**5. Metabolomics**

Metabolites are small molecules that can be measured in bodily fluids including blood and urine as well as in tissue samples. Of particular interest are low‐molecular‐weight metabolites that are involved in metabolic pathways where they act as substrates, intermediates or products. Such metabolites include hormones, fatty acids and amino acids [26]. Metabolomics therefore is the analysis of the metabolite profile in a given sample, cell or organ. There are a number of different subtypes of metabolomics including lipidomics, glycomics, fluxomics and peptidomics. While the genome and in turn the proteome set the scene as to what biological processes take place or are dominant in a cell, the actual activity and function encoded by the genes and proteins is carried out via metabolites [26]. Consequently, the metabolome is impacted by the accumulation of all the genetic variation, epigenetic status, gene and protein expression, enzymatic activity and environmental factors that are expressed in or exerted on an organism. Given that the large array of metabolites are formed in biological systems, this is a complex task. Most often, the techniques used to profile metabolomics are nuclear magnetic resonance spectroscopy (NMR) and gas chromatography mass spectrometry (GC‐MS). In general, metabolomics is a high‐throughput technology and while initial start‐up costs of purchasing equipment and providing the infrastructure is expensive, once established, on a per‐sample basis, it is relatively inexpensive. Short of this, there are many centres that specialise in metabolomic measures set up throughout the world. These centres often will provide metabolomics data on samples for a fee‐for‐service arrangement.

#### **5.1. Metabolomic analysis in obese humans**

Though adipose tissue is obviously important to study, other metabolic organs are also too important to study. Baiges et al. [24] investigated the effects of a high‐fat diet (HFD) on the rat liver proteome. Analysis identified 1,131 liver proteins and demonstrated that a high‐fat diet changed the expression of 90 of these proteins. As one would have hypothesised, many of these proteins are involved in glucose and lipid metabolism [24]. Proteomics can also be further enhanced by analysing sub‐cellular compartments such as mitochondria. In a study comparing lean versus obese mice, Nesteruk et al. [25] purified isolated mitochondria from both liver and skeletal muscle and analysed mitochondrial‐associated proteins via mass spectrometry. Analysis identified 1,675 liver and 704 muscle mitochondria‐associated proteins and of these, 221 liver and 44 muscle proteins were differentially expressed between the lean and obese groups [25]. Analysis of"sub‐proteomes" such as the ones at the mitochondrial proteome may be used more in the future to identify cellular compartment‐ specific changes associated with obesity. This type of analysis is also beneficial in the sense that it can increase the sensitivity of the process and allow the detection of lowly expressed

Looking into the future, the combination and integration of proteomics analysis with other"omic" platform analysis such as genomics, transcriptomics and metabolomics may provide a useful systems biology approach to further understand the regulation of whole‐body

Metabolites are small molecules that can be measured in bodily fluids including blood and urine as well as in tissue samples. Of particular interest are low‐molecular‐weight metabolites that are involved in metabolic pathways where they act as substrates, intermediates or products. Such metabolites include hormones, fatty acids and amino acids [26]. Metabolomics therefore is the analysis of the metabolite profile in a given sample, cell or organ. There are a number of different subtypes of metabolomics including lipidomics, glycomics, fluxomics and peptidomics. While the genome and in turn the proteome set the scene as to what biological processes take place or are dominant in a cell, the actual activity and function encoded by the genes and proteins is carried out via metabolites [26]. Consequently, the metabolome is impacted by the accumulation of all the genetic variation, epigenetic status, gene and protein expression, enzymatic activity and environmental factors that are expressed in or exerted on an organism. Given that the large array of metabolites are formed in biological systems, this is a complex task. Most often, the techniques used to profile metabolomics are nuclear magnetic resonance spectroscopy (NMR) and gas chromatography mass spectrometry (GC‐MS). In general, metabolomics is a high‐throughput technology and while initial start‐up costs of purchasing equipment and providing the infrastructure is expensive, once established, on a per‐sample basis, it is relatively inexpensive. Short of this, there are many centres that specialise in metabolomic measures set up throughout the world. These centres often will provide metabolomics data on samples for a

proteins.

fat mass.

**5. Metabolomics**

32 Adiposity - Omics and Molecular Understanding

fee‐for‐service arrangement.

For an in‐depth description of the amino acid metabolism, lipid metabolism, carbohydrate metabolism and nucleotide metabolism‐related metabolites that have been shown to be altered in the setting of obesity or diabetes or obesity with diabetes, we refer the readers to the review by Park et al. [27]. Herein, we will discuss some of these changes and provide a separate section on a popular sub‐branch of metabolomics that concentrates on studying lipids (lipidomics). In the 1970s, it was demonstrated that obese individuals have higher circulating levels of numerous amino acids including branched‐chain amino acids (BCAAs) [28]. Recently, utilising new analytical techniques, a number of studies have confirmed and expanded on these findings. In a study, utilising metabolomics and lipidomics of blood plasma and urine to investigate association of metabolites with adiposity, it was demonstrated that there were seven metabolites that were important in predicting visceral fat levels, which included the amino acids tyrosine and glutamine and the lipid species PC‐O 44:6, PC‐O 44:4, PC‐O 42:4, PC‐O 40:4 and PC‐O 40:3 [29]. In an alternative study conducted in obese Japanese individuals, plasma levels of amino acids are found to be associated with visceral fat accumulation. These amino acids included levels of alanine, glycine, glutamate, tryptophan and tyrosine [30]. In a metabolomic profiling study of 74 obese and 67 lean individuals, a number of differences in fatty and amino acids were reported [31]. Levels of free fatty acids C14:0, C16:0, C16:1, C18:1, C20:4 remained elevated in obese as compared to lean subjects. Levels of eight amino acids increased in obese as compared to lean individuals including alanine, arginine, asparagine, glutamine, leucine, phenylalanine, tyrosine and valine while conversely one decreased (glycine). Four acylcarnitine species (C3, C5, C6 and C8:1) were higher in the samples from obese individuals [31]. The data suggested the existence of a (BCAA)‐related metabolic signature in obesity and linking this to metabolic dysregulation, these changes were associated with insulin resistance [31].

Metabolomic profiling may assist in distinguishing different types of obesity. As metabolic abnormalities are associated with central obesity more so than they are with peripheral obesity, Gao et al. set out to identify via metabolomics whether serum metabolic markers differ in those with central versus peripheral obesity [32]. Five types of metabolites were verified to be higher in the central obesity group after multiple testing adjustments. These included the BCAAs leucine, isoleucine and valine as well as alpha‐aminoadipic acid and propionylcarnitine (C3 acylcarnitine) [32]. These metabolites may provide a useful mechanistic insight into determining the difference between metabolically healthy peripheral obesity and metabolically unhealthy central obesity. Another way to discriminate against metabolically healthy obesity and metabolically abnormal obesity is to divide patients on the basis of whether they have any form of hyperglycemia, hypertension or dyslipidemia. Chen et al. conducted such study in obese individuals from a weight‐loss clinic and could indeed identify differential metabolic profiles and metabolic pathways [33]. These groups differed in L‐kynurenine, glycerophosphocholine (GPC), glycerol 1‐phosphate, glycolic acid, tagatose, methyl palmitate and uric acid. The pathways that could distinguish between the obese metabolically healthy and unhealthy groups were pathways involved in fatty acid biosynthesis, phenylalanine metabolism, and valine, leucine and isoleucine degradation pathway [33].

Variants in the fat mass and obesity‐associated gene FTO has been identified as a risk factor for the accumulation of fat mass and the development of obesity [8, 34]. By correlating metabolites after stratification for whether or not an individual is a carrier of the FTO risk allele, genetic FTO‐induced changes in the metabolome can be assessed. Via utilising samples obtained as part of a Korean community‐based cohort (KARE cohort), Kim et al. [35] used serum metabolite quantification by targeted metabolomics to correlate FTO‐genotype with alterations to metabolites. This resulted in the analysis of 134 different metabolites (78 glycerophospholipids, 21 amino acids, 12 sphingolipids, 12 acylcarnitines, 10 biogenic amines and 1 hexose). Of these metabolites, the authors found that seven metabolites were associated with increased risk of obesity due to the presence of the rs9939609 FTO risk allele [35]. Most notably, of these seven metabolites, five were phosphatidylcholines (PCs) (C36:5, C36:6, C38:5, C38:5, C38:6 and C40:6) and these showed the strongest effect, while the monosaccharide (hexose) and amino acid (valine) were also associated. Similar future studies investigating the impact of genetic risk factors on the metabolome can provide insight into how genes impact metabolism and contribute to the development of obesity.

#### **5.2. Metabolomic and obesity in animal models**

Using mouse models that have had a genetic manipulation (knock‐out, knock‐down overexpression), metabolomics can be used to gain an idea as to the impact a genetic modification has on obesity or metabolic pathways. Neuroblast differentiation‐associated protein AHNAK knock‐out mice (AHNAK(‐/‐)) have been reported as having a phenotype whereby they have a strong resistance to high‐fat diet‐induced obesity. Consequently, Kim et al. [36] applied (1) H NMR‐based metabolomics to compare the altered metabolites in the urine from high‐ fat diet (HFD) fed wild‐type and AHNAK(‐/‐) mice. The profiling identified that the urinary metabolites of HFD‐fed AHNAK(‐/‐) mice gave higher levels of methionine, putrescine, tartrate, urocanate, sucrose, glucose, threonine and 3‐hydroxyisovalerate compared to wild‐type mice suggesting that the resistance to the HFD‐induced obesity may arise from alterations in amino acids [36]. Likewise, human ataxin‐2 (ATXN2) knock‐out mice display obesity, insulin resistance and dyslipidemia [37]. To understand the effects of the loss of ATXN2, Meierhofer and team used unbiased profiling approaches to quantify the global metabolome of ATXN2 knock‐out mice with label‐free mass spectrometry [38]. Significant down‐regulated pathways for branched chain and other amino acid metabolism, fatty acids and citric acid cycle provided evidence for the biological function of ATXN2 and the potential mechanism via which the lean phenotype is maintained [38]. Thus, metabolomics used in combination with genetic models offers a viable way to determine biological significance of genes and improve understanding of cellular pathways.
