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Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health

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초록

Over the past 70 years, the study of lipid metabolism has led to important discoveries in identifying the underlying mechanisms of chronic diseases. Advances in the use of stable isotopes and mass spectrometry in humans have expanded our knowledge of target molecules that contribute to pathologies and lipid metabolic pathways. These advances have been leveraged within two research paths, leading to the ability (1) to quantitate lipid flux to understand the fundamentals of human physiology and pathology and (2) to perform untargeted analyses of human blood and tissues derived from a single timepoint to identify lipidomic patterns that predict disease. This review describes the physiological and analytical parameters that influence these measurements and how these issues will propel the coming together of the two fields of metabolic tracing and lipidomics. The potential of data science to advance these fields is also discussed. Future developments are needed to increase the precision of lipid measurements in human samples, leading to discoveries in how individuals vary in their production, storage, and use of lipids. New techniques are critical to support clinical strategies to prevent disease and to identify mechanisms by which treatments confer health benefits with the overall goal of reducing the burden of human disease.

Abstract

Over the past 70 years, the study of lipid metabolism has led to important discoveries in identifying the underlying mechanisms of chronic diseases. Advances in the use of stable isotopes and mass spectrometry in humans have expanded our knowledge of target molecules that contribute to pathologies and lipid metabolic pathways. These advances have been leveraged within two research paths, leading to the ability (1) to quantitate lipid flux to understand the fundamentals of human physiology and pathology and (2) to perform untargeted analyses of human blood and tissues derived from a single timepoint to identify lipidomic patterns that predict disease. This review describes the physiological and analytical parameters that influence these measurements and how these issues will propel the coming together of the two fields of metabolic tracing and lipidomics. The potential of data science to advance these fields is also discussed. Future developments are needed to increase the precision of lipid measurements in human samples, leading to discoveries in how individuals vary in their production, storage, and use of lipids. New techniques are critical to support clinical strategies to prevent disease and to identify mechanisms by which treatments confer health benefits with the overall goal of reducing the burden of human disease.

참고문헌 (69)

  1. DeFronzo, R. A., "Fasting hyperglycemia in noninsulin-dependent diabetes mellitus : contributions of excessive hepatic glucose production and impaired tissue glucose uptake", Metabolism, 38, pp.387~395 (1989)
  2. Lambert, J. E., "Increased de novo lipogenesis is a distinct characteristic of individuals with nonalcoholic fatty liver disease", Gastroenterology, 146, pp.726~735 (2014)
  3. Ginsberg, H. N., "Triglyceride-rich lipoproteins and their remnants: metabolic insights, role in atherosclerotic cardiovascular disease, and emerging therapeutic strategies-a consensus statement from the European Atherosclerosis Society", Eur. Heart J, 42, pp.4791~4806 (2021)
  4. Wolfe, R. R., "Radioactive and stable isotope tracers in biomedicine" (1992), Wiley and Sons
  5. Berman, M., "Lipoprotein kinetics and modeling" (1982), Academic Press
  6. Berman, M., "Metabolism of apoB and apoC lipoproteins in man : kinetic studies in normal and hyperlipoproteinemic subjects", J. Lipid Res, 19, pp.38~56 (1978)
  7. Beltz, W. F., "Development of an integrated model for analysis of the kinetics of apolipoprotein B in plasma very low density lipoproteins, intermediate density lipoproteins, and low density lipoproteins", J. Clin. Invest, 76, pp.575~585 (1985)
  8. Barrett, P. H., "Thematic review series: patient-oriented research. Design and analysis of lipoprotein tracer kinetics studies in humans", J. Lipid Res, 47, pp.1607~1619 (2006)
  9. Ying, Q., "Unravelling lipoprotein metabolism with stable isotopes: tracing the flow", Metabolism, 124, pp.154887~ (2021)
  10. Beylot, M., "Use of stable isotopes to evaluate the functional effects of nutrients", Curr. Opin. Clin. Nutr. Metab. Care, 9, pp.734~739 (2006)
  11. Bier, D. M., "Stable isotopes in biosciences, their measurement and models for amino acid metabolism", Eur. J. Pediatr, 156, pp.S2~S8 (1997)
  12. Demant, T., "Studies of apolipoprotein B-100 metabolism using radiotracers and stable isotopes", Eur. J. Pediatr, 156, pp.S75~S77 (1997)
  13. Packard, C. J., "The role of stable isotopes in the investigation of plasma lipoprotein metabolism", Baillieres Clin. Endocrinol. Metab, 9, pp.755~772 (1995)
  14. Patterson, B. W., "Use of stable isotopically labeled tracers to measure very low density lipoproteintriglyceride turnover", J. Lipid Res, 43, pp.223~233 (2002)
  15. DeLany, J. P., "Differential oxidation of individual dietary fatty acids in humans", Am. J. Clin. Nutr, 72, pp.905~911 (2000)
  16. Raman, A., "Validation of deuterium-labeled fatty acids for the measurement of dietary fat oxidation during physical activity", J. Lipid Res, 45, pp.2339~2344 (2004)
  17. Hibi, M., "Fat utilization in healthy subjects consuming diacylglycerol oil diet : dietary and whole body fat oxidation", Lipids, 43, pp.517~524 (2008)
  18. Jones, P. J., "Whole body oxidation of dietary fatty acids : implications for energy utilization", Am. J. Clin. Nutr, 42, pp.769~777 (1985)
  19. Hodson, L., "Differences in partitioning of meal fatty acids into blood lipid fractions : a comparison of linoleate, oleate, and palmitate", Am. J. Physiol. Endocrinol. Metab, 296, pp.E64~E71 (2009)
  20. Heiling, V. J., "How valid are isotopic measurements of fatty acid oxidation?", Am. J. Physiol, 261, pp.E572~E577 (1991)
  21. Timlin, M. T., "Increased dietary substrate delivery alters hepatic fatty acid recycling in healthy men", Diabetes, 54, pp.2694~2701 (2005)
  22. Jacome-Sosa, M. M., "Fatty acid sources and their fluxes as they contribute to plasma triglyceride concentrations and fatty liver in humans", Curr. Opin. Lipidol, 25, pp.213~220 (2014)
  23. Mucinski, J. M., "High throughput LC–MS method to investigate postprandial lipemia : considerations for future precision nutrition research", Am. J. Physiol. Endocrinol. Metab, 320, pp.E702~E715 (2021)
  24. Knuth, N. D., "The elevation of ingested lipids within plasma chylomicrons is prolonged in men compared with women", J. Nutr, 136, pp.1498~1503 (2006)
  25. docosahexaenoic and other fatty acids across the human placenta 12 h after maternal oral intake, "Am. J. Clin. Nutr", 2010, 115~122
  26. Jackson, K. G., "Second meal effect : modified sham feeding does not provoke the release of stored triacylglycerol from a previous high-fat meal", Br. J. Nutr, 85, pp.149~156 (2001)
  27. Jacome-Sosa, M., "Human intestinal lipid storage through sequential meals reveals faster dinner appearance is associated with hyperlipidemia", JCI Insight, 6, pp.e148378~ (2021)
  28. Nelson, R. H., "Splanchnic spillover of extracellular lipase-generated fatty acids in overweight and obese humans", Diabetes, 56, pp.2878~2884 (2007)
  29. Barrows, B. R., "Spillover of dietary fatty acids and use of serum nonesterified fatty acids for the synthesis of VLDL-triacylglycerol under two different feeding regimens", Diabetes, 54, pp.2668~2673 (2005)
  30. Parks, E. J., "Meal-feeding studies in mice : effects of different diets on blood lipids and energy expenditure", Comp. Med, 55, pp.24~29 (2005)
  31. Barrows, B. R., "Contributions of different fatty acid sources to very low-density lipoprotein-triacylglycerol in the fasted and fed states", J. Clin. Endocrinol. Metab, 91, pp.1446~1452 (2006)
  32. Parks, E. J., "Thematic review series: patient-oriented research. Recent advances in liver triacylglycerol and fatty acid metabolism using stable isotope labeling techniques", J. Lipid Res, 47, pp.1651~1660 (2006)
  33. Baar, R. A., "Investigation of in vivo fatty acid metabolism in AFABP", aP2-, Am. J. Physiol. Endocrinol. Metab, pp.2004, 288[DOI]
  34. Donnelly, K. L., "Increased lipogenesis and fatty acid reesterification contribute to hepatic triacylglycerol stores in hyperlipidemic Txnip-", -mice, 2004, pp.134, 1475~1480
  35. Bastarrachea, R. A., "Protocol for the measurement of fatty acid and glycerol turnover in vivo in baboons", J. Lipid Res, 52, pp.1272~1280 (2011)
  36. Erkin-Cakmak, A., "Isocaloric fructose testriction reduces serum d-lactate concentration in children with obesity and metabolic syndrome", J. Clin. Endocrinol. Metab, 104, pp.3003~3011 (2019)
  37. Turner, S. M., "Measurement of TG synthesis and turnover in vivo by 2H2O incorporation into the glycerol moiety and application of MIDA", Am. J. Physiol. Endocrinol. Metab, 285, pp.E790~E803 (2003)
  38. Schoenheimer, R., "Deuterium as an indicator in the study of intermediary metabolism", Science, 82, pp.156~157 (1935)
  39. Castro-Perez, J., "In vivo D2O labeling to quantify static and dynamic changes in cholesterol and cholesterol esters by high resolution LC", MS, 2011, pp.52, 159~169
  40. Chen, Y., "Quantifying ceramide kinetics in vivo using stable isotope tracers and LC–MS", MS, 2018, pp.315, E416~e424
  41. White, U., "Adipose depotspecific effects of 16 weeks of pioglitazone on in vivo adipogenesis in women with obesity : a randomised controlled trial", Diabetologia, 64, pp.159~167 (2021)
  42. Zhou, H., "Quantifying apoprotein synthesis in rodents : coupling LC-MS", MS analyses with the administration of labeled water, 2012, pp.53, 1223~1231
  43. Puchalska, P., "Isotope tracing untargeted metabolomics reveals macrophage polarization-state-specific metabolic coordination across intracellular compartments", iScience, 9, pp.298~313 (2018)
  44. Downes, D. P., "Isotope fractionation during gas chromatography can enhance mass spectrometry-based measures of(2)H-labeling of small molecules", Metabolites, 10, pp.474~ (2020)
  45. Trötzmüller, M., "Determination of the isotopic enrichment of(13)C-and(2)Hlabeled tracers of glucose using high-resolution mass spectrometry : application to dual-and triple-tracer studies", Anal. Chem, 89, pp.12252~12260 (2017)
  46. Schuhmann, K., "Monitoring membrane lipidome turnover by metabolic(15)N labeling and shotgun ultra-high-resolution orbitrap fourier transform mass spectrometry", Anal. Chem, 89, pp.12857~12865 (2017)
  47. Triebl, A., "Analytical considerations of stable isotope labelling in lipidomics", Biomolecules, 8, pp.151~ (2018)
  48. Wang, M., "Selection of internal standards for accurate quantification of complex lipid species in biological extracts by electrospray ionization mass spectrometry-What, how and why?", Mass. Spectrom. Rev, 36, pp.693~714 (2017)
  49. Rampler, E., "LILY-lipidome isotope labeling of yeast : in vivo synthesis of(13)C labeled reference lipids for quantification by mass spectrometry", Analyst, 142, pp.1891~1899 (2017)
  50. Han, X., "The foundations and development of lipidomics", J. Lipid Res, 63, pp.100164~ (2022)
  51. Satapati, S., "Using measures of metabolic flux to align screening and clinical development : Avoiding pitfalls to enable translational studies", SLAS Disco, 27, pp.20~28 (2022)
  52. Tzafetas, M., "The intelligent knife(iKnife)and its intraoperative diagnostic advantage for the treatment of cervical disease", Proc. Natl Acad. Sci. USA, 117, pp.7338~7346 (2020)
  53. Balog, J., "Intraoperative tissue identification using rapid evaporative ionization mass spectrometry", Sci. Transl. Med, 5, pp.194ra193~ (2013)
  54. Sans, M., "Performance of the MasSpec Pen for rapid diagnosis of ovarian cancer", Clin. Chem, 65, pp.674~683 (2019)
  55. Zhou, X., "Recent advances in on-site mass spectrometry analysis for clinical applications", Trends Anal. Chem, 149, pp.116548~ (2022)
  56. Gorski, L. A., "Infusion therapy standards of practice, 8th Edition", J. Infus. Nurs, 44, pp.S1~S224 (2021)
  57. Melnikov, A. D., "Deep learning for the precise peak detection in high-resolution LC–MS data", Anal. Chem, 92, pp.588~592 (2020)
  58. Castañé, H., "Coupling machine learning and lipidomics as a tool to investigate metabolic dysfunction-associated fatty liver fisease. A general overview", Biomolecules, 11, pp.473~ (2021)
  59. Fujii, T. M. M., "FADS1 and ELOVL2 polymorphisms reveal associations for differences in lipid metabolism in a cross-sectional population-based survey of Brazilian men and women", Nutr. Res, 78, pp.42~49 (2020)
  60. Linke, V., "A large-scale genome-lipid association map guides lipid identification", Nat. Metab, 2, pp.1149~1162 (2020)
  61. Thangapandi, V. R., "Loss of hepatic Mboat7 leads to liver fibrosis", Gut, 70, pp.940~950 (2021)
  62. Bzdok, D., "Statistics versus machine learning", Nat. Methods, 15, pp.233~234 (2018)
  63. Berry, S. E., "Human postprandial responses to food and potential for precision nutrition", Nat. Med, 26, pp.964~973 (2020)
  64. "National Institute of Health Common Fund’s Nutrition for Precision Health" (2022)[URL]
  65. Liu, D., "Exploratory data mining for subgroup cohort discoveries and prioritization", IEEE J. Biomed. Health Inform, 24, pp.1456~1468 (2020)
  66. Al-Taie, Z., "Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential", J. Biomed. Inform, 118, pp.103792~ (2021)
  67. Faubert, B., "Stable isotope tracing to assess tumor metabolism in vivo", Nat. Protoc, 16, pp.5123~5145 (2021)
  68. Beysen, C., "Whole-body glycolysis measured by the deuterated-glucose disposal test correlates highly with insulin resistance in vivo", Diabetes Care, 30, pp.1143~1149 (2007)
  69. Diniz Behn, C., "Advances in stable isotope tracer methodology part 1 : Hepatic metabolism via isotopomer analysis and postprandial lipolysis modeling", J. Investig. Med, 68, pp.3~10 (2020)

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