ABSTRACT
Background
Ultra-processed food (UPF) consumption is linked to adverse health outcomes, including cardiovascular disease and all-cause mortality. Asian Americans (AAs) are the fastest growing ethnic group in the United States, yet their dietary patterns have seldom been described.
Objectives
The aim was to characterize UPF consumption among AAs and determine whether acculturation is associated with increased UPF consumption.
Methods
The NHANES is an annual, cross-sectional survey representative of the US population. We examined 2011–2018 NHANES data, which included 2404 AAs ≥18 y old with valid 24-h dietary recall. Using day 1 dietary recall data, we characterized UPF consumption as the percentage of caloric intake from UPFs, using the NOVA classification system. Acculturation was characterized by nativity status, nativity status and years in the United States combined, home language, and an acculturation index. We assessed the association between acculturation and UPF consumption using linear regression analyses adjusted for age, sex, marital status, education, income, self-reported health, and self-reported diet quality.
Results
UPFs provided, on average, 39.3% (95% CI: 38.1%, 40.5%) of total energy intake among AAs. In adjusted regression analyses, UPF consumption was 14% (95% CI: 9.5%, 17.5%; P < 0.05) greater among those with the highest compared with the lowest acculturation index score, 12% (95% CI: 8.5%, 14.7%: P < 0.05) greater among those who speak English only compared with non-English only in the home, 12% (95% CI: 8.6%, 14.7%: P < 0.05) greater among US-born compared with foreign-born AAs, and 15% (95% CI: 10.7%, 18.3%: P < 0.05) greater among US-born compared with foreign-born AAs with <10 y in the United States.
Conclusions
UPF consumption was common among AAs, and acculturation was strongly associated with greater proportional UPF intake. As the US-born AA population continues to grow, UPF consumption in this group is likely to increase. Further research on disaggregated AA subgroups is warranted to inform culturally tailored dietary interventions.
Keywords: ultra-processed food, Asian Americans, nutrition, diet, acculturation, NHANES
Introduction
Asian Americans (AAs) are the fastest growing racial or ethnic minority in the United States, with a population projected to nearly double to 46 million by 2060 (1). However, AAs remain significantly understudied in comparison to other racial and ethnic groups in the United States (2). The leading causes of death among AAs are noncommunicable diseases (NCDs), such as heart disease and diabetes (3, 4). AAs are disproportionately burdened by cardiovascular disease (CVD) risk factors, including hypertension, diabetes, and kidney failure, compared with their White, Black, and Hispanic counterparts (4). Poor diet has been implicated as a major contributor to this morbidity, and many AA subgroups report dietary patterns high in fat, salt, sugar, and refined grains (5–8).
To understand how dietary patterns contribute to adverse cardiometabolic health outcomes, emerging research has focused on the consumption of ultra-processed foods (UPFs). UPFs are defined broadly as foods containing salt, free sugars, oil, unhealthy fats, and substances not included in culinary preparations (e.g., coloring, sweeteners, emulsifiers, and other additives) (9). UPFs have been strongly linked to overweight and obesity, hypertension, metabolic syndrome, CVD, depression, cancer, and all-cause mortality (10, 11). In fact, recently updated dietary guidance from the American Heart Association recommends the consumption of minimally processed foods over UPFs for the promotion of cardiometabolic health (12). Yet, UPFs such as carbonated soft drinks, candies, fatty or salty packaged snacks, and other mass-produced products represent 57% of total energy intake among US adults (13). However, UPF consumption among AAs has yet to be characterized, despite the high cardiometabolic burden of disease in this population.
Age, sex, socioeconomic status (SES), and acculturation play an important role in shaping dietary patterns among AAs (14–16). Research in Asia has documented a recent increase in UPF consumption due to high economic growth rates, young and growing populations, and demands for convenience foods (17). It is unclear what impact, if any, SES in the United States has on the consumption of UPFs among AA populations. Further, AAs may be exposed to acculturative forces that influence diet. Although such drivers (e.g., language utilization, foreign-born status, and length of residence in the United States) may differ by ethnic subgroup (18), dietary acculturation among AAs can be understood as the adoption of Western dietary patterns, including a greater consumption of saturated fats, trans fats, and dietary cholesterol (19).
The goal of this work is 2-fold: 1) to describe UPF consumption among AAs, both overall and by sociodemographic and self-reported health characteristics (i.e., age, sex, marital status, educational level, income, general health, and diet), and 2) to determine whether acculturation is associated with UPF consumption among AAs. To do so, we utilized 24-h dietary recall data from the 2011–2018 NHANES and calculated UPF consumption, defined as the percentage of calories derived from UPFs (20), based on the NOVA framework (21).
Methods
Study design and population
NHANES is a cross-sectional, nationally representative survey that collects nutrition and health information from noninstitutionalized US residents selected through a complex, multistage probability sampling design (22). The current study combined data from NHANES cycles 2011–2012 through 2017–2018, during which AAs were oversampled. Of the 39,156 NHANES participants, our primary analytical sample included only AA adults above the age of 18 with at least one 24-h dietary recall (n = 2404). A supplementary analysis comparing UPF consumption across the different racial and ethnic groups included AA (n = 2404), non-Hispanic White (n = 7761), non-Hispanic Black (n = 4736), Hispanic (n = 4995), and “non-Hispanic other” (n = 784) US adults with valid dietary recall data.
UPF consumption
The NOVA classification system categorizes foods according to their level of processing and can be used to facilitate comparison between foods of different cultural backgrounds (20). The NOVA framework classifies foods into 4 mutually exclusive categories: unprocessed/minimally processed foods, processed culinary ingredients, processed foods, and UPFs. UPFs are combinations of processed ingredients that have undergone multiple levels of industrial alterations, such as hydrolysis and hydrogenation. UPFs include instant and canned soups, reconstituted meat and fish products, ready-made sauces, granola bars and protein bars, presweetened tea and coffee, flavored and/or sweetened yogurt, and industrially manufactured breads; the full list of UPFs can be found in previous work (23).
The outcome variable for this study was the mean percentage of total calories derived from UPFs. Participants were asked to complete two 24-h dietary recalls. Dietary recall interviews were performed by trained interviewers using the validated USDA Automated Multiple-Pass Method (24). Foods reported in the dietary recalls were categorized according to NOVA using the following NHANES variables: “Main Food Description,” “Additional Food Description,” “Source of Food,” “SR Code Description,” and “Combination Food Type.” These variables describe foods (Food Codes) and their underlying ingredients (SR codes). Homemade foods were classified according to their underlying ingredients to ensure an accurate NOVA categorization. To obtain the SR codes for each NHANES survey cycle, the corresponding versions were drawn from the USDA Food and Nutrient Database for Dietary Studies (FNDDS) (25). NHANES Food Code energy values were used to calculate the total daily calories consumed from UPFs for each participant. To calculate the percentage of calories derived from UPFs, we utilized the energy consumption in calories from NHANES day 1 dietary recall data. The UPF variable was captured as a percentage by multiplying the proportion by 100 and treating it as such throughout the analysis.
Acculturation
To characterize acculturation, we used 4 proxy measures:
Nativity status was defined as a self-reported US or foreign birth
Nativity status and years in the United States were defined by the following mutually exclusive categories: i) foreign-born and <10 y in the United States, ii) foreign-born and 10–19 y in the United States; iii) foreign-born and ≥20 y in the United States, or iv) US-born
Language spoken at home was also defined based on self-reported information and consisted of the following 3 categories: i) non-English only, ii) both another language and English equally or more English than non-English (representing “mixed”); or iii) English only
An acculturation index variable. We constructed the acculturation index variable by following methodology in the literature; the previous 2 variables (i.e., nativity status and years in the United States and language spoken at home) were summed to provide each participant with a total score ranging from 0–1, 2–3, and 4–5, representing “least,” “somewhat,” and “most acculturated,” respectively (15, 26).
Other variables
Demographic measures of interest were self-reported: sex (male or female), age (categorized as 18–24, 25–44, 45–64, and ≥65 y), and marital status (married, separated/divorced/widowed, or not married). Socioeconomic covariates included educational level [less than high school, high school graduate/General Equivalency Diploma (GED), some college, or college graduate or higher] and family income to poverty ratio, defined as the ratio of family income to the year-specific federal poverty threshold, categorized as <130%, 130–349%, or ≥350% (22). Measures of self-perceived general health status and dietary health were classified into 3 categories: excellent/very good, good, or fair/poor.
Statistical analysis
First, we described characteristics of the AA population (n, weighted %, and 95% CI of the weighted %) for each category of age, sex, marital status, educational level, family income to poverty ratio, general health status, dietary health, nativity status, nativity status and years lived in the United States, language spoken at home, and the acculturation index. We estimated the mean percentage of UPF intake overall and according to sociodemographic, self-reported health, and acculturation measures. We determined whether means differed across categories using linear regression analyses and visualized our findings with bar plots. Next, we used stepwise multivariable linear regression models to determine the association between UPF consumption and acculturation. For each acculturation proxy (i.e., nativity status, nativity status and years in the United States, language spoken at home, and the acculturation index), we included covariates in 4 stepwise models: 1) adjusted for age only, 2) additionally adjusted for sex and marital status, 3) additionally adjusted for educational level and family income to poverty ratio, and 4) additionally adjusted for self-reported general health status and dietary health. We reported regression coefficients and 95% CIs for each acculturation measure. As a supplementary analysis, we also estimated the mean percentage of UPF consumption across sociodemographic and self-reported health characteristics for each racial and ethnic group. We used ANOVAs to determine whether means statistically differed between AAs and the other racial and ethnic groups. For all analyses, statistical significance was set to α < 0.05. Data were analyzed using RStudio Desktop version 1.4.1717 (R Foundation for Statistical Computing).
Results
Most AA participants were female (52.8%), aged 25–44 y (44.3%), and married (64.9%) (Table 1). Most (54.5%) achieved a college education or greater, and 44.3% had an income to poverty ratio greater than 350%. Very few AAs indicated being in fair or poor health (10.8%) or consuming a fair or poor diet (14.8%). Most AAs were foreign-born (84.6%), foreign-born and living in the United States for at least 20 y (37.4%), and spoke only a non-English language in their home (43.4%). After applying the acculturation index to the sample, only 25.5% met the criteria for “most acculturated.”
TABLE 1.
Characteristics of non-Hispanic Asian-American adults with NOVA data from the NHANES, 2011–20181
| Non-Hispanic Asian-American adults (n = 2404) | ||
|---|---|---|
| n (weighted %) | 95% CI of weighted % | |
| Demographic measures | ||
| Sex | ||
| Female | 1219 (52.8) | (52.8, 52.9) |
| Male | 1185 (47.2) | (47.1, 47.2) |
| Age | ||
| 18–24 y | 313 (11.7) | (11.7, 11.8) |
| 25–44 y | 938 (44.3) | (44.2, 44.3) |
| 45–64 y | 821 (31.2) | (31.1, 31.2) |
| ≥65 y | 332 (12.9) | (12.8, 12.9) |
| Marital status | ||
| Married | 1546 (64.9) | (64.9, 64.9) |
| Separated/divorced/widowed | 224 (9.0) | (9.0, 9.1) |
| Not married | 511 (23.2) | (23.1, 23.2) |
| Socioeconomic measures | ||
| Highest level of education | ||
| < High school | 278 (10.8) | (10.8, 10.9) |
| High school graduate/GED | 293 (12.9) | (12.8, 12.9) |
| Some college | 439 (18.9) | (18.9, 19.0) |
| ≥ College graduate | 1271 (54.5) | (54.5, 54.6) |
| Family income to poverty ratio | ||
| <130% | 422 (16.7) | (16.6, 16.7) |
| 130–349% | 719 (29.0) | (28.9, 29.0) |
| ≥350% | 1015 (44.3) | (44.2, 44.3) |
| Self-reported health measures | ||
| General health status | ||
| Excellent/very good | 987 (41.6) | (41.5, 41.6) |
| Good | 928 (38.6) | (38.5, 38.6) |
| Fair/poor | 268 (10.8) | (10.7, 10.8) |
| Dietary health | ||
| Excellent/very good | 1059 (44.3) | (44.3, 44.4) |
| Good | 1003 (40.8) | (40.8, 40.9) |
| Fair/poor | 342 (14.8) | (14.8, 14.9) |
| Acculturation measures | ||
| Nativity status | ||
| US-born | 379 (15.4) | (15.3, 15.4) |
| Foreign-born | 2024 (84.6) | (84.5, 84.6) |
| Nativity status and years in United States | ||
| Foreign-born, <10 y | 586 (25.6) | (25.5, 25.6) |
| Foreign-born, 10–19 y | 505 (21.5) | (21.4, 21.5) |
| Foreign-born, ≥20 y | 917 (37.4) | (37.4, 37.5) |
| US-born | 379 (15.5) | (15.5, 15.6) |
| Language spoken at home | ||
| Non-English language only | 1066 (43.4) | (43.4, 43.5) |
| Mixed | 678 (29.1) | (29.1, 29.2) |
| English only | 653 (27.5) | (27.4, 27.5) |
| Acculturation index | ||
| Least acculturated | 806 (34.6) | (34.6, 34.7) |
| Somewhat acculturated | 961 (39.8) | (39.8, 39.9) |
| Most acculturated | 613 (25.5) | (25.5, 25.6) |
Percentages do not add up to 100 when there are missing values. GED, General Equivalency Diploma.
The mean percentage of caloric intake attributable to UPFs was 39.3% (95% CI: 38.1%, 40.5%) among all AAs (refer to Figure 1 for the distribution).
FIGURE 1.
Distribution of percentage of total calories derived from UPFs among Asian Americans, NHANES 2011–2018. UPF, ultra-processed food.
Mean UPF consumption did not differ by sex (P = 0.30) but was significantly lower with greater age (Figure 2 A). Mean UPF consumption was significantly lower among AAs aged 45–64 y (37.8% of kcal) and ≥65 y (33.4% of kcal) (P < 0.01) compared with AAs aged 18–24 y (44.5% of kcal). In addition, UPF consumption was higher among unmarried AAs (mean: 43.5% of kcal) compared with married AAs (37.5% of kcal) (P < 0.01). UPF consumption was also higher with greater educational level and greater income to poverty ratio (Figure 2B). While mean UPF consumption did not differ by general health status, it was significantly higher among those reporting a fair or poor diet (45.0% of kcal) compared with those reporting a very good or excellent diet (37.4% of kcal) (P < 0.01) (Figure 2C).
FIGURE 2.
Survey-weighted mean percentage of total calories derived from UPFs by (A) sociodemographic, (B) socioeconomic, and (C) self-reported health measures among non-Hispanic Asian American adults from the NHANES, 2011–2018. The numbers within each figure bar represent the n per group. Ref, reference; UPF, ultra-processed food. *Indicates estimate is significantly different (p < 0.05) compared to the reference category.
Acculturation measures
Mean UPF consumption was significantly higher among AAs born in the United States (51.5% of kcal) compared with foreign-born AAs (37.1% of kcal) (P < 0.01) (Figure 3). Similarly, those who had lived in the United States for at least 20 y consumed significantly more UPFs (38.2% of kcal) than AAs living in the United States for less than 10 y (35.0% of kcal) (P < 0.05). Moreover, those who speak only English at home consumed a significantly greater percentage of UPFs (47.4% of kcal) than AAs who speak only a non-English language (33.8% of kcal) (P < 0.05). Overall, classification using the acculturation index revealed that the “most acculturated” AAs consumed significantly more UPFs (48.8% for kcal) than their “least acculturated” counterparts (34.3% of kcal) (P < 0.05).
FIGURE 3.
Survey-weighted mean percentage of total calories derived from UPFs by acculturation measures among non-Hispanic Asian American adults from the National Health and Examination Survey (NHANES), 2011–2018. The numbers within each figure bar represent the n per group. Acc, Acculturation; Ref, reference; UPF, ultra-processed food. *Indicates estimate is significantly different (p < 0.05) compared to the reference category.
Results from the multivariable adjusted models showed that there was a strong association between greater levels of acculturation and UPF consumption (Table 2). The percentage of caloric intake attributable to UPFs was 11.7% greater among US-born AAs compared with those who were foreign-born (ß: 11.7), adjusting for age, sex, marital status, educational level, family income to poverty ratio, self-reported general health status, and self-reported dietary health (model 4). Consumption of UPFs was also significantly greater among foreign-born AAs who have been in the United States for greater than 20 y (ß: 5.13) and US-born AAs (ß: 14.5) compared with foreign-born AAs living in the United States for less than 10 y. In addition, consumption of UPFs was greater among AAs who speak only English in their home (ß: 11.6) or “mixed language” (ß: 4.86) compared with AAs who speak only a non-English language. Finally, consumption of UPFs was greater among “most acculturated” (ß: 13.5) and “somewhat acculturated” (ß: 4.55) AAs compared with “least acculturated” AAs.
TABLE 2.
Multivariable associations between measures of acculturation and UPF consumption among non-Hispanic Asian-American adults from the NHANES, 2011–20181
| Participants, n | Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|---|
| Nativity status | |||||
| Foreign-born | 2024 | Ref | Ref | Ref | Ref |
| US-born | 379 | 13.72 (10.9, 16.5) | 13.02 (10.0, 15.9) | 12.22 (9.1, 15.4) | 11.72 (8.6, 14.7) |
| Nativity status and years in United States | |||||
| Foreign-born, <10 y | 586 | Ref | Ref | Ref | Ref |
| Foreign-born, 10–19 y | 505 | 3.422 (0.34, 6.49) | 3.272 (0.19, 6.36) | 2.95 (–0.12, 6.03) | 2.99 (–0.14, 6.13) |
| Foreign-born, ≥20 y | 917 | 6.362 (3.23, 9.48) | 6.162 (3.01, 9.32) | 5.382 (1.99, 8.77) | 5.132 (1.79, 8.47) |
| US-born | 379 | 16.72 (13.5, 20.0) | 16.12 (12.5, 19.6) | 15.12 (11.2, 19.0) | 14.52 (10.7, 18.3) |
| Language spoken at home | |||||
| Non-English only | 1066 | Ref | Ref | Ref | Ref |
| Mixed | 678 | 5.522 (2.58, 8.46) | 5.452 (2.53, 8.37) | 5.012 (2.06, 7.97) | 4.862 (1.85, 7.86) |
| English only | 653 | 13.12 (10.3, 15.8) | 12.72 (9.9, 15.5) | 11.92 (8.8, 15.0) | 11.62 (8.5, 14.7) |
| Acculturation index | |||||
| Least acculturated | 806 | Ref | Ref | Ref | Ref |
| Somewhat acculturated | 961 | 5.412 (2.61, 8.22) | 5.192 (2.39, 7.98) | 4.762 (1.86, 7.67) | 4.552 (1.65, 7.46) |
| Most acculturated | 613 | 15.22 (11.8, 18.7) | 14.8 (11.2, 18.3) | 13.92 (10.0, 17.9) | 13.52 (9.5, 17.5) |
Values are linear regression coefficients (β) and 95% CIs. Model 1 adjusted for age; model 2 additionally adjusted for sex and marital status; model 3 additionally adjusted for educational level and family income to poverty ratio; model 4 additionally adjusted for general health status and dietary health. Ref, reference; UPF, ultra-processed food.
Different from the reference category, P < 0.05.
In our supplementary analysis (Supplemental Table 1), the percentage of calories attributable to UPFs was 39.3% among AAs, 57.7% among non-Hispanic Whites, 60.1% among non-Hispanic Blacks, and 52.7% among Hispanic US adults. For the “non-Hispanic other” group, mean UPF consumption was 57.7%. The percentage of calories attributable to UPFs was significantly lower among AAs compared with all other groups (P < 0.01).
Discussion
In this population-based study representative of AA adults in the United States, 39% of caloric intake was derived from UPFs. The percentage of UPF consumption was greater among AAs who were younger, unmarried, more educated, and had a higher income. Additionally, UPF consumption was greater at higher levels of acculturation, independent of important confounders. Given the fast growth rate of the AA population and the subsequent rise in the number of US-born AAs, UPF consumption is likely to increase among AAs (27). Future studies will be necessary to disentangle trends in dietary patterns across disaggregated AA subgroups as this heterogeneous cohort continues to grow and accounts for a greater share of the US population.
The current study revealed that UPF consumption differed across sociodemographic subgroups among AAs. As corroborated by other literature (28), we found that older age was associated with lower UPF consumption. This finding might be due to older adults being more health conscious and seeking to adopt diets that support a healthier lifestyle (29). Additionally, our finding of lower UPF consumption among married AAs is supported by research showing that married individuals have healthier eating habits, such as consuming less fast food and a greater amount of fruits and vegetables (30). Given that UPFs are convenient and cheaper than unprocessed foods (on a per kilocalorie basis, costing ∼$1.56 per 200 kcal less) (31, 32), we initially hypothesized greater UPF consumption among those of a lower SES. Although we found the opposite to be true, another NHANES analysis of the broader adult US population reported similar results (33). This observation may be explained by a higher demand for processed convenience foods (i.e., food that requires little preparation), such as protein bars and baby formula, among those highly educated/with high income levels (34), which require a greater amount of disposable income due to expense. Similarly, Asian countries such as India, Pakistan, and Indonesia have reported higher rates of UPF sales growth in recent years due to high economic growth rates, young and growing populations, and demand for convenience foods (35). Therefore, public health strategies should consider educational efforts that provide communities better tools to identify the diversity of UPFs consumed in daily life, including those which may be otherwise perceived as healthy.
Acculturation, or the process by which people adjust and adapt to a different culture (36), is particularly relevant for AAs, a group that is predominantly foreign-born. Dietary acculturation often results in shifts to US dietary practices, where 57.9% of total energy intake is attributed to UPFs (9, 37). Consistent with other studies (38–41), we found that acculturation (using multiple proxy measures) was associated with UPF consumption in a graded fashion. For instance, Asian immigrants and households with foreign-born individuals have been found to adhere to traditional food-preparation practices and express hesitancy towards those perceived as mainstream in the United States, such as fast food (21, 41–43). Home-prepared meals, which may involve less-processed foods, have been linked to balanced, healthy diets and represent a protective factor against CVD (44). While this study is the first to explore the association between acculturation and UPF consumption among AAs, other studies have demonstrated a clear link between acculturation, poor dietary quality, and adverse health outcomes, such as coronary artery disease, obesity, and type 2 diabetes among AAs (40, 45, 46). Given the expected rise in the number of acculturated AAs and data revealing that processed convenience foods are becoming more accessible to AA immigrants, dietary interventions addressing UPF intake among AAs will need to consider and adapt to the acculturative forces underpinning these dietary behaviors (41).
Of note, we found that UPF consumption was substantially lower among AAs compared with the other racial and ethnic groups across each investigated variable. Given that 71% of the AA adult population is foreign-born (47), the relatively low levels of UPF intake are not surprising. However, these findings should be interpreted with caution; the AA population is an exceptionally heterogeneous group consisting of numerous ethnicities with differing dietary practices, sociodemographic characteristics, and disease burdens (48–50). Although AAs comparatively consume fewer UPFs than other racial and ethnic groups, as acculturation dynamics change in coming years this problem will likely worsen. Therefore, properly contextualizing our findings would require disaggregation of AA data, which we aim to do in future analyses.
Other limitations of this work include its cross-sectional design. However, many of our measures of acculturation (exposures of interest) inherently precede the collection of dietary information (e.g., nativity status and years lived in the United States) and, therefore, could support a causal relationship. In addition, self-reported dietary recalls are subject to potential recall and social desirability bias. Finally, we were unable to utilize generational status as an acculturation measure, which has been linked to increased consumption of processed-meat intake among AAs (51). Despite such limitations, we believe our study has notable strengths. To the best of our knowledge, this was the first study to characterize UPF consumption among thousands of AAs using nationally representative data. Previous cross-sectional studies exploring UPF consumption in the United States have aggregated AAs into an “other race” category (21). Therefore, this analysis provides a “first look” at UPF consumption among AAs using the NOVA framework. Furthermore, this investigation used 4 proxy measures for acculturation, all with consistent results, which strengthens the scientific rigor of our conclusions. Last, we had access to a large sample of AAs with standardized 24-h dietary recalls after combining multiple years of data.
In conclusion, among AA adults, UPFs constituted a significant percentage of caloric intake, particularly among younger, unmarried, and higher-SES AAs. Additionally, we found a direct association between higher levels of acculturation and a greater percentage of diet derived from UPFs. Because diet is a major lifestyle risk factor that contributes to CVD and other NCDs, it is essential to promote healthy dietary behaviors among the more acculturated AAs through culturally tailored nutritional intervention programs that have been effectively implemented in similar groups (52–55). Further investigation into the subgroup diversity of UPF consumption among the AA population will aid in the development and implementation of such programs in an effort to mitigate the negative impacts of acculturation and UPF consumption on long-term CVD and NCD risk.
Supplementary Material
Acknowledgments
The authors greatly appreciate the insights and mentorship provided by the 2021 Stanford CARE Scholars team, including, but not limited to, Shozen Dan, Jaiveer Singh, Osika Tripathi, Nora Sharp, and Dr. Rita Popat. The authors’ responsibilities were as follows-LP, MSS, EY, FJ, and SHA provided a critical appraisal and review of the manuscript; KP, VS, ZR, RV, SHA, MSS, FJ, and TE: designed the research; KP, VS, ZR, RV, FJ, and TE: analyzed data; KP, VS, ZR, RV, SHA, and TE: wrote the manuscript; KP, VS, ZR, RV, and TE: had primary responsibility for final content; and all authors: read and approved the final manuscript.
Notes
TE is currently supported by a grant from the NIH/National Institute on Minority Health and Health Disparities (NIH/NIMHD; K01MD014158) and received support from the Rutgers University Asian Resource Center for Minority Aging Research under NIH/National Institute on Aging (NIH/NIA) grant P30-AG0059304.
Author disclosures: The authors report no conflicts of interest. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Supplemental Table 1 is available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/.
Abbreviations used: AA, Asian American; CVD, cardiovascular disease; NCD, noncommunicable disease; SES, socioeconomic status; UPF, ultra-processed food.
Contributor Information
Krithi Pachipala, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA.
Vishal Shankar, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA.
Zachary Rezler, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA.
Ranjana Vittal, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA.
Shahmir H Ali, Department of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY, USA.
Malathi S Srinivasan, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA.
Latha Palaniappan, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA.
Eugene Yang, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Division of Cardiology, University of Washington School of Medicine, Seattle, WA, USA.
Filippa Juul, Department of Public Health Policy and Management, New York University School of Global Public Health, New York, NY, USA.
Tali Elfassy, Stanford Center for Asian Health Research and Education (CARE), Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Katz Family Division of Nephrology and Hypertension, University of Miami Miller School of Medicine, Miami, FL, USA.
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