Mol. Cells 2015; 38(7): 587~596  https://doi.org/10.14348/molcells.2015.0126
A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes
Seokjae Park1,2,3, Krishna Chaitanya Sadanala2,3, and Eun-Kyoung Kim1,2,*
1Department of Brain & Cognitive Sciences, Daegu Gyeongbuk Institute of Science & Technology, Daegu 711-873, Korea, 2Neurometabolomics Research Center, Daegu Gyeongbuk Institute of Science & Technology, Daegu 711-873, Korea
*Correspondence: ekkim@dgist.ac.kr
Received May 15, 2015; Revised May 22, 2015; Accepted May 26, 2015.; Published online June 15, 2015.
© Korean Society for Molecular and Cellular Biology. All rights reserved.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit (http://creativecommons.org/licenses/by-nc-sa/3.0/).
ABSTRACT

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.

Keywords: biomarkers, diabetes, metabolites, metabolomics, obesity
INTRODUCTION

The term “metabolites” denotes all endogenous small molecules that are involved (as substrates, intermediates or products) in common metabolic reactions in biochemical pathways essential for growth, development, reproduction and stress response mechanisms (Goodacre et al., 2004). Metabolomics is defined as a technology aimed at measuring/profiling changes in the levels of metabolites present inside a cell, tissue, or organism in response to a genetic variation or pathophysiological stimuli (Oliver et al., 1998; Scarfe et al., 1998). Another aim of metabolomics is to evaluate the relative differences between biological samples on the basis of their metabolite profiles. It can deliver an immediate snapshot of the whole physiology of an organism. Current technological developments have allowed high-throughput profiling of a large number of metabolites in biological samples, with increasing applications in disease research (Shah et al., 2012). Metabolomics comprises qualitative and quantitative analysis of intracellular and intercellular metabolites using two distinct analytical approaches: (1) non-targeted metabolite profiling, i.e. identification and characterization of a large number of metabolites and their precursors; and (2) targeted metabolite profiling, with an emphasis on quantitative variations in metabolites of interest (e.g., amino acids, carbohydrates, lipids and nucleotides) based on a prior understanding of their biological roles in metabolic pathways (Sadanala et al., 2012).

In this review, we discuss the major metabolite changes observed in obesity and diabetes. The focus is on the altered metabolite profiles in each disorder and inter-related disease conditions. We also present a short list of metabolites commonly associated with both disorders. The concept of our review is based on metabolite classes such as amino acids, lipids, carbohydrates and nucleotides, in previous studies that proposed a cross-sectional relationship between obesity and diabetes.

ANALYTICAL APPLICATIONS IN METABOLOMIC STUDIES

The diversity in the physico-chemical properties of metabolites makes metabolomics analysis very challenging. Hence, different analytical methodologies are needed and are preferably combined with each other (Goodacre et al., 2004). Among the analytical techniques that can be used for metabolomics applications, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the most commonly used. High performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), gas chromatography (GC) and capillary electrophoresis (CE) can detect metabolites within a few minutes. Combinations of these advanced techniques with MS include GC-MS, liquid chromatography-mass spectrometry (LC-MS) and CE-MS. Application of these techniques to metabolomics has resulted in some promising outcomes (Carr et al., 2011; Chylla et al., 2011; Junot et al., 2010; Lin et al., 2010; Zhang et al., 2012). As one of the most widely-used spectroscopic analytical techniques, NMR can identify and quantify a large number of organic compounds simultaneously in the micromolar range. GC-MS has been extensively used for metabolomics and offers effective and reproducible analysis. However, nonvolatile compounds are not suitable for GC-MS analysis. For their separation on a GC column, a derivatization reaction is needed to generate volatile compounds. This limits the applicability of GC-MS in metabolomics. LC-MS techniques have been developed that use a soft ionization method, which makes this approach more suitable for routine use. Large-scale metabolomic technologies based on LC-MS are increasingly gaining attention for their applicability in the diagnosis of human disease (Courant et al., 2012). The UPLC-MS technology is important in biomolecular research and can be used to quantify the functions of signaling and metabolic pathways in a multiplex and comprehensive manner. UPLC also permits a more rapid analysis (in comparison with HPLC) without loss of resolution. CE-MS is a potent and promising high-resolution separation technique for charged metabolites in biological fluids (Barbas et al., 2011). CE-MS, as an analytical platform, has made substantial contributions in the progress of metabolomics research. Fourier transform mass spectrometry (FTMS) instruments are popular as they provide exact quantification (Carr et al., 2011). Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a rapidly evolving analytical tool that can analyze small molecules (less than 1000 Da; (van Kampen et al., 2011). Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS) may become a potent new technique for superior metabolomic analysis (Cho et al., 2015). In addition to high-resolution analysis methods, such as NMR and MS, metabolomics employs chemometric statistical tools, such as partial least squares (PLS) and principal component analysis (PCA), to develop an integrated picture of both endogenous and xenobiotic metabolism.

Role of metabolomics in investigation of biomarkers in the diseases

Metabolomics studies allow identification of metabolites involved in disease mechanisms by observing metabolite level variations in diseased individuals in comparison with healthy ones (Cheng et al., 2012; Goek et al., 2012; Newgard et al., 2009; Pietil?inen et al., 2007; Rhee et al., 2011; Shaham et al., 2008; Wang et al., 2011; Zhao et al., 2010). Furthermore, the metabolic phenotypes of human populations will greatly facilitate evaluation of the metabolic response of each patient to treatment, making possible personalized clinical treatment. Sample-specific metabolomes can be studied, such as serum metabolome or liver tissue metabolome. Urine and blood (serum and plasma) are the biofluids that are most often used for metabolomics studies because both contain a multitude of detectable metabolites and can be obtained without or with minimal invasion. A number of other fluids such as saliva, bile, gut aspirate, cerebrospinal fluid, seminal fluid, amniotic fluid and synovial fluid have also been analyzed (Bala et al., 2006; Bollard et al., 2005; Gowda et al., 2006). More recently, metabolic profiling of complete tissues and their lipid and aqueous metabolites has received more attention for biomarker detection (Griffin and Kauppinen, 2007). These studies have identified potential biomarkers or revealed pathophysiology of diseases such as obesity, diabetes, cardiovascular diseases and cancers (Beger, 2013; Du et al., 2013).

Metabolomics has been used for biomarker discovery for a number of clinical conditions (Madsen et al., 2010; Vinayavekhin et al., 2010). Biomarkers are regularly used in clinical practice to measure disease severity and also to provide essential prognostic information related to survival. Biomarkers have also been used as alternatives to clinical endpoints both in clinical practice and in research settings, predominantly in novel drug development. By identifying the definite early biomarkers of disease, metabolomics provides better understanding of disease progression and metabolic pathways (Zhang et al., 2013).

Obesity

Obesity is a medical condition, in which a large amount of fat accumulates in the body to the point that it may decrease the life span and result in such health problems as diabetes, cardiovascular disease, as well as bone and joint diseases (Haslam and James, 2005; Pataky et al., 2014). Obesity is responsible for about 5% of worldwide deaths, and the total economic losses from obesity are approximately $2 trillion, or 2.8% of global GDP, equivalent to the worldwide losses from smoking or armed violence, war and terrorism. Recently, several metabolites associated with obesity have been identified by metabolomics and confirmed to be disturbed extensively in both animal models and humans (Abu Bakar et al., 2015). Obesity affects the entire body and evidently involves metabolic variations, but the definite changes in metabolism during obesity and dysfunction of specific organs or cellular organelles related to obesity are not yet well understood (Kussmann et al., 2006). As metabolomics can swiftly detect subtle alterations in the metabolic network, it is exclusively poised to improve our knowledge of obesity and diseases related to obesity.

Diabetes

Diabetes mellitus (DM), usually called diabetes, is a collection of metabolic diseases characterized by elevated blood sugar levels over an extended period (Nyenwe et al., 2011). If untreated, diabetes can trigger many complications (Stolar, 1988). It is a chronic disease characterized by chronic hyperglycemia because of the absence or shortage of insulin, which may be due either to a gradual failure of pancreatic β-cell function and subsequently a lack of insulin production (type 1 diabetes, T1DM) or to the development of insulin resistance and consequently the loss of β-cell function (type 2 diabetes, T2DM). Using either animal models or oral glucose tolerance tests, metabolic studies have uncovered alterations in metabolites associated with pathways involved in the action of insulin, including lipolysis, ketogenesis, proteolysis and glucose metabolism (Friedrich, 2012). These results have revealed a switch from β-oxidation to glycolysis and fat storage in response to glucose intake. Moreover, metabolomics studies performed on diabetic patients and healthy subjects have revealed many significantly altered metabolic pathways and metabolic variations (Suhre et al., 2010).

METABOLOMICS IN OBESITY AND DIABETES

Metabolomics studies have offered new insights into the etiology of obesity and diabetes and individual dissimilarities. The identified metabolites and metabolite ratios might be considered as potential biomarkers useful to understand obesity-associated pathophysiological processes. A recent epidemiological study has found that plasma concentrations of metabolites, particularly branched-chain amino acids (BCAAs: isoleucine, leucine and valine), are linked to an increased risk of T2DM (Newgard et al., 2009). Other novel metabolite classes have also been associated with diabetes; these include short- and medium-chain acylcarnitines and the following lipid classes: sphingomyelins (SMs), lysophosphatidylcholines (LysoPC), phosphatidylcholines (PCs), and lysophosphatidylethanolamines (LysoPE).

Amino acid metabolism

Amino acids (AAs) are important biological compounds, which possess carboxylic and amine moieties as functional groups and serve as building blocks for proteins. They play key metabolic and physiological roles in all living organisms. It has been known that obesity is accompanied by an increase in circulating levels of several amino acids, including BCAAs (Chevalier et al., 2005; Felig et al., 1974; Krebs et al., 2007; Moore and Stein, 1954; Tremblay et al., 2007; Um et al., 2006). High fasting concentrations of BCAAs were found to be associated with obesity and low serum insulin levels.

The association between obesity and amino acid metabolism shows important physiological and clinical inferences. The concentrations of selected essential amino acids and their derivatives in blood, in specific BCAAs, tyrosine, phenylalanine and sulfur amino acids, are evidently changed with obesity (Adams, 2011). A distinctive metabolic picture related to BCAA catabolism was also found in obese subjects (Newgard et al., 2009). Reduced expression of mitochondrial branched chain amino transferase in adipose tissue is considered to be a reason for the increased levels of BCAAs in obesity (She et al., 2007). Decreased levels of glutamine and glycine are found in blood of obese individuals in comparison with lean controls (Xie et al., 2012), which was also observed in another previous study (Backman et al., 1975). Changes in the levels of a number of compounds involved in amino acid metabolism have been found in obese condition (Adams, 2011; Newgard et al., 2009). The levels of cysteine, glutamate, phenylalanine, threonine, tryptophan, tyrosine, pantothenic acid, α-hydroxyisovaleric acid, 3-methyl-2-oxobutyric acid, choline, glyceric acid, 2-ketoisocaproic acid, pipecolic acid, kynurenine, serotonin and isovaleryl carnitine are increased (Fiehn et al., 2010; Gogna et al., 2015; Moore et al., 2014; Zeng et al., 2010), whereas those of methionine, citrulline, 3-methylglutarylcarnitine and acylcarnitine are decreased (Kim et al., 2011; Mihalik et al., 2012; Oberbach et al., 2011; Wahl et al., 2012). Other metabolites such as, alanine, arginine, asparagine, glutamine, glycine, histidine, isoleucine, leucine, lysine, proline, valine, carnitines and hydroxycarnitines were increased in some studies (Fiehn et al., 2010; Kim et al., 2010; 2011; Moore et al., 2014; Oberbach et al., 2011; Wahl et al., 2012) and decreased in others (Mihalik et al., 2012).

Increased levels of amino acids can improve insulin secretion from primary islet cells and β-cell lines by diverse mechanisms (Brennan et al., 2002; Charles and Henquin, 1983; Dixon et al., 2003; Sener and Malaisse, 1980; Smith et al., 1997). The relationship between amino acids and insulin resistance has been known for decades (Felig et al., 1969; 1974; Gougeon et al., 2008), but with the arrival of comprehensive metabolomic profiling a more detailed picture emerges of how amino acids contribute to the progression of diabetes. The levels of cysteine, pantothenic acid, creatine, acetyl carnitine and butyryl carnitine are increased in diabetes patients (Bentley-Lewis et al., 2014; Fiehn et al., 2010; Guan et al., 2013; Suhre et al., 2010), whereas those of arginine, asparagine, glycine, methionine, citrulline, betaine, sarcosine, aspartic acid, benzoic acid, 4-hydroxyproline and 5-hydroxykynurenine are decreased (Bentley-Lewis et al., 2014; Diao et al., 2014; Fiehn et al., 2010; Floegel et al., 2013; Liu et al., 2013; Mihalik et al., 2012; Wang-Sattler et al., 2012). Other metabolites such as, alanine, glutamate, glutamine, histidine, isoleucine, leucine, lysine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, creatinine, 2-ketocaproic acid, pyroglutamic acid, choline, ornithine, carnitines and hydroxycarnitines were increased in some studies (Bentley-Lewis et al., 2014; Diao et al., 2014; Fiehn et al., 2010; Floegel et al., 2013; Liu et al., 2013; Mihalik et al., 2012; Palmer et al., 2015; Salek et al., 2007; Wang et al., 2011; Wang-Sattler et al., 2012; Zeng et al., 2010) and decreased in others (Bentley-Lewis et al., 2014; Diao et al., 2014; Fiehn et al., 2010; Liu et al., 2013; Mihalik et al., 2012; Palmer et al., 2015; Salek et al., 2007; Zeng et al., 2010). Glutamate and BCAAs are also found increased in T1DM (Oresic et al., 2008).

Lipid metabolism

Lipids are one of the main source of energy for metabolism. Blood lipids, obtained from food intake or from adipose tissue and liver, are mainly fatty acid derivatives and cholesterol. Obesity, which is characterized by fat deposits in tissues, is usually associated with elevated levels of plasma free fatty acids (FFAs) (Golay et al., 1986). Carnitine is an important metabolite linked to obesity, due to its involvement in fatty acid metabolism. It is well recognized that provision of FFAs can stimulate fatty acid oxidation (Randle, 1998). However, fatty acids can yield energy only via β-oxidation after esterification and transfer into the mitochondrion, which requires carnitine (Cha, 2008). Thus, higher levels of plasma FFAs in obesity may require more carnitine for effective β-oxidation. Consequently, the amount of carnitine in cells is the main factor regulating β-oxidation. Choline is a crucial dietary nutrient that is essential for the maintenance of cellular structure, methyl (one-carbon) metabolism, transport and metabolism of cholesterol (Blusztajn, 1998; Zeisel, 2000). Over 95% of the total choline present in most animal tissues is used for PC synthesis by the Kennedy pathway (Gibellini and Smith, 2010). The introduction of novel analytical and information technologies for management of large volumes of data has made it practical to characterize complex mixtures of lipids in body fluids and tissues (Lagarde et al., 2003; Wenk, 2005). Obesity and associated dyslipidemias may be caused by several factors (Despres et al., 1992). In obesity, the levels of non-essential fatty acids (oleic acid, palmitic acid, palmitoleic acid, stearic acid, stearoyl carnitine, 2-hydroxybutanoic acid and 3-hydroxybutanoic acid) are increased (Fiehn et al., 2010; Gogna et al., 2015; Kim et al., 2011; Moore et al., 2014), whereas those of ethanolamine and LysoPEs are decreased (Fiehn et al., 2010; Kim et al., 2011). LysoPCs, PCs, cholesteryl esters, ceramides, SMs and total fatty acids were increased in some studies (Kim et al., 2010; 2011; Moore et al., 2014; Pietil?inen et al., 2007; Samad et al., 2006; Wahl et al., 2012) and decreased in some others (Kim et al., 2010; 2011; Moore et al., 2014; Pietil?inen et al., 2007; Samad et al., 2006; Wahl et al., 2012)

Lipid abnormalities are common in diabetic patients, in particular in those with T2DM. Both the lipid profile and body fat mass have been reported to be the main predictors of metabolic disturbances and critical medical conditions, such as dyslipidemia, hypertension, diabetes and cardiovascular diseases (Du et al., 2013). Dyslipidemias make diabetic patients susceptible to heart disease and other problems related to atherosclerosis. A number of factors are likely to be responsible for diabetic dyslipidemia: the effects of insulin on apoprotein production in the liver, regulation of lipoprotein lipase, function of cholesteryl ester transfer protein and peripheral actions of insulin on adipose tissue and muscle (Mooradian et al., 2004; 2008). In diabetes, the levels of 1-monopalmitin, 1-monostearin, 1,2-distearoyl phosphatidylserine, ceramide, diacylglycerol, diacylphosphatidylcholines, fatty-acyl CoA, linoleic acid, myristic acid, oleic acid, palmitic acid, palmitoleic acid, stearic acid, tridecane, undecane, triacylglycerol, 2-hydroxybutanoic acid, and acetoacetic acid are increased (Fiehn et al., 2010; Liu et al., 2013; Salek et al., 2007; Villarreal-P?rez et al., 2014; Xu et al., 2013; Yan et al., 2014; Zeng et al., 2011), whereas those of acyl-alkyl-phosphatidylcholines and ethanolamine are decreased (Fiehn et al., 2010; Floegel et al., 2013; Suhre et al., 2010; Xu et al., 2013). Phosphatidylcholine, phosphatidylethanolamine, sphingomyelin and 3-hydroxybutanoic acid are increased in some studies (Drogan et al., 2015; Huang et al., 2011), and decreased in others (Floegel et al., 2013; Suhre et al., 2010; Xu et al., 2013). Phosphatidylcholine was also found decreased in T1DM (Oresic et al., 2008).

Carbohydrate metabolism

The progress of obesity is often explained by excessive consumption of high-fat and high-calorie diets (Saris, 2003; Siri-Tarino et al., 2010). Yet, the role of dietary carbohydrates in the development and maintenance of obesity is now also receiving increasing attention (Boling et al., 2009; Malik et al., 2010; Siri-Tarino et al., 2010; Wylie-Rosett and Davis, 2009). Obesity is associated with a decreased glucose disposal in adipose tissue. It has been reported that obesity leads to the development of hyperglycemia (Halaas et al., 1995), hyperlipidemia (Yamamoto et al., 2002), hyperinsulinemia (Berndt et al., 2005) and insulin resistance (Heilbronn et al., 2004). The pathways that metabolize carbohydrates include glycolysis, gluconeogenesis, the pentose phosphate pathway and the TCA cycle. Carbohydrate metabolism is vital to all metabolic processes. Glucose is catabolised via glycolysis to pyruvate, which under aerobic conditions is converted into acetyl coenzyme A, the entry point into the TCA cycle. Under anaerobic conditions, pyruvate is instead converted into lactate by lactate dehydrogenase. In obesity, the levels of glucose, lactic acid, fructose, glycerol, mannose, sorbitol, xylose, gluconic acid and glucuronic acid are increased (Fiehn et al., 2010; Gogna et al., 2015; Moore et al., 2014) whereas those of 1,5-anhydroglucitol and glycerol-3-phosphate are decreased (Fiehn et al., 2010; Moore et al., 2014).

Glucose concentration in blood reflects carbohydrate metabolism both in healthy people and in diabetics. In diabetes management, reduction in carbohydrate intake is the first and the most important step (Franz, 2008). Among compounds involved in carbohydrate metabolism, the levels of glucose, fructose, glycerol, inositol, mannose, sorbitol, xylose, 2,6-anhydrogalactose, 3,6-anhydrogalactose, gluconic acid, glucuronic acid, isopropanol, fumaric acid, malic acid and cis-aconitic acid are increased (Drogan et al., 2015; Fiehn et al., 2010; Gogna et al., 2015; Salek et al., 2007; Suhre et al., 2010; Xu et al., 2013; Zeng et al., 2010), whereas those of deoxygalactose, pyruvic acid, 1,5-anhydroglucitol and glycerol-3-phosphate are decreased in diabetes (Drogan et al., 2015; Fiehn et al., 2010; Suhre et al., 2010). Other metabolites such as acetic acid, deoxyglucose, lactic acid and citric acid were increased in some studies (Diao et al., 2014; Gogna et al., 2015; Salek et al., 2007; Suhre et al., 2010; Zeng et al., 2010) and decreased in others (Diao et al., 2014; Drogan et al., 2015). One metabolite, succinic acid is decreased in T1DM, though any significant changes are not observed in T2DM (Oresic et al., 2008).

Nucleotide metabolism

The study of nucleotide metabolism is vital for understanding of energy metabolism disorders, because nucleotides take part in most metabolic reactions and also act as coenzymes. Nucleotides are secreted from several body tissues and then build up in the blood circulation and other extracellular fluids. Release from cells and extracellular degradation both contribute to the nucleotide levels in the circulation. Persistently elevated blood nucleotide levels and constant purinergic signaling may play a pathophysiological role in metabolic disorders (Di Virgilio and Solini, 2002; Sellers et al., 2009; Sparks and Chatterjee, 2012). Nucleotide levels are extremely variable in obese subjects. High nucleotide levels observed in some morbidly obese subjects seem to be associated with the initial stage of insulin resistance (Sparks et al., 2014). Uric acid and uridine are strongly increased in obesity (Fiehn et al., 2010; Kim et al., 2011).

Analysis of the relationship between circulating nucleotide concentrations and clinical measures of T2DM demonstrated that changes in nucleotide metabolism are direct metabolic consequences of the disease and do not result from secondary complications (Dudzinska, 2014). In diabetes, compounds involved in nucleotide metabolism, including AMP, GMP, GTP, IMP, adenosine, guanosine and inosine are increased in both T1DM and T2DM (Dudzinska, 2014; Huang et al., 2011). Trimethylamine, glyoxylic acid and uridine are increased only in T2DM (Fiehn et al., 2010; Guan et al., 2013; Nikiforova et al., 2014; Padberg et al., 2014; Salek et al., 2007).

Besides the metabolites of major metabolic pathways, changes in the profiles of some other metabolites have been detected in obesity and diabetes. The levels of 3,6-anhydrogalactose, 7-ketodeoxycholic acid, heptadecanoic acid, inulobiose and myoinositol are increased, whereas those of, benzylalcohol, and a few glycerophospholipids are decreased in obesity (Fiehn et al., 2010; Gogna et al., 2015; Kim et al., 2011; Moore et al., 2014; Pietil?inen et al., 2007). The levels of cholesteryl-β-d-glucoside, dimethylamine, dimethylglucosamine, fructosamine and glycocholic acid are increased (Drogan et al., 2015; Fiehn et al., 2010; Huang et al., 2011; Liu et al., 2013; Padberg et al., 2014; Salek et al., 2007; Suhre et al., 2010).

METABOLITES SHOWING ALTERED CONCENTRATIONS IN BOTH OBESITY AND DIABETES

Obesity and diabetes have many similar causes and are related. These disorders often occur together, and most patients with T2DM are obese or were previously obese (Bray, 2004; Stumvoll et al., 2005). Obesity is usually considered as to strongly increase the risk of the later development of diabetes. The question arises whether obesity is not only a risk factor but also a cause of T2DM. A strong increase in obesity initiates a parallel upward trend in the incidence of diabetes. According to Ford et al. (1997) for every kilogram of weight gain, the risk of diabetes increases by between 4.5% and 9%. The relationship between obesity and insulin resistance is probably a cause-and-effect relationship, since human and animal studies show that weight loss or gain is closely associated with an increase or decrease in insulin sensitivity, respectively (Bak et al., 1992; Freidenberg et al., 1988; Sims et al., 1973). Longitudinal studies have shown that people with a genetic predisposition to compromised insulin secretion develop diabetes when they have developed insulin resistance due to obesity. Hence, T2DM develops once insulin secretion deteriorates to a level at which it cannot further compensate for insulin resistance. Also, in a longitudinal study of normal glucose tolerance offspring of two diabetic parents, Warram et al. (1990), revealed that patients presenting insulin resistance and extreme body weight were at increased risk of developing T2DM.

Several investigations have linked the phenotype and metabolism and identified serum or plasma metabolic markers that are involved independently in developing obesity (He et al., 2012; Williams et al., 2006) and diabetes (Bao et al., 2009; Chen et al., 2009). The occurrence of diabetes is positively associated with increased BMI across several ethnic groups (Chan et al., 2009; Maskarinec et al., 2009). Thirty nine metabolites are associated with both diabetes and high BMI or obesity (Table 1). Among compounds involved in amino acid metabolism, the levels of BCAAs, cysteine, glutamine, phenylalanine, proline, tyrosine, threonine, tryptophan, choline and pantothenic acid are increased, whereas those of asparagine, glycine, methionine and citrulline are decreased in both obesity and diabetes. Among compounds involved in lipid metabolism, the levels of 2-hydroxybutanoic acid, 3-hydroxybutanoic acid, phosphatidylcholine, stearic acid, oleic acid, palmitic acid and palmitoleic acid are increased, whereas those of ethanolamine, lysophosphatidylcholine 18:2, lysophosphatidylethanolamine and sphingomyelin are decreased. Among compounds involved in carbohydrate metabolism, the levels of fructose, glycerol, mannose, xylose, sorbitol, lactate, glucose, gluconic acid and glucuronic acid are increased, whereas those of 1,5-anhydroglucitol and glycerol-3-phosphate are decreased. Among compounds involved in nucleotide metabolism, the level of uridine is increased. All these metabolites along with the references are categorized and summarized in Table 1.

STRENGH AND LIMITATIONS

This study summarizes the major metabolites, metabolite classes and pathways associated with obesity and diabetes with a focus on metabolites associated with both disorders. The study also discusses some aspects of the development of these disorders and transformation of obesity into a diabetic state. Overall, metabolomics research can provide comprehensive information about metabolite changes in the studied disease using various sample types in minimum quantities and in a very short time. The limitations of the reviewed studies were the differences in the analytical platforms used, gender, age, diet, ethnicity and the source species of the samples, which may have led to some misinterpretations in the assessment. Other unknown diseases may also affect the outcome of metabolomics studies.

CONCLUSION

Obesity and diabetes are well-known to be associated. This study provides information on all major metabolites associated with obesity and diabetes and also common metabolites associated with both disorders. The classification and changes in metabolites in each comparison have been represented in Fig. 1. These metabolites can be considered as biomarkers for metabolomics studies of obesity and diabetes and can be a focus area of future research. This study can also provide some therapeutic clues for treatment of obesity-related diabetes. Further studies are required to test whether the selected metabolites might be useful as diagnostic tools and to reveal the biological mechanisms that result in changes in the levels of certain metabolites in the pathogenesis of these metabolic diseases.

FIGURES
Fig. 1. Summary of the metabolites changed in obesity and diabetes
TABLES

Table 1.

List of the metabolites changed in both obesity and diabetes

CategoryMetabolitesOriginSample typeUp/DownPlatformReferences
Amino acid metabolismAsparaginehumanserum/plasmaBentley-Lewis et al., 2014; Moore et al., 2014
Citrullinehumanserum/plasmaMihalik et al., 2012; Oberbach et al., 2011
Glycinehumanserum/plasmaFiehn et al., 2010; Floegel et al., 2013; Mihalik et al., 2012; Palmer et al., 2015; Wang-Sattler et al., 2012
Methioninehumanserum/plasmaBentley-Lewis et al., 2014; Mihalik et al., 2012
CholinehumanserumGogna et al., 2015
CysteinehumanplasmaBentley-Lewis et al., 2014; Fiehn et al., 2010
Glutaminehumanserum/plasma/urineGogna et al., 2015; Oberbach et al., 2011; Salek et al., 2007; Xu et al., 2013; Zeng et al., 2011
Isoleucinehumanserum/plasmaGogna et al., 2015; Moore et al., 2014; Suhre et al., 2010; Wang et al., 2011; Xu et al., 2013; Zeng et al., 2011
Leucinehumanserum/plasmaFiehn et al., 2010; Gogna et al., 2015; Kim et al., 2010; Moore et al., 2014; Oberbach et al., 2011; Palmer et al., 2015; Suhre et al., 2010; Villarreal-P?rez et al., 2014; Wang et al., 2011; Xu et al., 2013; Zeng et al., 2011
Pantothenic acidhumanserumGogna et al., 2015, Suhre et al., 2010
rodentserumKim et al., 2011
Phenylalaninehumanserum/plasmaFloegel et al., 2013; Gogna et al., 2015; Moore et al., 2014; Oberbach et al., 2011, Palmer et al., 2015; Wang et al., 2011; Xu et al., 2013
ProlinehumanserumGogna et al., 2015; Oberbach et al., 2011; Xu et al., 2013
ThreoninehumanserumGogna et al., 2015
Tryptophanhumanserum/plasmaKim et al., 2010; Wang et al., 2011
Tyrosinehumanserum/plasmaKim et al., 2010; Moore et al., 2014; Oberbach et al., 2011; Villarreal-P?rez et al., 2014; Wang et al., 2011
rodentserumKim et al., 2011
Valinehumanserum/plasmaGogna et al., 2015; Kim et al., 2010; Moore et al., 2014; Oberbach et al., 2011; Palmer et al., 2015; Suhre et al., 2010; Villarreal-P?rez et al., 2014; Wang et al., 2011; Xu et al., 2013
Lipid metabolismEthanolaminehumanplasmaFiehn et al., 2010
Lysophosphatidylcholine C18:2humanserumFloegel et al., 2013; Wahl et al., 2012; Wang-Sattler et al., 2012
LysophosphatidylethanolaminehumanplasmaLiu et al., 2013
rodentserumKim et al., 2011
SphingomyelinhumanserumFloegel et al., 2013; Xu et al., 2013
rodentadipose tissue/plasmaSamad et al., 2006
2-hydroxybutanoic acidhumanserumFiehn et al., 2010; Moore et al., 2014
3-hydroxybutanoic acidhumanserumFiehn et al., 2010; Gogna et al., 2015; Kim et al., 2010; Moore et al., 2014; Salek et al., 2007; Suhre et al., 2010; Zeng et al., 2011
Oleic acidhumanserum/plasmaFiehn et al., 2010; Liu et al., 2013; Xu et al., 2013; Yi et al., 2006; Zeng et al., 2011
Palmitic acidhumanserum/plasmaFiehn et al., 2010; Xu et al., 2013; Yi et al., 2006; Zeng et al., 2011
Palmitoleic acidhumanplasmaFiehn et al., 2010
PhosphatidylcholinehumanserumDrogan et al., 2015; Pietil?inen et al., 2007
rodentliver/serumHuang et al., 2011; Kim et al., 2011
Stearic acidhumanserum/plasmaFiehn et al., 2010; Xu et al., 2013; Yi et al., 2006; Zeng et al., 2011
Carbohydrate metabolism1,5-anhydroglucitolhumanserumDrogan et al., 2015; Moore et al., 2014; Suhre et al., 2010
Glycerol-3-phosphatehumanplasmaFiehn et al., 2010
Fructosehumanserum/plasmaFiehn et al., 2010; Xu et al., 2013
Glucosehumanserum/plasmaDrogan et al., 2015; Fiehn et al., 2010; Gogna et al., 2015
GlycerolhumanserumGogna et al., 2015; Moore et al., 2014
rodentserumDiao et al., 2014
Gluconic acidhumanplasmaFiehn et al., 2010
Glucuronic acidhumanserum/plasmaFiehn et al., 2010; Suhre et al., 2010
Lactic acidhumanserum/plasmaGogna et al., 2015; Wang et al., 2011; Zeng et al., 2011
MannosehumanserumMoore et al., 2014; Xu et al., 2013
SorbitolhumanserumGogna et al., 2015
XylosehumanplasmaFiehn et al., 2010
Nucleotide metabolismUridinehumanplasmaFiehn et al., 2010

, NMR; , LC/MS; , GC/MS


REFERENCES
  1. Abu Bakar, MH, Sarmidi, MR, Cheng, KK, Ali Khan, A, Suan, CL, Zaman Huri, H, and Yaakob, H (2015). Metabolomics - the complementary field in systems biology: a review on obesity and type 2 diabetes. Mol. Biosyst.. , .
  2. Adams, SH (2011). Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state. Adv. Nutr.. 2, 445-456.
    Pubmed KoreaMed CrossRef
  3. Backman, L, Hallberg, D, and Kallner, A (1975). Amino acid pattern in plasma before and after jejuno-ileal shunt operation for obesity. Scand J. Gastroenterol.. 10, 811-816.
  4. Bak, JF, Moller, N, Schmitz, O, Saaek, A, and Pedersen, O (1992). In vivo insulin action and muscle glycogen synthase activity in type 2 (non-insulin-dependent) diabetes mellitus: effects of diet treatment. Diabetologia. 35, 777-784.
  5. Bala, L, Ghoshal, UC, Ghoshal, U, Tripathi, P, Misra, A, Gowda, GA, and Khetrapal, CL (2006). Malabsorption syndrome with and without small intestinal bacterial overgrowth: a study on upper-gut aspirate using 1H NMR spectroscopy. Magn. Reson. Med.. 56, 738-744.
    Pubmed CrossRef
  6. Bao, Y, Zhao, T, Wang, X, Qiu, Y, Su, M, and Jia, W (2009). Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers. J. Proteome Res.. 8, 1623-1630.
    Pubmed CrossRef
  7. Barbas, C, Moraes, EP, and Villase?or, A (2011). Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples. J. Pharm. Biomed. Anal.. 55, 823-831.
    Pubmed CrossRef
  8. Beger, RD (2013). A review of applications of metabolomics in cancer. Metabolites. 3, 552-574.
    Pubmed KoreaMed CrossRef
  9. Bentley-Lewis, R, Xiong, G, Lee, H, Yang, A, Huynh, J, and Kim, C (2014). 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.. 1, 38-43.
    Pubmed KoreaMed CrossRef
  10. Berndt, J, Kloting, N, Kralisch, S, Kovacs, P, Fasshauer, M, Schon, MR, Stumvoll, M, and Bluher, M (2005). Plasma visfatin concentrations and fat depot-specific mRNA expression in humans. Diabetes. 54, 2911-2916.
    CrossRef
  11. Blusztajn, JK (1998). Choline, a vital amine. Science. 281, 794-795.
    CrossRef
  12. Boling, CL, Westman, EC, and Yancy, WS (2009). Carbohydrate-restricted diets for obesity and related diseases: an update. Curr. Atheroscler. Rep.. 11, 462-469.
    CrossRef
  13. Bollard, ME, Stanley, EG, Lindon, JC, Nicholson, JK, and Holmes, E (2005). NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed.. 18, 143-162.
    Pubmed CrossRef
  14. Bray, GA (2004). Medical consequences of obesity. J. Clin. Endocrinol. Metab.. 89, 2583-2589.
    Pubmed CrossRef
  15. Brennan, L, Shine, A, Hewage, C, Malthouse, JP, Brindle, KM, McClenaghan, N, Flatt, PR, and Newsholme, P (2002). A nuclear magnetic resonance-based demonstration of substantial oxidative L-alanine metabolism and L-alanine-enhanced glucose metabolism in a clonal pancreatic beta-cell line: metabolism of L-alanine is important to the regulation of insulin secretion. Diabetes. 51, 1714-1721.
    CrossRef
  16. Carr, PW, Stoll, DR, and Wang, X (2011). Perspectives on recent advances in the speed of high-performance liquid chromatography. Anal. Chem.. 83, 1890-1900.
    Pubmed KoreaMed CrossRef
  17. Cha, YS (2008). Effects of L-carnitine on obesity, diabetes, and as an ergogenic aid. Asia Pac. J. Clin. Nutr.. 17, 306-308.
  18. Chan, JC, Malik, V, Jia, W, Kadowaki, T, Yajnik, CS, Yoon, KH, and Hu, FB (2009). Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA. 301, 2129-2140.
    Pubmed CrossRef
  19. Charles, S, and Henquin, JC (1983). Distinct effects of various amino acids on 45Ca2+ fluxes in rat pancreatic islets. Biochem. J.. 214, 899-907.
    CrossRef
  20. Chen, J, Zhao, X, Fritsche, J, Yin, P, Schmitt-Kopplin, P, Wang, W, Lu, X, H?ring, HU, Schleicher, ED, and Lehmann, R (2009). Practical approach for the identification and isomer elucidation of biomarkers detected in a metabonomic study for the discovery of individuals at risk for diabetes by integrating the chromatographic and mass spectrometric information. Anal. Chem.. 80, 1280-1289.
  21. Cheng, S, Rhee, EP, Larson, MG, Lewis, GD, McCabe, EL, Shen, D, Palma, MJ, Roberts, LD, Dejam, A, and Souza, AL (2012). Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 125, 2222-2231.
    Pubmed KoreaMed CrossRef
  22. Chevalier, S, Marliss, EB, Morais, JA, Lamarche, M, and Gougeon, R (2005). Whole-body protein anabolic response is resistant to the action of insulin in obese women. Am. J. Clin. Nutr.. 82, 355-365.
  23. Cho, Y, Ahmed, A, Islam, A, and Kim, S (2015). Developments in FT-ICR MS instrumentation, ionization techniques, and data interpretation methods for petroleomics. Mass Spectrom. Rev.. 34, 248-263.
    Pubmed CrossRef
  24. Chylla, RA, Hu, K, Ellinger, JJ, and Markley, JL (2011). Deconvolution of two-dimensional NMR spectra by fast maximum likelihood reconstruction: application to quantitative metabolomics. Anal. Chem.. 83, 4871-4880.
    Pubmed KoreaMed CrossRef
  25. Courant, F, Royer, AL, Chereau, S, Morvan, ML, Monteau, F, Antignac, JP, and Le Bizec, B (2012). Implementation of a semi-automated strategy for the annotation of metabolomic fingerprints generated by liquid chromatography-high resolution mass spectrometry from biological samples. Analyst. 137, 4958-4967.
    Pubmed CrossRef
  26. Despres, JP, Moorjani, S, Lupien, PJ, Tremblay, A, Nadeau, A, and Bouchard, C (1992). Genetic aspects of susceptibility to obesity and related dyslipidemias. Mol. Cell Biochem.. 113, 151-169.
  27. Di Virgilio, F, and Solini, A (2002). P2 receptors: new potential players in atherosclerosis. Br J. Pharmacol.. 135, 831-842.
    Pubmed KoreaMed CrossRef
  28. Diao, C, Zhao, L, Guan, M, Zheng, Y, Chen, M, Yang, Y, Lin, L, Chen, W, and Gao, H (2014). Systemic and characteristic metabolites in the serum of streptozotocin-induced diabetic rats at different stages as revealed by a (1)H-NMR based metabonomic approach. Mol. Biosyst.. 10, 686-693.
    Pubmed CrossRef
  29. Dixon, G, Nolan, J, McClenaghan, N, Flatt, PR, and Newsholme, P (2003). A comparative study of amino acid consumption by rat islet cells and the clonal beta-cell line BRIN-BD11 - the functional significance of L-alanine. J. Endocrinol.. 179, 447-454.
    CrossRef
  30. Drogan, D, Dunn, WB, Lin, W, Buijsse, B, Schulze, MB, Langenberg, C, Brown, M, Floegel, A, Dietrich, S, and Rolandsson, O (2015). Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. Clin. Chem.. 61, 487-497.
    Pubmed CrossRef
  31. Du, F, Virtue, A, Wang, H, and Yang, XF (2013). Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark Res.. 1, 17.
    Pubmed KoreaMed CrossRef
  32. Dudzinska, W (2014). Purine nucleotides and their metabolites in patients with type 1 and 2 diabetes mellitus. J. Biomed. Sci. Eng.. 7, 38-44.
    CrossRef
  33. Felig, P, Marliss, E, and Cahill, GF (1969). Plasma amino acid levels and insulin secretion in obesity. N Engl. J. Med.. 281, 811-816.
    Pubmed CrossRef
  34. Felig, P, Wahren, J, Hendler, R, and Brundin, T (1974). Splanchnic glucose and amino acid metabolism in obesity. J. Clin. Invest.. 53, 582-590.
    Pubmed KoreaMed CrossRef
  35. Fiehn, O, Garvey, WT, Newman, JW, Lok, KH, Hoppel, CL, and Adams, SH (2010). Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 5, e15234.
    CrossRef
  36. Floegel, A, Stefan, N, Yu, Z, M?hlenbruch, K, Drogan, D, Joost, HG, Fritsche, A, H?ring, HU, Hrab? de Angelis, M, and Peters, A (2013). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 62, 639-648.
    Pubmed KoreaMed CrossRef
  37. Ford, ES, Williamson, DF, and Liu, S (1997). Weight change and diabetes incidence: findings from a national cohort of US adults. Am. J. Epidemiol.. 146, 214-222.
    CrossRef
  38. Franz, MJ (2008). Medical nutrition therapy for diabetes mellitus and hypoglycemia of nondiabetic origin. Food, Nutrition and Diet therapy. Canada: Saunders.
  39. Freidenberg, GR, Reichart, D, Olefsky, JM, and Henry, RR (1988). Reversibility of defective adipocyte insulin receptor kinase activity in non-insulin-dependent diabetes mellitus. Effect of weight loss. J. Clin. Invest.. 82, 1398-1406.
    CrossRef
  40. Friedrich, N (2012). Metabolomics in diabetes research. Metabolomics in human type 2 diabetes research. J. Endocrinol.. 215, 29-42.
    CrossRef
  41. Gibellini, F, and Smith, TK (2010). The Kennedy pathway--De novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life. 62, 414-428.
    CrossRef
  42. Goek, ON, Doring, A, Gieger, C, Heier, M, Koenig, W, Prehn, C, Romisch-Margl, W, Wang-Sattler, R, Illig, T, and Suhre, K (2012). Serum metabolite concentrations and decreased GFR in the general population. Am. J. Kidney Dis.. 60, 197-206.
    Pubmed CrossRef
  43. Gogna, N, Krishna, M, Oommen, AM, and Dorai, K (2015). Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. Mol. Biosyst.. 11, 595-606.
    Pubmed CrossRef
  44. Golay, A, Swislocki, AL, Chen, YD, Jaspan, JB, and Reaven, GM (1986). Effect of obesity on ambient plasma glucose, free fatty acid, insulin, growth hormone, and glucagon concentrations. J. Clin. Endocrinol. Metab.. 63, 481-484.
    Pubmed CrossRef
  45. Goodacre, R, Vaidyanathan, S, Dunn, WB, Harrigan, GG, and Kell, DB (2004). Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol.. 22, 245-252.
    Pubmed CrossRef
  46. Gougeon, R, Morais, JA, Chevalier, S, Pereira, S, Lamarche, M, and Marliss, EB (2008). Determinants of whole-body protein metabolism in subjects with and without type 2 diabetes. Diabetes Care. 31, 128-133.
    Pubmed CrossRef
  47. Gowda, GA, Ijare, OB, Somashekar, BS, Sharma, A, Kapoor, VK, and Khetrapal, CL (2006). Single-step analysis of individual conjugated bile acids in human bile using 1H NMR spectroscopy. Lipids. 41, 591-603.
    CrossRef
  48. Griffin, JL, and Kauppinen, RA (2007). Tumour metabolomics in animal models of human cancer. J. Proteome Res.. 6, 498-505.
    Pubmed CrossRef
  49. Guan, M, Xie, L, Diao, C, Wang, N, Hu, W, Zheng, Y, Jin, L, Yan, Z, and Gao, H (2013). Systemic perturbations of key metabolites in diabetic rats during the evolution of diabetes studied by urine metabonomics. PLoS One. 8, e60409.
    CrossRef
  50. Halaas, JL, Gajiwala, KS, Maffei, M, Cohen, SL, Chait, BT, Rabinowitz, D, Lallone, RL, Burley, SK, and Friedman, JM (1995). Weight-reducing effects of the plasma protein encoded by the obese gene. Science. 269, 543-546.
    CrossRef
  51. Haslam, DW, and James, WP (2005). Effect of obesity on the incidence of type 2 diabetes mellitus varies with age among Indian women. Lancet. 366, 1197-1209.
    CrossRef
  52. He, Q, Ren, P, Kong, X, Wu, Y, Wu, G, Li, P, Hao, F, Tang, H, Blachier, F, and Yin, Y (2012). Comparison of serum metabolite compositions between obese and lean growing pigs using an NMR-based metabonomic approach. J. Nutr. Biochem.. 23, 133-139.
    Pubmed CrossRef
  53. Heilbronn, LK, Rood, J, Janderova, L, Albu, JB, Kelley, DE, Ravussin, E, and Smith, SR (2004). Relationship between serum resistin concentrations and insulin resistance in nonobese, obese, and obese diabetic subjects. J. Clin. Endocrinol. Metab.. 89, 1844-1848.
    Pubmed CrossRef
  54. Huang, Q, Yin, P, Wang, J, Chen, J, Kong, H, Lu, X, and Xu, G (2011). Method for liver tissue metabolic profiling study and its application in type 2 diabetic rats based on ultra performance liquid chromatography-mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.. 879, 961-967.
    Pubmed CrossRef
  55. Junot, C, Madalinski, G, Tabet, JC, and Ezan, E (2010). Fourier transform mass spectrometry for metabolome analysis. Analyst. 135, 2203-2219.
    Pubmed CrossRef
  56. Kim, JY, Park, JY, Kim, OY, Ham, BM, Kim, HJ, Kwon, DY, Jang, Y, and Lee, JH (2010). Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J. Proteome Res.. 9, 4368-4375.
    Pubmed CrossRef
  57. Kim, HJ, Kim, JH, Noh, S, Hur, HJ, Sung, MJ, Hwang, JT, Park, JH, Yang, HJ, Kim, MS, and Kwon, DY (2011). Metabolomic analysis of livers and serum from high-fat diet induced obese mice. J. Proteome Res.. 10, 722-731.
    Pubmed CrossRef
  58. Krebs, M, Brunmair, B, Brehm, A, Artwohl, M, Szendroedi, J, Nowotny, P, Roth, E, Furnsinn, C, Promintzer, M, and Anderwald, C (2007). The Mammalian target of rapamycin pathway regulates nutrient-sensitive glucose uptake in man. Diabetes. 56, 1600-1607.
    Pubmed CrossRef
  59. Kussmann, M, Raymond, F, and Affolter, M (2006). OMICS-driven biomarker discovery in nutrition and health. J. Biotechnol.. 124, 758-787.
    Pubmed CrossRef
  60. Lagarde, M, G?lo?n, A, Record, M, Vance, D, and Spener, F (2003). Lipidomics is emerging. Biochim. Biophys. Acta. 1634, 61.
    CrossRef
  61. Lin, L, Yu, Q, Yan, X, Hang, W, Zheng, J, Xing, J, and Huang, B (2010). Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. Analyst. 135, 2970-2978.
    Pubmed CrossRef
  62. Liu, L, Wang, M, Yang, X, Bi, M, Na, L, Niu, Y, Li, Y, and Sun, C (2013). Fasting serum lipid and dehydroepiandrosterone sulfate as important metabolites for detecting isolated post-challenge diabetes: serum metabolomics via ultra-high-performance LC-MS. Clin. Chem.. 59, 1338-1348.
    Pubmed CrossRef
  63. Madsen, R, Lundstedt, T, and Trygg, J (2010). Chemometrics in metabolomics--a review in human disease diagnosis. Anal. Chim. Acta. 659, 23-33.
    Pubmed CrossRef
  64. Malik, VS, Popkin, BM, Bray, GA, Despres, JP, and Hu, FB (2010). Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation. 121, 1356-1364.
    Pubmed KoreaMed CrossRef
  65. Maskarinec, G, Grandinetti, A, Matsuura, G, Sharma, S, Mau, M, Henderson, BE, and Kolonel, LN (2009). Diabetes prevalence and body mass index differ by ethnicity: the Multiethnic Cohort. Ethn. Dis.. 19, 49-55.
  66. Mihalik, SJ, Michaliszyn, SF, de las Heras, J, Bacha, F, Lee, S, Chace, DH, DeJesus, VR, Vockley, J, and Arslanian, SA (2012). Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care. 35, 605-611.
    Pubmed KoreaMed CrossRef
  67. Mooradian, AD, Haas, MJ, and Wong, NC (2004). Transcriptional control of apolipoprotein A?I gene expression in diabetes. Diabetes. 53, 513-520.
    CrossRef
  68. Mooradian, AD, Haas, MJ, Wehmeier, KR, and Wong, NC (2008). Obesity-related changes in high-density lipoprotein metabolism. Obesity (Silver Spring). 16, 1152-1160.
    Pubmed CrossRef
  69. Moore, S, and Stein, WH (1954). Procedures for the chromatographic determination of amino acids on four per cent cross-linked sulfonated polystyrene resins. J. Biol. Chem.. 211, 893-906.
  70. Moore, SC, Matthews, CE, Sampson, JN, Stolzenberg-Solomon, RZ, Zheng, W, Cai, Q, Tan, YT, Chow, WH, Ji, BT, and Liu, DK (2014). Human metabolic correlates of body mass index. Metabolomics. 10, 259-269.
    Pubmed KoreaMed CrossRef
  71. Newgard, CB, An, J, Bain, JR, Muehlbauer, MJ, Stevens, RD, Lien, LF, Haqq, AM, Shah, SH, Arlotto, M, and Slentz, CA (2009). A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab.. 9, 311-326.
    Pubmed KoreaMed CrossRef
  72. Nikiforova, VJ, Giesbertz, P, Wiemer, J, Bethan, B, Looser, R, Liebenberg, V, Ruiz Noppinger, P, Daniel, H, and Rein, D (2014). Glyoxylate, a new marker metabolite of type 2 diabetes. J. Diabetes Res.. 2014, 685204.
    CrossRef
  73. Nyenwe, EA, Jerkins, TW, Umpierrez, GE, and Kitabchi, AE (2011). Management of type 2 diabetes: evolving strategies for the treatment of patients with type 2 diabetes. Metabolism. 60, 1-23.
    Pubmed KoreaMed CrossRef
  74. Oberbach, A, Bluher, M, Wirth, H, Till, H, Kovacs, P, Kullnick, Y, Schlichting, N, Tomm, JM, Rolle-Kampczyk, U, and Murugaiyan, J (2011). Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes. J. Proteome Res.. 10, 4769-4788.
    Pubmed CrossRef
  75. Oliver, SG, Winson, MK, Kell, DB, and Baganz, F (1998). Systematic functional analysis of the yeast genome. Trends Biotechnol.. 16, 373-378.
    CrossRef
  76. Oresic, M, Simell, S, Sysi-Aho, M, Nanto-Salonen, K, Seppanen-Laakso, T, Parikka, V, Katajamaa, M, Hekkala, A, Mattila, I, and Keskinen, P (2008). Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J. Exp. Med.. 205, 2975-2984.
    Pubmed KoreaMed CrossRef
  77. Padberg, I, Peter, E, Gonzalez-Maldonado, S, Witt, H, Mueller, M, Weis, T, Bethan, B, Liebenberg, V, Wiemer, J, and Katus, HA (2014). A new metabolomic signature in type-2 diabetes mellitus and its pathophysiology. PLoS One. 9, e85082.
    CrossRef
  78. Palmer, ND, Stevens, RD, Antinozzi, PA, Anderson, A, Bergman, RN, Wagenknecht, LE, Newgard, CB, and Bowden, DW (2015). Metabolomic profile associated with insulin resistance and conversion to diabetes in the insulin resistance atherosclerosis study. J. Clin. Endocrinol. Metab. 100, E463-468.
    CrossRef
  79. Pataky, Z, Armand, S, Muller-Pinget, S, Golay, A, and Allet, L (2014). Effects of obesity on functional capacity. Obesity (Silver Spring). 22, 56-62.
    Pubmed CrossRef
  80. Pietil?inen, KH, Sysi-Aho, M, Rissanen, A, Sepp?nen-Laakso, T, Yki-J?rvinen, H, Kaprio, J, and Oresic, M (2007). Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects--a monozygotic twin study. PLoS One. 2, e218.
    Pubmed KoreaMed CrossRef
  81. Randle, PJ (1998). Regulatory interactions between lipids and carbohydrates: the glucose fatty acid cycle after 35 years. Diabetes Metab. Rev.. 14, 263-283.
    CrossRef
  82. Rhee, EP, Cheng, S, Larson, MG, Walford, GA, Lewis, GD, McCabe, E, Yang, E, Farrell, L, Fox, CS, and O’Donnell, CJ (2011). Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J. Clin. Invest.. 121, 1402-1411.
    Pubmed KoreaMed CrossRef
  83. Sadanala, KC, Jeongae, L, Bong Chul, C, and Man Ho, C (2012). Targeted metabolite profiling: sample preparation techniques for GC-MS-based steroid analysis. Mass Spectrometry Lett.. 3, 4-9.
    CrossRef
  84. Salek, RM, Maguire, ML, Bentley, E, Rubtsov, DV, Hough, T, Cheeseman, M, Nunez, D, Sweatman, BC, Haselden, JN, and Cox, RD (2007). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol. Genomics. 29, 99-108.
  85. Samad, F, Hester, KD, Yang, G, Hannun, YA, and Bielawski, J (2006). Altered adipose and plasma sphingolipid metabolism in obesity: a potential mechanism for cardiovascular and metabolic risk. Diabetes. 55, 2579-2587.
    Pubmed CrossRef
  86. Saris, WH (2003). Sugars, energy metabolism, and body weight control. Am. J. Clin. Nutr.. 78, 850S-857S.
  87. Scarfe, GB, Wright, B, Clayton, E, Taylor, S, Wilson, I, Lindon, JC, and Nicholson, JK (1998). 19F-NMR and directly coupled HPLC-NMR-MS investigations into the metabolism of 2-bromo-4-trifluoromethylaniline in rat: a urinary excretion balance study without the use of radiolabelling. Xenobiotica. 28, 373-388.
    Pubmed CrossRef
  88. Sellers, MB, Tricoci, P, and Harrington, RA (2009). A new generation of antiplatelet agents. Curr. Opin. Cardiol.. 24, 307-312.
    Pubmed CrossRef
  89. Sener, A, and Malaisse, WJ (1980). L-leucine and a nonmetabolized analogue activate pancreatic islet glutamate dehydrogenase. Nature. 288, 187-189.
    CrossRef
  90. Shah, SH, Kraus, WE, and Newgard, CB (2012). Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation. 126, 1110-1120.
    Pubmed KoreaMed CrossRef
  91. Shaham, O, Wei, R, Wang, TJ, Ricciardi, C, Lewis, GD, Vasan, RS, Carr, SA, Thadhani, R, Gerszten, RE, and Mootha, VK (2008). Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol.. 4, 214.
    CrossRef
  92. She, P, Reid, TM, and Bronson, SK (2007). Disruption of BCATm in mice leads to increased energy expenditure associated with the activation of a futile protein turnover cycle. Cell Metab.. 6, 181-194.
    Pubmed KoreaMed CrossRef
  93. Sims, EA, Danforth, E, Horton, ES, Bray, GA, Glennon, JA, and Salans, LB (1973). Endocrine and metabolic effects of experimental obesity in man. Recent Prog. Horm. Res.. 29, 457-496.
    CrossRef
  94. Siri-Tarino, PW, Sun, Q, Hu, FB, and Krauss, RM (2010). Saturated fat, carbohydrate, and cardiovascular disease. Am. J. Clin. Nutr.. 91, 502-509.
    Pubmed KoreaMed CrossRef
  95. Smith, PA, Sakura, H, Coles, B, Gummerson, N, Proks, P, and Ashcroft, FM (1997). Electrogenic arginine transport mediates stimulus-secretion coupling in mouse pancreatic beta-cells. J. Physiol.. 499, 625-635.
    CrossRef
  96. Sparks, DL, and Chatterjee, C (2012). Purinergic signaling, dyslipidemia and inflammatory disease. Cell Physiol. Biochem.. 30, 1333-1339.
    Pubmed CrossRef
  97. Sparks, DL, Doelle, H, and Chatterjee, C (2014). Circulating nucleotide levels in health and disease. Receptors Clin. Invest.. 1, e344.
  98. Stolar, MW (1988). Atherosclerosis in diabetes: the role of hyperinsulinemia. Metabolism. 37, 1-9.
    CrossRef
  99. Stumvoll, M, Goldstein, BJ, and van Haeften, TW (2005). Type 2 diabetes: principles of pathogenesis and therapy. Lancet. 365, 1333-1346.
    CrossRef
  100. Suhre, K, Meisinger, C, Doring, A, Altmaier, E, Belcredi, P, Gieger, C, Chang, D, Milburn, MV, Gall, WE, and Weinberger, KM (2010). Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 5, e13953.
    Pubmed KoreaMed CrossRef
  101. Tremblay, F, Lavigne, C, Jacques, H, and Marette, A (2007). Role of dietary proteins and amino acids in the pathogenesis of insulin resistance. Annu. Rev. Nutr.. 27, 293-310.
    Pubmed CrossRef
  102. Um, SH, D'Alessio, D, and Thomas, G (2006). Nutrient overload, insulin resistance, and ribosomal protein S6 kinase 1, S6K1. Cell Metab.. 3, 393-402.
    Pubmed CrossRef
  103. van Kampen, JJ, Burgers, PC, de Groot, R, Gruters, RA, and Luider, TM (2011). Biomedical application of MALDI mass spectrometry for small-molecule analysis. Mass Spectrom Rev.. 30, 101-120.
    Pubmed CrossRef
  104. Villarreal-P?rez, JZ, Villarreal-Mart?nez, JZ, Lavalle-Gonz?lez, FJ, Torres-Sep?lveda, MdR, Ruiz-Herrera, C, Cerda-Flores, RM, Castillo-Garc?a, ER, Rodr?guez-S?nchez, IP, and Villarreal, LEMd (2014). Plasma and urine metabolic profiles are reflective of altered beta-oxidation in non-diabetic obese subjects and patients with type 2 diabetes mellitus. Diabetol. Metab. Syndr.. 6, 129-136.
    Pubmed KoreaMed CrossRef
  105. Vinayavekhin, N, Homan, EA, and Saghatelian, A (2010). Exploring disease through metabolomics. ACS Chem. Biol.. 5, 91-103.
    Pubmed CrossRef
  106. Wahl, S, Yu, Z, Kleber, M, Singmann, P, Holzapfel, C, He, Y, Mittelstrass, K, Polonikov, A, Prehn, C, and R?misch-Margl, W (2012). Childhood obesity is associated with changes in the serum metabolite profile. Obes. Facts. 5, 660-670.
    Pubmed CrossRef
  107. Wang, TJ, Larson, MG, Vasan, RS, Cheng, S, Rhee, EP, McCabe, E, Lewis, GD, Fox, CS, Jacques, PF, and Fernandez, C (2011). Metabolite profiles and the risk of developing diabetes. Nat. Med.. 17, 448-453.
    Pubmed KoreaMed CrossRef
  108. Wang-Sattler, R, Yu, Z, Herder, C, Messias, AC, Floegel, A, He, Y, Heim, K, Campillos, M, Holzapfel, C, and Thorand, B (2012). Novel biomarkers for pre-diabetes identified by metabolomics. Mol. Syst. Biol.. 8, 615-625.
    CrossRef
  109. Warram, JH, Martin, BC, Krolewski, AS, Soeldner, JS, and Kahn, CR (1990). Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the offspring of diabetic parent. Ann. Intern. Med.. 113, 909-915.
    CrossRef
  110. Wenk, MR (2005). The emerging field of lipidomics. Nat. Rev. Drug Discov.. 4, 594-610.
    Pubmed CrossRef
  111. Williams, R, Lenz, EM, Wilson, AJ, Granger, J, Wilson, ID, Major, H, Stumpf, C, and Plumb, R (2006). A multi-analytical platform approach to the metabonomic analysis of plasma from normal and Zucker (fa/fa) obese rats. Mol. Biosyst.. 2, 174-183.
    Pubmed CrossRef
  112. Wylie-Rosett, J, and Davis, NJ (2009). Low-carbohydrate diets: an update on current research. Curr. Diab. Rep.. 9, 396-404.
    CrossRef
  113. Xie, B, Waters, MJ, and Schirra, HJ (2012). Investigating potential mechanisms of obesity by metabolomics. J. Biomed. Biotechnol.. 2012, 805683.
    CrossRef
  114. Xu, F, Tavintharan, S, Sum, CF, Woon, K, Lim, SC, and Ong, CN (2013). Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J. Clin. Endocrinol. Metab.. 98, E1060-1065.
    CrossRef
  115. Yamamoto, Y, Hirose, H, Saito, I, Tomita, M, Taniyama, M, Matsubara, K, Okazaki, Y, Ishii, T, Nishikai, K, and Saruta, T (2002). Correlation of the adipocyte-derived protein adiponectin with insulin resistance index and serum high-density lipoproteincholesterol, independent of body mass index, in the Japanese population. Clin. Sci.. 103, 137-142.
    CrossRef
  116. Yan, Y, Wang, Q, Li, W, Zhao, Z, Yuan, X, Huang, Y, and Duan, Y (2014). Discovery of potential biomarkers in exhaled breath for diagnosis of type 2 diabetes mellitus based on GC-MS with metabolomics. RSC Adv.. 4, 25430-25439.
    CrossRef
  117. Yi, LZ, He, J, Liang, YZ, Yuan, DL, and Chau, FT (2006). Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA. FEBS Lett.. 580, 6837-6845.
    Pubmed CrossRef
  118. Zeisel, SH (2000). Choline: an essential nutrient for humans. Nutrition. 16, 669-671.
    CrossRef
  119. Zeng, M, Liang, Y, Li, H, Wang, M, Wang, B, Chen, X, Zhou, N, Cao, D, and Wu, J (2010). Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis. J Pharm Biomed Anal. 52, 265-272.
    Pubmed CrossRef
  120. Zeng, M, Liang, Y, Li, H, Wang, B, and Chen, X (2011). A metabolic profiling strategy for biomarker screening by GC-MS combined with multivariate resolution method and Monte Carlo PLS-DA. Analytical Methods. 3, 438-445.
    CrossRef
  121. Zhang, A, Sun, H, Wang, P, Han, Y, and Wang, X (2012). Modern analytical techniques in metabolomics analysis. Analyst. 137, 293-300.
    Pubmed CrossRef
  122. Zhang, AH, Sun, H, Qiu, S, and Wang, XJ (2013). Recent highlights of metabolomics in chinese medicine syndrome research. Evid Based Complement Alternat Med. 2013, 402159.
    CrossRef
  123. Zhao, X, Fritsche, J, Wang, J, Chen, J, Rittig, K, Schmitt-Kopplin, P, Fritsche, A, Haring, HU, Schleicher, ED, and Xu, G (2010). Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics. 6, 362-374.
    Pubmed KoreaMed CrossRef


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30 April 2017 Volume 40,
Number 4, pp. 243~314

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