Dietary Patterns Associated with the Prevalence of Cardiovascular Disease Risk Factors in Kuwaiti Adults

RESEARCH

Original Research: Brief

Dietary Patterns Associated with the Prevalence of Cardiovascular Disease Risk Factors in Kuwaiti Adults Badreya Al-Lahou, MS; Lynne M. Ausman, DSc, RD; José L. Peñalvo, PhD; Gordon S. Huggins, MD; Suad Al-Hooti, MS; Sameer Al-Zenki, PhD; Fang Fang Zhang, MD, PhD ARTICLE INFORMATION Article history: Submitted 17 January 2019 Accepted 6 September 2019

Keywords: Kuwait Dietary pattern Factor analysis Cardiovascular disease risk factors 2212-2672/Copyright ª 2019 by the Academy of Nutrition and Dietetics. https://doi.org/10.1016/j.jand.2019.09.012

ABSTRACT Background Kuwaiti adults have experienced a rapid increase in cardiovascular disease (CVD) and its risk factors. Dietary patterns in the Kuwaiti diet associated with the increasingly higher CVD burden have not been adequately evaluated. Objective The objective of this study was to identify the major dietary patterns in Kuwaiti adults and examine their associations with CVD risk factors. Design This cross-sectional study examined data from the 2008-2009 National Nutrition Survey of the State of Kuwait. Participants/setting The study included 555 Kuwaiti adults aged
20 years who completed a 24-hour dietary recall. Main outcome measures The outcome measures included CVD risk factors such as obesity (body mass index), abdominal obesity (waist circumference), elevated blood pressure, dyslipidemia (blood lipid levels), diabetes (glucose and glycated hemoglobin levels), and metabolic syndrome. Statistical analysis Dietary patterns were identified using principal component analysis. The associations between dietary patterns and CVD risk factors were analyzed using survey-weighted multivariable linear and logistic regression models. Results Three dietary patterns were identified: vegetable-rich, fast food, and refined grains/poultry. Younger adults had higher adherence to the fast-food or refined-grains/ poultry dietary patterns, whereas older adults had higher adherence to the vegetablerich dietary pattern. The fast-food dietary pattern was positively associated with body mass index (b¼.94, 95% CI 0.08 to 1.79), waist circumference (b¼2.05, 95% CI 0.20 to 3.90 cm), and diastolic blood pressure (b¼1.62, 95% CI 0.47 to 2.77 mm Hg). The refined grains/poultry dietary pattern was positively associated with plasma glucose levels (b¼1.02, 95% CI 1.002 to 1.04 mg/dL [0.056 to 0.058 mmol/L]). Individuals in the highest tertile of the fast-food or refined-grains/poultry dietary patterns had higher odds of metabolic syndrome than those in the lowest tertile. Conclusions The fast-food and refined grains/poultry dietary patterns were associated with high prevalence of CVD risk factors among Kuwaiti adults. The current findings underscore the need for prospective studies to further explore dietary pattern and CVD risk factor relationships among at-risk Kuwait adults. J Acad Nutr Diet. 2019;-(-):---.

D

IET PLAYS AN IMPORTANT ROLE IN PREVENTING cardiovascular disease (CVD).1 Traditional approaches of examining one or a few nutrients or foods in isolation have failed to consider the synergistic effects of nutrients and foods.2 A dietary pattern approach has potential to better account for the complexity of the human diet and represents a better approach to evaluate the role of diet in chronic disease.3 The 2015-2020 Dietary Guidelines for Americans4 have explicitly recommended healthy dietary patterns for chronic disease prevention, moving away from the recommendations on individual nutrients or foods.

ª 2019 by the Academy of Nutrition and Dietetics.

Although dietary patterns have been well characterized in Western populations, very few studies have examined dietary patterns among Middle East region populations.5-7 The Middle East region is composed of countries that are diverse in economic status, demographic characteristics, and eating habits.8 The Gulf Cooperation Council (GCC) countries, such as Kuwait, Saudi Arabia, Qatar, Bahrain, United Arab Emirates, and Oman, are high-income Middle East region countries.9 The introduction of Western foods in the GCC region may have shaped the traditional diet to resemble more of a Western-style diet.10 In parallel, the GCC countries have experienced a rapid increase in obesity and other CVD risk

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

1

RESEARCH factors such as diabetes and hypertension.11 For example, in Kuwait, more than three-fourths of the adult population is overweight or obese,12 nearly half of adults have prehypertension or hypertension,13 and 40% have prediabetes or diabetes.14 Although the association between dietary patterns and CVD and its related risk factors has been evaluated in other regions of the world,15,16 it is important to evaluate how dietary intake patterns contribute to the CVD burden in population of Middle East region countries given the alarmingly high CVD risk factors in this population. Evidence generated directly from Middle East region countries can play important roles in informing evidence-based priorities for implementation trials and prevention policies to reduce CVD burden in these countries as well as to reduce the global CVD burden. The aim of this study was to identify the major dietary patterns of the Kuwaiti adult population using dietary data collected from a national nutrition survey, and to further investigate the associations between dietary patterns and CVD risk factors, including obesity, abdominal obesity, elevated blood pressure (BP), dyslipidemia, and diabetes.

METHODS Study Design and Population The present study used data of adult participants aged 20 years or older in the National Nutrition Survey of the State of Kuwait (NNSSK). The NNSSK is a cross-sectional householdbased cluster survey that was conducted during 2008-2009 by the Kuwait Institute for Scientific Research and the Kuwaiti Ministry of Health.10 Based on the proportion to population size method, Kuwait was stratified into 54 localities considering the number of households and the percentage of Kuwaiti households. Of these, 82 clusters were identified with 20 households per cluster, including 1,640 households and 7,547 individuals. From these households, 545 agreed to participate, resulting in total of 1,830 individuals who participated in the survey (response rate¼24.25%). We included 1,021 adult participants aged 20 years or older. Pregnant (n¼27) or lactating (n¼5) women and participants with missing dietary information (n¼2) or unreliable reporting (n¼7), defined as total  3 standard deviations of the log-scale daily total energy intake, were excluded. Because of the concern that having chronic health conditions may result in change in dietary patterns, 425 participants with one or more diagnosed conditions of diabetes, hypertension, hypercholesterolemia, or heart problem, or were currently taking medications to treat these conditions were further excluded. These exclusions resulted in 555 adult participants included for the current analysis. The study was approved by the Ethics Committee of the Ministry of Health in Kuwait, and a written consent form was obtained from all participants.

Dietary Data An interview-based 24-hour dietary recall was administered by NNSSK-trained dietitians who asked participants about their food and beverage consumption in the previous 24 hours. Dietitians used a food instruction booklet developed by Kuwait Institute for Scientific Research on the basis of the US Department of Agriculture five-step multiple pass method 2

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

RESEARCH SNAPSHOT Research Question: What are the major dietary patterns of the Kuwaiti adult population? Are there any associations between the identified dietary patterns and cardiovascular disease risk factors? Key Findings: In this cross-sectional study that included 555 Kuwaiti adults aged 20 years or older, three major dietary patterns were identified: vegetable-rich pattern, fast-food pattern, and refined grains/poultry pattern. Higher adherence to the fast-food dietary pattern was positively associated with body mass index, waist circumference, and blood pressure. Higher adherence to the refined grains/ poultry dietary pattern was associated with higher glucose levels. to help participants recall the type and amount of food eaten.17 Dietitians also used food pictures and household measures to help participants estimate the amount of food consumed. Dietary data were analyzed using the ESHA Food Processor software version 10.318 that was uploaded with more than 100 recipes of traditional dishes and foods in the Kuwait market.10 Intakes of nutrients and total energy were derived from the ESHA software. Built-in recipes in ESHA were used to disaggregate traditional dishes into their ingredients.

CVD Risk Factors Weight, height, and waist circumference were measured based on standard protocols using a body composition analyzer (model TBF 310; Tanita), a vertical stadiometer (model 214; Seca), and a measuring tape, respectively.10 BP was measured using a sphygmomanometer or an electronic professional blood pressure monitor (Pro M, Spengler Electronic). Two measurements were taken, at least 10 minutes apart, while subjects were seated with their feet flat on the floor.13 All measurements were taken twice, and the average was used. Blood samples were collected at the cubital fossa after an overnight fast. Plasma glucose (PG), high-density lipoprotein (HDL) cholesterol, total cholesterol, and triglyceride (TG) levels were analyzed on a Dimension RxL automated clinical chemistry analyzer (DadeBehring [Siemens]) using the manufacturer’s kits.13 The bichromatic end point method was used to measure PG, HDL cholesterol, and TG levels and the interassay coefficients of variation ranged from 1.5% to 3.5%, 2% to 6%, and 1% to 1.5%, respectively. Total cholesterol level was measured by the trichromatic end point method and interassay coefficient of variation was <2%. Glycated hemoglobin (HbA1c) was measured on a Roche Cobas Integra 400 and the interassay coefficient of variation was 2.5%. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Obesity was defined as BMI
25.19 Abdominal obesity was defined as waist circumference
94 cm in men and
80 cm in women.20 Low-density lipoprotein (LDL) cholesterol level was calculated using the Friedewald equation: LDL cholesterol (mg/dL)¼total cholesterol (mg/dL)eHDL cholesterol (mg/dL)eTG (mg/dL)/5, for those with TG 400 mg/dL (4.4 mmol/L).21 Dyslipidemia was defined as LDL cholesterol level --

2019 Volume

-

Number

-

RESEARCH
130 mg/dL (3.37 mmol/L).22 Elevated BP was defined as diastolic BP
80 mm Hg and/or systolic BP
120 mm Hg.23 Prediabetes/diabetes was defined as fasting PG
100 mg/dL (5.6 mmol/L) or HbA1c
5.7%.24 Metabolic syndrome was defined as meeting any three of the five criteria: waist circumference
94 cm in men and
80 cm in women, TG
150 mg/dL (1.7 mmol/L); HDL cholesterol <40 mg/dL (1.0 mmol/L) in men and <50 mg/dL (1.3 mmol/L) in women, systolic BP
130 mm Hg and/or diastolic BP
85 mm Hg, and fasting PG
100 mg/dL (5.6 mmol/L).20

Covariates Sociodemographic information was collected using an interview-based questionnaire by trained interviewers. Information collected included date of birth, education level (less than high school, completed high school, some college, and completed college or beyond), and family monthly income in Kuwaiti dinar (KD) (<1,000 KD [$3,300], 1,000 to 2,000 KD [$3,300 to 6,600], and
2,000 KD [$6,600]). Smoking status and physical activity were assessed through a series of questions in a self-reported questionnaire. Individuals who had never smoked tobacco were defined as nonsmokers, individuals who had ever smoked were defined as former smokers, and individuals who were smoking at the time of the survey were defined as current smokers. For physical activity, individuals were asked to report their duration of moderate (defined as any activity that increases breathing to some extent) and strenuous physical activities (defined as any activity that requires great effort that caused sweating and difficulty in breathing, such as running, weight lifting, and strenuous activity). Participants who engaged in at least 150 minutes/week of moderate physical activity, 75 minutes/week of strenuous activity, or an equivalent combination of moderate and strenuous activities were classified as active.25

Identification of Dietary Patterns Food items were combined into 32 food groups based on nutrient profile. Food items with distinct characteristics or consumption, such as Arabic coffee and dates, constituted their own group (Figure). Intakes of food groups were adjusted for total energy intake using the density method as grams per 2,000 kcal, to account for individual differences in metabolic efficiency, body size, and physical activity and to reduce measurement error.26 Principal component analysis was conducted to identify the dietary patterns using the PROC FACTOR procedure in SAS version 9.4.27 Eigenvalues >1, evaluation of the scree plot, as well as interpretability were used to determine the common components to be retained. Retained components were rotated using orthogonal rotation method (VARIMAX procedure) to facilitate interpretability while keeping uncorrelated components. For each retained component, a score was estimated using the SCORE option in PROC FACTOR statement. Higher scores correspond to greater adherence to a specific dietary pattern.

Food or food group

Food item

Sugar-sweetened beverages

Regular soft drinks, fruit drinks

Nuts and seeds

Any kind of nuts, seeds, nuts/seeds butter

Dark green vegetables

Lettuce, spinach, broccoli, parsley, arugula

Red and orange vegetables

Carrots, pumpkin, sweet potatoes, red/orange bell peppers

Tomatoes

Tomatoes (fresh, cooked, sauce, paste, dried, pickled)

Starchy vegetables Corn, green peas Other vegetables

Cucumber, eggplant, zucchini, green peppers, cabbage, cauliflower, onion, garlic, mushrooms, zucchini

White potatoes

White potatoes

French fries

French fries, hash brown

Snacks

Potato chips, corn chips, tortilla chips, crackers, popcorn

Whole fruit

Apples, pears, berries, banana, citrus fruits, papaya, mango, pineapple, grapes, raisins, kiwifruit, fruit salads, figs, apricot, peach, watermelon, pomegranate

100% Fruit juice

100% fruit juice

Dates

All kinds of dates (fresh, immature, and mature)

Legumes

Lupin bean, fava bean, chickpea, hummus, lentil

Fish and shellfish

Fish, shrimp, tuna, salmon

Poultry

Chicken, turkey

Unprocessed red meat

Lamb, cow, and camel meat, organ meats such as liver, brain, kidney

Processed meat

Nuggets, mortadella, hot dogs, sausages

Egg

Eggs

Whole grains

Brown bread, toast, bun, shabura (rusk), brown rice (continued on next page)

Statistical Analysis Participants’ characteristics and intake of food groups and nutrients were compared by tertile of component scores for

--

2019 Volume

-

Number

-

Figure. Food groups included in the dietary pattern analysis among Kuwaiti adults, 2008-2009 National Nutrition Survey of the State of Kuwait.

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

3

RESEARCH Food or food group

Food item

Refined grains

White rice, white bread, toast, bun, cereal, pasta, croissant

Full-fat dairy products

Full fat milk (white or chocolate), buttermilk, yogurt or flavored yogurt, labna (creamy cheese made from strained yogurt), cheese

Low-fat dairy products

Skim (fat-free) or low-fat milk (white or chocolate), buttermilk, yogurt or flavored yogurt, labna (creamy cheese made from strained yogurt), cheese

Burgers and sandwiches

Burgers, sandwiches, meat and chicken patties

Pizza

Pizza

Sweet condiments Sugar, honey, molasses, syrup, jam, jelly Western sweet

Cookies, chocolate, cake, doughnut, cinnamon roll, Danish pastry, ice cream

Traditional sweet

All kinds of traditional sweets, including balaleet, khabees, alba, crème caramel, mehalabia, tamreia, rangena, rehash, lugamat, darabeel, gours egalee, zalabya, aseeda, ghourayba, baklava, kunafa, gatayef, halwa, semsemya

Arabic coffee

Arabic coffee

Western coffee

French, espresso, cappuccino, latte, mocha, Frappuccino,a instant coffee

Black tea

Black tea

Herbal Tea

Green, herbal teas

a

Starbucks Corporation.

Figure. (continued) Food groups included in the dietary pattern analysis among Kuwaiti adults, 2008-2009 National Nutrition Survey of the State of Kuwait. each dietary pattern using analysis of variance for continuous variables and c2 test for categorical variables. Logistic regression models were conducted to estimate the odds ratio (OR) and 95% CI of the association between dietary patterns and CVD risk factors, after adjustment of age (continuous), sex (men and women), total energy intake (continuous), place of living (six governorates), education (less than high school, completed high school, some college, and completed college and beyond), family income (<1,000 KD, 1000 to <2,000 KD, and
2,000 KD), cigarette smoking (current, 4

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

former, and nonsmoker), physical activity (active or sedentary), BMI (continuous), and multivitamin supplement use (yes or no). The three dietary patterns were adjusted in the same model to assess its independent effect. Further adjustment was made for family history of high cholesterol levels or hypertension (yes or no) when dyslipidemia or elevated BP was evaluated as the outcome, respectively. Significance of trend was examined by modeling diet quality tertile as an ordinal variable in the logistic regression models. Multivariable linear regression analyses were further conducted to estimate b coefficients and 95% CI of the association between dietary pattern scores (standardized component scores) and CVD risk factors as continuous variables after adjusting for the same confounding variables included in the logistic models. Skewed dependent variables such as PG and HbA1c were natural log-transformed before analyses. All statistical analyses were performed using SURVEY procedures in SAS version 9.4,27 to account for the complex sampling design of the NNSSK. Sampling weights have been adjusted for nonrespondents and matched to the 2005 Kuwaiti census. P values <0.05 was considered statistically significant.

RESULTS The meanstandard error of age of study participants was 34.50.65 years and 51.3% were women. The meanstandard error BMI was 28.90.59, and two-thirds were overweight (30.5%) or obese (36.8%). Three major dietary patterns were identified: the vegetable-rich dietary pattern loaded high in all subcategories of vegetables except for white potato; the fast-food dietary pattern loaded high in burgers/sandwiches, french fries, and sugar-sweetened beverages (SSBs); the refined-grains/poultry dietary pattern loaded high in refined grains and poultry and low in whole grains (Table 1). These three dietary patterns accounted for 2.5%, 2.5%, and 1.7% of the variance, respectively, and represent a total of 6.7% of the variance explained. Compared with those with a lower score, participants with a higher score for the vegetable-rich dietary pattern were older and more likely to be women and nonsmokers and those with a higher score of the fast-food dietary pattern were younger, more likely to be women, and receive higher levels of education. Participants with a higher score of the refined grains/poultry dietary pattern were younger compared with those with a lower score (Table 2). In multivariable logistic regression models, participants in the highest tertile of the fast-food dietary pattern scores had nearly twofold odds of being obese (OR 1.94, 95% CI 1.07 to 3.52) and having elevated BP (OR 2.38, 95% CI 1.13 to 4.99) and approximately three-fold odds of having metabolic syndrome (OR 2.66, 95% CI 1.29 to 5.47) compared with those in the lowest tertile (Table 3). Those with greater adherence to the refined grains/poultry dietary pattern also had higher odds of having dyslipidemia (OR 2.14, 95% CI 1.04 to 4.40) or metabolic syndrome (OR 1.95, 95% CI 0.99 to 3.84) than those with the lowest adherence. When the outcomes were analyzed in multivariable linear regression models, a 1 standard deviation increase in the fast-food dietary pattern score was associated with a 1.62 mm Hg increase in diastolic BP (b¼1.62, 95% CI 0.47 to 2.77 mm Hg), 0.94 increase in BMI (b¼.94, 95% CI 0.08 to 1.79), and 2.05 cm increase in waist circumference (b¼2.05, 95% CI 0.20 to 3.90 cm) (Table 4). --

2019 Volume

-

Number

-

--

2019 Volume

Table 1. Factor loadings and energy-adjusted intakes of key food groups and nutrients by tertile (T) of the dietary pattern among Kuwaiti adults aged
20 years in the 2008-2009 National Nutrition Survey of the State of Kuwait (N¼555) Vegetable-Rich Pattern

-

Number

Food group/nutrient

Factor loadinga

T1

T3

Fast-Food Pattern Factor loading

-

meanstandard errorb

T1

Refined-Grains/Poultry Pattern

T3

Factor loadinga

meanstandard errorb

T1

T3

meanstandard errorb

Fruit (g/d) 0.17

48.99.7

99.817.0**

e0.12

10716.8

Dates

e0.09

19.65.6

13.21.9

e0.38

36.36.4

100% fruit juice

e0.02

34.016.4

32.89.0

e0.08

44.619.2

17.86.3

79.89.3***

e0.03

29.54.0

39.08.5

Whole fruit

65.114.2 3.81.1***

e0.33

13518.1

e0.38

39.36.5

e0.17

68.120.8

28.416.2

e0.20

51.97.9

28.25.4*

28.25.4*** 5.91.4***

Vegetables (g/d) Dark green vegetables

0.76

4.81.6

Tomatoes

0.69

23.13.8

1197.4***

e0.10

76.75.6

56.97.0*

Other vegetables

0.76

26.63.7

1639.4***

e0.17

10510.8

73.89.2*

White potatoes

0.10

7.81.6

18.62.7***

e0.18

18.52.6

9.31.9**

64.07.6

74.54.6

e0.11

0.004

91.89.4

84.37.1

0.23

5.51.2

20.13.0***

Grains (g/d) 0.09

21.54.1

46.37.2**

e0.10

40.77.7

23.55.0

e0.41

82.911.5

11.45.1***

e0.01

1096.4

1369.9*

e0.14

1287.7

1119.5

0.71

60.94.4

1979.5***

0.22

30.47.4

11912.3***

e0.12

88.213.1

64.18.6

0.42

18.93.9

13512.1***

Unprocessed red meat

e0.13

46.48.0

25.25.0*

e0.25

63.39.3

11.23.0***

0.06

22.94.6

36.06.5

Processed meat

e0.07

8.53.1

3.91.7

0.21

0.30.2

12.43.1***

0.05

3.51.5

4.82.1

Whole grains Protein foods (g/d) Poultry

Fish/shellfish

0.04

15.64.7

18.55.5

0.04

12.54.5

25.76.6

e0.31

48.69.4

Legumes

0.02

10.13.3

10.62.1

e0.03

6.31.3

7.72.7

0.10

6.02.8

1.80.8*** 18.23.9*

Dairy products (g/d) Full fat

e0.03

15022.0

13017.9

e0.41

24430.0

48.26.0***

e0.19

16924.7

11717.1

Low fat

0.06

37.611.3

56.120.4

e0.01

37.314.2

44.017.0

e0.15

57.715.6

22.47.4

10.33.1***

0.63

1.81.6

61.49.7***

e0.20

40.57.7

9.82.4***

0.62

1.10.6

39.24.2***

e0.08

19.04.0

18826.2***

0.61

74.516.0

50941.0***

e0.05

33938.2

Fast food (g/d) Burgers/sandwiches

e0.13

49.47.7

French fries

e0.15

25.13.3

Sugar-sweetened beverages

e0.19

38550.7

9.32.8*** 9.62.0* 21021.1**

(continued on next page)

5

RESEARCH

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

Refined grains

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

Vegetable-Rich Pattern Food group/nutrient

Factor loadinga

T1

T3

Fast-Food Pattern Factor loading

meanstandard errorb

T1

Refined-Grains/Poultry Pattern

T3

Factor loadinga

meanstandard errorb

T1

T3

meanstandard errorb

Sweets/snacks (g/d) Snacks

e0.08

7.32.3

1.90.8*

0.19

1.30.6

11.62.7***

e0.01

7.22.2

5.11.7

Western sweets

e0.13

38.45.1

24.14.6*

Sweet condiments

e0.14

21.83.0

12.01.3**

0.19

13.81.8

50.26.8***

e0.17

37.55.4

16.54.7**

e0.46

28.12.7

7.91.5***

e0.04

18.42.9

13.41.5

Protein (% of energy)

14.80.5

18.20.6***

17.90.5

14.90.5***

15.10.5

18.20.5***

Carbohydrates (% of energy) Fat (% of energy)

52.51.0

50.30.9

51.71.2

52.60.9

56.31.1

51.11.1***

32.60.9

31.50.9

30.40.9

32.50.7

28.60.9

30.81.0

Nutrients

Saturated fats (% of energy) Sodium (mg/d) Fiber (g/d) Calcium (mg/d)

11.40.5

10.00.4*

9.30.4

10.30.4

2,81776.3

11.40.5

3,655160***

3,225157

3,222130

3,05499.0

3,271103

14.00.9

31.72.2***

25.21.7

20.22.0

25.11.6

20.91.4

70237.5

10.50.4

79740.2

Potassium (mg/d)

1,98893.1

2,659104***

Vitamin D (mg/d)

1.80.2

2.30.7

83246.8

59833.7***

82849.0

64131.3***

2,636102

2,01490.1***

2,728112

2,09368.5***

2.10.3

1.20.2***

Factor loadings with absolute value
0.40 are in boldface type. Values are energy-adjusted meanstandard error using a density method as a percentage of energy (carbohydrates, fat, and protein) or as amount per 2,000 kcal/day (food groups and micronutrients). *Significant difference between T1 and T3 at P<0.05. **Significant difference between T1 and T3 at P<0.01. ***Significant difference between T1 and T3 at P<0.0001. a

b

2.20.3

1.80.7

RESEARCH

6

Table 1. Factor loadings and energy-adjusted intakes of key food groups and nutrients by tertile (T) of the dietary pattern among Kuwaiti adults aged
20 years in the 2008-2009 National Nutrition Survey of the State of Kuwait (N¼555) (continued)

--

2019 Volume -

Number -

--

2019 Volume

Table 2. Characteristics of Kuwaiti adults aged
20 years (N¼555) for the lowest tertile (T1) and highest tertile (T3) of the dietary patterns, 2008-2009 National Nutrition Survey of the State of Kuwait

-

Number

Vegetable-Rich Pattern

-

T1

T3

Fast-Food Pattern

P value (T1 vs T3) T1

P value (T1 vs T3) T1

Total

Age, y

34.50.65 32.21.0

35.71.2

0.03

38.11.1

30.31.1

<0.0001

35.51.1

32.60.99

0.03

28.90.58 27.80.60

28.80.88

0.80

28.30.83

29.21.2

0.54

29.00.66

29.31.1

0.83

Body mass index

a

)meanstandard error/

ƒƒƒƒƒƒƒn (%)ƒƒƒƒƒƒƒ! Sex 286 (51.3)

75 (40.4)

116 (63.7)

Male

269 (48.7) 110 (59.6)

69 (36.3)

Education level

)meanstandard error/

ƒƒƒƒƒƒƒn (%)ƒƒƒƒƒƒƒ! 0.005

Female

T3

P value (T1 vs T3)

Characteristic

)meanstandard error/

T3

Refined-Grains/Poultry Pattern

ƒƒƒƒƒƒƒn (%)ƒƒƒƒƒƒƒ! 0.02

76 (40.0)

104 (55.1)

109 (60.0)

81 (44.9)

0.59

0.50 99 (53.8)

91 (46.5)

86 (46.2)

94 (53.5)

0.0005

0.67

Less than high school

115 (35.7)

37 (32.3)

34 (33.1)

58 (52.7)

20 (21.3)

38 (33.8)

41 (39.8)

High school

115 (19.7)

41 (23.9)

42 (19.5)

33 (13.9)

51 (27.0)

35 (16.0)

44 (21.9)

144 (22.3)

46 (19.6)

51 (26.0)

43 (17.7)

54 (26.3)

49 (26.0)

46 (19.3)

College or beyond

181 (22.4)

61 (24.1)

58 (21.5)

51 (15.7)

60 (25.4)

63 (24.3)

54 (19.0)

Monthly family income level (Kuwaiti dinarb)

0.26

0.66

0.31

<1,000

134 (26.2)

41 (23.7)

50 (23.7)

49 (25.6)

50 (29.2)

50 (28.3)

47 (28.8)

1,000-2,000

296 (49.9)

93 (51.2)

94 (47.1)

105 (55.2)

94 (46.4)

90 (50.2)

110 (51.9)


2,000

125 (23.9)

51 (25.1)

41 (29.2)

31 (19.2)

41 (24.4)

45 (21.5)

28 (19.2)

86 (16.2)

26 (11.4)

25 (15.7)

40 (23.7)

24 (12.6)

28 (15.9)

30 (14.5)

Governorate

c

Al Ahmadi

<0.0001

0.76

0.007

Al Asimah

96 (16.4)

28 (16.2)

33 (15.5)

21 (7.1)

30 (18.2)

41 (18.8)

19 (13.5)

Al Farwaniyah

91 (21.3)

28 (19.1)

40 (23.7)

39 (23.4)

23 (15.0)

30 (22.4)

40 (27.0)

Al Jahrah

80 (9.3)

26 (7.6)

25 (10.0)

35 (13.4)

22 (7.6)

24 (8.7)

35 (13.1)

Hawalli Mubarak Al Kabeer

116 (20.2)

43 (24.9)

36 (19.0)

23 (10.7)

60 (34.4)

49 (26.9)

28 (14.1)

86 (16.6)

34 (20.8)

26 (16.0)

27 (21.8)

26 (12.3)

13 (7.3)

33 (17.8) (continued on next page)

7

RESEARCH

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

Some college

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

Vegetable-Rich Pattern Characteristic

Total

T1

T3

ƒƒƒƒƒƒƒn (%)ƒƒƒƒƒƒƒ! Smoking status Nonsmoker

Fast-Food Pattern

P value (T1 vs T3) T1

T3

ƒƒƒƒƒƒƒn (%)ƒƒƒƒƒƒƒ! 0.0005

356 (63.9) 101 (51.2)

Refined-Grains/Poultry Pattern P value (T1 vs T3) T1

138 (78.5)

T3

ƒƒƒƒƒƒƒn (%)ƒƒƒƒƒƒƒ! 0.07

105 (55.6)

119 (63.1)

0.19 126 (69.3)

111 (57.0)

Former smoker

75 (12.5)

29 (16.3)

18 (8.0)

35 (18.5)

20 (10.4)

23 (9.1)

23 (13.2)

Current smoker

124 (23.6)

55 (32.5)

29 (13.5)

45 (25.9)

46 (26.5)

36 (21.5)

51 (29.8)

Active

114 (21.0)

43 (24.9)

37 (16.7)

31 (18.6)

47 (26.7)

45 (24.3)

41 (22.0)

Sedentary

433 (79.0) 140 (75.1)

145 (83.3)

153 (81.4)

134 (73.3)

138 (75.7)

141 (78.0)

Physical activityd

0.40

Weight statuse

0.21

0.33

0.33

0.47

0.74

Underweight/normal weight 163 (32.7)

59 (36.2)

46 (31.2)

58 (30.4)

55 (33.1)

50 (31.3)

60 (34.8)

Overweight

199 (30.5)

70 (35.0)

70 (30.9)

72 (36.7)

66 (30.3)

72 (32.1)

56 (25.6)

Obese

193 (36.8)

56 (28.9)

69 (38.0)

55 (32.9)

64 (36.6)

63 (36.6)

69 (39.6)

a

P value (T1 vs T3)

Body mass index was calculated as weight in kilograms divided by height in meters squared and was used to define weight status (underweight/normal weight [<25], overweight [25 to 29.9], and obesity [
30]). $1¼0.3 Kuwaiti dinar. c Kuwait is divided into six governorates or area of residence. Al Ahmadi is the most populated governorate (21.4%) followed by Al Asimah (19.1%), Hawalli (16.8%), Al Farwaniyah (16.7%), Al Jahrah (14.2%), and Mubarak Al Kabeer (11.7%). The governorates have similar sex distributions (approximately 49% men and 51% women). For age distribution, Al Ahmadi and Al Jahra are the two governorates with largest proportion of people younger than age 20 years (50.5% and 49.9%, respectively), and Al Asimah and Hawalli are the two governorates with largest proportion of people older than age 40 years (28.6% and 27.7%, respectively).44 d Physical activity was categorized into active (who reported engaging in moderate physical activity for 150 minutes per week, strenuous activity for 75 minutes per week, or an equivalent combination of moderate and strenuous activity) and inactive. e Body mass index was calculated as weight in kilograms divided by height in meters squared and was used to define weight status (underweight/normal weight [<25], overweight [25 to 29.9], and obese [
30]). b

RESEARCH

8

Table 2. Characteristics of Kuwaiti adults aged
20 years (N¼555) for the lowest tertile (T1) and highest tertile (T3) of the dietary patterns, 2008-2009 National Nutrition Survey of the State of Kuwait (continued)

--

2019 Volume -

Number -

--

2019 Volume -

Number

Table 3. Multivariable-adjusted odds ratios (ORs)a of cardiovascular disease (CVD) risk factorsb by tertile (T) of dietary pattern scores in Kuwaiti adults aged
20 years (N¼555), 2008-2009 National Nutrition Survey of the State of Kuwait Obesity

-

Score N (%)

OR (95% CI)

Abdominal Obesity N (%)

OR (95% CI)

Dyslipidemia N (%)

OR (95% CI)

Elevated Blood Pressure N (%)

OR (95% CI)

Prediabetes/Diabetes N (%)

OR (95% CI)

Metabolic Syndrome N (%)

OR (95% CI)

Vegetable-rich dietary pattern score T1

126 (63.8) 1.00

T2

127 (69.0) 0.94 (0.46-1.92) 119 (68.3) 1.37 (0.72-2.59) 67 (35.3) 0.63 (0.30-1.31) 126 (68.3) 0.71 (0.36-1.40) 91 (48.2) 1.48 (0.77-2.82) 66 (38.0) 1.44 (0.68-3.02)

110 (54.8) 1.00

T3

139 (68.8) 1.02 (0.54-1.90) 127 (62.7) 1.02 (0.60-1.74) 65 (32.4) 0.56 (0.32-0.99) 130 (76.4) 1.75 (0.81-3.79) 86 (45.4) 1.32 (0.66-2.65) 58 (31.4) 1.00 (0.39-2.56)

P value for trend 0.95

0.96

67 (40.0) 1.00

0.049

125 (67.7) 1.00

68 (34.9) 1.00

51 (21.9) 1.00

0.16

0.45

0.93

129 (71.2) 1.00

89 (44.8) 1.00

63 (31.5) 1.00

Fast-food dietary pattern score T1

127 (69.6) 1.00

T2

135 (65.3) 1.18 (0.62-2.24) 127 (65.5) 1.21 (0.62-2.34) 74 (35.6) 1.22 (0.55-2.71) 132 (73.1) 2.71 (1.29-5.73) 90 (50.3) 1.86 (1.04-3.33) 58 (31.5) 1.41 (0.76-2.61)

119 (62.2) 1.00

T3

130 (66.9) 1.94 (1.07-3.52) 110 (58.3) 1.28 (0.64-2.58) 61 (36.1) 1.55 (0.71-3.41) 120 (68.7) 2.38 (1.13-4.99) 66 (33.7) 1.16 (0.58-2.35) 54 (28.6) 2.66 (1.29-5.47)

P value for trend 0.03

64 (35.6) 1.00

0.26

0.03

0.67

126 (67.6) 1.00

55 (26.6) 1.00

133 (70.8) 1.00

77 (41.9) 1.00

0.008

Refined-grains/poultry dietary pattern score T1

135 (68.7) 1.00

T2

132 (68.1) 1.03 (0.50-2.12) 114 (59.1) 0.60 (0.33-1.12) 77 (44.7) 2.56 (1.20-5.47) 115 (67.3) 0.58 (0.29-1.13) 95 (51.8) 1.45 (0.76-2.79) 52 (27.5) 0.91 (0.44-1.88)

T3

125 (65.2) 0.96 (0.48-1.92) 116 (60.0) 0.72 (0.39-1.36) 67 (34.7) 2.14 (1.04-4.40) 133 (74.4) 0.90 (0.44-1.84) 73 (35.5) 0.77 (0.37-1.57) 68 (36.5) 1.95 (0.99-3.84)

P value for trend 0.91

0.33

0.03

0.75

0.43

55 (26.7) 1.00

0.04

Values were estimated from multivariable logistic models adjusted for age (years, continuous), sex (male vs female), place of living (six governorates), total energy intake (kcal/day, continuous), income level (<1,000 Kuwaiti dinars, 1,000 to <2,000 Kuwaiti dinars, and
2,000 Kuwaiti dinars), smoking status (current, former, and nonsmoker), education level (less than high school, high school, some college, and completed college and beyond), physical activity (active vs. sedentary), multivitamin supplement use (yes vs. no), other dietary pattern, family history (yes vs no) of high cholesterol (dyslipidemia model) or hypertension (elevated blood pressure model), and body mass index (dyslipidemia, elevated blood pressure, and prediabetes/ diabetes models). b Definition of CVD risk factors: obesity was defined as body mass index
25; abdominal obesity was defined as waist circumference
94 cm (men) or
80 cm (women); dyslipidemia was defined as low-density lipoprotein
130 mg/dL (calculated according to the Friedewald equation); elevated blood pressure was defined as diastolic blood pressure
80 mm Hg and/or systolic blood pressure
120 mm Hg; prediabetes/diabetes was defined as fasting plasma glucose
100 mg/dL [
5.55 mmol/L] or glycated hemoglobin
5.7%; and metabolic syndrome was defined as having any three of the five criteria: abdominal obesity, triglycerides
150 mg/dL [
1.69 mmol/L], high-density lipoprotein <40 mg/dL [<1.04 mmol/L] (men) or <50 mg/dL [<1.3 mmol/L] (women), systolic blood pressure
130 mm Hg and/or diastolic blood pressure
85 mm Hg, or fasting plasma glucose
100 mg/dL [
5.55 mmol/L]. a

9

RESEARCH

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

0.49

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

Variable

Body mass index

Waist circumference (cm)

LDL cholesterolbc (mg/dL)

HDL cholesterolcd (mg/dL)

Total cholesterolc (mg/dL)

Systolic blood pressure (mm Hg)

Diastolic blood pressure (mm Hg)

Glucoseef (mg/dL)

HbA1ce (%)

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ b (95% CI)ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ! Vegetable-rich dietary pattern score (per SDg)

b (95% CI) .57 (e.43 to 1.58) P value

0.26

1.61 (e.56 to 3.79)

e2.03 e.58 e3.46 0.45 .29 1.00 (e5.45 to 1.39) (e1.68 to 0.51) (e7.44 to 0.52) (e.86 to 1.76) (e.51 to 1.09) (.98 to 1.02)

1.00 (.98 to 1.01)

0.14

0.24

0.62

0.29

0.09

0.50

0.47

0.99

Fast-food dietary pattern score (per SD)

b (95% CI) .94 (.08 to 1.79) P value

0.03

2.05 (.20 to 3.90)

1.13 .37 .35 1.34 1.62 1.02 1.01 (e2.77 to 5.03) (e.74 to 1.47) (e3.40 to 4.11) (e.28 to 2.97) (.47 to 2.77) (1.00 to 1.04) (1.00 to 1.03)

0.03

0.57

0.51

0.85

0.10

0.006

0.07

0.11

Refined grains/poultry dietary pattern score (per SD)

b (95% CI) e.30 (e.94 to .35) P value

0.36

.15 2.07 e.31 1.95 e.24 e.34 1.02 1.01 (e1.18 to 1.47) (e1.14 to 5.29) (e1.58 to 0.96) (e1.60 to 5.50) (e1.87 to 1.38) (e1.57 to .90) (1.002 to 1.04) (0.99 to 1.02) 0.83

0.20

0.63

0.28

0.76

0.59

0.03

0.38

--

2019 Volume

a Values were estimated from multivariable linear regression models adjusted for age (years, continuous), sex (male vs female), place of living (six governorates), total energy intake (kcal/day, continuous), income level (<10,00 Kuwaiti dinars, 1,000 to <2,000 Kuwaiti dinars, and
2,000 Kuwaiti dinars), smoking status (current, former, and nonsmoker), education level (less than high school, high school, some college, and completed college or beyond), physical activity (active vs sedentary), multivitamin supplement use (yes vs no), other dietary patterns, family history (yes vs no) of high cholesterol (blood lipid models) or hypertension (blood pressure models), and body mass index (all models except when body mass index or waist circumference is the outcome). b LDL¼low-density lipoprotein and was calculated according to the Friedewald equation. c To convert mg/dL cholesterol to mmol/L, mulitpy mg/dL by 0.026. To convert mmol/L to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL¼5.00 mmol/L. d HDL¼high-density lipoprotein. e Glucose and HbA1c were log-transformed due to right skewness. b (95% CI) were converted back to its original unit, corresponding differences in glucose (mg/dL) and HbA1c (%) per 1 SD increase of dietary pattern scores. f To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL¼6.0 mmol/L. g SD¼standard deviation.

RESEARCH

10

Table 4. Multivariable-adjusted b coefficientsa of cardiovascular disease risk factors and dietary pattern scores in Kuwaiti adults aged
20 years (N¼555), 2008-2009 National Nutrition Survey of the State of Kuwait

-

Number -

RESEARCH A 1 standard deviation increase in the refined grains/poultry dietary pattern was associated with 1.02 mg/dL increase in plasma glucose (b¼1.02, 95% CI 1.002 to 1.04 mg/dL [0.026 to 0.027 mmol/L]).

DISCUSSION Among Kuwaiti adults who are at risk for CVD, three dietary patterns were identified: a fast-food dietary pattern, a refined-grains/poultry dietary pattern, and a vegetable-rich dietary pattern. High adherence to a fast-food dietary pattern among Kuwaiti adults was associated with elevated BP, BMI, and waist circumference; high adherence to a refined-grains/poultry dietary pattern was associated with increased glucose levels. Although dietary patterns can vary across populations due to cultural influences and societal factors,3 the fast-food dietary pattern identified among Kuwaiti adults is similar in food composition (eg, burgers, french fries, and SSBs) to the Western dietary pattern identified among US adults.28,29 Such a Western/fast-food dietary pattern has been previously identified among populations of the Middle East such as Iranian women5,6 and Lebanese adults,7 suggesting the westernization of dietary patterns among countries in the Middle East. As expected, the fast-food dietary pattern was associated with CVD risk factors such as obesity and elevated BP. The positive association between the fast-food dietary pattern and prevalence of obesity observed among Kuwaiti adults is in line with those reported by a meta-analysis of studies conducted in Europe, North and South America, and Asia.30 Participants with a high adherence to the fast-food dietary pattern also consumed high levels of energy-dense foods such as french fries and SSBs, and high consumption of empty calories are known risk factors for obesity.31,32 In addition, the fast-food dietary pattern was associated with elevated BP among Kuwaiti adults. The positive association between a Western dietary pattern and hypertension has been previously reported among Iranian women.5 Although sodium intake did not vary across tertiles of the fast-food dietary pattern among Kuwait adults, individuals who followed a fast-food dietary pattern also consumed low levels of vegetables and fruits that are rich sources of potassium.33 Findings from prior studies suggest that the ratio of sodium to potassium is more strongly associated with blood pressure than either nutrient alone.34 Given the rapid urbanization and increased availability of fast-food restaurants in Kuwait,9,35 the association between fast-food consumption and CVD risk and the potential mechanisms underlying this need to be further evaluated. In the case that studies indicate fast-food consumption results in increased CVD risk, policy options need to be considered to reduce fast-food consumption in Kuwait and other Middle Eastern countries for primary prevention of CVD. The refined-grains/poultry dietary pattern captured some aspects of a traditional Kuwaiti diet such as high consumption of refined grains and poultry, although other components of the traditional Kuwaiti diet such as dates, vegetables, and fish36 were negatively loaded. The refined-grains/poultry dietary pattern was associated with higher levels of plasma glucose among Kuwaiti adults. This finding may be explained by the high glycemic index foods (eg, refined grains and potatoes)37 characterized by this dietary pattern and the low --

2019 Volume

-

Number

-

consumption of the low glycemic index foods (eg, whole grains and whole fruits)37 by individuals with high adherence to this dietary pattern. The positive association between the refined-grains/poultry dietary pattern and plasma glucose may contribute to the alarmingly high prevalence of prediabetes (19.4%) and diabetes (18.8%) among Kuwaiti adults.14 It is important to note that the refined-grains/poultry dietary pattern maybe popular in Kuwait due to the subsidy of rice and poultry to all Kuwaiti families by the government.38 In the case that future longitudinal studies confirm the association between a refined-grains/poultry dietary pattern and plasma glucose, the current food subsidy policies may be revisited by shifting toward subsidizing healthier food options such as replacing refined grains with whole grains.10 The vegetable-rich dietary pattern was associated with lower odds of dyslipidemia but was not linked to CVD risk factors as continuous outcomes. Although other evidence suggests a high consumption of vegetables and fruits protecting against CVD risk factors,15,39 it is possible that among Kuwaiti adults, the vegetable-rich dietary pattern did not play as strong a role as the other two dietary patterns. Thus, discouraging unhealthful dietary patterns such as fast-food and refined-grains/poultry dietary patterns may be more important in reducing CVD risk factors among Kuwaiti adults than promoting a vegetable-rich dietary pattern. In Kuwait, vegetables are most commonly consumed as part of mixed dishes, in stews, soups, or salads. Further studies are needed to evaluate the role of vegetables types and cooking methods on CVD risk factors among Kuwaiti adults. The prevalence of metabolic syndrome is nearly 40% among Kuwaiti adults.13 Both the fast-food and refinedgrains/poultry dietary patterns that were identified in Kuwaiti adults were associated with a higher prevalence of metabolic syndrome. The positive association between consumption of a Western dietary pattern and metabolic syndromes has been reported in the literature.39-41 Such an association identified among Kuwaiti adults who were at high risk of metabolic syndrome and CVD underscore the importance of improving the dietary pattern to reduce the CVD burden in Kuwait. In line with our findings, the Global Burden of Disease project estimated that a suboptimal diet consisting of low intake of fruits, vegetables, whole grains, nuts/seeds, seafood n-3 fats, and polyunsaturated fats and a high intake of processed meats, unprocessed red meats, SSBs, sodium, and trans fats contributes to a substantial burden of CVD and diabetes in Kuwait and other GCC countries.42 In Kuwait, an estimated of 1,540 CVD deaths and 108 diabetes deaths were attributable to suboptimal diet, accounting for 57.1% of the total CVD and diabetes deaths that occurred in Kuwait in 2010.42 The younger generation in Kuwait had a much higher adherence to both the fast-food and refined-grains/poultry dietary patterns compared with the older generation. Indeed, Allafi and colleagues43 reported that among Kuwaiti adolescents, about 30% reported consuming fast foods more than 3 times a week, 60% reported consuming sweets and SSBs >3 times a week, and approximately 70% did not consume vegetables, fruits, and dairy products on a daily basis.43 More than 75% of the Kuwaiti population are younger than age 40 years.44 This highlights the importance of nutrition policies and interventions targeting Kuwaiti youth for reducing the CVD burden of the nation. In addition, JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

11

RESEARCH women were more likely to adhere to the fast-food dietary pattern and to the vegetable-rich dietary pattern than men after controlling for age. In Kuwait, women also have a higher obesity prevalence than men (44.0% vs 36.5%).12 In our study population, education was positively associated with adherence to the fast-food dietary pattern after controlling for age and income. A possible explanation for the observed result is that a high level of job involvement requires individuals to work longer hours leaving scarce time for individual to prepare or eat healthy food.45 The difference in dietary patterns by sex and education and its role in determining the risk of CVD in Kuwaiti warrant further investigation. The present study has limitations. First, although principal component analysis is widely used in nutritional epidemiology research,16,46 it involves some arbitrary decisions regarding combining food items into groups, determining the number of factors to retain, and naming of factors. Second, the cross-sectional design of this study precluded making causal inferences about the observed associations and the observed findings need to be confirmed in future prospective studies. To minimize reverse causation, individuals who were diagnosed by a physician as having diabetes, hypertension, or dyslipidemia before the survey were excluded in this analysis. Third, the assessment of dietary intake was based on a single 24-hour recall, which does not represent usual intake at the individual level.47 Fourth, dietary intake patterns tend to be correlated with socioeconomic status and lifestyle factors such as education and smoking.16 Although adjustments for known potential confounders were included in all regression models, residual confounding can still occur and bias the observed associations. Last, although the NNSSK is a national nutrition survey, only one-quarter of eligible participants completed the survey and of these, participants with pre-existing health conditions were excluded. Compared with the census data of Kuwaiti adults, the participants included in this study were younger and had a higher level of education. Thus, our findings may not be generalizable to the whole adult population in Kuwait. Despite these limitations, to our best knowledge, this study is among the first to evaluate associations between dietary patterns and CVD risk factors among Kuwaiti adults who have experienced a rapid increase in CVD diseases and risk factors.11-14

the Global Burden of Disease Study 2010. Lancet. 2012;380(9859): 2224-2260. 2.

Tapsell LC, Neale EP, Satija A, Hu FB. Foods, nutrients, and dietary patterns: Interconnections and implications for dietary guidelines. Adv Nutr. 2016;7(3):445-454.

3.

Hu FB. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3-9.

4.

US Departments of Health and Human Services and Agriculture. Dietary Guidelines for Americans 2015-2020. 8th ed. 2015. https://health.gov/ dietaryguidelines/2015/guidelines/. Accessed July 31, 2018.

5.

Esmaillzadeh A, Azadbakht L. Food intake patterns may explain the high prevalence of cardiovascular risk factors among Iranian women. J Nutr. 2008;138(8):1469-1475.

6.

Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr. 2008;138(2):358-363.

7.

Naja F, Nasreddine L, Itani L, Adra N, Sibai AM, Hwalla N. Association between dietary patterns and the risk of metabolic syndrome among Lebanese adults. Eur J Nutr. 2013;52(1):97-105.

8.

Sibai AM, Nasreddine L, Mokdad AH, Adra N, Tabet M, Hwalla N. Nutrition transition and cardiovascular disease risk factors in Middle East and North Africa Countries: Reviewing the evidence. Ann Nutr Metab. 2010;57:193-203.

9.

Musaiger AO, Takruri HR, Hassan AS, Abu-Tarboush H. Food-based dietary guidelines for the arab gulf countries. J Nutr Metab. 2012;2012:905303.

10.

Zaghloul S, Al-Hooti SN, Al-Hamad N, et al. Evidence for nutrition transition in Kuwait: Over-consumption of macronutrients and obesity. Public Health Nutr. 2013;16(4):596-607.

11.

Ng SW, Zaghloul S, Ali HI, Harrison G, Popkin BM. The prevalence and trends of overweight, obesity and nutrition-related non-communicable diseases in the Arabian Gulf States. Obes Rev. 2011;12(1):1-13.

12.

Weiderpass E, Botteri E, Longenecker JC, et al. The prevalence of overweight and obesity in an adult Kuwaiti population in 2014. Front Endocrinol (Lausanne). 2019;10:449.

13.

Al Zenki S, Al Omirah H, Al Hooti S, et al. High prevalence of metabolic syndrome among Kuwaiti Adults —a wake-up call for public health intervention. Int J Environ Res Public Health. 2012;9(5):19841996.

14.

Alkandari A, Longenecker JC, Barengo NC, et al. The prevalence of pre-diabetes and diabetes in the Kuwaiti adult population in 2014. Diabetes Res Clin Pract. 2018;144:213-223.

15.

Rodriguez-Monforte M, Flores-Mateo G, Sanchez E. Dietary patterns and CVD: A systematic review and meta-analysis of observational studies. Br J Nutr. 2015;114(9):1341-1359.

16.

Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc. 2004;104(4):615-635.

17.

US Department of Agriculture. USDA Automated Multiple-Pass Method. https://www.ars.usda.gov/northeast-area/beltsville-mdbhnrc/beltsville-human-nutrition-research-center/food-surveys-researchgroup/docs/ampm-usda-automated-multiple-pass-method/. Accessed March 12, 2019. Food Processor and Genesis SQL Database Sources version 10.3. Salem, OR: ESHA Research; 2006.

CONCLUSIONS

18.

Kuwait and other GCC countries have experienced rapid urbanization due to economic growth, and during the same period the burden of diet-related diseases has been increasing among all segments of the population in these countries. In this study of Kuwaiti adults, adherence to the fast-food and refined-grains/poultry dietary patterns was associated with CVD risk factors and metabolic syndrome. In the case that this is confirmed in future longitudinal studies, these findings point to an urgent need for population-based strategies to improve nutrition and reduce CVD burden in Kuwait and other GCC countries.

19.

World Health Organization. BMI classification. http://apps.who.int/ bmi/index.jsp?introPage¼intro_3.html. Accessed June 26, 2016.

20.

Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-1645.

21.

Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499-502.

22.

Jellinger PS, Dickey RA, Ganda OP, et al. AACE medical guidelines for clinical practice for the diagnosis and treatment of dyslipidemia and prevention of atherogenesis. Endocr Pract. 2000;6(2):162-213.

23.

Chobanian AV, Bakris GL, Black HR, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003;42(6):12061252.

References 1.

Lim SS, Vos T, Flaxman AD, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for

12

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

--

2019 Volume

-

Number

-

RESEARCH 24.

American Diabetes Association. Standards of medical care in diabetes—2012. Diabetes Care. 2012;35(suppl 1):S11-S63.

25.

US Department of Health and Human Services. 2008 Physical activity guidelines for Americans. 2008. https://health.gov/paguidelines/. Accessed November 13, 2016.

26.

Willett W. Nutritional Epidemiology. 3rd edition. Oxford University Press; 2012.

37.

Ludwig DS. The glycemic index: Physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA. 2002;287(18): 2414-2423.

38.

Al Hamad N. Nutrition Country Profile State of Kuwait. 2006. http:// www.fao.org/3/aq040e/aq040e.pdf. Accessed August 5, 2019.

39.

Rodriguez-Monforte M, Sanchez E, Barrio F, Costa B, Flores-Mateo G. Metabolic syndrome and dietary patterns: A systematic review and meta-analysis of observational studies. Eur J Nutr. 2017;56(3):925-947.

27.

SAS [computer program]. Version 9.4. Cary, NC: SAS Institute; 2013.

28.

Kerver JM, Yang EJ, Bianchi L, Song WO. Dietary patterns associated with risk factors for cardiovascular disease in healthy US adults. Am J Clin Nutr. 2003;78(6):1103-1110.

40.

Lutsey PL, Steffen LM, Stevens J. Dietary intake and the development of the metabolic syndrome: The Atherosclerosis Risk in Communities study. Circulation. 2008;117(6):754-761.

29.

Fung TT, Rimm EB, Spiegelman D, et al. Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr. 2001;73(1):61-67.

41.

30.

Mu M, Xu LF, Hu D, Wu J, Bai MJ. Dietary patterns and overweight/obesity: A review article. Iran J Public Health. 2017;46(7): 869-876.

Bahadoran Z, Mirmiran P, Hosseini-Esfahani F, Azizi F. Fast food consumption and the risk of metabolic syndrome after 3-years of follow-up: Tehran Lipid and Glucose Study. Eur J Clin Nutr. 2013;67(12):1303-1309.

42.

31.

Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain: A systematic review. Am J Clin Nutr. 2006;84(2): 274-288.

Afshin A, Micha R, Khatibzadeh S, et al. The impact of dietary habits and metabolic risk factors on cardiovascular and diabetes mortality in countries of the Middle East and North Africa in 2010: A comparative risk assessment analysis. BMJ Open. 2015;5(5): e006385

43.

32.

Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011;364(25):2392-2404.

Allafi A, Al-Haifi AR, Al-Fayez MA, et al. Physical activity, sedentary behaviours and dietary habits among Kuwaiti adolescents: gender differences. Public Health Nutr. 2014;17(9):2045-2052.

44.

33.

Slavin JL, Lloyd B. Health benefits of fruits and vegetables. Adv Nutr. 2012;3(4):506-516.

34.

Perez V, Chang ET. Sodium-to-potassium ratio and blood pressure, hypertension, and related factors. Adv Nutr. 2014;5(6):712741.

The Public Authority for Civil Information, Statistical Services System. The total population Kuwaiti and non-Kuwaiti by age group and gender. https://www.paci.gov.kw/stat/SubCategory.aspx?ID¼2. Accessed June 27, 2019.

45.

Hidaka BH, Hester CM, Bridges KM, Daley CM, Greiner KA. Fast food consumption is associated with higher education in women, but not men, among older adults in urban safety-net clinics: A crosssectional survey. Prev Med Rep. 2018;12:148-151.

46.

Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: A review. Nutr Rev. 2004;62(5):177-203.

47.

Shim JS, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health. 2014;36:e2014009

35.

Musaiger AO. Consumption, health attitudes and perception toward fast food among Arab consumers in Kuwait: Gender differences. Glob J Health Sci. 2014;6(6):136-143.

36.

Musaiger AO. Traditional foods in the Arabian Gulf Countries. 1st edition. Manama, Bahrain: FAO/RNEA-Egypt and Arabian Gulf Univeristy; 1993.

AUTHOR INFORMATION B. Al-Lahou is a doctoral candidate, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, and a research assistant, Kuwait Institute for Scientific Research, Kuwait City, Kuwait. L. M. Ausman is a professor, and F. F. Zhang is an associate professor, Friedman School of Nutrition Science and Policy, and G. S. Huggins is an associate professor, Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA. J. L. Peñalvo is an adjunct assistant professor, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, and a professor, Institute of Tropical Medicine Antwerp, Antwerpen, Belgium. S. Al-Hooti is retired; at the time of the study, she was a researcher, Food and Nutrition Program, Kuwait Institute for Scientific Research, Kuwait City, Kuwait. S. Al-Zenki is a science and technology director for Environment and Life Sciences Research Centers, Kuwait Institute for Scientific Research, Kuwait City, Kuwait. Address correspondence to: Fang Fang Zhang, MD, PhD, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Ave, Boston, MA 02111. E-mail: [email protected]

STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict of interest was reported by the authors.

FUNDING/SUPPORT This research was supported by Kuwait Institute for Scientific Research and supported partially by the Kuwait Foundation for the Advancement of Science (grant no. 2003-1202-02).

AUTHOR CONTRIBUTIONS B. Al-Lahou, L. M. Ausman, J. L. Peñalvo, G. S. Huggins, and F. F. Zhang designed this research; B. Al-Lahou analyzed data and performed statistical analysis; B. Al-Lahou, L. M. Ausman, and F. F. Zhang wrote the manuscript; and all authors contributed to the manuscript revisions and read and approved the manuscript.

--

2019 Volume

-

Number

-

JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS

13