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1 Using lipidomics in the study of obesity is very important to understanding the regulatory and diagnostic value of changes in the lipidome associated with obesity.
Ivan Liakh
 + 2058 word(s) 2058 2020-11-23 09:39:43 |
2 Format correct
Vicky Zhou
 Meta information modification 2058 2020-12-01 08:38:20 |

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Liakh, I.; Sledzinski, T.; Kaska, L.; Mozolewska, P.; Mika, A. Obesity and Lipids. Encyclopedia. Available online: https://encyclopedia.pub/entry/3270 (accessed on 18 November 2024).
Liakh I, Sledzinski T, Kaska L, Mozolewska P, Mika A. Obesity and Lipids. Encyclopedia. Available at: https://encyclopedia.pub/entry/3270. Accessed November 18, 2024.
Liakh, Ivan, Tomasz Sledzinski, Lukasz Kaska, Paulina Mozolewska, Adriana Mika. "Obesity and Lipids" Encyclopedia, https://encyclopedia.pub/entry/3270 (accessed November 18, 2024).
Liakh, I., Sledzinski, T., Kaska, L., Mozolewska, P., & Mika, A. (2020, November 30). Obesity and Lipids. In Encyclopedia. https://encyclopedia.pub/entry/3270
Liakh, Ivan, et al. "Obesity and Lipids." Encyclopedia. Web. 30 November, 2020.
Obesity and Lipids
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This entry is adapted from the peer-reviewed paper 10.3390/molecules25225307

Recently, lipidomics has become an important branch of medical/clinical sciences similar to proteomics and genomics. Due to the much higher lipid accumulation in obese patients and many alterations in the compositions of various groups of lipids, the methods used for sample preparations for lipidomic studies of samples from obese subjects sometimes have to be modified. Appropriate sample preparation methods allow for the identification of a wide range of analytes by advanced analytical methods, including mass spectrometry. This is especially the case in studies with obese subjects, as the amounts of some lipids are much higher, others are present in trace amounts, and obese subjects have some specific alterations of the lipid profile. 

sample preparation obesity lipids protein precipitation liquid–liquid extraction solid-phase extraction biological samples

1. Introduction

Obesity remains one of the pressing problems of modern society, therefore, studies of the mechanisms underlying its occurrence and the therapies used to treat it continue to be relevant. Depending on the hypothesis, a wide range of research methods can be used, ranging from purely assessing biochemical parameters to deep psychological research. However, research usually involves standard procedures such as measuring body mass index (BMI) or fat content in human subjects. Among the biochemical parameters, those that undergo the greatest changes with obesity (lipid profile, fasting glucose, insulin, etc.) are first examined.

A large amount of accumulated data on obesity allows for their meta-analysis and underlies a large number of systematic and retrospective reviews [1]. In particular, studies related to bariatric surgery are a powerful source of data because these types of surgery involve altering the stomach, intestines, or both to induce weight loss [2][3][4][5][6][7]. In addition, a large number of studies of obesity are associated with cardiovascular diseases [8][9][10][11], diabetes and metabolic syndrome [12][13][14]; many studies are in the field of diets, psychology and neurology [15][16][17][18][19][20][21][22][23].

While the number of parameters in the study of obesity itself is limited only by the imagination of scientists and the equipment available in the laboratory [24][25][26][27][28][29], the study of obesity in connection with other diseases is strictly subordinate to the study area and is often limited to several parameters, such as BMI and total fat content. [30][31][32][33]. In addition, determining the diagnosis of obesity is always primary in that work; for this purpose, the most commonly used method is the calculation of BMI. The World Health Organization used BMI to categorize humans into underweight (< 18.5), normal weight (18.5–24.9), overweight (25–29.9) and obese (BMI ≥ 30) categories [34]. Since BMI may not be a good indicator of obesity for bodybuilders and other groups of athletes [35][36], body fat [37][38][39] and total body water [40][41] can be determined in these groups, as can concomitant states of lipid alterations in blood (dyslipidaemia) [42], hyperinsulinemia [43], etc. Since obesity is directly related to lipid metabolism, it is interesting to study not only standard plasma parameters but also alterations in specific lipid groups in serum [44]. However, due to the much higher lipid accumulation in obesity (Figure 1) and many alterations in the lipid composition, the methods used for sample preparations for lipidomic studies in samples from obese subjects sometimes have to be modified.

Figure 1. Lipid alterations in obesity. Lipids in red are elevated in obesity, and lipids in green are reduced. BCAA—branched chain amino acids; BCFA—branched chain fatty acids; DAG—diacylglycerols; FFA—free fatty acids; HDL—high density lipoproteins; LDL—low density lipoproteins; LPP—lipid peroxidation products; MUFA—monounsaturated fatty acids; OCFA—odd chain fatty acids; SFA—saturated fatty acids; PUFA—polyunsaturated fatty acids; TAG—triacyclglycerols.

In the study of obesity, determining triglyceride (TG) and cholesterol levels is of great clinical importance. Basic blood test results for total cholesterol (TC), TG and cholesterol in lipoprotein fractions (low density lipoproteins (LDL) and high density lipoproteins (HDL)) should be considered together. To study these indicators and related and often required for standard clinical practice indicators (C-reactive protein, glucose, insulin levels, etc.), there are many standard methods and their modifications that make these analyses routine in clinical practice.

2. Methods of Sample Preparation for Lipidomic Studies

2.1. Sample Collection and Storage

Fat metabolism disorders are detected by determining the lipid spectrum of the blood. Blood for a study is taken from a vein, always on an empty stomach (12–14 h after eating); otherwise, the results of the study are distorted, since 1-4 h after eating, alimentary hyperlipaemia occurs [45]. During blood sampling, adverse events such as haemolysis, coagulation, and platelet activation should be avoided, but the class of anticoagulants used should also be taken into account since calcium-chelating coagulants (ethylenediaminetetraacetic acid (EDTA) and citrate) can cause the calcium-dependent formation or degradation of certain classes of lipids ex vivo [46].

Different classes of lipids are subject to different changes during storage. Long-term storage of plasma at room temperature (RT) leads to an increase in lysophosphatidylethanolamines (LPE), lysophosphatidylcholines (LPC) and fatty acids (FAs), while phosphatidylethanolamines (PE) and phosphatidylcholines (PC) decrease, which suggests the breakdown of ester bonds in these phospholipids [47]. Avoiding freeze-thaw cycles is no less important because with their increase, the number of lipid metabolites decreases significantly [48].

2.2. Pre-Extraction Additives

Additives used during or before extraction serve a variety of purposes. Internal standards are a measure of extraction efficiency. In many cases, lipidomic studies of obesity are accompanied by the determination of obesity-associated hormone levels (such as ghrelin, obestatin, glucagon, leptin, and adiponectin); therefore, protease inhibitor cocktails are added to serum/plasma samples to increase hormone stability [49]. In addition, various detergents serve to facilitate cell destruction during homogenization, and buffers are used to maintain a stable pH. The most commonly added substances to prevent oxidative processes during extraction are antioxidants and radical scavengers such as butylated hydroxytoluene (BHT) [50][51]. This is especially important when studying unstable compounds such as oxylipins [52][53][54], which are the metabolites of polyunsaturated fatty acids.

2.3. Sample Stability

It is generally assumed that lipids are highly stable at RT, while it is advisable to not allow them to overheat during homogenization and to prevent oxidation by the addition of antioxidants [55]. Despite this, many studies consider the effects of storage conditions, the number of freeze/thaw cycles and the behaviour of organic compounds in experimental conditions. Jiang et al. validated a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the determination of ceramides (Cer) in human plasma and determined the stability of each analyte at low- and high-quality control concentrations under long-term storage (39 days at -80 °C), freeze/thawing (five times), tabletop mode (14 h at RT before sample extraction) and autosampler conditions (3 days). The results showed that Cer (22:0) and Cer (24:0) were stable in human plasma under all conditions [56]. Ferreiro-Vera et al. assessed the stability of eicosanoids in serum under experimental conditions; every hour for 8 h, they analysed samples spiked with eicosanoids, and no significant differences in analyte concentrations were found [57]. Zeng and Cao also showed sufficient stability of short-chain FAs (SCFAs) and ketone body derivatives during autosampler storage (5 °C; 48 h), after 2 h at RT and after three freeze/thaw cycles [58]. Klawitter et al. showed that freeze/thaw cycles and long-term storage of plasma (6 h; RT) should be avoided to prevent changes in the composition of lipid classes of very low-density lipoprotein (VLDL) (loss of cholesterol esters and phospholipids), while free fatty acid (FFA) concentrations did not change under the same conditions [59]. Oxylipins are especially unstable in this regard, and improper collection and storage of samples can lead both to a significant decrease in their level and to an increase in their content due to enzymatic and non-enzymatic oxidation [60]. Some oxylipins (resolvins and prostanoids) are unstable even at −20 °C [61], so the manufacturers of their standards recommend storing them at −80 °C, while the concentration of prostaglandins can significantly decrease with prolonged storage at even −80 °C [62].

2.4. Extraction Methods

2.4.1. Protein Precipitation

Protein precipitation (PPT) is used to remove protein from samples, therefore, when carrying out PPT during sample preparation, it is important that the chosen solvent causes protein denaturation and, at the same time, is a good solvent for lipids [55]. In addition, precipitation of proteins that make up a large volume of the analysed matrix is necessary since some groups of lipids are present in the matrix in trace amounts. This helps to minimize the risk of lack of detection or misidentification and to release protein-bound compounds prior to target lipid extraction [63]. Most often, PPT is preceded by subsequent solid-phase extraction (SPE) and liquid-liquid extraction (LLE).

2.4.2. Liquid–Liquid Extraction

The high solubility of the hydrocarbon chains of lipids in organic solvents allows the use of LLE for the separation of lipids in various immiscible liquids. Widely used methods such as those of Folch [64] and Bligh and Dyer [65] have the drawback of using toxic solvents [66]; in addition, some classes of lipids (for example, lysophospholipids (LPL)) can remain in the aqueous phase [67]; however, many proposed modifications of these methods can overcome the above disadvantages, and these methods are still widely used in lipidomics of obesity samples [67][68][69][70][71][72][73]. Methyl tert-butyl ether (MTBE) extraction, which has been popular recently, is undergoing various modifications and shows very good efficiency over classical methods [66]. In the study of obesity, MTBE extraction is used to isolate lipids from liver tissue [74], skeletal muscle [75], adipose tissue [76] and plasma [50][69].

2.4.3. Solid-Phase Extraction

SPE is more suitable than LLE for target lipidomics because it allows fractionation of specific lipid classes after LLE [77][78]. Therefore, in a lipidomics study, SPE is resorted to when it is necessary to isolate specific lipid groups or species that are present in the sample in a small amount, such as eicosanoids [57], LPL [67], oxidized phospholipids [72], serum sterols [79] oxysterols, endocannabinoids, and Cer [80], non-esterified FA and oxylipins [53][81][82][83]. Due to the wide variety of SPE protocols and commercially available SPE columns, there are studies in which these parameters are compared, for example, in studies of fatty acid esters of hydroxy fatty acids (FAHFAs) in serum [84] or oxylipins in human plasma [85][86]. Additionally, SPE helps to separate lipids in complex matrices with a large lipid abundance, such as adipose tissue [80][87] or brain tissue [77].

2.4.4. Other Extraction Methods

In addition to the well-established routine extraction methods described above, such as LLE, SPE, and PPT, also more modern but at the same time rarer extraction methods are used in the studies of obesity, such as solid-phase microextraction (SPME), stir bar sorptive extraction (SBSE), dispersive liquid–liquid microextraction (DLLME) and their variants. The main disadvantage of the above-listed solvent extraction is the use of organic solvents that have such disadvantages as toxicity and harmfulness to the environment. In addition, they must be of high purity, which increases the cost of analysis [88]. However, SPME, SBSE, DLLME are a solvent-free sample preparation method that is easy to use, does not require preliminary sample preparation, and is easily automated [89].

The most widely used technique of above mentioned is SPME. In combination with gas chromatography-mass spectrometry (GC-MS) it can be used not only for analysis of volatile organic compounds, but also for the extraction of fatty acids and fatty acid esters from solid tissues and biofluids, which requires a very small sample volume and reduces the matrix effect [90]. Although SPME can be used for lipidomics studies, in obesity studies these methods are also used to study non-lipid compounds. SPME followed by GC/MS was used to analyze aroma compound headspace release from extra virgin olive oil after the interaction of saliva in obese and overweight individuals [91], to evaluate volatile organic compounds of gut microbiota of obese patients [92][93], and for urinary volatile organic compounds profiling in overweight children [94].

The SBSE method, like SPME, is a method of sample preparation without the use of solvents and with the use of a solid sorbent for preliminary concentration of the analyte before analysis. The surface area of the sorbing polymer is greater in SBSE than in SPME [95]. Eslami et al. used SBSE followed by HPLC for quantification of ghrelin in human plasma [96].

The DLLME method is based on the rapid mixing of dispersing and extraction solvents with an aqueous sample, resulting in the formation of an emulsion consisting of fine particles of the extraction solvent dispersed in the aqueous phase, then the solvent is separated from the sample by centrifugation [97]. Amin et al. used DLLME following GC/MS method for the evaluation of urinary Bisphenol A in obese subjects [98]. Krawczyńska et al. applied DLLME technique for the determination of vitamin D in obese patients plasma [99].

Thus, the relatively small number of studies in lipidomics using above methods is explained by their recent appearance, while such advantages as relative easiness of implementation, accuracy, small sample volume and lack of organic solvents make these extraction methods promising.

References

  1. Champion, J.D.; Collins, J.L. Retrospective Chart Review for Obesity and Associated Interventions Among Rural Mexican-American Adolescents Accessing Healthcare Services. J. Am. Assoc. Nurse Pract. 2013, 25, 604–610.
  2. Dogan, U.; Ellidag, H.Y.; Aslaner, A.; Cakir, T.; Oruc, M.T.; Koc, U.; Mayir, B.; Gomceli, I.; Bulbuller, N.; Yılmaz, N. The Impact of Laparoscopic Sleeve Gastrectomy on Plasma Obestatin and Ghrelin Levels. Eur. Rev. Med. Pharmacol. Sci. 2016, 20, 2113–2122.
  3. Blüher, S.; Raschpichler, M.; Hirsch, W.; Till, H. A Case Report and Review of the Literature of Laparoscopic Sleeve Gastrectomy in Morbidly Obese Adolescents: Beyond Metabolic Surgery and Visceral Fat Reduction. Metabolism 2013, 62, 761–767.
  4. Carswell, K.A.; Belgaumkar, A.P.; Amiel, S.A.; Patel, A.G. A Systematic Review and Meta-Analysis of the Effect of Gastric Bypass Surgery on Plasma Lipid Levels. Obes. Surg. 2016, 26, 843–855.
  5. Franco, J.V.A.; Ruiz, P.A.; Palermo, M.; Gagner, M. A Review of Studies Comparing Three Laparoscopic Procedures in Bariatric Surgery: Sleeve Gastrectomy, Roux-en-Y Gastric Bypass and Adjustable Gastric Banding. Obes. Surg. 2011, 21, 1458–1468.
  6. Howard, M.L.; Steuber, T.D.; Nisly, S.A. Glycemic Management in the Bariatric Surgery Population: A Review of the Literature. Pharmacotherapy 2018, 38, 663–673.
  7. Wijayatunga, N.N.; Sams, V.G.; Dawson, J.A.; Mancini, M.L.; Mancini, G.J.; Moustaid-Moussa, N. Roux-en-Y Gastric Bypass Surgery Alters Serum Metabolites and Fatty Acids in Patients with Morbid Obesity. Diabetes Metab. Res. Rev. 2018, 34, e3045.
  8. Hansen, D.; Marinus, N.; Remans, M.; Courtois, I.; Cools, F.; Calsius, J.; Massa, G.; Takken, T. Exercise Tolerance in Obese vs. Lean Adolescents: A Systematic Review and Meta-Analysis. Obes. Rev. 2014, 15, 894–904.
  9. Supariwala, A.; Makani, H.; Kahan, J.; Pierce, M.; Bajwa, F.; Dukkipati, S.S.; Teixeira, J.; Chaudhry, F.A. Feasibility and Prognostic Value of Stress Echocardiography in Obese, Morbidly Obese, and Super Obese Patients Referred for Bariatric Surgery. Echocardiography 2013, 31, 879–885.
  10. Sommer, A.; Twig, G. The Impact of Childhood and Adolescent Obesity on Cardiovascular Risk in Adulthood: A Systematic Review. Curr. Diab. Rep. 2018, 18, 91.
  11. Kiliçaslan, B.; Tigen, M.K.; Tekin, A.S.; Çiftçi, H. Cardiac Changes with Subclinical Hypothyroidism in Obese Women. Turk Kardiyol Dern Ars. 2013, 41, 471–477.
  12. Hajian-Tilaki, K.; Heidari, B. Variations in the Pattern and Distribution of Non-Obese Components of Metabolic Syndrome across Different Obesity Phenotypes among Iranian Adults’ Population. Diabetes Metab. Syndr. Clin. Res. Rev. 2019, 13, 2419–2424.
  13. Rao, R.S.; Yanagisawa, R.; Kini, S. Insulin Resistance and Bariatric Surgery. Obes. Rev. 2012, 13, 316–328.
  14. Sejooti, S.S.; Naher, S.; Hoque, M.M.; Zaman, M.S.; Aminur Rashid, H.M. Frequency of Insulin Resistance in Nondiabetic Adult Bangladeshi Individuals of Different Obesity Phenotypes. Diabetes Metab. Syndr. Clin. Res. Rev. 2019, 13, 62–67.
  15. Di Vincenzo, A.; Beghetto, M.; Vettor, R.; Rossato, M.; Bond, D.; Pagano, C. SAT-108 Effects of Bariatric and Non-Bariatric Weight Loss on Migraine Headache in Obesity. A Systematic Review and Meta-Analysis. J. Endocr. Soc. 2019, 3.
  16. Großschädl, F.; Freidl, W.; Rásky, É.; Burkert, N.; Muckenhuber, J.; Stronegger, W.J. A 35-Year Trend Analysis for Back Pain in Austria: The Role of Obesity. PLoS ONE 2014, 9, e107436.
  17. Chao, H.-L. Body Image Change in Obese and Overweight Persons Enrolled in Weight Loss Intervention Programs: A Systematic Review and Meta-Analysis. PLoS ONE 2015, 10, e0124036.
  18. Li, W.; Rukavina, P. A Review on Coping Mechanisms against Obesity Bias in Physical Activity/Education Settings. Obes. Rev. 2009, 10, 87–95.
  19. Budd, G.M.; Mariotti, M.; Graff, D.; Falkenstein, K. Health Care Professionals’ Attitudes about Obesity: An Integrative Review. Appl. Nurs. Res. 2011, 24, 127–137.
  20. Devoto, F.; Zapparoli, L.; Bonandrini, R.; Berlingeri, M.; Ferrulli, A.; Luzi, L.; Banfi, G.; Paulesu, E. Hungry Brains: A Meta-Analytical Review of Brain Activation Imaging Studies on Food Perception and Appetite in Obese Individuals. Neurosci. Biobehav. Rev. 2018, 94, 271–285.
  21. Amiri, S.; Behnezhad, S. Obesity and Anxiety Symptoms: A Systematic Review and Meta-Analysis. Neuropsychiatrie 2019, 33, 72–89.
  22. Kim, M.K.; Kim, W.; Kwon, H.-S.; Baek, K.-H.; Kim, E.K.; Song, K.-H. Effects of Bariatric Surgery on Metabolic and Nutritional Parameters in Severely Obese Korean Patients with Type 2 Diabetes: A Prospective 2-Year Follow Up. J. Diabetes Investig. 2014, 5, 221–227.
  23. Rashad, N.M.; Sayed, S.E.; Sherif, M.H.; Sitohy, M.Z. Effect of a 24-Week Weight Management Program on Serum Leptin Level in Correlation to Anthropometric Measures in Obese Female: A Randomized Controlled Clinical Trial. Diabetes Metab. Syndr. Clin. Res. Rev. 2019, 13, 2230–2235.
  24. Morais, L.C.; Rocha, A.P.R.; Turi-Lynch, B.C.; Ferro, I.S.; Koyama, K.A.K.; Araújo, M.Y.C.; Codogno, J.S. Health Indicators and Costs among Outpatients According to Physical Activity Level and Obesity. Diabetes Metab. Syndr. Clin. Res. Rev. 2019, 13, 1375–1379.
  25. Chen, C.M. Overview of Obesity in Mainland China. Obes. Rev. 2008, 9, 14–21.
  26. Nishide, R.; Ando, M.; Funabashi, H.; Yoda, Y.; Nakano, M.; Shima, M. Association of Serum Hs-CRP and Lipids with Obesity in School Children in a 12-Month Follow-Up Study in Japan. Environ. Health Prev. Med. 2015, 20, 116–122.
  27. Fernandes, L.A.; Braz, L.G.; Koga, F.A.; Kakuda, C.M.; Mõdolo, N.S.P.; De Carvalho, L.R.; Vianna, P.T.G.; Braz, J.R.C. Comparison of Peri-Operative Core Temperature in Obese and Non-Obese Patients. Anaesthesia 2012, 67, 1364–1369.
  28. Pérez-Pérez, R.; García-Santos, E.; Ortega-Delgado, F.J.; López, J.A.; Camafeita, E.; Ricart, W.; Fernández-Real, J.-M.; Peral, B. Attenuated Metabolism is a Hallmark of Obesity as Revealed by Comparative Proteomic Analysis of Human Omental Adipose Tissue. J. Proteomics 2012, 75, 783–795.
  29. Dolinková, M.; Dostálová, I.; Lacinová, Z.; Michalský, D.; Haluzíková, D.; Mráz, M.; Kasalický, M.; Haluzík, M. The Endocrine Profile of Subcutaneous and Visceral Adipose Tissue of Obese Patients. Mol. Cell. Endocrinol. 2008, 291, 63–70.
  30. Myung, Y.; Heo, C.-Y. Relationship between Obesity and Surgical Complications after Reduction Mammaplasty: A Systematic Literature Review and Meta-Analysis. Aesthetic Surg. J. 2017, 37, 308–315.
  31. Faucher, M.; Hastings-Tolsma, M.; Song, J.; Willoughby, D.; Bader, S.G. Gestational Weight Gain and Preterm Birth in Obese Women: A Systematic Review and Meta-Analysis. BJOG 2016, 123, 199–206.
  32. Freeman, C.M.; Woodle, E.S.; Shi, J.; Alexander, J.W.; Leggett, P.L.; Shah, S.A.; Paterno, F.; Cuffy, M.C.; Govil, A.; Mogilishetty, G.; et al. Addressing morbid obesity as a barrier to renal transplantation with laparoscopic sleeve gastrectomy. Am. J. Transplant. 2015, 15, 1360–1368.
  33. Rashad, N.M.; Al-sayed, R.M.; Yousef, M.S.; Saraya, Y.S. Kisspeptin and Body Weight Homeostasis in Relation to Phenotypic Features of Polycystic Ovary Syndrome; Metabolic Regulation of Reproduction. Diabetes Metab. Syndr. Clin. Res. Rev. 2019, 13, 2086–2092.
  34. Nuttall, F.Q. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr. Today 2015, 50, 117–128.
  35. Grier, T.; Canham-Chervak, M.; Sharp, M.; Jones, B.H. Does Body Mass Index Misclassify Physically Active Young Men. Prev. Med. Reports 2015, 2, 483–487.
  36. Van Marken Lichtenbelt, W.D.; Hartgens, F.; Vollaard, N.B.J.; Ebbing, S.; Kuipers, H. Body Composition Changes in Bodybuilders: A Method Comparison. Med. Sci. Sports Exerc. 2004, 36, 490–497.
  37. Sweeting, H.N. Measurement and Definitions of Obesity in Childhood and Adolescence: A Field Guide for the Uninitiated. Nutr. J. 2007, 6, 1–8.
  38. Deurenberg, P.; Yap, M. The Assessment of Obesity: Methods for Measuring Body Fat and Global Prevalence of Obesity. Baillieres Best Pract. Clin. Endocrinol. Metab. 1999, 13, 1–11.
  39. Peltz, G.; Aguirre, M.T.; Sanderson, M.; Fadden, M.K. The Role of Fat Mass Index in Determining Obesity. Am. J. Hum. Biol. 2010, 22, 639–647.
  40. Butte, N.F.; Brandt, M.L.; Wong, W.W.; Liu, Y.; Mehta, N.R.; Wilson, T.A.; Adolph, A.L.; Puyau, M.R.; Vohra, F.A.; Shypailo, R.J.; et al. Energetic Adaptations Persist after Bariatric Surgery in Severely Obese Adolescents. Obesity 2015, 23, 591–601.
  41. Sartorio, A.; Malavolti, M.; Agosti, F.; Marinone, P.G.; Caiti, O.; Battistini, N.; Bedogni, G. Body Water Distribution in Severe Obesity and Its Assessment from Eight-Polar Bioelectrical Impedance Analysis. Eur. J. Clin. Nutr. 2005, 59, 155–160.
  42. Vekic, J.; Zeljkovic, A.; Stefanovic, A.; Jelic-Ivanovic, Z.; Spasojevic-Kalimanovska, V. Obesity and Dyslipidemia. Metabolism 2019, 92, 71–81.
  43. Erion, K.A.; Corkey, B.E. Hyperinsulinemia: A Cause of Obesity? Curr. Obes. Rep. 2017, 6, 178–186.
  44. Mika, A.; Sledzinski, T. Alterations of Specific Lipid Groups in Serum of Obese Humans: A Review. Obes. Rev. 2017, 18, 247–272.
  45. Apryatin, S.A.; Sidorova, Y.S.; Shipelin, V.A.; Balakina, A.; Trusov, N.V.; Mazo, V.K. Neuromotor Activity, Anxiety and Cognitive Function in the In Vivo Model of Alimentary Hyperlipidemia and Obesity. Bull. Exp. Biol. Med. 2017, 163, 37–41.
  46. Burla, B.; Arita, M.; Arita, M.; Bendt, A.K.; Cazenave-Gassiot, A.; Dennis, E.A.; Ekroos, K.; Han, X.; Ikeda, K.; Liebisch, G.; et al. MS-Based Lipidomics of Human Blood Plasma: A Community-Initiated Position Paper to Develop Accepted Guidelines. J. Lipid Res. 2018, 59, 2001–2017.
  47. Jørgenrud, B.; Jäntti, S.; Mattila, I.; Pöhö, P.; Rønningen, K.S.; Yki-Järvinen, H.; Orešič, M.; Hyötyläinen, T. The Influence of Sample Collection Methodology and Sample Preprocessing on the Blood Metabolic Profile. Bioanalysis 2015, 7, 991–1006.
  48. Ishikawa, M.; Maekawa, K.; Saito, K.; Senoo, Y.; Urata, M.; Murayama, M.; Tajima, Y.; Kumagai, Y.; Saito, Y. Plasma and Serum Lipidomics of Healthy White Adults Shows Characteristic Profiles by Subjects’ Gender and Age. PLoS ONE 2014, 9, e91806.
  49. Kawano, Y.; Ohta, M.; Hirashita, T.; Masuda, T.; Inomata, M.; Kitano, S. Effects of Sleeve Gastrectomy on Lipid Metabolism in an Obese Diabetic Rat Model. Obes. Surg. 2013, 23, 1947–1956.
  50. Im, S.-S.S.; Park, H.Y.; Shon, J.C.; Chung, I.-S.S.; Cho, H.C.; Liu, K.-H.H.; Song, D.-K.K. Plasma Sphingomyelins Increase in Pre-Diabetic Korean Men with Abdominal Obesity. PLoS ONE 2019, 14, e0213285.
  51. Wang, J.; Zhang, L.; Xiao, R.; Li, Y.; Liao, S.; Zhang, Z.; Yang, W.; Liang, B. Plasma Lipidomic Signatures of Spontaneous Obese Rhesus Monkeys. Lipids Health Dis. 2019, 18, 8.
  52. Hernandez-Carretero, A.; Weber, N.; La Frano, M.R.; Ying, W.; Lantero Rodriguez, J.; Sears, D.D.; Wallenius, V.; Börgeson, E.; Newman, J.W.; Osborn, O. Obesity-Induced Changes in Lipid Mediators Persist after Weight Loss. Int. J. Obes. 2018, 42, 728–736.
  53. Pickens, C.A.; Sordillo, L.M.; Zhang, C.; Fenton, J.I.; Austin, C.; Sordillo, L.M.; Zhang, C.; Fenton, J.I. Obesity is Positively Associated with Arachidonic Acid-Derived 5-and 11-Hydroxyeicosatetraenoic Acid (HETE). Metabolism 2017, 70, 177–191.
  54. Fan, R.; Kim, J.; You, M.; Giraud, D.; Toney, A.M.; Shin, S.H.; Kim, S.Y.; Borkowski, K.; Newman, J.W.; Chung, S. α-Linolenic Acid-Enriched Butter Attenuated High Fat Diet-Induced Insulin Resistance and Inflammation by Promoting Bioconversion of n-3 PUFA and Subsequent Oxylipin Formation. J. Nutr. Biochem. 2020, 76, 108285.
  55. Rupasinghe, T.W.T. Lipidomics: Extraction Protocols for Biological Matrices; Humana Press Inc: Totowa, NJ, USA, 2013; Volume 1055, pp. 71–80. ISBN 9781627035767.
  56. Jiang, H.; Hsu, F.F.; Farmer, M.S.; Peterson, L.R.; Schaffer, J.E.; Ory, D.S.; Jiang, X. Development and Validation of LC-MS/MS Method for Determination of Very Long Acyl Chain (C22:0 and C24:0) Ceramides in Human Plasma. Anal. Bioanal. Chem. 2013, 405, 7357–7365.
  57. Ferreiro-Vera, C.; Priego-Capote, F.; Mata-Granados, J.M.; Luque De Castro, M.D. Short-Term Comparative Study of the Influence of Fried Edible Oils Intake on the Metabolism of Essential Fatty Acids in Obese Individuals. Food Chem. 2013, 136, 576–584.
  58. Zeng, M.; Cao, H. Fast Quantification of Short Chain Fatty Acids and Ketone Bodies by Liquid Chromatography-Tandem Mass Spectrometry after Facile Derivatization Coupled with Liquid-Liquid Extraction. J. Chromatogr. B 2018, 1083, 137–145.
  59. Klawitter, J.J.; Bek, S.; Zakaria, M.; Zeng, C.; Hornberger, A.; Gilbert, R.; Shokati, T.; Klawitter, J.J.; Christians, U.; Boernsen, K.O. Fatty Acid Desaturation Index in Human Plasma: Comparison of Different Analytical Methodologies for the Evaluation of Diet Effects. Anal. Bioanal. Chem. 2014, 406, 6399–6408.
  60. Liakh, I.; Pakiet, A.; Sledzinski, T.; Mika, A. Modern Methods of Sample Preparation for the Analysis of Oxylipins in Biological Samples. Molecules 2019, 24, 1639.
  61. Colas, R.A.; Shinohara, M.; Dalli, J.; Chiang, N.; Serhan, C.N. Identification and Signature Profiles for Pro-Resolving and Inflammatory Lipid Mediators in Human Tissue. Am. J. Physiol. Cell Physiol. 2014, 307, C39–C54.
  62. Golovko, M.Y.; Murphy, E.J. An Improved LC-MS/MS Procedure for Brain Prostanoid Analysis Using Brain Fixation with Head-Focused Microwave Irradiation and Liquid-Liquid Extraction. J. Lipid Res. 2008, 49, 893–902.
  63. Vuckovic, D. Current Trends and Challenges in Sample Preparation for Global Metabolomics Using Liquid Chromatography-Mass Spectrometry. Anal. Bioanal. Chem. 2012, 403, 1523–1548.
  64. Folch, J.; Lees, M.; Sloane Stanley, G.H. A Simple Method for the Isolation and Purification of Total Lipides from Animal Tissues. J. Biol. Chem. 1957, 226, 497–509.
  65. Bligh, E.G.; Dyer, W.J. A Rapid Method of Total Lipid Extraction and Purification. Can. J. Biochem. Physiol. 1959, 37, 911–917.
  66. Pizarro, C.; Arenzana-Rámila, I.; Pérez-del-Notario, N.; Pérez-Matute, P.; González-Sáiz, J.-M. Plasma Lipidomic Profiling Method Based on Ultrasound Extraction and Liquid Chromatography Mass Spectrometry. Anal. Chem. 2013, 85, 12085–12092.
  67. Wang, C.; Wang, M.; Han, X. Comprehensive and Quantitative Analysis of Lysophospholipid Molecular Species Present in Obese Mouse Liver by Shotgun Lipidomics. Anal. Chem. 2015, 87, 4879–4887.
  68. Al-Sulaiti, H.; Diboun, I.; Banu, S.; Al-Emadi, M.; Amani, P.; Harvey, T.M.; Dömling, A.S.; Latiff, A.; Elrayess, M.A. Triglyceride Profiling in Adipose Tissues from Obese Insulin Sensitive, Insulin Resistant and Type 2 Diabetes Mellitus Individuals. J. Transl. Med. 2018, 16, 175.
  69. Fernández-Arroyo, S.; Hernández-Aguilera, A.; de Vries, M.A.; Burggraaf, B.; van der Zwan, E.; Pouw, N.; Joven, J.; Castro Cabezas, M. Effect of Vitamin D3 on the Postprandial Lipid Profile in Obese Patients: A Non-Targeted Lipidomics Study. Nutrients 2019, 11, 1194.
  70. Yore, M.M.; Syed, I.; Moraes-Vieira, P.M.; Zhang, T.; Herman, M.A.; Homan, E.A.; Patel, R.T.; Lee, J.; Chen, S.; Peroni, O.D.; et al. Discovery of a Class of Endogenous Mammalian Lipids with Anti-Diabetic and Anti-Inflammatory Effects. Cell 2014, 159, 318–332.
  71. Choromańska, B.; Myśliwiec, P.; Razak Hady, H.; Dadan, J.; Myśliwiec, H.; Chabowski, A.; Mikłosz, A. Metabolic Syndrome is Associated with Ceramide Accumulation in Visceral Adipose Tissue of Women with Morbid Obesity. Obesity 2019, 27, 444–453.
  72. Serbulea, V.; Upchurch, C.M.; Schappe, M.S.; Voigt, P.; DeWeese, D.E.; Desai, B.N.; Meher, A.K.; Leitinger, N. Macrophage Phenotype and Bioenergetics are Controlled by Oxidized Phospholipids Identified in Lean and Obese Adipose Tissue. Proc. Natl. Acad. Sci. USA 2018, 115, E6254–E6263.
  73. León-Aguilar, L.F.; Croyal, M.; Ferchaud-Roucher, V.; Huang, F.; Marchat, L.A.; Barraza-Villarreal, A.; Romieu, I.; Ramakrishnan, U.; Krempf, M.; Ouguerram, K.; et al. Maternal Obesity Leads to Long-Term Altered Levels of Plasma Ceramides in the Offspring as Revealed by a Longitudinal Lipidomic Study in Children. Int. J. Obes. 2019, 43, 1231–1243.
  74. Jaramillo, M.G.; Lytle, K.A.; Spooner, M.H.; Jump, D.B.; García-Jaramillo, M.; Lytle, K.A.; Spooner, M.H.; Jump, D.B. A Lipidomic Analysis of Docosahexaenoic Acid (22:6, ω3) Mediated Attenuation of Western Diet Induced Nonalcoholic Steatohepatitis in Male Ldlr-/-Mice. Metabolites 2019, 9, 252.
  75. Eum, J.Y.; Lee, G.B.; Yi, S.S.; Kim, I.Y.; Seong, J.K.; Moon, M.H. Lipid Alterations in the Skeletal Muscle Tissues of Mice after Weight Regain by Feeding a High-Fat Diet Using Nanoflow Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry. J. Chromatogr. B 2020, 1141, 122022.
  76. Hu, T.; Lin, M.; Zhang, D.; Li, M.; Zhang, J. A UPLC/MS/MS Method for Comprehensive Profiling and Quantification of Fatty Acid Esters of Hydroxy Fatty Acids in White Adipose Tissue. Anal. Bioanal. Chem. 2018, 410, 7415–7428.
  77. Pakiet, A.; Jakubiak, A.; Czumaj, A.; Sledzinski, T.; Mika, A. The Effect of Western Diet on Mice Brain Lipid Composition. Nutr. Metab. 2019, 16, 81.
  78. Kim, H.; Salem, N. Separation of Lipid Classes by Solid Phase Extraction. J. Lipid Res. 1990, 31, 2285–2289.
  79. Cho, A.R.; Moon, J.Y.; Kim, S.; An, K.Y.; Oh, M.; Jeon, J.Y.; Jung, D.H.; Choi, M.H.; Lee, J.W. Effects of Alternate Day Fasting and Exercise on Cholesterol Metabolism in Overweight or Obese Adults: A Pilot Randomized Controlled Trial. Metabolism 2019, 93, 52–60.
  80. Mutemberezi, V.; Masquelier, J.; Guillemot-Legris, O.; Muccioli, G.G. Development and Validation of an HPLC-MS Method for the Simultaneous Quantification of Key Oxysterols, Endocannabinoids, and Ceramides: Variations in Metabolic Syndrome. Anal. Bioanal. Chem. 2016, 408, 733–745.
  81. Ramsden, C.E.; Hennebelle, M.; Schuster, S.; Keyes, G.S.; Johnson, C.D.; Kirpich, I.A.; Dahlen, J.E.; Horowitz, M.S.; Zamora, D.; Feldstein, A.E.; et al. Effects of Diets Enriched in Linoleic Acid and Its Peroxidation Products on Brain Fatty Acids, Oxylipins, and Aldehydes in Mice. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 2018, 1863, 1206–1213.
  82. Okada, K.; Hosooka, T.; Shinohara, M.; Ogawa, W. Modulation of Lipid Mediator Profile May Contribute to Amelioration of Chronic Inflammation in Adipose Tissue of Obese Mice by Pioglitazone. Biochem. Biophys. Res. Commun. 2018, 505, 29–35.
  83. Itariu, B.K.; Zeyda, M.; Hochbrugger, E.E.; Neuhofer, A.; Prager, G.; Schindler, K.; Bohdjalian, A.; Mascher, D.; Vangala, S.; Schranz, M.; et al. Long-Chain n−3 PUFAs Reduce Adipose Tissue and Systemic Inflammation in Severely Obese Nondiabetic Patients: A Randomized Controlled Trial. Am. J. Clin. Nutr. 2012, 96, 1137–1149.
  84. López-Bascón, M.A.; Calderón-Santiago, M.; Priego-Capote, F. Confirmatory and Quantitative Analysis of fatty acid esters of hydroxy fatty acids in serum by solid phase extraction coupled to liquid Chromatography Tandem Mass Spectrometry. Anal. Chim. Acta 2016, 943, 82–88.
  85. Ostermann, A.I.; Willenberg, I.; Schebb, N.H. Comparison of Sample Preparation Methods for the Quantitative Analysis of Eicosanoids and other Oxylipins in Plasma by Means of LC-MS/MS. Anal. Bioanal. Chem. 2015, 407, 1403–1414.
  86. Galvão, A.F.; Petta, T.; Flamand, N.; Bollela, V.R.; Silva, C.L.; Jarduli, L.R.; Malmegrim, K.C.R.; Simões, B.P.; de Moraes, L.A.B.; Faccioli, L.H. Plasma Eicosanoid Profiles Determined by High-Performance Liquid Chromatography Coupled with Tandem Mass Spectrometry in Stimulated Peripheral Blood from Healthy Individuals and Sickle Cell Anemia Patients in Treatment. Anal. Bioanal. Chem. 2016, 408, 3613–3623.
  87. Roberts, L.D.; West, J.A.; Vidal-Puig, A.; Griffin, J.L. Methods for Performing Lipidomics in White Adipose Tissue. In Methods in Enzymology. 2014; 538, 211–231. 538.
  88. Prosen, H. Applications of Liquid-Phase Microextraction in the Sample Preparation of Environmental Solid Samples. Molecules 2014, 19, 6776–6808.
  89. Ruiz-Rodriguez, A.; Reglero, G.; Ibañez, E. Recent Trends in the Advanced Analysis of Bioactive Fatty Acids. J. Pharm. Biomed. Anal. 2010, 51, 305–326.
  90. Teo, C.C.; Chong, W.P.K.; Tan, E.; Basri, N.B.; Low, Z.J.; Ho, Y.S. Advances in Sample Preparation and Analytical Techniques for Lipidomics Study of Clinical Samples. TrAC Trends Anal. Chem. 2015, 66, 1–18.
  91. Genovese, A.; Rispoli, T.; Sacchi, R. Extra Virgin Olive Oil Aroma Release after Interaction with Human Saliva from Individuals with Different Body Mass Index. J. Sci. Food Agric. 2018, 98, 3376–3383.
  92. Del Chierico, F.; Nobili, V.; Vernocchi, P.; Russo, A.; De Stefanis, C.; Gnani, D.; Furlanello, C.; Zandonà, A.; Paci, P.; Capuani, G.; et al. Gut Microbiota Profiling of Pediatric Nonalcoholic Fatty Liver Disease and Obese Patients Unveiled by an Integrated Meta-Omics-Based Approach. Hepatology 2017, 65, 451–464.
  93. Zamora-Gasga, V.M.; Montalvo-González, E.; Loarca-Piña, G.; Vázquez-Landaverde, P.A.; Tovar, J.; Sáyago-Ayerdi, S.G. Microbial Metabolites Profile during In Vitro Human Colonic Fermentation of Breakfast Menus Consumed by Mexican School Children. Food Res. Int. 2017, 97, 7–14.
  94. Cozzolino, R.; De Giulio, B.; Marena, P.; Martignetti, A.; Günther, K.; Lauria, F.; Russo, P.; Stocchero, M.; Siani, A. Urinary Volatile Organic Compounds in Overweight Compared to Normal-Weight Children: Results from the Italian I.Family Cohort. Sci. Rep. 2017, 7, 1–13.
  95. Marisol Encerrado Manriquez, A. Method Development For The Analysis Of Fatty Acids In Adipose Tissue Using Stir Bar Sorptive Extraction Coupled With Gas Chromatography-Mass Spectrometry. Masters’s Thesis, Biochemistry. The University of Texas at el El Paso, El Paso, TX, USA, August 2020.
  96. Eslami, Z.; Torabizadeh, M.; Talebpour, Z.; Talebpour, M.; Ghassempour, A.; Aboul-Enein, H.Y. Simple and Sensitive Quantification of Ghrelin Hormone in Human Plasma Using SBSE-HPLC/DAD-MS. J. Chromatogr. Sci. 2016, 54, 1652–1660.
  97. Rezaee, M.; Assadi, Y.; Milani Hosseini, M.-R.; Aghaee, E.; Ahmadi, F.; Berijani, S. Determination of Organic Compounds in Water Using Dispersive Liquid–Liquid Microextraction. J. Chromatogr. A 2006, 1116, 1–9.
  98. Amin, M.M.; Ebrahim, K.; Hashemi, M.; Shoshtari-Yeganeh, B.; Rafiei, N.; Mansourian, M.; Kelishadi, R. Association of Exposure to Bisphenol A with Obesity and Cardiometabolic Risk Factors in Children and Adolescents. Int. J. Environ. Health Res. 2019, 29, 94–106.
  99. Krawczyńska, A.; Konieczna, L.; Skrzypkowska, M.; Siebert, J.; Reiwer-Gostomska, M.; Gutknecht, P.; Kaska, Ł.; Bigda, J.; Proczko-Stepaniak, M.; Bączek, T. Decreased Level of Vitamin D in Obesity Patients Measured by the LC-MS/MS Method. In Proceedings of the CECE 2019—16th International Interdisciplinary Meeting on Bioanalysis, Gdańsk, Poland, 24–26 September 2019; p. 33.
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Subjects: Biochemistry & Molecular Biology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Ivan Liakh
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Tomasz Sledzinski
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Lukasz Kaska
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Paulina Mozolewska
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Adriana Mika
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Update Date: 01 Dec 2020
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