Abstract
Background:
While food allergies are considered common, relatively little is known about disparities in food allergy by race in the United States.
Objective:
To evaluate differences in reported food allergy and food-associated anaphylaxis among individuals enrolled in a longitudinal cohort study from metropolitan Detroit, Michigan.
Methods:
Participants in the Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) were asked about food allergies, including the inciting food and associated symptoms. Individuals were considered to have food-associated anaphylaxis if symptoms coincided with established clinical criteria. Logistic regression was used to assess whether race difference persisted after adjusting for and stratifying by potential confounders. African genetic ancestry was individually estimated among African American SAPPHIRE participants to assess whether ancestry was associated with food allergy.
Results:
Within the SAPPHIRE cohort, African American participants were significantly more likely to report food allergy (26.1% vs. 17%; P=3.47×10−18) and have food-associated anaphylactic symptoms (12.7% vs. 7%; P=4.65×10−14) when compared with European American participants. Allergy to seafood accounted for the largest difference (13.1% vs. 4.6%; P=1.38×10−31). Differences in food allergy by race persisted after adjusting for potential confounders including asthma status. Among African American participants, proportion of African ancestry was not associated with any outcome evaluated.
Conclusion:
Compared with European Americans, African Americans appear to be at higher risk for developing food allergy and food-associated anaphylaxis, particularly with regard to seafood allergy. The lack of association with genetic ancestry suggests that socio-environmental determinants may play a role in these disparities.
Keywords: food allergy, anaphylaxis, health status disparities, genetic ancestry
INTRODUCTION
According to National Institute of Allergy and Infectious Disease (NIAID) sponsored Guidelines for the Diagnosis and Management of Food Allergy in the United States, food allergy is broadly defined as “an adverse health effect arising from a specific immune response that occurs reproducibly on exposure to a given food.” 1 Nationwide surveys suggest that up to 10% of the U.S. population have an allergy to one or more foods, 2, 3 and Gupta et al. estimated that the economic cost of childhood food allergy alone in the U.S. exceeds $24 billion annually. 4
As with other atopic conditions, 5–7 food allergy appears to affect population groups differently. 8 For example, the prevalence of allergy to peanut, tree nut, and seafood is reportedly higher among African Americans when compared to non-Hispanic white individuals. 3, 9–11 A meta-analysis of food allergy surveys suggested that the rate increase from 1998 to 2011 was greater among African American children when compared with non-Hispanic white children in the U.S. 12 Jerschow et al. observed that African American males were the only U.S. demographic group with a significant increase in food-related anaphylaxis deaths between 1999 and 2010. 13
The Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) is a longitudinal cohort study of individuals with and without asthma from southeast Michigan and the Detroit metropolitan area. The study population is racially diverse, thereby permitting an assessment of population group differences. As part of this study, individuals provided a detailed history of symptoms related to food consumption and the types of foods eliciting this response. Symptoms were categorized into those consistent with anaphylaxis based on current clinical guidelines. 14 We used this information to measure difference in reported food allergy and food-induced anaphylaxis by race-ethnicity.
METHODS
Study participants and setting
This study was approved by the institutional review board (IRB) of Henry Ford Health System. Study participants provided informed written consent (as well as written assent for minors) at the time of study enrollment and prior to data collection. SAPPHIRE recruitment which began in 2007 has been described in detail elsewhere. 15–17 SAPPHIRE cohort participants were recruited from a large health care system serving the entirety of southeast Michigan, including the Detroit metropolitan area. Health plan members were broadly reflective of the demographic diversity of this region. Electronic medical records were first used to identify potentially eligible individuals. Eligible participants had to be between the age of 12–56 years at the time of recruitment and have no prior history of chronic obstructive pulmonary disease or congestive heart failure. All individuals meeting the above criteria with a prior diagnosis of asthma were invited to participate, and a random sample of individuals without asthma from the same geographic areas were recruited contemporaneously. Individuals were recruited from health system clinic sites distributed throughout the service area. For this analysis, we assessed individuals who self-identified as only African American or non-Hispanic white (henceforth referred to as European American); these two groups accounted for 91% of SAPPHIRE participants. We excluded population groups other than African Americans and European Americans from the analysis as they were too small in number to assess with granularity specific food allergies, as well as the association between food allergy and both race and genetic ancestry.
Data collection and food allergy determination
At the time of enrollment, participants underwent a research evaluation, which included completing a detailed study questionnaire, performing lung function testing, and submitting blood samples for biobanking. The staff-administered questionnaire included questions on food allergy, including the inciting food and the related symptoms. Food-related symptoms were categorized by a clinician into those consistent with anaphylaxis as defined using the National Institute of Allergy and Infectious Diseases and the Food Allergy and Anaphylaxis Network (NIAID/FAAN) clinical criteria. 14, 18, 19 Namely, an individual needed to report at least two of the following groups of signs or symptoms to be considered a likely anaphylactic response to food: 1) involvement of skin or mucosa, such as hives, swelling (e.g., lips, tongue, or uvula), or itching; 2) breathing symptoms, such as wheezing, shortness of breath, stridor, or hypoxia; 3) gastrointestinal symptoms, such as abdominal cramping, nausea, or vomiting; or 4) loss of blood pressure, such as sudden collapse, syncope, or incontinence. 14, 18, 19
DNA sequencing and genetic ancestry estimation
DNA was isolated from whole blood, and the resulting DNA was quantified by spectrophotometer and Quant-iT PicoGreen dsDNA assay (Thermo Fisher Scientific, Waltham, MA). Whole genome sequencing (WGS) was performed among African American SAPPHIRE participants as part of the Trans-Omics for Precision Medicine (TOPMed) program. Library preparation and WGS was performed at the Northwest Genomics Center. Libraries for sequencing were created using the TruSeq DNA PCR-free library preparation kit (Illumina, San Diego, CA); the targeted read depth was 30x with paired-end 150bp reads. The Illumina package, bcl2fastq v2 15.0, was used to generate the FASTQ files, and the TOPMed Data Coordinating Center at the Northwest Genomics Center aligned the raw sequence reads in FASTQ format to create binary sequence alignment (BAM) files. Variant calling was performed at the TOPMed Informatics Research Center at the University of Michigan.
For the analytic set, we removed individuals whose sequence or array data results did not meet pre-determined quality control standards, such as X chromosome heterozygosity results which did not match reported sex and low sequencing depth. The proportion of African ancestry among African American individuals was estimated using the program ADMIXTURE; the YRI and CEU reference populations from the 1000 Genomes project were used in estimating ancestral proportion. 20, 21
Statistical analyses
Baseline differences between African American and European American study participants were assessed using Welch’s t-test for continuous variables and chi-squared test for categorical variables. The Fisher’s exact test was used to compare differences in the number of individuals reporting food allergy and food-induced anaphylaxis by race-ethnicity. Food allergies were also categorized by food type (e.g., nuts, seeds and legumes, seafood, dairy, eggs, meat, fruits, vegetables). We also assessed whether population group differences in food allergy persisted after stratifying by asthma status.
Logistic regression analysis was used to assess differences in reported allergy to any food and to seafood. A similar analysis was conducted for the outcome of food-associated anaphylactic symptoms. All models adjusted for patient age, sex, body mass index (BMI), and asthma status. We also separately stratified our models by race and asthma status. Among African American study participants, we included an additional variable to assess the relationship between proportion of African ancestry and outcomes – reported food allergy and food-associated anaphylactic symptoms to any food and seafood.
Unless otherwise noted, analyses were performed using the statistical software R along with packages designed to run on this platform. 22, 23
RESULTS
Among the 7,627 SAPPHIRE participants included in this analysis, 4,558 individuals self-identified as African American and 2,391 self-identified as European American. Table 1 presents the characteristics of participants overall and stratified by race. African American participants were younger (34.1 vs. 35.5 years; P<0.001) and had a higher BMI (31.9 vs. 28.8; P< 0.001) when compared with European American participants. The former was also more likely to be female (66.0% vs. 62.5%; P= 0.004), to be a current smoker (20.0% vs. 12.4%; P< 0.001) and to have had early-onset asthma (57.1% vs. 55.3%) when compared with the latter. There were also significant differences in household income by race-ethnicity (P<0.001). The average proportions of African and European genetic ancestry in African American participants were 0.831 and 0.169, respectively (standard deviation [SD], 0.091); among European American participants, these proportions were 0.013 and 0.987, respectively (SD, 0.025). Of the 7,627 SAPPHIRE participants included in this analysis, 6,253 (82%) had a prior diagnosis of asthma, and 1,374 (18%) had no earlier history of asthma at the time of enrollment.
Table 1.
Characteristics of SAPPHIRE participants included in the analysis
| Characteristic | Total (n=7,627) |
African American (n=4,558) |
European American (n=2,391) |
P-value* |
|---|---|---|---|---|
| Age in years - mean ± SD | 34.3 ± 14.3 | 34.1 ± 14.0 | 35.5 ± 14.8 | < 0.001 |
| Female sex – no. (%) | 4,921 (64.5%) | 3,008 (66.0%) | 1,494 (62.5%) | 0.004 |
| Race-ethnicity – no. (%) | -- | -- | -- | -- |
| African American | 4,558 (59.8%) | 4,558 (100%) | -- | -- |
| European American | 2,391 (31.3%) | -- | 2,391 (100%) | -- |
| Latino | 242 (3.2%) | -- | -- | -- |
| Other† | 436 (5.7%) | -- | -- | -- |
| Ancestry – proportion African/European ± SD‡ | -- | 0.831/0.169 ± 0.091 | 0.013/0.987 ± 0.025 | <0.001 |
| Asthma – no. (%) | -- | -- | -- | < 0.001 |
| Childhood onset | 4,348 (57.0%) | 2,603 (57.1%) | 1,323 (55.3%) | -- |
| Adult onset | 1,804 (23.7%) | 1,024 (22.5%) | 646 (27.0%) | -- |
| BMI – mean ± SD | 30.7 ± 9.0 | 31.9 ± 9.3 | 28.8 ± 8.0 | < 0.001 |
| Smoking status | -- | -- | -- | < 0.001 |
| Never smoker | 5,422 (71.1%) | 3,203 (70.3%) | 1,709 (71.5%) | -- |
| Past smoker | 894 (11.7%) | 443 (9.7%) | 384 (16.1%) | -- |
| Current smoker | 1,299 (17.0%) | 911 (20.0%) | 297 (12.4%) | -- |
| Household income§ | -- | -- | -- | < 0.001 |
| <$10,000 | 756 (9.9%) | 643 (14.1%) | 75 (3.1%) | -- |
| $10,000 to $49,999 | 2,229 (29.2%) | 1,548 (34.0%) | 482 (20.2%) | -- |
| $50,000 to $89,999 | 1,639 (21.5%) | 867 (19.0%) | 628 (26.3%) | -- |
| $90,000 to $149,999 | 969 (12.7%) | 350 (7.7%) | 516 (21.6%) | -- |
| ≥$150,000 | 355 (4.7%) | 85 (1.9%) | 232 (9.7%) | -- |
SAPPHIRE denotes the Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity; SD, standard deviation; and BMI, body mass index.
P-values compared the differences between individuals reporting African American and European American race.
Individuals grouped into the “other” category included those whose self-reported race is shown in Supplementary Table E7 but not included here, individuals affiliating with multiple race groups, and individuals whose race was unknown or unreported.
Genetic ancestry could be estimated in participants who had existing whole genome sequence data or genome-wide array data; this included 4,150 African American participants and 1,145 European American participants.
Participant-reported total pre-tax household income in the preceding year.
As shown in Table 2, self-reported food allergy was significantly higher among the African American participants when compared with European American participants (26.1% vs. 17.0%, respectively; P=3.47×10−18). The largest driver of these between group differences was reported allergy to seafood (13.1% vs 4.6%, respectively; P=1.38×10−31), including shrimp (3.0% vs. 0.6%; P=5.50×10−13), all shellfish (6.0% vs. 3.1%; P=8.66×10−8), and fin fish (4.3% vs. 1.3%; P=8.38×10−13). African American participants were also more likely to report allergy to eggs (1.5% vs. 0.9%; P= 0.045), peanuts (3.4% vs. 1.8%; P=6.77×10−5), strawberries (1% vs. 0.5%; P=0.027), tomatoes (2.3% vs. 0.6%; P=3.72×10−8), and citrus fruits combined (1.4% vs. 0.5%; P=0.001). The only food categories for which African Americans were significantly less likely to report an allergy when compared to European American participants were gluten (0% vs. 0.4%; P=2.30×10−5), “seeds” (0.2% vs. 0.5%; P=0.013) and fruits such as avocado (0.1% vs 0.3%; P=0.038) and papaya (0% vs 0.1%; P= 0.041).
Table 2.
Differences in reported food allergy by race stratified by food type among African American and European American SAPPHIRE participants
| Food Category | Food type | African Americans (n=4,558) | European Americans (n=2,391) | P-value* |
|---|---|---|---|---|
| Tree Nuts and Nut Derivatives | Chocolate | 65 (1.4%) | 20 (0.8%) | 0.038 |
| Coconut | 9 (0.2%) | 4 (0.2%) | 1.000 | |
| Almond | 10 (0.2%) | 5 (0.2%) | 1.000 | |
| Brazil nut | 0 (0%) | 1 (0%) | 0.344 | |
| Cashew | 5 (0.1%) | 1 (0%) | 0.671 | |
| Hazel nut | 1 (0%) | 3 (0.1%) | 0.121 | |
| Macadamia nut | 4 (0.1%) | 0 (0%) | 0.306 | |
| Pecan | 9 (0.2%) | 2 (0.1%) | 0.350 | |
| Pistachio | 2 (0%) | 4 (0.2%) | 0.191 | |
| Walnut | 10 (0.2%) | 11 (0.5%) | 0.106 | |
| All nuts | 101 (2.2%) | 47 (2%) | 0.541 | |
| Grains, Seeds, and Legumes | Beans and Peas | 14 (0.3%) | 5 (0.2%) | 0.630 |
| Corn | 12 (0.3%) | 9 (0.4%) | 0.491 | |
| Gluten | 0 (0%) | 10 (0.4%) | 2.30×10−5 | |
| Legumes, other | 5 (0.1%) | 3 (0.1%) | 1.000 | |
| Peanut | 154 (3.4%) | 42 (1.8%) | 6.77×10−5 | |
| “Seeds” | 7 (0.2%) | 12 (0.5%) | 0.013 | |
| Soy | 10 (0.2%) | 7 (0.3%) | 0.612 | |
| Wheat bread | 21 (0.5%) | 20 (0.8%) | 0.068 | |
| All seeds and legumes | 193 (4.2%) | 79 (3.3%) | 0.059 | |
| Seafood † | Abalone | 0 (0%) | 1 (0%) | 0.344 |
| Clam | 2 (0%) | 0 (0%) | 0.549 | |
| Crab | 23 (0.5%) | 3 (0.1%) | 0.012 | |
| Crawfish | 2 (0%) | 0 (0%) | 0.549 | |
| Lobster | 21 (0.5%) | 3 (0.1%) | 0.029 | |
| Oyster | 2 (0%) | 0 (0%) | 0.549 | |
| Scallop | 0 (0%) | 1 (0%) | 0.344 | |
| Shrimp | 138 (3%) | 14 (0.6%) | 5.50×10−13 | |
| All shellfish | 274 (6%) | 75 (3.1%) | 8.66×10−8 | |
| Fin fish | 196 (4.3%) | 31 (1.3%) | 8.38×10−13 | |
| All seafood | 597 (13.1%) | 111 (4.6%) | 1.38×10−31 | |
| Dairy | Milk | 117 (2.6%) | 61 (2.6%) | 1.000 |
| Eggs | Egg | 69 (1.5%) | 22 (0.9%) | 0.045 |
| Meat | Meat | 24 (0.5%) | 22 (0.9%) | 0.062 |
| Fruits and Berries | Apples | 18 (0.4%) | 8 (0.3%) | 0.837 |
| Apricot | 1 (0%) | 1 (0%) | 1.000 | |
| Avocado | 3 (0.1%) | 7 (0.3%) | 0.038 | |
| Bananas | 47 (1%) | 17 (0.7%) | 0.234 | |
| Berries, other | 8 (0.2%) | 4 (0.2%) | 1.000 | |
| Blueberries | 1 (0%) | 0 (0%) | 1.000 | |
| Cantaloupe | 11 (0.2%) | 8 (0.3%) | 0.477 | |
| Cherry | 5 (0.1%) | 1 (0%) | 0.671 | |
| Citrus fruits | 64 (1.4%) | 13 (0.5%) | 0.001 | |
| Cucumber | 7 (0.2%) | 1 (0%) | 0.277 | |
| Date | 0 (0%) | 1 (0%) | 0.344 | |
| Eggplant | 2 (0%) | 3 (0.1%) | 0.347 | |
| Fig | 0 (0%) | 1 (0%) | 0.344 | |
| Grapes | 7 (0.2%) | 4 (0.2%) | 1.000 | |
| Green pepper | 6 (0.1%) | 5 (0.2%) | 0.528 | |
| Honeydew | 3 (0.1%) | 1 (0%) | 1.000 | |
| Kiwi | 12 (0.3%) | 6 (0.3%) | 1.000 | |
| Mango | 6 (0.1%) | 4 (0.2%) | 0.745 | |
| Melon, other | 7 (0.2%) | 7 (0.3%) | 0.262 | |
| Olive | 3 (0.1%) | 0 (0%) | 0.556 | |
| Papaya | 0 (0%) | 3 (0.1%) | 0.041 | |
| Peach | 11 (0.2%) | 4 (0.2%) | 0.599 | |
| Pear | 1 (0%) | 0 (0%) | 1.000 | |
| Pineapple | 19 (0.4%) | 7 (0.3%) | 0.537 | |
| Plum | 2 (0%) | 0 (0%) | 0.549 | |
| Pomegranate | 0 (0%) | 1 (0%) | 0.344 | |
| Squash | 2 (0%) | 3 (0.1%) | 0.347 | |
| Strawberry | 46 (1%) | 12 (0.5%) | 0.027 | |
| Tomatoes | 107 (2.3%) | 15 (0.6%) | 3.72×10−8 | |
| Watermelon | 13 (0.3%) | 5 (0.2%) | 0.629 | |
| Fruit, other | 16 (0.4%) | 8 (0.3%) | 1.000 | |
| All fruits | 318 (7%) | 112 (4.7%) | 1.55×10−4 | |
| Non-fruit Vegetables | Asparagus | 2 (0%) | 4 (0.2%) | 0.191 |
| Broccoli | 0 (0%) | 1 (0%) | 0.344 | |
| Cabbage | 1 (0%) | 1 (0%) | 1.000 | |
| Carrots | 4 (0.1%) | 1 (0%) | 0.666 | |
| Cauliflower | 0 (0%) | 1 (0%) | 0.344 | |
| Celery | 4 (0.1%) | 6 (0.3%) | 0.103 | |
| Kale | 1 (0%) | 0 (0%) | 1.000 | |
| Lettuce | 5 (0.1%) | 1 (0%) | 0.671 | |
| Okra | 2 (0%) | 0 (0%) | 0.549 | |
| Onion | 9 (0.2%) | 5 (0.2%) | 1.000 | |
| Spinach | 0 (0%) | 1 (0%) | 0.344 | |
| Sweet potato | 2 (0%) | 1 (0%) | 1.000 | |
| Tuber, other | 2 (0%) | 2 (0.1%) | 0.612 | |
| Vegetable, other | 6 (0.1%) | 3 (0.1%) | 1.000 | |
| All vegetables | 33 (0.7%) | 23 (1%) | 0.323 | |
| Spices | Basil | 1 (0%) | 0 (0%) | 1.000 |
| Black Pepper | 1 (0%) | 1 (0%) | 1.000 | |
| Capers | 0 (0%) | 1 (0%) | 0.344 | |
| Cinnamon | 5 (0.1%) | 2 (0.1%) | 1.000 | |
| Dill | 0 (0%) | 1 (0%) | 0.344 | |
| Garlic | 1 (0%) | 1 (0%) | 1.000 | |
| Ginger | 2 (0%) | 2 (0.1%) | 0.612 | |
| Horseradish | 1 (0%) | 1 (0%) | 1.000 | |
| Mustard | 7 (0.2%) | 0 (0%) | 0.104 | |
| Oregano | 0 (0%) | 1 (0%) | 0.344 | |
| Paprika | 1 (0%) | 1 (0%) | 1.000 | |
| Other | Honey | 3 (0.1%) | 2 (0.1%) | 1.000 |
| Mushroom | 15 (0.3%) | 11 (0.5%) | 0.412 | |
| All Foods | 1190 (26.1%) | 407 (17%) | 3.47×10−18 |
SAPPHIRE denotes the Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity.
P-value for the comparison between African American and European American individuals.
Some participants reported allergy to seafood without specifying fin fish or shellfish.
Food-associated anaphylactic symptoms demonstrated similar patterns to self-reported food allergy (Table 3). Anaphylactic symptoms to seafood constituted the largest difference between African Americans and European Americans (7.7% vs. 2.4%, respectively; P=2.40×10−21), and these between group differences included both shellfish (3.3% vs. 1.8%, P=1.52×10−4) and fin fish (2.5% vs. 0.7%, P=5.01×10−9). African American participants were also more likely to report anaphylactic symptoms to eggs (0.8% vs. 0.4%; P=0.030), peanuts (1.8% vs. 1.0%, P=0.007), and tomatoes (1.1% vs. 0.3%, P=1.37×10−4) when compared with European Americans. Despite small numbers, anaphylactic symptoms to gluten (0% vs. 0.1%, P=0.041) and seeds (0.1% vs. 0.3%, P=0.011) were significantly less common among African Americans.
Table 3.
Differences in reported food-associated anaphylaxis by race stratified by food type among African American and European American SAPPHIRE participants*
| Food Category | Food type | African American (n= 4558) | European American (n=2391) | P-value† |
|---|---|---|---|---|
| Tree Nuts and Nut Derivatives | Chocolate | 29 (0.6%) | 7 (0.3%) | 0.077 |
| Coconut | 6 (0.1%) | 2 (0.1%) | 0.723 | |
| Almond | 5 (0.1%) | 4 (0.2%) | 0.505 | |
| Brazil nut | 0 (0%) | 1 (0%) | 0.344 | |
| Cashew | 2 (0%) | 1 (0%) | 1.000 | |
| Hazel nut | 0 (0%) | 2 (0.1%) | 0.118 | |
| Macadamia nut | 3 (0.1%) | 0 (0%) | 0.556 | |
| Pecan | 5 (0.1%) | 0 (0%) | 0.172 | |
| Pistachio | 1 (0%) | 1 (0%) | 1.000 | |
| Walnut | 6 (0.1%) | 4 (0.2%) | 0.745 | |
| All nuts | 48 (1.1%) | 21 (0.9%) | 0.526 | |
| Grains, Seeds, and Legumes | Beans and Peas | 7 (0.2%) | 4 (0.2%) | 1.000 |
| Corn | 7 (0.2%) | 3 (0.1%) | 1.000 | |
| Gluten | 0 (0%) | 3 (0.1%) | 0.041 | |
| Legumes, other | 3 (0.1%) | 1 (0%) | 1.000 | |
| Peanut | 82 (1.8%) | 23 (1%) | 0.007 | |
| “Seeds” | 3 (0.1%) | 8 (0.3%) | 0.011 | |
| Soy | 4 (0.1%) | 4 (0.2%) | 0.459 | |
| Wheat bread | 8 (0.2%) | 6 (0.3%) | 0.576 | |
| All seeds and legumes | 100 (2.2%) | 41 (1.7%) | 0.210 | |
| Seafood ‡ | Abalone | 0 (0) | 0 (0) | -- |
| Clam | 2 (0%) | 0 (0%) | 0.549 | |
| Crab | 10 (0.2%) | 2 (0.1%) | 0.239 | |
| Crawfish | 1 (0%) | 0 (0%) | 1.000 | |
| Lobster | 10 (0.2%) | 1 (0%) | 0.111 | |
| Oyster | 1 (0%) | 0 (0%) | 1.000 | |
| Scallop | 0 (0) | 0 (0) | -- | |
| Shrimp | 73 (1.6%) | 4 (0.2%) | 1.40×10−9 | |
| All shellfish | 151 (3.3%) | 42 (1.8%) | 1.52×10−4 | |
| Fin fish | 116 (2.5%) | 16 (0.7%) | 5.01×10−9 | |
| All seafood | 349 (7.7%) | 57 (2.4%) | 2.40×10−21 | |
| Dairy | Milk | 46 (1%) | 20 (0.8%) | 0.518 |
| Eggs | Egg | 38 (0.8%) | 9 (0.4%) | 0.030 |
| Meat | Meat | 14 (0.3%) | 12 (0.5%) | 0.218 |
| Fruits and Berries | Apples | 6 (0.1%) | 1 (0%) | 0.434 |
| Apricot | 1 (0%) | 0 (0%) | 1.000 | |
| Avocado | 1 (0%) | 4 (0.2%) | 0.051 | |
| Bananas | 22 (0.5%) | 10 (0.4%) | 0.852 | |
| Berries, other | 4 (0.1%) | 1 (0%) | 0.666 | |
| Blueberries | 1 (0%) | 0 (0%) | 1.000 | |
| Cantaloupe | 7 (0.2%) | 2 (0.1%) | 0.727 | |
| Cherry | 2 (0%) | 0 (0%) | 0.549 | |
| Citrus fruits | 26 (0.6%) | 6 (0.3%) | 0.064 | |
| Cucumber | 1 (0%) | 1 (0%) | 1.000 | |
| Date | 0 (0) | 0 (0) | -- | |
| Eggplant | 1 (0%) | 0 (0%) | 1.000 | |
| Fig | 0 (0%) | 1 (0%) | 0.344 | |
| Grapes | 6 (0.1%) | 1 (0%) | 0.434 | |
| Green pepper | 4 (0.1%) | 3 (0.1%) | 0.698 | |
| Honeydew | 2 (0%) | 0 (0%) | 0.549 | |
| Kiwi | 7 (0.2%) | 4 (0.2%) | 1.000 | |
| Mango | 4 (0.1%) | 2 (0.1%) | 1.000 | |
| Melon, other | 4 (0.1%) | 3 (0.1%) | 0.698 | |
| Olive | 1 (0%) | 0 (0%) | 1.000 | |
| Papaya | 0 (0%) | 1 (0%) | 0.344 | |
| Peach | 7 (0.2%) | 3 (0.1%) | 1.000 | |
| Pear | 1 (0%) | 0 (0%) | 1.000 | |
| Pineapple | 8 (0.2%) | 1 (0%) | 0.178 | |
| Plum | 2 (0%) | 0 (0%) | 0.549 | |
| Pomegranate | 0 (0) | 0 (0) | -- | |
| Squash | 0 (0%) | 2 (0.1%) | 0.118 | |
| Strawberry | 25 (0.5%) | 8 (0.3%) | 0.272 | |
| Tomatoes | 48 (1.1%) | 6 (0.3%) | 1.37×10−4 | |
| Watermelon | 9 (0.2%) | 3 (0.1%) | 0.762 | |
| Fruit, other | 10 (0.2%) | 5 (0.2%) | 1.000 | |
| All fruits | 146 (3.2%) | 47 (2%) | 0.003 | |
| Non-fruit Vegetables | Asparagus | 0 (0%) | 1 (0%) | 0.344 |
| Broccoli | 0 (0%) | 1 (0%) | 0.344 | |
| Cabbage | 0 (0%) | 1 (0%) | 0.344 | |
| Carrots | 0 (0) | 0 (0) | -- | |
| Cauliflower | 0 (0%) | 1 (0%) | 0.344 | |
| Celery | 2 (0%) | 1 (0%) | 1.000 | |
| Kale | 0 (0) | 0 (0) | -- | |
| Lettuce | 2 (0%) | 0 (0%) | 0.549 | |
| Okra | 1 (0%) | 0 (0%) | 1.000 | |
| Onion | 4 (0.1%) | 4 (0.2%) | 0.459 | |
| Spinach | 0 (0) | 0 (0) | -- | |
| Sweet potato | 1 (0%) | 1 (0%) | 1.000 | |
| Tuber, other | 0 (0%) | 1 (0%) | 0.344 | |
| Vegetable, other | 5 (0.1%) | 3 (0.1%) | 1.000 | |
| All vegetables | 14 (0.3%) | 13 (0.5%) | 0.155 | |
| Spices | Basil | 1 (0%) | 0 (0%) | 1.000 |
| Black Pepper | 0 (0) | 0 (0) | -- | |
| Capers | 0 (0) | 0 (0) | -- | |
| Cinnamon | 1 (0%) | 2 (0.1%) | 0.274 | |
| Dill | 0 (0%) | 1 (0%) | 0.344 | |
| Garlic | 1 (0%) | 0 (0%) | 1.000 | |
| Ginger | 0 (0%) | 2 (0.1%) | 0.118 | |
| Horseradish | 1 (0%) | 1 (0%) | 1.000 | |
| Mustard | 4 (0.1%) | 0 (0%) | 0.306 | |
| Oregano | 0 (0) | 0 (0) | -- | |
| Paprika | 0 (0) | 0 (0) | -- | |
| Other | Honey | 2 (0%) | 1 (0%) | 1.000 |
| Mushroom | 8 (0.2%) | 5 (0.2%) | 0.775 | |
| All Foods | 580 (12.7%) | 167 (7%) | 4.65×10−14 |
SAPPHIRE denotes the Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity.
Patient with symptoms consistent with NIAID/FAAN clinical criteria in at least two categories (see Methods Section) were categorized as having food-associated anaphylaxis.
P-value for the comparison between African American and European American individuals.
P-value for the comparison between African American and European American individuals.
Some participants reported allergy to seafood without specifying fin fish or shellfish.
Tables E1–E4 of the online repository show the difference in food allergy and food-associated anaphylactic symptom prevalence by self-reported race after stratification by asthma status. Despite the smaller numbers, findings were consistent within the stratified analyses. Prevalence rates of food allergy and food-associated anaphylactic symptoms were significantly higher among African Americans when compared with European Americans (P=0.001 for differences in reported food allergy by race among individuals without asthma [Table E1; n=1,340]; P=0.003 for differences in anaphylactic symptoms by race among individuals without asthma [Table E2; n=1,340]; P=5.24×10−18 for differences in reported food allergy by race among individuals with asthma [Table E3; n=5,609]; and P=1.11×10−13 for differences in for anaphylactic symptoms by race among individuals with asthma [Table E4; n=5,609]). Seafood was again the most significant category in all of these stratified analyses (P=0.004 for reported seafood allergy among individuals without asthma [Table E1]; P=0.006 for seafood-associated anaphylactic symptoms among individuals without asthma [Table E2]; P=2.98×10−31 for reported seafood allergy among individuals with asthma [Table E3]; and 1.40×10−20 for seafood-associated anaphylactic symptoms among individuals with asthma [Table E4]).
As shown in Tables 4 and 5, race was an independent risk factor for both self-reported food allergy (adjusted odds ratio [aOR] 1.79; P=1.15 ×10−18) and food-associated anaphylaxis (aOR 1.99; P=2.25 ×10−13 respectively) to any food, even after adjusting for age, sex, BMI, and asthma status. This was further confirmed after stratifying by asthma status and demonstrating consistent increased odds ratios for food allergy among African Americans with (aOR 1.76; P=1.73×10−16) and without asthma (aOR 2.10; P=0.002) (Table 4). Similar findings were observed for food-associated anaphylaxis (Table 5) with African Americans appearing to be at risk when compared with European Americans regardless of asthma status (aOR 1.93 for race among individuals with asthma and aOR 3.85 for race among individuals without asthma).
Table 4.
Factors associated with reported food allergy among SAPPHIRE participants stratified by race and asthma status*
| Factors | All participants (n=6,949) | African Americans (n=4,150) | European Americans (n= 2,391) | Individuals with asthma (n= 5,609) | Individuals without asthma (n= 1,340) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | |
| Age (per 10-year increment) | 1.04 (1.00–1.09) | 0.065 | 1.00 (0.95–1.06) | 0.900 | 1.13 (1.05–1.23) | 0.002 | 1.03 (0.98–1.08) | 0.214 | 1.17 (1.01–1.35) | 0.035 |
| Sex (female referent) | 0.85 (0.75–0.96) | 0.009 | 0.91 (0.78–1.06) | 0.230 | 0.66 (0.51–0.83) | 5.82×10−4 | 0.84 (0.73–0.95) | 0.008 | 0.91 (0.61–1.34) | 0.645 |
| Race (European American referent) | 1.79 (1.57–2.04) | 1.15×10−18 | -- | -- | -- | -- | 1.76 (1.54–2.02) | 1.73×10−16 | 2.10 (1.35–3.40) | 0.002 |
| BMI (per unit increase) | 1.00 (1.00–1.01) | 0.456 | 1.00 (0.99–1.01) | 0.558 | 1.01 (0.99–1.02) | 0.376 | 1.00 (1.00–1.01) | 0.377 | 1.00 (0.97–1.02) | 0.746 |
| Asthma status | 3.37 (2.80–4.10) | 6.69×10−36 | 3.27 (2.62–4.12) | 7.48×10−25 | 3.85 (2.57–6.02) | 4.23×10−10 | -- | -- | -- | -- |
| Proportion African ancestry (per 10% increase)‡ | ND | ND | 0.97 (0.90–1.05) | 0.485 | ND | ND | ND | ND | ND | ND |
SAPPHIRE denotes Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-ethnicity; aOR, adjusted odds ratio; CI, confidence interval; BMI, body mass index; and ND, not done.
Outcome was any reported food allergy. Race categories were based on participant self-report and asthma status was based on a documented clinical diagnosis with concordance by patient report.
Logistic regression was used to adjust for all of the variables shown. Odds ratio for age were reported for 10-year increments while the models adjusted for each year increase. Sex (female=0, male=1), race (European American=0, African American=1), and asthma status (no history=0, positive history=1) were all coded as dichotomous variables.
Proportion of African ancestry was assessed using existing whole genome sequence data on SAPPHIRE participants and the YRI and CEU reference populations from the 1000 Genomes Project. African ancestry was only assessed in individuals who identified as African American, and was only included in these stratified models. Odds ratios for ancestry were reported for 10% increments of African ancestry while the models adjusted for each percentage increase.
Table 5.
Factors associated with reported food-associated anaphylaxis among SAPPHIRE participants stratified by race and asthma status*
| Factors | All participants (n=6,949) | African Americans (n=4,150) | European Americans (n= 2,391) | Individuals with asthma (n= 5,609) | Individuals without asthma (n=1,340) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | |
| Age (per 10-year increment) | 1.03 (0.97–1.09) | 0.280 | 1.00 (0.93–1.07) | 0.963 | 1.10 (0.98–1.23) | 0.120 | 1.03 (0.97–1.09) | 0.373 | 1.10 (0.87–1.40) | 0.448 |
| Sex (female referent) | 0.81 (0.68–0.96) | 0.016 | 0.84 (0.68–1.03) | 0.096 | 0.63 (0.43–0.90) | 0.014 | 0.79 (0.66–0.95) | 0.011 | 1.06 (0.54–1.98) | 0.860 |
| Race (European American referent) | 1.99 (1.66–2.39) | 2.25×10−13 | -- | -- | -- | -- | 1.93 (1.60–2.33) | 7.63×10−12 | 3.85 (1.63–11.32) | 0.005 |
| BMI (per unit increase) | 1.01 (1.00–1.01) | 0.223 | 1.01 (1.00–1.02) | 0.252 | 1.02 (1.00–1.04) | 0.023 | 1.01 (1.00–1.01) | 0.179 | 0.99 (0.95–1.03) | 0.639 |
| Asthma status | 4.36 (3.23–6.02) | 1.28×10−20 | 3.94 (2.82–5.68) | 1.50×10−14 | 7.23 (3.27–20.50) | 1.62×10−5 | -- | -- | -- | -- |
| Proportion African ancestry (per 10% increase)‡ | ND | ND | 1.00 (0.90–1.10) | 0.936 | ND | ND | ND | ND | ND | ND |
SAPPHIRE denotes Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-ethnicity; aOR, adjusted odds ratio; CI, confidence interval; BMI, body mass index; and ND, not done.
Outcome was any reported food-associated anaphylaxis. Patient with symptoms consistent with NIAID/FAAN clinical criteria in at least two categories (see Methods Section) were categorized as having food-associated anaphylaxis. Race categories were based on participant self-report and asthma status was based on a documented clinical diagnosis with concordance by patient report.
Logistic regression was used to adjust for all of the variables shown. Odds ratio for age were reported for 10-year increments while the models adjusted for each year increase. Sex (female=0, male=1), race (European American=0, African American=1), and asthma status (no history=0, positive history=1) were all coded as dichotomous variables.
Proportion of African ancestry was assessed using existing whole genome sequence data on SAPPHIRE participants and the YRI and CEU reference populations from the 1000 Genomes Project. African ancestry was only assessed in individuals who identified as African American, and was only included in these stratified models. Odds ratios for ancestry were reported for 10% increments of African ancestry while the models adjusted for each percentage increase.
Given the apparent prevalence difference in seafood allergy by race, we conducted similar adjusted analyses for seafood allergy (Table 6) and seafood-associated anaphylaxis (Table 7). African American individuals appeared to be at increased risk of seafood allergy when compared with European American individuals in all study participants (aOR=3.14; P=3.76×10−26), those with asthma (aOR=3.14; P=1.83×10−24), and those without asthma (aOR=3.17; P=0.005) (Table 6). Similar findings were observed for seafood-associated anaphylaxis (Table 7) with African Americans appearing to be at risk when compared with European Americans regardless of asthma status (aOR 3.32 for race among individuals with asthma and aOR 5.48 for race among individuals without asthma).
Table 6.
Factors associated with reported seafood allergy among SAPPHIRE participants stratified by race and asthma status*
| Factors | All participants (n=6,949) | African Americans (n=4,150) | European Americans (n= 2,391) | Individuals with asthma (n=5,609) | Individuals without asthma (n=1,340) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | |
| Age (per 10-year increment) | 0.99 (0.93–1.05) | 0.697 | 0.97 (0.90–1.04) | 0.353 | 1.12 (0.97–1.29) | 0.125 | 0.98 (0.92–1.04) | 0.538 | 1.08 (0.87–1.34) | 0.492 |
| Sex (female referent) | 1.15 (0.97–1.36) | 0.104 | 1.17 (0.97–1.42) | 0.106 | 0.99 (0.65–1.48) | 0.960 | 1.14 (0.95–1.36) | 0.154 | 1.27 (0.71–2.24) | 0.410 |
| Race (European American referent) | 3.14 (2.55–3.90) | 3.76×10−26 | -- | -- | -- | -- | 3.14 (2.53–3.93) | 1.83×10−24 | 3.17 (1.50–7.83) | 0.005 |
| BMI (per unit increase) | 1.01 (1.00–1.02) | 0.075 | 1.00 (0.99–1.01) | 0.356 | 1.02 (1.00–1.05) | 0.040 | 1.01 (1.00–1.02) | 0.067 | 1.00 (0.96–1.03) | 0.971 |
| Asthma status | 3.29 (2.49–4.43) | 4.49×10−16 | 3.21 (2.36–4.48) | 9.75×10−13 | 3.26 (1.61–7.80) | 0.003 | -- | -- | -- | -- |
| Proportion African ancestry (per 10% increase)‡ | ND | ND | 1.00 (0.91–1.11) | 1.000 | ND | ND | ND | ND | ND | ND |
SAPPHIRE denotes Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-ethnicity; aOR, adjusted odds ratio; CI, confidence interval; BMI, body mass index; and ND, not done.
Outcome was a reported food allergy to seafood, including both fin fish and shellfish. Race categories were based on participant self-report and asthma status was based on a documented clinical diagnosis with concordance by patient report.
Logistic regression was used to adjust for all of the variables shown. Odds ratio for age were reported for 10-year increments while the models adjusted for each year increase. Sex (female=0, male=1), race (European American=0, African American=1), and asthma status (no history=0, positive history=1) were all coded as dichotomous variables.
Proportion of African ancestry was assessed using existing whole genome sequence data on SAPPHIRE participants and the YRI and CEU reference populations from the 1000 Genomes Project. African ancestry was only assessed in individuals who identified as African American, and was only included in these stratified models. Odds ratios for ancestry were reported for 10% increments of African ancestry while the models adjusted for each percentage increase.
Table 7.
Factors associated with reported seafood-associated anaphylaxis among SAPPHIRE participants stratified by race and asthma status*
| Factors | All participants (n=6,949) | African Americans (n=4,150) | European Americans (n= 2,391) | Individuals with asthma (n=5,609) | Individuals without asthma (n= 1,340) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | aOR (95% CI)† | P-value | |
| Age (per 10-year increment) | 1.00 (0.92–1.08) | 0.934 | 0.98 (0.89–1.06) | 0.587 | 1.07 (0.88–1.29) | 0.511 | 0.99 (0.92–1.07) | 0.857 | 1.05 (0.78–1.43) | 0.763 |
| Sex (female referent) | 1.04 (0.83–1.29) | 0.729 | 1.03 (0.80–1.32) | 0.806 | 1.19 (0.68–2.06) | 0.534 | 1.02 (0.81–1.28) | 0.879 | 1.35 (0.58–2.96) | 0.467 |
| Race (European American referent) | 3.40 (2.57–4.58) | 8.10×10−17 | -- | -- | -- | -- | 3.32 (2.50–4.50) | 1.29×10−15 | 5.48 (1.59–34.47) | 0.022 |
| BMI (per unit increase) | 1.01 (1.00–1.02) | 0.056 | 1.01 (1.00–1.02) | 0.072 | 1.03 (0.99–1.06) | 0.106 | 1.01 (1.00–1.02) | 0.078 | 1.02 (0.97–1.06) | 0.440 |
| Asthma status | 3.70 (2.53–5.63) | 1.22×10−10 | 3.34 (2.23–5.24) | 2.91×10−8 | 5.89 (1.82–36.14) | 0.014 | -- | -- | -- | -- |
| Proportion African ancestry (per 10% increase)‡ | ND | ND | 1.05 (0.93–1.20) | 0.430 | ND | ND | ND | ND | ND | ND |
SAPPHIRE denotes Study of Asthma-related Phenotypes and Pharmacogenomic Interactions by Race-ethnicity; aOR, adjusted odds ratio; CI, confidence interval; BMI, body mass index; and ND, not done.
Outcome was any reported seafood-associated anaphylaxis, including to both fin fish and shellfish. Patient with symptoms consistent with NIAID/FAAN clinical criteria in at least two categories (see Methods Section) were categorized as having food-associated anaphylaxis. Race categories were based on participant self-report and asthma status was based on a documented clinical diagnosis with concordance by patient report.
Logistic regression was used to adjust for all of the variables shown. Odds ratio for age were reported for 10-year increments while the models adjusted for each year increase. Sex (female=0, male=1), race (European American=0, African American=1), and asthma status (no history=0, positive history=1) were all coded as dichotomous variables.
Proportion of African ancestry was assessed using existing whole genome sequence data on SAPPHIRE participants and the YRI and CEU reference populations from the 1000 Genomes Project. African ancestry was only assessed in individuals who identified as African American, and was only included in these stratified models. Odds ratios for ancestry were reported for 10% increments of African ancestry while the models adjusted for each percentage increase.
We also evaluated the relationship between race and both non-seafood food allergy and non-seafood food-associated anaphylaxis (Supplementary Tables E5 and E6, respectively). African American individuals appeared to be at increased risk for non-seafood food allergy (aOR=1.23; P=0.008) and non-seafood food-associated anaphylaxis (aOR=1.28; P=0.043) when compared with European American participants.
We assessed whether African ancestry was associated with food allergy among African American individuals. In none of the adjusted analyses was African ancestry associated with food allergy or food-associated anaphylaxis among African Americans (Tables 4–7 and Supplementary Tables E5 and E6). In addition, a post-hoc analysis adjusting for household income found that income did not abrogate the statistically significant relationship observed between race and food allergy in any of the regression analyses (data not shown).
DISCUSSION
Our analysis found that African American individuals report much higher rates of food allergy when compared with European Americans. A similar between group difference was observed for anaphylaxis, the most severe and concerning of food-related symptoms. Seafood (i.e., both shellfish and fin fish) appeared to be the largest driver of the between group differences in food-related symptoms.
Our findings are in keeping with the results of others. The National Health and Nutrition Examination Survey (NHANES) found that African American individuals were more likely to be sensitized to food allergens (i.e., have a serum-specific IgE) when compared with European American individuals (OR=3.06, 95% CI: 2.14, 4.36). 24 In a cross-sectional food allergy survey, Wang et al. also found that African Americans were significantly more likely to report an allergy to shellfish as compared with European Americans (OR=2.3; 95% CI: 1.6–3.4). 25 Other investigators have also observed higher rates of food allergy and food allergen sensitization among African Americans when compared to European Americans, including to peanut, tree nuts, and egg. 10, 26–28
Rather than assess the relationship between genetic ancestry and food allergy across different population groups, 29 we performed a stratified analysis to assess the within group effect of African ancestry among African Americans. Our approach was used to estimate whether genetic ancestry had an additional independent effect on food allergy risk (i.e., an effect beyond the association already captured by self-reported race). Interestingly, among African American individuals, neither food allergy nor food-related anaphylaxis were associated with African genetic ancestry. These findings suggest that non-genetic factors may also play an important role in the between group differences in food allergy prevalence. However, our findings do not discount the overall importance of genetics to food allergy development, nor do they imply that the genetic risk factors are the same between population groups; however, our results highlight the importance of also exploring socio-environmental contributions to food allergy disparities. To our knowledge, this is the first study analyzing the relationship between food allergy and genetic ancestry; therefore, our results merit replication in other large and diverse cohorts. It is worth noting that our estimated proportions of African and European ancestry in African American and European American SAPPHIRE participants was similar to the proportion of genetic ancestry independently measured by other investigators throughout the U.S. Hence, our study population did not appear to be unique with respect to genetic ancestry.
Existing studies on the heritability of food allergy provide a wide range of estimates. In a twin study of peanut allergy, Sicherer et al. observed a pairwise concordance of 64.3% among monozygotic twins and 6.8% among dizygotic twins for a heritability estimate of 81.6%.30 In contrast, Tsai and colleagues evaluated the heritability of food sensitization (i.e., the presence of food-specific IgE levels) among participants in the Chicago Food Allergy Study; the estimated heritability for sensitization to peanut, milk, cod, egg, walnut, sesame, shrimp, soy, and wheat ranged from 15% to 35%.31 In contrast, Liu et al. performed a twin study of allergic sensitization to food allergens based on skin prick test results (i.e., to egg, fish, milk, peanut, soy, sesame, shellfish, walnut, and wheat) and found a heritability of 56% to any food allergen, 51% for peanut, and 54% for shellfish. 32 In sum, it appears that non-genetic causes may account for a sizable proportion of the variance in food allergy development.
Unfortunately, the evidence linking environmental exposures to food allergy development is sparse and observational. Globally, higher socioeconomic status and a westernized lifestyle have been associated with food allergy prevalence. 33–35 Other potential environmental factors included exposure to air pollution, 36, 37 tobacco smoke, 38–40 in utero heavy metal exposure, 41 a maternal diet high in baked and sugary foods in the first two trimesters of pregnancy, 42 less healthy diets during infancy, 43 and early childhood antibiotic use. 44–46 Perinatal factors, such as Caesarian delivery, 47–49 premature birth, 50 low birthweight, 51 and breastfeeding52, 53 may also contribute to food allergy development. Possible causes include disruption or alteration of gut permeability and microbial composition; 54, 55 however, the mechanistic link to food allergy has yet to be fully elucidated.
Our study must be assessed in light of its limitations. First, our assessment of food allergy and anaphylaxis depended on patient self-report. Ideally, we would have assessed immediate-type food allergy based on reproducible symptoms elicited via a double-blind placebo-controlled food challenge (DBPCFC). 56 However, given the expense and time requirements of DBPCFC, this approach is not feasible for large epidemiologic studies, such as this one. Moreover, our large size and in-person assessment allowed us to evaluate participant reports of food allergy with greater depth and granularity than is permittable by either mailed survey or DBPCFC. Because a large portion of SAPPHIRE participants have asthma, this could have upwardly biased or confounded our main study findings, particularly with regard to our prevalence estimates and racial differences. Nevertheless, our main findings of food allergy differences by race were still observed after stratifying by asthma status. As our findings represent those of a single large cohort study from southeast Michigan, our results may not be broadly generalizable. However, unlike many of the existing genetic studies of food allergy to date, our study had sufficient diversity to assess differences by race and genetic ancestry which makes our study unique. The SAPPHIRE cohort reflects the diversity of southeast Michigan and the Detroit metropolitan area. As shown in Table 1, we did not have sufficient representation from “other” groups including Latinos, Asians, indigenous populations, and individuals reporting multiple races to independently investigate the relationship between food allergy and both race-ethnicity and genetic ancestry in these groups (the survey questions used to assess race-ethnicity in SAPPHIRE are shown in Supplementary Table E7). Finally, the average age of SAPPHIRE participants was 34 years. Although we did not place a time limit to the age at which study participants experienced food allergy symptoms, it is possible that the differences we observed are biased to reflect those food allergies which persist or appear in adulthood. 3, 10, 57–60
In summary, our study identified significant differences in food allergy and food associated anaphylaxis by race. These associations appeared to be independent of asthma status and genetic ancestry. The latter suggests that socio-environmental factors may play a substantial role in the difference in food allergy prevalence by race. The disproportionate contribution of seafood allergy to between group racial differences deserves particular attention to determine if these findings persist in other locations and to better understand the underlying mechanism.
Supplementary Material
What is already known about this topic?
While U.S. national surveys suggest significant differences in food allergy by race-ethnicity, little is known about the relative importance of different food allergens and the mechanism by which they contribute to disparities.
What does this article add to our knowledge?
This study demonstrated significant race-ethnic differences in both reported food allergy and food-associated anaphylaxis between African Americans and non-Hispanic white individuals with seafood allergy being a major contributor to these differences. The study also found that genetic ancestry was not significantly associated with reported food allergy, suggesting that socio-environmental factors play an important role in food allergy disparities.
How does this study impact current management guidelines?
This study does not directly impact current guidelines, but it does highlight the importance of considering potential food allergens more broadly in different race-ethnic groups. The study also emphasizes the importance of future studies of socio-environmental drivers of food allergy.
SOURCES OF SUPPORT:
Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). WGS for “NHLBI TOPMed: Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity” (phs001467.v1.p1) was performed at University of Washington Northwest Genome Center (HHSN268201600032I). Centralized read mapping and genotype calling along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample-identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1; contract HHSN268201800001I).
This work was supported by the Fund for Henry Ford Hospital (L. K. Williams); the American Asthma Foundation (E. G. Burchard, L. K. Williams); the Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program (E. G. Burchard); and the following institutes of the National Institutes of Health (NIH): National Institute of Allergy and Infectious Diseases (NIAID: R56AI165903 to E. G. Burchard; R01AI079139, R01AI061774, and R56AI165903 to L. K. Williams), the National Heart Lung and Blood Institute (NHLBI: R01HL117004, R01HL128439, R01HL135156, R01HL141992, R01HL155024, and X01HL134589 to E. G. Burchard; R01HL079055, R01HL118267, R01HL141845, and X01HL134589 to L. K. Williams), the National Institute on Minority Health and Health Disparities (NIMHD: R01MD010443 and R56MD013312 to E. G. Burchard), the National Institute of General Medical Sciences (NIGMS: T32GM007546 to E. G. Burchard), the National Institute of Environmental Health Sciences (NIEHS: R01ES015794 and R21ES024844 to E. G. Burchard), the National Human Genome Research Institute (NHGRI: U01HG009080 to E. G. Burchard), and the National Institute of Diabetes and Digestive and Kidney diseases (NIDDK: R01DK064695 and R01DK113003 to L. K. Williams).
Abbreviations:
- aOR
Adjusted Odds Ratio
- CEU
Utah residents with Northern and Western European ancestry from the Center for the Study of Human Polymorphisms (Centre d’Etude du Polymorphisme Humain [CEPH], Paris, France)
- DBPCFC
Double-Blind Placebo-Controlled Food Challenge
- FAAN
Food Allergy and Anaphylaxis Network
- NIAID
National Institute of Allergy and Infectious Disease
- NHANES
National Health and Nutrition Examination Survey
- SAPPHIRE
Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity
- TOPMed
Trans-Omics for Precision Medicine
- WGS
Whole Genome Sequencing
- YRI
Yoruba in Ibadan, Nigeria
Footnotes
CONFLICT OF INTEREST STATEMENT
Dr. Martinez reports funding from the NHLBI, NIAID, NIEHS, and Office of the Director, NIH unrelated to the current work. Dr. Hakonarson reports funding from NHLBI and Children’s Hospital of Philadelphia related to the current work. Dr. Kumar reports NIH funding, consultant work for Regeneron, and honoraria from Indiana University and the Hospital for Sick Children (Toronto, Canada) unrelated to the current work. Dr. Burchard reports funding from the Sandler Family Foundation, the RWJF Amos Medical Faculty Development Program, and the NHLBI, the NIMHD, the NIGMS, NHGRI, and the NIEHS of the NIH related to the current work. Dr. Williams reports funding from the NIAID, the NHLBI, and the NIDDK related to the current work.
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