. Endocrinol. Invest. 36, 965–969 (2013).

    PubMed  Google Scholar 

  65. Stevenson, J. L., Paton, C. M. & Cooper, J. A. Hunger and satiety responses to high-fat meals after a high-polyunsaturated fat diet: a randomized trial. Nutrition 41, 14–23 (2017).

    CAS  PubMed  Google Scholar 

  66. Cases, S. et al. Cloning of DGAT2, a second mammalian diacylglycerol acyltransferase, and related family members. J. Biol. Chem. 276, 38870–38876 (2001).

    CAS  PubMed  Google Scholar 

  67. Hostetler, H. A., Petrescu, A. D., Kier, A. B. & Schroeder, F. Peroxisome proliferator-activated receptor α interacts with high affinity and is conformationally responsive to endogenous ligands. J. Biol. Chem. 280, 18667–18682 (2005).

    CAS  PubMed  Google Scholar 

  68. Kliewer, S. A. et al. Fatty acids and eicosanoids regulate gene expression through direct interactions with peroxisome proliferator-activated receptors α and γ. Proc. Natl Acad. Sci. USA 94, 4318–4323 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Soumura, M. et al. Oleate and eicosapentaenoic acid attenuate palmitate-induced inflammation and apoptosis in renal proximal tubular cell. Biochem. Biophys. Res. Commun. 402, 265–271 (2010).

    CAS  PubMed  Google Scholar 

  70. Kris-Etherton, P. M., Harris, W. S. & Appel, L. J. Nutrition Committee Fish consumption, fish oil, omega-3 fatty acids, and cardiovascular disease. Circulation 106, 2747–2757 (2002).

    PubMed  Google Scholar 

  71. Kim, W. et al. Polyunsaturated fatty acid desaturation is a mechanism for glycolytic NAD+ recycling. Cell Metab. 29, 856–870.e7 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Crescenzo, R. et al. Fat quality influences the obesogenic effect of high fat diets. Nutrients 7, 9475–9491 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Ruiz-Ramírez, A., Barrios-Maya, M.-A., López-Acosta, O., Molina-Ortiz, D. & El-Hafidi, M. Cytochrome c release from rat liver mitochondria is compromised by increased saturated cardiolipin species induced by sucrose feeding. Am. J. Physiol. Endocrinol. Metab. 309, E777–E786 (2015).

    PubMed  Google Scholar 

  74. Oemer, G. et al. Phospholipid acyl chain diversity controls the tissue-specific assembly of mitochondrial cardiolipins. Cell Rep. 30, 4281–4291.e4 (2020).

    CAS  PubMed  Google Scholar 

  75. Szeto, H. H. Pharmacologic approaches to improve mitochondrial function in AKI and CKD. J. Am. Soc. Nephrol. 28, 2856–2865 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Cavaliere, G. et al. Polyunsaturated fatty acids attenuate diet induced obesity and insulin resistance, modulating mitochondrial respiratory uncoupling in rat skeletal muscle. PLoS One 11, e0149033 (2016).

    PubMed  PubMed Central  Google Scholar 

  77. Cortie, C. H. & Else, P. L. Dietary docosahexaenoic acid (22:6) incorporates into cardiolipin at the expense of linoleic acid (18:2): analysis and potential implications. Int. J. Mol. Sci. 13, 15447–15463 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Khairallah, R. J. et al. Improved mitochondrial function with diet-induced increase in either docosahexaenoic acid or arachidonic acid in membrane phospholipids. PLoS One 7, e34402 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Herbst, Ea. F. et al. Omega-3 supplementation alters mitochondrial membrane composition and respiration kinetics in human skeletal muscle. J. Physiol. 592, 1341–1352 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Mulligan, C. M. et al. Dietary linoleate preserves cardiolipin and attenuates mitochondrial dysfunction in the failing rat heart. Cardiovasc. Res. 94, 460–468 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Abdelhamid, A. S. et al. Polyunsaturated fatty acids for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst. Rev. 7, CD012345 (2018).

    PubMed  Google Scholar 

  82. Clifton, P. M. & Keogh, J. B. A systematic review of the effect of dietary saturated and polyunsaturated fat on heart disease. Nutr. Metab. Cardiovasc. Dis. 27, 1060–1080 (2017).

    CAS  PubMed  Google Scholar 

  83. St-Onge, M.-P., Zhang, S., Darnell, B. & Allison, D. B. Baseline serum C-reactive protein is associated with lipid responses to low-fat and high-polyunsaturated fat diets. J. Nutr. 139, 680–683 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Ibarra-González, I. et al. Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics. Acta Diabetol. 55, 1151–1161 (2018).

    PubMed  Google Scholar 

  85. Liu, J.-J. et al. 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. 2, 470–480 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. Kordalewska, M. et al. Multiplatform metabolomics provides insight into the molecular basis of chronic kidney disease. J. Chromatogr. B 1117, 49–57 (2019).

    CAS  Google Scholar 

  87. Reuter, S. E. et al. Impact of haemodialysis on individual endogenous plasma acylcarnitine concentrations in end-stage renal disease. Ann. Clin. Biochem. 42, 387–393 (2005).

    CAS  PubMed  Google Scholar 

  88. Kalim, S. et al. A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients. J. Am. Heart Assoc. Cardiovasc. Cerebrovasc. Dis. 2, e000542 (2013).

    Google Scholar 

  89. Looker, H. C. et al. Biomarkers of rapid chronic kidney disease progression in type 2 diabetes. Kidney Int. 88, 888–896 (2015).

    CAS  PubMed  Google Scholar 

  90. Niewczas, M. A. et al. Uremic solutes and risk of end stage renal disease in type 2 diabetes. Kidney Int. 85, 1214–1224 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. van der Kloet, F. M. et al. Discovery of early-stage biomarkers for diabetic kidney disease using MS-based metabolomics (FinnDiane study). Metabolomics 8, 109–119 (2012).

    CAS  PubMed  Google Scholar 

  92. Goek, O.-N. et al. Serum metabolite concentrations and decreased GFR in the general population. Am. J. Kidney Dis. 60, 197–206 (2012).

    CAS  PubMed  Google Scholar 

  93. Wang, F. et al. Associations of plasma amino acid and acylcarnitine profiles with incident reduced glomerular filtration rate. Clin. J. Am. Soc. Nephrol. 13, 560–568 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Koves, T. R. et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 7, 45–56 (2008).

    CAS  PubMed  Google Scholar 

  95. Brass, E. P. Pharmacokinetic considerations for the therapeutic use of carnitine in hemodialysis patients. Clin. Ther. 17, 176–185 (1995).

    CAS  PubMed  Google Scholar 

  96. Xu, G. et al. Liver and muscle contribute differently to the plasma acylcarnitine pool during fasting and exercise in humans. J. Clin. Endocrinol. Metab. 101, 5044–5052 (2016).

    CAS  PubMed  Google Scholar 

  97. Fouque, D. et al. Relationship between serum carnitine, acylcarnitines, and renal function in patients with chronic renal disease. J. Ren. Nutr. 16, 125–131 (2006).

    PubMed  Google Scholar 

  98. Murphy, W. J. A. et al. Altered carnitine metabolism in dialysis patients with reduced physical function may be due to dysfunctional fatty acid oxidation. Nephrol. Dial. Transplant. 27, 304–310 (2012).

    CAS  PubMed  Google Scholar 

  99. Kamei, Y., Kamei, D., Tsuchiya, K., Mineshima, M. & Nitta, K. Association between 4-year all-cause mortality and carnitine profile in maintenance hemodialysis patients. PLoS One 13, e0201591 (2018).

    PubMed  PubMed Central  Google Scholar 

  100. Schooneman, M. G., Vaz, F. M., Houten, S. M. & Soeters, M. R. Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes 62, 1–8 (2013).

    CAS  PubMed  Google Scholar 

  101. Muoio, D. M. et al. Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility. Cell Metab. 15, 764–777 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Kruger, C. et al. Proximal tubular cell-specific ablation of carnitine acetyltransferase causes tubular disease and secondary glomerulosclerosis. Diabetes 68, 819 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Chen, Y. et al. L-Carnitine supplementation for adults with end-stage kidney disease requiring maintenance hemodialysis: a systematic review and meta-analysis. Am. J. Clin. Nutr. 99, 408–422 (2014).

    CAS  PubMed  Google Scholar 

  104. Huang, H. et al. Influence of L-carnitine supplementation on serum lipid profile in hemodialysis patients: a systematic review and meta-analysis. Kidney Blood Press. Res. 38, 31–41 (2013).

    PubMed  Google Scholar 

  105. Naini, A. E. et al. Effects of oral L-carnitine supplementation on lipid profile, anemia, and quality of life in chronic renal disease patients under hemodialysis: a randomized, double-blinded, placebo-controlled trial. J. Nutr. Metab. 2012, 510483 (2012).

    Google Scholar 

  106. Mikolasevic, I., Žutelija, M., Mavrinac, V. & Orlic, L. Dyslipidemia in patients with chronic kidney disease: etiology and management. Int. J. Nephrol. Renov. Dis. 10, 35–45 (2017).

    CAS  Google Scholar 

  107. Ho, C.-I. et al. Relationship between TG/HDL-C ratio and metabolic syndrome risk factors with chronic kidney disease in healthy adult population. Clin. Nutr. 34, 874–880 (2015).

    CAS  PubMed  Google Scholar 

  108. Zoppini, G. et al. Triglyceride–high-density lipoprotein cholesterol is associated with microvascular complications in type 2 diabetes mellitus. Metabolism 61, 22–29 (2012).

    CAS  PubMed  Google Scholar 

  109. Lamprea-Montealegre, J. A. et al. Apolipoprotein B, triglyceride-rich lipoproteins, and risk of cardiovascular events in persons with CKD. Clin. J. Am. Soc. Nephrol. 15, 47–60 (2020).

    CAS  PubMed  Google Scholar 

  110. Rhee, E. P. et al. Metabolite profiling identifies markers of uremia. J. Am. Soc. Nephrol. 21, 1041–2051 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Chang, T. I. et al. Association of serum triglyceride to HDL cholesterol ratio with all-cause and cardiovascular mortality in incident hemodialysis patients. Clin. J. Am. Soc. Nephrol. 12, 591–602 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Zhan, X. et al. Triglyceride to high-density lipoprotein cholesterol ratio is associated with increased mortality in older patients on peritoneal dialysis. Lipids Health Dis. 18, 199 (2019).

    PubMed  PubMed Central  Google Scholar 

  113. Longo, N., Frigeni, M. & Pasquali, M. Carnitine transport and fatty acid oxidation. Biochim. Biophys. Acta 1863, 2422–2435 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Davis, T. M. E. et al. Effects of fenofibrate on renal function in patients with type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. Diabetologia 54, 280–290 (2011).

    CAS  PubMed  Google Scholar 

  115. Jun, M. et al. Effects of fibrates in kidney disease: a systematic review and meta-analysis. J. Am. Coll. Cardiol. 60, 2061–2071 (2012).

    CAS  PubMed  Google Scholar 

  116. Sas, K. M. et al. Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease. Metabolomics 17, 65 (2021).

    CAS  PubMed  Google Scholar 

  117. Luo, S. et al. Serum metabolomic alterations associated with proteinuria in CKD. Clin. J. Am. Soc. Nephrol. 14, 342–353 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Afshinnia, F. et al. Plasma lipidomic profiling identifies a novel complex lipid signature associated with ischemic stroke in chronic kidney disease. J. Transl. Sci. 6, 419 (2020).

    PubMed  PubMed Central  Google Scholar 

  119. van der Veen, J. N. et al. The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. Biochim. Biophys. Acta 1859, 1558–1572 (2017).

    Google Scholar 

  120. Vance, J. E. & Tasseva, G. Formation and function of phosphatidylserine and phosphatidylethanolamine in mammalian cells. Biochim. Biophys. Acta 1831, 543–554 (2013).

    CAS  PubMed  Google Scholar 

  121. Chan, E. Y. L. & McQuibban, G. A. Phosphatidylserine decarboxylase 1 (Psd1) promotes mitochondrial fusion by regulating the biophysical properties of the mitochondrial membrane and alternative topogenesis of mitochondrial genome maintenance protein 1 (Mgm1). J. Biol. Chem. 287, 40131–40139 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Friedman, J. R. et al. Lipid homeostasis is maintained by dual targeting of the mitochondrial PE biosynthesis enzyme to the ER. Dev. Cell 44, 261–270.e6 (2018).

    CAS  PubMed  Google Scholar 

  123. Tasseva, G. et al. Phosphatidylethanolamine deficiency in mammalian mitochondria impairs oxidative phosphorylation and alters mitochondrial morphology. J. Biol. Chem. 288, 4158–4173 (2013).

    CAS  PubMed  Google Scholar 

  124. Newsom, S. A. et al. Skeletal muscle phosphatidylcholine and phosphatidylethanolamine are related to insulin sensitivity and respond to acute exercise in humans. J. Appl. Physiol. 120, 1355–1363 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Lee, S. et al. Skeletal muscle phosphatidylcholine and phosphatidylethanolamine respond to exercise and influence insulin sensitivity in men. Sci. Rep. 8, 6531 (2018).

    PubMed  PubMed Central  Google Scholar 

  126. van der Veen, J. N., Lingrell, S., da Silva, R. P., Jacobs, R. L. & Vance, D. E. The concentration of phosphatidylethanolamine in mitochondria can modulate ATP production and glucose metabolism in mice. Diabetes 63, 2620–2630 (2014).

    PubMed  Google Scholar 

  127. Watanabe, M. et al. Pemt deficiency ameliorates endoplasmic reticulum stress in diabetic nephropathy. PLoS One 9, e92647 (2014).

    PubMed  PubMed Central  Google Scholar 

  128. Bhat, O. M., Yuan, X., Li, G., Lee, R. & Li, P.-L. Sphingolipids and redox signaling in renal regulation and chronic kidney diseases. Antioxid. Redox Signal. 28, 1008–1026 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Shayman, J. A. Targeting glucosylceramide synthesis in the treatment of rare and common renal disease. Semin. Nephrol. 38, 183–192 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Mesicek, J. et al. Ceramide synthases 2, 5, and 6 confer distinct roles in radiation-induced apoptosis in HeLa cells. Cell. Signal. 22, 1300–1307 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Rudd, A. K. & Devaraj, N. K. Traceless synthesis of ceramides in living cells reveals saturation-dependent apoptotic effects. Proc. Natl Acad. Sci. USA 115, 7485–7490 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Mantovani, A. et al. Association between increased plasma ceramides and chronic kidney disease in patients with and without ischemic heart disease. Diabetes Metab. 47, 101152 (2021).

    CAS  PubMed  Google Scholar 

  133. Klein, R. L. et al. Decreased plasma levels of select very long chain ceramide species are associated with the development of nephropathy in type 1 diabetes. Metabolism 63, 1287–1295 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Sas, K. M. et al. Targeted lipidomic and transcriptomic analysis identifies dysregulated renal ceramide metabolism in a mouse model of diabetic kidney disease. J. Proteom. Bioinform. S14, 002 (2015).

    Google Scholar 

  135. Woo, C.-Y. et al. Inhibition of ceramide accumulation in podocytes by myriocin prevents diabetic nephropathy. Diabetes Metab. J. 44, 581–591 (2020).

    PubMed  Google Scholar 

  136. Dupre, T. V. et al. Inhibiting glucosylceramide synthase exacerbates cisplatin-induced acute kidney injury. J. Lipid Res. 58, 1439–1452 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Turpin-Nolan, S. M. et al. CerS1-derived C18:0 ceramide in skeletal muscle promotes obesity-induced insulin resistance. Cell Rep. 26, 1–10.e7 (2019).

    CAS  PubMed  Google Scholar 

  138. Raichur, S. et al. CerS2 haploinsufficiency inhibits β-oxidation and confers susceptibility to diet-induced steatohepatitis and insulin resistance. Cell Metab. 20, 687–695 (2014).

    CAS  PubMed  Google Scholar 

  139. Mitrofanova, A. et al. SMPDL3b modulates insulin receptor signaling in diabetic kidney disease. Nat. Commun. 10, 2692 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Yoo, T.-H. et al. Sphingomyelinase-like phosphodiesterase 3b expression levels determine podocyte injury phenotypes in glomerular disease. J. Am. Soc. Nephrol. 26, 133–147 (2015).

    CAS  PubMed  Google Scholar 

  141. Jimenez, F., Monte, M. J., El-Mir, M. Y., Pascual, M. J. & Marin, J. J. G. Chronic renal failure-induced changes in serum and urine bile acid profiles. Dig. Dis. Sci. 47, 2398–2406 (2002).

    CAS  PubMed  Google Scholar 

  142. Li, R. et al. Targeted metabolomics study of serum bile acid profile in patients with end-stage renal disease undergoing hemodialysis. PeerJ 7, e7145 (2019).

    PubMed  PubMed Central  Google Scholar 

  143. Jovanovich, A. et al. Deoxycholic acid, a metabolite of circulating bile acids, and coronary artery vascular calcification in CKD. Am. J. Kidney Dis. 71, 27–34 (2018).

    CAS  PubMed  Google Scholar 

  144. Chu, L., Zhang, K., Zhang, Y., Jin, X. & Jiang, H. Mechanism underlying an elevated serum bile acid level in chronic renal failure patients. Int. Urol. Nephrol. 47, 345–351 (2015).

    CAS  PubMed  Google Scholar 

  145. Wang, X. et al. Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. Gut 69, 2131–2142 (2020).

    CAS  PubMed  Google Scholar 

  146. Wang, X. X. et al. G protein-coupled bile acid receptor TGR5 activation inhibits kidney disease in obesity and diabetes. J. Am. Soc. Nephrol. 27, 1362–1378 (2016).

    CAS  PubMed  Google Scholar 

  147. Wang, X. X. et al. FXR/TGR5 dual agonist prevents progression of nephropathy in diabetes and obesity. J. Am. Soc. Nephrol. 29, 118–137 (2018).

    CAS  PubMed  Google Scholar 

  148. Wang, X. X. et al. The farnesoid X receptor modulates renal lipid metabolism and diet-induced renal inflammation, fibrosis, and proteinuria. Am. J. Physiol. Ren. Physiol. 297, F1587–F1596 (2009).

    CAS  Google Scholar 

  149. Jiang, T. et al. Farnesoid X receptor modulates renal lipid metabolism, fibrosis, and diabetic nephropathy. Diabetes 56, 2485–2493 (2007).

    CAS  PubMed  Google Scholar 

  150. Chianelli, D. et al. Nidufexor (LMB763), a novel FXR modulator for the treatment of nonalcoholic steatohepatitis. J. Med. Chem. 63, 3868–3880 (2020).

    CAS  PubMed  Google Scholar 

  151. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03804879 (2021).

  152. Malhi, H. & Camilleri, M. Modulating bile acid pathways and TGR5 receptors for treating liver and GI diseases. Curr. Opin. Pharmacol. 37, 80–86 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Sas, K. M. et al. Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model. J. Lipid Res. 59, 173–183 (2018).

    CAS  PubMed  Google Scholar 

  154. Eum, J. Y. et al. Aging-related lipidomic changes in mouse serum, kidney, and heart by nanoflow ultrahigh-performance liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 1618, 460849 (2020).

    CAS  PubMed  Google Scholar 

  155. Tan, S. M. et al. Complement C5a induces renal injury in diabetic kidney disease by disrupting mitochondrial metabolic agility. Diabetes 69, 83–98 (2020).

    CAS  PubMed  Google Scholar 

  156. Zhang, G. et al. DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules. Metabolomics 16, 11 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Mizuguchi, Y. et al. A novel cell-permeable antioxidant peptide decreases renal tubular apoptosis and damage in unilateral ureteral obstruction. Am. J. Physiol. Ren. Physiol. 295, F1545–F1553 (2008).

    CAS  Google Scholar 

  158. Hou, Y. et al. Mitochondria-targeted peptide SS-31 attenuates renal injury via an antioxidant effect in diabetic nephropathy. Am. J. Physiol. Ren. Physiol. 310, F547–F559 (2015).

    Google Scholar 

  159. Mitrofanova, A. et al. Hydroxypropyl-β-cyclodextrin protects from kidney disease in experimental Alport syndrome and focal segmental glomerulosclerosis. Kidney Int. 94, 1151–1159 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Ryu, J.-H. et al. APOL1 renal risk variants promote cholesterol accumulation in tissues and cultured macrophages from APOL1 transgenic mice. PLoS One 14, e0211559 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Isaac, G. in Metabolic Profiling: Methods and Protocols (ed. Metz, T. O.) 259–275 (Humana Press, 2011).

  162. Qiao, X. et al. A tandem mass spectrometric study of bile acids: interpretation of fragmentation pathways and differentiation of steroid isomers. Steroids 77, 204–211 (2012).

    CAS  PubMed  Google Scholar 

  163. van der Hooft, J. J. J., Ridder, L., Barrett, M. P. & Burgess, K. E. V. Enhanced acylcarnitine annotation in high-resolution mass spectrometry data: fragmentation analysis for the classification and annotation of acylcarnitines. Front. Bioeng. Biotechnol. 3, 204 (2015).

    Google Scholar 

  164. Ren, J. L., Zhang, A. H., Kong, L. & Wang, X. J. Advances in mass spectrometry-based metabolomics for investigation of metabolites. RSC Adv. 8, 22335–22350 (2018).

    CAS  Google Scholar 

  165. Abbas, I. et al. Kidney lipidomics by mass spectrometry imaging: a focus on the glomerulus. Int. J. Mol. Sci. 20, 1623 (2019).

    CAS  PubMed Central  Google Scholar 

  166. Wernisch, S. & Pennathur, S. Application of differential mobility-mass spectrometry for untargeted human plasma metabolomic analysis. Anal. Bioanal. Chem. 411, 6297–6308 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors’ work is supported by the NIH grants 5F30DK121463, T32GM007863, T32GM008322, 5T32DK101357, K08DK106523, R03DK121941, R56DK126647, R24 DK082841, P30DK089503, P30DK081943, P30DK020572, 1R01DK110541-01A1, 5U01CA235487-03 and 5R01GM114029-05, and the JDRF Center for Excellence (5-COE-2019-861-S-B). We apologize to our colleagues whose work could not be cited due to space constraints.

Author information

Authors and Affiliations

  1. Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA

    Judy Baek & Subramaniam Pennathur

  2. Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Chenchen He, Farsad Afshinnia & Subramaniam Pennathur

  3. Department of Statistics, University of Florida, Gainesville, FL, USA

    George Michailidis

Authors
  1. Judy Baek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenchen He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farsad Afshinnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. George Michailidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Subramaniam Pennathur
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.B. wrote the first draft of the article. All authors contributed equally to all other aspects of the article.

Corresponding author

Correspondence to Subramaniam Pennathur.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Nephrology thanks A. Fornoni, who co-reviewed with Y. Drexler; M. Levi; and Y.-Y. Zhao for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

LIPID MAPS database: https://www.lipidmaps.org/

Glossary

Stereospecific number

(sn). Nomenclature that specifies the location of glycerol derivative side chains.

Liquid chromatography–mass spectrometry

(LC–MS). A metabolomics and lipidomics technique that combines liquid chromatography separation with a mass spectrometry detection method.

Features

Signals identified by the metabolomics study (e.g. in peaks in a LC–MS study defined by LC retention time and MS signal intensity).

Isotopologues

Compounds that differ from each other only by a difference in the number of neutrons.

Primary metabolic pathways

Metabolic pathways that generate products crucial to the biological function of the cell.

Secondary metabolic pathways

Metabolic pathways that generate products that are not necessarily crucial to biological function but serve to supplement normal function (e.g. in the context of disease).

Acylcarnitine

A conjugate of acyl-CoA and carnitine that can be exported between cellular membranes.

Principal component analysis

A data dimensionality reduction method in which the number of variables is reduced by selecting the most significant variables.

Cardiolipins

Phospholipids specific to the mitochondria that serve numerous functions including the regulation and structural organization of the electron transport chain.

Pearson correlation coefficients

Measurements of the linear relationship between two continuous variables.

Neutral lipids

Lipids that lack charged groups, such as triacylglycerols.

Non-esterified fatty acids

Also known as free fatty acids. Fatty acids that are not conjugated to glycerol backbone.

Kennedy pathway

An oestrogen receptor-based de novo phosphatidylcholine and phosphatidylethanolamine synthesis pathway.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baek, J., He, C., Afshinnia, F. et al. Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nat Rev Nephrol 18, 38–55 (2022). https://doi.org/10.1038/s41581-021-00488-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41581-021-00488-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research