Abstract
Rationale: Dietary patterns may alter immune responses and increase asthma risk or affect lung function.
Objectives: To examine whether a proinflammatory diet (assessed by the energy-adjusted Dietary Inflammatory Index [E-DII]) or high dietary quality (assessed by the Alternative Healthy Eating Index [AHEI-2010]) are associated with current asthma, current asthma symptoms, and lung function in Hispanic adults.
Methods: This was a cross-sectional study of 12,687 adults aged 18 to 76 years who participated in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The E-DII and AHEI-2010 were calculated based on two 24-hour dietary recalls. Logistic or linear regression was used for the multivariable analysis of E-DII or AHEI-2010 scores and current asthma, asthma symptoms, and lung function measures, adjusting for age, sex, annual household income, study center, Hispanic/Latino background, smoking status, and other covariates.
Results: A higher E-DII score was associated with current asthma (odds ratio [OR] for quartile 4 vs. 1, 1.35; 95% confidence interval [CI], 0.97–1.90) and asthma symptoms (OR for quartile 4 vs. 1, 1.42; 95% CI, 1.12–1.81). The AHEI-2010 score was not associated with current asthma or asthma symptoms. Among adults without asthma, a higher E-DII score was associated with lower forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC), and a higher AHEI-2010 score was associated with higher FEV1 and FVC.
Conclusions: Our findings suggest that a proinflammatory diet increases the risk of asthma and asthma symptoms in Hispanic adults. An antiinflammatory diet (indicated by a lower E-DII or a higher AHEI-2010 score) may positively influence lung function in Hispanic adults without asthma.
Keywords: Dietary Inflammatory Index, AHEI-2010, asthma, lung function, HCHS/SOL
Asthma affects ∼26 million people in the United States (1). The burden from asthma is more variable among Hispanic subgroups than across other broadly defined racial or ethnic groups (2). The prevalence of asthma (3–5) or asthma morbidity (6) is highest in Puerto Ricans and lowest in Mexican Americans or Central Americans (3, 7). Indeed, Puerto Ricans have the highest prevalence of physician-diagnosed asthma among adults in the HCHS/SOL (8). Dietary patterns may partly explain this finding, as Puerto Ricans have lower dietary intake of fruits and vegetables than members of other Hispanic subgroups in HCSH/SOL (9).
An imbalanced diet could modulate allergic airway inflammation by affecting innate and adaptive immune responses (10, 11). Two evidence-based dietary indices, the Dietary Inflammatory Index (DII) (12) and the Alternative Healthy Eating Index (AHEI)-2010 (13), can be used to study the health effects of dietary patterns, which can account for the combined effects of nutrients and vitamins. In a population-based study of U.S. residents, we previously showed that a higher DII score (indicating a proinflammatory diet) is associated with current wheeze in adults and atopic asthma in children (14). Consistent with dietary effects on airway diseases, a higher AHEI-2010 score (reflecting a better diet quality) has been associated with improved symptoms and better control in French adults with asthma (15) and with lower risk of chronic obstructive pulmonary disease (COPD) in U.S. adults (16).
To date, no study has examined the relation between dietary patterns and asthma or lung function among adults in well-defined Hispanic subgroups. We examined whether such dietary patterns are associated with asthma, asthma symptoms, and lung function among Hispanics in HCHS/SOL.
Methods
Subject Recruitment and Study Population
HCHS/SOL is a population-based cohort study of 16,415 adults aged 18 to 74 years who self-identified as having Hispanic/Latino background at screening visits of households in four U.S. field centers (Chicago, IL; Miami, FL; Bronx, NY; and San Diego, CA) (17). The goals of HCHS/SOL are to describe the prevalence of risk and protective factors for chronic conditions via a baseline examination (performed between 2008 and 2011), including questionnaire, interview, and physical examination (17), and a yearly telephone follow-up assessment. The sample design and cohort selection have been previously described (18). In brief, a stratified two-stage probability sample of household addresses was selected in each of the four centers to provide a representative sample of the target population. On the basis of census block groups, both stages oversampled strata that had a higher proportion of Hispanic residents, to increase the likelihood that a selected address yielded a Hispanic/Latino household. The study oversampled the 45- to 74-year age group (n = 9,714; 59.2%) to facilitate examination of target outcomes. This study was approved by the institutional review boards at the data coordinating center (University of North Carolina) and at each center, and all participants gave written informed consent.
Dietary Assessment
Dietary patterns were assessed using two 24-hour dietary recalls. The first dietary recall was administered in person at the time of the baseline interview. The second dietary recall was mostly administered during a phone call (about 15% were administered in person) and performed within 45 days of the baseline interview. Participants estimated portion sizes with the use of food models (in person) or a food-amount booklet (for telephone interviews). Both 24-hour dietary recalls were conducted using the Nutrition Data System for Research (NDSR) software (version 11) developed by the Nutrition Coordinating Center at the University of Minnesota, which uses the multiple-pass method (details at http://www.ncc.umn.edu/). The NDSR contains more than 18,000 foods, 8,000 brand-name products, and many ethnic foods, supplements, and vitamins. The program provides values for 158 nutrients, nutrient ratios, and other food components. NDSR calculates nutrient intake and presents the data in several formats, including daily nutrient totals.
The Dietary Inflammatory Index
The DII was first created to quantify the overall effects of diet on chronic inflammation. The development and validation of the DII have been previously described (12, 14, 19). In brief, peer-reviewed literature published between 1950 and 2010 was evaluated, and 1,943 articles linking inflammation to 45 individual food parameters (mostly micro- and macronutrients) were identified and scored. Points were assigned to each of these parameters according to whether they increased, decreased, or had no effect on six biomarkers of inflammation. On the basis of the study design and total number of research articles, an overall inflammatory effect score was calculated for each food parameter. The dietary data are first linked to a regionally representative world database, which contains standard means and deviations for the 45 food parameters from 11 populations around the world (12). The z-scores are then converted to centered proportions, which are then multiplied by food parameter–specific inflammation effect score to obtain food parameter–specific DII scores. These are then summed to get the overall DII score. The DII thus reflects a robust literature base and standardization of individual intakes to global reference values.
Of the 45 DII food parameters, 29 were available in HCHS/SOL, as follows: carbohydrates; proteins; fiber; cholesterol; total, trans, saturated, monounsaturated, and polyunsaturated fats (PUFAs); omega-3 and omega-6 PUFAs; niacin; vitamins A, B1, B2, B6, B12, C, D, and E; iron; magnesium; zinc; selenium; folic acid; β-carotene; caffeine; alcohol; and total energy. Higher (i.e., more positive) scores indicate a more proinflammatory diet, and more negative scores indicate a more antiinflammatory diet. To control for total energy intake, the energy-adjusted DII (E-DII) score was calculated per 1,000 calories of food consumed (using a calorie-adjusted version of the global database for comparison). The mean of two E-DII scores calculated separately from the two dietary recalls was used for the analysis. A separate E-DII score accounting for 24-hour dietary supplements was also calculated.
Alternative Healthy Eating Index
The AHEI was created in 2002 and updated in 2010, as an alternative to the Healthy Eating Index that assesses the degree to which participants adhere to U.S. Dietary Guidelines. It was based on a comprehensive review of the relevant literature to include foods and nutrients predictive of chronic diseases, including cardiovascular disease, diabetes, and cancer (13). In HCHS/SOL, the AHEI-2010 was calculated using data from two 24-hour dietary recalls, as previously described (20). The AHEI-2010 is derived using the following 11 components: vegetables without potatoes, whole fruit (not including fruit juice), whole grains, sugar-sweetened beverages and fruit juices, nuts and legumes, red/processed meats, trans fats, long-chain (omega-3) fat, PUFA, sodium, and alcohol. Each component was created by adding the corresponding NDSR food subgroups. Predicted usual intake amounts for each component were then estimated using the National Cancer Institute method (21). The AHEI-2010 score is the sum of the 11 individual components’ scores, each with a range from 0 (worst) to 10 (best). Hence, AHEI-2010 takes values from 0 to 110: higher scores represent healthy eating habits (higher intake of vegetables, whole fruit, whole grains, polyunsaturated fatty acids, nuts, and long-chain omega-3 fats and lower intake of red/processed meats, trans fat, refined grains, and sugar-sweetened drinks), and lower scores represent unhealthy eating habits.
Outcomes
Current asthma was defined by a positive answer to all following questions: “Have you ever had asthma?”, “Was it diagnosed by a doctor or other health care professional?”, and “Do you still have it?” For the analysis of current asthma, control subjects were those responding “No” to any of these questions. Current asthma symptoms were independent of a physician’s diagnosis of asthma and were defined by a positive answer to either of these questions: “In the last 12 months, have you had wheezing or whistling in your chest at any time?” or “In the last 12 months, have you been awakened from sleep either by coughing (apart from a cough associated with a cold or chest infection) or by shortness of breath or a feeling of tightness in your chest?” For the analysis of current asthma symptoms, control subjects were those answering “No” to both questions. Atopy was defined by a positive answer to any of the following questions: “When you are near animals (such as cats, dogs, or horses) or near feathers (including pillows, quilts, or comforters) or in a dusty or moldy part of the house, do you ever get a runny or stuffy nose or start to sneeze, or get itching or watering eyes?”, “When you are near trees, grass, or flowers, or when there is a lot of pollen in the air, do you ever get a runny or stuffy nose or start to sneeze, or get itching or watering eyes?”, and “Have you ever had hay fever (allergy involving the nose and/or eyes)?”
Pulmonary function testing was performed according to American Thoracic Society recommendations (22). Standard digitized spirometric measurements of timed pulmonary function, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC) were obtained using the SensorMedics model 1022 dry-rolling seal volume spirometer (SensorMedics/Viasys). Percent predicted values for lung function measures were calculated based on spirometry reference equations from HCHS/SOL (23).
Statistical Analysis
Because of the complex design of HCHS/SOL, sampling weights, stratification, and clusters provided in the HCHS/SOL dataset were incorporated into the analysis, to obtain proper estimates and their standard errors. Two-sided Wald chi-square tests and t tests were used for bivariate analyses of binary and continuous variables, respectively. Analysis of variance was used to compare mean E-DII or AHEI-2010 score across Hispanic/Latino subgroups. Logistic and linear regression were used for the multivariable analysis of the E-DII or AHEI-2010 scores and the outcomes of interest (current asthma, current asthma symptoms, and lung function), which was adjusted for known or potential confounders. All multivariable models were adjusted for age, sex, Hispanic/Latino background (Cuban, Mexican, Puerto Rican, and others [including Dominicans, Central Americans, South Americans, other Hispanics, and those with more than one heritage—these subgroups had small sample size and similar effect estimates on bivariate analyses and were thus grouped together]), study center, annual household income, body mass index (BMI), level of total physical activity (low, moderate, and high, based on the global physical activity questionnaire, http://www.who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf), family history of asthma, place of birth (born in the United States or a U.S. territory), smoking status (never, former, current), and pack-years of cigarette smoking. Given the number of covariates, we assessed multicollinearity by examining the variance inflation factor and tolerance, which were <5 and >0.1, respectively, in all models. Model fitness was assessed using standard approaches.
All statistical analyses were conducted using the SAS SURVEY procedure and SAS 9.4 software (SAS Institute Inc.).
Results
Of the 16,415 HCHS/SOL participants, 16,289 and 15,424 had data for the first and second dietary recall, respectively. We excluded unreliable dietary recalls (according to the interviewer) and extreme observed daily energy intake (defined as below sex-specific first percentile or above the 99th percentile). Of the 14,160 participants with both valid dietary recalls, 1,473 individuals were further excluded because of lack of information on asthma or key covariates. Thus, 12,687 subjects were included in the current analysis (see Figure E1 in the online supplement).
The overall prevalence of current asthma in the 12,687 study participants was 6.8%. The weighted distribution of the characteristics of these participants is shown in Table 1. Compared with subjects without current asthma, those with current asthma were more likely to be older, female, Puerto Rican, current smokers, atopic, recruited at the Bronx study center, and covered by health insurance, and more likely to have: a family history of asthma; higher BMI, and pack-years of smoking; but less physical activity and lower annual household income and lung function. The E-DII was significantly higher and the AHEI-2010 was significantly lower in subjects with current asthma than in those without current asthma. We found a moderate correlation between the E-DII and AHEI-2010 scores in subjects with (coefficient correlation, r = −0.58; P < 0.01) and without ( r = −0.53; P < 0.01) asthma (Figure 1). The distribution of mean E-DII and AHEI-2010 scores in each Hispanic/Latino subgroup is shown in Figure 2. Participants of Puerto Rican descent had significantly higher mean E-DII (P < 0.01) and lower mean AHEI-2010 (P < 0.01) than participants in other Hispanic/Latino subgroups.
Table 1.
Main characteristics of study participants, by current asthma
| Characteristics | No Current Asthma (n = 11,725) | Current Asthma (n = 962) |
|---|---|---|
| Age, yr | 41.4 ± 0.3 | 43.6 ± 0.8 |
| Female sex | 6,911 (51.0) | 727 (66.7) |
| Hispanic/Latino background | ||
| Dominican | 982 (9.1) | 92 (9.4) |
| Central American | 1,278 (7.5) | 53 (3.9) |
| Cuban | 1,662 (20.0) | 128 (19.1) |
| Mexican | 5,048 (41.2) | 184 (16.4) |
| Puerto Rican | 1,570 (12.8) | 442 (44.9) |
| South American | 875 (5.6) | 29 (2.7) |
| Other or more than one heritage | 321 (3.8) | 34 (3.5) |
| Study center | ||
| Bronx, NY | 2,430 (24.4) | 451 (50.3) |
| Chicago, IL | 3,229 (17.6) | 210 (13.6) |
| Miami, FL | 2,945 (29.6) | 172 (24.5) |
| San Diego, CA | 3,121 (28.3) | 129 (11.6) |
| Educational attainment | ||
| No high school diploma or GED | 4,223 (30.0) | 377 (32.7) |
| At most a high school diploma or GED | 3,045 (28.5) | 342 (27.2) |
| Greater then high school (or GED) education | 4,443 (41.5) | 343 (40.1) |
| Have health insurance coverage | 5,605 (48.2) | 702 (72.0) |
| Annual household income < $30,000 | 7,857 (64.0) | 742 (73.7) |
| Family history of asthma | 2,124 (19.9) | 458 (49.6) |
| Atopy | 4,590 (38.1) | 763 (78.2) |
| Body mass index, kg/m2 | 29.2 ± 0.1 | 32.5 ± 0.3 |
| Physical activity | ||
| Low | 5,139 (40.7) | 464 (45.8) |
| Moderate | 5,285 (45.3) | 417 (44.4) |
| High | 1,301 (14.0) | 81 (9.8) |
| Smoking status | ||
| Never | 7,439 (63.9) | 529 (54.0) |
| Former | 2,270 (17.0) | 192 (22.2) |
| Current | 2,016 (19.1) | 241 (23.8) |
| Cigarette pack-years | 4.8 ± 0.2 | 7.5 ± 0.8 |
| Born in 50 U.S. states/DC | 1,797 (20.3) | 301 (35.4) |
| Energy-adjusted Dietary inflammatory index | 0.23 ± 0.03 | 0.67 ± 0.08 |
| Alternative Healthy Eating Index 2010 | 48.1 ± 0.2 | 44.5 ± 0.4 |
| FEV1*, % predicted | 95.3 ± 0.2 | 81.0 ± 1.5 |
| FVC*, % predicted | 96.0 ± 0.2 | 87.7 ± 1.1 |
| FEV1/FVC*,% predicted | 99.1 ± 0.1 | 91.5 ± 1.1 |
Definition of abbreviations: FEV1 = forced expiratory volume in 1 second; FVC = forced vital capacity; GED = General Educational Development.
Results are shown as mean ± standard error for continuous variables and as n (%) for binary variables.
n = 11,817.
Figure 1.
Correlation between the energy-adjusted Dietary Inflammatory Index (E-DII) and the Alternative Healthy Eating Index 2010 (AHEI-2010), by current asthma.
Figure 2.
Distribution of energy-adjusted Dietary Inflammatory Index (E-DII, upper panel) and Alternative Healthy Eating Index 2010 (AHEI-2010, lower panel) scores by Hispanic/Latino subgroup.
Table 2 presents the results of the multivariable analysis of E-DII or AHEI-2010 scores (as quartiles) and current asthma and asthma symptoms. In this analysis, subjects whose E-DII score was in the second to fourth quartile had significantly higher odds of current asthma symptoms than those whose E-DII score was in the first quartile (e.g., odds ratio [OR] for quartile 4 vs. 1, 1.42; 95% confidence interval [CI], 1.12–1.81; P for trend < 0.01). Similarly, subjects whose E-DII score was in the third or fourth quartile had increased odds of current asthma (with P for linear trend < 0.05). In this analysis, subjects of Mexican, Dominican, Central American, and South American heritage or who had more than one heritage had lower odds of current asthma than Cubans (e.g., OR for Mexicans vs. Cubans, 0.49; 95% CI, 0.28–0.86). In contrast, Puerto Ricans had higher odds of current asthma than Cubans (OR, 1.72; 95% CI, 1.01–2.92). Similar results were found in multivariable models including the E-DII or AHEI-2010 as continuous variables (Table E1). Our findings for E-DII scores and asthma or asthma symptoms were unchanged when we used an E-DII version that accounted for 24-hour intake of dietary supplements (Table E2).
Table 2.
Multivariable analysis of the relation between the E-DII or the AHEI-2010 and current asthma and current asthma symptoms
| Predictors | Current Asthma (n = 962) | Current Asthma Symptoms (n = 2,085) |
|---|---|---|
| E-DII | ||
| Quartile 1 | 1.0 (reference) | 1.0 (reference) |
| Quartile 2 | 1.08 (0.76–1.52) | 1.32 (1.03–1.69) |
| Quartile 3 | 1.33 (0.97–1.82) | 1.37 (1.10–1.71) |
| Quartile 4 | 1.35 (0.97–1.90)* | 1.42 (1.12–1.81)* |
| Hispanic/Latino background | ||
| Cuban | 1.0 (reference) | 1.0 (reference) |
| Mexican | 0.49 (0.28–0.86) | 0.74 (0.51–1.08) |
| Puerto Rican | 1.72 (1.01–2.92) | 1.02 (0.74–1.43) |
| Others | 0.61 (0.40–0.92) | 0.90 (0.68–1.19) |
| AHEI-2010 | ||
| Quartile 1 | 1.0 (reference) | 1.0 (reference) |
| Quartile 2 | 1.11 (0.84–1.48) | 0.96 (0.75–1.23) |
| Quartile 3 | 0.90 (0.64–1.28) | 0.85 (0.62–1.16) |
| Quartile 4 | 0.94 (0.61–1.43) | 0.88 (0.63–1.21) |
| Hispanic/Latino background | ||
| Cuban | 1.0 (reference) | 1.0 (reference) |
| Mexican | 0.49 (0.27–0.89) | 0.76 (0.52–1.11) |
| Puerto Rican | 1.74 (1.02–2.94) | 1.02 (0.72–1.42) |
| Others | 0.61 (0.40–0.92) | 0.91 (0.68–1.21) |
Definition of abbreviations: AHEI-2010 = Alternative Healthy Eating Index 2010; E-DII = energy-adjusted Dietary Inflammatory Index; OR = odds ratio.
Data presented as OR (95% confidence interval). All models adjusted for adjusted for age, sex, study center, annual household income, body mass index, physical activity, family history of asthma, place of birth, smoking status, and pack-years of cigarette smoking. Effect estimates (ORs) with P < 0.05 are shown in bold typeface.
P for trend < 0.05.
In a multivariable analysis adjusting for Hispanic/Latino background and other covariates, AHEI-2010 scores were not significantly associated with current asthma or asthma symptoms. We found no significant modification of the estimated effects of E-DII or AHEI-2010 scores on current asthma or asthma symptoms by age, sex, BMI, or Hispanic/Latino subgroup.
To assess potential misclassification of COPD as asthma, we repeated the multivariable analysis of current asthma and asthma symptoms after stratification by smoking status. In this analysis, E-DII scores remained associated with current asthma (P for linear trend < 0.05, Table 3) and asthma symptoms (Table E3) among never and former smokers. Among never and former smokers, Mexicans and others (including Dominicans, Central and South Americans, and those with more than one heritage) had lower odds of current asthma than Cubans. In the multivariable analysis among current smokers, E-DII scores were not significantly associated with current asthma or asthma symptoms (Table 3 and Table E1). Among current smokers, Puerto Ricans had significantly higher odds of current asthma than Cubans (Table 3). AHEI-2010 scores were not significantly associated with current asthma or asthma symptoms among study participants, regardless of smoking status.
Table 3.
Multivariable analysis of the relation between the E-DII or AHEI-2010 and current asthma, stratified by smoking status
| Predictors | Current Asthma |
|
|---|---|---|
| Never/Former Smokers (n = 10,430) | Current Smokers (n = 2,257) | |
| E-DII | ||
| Quartile 1 | 1.0 (reference) | 1.0 (reference) |
| Quartile 2 | 0.98 (0.67–1.46) | 1.35 (0.69–2.64) |
| Quartile 3 | 1.37 (0.94–1.99) | 1.13 (0.58–2.18) |
| Quartile 4 | 1.40 (0.92–2.14)* | 1.18 (0.63–2.21) |
| Hispanic/Latino background | ||
| Cuban | 1.0 (reference) | 1.0 (reference) |
| Mexican | 0.51 (0.28–0.93) | 0.50 (0.19–1.31) |
| Puerto Rican | 1.47 (0.84–2.57) | 3.51 (1.38–8.93) |
| Other or more than one heritage | 0.54 (0.35–0.84) | 1.20 (0.54–2.64) |
| AHEI-2010 | ||
| Quartile 1 | 1.0 (reference) | 1.0 (reference) |
| Quartile 2 | 1.02 (0.70–1.49) | 1.44 (0.87–2.37) |
| Quartile 3 | 0.89 (0.57–1.39) | 0.87 (0.44–1.70) |
| Quartile 4 | 0.91 (0.55–1.52) | 1.09 (0.37–3.22) |
| Hispanic/Latino background | ||
| Cuban | 1.0 (reference) | 1.0 (reference) |
| Mexican | 0.51 (0.27–0.97) | 0.53 (0.19–1.52) |
| Puerto Rican | 1.48 (0.85–2.58) | 3.73 (1.44–9.63) |
| Other or more than one heritage | 0.54 (0.35–0.85) | 1.19 (0.53–2.69) |
Definition of abbreviations: AHEI-2010 = Alternative Healthy Eating Index 2010; E-DII = energy-adjusted Dietary Inflammatory Index; OR = odds ratio.
Data presented as OR (95% confidence interval). All models adjusted for adjusted for age, sex, Hispanic/Latino background, study center, health insurance coverage, body mass index, physical activity, family history of asthma, place of birth, and pack-years of cigarette smoking. Effect estimates (ORs) with P < 0.05 are shown in bold typeface.
P for trend < 0.05.
In an exploratory analysis, we examined individual components of the AHEI-2010 score and current asthma or asthma symptoms. In this analysis, each one-point increment in the score for whole grains was significantly associated with 10% decreased odds of current asthma, whereas each one-point increment in the score for percentage trans fats from energy or sodium was associated with 3% to 18% decreased odds of current asthma symptoms (Table E4).
Table 4 shows the results of the multivariable analysis of E-DII or AHEI-2010 scores (as ordinal variables, in quartiles) and lung function, which was stratified by current asthma on the basis of findings from previous studies. Among subjects without current asthma, each quartile increment in the E-DII score was significantly associated with 0.71% to 0.58% decrements in percent predicted FEV1 (β = −0.71; 95% CI, −1.02 to −0.41) and percent predicted FVC (β = −0.58; 95% CI, −0.88 to −0.29). In contrast, each quartile increment in the AHEI-2010 score was significantly associated with 0.77% to 0.55% increments in percent predicted FEV1 (β = 0.77; 95% CI, 0.40 to 1.14) and percent predicted FVC (β = 0.55; 95% CI, 0.17 to 0.92). In subjects with current asthma, neither E-DII scores nor AHEI-2010 scores were significantly associated with lung function measures.
Table 4.
Multivariable analysis of the relation between the E-DII or the AHEI-2010 scores and lung function measures
| Lung Function Measures | E-DII | AHEI-2010 |
|---|---|---|
| All participants (n = 11,817) | ||
| FEV1, % predicted | −0.75 (−1.07 to −0.42) | 0.76 (0.31 to 1.20) |
| FVC, % predicted | −0.58 (−0.87 to −0.29) | 0.55 (0.16 to 0.94) |
| FEV1/FVC, % predicted | −0.18 (−0.42 to 0.06) | 0.23 (−0.05 to 0.51) |
| Participants without asthma (n = 10,948) | ||
| FEV1, % predicted | −0.71 (−1.02 to −0.41) | 0.77 (0.40 to 1.14) |
| FVC, % predicted | −0.58 (−0.88 to −0.29) | 0.55 (0.17 to 0.92) |
| FEV1/FVC, % predicted | −0.11 (−0.30 to 0.08) | 0.21 (−0.01 to 0.43) |
| Participants with asthma (n = 869) | ||
| Prebronchodilator FEV1, % predicted | −0.80 (−2.73 to 1.14) | −0.24 (−2.52 to 2.05) |
| Prebronchodilator FVC, % predicted | −0.12 (−1.52 to 1.29) | −0.43 (−2.00 to 1.15) |
| Prebronchodilator FEV1/FVC, % predicted | −1.05 (−2.53 to 0.44) | 0.56 (−1.26 to 2.37) |
Definition of abbreviations: AHEI-2010 = Alternative Healthy Eating Index 2010; E-DII = energy-adjusted Dietary Inflammatory Index; FEV1 = forced expiratory volume in 1 second; FVC = forced vital capacity.
Data are presented as β (95% confidence interval). All models adjusted for study center, annual household income, body mass index, physical activity, family history of asthma, place of birth, smoking status, pack-years of cigarette smoking, and (in all participants) current asthma. β indicates the estimated effect of a one-quartile increment in the E-DII or the AHEI-2010 on the corresponding lung function measure. βs with P < 0.05 are shown in bold typeface.
Discussion
To our knowledge, this is the first population-based study of dietary patterns and asthma or lung function among adults in well-defined Hispanic/Latino subgroups. We found that a higher E-DII score, but not the AHEI-2010 score, was associated with current asthma and asthma symptoms. Among adults without asthma, a higher E-DII score was associated with lower FEV1 and FVC, whereas a higher AHEI-2010 score was associated with higher FEV1 and FVC. Thus, the E-DII and AHEI-2010 yielded consistent results for lung function, but not for asthma, among Hispanics in HCHS/SOL.
The DII or E-DII have been positively correlated with inflammatory biomarkers such as CRP (C-reactive protein) (19), IL (interleukin)-6 (24, 25), and TNF-α (tumor necrosis factor-α) (25). Consistent with our findings, a case–control study of 160 Australian adults reported that a higher DII was associated with asthma, lower FEV1, and higher serum IL-6 (24). On the other hand, a cross-sectional study of 22,294 U.S. adults found that a higher DII score was associated with current wheeze but not with current asthma (14). As in the current study, that prior study found that the DII was associated with lower FEV1 and FVC in adults without current wheeze or asthma (14) but not in those with current wheeze or asthma. The negative results for lung function among subjects with asthma in that study and in this report may be due to reduced statistical power because of small sample size or to reduced effects of diet on lung function in subjects with asthma and airway inflammation.
A higher DII generally reflects higher intake of saturated and trans fats, and a high-fat diet has been shown to modify the gut microbiome, leading to an increased proportion of invasive bacteria and decreased concentration of short-chain fatty acids, which could alter immune responses and lead to asthma (26, 27). A high-fat and high–refined carbohydrate diet also leads to excess energy intake, and ultimately obesity, which has been associated with asthma and lower lung function (28). On the other hand, nutrients that contribute to a lower DII (i.e., β-carotene, riboflavin, vitamin C, and fiber) are mainly derived from fruits and vegetables, and a higher intake of fruits and vegetables has been linked to lower risk of asthma and allergies, perhaps through downregulation of T-helper cell type 2 (Th2) immune responses and antioxidant effects (29). Moreover, high fiber intake can affect the gut microbiome and has been associated with lower risk of allergic diseases (30, 31).
The AHEI-2010, a measure of diet quality on the basis of foods and nutrients predictive of chronic disease risk (13), has been inversely associated with CRP (32) and IL-6 levels (33). Consistent with our negative results for the AHEI-2010 and asthma, the U.S.-based Nurses’ Health Study showed that high AHEI-2010 scores were associated with decreased risk of COPD but not with adult-onset asthma (16). In contrast to our negative results for the AHEI-2010 and asthma symptoms, two studies reported that a higher AHEI-2010 score was associated with improved symptoms and better disease control in adults with asthma (15, 34). One of those studies also showed an inverse association between the AHEI-2010 and asthma symptoms or control in never smokers, but not in former or current smokers (which may be due to greater difficulty in detecting dietary effects on asthma in current smokers at risk of COPD [34]).
Among the components included in the AHEI-2010, higher intake of whole grains and lower intake of percentage trans fat from energy or sodium were associated with lower odds of asthma or asthma symptoms in HCHS/SOL. This is consistent with our previous findings in Puerto Rican children, in whom frequent consumption of vegetables and grains, along with less frequent consumption of dairy products and sweets or snacks, was associated with lower risk of asthma in all subjects, as well as with higher FEV1 and FVC in subjects without asthma (35, 36). In further support of a protective role of a “healthy diet” against asthma, a pilot trial of 90 adults with persistent asthma reported that a 6-month healthy eating intervention on the basis of Dietary Approaches to Stop Hypertension recommendations (emphasizing intake of vegetables, fruits, and whole grains, while limiting intake of saturated fats and sugar-sweetened beverages) was associated with improved asthma control (37).
We found a moderate correlation between the E-DII and AHEI-2010 scores, and both indices were associated with lung function in subjects without asthma. However, only the E-DII was associated with asthma or asthma symptoms and may thus be a better indicator of dietary patterns leading to airway inflammation than the AHEI-2010. Of interest, adjustment for either the E-DII or the AHEI-2010 in multivariable models had little impact on the positive association between Puerto Rican ethnicity and asthma or the inverse association between Mexican ethnicity and current asthma. This suggests that risk or protective factors other than diet (e.g., psychosocial stressors, access to and quality of health care, and racial ancestry) (2) explain this “Hispanic paradox” for asthma.
We recognize several study limitations. First, we cannot examine temporal relationships between dietary indices and asthma or lung function in a cross-sectional analysis. Second, 24-hour dietary recalls may not reflect day-to-day or seasonal variability in dietary patterns. However, we obtained similar results in multivariable analyses with additional adjustment for the season in which 24-hour dietary recalls were obtained (Table E5). Moreover, both the DII (38) and AHEI (39) are relatively consistent over calendar seasons. Third, data were lacking on a few parameters (i.e., flavones, anthocyanidins) in the dietary recalls and could not be included in the E-DII calculations. Whether the 29 parameters included in this analysis fully capture variation in dietary inflammatory patterns warrants further investigation. Fourth, our analysis was adjusted for only one indicator of socioeconomic status (annual household income), as we could not include both income and health insurance in the same multivariable models because of collinearity. However, we obtained similar results in multivariable models adjusting for health insurance coverage instead of annual income (Table E6). Last, we had no data on some potential confounders or modifiers of the relation between diet and asthma, including objective measures of atopy (i.e., skin test reactivity to allergens) or outdoor air pollution. Finally, we lack statistical power to conduct analyses stratified by Hispanic or Latino subgroup.
In summary, our findings for the E-DII suggest that a proinflammatory diet increases the risk of asthma and asthma symptoms in Hispanic adults. Moreover, our results for the E-DII and the AHEI-2010 suggest that healthy dietary patterns positively influence lung function in Hispanic adults without asthma.
Acknowledgments
Acknowledgment
The authors thank the staff and participants of HCHS/SOL for their important contributions.
Footnotes
Supported by U.S. National Institutes of Health (NIH) grants MD011764 (Y.-Y.H.); HL125666 (E.F.); and HL117191, HL119952, and MD011764 (J.C.C.). The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) was performed as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following institutes/centers/offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the Office of Dietary Supplements. None of the funding sponsors had any role in study design, data analysis, or manuscript preparation or approval.
Author Contributions: Conception and study design: Y.-Y.H., E.J., Y.M.-R., R.C.K., and J.C.C.; data collection and recruitment: K.M.P., D.S.-A., M.A., N.M.P., and B.T.; data analysis and interpretation: Y.-Y.H., E.F., S.H., Y.M.-R., N.S., and J.R.H.; and drafting of the manuscript for intellectual content: Y.-Y.H., E.J., E.F., N.S., J.R.H., R.C.K., and J.C.C. All authors approved the final version of the manuscript before submission. Y.-Y.H. and J.C.C. had full access to all of the data and take responsibility for the integrity and accuracy of the analysis.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Author disclosures are available with the text of this article at www.atsjournals.org.
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