Advertisement

Diabetologia

, Volume 61, Issue 4, pp 821–830 | Cite as

Association of NEFA composition with insulin sensitivity and beta cell function in the Prospective Metabolism and Islet Cell Evaluation (PROMISE) cohort

  • Luke W. Johnston
  • Stewart B. Harris
  • Ravi Retnakaran
  • Adria Giacca
  • Zhen Liu
  • Richard P. Bazinet
  • Anthony J. Hanley
Article

Abstract

Aims/hypothesis

Our aim was to determine the longitudinal associations of individual NEFA with the pathogenesis of diabetes, specifically with differences in insulin sensitivity and beta cell function over 6 years in a cohort of individuals who are at risk for diabetes.

Methods

In the Prospective Metabolism and Islet Cell Evaluation (PROMISE) longitudinal cohort, 477 participants had serum NEFA measured at the baseline visit and completed an OGTT at three time points over 6 years. Outcome variables were calculated using the OGTT values. At each visit, insulin sensitivity was assessed using the HOMA2 of insulin sensitivity (HOMA2-%S) and the Matsuda index, while beta cell function was assessed using the insulinogenic index over HOMA-IR (IGI/IR) and the insulin secretion-sensitivity index-2 (ISSI-2). Generalised estimating equations were used, adjusting for time, waist, sex, ethnicity, baseline age, alanine aminotransferase (ALT) and physical activity. NEFA were analysed as both concentrations (nmol/ml) and proportions (mol%) of the total fraction.

Results

Participants’ (73% female, 70% with European ancestry) insulin sensitivity and beta cell function declined by 14–21% over 6 years of follow-up. In unadjusted models, several NEFA (e.g. 18:1 n-7, 22:4 n-6) were associated with lower insulin sensitivity, however, nearly all of these associations were attenuated in fully adjusted models. In adjusted models, total NEFA, 16:0, 18:1 n-9 and 18:2 n-6 (as concentrations) were associated with 3.7–8.0% lower IGI/IR and ISSI-2, while only 20:5 n-3 (as mol%) was associated with 7.7% higher HOMA2-%S.

Conclusions/interpretation

Total NEFA concentration was a strong predictor of lower beta cell function over 6 years. Our results suggest that the association with beta cell function is due to the absolute size of the serum NEFA fraction, rather than the specific fatty acid composition.

Keywords

Beta cell function Diabetes pathogenesis Fatty acid composition Insulin sensitivity Longitudinal cohort Non-esterified fatty acids 

Abbreviations

ALT

Alanine aminotransferase

EPA

Eicosapentaenoic acid

FDR

False discovery rate

GEE

Generalised estimating equations

HOMA2-%S

HOMA2 of insulin sensitivity

IFG

Impaired fasting glucose

IGI/IR

Insulinogenic index over HOMA-IR

IGT

Impaired glucose tolerance

ISI

Insulin sensitivity index (Matsuda index)

ISSI-2

Insulin secretion-sensitivity index-2

MAQ

Modified activity questionnaire

MET

Metabolic equivalent of task

PLS

Partial least squares regression

PROMISE

Prospective Metabolism and Islet Cell Evaluation Cohort

PUFA

Polyunsaturated fatty acids

QIC

Quasi-likelihood information criterion

TAG

Triacylglycerol

WC

Waist circumference

Notes

Acknowledgements

The authors thank J Neuman, PV Nostrand, S Kink and A Barnie (all of the Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Canada) and S Porter and M Marin (both of the Centre for Studies in Family Medicine, University of Western Ontario, Canada) for their expert technical assistance and dedication in their work for PROMISE.

Contribution statement

The authors had the following responsibility: LWJ analysed and interpreted the data and drafted the article; RR and SBH contributed to the conception and design and revised the article intellectual content; ZL acquired the data and revised the article intellectual content; AG assisted with interpretation of the data and revised the article intellectual content; AJH and RPB substantially contributed to the study conception and design, assisted with interpretation of the data and revised the article intellectual content. All authors read and approved the final version. LWJ and AJH have primary responsibility for final content and are the guarantors of this work.

Funding

This study was supported by grants from the Canadian Diabetes Association (CDA; Grant number: OG-3-14-4574-AH), the Canadian Institutes for Health Research (Grant number: MOP-130458) and the University of Toronto Banting and Best Diabetes Centre. LWJ was supported by a CDA Doctoral Student Research Award; RR was supported by a Heart and Stroke Foundation of Ontario Mid-Career Investigator Award; SBH holds the CDA Chair in National Diabetes Management and the Ian McWhinney Chair of Family Medicine Studies at the University of Western Ontario; RPB holds a Tier II Canada Research Chair in Brain Lipid Metabolism and AJH holds a Tier II Canada Research Chair in Diabetes Epidemiology.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2017_4534_MOESM1_ESM.pdf (1.4 mb)
ESM (PDF 1478 kb)

References

  1. 1.
    Rachek LI (2014) Free fatty acids and skeletal muscle insulin resistance. Prog Mol Biol Transl Sci 121:267–292CrossRefPubMedGoogle Scholar
  2. 2.
    Cnop M (2008) Fatty acids and glucolipotoxicity in the pathogenesis of Type 2 diabetes. Biochem Soc Trans 36:348–352CrossRefPubMedGoogle Scholar
  3. 3.
    Paolisso G, Tataranni PA, Foley JE et al (1995) A high concentration of fasting plasma non-esterified fatty acids is a risk factor for the development of NIDDM. Diabetologia 38:1213–1217CrossRefPubMedGoogle Scholar
  4. 4.
    Salgin B, Ong KK, Thankamony A et al (2012) Higher fasting plasma free fatty acid levels are associated with lower insulin secretion in children and adults and a higher incidence of type 2 diabetes. J Clin Endocrinol Metab 97:3302–3309CrossRefPubMedGoogle Scholar
  5. 5.
    Kato T, Shimano H, Yamamoto T et al (2008) Palmitate impairs and eicosapentaenoate restores insulin secretion through regulation of SREBP-1c in pancreatic islets. Diabetes 57:2382–2392CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Maris M, Robert S, Waelkens E et al (2013) Role of the saturated nonesterified fatty acid palmitate in beta cell dysfunction. J Proteome Res 12:347–362CrossRefPubMedGoogle Scholar
  7. 7.
    Deguil J, Pineau L, Rowland Snyder EC et al (2011) Modulation of lipid-induced ER stress by fatty acid shape. Traffic 12:349–362CrossRefPubMedGoogle Scholar
  8. 8.
    Szendroedi J, Frossard M, Klein N et al (2012) Lipid-induced insulin resistance is not mediated by impaired transcapillary transport of insulin and glucose in humans. Diabetes 61:3176–3180CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Daniele G, Eldor R, Merovci A et al (2014) Chronic reduction of plasma free fatty acid improves mitochondrial function and whole-body insulin sensitivity in obese and type 2 diabetic individuals. Diabetes 63:2812–2820CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Liang H, Tantiwong P, Sriwijitkamol A et al (2013) Effect of a sustained reduction in plasma free fatty acid concentration on insulin signalling and inflammation in skeletal muscle from human subjects. J Physiol 591:2897–2909CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kehlenbrink S, Koppaka S, Martin M et al (2012) Elevated NEFA levels impair glucose effectiveness by increasing net hepatic glycogenolysis. Diabetologia 55:3021–3028CrossRefPubMedGoogle Scholar
  12. 12.
    Miller MR, Pereira RI, Langefeld CD et al (2012) Levels of free fatty acids (FFA) are associated with insulin resistance but do not explain the relationship between adiposity and insulin resistance in Hispanic Americans: The IRAS Family Study. J Clin Endocrinol Metab 97:3285–3291CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Johns I, Goff L, Bluck LJ et al (2014) Plasma free fatty acids do not provide the link between obesity and insulin resistance or beta-cell dysfunction: Results of the Reading, Imperial, Surrey, Cambridge, Kings (RISCK) study. Diabet Med 31:1310–1315CrossRefPubMedGoogle Scholar
  14. 14.
    Charles MA, Eschwège E, Thibult N et al (1997) The role of non-esterified fatty acids in the deterioration of glucose tolerance in Caucasian subjects: Results of the Paris prospective study. Diabetologia 40:1101–1106CrossRefPubMedGoogle Scholar
  15. 15.
    WHO, IDF (2006) Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: Report of a WHO/IDF consultation. Available from: http://whqlibdoc.who.int/publications/2006/9241594934_eng.pdf
  16. 16.
    Matthan NR, Ip B, Resteghini N et al (2010) Long-term fatty acid stability in human serum cholesteryl ester, triglyceride, and phospholipid fractions. J Lipid Res 51:2826–2832CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509PubMedGoogle Scholar
  18. 18.
    Nishi S, Kendall CWC, Gascoyne A-M et al (2014) Effect of almond consumption on the serum fatty acid profile: a dose-response study. Br J Nutr 112:1137–1146CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Johnston LW, Harris SB, Retnakaran R et al (2016) Longitudinal associations of phospholipid and cholesteryl ester fatty acids with disorders underlying diabetes. J Clin Endocrinol Metab 101:2536–2544CrossRefPubMedGoogle Scholar
  20. 20.
    Levy JC, Matthews DR, Hermans MP (1998) Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 21:2191–2192CrossRefPubMedGoogle Scholar
  21. 21.
    Matsuda M, DeFronzo R (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: Comparison with the euglycemic insulin clamp. Diabetes Care 22:1462–1470CrossRefPubMedGoogle Scholar
  22. 22.
    Abdul-Ghani M, Matsuda M, Balas B, DeFronzo R (2007) Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test. Diabetes Care 30:89–94CrossRefPubMedGoogle Scholar
  23. 23.
    Wareham N, Phillips D, Byrne C, Hales C (1995) The 30 minute insulin incremental response in an oral glucose tolerance test as a measure of insulin secretion. Diabet Med 12:931CrossRefPubMedGoogle Scholar
  24. 24.
    Retnakaran R, Qi Y, Goran M, Hamilton J (2009) Evaluation of proposed oral disposition index measures in relation to the actual disposition index. Diabet Med 26:1198–1203CrossRefPubMedGoogle Scholar
  25. 25.
    Hermans MP, Levy JC, Morris RJ, Turner RC (1999) Comparison of insulin sensitivity tests across a range of glucose tolerance from normal to diabetes. Diabetologia 42:678–687CrossRefPubMedGoogle Scholar
  26. 26.
    Kriska A, Knowler W, LaPorte R et al (1990) Development of questionnaire to examine relationship of physical activity and diabetes in Pima Indians. Diabetes Care 13:401–411CrossRefPubMedGoogle Scholar
  27. 27.
    Zeger SL, Liang KY (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42:121–130CrossRefPubMedGoogle Scholar
  28. 28.
    Greenland S, Pearl J, Robins JM (1999) Causal diagrams for epidemiologic research. Epidemiology 10:37–48CrossRefPubMedGoogle Scholar
  29. 29.
    Textor J, Hardt J, Knüppel S (2011) DAGitty: A graphical tool for analyzing causal diagrams. Epidemiology 22:745CrossRefPubMedGoogle Scholar
  30. 30.
    Shrier I, Platt RW (2008) Reducing bias through directed acyclic graphs. BMC Med Res Methodol 8:70CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Pan W (2001) Akaike’s information criterion in generalized estimating equations. Biometrics 57:120–125CrossRefPubMedGoogle Scholar
  32. 32.
    Barrows BR, Parks EJ (2006) Contributions of different fatty acid sources to very low-density lipoprotein-triacylglycerol in the fasted and fed states. J Clin Endocrinol Metab 91:1446–1452CrossRefPubMedGoogle Scholar
  33. 33.
    R Core Team (2015) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  34. 34.
    Højsgaard S, Halekoh U, Yan J (2006) The R package geepack for generalized estimating equations. J Stat Softw 15(2):1–11Google Scholar
  35. 35.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 57:289–300Google Scholar
  36. 36.
    Vandenbroucke JP, von Elm E, Altman DG et al (2007) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. PLoS Med 4:e297CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Pankow JS, Duncan BB, Schmidt MI et al (2004) Fasting plasma free fatty acids and risk of type 2 diabetes: The atherosclerosis risk in communities study. Diabetes Care 27:77–82CrossRefPubMedGoogle Scholar
  38. 38.
    Rebelos E, Seghieri M, Natali A et al (2015) Influence of endogenous NEFA on beta cell function in humans. Diabetologia 58:2344–2351CrossRefPubMedGoogle Scholar
  39. 39.
    Giacca A, Xiao C, Oprescu AI et al (2011) Lipid-induced pancreatic beta-cell dysfunction: Focus on in vivo studies. Am J Physiol Endocrinol Metab 300:E255–E262CrossRefPubMedGoogle Scholar
  40. 40.
    Xiao C, Giacca A, Lewis GF (2009) The effect of high-dose sodium salicylate on chronically elevated plasma nonesterified fatty acid-induced insulin resistance and beta-cell dysfunction in overweight and obese nondiabetic men. Am J Physiol Endocrinol Metab 297:E1205–E1211CrossRefPubMedGoogle Scholar
  41. 41.
    Morita S, Shimajiri Y, Sakagashira S et al (2012) Effect of exposure to non-esterified fatty acid on progressive deterioration of insulin secretion in patients with type 2 diabetes: a long-term follow-up study. Diabet Med 29:980–985CrossRefPubMedGoogle Scholar
  42. 42.
    Nielsen S, Karpe F (2012) Determinants of VLDL-triglycerides production. Curr Opin Lipidol 23:321–326CrossRefPubMedGoogle Scholar
  43. 43.
    Martins AR, Nachbar RT, Gorjao R et al (2012) Mechanisms underlying skeletal muscle insulin resistance induced by fatty acids: Importance of the mitochondrial function. Lipids Health Dis 11:30CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Ebbert JO, Jensen MD (2013) Fat depots, free fatty acids, and dyslipidemia. Nutrients 5:498–508CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Capurso C, Capurso A (2012) From excess adiposity to insulin resistance: The role of free fatty acids. Vasc Pharmacol 57:91–97CrossRefGoogle Scholar
  46. 46.
    Steffen BT, Steffen LM, Zhou X et al (2015) n-3 Fatty acids attenuate the risk of diabetes associated with elevated serum nonesterified fatty acids: The multi-ethnic study of atherosclerosis. Diabetes Care 38:575–580PubMedPubMedCentralGoogle Scholar
  47. 47.
    Virtanen JK, Mursu J, Voutilainen S et al (2014) Serum omega-3 polyunsaturated fatty acids and risk of incident type 2 diabetes in men: The kuopio ischemic heart disease risk factor study. Diabetes Care 37:189–196CrossRefPubMedGoogle Scholar
  48. 48.
    Sarbolouki S, Javanbakht MH, Derakhshanian H et al (2013) Eicosapentaenoic acid improves insulin sensitivity and blood sugar in overweight type 2 diabetes mellitus patients: A double-blind randomised clinical trial. Singap Med J 54:387–390CrossRefGoogle Scholar
  49. 49.
    Calder PC (2009) Polyunsaturated fatty acids and inflammatory processes: New twists in an old tale. Biochimie 91:791–795CrossRefPubMedGoogle Scholar
  50. 50.
    Magkos F, Fabbrini E, Conte C et al (2012) Relationship between adipose tissue lipolytic activity and skeletal muscle insulin resistance in nondiabetic women. J Clin Endocrinol Metab 97:E1219–E1223CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Almandoz JP, Singh E, Howell LA et al (2013) Spillover of fatty acids during dietary fat storage in type 2 diabetes: relationship to body fat depots and effects of weight loss. Diabetes 62:1897–1903CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Dai L, Gonçalves CMV, Lin Z et al (2015) Exploring metabolic syndrome serum free fatty acid profiles based on GC-SIM-MS combined with random forests and canonical correlation analysis. Talanta 135:108–114CrossRefPubMedGoogle Scholar
  53. 53.
    Liu L, Li Y, Guan C et al (2010) Free fatty acid metabolic profile and biomarkers of isolated post-challenge diabetes and type 2 diabetes mellitus based on GC-MS and multivariate statistical analysis. J Chromatogr B Anal Technol Biomed Life Sci 878:2817–2825CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Luke W. Johnston
    • 1
  • Stewart B. Harris
    • 2
  • Ravi Retnakaran
    • 3
    • 4
  • Adria Giacca
    • 5
  • Zhen Liu
    • 1
  • Richard P. Bazinet
    • 1
  • Anthony J. Hanley
    • 1
    • 3
    • 6
  1. 1.Department of Nutritional Sciences, Faculty of MedicineUniversity of TorontoTorontoCanada
  2. 2.Centre for Studies in Family MedicineUniversity of Western OntarioLondonCanada
  3. 3.Division of EndocrinologyUniversity of TorontoTorontoCanada
  4. 4.Lunenfeld Tanenbaum Research InstituteMount Sinai HospitalTorontoCanada
  5. 5.Department of PhysiologyUniversity of TorontoTorontoCanada
  6. 6.Dalla Lana School of Public HealthUniversity of TorontoTorontoCanada

Personalised recommendations