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. 2025 Mar 22;31:101777. doi: 10.1016/j.ssmph.2025.101777

Immigration, acculturation, and diabetes: A comparative study of diabetes prevalence among Asian Indian immigrants living in the United States and native-born populations in India and the United States

Emma Nichols a,b,⁎, Hunter Green a, Haomiao Jin c, David Flood d, Elizabeth Rose Mayeda e, M Maria Glymour f, Namratha R Kandula g,h, Alka M Kanaya i, Jinkook Lee a,j
PMCID: PMC12301973  PMID: 40727713

Abstract

Despite evidence that Indian immigrants in high-income countries have higher diabetes risk, few studies have directly compared Indian immigrants to both Indians in India and the general population. We compared diabetes prevalence in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study (Indian immigrants) (N = 686), the Longitudinal Aging Study in India (LASI) (Indians in India) (N = 40,496), and the Health and Retirement Study (HRS) (general US population) (N = 7643), accounting for selective immigration using propensity score matching. We used generalized regression models to assess associations between diabetes and acculturation in MASALA and compare correlates of diabetes across studies. After matching, Indians in India had a higher prevalence of diabetes (37.9 % [35.4–40.5]) than Indian immigrants in the US (26.7 % [23.5–30.1]) and the general US population (19.6 % [17.6–21.8]). Higher acculturation was associated with a lower diabetes prevalence (prevalence ratio [PR]: 0.68 [0.45–1.04], P = 0.078) and lower HbA1c (difference: 0.205 % [-0.408 to −0.001], P = 0.049). We also identified differences in the magnitude of correlations between diabetes and risk factors, including abdominal obesity (MASALA PR: 1.41 [1.09–1.81], LASI PR: 2.41 [2.29–2.54], HRS PR: 2.52 [2.17–2.93]). Cultural factors, including differences in lifestyle and diet, may play an important role in the high diabetes risk among Indian immigrants and explaining racial disparities in diabetes burden in the US.

Highlights

  • •

    Researchers do not currently understand why Indian immigrants have higher risk of diabetes.

  • •

    We compared diabetes prevalence among Indian immigrants in the US, Indians living in India, and the general US population.

  • •

    Accounting for selection factors, prevalence was highest in Indians living in India, followed by Indian immigrants.

  • •

    Cultural factors may play an important role in the higher observed diabetes prevalence in Indian immigrants.

  • •

    Findings highlight the potential importance of cultural factors in explaining racial disparities.

1. Introduction

The burden of diabetes is expected to increase both in the United States (US) and globally due to population aging and trends in the prevalence of obesity and other risk factors for diabetes (Sun et al., 2022). However, the burden of diabetes is not equally distributed (Cheng et al., 2019; McBean et al., 2004; Schmidt et al., 2014). Consistent evidence from high-income countries documents the high prevalence of diabetes among many immigrant and migrant groups (Araneta; Jenum et al., 2012; Mather & Keen, 1985; Ujcic-Voortman et al., 2009), including Asian Indian (Indian) immigrants in the US (Cheng et al., 2019; Gupta et al., 2011; Kanaya et al., 2014; Lee et al., 2020; Misra et al., 2023; Oza-Frank & Narayan, 2010).

A better understanding of the reasons for the observed higher prevalence of diabetes among Indians could generate biological and epidemiologic insights that may subsequently lead to improvements in both disease prevention and treatment in this growing population. While some researchers suggest biological or genetic explanations for elevated diabetes risk among Asian Indians (Bajaj & Banerji, 2004; Narayan et al., 2021), contextual differences, including differences in exposures or characteristics intrinsic to the surrounding environment, or sociocultural differences between India and high-income settings are also plausible. However, differences between biological or genetic, contextual, and cultural factors are challenging to disentangle.

Differences identified in studies comparing Indians in India to the overall population of high-income countries in Europe and North America may be due to differences in culture, study context, or biological factors including genetics. In contrast, studies comparing Indian immigrants with the general population hold current context constant, but culture and genetics vary between the groups. Comparisons of Indian immigrants and Indians in India would be helpful to further disentangle the potential roles of these three factors; however, few such studies exist. A comparison between the Mediators of Atherosclerosis in South Asians Living in America (MASALA) and Centre for cArdiometabolic Risk Reduction in South-Asia (CARRS) studies found that Indians immigrants in the US had a higher prevalence of prediabetes but lower prevalence of diabetes compared to Indians living in Chennai, India (Gujral).

Comparisons across all three groups would provide strong evidence to help shed light on underlying reasons for the high prevalence of diabetes among Indians. However, to our knowledge, only one prior study has compared all three groups, using the United Kingdom (UK) as the setting of interest (comparing the general UK population, Indian immigrants in the UK, and Indians in India). Though researchers found that diabetes prevalence was highest in Indians in India, followed by Indian immigrants in the UK, with participants from the general UK population having the lowest observed prevalence, the sample size of the study was small, and patients were recruited from outpatient clinics and were therefore not representative of underlying populations of interest (Dhawan).

The current study builds on this prior work by using population-representative or community-based samples of older adults in the US, India, and Indian immigrants in the US to quantify diabetes prevalence across the three groups. We additionally compare correlates of diabetes prevalence to better understand the underlying factors that lead to disparities.

2. Methods

2.1. Samples

The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a community-based sample of 1164 South Asians in the greater San Francisco and Chicago regions (Kanaya, 2013). Adults aged 40–84 years and older free from existing cardiovascular disease (heart attack, stroke, heart failure, atrial fibrillation) were included at the baseline assessments conducted in 2010–2013 and 2017–2018. Approximately 98 % of participants were immigrants and 83 % were Indian immigrants. Most interviews were completed in English; 4 % were conducted in Hindi or Urdu. Prior comparisons found that the demographic characteristics of the MASALA cohort, including high socioeconomic attainment, are roughly representative of middle-aged and older South Asian adults in the US (Kanaya).

The Longitudinal Ageing Study in India (LASI) is a nationally-representative study of over 73,000 community-dwelling adults aged 45 years and older and their spouses in India (Perianayagam). Wave 1 data were collected in 2017–2019. The English version of the LASI instrument was translated and back-translated into 16 regional languages and participants completed the survey in their language of choice.

The Health and Retirement Survey (HRS) includes a nationally-representative sample of adults aged 51 years and older and their spouses in the United States (Sonnega). Baseline data were collected in 1992, with follow-up waves conducted every 2 years. Participants from new birth cohorts have been added over time to ensure the sample remains representative of older adults in the United States. The present analysis used data from follow-up visits of participants during the 2014 or 2016 waves (HRS participants are randomized to receive the full battery of physical and biological measures every other cycle). Among these participants, 60 % were non-Hispanic White, 20 % were non-Hispanic Black, 15 % were Hispanic or Latino, and 4 % were of other or unknown race and ethnicity.

To maximize comparability between the included samples, we excluded individuals under 51 years old (MASALA N = 331; LASI N = 23,384; HRS N = 818). We also excluded those with existing cardiovascular disease (self-reported stroke, heart attack, congestive heart failure, or other chronic heart problem) or missing cardiovascular disease status in both the LASI (N = 3220) and HRS (N = 5550) samples, as individuals with these conditions were excluded from the MASALA study by design. We excluded MASALA participants who were not first-generation Indian immigrants (N = 143). In the LASI and HRS samples, we excluded individuals who were born in other countries or missing birth country information (LASI N = 671; HRS N = 2373). We also excluded individuals with missing marital status, diabetes diagnosis, hemoglobin A1c (HbA1c), and missing or zero survey weights in MASALA (N = 4), LASI (N = 5637), and HRS (N = 3403), as these data were required for the assessment of diabetes (Appendix A). The final samples included 686 participants in MASALA, 40,496 participants in LASI, and 7643 participants in HRS. Participants gave written informed consent; illiterate participants in LASI gave consent via thumb print. All studies were approved by institution-specific Review Boards.

2.2. Assessment of diabetes

In the main analysis, we defined diabetes in the three samples as either self-report of a diabetes diagnosis or having measured HbA1c ≥ 6.5 % (48 mmol/mol), based on measures available in all 3 studies. In HRS and LASI, HbA1c was assessed using dried blood spots (DBS), while in MASALA the assays were conducted using whole blood samples (Crimmins, 2017; Flood et al., 2022; Gujral et al., 2019). As the clinical HbA1c cut-off point for diabetes diagnosis is based on whole blood and not DBS values, we converted HRS and LASI DBS HbA1c to whole blood equivalent values (Crimmins, 2017; Flood et al., 2022). In a sensitivity analyses, we used an alternative definition of diabetes using only self-reported diagnoses. In HRS and LASI, questions about diabetes medications were only asked of those who self-reported diabetes diagnosis, while the MASALA study conducted a medication inventory. In MASALA, additional inclusion of those who reported diabetes medication without self-reporting diabetes diagnosis as diabetes cases did not substantially alter prevalence estimates (26.7 % [95 % CI: 23.5 to 30.1] vs. 26.8 % [23.6 to 30.3]).

2.3. Assessment of acculturation in the MASALA cohort

We used two basic measures of acculturation: age at immigration and percent of life lived in the United States. We also used a more complex measure of acculturation, based on a latent class analysis of 12 measures describing attributes regarding the practice of South Asian traditions, frequency of fasting, foods typically eaten both at home and in restaurants, and ethnic composition of friends (Needham et al., 2017). This analysis was guided by Berry's acculturation framework (Berry, 1997), which hypothesizes four acculturation strategies: integration (bicultural, simultaneous maintenance heritage culture and adoption of host culture), assimilation (most acculturated, rejection of heritage culture and assumption of host culture), separation (less acculturated, maintenance of heritage culture and rejection of host culture), and marginalization (less acculturated, rejection of both heritage and host cultures). However, the data-driven latent class analysis identified three latent classes: integration (54 % of sample), assimilation (23 % of sample), and separation (22 % of sample). Additional details of this latent class analysis have been previously published (Needham et al., 2017).

2.4. Measurement of other included measures

Age, gender, marital status (married or partnered vs. not married or partnered), educational attainment (primary school or less, less than bachelors, bachelors or more) and smoking status (never, former, current smoker) were self-reported. Body-mass index was calculated based on measured height and weight and categorized as underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥30 kg/m2). Interviewers measured waist circumference using a measuring tape applied over undergarments or light clothing. Abdominal obesity was defined as waist circumference greater than 102 cm in men or 88 cm in women. In sensitivity analyses, we replicated analyses using BMI (underweight: <18 kg/m2; normal: 18.0–22.9 kg/m2, overweight: 23.0–24.9 kg/m2, obese: >25 kg/m2) and abdominal obesity (90 cm in men or 80 cm in women) categories based on guidelines for Asian Indians (Misra).

2.5. Statistical analysis

We described and compared the included samples using proportions and associated uncertainty intervals; descriptive statistics for the LASI and HRS samples were weighted to account for the survey design. To estimate the prevalence of diabetes in a manner that would allow for comparisons across studies in light of large differences between samples in characteristics such as educational attainment or marital status, we matched the MASALA sample to both the LASI and HRS samples using the propensity score matching (Austin, 2011). We estimated propensity scores using logistic regression with inclusion in the MASALA sample as the dependent variable and age, gender, educational attainment, and marital status as covariates. We assessed balance using standardized mean differences. We selected 2:1 (2 LASI/HRS participants to 1 MASALA participant) nearest neighbor matching, as this allowed us to increase the available sample size without sacrificing covariate balance (Appendix B). We estimated diabetes prevalence using multiple definitions with both unmatched and matched data.

Within the MASALA sample, we assessed the association between acculturation and both diabetes and HbA1c. We used percent of life lived in the United States, age at immigration, and acculturation latent class as markers of acculturation. We used Poisson regression with robust standard errors to estimate the association between acculturation and diabetes and linear regression to estimate the association between acculturation and HbA1c. To compare diabetes prevalence between individuals in this latent class with the highest assimilation levels and corresponding matched HRS participants we estimated a prevalence ratio and used a chi-squared test to assess statistical significance.

We assessed correlates of diabetes prevalence within the three samples using Poisson regression with robust standard errors to estimate prevalence ratios. Models in LASI and HRS accounted for the complex survey design. All regression models controlled for age, gender, educational attainment, and marital status. Because we controlled for all matching variables in regression models assessing correlates of diabetes prevalence, we used the unmatched rather than matched analytic samples to maximize sample size. We evaluated all matching variables (age, gender, educational attainment, and marital status), as well as BMI category, abdominal obesity, and smoking status as correlates of diabetes prevalence using separate regression models.

All analyses were conducted in Stata version 18.0 and R version 4.3.3. Propensity score matching was conducted using the MatchIt package (Ho et al., 2011).

3. Results

There were large differences between the cohorts on a range of demographic factors and cardiovascular disease risk factors (Table 1, Table C.1). Although distributions of age and gender were similar, educational attainment was highest in MASALA, followed by HRS, and was lowest in LASI. The prevalence of obese BMI and abdominal obesity was highest in HRS, followed by MASALA, and was lowest in LASI, which had the highest prevalence of normal and underweight BMI.

Table 1.

Sample characteristics of the Longitudinal Aging Study in India (LASI) (India, 2017–2019), Mediators of Atherosclerosis in South Asians Living in America (MASALA) (Indian immigrants in the United States, 2010–2018), and Health and Retirement Survey (HRS) (United States, 2014–2016).

Characteristic LASI (n = 40,496)a MASALA (n = 686) HRS (n = 7643)a
Age (years)
 Mean (SD) 62.6 (8.6) 61.3 (7.1) 64.5 (7.5)
 51-59 43.0 (42.4–43.6) 44.2 (40.5–47.9) 35.9 (34.5–37.4)
 60-69 35.7 (35.1–36.2) 40.4 (36.8–44.1) 38.3 (37.0–39.7)
 ≥70 21.4 (20.9–21.8) 15.5 (12.9–18.4) 25.7 (24.7–26.8)
Gender
 Women 50.2 (49.6–50.8) 46.2 (42.5–50.0) 55.1 (53.7–56.6)
 Men 49.8 (49.2–50.4) 53.8 (50.0–57.5) 44.9 (43.4–46.3)
Education
 Primary or less 83.9 (83.4–84.3) 1.5 (0.8–2.7) 2.1 (1.8–2.5)
 Less than bachelors 11.7 (11.3–12.1) 11.8 (9.6–14.4) 66.6 (65.2–67.9)
 Bachelors or more 4.5 (4.2–4.7) 86.7 (84.0–89.1) 31.3 (30.0–32.7)
Marital status
 Not married or partnered 27.2 (26.6–27.7) 10.3 (8.3–12.9) 33.9 (32.6–35.2)
 Married or partnered 72.8 (72.3–73.4) 89.7 (87.1–91.7) 66.1 (64.8–67.4)
Body mass index
 Mean (SD) 22.2 (4.6) 25.8 (3.8) 29.9 (5.0)
 <18.5 (Underweight) 22.5 (22.0–23.0) 1.3 (0.7–2.5) 1.0 (0.8–1.3)
 18.5–24.9 (Normal) 51.4 (50.8–52.0) 43.4 (39.8–47.2) 18.6 (17.5–19.7)
 25.0–29.9 (Overweight) 19.2 (18.7–19.6) 42.4 (38.8–46.2) 32.8 (31.5–34.2)
 ≥30.0+ (Obese) 6.1 (5.8–6.4) 12.8 (10.5–15.6) 41.6 (40.1–43.0)
 Missing 0.9 (0.8–1.0) – 6.1 (5.4–6.8)
Abdominal obesityb
 Mean (SD) 84.7 (12.8) 93.8 (9.9) 102.7 (14.0)
 Not obese 75.3 (74.8–75.8) 59.6 (55.9–63.2) 31.9 (30.6–33.2)
 Obese 23.8 (23.3–24.3) 40.4 (36.8–44.1) 65.2 (63.8–66.5)
 Missing 0.9 (0.8–1.1) – 2.9 (2.5–3.4)
Smoking status
 Never 80.2 (79.7–80.7) 83.2 (80.2–85.9) 46.0 (44.6–47.5)
 Former 4.4 (4.1–4.6) 13.6 (11.2–16.3) 39.2 (37.8–40.6)
 Current 15.4 (14.9–15.8) 3.2 (2.1–4.8) 14.5 (13.5–15.6)
 Missing 0.1 (0.1–0.1) – 0.3 (0.2–0.4)
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Data are means (standard deviations) or percentages (95 % confidence intervals).

a

Values were estimated using survey weights.

b

Abdominal obesity was defined as waist circumference greater than 102 cm in men and 88 cm in women. IQR = interquartile range.

Despite substantial imbalance prior to matching, propensity score matching improved the balance between LASI, MASALA, and HRS on age, gender, education, and marital status (all standardized mean differences below 0.1, Appendix B). Table 2 shows diabetes prevalence before and after matching. Before matching, Indian immigrants in the US had a higher prevalence of diabetes (26.7 % [23.5 to 30.1]) than Indians in India (20.4 % [19.9 to 20.9]) or the general US population (20.9 % [19.8 to 22.0]) over age 50 years. However, after matching, Indians in India had a highest prevalence of diabetes (37.9 % [35.4 to 40.5]), followed by Indian immigrants in the US (26.7 % [23.5 to 30.1]), and the general US population (19.6 % [17.6 to 21.8]) (Fig. 1). Though the prevalence of diabetes in all cohorts decreased when using an alternative definition based only on self-report diagnoses, the patterns remained largely consistent (Table 2).

Table 2.

Diabetes prevalence before and after propensity score matching in the Longitudinal Aging Study in India (LASI) (India, 2017–2019), Mediators of Atherosclerosis in South Asians Living in America (MASALA) (Indian immigrants in the United States, 2010–2018), and Health and Retirement Survey (HRS) (United States, 2014–2016).

LASI (matched N = 1372) MASALA (matched N = 686) HRS (matched N = 1372)
Before matchinga
 Prevalence (self-reported diagnosis or by HbA1c) 20.4 (19.9–20.9) 26.7 (23.5–30.1) 20.9 (19.8–22.0)
 Prevalence (self-reported diagnosis) 12.4 (12.1–12.8) 22.3 (19.3–25.6) 17.9 (16.9–18.9)
After matchingb
 Prevalence (self-reported diagnosis or by HbA1c) 37.9 (35.4–40.5) 26.7 (23.5–30.1) 19.6 (17.6–21.8)
 Prevalence (self-reported diagnosis) 26.2 (24.0–28.6) 22.3 (19.3–25.6) 17.2 (15.3–19.3)
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Data are percentages (95 % confidence intervals).

a

LASI and HRS values were estimated using survey weights.

b

Samples matched on age, gender, educational attainment, and marital status. HbA1c = hemoglobin A1c.

Fig. 1.

Fig. 1

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Diabetes prevalence across cohorts (LASI, MASALA, and HRS) among propensity score matched samples by both age group (panel A) and continuous age (panel B). In panel A, chi squared tests for comparison by age group yielded the following P values: 51–59 = <0.001, 60–69 = <0.001, ≥70 = 0.003. In panel B, estimates were derived using Poisson regression with robust standard errors and a restricted cubic spline on age with one internal knot at the 50th percentile of the data distribution and two external knots at the 10th and 90th percentiles of the data distribution. Error bars in panel A and shaded bands in panel B represent 95 % confidence intervals. HRS=Health and Retirement Study. LASI = Longitudinal Aging Study in India. MASALA = Mediators of Atherosclerosis in South Asians Living in America.

Among Asian Indian immigrants, age at immigration, and percent of life lived in the US were not associated with HbA1c or diabetes (Appendix D). Compared to those with the lowest level of acculturation to US cultural norms and practices (separation class from latent class analysis), immigrants with the greatest level of acculturation (assimilation class) had lower HbA1c (difference: 0.205 % [−0.408 to −0.001], P = 0.049) and were less likely to have diabetes (prevalence ratio [PR]: 0.68 [0.45 to 1.04], P = 0.078). In addition, among those with the greatest level of acculturation (assimilation class), diabetes prevalence was not statistically significantly different from diabetes prevalence in the general US population after matching (PR: 0.94 [0.66 to 1.33], P = 0.708).

We identified some notable differences in associations between diabetes and considered demographic characteristics and cardiovascular disease risk factors. In all three samples, older age and abdominal obesity were associated with diabetes (Fig. 2). However, the association between diabetes and abdominal obesity was weaker in MASALA (PR: 1.41 [1.09 to 1.81]) than the other cohorts (HRS PR: 2.52 [2.17 to 2.93], LASI PR: 2.41 [2.29 to 2.54]). In a sensitivity analysis using an alternative definition of abdominal obesity based on guidelines for Asian Indians, the association between abdominal obesity and diabetes remained weaker in the MASALA cohort and was not statistically significant, although estimates were imprecise due to the smaller available sample size in the MASALA cohort (Appendix C). Higher categories of BMI were also associated with diabetes in both LASI and HRS, whereas estimates were again smaller and not statistically significant in MASALA. Using alternative cut-points for BMI categories based on guidelines for Asian Indians, this association strengthened in LASI and attenuated in HRS (Figure C.1). The prevalence of diabetes was higher among men compared to women in both MASALA and HRS, but higher among women in LASI. However, this observed association in LASI flipped directions in subsequent models controlling for either BMI category or abdominal obesity. Higher educational attainment was associated with a greater likelihood of having diabetes in LASI but a lower likelihood in HRS. Among Indians in India, current smokers were less likely to have diabetes than never smokers (PR: 0.68 [0.62 to 0.74]).

Fig. 2.

Fig. 2

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Correlates of diabetes across cohorts (LASI, MASALA, and HRS). Estimates were derived using Poisson regression models with robust standard errors and adjusted for age, gender, educational attainment, and marital status. LASI and HRS values were estimated using survey weights. Error bars represent 95 % confidence intervals. The dashed line represents a prevalence ratio equal to 1. Abdominal obesity was defined as waist circumference greater than 102 cm in men or 88 cm in women. HRS=Health and Retirement Study. LASI = Longitudinal Aging Study in India. MASALA = Mediators of Atherosclerosis in South Asians Living in America.

4. Discussion

In a cross-sectional analysis comparing a community-based sample of Indian immigrants in the US to demographically matched population of middled aged adults in the US and in India, we found that Indians living in India had the highest diabetes prevalence, followed by Indians living in the US, and then the general US population. Among Indian immigrants, those with the greater levels of acculturation towards US cultural norms and practices had lower HbA1c levels, were less likely to have diabetes, and had diabetes prevalence that was more similar to the prevalence observed in the general US population. We also found divergent associations between diabetes and risk factors including obesity and educational attainment across samples. Diabetes prevalence was greater among those with higher educational attainment in India, whereas diabetes prevalence was lower among those with higher educational attainment in the US general population; in Indian immigrants, estimates of associations were closer in magnitude to those observed in the US general population, although estimates were imprecise. Our findings on the comparative diabetes prevalence across populations, associations with acculturation and HbA1c among Indian immigrants, and differences in associations between diabetes and risk factors across populations further our understanding of patterns of diabetes prevalence in the US, in India, and among Indian immigrants in the US and help disentangle the impacts of biology due to shared ancestry and genetics, context, and culture on diabetes risk.

Our results build on previous research that has reported higher diabetes prevalence in Indian immigrants than the general population of the United States and other high-income countries (Gupta et al., 2011; Jenum et al., 2012; Oza-Frank & Narayan, 2010), and a prior study that has reported a higher prevalence of diabetes in Indians living in India compared to the general US population (Narayan et al., 2021). Study findings also are generally consistent with a prior study which found higher prevalence of diabetes among Indians living in India compared Indian immigrants in the US (the opposite was true for pre-diabetes), although this study only included participants from one Indian mega-city (Chennai), rather than using a national sample (Gujral). To our knowledge, only one existing study conducted a similar three-way comparison and found that Indian men in the United Kingdom (U.K.) and in India had greater unadjusted diabetes prevalence than men from the general population of the U.K. While findings were substantially limited by the use of unadjusted analyses from a convenience sample of fewer than 400 clinic patients (Dhawan), our work replicates the ordering observed in this study.

Our finding that Indian immigrants in the US have lower diabetes prevalence than Indians in India may suggest an important role for context and culture in explaining differences in diabetes prevalence beyond potential differences due to biological factors related to shared ancestry and genetic factors. When comparing those with high (assimilation class) vs. low (separation class) acculturation based on measures describing the practice of South Asian traditions, frequency of fasting, foods eaten at home and in restaurants, and ethnic composition of friends, we also found some evidence that Indian immigrants with higher levels of acculturation had lower diabetes prevalence than those with low levels of acculturation. Furthermore, diabetes prevalence among those with high acculturation was similar to diabetes prevalence in the general US population. This result further highlights the potential impact of culture and culturally-governed behaviors.

Our finding that more acculturated immigrants had lower risk of diabetes is counter to evidence in other immigrant groups, including Mexican immigrants in the US (Anderson et al., 2016; Commodore-Mensah et al., n.d.; Kandula et al., 2008). Existing evidence in Indian immigrant populations is sparse but mixed (Al-Sofiani et al., 2020; Lee et al., 2020); however, prior work reporting a positive association between acculturation and diabetes risk used a self-reported measure of diabetes, which may lead to biased estimates (Lee et al., 2020). Our study agrees with prior findings in the MASALA cohort, which suggest that those with higher acculturation levels have a more favorable cardiometabolic profile (Al-Sofiani et al., 2020). Though the current analysis was limited to broad measures of acculturation and acculturation class, future research should seek to understand the specific aspects of acculturation (e.g., diet, social activities, lifestyle factors) that drove observed findings. Additionally, analyses probing the intersection of acculturation and other related factors including gender or caste may yield important insights, though larger sample sizes are needed to explore these interactions. Future studies should seek to collect detailed data on specific components of acculturation and ensure adequate sample sizes to assess mechanistic explanations for observed findings and explore interactions.

Several mechanisms have been previously proposed to explain observed differences in diabetes prevalence between Asian Indians and the general US population. There may be a role for genetic factors, including genetic differences in lean mass, which tends to be lower among Indians and can affect peripheral glucose uptake and clearance (DeFronzo; Narayan & Kanaya, 2020). However, if one assumes biological factors related to shared ancestry and genetics are more similar comparing Indians in the US and Indians in India compared to the general US population, our results suggest the importance of non-genetic influences, including differences in culture and context. Yet, it is important to note that without genetic data, it is impossible to confirm the genetic similarity of the groups included in the present study, and other factors such as early-life experiences may be shared between Indians in India and Indian immigrants in the US.

Prior evidence also has shown that Indians have reduced β-cell function compared to Western populations, leading to deficiencies in the secretion of insulin and lower glucose uptake across recipient organs including the muscle, brain and liver (Jainandunsing; Sharma et al., 2022). While such deficiencies may be partially genetic, an estimated polygenic risk score only explained 2 % of the total variation in β-cell functioning in a recent study (Siddiqui et al., 2022). This finding, alongside results from the present study, indicate that factors associated with culture and context likely also play a role in observed differences in β-cell function. While biological processes have some genetic underpinnings, they are also both caused and moderated by social and contextual factors. Environment-gene interaction through epigenetic modification may also influence lower β-cell function and lead to higher diabetes risk in Indians (Chambers et al., 2015). Further, additional evidence suggests that differences in diabetes prevalence may also be partially attributable to higher hepatic fat levels in South Asians, which could be due to higher saturated fat and carbohydrate content in traditional South Asian diets (Misra; Narayan & Kanaya, 2020).

The complexity of teasing apart differences that could plausibly be due to contextual, cultural, or biological and genetic factors has also been an important topic of discussion in studies investigating racial/ethnical disparities more broadly and in particular between White and Black racial groups in the United States (Borrell). However, the three-way comparison design applied in this study helps understand these factors and cannot be applied to more general racial comparisons when there is little or no active immigration. In spite of the challenge associated with disentangling potential causes, research comparing racial groups in the US has also suggested a more limited role for genetic differences in comparison to the broader impact of social factors on health disparities (Kaufman et al., 2015; Kramer & Hogue, 2009). This literature also highlights a number of issues that should be considered in future research on Indian immigrants, including the potential role of discrimination, which is currently subsumed into the broader category of “contextual” differences. Prior research has found associations between various measures of discrimination and chronic health conditions or risk factors, including HbA1c, highlighting the need to future work to further disentangle the role of discrimination from other “contextual” differences (Gee et al., 2007; Harris et al., 2006; Piette et al., 2006).

In this study, we also observed differences in correlates of diabetes across settings, which can provide additional insights into differences in the development of diabetes. In line with prior work, we found that the association between BMI and diabetes was weaker in Asian Indians as compared to the general US population (Ghai; Gupta et al., 2011). In this study, this attenuated association was most pronounced among Indian immigrants in the US, perhaps due to strong confounding by SES-related factors within the Indian context (those with higher SES may be more likely to be obese and have diabetes). Results point to the existence of alternate social and biological mechanisms leading to diabetes among Asian Indians in comparison to other populations; more research is needed to better understand these pathways, which may be related to reduced β-cell function.

In contrast to results from the US general population, among Indians living in India, those at higher levels of educational attainment were more likely to have diabetes. Observed discrepancies by educational attainment align with existing evidence (Corsi; Lamb et al., 2021) and observed patterns by other related demographic factors, such as urbanicity (Ranasinghe). Altogether, evidence suggests that lifestyle differences by demographic factors such as education in India are important. Prior work has hypothesized that these disparities may be attributable to the adoption of Westernized practices, including more sedentary lifestyles, and increased fast food and soft drink consumption (Blakely et al., 2005; Hosseinpoor et al., 2012). Stark increases in the prevalence of diabetes in India and other Asian countries further supports the modernization hypothesis (Diamond, 2011; Ramachandran et al., 2004, 2012). Future studies should directly compare the impact of adopting Westernized practices in India vs. in the US, in light of our finding that acculturation within the US context was associated with lower diabetes risk.

Strengths of this study include the three-way comparison design, the use of large, nationally-representative studies in India and the US, the use of matching to control for demographic differences in comparisons, and the ability to probe the associations with acculturation among Indian immigrants in the US. However, some limitations should be considered. First, the smaller available sample size in the MASALA cohort given study design and sample exclusions led to imprecision in estimates, which was particularly impactful in the assessment of associations with correlates of diabetes prevalence and could increase the chance of issues with confounder adjustment due to sparse cells and positivity violations. In acknowledgement of the imprecision of findings from MASALA, we focused our interpretation on estimated effect sizes with consideration of confidence intervals and P-values, rather than relying solely on P-values alone. Because we required hypothesized confounders included in analyses to be available in all three studies, we relied on educational attainment and marital status to represent social determinants of health. To the extent that this leaves important facets of socioeconomic status unmeasured, the impact of unmeasured confounding and healthy immigrant bias (Markides) should be considered. However, many of the observed differences in diabetes prevalence and in associations with covariates were large, and therefore unmeasured confounders would have to be quite strong to explain away observed patterns.

Third, some evidence has suggested that HbA1c may be a more sensitive marker of diabetes in Asian Indians than in other ethnic groups, with a prior analysis showing that 1.3 % and 3.5 % of Asian Indians met multifactorial criteria for diabetes solely based on elevated HbA1c but not other markers, whereas in other populations, diabetes prevalence is lower on HbA1c as compared to other markers (Gujral; NCD Risk Factor Collaboration (NCD-RisC), 2015). However, these margins are smaller than observed differences between groups in this study and would not be expected to impact observed differences between Indian immigrants in the US compared to Indians in India or differences in groups with varying levels of acculturation. Fourth, while comparisons between Indian immigrants and the general US population hold current context constant, there may be differences in early-life contextual factors that could shape biological risk for diabetes. However, these differences are held constant in analyses examining factors across acculturation levels of Indian immigrants in the US. Finally, we used cross-sectional data for analyses; comparisons of diabetes prevalence may be due to differences in disease risk or differences in disease duration. Future studies should extend comparisons by using longitudinal data to compare diabetes incidence and correlates of incidence.

Comparisons from this study highlight the importance of context and culture in understanding the observed differences in diabetes prevalence between Asian Indians and high-income Western populations. Findings on acculturation in Indians immigrants in the US provides further evidence on the importance of culture, which may impact diabetes prevalence through differences in lifestyle, diet, or epigenetic interactions between lifestyle, life experiences, and genetics. Future research should further explore the specific components of acculturation and lifestyle that have the strongest associations with diabetes risk. Better understanding of the cultural and environmental factors associated with diabetes risk among Indians can inform the design of public health interventions to reduce the burden of diabetes in this population. Insights gleaned from this population may also contribute to our understanding of the development of diabetes more broadly.

CRediT authorship contribution statement

Emma Nichols: Writing – review & editing, Writing – original draft, Visualization, Supervision, Methodology, Investigation. Hunter Green: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis. Haomiao Jin: Writing – review & editing, Investigation. David Flood: Writing – review & editing, Investigation. Elizabeth Rose Mayeda: Writing – review & editing, Investigation, Conceptualization. M. Maria Glymour: Writing – review & editing, Investigation, Conceptualization. Namratha R. Kandula: Writing – review & editing, Project administration, Investigation. Alka M. Kanaya: Writing – review & editing, Project administration, Investigation, Funding acquisition, Conceptualization. Jinkook Lee: Writing – review & editing, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.

Ethical statement

The MASALA study received ethical approval from the Institutional Review Boards of the University of California San Francisco and Northwestern University. The LASI study received ethical approval from the following collaborating organizations: Indian Council of Medical Research (ICMR), Delhi; IRB, International Institute for Population Sciences (IIPS), Mumbai; IRB, Harvard T.H. Chan School of Public Health (HSPH), Boston; IRB, University of Southern California (USC), Los Angeles; IRB, ICMR-National AIDS Research Institute (NARI), Pune; and IRB, Regional Geriatric Centres (RGCs), MoHFW. The HRS received ethical approval from the University of Michigan Institutional Review Board. The present study was a secondary data analysis of existing de-identified data, and is therefore not human subjects research.

Data statement

LASI data used in this study, with the exception of DBS biomarkers, are publicly available on the websites of the Gateway to Global Aging (https://g2aging.org/) and International Institute for Population Sciences (IIPS; https://www.iipsindia.ac.in/content/LASI-data). DBS biomarkers will be made public after the data are submitted to the Ministry of Health and Family Welfare, Government of India. To access data, users must register, provide an email address, and sign a data use agreement. HRS data used in this study are publicly available on the HRS project website (https://hrs.isr.umich.edu/). To access data, users must register, provide an email address, and sign a data use agreement. MASALA data are available for analysis following submission and approval of a manuscript proposal, with IRB approval and a signed data use agreement. Instructions and guidelines can be accessed at the MASALA website (https://www.masalastudy.org/for-researchers).

Funding

Data collection for the MASALA cohort was supported by National Heart Lung and Blood Institute (National Institutes of Health [NIH]) grants R01HL093009 and K24HL112827. Data collection at the University of California, San Francisco, was supported by NIH/NCRR grant UL1 RR024131. Data collection for the HRS study was funded by NIH grant U01AG009740 and by the Social Security Administration. Data collection for the LASI study was funded by the National Institute on Aging (NIA) (R01AG042778) and the Ministry of Health and Family Welfare, Government of India (T22011/02/2015-NCD). The preparation of this paper was funded by the NIA (R01AG030153). DF was supported by grant K23HL161271 from the National Heart, Lung, and Blood Institute and grant AG024824 from the National Institute on Aging Pepper Center.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssmph.2025.101777.

Contributor Information

Emma Nichols, Email: emmanich@usc.edu.

Hunter Green, Email: huntgreen2@gmail.com.

Haomiao Jin, Email: h.jin@surrey.ac.uk.

David Flood, Email: dcflood@umich.edu.

Elizabeth Rose Mayeda, Email: ermayeda@ph.ucla.edu.

M. Maria Glymour, Email: mglymour@bu.edu.

Namratha R. Kandula, Email: kandula@northwestern.edu.

Alka M. Kanaya, Email: alka.kanaya@ucsf.edu.

Jinkook Lee, Email: jinkookl@usc.edu.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.pdf (513.2KB, pdf)

Data availability

Data are available on study webpages as detailed in the data statement.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.pdf (513.2KB, pdf)

Data Availability Statement

Data are available on study webpages as detailed in the data statement.

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