Keywords
Body mass index
Glycated hemoglobin
Anthropometry
Biomarkers
Continuous glucose monitoring
Obesity
Risk assessment
How to Cite
Abstract
Context: Metabolic syndrome (MS) is a major public health problem due to its high prevalence and association with cardiovascular disease and type 2 diabetes mellitus. Obesity is its main component, and body mass index (BMI) has traditionally been used as a diagnostic measure, despite its limitations. Additionally, glycated hemoglobin (HbA1c) is widely used in metabolic assessment, although its interpretation in isolation may be insufficient.
Objective: To analyze and compare the evidence on body mass index and glycated hemoglobin as diagnostic and monitoring parameters for metabolic syndrome, identifying their limitations and exploring accurate, clinically applicable alternatives for clinical practice.
Methodology: A narrative review of the literature published between 2015 and 2025 was conducted in PubMed, ScienceDirect, SpringerNature, and Google Scholar, using MeSH terms related to “Body Mass Index”, “Glycosylated Hemoglobin”, and “Metabolic Syndrome”. Original articles and reviews in English and Spanish were included, focusing on adults and the diagnostic evaluation of metabolic syndrome. Publications in other languages, pediatric studies, and publications unrelated to the objective of the review were excluded. A total of 82 articles were included. No formal risk of bias assessment or quantitative analysis of the evidence was performed.
Results: Body mass index and glycated hemoglobin, although accessible and widely used, have limitations in the assessment of metabolic syndrome. Complementary anthropometric alternatives and biomarkers that could improve cardiometabolic risk stratification are described.
Conclusions: body mass index and glycated hemoglobin remain useful, but they should not be used as the sole tools in the assessment of metabolic syndrome.
References
Mohamed SM, Shalaby MA, El-Shiekh RA, El-Banna HA, Emam SR, Bakr AF. Metabolic syndrome: Risk factors, diagnosis, pathogenesis, and management with natural approaches. Food Chem Adv. 2023;3(100335):100335. http://doi.org/10.1016/j.focha.2023.100335
Escudero Villarroel TE, Paredes Quispe JR, Espinosa Arreaga GB, Alvarado Alvarado R de las M. Síndrome metabólico: una mirada a los factores de riesgo y su abordaje integral. Una revisión sistemática. RECIMUNDO. 2025;9(1):174-186. https://doi.org/10.26820/recimundo/9.(1).enero.2025.174-186
Jani? M, Janež A, El-Tanani M, Rizzo M. Obesity: Recent advances and future perspectives. Biomedicines. 2025;13(2):368. https://doi.org/10.3390/biomedicines13020368
Wong JC, O’Neill S, Beck BR, Forwood MR, Khoo SK. Comparison of obesity and metabolic syndrome prevalence using fat mass index, body mass index and percentage body fat. PLoS One. 2021;16(1):e0245436. https://doi.org/10.1371/journal.pone.0245436
Potter AW, Chin GC, Looney DP, Friedl KE. Defining overweight and obesity by percent body fat instead of body mass index. J Clin Endocrinol Metab. 2025;110(4):e1103–1107. https://doi.org/10.1210/clinem/dgae341
Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A, et al. Glucose management indicator (GMI): A new term for estimating A1C from continuous glucose monitoring. Diabetes Care. 2018;41(11):2275–2280. http://doi.org/10.2337/dc18-1581
Jaramillo Nieto A, Medina Orjuela A, Rosselli San Martin C, Rojas García W, Centeno García CD, Montoya Quesada LM. Monitoreo continuo de glucosa de seis días en pacientes diabéticos tipo 2 bajo hemodiálisis en tratamiento con insulinas en el Hospital de San José. (Bogotá). Rev Colomb Endocrinol Diabet Metab. 2018;5(4):13–20. https://doi.org/10.53853/encr.5.4.449
Pigeot I, Ahrens W. Epidemiology of metabolic syndrome. Pflugers Arch. 2025;477(5):669–680. http://doi.org/10.1007/s00424-024-03051-7
Patial R, Batta I, Thakur M, Sobti RC, Agrawal DK. Etiology, pathophysiology, and treatment strategies in the prevention and management of metabolic Syndrome. Arch Intern Med Res. 2024;7(4):273–283. https://doi.org/10.26502/aimr.0184
Giangregorio F, Mosconi E, Debellis MG, Provini S, Esposito C, Garolfi M, et al. A systematic review of metabolic syndrome: Key correlated pathologies and non-invasive diagnostic approaches. J Clin Med. 2024;13(19):5880.http://doi.org/10.3390/jcm13195880
Peterseim CM, Jabbour K, Kamath Mulki A. Metabolic syndrome: An updated review on diagnosis and treatment for primary care clinicians. J Prim Care Community Health. 2024;15:1177. http://doi.org/10.1177/21501319241309168
Massy ZA, Drueke TB. Combination of cardiovascular, kidney, and metabolic diseases in a syndrome named cardiovascular-kidney-metabolic, with new risk prediction equations. Kidney Int Rep. 2024;9(9):2608–2618. http://doi.org/10.1016/j.ekir.2024.05.033
Driesen K, Witters P. Understanding inborn errors of metabolism through metabolomics. Metabolites. 2022;12(5):398. http://doi.org/10.3390/metabo12050398
Wu Y, Li D, Vermund SH. Advantages and limitations of the body mass index (BMI) to assess adult obesity. Int J Environ Res Public Health. 2024;21(6):757. http://doi.org/10.3390/ijerph21060757
Al-Bachir M, Bakir MA. Predictive value of body mass index to metabolic syndrome risk factors in Syrian adolescents. J Med Case Rep. 2017;11(1):170. http://doi.org/10.1186/s13256-017-1315-2
Weir CB, Jan A. BMI classification percentile and cut off points. En: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. PMID: 31082114. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK541070/
Shukohifar M, Mozafari Z, Rahmanian M, Mirzaei M. Performance of body mass index and body fat percentage in predicting metabolic syndrome risk factors in diabetic patients of Yazd, Iran. BMC Endocr Disord. 2022;22(1):216. http://doi.org/10.1186/s12902-022-01125-0
Wang M, Hng T-M. HbA1c: More than just a number. Aust J Gen Pract. 2021;50(9):628–632. http://doi.org/10.31128/AJGP-03-21-5866
Chen Z, Shao L, Jiang M, Ba X, Ma B, Zhou T. Interpretation of HbA1c lies at the intersection of analytical methodology, clinical biochemistry and hematology (Review). Exp Ther Med. 2022;24(6):707. http://doi.org/10.3892/etm.2022.11643
Eyth E, Zubair M, Naik R. Hemoglobin A1C. En: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. PMID: 31747223. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK549816/
Australian Diabetes Educators Association, National Diabetes Services Scheme. Understanding HbA1c: A guide for health professionals [Internet]. Canberra: NDSS; 2025 Jul. Disponible en: https://www.ndss.com.au/wp-content/uploads/resources/understanding-hba1c-measurements.pdf
Zhao L, Li C, Lv H, Zeng C, Peng Y. Association of hemoglobin glycation index with all-cause and cardio-cerebrovascular mortality among people with metabolic syndrome. Front Endocrinol (Lausanne). 2024;15:1447184. http://doi.org/10.3389/fendo.2024.1447184
Mastrototaro L, Roden M. Insulin resistance and insulin sensitizing agents. Metabolism. 2021;125(154892):154892. http://doi.org/10.1016/j.metabol.2021.154892
American Diabetes Association Professional Practice Committee. 6. Glycemic targets: Standards of Medical Care in diabetes—2022. Diabetes Care. 2022;45(Suppl 1):S83–96. http://doi.org/10.2337/dc22-s006
American Diabetes Association Professional Practice Committee. 7. Diabetes technology: Standards of care in diabetes-2025. Diabetes Care. 2025;48(Suppl 1):S146–166.http://doi.org/10.2337/dc25-S007
Litwak L, Querzoli I, Musso C, Daín A, Houssay S, Gil JC. Monitoreo contínuo de glucosa: utilidad e indicaciones. Medicina (B Aires). 2019;79(1):44–52.
American Diabetes Association Professional Practice Committee. 7. Diabetes technology: Standards of care in diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S126–44. http://doi.org/10.2337/dc24-S007
Hirsch I. Introduction: History of glucose monitoring. En: Role of Continuous Glucose Monitoring in Diabetes Treatment [Internet]. Alexandria (VA): American Diabetes Association; 2018. p. 1–1. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK538968/
Gomez-Peralta F, Dunn T, Landuyt K, Xu Y, Merino-Torres JF. Flash glucose monitoring reduces glycemic variability and hypoglycemia: Real-world data from Spain. BMJ Open Diabetes Res Care. 2020;8(1):e001052. http://doi.org/10.1136/bmjdrc-2019-001052
Litwak L, Carreño N, Carnero R, Daín A, Grosembacher L, Musso C, et al. Monitoreo continuo de glucosa: indicaciones, interpretación de datos y toma de decisiones terapéuticas. Rev Soc Argent Diabetes. 2020;54(3):140-54. https://doi.org/10.47196/diab.v54i3.455
Holzer R, Bloch W, Brinkmann C. Continuous glucose monitoring in healthy adults-possible applications in health care, wellness, and sports. Sensors (Basel). 2022;22(5):2030. http://doi.org/10.3390/s22052030
Selvin E. The glucose management indicator: Time to change course? Diabetes Care. 2024;47(6):906–914. http://doi.org/10.2337/dci23-0086
Padilla CJ, Ferreyro FA, Arnold WD. Anthropometry as a readily accessible health assessment of older adults. Exp Gerontol. 2021;153:111464. http://doi.org/10.1016/j.exger.2021.111464
Casadei K, Kiel J. Anthropometric measurement. En: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK537315/
Zeñas-Trujillo GZ, Vera-Ponce VJ, Trujillo-Ramírez I. Rendimiento diagnóstico de tres índices antropométricos de peso y talla para síndrome metabólico en trabajadores. Rev Cubana Med Mil. 2023;52(2):e02302556.
Rodríguez-Guerrero E. Métodos mínimamente invasivos para el diagnóstico del síndrome metabólico en ancianos [tesis doctoral]. Córdoba: Universidad de Córdoba, UCOPress; 2021.
Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, et al. Waist circumference as a vital sign in clinical practice: A consensus statement from the IAS and ICCR working group on visceral obesity. Nat Rev Endocrinol. 2020;16(3):177–189. http://doi.org/10.1038/s41574-019-0310-7
Garvey WT, Mechanick JI, Brett EM, Garber AJ, Hurley DL, Jastreboff AM, et al. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract. 2016;22(Suppl 3):1–203. https://doi.org/10.4158/EP161365.GL
Chen X, Zhao Y, Sun J, Jiang Y, Tang Y. Identification of metabolic syndrome using lipid accumulation product and cardiometabolic index based on NHANES data from 2005 to 2018. Nutr Metab (Lond). 2024;21(1):96. http://doi.org/10.1186/s12986-024-00864-2
Christakoudi S, Tsilidis KK, Muller DC, Freisling H, Weiderpass E, Overvad K, et al. A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: Results from a large European cohort. Sci Rep. 2020;10(1):14541. https://doi.org/10.1038/s41598-020-71302-5
Darbandi M, Pasdar Y, Moradi S, Mohamed HJJ, Hamzeh B, Salimi Y. Discriminatory capacity of anthropometric indices for cardiovascular disease in adults: A systematic review and meta-analysis. Prev Chronic Dis. 2020;17:200112. https://doi.org/10.5888/pcd17.200112
Ramírez-Vélez R, Correa-Bautista J, Carrillo H, González-Jiménez E, Schmidt-RioValle J, Correa-Rodríguez M, et al. Tri-ponderal mass index vs. Fat Mass/Height³ as a screening tool for metabolic syndrome prediction in Colombian children and young people. Nutrients. 2018;10(4):412. https://doi.org/10.3390/nu10040412
Yin M, Liu C. A NEW Body Mass Index (NBMI) [Preprint]. Research Square;2023. https://doi.org/10.21203/rs.3.rs-2719634/v1
Spinoza ED, Fonte FK, Carvalho VA, Dos Santos RA, Colleoni GWB, Cendoroglo MS. Body adiposity index as a predictor of body fat in an oldest old and independent cohort of Brazilian older adults. Ann Geriatr Med Res. 2024;28(3):284–290. https://doi.org/10.4235/agmr.24.0008
Oliveira RAR, de Moreira OC, Mota Júnior RJ, Marins JCB. Association between body adiposity index and cardiovascular risk factors in teachers. Rev Bras Cineantropom Desempenho Hum. 2020;22:e59010. https://doi.org/10.1590/1980-0037.2020v22e59010
Grams AC, Acevedo AM, Price P, Alvarez K, Nowlen M, Morton R, et al. Body mass index superior to body Adiposity Index in predicting adiposity in female collegiate athletes. Int J Exerc Sci. 2023;16(4):1487–1498. https://doi.org/10.70252/CJWQ8241
García AI, Niño-Silva LA, González-Ruíz K, Ramírez-Vélez R. Utilidad del índice de adiposidad corporal como indicador de obesidad y predictor de riesgo cardiovascular en adultos de Bogotá, Colombia. Endocrinol Nutr. 2015;62(3):130–137. https://doi.org/10.1016/j.endonu.2014.11.007
Aquino Ramírez AI. Relación entre índice de forma corporal y factores de riesgo de enfermedades cardiovasculares en adultos del distrito de Los Olivos-Lima 2017 [tesis de grado]. Lima: Universidad Nacional Mayor de San Marcos; 2021. Disponible en: https://hdl.handle.net/20.500.12672/17462
De la Osa Andrés G, Calderón García JF, Martín SR. Asociación de la obesidad central medida mediante un nuevo índice antropométrico con el riesgo de sufrir un evento cardiovascular. Arch Nurs Res. 2024;6(1):8-19.
Zhang X, Ma N, Lin Q, Chen K, Zheng F, Wu J, et al. Body roundness index and all-cause mortality among US adults. JAMA Network Open. 2024;7(6):e2415051. https://doi.org/10.1001/jamanetworkopen.2024.15051
Dang AK, Truong MT, Le HT, Nguyen KC, Le MB, Nguyen LT, et al. Anthropometric Cut-Off values for detecting the presence of metabolic syndrome and its multiple components among adults in Vietnam: The role of novel indices. Nutrients. 2022;14(19):4024. https://doi.org/10.3390/nu14194024
de Luis D, Muñoz M, Izaola O, Lopez Gomez JJ, Rico D, Primo D. Body roundness index (BRI) predicts metabolic syndrome in postmenopausal women with obesity better than insulin resistance. Diabetology (Basel). 2025;6(7):60. https://doi.org/10.3390/diabetology6070060
Woolcott OO, Samarasundera E, Heath AK. Association of relative fat mass (RFM) index with diabetes-related mortality and heart disease mortality. Sci Rep. 2024;14(1):30823. https://doi.org/10.1038/s41598-024-81497-6
Suthahar N, Wang K, Zwartkruis VW, Bakker SJL, Inzucchi SE, Meems LMG, et al. Associations of relative fat mass, a new index of adiposity, with type-2 diabetes in the general population. Eur J Intern Med. 2023;109:73–78. https://doi.org/10.1016/j.ejim.2022.12.024
Palumbo AM, Jacob CM, Khademioore S, Sakib MN, Yoshida-Montezuma Y, Christodoulakis N, et al. Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis. Obes Rev. 2025;26(6): e13894. https://doi.org/10.1111/obr.13894
Morris-Murray M, Frazzitta M. Using continuous glucose monitoring to measure and improve quality metrics: Updates on the Healthcare Effectiveness Data and Information Set 2024 Glucose Management Indicator measure. J Manag Care Spec Pharm. 2024;30(Supl. 10-b):S30–S39. https://doi.org/10.18553/jmcp.2024.30.10-b.s30
Grimsmann JM, von Sengbusch S, Freff M, Ermer U, Placzek K, Danne T, et al. Glucose management indicator based on sensor data and laboratory HbA1c in people with type 1 diabetes from the DPV database: Differences by sensor type. Diabetes Care. 2020;43(9):e111–e112. https://doi.org/10.2337/dc20-0259
Riveline JP, Prevost G, Andrieu A, Joubert M, Oriot P, Penfornis A, et al. 1020-P: Evolution over time of the discrepancy between HbA1c and Glucose Management Indicator—findings from a Franco-Belgian cohort of 347 patients. Diabetes. 2024;73(Supl. 1):1020. https://doi.org/10.2337/db24-1020-p
Fang M, Wang D, Rooney MR, Echouffo-Tcheugui JB, Coresh J, Aurora RN, et al. Performance of the glucose management indicator (GMI) in type 2 diabetes. Clin Chem. 2023;69(4):422–428. https://doi.org/10.1093/clinchem/hvac210
Perlman JE, Gooley TA, McNulty B, Meyers J, Hirsch IB. HbA1c and glucose management indicator discordance: A real-world analysis. Diabetes Technol Ther. 2020;23(4):253–258. https://doi.org/10.1089/dia.2020.0501
Yang D, Ling P, Wang C, Zheng X, Deng H, Yang X, et al. Pregnancy-specific Glucose Management Index predicts preterm birth and pre-eclampsia superior to HbA1c in women with type 1 diabetes mellitus. Diabetes-Metab Res. 2025;41(5):e70048. https://doi.org/10.1002/dmrr.70048
Ji XL, Yin M, Deng C, Fan L, Xie YT, Huang FS, et al. Hemoglobin glycation index among adults with type 1 diabetes: Association with double diabetes features. World J Diabetes. 2025;16(4):100917. https://doi.org/10.4239/wjd.v16.i4.100917
Oriot P, Viry C, Vandelaer A, Grigioni S, Roy M, Philips JC, et al. Discordance between glycated hemoglobin A1c and the glucose management indicator in people with diabetes and chronic kidney disease. J Diabetes Sci Technol. 2023;17(6):1553–1562. https://doi.org/10.1177/19322968221092050
Yoo JH, Moon SJ, Park CY, Kim JH. Differences between glycated hemoglobin and glucose management indicator in real-time and intermittent scanning continuous glucose monitoring in adults with type 1 diabetes. J Diabetes Sci Technol. 2024;20(1). https://doi.org/10.1177/19322968241262106
Angellotti E, Muppavarapu S, Siegel RD, Pittas AG. The calculation of the glucose management indicator is influenced by the continuous glucose monitoring system and patient race. Diabetes Technol Ther. 2020;22(9):651–657. https://doi.org/10.1089/dia.2019.0405
Jávorfi T, Kocsis G, Svébis MM, Ferencz V, Domján BA, Kézdi Á, et al. Glucose management indicator: Do we need device-specific equations? Diabetes Metab. 2025;51(4):101661. https://doi.org/10.1016/j.diabet.2025.101661
Castañeda J, de Galan BE, van Kuijk SMJ, Arrieta A, van den Heuvel T, Cohen O. The interdependence of targets for continuous glucose monitoring outcomes in type 1 diabetes with automated insulin delivery. Diabetes Obes Metab. 2024;26(12):5836–5844. https://doi.org/10.1111/dom.15955
Salton N, Kern S, Interator H, Lopez A, Moran-Lev H, Lebenthal Y, et al. Muscle-to-fat ratio for predicting metabolic syndrome components in children with overweight and obesity. Child Obes. 2021;18(2):132–142. https://doi.org/10.1089/chi.2021.0157
Chakrabarti D, Pal PS. Estimation of proinflammatory cytokines and mediator CD40 Ligand levels in young tribal subjects of Tripura- an observational study. J Clin Diagn. 2022;16(3):BC07-BC11. https://doi.org/10.7860/jcdr/2022/51602.16119
Murkamilov IT, Aitbaev KA, Fomin VV, Murkamilova ZA, Yusupova ZF, Yusupova T, et al. Assessment of inflammatory biomarkers and risk factors for cardiovascular diseases in overweight and obesity. Cardiovasc Ther Prev. 2024;23(3):3733. https://doi.org/10.15829/1728-8800-2024-3733
Gupta MK, Dutta G, Sridevi G, Raghav P, Dhanesh Goel A, Bhardwaj P, et al. Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as metabolic syndrome prediction tools. PloS One. 2023;18(3): e0283263. https://doi.org/10.1371/journal.pone.0283263
Lingvay I, Deanfield J, Kahn SE, Weeke PE, Toplak H, Scirica BM, et al. Semaglutide and cardiovascular outcomes by baseline HbA1c and change in HbA1c in people with overweight or obesity but without diabetes in SELECT. Diabetes Care. 2024;47(8):1360–1369. https://doi.org/10.2337/dc24-0764
Vassallo P, Driver SL, Stone NJ. Metabolic syndrome: An evolving clinical construct. Prog Cardiovasc Dis. 2016;59(2):172–177. https://doi.org/10.1016/j.pcad.2016.07.012
Lundholm MD, Emanuele MA, Ashraf A, Nadeem S. Applications and pitfalls of hemoglobin A1C and alternative methods of glycemic monitoring. J Diabetes Complicat. 2020;34(8);107585. https://doi.org/10.1016/j.jdiacomp.2020.107585
Rodacki M, Zajdenverg L, da Silva Júnior WS, Giacaglia L, Negrato CA, Cobas RA, et al. Brazilian guideline for screening and diagnosis of type 2 diabetes: A position statement from the Brazilian Diabetes Society. Diabetol Metab Syndr. 2025;17(1):78. https://doi.org/10.1186/s13098-024-01572-w
Vega-Vázquez MA, Ramírez-Vick M, Muñoz-Torres FJ, González-Rodríguez LA, Joshipura K. Comparing glucose and hemoglobin A1c diagnostic tests among a high metabolic risk Hispanic population. Diabetes-Metab Res. 2016;33(4):e2874. https://doi.org/10.1002/dmrr.2874
Wysham CH, Kruger DF. Practical considerations for initiating and utilizing flash continuous glucose monitoring in clinical practice. J Endocr Soc. 2021;5(9):bvab064. https://doi.org/10.1210/jendso/bvab064
Dunn TC, Xu Y, Hayter G, Ajjan RA. Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: A European analysis of over 60 million glucose tests. Diabetes Res Clin Pract. 2018;137:37–46. https://doi.org/10.1016/j.diabres.2017.12.015
Mendivil CO. Nuevas métricas de control glucémico [diapositivas]. Presentado en: Simposio Controversias y Nuevos Paradigmas en Endocrinología, Asociación Colombiana de Endocrinología, Diabetes y Metabolismo; 2025 ago 22; Bogotá, Colombia.
Gugelmo G, Maines E, Boscari F, Lenzini L, Fadini GP, Burlina A, et al. Continuous glucose monitoring in patients with inherited metabolic disorders at risk for hypoglycemia and nutritional implications. Rev Endocr Metab Disord. 2024;25(5):897–910. https://doi.org/10.1007/s11154-024-09903-y
Bellido V, Aguilera E, Cardona-Hernandez R, Diaz-Soto G, González Pérez de Villar N, Picón-César MJ, et al. Expert recommendations for using time-in-range and other continuous glucose monitoring metrics to achieve patient-centered glycemic control in people with diabetes. J Diabetes Sci Technol. 2022;17(5):1326–1336. https://doi.org/10.1177/19322968221088601
Frost AP, Norman Giest T, Ruta AA, Snow TK, Millard-Stafford M. Limitations of body mass index for counseling individuals with unilateral lower extremity amputation. Prosthet Orthot Int. 2022;41(2):186–193. https://doi.org/10.1177/0309364616650079
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