Association between body roundness index and non‐alcoholic fatty liver disease detected by Fibroscan in America
Open accessAbstract
Background: The prevalence of non‐alcoholic fatty liver disease (NAFLD) and obesity is worldwide on the rise. Body roundness index (BRI), as a newly developed anthropometric indicator, has been recently reported to identify obesity. However, it is still unclear whether BRI is associated with the prevalence of NAFLD. Methods: Data were from the National Health and Nutrition Examination Survey (NHANES) 2017–2018. NAFLD was diagnosed based on hepatic steatosis defined by CAP values ≥274 dB/m. Multivariable logistic regression analysis was performed to detect the association between BRI and the odds of NAFLD. Subgroup analysis stratified by age, gender, BMI, and race was further conducted. To explore the potential ability of BRI in predicting NAFLD, the area under the curve (AUC) of BRI was calculated by receiver operating characteristic (ROC) analysis. Results: Among the 4467 study participants, 1718 (38.5%) were diagnosed with NAFLD. Compared to the non‐NAFLD group, participants with NAFLD had a higher level of BRI. The positive associations between BRI and NAFLD were detected in all three models (mode 1: OR = 1.71, 95% CI: 1.65–1.78, p < 0.0001; mode 2: OR = 1.78, 95% CI: 1.71–1.86, p < 0.0001; mode3: OR = 1.23, 95% CI: 1.11–1.35, p < 0.0001). The positive association steadily existed in different subgroups after stratified by age, gender, and BMI. Moreover, the non‐linear association between BRI and NAFLD was detected, presenting inverted U‐shaped curves. Furthermore, BRI had a high predictive value (AUC = 0.807) in identifying NAFLD. Conclusions: BRI was positively associated with the prevalence of NAFLD among individuals in America, regardless of age, gender, and BMI. Besides, BRI presented a high ability for identifying NAFLD.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
BRI outperformed other common anthropometric obesity-related measures, including BMI, WC, and ABSI, in assessing NAFLD risk.
“Additionally, BRI outperformed other common anthropometric obesity‐related measures, including BMI, WC, and a body shape index (ABSI), in assessing NAFLD risk [13].”
From Effectiveness of body roundness index for the prediction of nonalcoholic fatty liver disease: a systematic review and meta-analysis (2025) · cites this paper as [13]
The prevalence of NAFLD was found to have a positive association with BRI, regardless of age, gender, and BMI.
“A study using data from a large cross-sectional survey found that participants with NAFLD had a higher BRI level compared to those in the non‐NAFLD group, and the prevalence of NAFLD was found to have a positive association with BRI, regardless of age, gender, and BMI.”
From Effectiveness of body roundness index for the prediction of nonalcoholic fatty liver disease: a systematic review and meta-analysis (2025) · cites this paper as [13]
A study using data from a large cross-sectional survey found that participants with NAFLD had a higher BRI level compared to those in the non-NAFLD group.
“A study using data from a large cross-sectional survey found that participants with NAFLD had a higher BRI level compared to those in the non‐NAFLD group, and the prevalence of NAFLD was found to have a positive association with BRI, regardless of age, gender, and BMI.”
From Effectiveness of body roundness index for the prediction of nonalcoholic fatty liver disease: a systematic review and meta-analysis (2025) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Younossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., & Wymer, M. (2016) · Hepatology · doi:10.1002/hep.28431 · Cited by 10,781 · see citing works
Byrne, C. D., & Targher, G. (2015) · Journal of Hepatology · doi:10.1016/j.jhep.2014.12.012 · Cited by 3,082 · see citing works
Ahluwalia, N., Dwyer, J., Terry, A. L., Moshfegh, A., & Johnson, C. L. (2016) · Advances in Nutrition · doi:10.3945/an.115.009258 · Cited by 983 · see citing works
Increased overall mortality and liver-related mortality in non-alcoholic fatty liver disease
Ong, J. P., Pitts, A., & Younossi, Z. M. (2008) · Journal of Hepatology · doi:10.1016/j.jhep.2008.06.018 · Cited by 636 · see citing works
The epidemiology of non‐alcoholic fatty liver disease
Bellentani, S. (2017) · Liver International · doi:10.1111/liv.13299 · Cited by 607 · see citing works
Extrahepatic complications of nonalcoholic fatty liver disease
Armstrong, M. J., Adams, L. A., Canbay, A. E., & Syn, W. (2013) · Hepatology · doi:10.1002/hep.26717 · Cited by 602 · see citing works
Rico‐Martín, S., Calderón‐García, J. F., Sánchez‐Rey, P., Franco‐Antonio, C., Martínez Alvarez, M., & Sánchez Muñoz‐Torrero, J. F. (2020) · Obesity Reviews · doi:10.1111/obr.13023 · Cited by 355 · see citing works
Review article: the epidemiologic burden of non‐alcoholic fatty liver disease across the world
Henry, L. L., Paik, J. M., & Younossi, Z. M. (2022) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.17158 · Cited by 217 · see citing works
Sheng, G., Lu, S., Xie, Q., Peng, N., Kuang, M., & Zou, Y. (2021) · Lipids in Health and Disease · doi:10.1186/s12944-021-01561-2 · Cited by 198 · cited 1× · see citing works
“17 In contradiction with the above conclusion, a cross-sectional study comprehensively evaluated the predictive performance of anthropometric and metabolic indices to identify NAFLD in Chinese adults, but found BRI had the best diagnostic ability than other obesity-related indices.”
BackgroundWu, L., Zhu, W., Qiao, Q., Huang, L., Li, Y., & Chen, L. (2021) · Nutrition & Metabolism · doi:10.1186/s12986-020-00536-x · Cited by 108 · see citing works
Stefan, N., Häring, H., Hu, F., & Schulze, M. (2016) · The Lancet Diabetes and Endocrinology · doi:10.1016/s2213-8587(15)00474-x · Cited by 108 · see citing works
Ciardullo, S., Monti, T., & Perseghin, G. (2020) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2020.06.048 · Cited by 107 · cited 1× · see citing works
“21 There were also many confounding variables, including continuous and categorical variables, that might affect the relationship between the independent variable BRI and the dependent variable NAFLD status.”
BackgroundMotamed, N., Rabiee, B., Hemasi, G. R., Ajdarkosh, H., Khonsari, M. R., Maadi, M., Keyvani, H., & Zamani, F. (2016) · Hepatitis Monthly · doi:10.5812/hepatmon.39575 · Cited by 80 · see citing works
Epidemiology and risk factors of nonalcoholic fatty liver disease (NAFLD)
Duseja, A., & Chalasani, N. (2013) · Hepatology International · doi:10.1007/s12072-013-9480-x · Cited by 74 · see citing works
Singal, A., Arsalan, A., Dunn, W., Arab, J., Wong, R., Kuo, Y., Kamath, P., & Shah, V. (2021) · Alimentary Pharmacology and Therapeutics · doi:10.1111/apt.16461 · Cited by 72 · see citing works
Lin, I.-T., Lee, M.-Y., Wang, C.-W., Wu, D.-W., & Chen, S.-C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18030857 · Cited by 69 · see citing works
Chiu, T.-H., Huang, Y.-C., Chiu, H., Wu, P.-Y., Chiou, H., Huang, J.-C., & Chen, S.-C. (2020) · Diagnostics · doi:10.3390/diagnostics10121081 · Cited by 44 · see citing works
Association of serum vitamin C with liver fibrosis in adults with nonalcoholic fatty liver disease
Zhao, Y., & Li, H. (2022) · Scandinavian Journal of Gastroenterology · doi:10.1080/00365521.2022.2041085 · Cited by 39 · see citing works
Xie, F., Pei, Y., Zhou, Q., Cao, D., & Wang, Y. (2021) · Lipids in Health and Disease · doi:10.1186/s12944-021-01560-3 · Cited by 34 · cited 2× · see citing works
“18 These inconsistent findings among the studies can be partly attributed to the difference in anthropometric characteristics among various ethnic and racial groups, different study designs, the method to diagnose NAFLD, and the study sample size.”
BackgroundProcino, F., Misciagna, G., Veronese, N., Caruso, M., Chiloiro, M., Cisternino, A., Notarnicola, M., Bonfiglio, C., Bruno, I., Buongiorno, C., et al. (2018) · Liver international (Print) · doi:10.1111/liv.13970 · Cited by 28 · cited 1× · see citing works
“16 Sheng et al. found TyG index-related parameters showed a better predictive potential for NAFLD diagnosed by abdominal ultrasound than BRI.”
BackgroundYang, N., Lu, Y., Liwen, C., & Lu, M. (2022) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.24225 · Cited by 28 · see citing works
Self-citationSomda, S., Lebrun, A., Tranchart, H., Lamouri, K., Prevot, S., Njiké‐Nakseu, M., Gaillard, M., Laïnas, P., Balian, A., Dagher, I., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217093 · Cited by 19 · see citing works
Wung, C.-H., Lee, M.-Y., Wu, P.-Y., Huang, J.-C., & Chen, S.-C. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11060533 · Cited by 16
Flagged first, then by citations
Cited by
Sorted by citations
Zhang, J.-Q., & Zhang, H.-F. (2025) · Lipids in Health and Disease · doi:10.1186/s12944-025-02450-8 · Cited by 23 · see citing works
“Since its proposal, several studies have shown that BRI is superior to BMI in predicting cardio-metabolic abnormalities [15], gallstones [16], hyperuricemia and gout [17], non - alcoholic fatty liver disease (NAFLD) [18], kidney stones [19], and urinary incontinence [20].”
Khanmohammadi, S., Fallahtafti, P., Habibzadeh, A., Ezzatollahi Tanha, A., Alamdari, A. A., Fallahtafti, P., & Shafi Kuchay, M. (2025) · Lipids in Health and Disease · doi:10.1186/s12944-025-02544-3 · Cited by 19 · see citing works
“This insulin resistance impairs the liver’s capability to metabolize free fatty acids [13].”
InfluentialAssociation of body roundness index with female infertility: 2013–2018 NHANES
Wang, W., Hou, S., Wang, K., Ling, B., & Yu, H. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1416637 · Cited by 12 · see citing works
Cao, H.-L., Shi, C., Aihemaiti, Z., Dai, X., Yu, F., & Wang, S. (2024) · International Urology and Nephrology · doi:10.1007/s11255-024-04275-3 · Cited by 12 · see citing works
Huang, C., Gao, Z., Zhenxia, H., & Xu, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-99540-5 · Cited by 9 · see citing works
“Due to the significant correlation between obesity and MASLD, BRI has emerged as a potential predictor of MASLD presence in the American population 19 .”
Liao, X., Li, Y., Zhang, Z., Xiao, Y., Yu, X., Huang, R., & Zhong, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-01383-7 · Cited by 8 · see citing works
The nonlinear association between body roundness index and infertility in married women
Fang, Y., Ren, D., Pan, J., & Xu, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-86948-2 · Cited by 5 · see citing works
Xu, Y.-Y., Chen, L., & Zhang, J.-Q. (2025) · Journal of Health, Population and Nutrition · doi:10.1186/s41043-025-01052-5 · Cited by 5 · see citing works
“…put forward, multiple investigations have indicated that the BRI has an edge over the BMI in anticipating cardiometabolic irreg-ularities [12], gallstones [13], hyperuricemia and gout [14], non-alcoholic fatty liver disease (NAFLD) [15, 16], kidney stones [17], as well as urinary incontinence [18].”
Ye, M.-Y., Zhang, D., Wu, L., Gao, J.-Z., Yi, Q.-M., Chen, J.-J., & Luo, J. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06834-z · Cited by 4 · see citing works
“…that the BRI, as a comprehensive assessment of physical obesity, is effective in predicting the probability of cardiovascular disease [20], diabetes mellitus [36, 37], metabolic syndrome [15, 38], certain cancers [39], respiratory disease [13], liver disease [40], and mental health problems [20].”
Li, X., Liu, S.-P., Zhao, Q., An, M., Hou, C., Hu, S., & Niu, Y.-C. (2025) · HORMONES · doi:10.1007/s42000-025-00721-8 · Cited by 3 · see citing works
Yu, J.-N., Ge, H., Gao, Z.-W., Feng, J.-X., Gao, Y., Cong, J., Zhu, M.-Y., Shi, B.-C., Guan, M., Gao, J.-S., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1705555 · Cited by 1 · see citing works
Sun, Y.-Z., Sun, P., Liu, J.-F., Cui, S., Li, Y., & Zuo, Y. (2025) · Reviews in cardiovascular medicine · doi:10.31083/rcm39048 · Cited by 1 · see citing works
Jia, W.-A., Wang, H.-M., Yong, F.-F., Liu, W., Shi, J.-P., & Jia, H. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003683 · Cited by 1 · see citing works
“The BRI is associated with many diseases, including cardiovascular [ 16 ] , metabolic [ 17 , 18 ] , non-alcoholic fatty liver [ 19 , 20 ] , and kidney stone diseases [ 21 ] .”
Shen, Y.-H., Lee, Y.-C., Chou, T.-J., Chang, H.-H., Lu, C.-W., & Huang, K.-C. (2026) · Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy · doi:10.2147/dmso.s630425
“7,8,38 The new nomenclature and criteria of MASLD have replaced those of NAFLD.”
Huang, H., Lu, Y., Luo, T., Wu, H., Li, J., Tang, T., Xv, X., Wu, J., & Chen, M. Y. (2026) · JGH Open · doi:10.1002/jgh3.70435
“%) and BMI (AUC = 0.79; sensitivity, 72%), findings consistent with those reported by Jiang and Zhao [26, 27].”
InfluentialKarimi, E., Hemami, R. M., Somi, M., Gilani, N., Ostadrahimi, A., Shirmohamadi, M., Kohneloo, A. J., Tavakkoli, S., Faramarzi, E., & Safaei, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-40671-8
“In this context, indices such as the visceral adiposity index (VAI), weight-adjusted waist index (WWI), and abdominal volume index (AVI) have been proposed as more sensitive and specific markers for NAFLD screening 20,21 .”
Gervacio-Carrión, C. N., Saraguro-Sotamba, K. P., Quezada, T. D. C. N., & Solano-Maza, L. O. (2025) · Innova Science Journal · doi:10.63618/omd/isj/v3/ne1/178
Zhao, Z.-Y., Ji, H.-X., Liu, W., Wang, Z.-D., Ren, S., Liu, C., Wu, C., Wang, J., & Ding, X. (2025) · Medicine · doi:10.1097/md.0000000000043750
Maghbouli, N., Rashnavadi, H., Pourhassan, S., & Madreseh, E. (2025) · JMIR Research Protocols · doi:10.2196/71656
Pérez-Mingan, G., Sierra-Merlano, R., Yepes, I., Vergara, M., Ortiz, M., Peña, B., Cano-Pérez, E., & Gómez-Camargo, D. (2024) · Genes · doi:10.3390/genes15121639
- Authors
- Jiang, N., Zhang, S., Chu, J., Yang, N., & Lu, M.
- Affiliations
- Ningbo No. 2 Hospital (CN)
- Ruian People's Hospital (CN)
- Ningbo University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Corresponding author
- Chu, Jinguo
- Yang, Naibin
- Lu, Mingqin
- Venue
- Journal of Clinical Laboratory Analysis
- Publisher
- Wiley
- Published
- 2023-10
- Volume
- 37
- Issue
- 19-20
- Pages
- e24973-e24973
- ISSN
- 0887-8013 (ISSN-L)
- 1098-2825
- PMC
- PMC10681427
- OpenAlex
- W4387729044
- Cited by
- 20
- References
- 23
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 3 · 2023: 1
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Clinical Laboratory Analysis (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Zhejiang University
- Chinese Foundation for Hepatitis Prevention and Control
- Topics
- Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- body roundness index, non-alcoholic fatty liver disease, hepatic steatosis, anthropometric indicators, obesity, NAFLD prevalence, NAFLD prediction, inverted U-shaped relationship, NHANES
- MeSH terms
- Anthropometry, Humans, Nutrition Surveys, Obesity, Body Mass Index, Non-alcoholic Fatty Liver Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
