Utilidad de indicadores antropométricos de adiposidad abdominal no convencionales como predictores de riesgo cardiometabólico en adultos jóvenes

Mathías Senen  Figueredo León; Kiara Monserrat   Galeano Manfredi; Juan Andrés  Sosa Leguizamón; Ada Liz  Ojeda Benitez; María Lourdes  Falcó de Ayala; Claudia Patricia  Fleitas Almirón; María Liz  Hermosilla de Sánchez; Lilian Elena  Mendieta de Servián; Andrea  Benítez; Isaura Pamela C.  Gamarra Salinas; Maccarena Monserrat  Troche Estigarribia; Fidel Jorge  Zenteno Armoa; Julio Torales; Eliana Romina Meza Miranda

doi:10.18004/

Autores/as

Mathías Senen Figueredo León Universidad Nacional de Asunción, Centro Multidisciplinario de Investigaciones Tecnológicas, Laboratorio de Biotecnología. San Lorenzo, Paraguay https://orcid.org/0009-0002-1500-6125
Kiara Monserrat Galeano Manfredi Universidad Nacional de Asunción, Centro Multidisciplinario de Investigaciones Tecnológicas, Laboratorio de Biotecnología. San Lorenzo, Paraguay https://orcid.org/0009-0004-2409-474X
Juan Andrés Sosa Leguizamón Universidad Nacional de Asunción, Centro Multidisciplinario de Investigaciones Tecnológicas, Laboratorio de Biotecnología. San Lorenzo, Paraguay https://orcid.org/0009-0008-0093-1151
Ada Liz Ojeda Benitez Universidad del Norte, Facultad de Medicina, Nutrición Sede Itauguá. Itauguá, Paraguay https://orcid.org/0009-0000-5811-3436
María Lourdes Falcó de Ayala Universidad del Norte, Facultad de Medicina, Nutrición Sede Itauguá. Itauguá, Paraguay https://orcid.org/0000-0002-9141-2419
Claudia Patricia Fleitas Almirón Universidad del Norte, Facultad de Medicina, Nutrición Sede Itauguá. Itauguá, Paraguay https://orcid.org/0009-0008-7984-8499
María Liz Hermosilla de Sánchez Universidad del Norte, Facultad de Medicina, Nutrición Sede Itauguá. Itauguá, Paraguay https://orcid.org/0009-0006-8919-1289
Lilian Elena Mendieta de Servián Universidad del Norte, Facultad de Medicina, Nutrición Sede Itauguá. Itauguá, Paraguay https://orcid.org/0009-0006-4268-924X
Andrea Benítez Universidad Nacional de Asunción, Centro Multidisciplinario de Investigaciones Tecnológicas, Laboratorio de Biotecnología. San Lorenzo, Paraguay https://orcid.org/0009-0003-2507-2609
Isaura Pamela C. Gamarra Salinas Universidad Nacional de Asunción, Centro Multidisciplinario de Investigaciones Tecnológicas, Laboratorio de Biotecnología. San Lorenzo, Paraguay https://orcid.org/0009-0008-5481-5738
Maccarena Monserrat Troche Estigarribia Universidad Técnica de Comercialización y Desarrollo, Facultad de Ciencias de la Salud. San Lorenzo, Paraguay https://orcid.org/0009-0004-4332-9110
Fidel Jorge Zenteno Armoa Universidad Técnica de Comercialización y Desarrollo, Facultad de Ciencias de la Salud. San Lorenzo, Paraguay https://orcid.org/0009-0009-4540-4862
Julio Torales Universidad Nacional de Asunción, Facultad de Ciencias Médicas, Grupo de Investigación sobre Epidemiología de los Trastornos Mentales, Psicopatología y Neurociencias. San Lorenzo, Paraguay https://orcid.org/0000-0003-3277-7036
Eliana Romina Meza Miranda Universidad Nacional de Asunción, Centro Multidisciplinario de Investigaciones Tecnológicas, Laboratorio de Biotecnología. San Lorenzo, Paraguay https://orcid.org/0000-0001-9791-8835

DOI:

https://doi.org/10.18004/

Palabras clave:

riesgo cardiometabólico, índices antropométricos, obesidad, adultos jóvenes

Resumen

La adiposidad abdominal es un predictor de riesgo cardiometabólico (RC). Se evaluó la utilidad de los índices de forma corporal (ABSI), de redondez corporal (BRI) y de conicidad (CI) como predictor de riesgo cardiometabólico en comparación a la circunferencia de cintura (CC) y el índice cintura-talla (ICT) en adultos jóvenes residentes del Departamento Central de Paraguay. Estudio de diseño observacional analítico de corte transversal en el que se evaluaron 280 adultos jóvenes de ambos sexos de entre 20 y 45 años. Se evaluaron variables sociodemográficas, antropométricas (peso, talla, circunferencia de cintura). Se evaluó la sensibilidad y especificidad de los índices ABSI, BRI y CI en relación con la CC y el ICT. El 68,2% fue del sexo femenino con edad promedio de 36,48±13,2 años. El RC según la CC, el ICT y el CI fue alto en un 21,8%; 11,2% y 15,7% respectivamente, mientras que según el ABSI y el BRI fue muy alto en un 21,4% y 1,4% respectivamente. La sensibilidad del ABSI, BRI y CI respecto a la CC e ICT fue de 71,0%, y 74,5%; 71,0% y 72% y de 40,2% y 38,6% respectivamente. En cuanto a la especificidad con relación al CC e ICT del ABSI, BRI y CI fue de 50,3% y 53,6%; 96,5% y 100% y 99,4% y 100% respectivamente. El ABSI y el BRI tienen una alta sensibilidad para predecir el riesgo cardiometabólico, mientras que el CI fue el menos eficiente en la predicción del mismo.

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Referencias

1. Sattar N, Gill JMR, Alazawi W. Improving prevention strategies for cardiometabolic disease. Nat Med. 2020; 26(3): 320-325. doi: 10.1038/s41591-020-0786-7.

2. Miranda JJ, Barrientos-Gutiérrez T, Corvalan C, Hyder AA, Lazo-Porras M, Oni T, et al. Understanding the rise of cardiometabolic diseases in low- and middle-income countries. Nat Med. 2019; 25(11): 1667-1679. doi: 10.1038/s41591-019-0644-7.

3. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022; 183(109119): 109119. doi: 10.1016/j.diabres.2021.109119.

4. Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The global burden of cardiovascular diseases and risk: a compass for future health. J Am Coll Cardiol. 2022; 80(25): 2361-2371. doi: 10.1016/j.jacc.2022.11.005.

5. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update From the GBD 2019 Study. J Am Coll Cardiol. 2020; 76(25): 2982-3021. doi: 10.1016/j.jacc.2020.11.010.

6. Akbar Z, Fituri S, Ouagueni A, Alalwani J, Sukik A, Al-Jayyousi GF, et al. Associations of the MIND Diet with Cardiometabolic Diseases and Their Risk Factors: A Systematic Review. Diabetes Metab Syndr Obes. 2023; 16: 3353-3371. doi: 10.2147/dmso.s427412.

7. Li Y, Zhou H, Xu D, Wang H, Zhao Y, Jiang H. Independent and joint associations of physical activity and a body shape index with sleep disorders in older adults with cardiometabolic multimorbidity: a cross-sectional study. BMC Geriatr. 2025; 25(1): 519. doi: 10.1186/s12877-025-06170-7.

8. Quirino-Vela L, Mayoral-Chavez M, Pérez-Cervera Y, Ildefonso-García O, Cruz-Altamirano E, Ruiz-García M, et al. Cardiometabolic risk assessment by anthropometric and biochemical indices in Mexican population. Front Endocrinol (Lausanne). 2025; 16: 1588469. doi: 10.3389/fendo.2025.1588469.

9. Garofallo SB, Portal VL, Markoski MM, Dias LD, de Quadrosa AS, Marcadenti A. Correlations between traditional and nontraditional indicators of adiposity, inflammation, and monocyte subtypes in patients with stable coronary artery disease. J Obes. 2019; 2019: 1-11. doi: 10.1155/2019/3139278.

10. Neeland IJ, Ross R, Després JP, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019; 7: 715-25. doi: 10.1016/s2213-8587(19)30084-1.

11. Golabi S, Ajloo S, Maghsoudi F, Adelipour M, Naghashpour M. Associations between traditional and non-traditional anthropometric indices and cardiometabolic risk factors among inpatients with type 2 diabetes mellitus: a cross-sectional study. J Int Med Res. 2021; 49. doi: 10.1177/03000605211049960.

12. 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:E131. doi: 10.5888/pcd17.200112.

13. Xie L, Kim J, Almandoz JP, Clark J, Mathew MS, Cartwright BR, et al. Anthropometry for predicting cardiometabolic disease risk factors in adolescents. Obesity (Silver Spring). 2024; 32(8): 1558-1567. doi: 10.1002/oby.24090.

14. Adab P, Pallan M, Whincup PH. Is BMI the best measure of obesity? BMJ. 2018; 360: k1274. doi: 10.1136/bmj.k1274.

15. Ashwell M, Gibson S. Waist-to-height ratio as an indicator of 'early health risk': simpler and more predictive than using a 'matrix' based on BMI and waist circumference. BMJ Open. 2016; 6(3): e010159. doi: 10.1136/bmjopen-2015-010159.

16. Mohammadian Khonsari N, Khashayar P, Shahrestanaki E, Kelishadi R, Mohammadpoor Nami S, Heidari-Beni M, et al. Normal Weight Obesity and Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis. Front Endocrinol (Lausanne). 2022; 13: 857930. doi: 10.3389/fendo.2022.857930.

17. Shirasawa T, Ochiai H, Yoshimoto T, Nagahama S, Kobayashi M, Ohtsu I, et al. Associations between normal weight central obesity and cardiovascular disease risk factors in Japanese middle-aged adults: a cross-sectional study. J Health Popul Nutr. 2019; 38(1): 1-7. doi: 10.1186/s41043-019-0201-5.

18. Suliga E, Kozieł D, Głuszek S. Prevalence of metabolic syndrome in normal weight individuals. Ann Agric Environ Med. 2016; 23(4): 631-5. doi: 10.5604/12321966.1226858.

19. Millar SR, Perry IJ, Phillips CM. Assessing cardiometabolic risk in middle-aged adults using body mass index and waist-height ratio: are two indices better than one? A cross-sectional study. Diabetol Metab Syndr. 2015; 7: 73. doi: 10.1186/s13098-015-0069-5.

20. Amirabdollahian F, Haghighatdoost F. Anthropometric Indicators of Adiposity Related to Body Weight and Body Shape as Cardiometabolic Risk Predictors in British Young Adults: Superiority of Waist-to-Height Ratio. J Obes. 2018; 2018: 8370304. doi: 10.1155/2018/8370304.

21. Padilla CJ, Ferreyro FA, Arnold WD. Anthropometry as a readily accessible health assessment of older adults. Exp Gerontol. 2021; 153: 111464. doi: 10.1016/j.exger.2021.111464.

22. Song B, Zhao X, Yao T, Lu W, Zhang H, Liu T, et al. Triglyceride glucose-body mass index and risk of incident type 2 diabetes mellitus in Japanese people with normal glycemic level: A population-based longitudinal cohort study. Front Endocrinol (Lausanne). 2022; 13. doi: 10.3389/fendo.2022.907973.

23. Tahir UA, Gerszten RE. Molecular biomarkers for cardiometabolic disease: risk assessment in young individuals. Circ Res. 2023; 132: 1663-73. doi: 10.1161/circresaha.123.322000.

24. Er LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PloS One. 2016; 11: e0149731. doi: 10.1371/journal.pone.0149731.

25. Xiao Q, Xue A, Huang W, Yuan J. Evaluating the visceral adiposity inflammatory index for enhanced stroke risk assessment. Sci Rep. 2025; 15: 14971. doi: 10.1038/s41598-025-99024-6.

26. Xu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, et al. Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 Eastern-China adults. BMC Cardiovasc Disord. 2021; 21: 97. doi: 10.1186/s12872-021-01905-x.

27. Li Q, Qie R, Qin P, Zhang D, Guo C, Zhou Q, et al. Association of weight-adjusted-waist index with incident hypertension: the Rural Chinese Cohort Study. Nutr Metab Cardiovasc Dis. 2020; 30: 1732-41. doi: 10.1016/j.numecd.2020.05.033.

28. Anoop SS, Dasgupta R, Rebekah G, Jose A, Inbakumari MP, Finney G, et al. Lipid accumulation product (LAP) as a potential index to predict risk of insulin resistance in young, non-obese Asian Indian males from Southern India: observations from hyperinsulinemic-euglycemic clamp studies. BMJ Open Diabetes Res Care. 2021; 9: e002414. doi: 10.1136/bmjdrc-2021-002414.

29. Qin Z, Jiang L, Sun J, Geng J, Chen S, Yang Q, et al. Higher visceral adiposity index is associated with increased likelihood of abdominal aortic calcification. Clinics. 2022; 77: 100114. doi: 10.1016/j.clinsp.2022.100114.

30. Cho HW, Chung W, Moon S, Ryu OH, Kim MK, Kang JG. Effect of sarcopenia and body shape on cardiovascular disease according to obesity phenotypes. Diabetes Metab J. 2021; 45: 209-18. doi: 10.4093/dmj.2019.0223.

31. Anoop S, Krakauer J, Krakauer N, Misra A. A Body shape index significantly predicts MRI-defined abdominal adipose tissue depots in non-obese Asian Indians with type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2020; 8: e001324. doi: 10.1136/bmjdrc-2020-001324.

32. Bertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et al. Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PloS One. 2017; 12: e0185013. doi: 10.1371/journal.pone.0185013.

33. Ofstad AP, Sommer C, Birkeland KI, Bjørgaas MR, Gran JM, Gulseth HL, et al. Comparison of the associations between non-traditional and traditional indices of adiposity and cardiovascular mortality: an observational study of one million person-years of follow-up. Int J Obes. 2019; 43: 1082-92. doi: 10.1038/s41366-019-0353-9.

34. Nagayama D, Fujishiro K, Watanabe Y, Yamaguchi T, Suzuki K, Saiki A, et al. A body shape index (ABSI) as a variant of conicity index not affected by the obesity paradox: A cross-sectional study using arterial stiffness parameter. J Pers Med. 2022; 12: 2014. doi: 10.3390/jpm12122014.

35. Sousa NC, Marques FRDM, Pires GAR, Scardoelli MGDC, Rêgo ADS, Radovanovic CAT, et al. Conicity index in people with hypertension followed in the Brazil's Family Health Strategy. Rev Bras Enferm. 2020; 73(5): e20190484. doi: 10.1590/0034-7167-2019-0484.

36. Klisic A, Radoman Vujačić I, Kostadinovic J, Patoulias D, Ninic A. Novel anthropometric parameters in the adult population with prediabetes. Eur Rev Med Pharmacol Sci. 2023; 27: 11063-72. doi: 10.26355/eurrev_202311_34475.

37. Calderón-García JF, Roncero-Martín R, Rico-Martín S, De Nicolás-Jiménez JM, López-Espuela F, Santano-Mogena E, et al. Effectiveness of body roundness index (BRI) and a body shape index (ABSI) in predicting hypertension: A systematic review and meta-analysis of observational studies. Int J Environ Res Public Health. 2021; 18: 11607. doi: 10.3390/ijerph182111607.

38. Baveicy K, Mostafaei S, Darbandi M, Hamzeh B, Najafi F, Pasdar Y. Predicting metabolic syndrome by visceral adiposity index, body roundness index and a body shape index in adults: A cross-sectional study from the Iranian raNCD cohort data. Diabetes Metab Syndr Obes. 2020; 13: 879-87. doi: 10.2147/dmso.s238153.

39. 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. doi: 10.1038/s41574-019-0310-7.

40. Bohmann P, Stein MJ, Amadou A. WHO guidelines on waist circumference and physical activity and their joint association with cancer risk. Br J Sports Med. 2025; 59: 360-366. doi: 10.1136/bjsports-2024-108708.

41. Montoya Castillo M, Martínez Quiroz WJ, Suarez-Ortegón MF, Higuita-Gutiérrez LF. Waist-to-Height Ratio, Waist Circumference, and Body Mass Index in Relation to Full Cardiometabolic Risk in an Adult Population from Medellin, Colombia. J Clin Med. 2025; 14(7): 2411. doi: 10.3390/jcm14072411.

42. Witarto BS, Witarto AP, Visuddho V, Wungu CDK, Maimunah U, Rejeki PS, et al. Gender-specific accuracy of lipid accumulation product index for the screening of metabolic syndrome in general adults: a meta-analysis and comparative analysis with other adiposity indicators. Lipids Health Dis. 2024; 23(1): 198. doi: 10.1186/s12944-024-02190-1.

43. 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 Netw Open. 2024; 7(6): e2415051. doi: 10.1001/jamanetworkopen.2024.15051.

44. Chen Y-R, Chu N-F, Wu D-M, Shen W-C. Trends in Anthropometric and Cardiometabolic Risk Factor Changes Among Health Professionals: A 3-Year Follow-Up Study in Taiwan. Obesities. 2025; 5(4): 89. doi: 10.3390/obesities5040089.

45. Daniels NF, Burrin C, Chan T, Fusco F. A Systematic Review of the Impact of the First Year of COVID-19 on Obesity Risk Factors: A Pandemic Fueling a Pandemic? Curr Dev Nutr. 2022; 6(4): nzac011. doi: 10.1093/cdn/nzac011.

46. Safaei M, Sundararajan EA, Driss M, Boulila W, Shapi'i A. 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. doi: 10.1016/j.compbiomed.2021.104754.

47. Anto EO, Frimpong J, Boadu WIO, Tamakloe VCKT, Hughes C, Acquah B, et al. Prevalence of Cardiometabolic Syndrome and its Association With Body Shape Index and A Body Roundness Index Among Type 2 Diabetes Mellitus Patients: A Hospital-Based Cross-Sectional Study in a Ghanaian Population. Front Clin Diabetes Healthc. 2022; 2: 807201. doi: 10.3389/fcdhc.2021.807201.

48. Kim MY, Kim K, Hong CH, Lee SY, Jung YS. Sex Differences in Cardiovascular Risk Factors for Dementia. Biomol Ther (Seoul). 2018; 26(6): 521-532. doi: 10.4062/biomolther.2018.159.

49. Ji M, Zhang S, An R. Effectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis. Obes Rev. 2018; 19(5): 737-759. doi: 10.1111/obr.12666.

50. Li Y, Gui J, Liu H, Guo LL, Li J, Lei Y, et al. Predicting metabolic syndrome by obesity- and lipid-related indices in mid-aged and elderly Chinese: a population-based cross-sectional study. Front Endocrinol (Lausanne). 2023; 14: 1201132. doi: 10.3389/fendo.2023.1201132.

51. Kunutsor SK, Bhattacharjee A, Jae SY, Laukkanen JA. Body roundness index and cardiometabolic multimorbidity: Findings from the English Longitudinal Study of Ageing. Nutr Metab Cardiovasc Dis. 2026; 36(4): 104475. doi: 10.1016/j.numecd.2025.104475.

52. Abboud M, Haidar S, Mahboub N, Papandreou D, Rizk R. Abdominal volume index, waist-to-height ratio, and waist circumference are optimal predictors of cardiometabolic abnormalities in a sample of Lebanese adults: A cross-sectional study. PLOS Glob Public Health. 2023; 3(12): e0002726. doi: 10.1371/journal.pgph.0002726.