Journal of Diabetes Research

Journal of Diabetes Research / 2018 / Article

Review Article | Open Access

Volume 2018 |Article ID 6270875 |

Paulina Samczuk, Michal Ciborowski, Adam Kretowski, "Application of Metabolomics to Study Effects of Bariatric Surgery", Journal of Diabetes Research, vol. 2018, Article ID 6270875, 13 pages, 2018.

Application of Metabolomics to Study Effects of Bariatric Surgery

Academic Editor: Andrea Tura
Received12 Aug 2017
Revised30 Dec 2017
Accepted28 Jan 2018
Published11 Mar 2018


Bariatric surgery was born in the 1950s at the University of Minnesota. From this time, it continues to evolve and, by the same token, gives new or better possibilities to treat not only obesity but also associated comorbidities. Metabolomics is also a relatively young science discipline, and similarly, it shows great potential for the comprehensive study of the dynamic alterations of the metabolome. It has been widely used in medicine, biology studies, biomarker discovery, and prognostic evaluations. Currently, several dozen metabolomics studies were performed to study the effects of bariatric surgery. LC-MS and NMR are the most frequently used techniques to study main effects of RYGB or SG. Research has yield many interesting results involving not only clinical parameters but also molecular modulations. Detected changes pertain to amino acid, lipids, carbohydrates, or gut microbiota alterations. It proves that including bariatric surgery to metabolic surgery is warranted. However, many molecular modulations after those procedures remain unexplained. Therefore, application of metabolomics to study this field seems to be a proper solution. New findings can suggest new directions of surgery technics modifications, contribute to broadening knowledge about obesity and diseases related to it, and perhaps develop nonsurgical methods of treatment in the future.

1. Introduction

Based on historical reports, origins of bariatric surgery date back to the 10th century, when King of León, Sancho I (called the Fat), was treated by famous Jewish doctor, Hasdai ibn Shaprut [1]. The homeland of bariatric surgery in modern meaning is the University of Minnesota. The first metabolic surgery was the jejunoileal bypass performed there by Arnold J. Kremen in 1954 [1, 2]. Nevertheless, success has many parents; therefore, many great minds were involved in the bariatric surgery development. As the pioneers in this medicine field, Henry Buchwald, Richard L. Varco, Edward E. Manson (long recognized as the father of bariatric surgery), Allan C. Wittgrove, Nicola Scopinaro, Walter J. Pories, Picard Marceau, and Douglas S. Hess have to be mentioned [1, 3]. Of course, they are a small group from all people developing bariatric surgery discipline, but their contributions to this area are incontrovertible.

Bariatric surgery has come a long way since the first procedures. Its evolution can be seen from several perspectives. Henry Buchwald spotlights the fact that bariatric surgery is metabolic surgery. In 1978, he and Richard L. Varco published a book entitled Metabolic Surgery in which they defined it as “the operative manipulation of a normal organ system to achieve a biological result for a potential health gain.” Therefore, in the early beginnings of bariatric surgery, not only weight loss but also general health improvement was already expected. Other years carried out other studies, and new reports about benefits are evoked by bariatric procedures. Toward the end of the previous century, it was communicated that bariatric surgery can resolve type 2 diabetes mellitus. What is really interesting is that the remission of T2DM can occur very fast, even before the patients can reduce their weight [46]. It has also been pointed out that bariatric procedures improve many other clinical parameters (BMI, HbA1c, glucose and cholesterol levels, insulin resistance, and modulations of gut hormones) [7]. However, those modulations are not the core of this paper. Evidently, like every surgical intervention, it is related to some risks and disadvantages. Depending on the type of surgery, patients can be exposed to, for example, dumping syndrome or be forced to lifetime vitamin supplementation [6]. However, the growth of laparoscopic surgery with its reduced complications such as wound infection, incisional hernias, and lower early postoperative morbidity and mortality resulted in shorter hospital stay, faster recovery, lower morbidity, and improved effects, which has led to an ever-increasing patient demand [1, 8, 9]. Therefore, bariatric surgery has become the most effective treatment of morbid obesity and associated comorbidities, such as sleep apnea, hypertension, dyslipidaemia, and type 2 diabetes [8, 10, 11]. Thus, these kinds of surgical procedures not only reduce overall mortality but also improve patients’ quality of life [9, 12].

The history of bariatric surgery is dominated by six procedures, which are jejunoileal bypass (JIB), Roux-en-Y gastric bypass (RYGB), vertical-banded gastroplasty (VBG), biliopancreatic diversion (BPD) or its familiar duodenal switch (DS), adjustable gastric banding (AGB), and sleeve gastrectomy (SG) [3, 6]. Nowadays, Roux-en-Y gastric bypass and sleeve gastrectomy are considered as the “gold standard” bariatric interventions [9, 1316].

Similarly to those of bariatric surgery, the origins of metabolomics can be found far back in the past—in ancient Greece. Likewise, the beginning of metabolomics in modern meaning is estimated to be in the 1960s, when during metabolic-control analysis, the mathematical method for cell metabolism modelling was developed. The second starting point was the development of nuclear magnetic resonance (NMR) spectroscopy [17]. Currently, Oliver Fiehn and Jeremy K. Nicholson are considered as pioneers in the metabolomics (metabonomics) field [1820]. Nowadays, both terms are used interchangeably. Jeremy K. Nicholson described differences between these terms as philosophical rather than technical. Metabolomics looks for an analytical description of complex biological samples. Its aim is to characterize and quantify all the small molecules in the studied sample. In the meantime, metabonomics is described as global measurement of dynamic metabolic response of living systems to biological stimuli or genetic manipulation. It is focused on understanding systemic modulations of complex multicellular systems through the time. Actually, modelling procedures for both of them are the same [17]. Metabolomics analyses are based on stand-alone hydrogen nuclear magnetic resonance (1H NMR) technique or mass spectrometry technique combined with different metabolite chromatographic separation methods, that is, liquid chromatography (LC), gas chromatography (GC), or capillary electrophoresis (CE). This range of analytical platforms enable detection, characterization, and quantification of low-molecular-weight metabolites from different classes, for example, lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols, carbohydrates, and many other metabolites in which mentioned species can be metabolised. NMR can uniquely identify and simultaneously quantify a wide range of organic compounds in the micromolar range. It has been used for analysis of amino acids, nucleotides and nucleosides, vitamins, thiols, carbohydrates, and peptides. The LC-MS method has become a useful tool for the analysis of hundreds of polar metabolites in a complex sample. It is an important tool used for targeted or nontargeted metabolomics. Liquid chromatography separation is better suited for the analysis of labile and nonvolatile polar (hydrophilic interaction liquid chromatography (HILIC)) and nonpolar (reversed-phase chromatography) compounds in their native forms. Additionally, MS and LC are commonly used for compound characterization and to obtain structural information. GC-MS has been used as a platform especially for hydrophilic metabolites. Using this approach, one can directly separate and quantify the volatile metabolites. It allows to profile several hundreds of compounds including organic acids, most amino acids, sugars, sugar alcohols, aromatic amines, and fatty acids. CE-MS has been used for both targeted and nontargeted analyses of polar and ionic metabolites, including analysis of inorganic ions, organic acids, amino acids, nucleotides and nucleosides, vitamins, thiols, carbohydrates, and peptides [21, 22].

Therefore, metabolomics is a powerful tool for the comprehensive study of the dynamic alterations of the metabolome. It has been widely used in the areas of medicine, biology, and physiology for biomarker discovery or for prognostic evaluations [2327].

The aim of this study was to find and classify all studies in which the metabolomics approach was used to study the metabolic effects of bariatric surgery published up till now. Additionally, detected metabolites were investigated together to obtain conclusions about the impact of bariatric surgery on particular biochemical pathways.

2. Methods

2.1. Data Source and Study Selection

PubMed was searched for keywords such as metabolomics, bariatric surgery, LC-MS, GC-MS, CE-MS, NMR, LSG, and RYGB. The last search was performed in July 24, 2017, and only publications up to this date are included. Additionally, results were limited to papers written in the English language. All studies, regardless of species (humans, rats, and mice) and biological samples used (blood, urine, and tissues), were included in this review. Intervention trials in which metabolomics techniques were used to study changes after bariatric surgery were investigated. In one study, influence of diet and surgery was compared, but only blood taken during bariatric procedure was analyzed [28]. This study (and animal studies) is reviewed, but its results were not considered during the MetaboAnalyst analysis.

2.2. Metabolomics Data Analysis with MetaboAnalyst 3.0

MetaboAnalyst 3.0 was used to perform biochemical interpretation of all altered metabolites. This online tool ( allows to analyze impact of particular compounds on biochemical pathways. In MetaboAnalyst 3.0, there are currently 15 pathway libraries supported, with a total of 1173 pathways (80 for Homo sapiens). The pathway analysis module combines results from the powerful pathway enrichment analysis with those from the pathway topology analysis. Pathway analysis accepts a list of compound labels (common names, HMDB IDs, or KEGG IDs). Next, Fisher’s exact test or hypergeometric test is used. The results from the pathway analysis are presented graphically as well as in a detailed table [29].

3. Results

Of all initially retrieved studies, 30 successfully fit the criteria for this review. The first study on the application of NMR to examine effects of biliopancreatic diversion and Roux-en-Y gastric bypass was published in 2010. Since then, the number of papers per year is still rising (Figure 1). Additionally, also other metabolomics platforms were used to investigate the influence of different types of surgeries on metabolome. This proves the growing interest on bariatric surgery and applicability of metabolomics to investigate this metabolic surgery.

Among all examined procedures [7, 11, 13, 25, 26, 28, 3039], Roux-en-Y gastric bypass comprised over half (53.7%). The second most studied was sleeve gastrectomy (29.3%) [7, 26, 27, 30, 31, 3844]. Investigations on other techniques—duodenal-jejunal endoluminal bypass (4.9%) [37, 45], laparoscopic gastric banding (4.9%) [7, 46], biliopancreatic diversion (4.9%) [47, 48], and duodenal-jejunal endoluminal bypass liner (2.4%) [49]—were definitely less often.

Liquid chromatography coupled with mass spectrometry [7, 11, 13, 25, 3032, 4144, 48, 50, 51] and hydrogen nuclear magnetic resonance [11, 26, 33, 35, 3740, 4547, 49] were the most commonly used analytical platforms (Figure 1, Table 1). Gas chromatography was also a rather commonly used technique [7, 27, 28, 3437, 42, 44, 52]. Hitherto, capillary electrophoresis was not applied to study the effects of bariatric procedures. There was no domination of targeted (51%) or untargeted (49%) type of metabolomics analysis.

YearFirst authorRefMetabolomics techniqueSample typeSurgery typeStudied diseaseGroup sizeTime points

2017Liu, Ruixin[42]Untargeted and targeted (amino acids)HPLC-MS and GC-MSHuman and animal (mice C57BL/6)SerumSGObesity29 (23 obese and 6 cases)Before and one and three months after surgery
2016Zhao, Linjing[51]Targeted (FFA)UPLC-QTOF MSHumanSerumRYGBObesity and T2DM419 individuals (38 obese and diabetic after RYGB and 381 diabetic and nondiabetic with overweight or obesity)Baseline, 1 year after RYGB
2016Narath, Sophie H.[32]UntargetedLC-HRMS, HILICHumanSerumRYGBObesity44 patients2–4 weeks before surgery (PRE), 1–3 weeks after surgery (POST), and one year after surgery follow-up (FU)
2016Lopes, Thiago I.B.[33]Untargeted1H NMRHumanPlasmaRYGBObesity and T2DM10 subjectsBefore and 12 months after surgery
2016Sarosiek, Konrad[44]UntargetedUHPLC-MS/MS and GC-MSHumanSerumSG or full GBObesity and T2DM15 patients (nondiabetic with SG and diabetic with SG or GB)Baseline (prior to diet/surgery) and 14 and 28 days after surgery)
2016Jung, Jeeyoun[45]Untargeted and targeted1H NMRAnimal (rats, 12-wk-old male Sprague-Dawley)Serum and liver tissueDJBSurgery impact15 serum (7 sham, 8 DJB), 16 tissue (7 sham, 9 DJB)No time points, controls versus DJB rats
2016Chouiali, Ahlem[48]Targeted (vit. D)LC-MS (QQQ)HumanSerumBPDBariatric population supplemented with vit. D48 healthy subjects, post-BPD: 44 supplemented with vit. D3 and 30 with vit. D2During patients follow-up
2016Nemati, Reza[30]Targeted (NEFAs)LC-MS/MS (QQQ)HumanPlasmaLGB (Roux), LSGObesity and T2DM38 obese patients—11 GBP, 14 SG, 13 VLCDBefore and 3 days after intervention
2016García-Alonso, Veronica[50]Targeted (eicosanoids)LC-MS/MS (QQQ)HumanOmental adipose tissue“Laparoscopic bariatric surgery”Regulatory actions of PGs in human omental WAT from obese patients12 obese individuals after bariatric surgery and 10 patients without obesity after laparoscopic cholecystectomySurgery versus controls
2016Bankoglu, Ezgi Eyluel[13]Targeted (8-oxoGua, 8-oxodG, 8-oxoGuo)LC-MS/MS (QQQ)Animal (rats, 12-wk-oldmale Zucker)UrineRYGBObesity, oxidative/nitrative stress, genomic damage15 RYGB, 17 sham surgery0 and 27 days
2016Stratmann, B.[49]Targeted (lipoproteins)1H NMRHumanSerumDJBLMorbid obesity and T2DM18 subjects, finally 16; lipidome for 10 patientsNMR lipidomics baseline and 12 months
2016Luo, Ping[25]UntargetedUPLC-MSHumanSerumRYGBObesity and T2DM35 subjects—23 remission, 12 nonremission of T2DMAt baseline and 6 and 12 months after RYGB
2015Gralka, Ewa[38]Untargeted1H NMRHumanSerumSG, proximal or distal RYGBObesity106 obese patients (19 SG, 27 proximal RYGB, 60 distal RYGB), 19 normal weight volunteers, 30 subject with matched BMIBefore and 3, 6, 9, and 12 months after procedures
2015Bojsen-Moller, Kristine N.[34]Targeted (leucine, phenylalanine)GC-MSHumanPlasmaRYGBObesity10 obese subject after RYGB (1 out BCZ complications); 9 obese glucose-tolerantBefore and 3 months after surgery
2015Calvo, Nahum[40]Untargeted1H NMRHumanHepatic tissueLSGNonalcoholic fatty liver disease (NAFLD)47 patients, finally included 19Before (<24 h) and 12 months after surgery
2015Modesitt, Susan C.[7]UntargetedGC-MS and UPLC-MS/MSHuman (women only)SerumLRYGB, LSG, LGB, open RYGBEndometrial histology, obesity71 (41 LRYGB, 17 LSG, 8 LGB, 2 open RYGB, 3 no surgery)Pre- and postoperatively
2015Arora, Tulika[36]UntargetedGC-MS and UPLC-MSHumanPlasmaRYGBInsulin-resistant morbidly obese subjects, some with diabetes16 patientsPresurgery and 4 and 42 days after surgery
2015Lopes, Thiago I.B.[35]Untargeted and targeted (FA)1H NMR and GC-MSHumanPlasmaRYGBObesity and T2DM10 patientsBefore and 12 months after surgery
2015Hertel, Johannes[26]Untargeted1H NMRHumanUrineSG, RYGBMeasurement of biological age based on metabolomics profiles, expanded on clinical samples of obese patients after bariatric surgery4068 individuals from SHIP-0 at baseline, 996 from SHIP-TREND;38 individuals after surgeryFor operated patients: preoperative and postoperative (median follow-up 366.5 days)
2014Kwon, Hyuk Nam[37]Untargeted and targeted (3-HB)1H NMR and GC-MSHumanPlasmaRYGB, DJBPatients who underwent surgery for uncontrolled diabetes22 patientsMetabolic profile 7 days after surgery
2014Oberbach, Andreas[31]Targeted (sUA)LC-MS/MSHumanSerumLSG and RYGBObesity10 severely obese adolescents (5 LSG, 5 RYGB) and 17 normal weightPre- and 12 months postoperatively
2014Kaska, Lukasz[52]Targeted (FA)GC-MSHumanSerumAnalysis before the surgeryMorbid obesity, nondiabetic16 womenSamples from the day of surgery
2014Jüllig, Mia[27]UntargetedGC-MSHumanPlasmaGB, SGSubject with T2D undergoing GBP or SG15 subjects (8 GBP, 7 SG)3 days before and 3 days after surgery
2014Heffron, Sean P.[46]TargetedNMR spectroscopyHumanFasting blood samplesLGBObesity and obesity-relatedcomorbidity50 obese patients, 47 with completed follow-upBaseline and 1, 2, 3, 4, and 5 years after operation
2013Sledzinski, Tomasz[28]Targeted (FA)GC-MSHuman and animal (Rats, 10-wk-old male Wistar)Serum and adipose tissueRYGBObesity16 obese nondiabetic women after RYGB, 20 rats (10 controls, 10 diet)Tissue from women during surgery, rats one month after starting diet
2013Ashrafian, Hutan[11]Untargeted1H NMR and UPLC-MSAnimal (rats, male Wistar)Plasma and heart tissue extractsRYGBBariatric surgical on cardiac metabolites27 rats—13 RYGB, 14 sham-operated8 weeks postoperation
2012Oberbach, Andreas[41]TargetedLC-MSHumanSerumLSGMorbid obesity (children)6 obese childrenPre- and 6 months postoperatively
2012Friedrich, Nele[39]Untargeted and targeted1H NMRHumanUrineSG, RYGBObesity50 patients (39 SG, 11 RYGB) 50 controls; finally 47 preoperation, 45 postoperation, 48 controlsPre- and postoperatively (3, 4, 5, 6, 7, and 9 days postsurgery)
2011Oberbach, Andreas[43]TargetedLC-MSHumanSerumSGObesity14 obese after SG, 12 on hypocaloric diet, 17 healthy subjectsComparison obese subjects and controls, samples six month after treatment
2010Calvani, R.[47]Untargeted1H NMRHumanUrineBPD, RYGBMorbid obesity (insulin resistant)15 obese subjects, 10 matched controls in general, 2 subjects after bariatric surgery30 and 90 days after surgery

Most of the studies were focused on human samples; only few were performed on rats [11, 13, 28, 45] or mice [42]. In some of them, animal and human studies were combined [28, 42].

Serum (47%) [7, 25, 28, 31, 32, 38, 4145, 48, 49, 51, 52] and plasma (25%) [11, 27, 30, 33, 34, 37] were the most commonly used biological materials. Other analyzed samples were urine (12%) [13, 26, 37, 39] and different types of tissues (16%)—heart [11], liver [40, 45], or adipose tissue [28, 50]. Interestingly, a study on atypical material (omental adipose tissue) was performed by García-Alonso et al. [50].

Most of the studies were focused on obesity, including morbid obesity, and type 2 diabetes. Interestingly, in one research, another disease, that is, nonalcoholic fatty liver disease (NAFLD), was examined [40]. Some of the studies were focused only on effects of bariatric surgery. In one interesting study, measurement of biological age was performed on a group of obese patients after bariatric surgery [26].

Usually, researches were performed on medium-sized groups, that is, 10–20 patients. There were also few studies on really small (below 10), big (30–50), or really big (up to 100) [38] groups of patients. For animal study standards, a really large group of rats (27 animals) was used to study RYGB surgery effects [11].

Considering time intervals, very different time points were examined. Most of the studies contained baseline and then one or more follow-up points. The shortest difference between baseline and the first analyzed time point (below 7 days after surgery) was presented by Jüllig et al. [27], Nemati et al. [30], Arora et al. [36], Gralka et al. [38], and Friedrich et al. [39]. The longest follow-up was presented in the study performed by Heffron—1, 2, 3, 4, and 5 years after the surgery [46].

From examined studies, over 300 metabolites were selected. After removing duplicates, standardizing names (taking into account synonyms), and assigning HMDB IDs, we introduced 224 compounds into MetaboAnalyst 3.0 software. Finally, the software used 211 of them for pathway analysis. Obtained results showed that metabolites altered by bariatric surgery belong to 63 biochemical pathways. A statistically significant influence was exhibited for 23 of them. Based on the MetaboAnalyst results, the most impacted pathways after bariatric interventions are aminoacyl-tRNA biosynthesis; glycine, serine, and threonine metabolism; nitrogen metabolism; phenylalanine metabolism; cysteine and methionine metabolism; TCA cycle (citrate cycle); taurine and hypotaurine metabolism; valine, leucine, and isoleucine biosyntheses; propanoate metabolism; and nicotinate and nicotinamide metabolism (Figure 2).

All the above-described data are detailed in Tables 1 and 2.

Pathway nameFDRImpact

Aminoacyl-tRNA biosynthesis0.16902
Glycine, serine, and threonine metabolism0.48394
Nitrogen metabolism
Phenylalanine metabolism0.011770.20468
Cysteine and methionine metabolism0.0152990.54182
Citrate cycle (TCA cycle)0.00148740.0154530.28353
Taurine and hypotaurine metabolism0.00148740.0154530.46583
Valine, leucine, and isoleucine biosyntheses0.00154670.0154530.12084
Propanoate metabolism0.00173840.0154530.07344
Nicotinate and nicotinamide metabolism0.00207640.0166110.06485
Alanine, aspartate, and glutamate metabolism0.00412180.0299770.53182
Arginine and proline metabolism0.00451570.0301040.4641
Synthesis and degradation of ketone bodies0.00514670.0316720.7
Pyrimidine metabolism0.00576440.0329390.22308
Methane metabolism0.00627920.0334890.18217
Glutathione metabolism0.0117330.0586660.34321
Glyoxylate and dicarboxylate metabolism0.0167070.0775240.1897
Pyruvate metabolism0.0177330.0775240.42654
Purine metabolism0.0184120.0775240.30417
Pantothenate and CoA biosyntheses0.0312740.12510.0
D-Glutamine and D-glutamate metabolism0.0330630.125960.35294
Valine, leucine, and isoleucine degradation0.0482330.167770.0835
Butanoate metabolism0.0482330.167770.18589
Glycolysis or gluconeogenesis0.0530560.176850.09576
Linoleic acid metabolism0.0748810.239620.65625
Cyanoamino acid metabolism0.0877760.270080.0
Primary bile acid biosynthesis0.0919950.272580.08068
D-Arginine and D-ornithine metabolism0.0964270.275510.0
Sulfur metabolism0.116090.313150.06614
Glycerophospholipid metabolism0.117430.313150.17061
Galactose metabolism0.137570.355030.24385
Vitamin B6 metabolism0.164540.398890.24174
Glycerolipid metabolism0.164540.398890.27975
Thiamine metabolism0.216330.509010.0
Fatty acid biosynthesis0.230920.521230.0
Sphingolipid metabolism0.234550.521230.04244
Tyrosine metabolism0.247720.53560.12506
Phenylalanine, tyrosine, and tryptophan biosyntheses0.271740.572080.008
beta-Alanine metabolism0.290580.596050.0
alpha-Linolenic acid metabolism0.309510.619020.20335
Histidine metabolism0.343480.670210.21313
Selenoamino acid metabolism0.441980.841860.00321
Biotin metabolism0.53610.99740.0
Ascorbate and aldarate metabolism0.593251.00.13047
Arachidonic acid metabolism0.609981.00.2595
Lysine degradation0.623331.00.14675
Glycosylphosphatidylinositol- (GPI-) anchor biosynthesis0.624011.00.0
Tryptophan metabolism0.624321.00.24863
Pentose phosphate pathway0.645911.00.0
Lysine biosynthesis0.645911.00.09993
Starch and sucrose metabolism0.665551.00.0765
Pentose and glucuronate interconversions0.704261.00.02401
Porphyrin and chlorophyll metabolism0.713741.00.05249
Inositol phosphate metabolism0.750541.00.18387
Riboflavin metabolism0.769961.00.0
Caffeine metabolism0.769961.00.0305
Fatty acid elongation in mitochondria0.849211.00.0
Terpenoid backbone biosynthesis0.901261.00.0
Ubiquinone and other terpenoid-quinone biosyntheses0.920131.00.0
Amino sugar and nucleotide sugar metabolism0.94391.00.0
Fructose and mannose metabolism0.965911.00.0
Steroid hormone biosynthesis0.968781.00.10049
Fatty acid metabolism0.970431.00.02959

Raw is the original value calculated from the enrichment analysis; the FDR is the value adjusted using false discovery rate; the Impact is the pathway impact value calculated from pathway topology analysis.

4. Discussion

Despite the substantial research activities in the last years, many molecular aspects of bariatric surgery consequences leading to observed surgery effects are still unknown. However, alterations of particular metabolite groups detected till now allow to deduce about general changes associated with this kind of surgical procedure.

4.1. Amino Acid Alterations

One of the biggest group of metabolites altered by bariatrics procedures are amino acids (AA) [25]. Changes in the level of alanine [26, 27, 32, 38, 40, 42, 45], arginine [38], cysteine [45], glutamate [7, 33, 35, 40, 42], glutamine [11, 35, 38, 42, 43, 45], glycine [26, 32, 38, 39, 42, 43], histidine [26, 27, 38, 44], homocysteine [45], proline [36], lysine [11, 26, 40, 42], methionine [36, 41, 42, 45], ornithine [32], phenylalanine [7, 25, 27, 32, 34, 38, 40], proline [27], serine [42, 45], threonine [26, 27, 35], and tyrosine [25, 26, 38, 4042] were observed. Among AA, the frequently modulated group was branched-chain amino acids (BCAA)—isoleucine [7, 25, 32, 33, 35, 36, 38, 40, 42, 45], leucine [7, 32, 3436, 38, 40, 42], and valine [7, 25, 26, 32, 33, 35, 36, 38, 40, 42, 45]. Also small peptides such as glutathione [44, 45], amino acid derivatives [25], or products of their chemical modulations like phenylacetyl-L-glutamine (PAGN) [25] were found to be altered.

Higher serum concentrations of phenylalanine, tyrosine, leucine, isoleucine, valine, and glutamate were noticed in obese individuals. Roux-en-Y gastric bypass caused a decrease of circulating aromatic amino acids (AAA): methionine, alanine, and lysine. Serum concentrations of serine and glycine were found to be increased after sleeve gastrectomy [42]. RYGB accelerates caseinate digestion and amino acid absorption, resulting in a faster and higher but also more transient postprandial elevation of plasma amino acids [34]. In the group of patients with diabetes remission, relatively to nonremission significant decrease in alanine after one year was observed [32]. Branched-chain amino acid levels were found to be correlated with decreased insulin resistance [7].

4.2. Lipids Modulation

Another large group of metabolites modulated by bariatric procedures is lipids. Among them, alterations of phosphatidylcholines [11, 25, 30, 36, 41], lysophosphatidylcholines [11], phosphatidylethanolamines [7, 25, 40], lysophosphatidylethanolamines [11], phosphatidylinositol [40], sphingomyelins (SM) [25, 36, 41], cholesterol and its fractions [33, 35, 46], triglycerides [36, 40], and monoacylglycerols [44] were observed. An important group of affected lipids is composed of the fatty acids (FA) [52], especially free fatty acids (FFA) [25] and their esters (FAME) [28]. Modulations of monounsaturated (MUFAs) [44] and polyunsaturated fatty acids (PUFAs) [30, 40] were also described. Alterations in the levels of palmitoleic acid [11, 30], eicosadienoic acid [51], linoleic acid [30, 40, 51], stearic acid [30, 36, 51], or palmitic acid [30, 36, 51] were highlighted.

Higher baseline stearic acid/palmitic acid ratio was associated with greater probability of diabetes remission after RYGB and may serve as a diagnostic marker in preoperative patient assessment. Correlation analysis demonstrated that the stearic acid/palmitic acid ratio negatively correlated with HbA1c, TG, TC, LDL-c, and HOMA-IR and positively correlated with HDL-c in overweight and obese subjects [51]. Arora et al. reported early alterations in the metabolome and lipidome after gastric bypass in insulin-resistant morbidly obese subjects. The beneficial effects of surgery included a reduction in BCAA metabolites and short-chain TGs [36]. Data obtained by Oberbach et al. showed that LSG affects the amino acid and lipid metabolism. It leads to modification of amino acids and lipid metabolism as indicated by changes in glycerol-phosphatidylcholines and SM levels [41].

4.3. Gut Microbiota-Related Metabolites

Gut microbiota plays an important role in various processes including energy metabolism, lipid accumulation, homeostasis, regulation of brain function, and behavior. Its modulation is also one of the mechanisms by which bariatric surgery promotes weight loss and type 2 diabetes remission [53]. The human gastrointestinal tract (GIT) is dominated by two bacterial phyla, the Bacteroidetes and the Firmicutes, and the proportion of this phyla to each other has been already linked to obesity and type 2 diabetes mellitus [5356]. Accordingly, alterations of compounds which can be linked to gut microbes and their modulations by surgery are important and interesting findings [38]. In several studies, changes in levels of SCFAs (i.e., butyric acid) [26, 27, 3739, 44, 47], lactate [7, 26, 33, 35, 45], indole [36], and 3-indoxyl sulfate [7, 26] after bariatric procedures were observed. Alterations in the level of sulfate-containing metabolites can be expected, as the largest group of sulfate-reducing bacteria is found among the Proteobacteria, present mainly in the duodenum, which is modified in some bariatric procedures [57]. Also levels of mentioned cholesterol [33, 35, 46], L-carnitine [7, 11, 26, 43], and niacin or choline [7, 25, 26, 32, 45] can be linked to gut microbes [58]. Modesitt et al. linked perturbations in tryptophan, phenylalanine, and heme metabolism with decreased inflammation and alterations in the intestinal microbiome [7]. Moreover, tyrosine or phenylalanine fermentation by intestinal bacteria generates p-cresol. Bacteroides fragilis is one of the bacteria that have been shown to produce it [59]. Serum glutamate concentration was inversely correlated with the abundance of some Bacteroides species as well [42]. Changes in histidine and its metabolites following surgery might be an indication of altered gut microbiome ecology or liver function [44]. Another significant metabolite in which its modification can be linked to microbiota modification is beta-hydroxybutyrate (β-OHB). It is derived mainly from the oxidation of fatty acids and is the first ketone produced in the fasting state. Additionally, it is also produced in the form of poly-β-OHB by prokaryotes when carbon sources are freely available but other nutrients are limited [60].

4.4. Other Compounds

Some of the metabolites reported in reviewed studies as significantly changing after bariatric surgery do not belong to the above-described groups of compounds. Therefore, in this section, metabolites from other biochemical pathways altered by metabolic surgery will be presented.

Examples of such compounds are 8-oxoGua, 8-oxoGuo, and 8-oxodG, markers of DNA and RNA damage studied by Bankoglu et al. [13]. Alterations in the concentration of these compounds indicate the association of obesity with increased oxidative stress and DNA damage. Moreover, it was said that RYGB or caloric restriction can significantly reduce elevated oxidative or nitrative stress as well as genomic damage in obese subjects. Results obtained by Sarosiek et al. also suggested that bariatric surgery might promote antioxidant defence and insulin sensitivity through both increased heme synthesis and heme oxygenase (HO) activity or expression [44]. Modification of nucleotide metabolism after bariatric intervention was evaluated by adenosine, inosine, hypoxanthine, xanthine, urate, and allantoin profiling [7]. DJB surgery enhanced trans-sulfuration and its consecutive reactions such as detoxification and the scavenging activities of reactive oxygen species [45]. Metabolites detected by Narath et al. (trimethylamine N-oxide, alanine, phenylalanine, and indoxyl sulfate) are known as cardiovascular disease risk markers [32].

An important group of altered metabolites is compounds connected with energetic processes. Pyruvate [7, 11, 38, 44, 45], citric acid [11, 26, 27, 38], carnitines [7, 11, 26, 43], or the above-mentioned fatty acids belong to this group. Calvo et al. observed that the presence of moderate NAFLD is common in young patients with morbid obesity. Their data may be useful to explain the dissociation between excess lipid storage in adipose tissue, NAFLD, and insulin resistance [40]. After surgeries, energy metabolism, glucose homeostasis, and glycemic markers showed marked improvements, which manifested with reduced levels of glucose and the glycolytic end products of pyruvate and lactate. An increased level of chiro-inositol may be associated with improved insulin signaling [7]. Narath et al. reported the decrease of lactate (Krebs’ intermediate cycle) after RYGB. They also observed the higher levels of the high-density lipoprotein and phosphatidylcholine after bariatric surgery [32].

An interesting study about serum uric acid (sUA) was performed by Oberbach et al. An elevated level of sUA was observed in obese patients. However, twelve months after LSG and RYGB, a significant decrease in sUA and other parameters such as BMI, CVD risk factors, hepatic transaminases, and HOMA-IR was observed. Kwon et al. suggested new criteria—7-day metabolomics profile and 3-hydroxybutyrate to glucose ratio for the prediction of 3-month HbA1c. They suggested that this finding could augment current prognostic modalities and help clinicians decide if drug therapy is necessary [37]. Chouiali et al. performed also an interesting but more methodological study. The authors compared two methods (electro-chemiluminescence immunoassay and LC-MS/MS) for measurement of serum 25(OH)D by applying them to the bariatric population [48].

It has to be mentioned that changes presented above are only part of all detected modulations in all investigated studies. They were subjectively chosen by authors as the most interesting. There are still some intriguing modulations which are not described here but can be found in referred articles.

4.5. Animal Studies

The animal studies are an important part of the bariatric surgery research. The animal models allow not only to follow the general metabolic changes in blood and urine but also to focus on modulations, in particular organs, by analyses of tissue samples. Five [11, 13, 28, 42, 45] of all presented studies were performed on an animal model. In mentioned studies, not only blood or urine but also liver, heart, and adipose tissue samples were examined. Animal models in this kind of studies are also important because of the fact that they allows to compare particular bariatric procedures between themselves, as well as with sham operations, which cannot be performed in humans. Additionally, the animal models are characterized by high repeatability, which is meaningful when using the metabolomics approach [61].

4.6. From Metabolite to Metabolome—General Metabolic Effects of Bariatric Intervention

All above-described alterations in combination with results from MetaboAnalyst showed that bariatric procedures have a huge impact on patients’ metabolism. This influence can be observed by following clinical as well as molecular parameters. Investigation of clinical parameters in combination with multiple metabolites provides a broader picture than does evaluation of changes in selected metabolites. Thus, metabolomics is a perfect tool to study global effects evoked by bariatric surgery.

Of course, even the “image” of metabolic changes obtained here, based on all mentioned studies, is still incomplete. There are still some blank areas on this biochemical map. But step by step, using metabolomics techniques (especially combined together) to examine different procedures can bring us more interesting and useful knowledge. Although providing a wide spectrum of information, the metabolomics approach has some limitations. In case of LC-MS, the identification of metabolites could be improved—a relatively large percent of detected metabolites have remained unidentified. In case of GC-MS, identification is based on libraries, so the number of detected metabolites is always limited. NMR in comparison to MS-based platforms is less sensitive. Drawing conclusions is also limited by use of software for pathways analysis. In each software, the number of available metabolites and pathways is limited. MetaboAnalyst library contains 80 biochemical pathways for humans and similar or even less for other species. Therefore, many metabolites and pathways which can be affected by bariatric intervention are not included in such analysis. It is also worthy to collect more data for a particular procedure and to try to analyze them separately. It is very possible that different procedures will evoke distinct impact on biochemical pathways.

5. Conclusion

Bariatric procedure strongly influences the metabolism. Detected changes are tied with many compounds and biochemical pathways such as amino acids, lipids, carbohydrates, or gut microbiota alterations. It proves that classification of bariatric intervention as metabolic surgery is appropriate. However, many molecular modulations after those procedures are still unexplained. Therefore, the application of metabolomics in this field of medicine seems to be a right choice. New findings can suggest new directions for surgery technique modifications, contribute to broaden knowledge about obesity and related diseases, and perhaps develop nonsurgical methods of treatment in the future.

Conflicts of Interest

The authors declare no conflicts of interest.


The authors are indebted to Mr. Dan Cherry for his careful English editing and proofreading. The authors were supported by funding from the leading National Research Centre in Poland (KNOW 2012–2017).


  1. G. R. Faria, “A brief history of bariatric surgery,” Porto Biomedical Journal, vol. 2, no. 3, pp. 90–92, 2017. View at: Publisher Site | Google Scholar
  2. A. J. Kremen, J. H. Linner, and C. H. Nelson, “An experimental evaluation of the nutritional importance of proximal and distal small intestine,” Annals of Surgery, vol. 140, no. 3, pp. 439–448, 1954. View at: Publisher Site | Google Scholar
  3. H. Buchwald, “The evolution of metabolic/bariatric surgery,” Obesity Surgery, vol. 24, no. 8, pp. 1126–1135, 2014. View at: Publisher Site | Google Scholar
  4. S. R. Kashyap, P. Gatmaitan, S. Brethauer, and P. Schauer, “Bariatric surgery for type 2 diabetes: weighing the impact for obese patients,” Cleveland Clinic Journal of Medicine, vol. 77, no. 7, pp. 468–476, 2010. View at: Publisher Site | Google Scholar
  5. B. J. Slater, N. Bellatorre, and D. Eisenberg, “Early postoperative outcomes and medication cost savings after laparoscopic sleeve gastrectomy in morbidly obese patients with type 2 diabetes,” Journal of Obesity, vol. 2011, Article ID 350523, 5 pages, 2011. View at: Publisher Site | Google Scholar
  6. P. Nalepa, A. Piechnik, and A. Kiersztan, “Influence of bariatric surgery on remission of type 2 diabetes,” Postȩpy Higieny i Medycyny Doświadczalnej, vol. 65, pp. 804–818, 2011. View at: Publisher Site | Google Scholar
  7. S. C. Modesitt, P. T. Hallowell, J. K. Slack-Davis et al., “Women at extreme risk for obesity-related carcinogenesis: baseline endometrial pathology and impact of bariatric surgery on weight, metabolic profiles and quality of life,” Gynecologic Oncology, vol. 138, no. 2, pp. 238–245, 2015. View at: Publisher Site | Google Scholar
  8. J. I. Mechanick, A. Youdim, D. B. Jones et al., “Clinical practice guidelines for the perioperative nutritional, metabolic, and nonsurgical support of the bariatric surgery patient—2013 update: cosponsored by American Association of Clinical Endocrinologists, the Obesity Society, and American Society for Metabolic & Bariatric Surgery,” Obesity, vol. 21, Supplement 1, pp. S1–S27, 2013. View at: Publisher Site | Google Scholar
  9. K. J. Neff, T. Olbers, and C. W. le Roux, “Bariatric surgery: the challenges with candidate selection, individualizing treatment and clinical outcomes,” BMC Medicine, vol. 11, no. 1, p. 8, 2013. View at: Publisher Site | Google Scholar
  10. R. S. Ahima, “Digging deeper into obesity,” The Journal of Clinical Investigation, vol. 121, no. 6, pp. 2076–2079, 2011. View at: Publisher Site | Google Scholar
  11. H. Ashrafian, J. V. Li, K. Spagou et al., “Bariatric surgery modulates circulating and cardiac metabolites,” Journal of Proteome Research, vol. 13, no. 2, pp. 570–580, 2014. View at: Publisher Site | Google Scholar
  12. M. E. Piche, A. Auclair, J. Harvey, S. Marceau, and P. Poirier, “How to choose and use bariatric surgery in 2015,” Canadian Journal of Cardiology, vol. 31, no. 2, pp. 153–166, 2015. View at: Publisher Site | Google Scholar
  13. E. E. Bankoglu, F. Seyfried, L. Rotzinger et al., “Impact of weight loss induced by gastric bypass or caloric restriction on oxidative stress and genomic damage in obese Zucker rats,” Free Radical Biology & Medicine, vol. 94, pp. 208–217, 2016. View at: Publisher Site | Google Scholar
  14. S. A. Brethauer, B. Chand, and P. R. Schauer, “Risks and benefits of bariatric surgery: current evidence,” Cleveland Clinic Journal of Medicine, vol. 73, no. 11, pp. 993–1007, 2006. View at: Publisher Site | Google Scholar
  15. W. J. Lee, A. Almulaifi, J. J. Tsou, K. H. Ser, Y. C. Lee, and S. C. Chen, “Laparoscopic sleeve gastrectomy for type 2 diabetes mellitus: predicting the success by ABCD score,” Surgery for Obesity and Related Diseases, vol. 11, no. 5, pp. 991–996, 2015. View at: Publisher Site | Google Scholar
  16. D. Benaiges, A. Más-Lorenzo, A. Goday et al., “Laparoscopic sleeve gastrectomy: more than a restrictive bariatric surgery procedure?” World Journal of Gastroenterology, vol. 21, no. 41, pp. 11804–11814, 2015. View at: Publisher Site | Google Scholar
  17. J. K. Nicholson and J. C. Lindon, “Systems biology: metabonomics,” Nature, vol. 455, no. 7216, pp. 1054–1056, 2008. View at: Publisher Site | Google Scholar
  18. J. K. Nicholson, J. C. Lindon, and E. Holmes, “‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data,” Xenobiotica, vol. 29, no. 11, pp. 1181–1189, 1999. View at: Publisher Site | Google Scholar
  19. O. Fiehn, J. Kopka, P. Dormann, T. Altmann, R. N. Trethewey, and L. Willmitzer, “Metabolite profiling for plant functional genomics,” Nature Biotechnology, vol. 18, no. 11, pp. 1157–1161, 2000. View at: Publisher Site | Google Scholar
  20. O. Fiehn, “Metabolomics—the link between genotypes and phenotypes,” Plant Molecular Biology, vol. 48, no. 1-2, pp. 155–171, 2002. View at: Publisher Site | Google Scholar
  21. A. Zhang, H. Sun, P. Wang, Y. Han, and X. Wang, “Modern analytical techniques in metabolomics analysis,” The Analyst, vol. 137, no. 2, pp. 293–300, 2012. View at: Publisher Site | Google Scholar
  22. Z. Lei, D. V. Huhman, and L. W. Sumner, “Mass spectrometry strategies in metabolomics,” Journal of Biological Chemistry, vol. 286, no. 29, pp. 25435–25442, 2011. View at: Publisher Site | Google Scholar
  23. J. L. Spratlin, N. J. Serkova, and S. G. Eckhardt, “Clinical applications of metabolomics in oncology: a review,” Clinical Cancer Research, vol. 15, no. 2, pp. 431–440, 2009. View at: Publisher Site | Google Scholar
  24. A. D. Eckhart, K. Beebe, and M. Milburn, “Metabolomics as a key integrator for “omic” advancement of personalized medicine and future therapies,” Clinical and Translational Science, vol. 5, no. 3, pp. 285–288, 2012. View at: Publisher Site | Google Scholar
  25. P. Luo, H. Yu, X. Zhao et al., “Metabolomics study of roux-en-Y gastric bypass surgery (RYGB) to treat type 2 diabetes patients based on ultraperformance liquid chromatography–mass spectrometry,” Journal of Proteome Research, vol. 15, no. 4, pp. 1288–1299, 2016. View at: Publisher Site | Google Scholar
  26. J. Hertel, N. Friedrich, K. Wittfeld et al., “Measuring biological age via metabonomics: the metabolic age score,” Journal of Proteome Research, vol. 15, no. 2, pp. 400–410, 2016. View at: Publisher Site | Google Scholar
  27. M. Jüllig, S. Yip, A. Xu et al., “Lower fetuin-A, retinol binding protein 4 and several metabolites after gastric bypass compared to sleeve gastrectomy in patients with type 2 diabetes,” PLoS One, vol. 9, no. 5, article e96489, 2014. View at: Publisher Site | Google Scholar
  28. T. Sledzinski, A. Mika, P. Stepnowski et al., “Identification of cyclopropaneoctanoic acid 2-hexyl in human adipose tissue and serum,” Lipids, vol. 48, no. 8, pp. 839–848, 2013. View at: Publisher Site | Google Scholar
  29. J. Xia and D. S. Wishart, “MetPA: a web-based metabolomics tool for pathway analysis and visualization,” Bioinformatics, vol. 26, no. 18, pp. 2342–2344, 2010. View at: Publisher Site | Google Scholar
  30. R. Nemati, J. Lu, A. Tura, G. Smith, and R. Murphy, “Acute changes in non-esterified fatty acids in patients with type 2 diabetes receiving bariatric surgery,” Obesity Surgery, vol. 27, no. 3, pp. 649–656, 2017. View at: Publisher Site | Google Scholar
  31. A. Oberbach, J. Neuhaus, T. Inge et al., “Bariatric surgery in severely obese adolescents improves major comorbidities including hyperuricemia,” Metabolism, vol. 63, no. 2, pp. 242–249, 2014. View at: Publisher Site | Google Scholar
  32. S. H. Narath, S. I. Mautner, E. Svehlikova et al., “An untargeted metabolomics approach to characterize short-term and long-term metabolic changes after bariatric surgery,” PLoS One, vol. 11, no. 9, article e0161425, 2016. View at: Publisher Site | Google Scholar
  33. T. I. B. Lopes, B. Geloneze, J. C. Pareja, A. R. Calixto, M. M. C. Ferreira, and A. J. Marsaioli, ““Omics” prospective monitoring of bariatric surgery: Roux-en-Y gastric bypass outcomes using mixed-meal tolerance test and time-resolved 1H NMR-based metabolomics,” OMICS: A Journal of Integrative Biology, vol. 20, no. 7, pp. 415–423, 2016. View at: Publisher Site | Google Scholar
  34. K. N. Bojsen-Moller, S. H. Jacobsen, C. Dirksen et al., “Accelerated protein digestion and amino acid absorption after Roux-en-Y gastric bypass,” The American Journal of Clinical Nutrition, vol. 102, no. 3, pp. 600–607, 2015. View at: Publisher Site | Google Scholar
  35. T. I. B. Lopes, B. Geloneze, J. C. Pareja, A. R. Calixto, M. M. C. Ferreira, and A. J. Marsaioli, “Blood metabolome changes before and after bariatric surgery: a 1H NMR-based clinical investigation,” OMICS: A Journal of Integrative Biology, vol. 19, no. 5, pp. 318–327, 2015. View at: Publisher Site | Google Scholar
  36. T. Arora, V. Velagapudi, D. J. Pournaras et al., “Roux-en-Y gastric bypass surgery induces early plasma metabolomic and lipidomic alterations in humans associated with diabetes remission,” PLoS One, vol. 10, no. 5, article e0126401, 2015. View at: Publisher Site | Google Scholar
  37. H. N. Kwon, Y. J. Lee, J. H. Kang et al., “Prediction of glycated hemoglobin levels at 3 months after metabolic surgery based on the 7-day plasma metabolic profile,” PLoS One, vol. 9, no. 11, article e109609, 2014. View at: Publisher Site | Google Scholar
  38. E. Gralka, C. Luchinat, L. Tenori, B. Ernst, M. Thurnheer, and B. Schultes, “Metabolomic fingerprint of severe obesity is dynamically affected by bariatric surgery in a procedure-dependent manner,” The American Journal of Clinical Nutrition, vol. 102, no. 6, pp. 1313–1322, 2015. View at: Publisher Site | Google Scholar
  39. N. Friedrich, K. Budde, T. Wolf et al., “Short-term changes of the urine metabolome after bariatric surgery,” OMICS: A Journal of Integrative Biology, vol. 16, no. 11, pp. 612–620, 2012. View at: Publisher Site | Google Scholar
  40. N. Calvo, R. Beltrán-Debón, E. Rodríguez-Gallego et al., “Liver fat deposition and mitochondrial dysfunction in morbid obesity: an approach combining metabolomics with liver imaging and histology,” World Journal of Gastroenterology, vol. 21, no. 24, pp. 7529–7544, 2015. View at: Publisher Site | Google Scholar
  41. A. Oberbach, M. von Bergen, S. Blüher, S. Lehmann, and H. Till, “Combined serum proteomic and metabonomic profiling after laparoscopic sleeve gastrectomy in children and adolescents,” Journal of Laparoendoscopic & Advanced Surgical Techniques, vol. 22, no. 2, pp. 184–188, 2012. View at: Publisher Site | Google Scholar
  42. R. Liu, J. Hong, X. Xu et al., “Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention,” Nature Medicine, vol. 23, no. 7, pp. 859–868, 2017. View at: Publisher Site | Google Scholar
  43. A. Oberbach, M. Blüher, H. Wirth et al., “Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes,” Journal of Proteome Research, vol. 10, no. 10, pp. 4769–4788, 2011. View at: Publisher Site | Google Scholar
  44. K. Sarosiek, K. L. Pappan, A. V. Gandhi et al., “Conserved metabolic changes in nondiabetic and type 2 diabetic bariatric surgery patients: Global Metabolomic Pilot Study,” Journal of Diabetes Research, vol. 2016, Article ID 3467403, 10 pages, 2016. View at: Publisher Site | Google Scholar
  45. J. Jung, T. K. Ha, J. Lee et al., “Changes in one-carbon metabolism after duodenal-jejunal bypass surgery,” American Journal of Physiology-Endocrinology and Metabolism, vol. 310, no. 8, pp. E624–E632, 2016. View at: Publisher Site | Google Scholar
  46. S. P. Heffron, A. Singh, J. Zagzag et al., “Laparoscopic gastric banding resolves the metabolic syndrome and improves lipid profile over five years in obese patients with body mass index 30–40 kg/m2,” Atherosclerosis, vol. 237, no. 1, pp. 183–190, 2014. View at: Publisher Site | Google Scholar
  47. R. Calvani, A. Miccheli, G. Capuani et al., “Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype,” International Journal of Obesity, vol. 34, no. 6, pp. 1095–1098, 2010. View at: Publisher Site | Google Scholar
  48. A. Chouiali, P.-L. Mallet, G. Fink, S. Biron, and M.-F. Langlois, “Comparison of two methods for measuring 25-OH vitamin D in the follow-up of patients after bilio-pancreatic diversion bariatric surgery,” Clinical Biochemistry, vol. 50, no. 4-5, pp. 210–216, 2017. View at: Publisher Site | Google Scholar
  49. B. Stratmann, Y. Krepak, E. Schiffer et al., “Beneficial metabolic effects of duodenal jejunal bypass liner for the treatment of adipose patients with type 2 diabetes mellitus: analysis of responders and non-responders,” Hormone and Metabolic Research, vol. 48, no. 10, pp. 630–637, 2016. View at: Publisher Site | Google Scholar
  50. V. García-Alonso, E. Titos, J. Alcaraz-Quiles et al., “Prostaglandin E2 exerts multiple regulatory actions on human obese adipose tissue remodeling, inflammation, adaptive thermogenesis and lipolysis,” PLoS One, vol. 11, no. 4, article e0153751, 2016. View at: Publisher Site | Google Scholar
  51. L. Zhao, Y. Ni, H. Yu et al., “Serum stearic acid/palmitic acid ratio as a potential predictor of diabetes remission after Roux-en-Y gastric bypass in obesity,” The FASEB Journal, vol. 31, no. 4, pp. 1449–1460, 2017. View at: Publisher Site | Google Scholar
  52. L. Kaska, A. Mika, P. Stepnowski et al., “The relationship between specific fatty acids of serum lipids and serum high sensitivity C-reactive protein levels in morbidly obese women,” Cellular Physiology and Biochemistry, vol. 34, no. 4, pp. 1101–1108, 2014. View at: Publisher Site | Google Scholar
  53. R. Murphy, P. Tsai, M. Jullig, A. Liu, L. Plank, and M. Booth, “Differential changes in gut microbiota after gastric bypass and sleeve gastrectomy bariatric surgery vary according to diabetes remission,” Obesity Surgery, vol. 27, no. 4, pp. 917–925, 2017. View at: Publisher Site | Google Scholar
  54. G. P. Donaldson, S. M. Lee, and S. K. Mazmanian, “Gut biogeography of the bacterial microbiota,” Nature Reviews Microbiology, vol. 14, no. 1, pp. 20–32, 2016. View at: Publisher Site | Google Scholar
  55. G. Blandino, R. Inturri, F. Lazzara, M. Di Rosa, and L. Malaguarnera, “Impact of gut microbiota on diabetes mellitus,” Diabetes & Metabolism, vol. 42, no. 5, pp. 303–315, 2016. View at: Publisher Site | Google Scholar
  56. H. Liu, C. Hu, X. Zhang, and W. Jia, “Role of gut microbiota, bile acids and their cross-talk in the effects of bariatric surgery on obesity and type 2 diabetes,” Journal of Diabetes Investigation, vol. 9, no. 1, pp. 13–20, 2018. View at: Publisher Site | Google Scholar
  57. C. L. Ohland and C. Jobin, “Microbial activities and intestinal homeostasis: a delicate balance between health and disease,” Cellular and Molecular Gastroenterology and Hepatology, vol. 1, no. 1, pp. 28–40, 2015. View at: Publisher Site | Google Scholar
  58. W.-J. Lee and K. Hase, “Gut microbiota–generated metabolites in animal health and disease,” Nature Chemical Biology, vol. 10, no. 6, pp. 416–424, 2014. View at: Publisher Site | Google Scholar
  59. Y. J. Zhang, S. Li, R. Y. Gan, T. Zhou, D. P. Xu, and H. B. Li, “Impacts of gut bacteria on human health and diseases,” International Journal of Molecular Sciences, vol. 16, no. 12, pp. 7493–7519, 2015. View at: Publisher Site | Google Scholar
  60. E. N. Dedkova and L. A. Blatter, “Role of β-hydroxybutyrate, its polymer poly-β-hydroxybutyrate and inorganic polyphosphate in mammalian health and disease,” Frontiers in Physiology, vol. 5, p. 260, 2014. View at: Publisher Site | Google Scholar
  61. F. Musshoff, H. Klotzbach, W. Block, F. Traeber, H. Schild, and B. Madea, “Comparison of post-mortem metabolic changes in sheep brain tissue in isolated heads and whole animals using 1H-MR spectroscopy—preliminary results,” International Journal of Legal Medicine, vol. 125, no. 5, pp. 741–744, 2011. View at: Publisher Site | Google Scholar

Copyright © 2018 Paulina Samczuk et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.