Adaptation and validity assessment of a diet quality index for patients with type 2 diabetes

Abstract

Purpose

As diet quality indices (DQI) are likely to be influenced by disease background, adapting the existing indices for each disease is crucial. No study has been adapted a DQI for patients with type 2 diabetes. We aimed to adapt healthy eating index and assess its validity for Iranian patients with type 2 diabetes mellitus (T2DM).

Methods

In this cross-sectional study, the analysis was conducted on 489 adults with T2DM. We adapted HEI and assessed its validity using construct validity. Construct validity was assessed using a 168-item semi-quantitative food-frequency questionnaire (FFQ). General linear model was used to assess associations between adapted HEI scores and demographic characteristics, anthropometric indices, physical activity and food and nutrient intakes.

Results

Adapted HEI were examined on 489 subjects (163 men and 326 women). Findings showed that in older subjects the mean adapted HEI score was greater than the younger ones. However, it was significant only in women (p = 0.01). Women with higher education level obtained the greater score (p < 0.001). The greatest mean score of the adapted HEI score in men was related to non-smokers. The mean score in both genders were raised following the increase in physical activity level (p < 0.05). Moreover, a reduction in the mean score of adapted HEI was observed in men with higher BMI compared to those with the lower one (p = 0.001).

Conclusion

Adapted HEI could successfully discriminate diet quality in patients with T2DM. Older and high-educated women were adhered greatly to high quality diets. The adapted DQI linked with greater physical activity level and non-smokers diabetic men with lower BMI.

Electronic supplementary material

The online version of this article (10.1007/s40200-020-00601-5) contains supplementary material, which is available to authorized users.

Introduction

Type 2 diabetes mellitus (T2DM) is a main non-communicable disorder that is growing dramatically across the world [1]. The prevalence of diabetes mellitus (DM) among adults (older than 18 years) has increased from 4.7% in 1980 to 8.5% in 2014 [2]. Based on the World Health Organization (WHO), DM will probably become the 7^thcause of death in 2030 [2]. Therefore, studying on T2DM to find out efficient strategies to prevent T2DM and its complications is a key procedure toward decreasing the economical and psychological burden of this metabolic disorder [3, 4].

Lifestyle changes including adherence to healthy dietary patterns and increasing physical activity level play promoting roles in both the treatment and prevention of T2DM [5, 6]. Earlier studies have revealed the associations between diet and controlling T2DM [5, 7, 8]. The influence of numerous foods [9–11] and nutrients in isolation [12–15] on DM have been studied; however, studying on the effects of overall diet quality is being conspicuous [16–18]. Since certain food or nutrient is not consumed alone, the kinship between diet and T2DM can be clarified by taking into account diet as a whole [19].

There are various dietary quality indices (DQI) including dietary diversity score (DDS), Mediterranean diet and Healthy Eating Index (HEI) [20, 21]. However, they were developed based on healthy populations. As DQI is likely to be influenced by disease background, determining a specific DQI or adapting the existing indices is crucial. Moreover, due to different cultural, behavioral and social factors in each society, developing a DQI considering such environmental parameters can be substantially helpful to assess and change less healthy dietary patterns [22]. Consequently, controlling and preventing metabolic disorders following T2DM can become much easier.

It seems that limited studies have developed a DQI for a certain society [22–24]. Zarrin et al., developed a new DQI for Australians (Aussie-DQI). They found that there was a link between Aussie-DQI and being older, being women and having normal range of BMI [22]. Drake et al., also developed a DQI which examined the adherence to the Swedish nutrition recommendations and guidelines [23]. They reported that Individuals with higher score suffered from diabetes or experienced a cardiovascular disease. Based on Stookey et al. study., Chinese DQI was sensitive to both under- and over-nutrition and socio-demographic properties [24].

To the best of our knowledge, no study has adapted a DQI for patients with T2DM, thus far. Because the components of the newer versions of HEI (2010 and 2015) are totally different from those of HEI 1995 developed by Kennedy el al., For example the cut off points used for “whole fruit”, “whole grain” or “total protein foods” in the HEI version 2010 and 2015 are not considered suitable for diabetes and According to the great importance of diet on the management of T2DM and limited studies on this topic, we decided to adapt and apply the original version of the HEI for Iranian diabetes.

Methodology

Study design and participants

In 2017, the Urmia Diabetes Society (UDS) performed a cross-sectional study of 619 adult subjects with T2DM. Random sampling was used. 130 out of the 619 individuals were excluded from the study based on a selective criteria: those not on diabetic medication (n = 23); those with a diagnosed medical condition, not including hypertension and/or dyslipidemia (n = 66); women who were pregnant or lactating (n = 3); those with more than 40.0% blank answers on the food frequency questionnaire (n = 27); and those who’s total energy intake were outside the ranges of 800–4200 kcal/d or 3347–17,573 kJ/d (n = 11), the study was performed on the remaining 489 subjects.

The study protocol was reviewed and approved by the ethical committee of Urmia University of Medical Science (reference number: ir.umsu.rec.1393.150). The study procedures were explained for the selected sample. All eligible patients filled the consents form. The present study has been approved by the ethics committee of Urmia University of Medical Sciences in accordance with the Helsinki Declaration.

Dietary assessment

To assess the dietary intake of participants, a168-item semi-quantitative willet format food-frequency questionnaire (FFQ), was used. In the last investigations the validity and reliability of this questionnaire about evaluating nutrients and food groups intake in Iranian adults has been demonstrated [25–27]. The FFQ was filled out through a face to face interview and it was completed by trained dietitians. This questionnaire provides a list of foods to identify the frequency (daily, weekly, monthly) of foods that were consumed throughout the past year. Eventually, the reported amounts of each food item were converted to gram by using the Iranian Manual for Household Measures, Cooking Yield Factors, and Edible Portion of Foods [28]. The Data of dietary intake were analyzed using the modified version of Nutritionist IV (First Databank Inc., Hearst Corp., San Bruno, CA, USA) for Iranian foods.

HEI component

According to the method of Kennedy et al., [29] HEI is constituent of ten different components as follows: the amounts of five groups including milk, grains, fruits, vegetables, and meat, the percentage of total fat, saturated fatty acids and cholesterol intakes, the scores of dietary variety and sodium intake. Subject who consumes less than 10% of saturated fatty acid (SFA), less than 30% of total energy from total fat obtains all 10 points (a maximum score). Additionally, the consumption of cholesterol ˂300 mg/day led to get maximum score as well.

Adaptation of HEI

Iran does not have an official dietary guideline for patients with type 2 diabetes and the fact that there are diverse dietary patterns across regions of Iran, the authors found the original versions of HEI more compatible to the existing dietary recommendations of type 2 diabetes and adopted it for Iranian diabetes. Adapted HEI consists of 9 components including grain, meat, dairy, fruits, vegetables, salt, fatty acid (unsaturated fatty acids to saturated fatty acid (SFA) ratio), calorie from solid fat and added sugar. In adapted HEI, dietary diversity score contains vegetables, fruits, whole grains and marine products (Table 1). Differences between HEI and adapted HEI components are as follows: 1) total fat, saturated fatty acids and dietary cholesterol were replaced by the ratio of poly-(PUFA) and mono- unsaturated fatty acids (MUFA) to saturated fatty acids (SFA) and 2) calorie from solid fat and sugar were considered.

Table 1.

Adapted healthy eating index components

Components	Score range	Groups	Max score	Min Score
Grains	0–10	Men:19–50 yrs	11 serving/day	0 or ≥ 13.5 serving/day
		Men>51 yrs	9.1 serving/day
		Women:18–50 yrs	9 serving/day
		Women>51 yrs	7.4 serving/day
Meat	0–10	Men:19–50 yrs	2.8 serving/day	0 or ≥ 3.4 serving/day
		Men>51 yrs	2.5 serving/day
		Women:18–50 yrs	2.4 serving/day
		Women>51 yrs	2.2 serving/day
Dairy	0–10	Men:19-24 yrs	3 serving/day	0 or ≥ 3 serving/day
		Men>25 yrs	2 serving/day
		Women:18-24 yrs	3 serving/day
		Women>25 yrs	2 serving/day
Fruits	0–10	Men:19–50 yrs	4 serving/day	0 or ≥ 4.9 serving/day
		Men>51 yrs	3.2 serving/day
		Women:18–50 yrs	3.0 serving/day
		Women>51 yrs	2.5 serving/day
Vegetables1	0–10	Men:19-50 yrs	≥ 5 serving/day	0 serving/day
		Men>51 yrs	≥ 4.2 serving/day
		Men:19–50 yrs	≥ 4 serving/day
		Men>51 yrs	≥ 3.5 serving/day
Salt2	0–10	negative answer to two questions (No)		positive answer to two questions (Yes)
Fatty acids	0–10	(MUFA+PUFA)/SFA > 2.5		(MUFA+PUFA)/SFA < 1.2
Calorie from solid fat and added sugar	0–20	≤19% kcal		≥50%kcal
Dietary diversity
Vegetables3	0–5	≥3 types of vegetables/day		0
Fruits4	0–5	≥2 types of fruits/day		≤1 type/day
Whole grain	0–5	Whole grain/day		0 serving/day
Fish	0–5	Fish or marine products in week		0 serving/day

¹Potatowas considered as grain not vegetable (due to its high glycemic index)

²1) do you add salt to food? 2) Is salt added to food throughout cooking?

If person answered ((sometimes or don’t know)) received 2.5 point

³Score for beans = 2; green leafy vegetables = 1.5; tomato, carrot, and vegetables similar to fruits = 1.5

⁴classified into three groups: berries, citrus & tropical fruits, dried fruits and other fruits

We omitted the score for total fat and cholesterol because there is a positive relationship between total fat and cholesterol with SFA. Additionally, the restriction of SFA due to its greater consumption compared to trans fatty acid and cholesterol (78) are important. Moreover, this ratio emphasizes on the replacement of SFA with unsaturated fatty acids and the balance between them. Therefore, total fat and cholesterol were not considered as separate items in the adapted HEI. We also added calorie of solid fat and sugar which impact upon the dietary quality. They are full of energy while containing minimum nutrients. They can disturb the balance of diet; therefore, considering such foods seems necessary to determine diet quality.

We used the Food Guide Pyramid 1992 (https://www.cnpp.usda.gov/sites/default/files/archived_projects/FGPPamphlet.pdf) to calculate the servings of food items based on the amounts of food (gr/day) obtained from FFQ and then they were scored. The details of scoring are presented in Table 1. Briefly, 10 point is given to all divisions of adapted HEI except dietary diversity and calories from solid fat which given 20 point. In grain, meat, dairy products and fruits groups maximum point (10) is given to those that consumed recommended serving according to age and gender, while minimum point (0) is given to receiving zero serving or more than maximum serving (85th percentile for population). Scoring is done proportionally between zero up to recommended serving and recommended serving up to maximum serving (85 percentile) (0–10 point). Through vegetable groups to emphasize the importance of consuming this group in diabetes 2 patient the maximum serving isn’t considered. Because exact computing of received salt is impossible, we applied two qualitative questions each one score was 5 point. Dietary diversity consist of 4 parts and each one’s score was 0–5 points; sum of this parts score result dietary diversity score as shown in Table 1. Points were summed up to obtain total score for each subject ranging from 0 to 110.

Non-dietary information

Demographic information including age, sex, education level, smoking, and disease history were collected via face-to-face interview. Weight and height were measured using standard method without shoes. Then, body mass index (BMI) was calculated by dividing body weight (kg) to the squared of straight height (m). The valid and reliable Persian version of the International Physical Activity Questionnaire-Short Form (IPAQ-SF) is a self-administered 7-item instrument for evaluating the rates of physical activity of people between 15 and 69 years of age. IPAQ-SF estimated Physical activity levels during the last week. It was reported based on MET/min/week [30–32].

Validity assessment

To assess the validity of DQI, construct validity was used. In construct validity, the association between the score of DQI with demographic characteristics, anthropometric indices, physical activity and foods and nutrients were examined [33–35]. In this study, it has also been used construct validity to examine the validity of adapted HEI. The highest adapted HEI scores were in older people (women only), people with higher education (women only), people with higher physical activity, men with lower BMI and non-smokers (Table 2). High adapted HEI score was related with high score of consumption of grains, meat, dairy, fruits, vegetables, fatty acids (only women), variety of foods and sodium intake in men and women (Table 3). Cronbach’s alpha of the whole questionnaire was also calculated to be 0.811.

Table 2.

Comparison of Adapted healthy eating index in the study participants using GLM test¹

Category		Men (n = 163)			Women (n = 326)
		number	Mean ± SE2	p value	number	Mean ± SE	p value
Age	18-44 yrs	11	62.78 ± 3.3	Ref.	29	69.25 ± 1.6	Ref.
	45–64 yrs	124	66.77 ± 1.0	0.51	253	67.04 ± 0.6	0.73
	≥65 yrs	28	68.92 ± 2.1	0.28	44	71.68 ± 1.4	0.06
P-trend	0.29				0.005
Education level	Illiterate	8	62.89 ± 4.0	Ref.	74	64.13 ± 1.1	Ref.
	under diploma	37	64.68 ± 1.8	0.72	104	68.36 ± 0.9	0.003
	till diploma	61	66.46 ± 1.4	0.53	110	69.59 ± 0.9	<0.001
	Over diploma	57	69.28 ± 1.4	0.14	38	68.77 ± 1.5	0.001
P-trend	0.17				0.001
Smoking	Yes	34	62.40 ± 1.8	Ref.	3	70.20 ± 5.2	Ref.
	Ex-smoker	20	63.81 ± 2.4	0.37	3	64.15 ± 5.2	0.29
	No	109	68.82 ± 1.0	0.001	320	67.87 ± 0.5	0.78
P-trend	0.003				0.69
Physical activity (MET/min/week)	zero	29	59.86 ± 2.0	Ref.	116	65.86 ± 0.8	Ref.
	between zero and ≤ 462	22	65.43 ± 2.3	0.06	80	67.71 ± 1.0	0.98
	between 462 and ≤ 1386	66	67.50 ± 1.3	0.002	105	68.81 ± 0.9	0.06
	>1386	46	71.07 ± 1.5	<0.001	25	73.66 ± 1.8	0.001
P-trend	<0.001				0.001
BMI (kg/m2)	<25	36	73.86 ± 2.2	Ref.	21	72.92 ± 2.2	Ref.
	between 25 and 30	86	65.69 ± 1.1	<0.001	102	67.67 ± 1.0	0.05
	≥30	41	63.19 ± 2.2	0.001	203	67.43 ± 0.7	0.10
P-trend	0.004				0.05
Waist circumference (WC)*	With abdominal obesity	102	66.51 ± 1.2	–	59	67.94 ± 1.3	–
	Without abdominal abesity	61	67.47 ± 1.7	–	267	67.84 ± 0.6	–
P-trend	0.69				0.94
Category		Men (n = 163)			Women (n = 326)
		number	Mean ± SE	p value	number	Mean ± SE	p value
FBS (mg/dl)	<128	53	69.12 ± 1.5	Ref.	106	72.22 ± 0.9	Ref.
	128–157	60	67.45 ± 1.4	0.3	108	67.89 ± 0.8	<0.001
	≥157	50	63.78 ± 1.6	0.001	112	63.71 ± 0.9	<0.001
P-trend	0.05				<0.001
HbA1C (%)	<6.8	53	70.46 ± 1.5	Ref.	96	72.54 ± 1.0	Ref.
	6.8–7.8	66	67.22 ± 1.3	0.09	118	67.88 ± 0.8	<0.001
	≥7.8	44	62.01 ± 1.6	<0.001	112	63.83 ± 0.9	<0.001
P-trend	0.001				<0.001

*Abdominal obesity defined as waist circumference equal or more than 102 for men and 88 for women, otherwise it considered as no abdominal obesity;¹General Linear Model analysis of the adapted HEI score for selected characteristics adjusted for all other variables; ²Standard Error

Table 3.

Comparison of energy and food intake across the Adopted healthy eating index quartiles using GLM test¹

Variables	Total	Q1 (<59.3) (n = 44)	Q2 (59.3≤, <68.2) (n = 38)	Q3 (≤68.2, <75.8) (n = 47)	Q4 (≥75.8) (n = 34)	P-trend
Men (n = 163)
Energy (kcal/day)	2605 ± 679*	2946 ± 659	2511 ± 669	2461 ± 572	2467 ± 723	0.001
Grain serving	10.89 ± 4.3	12.99 ± 4.1	10.82 ± 4.1	10.15 ± 3.1	8.99 ± 1.5	<0.001
Grain score	5.46 ± 3.4	2.52 ± 3.0	4.80 ± 3.0	6.80 ± 2.8	8.15 ± 1.4	<0.001
Meat serving	2.53 ± 1.2	2.97 ± 1.3	2.42 ± 1.1	2.32 ± 0.9	2.26 ± 0.5	0.01
Meat score	5.38 ± 3.3	3.05 ± 3.3	4.76 ± 3.0	6.34 ± 2.8	7.75 ± 2.1	<0.001
Dairy serving	2.11 ± 0.9	2.45 ± 1.2	2.27 ± 0.8	2.13 ± 0.7	1.95 ± 0.4	0.07
Dairy score	5.76 ± 3.5	3.16 ± 3.2	5.49 ± 3.2	6.72 ± 3.1	8.12 ± 2.2	<0.001
Fruit serving	3.62 ± 1.8	3.88 ± 1.7	3.13 ± 1.5	3.02 ± 1.2	2.60 ± 0.7	<0.001
Fruit score	5.82 ± 2.8	4.09 ± 3.3	5.90 ± 2.8	6.50 ± 2.6	7.01 ± 1.3	<0.001
Vegetable serving	5.13 ± 1.9	4.89 ± 2.0	4.96 ± 3.0	5.31 ± 2.5	6.94 ± 3.8	0.01
Vegetable score	8.92 ± 1.7	8.57 ± 1.8	8.50 ± 2.1	8.97 ± 1.7	9.78 ± 0.7	0.01
Calorie from solid fat and added sugar	326.76 ± 230.4	429.50 ± 280.6	326.46 ± 201.6	293.70 ± 203.1	230.30 ± 163.7	0.001
Calorie from solid fat and added sugar score	19.07 ± 3.0	17.92 ± 4.4	19.29 ± 1.6	19.32 ± 3.0	19.98 ± 0.1	0.02
Sodium score	3.79 ± 3.3	1.48 ± 2.4	4.01 ± 3.2	4.31 ± 3.1	5.81 ± 3.1	<0.001
Fatty acid score	5.43 ± 3.2	4.93 ± 3.1	4.80 ± 3.2	5.64 ± 3.2	6.51 ± 3.2	0.09
Dietary diversity score	7.24 ± 5.1	5.69 ± 4.9	5.89 ± 5.1	7.20 ± 4.7	10.78 ± 4.4	<0.001
Variables	Total	Q1 (<59.3) (n = 77)	Q2 (59.3≤, <68.2) (n = 85)	Q3 (≤68.2, <75.8) (n = 77)	Q4 (≥75.8) (n = 87)	P-trend
Women (n = 326)
Energy (kcal/day)	2466 ± 531	2722 ± 529	2404 ± 481	2448 ± 519	2316 ± 519	<0.001
Grain serving	9.65 ± 3.7	12.14 ± 3.5	9.71 ± 3.6	9.43 ± 3.4	8.33 ± 1.9	<0.001
Grain score	5.50 ± 3.5	2.50 ± 2.7	4.78 ± 3.1	6.50 ± 3.2	7.95 ± 2.1	<0.001
Meat serving	2.19 ± 1.0	2.54 ± 1.4	2.28 ± 1.2	2.08 ± 0.7	2.14 ± 0.6	0.03
Meat score	5.89 ± 3.0	3.73 ± 3.1	5.59 ± 2.9	6.48 ± 2.8	7.56 ± 1.9	<0.001
Dairy serving	2.08 ± 0.9	2.58 ± 1.0	2.03 ± 0.7	1.98 ± 0.7	1.99 ± 0.5	<0.001
Dairy score	5.87 ± 3.2	2.98 ± 3.1	6.03 ± 3.1	6.82 ± 2.8	7.43 ± 2.0	<0.001
Fruit serving	3.87 ± 1.8	4.03 ± 1.5	3.44 ± 1.6	3.32 ± 1.3	2.84 ± 1.0	<0.001
Fruit score	5.80 ± 3.3	3.66 ± 3.3	5.76 ± 3.1	6.17 ± 3.1	7.39 ± 2.6	<0.001
Vegetable serving	5.23 ± 2.1	5.14 ± 2.4	5.00 ± 2.4	5.60 ± 2.8	6.05 ± 2.8	0.04
Vegetable score	9.44 ± 1.3	9.15 ± 1.8	9.26 ± 1.5	9.55 ± 1.1	9.77 ± 0.8	0.01
Calorie from solid fat and added sugar	329.52 ± 242.8	393.71 ± 259.0	382.32 ± 273.8	305.74 ± 224.5	245.90 ± 181.1	<0.001
Calorie from solid fat and added sugar score	18.91 ± 3.0	18.75 ± 3.3	18.03 ± 4.3	19.19 ± 2.3	19.67 ± 1.0	0.003
Sodium score	3.59 ± 3.1	2.27 ± 2.4	3.18 ± 3.1	3.70 ± 3.2	5.06 ± 2.9	<0.001
Fatty acid score	5.44 ± 3.1	4.36 ± 3.2	5.88 ± 3.2	5.30 ± 3.0	6.10 ± 2.8	0.002
Dietary diversity score	7.43 ± 5.1	5.11 ± 4.1	5.49 ± 4.2	8.07 ± 5.2	10.82 ± 4.5	<0.001

*Mean ± SD; ¹General Linear Model

Ethics statementttt

Written informed consent was obtained from all participants. This study was approved by School of Medicine, Urmia University of Medical Sciences.

Statistical analysis

The possible normality of variables was evaluated using Kolmogorov-Smirnov test. To assess the validity, mean differences of adapted HEI score were examined using general linear regression models (GLM) across the categories of educational level, age groups, smoking status, BMI, WC and physical activity. To compare the mean score and food servings across the adapted HEI quartiles the following ranges were considered: Q1:<59.3, Q2: 59.3 ≤ to <68.2, Q3:68.2 ≤ to <75.8, Q4: ≥75.8. We categorized demographic characteristics, anthropometric indices and physical activity level; the first category of each aforesaid variable was considered as reference group. All data analyses were performed using SPSS statistical software (version 20.0). P value less than 0.05 considered significant. In analysis of study, individuals with implausible energy intake (total energy intake below 500 Kcal for females and 800 Kcal for males or more than 3500 Kcal for females and 4000 Kcal for males) as well as pregnant and lactating women were excluded.

Results

In the present study, 163 (33.3%) and 326 (66.7%) of men and women were included, respectively. The total energy intake in 11 participants were out of the defined range; therefore, all analysis was conducted on 489 subjects. Among the patients with T2DM, 90.2% (89.6% of men, 90.5% of women) consumed anti-diabetic medications.

Mean adapted HEI score is indicated for the categories of demographic, anthropometric characteristics and physical activity level are shown in Table 2. Findings showed that in older subjects the mean adapted HEI score was greater than the younger ones. However, it was significant only in women (p = 0.01). There were no significant differences in age groups among men. Additionally, participants in both genders with higher education level obtained greater score. However, it was significant only in women (p < 0.001). The greatest mean score of the adapted HEI score in men was related to non-smokers (mean score: 68.9 ± 1.1). The mean score in both genders were raised following the increase in physical activity level (p < 0.05). Moreover, a reduction in the mean score of adapted HEI was observed in men with higher BMI compared to those with the lower group (p = 0.001).

The mean food group servings (components of adapted HEI) and their scores were represented in Table 3. The scores of all the components in the top quartile among men were greater than the bottom ones (p < 0.05 for all variables). However, subjects in the first quartile consumed greater total energy, grain, meat, dairy and fruits servings as well as calorie from solid fat and added sugar compared to those in the forth ones (p < 0.05 for all variables). There were no significant differences in the servings of dairy products (p = 0.07) and fatty acid (p = 0.09) scores across the adapted HEI quartiles. As presented in Table 3, the scores of all components in the top quartile of adapted HEI among women were higher than the bottom ones (p < 0.05 for all variables). Participants who were assigned in the first quartile consumed more energy compared to those in the forth one (p < 0.05 for all variables). The consumed servings of all adapted HEI except vegetables in the first quartile were greater than the forth one (p < 0.05 for all variables). Furthermore, participants in the top quartile consumed more servings of vegetables compared to the bottom one.

The mean daily intake of macro and micro nutrients in relation with adapted HEI score quartiles were represented in Table 4. Intake of the macro nutrients (carbohydrate, fat and protein) increase as increasing in adapted HEI score quartiles (P-trend ≤0.001). In micronutrient, significant differences was observed in mean daily intake of vitamin A, D, E, K, C, B₉, Iron and Selenium (P-trend ≤0.001) but there were no significant differences in the mean daily intake of vitamin B₁₂, calcium, magnesium, copper, manganese and chromium across the adapted HEI quartiles.

Table 4.

Weighted mean daily intakes of macro- and micronutrients by quartile category of the HEI

Variables	Total	Q1 (<59.3) (n = 121)	Q2 (59.3≤, <68.2) (n = 123)	Q3 (≤68.2, <75.8) (n = 124)	Q4 (≥75.8) (n = 121)	P-trend
Carbohydrate (gr/d)	346.52 ± 98.1	385.07 ± 102.1	342.06 ± 84.8	331.56 ± 89.0	322.29 ± 107.7	0.01
Protein (gr/d)	84.71 ± 10.9	89.67 ± 11.8	84.87 ± 9.6	82.56 ± 8.5	81.09 ± 11.9	0.002
Total fat (gr/d)	101.30 ± 30.0	119.45 ± 29.80	99.00 ± 31.3	93.95 ± 26.4	90.52 ± 23.4	<0.001
SFA (gr/d)	27.22 ± 8.9	31.92 ± 11.0	27.80 ± 7.9	24.99 ± 6.9	23.57 ± 6.8	<0.001
PUFA (gr/d)	18.01 ± 3.8	19.86 ± 3.6	17.75 ± 3.9	17.43 ± 3.2	16.71 ± 4.2	0.001
Vitamin A (μg/d)	316.45 ± 169.7	140.65 ± 53.1	247.76 ± 108.2	355.07 ± 68.0	567.35 ± 67.1	<0.001
Vitamin D (μg/d)	1.78 ± 0.4	1.40 ± 0.3	1.72 ± 0.2	1.83 ± 0.3	2.26 ± 0.4	<0.001
Vitamin E (mg/d)	8.4 ± 2.4	6.58 ± 1.8	7.74 ± 1.6	9.16 ± 2.0	10.84 ± 2.3	<0.001
Vitamin K (μg /d)	149 ± 51.9	128.88 ± 43.6	142.74 ± 52.7	158.37 ± 52.7	167.32 ± 50.2	<0.001
Vitamin C (mg/d)	110.50 ± 54.6	60.70 ± 15.5	95.61 ± 46.2	128.20 ± 38.0	167.13 ± 50.8	<0.001
Vitamin B9 (μg /d)	517.30 ± 121.6	383.33 ± 80.7	536.73 ± 64.1	568.75 ± 83.4	597.81 ± 122.6	<0.001
Vitamin B12 (μg /d)	3.10 ± 1.0	3.23 ± 1.0	3.01 ± 1.0	3.07 ± 1.0	3.11 ± 1.0	0.37
Iron (mg/d)	18.41 ± 2.0	18.95 ± 1.5	18.75 ± 2.2	18.08 ± 2.0	17.85 ± 1.9	<0.001
Calcium (mgr/d)	1082.08 ± 302.9	1087.63 ± 290.0	1072.82 ± 306.4	1080.97 ± 323.3	1086.78 ± 299.4	0.99
Magnesium (mg/d)	356.90 ± 64.3	338.21 ± 72.5	354.82 ± 72.6	367.14 ± 44.0	369.27 ± 63.5	0.10
Copper (mg/d)	1.63 ± 0.3	1.67 ± 0.3	1.67 ± 0.4	1.63 ± 0.3	1.53 ± 0.3	0.25
Zinc (mg/d)	10.47 ± 1.9	10.88 ± 1.8	10.11 ± 2.1	10.41 ± 1.9	10.43 ± 2.0	0.34
Manganese (mg/d)	5.65 ± 1.7	5.35 ± 1.6	5.53 ± 2.1	5.85 ± 1.7	5.91 ± 1.6	0.41
Selenium (μg /d)	121.12 ± 32.1	89.53 ± 27.7	122.70 ± 20.0	129.96 ± 24.2	148.03 ± 24.8	<0.001
Chromium (mg /d)	0.15 ± 0.1	0.14 ± 0.1	0.15 ± 0.1	0.15 ± 0.1	0.15 ± 0.1	0.73

Associations between index scores and intake of various foods and nutrients adjusted for age, sex, and energy intake was shown in Table 5; There is a significant strength inverse relation between intake of bread, rice, potato and red meat with the adapted HEI score; inverse relation was observed between intake of pasta and yogurt with the adapted HEI score but that was not significant. The positive strength significant relation among intake of soy bean, haricot bean, fish, vegetables, low fat milk with the adapted HEI score; however, positive relation between bologna and poultry with adapted HEI score was not significant.

Table 5.

Associations between index scores and intake of various foods and nutrients adjusting for age, sex, and energy intake

Variables*	Adapted HEI P value	r
Bread(gr/d)	<0.001	−0.35
Rice (gr/d)	<0.001	−0.24
Pasta (gr/d)	0.44	−0.24
Potato(gr/d)	0.001	−0.28
Haricot bean (gr/d)	0.003	0.27
Soy bean (gr/d)	0.01	0.26
Red meat(gr/d)	0.01	−0.26
Poultry (gr/d)	0.69	0.24
Fish (gr/d)	0.002	0.27
Bologna (gr/d)	0.32	0.24
Low fat milk(gr/d)	0.001	0.28
Yoghurt (gr/d)	0.30	−0.24
Tomato(gr/d)	0.005	0.27
Cucumber (gr/d)	<0.001	0.30
Vegetables (gr/d)	<0.001	0.35
Garlic (gr/d)	0.03	0.26
Onion (gr/d)	0.02	0.26
Apple (gr/d)	0.001	0.28
Cherry (gr/d)	0.001	0.27

*calculated using weighted multivariate linear regression

Discussion

In the current study, we found that the highest score for the adapted HEI was associated with older and high-educated women with T2DM. It also linked with greater physical activity level, non-smokers diabetic men with lower BMI. Furthermore, there was a positive association between the higher score of adapted HEI with the scores of grains, meats, dairy products, fruits and vegetables, dietary diversity and sodium intake. However, the association between adapted HEI and fatty acids were found in only women. In addition, an inverse association was found between solid fat & added sugar with the index in both genders.

To the best of our knowledge, the current study was the first to adapt a DQI for patients with T2DM. However, earlier studies have conducted on the association between developed DQI with demographic characteristics and dietary intake in non-diabetic subjects [22, 36]. Looman et al., reported that the Dutch Healthy Diet index was inversely associated with BMI, smoking, and the intakes of energy, total fat and SFA. Furthermore, they found a positive trend across sex-specific quintiles of this dietary index [36]. However, in our study there was an inverse relationship between BMI and the DQI only in men. Our study was in line with Zarrin et al., regarding the association between DQI, gender, age and smoking status. Based on Zarrin et al., who used a novel Aussie-DQI, men, younger adults, overweight/obese subjects and current smokers consumed less than dietary recommendations. They also showed that high quality diet is related to decreased risk of cancer mortality in men [22]. However, as the present study had a cross-sectional design, it was not possible to clarify the association of the adapted HEI with the risk of diseases or mortality.

In the present research, subjects with higher physical activity level and non-smoker men adhered more to the healthy dietary pattern. It revealed that subjects with higher score for DQI had totally healthy lifestyle. In addition, as older subjects are at risk for diseases greater than younger ones, they paid more attention to health maintenance. Hence, they might adhere greatly to healthy dietary pattern. Similar findings were reached in the previous studies [33, 34, 37].

In the present study one of the components of DQI was sodium intake. As we did not obtain necessary information about sodium intake from FFQ, we estimated the consumed sodium by asking two following questions: 1) do you add salt when a meal served? 2) do you add salt while you are cooking? Estimation and not reporting precise salt intake is one of the limitations of the present study.

There are two common methods to examine the validity of a dietary index, namely construct validity and criterion validity. In construct validity used for the present study, the associations of a dietary index with demographic characteristics, anthropometric indices, foods and nutrients are evaluated. In the previous studies, the score of DQI in women were greater than men [33–35]. It has been reported that there was an inverse association between DQI score and smoking [34, 38], while the link of DQI with age [34, 39], physical activity level [40], fruits and vegetables consumption [35, 41] were positive.

Another validity assessment method was criterion validity in which the ability of DQI for prediction of disease incidence and mortality are examined [22, 42, 43]. As our study was a cross-sectional study, it did not allow us to assess the predicting power of any health outcome including the occurrence of disease or mortality.

There are some limitations for the present study. As it had a cross-sectional study, we were not able to evaluate the association between of the adapted HEI with diseases or mortality. We estimated the salt intake and we could not report sodium intake, precisely. Moreover, we did not examine the association of the novel index with glycemic status and lipid profile in patients with T2DM. The strengths of the current study were as follows: 1) it was the first study to adapt a DQI for patients with T2DM, 2) the validity of the developed DQI was evaluated and 3) findings were adjusted for some known covariates. For the future study, development or adapted DQI in each society in patients with diabetes or other non-communicable diseases are suggested.

Conclusion

We found that the adapted HEI can successfully show diet quality in patients with T2DM. Based on our findings, older and high-educated women were adhered greatly to high quality diets. The adapted DQI linked with greater physical activity level and non-smokers diabetic men with lower BMI. Furthermore, there was a positive association between the higher score of adapted HEI with the scores of grains, meats, dairy products, fruits and vegetables, dietary diversity and sodium intake. However, the association between adapted HEI and fatty acids were found in only women. In addition, an inverse association was found between solid fat & added sugar with the index in both genders.

Author’s contribution

MG and RZ conceived, designed and supervised the study; PA AND MG carried out power and sample size calculation; MG and RZ AND PA interpreted the data and drafted the manuscript; MG AND RZ carried out data collection, monitoring and evaluation of participants, and project administration. All authors read and approved the final manuscript.

Funding information

This study was financially sponsored by Urmia University of Medical Sciences, Urmia, Iran, with grant number ir.umsu.rec.1393.150.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.

Ethics approval and consent participate

The study protocol was reviewed and approved by the ethical committee of Urmia University of Medical Science (reference number: ir.umsu.rec.1393.150)