Obesity-Related Metabolomic Profiles and Discrimination of

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Obesity-related metabolomic profiles and discrimination of metabolically unhealthy obesity Minoo Bagheri, Farshad Farzadfar, Lu Qi, Mir Saeed Yekaninejad, Maryam Chamari, Oana A. Zeleznik, Zahra Kalantar, Zarin Ebrahimi, Ali Sheidaie, Berthold Koletzko, Olaf Uhl, and Abolghasem Djazayery J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00802 • Publication Date (Web): 01 Mar 2018 Downloaded from http://pubs.acs.org on March 2, 2018

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Journal of Proteome Research

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Obesity-related metabolomic profiles and discrimination of metabolically unhealthy obesity

Authors: Minoo Bagheri, MSc1, Farshad Farzadfar, MD, ScD2, Lu Qi, MD, PhD3, Mir Saeed Yekaninejad, PhD4, Maryam Chamari, MSc 1, Oana A. Zeleznik, PhD5, Zahra Kalantar, MSc 6, Zarin Ebrahimi, MSc 7, Ali Sheidaie, MSc 2, Berthold Koletzko, MD, PhD8, Olaf Uhl, PhD8, Abolghasem Djazayery, PhD1*

Affiliation: 1. Department of Community Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran, Iran (ORCI: 0000-0002-6093-613X) 2. Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran 3. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, US 4. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 5. Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, US 6. Department of Cellular and Molecular Nutrition School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran, Iran 7. Department of Nutrition, Faculty of Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 8. Ludwig-Maximilians-Universität München, Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, 80337 Munich, Germany

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*Address for correspondence: Dr. Abolgasem Djazayery, Department of Community Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, No 44, Hojjat-dost Alley, Naderi St., Keshavarz Blvd, Tehran 1416-643931, Iran. Email: [email protected], [email protected] Telephone number: +9821-88955742 Fax Number: +9821-88955569

Disclosure None


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Abstract

Background: A particular sub-group of obese adults, considered as metabolically healthy obese (MHO), have a reduced risk of metabolic complications. However, the molecular basis contributing to this healthy phenotype remains unclear. Objective: To identify obesity-related metabolite patterns differed between MHO and metabolically unhealthy obese (MUHO) groups and examine whether these patterns are associated with the development of cardiometabolic disorders in a sample of Iranian adult population aged 18-50 years. Valid metabolites were defined as metabolites that passed the quality control analysis of the study. In this case-control study, 104 valid metabolites of 107 MHO and 100 MUHO patients were separately compared to those of 78 normal-weight metabolically healthy (NWMH) adults. Multivariable linear regression was used to investigate all potential relations in the study. A targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry was employed to profile plasma metabolites. Results: The study revealed that, after Bonferroni correction, branched-chain amino-acids, tyrosine, glutamic acid, diacyl-phosphatidylcholines C32:1 and C38:3 were directly and acyl-carnitine C18:2, acyl-lysophosphatidylcholines C18:1 and C18:2, and alkyl-lysophosphatidylcholines C18.0 were inversely associated with MHO phenotype. The same patterns were observed in MUHO patients except for the acyl-carnitine and

lysophosphatidylcholine

profiles

where

acyl-carnitine

C3:0

and

acyl-

lysophosphatidylcholine C16:1 were higher and acyl-lysophosphatidylcholines C18:1, C18:2 were lower in this phenotype. Furthermore, proline, and diacyl-phosphatidylcholines C32:2 and C34:2 were directly and serine, asparagines and acyl-alkyl-phosphatidylcholine C34:3 were negatively linked to MUHO group. Factors composed of amino acids were directly and those



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containing lysophosphatidylcholines were inversely related to cardiometabolic biomarkers in both phenotypes. Interestingly, the diacyl-phosphatidylcholines-containing factor was directly associated with cardiometabolic disorders in the MUHO group. Conclusions: A particular pattern of amino acids and choline-containing phospholipids may aid in the identification of metabolic health among obese patients.

Keywords: branched-chain amino acids, metabolomics, obesity, phospholipids, untargeted

Introduction

Obesity, widely recognized as a serious public health problem, is associated with the development of several metabolic disorders including type 2 diabetes, hypertension, dyslipidemia, cancer

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and mid-life mortality.3 However, according to the recent findings, a

particular sub-group of obese patients, considered as metabolically healthy obese (MHO), do not develop the downstream diseases associated with obesity.2, 4 MHO phenotype, estimated to be 20-40% of obese patients,4-7 bear healthy metabolic characteristics like reduced blood pressure, lipid profiles and insulin resistance and elevated high-density lipoprotein cholesterol (HDL-C).2, 4-5, 8 Despite having high body fat mass, patients with MHO phenotype were less likely to be afflicted with cardiovascular disease (CVD) or related disorders.2 However, some studies reported that MHO phenotype is not protected from CVD risk and other cardiometabolic complications.9-10 Although the extent to which MHO status is protected against metabolic disorders is not clear, it is crucial to identify molecular mechanisms underlying reduced metabolic complications in this group.11 This may lead to selecting more appropriate health care strategies and avoiding invasive therapeutic methods in public health.2, 6, 11


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The currently evolving metabolomics measuring techniques are highly sensitive methods with the capability of a quantitative analysis and an accurate detection of small-molecule metabolites that represent the end point of genes and proteins.12 Metabolite profiling may help to identify biomarkers that improve distinction between MHO and their metabolically unhealthy obese (MUHO) counterparts.11-12

In prior animal and human studies, amino acids, particularly branched-chain amino acids (BCAAs), were identified as potential biomarkers of obesity and type 2 diabetes.12-14 The human studies which included African American, Caucasian, and South Korean population uncovered metabolites that discriminated lean from obese individuals and diabetic from non-diabetic adults. Obesity, metabolic syndrome and type 2 diabetes were linked to changes in plasma amino acids.15 Several acyl-carnitines alterations have been observed in obesity, per-diabetic and diabetic patients.16-17 Studies that compared metabolite profiles in individuals characterized as metabolically healthy and metabolically unhealthy suggested that amino acids and acyl-carnitine significantly differed between these two groups.18-19 Using different criteria to define metabolically healthy status, Chen et al.20 reported a cluster of metabolites distinguishing obesity phenotypes.

It was reported that energy and macronutrient intake, physical activity, smoking, and alcohol consumption did not significantly differ between MHO and MUHO.7 Therefore, potential differences in metabolite concentrations between these two groups might originate from their dissimilar homeostasis and molecular mechanism involved in metabolism regulation. The accumulation of amino acids and acyl-carnitines in the body was linked to mitochondrial



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dysfunction to effectively transfer cells’ energy.14 Along with amino acids and acyl-carnitines, studies revealed that phospholipids and fatty acids were altered in obese compared to normalweight individuals.21 A metabolomic study that compared obese with lean individuals has provided evidence highlighting sphingomyelins and lysophosphatidylcholines as the lipidomic biomarkers associated with obesity.22 This might be due to an abnormal metabolism of energy and lipids leading to lipid accumulation through decreasing fatty acids β-oxidation.21 To date, most studies have assessed amino acid and acyl-carnitine profiles rather than phospholipids when phenotypes of obesity were compared to each other. It currently remains unknown if these three profiles differ between MHO and MUHO groups compared to normal-weight metabolically healthy (NWMH) individuals and, which category can act as metabolic biomarkers in each of these two phenotypes.

Therefore, our primary aim was to simultaneously measure all three groups of obesityassociated metabolites (amino acids, acyl-carnitines and phospholipids) to identify novel biomarkers related to MHO and MUHO. As metabolites can be regarded as predictors of cardiometabolic-associated biomarkers,8,

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the identified metabolites were then further

assessed to elucidate the molecular basis of the relationship between metabolomic profiles and disease progression.

Methods

Study design and population


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In our case-control study, the sample size was estimated based on the comparison of BCAAs between groups in a study by Badoud et al.8 To assess metabolimics profiles in MHO and MUHO phenotypes, we conducted two case-control studies in which 107 MHO and 100 MUHO patients (cases) were compared to 78 participants with NWMH status (controls) on the basis of obesityrelated plasma metabolites. Standard adult criteria

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was used to define obesity and normal

weight range (BMI≥30 kg/m2 for MHO and MUHO, and 18.5≤BMI

Obesity-Related Metabolomic Profiles and Discrimination of

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