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Lipidomics in the Study of Hypertension in Metabolic Syndrome

  • Hypertension and Metabolic Syndrome (J Sperati, Section Editor)
  • Published:
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Abstract

Purpose of Review

The entire gamut of changes in the lipid profile that precede, predict, and correlate with hypertension in metabolic syndrome is unknown.

Recent Findings

The power, resolution, and accuracy of lipidomic assay technologies have brought us to the threshold of another information explosion. Understanding of hypertension and its pathophysiology especially within the setting of metabolic syndrome has been greatly improved by recent lipidomic studies. Hypertension in metabolic syndrome differs from other forms of hypertension, and recent studies have highlighted this difference in many interesting ways. Mounting evidence points towards a derangement of the sphingolipid pathway that may trigger the precursor clinical conditions of hypertension as well as hypertension itself. In this review, we summarize the available published literature in this field and propose a unifying hypothesis based on the published evidence.

Summary

Recent studies have created substantial interest and advances in the understanding of hypertension in metabolic syndrome. Studies that directly test these concepts within a lipidomic framework are urgently needed.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Lam DW, LeRoith D. In: De Groot LJ, Beck-Peccoz P, Chrousos G, Dungan K, Grossman A, Hershman JM, et al., editors. Metabolic syndrome. South Dartmouth (MA): Endotext; 2000.

    Google Scholar 

  2. Lin CC, Liu CS, Li CI, Lin WY, Lai MM, Lin T, et al. The relation of metabolic syndrome according to five definitions to cardiovascular risk factors—a population-based study. BMC Public Health. 2009;9:484. doi:10.1186/1471-2458-9-484.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Yanai H, Tomono Y, Ito K, Furutani N, Yoshida H, Tada N. The underlying mechanisms for development of hypertension in the metabolic syndrome. Nutr J. 2008;7:10. doi:10.1186/1475-2891-7-10.

    Article  PubMed  PubMed Central  Google Scholar 

  4. • Meikle PJ, Christopher MJ. Lipidomics is providing new insight into the metabolic syndrome and its sequelae. Curr Opin Lipidol. 2011;22(3):210–5. doi:10.1097/MOL.0b013e3283453dbe. This is an excellent review of lipidomics technology and how it is enriching our understanding of complex diseases

    Article  CAS  PubMed  Google Scholar 

  5. Hoefer IE, Steffens S, Ala-Korpela M, Back M, Badimon L, Bochaton-Piallat ML, et al. Novel methodologies for biomarker discovery in atherosclerosis. Eur Heart J. 2015;36(39):2635–42. doi:10.1093/eurheartj/ehv236.

    Article  PubMed  Google Scholar 

  6. Goldenberg NA, Everett AD, Graham D, Bernard TJ, Nowak-Gottl U. Proteomic and other mass spectrometry based “omics” biomarker discovery and validation in pediatric venous thromboembolism and arterial ischemic stroke: current state, unmet needs, and future directions. Proteomics Clin Appl. 2014;8(11–12):828–36. doi:10.1002/prca.201400062.

    Article  CAS  PubMed  Google Scholar 

  7. Meikle PJ, Wong G, Barlow CK, Kingwell BA. Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease. Pharmacol Ther. 2014;143(1):12–23. doi:10.1016/j.pharmthera.2014.02.001.

    Article  CAS  PubMed  Google Scholar 

  8. Hla T. Genomic insights into mediator lipidomics. Prostaglandins & other lipid mediators. 2005;77(1–4):197–209. doi:10.1016/j.prostaglandins.2005.06.008.

    Article  CAS  Google Scholar 

  9. Overgaard AJ, Weir JM, De Souza DP, Tull D, Haase C, Meikle PJ, et al. Lipidomic and metabolomic characterization of a genetically modified mouse model of the early stages of human type 1 diabetes pathogenesis. Metabolomics : Official journal of the Metabolomic Society. 2016;12(1):13. doi:10.1007/s11306-015-0889-1.

    Article  Google Scholar 

  10. •• Alshehry ZH, Barlow CK, Weir JM, Zhou Y, McConville MJ, Meikle PJ. An efficient single phase method for the extraction of plasma lipids. Metabolites. 2015;5(2):389–403. doi:10.3390/metabo5020389. This is another extensive discussion of the technological details of single phase extraction methods used for lipid separation.

  11. •• Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, et al. Plasma lipid profiling in a large population-based cohort. J Lipid Res. 2013;54(10):2898–908. doi:10.1194/jlr.P035808. This report describes an effective single-method lipid extraction assay that was practically used for high-scale, population-level lipidomic profiling of human plasma lipidome

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Murphy RC, James PF, McAnoy AM, Krank J, Duchoslav E, Barkley RM. Detection of the abundance of diacylglycerol and triacylglycerol molecular species in cells using neutral loss mass spectrometry. Anal Biochem. 2007;366(1):59–70. doi:10.1016/j.ab.2007.03.012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Smyth I, Hacking DF, Hilton AA, Mukhamedova N, Meikle PJ, Ellis S, et al. A mouse model of harlequin ichthyosis delineates a key role for Abca12 in lipid homeostasis. PLoS Genet. 2008;4(9):e1000192. doi:10.1371/journal.pgen.1000192.

    Article  PubMed  PubMed Central  Google Scholar 

  14. •• Zhao YY, Wu SP, Liu S, Zhang Y, Lin RC. Ultra-performance liquid chromatography-mass spectrometry as a sensitive and powerful technology in lipidomic applications. Chem Biol Interact. 2014;220:181–92. doi:10.1016/j.cbi.2014.06.029. This excellent review presents state-of-the-art understainding of the evolving techniques used for lipidomic profiling.

    Article  CAS  PubMed  Google Scholar 

  15. Han X, Yang K, Gross RW. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom Rev. 2012;31(1):134–78. doi:10.1002/mas.20342.

    Article  CAS  PubMed  Google Scholar 

  16. • Graessler J, Schwudke D, Schwarz PE, Herzog R, Shevchenko A, Bornstein SR. Top-down lipidomics reveals ether lipid deficiency in blood plasma of hypertensive patients. PLoS One. 2009;4(7):e6261. doi:10.1371/journal.pone.0006261. This is the first study of lipidomics in hypertension.

    Article  PubMed  PubMed Central  Google Scholar 

  17. •• Hu C, Kong H, Qu F, Li Y, Yu Z, Gao P, et al. Application of plasma lipidomics in studying the response of patients with essential hypertension to antihypertensive drug therapy. Mol BioSyst. 2011;7(12):3271–9. doi:10.1039/c1mb05342f. This is a very interesting study that compares the lipidomic profiles of three groups: untreated hypertensive, treated hypertensive, and normotensive individuals.

    Article  CAS  PubMed  Google Scholar 

  18. •• Kulkarni H, Meikle PJ, Mamtani M, Weir JM, Barlow CK, Jowett JB, et al. Plasma lipidomic profile signature of hypertension in Mexican American families: specific role of diacylglycerols. Hypertension. 2013;62(3):621–6. doi:10.1161/HYPERTENSIONAHA.113.01396. This is the largest, human population lipidomic profile study of hypertension in the context of metabolic syndrome which also has an in-built longitudinal component predicting incident hypertension. The finding of the study has also been discussed in an accompanying Editorial Commentary.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. • Spijkers LJ, van den Akker RF, Janssen BJ, Debets JJ, De Mey JG, Stroes ES, et al. Hypertension is associated with marked alterations in sphingolipid biology: a potential role for ceramide. PLoS One. 2011;6(7):e21817. doi:10.1371/journal.pone.0021817. This is one of the earlier reports of the studies on the role of lipidomics in human hypertension.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Au A, Cheng KK, Wei LK. Metabolomics, lipidomics and pharmacometabolomics of human hypertension. Adv Exp Med Biol. 2016; doi:10.1007/5584_2016_79.

    Google Scholar 

  21. •• Hinterwirth H, Stegemann C, Mayr M. Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease. Circulation Cardiovascular genetics. 2014;7(6):941–54. doi:10.1161/CIRCGENETICS.114.000550. An impressive overview of the role of lipidomics in complex diseases with a focus on cardiovascular diseases

    Article  CAS  PubMed  Google Scholar 

  22. Perona JS, Ruiz-Gutierrez V. Virgin olive oil normalizes the altered triacylglycerol molecular species composition of adipose tissue in spontaneously hypertensive rats. J Agric Food Chem. 2004;52(13):4227–33. doi:10.1021/jf0498923.

    Article  CAS  PubMed  Google Scholar 

  23. Kotronen A, Velagapudi VR, Yetukuri L, Westerbacka J, Bergholm R, Ekroos K, et al. Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations. Diabetologia. 2009;52(4):684–90. doi:10.1007/s00125-009-1282-2.

    Article  CAS  PubMed  Google Scholar 

  24. Pietilainen KH, Sysi-Aho M, Rissanen A, Seppanen-Laakso T, Yki-Jarvinen H, Kaprio J, et al. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects—a monozygotic twin study. PLoS One. 2007;2(2):e218. doi:10.1371/journal.pone.0000218.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Archer A, Stolarczyk E, Doria ML, Helguero L, Domingues R, Howard JK, et al. LXR activation by GW3965 alters fat tissue distribution and adipose tissue inflammation in ob/ob female mice. J Lipid Res. 2013;54(5):1300–11. doi:10.1194/jlr.M033977.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mamtani M, Meikle PJ, Kulkarni H, Weir JM, Barlow CK, Jowett JB, et al. Plasma dihydroceramide species associate with waist circumference in Mexican American families. Obesity (Silver Spring). 2014;22(3):950–6. doi:10.1002/oby.20598.

    Article  CAS  Google Scholar 

  27. Jove M, Moreno-Navarrete JM, Pamplona R, Ricart W, Portero-Otin M, Fernandez-Real JM. Human omental and subcutaneous adipose tissue exhibit specific lipidomic signatures. FASEB journal : official publication of the Federation of American Societies for Experimental Biology. 2014;28(3):1071–81. doi:10.1096/fj.13-234419.

    Article  CAS  Google Scholar 

  28. Scherer M, Montoliu I, Qanadli SD, Collino S, Rezzi S, Kussmann M, et al. Blood plasma lipidomic signature of epicardial fat in healthy obese women. Obesity (Silver Spring). 2015;23(1):130–7. doi:10.1002/oby.20925.

    Article  CAS  Google Scholar 

  29. Miao H, Zhao YH, Vaziri ND, Tang DD, Chen H, Khazaeli M, et al. Lipidomics biomarkers of diet-induced hyperlipidemia and its treatment with Poria cocos. J Agric Food Chem. 2016;64(4):969–79. doi:10.1021/acs.jafc.5b05350.

    Article  CAS  PubMed  Google Scholar 

  30. Oresic M, Seppanen-Laakso T, Sun D, Tang J, Therman S, Viehman R, et al. Phospholipids and insulin resistance in psychosis: a lipidomics study of twin pairs discordant for schizophrenia. Genome medicine. 2012;4(1):1. doi:10.1186/gm300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kien CL, Bunn JY, Poynter ME, Stevens R, Bain J, Ikayeva O, et al. A lipidomics analysis of the relationship between dietary fatty acid composition and insulin sensitivity in young adults. Diabetes. 2013;62(4):1054–63. doi:10.2337/db12-0363.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Goto-Inoue N, Yamada K, Inagaki A, Furuichi Y, Ogino S, Manabe Y, et al. Lipidomics analysis revealed the phospholipid compositional changes in muscle by chronic exercise and high-fat diet. Scientific reports. 2013;3:3267. doi:10.1038/srep03267.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rauschert S, Uhl O, Koletzko B, Kirchberg F, Mori TA, Huang RC, et al. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab. 2016;101(3):871–9. doi:10.1210/jc.2015-3525.

    Article  CAS  PubMed  Google Scholar 

  34. Tonks KT, Coster AC, Christopher MJ, Chaudhuri R, Xu A, Gagnon-Bartsch J, et al. Skeletal muscle and plasma lipidomic signatures of insulin resistance and overweight/obesity in humans. Obesity (Silver Spring). 2016;24(4):908–16. doi:10.1002/oby.21448.

    Article  CAS  Google Scholar 

  35. Kopprasch S, Dheban S, Schuhmann K, Xu A, Schulte KM, Simeonovic CJ, et al. Detection of independent associations of plasma lipidomic parameters with insulin sensitivity indices using data mining methodology. PLoS One. 2016;11(10):e0164173. doi:10.1371/journal.pone.0164173.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Mamtani M, Kulkarni H, Wong G, Weir JM, Barlow CK, Dyer TD, et al. Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts. Lipids Health Dis. 2016;15:67. doi:10.1186/s12944-016-0234-3.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Jove M, Naudi A, Portero-Otin M, Cabre R, Rovira-Llopis S, Banuls C, et al. Plasma lipidomics discloses metabolic syndrome with a specific HDL phenotype. FASEB journal : official publication of the Federation of American Societies for Experimental Biology. 2014;28(12):5163–71. doi:10.1096/fj.14-253187.

    Article  CAS  Google Scholar 

  38. Bellis C, Kulkarni H, Mamtani M, Kent Jr JW, Wong G, Weir JM, et al. Human plasma lipidome is pleiotropically associated with cardiovascular risk factors and death. Circulation Cardiovascular genetics. 2014;7(6):854–63. doi:10.1161/CIRCGENETICS.114.000600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hoene M, Li J, Haring HU, Weigert C, Xu G, Lehmann R. The lipid profile of brown adipose tissue is sex-specific in mice. Biochim Biophys Acta. 2014;1842(10):1563–70. doi:10.1016/j.bbalip.2014.08.003.

    Article  PubMed  Google Scholar 

  40. Huang JP, Cheng ML, Wang CH, Shiao MS, Chen JK, Hung LM. High-fructose and high-fat feeding correspondingly lead to the development of lysoPC-associated apoptotic cardiomyopathy and adrenergic signaling-related cardiac hypertrophy. Int J Cardiol. 2016;215:65–76. doi:10.1016/j.ijcard.2016.03.239.

    Article  PubMed  Google Scholar 

  41. •• Gross RW, Han X. Lipidomics in diabetes and the metabolic syndrome. Methods Enzymol. 2007;433:73–90. doi:10.1016/S0076-6879(07)33004-8. This review is directly relevant from the point of view of metabolic syndrome

    Article  CAS  PubMed  Google Scholar 

  42. Kotronen A, Seppanen-Laakso T, Westerbacka J, Kiviluoto T, Arola J, Ruskeepaa AL, et al. Comparison of lipid and fatty acid composition of the liver, subcutaneous and intra-abdominal adipose tissue, and serum. Obesity (Silver Spring). 2010;18(5):937–44. doi:10.1038/oby.2009.326.

    Article  CAS  Google Scholar 

  43. Frangioudakis G, Diakanastasis B, Liao BQ, Saville JT, Hoffman NJ, Mitchell TW, et al. Ceramide accumulation in L6 skeletal muscle cells due to increased activity of ceramide synthase isoforms has opposing effects on insulin action to those caused by palmitate treatment. Diabetologia. 2013;56(12):2697–701. doi:10.1007/s00125-013-3035-5.

    Article  CAS  PubMed  Google Scholar 

  44. Frangioudakis G, Garrard J, Raddatz K, Nadler JL, Mitchell TW, Schmitz-Peiffer C. Saturated- and n-6 polyunsaturated-fat diets each induce ceramide accumulation in mouse skeletal muscle: reversal and improvement of glucose tolerance by lipid metabolism inhibitors. Endocrinology. 2010;151(9):4187–96. doi:10.1210/en.2010-0250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Heilbronn LK, Coster AC, Campbell LV, Greenfield JR, Lange K, Christopher MJ, et al. The effect of short-term overfeeding on serum lipids in healthy humans. Obesity (Silver Spring). 2013;21(12):E649–59. doi:10.1002/oby.20508.

    Article  CAS  Google Scholar 

  46. Nestel PJ, Straznicky N, Mellett NA, Wong G, De Souza DP, Tull DL, et al. Specific plasma lipid classes and phospholipid fatty acids indicative of dairy food consumption associate with insulin sensitivity. Am J Clin Nutr. 2014;99(1):46–53. doi:10.3945/ajcn.113.071712.

    Article  CAS  PubMed  Google Scholar 

  47. Coen PM, Goodpaster BH. Role of intramyocelluar lipids in human health. Trends in endocrinology and metabolism: TEM. 2012;23(8):391–8. doi:10.1016/j.tem.2012.05.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kolovou G, Kolovou V, Mavrogeni S. Lipidomics in vascular health: current perspectives. Vasc Health Risk Manag. 2015;11:333–42. doi:10.2147/VHRM.S54874.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Laaksonen R, Janis MT, Oresic M. Lipidomics-based safety biomarkers for lipid-lowering treatments. Angiology. 2008;59(2 Suppl):65S–8S. doi:10.1177/0003319708321106.

    Article  PubMed  Google Scholar 

  50. Chen F, Maridakis V, O'Neill EA, Hubbard BK, Strack A, Beals C, et al. The effects of simvastatin treatment on plasma lipid-related biomarkers in men with dyslipidaemia. Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals. 2011;16(4):321–33. doi:10.3109/1354750X.2011.561367.

    Article  CAS  Google Scholar 

  51. Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, Nguyen UT, et al. Lipidomic analysis of variation in response to simvastatin in the cholesterol and pharmacogenetics study. Metabolomics : Official journal of the Metabolomic Society. 2010;6(2):191–201. doi:10.1007/s11306-010-0207-x.

    Article  CAS  Google Scholar 

  52. Yetukuri L, Huopaniemi I, Koivuniemi A, Maranghi M, Hiukka A, Nygren H, et al. High density lipoprotein structural changes and drug response in lipidomic profiles following the long-term fenofibrate therapy in the FIELD substudy. PLoS One. 2011;6(8):e23589. doi:10.1371/journal.pone.0023589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. • Kang SC, Kim BR, Lee SY, Park TS. Sphingolipid metabolism and obesity-induced inflammation. Front Endocrinol. 2013;4:67. doi:10.3389/fendo.2013.00067. Informative overview of sphingolipid pathway.

    Article  Google Scholar 

  54. • Huang C, Freter C. Lipid metabolism, apoptosis and cancer therapy. Int J Mol Sci. 2015;16(1):924–49. doi:10.3390/ijms16010924. Another very informative piece on sphingolipid pathway

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Merrill Jr AH, Schmelz EM, Dillehay DL, Spiegel S, Shayman JA, Schroeder JJ, et al. Sphingolipids—the enigmatic lipid class: biochemistry, physiology, and pathophysiology. Toxicol Appl Pharmacol. 1997;142(1):208–25. doi:10.1006/taap.1996.8029.

    Article  CAS  PubMed  Google Scholar 

  56. Bikman BT, Guan Y, Shui G, Siddique MM, Holland WL, Kim JY, et al. Fenretinide prevents lipid-induced insulin resistance by blocking ceramide biosynthesis. J Biol Chem. 2012;287(21):17426–37. doi:10.1074/jbc.M112.359950.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Deng X, Lin F, Zhang Y, Li Y, Zhou L, Lou B, et al. Identification of small molecule sphingomyelin synthase inhibitors. Eur J Med Chem. 2014;73:1–7. doi:10.1016/j.ejmech.2013.12.002.

    Article  CAS  PubMed  Google Scholar 

  58. Li Z, Zhang H, Liu J, Liang CP, Li Y, Teitelman G, et al. Reducing plasma membrane sphingomyelin increases insulin sensitivity. Mol Cell Biol. 2011;31(20):4205–18. doi:10.1128/MCB.05893-11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Pandolfi R, Barreira B, Moreno E, Lara-Acedo V, Morales-Cano D, Martinez-Ramas A, et al. Role of acid sphingomyelinase and IL-6 as mediators of endotoxin-induced pulmonary vascular dysfunction. Thorax. 2016; doi:10.1136/thoraxjnl-2015-208067.

    PubMed  Google Scholar 

  60. Moreno L, Moral-Sanz J, Morales-Cano D, Barreira B, Moreno E, Ferrarini A, et al. Ceramide mediates acute oxygen sensing in vascular tissues. Antioxid Redox Signal. 2014;20(1):1–14. doi:10.1089/ars.2012.4752.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Aslan M, Ozcan F, Tuzcu H, Kirac E, Elpek GO. Inhibition of neutral sphingomyelinase decreases arachidonic acid mediated inflammation in liver ischemia-reperfusion injury. International journal of clinical and experimental pathology. 2014;7(11):7814–23.

    PubMed  PubMed Central  Google Scholar 

  62. Babenko NA, Kharchenko VS. Effects of inhibitors of key enzymes of sphingolipid metabolism on insulin-induced glucose uptake and glycogen synthesis in liver cells of old rats. Biochemistry Biokhimiia. 2015;80(1):104–12. doi:10.1134/S0006297915010125.

    Article  CAS  PubMed  Google Scholar 

  63. Ludwig EH, Mahley RW, Palaoglu E, Ozbayrakci S, Balestra ME, Borecki IB, et al. DGAT1 promoter polymorphism associated with alterations in body mass index, high density lipoprotein levels and blood pressure in Turkish women. Clin Genet. 2002;62(1):68–73.

    Article  PubMed  Google Scholar 

  64. Tsuda N, Kumadaki S, Higashi C, Ozawa M, Shinozaki M, Kato Y, et al. Intestine-targeted DGAT1 inhibition improves obesity and insulin resistance without skin aberrations in mice. PLoS One. 2014;9(11):e112027. doi:10.1371/journal.pone.0112027.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Gooley JJ. Circadian regulation of lipid metabolism. Proc Nutr Soc. 2016:1–11. doi:10.1017/S0029665116000288.

  66. Gooley JJ, Chua EC. Diurnal regulation of lipid metabolism and applications of circadian lipidomics. Journal of genetics and genomics =. Yi chuan xue bao. 2014;41(5):231–50. doi:10.1016/j.jgg.2014.04.001.

    Article  CAS  PubMed  Google Scholar 

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  1. South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA

    Hemant Kulkarni, Manju Mamtani, John Blangero & Joanne E. Curran

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  1. Hemant Kulkarni
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  2. Manju Mamtani
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Correspondence to Hemant Kulkarni.

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Drs. Kulkarni, Mamtani, Blangero, and Curran declare no conflicts of interest relevant to this manuscript.

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This article is part of the Topical Collection on Hypertension and Metabolic Syndrome

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Kulkarni, H., Mamtani, M., Blangero, J. et al. Lipidomics in the Study of Hypertension in Metabolic Syndrome. Curr Hypertens Rep 19, 7 (2017). https://doi.org/10.1007/s11906-017-0705-6

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