从BMI到体脂百分比:精准医疗时代肥胖诊断的重新定义
From BMI to Body Fat Percentage: Redefining Obesity Diagnosis in the Era of Precision Medicine
摘要: 肥胖已成全球重大公共卫生挑战。长期作为核心筛查工具的身体质量指数(BMI)因无法区分脂肪与瘦体重、忽视脂肪分布异质性及存在种族差异,在精准医疗背景下局限性凸显。本综述系统阐述肥胖诊断从“以BMI为中心”向“以体脂为核心”的范式演进,重点解析2025年《柳叶刀》委员会提出的“临床肥胖”新框架——该框架将肥胖界定为脂肪过量直接导致的慢性疾病,区分“临床前肥胖”与“临床肥胖”,推动诊断从“体重评估”转向“脂肪与功能联合评估”。文章评价体脂百分比、腰围、腰高比、相对脂肪质量等替代指标效能,阐明正常体重肥胖等BMI掩盖表型的临床意义,并结合中国人群特征与医疗资源配置现状,提出分层落地路径,为构建适配中国人群的精准肥胖诊断体系提供参考。
Abstract: The global obesity epidemic is a major public health challenge. The long-standing screening tool, Body Mass Index (BMI), is limited in the era of precision medicine due to its inability to distinguish fat from lean mass, ignore fat distribution heterogeneity, and vary across ethnicities. This review outlines the paradigm shift in obesity diagnosis from BMI-centric to adiposity-centered assessment, with a focus on the 2025 Lancet Commission’s novel “clinical obesity” framework. This framework defines obesity as a chronic disease caused by excess adiposity, differentiates between pre-clinical and clinical obesity, and shifts diagnosis from weight-focused to integrated adiposity and functional evaluation. We appraise alternative metrics including body fat percentage, waist circumference, waist-to-height ratio, and relative fat mass, clarify clinical implications of BMI-masked phenotypes such as normal-weight obesity, and propose a tiered implementation pathway tailored to Chinese populations to support precise obesity diagnosis.
文章引用:宗蓉蓉. 从BMI到体脂百分比:精准医疗时代肥胖诊断的重新定义[J]. 临床医学进展, 2026, 16(8): 1399-1407. https://doi.org/10.12677/acm.2026.1682916

参考文献

[1] Phelps, N.H., Singleton, R.K., Zhou, B., Heap, R.A., Mishra, A., Bennett, J.E., et al. (2024) Worldwide Trends in Underweight and Obesity from 1990 to 2022: A Pooled Analysis of 3663 Population-Representative Studies with 222 Million Children, Adolescents, and Adults. The Lancet, 403, 1027-1050.
https://doi.org/10.1016/s0140-6736(23)02750-2
[2] Pan, X., Fang, Z., Zhang, L. and Pan, A. (2026) Obesity in China: Current Progress and Future Prospects. The Lancet Diabetes & Endocrinology, 14, 178-186.
https://doi.org/10.1016/s2213-8587(25)00357-2
[3] WHO Expert Committee (1995) Physical Status: The Use and Interpretation of Anthropometry. World Health Organization Technical Report Series, 854, 452.
https://extranet.who.int/prequal/content/who-technical-report-series
[4] Keys, A., Fidanza, F., Karvonen, M.J., Kimura, N. and Taylor, H.L. (2014) Indices of Relative Weight and Obesity. International Journal of Epidemiology, 43, 655‑65.
https://doi.org/10.1093/ije/dyu058
[5] Pan, W.H. and Yeh, W.T. (2008) How to Define Obesity? Evidence-Based Multiple Action Points for Public Awareness, Screening, and Treatment: An Extension of Asian-Pacific Recommendations. Asia Pacific Journal of Clinical Nutrition, 17, 370-374.
http://apjcn.nhri.org.tw/index.php
[6] Rubino, F., Cummings, D.E., Eckel, R.H., Cohen, R.V., Wilding, J.P.H., Brown, W.A., et al. (2025) Definition and Diagnostic Criteria of Clinical Obesity. The Lancet Diabetes & Endocrinology, 13, 221-262.
https://doi.org/10.1016/s2213-8587(24)00316-4
[7] Busetto, L., Dicker, D., Frühbeck, G., Halford, J.C.G., Sbraccia, P., Yumuk, V., et al. (2024) A New Framework for the Diagnosis, Staging and Management of Obesity in Adults. Nature Medicine, 30, 2395-2399.
https://doi.org/10.1038/s41591-024-03095-3
[8] Ross, R., Neeland, I.J., Yamashita, S., Shai, I., Seidell, J., Magni, P., et al. (2020) Waist Circumference as a Vital Sign in Clinical Practice: A Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nature Reviews Endocrinology, 16, 177-189.
https://doi.org/10.1038/s41574-019-0310-7
[9] De Lorenzo, A., Gualtieri, P., Frank, G., Palma, R., Cianci, R., Romano, L., et al. (2025) Normal Weight Obesity Overview and Update: A Narrative Review. Current Obesity Reports, 14, Article No. 50.
https://doi.org/10.1007/s13679-025-00641-z
[10] Cota, B.C., Ribeiro, S.A.V., Priore, S.E., Juvanhol, L.L., de Faria, E.R., de Faria, F.R., et al. (2021) Anthropometric and Body Composition Parameters in Adolescents with the Metabolically Obese Normal-Weight Phenotype. British Journal of Nutrition, 127, 1458-1466.
https://doi.org/10.1017/s0007114521002427
[11] Lee, J., Min, S., Oh, S., Oh, S., Lee, Y., Kwon, H., et al. (2023) Association of Intraabdominal Fat with the Risk of Incident Chronic Kidney Disease According to Body Mass Index among Korean Adults. PLOS ONE, 18, e0280766.
https://doi.org/10.1371/journal.pone.0280766
[12] Tchernof, A. and Després, J. (2013) Pathophysiology of Human Visceral Obesity: An Update. Physiological Reviews, 93, 359-404.
https://doi.org/10.1152/physrev.00033.2011
[13] Jayedi, A., Soltani, S., Motlagh, S.Z., Emadi, A., Shahinfar, H., Moosavi, H., et al. (2022) Anthropometric and Adiposity Indicators and Risk of Type 2 Diabetes: Systematic Review and Dose-Response Meta-Analysis of Cohort Studies. BMJ, 376, e067516.
https://doi.org/10.1136/bmj-2021-067516
[14] Powell-Wiley, T.M., Poirier, P., Burke, L.E., Després, J., Gordon-Larsen, P., Lavie, C.J., et al. (2021) Obesity and Cardiovascular Disease: A Scientific Statement from the American Heart Association. Circulation, 143, e984-e1010.
https://doi.org/10.1161/cir.0000000000000973
[15] Morys, F., Dadar, M. and Dagher, A. (2021) Association between Midlife Obesity and Its Metabolic Consequences, Cerebrovascular Disease, and Cognitive Decline. The Journal of Clinical Endocrinology & Metabolism, 106, e4260-e4274.
https://doi.org/10.1210/clinem/dgab135
[16] Di Angelantonio, E., Bhupathiraju, S.N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A.B., et al. (2016) Body-Mass Index and All-Cause Mortality: Individual-Participant-Data Meta-Analysis of 239 Prospective Studies in Four Continents. The Lancet, 388, 776-786.
https://doi.org/10.1016/s0140-6736(16)30175-1
[17] Erdoğan, O., Erdoğan, T., Önür, N.H., Özkök, S., Karan, M.A. and Bahat, G. (2025) Beyond BMI: Central Obesity Measures and Cardiovascular Risk in Late Life. Aging Clinical and Experimental Research, 37, Article No. 287.
https://doi.org/10.1007/s40520-025-03197-z
[18] Zeng, Q., Dong, S.Y., Sun, X.N., et al. (2018) Percent Body Fat Is a Better Predictor of Cardiovascular Risk Factors than Body Mass Index in Chinese Non-Obese Adults. European Journal of Nutrition, 57, 2557-2568.
[19] WHO Expert Consultation (2004) Appropriate Body-Mass Index for Asian Populations and Its Implications for Policy and Intervention Strategies. Lancet (London, England), 363, 157-163.
https://doi.org/10.1016/S0140-6736(03)15268-3
[20] Choe, H.J., Després, J., Wilding, J.P.H., Ryan, D.H. and Lim, S. (2026) Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023. Diabetes & Metabolism Journal, 50, 127-138.
https://doi.org/10.4093/dmj.2025.0697
[21] Zahid, S., Yao, Z., Grimes, S.N., Kim, A., Peng, A.W., Blumenthal, R.S., et al. (2026) Epidemiology and Natural History of Preclinical and Clinical Obesity: Insights from a UK Cohort. Obesity, 34, 738-747.
https://doi.org/10.1002/oby.70126
[22] Wang, Z., Du, W., Wei, X., Li, S., Zhang, J., Ju, L., et al. (2026) Clinical Obesity among Chinese Adults: Prevalence, Multimorbidity Burden, and Associations with Physical Activity. Nutrients, 18, Article No. 983.
https://doi.org/10.3390/nu18060983
[23] Wu, R., Li, M., Liao, Y., Zhang, J., Xu, C. and Yan, X. (2025) Prevalence and Characteristics of Sarcopenic Obesity and Normal Weight Obesity in Chinese Women: A Cross-Sectional Study Based on Body Fat Percentage. BMC Public Health, 25, Article No. 2817.
https://doi.org/10.1186/s12889-025-24086-0
[24] Leitzmann, M.F., Stein, M.J., Baurecht, H. and Freisling, H. (2025) Excess Adiposity and Cancer: Evaluating a Preclinical-Clinical Obesity Framework for Risk Stratification. eClinicalMedicine, 83, Article ID: 103247.
https://doi.org/10.1016/j.eclinm.2025.103247
[25] Ward, L.C. (2018) Bioelectrical Impedance Analysis for Body Composition Assessment: Reflections on Accuracy, Clinical Utility, and Standardisation. European Journal of Clinical Nutrition, 73, 194-199.
https://doi.org/10.1038/s41430-018-0335-3
[26] Gallagher, D., Heymsfield, S.B., Heo, M., Jebb, S.A., Murgatroyd, P.R. and Sakamoto, Y. (2000) Healthy Percentage Body Fat Ranges: An Approach for Developing Guidelines Based on Body Mass Index. The American Journal of Clinical Nutrition, 72, 694-701.
https://doi.org/10.1093/ajcn/72.3.694
[27] Zeng, Q., Dong, S., Sun, X., Xie, J. and Cui, Y. (2012) Percent Body Fat Is a Better Predictor of Cardiovascular Risk Factors than Body Mass Index. Brazilian Journal of Medical and Biological Research, 45, 591-600.
https://doi.org/10.1590/s0100-879x2012007500059
[28] Gu, D., Reynolds, K., Wu, X., Chen, J., Duan, X., Reynolds, R.F., et al. (2005) Prevalence of the Metabolic Syndrome and Overweight among Adults in China. The Lancet, 365, 1398-1405.
https://doi.org/10.1016/s0140-6736(05)66375-1
[29] Flegal, K.M., Graubard, B.I. and Yi, S. (2017) Comparative Effects of the Restriction Method in Two Large Observational Studies of Body Mass Index and Mortality among Adults. European Journal of Clinical Investigation, 47, 415-421.
https://doi.org/10.1111/eci.12756
[30] Lv, M., Li, Y., Guo, Z., Ma, L. and Zhang, L. (2025) Bidirectional Associations between Adiposity and Mental Health: A Prospective Cohort Study of the UK Biobank. Obesity, 33, 1195-1206.
https://doi.org/10.1002/oby.24296
[31] Wang, T., Zhang, L., Chen, P., Chen, C., Xu, L. and Guo, M. (2025) Weight-Adjusted Waist Index Outperforms Other Obesity Indices for Cardiovascular Disease Prediction in Cardiovascular-Kidney-Metabolic Syndrome: Insights from UK Biobank. BMC Public Health, 26, Article No. 160.
https://doi.org/10.1186/s12889-025-25830-2
[32] Suthahar, N., Lau, E.S. and Savarese, G. (2025) Relative Fat Mass: Refining Adiposity Measurement in the Era beyond Body Mass Index. Current Heart Failure Reports, 22, Article No. 22.
https://doi.org/10.1007/s11897-025-00709-w
[33] Tomiyama, A.J., Carr, D., Granberg, E.M., Major, B., Robinson, E., Sutin, A.R., et al. (2018) How and Why Weight Stigma Drives the Obesity “Epidemic” and Harms Health. BMC Medicine, 16, Article No. 123.
https://doi.org/10.1186/s12916-018-1116-5
[34] Woolcott, O.O. and Bergman, R.N. (2020) Defining Cutoffs to Diagnose Obesity Using the Relative Fat Mass (RFM): Association with Mortality in NHANES 1999-2014. International Journal of Obesity, 44, 1301-1310.
https://doi.org/10.1038/s41366-019-0516-8
[35] Santhanam, P., Nath, T., Peng, C., Bai, H., Zhang, H., Ahima, R.S., et al. (2023) Artificial Intelligence and Body Composition. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 17, Article ID: 102732.
https://doi.org/10.1016/j.dsx.2023.102732
[36] Park, M.J. and Choi, K.M. (2023) Interplay of Skeletal Muscle and Adipose Tissue: Sarcopenic Obesity. Metabolism, 144, Article ID: 155577.
https://doi.org/10.1016/j.metabol.2023.155577