**6.3. Plasma lipidomics in obese rodent models**

In one study using shotgun lipidomics that captured 252 individual lipid species over 14 different classes, the authors aimed to link the circulating levels of blood plasma lipids to fat accumulated in various parts of the body including visceral adiposity and epicardial adipose tissue (EAT) (which are both cardiac disease risk factors) [44]. Via modelling analysis, a strong association was identified between visceral adiposity and plasma diacylglycerol (DAG) and EAT and triacylglycerol (TAG) (both DAG and TAG are composed of saturated fatty acids) [44]. In this study, EAT is also correlated with increased levels of phosphatidylglycerol (PG) species including PG 20:3/20:3 and PG 22:5/18:1 and with decreased levels of ether phosphatidylethanolamine (PE‐O) lipid species that are mainly composed of plasmalogens [44]. Additionally, Perreault and colleagues set out to determine the ability of lipidomics performed in the serum to predict ectopic lipid accumulation in skeletal muscle, in particular, the ability to predict TAG, DAG and ceramide. After analysis of 215 serum lipids, they found that in obese individuals, ganglioside C22:0 and lactosylceramide C14:0 levels in the serum predicted muscle TAG levels while serum DAG C36:1 and free fatty acid (FFA) C18:4 could predict muscle TAG levels. Furthermore, serum TAG C58:5, cholesterol ester C24:1, phosphatidylcholine C38:1 and FFA C14:2 were good predictors of the ceramide levels in muscle. Moving forward, confirmation of such findings could allow for a panel of plasma lipids to accurately depict the state of ectopic lipid deposition in peripheral tissues such as skeletal

While plasma is an obvious location to identify a prognostic marker, studying the lipidomic profile in adipose tissue itself or in other metabolic organs is of great interest to understand the biology of the condition. In a study of 20 obese, but otherwise healthy women, lipidomics was carried out on subcutaneous adipose tissue samples. Participants were divided into those with high content of liver fat or those with a low content of liver fat to determine if adipose tissue is altered in those discordant for intrahepatic lipid content. Analysis of 154 lipid species revealed increased concentrations of TAGs particularly long chain, and ceramides, specifically Cer (d18:1/24:1) in the group with more liver fat [46]. In another study carried out in obese insulin‐resistant women compared to obese women with normal insulin levels, lipid profiling revealed an increase in GM3 ganglioside and phosphatidylethanolamine (PE) lipid species in omental adipose tissue [47]. These findings corresponded to an increase in ST3GAL5, the synthesis enzyme for GM3 ganglioside, and a decrease in phosphatidylethanolamine methyl transferase (PEMT), the degradation enzyme of PEs [47]. Thus, these changes may contribute

In a human lipidomics screening of plasma and skeletal muscle samples comparing lean individuals with those who were obese or overweight but insulin sensitive (as defined by glucose infusion rate during a hyperinsulinemic‐euglycemic clamp) and those who were obese and overweight but insulin resistant, demonstrated that there was no defining difference in the skeletal muscle of those who were lean as compared to those that were overweight or obese [48]. The plasma samples did demonstrate a higher quantity of TAG and lower plasmalogen species in those who were overweight or obese compared to lean. However, in individuals who were overweight or had obesity but were discordant for insulin resistance those who

muscle and prove useful in the clinical setting [45].

**6.2. Tissue lipidomics in human obesity**

36 Adiposity - Omics and Molecular Understanding

to the obesity‐induced insulin‐resistant state.

Plasma lipidomics has been assessed in various rodent models of obesity and to investigate the effect on the lipidome of various intervention studies. Barber and colleagues performed lipidomics in plasma samples from mice fed a high‐fat diet for 12 weeks. Compared to low‐fat diet control mice, these mice had increased levels of TAG, DAG and sphingolipid species, while there was a reduction in LPC levels [43]. To describe these LPC effects further, a high‐ fat feeding time course study was completed which noted an increase in LPC 18:0 and 20:0 after just 1 week of high‐fat feeding. However, LPC 15:0, 16:1, 18:1, 18:2, 20:1 and 20:5 were all significantly decreased from baseline at 1 week and continued to be decreased out to 6 weeks of high‐fat feeding [43]. While most of the LPC species were decreased some species such as 18:0 were elevated. Also utilising a high‐fat diet to induce obesity, Li and colleagues identified LPC 18:0 as a potential biomarker of obesity [49] confirming the findings of Barber and authors.

#### **6.4. Lipidomic analysis in tissues from rodents**

Gaining access to rodent organ samples is far more convenient than human tissues and thus can be used more readily to access intra‐tissue lipid content. Sixteen‐week old wild type (WT) and the leptin‐deficient *ob/ob* mice, a genetic model of obesity that becomes overweight due to hyperphagia, were studied for their hepatic lipidomic profiles using non‐targeted analysis via ultra‐performance liquid chromatography (UPLC) coupled to quadrupole mass spectrometry (MS). The obese mice had an increased level of TAG and DAG lipid species in the liver as well as diacylphosphoglycerols and ceramide species, while there was a decrease in the sphingomyelins [50]. Using high‐fat feeding to induce obesity, Turner et al. performed a tissue lipid profiling study over a course of time of the high‐fat dietary intervention [51]. Liver TAG was increased after 1 week of high‐fat feeding and peaked at 16 weeks of high‐fat feeding whilst numerous DAG species were elevated in the liver at 1, 3 and 16 weeks of feeding. Hepatic ceramide content was unchanged after 1 week of high‐fat feeding, the 20:0 and 22:0 species elevated at 3 weeks of feeding and the 18:0 and 20:0 species increased at 16 weeks of high‐fat feeding. Whilst these species were increased with high‐fat feeding, the 24:1 and 24:0 species were significantly decreased at both 3 and 16 weeks of high‐fat feeding [51]. In the epididymal adipose tissue, TAG levels and numerous DAG species were elevated at 16 weeks of high‐fat feeding. Ceramide and sphingomyelin species were increased at both 1 and 16 weeks of high‐fat feeding. Analysis of the skeletal muscle revealed an increase in TAG and DAG levels at 3 and 16 weeks post‐high‐fat feeding, whilst in terms of ceramide levels, only the 18:0 ceramide species was increased at 3 and 16 weeks [51]. These studies have highlighted the lipid changes that occur in tissues during obesity induced by different means.

The use of metabolomics and lipidomics is set to continue as they may prove useful in providing indications of metabolic health in the obese setting. Identification of metabolites as"biomarkers" may make it possible to identify individuals at risk of developing obesity, which could be useful as a way of tracking disease progression and provide a predictive tool for diseases associated with obesity. These measures could also theoretically be used to give an indication of the effectiveness of new weight‐loss therapies and their impact on metabolism. Studies have already taken place investigating bariatric surgery, in particular, Roux‐en‐Y gastric bypass surgery. How this type of surgery impacts the metabolome was not very well described so Arora et al. analysed the plasma metabolome and lipidome of morbidly obese individuals prior to and after surgery to describe the effects of surgery [52]. From 96 metabolites and 192 molecular lipid species compared, the factors that were most different at 42 days post‐surgery were decanoic acid and octanoic acid whose levels increased and the sphingomyelins 18:1, 21:0 and 18:1, 22:3 whose levels decreased [52]. Moving forward, it is likely that a greater understanding of fluxomics will be utilised. Fluxomics is an analytical method that describes the rates of metabolic reactions within a sample. This is important as metabolism is a dynamic process, and metabolomics and lipidomics analysis will only provide a snapshot or static picture of the metabolic reactions in the cell. It could be stated well that it is not the quantity of metabolites that is important in the obese setting but the rates at which they undergo metabolic reactions.
