Abstract
OBJECTIVE
To examine the association between race/ethnicity and type 2 diabetes risk in women and assess the interaction between race/ethnicity and BMI.
RESEARCH DESIGN AND METHODS
We analyzed individual-level data from 730,408 women across 15 cohort studies. Six racial/ethnic groups were identified: White, Chinese, Japanese, South/Southeast Asian, Black, and mixed/other. Cox proportional hazards models with study as a random effect were used to estimate hazard ratios (HRs) for type 2 diabetes associated with race/ethnicity. The joint association of race/ethnicity and BMI was assessed using BMI categories incorporating Asian-specific cutoffs (<18.5, 18.5–22.9, 23.0–24.9, 25.0–27.4, 27.5–29.9, and ≥30 kg/m2), with White women having a BMI of 18.5–22.9 kg/m2 as the reference.
RESULTS
Overall, 37,329 women (5.1%) were diagnosed with type 2 diabetes. By age 70, the cumulative incidence was highest among South/Southeast Asian (24.6%) and Black women (23.6%), with baseline obesity rates of 40.0% (BMI ≥27.5 kg/m2) and 45.6% (BMI ≥30 kg/m2), respectively. After adjusting for BMI, South/Southeast Asian women had the highest diabetes risk compared with White women (HR 4.13, 95% CI 3.78–4.51), while other racial/ethnic groups had about twice the risk. Joint effect analysis showed South/Southeast Asian women with a BMI ≥23 kg/m2 had a substantially greater diabetes risk than other racial/ethnic groups with the same BMI, especially those with BMI 27.5–29.9 kg/m2 (HR 23.17, 19.21–27.95) and ≥30 kg/m2(HR 35.52, 30.57–41.28).
CONCLUSIONS
South/Southeast Asian women have a markedly elevated risk of type 2 diabetes, further amplified by modestly higher BMI, highlighting the need for ethnicity-specific diabetes prevention strategies for women.
Graphical Abstract
Introduction
Diabetes is a critical global health challenge, with its prevalence reaching alarming levels worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated that 529 million people were living with diabetes in 2021, a figure projected to rise to 1.31 billion by 2050 (1). The global age-standardized prevalence of diabetes was 6.1% in 2021 (1), but substantial variations exist across regions and countries, particularly among diverse racial and ethnic groups (2,3). Studies conducted in the U.S., U.K., and Europe have reported higher diabetes prevalence among racial/ethnic minority groups, including Asian, Black, Hispanic, and mixed populations, compared with White populations (2–7). Only one U.S. cohort study has specifically examined racial/ethnic differences in diabetes among women, highlighting that increasing BMI and greater weight gain are particularly detrimental for Asian women (4). However, population-based evidence remains limited for specific Asian subgroups, such as South Asian, Chinese, and Japanese women, as well as for comparisons between Asian women residing in Asia and those living in Western countries (8,9).
Although the global prevalence of diabetes is slightly lower in women than in men (10.2% vs. 10.8%) (10), women often face a greater burden of risk factors at diagnosis, with obesity being particularly prominent (11). Across their lifespan, women experience greater hormone fluctuations and weight changes than men, largely due to reproductive events such as pregnancy and menopause (11). Adverse reproductive outcomes may disproportionately increase the risk of type 2 diabetes among certain racial/ethnic groups (12–14). These racial/ethnic disparities likely reflect the interplay of genetic, social, and environmental factors, with obesity playing a key role (15). Previous studies have demonstrated that non-White populations experience a greater burden of diabetes at lower BMI thresholds compared with White populations (16–20). However, the interaction of race/ethnicity and BMI on diabetes risk remains unclear (additive or multiplicative), particularly in women.
The International Collaboration Approach to Reproductive Health and Chronic Disease Events (InterLACE) consortium pools individual-level data from multiple cohort studies of women. Using data from 15 cohorts, we aimed to examine the association between race/ethnicity and type 2 diabetes risk in women, including three Asian subgroups, and to compare the risk between Asian women living in their countries of origin and those living in Western countries. We further investigated the interaction (joint effect) between race/ethnicity and BMI in relation to diabetes risk.
Research Design and Methods
Ethics
Ethical approval was obtained from the Institutional Review Board or Human Research Ethics Committee at each participating institution, and all participants provided informed consent. InterLACE used nonidentifiable data from existing studies. The InterLACE project had been reviewed by the Research Ethics and Integrity, The University of Queensland, and was deemed to be exempt from ethics review under the National Statement on Ethical Conduct in Human Research (2024/HE000390).
Study Design and Participants
The data for this study were obtained from InterLACE, an ongoing women’s health consortium integrating 27 observational studies on women’s reproductive health and chronic disease. The study design and data harmonization have been previously published (21). For this analysis, we excluded 10 studies that did not collect data on diabetes or diagnosis age and 2 studies with participants who were significantly younger (all born in 1970 or later). The sample included individual-level data from 15 studies (n = 760,478) across seven countries, with information on race/ethnicity, BMI, and type 2 diabetes. These studies included the Australian Longitudinal Study on Women’s Health (ALSWH), Melbourne Collaborative Cohort Study (MCCS), Healthy Ageing of Women Study (HOW), Medical Research Council National Survey of Health and Development (NSHD), National Child Development Study (NCDS), English Longitudinal Study of Aging (ELSA), Whitehall II, Southall and Brent Revisited (SABRE), UK Biobank, Women’s Lifestyle and Health Study (WLHS), Prospect cohort of the European Prospective Investigation Into Cancer and Nutrition (Prospect-EPIC), Study of Women’s Health Across the Nation (SWAN), Seattle Middle Women’s Health Study (SMWHS), Japanese Nurses’ Health Study (JNHS), and China Kadoorie Biobank. Study characteristics are summarized in Supplementary Table 1.
Women were excluded from the study if there was insufficient information to define diabetes (n = 8,943), they had been diagnosed with type 1 diabetes, reported a diagnosis age younger than 30 years (n = 3,793), or were younger than 30 years at the last follow-up (n = 294). Women with missing data on key variables, including race/ethnicity, cohort entry age, BMI, birth year, education level, and smoking status, were also excluded (n = 17,040). The analytic sample comprised 730,408 women with complete data.
Exposure and Outcome Variables
Race/ethnicity was self-identified in eight studies (MCCS, NCDS, ELSA, Whitehall II, SABRE, UK Biobank, SWAN, and SMWHS), accounting for 310,330 participants (42.5% of the sample). Three studies (Prospect-EPIC [22], JNHS [23], and China Biobank [24]) did not collect data on race/ethnicity due to the homogeneity of their source populations at the time of recruitment, where >95% of participants identified as Dutch, Japanese, or Chinese, respectively (n = 359,540 [49.2%]). The remaining four studies (ALSWH, HOW, NSHD, and WLHS) relied on proxy indicators, such as country of birth, country of childhood residency, or language spoken at home, to infer ethnicity (n = 60,538 [8.3%]). For the analysis, race/ethnicity was categorized into six groups: White, Chinese, Japanese, South/Southeast Asian, Black (including African American, Caribbean, and other Black identities), and mixed/other (including a small sample of Hispanic/Latino, Middle Eastern, Indigenous, or Pacific Islander).
BMI was calculated as weight (in kilograms) divided by the square of height (in meters), based on data collected at cohort entry (or by participants’ early 40s in birth cohorts), which served as the study baseline. Weight and height were measured in eight studies (MCCS, NSHD, NCDS, ELSA, WHITEHALL, SABRE, UK Biobank, and China Biobank; n = 603,501 [82.6%]). The remaining seven studies relied on self-reported data. BMI was classified using standard World Health Organization categories, with additional cutoffs at 23.0 and 27.5 kg/m2 recommended for public health actions in Asian populations (25). For analysis, BMI was grouped into six categories across all racial/ethnic groups: <18.5, 18.5–22.9 (reference), 23.0–24.9, 25.0–27.4, 27.5–29.9, and ≥30 kg/m2 (25).
The primary outcome was time to onset of type 2 diabetes. Information on physician-diagnosed type 2 diabetes and/or glucose-lowering medication use (after age 30) was obtained through self-reported questionnaires across all studies. Additionally, ALSWH, UK Biobank, WLHS, and Prospect-EPIC provided administrative health data, including hospital, emergency department, and death records, where type 2 diabetes was identified using ICD-10 code E11 or ICD-9 code 250.x0 or 250.x2 (x = 0–9). In cases where subtype details were unavailable, broader diagnosis codes (ICD-10: E13, E14; ICD-9: 250 unspecified) were used, as diabetes diagnosed after age 30 was less likely to be type 1 diabetes. Women diagnosed with diabetes before age 30 were excluded. ALSWH also provided pharmaceutical data (Anatomical Therapeutic Chemical code A10: glucose-lowering therapies, including biguanides, sulfonylureas, and insulins, with the first prescription after age 30 years) and aged care records (Aged Care Assessment Program code 403: type 2 diabetes). Age at onset of type 2 diabetes was defined as the earliest recorded age of diagnosis, based on either self-report or administrative health data (available for four studies).
Covariates
We harmonized sociodemographic and lifestyle factors collected at baseline (i.e., cohort entry or early 40s for birth cohorts) as potential confounding factors for racial/ethnic differences. These included baseline age (continuous), birth year (<1940, 1940–1949, 1950–1959, or ≥1960), education level (no formal qualification, year 10, year 12, trade/other, or university degree), and smoking status (never, past, or current smoker).
Statistical Analyses
We analyzed pooled individual-level data from all cohorts. Participants contributed person-years from 30 years of age until diagnosis of type 2 diabetes, loss to follow-up, death, or end of follow-up, whichever occurred first. The cumulative incidence (%) between ages 30 and 70 years was estimated by race/ethnicity using the Kaplan-Meier method.
Because most cohorts recruited participants in midlife (ages 40–60 years), a substantial proportion of women with type 2 diabetes had been diagnosed prior to baseline (18,423 of 37,329 [49.4%]). These women were excluded from the following perspective analyses, leaving 711,987 participants. Incidence rates (per 1,000 person-years) after baseline were calculated by race/ethnicity. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for the association between race/ethnicity and diabetes risk, with White women as the reference group. Study-level variability was included in the model as a random effect. The proportional hazards assumption was assessed using log-cumulative hazard plots, with no violations detected. Models were sequentially adjusted for baseline age (model 1), birth year, education level, and smoking status (model 2), with further adjustment for BMI (model 3). For Chinese and Japanese women, we conducted separate Cox models to compare diabetes risk between those residing in their countries of origin and those living in Western countries.
To assess whether the joint effect of race/ethnicity and BMI was additive or multiplicative, we included an interaction term in the Cox model. Where there was strong evidence of interaction, we generated a combined variable with 36 categories (6 race/ethnicity groups × 6 BMI groups) to aid the interpretation of the joint effect, using White women with a BMI of 18.5–22.9 kg/m2 as the reference.
Sensitivity Analyses
First, evidence suggests that central adiposity is a stronger predictor of type 2 diabetes than overall obesity, particularly among women (26). We substituted BMI with waist circumference as the measure of adiposity. Eight cohorts (MCCS, NSHD, ELSA, UK Biobank, Prospect-EPIC, SWAN, JNHS, and China Biobank) that collected waist circumference data at baseline were included in this analysis (n = 638,580). Waist circumference was categorized using an additional cutoff recommended for Asian women: <80 (reference), 80–87, and ≥88 cm (26,27). Second, given nearly half of diabetes cases were diagnosed prior to baseline and excluded from the main analyses, we conducted a sensitivity analysis including these women for comparison (n = 730,408).
Data and Resource Availability
The data sets generated for this pooled analysis are not publicly available because of the data transfer agreements or restrictions under license for the current study. However, data from some studies can be accessed by submitting an application (e.g., ALSWH [https://alswh.org.au/for-data-users/applying-for-data/] and UK Biobank [https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access]).
Results
Study Characteristics
Overall, data from 730,408 women from 15 studies across seven countries were included (Supplementary Table 1). Six racial/ethnic groups were identified: White (n = 370,937 [50.8%]), Chinese (n = 297,550 [40.7%]), Japanese (n = 46,759 [6.4%]), South/Southeast Asian (n = 5,162; [0.7%]), Black (n = 6,430 [0.9%]), and mixed/other (n = 3,570 [0.5%]). More than 98% of the Chinese and Japanese participants were derived from cohorts based in their respective countries, specifically the China Kadoorie Biobank and the JNHS), with only 493 Japanese (1.1%) and 1,242 Chinese women (0.4%) living in Western countries. In contrast, other non-White groups were represented in studies conducted in Western countries. Table 1 presents the baseline characteristics stratified by race/ethnicity. The median age at baseline was 51 years (interquartile range 44–60). Overall, 37,329 (5.1%) women had been diagnosed with type 2 diabetes, with half (49.4%) of the women being diagnosed prior to baseline and half (52.2%) identified through administrative records. The proportion of women living with obesity (BMI ≥30 kg/m2) was highest among Black women (45.6%), followed by mixed/other (25.0%) and South/Southeast Asian women (23.0%). In contrast, obesity rates were notably lower among Chinese (4.7%) and Japanese women (1.9%). When applying the Asian-specific cutoff (BMI ≥27.5 kg/m2), the prevalence of obesity increased to 40.0% among South/Southeast Asians, 14.1% among Chinese, and 5.0% among Japanese.
Table 1.
Baseline characteristics based on race/ethnicity (n = 730,408)
| Baseline characteristics | Overall | White | Chinese | Japanese | South/Southeast Asian | Black | Mixed/other |
|---|---|---|---|---|---|---|---|
| Women, n | 730,408 | 370,937 | 297,550 | 46,759 | 5,162 | 6,430 | 3,570 |
| Age at baseline (years) | 51 (44–60) | 54 (46–62) | 50 (42–58) | 41 (35–48) | 52 (46–59) | 50 (45–56) | 49 (45–55) |
| Birth year | |||||||
| Born before 1940 | 67,936 (9.3) | 37,217 (10.0) | 29,975 (10.1) | 117 (0.3) | 132 (2.6) | 295 (4.6) | 200 (5.6) |
| Born 1940–1949 | 228,638 (31.3) | 158,785 (42.8) | 60,617 (20.4) | 5,053 (10.8) | 1,559 (30.2) | 1,552 (24.1) | 1,072 (30.0) |
| Born 1950–1959 | 236,298 (32.4) | 119,134 (32.1) | 95,844 (32.2) | 15,757 (33.7) | 1,866 (36.1) | 2,483 (38.6) | 1,214 (34.0) |
| Born 1960 or later | 197,536 (27.0) | 55,801 (15.0) | 111,114 (37.3) | 25,832 (55.2) | 1,605 (31.1) | 2,100 (32.7) | 1,084 (30.4) |
| Education level | |||||||
| No formal qualification | 96,827 (13.3) | 21,283 (5.7) | 74,687 (25.1) | 3 (0.0) | 342 (6.6) | 223 (3.5) | 289 (8.1) |
| Year 10 | 334,315 (45.8) | 160,274 (43.2) | 168,549 (56.6) | 16 (0.0) | 1,813 (35.1) | 2,489 (38.7) | 1,174 (32.9) |
| Year 12 | 100,682 (13.8) | 57,977 (15.6) | 40,314 (13.5) | 415 (0.9) | 689 (13.3) | 760 (11.8) | 527 (14.8) |
| Trade/diploma/other | 87,769 (12.0) | 32,383 (8.7) | 8,870 (3.0) | 44,538 (95.3) | 533 (10.3) | 1,068 (16.6) | 377 (10.6) |
| University degree | 110,815 (15.2) | 99,020 (26.7) | 5,130 (1.7) | 1,787 (3.8) | 1,785 (34.6) | 1,890 (29.4) | 1,203 (33.7) |
| Smoking status | |||||||
| Never | 533,589 (73.1) | 206,249 (55.6) | 282,569 (95.0) | 33,107 (70.8) | 4,678 (90.6) | 4,819 (74.9) | 2,167 (60.7) |
| Past | 130,113 (17.8) | 119,799 (32.3) | 2,624 (0.9) | 5,608 (12.0) | 302 (5.9) | 900 (14.0) | 880 (24.6) |
| Current | 66,706 (9.1) | 44,889 (12.1) | 12,357 (4.2) | 8044 (17.2) | 182 (3.5) | 711 (11.1) | 523 (14.6) |
| BMI (kg/m2) | |||||||
| <18.5 | 20,812 (2.8) | 3,571 (1.0) | 12,808 (4.3) | 4,327 (9.3) | 55 (1.1) | 20 (0.3) | 31 (0.9) |
| 18.5–22.9 | 239,182 (32.7) | 91,778 (24.7) | 116,233 (39.1) | 29,016 (62.1) | 929 (18.0) | 524 (8.1) | 702 (19.7) |
| 23.0–24.9 | 150,313 (20.6) | 72,889 (19.7) | 67,839 (22.8) | 7,322 (15.7) | 914 (17.7) | 685 (10.7) | 664 (18.6) |
| 25.0–27.4 | 142,028 (19.4) | 76,525 (20.6) | 58,772 (19.8) | 3,754 (8.0) | 1,203 (23.3) | 1,060 (16.5) | 714 (20.0) |
| 27.5–29.9 | 83,387 (11.4) | 51,451 (13.9) | 27,823 (9.4) | 1,463 (3.1) | 876 (17.0) | 1,207 (18.8) | 567 (15.9) |
| ≥30 | 94,686 (13.0) | 74,723 (20.1) | 14,075 (4.7) | 877 (1.9) | 1,185 (23.0) | 2,934 (45.6) | 892 (25.0) |
| Mean ± SD | 25.1 ± 4.6 | 26.4 ± 5.0 | 23.8 ± 3.4 | 21.8 ± 3.0 | 27.0 ± 4.9 | 30.3 ± 6.1 | 27.3 ± 5.4 |
| Type 2 diabetes | |||||||
| No | 693,079 (94.9) | 348,058 (93.8) | 287,047 (96.5) | 45,353 (97.0) | 4,124 (79.9) | 5,337 (83.0) | 3,160 (88.5) |
| Yes | 37,329 (5.1) | 22,879 (6.2) | 10,503 (3.5) | 1,406 (3.0) | 1,038 (20.1) | 1,093 (17.0) | 410 (11.5) |
| Identified at baseline | 18,423 (49.4) | 7,618 (33.3) | 9,222 (87.8) | 533 (37.9) | 492 (47.4) | 433 (39.6) | 125 (30.5) |
| Identified by linked records | 19,495 (52.2) | 17,503 (76.5) | 57 (0.5) | 4 (0.3) | 890 (85.7) | 776 (71.0) | 265 (64.6) |
Data are presented as median (interquartile range) or n (%), unless indicated otherwise.
Race/Ethnicity and Risk of Type 2 Diabetes
Figure 1 illustrates the cumulative incidence between ages 30 and 70 years across racial/ethnic groups. By age 70, nearly one in four South/Southeast Asian and Black women had diabetes, compared with 7% of White women (Supplementary Table 2).
Figure 1.
Cumulative incidence (proportion) of type 2 diabetes between ages 30 and 70 years across racial/ethnic groups (n = 730,408).
For the prospective analyses, we excluded women with type 2 diabetes at baseline (n = 711,987) (Table 2). During follow-up, the incidence of type 2 diabetes was highest among South/Southeast Asian and Black women at 3.4 per 1,000 person-years compared with 1.1 per 1,000 person-years among White women. In the multivariable-adjusted model excluding BMI (model 2), South/Southeast Asian (HR 3.96, 95% CI 3.63–4.33) and Black women (HR 3.80, 3.49–4.15) had nearly a fourfold increased diabetes risk compared with White women. Mixed/other (HR 2.33, 2.07–2.63) and Chinese women (HR 1.43, 1.11–1.85) also had an elevated risk of diabetes, whereas no clear evidence was observed for Japanese women (HR 1.13, 0.75–1.69). When additionally adjusted for BMI (model 3), the fourfold increased risk for South/Southeast Asian women remained unchanged (HR 4.13, 3.78–4.51. However, the risk for Black women was attenuated, decreasing from an HR of 3.80 (3.49–4.15) to 2.61 (2.40–2.85), suggesting that high BMI partially explained the association. Conversely, the diabetes risk for Chinese (HR 2.77, 2.14–3.58) and Japanese women (HR 2.29, 1.53–3.45) was more than doubled after accounting for their lower BMI levels. Furthermore, Chinese and Japanese women living in Western countries seemed to have a lower diabetes risk compared with those residing in their countries of origin, although the 95% CIs were wide due to the small sample size (Supplementary Table 3).
Table 2.
The association between race/ethnicity and risk of incident type 2 diabetes (n = 711,985)*
| Race/ethnicity | Women, n | Person- years | Women with incident diabetes, n | IR/1,000 person-years | BMI at baseline (kg/m2), mean ± SD | Model 1, HR (95% CI) | Model 2, HR (95% CI) | Model 3, HR (95% CI) |
|---|---|---|---|---|---|---|---|---|
| White | 363,319 | 13,627,841 | 15,261 | 1.1 | 26.3 ± 4.9 | Reference | Reference | Reference |
| Chinese | 288,328 | 6,118,740 | 1,281 | 0.2$ | 23.7 ± 3.4 | 1.27 (0.98–1.63) | 1.43 (1.11–1.85) | 2.77 (2.14–3.58) |
| Japanese | 46,226 | 713,285 | 873 | 1.2 | 21.8 ± 3.0 | 0.99 (0.67–1.47) | 1.13 (0.75–1.69) | 2.29 (1.53–3.45) |
| South/Southeast Asian | 4,670 | 161,450 | 546 | 3.4 | 26.8 ± 4.7 | 3.49 (3.20–3.81) | 3.96 (3.63–4.33) | 4.13 (3.78–4.51) |
| Black | 5,997 | 193,417 | 660 | 3.4 | 30.1 ± 6.0 | 3.75 (3.44–4.09) | 3.80 (3.49–4.15) | 2.61 (2.40–2.85) |
| Mixed/other | 3,445 | 112,292 | 285 | 2.5 | 27.1 ± 5.2 | 2.28 (2.02–2.58) | 2.33 (2.07–2.63) | 2.05 (1.81–2.31) |
IR, incidence rate.
Cox proportional hazards models were used to estimate HRs and 95% CIs for the incidence of type 2 diabetes, with study as a random effect. Model 1: Adjusted for baseline age. Model 2: Adjusted for baseline age, birth year, education level, and smoking status. Model 3: Adjusted for baseline age, birth year, education level, smoking status, and BMI.
*Women with type 2 diabetes at baseline (n = 18,423) were excluded. $The China Biobank provided only baseline data and limited follow-up for 5% of participants. The low incidence rate among Chinese women was attributed to the short follow-up duration in a subcohort, which was accounted for in the Cox regression model.
Joint Effect of Race/Ethnicity and BMI
There was a statistically significant interaction between race/ethnicity and BMI on type 2 diabetes risk (P < 0.001). To assess the joint effects, we compared each race/ethnicity and BMI category with the reference category of White women with a BMI of 18.5–22.9 kg/m2 within a single model. South/Southeast Asian women with a BMI ≥30 kg/m2 had an exceptionally high diabetes risk, with a 35-fold increased risk (HR 35.52, 95% CI 30.57–41.28) (Fig. 2). Black women with a BMI ≥30 kg/m2 showed a 25-fold increased risk (HR 24.15, 21.45–27.19), and Japanese (HR 19.67, 12.45–31.08) and mixed/other women (HR 15.74, 12.99–19.06) with a BMI ≥30 kg/m2 exhibited approximately 15- to 20-fold higher risks. In contrast, Chinese and White women with a BMI ≥30 kg/m2 had an ∼10-fold increased risk compared with the reference group. Importantly, South/Southeast Asian women with a BMI of 23.0–29.9 kg/m2 also had markedly higher risks than other ethnic groups with the same BMI.
Figure 2.
The joint effect between race/ethnicity and BMI on the risk of incident type 2 diabetes (T2D) (n = 711,985). The model was adjusted for baseline age, birth year, education level, and smoking status. N/A, not available.
Sensitivity Analyses
When BMI was replaced with waist circumference, the association between race/ethnicity and diabetes remained consistent, with South/Southeast Asian women showing the highest risk (HR 4.30, 95% CI 3.90–4.74), while other racial/ethnic groups had more than double the risk compared with White women (Supplementary Table 4). A significant interaction was observed between race/ethnicity and waist circumference (P < 0.001). Joint effect analysis further revealed that South/Southeast Asian women with a waist circumference of 80–87 cm (HR 13.12, 10.60–16.22) and ≥88 cm (HR 32.16, 28.26–36.59) had an exceptionally higher risk of diabetes than other racial/ethnic groups with the same waist circumference, compared with White women with a waist circumference <80 cm (Supplementary Fig. 1). When women with diabetes at baseline were included, the association between race/ethnicity and diabetes (Supplementary Table 5) and the joint effects of race/ethnicity and BMI (Supplementary Fig. 2) remained consistent.
Conclusions
This pooled analysis of 15 cohort studies demonstrated that South/Southeast Asian women had the highest risk of type 2 diabetes, with a fourfold increased risk compared with White women, even after accounting for BMI or waist circumference. Black, Chinese, Japanese, and mixed/other women had more than double the risk. Among Chinese and Japanese women, those residing in their countries of origin had a higher diabetes risk compared with their counterparts living in Western countries; however, these results were inconclusive due to the small number of migrants. Joint effect analysis showed that South/Southeast Asian women with a BMI ≥23 kg/m2 had a substantially greater diabetes risk than other racial/ethnic groups with the same BMI compared with White women with a BMI of 18.5–22.9 kg/m2. For instance, South/Southeast Asian women with a BMI ≥30 kg/m2 had a 35-fold increased risk compared with a 10-fold risk among White women with the same BMI. The results were consistent when BMI ≥30 kg/m2 was substituted with waist circumference ≥88 cm.
Our findings confirm significant racial/ethnic disparities in type 2 diabetes risk among women. These results align with those from the U.S. Nurses’ Health Study (n = 78,419 women), which demonstrated that after adjusting for age and BMI, Asian (relative risk [RR] 2.26, 95% CI, 1.70–2.99), Hispanic (RR 2.18, 1.82–2.61), and Black women (RR 1.34, 1.12–1.61) were at a higher type 2 diabetes risk compared with White women (4). Similarly, a large U.K. cross-sectional study (n = 404,318 [51% women]) found that Asian (odds ratio [OR] 2.36, 95% CI 2.26–2.47), Black (OR 1.65, 1.56–1.73), and mixed/other ethnic groups (OR 1.17, 1.08–1.27) had an elevated risk of diabetes compared with the White group, although this study did not adjust for BMI (3). Asians are generally considered a high-risk group for type 2 diabetes; however, limited population-based evidence is available for specific Asian subgroups. A systemic review and meta-analysis of ethnic minority groups in Europe (20 studies) revealed that South Asians had the highest odds of diabetes (OR 3.74, 2.74–5.12) compared with White Europeans, but data on other Asian subgroups were unavailable (7). The study also suggested the association may be stronger in women (OR 4.40, 3.06–6.31) than in men (OR 3.45, 2.52–4.71) (7). South Asians in Western countries tended to experience an earlier onset of diabetes, with a mean age at diagnosis of 46–49 years compared with 58 years in White Europeans (28,29). Studies conducted in Asian countries further indicated that Indians were at higher diabetes risk compared with Chinese and Japanese individuals (8,9), with the peak prevalence of diabetes occurring ∼10 years earlier in Indian populations (8). Our pooled analysis from 15 cohort studies showed a similar effect estimate of a fourfold increased diabetes risk for South Asian women (residing in Western countries) compared with White women. Additionally, Chinese and Japanese women residing in China and Japan may have a higher diabetes risk than their counterparts living in Western countries, suggesting that environmental, lifestyle, sociocultural factors, along with differences in health care access and screening may influence the risk.
Ethnicity-specific studies have shown that South Asian and Chinese populations reach an equivalent age- and sex-adjusted incidence of type 2 diabetes at lower BMI thresholds of 23.9 kg/m2 and 26.9 kg/m2, respectively, compared with a BMI of 30 kg/m2 in White populations (17). A key novelty of the current study is its exploration of the joint effect of race/ethnicity and BMI on diabetes risk in women. Our findings demonstrated that the diabetes risk was substantially amplified among South/Southeast Asian women with elevated BMI levels, with a nearly 10-fold increased risk for those with a BMI ≥23 kg/m2. Specifically, South/Southeast Asian women with a BMI ≥30 kg/m2 had a 35-fold increased risk of diabetes, compared with a 10-fold risk among Chinese and White women with the same BMI, relative to White women with a BMI of 18.5–22.9 kg/m2. In this study, 80% of South/Southeast Asian women had a BMI ≥23 kg/m2, and nearly 25% had a BMI ≥30 kg/m2, highlighting a substantial proportion of this population at elevated risk of diabetes.
Asians, particularly South Asians and women, tend to have a phenotype characterized by higher fat mass (especially ectopic fat), lower lean mass, and larger adipocyte size for a given BMI compared with White and Black populations (30–33). These traits may account for elevated fasting insulin levels, increased insulin resistance, and lower levels of HDL-cholesterol and adiponectin, which may increase diabetes risk (30–33). Studies suggest that South Asians consistently exhibit low bone breadth relative to length, reflecting low lean mass for their stature; however, whether this arises from genetic or epigenetic mechanisms remains unknown (34). Low skeletal muscle mass is strongly associated with poor insulin sensitivity and a higher risk of prediabetes (35). A recent review also identified sarcopenic obesity as an emerging public health challenge in the Asia-Pacific region (36). Beyond impaired insulin action due to low lean mass, South Asians may have reduced β-cell function, which limits their insulin secretion capacity and reduces their compensatory reserves when exposed to unhealthy lifestyle factors (37). Therefore, prevention strategies in Asian populations should not only address central obesity but also focus on preserving lean muscle mass through lifestyle modifications, such as regular resistance training, adequate protein intake, and balanced nutrition (36). In contrast, Black women are disproportionately affected by obesity (38), with 45% of Black women in the current study classified as having a BMI ≥30 kg/m2. The elevated risk of diabetes in Black women was largely attenuated after adjusting for BMI, indicating that living with obesity plays a substantial role.
The strengths of our study include: 1) a large number of women with type 2 diabetes from 15 cohort studies, with nearly 40% being non-White; 2) representation of diverse racial/ethnic groups, including three distinct Asian subgroups; 3) harmonization of variables across studies to ensure consistency; 4) adjustment for a range of confounding factors; and 5) the use of a robust analytical approach to explore joint effect of race/ethnicity and BMI.
However, several limitations need to be acknowledged. First, >98% of the Chinese and Japanese participants were drawn from cohorts based in their countries of origin, whereas other non-White groups were primarily represented in cohorts from Western countries. Although the numbers of Japanese, South/Southeast Asian, and Black women were considerably smaller than those of White women, the narrow and statistically significant 95% CIs for HRs support the precision of our estimates and suggest that the study had sufficient power to detect racial/ethnic differences in diabetes risk. Our findings suggest that Chinese and Japanese women residing in their countries of origin may have a higher risk of diabetes compared with their counterparts living in Western countries; however, these results should be interpreted with caution due to limited statistical power (<50%), as indicated by the wide 95% CIs. Future studies should include large samples of Asian women in Western countries to ensure adequate power. Furthermore, some of the observed racial/ethnic differences may reflect variations between studies, including sources of data and health system differences.
We accounted for study heterogeneity by modeling it as a random effect in the Cox model.
Second, although half of the diabetes cases were self-reported, data from four studies with linked health records demonstrated moderate to substantial agreement (κ = 54–72%) between administrative and self-reported data (12), supporting the reasonable validity of our findings. Additionally, the Prospect-EPIC study verified diabetes diagnosis using general practitioner and pharmacist records and reported high confirmation rates (39).
Third, BMI can change with age, particularly around menopause. However, longitudinal BMI data were not consistently collected across cohorts or survey intervals, and in some cases, diabetes onset occurred before follow-up assessments. As a result, we were limited to using baseline BMI in this pooled analysis.
Fourth, BMI cannot differentiate between visceral and subcutaneous fat, which may vary by ethnicity. We conducted a sensitivity analysis substituting BMI with waist circumference, a marker more closely associated with visceral fat. The results remained consistent, suggesting that the observed racial/ethnic differences in diabetes risk were not solely attributable to this limitation.
Finally, while obesity is a key risk factor for type 2 diabetes among women across racial/ethnic groups, unmeasured reproductive and sociocultural factors, such as socioeconomic status, diet, physical activity, stress, and access to health care, may also contribute to the observed racial/ethnic disparities in diabetes risk among women. These factors could not be examined in our analysis due to inconsistent data collection across studies.
Given the greater burden of risk factors and complications associated with type 2 diabetes in women compared with men (11), and in non-White compared with White populations, prevention strategies should be specific to women and non-White populations. Our findings suggest that Asian women, particularly South/Southeast Asians, may have unique risk profiles for diabetes, highlighting the need for targeted prevention and treatment strategies. In particular, ethnicity-specific approaches that extend beyond obesity alone, such as interventions to increase and preserve lean muscle mass and improve metabolic health, are essential for Asian women (36).
In conclusion, this study represents the largest investigation to date, demonstrating that non-White women were at a higher risk of type 2 diabetes compared with White women, with South/Southeast Asian women being particularly vulnerable. The joint effect analysis showed that South/Southeast Asian women with a BMI ≥23 kg/m2 had a markedly higher diabetes risk than other racial/ethnic groups at the same BMI levels. Early and targeted diabetes screening should prioritize South/Southeast Asian women, including those with a normal or modestly elevated BMI, to facilitate timely intervention and reduce the burden of diabetes-related complications.
This article contains supplementary material online at https://doi.org/10.2337/figshare.30369370.
Article Information
Acknowledgments. The data on which this research is based were drawn from 15 observational studies. The research included data from the Australian Longitudinal Study on Women’s Health (ALSWH), by the University of Newcastle and the University of Queensland. The authors are grateful to the Australian Government Department of Health and Aged Care for funding and to the women who provided the survey data. The authors acknowledge the Australian Government Department of Health and Aged Care for providing Pharmaceutical Benefits Scheme and Aged Care data, and the Australian Institute of Health and Welfare (AIHW) as the integrating authority. The authors acknowledge the assistance of the Data Linkage Unit at the Australian Institute of Health and Welfare (AIHW) for undertaking the data linkage to the National Death Index. The authors acknowledge the following:
The Centre for Health Record Linkage (CHeReL), New South Wales (NSW) Ministry of Health and Australian Capital Territory (ACT) Health, for the NSW Admitted Patients and Emergency Department Data Collections; and the ACT Admitted Patient Care and Emergency Department Data Collections.
Queensland Health as the source for Queensland Hospital Admitted Patient and Emergency Data Collections; and the Statistical Analysis and Linkage Unit (Queensland Health) for the provision of data linkage.
The Department of Health Western Australia (WA), including Data Linkage Services WA and the Data Custodians of the WA Hospital Morbidity and Emergency Department Data Collections.
South Australia (SA) Northern Territory (NT) DataLink, SA Health, and Northern Territory Department of Health, for the SA Public Hospital Separations, SA Public Hospital Emergency Department, NT Public Hospital Inpatient Activity, and NT Public Hospital Emergency Department Data Collections.
The Department of Health Tasmania, and the Tasmanian Data Linkage Unit, for the Public Hospital Admitted Patient Episodes and Tasmanian Emergency Department Presentations.
Victorian Department of Health as the source of the Victorian Admitted Episodes Dataset and the Victorian Emergency Minimum Dataset; and the Centre for Victorian Data Linkage (Victorian Department of Health) for the provision of data linkage.
The Study of Women’s Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), Department of Health and Human Services, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women’s Health (ORWH) (grants U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH.
Clinical Centers. University of Michigan, Ann Arbor–Carrie Karvonen-Gutierrez, primary investigator (PI) 2021–present, Siobán Harlow, PI 2011–2021, MaryFran Sowers, PI 1994–2011; Massachusetts General Hospital, Boston, MA–Sherri‐Ann Burnett‐Bowie, PI 2020–present; Joel Finkelstein, PI 1999–2020; Robert Neer, PI 1994–1999; Rush University, Rush University Medical Center, Chicago, IL–Imke Janssen, PI 2020–present; Howard Kravitz, PI 2009–2020; Lynda Powell, PI 1994–2009; University of California, Davis/Kaiser–Elaine Waetjen and Monique Hedderson, PIs 2020–present; Ellen Gold, PI 1994–2020; University of California, Los Angeles–Arun Karlamangla, PI 2020–present; Gail Greendale, PI 1994–2020; Albert Einstein College of Medicine, Bronx, NY–Carol Derby, PI 2011–present, Rachel Wildman, PI 2010–2011; Nanette Santoro, PI 2004–2010; University of Medicine and Dentistry–New Jersey Medical School, Newark–Gerson Weiss, PI 1994–2004; and the University of Pittsburgh, Pittsburgh, PA–Rebecca Thurston, PI 2020–present; Karen Matthews, PI 1994–2020.
NIH Program Office. National Institute on Aging, Bethesda, MD–Rosaly Correa-de-Araujo 2020–present; Chhanda Dutta 2016–present; Winifred Rossi 2012–2016; Sherry Sherman 1994–2012; Marcia Ory 1994–2001; National Institute of Nursing Research, Bethesda, MD–program officers.
Central Laboratory. University of Michigan, Ann Arbor–Daniel McConnell (Central Ligand Assay Satellite Services).
Coordinating Center. University of Pittsburgh, Pittsburgh, PA–Maria Mori Brooks, PI 2012–present; Kim Sutton-Tyrrell, PI 2001–2012; New England Research Institutes, Watertown, MA–Sonja McKinlay, PI 1995–2001.
Steering Committee. Susan Johnson, Current Chair; Chris Gallagher, Former Chair.
Healthy Ageing of Women Study (HOW) was supported by the Queensland University of Technology Early Career Research Grant and the Japan Society for the Promotion of Science (JSPS) Grant-in-aid for Scientific Research. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. Women’s Lifestyle and Health Study (WLHS) was funded by a grant from the Swedish Research Council (grant no. 521-2011-2955). MRC National Survey of Health Development (NSHD) has core funding from the U.K. Medical Research Council (MC UU 12019/1). National Child Development Study (NCDS) is funded by the U.K. Economic and Social Research Council. English Longitudinal Study of Ageing (ELSA) is funded by the National Institute on Aging (grants 2RO1AG7644 and 2RO1AG017644-01A1) and a consortium of U.K. government departments coordinated by the Office for National Statistics. The Whitehall II study is supported by grants from the Medical Research Council (K013351), British Heart Foundation (BHF) (RG/16/11/32334), National Institutes of Health NIA (R01AG013196, R01AG034454), and Economic and Social Research Council (UKRI grant ES/T014377/1). Southall And Brent Revisited (SABRE) study was supported at baseline by the U.K. Medical Research Council, Diabetes UK, and BHF and at follow-up by the Wellcome Trust (082464/Z/07/Z) and BHF (SP/07/001/23603, PG/08/103, and PG/12/29/29497). Seattle Midlife Women’s Health Study (SMWHS) was supported in part by grants from the National Institute of Nursing Research (P50-NU02323, P30-NR04001, and R01-NR0414). Japan Nurses’ Health Study (JNHS) was supported in part by JSPS Grants-in-Aid for Scientific Research National Institute of Informatics (KAKENHI) (JP14370133, JP18390195, JP18H04069, JP19KK0235, and JP23KK0167), by the Japan Agency for Medical Research and Development (JP24gk0210038), and by the grants from the Japan Menopause Society. Prospect–EPIC Utrecht is financed by the European Commission–Europe Against Cancer: World Health Organization (WHO) AEP/90/05, The Dutch Ministry of Health, The Dutch Prevention Funds, and the World Cancer Research Fund (WCRF) funds (WCRF 98A04 and WCRF 2000/30). This research has been conducted using the UK Biobank resource under Application 80681. This work uses data provided by patients and collected by the National Health Service as part of their care and support. The China Kadoorie Biobank has grant support from the Kadoorie Charitable Foundation in Hong Kong, the Wellcome Trust in the U.K. (088158/Z/09/Z), and the Chinese Ministry of Science and Technology (2011BAI09B01). The UK Medical Research Council, the BHF and Cancer Research UK also provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University for the project.
All study teams thank the participants for volunteering their time to be involved in the respective studies. The findings and views in this article are not those from the original studies or their respective funding agencies.
The study funders had no role in study design, analysis, interpretation of data, or writing of the report or decision to submit the manuscript for publication. The authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer or World Health Organization.
Duality of Interest. No potential conflicts of interest relevant to this article are reported.
Author Contributions. K.N. conducted statistical analyses and wrote the initial draft of the manuscript. H.F.C. conducted data harmonization across studies, supervised statistical analysis and data interpretation, and wrote/revised the manuscript. H.-F.C. and G.D.M. conceptualized the research question and study design. K.H., A.J.D., S.S., and G.D.M. critically reviewed the statistical methods and edited the manuscript. All the other authors contributed study data and provided critical revision of the manuscript for intellectual content. All authors approved the final manuscript. H.-F.C. and G.D.M. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Steven E. Kahn and Jonathan E. Shaw.
Funding Statement
The InterLACE Consortium is funded by the Australian National Health and Medical Research Council project grant (APP1027196) and Centres of Research Excellence (APP1153420). G.D.M. is supported by the Australian National Health and Medical Research Council Leadership Fellowship (APP2009577). This research was supported in part by the JSPS KAKENHI (19KK0235 and 23KK0167).
Supporting information
References
- 1. GBD Diabetes Collaborators . Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2023;402:203–234 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Cheng YJ, Kanaya AM, Araneta MRG, et al. Prevalence of diabetes by race and ethnicity in the United States, 2011–2016. JAMA 2019;322:2389–2398 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Pham TM, Carpenter JR, Morris TP, Sharma M, Petersen I. Ethnic differences in the prevalence of type 2 diabetes diagnoses in the UK: cross-sectional analysis of the Health Improvement Network Primary Care Database. Clin Epidemiol 2019;11:1081–1088 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Shai I, Jiang R, Manson JE, et al. Ethnicity, obesity, and risk of type 2 diabetes in women: a 20-year follow-up study. Diabetes Care 2006;29:1585–1590 [DOI] [PubMed] [Google Scholar]
- 5. Goff LM. Ethnicity and Type 2 diabetes in the UK. Diabet Med 2019;36:927–938 [DOI] [PubMed] [Google Scholar]
- 6. McNeely MJ, Boyko EJ. Type 2 diabetes prevalence in Asian Americans: results of a national health survey. Diabetes Care 2004;27:66–69 [DOI] [PubMed] [Google Scholar]
- 7. Meeks KAC, Freitas-Da-Silva D, Adeyemo A, et al. Disparities in type 2 diabetes prevalence among ethnic minority groups resident in Europe: a systematic review and meta-analysis. Intern Emerg Med 2016;11:327–340 [DOI] [PubMed] [Google Scholar]
- 8. Qiao Q, Hu G, Tuomilehto J, et al.; DECODA Study Group . Age- and sex-specific prevalence of diabetes and impaired glucose regulation in 11 Asian cohorts. Diabetes Care 2003;26:1770–1780 [DOI] [PubMed] [Google Scholar]
- 9. Seah JYH, Sim X, Khoo CM, Tai ES, van Dam RM. Differences in type 2 diabetes risk between East, South, and Southeast Asians living in Singapore: the multi-ethnic cohort. BMJ Open Diabetes Res Care 2023;11:e003385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Sun H, Saeedi P, Karuranga S, 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Kautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia 2023;66:986–1002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Chung H-F, Dobson AJ, Hayashi K, et al. Ethnic differences in the association between age at natural menopause and risk of type 2 diabetes among postmenopausal women: a pooled analysis of individual data from 13 cohort studies. Diabetes Care 2023;46:2024–2034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Engmann L, Jin S, Sun F, et al.; Reproductive Medicine Network . Racial and ethnic differences in the polycystic ovary syndrome metabolic phenotype. Am J Obstet Gynecol 2017;216:493.e1–493.e13 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Xiang AH, Li BH, Black MH, et al. Racial and ethnic disparities in diabetes risk after gestational diabetes mellitus. Diabetologia 2011;54:3016–3021 [DOI] [PubMed] [Google Scholar]
- 15. Lee A, Cardel M, Donahoo WT. Social and environmental factors influencing obesity. Feingold KR, Ahmed SF, Anawalt B, et al. Eds. In Endotext. South Dartmouth, MDText.com, Inc., 2000 [Google Scholar]
- 16. Zhu Y, Sidell MA, Arterburn D, et al. Racial/ethnic disparities in the prevalence of diabetes and prediabetes by BMI: Patient Outcomes Research To Advance Learning (PORTAL) multisite cohort of adults in the U.S. Diabetes Care 2019;42:2211–2219 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Caleyachetty R, Barber TM, Mohammed NI, et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol 2021;9:419–426 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Aggarwal R, Bibbins-Domingo K, Yeh RW, et al. Diabetes screening by race and ethnicity in the United States: equivalent body mass index and age thresholds. Ann Intern Med 2022;175:765–773 [DOI] [PubMed] [Google Scholar]
- 19. Nakagami T, Qiao Q, Carstensen B, et al.; DECODE-DECODA Study Group . Age, body mass index and Type 2 diabetes-associations modified by ethnicity. Diabetologia 2003;46:1063–1070 [DOI] [PubMed] [Google Scholar]
- 20. Hedderson M, Ehrlich S, Sridhar S, Darbinian J, Moore S, Ferrara A. Racial/ethnic disparities in the prevalence of gestational diabetes mellitus by BMI. Diabetes Care 2012;35:1492–1498 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Mishra GD, Chung H-F, Pandeya N, et al. The InterLACE study: design, data harmonization and characteristics across 20 studies on women’s health. Maturitas 2016;92:176–185 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. IBC 50K CAD Consortium . Large-scale gene-centric analysis identifies novel variants for coronary artery disease. PLoS Genet 2011;7:e1002260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Hayashi K, Mizunuma H, Fujita T, et al. Design of the Japan Nurses' Health Study: a prospective occupational cohort study of women’s health in Japan. Ind Health 2007;45:679–686 [DOI] [PubMed] [Google Scholar]
- 24. Chen Z, Chen J, Collins R, et al.; China Kadoorie Biobank (CKB) collaborative group . China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol 2011;40:1652–1666 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. WHO Expert Consultation . Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–163 [DOI] [PubMed] [Google Scholar]
- 26. Seo D-C, Choe S, Torabi MR. Is waist circumference ≥102/88cm better than body mass index ≥30 to predict hypertension and diabetes development regardless of gender, age group, and race/ethnicity? Meta-analysis. Prev Med 2017;97:100–108 [DOI] [PubMed] [Google Scholar]
- 27. Alberti KGMM, Zimmet P, Shaw J. Metabolic syndrome–a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006;23:469–480 [DOI] [PubMed] [Google Scholar]
- 28. Paul SK, Owusu Adjah ES, Samanta M, et al. Comparison of body mass index at diagnosis of diabetes in a multi-ethnic population: a case-control study with matched non-diabetic controls. Diabetes Obes Metab 2017;19:1014–1023 [DOI] [PubMed] [Google Scholar]
- 29. Chiu M, Austin PC, Manuel DG, Shah BR, Tu JV. Deriving ethnic-specific BMI cutoff points for assessing diabetes risk. Diabetes Care 2011;34:1741–1748 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Lear SA, Kohli S, Bondy GP, Tchernof A, Sniderman AD. Ethnic variation in fat and lean body mass and the association with insulin resistance. J Clin Endocrinol Metab 2009;94:4696–4702 [DOI] [PubMed] [Google Scholar]
- 31. Anand SS, Tarnopolsky MA, Rashid S, et al. Adipocyte hypertrophy, fatty liver and metabolic risk factors in South Asians: the Molecular Study of Health and Risk in Ethnic Groups (mol-SHARE). PLoS One 2011;6:e22112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Sulistyoningrum DC, Gasevic D, Lear SA, Ho J, Mente A, Devlin AM. Total and high molecular weight adiponectin and ethnic-specific differences in adiposity and insulin resistance: a cross-sectional study. Cardiovasc Diabetol 2013;12:170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Shah AD, Kandula NR, Lin F, et al. Less favorable body composition and adipokines in South Asians compared with other US ethnic groups: results from the MASALA and MESA studies. Int J Obes (Lond) 2016;40:639–645 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Pomeroy E, Mushrif-Tripathy V, Cole TJ, Wells JCK, Stock JT. Ancient origins of low lean mass among South Asians and implications for modern type 2 diabetes susceptibility. Sci Rep 2019;9:10515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Srikanthan P, Karlamangla AS. Relative muscle mass is inversely associated with insulin resistance and prediabetes. Findings from the third National Health and Nutrition Examination Survey. J Clin Endocrinol Metab 2011;96:2898–2903 [DOI] [PubMed] [Google Scholar]
- 36. Huang C-F, Wu C-H. Sarcopenic obesity in the Asia-Pacific region: epidemiology, risk factors, and management. Osteoporos Sarcopenia 2025;11:40–49 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Narayan KMV, Kanaya AM. Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways. Diabetologia 2020;63:1103–1109 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Agyemang P, Powell-Wiley TM. Obesity and Black women: special considerations related to genesis and therapeutic approaches. Curr Cardiovasc Risk Rep 2013;7:378–386 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Sluijs I, van der A DL, Beulens JWJ, et al. Ascertainment and verification of diabetes in the EPIC-NL study. Neth J Med 2010;68:333–339 [PubMed] [Google Scholar]
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