Abstract
Objectives:
Examine associations of socio-demographic and lifestyle factors with diet quality in a multiethnic population.
Methods:
The analysis included 160,353 African American, Native Hawaiian, Japanese American, Latino, and non-Hispanic white participants aged 45-75 years, who entered the Multiethnic Cohort study by completing a comprehensive questionnaire in 1993-1996 and did not report cancer or heart disease. Diet quality was assessed using four diet quality indexes (DQIs): the Healthy Eating Index 2010 (HEI-2010), the Alternative Healthy Eating Index 2010 (AHEI-2010), the alternate Mediterranean Diet (aMED), and the Dietary Approaches to Stop Hypertension (DASH).
Results:
For the three DQIs, HEI-2010, AHEI-2010, and DASH, mean scores were significantly higher in women than men, while mean score of aMED was significantly higher in men than women. In both men and women, older age, higher education, being physically active, and multivitamin use were associated with scores above the median of DQIs, while overweight/obesity, currently smoking, and heavy alcohol consumption (≥2 drinks/day) were associated with scores below the median of DQIs. Race/ethnicity showed inconsistent associations according to the DQIs. Being widowed, previous smoking, and lower body mass index (BMI, < 20 kg/m2) were associated with scores below the median of DQIs in men, but not in women.
Conclusions:
Diet quality was associated with socio-demographic and lifestyle characteristics in men and women. The associations with several factors such as marital status, BMI, and smoking status differed by gender. These findings may help to identify at-risk populations for nutritional screening, and to develop nutritional intervention strategies and educational materials.
Keywords: Diet quality, socio-demographic factors, lifestyle factors, food frequency questionnaire, Hawaii – Los Angeles Multiethnic Cohort Study
Introduction
The concepts of a high quality diet usually address items to limit such as sodium, solid fats, added sugars, and refined grains [1-5]. Conversely, these high quality diet recommendations emphasize inclusion of nutrient-dense foods and beverages - vegetables, fruits, whole grains, fat-free or low-fat milk and milk products, seafood, lean meats and poultry, eggs, beans and peas, and nuts and seeds [1-5]. Dietary recommendations, such as the US Dietary Guidelines for Americans emphasize diet quality as an important factor to maintain a healthy weight, reduce risk of chronic disease, and promote overall health [1]. Results from epidemiological studies in the United States suggest high quality diets are associated with lower risk of mortality from all causes, cardiovascular disease, and cancer, as well as a lower risk of colorectal cancer for men and women [6-10]. Further, an association between high quality diet and reduced risk of colorectal, esophageal, pancreatic, and prostate cancer was reported from a systematic meta-analysis of cohort studies [10].
Socio-demographic and lifestyle factors are important covariates in diet-disease relationships. Previous studies have reported diet quality was associated with socio-demographic and lifestyle factors including age, sex, race/ethnicity, education, and energy intake [11-15]. For instance, a study from the 2003-2004 National Health and Nutrition Examination Survey showed older adults had better-quality diets than younger and middle-aged adults, women had better-quality diets than men, and Hispanic men and women had better-quality diets than African American men and women, but not higher than their non-Hispanic white counterparts [11]. A study in four European countries reported education, not living alone, and being a woman were positively associated with diet quality among elderly individuals [12].
Although many studies have been published on the associations between diet quality and socio-demographic characteristics [11-15], they were mostly conducted in racially/ethnically homogeneous populations. The present study aimed to examine the associations of diet quality assessed by four diet quality indexes (DQIs) [16], the Healthy Eating Index 2010 (HEI-2010) [2], the Alternative Healthy Eating Index 2010 (AHEI-2010) [3], the alternate Mediterranean Diet (aMED) [4], and the Dietary Approaches to Stop Hypertension (DASH) score [5], with socio-demographic and lifestyle factors in men and women of a multiethnic population.
Methods
Study population
The Multiethnic Cohort Study (MEC) was established between 1993 and 1996 to study diet and cancer in Hawaii and California (primarily Los Angeles County) [17]. Details of the study design and implementation have been described previously [17]. Briefly, the cohort consisted of more than 215,000 men and women (age 45 – 75 years at baseline) who were primarily of five major race/ethnic groups: African American, Native Hawaiian, Japanese American, Latino, and non-Hispanic white [17]. At cohort entry, participants completed a self-administered 26-page questionnaire including a quantitative food frequency questionnaire (QFFQ), demographic factors, lifestyle factors, history of prior medical conditions, use of medications, reproductive history and use of replacement estrogens/oral contraceptives, and a brief family history of cancer [17]. The study protocol was approved by the institutional review boards at the University of Hawaii and the University of Southern California.
For the current analysis, we excluded participants who did not self-report as one of the five major race/ethnic groups (n = 13,986), had any previous cancer reported on the baseline questionnaire (n = 17,645) or from tumor registries (n = 2,070), had a previous heart attack or angina reported on the baseline questionnaire (n = 14,979), or reported implausible diet based on total energy or its components intakes (n = 6,617). Among records with complete dietary information, dietary extremes were based on energy intakes (kcal) and its components. The ranges for implausible energy intakes were developed using the top and bottom 10% tails of the logged energy distribution which were excluded, and the variance (VMkcal) was computed based on the middle 80% of the data. An overall variance was computed as VAkcal = VMkcal * 1.5. This was based on the variance for the truncated normal distribution from the 10th to the 90th percentile (−1.28 to 1.28 for the standard normal) being 1/1.5 times the variance for the untruncated normal distribution. This adjustment allowed the clearly incorrect data (e.g., energy of 100 kcal) in the tails to not contribute to the variance estimate. Then, all energy less than [meankcal −3SDAkcal] or greater than [meankcal+3SDAkcal] were excluded, where SDAkcal=sqrt(VAkcal). The estimate for average energy (meankcal) was based on all cohort members rather than the middle 80%. These exclusions were made separately by sex and ethnic group. The final sample included 160,353 participants.
Dietary assessment and calculation of dietary indexes
The QFFQ with more than 180 food items was developed using 3-day measured food records from approximately 60 men and women, 45 to 75 years of age, from each of the five main ethnic groups in the study [17]. A sub study with validation and calibration purposes showed acceptable correlations ranging from 0.55 to 0.74 between the QFFQ and three 24-hour recalls among 1,606 cohort members [18]. Daily nutrient and food intakes from the QFFQ were calculated by using the MEC food-composition tables including commonly consumed foods in multi-ethnic populations of Hawaii, California, and the Pacific Region [19,20].
Four DQIs (HEI-2010, AHEI-2010, aMED, and DASH score) were calculated for the MEC as part of the Dietary Patterns Method Project (DPMP) [16], which has been described in detail previously [21,8,9]. Briefly, HEI-2010 is a measure of diet quality in terms of conformance to the Dietary Guidelines for Americans 2010 and has 12 components, including 9 adequacy components, i.e., total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, and fatty acids; and 3 moderation components i.e., refined grains, sodium, and empty calories [2,22]. The maximum score for the HEI-2010 is 100 and higher scores indicate closer conformance with dietary guidelines [2,22]. The AHEI-2010 is based on foods and nutrients predictive of chronic disease risk and includes 11 components which are scored 0 (worst) to 10 (best). The components are vegetables, fruit, whole grains, sugar-sweetened beverages and fruit juice, nuts and legumes, red/processed meat, trans fat, long-chain (n-3) fats (EPA + DHA), PUFA, sodium, and alcohol [3]. The total AHEI-2010 score ranges from 0 (nonadherence) - 110 (perfect adherence) and was associated inversely with chronic disease risk [3,23-25]. The aMED , as a modification from the Mediterranean diet scale of Trichopoulou et al [26,27] based on dietary patterns and eating behaviors that have been consistently associated with chronic disease risk [4]. The possible scores on the aMED range from 0 to 9 and include 9 components which are scored 0 (worst) to 1 (best) for the components vegetables, legumes, fruit, nuts, whole grains, red and processed meats, fish, ratio of monounsaturated to saturated fat, and ethanol [4]. The DASH score as constructed by Fung et al. [5] is based on food and nutrients emphasized in the DASH diet designed for hypertension management and includes 8 components focusing on higher intakes, i.e., fruits, vegetables, nuts and legumes, low-fat dairy products, and whole grains; and lower intakes, i.e., sodium, sweetened beverages, and red and processed meats [5]. The overall DASH score ranges from 8 to 40 and all 8 components are scored 1 (worst) – 5 (best) [5].
Socio-demographic and lifestyle characteristics
For the MEC baseline questionnaire, participants provided comprehensive socio-demographic and lifestyle information including sex, date of birth, racial/ethnic background, education, marital status, smoking status, weight, height, multivitamin use, menopausal hormone therapy (MHT) use, and family history of cancer. Body mass index (BMI, kg/m2) was calculated from self-reported weight and height. Physical activity was calculated as hours spent in vigorous work or sports per day. Selected characteristics for this study represent variables used as adjustment factors in analyzing the relationship between diet and disease in previous studies [6-9].
Statistical analysis
Selected characteristics are presented as means ± SD for quantitative variables and n (%) for categorical variables for men and women separately. Differences of the characteristics between men and women were tested using t-test for quantitative variables and chi-square test for categorical variables. Associations between dietary indexes and selected socio-demographic and lifestyle characteristics were assessed using multivariate logistic regression in men and women separately. The total score of each dietary index (HEI-2010, AHEI-2010, aMED, and DASH score) was dichotomized for men and women based on the median value and the odds of being above the median scores were modeled as dependent variables. The multivariate logistic regression was performed with participants who provided information on all covariates (men = 66,940; women = 77,203), including race/ethnicity (non-Hispanic White, African American, Native Hawaiian, Japanese American, Latino), age (45 – 54; 55 – 64; 65 – 75), educational status (≤ 12th grade, post-high school/vocational, ≥ college graduate), marital status (married, separated/divorced, widowed, never married), BMI group(< 20 kg/m2, 20 to < 25 kg/m2, 25 to < 30 kg/m2, 30 to < 35 kg/m2, ≥ 35 kg/m2), smoking status (never smoking, former smoking, current smoking), physical activity (< 0.1 hours, 0.1 to 0.3 hours, > 0.3 hours), multivitamin use (yes/no), family history of cancer (yes/no), menopausal hormone therapy use (yes/no) for women only, energy intake (< 1617 kcal, 1617 to 2381 kcal, > 2381 kcal), and alcohol intake (non-drinkers, < 1 drink (14g of ethanol), 1 to < 2 drinks, ≥ 2 drinks). Data were analyzed using SAS software (ver. 9.4, SAS Institute, Inc., Cary, NC, USA) and differences were considered significant at p < 0.05.
Results
Characteristics of the study participants are presented in Table 1. Among the 160,353 participants included in this analysis, 55% were women. The distribution of the study population’s race/ethnicity stratified by sex is as follows: for men, 24.6% non-Hispanic white, 13.1% African American, 7.1% Native Hawaiian, 30.4% Japanese American, and 24.9% Latino; and for women, 23.7% non-Hispanic white, 18.8% African American, 7.4% Native Hawaiian, 28.5% Japanese American, and 21.6% Latino. There was no significant difference in age group distribution between men and women. About 76.6% of men and 59.9% of women were married and 33.2% of men and 38.0% of women had a family history of cancer. Among women, 46.1% had used menopausal hormone therapy. Men were more likely to be educated, former or current smokers, and physically active (p <.001). On the other hand, women were more likely to be multivitamin users and non-drinkers of alcohol (p <.001). Mean BMI of men (26.6 kg/m2) was significantly higher than that of women (26.3 kg/m2) (p <.001). Mean energy and alcohol intakes among drinkers were higher in men than in women (p <.001). For the three DQIs, HEI-2010, AHEI-2010, and DASH, mean scores were significantly higher in women (68.6, 65.1, and 24.0, respectively) than men (64.3, 64.0, and 23.9, respectively), while mean score of aMED was significantly higher in men (4.14) than women (4.08) (all P’s <0.001). Scores of each components for the four DQIs were also calculated (Supplemental Tables 1-4). There were variation in scores of each components for the four DQIs by race/ethnicity, since the four DQIs included a unique combination of dietary constituents.
Table 1.
Baseline characteristics of participants in the Multiethnic Cohort Study 1993 –1996a
| Men (n = 71,894) | Women (n= 88,459) | |
|---|---|---|
| Race/ethnicity | ||
| non-Hispanic white | 17663 (24.6) | 21009 (23.7) |
| African American | 9384 (13.1) | 16663 (18.8) |
| Native Hawaiian | 5096 (7.1) | 6514 (7.4) |
| Japanese American | 21840 (30.4) | 25182 (28.5) |
| Latino | 17911 (24.9) | 19091 (21.6) |
| Age group (years) | ||
| 45 - < 55 | 24556 (34.2) | 30528 (34.5) |
| 55 - < 65 | 24131 (33.6) | 29649 (33.5) |
| 65 – 75 | 23207 (32.3) | 28282 (32.0) |
| Educational status | ||
| ≤ 12th grade | 28937 (40.7) | 39942 (45.7) |
| Vocational school/some college | 20709 (29.1) | 25846 (29.6) |
| ≥ College graduate | 21464 (30.2) | 21569 (24.7) |
| Marital Status | ||
| Married | 54692 (76.6) | 52451 (59.9) |
| Separated/divorced | 8995 (12.6) | 16682 (19.0) |
| Widowed | 2489 (3.5) | 12832 (14.6) |
| Never married | 5218 (7.3) | 5672 (6.5) |
| Body mass index (kg/m2) | 26.6 ± 4.0 | 26.3 ± 5.4 |
| Body mass index (kg/m2) | ||
| <20 | 1468 (2.1) | 6875 (7.9) |
| 20 - < 25 | 24189 (33.9) | 34020 (39.1) |
| 25 - < 30 | 33649 (47.1) | 27727 (31.9) |
| 30 - < 35 | 9443 (13.2) | 12038 (13.8) |
| ≥ 35 | 2666 (3.7) | 6336 (7.3) |
| Smoking status | ||
| Never | 22077 (31.1) | 49283 (56.8) |
| Former | 35627 (50.1) | 25012 (28.8) |
| Current | 13351 (18.8) | 12472 (14.4) |
| Physical activityb (hours) | 0.6 ± 1.1 | 0.2 ± 0.6 |
| Multivitamin use | 33634 (47.6) | 46554 (54.0) |
| Family history of cancer | 23884 (33.2) | 33600 (38.0) |
| MHT ever use among women | - | 39528 (46.1) |
| Energy intake (kcal) | 2444 ± 1134 | 1979 ± 963 |
| Alcohol non-drinker | 26553 (36.9) | 54369 (61.5) |
| Alcohol intake among drinkers (g/day) | 23.9 ± 39.0 | 11.1 ± 22.1 |
| Dietary quality indexes | ||
| HEI-2010 | 64.3 ± 11.0 | 68.6 ± 10.9 |
| AHEI-2010 | 64.0 ± 9.8 | 65.1 ± 9.2 |
| aMED | 4.14 ± 1.78 | 4.08 ± 1.78 |
| DASH | 23.9 ± 4.4 | 24.0 ± 4.4 |
MHT, menopausal hormone therapy
Values are n (%) or mean ± SD. All distributions and means were significantly different between men and women (P<0.001) except for age group (P=0.27).
Hours spent in vigorous work or sports per day
Independent associations between the four DQIs and selected socio-demographic and lifestyle factors are presented in Table 2 for men and women separately. Older age, higher educational status, physical activity, and multivitamin use were associated with scores above the median for all four DQIs in both men and women. Ever use of menopausal hormone therapy among women was also associated with scores above the median for all four DQIs.
Table 2.
Odds ratios and 95% confidence intervals from multivariate analyses of socio-demographic and lifestyle factors associated with Dietary Quality Indexes (DQIs) in the Multiethnic Cohort Study 1993–1996a
| Men (n= 66,940) | Women (n = 77,203) | |||||||
|---|---|---|---|---|---|---|---|---|
| HEI-2010 | AHEI-2010 | aMED | DASH | HEI-2010 | AHEI-2010 | aMED | DASH | |
| Race/ethnicity | ||||||||
| White | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| African American | 1.09 (1.02-1.15) | 0.90 (0.85-0.95) | 1.06 (1.00-1.13) | 0.55 (0.52-0.59) | 1.27 (1.21-1.34) | 1.05 (1.00-1.10) | 1.18 (1.12-1.25) | 0.57 (0.54-0.60) |
| Native Hawaiian | 0.78 (0.72-0.83) | 1.05 (0.98-1.13) | 1.02 (0.94-1.10) | 0.43 (0.40-0.46) | 0.90 (0.85-0.96) | 1.06 (1.00-1.13) | 1.20 (1.12-1.29) | 0.48 (0.45-0.51) |
| Japanese American | 0.59 (0.56-0.62) | 1.10 (1.05-1.15) | 1.05 (1.00-1.10) | 0.33 (0.31-0.34) | 0.63 (0.60-0.66) | 1.34 (1.28-1.40) | 1.19 (1.14-1.25) | 0.37 (0.36-0.39) |
| Latino | 0.58 (0.55-0.61) | 0.62 (0.59-0.65) | 0.71 (0.67-0.75) | 0.72 (0.68-0.75) | 0.57 (0.54-0.60) | 0.62 (0.59-0.66) | 0.74 (0.70-0.78) | 0.71 (0.68-0.75) |
| Age group (years) | ||||||||
| 45 - < 55 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 55 - < 65 | 1.56 (1.50-1.63) | 1.55 (1.48-1.61) | 1.38 (1.32-1.44) | 1.70 (1.64-1.77) | 1.67 (1.61-1.73) | 1.62 (1.56-1.69) | 1.58 (1.52-1.65) | 1.80 (1.74-1.87) |
| 65 – 75 | 2.25 (2.15-2.35) | 2.19 (2.10-2.29) | 1.93 (1.84-2.02) | 2.99 (2.86-3.13) | 2.54 (2.44-2.65) | 2.21 (2.12-2.30) | 2.08 (1.99-2.18) | 3.04 (2.91-3.17) |
| Educational status | ||||||||
| ≤ 12th grade | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| post-high school/vocational | 1.27 (1.22-1.32) | 1.14 (1.10-1.19) | 1.13 (1.08-1.18) | 1.21 (1.16-1.26) | 1.41 (1.36-1.47) | 1.19 (1.14-1.23) | 1.16 (1.12-1.21) | 1.36 (1.31-1.42) |
| ≥ College graduate | 1.61 (1.55-1.68) | 1.41 (1.35-1.47) | 1.37 (1.31-1.44) | 1.62 (1.55-1.70) | 1.71 (1.64-1.78) | 1.36 (1.31-1.42) | 1.37 (1.31-1.44) | 1.76 (1.69-1.84) |
| Marital Status | ||||||||
| married | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| separated/divorced | 0.95 (0.90-0.99) | 1.01 (0.96-1.06) | 1.01 (0.96-1.07) | 0.97 (0.92-1.02) | 0.99 (0.95-1.03) | 0.97 (0.93-1.01) | 0.93 (0.89-0.98) | 1.05 (1.01-1.10) |
| widowed | 0.87 (0.80-0.96) | 0.91 (0.83-1.00) | 0.90 (0.81-0.99) | 0.89 (0.81-0.97) | 0.99 (0.95-1.04) | 0.98 (0.93-1.02) | 0.96 (0.91-1.01) | 1.06 (1.01-1.11) |
| never married | 1.14 (1.08-1.22) | 1.14 (1.07-1.22) | 1.05 (0.99-1.13) | 1.15 (1.08-1.23) | 1.14 (1.07-1.21) | 1.03 (0.97-1.10) | 1.03 (0.96-1.10) | 1.18 (1.11-1.26) |
| Body mass index (kg/m2) | ||||||||
| < 20 | 0.77 (0.69-0.87) | 0.91 (0.81-1.02) | 0.84 (0.74-0.95) | 0.93 (0.83-1.05) | 1.04 (0.98-1.10) | 1.12 (1.05-1.18) | 1.14 (1.07-1.21) | 1.22 (1.15-1.29) |
| 20 - < 25 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 25 - <30 | 0.90 (0.87-0.93) | 0.88 (0.85-0.91) | 0.90 (0.86-0.93) | 0.84 (0.81-0.88) | 0.89 (0.86-0.92) | 0.88 (0.85-0.91) | 0.88 (0.84-0.91) | 0.81 (0.78-0.84) |
| 30 - < 35 | 0.81 (0.77-0.85) | 0.82 (0.78-0.87) | 0.86 (0.81-0.91) | 0.73 (0.69-0.77) | 0.75 (0.71-0.78) | 0.78 (0.75-0.82) | 0.75 (0.71-0.79) | 0.68 (0.65-0.72) |
| ≥ 35 | 0.75 (0.69-0.82) | 0.85 (0.77-0.92) | 0.84 (0.76-0.93) | 0.73 (0.67-0.80) | 0.66 (0.62-0.71) | 0.75 (0.71-0.80) | 0.71 (0.67-0.76) | 0.59 (0.55-0.63) |
| Smoking status | ||||||||
| never | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| former | 0.87 (0.83-0.90) | 0.98 (0.95-1.02) | 0.92 (0.88-0.96) | 0.88 (0.85-0.91) | 0.97 (0.94-1.00) | 1.07 (1.04-1.11) | 1.01 (0.97-1.05) | 0.98 (0.95-1.02) |
| current | 0.38 (0.36-0.40) | 0.52 (0.50-0.55) | 0.51 (0.49-0.54) | 0.41 (0.39-0.43) | 0.48 (0.46-0.50) | 0.58 (0.55-0.61) | 0.56 (0.53-0.59) | 0.44 (0.42-0.47) |
| Physical activityb | ||||||||
| Tertile 1 (< 0.1 hours) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Tertile 2 (0.1 – 0.3 hours) | 1.15 (1.10-1.20) | 1.15 (1.10-1.20) | 1.20 (1.14-1.26) | 1.13 (1.08-1.18) | 1.16 (1.12-1.21) | 1.16 (1.12-1.21) | 1.20 (1.15-1.25) | 1.16 (1.12-1.21) |
| Tertile 3 (> 0.3 hours) | 1.42 (1.37-1.48) | 1.33 (1.28-1.38) | 1.50 (1.44-1.56) | 1.42 (1.36-1.47) | 1.47 (1.41-1.53) | 1.48 (1.42-1.54) | 1.52 (1.46-1.59) | 1.57 (1.51-1.63) |
| Multivitamin use | 1.42 (1.37-1.47) | 1.33 (1.29-1.37) | 1.37 (1.32-1.42) | 1.47 (1.42-1.52) | 1.38 (1.34-1.42) | 1.35 (1.31-1.39) | 1.37 (1.33-1.42) | 1.44 (1.39-1.48) |
| Family history of cancer | 1.06 (1.03-1.10) | 1.06 (1.02-1.10) | 1.05 (1.01-1.09) | 1.03 (1.00-1.07) | 1.06 (1.03-1.09) | 1.01 (0.98-1.05) | 1.02 (0.99-1.05) | 1.03 (1.00-1.06) |
| MHT ever use among women | - | - | - | - | 1.24 (1.20-1.28) | 1.17 (1.13-1.20) | 1.14 (1.11-1.18) | 1.24 (1.20-1.28) |
| Energy intake | ||||||||
| Tertile 1 (< 1617 kcal) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Tertile 2 (1617 – 2381 kcal) | 1.07 (1.03-1.12) | 1.64 (1.57-1.72) | 3.00 (2.87-3.14) | 1.67 (1.60-1.75) | 1.03 (1.00-1.07) | 1.90 (1.84-1.97) | 3.72 (3.59-3.86) | 1.80 (1.73-1.86) |
| Tertile 3 (> 2381 kcal) | 0.95 (0.91-0.99) | 1.88 (1.80-1.97) | 10.09 (9.61-10.60) | 2.70 (2.59-2.83) | 0.89 (0.86-0.93) | 2.44 (2.34-2.53) | 13.61 (12.93-14.32) | 3.40 (3.26-3.54) |
| Alcohol intake | ||||||||
| Non-drinkers | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| < 1 drink (14g of ethanol) | 1.08 (1.04-1.12) | 1.50 (1.44-1.56) | 1.23 (1.18-1.28) | 0.89 (0.86-0.93) | 1.04 (1.01-1.08) | 1.30 (1.26-1.35) | 1.35 (1.30-1.40) | 0.95 (0.92-0.99) |
| 1 - < 2 drinks | 1.19 (1.12-1.25) | 3.76 (3.54-3.99) | 2.23 (2.09-2.38) | 0.79 (0.75-0.84) | 1.20 (1.12-1.30) | 3.14 (2.90-3.41) | 1.01 (0.93-1.09) | 0.88 (0.82-0.95) |
| ≥ 2 drinks | 0.72 (0.69-0.76) | 0.62 (0.59-0.65) | 0.65 (0.62-0.68) | 0.55 (0.52-0.58) | 0.57 (0.53-0.62) | 0.41 (0.37-0.44) | 0.57 (0.52-0.61) | 0.56 (0.52-0.61) |
MHT, menopausal hormone therapy
Odds ratio of being above the median score and 95% confidence interval. Multivariate logistic regression was performed in participants with complete information on covariates (66,940 men and 77,203 women).Therefore, for each factor, e.g., race/ethnicity, all other factors in the table were adjusted for. This was repeated for every factor in the table.
Hours spent in vigorous work or sports per day
Compared with non-Hispanic white men, African American men were more likely to be in the upper half of HEI-2010 and aMED scores but in the lower half of AHEI-2010 and DASH score. Japanese American men were associated with scores above the median for AHEI-2010 and aMED and scores below the median for HEI-2010 and DASH. Native Hawaiian men were associated with scores below the median for HEI-2010 and DASH, and Latino men were associated with scores below the median for all four DQIs than non-Hispanic white men. Among women participants, race/ethnic group also showed inconsistent associations according to the DQIs. For instance, compared with non-Hispanic white women, African American women were more likely to be in the upper half of HEI-2010, AHEI-2010, and aMED but in the lower half of DASH score. Native Hawaiian ethnicity and Japanese American ethnicity were associated with scores above the median for AHEI-2010 and aMED but with scores below the median for HEI-2010 and DASH scores. Latino ethnicity was associated with scores below the median for all four DQIs, compared with non-Hispanic white women.
For marital status, widowed men tended to be in the lower half of all four DQI scores compared with married men. In contrast, widowed women were in the upper half of DASH score and had no significant associations with HEI-2010, AHEI-2010, and aMED. Compared with BMI of 20 to < 25 kg/m2, lower BMI (< 20 kg/m2) was associated with scores below the median for HEI-2010 and aMED in men, but with scores above the median for AHEI-2010, aMED, and DASH in women. On the other hand, classification as overweight/obese was associated with scores below the median for all four DQIs in both men and women. In addition, former smokers were more likely to be in the lower half of HEI-2010, aMED, and DASH compared with never smokers in men. While in women, former smokers were more likely to be in the upper half of AHEI-2010 and no associations with the other three DQIs. Current smokers were more likely to be in the lower half of all four DQIs compared with never smokers in both men and women.
Among both men and women, higher energy intakes (>2381 kcal) were associated with scores above the median for AHEI-2010, aMED, and DASH, but with a score below the median for HEI-2010, in which density (intake per 1000 kcal) approach was used. The positive associations with energy intake were much stronger for aMED compared with the other three DQIs. Compared with non-drinkers of alcohol, alcohol consumption ≥ 2 drinks/day (1 drink = 14 g of ethanol) were associated with scores below the median for all four DQIs in both men and women. On the other hand, alcohol drinkers reporting < 2 drinks per day were associated with scores above the median for HEI-2010, AHEI-2010, and aMED, but with a score below the median for DASH in both men and women.
Discussion
This study examined the associations between four DQIs and socio-demographic and lifestyle factors in a large multiethnic population. Older age, higher education, physical activity, and multivitamin use were associated with scores above the median and overweight/obesity, current smoking, and heavy alcohol consumption were associated with scores below the median in both men and women. Being widowed, underweight, and former smoking were related to be in the lower half of DQI scores in men but not in women. Race/ethnicity was inconsistently associated with diet quality across the DQIs.
In our analysis, women tend to have better diet quality than men. This finding is consistent with other research [11,28]. For instance, in National Health and Nutrition Examination Survey (NHANES), a nation-wide survey, women had significantly higher mean AHEI-2010 score than men in the 1999-2010 NHANES [28]. Also, women had a higher total HEI-2005 score than men in the 2003-2004 NHANES [11]. In our results, only aMED score was higher in men than women. The aMED was most strongly associated with energy intake among four DQIs. The aMED does not standardize its components to 2000/1500 kcal before computing a score, so naturally higher intake means higher score since there are more adequacy components than moderation components. This coincides with Trichopoulou et al. study that assessed diet quality using the original Mediterranean-diet scale (range of scores, 0 to 9) among adult men and women in Greece [26].
In previous studies conducted in US and other countries such as Australia, Brazil, and European countries, that analyzed the association between socio-demographic and lifestyle characteristics and diet quality, diet quality was positively associated with factors including older age, higher education, not living alone and negatively associated with factors including, obesity and smoking [12,13,15,29]. Similarly, we found that older age, higher educational status, engaging in regular physical activity, and multivitamin use were associated with scores above the median for all four DQIs in both men and women.
Race/ethnicity differences were inconsistent among the four DQI scores. These may be partly due to the differences in components and scoring system by each DQIs. For example, Japanese American men and women in the MEC were associated with scores above the median for AHEI-2010 and aMED but in the lower half of HEI-2010 and DASH. AHEI-2010 includes the consumption of soy as a part of a component and does not include the consumption of dairy products [3] which represent traditional dietary practices of Japanese Americans. In aMED, legumes are included as a component and dairy products are not included, and a high score (1, between 0 and 1) is obtained when the intake of each components are higher than the median value [4,26,27]. For DASH, the intake of legumes and low-fat dairy products are included as components, and the highest value (5, from 1 to 5) is given when classified to the highest quintile of intake [5]. Latino men and women were associated with scores below the median for all four DQIs than non-Hispanic white men and women, respectively. Few other studies have reported the associations between race/ethnicity and diet quality, and have shown mixed results. For instance, one study focusing on the diet quality of urban older adults ages 60 to 99 years in the U.S. using HEI-2005 showed Hispanic participants were more likely to have higher HEI-2005 scores than African American participants, and non-Hispanic white participants had no significant difference compared to African American participants [13]. Another study showed diet quality assessed using the AHEI-2010 differed by Hispanic or Latino background, with higher AHEI-2010 among those with origins associated with Mexico and lower AHEI-2010 among those with origins associated with Puerto Rico [30].
The associations between the DQIs and several factors varied between men and women. Among marital status, widowed men tended to be in the lower half of all four DQI scores compared with those who were married men, but widowed women tended to be in the upper half of DASH and showed no associations in three DQIs (HEI-2010, AHEI-2010, and aMED). In a previous study reporting gender differences in bereavement, men believed women were better equipped to deal with widowhood due to women’s domestic abilities and social skills, and men’s inability to talk about their emotions [31]. Hughes et al. also described poor cooking skills and low motivation to change eating habits may constitute barriers to improving healthy eating in older men living alone [32].
Several studies have examined the association between smoking status and diet quality, and have reported smoking being inversely associated with overall diet quality [15,33]. In our results, individuals currently smoking were associated with scores below the median for all four DQIs in both men and women. As with current smoking, previous smoking was associated with scores below the median for diet quality in men, but not in women. In addition, classification as overweight/obese was associated with scores below the median for all four DQIs in both men and women. However, compared with BMI of 20 – 25 kg/m2, lower BMI (< 20 kg/m2) was associated with scores below the median for HEI-2010 and aMED in men, but with scores above the median for three DQIs in women. In previous studies reporting about eating differences in men and women, women have a higher awareness and better knowledge of nutrition than men and women usually attach greater importance to healthy eating [34,35]. Also, women are less satisfied with their weight and the motivation of weight control is more prominent in women and they are more likely to diet or restrain their eating behavior [34,35]. Therefore, these differences in men and women may in part explain the variation by sex for the associations with DQIs.
Several limitations should be taken into consideration. For the current analysis, we excluded participants who had any previous cancer and cardiovascular disease in order to examine the relationship between diet quality and socio-demographic and lifestyle factors of healthy participants, but could not completely rule out disease-induced dietary changes. Also, dietary data based on a self-administered QFFQ are subject to measurement error inherent in all dietary assessment methods including QFFQ [18,36]. Lastly, this study was a cross-sectional analysis, so we cannot infer causality from our results. Nevertheless, this study is the first to estimate the associations between sociodemographic and lifestyle characteristics and dietary quality using four indexes among healthy African-American, Native Hawaiian, Latino, Japanese-American, and non-Hispanic white adults for whom all data were collected in a uniform manner.
In conclusion, in a multiethnic population, we found older age, higher education, engaging in physical activity, and multivitamin use were positively associated with diet quality in men and women. The relationship between diet quality and several factors such as race/ethnicity, marital status, BMI status, and smoking status were found to be different in men and women. These findings are important to both the clinical and research areas in adult populations, because it may help to identify at-risk populations for nutritional screening such as men who live alone, underweight men, overweight/obese men and women, former smoking men, and currently smoking men and women. Furthermore, findings from this study can support targeted effort in developing nutritional intervention strategies and educational materials across ethnic minority groups.
Supplementary Material
Highlights.
Diet quality was associated with socio-demographic and lifestyle characteristics in men and women.
Widowed, previous smoking, lower body mass index (<20 kg/m2) were associated with lower diet quality in men, not in women.
In both men and women, older age, higher education, physically active, multivitamin use were associated with higher diet quality.
Obesity, currently smoking, and heavy alcohol consumption were associated with lower diet quality.
Acknowledgments
MK, SYP, CJB, and HYP contributed to the conception and design, analysis, interpretation of data, and manuscript drafting. YBS contributed to analysis and interpretation of data and critically reviewed the draft manuscript. LRW and LLM designed the overall cohort study and were responsible for the data. All authors read and approved the final manuscript.
Grant support:
Supported by the National Cancer Institute at the National Institutes of Health (UO1 CA164973, NCI HHSN261201200423P, P30 CA071789)
Supported by Support Program for Women in Science, Engineering and Technology through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (Grant no. 2016H1C3A1903202).
Footnotes
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Conflict of interest: The authors declare no conflict of interest
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