Advances in Biomedical Engineering for Understanding and Managing Metabolic Syndrome: A Comprehensive Review
DOI:
https://doi.org/10.37155/2972-449X-0102-7Keywords:
Metabolic syndrome, Diabetes, Obesity, BiomedicalAbstract
Metabolic syndrome (MetS) is a complex disorder characterized by a set of interrelated metabolic abnormalities, such as central obesity, hypertension, dyslipidemia, and insulin resistance. It constitutes a major public health problem worldwide due to its association with an increased risk of cardiovascular disease, type 2 diabetes mellitus (T2DM) and other chronic diseases. Biomedical engineering (BME), through its interdisciplinary nature, has contributed significantly to the understanding, diagnosis, and treatment of MetS. The aim of this review article is to provide a comprehensive overview of the current state of research and advances in BME approaches to the study and management of MetS. The article will delve into diverse approaches, including computational and omics models, that have been used to improve our understanding of MetS. In addition, it will provide an overview of specialized devices that have been designed for the non-invasive assessment of individuals with MetS.
References
Samson SL, Garber AJ. Metabolic Syndrome. Endocrinology and Metabolism Clinics, 2014;43(1):1-23.
Mottillo S, Filion KB, Genest J, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol, 2010;56(14):1113-32.
Silveira Rossi JL, Barbalho SM, Reverete de Araujo R, et al. Metabolic syndrome and cardiovascular diseases: Going beyond traditional risk factors. Diabetes Metab Res Rev, 2022;38(3):e3502.
James M, Varghese TP, Sharma R, et al. Association Between Metabolic Syndrome and Diabetes Mellitus According to International Diabetic Federation and National Cholesterol Education Program Adult Treatment Panel III Criteria: a Cross-sectional Study. Journal of Diabetes & Metabolic Disorders, 2020;19(1):437-43.
Battelli MG, Bortolotti M, Polito L, et al. Metabolic syndrome and cancer risk: The role of xanthine oxidoreductase. Redox Biology, 2019;21:101070.
Yu Y, Gong L, Ye J. The Role of Aberrant Metabolism in Cancer: Insights Into the Interplay Between Cell Metabolic Reprogramming, Metabolic Syndrome, and Cancer. Front Oncol, 2020;10:942.
Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech, 2009;2(5-6):231-7.
Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep, 2018;20(2):12.
Kao TW, Huang CC. Recent Progress in Metabolic Syndrome Research and Therapeutics. Int J Mol Sci, 2021;22(13).
Bahadori E, Farjami Z, Rezayi M, et al. Recent advances in nanotechnology for the treatment of metabolic syndrome. Diabetes Metab Syndr, 2019;13(2):1561-8.
Yu Q, Huang S, Xu TT, et al. Measuring Brown Fat Using MRI and Implications in the Metabolic Syndrome. J Magn Reson Imaging, 2021;54(5):1377-92.
Ambroselli D, Masciulli F, Romano E, et al. New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food. Nutrients, 2023;15(3).
Aurich MK, Thiele I. Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine. Methods Mol Biol, 2016;1386:253-81.
Lusis AJ, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet, 2008;9(11):819-30.
Ismail SNA, Nayan NA, Jaafar R, et al. Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach. Sensors (Basel), 2022;22(16).
Hsu N-W, Chou K-C, Wang Y-TT, et al. Building a model for predicting metabolic syndrome using artificial intelligence based on an investigation of whole-genome sequencing. Journal of Translational Medicine, 2022;20(1):190.
Rozendaal YJW, Wang Y, Hilbers PAJ, et al. Computational modelling of energy balance in individuals with Metabolic Syndrome. BMC Systems Biology, 2019;13(1):24.
Benmohammed K, Valensi P, Omri N, et al. Metabolic syndrome screening in adolescents: New scores AI_METS based on artificial intelligence techniques. Nutr Metab Cardiovasc Dis, 2022;32(12):2890-9.
Chen D, Zhao X, Sui Z, et al. A multi-omics investigation of the molecular characteristics and classification of six metabolic syndrome relevant diseases. Theranostics, 2020;10(5):2029-46.
Wu Q, Li J, Sun X, et al. Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants. EBioMedicine, 2021;74:103707.
Bioengineering and Metabolism Voices. Cell Metabolism, 2019;29(3):506-12.
Volkova S, Matos MRA, Mattanovich M, et al. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites, 2020;10(8):303.
Bordbar A, Monk JM, King ZA, et al. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet, 2014;15(2):107-20.
Fell DA, Small JR. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J, 1986;238(3):781-6.
Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol, 2012;10(4):291-305.
Dandekar T, Schuster S, Snel B, et al. Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem J, 1999;343 Pt 1(Pt 1):115-24.
Moulin C, Tournier L, Peres S. Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics. Processes, 2021;9(10):1701.
Stelling J, Klamt S, Bettenbrock K, et al. Metabolic network structure determines key aspects of functionality and regulation. Nature, 2002;420(6912):190-3.
Almaas E, Kovács B, Vicsek T, et al. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature, 2004;427(6977):839-43.
Yu H, Blair RH. Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer’s disease. BMC Bioinformatics, 2019;20(1):386.
Meeson KE, Schwartz J-M. Constraint-based modeling predicts metabolic signatures of low- and high-grade serous ovarian cancer. bioRxiv, 2023:2023.03.09.531870.
Mao L, Nicolae A, Oliveira MAP, et al. A constraint-based modelling approach to metabolic dysfunction in Parkinson's disease. Computational and Structural Biotechnology Journal, 2015;13:484-91.
Strutz J, Martin J, Greene J, et al. Metabolic kinetic modeling provides insight into complex biological questions, but hurdles remain. Curr Opin Biotechnol, 2019;59:24-30.
Islam MM, Schroeder WL, Saha R. Kinetic modeling of metabolism: Present and future. Current Opinion in Systems Biology, 2021;26:72-8.
Saa PA, Nielsen LK. Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnol Adv, 2017;35(8):981-1003.
Lu H, Chen Y, Nielsen J, et al. Kinetic Models of Metabolism. Metabolic Engineering, 2021;p. 153-70.
Rozendaal YJW, Wang Y, Paalvast Y, et al. In vivo and in silico dynamics of the development of Metabolic Syndrome. PLoS Comput Biol, 2018;14(6):e1006145.
Paalvast Y, Zhou E, Rozendaal YJW, et al. A Systems Analysis of Phenotype Heterogeneity in APOE*3Leiden. CETP Mice Induced by Long-Term High-Fat High-Cholesterol Diet Feeding. Nutrients, 2022;14(22):4936.
Del Giacco L, Cattaneo C. Introduction to genomics. Methods Mol Biol, 2012;823:79-88.
Williams CG, Lee HJ, Asatsuma T, et al. An introduction to spatial transcriptomics for biomedical research. Genome Medicine, 2022;14(1):68.
Al-Amrani S, Al-Jabri Z, Al-Zaabi A, et al. Proteomics: Concepts and applications in human medicine. World J Biol Chem, 2021;12(5):57-69.
Clish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud, 2015;1(1):a000588.
Wang KC, Chang HY. Epigenomics: Technologies and Applications. Circ Res, 2018;122(9):1191-9.
Hu C, Jia W. Multi-omics profiling: the way towards precision medicine in metabolic diseases. J Mol Cell Biol, 2021;13(8):576-93.
Taylor JY, Kraja AT, de Las Fuentes L, et al. An overview of the genomics of metabolic syndrome. J Nurs Scholarsh, 2013;45(1):52-9.
Mir FA, Mall R, Ullah E, et al. An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case-control study. J Transl Med, 2023;21(1):229.
Ozhan H, Alemdar R, Caglar O, et al. Performance of bioelectrical impedance analysis in the diagnosis of metabolic syndrome. J Investig Med, 2012;60(3):587-91.
Stramaglia G, Greco A, Guglielmi G, et al. Echocardiography and dual-energy x-ray absorptiometry in the elderly patients with metabolic syndrome: a comparison of two different tecniques to evaluate visceral fat distribution. J Nutr Health Aging, 2010;14(1):6-10.
Buscemi S, Verga S, Cottone S, et al. Glycaemic variability and inflammation in subjects with metabolic syndrome. Acta Diabetol, 2009;46(1):55-61.
Madhurantakam S, Babu KJ, Rayappan JBB, et al. Nanotechnology-based electrochemical detection strategies for hypertension markers. Biosens Bioelectron, 2018;116:67-80.
Melibeu Bentes C, Luiz Bezerra da Silveira A, Di Masi F, et al. Reliability of bioimpedance and indirect calorimetry to evaluate resting metabolic rate in Brazilian women with metabolic syndrome. Diabetes Metab Syndr, 2021;15(2):493-7.
Pi-Sunyer X. Changes in body composition and metabolic disease risk. Eur J Clin Nutr, 2019;73(2):231-5.
Lee YC, Lee YH, Chuang PN, et al. The utility of visceral fat level measured by bioelectrical impedance analysis in predicting metabolic syndrome. Obes Res Clin Pract, 2020;14(6):519-23.
Mulasi U, Kuchnia AJ, Cole AJ, et al. Bioimpedance at the bedside: current applications, limitations, and opportunities. Nutr Clin Pract, 2015;30(2):180-93.
Jaffrin MY, Morel H. Body fluid volumes measurements by impedance: A review of bioimpedance spectroscopy (BIS) and bioimpedance analysis (BIA) methods. Med Eng Phys, 2008;30(10):1257-69.
Pouragha H, Amiri M, Saraei M, et al. Body impedance analyzer and anthropometric indicators; predictors of metabolic syndrome. J Diabetes Metab Disord, 2021;20(2):1169-78.
Kim SH, Kang HW, Jeong JB, et al. Association of obesity, visceral adiposity, and sarcopenia with an increased risk of metabolic syndrome: A retrospective study. PLoS One, 2021;16(8):e0256083.
Wong SK, Chin KY, Suhaimi FH, et al. The Relationship between Metabolic Syndrome and Osteoporosis: A Review. Nutrients, 2016;8(6).
Xue P, Gao P, Li Y. The association between metabolic syndrome and bone mineral density: a meta-analysis. Endocrine, 2012;42(3):546-54.
Kaul S, Rothney MP, Peters DM, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring), 2012;20(6):1313-8.
Goh VH, Hart WG. Association of general and abdominal obesity with age, endocrine and metabolic factors in Asian men. Aging Male, 2016;19(1):27-33.
Mesinovic J, McMillan LB, Shore-Lorenti C, et al. Metabolic Syndrome and Its Associations with Components of Sarcopenia in Overweight and Obese Older Adults. J Clin Med, 2019;8(2).
Fahed G, Aoun L, Bou Zerdan M, et al. Metabolic Syndrome: Updates on Pathophysiology and Management in 2021. Int J Mol Sci, 2022;23(2).
Mendrick DL, Diehl AM, Topor LS, et al. Metabolic Syndrome and Associated Diseases: From the Bench to the Clinic. Toxicol Sci, 2018;162(1):36-42.
Bovolini A, Garcia J, Andrade MA, et al. Metabolic Syndrome Pathophysiology and Predisposing Factors. Int J Sports Med, 2021;42(3):199-214.
Holzer R, Bloch W, Brinkmann C. Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports. Sensors, 2022;22(5):2030.
Krakauer M, Botero JF, Lavalle-González FJ, et al. A review of flash glucose monitoring in type 2 diabetes. Diabetology & Metabolic Syndrome, 2021;13(1):42.
Zhou M-S, Wang A, Yu H. Link between insulin resistance and hypertension: What is the evidence from evolutionary biology? Diabetology & Metabolic Syndrome, 2014;6(1):12.
Kishi T, Hirooka Y. Sympathoexcitation associated with Renin-Angiotensin system in metabolic syndrome. Int J Hypertens, 2013;2013:406897.
Stanciu S, Rusu E, Miricescu D, et al. Links between Metabolic Syndrome and Hypertension: The Relationship with the Current Antidiabetic Drugs. Metabolites, 2023;13(1).
Meidert AS, Saugel B. Techniques for Non-Invasive Monitoring of Arterial Blood Pressure. Frontiers in Medicine, 2018;4.
Chen Y, Lei L, Wang JG. Methods of Blood Pressure Assessment Used in Milestone Hypertension Trials. Pulse (Basel), 2018;6(1-2):112-23.
Huang JF, Li Y, Shin J, et al. Characteristics and control of the 24-hour ambulatory blood pressure in patients with metabolic syndrome. J Clin Hypertens (Greenwich), 2021;23(3):450-6.
Ukkola O, Vasunta R-L, Kesäniemi YA. Non-dipping pattern in ambulatory blood pressure monitoring is associated with metabolic abnormalities in a random sample of middle-aged subjects. Hypertension Research, 2009;32(11):1022-7.
Casiglia E, Tikhonoff V, Albertini F, et al. Poor Reliability of Wrist Blood Pressure Self-Measurement at Home: A Population-Based Study. Hypertension, 2016;68(4):896-903.
Hoffmann U, Drey M, Thrun JM, et al. The role of wrist monitors to measure blood pressure in older adults. Aging Clin Exp Res, 2019;31(9):1227-31.
Sayer G, Piper G, Vorovich E, et al. Continuous Monitoring of Blood Pressure Using a Wrist-Worn Cuffless Device. Am J Hypertens, 2022;35(5):407-13.
O'Brien E, White WB, Parati G, et al. Ambulatory blood pressure monitoring in the 21st century. J Clin Hypertens (Greenwich), 2018;20(7):1108-11.
Mengden T, Weisser B. Monitoring of Treatment for Arterial Hypertension–The Role of Office, Home, and 24 h Ambulatory Blood Pressure Measurement. Dtsch Arztebl Int, 2021;118(27-28):473-8.
Yetisen AK, Martinez-Hurtado JL, Ünal B, et al. Wearables in Medicine. Advanced Materials, 2018;30(33):1706910.
Keshet A, Reicher L, Bar N, et al. Wearable and digital devices to monitor and treat metabolic diseases. Nature Metabolism, 2023;5(4):563-71.
Huh U, Tak YJ, Song S, et al. Feedback on Physical Activity Through a Wearable Device Connected to a Mobile Phone App in Patients With Metabolic Syndrome: Pilot Study. JMIR Mhealth Uhealth, 2019;7(6):e13381.
Yamaga Y, Svensson T, Chung U-i, et al. Association between Metabolic Syndrome Status and Daily Physical Activity Measured by a Wearable Device in Japanese Office Workers. International Journal of Environmental Research and Public Health, 2023;20(5):4315.
Reeder B, David A. Health at hand: A systematic review of smart watch uses for health and wellness. J Biomed Inform, 2016;63:269-76.
Fagherazzi G, Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes Metab, 2019;45(4):322-9.
Canali S, Schiaffonati V, Aliverti A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digit Health, 2022;1(10):e0000104.
Guimarães D, Cavaco-Paulo A, Nogueira E. Design of liposomes as drug delivery system for therapeutic applications. Int J Pharm, 2021;601:120571.
Chen Q, Guo C, Liu Z, et al. Multifunctional nanoparticles with anti-inflammatory effect for improving metabolic syndromes. J Drug Target, 2023;31(3):286-95.
Hu F, Sun DS, Wang KL, et al. Nanomedicine of Plant Origin for the Treatment of Metabolic Disorders. Front Bioeng Biotechnol, 2021;9:811917.
Li T, Zhu L, Zhu L, et al. Recent Developments in Delivery of MicroRNAs Utilizing Nanosystems for Metabolic Syndrome Therapy. Int J Mol Sci, 2021;22(15).
Şaman E, Cebova M, Barta A, et al. Combined Therapy with Simvastatin- and Coenzyme-Q10-Loaded Nanoparticles Upregulates the Akt-eNOS Pathway in Experimental Metabolic Syndrome. Int J Mol Sci, 2022;24(1).
Beuzelin D, Kaeffer B. Exosomes and miRNA-Loaded Biomimetic Nanovehicles, a Focus on Their Potentials Preventing Type-2 Diabetes Linked to Metabolic Syndrome. Front Immunol, 2018;9:2711.
El-Say KM, Felimban RI, Tayeb HH, et al. Pairing 3D-Printing with Nanotechnology to Manage Metabolic Syndrome. Int J Nanomedicine, 2022;17:1783-801.
Sghaireen MG, Al-Smadi Y, Al-Qerem A, et al. Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification. Diagnostics (Basel), 2022;12(12).
Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine. J Family Med Prim Care, 2019;8(7):2328-31.
Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med, 2016;375(13):1216-9.
Karimi-Alavijeh F, Jalili S, Sadeghi M. Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atheroscler, 2016;12(3):146-52.
Olveres J, González G, Torres F, et al. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg, 2021;11(8):3830-53.
Daniel Tavares L, Manoel A, Henrique Rizzi Donato T,et al. Prediction of metabolic syndrome: A machine learning approach to help primary prevention. Diabetes Res Clin Pract, 2022;191:110047.
Safaei M, Sundararajan EA, Driss M, et al. A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Comput Biol Med, 2021;136:104754.
Javaid A, Zghyer F, Kim C, et al. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. American Journal of Preventive Cardiology, 2022;12:100379.
Ansari RM, Harris MF, Hosseinzadeh H, et al. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare, 2023;11(6):903.
Lee S, Lee SK, Kim JY, et al. Sasang constitutional types for the risk prediction of metabolic syndrome: a 14-year longitudinal prospective cohort study. BMC Complement Altern Med, 2017;17(1):438.
Li G, Esangbedo IC, Xu L, et al. Childhood retinol-binding protein 4 (RBP4) levels predicting the 10-year risk of insulin resistance and metabolic syndrome: the BCAMS study. Cardiovascular Diabetology, 2018;17(1):69.
Khotimchenko M, Brunk NE, Hixon MS, et al. Combinatorial approaches using an AI/ML-driven drug development platform targeting insulin resistance, lipid dysregulation, and inflammation for the amelioration of metabolic syndrome in patients. bioRxiv, 2021:2021.09.01.458488.
Jiang X, Yang Z, Wang S, et al. "Big Data" Approaches for Prevention of the Metabolic Syndrome. Front Genet, 2022;13:810152.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Mariana M Ramírez-Mejía, Nahum Méndez-Sánchez
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on any open-access article in a journal published by Globasci Publishing House Pte. Ltd. is retained by the authors. Authors grant Globasci Publishing House Pte. Ltd. a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its integrity is maintained and its original authors, citation details and publisher are identified. The Creative Commons Attribution-NonCommercial 4.0 International License formalizes these and other terms and conditions of publishing articles.