Modulation of the lipidomic profile due to a lipid challenge and fitness level: a postprandial study

Lipids in Health and Disease

Lorraine Brennan²  Eileen R. Gibney¹  Helen M. Roche²  Michael J. Gibney¹  Miriam F. Ryan¹  Colm M. O’Grada²  Ciara Morris² 

[1] UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland;UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland

关键词: Fitness level;    Oral lipid tolerance test;    Lipidomics;   

Others  :  1217217
DOI  :  10.1186/s12944-015-0062-x

 received in 2015-01-13, accepted in 2015-06-17,  发布年份 2015

【 摘 要 】

Background

The lipid composition of plasma is known to vary due to both phenotypic factors such as age, gender and BMI as well as with various diseases including cancer and neurological disorders. However, there is little investigation into the variation in the lipidome due to exercise and/ or metabolic challenges. The objectives of this present study were (i) To identify the glycerophospholipid, sphingolipids and ceramide changes in response to an oral lipid tolerance test (OLTT) in healthy adults and (ii) To identify the effect of aerobic fitness level on lipidomic profiles.

Methods

214 healthy adults aged 18–60 years were recruited as part of a metabolic challenge study. A sub-group of 40 volunteers were selected for lipidomic analysis based on their aerobic fitness level. Ceramides, glycerophospholipids and sphingomyelins were quantified in baseline fasting plasma samples as well as at 60, 120, 180, 240 and 300 min following a lipid challenge using high-throughput flow injection ESI-MS/MS.

Results

Mixed model repeated measures analysis identified lipids which were significantly changing over the time course of the lipid challenge. Included in these lipids were lysophosphoethanolamines (LPE), phosphoethanolamines (PE), phosphoglycerides (PG) and ceramides (Cer). Five lipids (LPE a C18:2, LPE a C18:1, PE aa C36:2, PE aa C36:3 and N-C16:1-Cer) had a fold change > 1.5 at 120 min following the challenge and these lipids remained elevated. Furthermore, three of these lipids (LPE a C18:2, PE aa C36:2 and PE aa C36:3) were predictive of fasting and peak plasma TAG concentrations following the OLTT. Further analysis revealed that fitness level has a significant impact on the response to the OLTT: in particular significant differences between fitness groups were observed for phosphatidylcholines (PC), sphingomyelins (SM) and ceramides.

Conclusion

This study identified specific lipids which were modulated by an acute lipid challenge. Furthermore, it identified a series of lipids which were modulated by fitness level. Future lipidomic studies should take into account environmental factors such as diet and fitness level during biomarker discovery work.

Trial registration

Data, clinicaltrials.gov, NCT01172951

【 授权许可】

   
2015 Morris et al.

【 图 表 】

Fig. 1.

【 参考文献 】

• [1]Wenk MR. The emerging field of lipidomics. Nat Rev Drug Discov. 2005; 4:594-610.
• [2]Weiss JN, Yang L, Qu ZL. Thematic review series: systems biology approaches to metabolic and cardiovascular disorders - Network perspectives of cardiovascular metabolism. J Lipid Res. 2006; 47:2355-66.
• [3]Han X, Gross RW. Shotgun lipidomics: multidimensional MS analysis of cellular lipidomes. Expert Rev Proteomics. 2005; 2:253-64.
• [4]Watson AD. Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems. J Lipid Res. 2006; 47:2101-11.
• [5]Postle AD. Lipidomics. Curr Opin Clin Nutr Metab Care. 2012; 15:127-33.
• [6]Postle AD. Phospholipid lipidomics in health and disease. Eur J Lipid Sci Technol. 2009; 111:2-13.
• [7]Coen PM, Goodpaster BH. Role of intramyocelluar lipids in human health. Trends Endocrinol Metab. 2012; 23:391-8.
• [8]Sorensen CM, Ding J, Zhang Q, Alquier T, Zhao R, Mueller PW, Smith RD, Metz TO. Perturbations in the lipid profile of individuals with newly diagnosed type 1 diabetes mellitus: lipidomics analysis of a Diabetes Antibody Standardization Program sample subset. Clin Biochem. 2010; 43:948-56.
• [9]Georgiopoulou VV, Denker A, Bishop KL, Brown JM, Hirsh B, Wolfenden L, Sperling L. Metabolic abnormalities in adults with cystic fibrosis. Respirology. 2006; 15:823-9.
• [10]Lankinen M, Schwab U, Erkkila A, Seppanen-Laakso T, Hannila ML, Mussalo H, Lehto S, Uusitupa M, Gylling H, Oresic M. Fatty fish intake decreases lipids related to inflammation and insulin signaling--a lipidomics approach. PLoS One. 2009; 4: Article ID e5258
• [11]Schwab U, Seppanen-Laakso T, Yetukuri L, Agren J, Kolehmainen M, Laaksonen DE, Ruskeepaa AL, Gylling H, Uusitupa M, Oresic M. Triacylglycerol fatty acid composition in diet-induced weight loss in subjects with abnormal glucose metabolism--the GENOBIN study. PLoS One. 2008; 3: Article ID e2630
• [12]Hyotylainen T, Bondia-Pons I, Oresic M. Lipidomics in nutrition and food research. Mol Nutr Food Res. 2013; 57:1306-18.
• [13]Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O’Donnell CJ et al.. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011; 121:1402-11.
• [14]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; 143:12-23.
• [15]O’Gorman A, Morris C, Ryan M, O’Grada CM, Roche HM, Gibney ER, Gibney MJ, Brennan L. Habitual dietary intake impacts on the lipidomic profile. J Chromatogr B Analyt Technol Biomed Life Sci. 2014; 966:140-6.
• [16]Yore MM, Syed I, Moraes-Vieira PM, Zhang T, Herman MA, Homan EA, Patel RT, Lee J, Chen S, Peroni OD et al.. Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell. 2014; 159:318-32.
• [17]Zhao X, Peter A, Fritsche J, Elcnerova M, Fritsche A, Haring HU, Schleicher ED, Xu G, Lehmann R. Changes of the plasma metabolome during an oral glucose tolerance test: is there more than glucose to look at? Am J Physiol Endocrinol Metab. 2009; 296:E384-93.
• [18]Krug S, Kastenmuller G, Stuckler F, Rist MJ, Skurk T, Sailer M, Raffler J, Romisch-Margl W, Adamski J, Prehn C et al.. The dynamic range of the human metabolome revealed by challenges. Faseb J. 2012; 26:2607-19.
• [19]Bentley-Lewis R, Xiong G, Lee H, Yang A, Huynh J, Kim C. Metabolomic analysis reveals amino acid responses to an oral glucose tolerance test in women with prior history of gestational diabetes mellitus. J Clin Transl Endocrinol. 2014; 1:38-43.
• [20]Moazzami AA, Shrestha A, Morrison DA, Poutanen K, Mykkanen H. Metabolomics reveals differences in postprandial responses to breads and fasting metabolic characteristics associated with postprandial insulin demand in postmenopausal women. J Nutr. 2014; 144:807-14.
• [21]Wopereis S, Wolvers D, van Erk M, Gribnau M, Kremer B, van Dorsten FA, Boelsma E, Garczarek U, Cnubben N, Frenken L et al.. Assessment of inflammatory resilience in healthy subjects using dietary lipid and glucose challenges. BMC Med Genomics. 2013; 6:44. BioMed Central Full Text
• [22]van Ommen B, van der Greef J, Ordovas JM, Daniel H. Phenotypic flexibility as key factor in the human nutrition and health relationship. Genes Nutr. 2014; 9:423.
• [23]Pagels P, Raustorp A, Archer T, Lidman U, Alricsson M. Influence of moderate, daily physical activity on body composition and blood lipid profile in Swedish adults. J Phys Act Health. 2012; 9:867-74.
• [24]Guzel NA, Pinar L, Colakoglu F, Karacan S, Ozer C. Long-term callisthenic exercise-related changes in blood lipids, homocysteine, nitric oxide levels and body composition in middle-aged healthy sedentary women. Chin J Physiol. 2012; 55:202-9.
• [25]Hunter GR, Chandler-Laney PC, Brock DW, Lara-Castro C, Fernandez JR, Gower BA. Fat distribution, aerobic fitness, blood lipids, and insulin sensitivity in African-American and European-American women. Obesity (Silver Spring). 2010; 18:274-81.
• [26]Kostka T, Lacour JR, Berthouze SE, Bonnefoy M. Relationship of physical activity and fitness to lipid and lipoprotein (a) in elderly subjects. Med Sci Sports Exerc. 1999; 31:1183-9.
• [27]Austin A, Warty V, Janosky J, Arslanian S. The relationship of physical fitness to lipid and lipoprotein(a) levels in adolescents with IDDM. Diabetes Care. 1993; 16:421-5.
• [28]Pellis L, van Erk MJ, van Ommen B, Bakker GC, Hendriks HF, Cnubben NH, Kleemann R, van Someren EP, Bobeldijk I, Rubingh CM, Wopereis S. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics. 2012; 8:347-59.
• [29]Steenbergen R, Nanowski TS, Beigneux A, Kulinski A, Young SG, Vance JE. Disruption of the phosphatidylserine decarboxylase gene in mice causes embryonic lethality and mitochondrial defects. J Biol Chem. 2005; 280:40032-40.
• [30]Hailey DW, Rambold AS, Satpute-Krishnan P, Mitra K, Sougrat R, Kim PK, Lippincott-Schwartz J. Mitochondria supply membranes for autophagosome biogenesis during starvation. Cell. 2010; 141:656-67.
• [31]Vance JE, Tasseva G. Formation and function of phosphatidylserine and phosphatidylethanolamine in mammalian cells. Biochim Biophys Acta. 1831; 2013:543-54.
• [32]Boullart AC, de Graaf J, Stalenhoef AF. Serum triglycerides and risk of cardiovascular disease. Biochim Biophys Acta. 1821; 2012:867-75.
• [33]Ginsberg HN. Is hypertriglyceridemia a risk factor for atherosclerotic cardiovascular disease? A simple question with a complicated answer. Ann Intern Med. 1997; 126:912-4.
• [34]Hyson D, Rutledge JC, Berglund L. Postprandial lipemia and cardiovascular disease. Curr Atheroscler Rep. 2003; 5:437-44.
• [35]Rojas-Garcia R, Scott KM, Roche JC, Scotton W, Martin N, Janssen A, Goldstein LH, Leigh PN, Ellis CM, Shaw CE, Al-Chalabi A. No evidence for a large difference in ALS frequency in populations of African and European origin: a population based study in inner city London. Amyotroph Lateral Scler. 2012; 13:66-8.
• [36]Zhao X, Fritsche J, Wang J, Chen J, Rittig K, Schmitt-Kopplin P, Fritsche A, Häring H-U, Schleicher E, Xu G, Lehmann R. Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics. 2010; 6:362-74.
• [37]Lehmann R, Franken H, Dammeier S, Rosenbaum L, Kantartzis K, Peter A, Zell A, Adam P, Li J, Xu G et al.. Circulating lysophosphatidylcholines are markers of a metabolically benign nonalcoholic fatty liver. Diabetes Care. 2013; 36(8):2331-8.
• [38]Barber MN, Risis S, Yang C, Meikle PJ, Staples M, Febbraio MA, Bruce CR. Plasma lysophosphatidylcholine levels are reduced in obesity and type 2 diabetes. PLoS One. 2012; 7: Article ID e41456
• [39]Rancoule C, Attane C, Gres S, Fournel A, Dusaulcy R, Bertrand C, Vinel C, Treguer K, Prentki M, Valet P, Saulnier-Blache JS. Lysophosphatidic acid impairs glucose homeostasis and inhibits insulin secretion in high-fat diet obese mice. Diabetologia. 2013; 56:1394-402.
• [40]Bye A, Vettukattil R, Aspenes ST, Giskeodegard GF, Gribbestad IS, Wisloff U, et al. Serum levels of choline-containing compounds are associated with aerobic fitness level: the HUNT-study. Plos One. 2012;7.
• [41]Gill JMR, Hardman AE. Exercise and postprandial lipid metabolism: an update on potential mechanisms and interactions with high-carbohydrate diets (Review). J Nutr Biochem. 2003; 14:122-32.
• [42]Gill JMR. Exercise and postprandial lipid metabolism - an analysis of the current evidence. Eur J Lipid Sci Technol. 2004; 106:110-21.
• [43]Harrison M, O’Gorman DJ, McCaffrey N, Hamilton MT, Zderic TW, Carson BP, Moyna NM. Influence of acute exercise with and without carbohydrate replacement on postprandial lipid metabolism. J Appl Physiol. 2009; 106:943-9.
• [44]Katsanos CS. Prescribing aerobic exercise for the regulation of postprandial lipid metabolism - Current research and recommendations. Sports Med. 2006; 36:547-60.
• [45]Hardman AE, Herd SL. Exercise and postprandial lipid metabolism. Proc Nutr Soc. 1998; 57:63-72.
• [46]Kien CL, Bunn JY, Poynter ME, Stevens R, Bain J, Ikayeva O, Fukagawa NK, Champagne CM, Crain KI, Koves TR, Muoio DM. A lipidomics analysis of the relationship between dietary fatty acid composition and insulin sensitivity in young adults. Diabetes. 2013; 62:1054-63.
• [47]Amati F, Dube JJ, Alvarez-Carnero E, Edreira MM, Chomentowski P, Coen PM, Switzer GE, Bickel PE, Stefanovic-Racic M, Toledo FG, Goodpaster BH. Skeletal muscle triglycerides, diacylglycerols, and ceramides in insulin resistance: another paradox in endurance-trained athletes? Diabetes. 2011; 60:2588-97.
• [48]Erion DM, Shulman GI. Diacylglycerol-mediated insulin resistance. Nat Med. 2010; 16:400-2.
• [49]Ryan MF, Grada CO, Morris C, Segurado R, Walsh MC, Gibney ER, Brennan L, Roche HM, Gibney MJ. Within-person variation in the postprandial lipemic response of healthy adults. Am J Clin Nutr. 2013; 97:261-7.
• [50]Morris C, Grada CO, Ryan M, Roche HM, De Vito G, Gibney MJ, Gibney ER, Brennan L. The relationship between aerobic fitness level and metabolic profiles in healthy adults. Mol Nutr Food Res. 2013; 57:1246-54.
• [51]Biaggi RR, Vollman MW, Nies MA, Brener CE, Flakoll PJ, Levenhagen DK, Sun M, Karabulut Z, Chen KY. Comparison of air-displacement plethysmography with hydrostatic weighing and bioelectrical impedance analysis for the assessment of body composition in healthy adults. Am J Clin Nutr. 1999; 69:898-903.
• [52]Jones NL, Makrides L, Hitchcock C, Chypchar T, McCartney N. Normal standards for an incremental progressive cycle ergometer test. Am Rev Respir Dis. 1985; 131:700-8.
• [53]Wallace M, Morris C, O’Grada CM, Ryan M, Dillon ET, Coleman E, Gibney ER, Gibney MJ, Roche HM, Brennan L. Relationship between the lipidome, inflammatory markers and insulin resistance. Mol Biosyst. 2014; 10:1586-95.
• [54]Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, van Ommen B, Smilde AK. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem. 2006; 78:567-74.