Molecular, Metabolic, and Nutritional Changes after Metabolic Surgery in Obese Diabetic Patients (MoMen): A Protocol for a Multicenter Prospective Cohort Study

Metabolic surgery is an essential option in the treatment of obese patients with type 2 diabetes (T2D). Despite its known advantages, this surgery still needs to be introduced in Malaysia. In this prospective study, the pathophysiological mechanisms at the molecular level will be studied and the metabolomics pathways of diabetes remission will be explored. The present study aims to evaluate the changes in the anthropometric measurements, body composition, phase angle, diet intake, biochemistry parameters, adipokines, microRNA, and metabolomics, both pre- and post-surgery, among obese diabetic patients in Malaysia. This is a multicenter prospective cohort study that will involve obese patients (n = 102) with a body mass index (BMI) of ≥25 kg/m2 (Asian BMI categories: WHO/IASO/IOTF, 2000) who will undergo metabolic surgery. They will be categorized into three groups: non-diabetes, prediabetes, and diabetes. Their body composition will be measured using a bioimpedance analyzer (BIA). The phase angle (PhA) data will be analyzed. Venous blood will be collected from each patient for glycated hemoglobin (HbA1c), lipids, liver, renal profile, hormones, adipokines, and molecular and metabolomics analyses. The serum microRNA will be measured. A gene expression study of the adipose tissue of different groups will be conducted to compare the groups. The relationship between the 1HNMR-metabolic fingerprint and the patients’ lifestyles and dietary practices will be determined. The factors responsible for the excellent remission of T2D will be explored in this study.

[1]  T. Nakagata,et al.  Phase angle obtained via bioelectrical impedance analysis and objectively measured physical activity or exercise habits , 2022, Scientific Reports.

[2]  A. A. Zainuddin,et al.  Prevalence of Obesity and Its Associated Factors Among Malaysian Adults: Finding From the National Health and Morbidity Survey 2019 , 2022, Asia-Pacific journal of public health.

[3]  H. Murphy,et al.  Dietary Patterns, Metabolomic Profile, and Nutritype Signatures Associated with Type 2 Diabetes in Women with Postgestational Diabetes Mellitus: MyNutritype Study Protocol , 2022, Metabolites.

[4]  Se-Ho Chang,et al.  Impact of phase angle and sarcopenia estimated by bioimpedance analysis on clinical prognosis in patients undergoing hemodialysis , 2022, Medicine.

[5]  H. Berthoud,et al.  Regulation of body weight: Lessons learned from bariatric surgery , 2022, Molecular metabolism.

[6]  Ahmed Rabiee,et al.  Predictors of type-2 diabetes remission following bariatric surgery after a two-year follow up. , 2022, Asian journal of surgery.

[7]  Chao-Yung Wang,et al.  Serum microRNA panels predict bariatric surgery outcomes , 2022, Obesity.

[8]  M. Kretowski,et al.  Exploring microRNAs as predictive biomarkers for type 2 diabetes mellitus remission after sleeve gastrectomy: A pilot study , 2022, Obesity.

[9]  Y. Cho,et al.  East Asian perspectives in metabolic and bariatric surgery , 2022, Journal of diabetes investigation.

[10]  M. Alqunai,et al.  Bariatric surgery for the management of type 2 diabetes mellitus-current trends and challenges: a review article. , 2022, American journal of translational research.

[11]  A. Tahrani,et al.  Prognostic Models for Predicting Remission of Diabetes Following Bariatric Surgery: A Systematic Review and Meta-analysis , 2021, Diabetes Care.

[12]  A. V. van Zanten,et al.  Bioelectric impedance body composition and phase angle in relation to 90-day adverse outcome in hospitalized COVID-19 ward and ICU patients: The prospective BIAC-19 study , 2021, Clinical Nutrition ESPEN.

[13]  Fabien Jourdan,et al.  Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis , 2021, PLoS Comput. Biol..

[14]  A. Pineda-Lucena,et al.  Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments , 2021, Cancers.

[15]  B. Brandao,et al.  Extracellular miRNAs as mediators of obesity‐associated disease , 2021, The Journal of physiology.

[16]  Y. Vander Heyden,et al.  Improved multi-class discrimination by Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis. , 2021, Talanta.

[17]  A. V. van Zanten,et al.  Bioelectric impedance analysis for body composition measurement and other potential clinical applications in critical illness , 2021, Current opinion in critical care.

[18]  Qaiser Abbas,et al.  Shape and texture based classification of citrus using principal component analysis , 2021 .

[19]  J. Aberle,et al.  Long-Term Improvement of Chronic Low-Grade Inflammation After Bariatric Surgery , 2021, Obesity Surgery.

[20]  U. P. Flato,et al.  Adipokines, Myokines, and Hepatokines: Crosstalk and Metabolic Repercussions , 2021, International journal of molecular sciences.

[21]  S. Dash,et al.  Obesity as a multisystem disease: Trends in obesity rates and obesity‐related complications , 2021, Diabetes, obesity & metabolism.

[22]  Jingjing Zhang,et al.  Changes in fasting bile acid profiles after Roux-en-Y gastric bypass and sleeve gastrectomy , 2021, Medicine.

[23]  S. Hunt,et al.  Associations of Visceral, Subcutaneous, Epicardial, and Liver Fat with Metabolic Disorders up to 14 Years After Weight Loss Surgery. , 2020, Metabolic syndrome and related disorders.

[24]  E. Ferretti,et al.  Tissue and circulating microRNAs as biomarkers of response to obesity treatment strategies , 2020, Journal of Endocrinological Investigation.

[25]  C. Reissfelder,et al.  Handgrip Strength and Phase Angle Predict Outcome After Bariatric Surgery , 2020, Obesity Surgery.

[26]  A. Streb,et al.  Phase angle associated with different indicators of health-related physical fitness in adults with obesity , 2020, Physiology & Behavior.

[27]  P. Elliott,et al.  Identifying unknown metabolites using NMR-based metabolic profiling techniques , 2020, Nature Protocols.

[28]  T. Karupaiah,et al.  Exploring Metabolic Signature of Protein Energy Wasting in Hemodialysis Patients , 2020, Metabolites.

[29]  M. Nieuwdorp,et al.  Small intestinal physiology relevant to bariatric and metabolic endoscopic therapies: Incretins, bile acid signaling, and gut microbiome , 2020 .

[30]  K. Rieger-Christ,et al.  Can genetics help predict efficacy of bariatric surgery? An analysis of microRNA profiles. , 2020, Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery.

[31]  Muhammad Aslam,et al.  Test of Association in the Presence of Complex Environment , 2020, Complex..

[32]  P. Schauer,et al.  Bariatric Surgery as a Long-Term Treatment for Type 2 Diabetes/Metabolic Syndrome. , 2020, Annual review of medicine.

[33]  J. DeLany,et al.  Bariatric Surgery vs. Lifestyle Intervention for Diabetes Treatment: Five Year Outcomes From a Randomized Trial. , 2020, The Journal of clinical endocrinology and metabolism.

[34]  A. Gangemi,et al.  Physiologic Mechanisms of Type II Diabetes Mellitus Remission Following Bariatric Surgery: a Meta-analysis and Clinical Implications , 2020, Journal of Gastrointestinal Surgery.

[35]  N. Esfandiari,et al.  Bariatric Surgery in the Treatment of Type 2 Diabetes , 2019, Current Diabetes Reports.

[36]  B. Naziruddin,et al.  MicroRNA Signatures as Future Biomarkers for Diagnosis of Diabetes States , 2019, Cells.

[37]  G. Frühbeck,et al.  Relevance of Leptin and Other Adipokines in Obesity-Associated Cardiovascular Risk , 2019, Nutrients.

[38]  M. Arshad,et al.  Two Years Remission of Type 2 Diabetes Mellitus after Bariatric Surgery. , 2019, Journal of the College of Physicians and Surgeons--Pakistan : JCPSP.

[39]  H. Lukaski,et al.  Classification of Hydration in Clinical Conditions: Indirect and Direct Approaches Using Bioimpedance , 2019, Nutrients.

[40]  Chao-Yung Wang,et al.  MiR-122 marks the differences between subcutaneous and visceral adipose tissues and associates with the outcome of bariatric surgery. , 2018, Obesity research & clinical practice.

[41]  D. Brennan,et al.  Gut adaptation after metabolic surgery and its influences on the brain, liver and cancer , 2018, Nature Reviews Gastroenterology & Hepatology.

[42]  A. Pucci,et al.  Mechanisms underlying the weight loss effects of RYGB and SG: similar, yet different , 2018, Journal of Endocrinological Investigation.

[43]  Arthur S Edison,et al.  Alternatives to Nuclear Overhauser Enhancement Spectroscopy Presat and Carr–Purcell–Meiboom–Gill Presat for NMR-Based Metabolomics , 2017, Analytical chemistry.

[44]  Safdar Ali,et al.  Identification of new spectral signatures associated with dengue virus infected sera , 2017 .

[45]  P. Kienle,et al.  The Phase Angle of the Bioelectrical Impedance Analysis as Predictor of Post-Bariatric Weight Loss Outcome , 2017, Obesity Surgery.

[46]  C. Cogoni,et al.  MicroRNA in Control of Gene Expression: An Overview of Nuclear Functions , 2016, International journal of molecular sciences.

[47]  R. Sinha,et al.  Comparing metabolite profiles of habitual diet in serum and urine. , 2016, The American journal of clinical nutrition.

[48]  Thiago I. B. Lopes,et al.  "Omics" Prospective Monitoring of Bariatric Surgery: Roux-En-Y Gastric Bypass Outcomes Using Mixed-Meal Tolerance Test and Time-Resolved (1)H NMR-Based Metabolomics. , 2016, Omics : a journal of integrative biology.

[49]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[50]  L. Samavati,et al.  Metabolomics connects aberrant bioenergetic, transmethylation, and gut microbiota in sarcoidosis , 2016, Metabolomics.

[51]  C. Emanueli,et al.  Noncoding RNAs in diabetes vascular complications. , 2015, Journal of molecular and cellular cardiology.

[52]  M. Rodicio,et al.  Detection methods for microRNAs in clinic practice. , 2013, Clinical biochemistry.

[53]  Dean P. Jones,et al.  Metabolomic Analysis Reveals Extended Metabolic Consequences of Marginal Vitamin B-6 Deficiency in Healthy Human Subjects , 2013, PloS one.

[54]  D. Huhman,et al.  Mass Spectrometry Strategies in Metabolomics* , 2011, The Journal of Biological Chemistry.

[55]  T. Sørensen,et al.  Chemometric strategies to assess metabonomic imprinting of food habits in epidemiological studies , 2010 .

[56]  B. Poh,et al.  Food Consumption Patterns: Findings from the Malaysian Adult Nutrition Survey (MANS). , 2008, Malaysian journal of nutrition.

[57]  T. Ebbels,et al.  Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts , 2007, Nature Protocols.

[58]  Ho-Young Son,et al.  Epidemic obesity and type 2 diabetes in Asia , 2006, The Lancet.

[59]  Eric Altermann,et al.  PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database , 2005, BMC Genomics.

[60]  W. Scherbaum,et al.  Diabetes Mellitus Type 2 , 2007, Deutsche medizinische Wochenschrift.