Hostname: page-component-55f67697df-7l9ct Total loading time: 0 Render date: 2025-05-08T08:47:28.965Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-linear relationship between the body roundness index and metabolic syndrome: data from National Health and Nutrition Examination Survey (NHANES) 1999–2018

Published online by Cambridge University Press:  15 February 2024

Zhenhan Li Open the ORCID record for Zhenhan Li [Opens in a new window] ,
Chunhua Fan ,
Jun Huang ,
Zhongpei Chen ,
Xiaoxia Yu  and
Jun Qian
Zhenhan Li
Affiliation:
Department of Endocrinology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, People’s Republic of China
Chunhua Fan
Affiliation:
Department of Anatomy, Chongqing Medical and Pharmaceutical College, Chongqing, People’s Republic of China
Jun Huang
Affiliation:
Department of Endocrinology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, People’s Republic of China
Zhongpei Chen
Affiliation:
Department of Endocrinology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, People’s Republic of China
Xiaoxia Yu
Affiliation:
Department of Endocrinology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, People’s Republic of China
Jun Qian*
Affiliation:
Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200092, People’s Republic of China
*
*Corresponding author: Jun Qian, email [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Obesity is an important characteristic manifestation of metabolic syndrome (MetS), and body roundness index (BRI) is one of the anthropometric indicators associated with obesity. However, studies on the relationship between BRI and MetS risk are limited. We aimed to explore the relationship between baseline BRI and MetS in the USA population. Our study used data from the National Health and Nutrition Examination Survey from 1999 to 2018, ultimately enrolling and analysing 47 303 participants. Data-driven tertiles were used to categorise BRI levels, and multivariate logistic regression models were fitted to investigate the association of BRI with MetS in adults. In addition, receiver operating characteristic curve analysis was used to assess the ability of BRI to predict MetS. The distribution of BRI was different across ethnic groups with a gradual decrease in the proportion of non-Hispanic Whites and other races. In addition, BRI was significantly associated with traditional cardiovascular risk factors. Univariate regression analysis indicated BRI to be a moderate risk factor for MetS, and multivariate logistic regression analysis found that BRI remained an independent risk factor for MetS. After adjusting for confounding variables, a non-linear relationship was found between BRI levels and the prevalence of MetS. More importantly, BRI predicted MetS with the largest AUC among anthropometric measures. In summary, elevated baseline BRI levels are independently associated with the development of MetS, and baseline BRI may assist in identifying patients at risk for MetS, leading to early and optimal treatment to improve their outcomes.

Keywords

Body roundness indexMetabolic syndromeNon-linear relationship
Type
Research Article
Information
British Journal of Nutrition , Volume 131 , Issue 11 , 14 June 2024 , pp. 1852 - 1859
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Nutrition Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

†

These authors contributed equally and shared the first authorship

‡

These authors contributed equally to this work

References

Saklayen, MG (2018) The global epidemic of the metabolic syndrome. Curr Hypertens Rep 20, 12.CrossRefGoogle ScholarPubMed
Eckel, RH, Grundy, SM & Zimmet, PZ (2005) The metabolic syndrome. Lancet 365, 1415–1428.CrossRefGoogle ScholarPubMed
Medina, G, Vera-Lastra, O, Peralta-Amaro, AL, et al. (2018) Metabolic syndrome, autoimmunity and rheumatic diseases. Pharmacol Res 133, 277–288.CrossRefGoogle ScholarPubMed
Hirode, G & Wong, RJ (2020) Trends in the prevalence of metabolic syndrome in the United States, 2011–2016. JAMA 323, 2526–2528.CrossRefGoogle Scholar
Pucci, G, Alcidi, R, Tap, L, et al. (2017) Sex- and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: a review of the literature. Pharmacol Res 120, 34–42.CrossRefGoogle Scholar
Tune, JD, Goodwill, AG, Sassoon, DJ, et al. (2017) Cardiovascular consequences of metabolic syndrome. Transl Res 183, 57–70.CrossRefGoogle ScholarPubMed
Alberti, KG, Eckel, RH, Grundy, SM, et al. (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120, 1640–1645.CrossRefGoogle Scholar
Saklayen, MG (2018) The global epidemic of the metabolic syndrome. Curr Hypertens Rep 20, 12.CrossRefGoogle ScholarPubMed
Tchernof, A & Després, JP (2013) Pathophysiology of human visceral obesity: an update. Physiol Rev 93, 359–404.CrossRefGoogle ScholarPubMed
Elagizi, A, Kachur, S, Lavie, CJ, et al. (2018) An overview and update on obesity and the obesity paradox in cardiovascular diseases. Prog Cardiovasc Dis 61, 142–150.CrossRefGoogle ScholarPubMed
Wu, L, Zhu, W, Qiao, Q, et al. (2021) Novel and traditional anthropometric indices for identifying metabolic syndrome in non-overweight/obese adults. Nutr Metab (Lond) 18, 3.CrossRefGoogle Scholar
Thomas, DM, Bredlau, C, Bosy-Westphal, A, et al. (2013) Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring) 21, 2264–2271.CrossRefGoogle ScholarPubMed
Rico-Martín, S, Calderón-García, JF, Sánchez-Rey, P, et al. (2020) Effectiveness of body roundness index in predicting metabolic syndrome: a systematic review and meta-analysis. Obes Rev 21, e13023.CrossRefGoogle ScholarPubMed
Johnson, CL, Paulose-Ram, R, Ogden, CL, et al. (2013) National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat 2 2013, 1–24.Google Scholar
Grundy, SM (2005) Metabolic syndrome scientific statement by the American Heart Association and the National Heart, Lung, and Blood Institute. Arterioscler Thromb Vasc Biol 25, 2243–2244.CrossRefGoogle ScholarPubMed
Ahluwalia, N, Raghavan, R, Zhang, G, et al. (2022) Vitamin D status and prevalence of metabolic syndrome by race and Hispanic origin in U.S. adults: findings from 2007–2014 NHANES. Am J Clin Nutr 116, 1400–1408.CrossRefGoogle ScholarPubMed
Wu, L, Pu, H, Zhang, M, et al. (2022) Non-linear relationship between the body roundness index and incident type 2 diabetes in Japan: a secondary retrospective analysis. J Transl Med 20, 110.CrossRefGoogle ScholarPubMed
Ingram, DD, Malec, DJ, Makuc, DM, et al. (2018) National center for health statistics guidelines for analysis of trends. Vital Health Stat 2 2018, 1–71.Google Scholar
Gu, Z, Li, D, He, H, et al. (2018) Body mass index, waist circumference, and waist-to-height ratio for prediction of multiple metabolic risk factors in Chinese elderly population. Sci Rep 8, 385.CrossRefGoogle ScholarPubMed
Geraci, G, Zammuto, M, Gaetani, R, et al. (2019) Relationship of a Body Shape Index and Body Roundness Index with carotid atherosclerosis in arterial hypertension. Nutr Metab Cardiovasc Dis 29, 822–829.CrossRefGoogle ScholarPubMed
Vásquez, FD, Corvalán, CL, Uauy, RE, et al. (2017) Anthropometric indicators as predictors of total body fat and cardiometabolic risk factors in Chilean children at 4, 7 and 10 years of age. Eur J Clin Nutr 71, 536–543.CrossRefGoogle Scholar
Aguilar-Morales, I, Colin-Ramirez, E, Rivera-Mancía, S, et al. (2018) Performance of waist-to-height ratio, waist circumference, and body mass index in discriminating cardio-metabolic risk factors in a sample of school-aged Mexican children. Nutrients 10, 1850.CrossRefGoogle Scholar
Suliga, E, Ciesla, E, Głuszek-Osuch, M, et al. (2019) The usefulness of anthropometric indices to identify the risk of metabolic syndrome. Nutrients 11, 2598.CrossRefGoogle Scholar
Chang, Y, Guo, X, Li, T, et al. (2016) A body shape index and body roundness index: two new body indices to identify left ventricular hypertrophy among rural populations in northeast China. Heart Lung Circ 25, 358–364.CrossRefGoogle ScholarPubMed
Dong, J, Wang, SS, Chu, X, et al. (2019) Optimal cut-off point of waist to height ratio in Beijing and its association with clusters of metabolic risk factors. Curr Med Sci 39, 330–336.CrossRefGoogle ScholarPubMed
Li, G, Wu, HK, Wu, XW, et al. (2019) The feasibility of two anthropometric indices to identify metabolic syndrome, insulin resistance and inflammatory factors in obese and overweight adults. Nutrition 57, 194–201.CrossRefGoogle ScholarPubMed
Zhao, Q, Zhang, K, Li, Y, et al. (2018) Capacity of a body shape index and body roundness index to identify diabetes mellitus in Han Chinese people in Northeast China: a cross-sectional study. Diabet Med 35, 1580–1587.CrossRefGoogle Scholar
Stefanescu, A, Revilla, L, Lopez, T, et al. (2020) Using a Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res 48, 300060519848854.CrossRefGoogle ScholarPubMed
Liu, PJ, Ma, F, Lou, HP, et al. (2017) Comparison of the ability to identify cardiometabolic risk factors between two new body indices and waist-to-height ratio among Chinese adults with normal BMI and waist circumference. Public Health Nutr 20, 984–991.CrossRefGoogle ScholarPubMed
Xu, J, Zhang, L, Wu, Q, et al. (2021) 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 21, 97.CrossRefGoogle ScholarPubMed
Endukuru, CK, Gaur, GS, Dhanalakshmi, Y, et al. (2021) Cut-off values and clinical efficacy of body roundness index and other novel anthropometric indices in identifying metabolic syndrome and its components among Southern-Indian adults. Diabetol Int 13, 188–200.CrossRefGoogle ScholarPubMed