BMI Cut Points to Identify At-Risk Asian Americans for Type 2 Diabetes Screening
Open accessAbstract
According to the U.S. Census Bureau, an Asian is a person with origins from the Far East (China, Japan, Korea, and Mongolia), Southeast Asia (Cambodia, Malaysia, the Philippine Islands, Thailand, Vietnam, Indonesia, Singapore, Laos, etc.), or the Indian subcontinent (India, Pakistan, Bangladesh, Bhutan, Sri Lanka, and Nepal); each region has several ethnicities, each with a unique culture, language, and history.In 2011, 18.2 million U.S. residents self-identified as Asian American, with more than two-thirds foreign-born (1).In 2012, Asian Americans were the nation's fastestgrowing racial or ethnic group, with a growth rate over four times that of the total U.S. population.International migration has contributed .60% of the growth rate in this population (1).Among Asian Americans, the Chinese population was the largest (4.0 million), followed by Filipinos (3.4 million), Asian Indians (3.2 million), Vietnamese (1.9 million), Koreans (1.7 million), and Japanese (1.3 million).Nearly three-fourths of all Asian Americans live in 10 statesdCalifornia, New York, Texas, New Jersey, Hawaii, Illinois, Washington, Florida, Virginia, and Pennsylvania (1).By 2060, the Asian American population is projected to more than double to 34.4 million, with its share of the U.S. population climbing from 5.1 to 8.2% in the same period (2).
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In the Asian population, the prevalence of diabetes and cardiovascular disease is higher at a lower BMI than in the non-Asian population.
“In the Asian population the prevalence of diabetes and cardiovascular disease is higher at a lower BMI than in the non-Asian population.”
From 2022 American Society of Metabolic and Bariatric Surgery (ASMBS) and International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) Indications for Metabolic and Bariatric Surgery (2023) · cites this paper as [41]
The ‘Overweight/Obesity and Type 2 Diabetes Risk for Asian Americans’ section says diabetes risk in Asians shifts to lower BMI values and diabetes prevalence is higher than in whites at similar BMI levels. The paper does not establish the claimed higher prevalence of cardiovascular disease at lower BMI in Asians than in non-Asians; its cardiovascular references and general discussion of obesity-related disorders do not make that comparison. (LLM, conf=0.88)
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From race-based to race-conscious medicine: how anti-racist uprisings call us to act
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2. Classification and Diagnosis of Diabetes
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“Screening” (26) suggest that the BMI cut point should be lower for the Asian American population.”
BackgroundThe CardioMetabolic Health Alliance
Sperling, L. S., Mechanick, J. I., Neeland, I. J., Herrick, C. J., Després, J.-P., Ndumele, C. E., Vijayaraghavan, K., Handelsman, Y., Puckrein, G. A., Araneta, M. R. G., et al. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.06.1328 · Cited by 294 · see citing works
“First, the correlation among BMI, waist circumference, and VAT is highly variable among different racial groups, prompting the American Diabetes Association and the International Diabetes Federation to define different cutoffs for abnormal BMI and waist circumference, respectively, in Asian populations (49,50).”
BackgroundSelf-citationMisra, A. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0007 · Cited by 292 · see citing works
“Although lower cut-points of BMI for defining overweight/obesity have been used ‘‘unofficially’’ by some hospital/diabetes centers in the United States for some time now, a review of recent studies conducted on Asian Americans by Hsu et al.(35) showed occurrence of T2DM at a relatively lower BMI cut-points.”
BackgroundResultInfluentialLiu, B., Du, Y., Wu, Y., Snetselaar, L. G., Wallace, R. B., & Bao, W. (2021) · BMJ · doi:10.1136/bmj.n365 · Cited by 313 · see citing works
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“Individuals were eligible if they were aged between 30 and 69 years with a BMI of at least 27 kg/m(2) (BMI >25 kg/m(2) for Asian participants) [20], spoke English, were not taking diabetes medications, had access to email and Internet, and had either fasting glucose or HbA1c in the prediabetes range (glucose: 5.”
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“Based on these and other publications from around the world [15, 16], a definition for obesity specific to the Japanese population was proposed as BMI ≥ 25 kg/m(2) [17], which differed from that for Europeans (BMI ≥ 30 kg/m(2)).”
BackgroundRacial/Ethnic Differences in Gestational Diabetes Prevalence and Contribution of Common Risk Factors
Pu, J., Zhao, B.-N., Wang, E., Nimbal, V. C., Osmundson, S., Kunz, L., Popat, R., Chung, S., & Palaniappan, L. (2015) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.12209 · Cited by 161 · see citing works
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Diabetes mellitus and hearing loss: A review
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Epidemiology of Obesity and Associated Comorbidities
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“As a result, the American Diabetes Association recently lowered the BMI from 25 to 23 kg/m(2) at which they recommend screening Asian Americans for type 2 diabetes [7].”
BackgroundFukuda, T., Hamaguchi, M., Kojima, T., Hashimoto, Y., Ohbora, A., Kato, T., Nakamura, N., & Fukui, M. (2015) · Liver International · doi:10.1111/liv.12912 · Cited by 156 · see citing works
“(27) also recommended a cut-off point of BMI 23 for the Asian American population, because this population was susceptible to overweight on incident T2DM.”
BackgroundBoutari, C., DeMarsilis, A. J., & Mantzoros, C. S. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110773 · Cited by 170 · see citing works
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“Total body fat is widely variable and correlates to body weight, height, and sex [48,49,51,52] Visceral and/or android fat directly correlate to adiposopathic metabolic diseases (e.g., diabetes mellitus, hypertension, dyslipidemia, cardiovascular disease, and cancer), perhaps more so than waist…”
BackgroundMehta, L. S., Velarde, G., Lewey, J., Sharma, G. V., Bond, R. M., Navas‐Acién, A., Fretts, A. M., Magwood, G., Yang, E., Blumenthal, R. S., et al. (2023) · Circulation · doi:10.1161/cir.0000000000001139 · Cited by 193 · see citing works
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“Systolic blood pressure, mean (SD), mm Hg 128 (20) 132 (20) 138 (21)”
Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes
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Body fat, metabolic syndrome and hyperglycemia in South Asians.
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Improving diversity in medical research
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Malnutrition in obesity before and after bariatric surgery.
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Dapagliflozin Reduces Fat Mass without Affecting Muscle Mass in Type 2 Diabetes
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“However, BMI as a risk factor for developing T2DM has been shown to vary between ethnic groups(40) and the BMI threshold for T2DM screening of American Asians has been modified to 23 kg/m(2) by the American Diabetes Association.(41) Although the percentage of individuals covered by insurance was significantly lower in…”
BackgroundFei, K., Rodriguez-Lopez, J., Ramos, M., Islam, N., Trinh-Shevrin, C., Yi, S. S., Chernov, C., Perlman, S., & Thorpe, L. (2017) · Preventing Chronic Disease · doi:10.5888/pcd14.160478 · Cited by 91 · see citing works
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“Asians are known to develop diabetes at lower BMI levels compared to other racial and ethnic groups (Hsu et al. 2015).”
BackgroundMetabolic Dysfunction, Obesity, and Survival Among Patients With Early-Stage Colorectal Cancer
Feliciano, E. M. C., Kroenke, C. H., Meyerhardt, J. A., Prado, C. M. M., Bradshaw, P. T., Dannenberg, A. J., Kwan, M. L., Xiao, J., Quesenberry, C. P., Weltzien, E. K., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2016.67.4473 · Cited by 78 · see citing works
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Perspective on Diagnostic Criteria for Obesity and Abdominal Obesity in Korean Adults
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New research directions on disparities in obesity and type 2 diabetes
Thornton, P. L., Kumanyika, S., Gregg, E. W., Araneta, M. R. G., Baskin, M. L., Chin, M. H., Crespo, C. J., de Groot, M., Garcia, D. O., Haire-Joshu, D. L., et al. (2019) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14270 · Cited by 73 · see citing works
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“Adjusting for all covariates in our full model (age, gender, and the social determinants of educational, marital, poverty, and health insurance status), full model odds for obesity were significantly higher (P 0.05) for all minority groups, excluding Asian Americans, than for non-Hispanic white adults.”
BackgroundInfluentialLiu, Y., Andersen, S. W., Wen, W., Gao, Y.-T., Lan, Q., Rothman, N., Ji, B., Yang, G., Xiang, Y.-B., Shu, X., et al. (2016) · International Journal of Cancer · doi:10.1002/ijc.30187 · Cited by 65 · see citing works
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Ethnic Differences in Iron Status
Kang, W., Barad, A., Clark, A. G., Wang, Y., Xu, L., Gu, Z., & O'Brien, K. O. (2021) · Advances in Nutrition · doi:10.1093/advances/nmab035 · Cited by 73 · see citing works
Lim, S. L., Ong, K. W., Johal, J., Han, C. Y., Yap, Q. V., Chan, Y. H., Zhang, Z. P., Chandra, C. C., Thiagarajah, A. G., & Khoo, C. M. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2021.780567 · Cited by 61 · see citing works
“It is well-established that Asian populations have a higher risk of developing diabetes at a lower body mass index (BMI) due to the higher predisposition to storing visceral fat than Caucasians (6).”
BackgroundRacial and Ethnic Differences in Anthropometric Measures as Risk Factors for Diabetes
Luo, J.-H., Hendryx, M., Laddu, D., Phillips, L., Chlebowski, R., Leblanc, E. S., Allison, D., Nelson, D., Li, Y., Rosal, M., et al. (2018) · Diabetes Care · doi:10.2337/dc18-1413 · Cited by 60 · see citing works
“© 2018 by the American Diabetes Association.”
ResultInfluentialEthnic Differences in Risk Factors for Obesity among Adults in California, the United States
Wang, L., Southerland, J. L., Wang, K., Bailey, B. A., Alamian, A., Stevens, M., & Wang, Y. (2017) · Journal of Obesity · doi:10.1155/2017/2427483 · Cited by 69 · see citing works
“Due to small sample sizes for Pacific Islander (𝑛 = 73, 0.17%), American Indian/Alaskan native (𝑛 = 636, 1.48%), and other single/multiple racial groups (𝑛 = 3,065; 7.14%), we combined them as “other.””
InfluentialA Review of Cutoffs for Nutritional Biomarkers.
Raghavan, R., Sakr-Ashour, F. A., & Bailey, R. L. (2016) · Advances in Nutrition · doi:10.3945/an.115.009951 · Cited by 59 · see citing works
“, their risk of health conditions such as diabetes increases at a much lower BMI compared with non-Hispanic whites (45)].”
BackgroundLifestyle and socioeconomic determinants of diabetes: Evidence from country-level data
Richards, S. E., Wijeweera, C., & Wijeweera, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270476 · Cited by 61 · see citing works
“Current policy recommendations encourage setting a threshold BMI of� 23 kg/m2 to denote “overweight” in Chinese Asians [38].”
BackgroundInfluentialTakeno, K., Tamura, Y., Kawaguchi, M., Kakehi, S., Watanabe, T., Funayama, T., Furukawa, Y., Kaga, H., Yamamoto, R., Kim, M., et al. (2016) · Journal of Clinical Endocrinology and Metabolism · doi:10.1210/jc.2016-1650 · Cited by 58 · see citing works
Yu, H., Ho, M. M., Liu, X., Yang, J., Chau, P. H., & Fong, D. (2022) · International Journal of Obesity · doi:10.1038/s41366-022-01096-1 · Cited by 64 · see citing works
“Notably, the Asian population had a much lower threshold of BMI for similar T2DM risk compared to the non-Asian population [20, 21].”
ResultAgbim, U., Carr, R., Pickett-Blakely, O., & Dagogo-Jack, S. (2019) · Current Obesity Reports · doi:10.1007/s13679-019-00349-x · Cited by 56 · see citing works
Bariatric/Metabolic Surgery in the Asia-Pacific Region: APMBSS 2018 Survey
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“Further, Asian Americans are at greater risk of developing type 2 diabetes at a lower BMI (in kg/m 2 ) compared with other groups [17].”
Amin, F., Fatima, S. S., Islam, N., & Gilani, A. H. (2015) · BMC Obesity · doi:10.1186/s40608-015-0044-6 · Cited by 46 · see citing works
“Therefore, using WC or BMI alone to classify individuals according to fatness may result in misclassification because of the varying contributions of body composition [10-12].”
MethodDiabetes Diagnosis and Control: Missed Opportunities to Improve Health
Cowie, C. C. (2019) · Diabetes Care · doi:10.2337/dci18-0047 · Cited by 63 · see citing works
“to consider diabetes testing for all Asian Americans with a BMI $23 kg/m(2) (8).”
BackgroundHruschka, D. J., & Hadley, C. (2016) · Obesity Reviews · doi:10.1111/obr.12449 · Cited by 56 · see citing works
Body Mass Index and Mortality in a Very Large Cohort: Is It Really Healthier to Be Overweight?
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Renal UTX-PHGDH-serine axis regulates metabolic disorders in the kidney and liver
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“Using IDEO, we recruited both lean and obese W and EA individuals 565 into this study based on World Health Organization cut-offs: W/EA BMI≤24.9 kg/m 2 (lean); W 566 BMI≥30 kg/m 2 (obese); and EA BMI≥27.5 kg/m 2 (obese) (Hsu et al., 2015; Jih et al., 2014; 567 WHO Expert Consultation, 2004).”
MethodInfluentialBouchi, R., Kondo, T., Ohta, Y., Goto, A., Tanaka, D., Satoh, H., Yabe, D., Nishimura, R., Harada, N., Kamiya, H., et al. (2022) · Journal of Diabetes Investigation · doi:10.1111/jdi.13960 · Cited by 49 · see citing works
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“Using more conservative BMI cutoff points suggested for Asians (28) did not meaningfully change the HRs for breast cancer, but increased the proportion of overweight/obese women which slightly increased the PARPS.”
BackgroundMethodInfluentialBody Composition and Diabetes Risk in South Asians: Findings From the MASALA and MESA Studies
Flowers, E., Lin, F., Kandula, N., Allison, M., Carr, J., Ding, J.-Z., Shah, R. V., Liu, K., Herrington, D., & Kanaya, A. (2019) · Diabetes Care · doi:10.2337/dc18-1510 · Cited by 49 · see citing works
“Measuresof ectopic fat regions from computed tomography (CT) scans have demonstrated stronger associations with metabolic risk when comparedwithbodyweight or BMI in Asians (10,11,13).”
BackgroundSelf-citationJia, G., Shu, X., Liu, Y., Li, H.-L., Cai, H., Gao, J., Gao, Y.-T., Wen, W., Xiang, Y.-B., & Zheng, W. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.17371 · Cited by 48 · see citing works
The “A to Z” of Managing Type 2 Diabetes in Culturally Diverse Populations
Caballero, A. E. (2018) · Frontiers in Endocrinology · doi:10.3389/fendo.2018.00479 · Cited by 71 · see citing works
“Particular attention must be paid to Asian patients who may not be considered overweight or obese according to traditional standards but who tend to accumulate high levels of intra-abdominal or visceral fat leading to increased insulin resistance (34).”
BackgroundYang, J., Park, K., & Cho, S. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.002405 · Cited by 48 · see citing works
“In addition, a significantly higher incidence of undiagnosed diabetes in Asians (48) suggests that a treatment strategy targeting populations genetically at risk for developing obesity-associated metabolic dysfunction may be beneficial.”
BackgroundInfluentialAggarwal, R., Bibbins-Domingo, K., Yeh, R., Song, Y., Chiu, N., Wadhera, R., Shen, C.-Y., & Kazi, D. S. (2022) · Annals of Internal Medicine · doi:10.7326/m20-8079 · Cited by 47 · see citing works
Becerra, M. B., & Becerra, B. J. (2015) · Preventing Chronic Disease · doi:10.5888/pcd12.150006 · Cited by 47 · see citing works
Diabetes and Cardiometabolic Risk in South Asian Youth: A Review
Wolf, R., Nagpal, M., & Magge, S. (2020) · Pediatric Diabetes · doi:10.1111/pedi.13078 · Cited by 46 · see citing works
1. Strategies for Improving Care
American Diabetes Association (2015) · Diabetes Care · doi:10.2337/dc16-s004 · Cited by 108 · see citing works
“Recent studies have recommended lowering the BMI cut point for testing for Asian Americans to $23 kg/m(2) (44).”
BackgroundSocial, cultural and ethnic determinants of obesity: From pathogenesis to treatment.
Capoccia, D., Milani, I., Colangeli, L., Parrotta, M. E., Leonetti, F., & Guglielmi, V. (2025) · NMCD. Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2025.103901 · Cited by 45 · see citing works
Epidemiology of diabetes among South Asians in the United States: lessons from the MASALA study
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“Chinese patients were the oldest among Asian patients (median [IQR] age, 53 [38-64]) and among the oldest of all racial and ethnic groups.”
BackgroundImpact of risk factors for gestational diabetes (GDM) on pregnancy outcomes in women with GDM
Filardi, T., Tavaglione, F., Stasio, M., Fazio, V., Lenzi, A., & Morano, S. (2018) · Journal of Endocrinological Investigation · doi:10.1007/s40618-017-0791-y · Cited by 43 · see citing works
“The WHO and the ADA have recommended the use of lower body mass index (BMI) cut-off points for Asians (< 23 kg/m2 for normal weight) as the lower BMI cutoffs better identify health risks for T2D and cardiovascular disease among Asians [31, 32].”
InfluentialConroy, S., Shariff-Marco, S., Yang, J., Hertz, A., Cockburn, M., Shvetsov, Y. B., Clarke, C., Abright, C. L., Haiman, C., Marchand, L. L., et al. (2017) · Cancer Causes and Control · doi:10.1007/s10552-017-0980-1 · Cited by 44 · see citing works
Kalra, D. K., Vijayaraghavan, K., Sikand, G., Desai, N. R., Joshi, P. H., Mehta, A., Karmally, W., Vani, A. K., Sitafalwalla, S. J., Puri, R., et al. (2021) · Journal of clinical lipidology · doi:10.1016/j.jacl.2021.03.007 · Cited by 48 · see citing works
Tek, C. (2016) · Patient Preference and Adherence · doi:10.2147/ppa.s84778 · Cited by 40 · see citing works
“Also, for people of Asian ethnic origin, BMI cutoffs should be lowered by 2 or 3 points.(26,27) Furthermore, this writer is of the opinion that healthy overweight people with a BMI over 27 should be treated if their weight over the past few years has been consistently trending up.”
- Authors
- Hsu, W. C., Araneta, M. R. G., Kanaya, A. M., Chiang, J. L., & Fujimoto, W.
- Affiliations
- Joslin Diabetes Center (US)
- University of California San Diego (US)
- University of California, San Francisco (US)
- American Diabetes Association (US)
- University of Washington (US)
- Corresponding author
- Hsu, William C.
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2014-12-13
- Volume
- 38
- Issue
- 1
- Pages
- 150-158
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC4392932
- OpenAlex
- W2138127318
- Cited by
- 500
- References
- 60
- Citations by year
- 2026: 23 · 2025: 20 · 2024: 36 · 2023: 40 · 2022: 50 · 2021: 62 · 2020: 48 · 2019: 52 · 2018: 45 · 2017: 52 · 2016: 37 · 2015: 33 · 2014: 1
- FWCI
- 6.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- Diabetes Action Research and Education Foundation
- World Health Organization
- Women's Health Initiative
- National Heart, Lung, and Blood Institute (K24HL112827, R01HL093009)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063720)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- Asian Americans, type 2 diabetes, diabetes screening, body mass index, vulnerable populations, ethnic diversity, population growth
- MeSH terms
- Adult, Aged, Asian, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Obesity, Reference Values, Risk Factors, Body Mass Index, Prevalence, Overweight, Waist Circumference
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 ›
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