METABOLOMICS-GUIDED INSIGHTS ON BARIATRIC SURGERY: A MULTIVARIATE DATA ANALYSIS OVER 1H NMR SPECTRA FROM SERUM SAMPLES

MARTINEZ BILESIO, ANDRÉS R.; ARGUELLO, MARÍA A.; MATELLICANI, GUSTAVO; NASURDI, ALEJANDRO; BARRERA, MARIA M.; ROCCA, LILIANA; PAZ, MAXIMILIANO; SCIARA MARIELA; FAY, FABIAN; JAUMOT SOLER JOAQUIM; RASIA, RODOLFO M.; GARCÍA REIRIZ, ALEJANDRO G.; BURDISSO, PAULA

Congreso; Metabolomics 2021 Online; 2021

Metabolomics Society

Bariatric surgery is considered the most efficient treatment for diseases relatedto morbid obesity [1]. To date, this surgical procedure has proven to be successful forweight loss, but also for the progression control of type 2 diabetes considering itsmetabolic impacts [2]. However, the effect of bariatric surgery on metabolism is still notwell defined. In this sense, metabolomics has emerged in biomedical research as afield able to shed some light on obesity-related metabolic diseases [3-7]. The analysisof the metabolome through high-throughput nuclear magnetic resonance (NMR)provides a picture of the main metabolic processes at a particular time and allowsdefining metabolic phenotypes (metabotypes) of responses affected by a certaintreatment.Hence, in the present study, we aimed to discriminate metabolic signatureslinked to bariatric surgery and determine potential adaptations according to themetabolic evolution and the clinical parameters of different patients.A 1 H NMR metabolomic approach was used in order to analyze serum samplesof subjects with morbid obesity (n = 15), before (2-3 weeks) and after (48 hours, 5days, 1 month, 6 months and 12 months) bariatric surgery. Different multivariateanalyses including exploratory analysis (principal component analysis, PCA), statisticalassessment of the effects of the studied factors (ANOVA simultaneous componentanalysis, ASCA) and sample discrimination (partial least squares discriminant analysis,PLS-DA) allowed to identify the main metabolic responses associated with the bariatricsurgery. We have defined two metabotypes of response independently of gender, ageor body mass index (BMI). In addition, it was possible to elucidate general metabolicprofiles over time, distinguishing 3 primary temporal trends throughout the bariatricsurgery evolution.Although further studies are needed, our results open new hypotheses in thestudy of obesity-linked co-morbidities and provide a comprehensive view of themetabolic changes after the surgery.[1] Rubino F, Nathan DM, Eckel RH, Schauer PR, Alberti KGMM, Zimmet PZ, et al. Metabolic surgery inthe treatment algorithm for type 2 diabetes: a joint statement by international diabetes organizations.Diabetes Care. 2016;39: 861?877. pmid:27222544[2] Mingrone G, Panunzi S, De Gaetano A, Guidone C, Iaconelli A, Nanni G, et al. Bariatric?metabolicsurgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up ofan open-label, single-centre, randomised controlled trial. Lancet. Elsevier Ltd; 2015;386: 964?973.[3] Palau-Rodriguez M, Tulipani S, Marco-Ramell A, Miñarro A, Jáuregui O, Sanchez-Pla A, Ramos-Molina B, Tinahones FJ, Andres-Lacueva C. Metabotypes of response to bariatric surgery independent ofthe magnitude of weight loss. PLoS One. 2018;13(6): e0198214[4] Gralka E, Luchinat C, Tenori L, Ernst B, Thurnheer M, Schultes B. Metabolomic fingerprint of severeobesity is dynamically affected by bariatric surgery in a procedure-dependent manner. Am J Clin Nutr.2015;102: 1313?1322. pmid:26581381[5] Mutch DM, Fuhrmann JC, Rein D, Wiemer JC, Bouillot J-L, Poitou C, et al. Metabolite profilingidentifies candidate markers reflecting the clinical adaptations associated with Roux-en-Y gastric bypasssurgery. Calbet JAL, editor. PLoS One. 2009;4: e7905. pmid:19936240[6] Luo P, Yu H, Zhao X, Bao Y, Hong CS, Zhang P, et al. Metabolomics study of Roux-en-Y gastricbypass surgery (RYGB) to treat type 2 diabetes patients based on ultraperformance liquidchromatography?mass spectrometry. J Proteome Res. 2016;15: 1288?1299. pmid:26889720[7] Nicholson JK, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon JC. Metabolic phenotyping in clinicaland surgical environments. Nature. 2012;491: 384?92. pmid:23151581