A comprehensive metabolic profiling of the metabolically healthy obesity phenotype

Vibeke H Telle-Hansen, Jacob J Christensen, Gulla Aase Formo, Kirsten B Holven, Stine M Ulven, Vibeke H Telle-Hansen, Jacob J Christensen, Gulla Aase Formo, Kirsten B Holven, Stine M Ulven

Abstract

Background: The ever-increasing prevalence of obesity constitutes a major health problem worldwide. A subgroup of obese individuals has been described as "metabolically healthy obese" (MHO). In contrast to metabolically unhealthy obese (MUO), the MHO phenotype has a favorable risk profile. Despite this, the MHO phenotype is still sub-optimally characterized with respect to a comprehensive risk assessment. Our aim was to increase the understanding of metabolic alterations associated with healthy and unhealthy obesity.

Methods: In this cross-sectional study, men and women (18-70 years) with obesity (body mass index (BMI) ≥ 30 kg/m2) or normal weight (NW) (BMI ≤ 25 kg/m2) were classified with MHO (n = 9), MUO (n = 10) or NW (n = 11) according to weight, lipid profile and glycemic regulation. We characterized individuals by comprehensive metabolic profiling using a commercial available high-throughput proton NMR metabolomics platform. Plasma fatty acid profile, including short chain fatty acids, was measured using gas chromatography.

Results: The concentrations of very low density lipoprotein (VLDL), intermediate density lipoprotein (IDL) and low density lipoprotein (LDL) subclasses were overall significantly higher, and high density lipoprotein (HDL) subclasses lower in MUO compared with MHO. VLDL and IDL subclasses were significantly lower and HDL subclasses were higher in NW compared with MHO. The concentration of isoleucine, leucine and valine was significantly higher in MUO compared with MHO, and the concentration phenylalanine was lower in NW subjects compared with MHO. The fatty acid profile in MHO was overall more favorable compared with MUO.

Conclusions: Comprehensive metabolic profiling supports that MHO subjects have intermediate-stage cardiovascular disease risk marker profile compared with NW and MUO subjects.

Clinical trial registration number: NCT01034436, Fatty acid quality and overweight (FO-study).

Keywords: Diet; Fatty acids; Glycemic regulation; Lipoprotein; Metabolic profiling; Metabolically healthy obesity; Metabolically unhealthy obesity; Obese; SCFA.

Conflict of interest statement

Mills DA partially funded the study and VHTH has been employed at Mills DA. She does not owns any stocks in the company, and the work performed in this paper was done after she left the company. KBH has received research grant from TINE BA, Olympic Seafood, Amgen, Sanofi, Kaneka and Pronova. SMU has received research grant from TINE BA and Olympic Seafood. None of these grants or honoraria are related to the content of this manuscript.

Figures

Fig. 1
Fig. 1
Atherogenic lipoprotein particles were increased in MUO, and reduced in NW, compared with MHO. The forest plot displays the β regression coefficients (mean difference) and 95% confidence interval for MUO vs MHO subjects (circles) and NW vs MHO subjects (squares). Estimates on the right and left side of the zero-line translates to higher and lower than MHO subjects, respectively. Color denotes nominal significance level. Abbreviations: ApoA-I, Apolipoprotein A-I; ApoB-ApoA-I ratio, Ratio of apolipoprotein B to apolipoprotein A-I; ApoB, Apolipoprotein B; Est-C, Esterified cholesterol; Free-C, Free cholesterol; HDL-C, Total cholesterol in HDL; HDL-TG, Triglycerides in HDL; HDL, High-density lipoprotein; HDL2-C, Total cholesterol in HDL2; HDL3-C, Total cholesterol in HDL3; IDL, Intermediate-density lipoprotein; L, Large; LDL-C, Total cholesterol in LDL; LDL-TG, Triglycerides in LDL; LDL, Low-density lipoprotein; M, Medium; MHO, Metabolically healthy obese subjects; MUO, Metabolically unhealthy obese subjects; NW, Normal weight subjects; PC-cholines, Phosphatidylcholine and other cholines; Remnant-C, Remnant cholesterol (non-HDL, non-LDL -cholesterol); S, Small; SphingoM, Sphingomyelins; T-cholines, Total cholines; T-PG, Total phosphoglycerides; TC, Serum total cholesterol; TG-PG ratio, Ratio of triglycerides to phosphoglycerides; TG, Serum total triglycerides; VLDL-C, Total cholesterol in VLDL; VLDL-TG, Triglycerides in VLDL; VLDL, Very low-density lipoprotein; XL, Extra-large; XS, Extra-small; XXL, Extremely large
Fig. 2
Fig. 2
Branched-chain amino acids were generally higher in MUO vs MHO, whereas inflammation markers are lower in NW vs MHO. The forest plot displays the β regression coefficients (mean difference) and 95% confidence interval for MUO vs MHO subjects (circles) and NW vs MHO subjects (squares). Estimates on the right and left side of the zero-line translates to higher and lower than MHO subjects, respectively. Color denotes nominal significance level. Abbreviations: CRP, C-reactive protein; Gp-acetyls, Glycoprotein acetyls, mainly a1-acid glycoprotein; MHO, Metabolically healthy obese subjects; MUO, Metabolically unhealthy obese subjects; NW, Normal weight subjects
Fig. 3
Fig. 3
MUFAs were higher, whereas other fatty acids were lower in MUO subjects. The forest plot displays the β regression coefficients (mean difference) and 95% confidence interval for MUO vs MHO subjects (circles). Estimates on the right and left side of the zero-line translates to higher and lower than MHO subjects, respectively. Color denotes nominal significance level. Abbreviations: 16:1/16:0 ratio, Ratio of palmitoleic acid to palmitic acid; 18:1/18:0 ratio, Ratio of oleic acid to stearic acid; Acetate, Acetate; Butyrate, Butyrate; C14:0, Myristic acid; C15:0, Pentadecylic acid; C16:0, Palmitic acid; C16:1, Palmitoleic acid; C18:0, Stearic acid; C18:1,c11, NA; C18:1,c9, Oleic acid; C18:1,t6–11, Vaccenic acid; C18:2,n-6, Linoleic acid (LA); C18:3,n-6, Gamma-Linolenic acid (GLA); C20:0, Arachidic acid; C20:2,n-6, Dihomolinoleic acid; C20:3,n-6, Dihomo-γ-linolenic acid; C20:4,n-6, Arachidonic acid (AA); C20:5,n-3, Eicosapentaenoic acid (EPA); C22:0, Behenic acid; C22:4,n-6, Adrenic acid (AdA); C22:5,n-3, Docosapentaenoic acid (DPA); C22:5,n-6, Docosapentaenoic acid (Osbond acid); C22:6,n-3, Docosahexaenoic acid (DHA); C23:0, Tricosylic acid; C24:0, Lignoceric acid; C24:1,n-9, Nervonic acid; d5desat, Delta 5-desaturase; d6desat, Delta 6-desaturase; MHO, Metabolically healthy obese subjects; MUFA, Monounsaturated fatty acids; MUO, Metabolically unhealthy obese subjects; n6/n3 ratio, NA; Propionate, Propionate; PUFA, Polyunsaturated fatty acids; SFA, Saturated fatty acids
Fig. 4
Fig. 4
Biological markers differentially regulated in MUO, MHO and NW subjects associate with a number of clinical variables, especially body composition- and lipid-related. The heatmap displays Spearman’s rho (ρ) correlation coefficient for clinical variables (x axis) vs significant variables (y axis), as seen in Figs. 1, 2 and 3. Correlations were calculated using all three groups combined. Abbreviations: 16:1/16:0 ratio, Ratio of palmitoleic acid to palmitic acid; 18:1/18:0 ratio, Ratio of oleic acid to stearic acid; ALAT, Alanine aminotransferase; ALP, Alkaline phosphatase; ApoA-I, Apolipoprotein A-I; ApoB-ApoA-I ratio, Ratio of apolipoprotein B to apolipoprotein A-I; ApoB, Apolipoprotein B; ASAT, Aspartate aminotransferase; BMI, Body mass index; C16:0, Palmitic acid; C16:1, Palmitoleic acid; C18:1,c11, NA; C18:1,c9, Oleic acid; C18:2,n-6, Linoleic acid (LA); C20:0, Arachidic acid; C22:0, Behenic acid; C22:5,n-3, Docosapentaenoic acid (DPA); C23:0, Tricosylic acid; CRP, C-reactive protein; gGT, gamma-Glutamyltransferase; Gp-acetyls, Glycoprotein acetyls, mainly a1-acid glycoprotein; HbA1c, Glycated hemoglobin A1c; HDL-C, HDL cholesterol; HDL-C, Total cholesterol in HDL; HDL-TG, Triglycerides in HDL; HDL, high-density lipoprotein; HDL2-C, Total cholesterol in HDL2; Hip-c, Hip circumference; IDL, Intermediate-density lipoprotein; Isoleucine, Isoleucine; L, Large; LDL-C, LDL cholesterol; LDL-TG, Triglycerides in LDL; LDL, low-density lipoprotein; Leucine, Leucine; M, Medium; MHO, Metabolically healthy obese subjects; MUFA, Monounsaturated fatty acids; MUO, Metabolically unhealthy obese subjects; Phenylalanine, Phenylalanine; Propionate, Propionate; PUFA, Polyunsaturated fatty acids; NW, normal weight subjects; Remnant-C, Remnant cholesterol (non-HDL, non-LDL -cholesterol); S, Small; TC, Total cholesterol; TG-PG ratio, Ratio of triglycerides to phosphoglycerides; TG, Serum total triglycerides; TG, Triglycerides; Valine, Valine; VLDL-C, Total cholesterol in VLDL; VLDL-TG, Triglycerides in VLDL; VLDL, Very low-density lipoprotein; Waist-c, Waist circumference; WH ratio, Waist-hip ratio; XL, Extra-large; XS, Extra-small; XXL, Extremely large
Fig. 5
Fig. 5
Biomarkers generally go in opposite direction in MUO and NW subject, compared with MHO subjects. The correlation plot displays the bivariate distribution between β regression coefficients (mean difference) and 95% confidence interval for all variables under study, for MUO vs MHO subjects (x axis) and NW vs MHO subjects (y axis). In this figure, significance level cut-off is set to P < 0.01. Non-significant variables are grey; those significantly different for MUO vs MHO are green; those significantly different for NW vs MHO are orange; those significantly different in both comparisons are in purple. The correlation coefficient and regression line indicates that biomarkers generally go in opposite direction in MUO and NW subject, compared with MHO subjects, with some exceptions in the upper right and lower left quadrant. Abbreviations: MUO, Metabolically unhealthy obese subjects; MHO, Metabolically healthy obese subjects; NW, Normal weight subjects; r, Spearman’s correlation coefficient; R2, Explained variance; SE, Standard error

References

    1. Bluher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15(5):288–298. doi: 10.1038/s41574-019-0176-8.
    1. Collaboration NCDRF Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(10026):1377–1396. doi: 10.1016/S0140-6736(16)30054-X.
    1. Karelis AD, Brochu M, Rabasa-Lhoret R. Can we identify metabolically healthy but obese individuals (MHO)? Diabetes Metab. 2004;30(6):569–572. doi: 10.1016/S1262-3636(07)70156-8.
    1. Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: a systematic review and meta-analysis. Ann Intern Med. 2013;159(11):758–769. doi: 10.7326/0003-4819-159-11-201312030-00008.
    1. Caleyachetty R, Thomas GN, Toulis KA, Mohammed N, Gokhale KM, Balachandran K, et al. Metabolically healthy obese and incident cardiovascular disease events among 3.5 million men and women. J Am Coll Cardiol. 2017;70(12):1429–1437. doi: 10.1016/j.jacc.2017.07.763.
    1. Chen HH, Tseng YJ, Wang SY, Tsai YS, Chang CS, Kuo TC, et al. The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity. Int J Obes. 2015;39(8):1241–1248. doi: 10.1038/ijo.2015.65.
    1. Ho JE, Larson MG, Ghorbani A, Cheng S, Chen MH, Keyes M, et al. Metabolomic profiles of body mass index in the Framingham heart study reveal distinct Cardiometabolic phenotypes. PLoS One. 2016;11(2):e0148361. doi: 10.1371/journal.pone.0148361.
    1. Park S, Sadanala KC, Kim EK. A Metabolomic approach to understanding the metabolic link between obesity and diabetes. Mol Cells. 2015;38(7):587–596. doi: 10.14348/molcells.2015.0126.
    1. Telle-Hansen VH, Narverud I, Retterstol K, Wesseltoft-Rao N, Mosdol A, Granlund L, et al. Substitution of TAG oil with diacylglycerol oil in food items improves the predicted 10 years cardiovascular risk score in healthy, overweight subjects. J Nutr Sci. 2012;1:e17. doi: 10.1017/jns.2012.18.
    1. Telle-Hansen VH, Halvorsen B, Dalen KT, Narverud I, Wesseltoft-Rao N, Granlund L, et al. Altered expression of genes involved in lipid metabolism in obese subjects with unfavourable phenotype. Genes Nutr. 2013;8(4):425–434. doi: 10.1007/s12263-012-0329-z.
    1. Soininen P, Kangas AJ, Wurtz P, Suna T, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015;8(1):192–206. doi: 10.1161/CIRCGENETICS.114.000216.
    1. Lillegaard IT, Andersen LF. Validation of a pre-coded food diary with energy expenditure, comparison of under-reporters v. acceptable reporters. Br J Nutr. 2005;94(6):998–1003. doi: 10.1079/BJN20051587.
    1. Jump DB. Fatty acid regulation of gene transcription. Crit Rev Clin Lab Sci. 2004;41(1):41–78. doi: 10.1080/10408360490278341.
    1. Gonzalez-Becerra K, Ramos-Lopez O, Barron-Cabrera E, Riezu-Boj JI, Milagro FI, Martinez-Lopez E, et al. Fatty acids, epigenetic mechanisms and chronic diseases: a systematic review. Lipids Health Dis. 2019;18(1):178. doi: 10.1186/s12944-019-1120-6.
    1. Warensjo E, Riserus U, Vessby B. Fatty acid composition of serum lipids predicts the development of the metabolic syndrome in men. Diabetologia. 2005;48(10):1999–2005. doi: 10.1007/s00125-005-1897-x.
    1. Warensjo E, Riserus U, Gustafsson IB, Mohsen R, Cederholm T, Vessby B. Effects of saturated and unsaturated fatty acids on estimated desaturase activities during a controlled dietary intervention. Nutr Metab Cardiovasc Dis. 2008;18(10):683–690. doi: 10.1016/j.numecd.2007.11.002.
    1. Telle-Hansen VH, Larsen LN, Hostmark AT, Molin M, Dahl L, Almendingen K, et al. Daily intake of cod or salmon for 2 weeks decreases the 18:1n-9/18:0 ratio and serum triacylglycerols in healthy subjects. Lipids. 2012;47(2):151–160. doi: 10.1007/s11745-011-3637-y.
    1. Wurtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation. 2015;131(9):774–785. doi: 10.1161/CIRCULATIONAHA.114.013116.
    1. Cohn JS, Johnson EJ, Millar JS, Cohn SD, Milne RW, Marcel YL, et al. Contribution of apoB-48 and apoB-100 triglyceride-rich lipoproteins (TRL) to postprandial increases in the plasma concentration of TRL triglycerides and retinyl esters. J Lipid Res. 1993;34(12):2033–2040.
    1. Nakajima K, Tokita Y, Tanaka A. Hypothesis: postprandial remnant lipoproteins are the causal factors that induce the insulin resistance associated with obesity. Clin Chim Acta. 2018;485:126–132. doi: 10.1016/j.cca.2018.06.029.
    1. Masuda D, Yamashita S. Postprandial hyperlipidemia and remnant lipoproteins. J Atheroscler Thromb. 2017;24(2):95–109. doi: 10.5551/jat.RV16003.
    1. Robinson JG, Williams KJ, Gidding S, Boren J, Tabas I, Fisher EA, et al. Eradicating the burden of atherosclerotic cardiovascular disease by lowering Apolipoprotein B lipoproteins earlier in life. J Am Heart Assoc. 2018;7(20):e009778. doi: 10.1161/JAHA.118.009778.
    1. Candi E, Tesauro M, Cardillo C, Lena AM, Schinzari F, Rodia G, et al. Metabolic profiling of visceral adipose tissue from obese subjects with or without metabolic syndrome. Biochem J. 2018;475(5):1019–1035. doi: 10.1042/BCJ20170604.
    1. Hanamatsu H, Ohnishi S, Sakai S, Yuyama K, Mitsutake S, Takeda H, et al. Altered levels of serum sphingomyelin and ceramide containing distinct acyl chains in young obese adults. Nutr Diabetes. 2014;4:e141. doi: 10.1038/nutd.2014.38.
    1. Arany Z, Neinast M. Branched chain amino acids in metabolic disease. Curr Diab Rep. 2018;18(10):76. doi: 10.1007/s11892-018-1048-7.
    1. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–326. doi: 10.1016/j.cmet.2009.02.002.
    1. Kim M, Yoo HJ, KO J, Lee HJ. Metabolically unhealthy overweight individuals have high lysophosphatide levels, phospholipase activity, and oxidative stress. Clin Nutr. 2020;39(4):1137–45.
    1. Palmer ND, Stevens RD, Antinozzi PA, Anderson A, Bergman RN, Wagenknecht LE, et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the insulin resistance atherosclerosis study. J Clin Endocrinol Metab. 2015;100(3):E463–E468. doi: 10.1210/jc.2014-2357.
    1. Krebs M, Krssak M, Bernroider E, Anderwald C, Brehm A, Meyerspeer M, et al. Mechanism of amino acid-induced skeletal muscle insulin resistance in humans. Diabetes. 2002;51(3):599–605. doi: 10.2337/diabetes.51.3.599.
    1. Everman S, Mandarino LJ, Carroll CC, Katsanos CS. Effects of acute exposure to increased plasma branched-chain amino acid concentrations on insulin-mediated plasma glucose turnover in healthy young subjects. PLoS One. 2015;10(3):e0120049. doi: 10.1371/journal.pone.0120049.
    1. Liu J, Semiz S, van der Lee SJ, van der Spek A, Verhoeven A, van Klinken JB, et al. Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study. Metabolomics. 2017;13(9):104. doi: 10.1007/s11306-017-1239-2.
    1. Shah SH, Kraus WE, Newgard CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation. 2012;126(9):1110–1120. doi: 10.1161/CIRCULATIONAHA.111.060368.
    1. Yang RY, Wang SM, Sun L, Liu JM, Li HX, Sui XF, et al. Association of branched-chain amino acids with coronary artery disease: a matched-pair case-control study. Nutr Metab Cardiovasc Dis. 2015;25(10):937–942. doi: 10.1016/j.numecd.2015.06.003.
    1. Ntzouvani A, Nomikos T, Panagiotakos D, Fragopoulou E, Pitsavos C, McCann A, et al. Amino acid profile and metabolic syndrome in a male Mediterranean population: a cross-sectional study. Nutr Metab Cardiovasc Dis. 2017;27(11):1021–1030. doi: 10.1016/j.numecd.2017.07.006.
    1. Mangge H, Zelzer S, Pruller F, Schnedl WJ, Weghuber D, Enko D, et al. Branched-chain amino acids are associated with cardiometabolic risk profiles found already in lean, overweight and obese young. J Nutr Biochem. 2016;32:123–127. doi: 10.1016/j.jnutbio.2016.02.007.
    1. Ruiz-Canela M, Toledo E, Clish CB, Hruby A, Liang L, Salas-Salvado J, et al. Plasma branched-chain amino acids and incident cardiovascular disease in the PREDIMED trial. Clin Chem. 2016;62(4):582–592. doi: 10.1373/clinchem.2015.251710.
    1. Delles C, Rankin NJ, Boachie C, McConnachie A, Ford I, Kangas A, et al. Nuclear magnetic resonance-based metabolomics identifies phenylalanine as a novel predictor of incident heart failure hospitalisation: results from PROSPER and FINRISK 1997. Eur J Heart Fail. 2018;20(4):663–673. doi: 10.1002/ejhf.1076.
    1. Swierczynski J, Sledzinski T, Slominska E, Smolenski R, Sledzinski Z. Serum phenylalanine concentration as a marker of liver function in obese patients before and after bariatric surgery. Obes Surg. 2009;19(7):883–889. doi: 10.1007/s11695-008-9521-z.
    1. Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017;127(1):1–4. doi: 10.1172/JCI92035.
    1. Connelly MA, Otvos JD, Shalaurova I, Playford MP, Mehta NN. GlycA, a novel biomarker of systemic inflammation and cardiovascular disease risk. J Transl Med. 2017;15(1):219. doi: 10.1186/s12967-017-1321-6.
    1. Lawler PR, Akinkuolie AO, Chandler PD, Moorthy MV, Vandenburgh MJ, Schaumberg DA, et al. Circulating N-linked glycoprotein acetyls and longitudinal mortality risk. Circ Res. 2016;118(7):1106–1115. doi: 10.1161/CIRCRESAHA.115.308078.
    1. Warensjo E, Ohrvall M, Vessby B. Fatty acid composition and estimated desaturase activities are associated with obesity and lifestyle variables in men and women. Nutr Metab Cardiovasc Dis. 2006;16(2):128–136. doi: 10.1016/j.numecd.2005.06.001.
    1. Yu EA, Hu PJ, Mehta S. Plasma fatty acids in de novo lipogenesis pathway are associated with diabetogenic indicators among adults: NHANES 2003-2004. Am J Clin Nutr. 2018;108(3):622–632. doi: 10.1093/ajcn/nqy165.
    1. Mayneris-Perxachs J, Guerendiain M, Castellote AI, Estruch R, Covas MI, Fito M, et al. Plasma fatty acid composition, estimated desaturase activities, and their relation with the metabolic syndrome in a population at high risk of cardiovascular disease. Clin Nutr. 2014;33(1):90–97. doi: 10.1016/j.clnu.2013.03.001.
    1. AL AM, Syed DN, Ntambi JM. Insights into Stearoyl-CoA Desaturase-1 regulation of systemic metabolism. Trends Endocrinol Metab. 2017;28(12):831–842. doi: 10.1016/j.tem.2017.10.003.
    1. Zhao L, Ni Y, Ma X, Zhao A, Bao Y, Liu J, et al. A panel of free fatty acid ratios to predict the development of metabolic abnormalities in healthy obese individuals. Sci Rep. 2016;6:28418. doi: 10.1038/srep28418.
    1. Kamal S, Saleem A, Rehman S, Bibi I, Iqbal HMN. Protein engineering: regulatory perspectives of stearoyl CoA desaturase. Int J Biol Macromol. 2018;114:692–699. doi: 10.1016/j.ijbiomac.2018.03.171.

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