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

10 March 2023


Mansor Fazliana, Zubaidah Nor Hanipah, Barakatun Nisak Mohd Yusof, Nur Azlin Zainal Abidin, You Zhuan Tan, Farah Huda Mohkiar, Ahmad Zamri Liyana, Mohd Nawi Mohd Naeem, Norazlan Mohmad Misnan, Haron Ahmad, Mohd Shazli Draman, Poh Yue Tsen, Shu Yu Lim, Tikfu Gee


Abstract

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.


Reference

  1. AbdAlla Salman, M., Rabiee, A., Salman, A., Elewa, A., Tourky, M., Mahmoud, A. A., Moustafa, A., El-Din Shaaban, H., Ismail, A. A., Noureldin, K., & Tourky, M. (2022). Predictors of type-2 diabetes remission following bariatric surgery after a two-year follow up. Asian Journal of Surgery, 45(12), 2645–2650. https://doi.org/10.1016/j.asjsur.2022.01.114
  2. Affinati, A. H., Esfandiari, N. H., Oral, E. A., & Kraftson, A. T. (2019). Bariatric surgery in the treatment of type 2 diabetes. Current Diabetes Reports, 19(12), Article 156. https://doi.org/10.1007/s11892-019-1235-3
  3. Akhtar, N., Idrees, M., Rehman, F. U., Ilyas, M., Abbas, Q., & Luqman, M. (2021). Shape and texture based classification of citrus using principal component analysis. International Journal of Agricultural Extension, 9(2), 229–238. https://doi.org/10.33687/ijae.009.02.3719
  4. Albaugh, V. L., He, Y., Münzberg, H., Morrison, C. D., Yu, S., & Berthoud, H. -R. (2022). Regulation of body weight: Lessons learned from bariatric surgery. Molecular Metabolism, 68, Article 101517. https://doi.org/10.1016/j.molmet.2022.101517
  5. Alqunai, M. S., & Alrashid, F. F. (2022). Bariatric surgery for the management of type 2 diabetes mellitus-current trends and challenges: A review article. American Journal of Translational Research, 14(2), 1160–1171.
  6. Altermann, E., & Klaenhammer, T. R. (2005). PathwayVoyager: Pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics, 6, Article 60. https://doi.org/10.1186/1471-2164-6-60
  7. Amin, A., Ghouri, N., Ali, S., Ahmed, M., Saleem, M., & Qazi, J. (2017). Identification of new spectral signatures associated with dengue virus infected sera. Journal of Raman Spectroscopy, 48(5), 705–710. https://doi.org/10.1002/jrs.5111
  8. Andries, J. P. M., & Vander Heyden, Y. (2021). Improved multi-class discrimination by common-subset-of-independent-variables partial-least-squares discriminant analysis. Talanta, 234, Article 122595. https://doi.org/10.1016/j.talanta.2021.122595
  9. Aslam, M., & Arif, O. H. (2020). Test of association in the presence of complex environment. Complexity, 2020, Article e2935435. https://doi.org/10.1155/2020/2935435
  10. Bae, E., Lee, T. W., Bae, W., Kim, S., Choi, J., Jang, H. N., Chang, S. -H., & Park, D. J. (2022). Impact of phase angle and sarcopenia estimated by bioimpedance analysis on clinical prognosis in patients undergoing hemodialysis: A retrospective study. Medicine, 101(23), Article e29375. https://doi.org/10.1097/MD.0000000000029375
  11. Beckonert, O., Keun, H. C., Ebbels, T. M. D., Bundy, J., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2007). Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols, 2(11), 2692–2703. https://doi.org/10.1038/nprot.2007.376
  12. Beltrami, C., Angelini, T. G., & Emanueli, C. (2015). Noncoding RNAs in diabetes vascular complications. Journal of Molecular and Cellular Cardiology, 89(Pt. A), 42–50. https://doi.org/10.1016/j.yjmcc.2015.10.019
  13. Brandao, B. B., Lino, M., & Kahn, C. R. (2022). Extracellular miRNAs as mediators of obesity-associated disease. The Journal of Physiology, 600(5), 1155–1169. https://doi.org/10.1113/JP280918
  14. Catalanotto, C., Cogoni, C., & Zardo, G. (2016). MicroRNA in control of gene expression: An overview of nuclear functions. International Journal of Molecular Sciences, 17(10), Article 1712. https://doi.org/10.3390/ijms17101712
  15. Catanzaro, G., Filardi, T., Sabato, C., Vacca, A., Migliaccio, S., Morano, S., & Ferretti, E. (2021). Tissue and circulating microRNAs as biomarkers of response to obesity treatment strategies. Journal of Endocrinological Investigation, 44(6), 1159–1174. https://doi.org/10.1007/s40618-020-01441-w
  16. Chong, C. T., Lai, W. K., Zainuddin, A. A., Pardi, M., Mohd Sallehuddin, S., & Ganapathy, S. S. (2022). Prevalence of obesity and its associated factors among Malaysian adults: Finding from the National Health and Morbidity Survey 2019. Asia Pacific Journal of Public Health, 34(8), 786–792. https://doi.org/10.1177/10105395221124450
  17. Courcoulas, A. P., Gallagher, J. W., Neiberg, R. H., Eagleton, E. B., DeLany, J. P., Lang, W., Punchai, S., Gourash, W., & Jakicic, J. M. (2020). Bariatric surgery vs lifestyle intervention for diabetes treatment: 5-year outcomes from a randomized trial. The Journal of Clinical Endocrinology & Metabolism, 105(6), dgaa006. https://doi.org/10.1210/clinem/dgaa006
  18. de Oliveira Dos Santos, A. R., de Oliveira Zanuso, B., Miola, V. F. B., Barbalho, S. M., Santos Bueno, P. C., Flato, U. A. P., Detregiachi, C. R. P., Buchaim, D. V., Buchaim, R. L., Tofano, R. J., & Bishayee, A. (2021). Adipokines, myokines, and hepatokines: Crosstalk and metabolic repercussions. International Journal of Molecular Sciences, 22(5), Article 2639. https://doi.org/10.3390/ijms22052639
  19. de Planell-Saguer, M., & Rodicio, M. C. (2013). Detection methods for microRNAs in clinic practice. Clinical Biochemistry, 46(10-11), 869–878. https://doi.org/10.1016/j.clinbiochem.2013.02.004
  20. Doyon, L., Das, S., Sullivan, T., Rieger-Christ, K., Sherman, J., Roque, S., & Nepomnayshy, D. (2020). Can genetics help predict efficacy of bariatric surgery? An analysis of microRNA profiles. Surgery for Obesity and Related Diseases, 16(11), 1802–1807. https://doi.org/10.1016/j.soard.2020.07.003
  21. Garcia-Perez, I., Posma, J. M., Serrano-Contreras, J. I., Boulangé, C. L., Chan, Q., Frost, G., Stamler, J., Elliott, P., Lindon, J. C., Holmes, E., & Nicholson, J. K. (2020). Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nature Protocols, 15(8), 2538–2567. https://doi.org/10.1038/s41596-020-0343-3
  22. Geamanu, A., Gupta, S. V., Bauerfeld, C., & Samavati, L. (2016). Metabolomics connects aberrant bioenergetic, transmethylation, and gut microbiota in sarcoidosis. Metabolomics: Official Journal of the Metabolomic Society, 12, Article 35. https://doi.org/10.1007/s11306-016-0951-6
  23. Gerken, A. L. H., Rohr-Kräutle, K. -K., Weiss, C., Seyfried, S., Reissfelder, C., Vassilev, G., & Otto, M. (2021). Handgrip strength and phase angle predict outcome after bariatric surgery. Obesity Surgery, 31(1), 200–206. https://doi.org/10.1007/s11695-020-04875-1
  24. Gómez-Cebrián, N., Domingo-Ortí, I., Poveda, J. L., Vicent, M. J., Puchades-Carrasco, L., & Pineda-Lucena, A. (2021). Multi-omic approaches to breast cancer metabolic phenotyping: Applications in diagnosis, prognosis, and the development of novel treatments. Cancers, 13(18), Article 4544. https://doi.org/10.3390/cancers13184544
  25. Goyal, R., & Jialal, I. (2022). Diabetes mellitus type 2. StatPearls Publishing.
  26. Gregory, J. F., Park, Y., Lamers, Y., Bandyopadhyay, N., Chi, Y. -Y., Lee, K., Kim, S., da Silva, V., Hove, N., Ranka, S., & Theriaque, D. W. (2013). Metabolomic analysis reveals extended metabolic consequences of marginal vitamin B-6 deficiency in healthy human subjects. PLoS ONE, 8(6), Article e63544. https://doi.org/10.1371/journal.pone.0063544
  27. Hanipah, Z. N., & Schauer, P. R. (2020). Bariatric surgery as a long-term treatment for type 2 diabetes/metabolic syndrome. Annual Review of Medicine, 71(1), 1–15. https://doi.org/10.1146/annurev-med-053118-011443
  28. Hasbullah, F. Y., Yusof, B. -N. M., Ghani, R. A., Daud, Z., Azuan, M., Appannah, G., Abas, F., Shafie, N. H., Khir, H. I. M., & Murphy, H. R. (2022). Dietary patterns, metabolomic profile, and nutritype signatures associated with type 2 diabetes in women with postgestational diabetes mellitus: MyNutritype study protocol. Metabolites, 12(9), Article 843. https://doi.org/10.3390/metabolites12090843
  29. Hunt, S. C., Davidson, L. E., Adams, T. D., Ranson, L., McKinlay, R. D., Simper, S. C., & Litwin, S. E. (2021). Associations of visceral, subcutaneous, epicardial, and liver fat with metabolic disorders up to 14 years after weight loss surgery. Metabolic Syndrome and Related Disorders, 19(2), 83–92. https://doi.org/10.1089/met.2020.0076
  30. Institute for Public Health. (2020). National Health and Morbidity Survey (NHMS) 2019: Non-communicable diseases, healthcare demand, and health literacy—Key findings. Institute for Public Health, Ministry of Health Malaysia.
  31. Institute for Public Health Malaysia. (2016). National Health and Morbidity Survey 2014: Malaysian adult (Vol. 2). Ministry of Health Malaysia.
  32. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2060), Article 20150202. https://doi.org/10.1098/rsta.2015.0202
  33. Landecho, M. F., Tuero, C., Valentí, V., Bilbao, I., de la Higuera, M., & Frühbeck, G. (2019). Relevance of leptin and other adipokines in obesity-associated cardiovascular risk. Nutrients, 11(11), Article 2664. https://doi.org/10.3390/nu11112664
  34. Lautenbach, A., Stoll, F., Mann, O., Busch, P., Huber, T. B., Kielstein, H., Bähr, I., & Aberle, J. (2021). Long-term improvement of chronic low-grade inflammation after bariatric surgery. Obesity Surgery, 31(7), 2913–2920. https://doi.org/10.1007/s11695-021-05315-y
  35. Le Guennec, A., Tayyari, F., & Edison, A. S. (2017). Alternatives to nuclear Overhauser enhancement spectroscopy presat and Carr-Purcell-Meiboom-Gill presat for NMR-based metabolomics. Analytical Chemistry, 89(16), 8582–8588. https://doi.org/10.1021/acs.analchem.7b02131
  36. Lei, Z., Huhman, D. V., & Sumner, L. W. (2011). Mass spectrometry strategies in metabolomics. Journal of Biological Chemistry, 286(29), 25435–25442. https://doi.org/10.1024/jbc.R111.238691
  37. Liao, C. -H., Wang, C. -Y., Liu, K. -H., Liu, Y. -Y., Wen, M. -S., & Yeh, T. -S. (2018). MiR-122 marks the differences between subcutaneous and visceral adipose tissues and associates with the outcome of bariatric surgery. Obesity Research & Clinical Practice, 12(6), 570–577. https://doi.org/10.1016/j.orcp.2018.06.008
  38. Lopes, T. I. B., Geloneze, B., Pareja, J. C., Calixto, A. R., Ferreira, M. M. C., & Marsaioli, A. J. (2016). “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. OMICS: A Journal of Integrative Biology, 20(7), 415–423. https://doi.org/10.1089/omi.2016.0048
  39. Lukaski, H. C., Vega Diaz, N., Talluri, A., & Nescolarde, L. (2019). Classification of hydration in clinical conditions: Indirect and direct approaches using bioimpedance. Nutrients, 11(4), Article 809. https://doi.org/10.3390/nu11040809
  40. Manaf, Z. A. (2015). The atlas of food exchange and portion sizes / Atlas makanan: Saiz pertukaran & porsi (3rd ed.). MDC Publishers Sdn Bhd.
  41. Ministry of Health Malaysia. (2020). Clinical practice guideline—Management of type 2 diabetes mellitus (6th ed.). Ministry of Health Malaysia.
  42. Moonen, H. P. F. X., Bos, A. E., Hermans, A. J. H., Stikkelman, E., van Zanten, F. J. L., & van Zanten, A. R. H. (2021). 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. Clinical Nutrition ESPEN, 46, 185–192. https://doi.org/10.1016/j.clnesp.2021.09.014
  43. Moonen, H. P. F. X., & Van Zanten, A. R. H. (2021). Bioelectric impedance analysis for body composition measurement and other potential clinical applications in critical illness. Current Opinion in Critical Care, 27(4), 344–353. https://doi.org/10.1097/MCC.0000000000000840
  44. Norimah, A. K., Safiah, M., Jamal, K., Haslinda, S., Zuhaida, H., Rohida, S., Fatimah, S., Norazlin, S., Poh, B. K., Kandiah, M., & ... & MANS Steering Committee. (2008). Food consumption patterns: Findings from the Malaysian Adult Nutrition Survey (MANS). Malaysian Journal of Nutrition, 14(1), 25–39.
  45. Oh, T. J., Lee, H. -J., & Cho, Y. M. (2022). East Asian perspectives in metabolic and bariatric surgery. Journal of Diabetes Investigation, 13(5), 756–761. https://doi.org/10.1111/jdi.13768
  46. Pauzi, F. A., Sahathevan, S., Khor, B. -H., Narayanan, S. S., Zakaria, N. F., Abas, F., Karupaiah, T., & Daud, Z. A. M. (2020). Exploring metabolic signature of protein energy wasting in hemodialysis patients. Metabolites, 10(7), Article 291. https://doi.org/10.3390/metabolites10070291
  47. Peré-Trepat, E., Ross, A. B., Martin, F. -P., Rezzi, S., Kochhar, S., Hasselbalch, A. L., Kyvik, K. O., & Sørensen, T. I. A. (2010). Chemometric strategies to assess metabonomic imprinting of food habits in epidemiological studies. Chemometrics and Intelligent Laboratory Systems, 104(1), 95–100. https://doi.org/10.1016/j.chemolab.2010.05.008
  48. Playdon, M. C., Sampson, J. N., Cross, A. J., Sinha, R., Guertin, K. A., Moy, K. A., Rothman, N., Irwin, M. L., Mayne, S. T., Stolzenberg-Solomon, R., & ... & Moore, S. C. (2016). Comparing metabolite profiles of habitual diet in serum and urine. The American Journal of Clinical Nutrition, 104(3), 776–789. https://doi.org/10.3945/ajcn.116.134502
  49. Pucci, A., & Batterham, R. L. (2019). Mechanisms underlying the weight loss effects of RYGB and SG: Similar, yet different. Journal of Endocrinological Investigation, 42(2), 117–128. https://doi.org/10.1007/s40618-018-0892-2
  50. Russel, S. M., Valle, V., Spagni, G., Hamilton, S., Patel, T., Abdukadyrov, N., Dong, Y., & Gangemi, A. (2020). Physiologic mechanisms of type II diabetes mellitus remission following bariatric surgery: A meta-analysis and clinical implications. Journal of Gastrointestinal Surgery, 24(3), 728–741. https://doi.org/10.1007/s11605-019-04481-2
  51. Sarma, S., Sockalingam, S., & Dash, S. (2021). Obesity as a multisystem disease: Trends in obesity rates and obesity-related complications. Diabetes, Obesity and Metabolism, 23(Suppl. 1), 3–16. https://doi.org/10.1111/dom.14290
  52. Sinclair, P., Brennan, D. J., & le Roux, C. W. (2018). Gut adaptation after metabolic surgery and its influences on the brain, liver and cancer. Nature Reviews Gastroenterology & Hepatology, 15(10), 606–624. https://doi.org/10.1038/s41575-018-0051-4
  53. Singh, P., Adderley, N. J., Hazlehurst, J., Price, M., Tahrani, A. A., Nirantharakumar, K., & Bellary, S. (2021). Prognostic models for predicting remission of diabetes following bariatric surgery: A systematic review and meta-analysis. Diabetes Care, 44(11), 2626–2641. https://doi.org/10.2337/dc21-0731
  54. Streb, A. R., Hansen, F., Gabiatti, M. P., Tozetto, W. R., & Del Duca, G. F. (2020). Phase angle associated with different indicators of health-related physical fitness in adults with obesity. Physiology & Behavior, 225, Article 113104. https://doi.org/10.1016/j.physbeh.2020.113104
  55. van Olst, N., Meiring, S., de Brauw, M., Bergman, J. J. G. H. M., Nieuwdorp, M., van der Peet, D. L., & Gerdes, V. E. A. (2020). Small intestinal physiology relevant to bariatric and metabolic endoscopic therapies: Incretins, bile acid signaling, and gut microbiome. Techniques and Innovations in Gastrointestinal Endoscopy, 22(3), 109–119. https://doi.org/10.1016/j.tige.2020.03.003
  56. Vasu, S., Kumano, K., Darden, C. M., Rahman, I., Lawrence, M. C., & Naziruddin, B. (2019). MicroRNA signatures as future biomarkers for diagnosis of diabetes states. Cells, 8(12), Article 1533. https://doi.org/10.3390/cells8121533
  57. Vassilev, G., Hasenberg, T., Krammer, J., Kienle, P., Ronellenfitsch, U., & Otto, M. (2017). The phase angle of the bioelectrical impedance analysis as predictor of post-bariatric weight loss outcome. Obesity Surgery, 27(3), 665–669. https://doi.org/10.1007/s11695-016-2337-1
  58. Wazir, N., Arshad, M. F., Finney, J., Kirk, K., & Dewan, S. (2019). Two years remission of type 2 diabetes mellitus after bariatric surgery. Journal of the College of Physicians and Surgeons Pakistan, 29(10), 967–971. https://doi.org/10.29271/jcpsp.2019.10.967
  59. Wieder, C., Frainay, C., Poupin, N., Rodríguez-Mier, P., Vinson, F., Cooke, J., Lai, R. P., Bundy, J. G., Jourdan, F., & Ebbels, T. (2021). Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis. PLoS Computational Biology, 17(9), Article e1009105. https://doi.org/10.1371/journal.pcbi.1009105
  60. Wojciechowska, G., Szczerbinski, L., Kretowski, M., Niemira, M., Hady, H. R., & Kretowski, A. (2022). Exploring microRNAs as predictive biomarkers for type 2 diabetes mellitus remission after sleeve gastrectomy: A pilot study. Obesity, 30(2), 435–446. https://doi.org/10.1002/oby.23348
  61. World Health Organization, & Regional Office for the Western Pacific. (2000). The Asia-Pacific perspective: Redefining obesity and its treatment. Health Communications Australia.
  62. Yamada, Y., Yoshida, T., Murakami, H., Kawakami, R., Gando, Y., Ohno, H., Tanisawa, K., Konishi, K., Julien, T., Kondo, E., & ... & Section of the National Health and Nutrition Survey. (2022). Phase angle obtained via bioelectrical impedance analysis and objectively measured physical activity or exercise habits. Scientific Reports, 12(1), Article 17274. https://doi.org/10.1038/s41598-022-21443-4
  63. Yeh, J. -K., Chen, C. -C., Liu, K. -H., Peng, C. -C., Lin, T. -A., Chang, Y. -S., Wen, M. -S., Yeh, T. -S., & Wang, C. -Y. (2022). Serum microRNA panels predict bariatric surgery outcomes. Obesity, 30(2), 389–399. https://doi.org/10.1002/oby.23337
  64. Yoon, K. -H., Lee, J. -H., Kim, J. -W., Cho, J. H., Choi, Y. -H., Ko, S. -H., Zimmet, P., & Son, H. -Y. (2006). Epidemic obesity and type 2 diabetes in Asia. The Lancet, 368(9548), 1681–1688. https://doi.org/10.1016/S0140-6736(06)69703-1
  65. Zhang, C., Zhang, J., & Zhou, Z. (2021). Changes in fasting bile acid profiles after Roux-en-Y gastric bypass and sleeve gastrectomy. Medicine, 100(4), Article e23939. https://doi.org/10.1097/MD.0000000000023939

Cite

Fazliana, M., Nor Hanipah, Z., Mohd Yusof, B. N., Zainal Abidin, N. A., Tan, Y. Z., Mohkiar, F. H., Liyana, A. Z., Mohd Naeem, M. N., Mohmad Misnan, N., Ahmad, H., Draman, M. S., Tsen, P. Y., Lim, S. Y., & Gee, T. (2023). Molecular, Metabolic, and Nutritional Changes after Metabolic Surgery in Obese Diabetic Patients (MoMen): A Protocol for a Multicenter Prospective Cohort Study. Metabolites13(3), 413. https://doi.org/10.3390/metabo13030413

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