The association between diet quality and obesity-related metabolic risks

Abstract

BACKGROUND:

The aim of this study was to examine the association between dietary quality measured by inflammatory potential of a diet and Alternative Healthy Eating Index-2010 (AHEI-2010), and obesity-related metabolic risks in a representative sample of Iranian obese adults.

METHOD:

This cross-sectional study was conducted on 300 obese adults. Dietary intake was assessed using a validated 168-item semi-quantitative food frequency questionnaire (FFQ). Diet quality was measured using AHEI-2010 and Empirical Dietary Inflammatory Pattern (EDIP) scores.

RESULTS:

Those in the upper quartile of AHEI-2010 were associated with lower serum level of triglycerides (TG), and higher body mass index (BMI), compared to participants in the lower quartile. Those in the higher quartile of EDIP score were associated with higher serum level of TG. Greater adherence to AHEI-2010 had 70% lower odds of high fasting blood glucose (FBG), compared with those in the first quartile [Q4 vs Q1: OR, 0.3 (95% CI: 0.1–0.8), $p$ trend $=$ 0.02] Those in the highest quartile of EDIP score had a 60% higher odds of high diastolic blood pressure, compared with those in the lowest quartile [Q4 vs Q1: OR, 2.4 (95% CI: 1.1–5.5), $p$ trend $=$ 0.05] after controlling for potential covariates.

CONCLUSIONS:

High diet quality incorporating more anti-inflammatory diet may have a potential benefit in reducing obesity-related metabolic risks.

Keywords

Alternative Healthy Eating Index Empirical Dietary Inflammatory Pattern diet quality metabolic risks

1. Introduction

The presence of metabolic risk factors such as high serum level of fasting blood glucose (FBG), triglycerides (TG), high systolic and diastolic blood pressure (SBP & DBP), high body mass index (BMI), and low level of high density lipoprotein cholesterol (HDL-c) in adults, are known to increase their risk for cardiovascular diseases (CVD) and type 2 diabetes [12]. These metabolic risks are becoming more prevalent in the world, and approximately 90% of Americans with obesity had at least one metabolic risk factors [23]. Reports of a systematic review from Iran have revealed that metabolic abnormalities were noted in about 31% of the population and more prevalent among women [3].

A large body of evidence has indicated that dietary factors are the main modifiable risk factors for obesity and obesity-related metabolic risks. Diet quality indices represent a broader picture of food and nutrient consumption, therefore, measurement of overall diet quality is employed as an alternative method to assess diet-disease relations [24, 32]. Adiposity and the risk of obesity are highly affected by the overall quality of a diet than a relative macronutrient [13, 16, 21]. Epidemiological studies have suggested that adherence to healthier score of Alternative Healthy Eating Index-2010 (AHEI-2010) was associated with reduced risk of CVD and type 2 diabetes, inversely associated with obesity and abdominal obesity [10, 26], and concentrations of metabolic risk markers.

An empirically derived dietary inflammatory pattern (EDIP) score based exclusively on food groups was developed to assess the inflammatory potential of a diet as the net effect of the anti- and pro-inflammatory foods in whole diets [34]. The validity of EDIP score was evaluated in previous research [34]. The EDIP score has been associated with concentrations of inflammatory plasma biomarkers, and higher EDIP scores were related to increased risk of colorectal cancer [15].

Although earlier studies in Iran have reported a protective association between healthy eating pattern and metabolic risks [8, 28], they were used health eating index (HEI) for diet quality assessment and focused on the general population. To the best of our knowledge no studies examined the relationship between diet quality and obesity-related metabolic risks in obese Iranian adults. Establishing the association between these two factors is important for the promotion of the healthy eating according to dietary guidelines, and to manage the development and progression of CVD and type 2 diabetes among this group of the population. Therefore, we sought to examine the association between diet quality measured by inflammatory potential of a diet and Alternative Healthy Eating Index-2010 (AHEI-2010), and obesity-related metabolic risks in a sample of obese Iranian adults.

2. Materials and methods

This cross-sectional study was conducted among 300 obese adults aged 18 to 59 years in Tehran. The study was conducted from July to October 2017 in the Southern area of Tehran. A stratified random sampling technique was employed to select the required households. In the first stage, ten public health centres were selected from a total of 33 public health centres using a random sampling technique. Following, obese persons were randomly selected with a probability proportional to their population from a sampling frame of a telephone directory database found at the nearby health centre. Then study participants were recruited via a phone call to come to the nearby cluster. When there were two eligible obese individuals in the household, only one person was invited randomly. In case, there was no eligible person in the selected household, the next household was contacted. Eligible participants were 19–59 years of age, had BMI of $\geqslant$ 30 kg/m ${}^{2}$ , and lived in the area for 6 months and more.

The sample size was determined based on WHO estimated prevalence of obesity in adults of Iran [25], which was 26.1%. Thus using the formula for estimation of single proportion; $n=(Z2\times P(1-P))/d2$ , the total sample was $\sim$ 300 obese people.

2.1 Anthropometric and biochemical assessment

Body weight (in kg) was measured to the nearest 0.1 kg while the subjects wearing light clothing without shoes and height (in cm) was measured to the nearest 0.5 cm by using a stadiometer. Body mass index (BMI) is calculated as weight divided by height squared (in kg/m ${}^{2}$ ). Waist circumference (WC) was measured at midway between the costal margin and the iliac crest to the nearest 0.5 cm using an inextensible measuring tape. Blood pressure (BP) was measured by a digital sphygmomanometer (OMRON HEM-780, Osaka, Japan) using the right arm twice in the seated position, and the mean values were used in all analyses.

A 12-hour fasting venous blood sample was used to measure all biochemical markers following standard chemical procedures. Serum glucose was measured using a modified hexokinase enzymatic method. Serum level of high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) was measured using a Hitachi 704 analyser (Boehringer-Mannheim Diagnostics).

2.2 Dietary intake assessment

Dietary intake data was obtained using a validated semi-quantitative food-frequency questionnaire (FFQ) [20], consisting of 168-item. The FFQ consisted of a list of foods with standard serving sizes. Participants were asked to report their frequency and amount of each food item consumed during the previous year. Portion sizes of the consumed foods were converted to grams using household measurements. Nutritionist IV computer software was used for the nutrient analysis of the diets. The database of this software is modified for Iranian foods. Then, all items were converted to daily intakes based on grams. Dietary data was collected by trained nutritionist. Dietary data from the FFQ were used to calculate both AHEI-2010 and EDIP scores.

2.3 Diet quality indices

2.3.1 The Alternate Healthy Eating Index (AHEI-2010)

The scoring criteria for the AHEI-2010 is according to a method described by Chiuve et al. [2]. The AHEI-2010 is based on 1 components: six components for which the highest intakes were supposed to be ideal (vegetables, fruit, whole grains, nuts and legumes, long chain omega-3 fats, and polyunsaturated fatty acids), and four components for which avoidance or lowest intake were supposed to be ideal (sugar sweetened drinks and fruit juice, red and processed meat, trans fat, and sodium). In the present study, alcohol consumption was not included into the score, due to lack of information in the original dataset. Each component is given a minimal score of 0 and a maximal score of 10, with intermediate values scored proportionally, and has the potential to contribute 0–10 points to the total score. All the component scores are summed to obtain a total AHEI-2010 score, which ranges from 0 to 100, with a higher score representing a healthier diet.

2.3.2 An empirically derived dietary inflammatory pattern (EDIP) score

EDIP is calculated using the method proposed by Tabung et al. [34]. The goal for developing the Empirical Dietary Inflammatory Pattern (EDIP) score was to empirically create an overall score to assess the inflammatory potential of whole diets defined by using food groups. The details of the development of the EDIP score has been described previously [34]. Although the EDIP score has 18 component, the present study limited on 16 components by excluding beer and wine components from the score due to lack of information in the original dataset. The EDIP score assesses the inflammatory potential of an individual’s diet on a continuum from maximally anti-inflammatory to maximally proinflammatory, with higher (more positive) scores indicating more proinflammatory diets and lower (more negative) scores indicating anti-inflammatory diets [34].

2.4 Assessment of covariates

Covariates were identified using the existing knowledge base, including the investigative team’s knowledge and published literature. And included BMI, age, sex, physical activity level (measured as light, moderate, and vigorous), and history of chronic disease (yes/no). BMI was categorized in to tree groups; Obese I (BMI 30.00–34.99 kg/m ${}^{2}$ ), Obese II (BMI 35.00–39.99 kg/m ${}^{2}$ ), and Obese III (BMI $\geqslant$ 40 kg/m ${}^{2}$ ).

Metabolic risks were defined according to the Adult Treatment Panel III (ATP-III) Guidelines [6]; by which abdominal obesity defined as waist circumference $>$ 102 cm in men and $>$ 88 cm in women; High DBP: defined as diastolic blood pressure $\geqslant$ 85 mmHg; High SBP: defined as systolic blood pressure $\geqslant$ 130 mmHg; High blood pressure (hypertension): defined as diastolic blood pressure and systolic blood pressure $\geqslant$ 85/130 mmHg; High TG: defined as values $\geqslant$ 200 mg/dL; Low HDL-c: defined as values $<$ 40 mg/dL for men and $<$ 50 mg/dL for wome; High FBG (type 2 diabetes): defined as fasting serum level of $\geqslant$ 126 mg/dL.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethical Committee of Tehran University of Medical Sciences (TUMS) (Date of Approval: May 19, 2017; Approval Number IR.TUMS.VCR. REC.1396.2157). Written informed consent was obtained from all participants and the study was conducted in accordance with the Helsinki Declaration.

Table 1
Descriptive characteristics of participants ( $n=$ 300) based on lower and upper quartiles of the AHEI-2010 and EDIP scores

	Overall	AHEI-2010 score			EDIP score
		Q1 ( $n=$ 81)	Q4 ( $n=$ 71)	$P$ trend	Q1 ( $n=$ 75)	Q4 ( $n=$ 75)	$P$ trend
		( $<$ 59)	( $>$ 74)		( $<-$ 0.38)	( $>$ 0.71)
Age, years	42.9 $\pm$ 10.9	44.2 $\pm$ 11.4	39.9 $\pm$ 9.7	0.01	43.2 $\pm$ 10.3	44.2 $\pm$ 11.5	0.34
Sex, %				0.85			0.17
Male	46 (15.3)	10 (21.7)	9 (19.6)		8 (17.4)	16 (34.8)
Female	254 (84.7)	71 (27.9)	62 (24.4)		67 (26.4)	59 (23.2)
Marital status, %				0.11			0.27
Single	23 (7.7)	12 (52.2)	0 (0.0)		4(17.4)	9(39.1)
Married	261 (87.0)	66 (25.3)	68 (26.1)		66 (25.3)	62 (23.7)
Divorced	1 (0.3)	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
Widowed	15 (5.0)	3 (20.0)	3 (20.0)		5 (33.3)	4 (26.7)
Education, %				0.08			0.01
$<$ high school	91 (30.3)	19 (20.9)	25 (27.5)		29 (31.9)	20 (21.9)
$\geqslant$ high school	209 (69.7)	62 (29.7)	46 (22.0)		46 (22.0)	55 (26.3)
Occupation				0.01			0.001
Employed	95 (31.7)	30 (31.6)	12 (12.6)		16 (16.8)	34 (35.8)
Unemployed	205 (68.3)	51 (62.9)	59 (83.1)		59 (28.8)	41 (20.0)
Smoking, %				0.22			0.53
Current	33 (11.0)	11 (33.3)	4 (12.1)		10 (30.3)	13 (39.4)
Former	24 (8.0)	6 (25.0)	5 (20.8)		7 (29.2)	7 (29.2)
Never	243 (81.0)	64 (26.3)	62 (25.5)		58 (23.9)	55 (22.6)
Physical activity, %				0.28			0.97
Low PA	174 (58.0)	49 (28.2)	37 (21.3)		46 (26.4)	43 (24.7)
Moderate PA	116 (38.7)	28 (24.1)	31 (26.7)		26 (22.4)	28 (24.1)
High PA	10 (3.3)	4 (40.0)	3 (30.0)		3 (30.0)	4 (40.0)
BMI (kg/m ${}^{2}$ )				0.001			0.57
30.0–34.9	183 (61.0)	54 (29.5)	32 (17.5)		47 (25.7)	50 (27.3)
35.0–39.9	84 (28.0)	20 (23.8)	26 (30.9)		19 (22.6)	16 (19.0)
$\geqslant$ 40	33 (11.0)	7 (21.2)	13 (39.4)		9 (27.3)	9 (27.3)

Data are expressed by mean $\pm$ SD for continues variables and $n$ (%) for categorical variables. Linear regression analyses (continuous variables) and an extension of the Wilcoxon rank-sum test (categorical variables) were used to test for trends across quartiles of diet indices.

2.5 Statistical analysis

Normal distribution of continuous variables was checked by Kolmorov-Smirnov test. Categorical variables were presented as a number and percentage, and continuous variables were presented as mean and SD. Population characteristics were compared across AHEI-2010 and EDIP quartiles using $\chi^{2}$ test for categorical measures or trend tests for continuous measures. The ANOVA test was used to compare continuous variable a across AHEI-2010 and EDIP quartiles. Multinomial logistic regression models were applied to find determinates of metabolic risk factors using univariate and multivariate models. In the multivariate model, the variables that are significantly associated with the outcome variable on the bivariate analysis ( $p\leqslant$ 0.20) were included in the model. Association between independent variables with metabolic risks were assessed using univariate and multivariate binary logistic regression. Results of binary and multinomial logistic regression was presented as odds ratios (OR) with 95% confidence intervals (CI). We compared last quartiles of AHEI-2010 and EDIP with the first quartiles (reference category) in all analysis. Two-tailed $P$ values of $<$ 0.05 were considered to be statistically significant. All analyses were performed using Stata (release 11.2 for Windows, Stata Corp LP).

3. Results

Descriptive characteristics of participants ( $n=$ 300) based on lower and upper quartiles of the AHEI-2010 and EDIP score are showed in Table 1. The majority of the population was primarily female (84.7%) with an average age of 42.9 $\pm$ 10.9 years, BMI of 34.2 $\pm$ 3.9 kg/m ${}^{2}$ , and 87% were married. Around 70% of the population have completed educational level more than and equal to high school, and 58% were physically inactive. The average scores for AHEI-2010 and EDIP in this study were 66.1 $\pm$ 10.5 and 0.24 $\pm$ 0.81, respectively.

Those in the highest quartile of AHEI-2010 were more likely to be unemployed (83.1% vs 62.9%, $p$ for trend $=$ 0.01), and obese in category III [ $\geqslant$ 40 kg/m ${}^{2}$ ] (39.4% vs 21.2%, $p$ for trend $=$ 0.001), compared with participants in the lower quartile. While age tended to decrease with increasing AHEI-2010 score, i.e. young participants had shown greater compliance with dietary guidelines. Those in the highest quartile of EDIP score (i.e. more pro-inflammatory) were more likely to be employed (35.8% vs 16.8%, $p$ for trend $=$ 0.01) and educated ( $\geqslant$ high school) (26.3% vs 22.0%, $p$ for trend $=$ 0.001), compared with participants in the lower quartile (i.e. anti-inflammatory).

Table 2
Metabolic risk factors based on lower and upper quartiles of the AHEI-2010 and EDIP score among study participants

	AHEI-2010 score								EDIP score
	Q1 ( $n=$ 81)		Q4 ( $n=$ 71)		$P$		$R$		Q1 ( $n=$ 75)		Q4 ( $n=$ 75)		$P$		$r$
	( $<$ 59)		( $>$ 74)						( $<-$ 0.38)		( $>$ 0.71)
BMI (kg/m ${}^{2}$ )	33.8	$\pm$ 3.7	35.6	$\pm$ 3.8	$<$ 0.	001	0.	2 ${}^{*}$	34.3	$\pm$ 4.0	34.1	$\pm$ 3.7	0.	93	$-$ 0.	0
WC (cm)	103.5	$\pm$ 11.2	106.7	$\pm$ 12.4	0.	01	0.	1	103.9	$\pm$ 12.6	101.3	$\pm$ 12.4	0.	51	$-$ 0.	1
DBP (mmhg)	77.9	$\pm$ 14.1	77.6	$\pm$ 9.9	0.	94	0.	0	80.1	$\pm$ 12.3	77.6	$\pm$ 12.3	0.	45	$-$ 0.	1
SBP (mmhg)	122.1	$\pm$ 13.9	122.0	$\pm$ 16.6	0.	56	$-$ 0.	0	122.8	$\pm$ 16.0	120.2	$\pm$ 14.8	0.	16	$-$ 0.	1
FBG (mg/dl)	99.5	$\pm$ 1.2	99.5	$\pm$ 1.3	0.	86	0.	0	99.5	$\pm$ 1.3	99.5	$\pm$ 0.12	0.	79	0.	0
TG (mg/dl)	134.3	$\pm$ 1.6	121.5	$\pm$ 1.5	0.	08	$-$ 0.	1	121.5	$\pm$ 1.6	148.4	$\pm$ 1.6	0.	05	0.	1 ${}^{*}$
HDL (mg/dl)	49.4	$\pm$ 1.2	44.7	$\pm$ 1.2	0.	01	$-$ 0.	1 ${}^{*}$	44.7	$\pm$ 1.2	49.4	$\pm$ 1.3	0.	96	0.	1

Values are presented as means $\pm$ SD. $P$ values obtained by one-way analysis of variance. $r$ correlation with the diet indices. Partial correlation coefficient (controlled for age). ${}^{*}$ Significant association ( $p<$ 0.05). Abbreviations: BMI Body mass index, DBS Diastolic blood pressure, FBG Fasting blood glucose, HDL High density lipoprotein, SBP Systolic blood pressure, TG Triglyceride, WC Waist circumference.

Table 3

Multivariable linear regression analysis for the association of diet quality indices with metabolic risk factors

	AHEI-2010 score					EDIP score
	Q2 ( $n=$ 72)		Q4 ( $n=$ 71)		$P$ trend	Q2 ( $n=$ 75)		Q4 ( $n=$ 75)		$P$ trend
	(59–64)		( $>$ 74)			( $-$ 0.38–0.03)		( $>$ 0.71)
BMI (kg/m ${}^{2}$ )	$-$ 0.85	(0.6)	1.38	(0.7) ${}^{*}$	0.04	0.13	(0.6)	0.13	(0.6)	0.85
WC (cm)	$-$ 3.92	(1.9)	2.64	(2.3)	0.27	$-$ 0.69	(1.9)	$-$ 2.48	(2.0)	0.23
DBP (mmhg)	0.74	(1.9)	1.21	(2.3)	0.54	$-$ 3.06	(1.9)	$-$ 3.30	(1.9)	0.15
SBP (mmhg)	$-$ 0.001	(0.0)	$-$ 0.001	(0.0)	0.31	$-$ 0.001	(0.0)	0.001	(0.0)	0.07
FBG (mg/dl)	0.01	(0.04)	0.01	(0.05)	0.42	$-$ 0.02	(0.04)	$-$ 0.006	(0.04)	0.89
TG (mg/dl)	$-$ 0.08	(0.1)	$-$ 0.17	(0.1) ${}^{*}$	0.05	0.12	(0.1)	0.18	(0.1) ${}^{*}$	0.05
HDL (mg/dl)	0.04	(0.04)	$-$ 0.04	(0.1)	0.30	0.003	(0.04)	0.01	(0.04)	0.92

Quartile 1 is reference for each diet indices. Values represent regression coefficients (SE). ${}^{*}$ Significant association ( $p<$ 0.05). $P$ values for trend obtained by linear regression analyses. Abbreviations: BMI Body mass index, DBS Diastolic blood pressure, FBG Fasting blood glucose, HDL High density lipoprotein, SBP Systolic blood pressure, TG Triglyceride, WC Waist circumference. Model adjusted for age (years), sex, BMI, history of chronic diseases, physical activity level, smoking status and total calorie intake (kcal/day).

Metabolic risk factors based on lower and upper quartiles of the AHEI-2010 and EDIP scores among study participants are presented in Table 2. The analysis of variance showed that significant differences were observed on the BMI, waist circumference, and serum levels of HDL-C between higher and lower quartile of the AHEI-2010 score. Whereas, significant differences were observed on serum level of TG between higher and lower quartile of EDIP score. However, no differences were shown between diet quality indices with respect to other metabolic risk factors. The results of a partial correlation, when we controlled age on the association between metabolic risk factors with dietary quality indices, revealed that quartiles of AHEI-2010 was significantly correlated to BMI and inversely correlated with serum level of HDL-c. Whereas, quartiles of EDIP score positively significantly correlated with serum level of TG.

Multivariable linear regression revealed that in the adjusted model, participants in the upper quartile of the AHEI-2010 score were associated with lower serum level of TG and higher BMI. There was also a trend between quartiles of AHEI-2010 score and serum levels of TG and HDL-c. Moreover, higher EDIP score was associated with higher level of TG, and trend test was also statistically significant. However, no other significant associations between dietary quality indices with metabolic risk factors were observed (Table 3).

A multinomial logistic regression was performed to model the relationship between the AHEI-2010 and EDIP scores with obesity-related metabolic risks. The analysis showed that greater adherence to AHEI-2010 had a 70% lower odds of high FBG, compared with those in the first quartile [Q4 vs Q1: OR, 0.3 (95% CI: 0.1–0.8), $p$ trend $=$ 0.02]. On the other hand, nonlinear association was observed between those participants in the third vs first quartiles of AHEI-2010 score and high blood pressure [Q4 vs Q1: OR, 0.5 (95% CI: 0.2–0.9)] (data not shown), Whereas, those in the highest quartile of EDIP score had a 60% higher odds of high DBP, compared with those in the lowest quartile [Q4 vs Q1: OR, 2.4 (95% CI: 1.1–5.5), $p$ trend $=$ 0.05] after controlling for potential covariates. We did not observe any protective effects between quartiles of diet quality indices and other obesity-related metabolic risks (Table 4).

Table 4

Odds ratios and 95% confidence intervals for the association between quartiles of AHEI-2010 and EDIP scores and obesity-related metabolic risks

	AHEI-2010 score			EDIP score
	Q2 ( $n=$ 72)	Q4 ( $n=$ 71)	$P$ trend	Q2 ( $n=$ 75)	Q4 ( $n=$ 75)	$P$ trend
	(59–64)	( $>$ 74)		( $-$ 0.38–0.03)	( $>$ 0.71)
Central obesity	1.6 (0.6–4.6)	0.8 (0.2–3.1)	0.85	1.7 (0.5–5.2)	2.3 (0.8–6.9)	0.22
High DBP	1.4 (0.6–3.2)	0.7 (0.3–1.9)	0.45	2.1 (0.9–4.5)	2.4 (1.1–5.5) ${}^{*}$	0.05
High SBP	0.9 (0.4–1.8)	0.8 (0.3–1.7)	0.43	0.7 (0.4–1.4)	1.5 (0.7–3.0)	0.19
HTN	0.6 (0.3–1.2)	0.5 (0.2–1.1)	0.05	0.9 (0.4–1.8)	1.3 (0.6–2.6)	0.35
Raised FBG	0.9 (0.3–2.2)	0.3 (0.1–0.8) ${}^{*}$	0.02	1.3 (0.6–2.9)	2.1 (0.9–4.9)	0.08
Raised TG	1.2 (0.6–2.8)	0.7 (0.3–1.7)	0.22	0.8 (0.4–1.5)	1.0 (0.5–2.1)	0.40
Low HDL	0.9 (0.5–1.9)	0.7 (0.3–1.8)	0.89	1.0 (0.5–2.2)	1.1 (0.5–2.3)	0.46

Quartile 1 is reference for each diet indices. Values are presented as odds ratio (95% confidence interval) using logistic regression model. $P$ values for trend obtained by logistic regression analyses. Abbreviations: BMI Body mass index, DBS Diastolic blood pressure, FBG Fasting blood glucose, HDL High density lipoprotein, SBP Systolic blood pressure, HTN Hypertension, TG Triglyceride, WC Waist circumference. Model adjusted for age (years), sex, BMI, history of chronic diseases, physical activity level, smoking status and total calorie intake (kcal/day). Abdominal obesity defined as waist circumference $>$ 102 cm in men and $>$ 88 cm in women; High DBP: defined as diastolic blood pressure $\geqslant$ 85 mmHg; High SBP: defined as systolic blood pressure $\geqslant$ 130 mmHg; High blood pressure (HTN): defined as DBP and SBP $\geqslant$ 85/130 mmHg; High TG: defined as values $\geqslant$ 200 mg/dL; Low HDL-c: defined as values $<$ 40 mg/dL for men and $<$ 50 mg/dL for wome; High FBG: defined as fasting serum level of $\geqslant$ 126 mg/dL.

4. Discussion

The aim of this study was to examine the association between dietary quality and obesity-related metabolic risks in a representative sample of Iranian obese adults. We found that greater adherence to AHEI-2010 was inversely associated with high FBG, whereas, higher EDIP score was associated with high DBP after controlling for potential covariates. Likewise, a significant but nonlinear association was observed in the third vs first quartiles of AHEI-2010 score with high blood pressure. Our study also found that the healthier score of AHEI-2010 was associated with lower serum level of TG and higher BMI. Similarly, the pro-inflammatory score of EDIP was associated with higher level of TG. To our knowledge, this is the first study to demonstrate a relationship between diet quality measured by AHEI-2010 and EDIP scores with obesity-related metabolic risks in obese Iranian adults.

The AHEI was an alternative to the health eating Index (HEI) and was based on foods and nutrients predictive of chronic disease risk [2]. The AHEI, based on features of the original HEI, used an absolute intake approach in contrast to a nutrient density basis [17]. Since the formation of the AHEI, extensive evidence has appeared to support a role of additional dietary factors in the advancement of chronic disease. Thus, the AHEI-2010 was created based on a wide-ranging review of the pertinent literature and discussions with other nutrition investigators to detect foods and nutrients that have been associated consistently with lower risk of chronic disease in clinical and epidemiologic studies, including evidence from the original AHEI [2, 17]. For this reason in this study, we used the most recent versions of the AHEI-2010.

Results of the present study, a greater adherence to higher AHEI-2010 score was associated with 70% lower risk of type 2 diabetes, support the findings of the previous studies from the Nurses’ Health Study, Men from the Health Professionals Follow-Up Study, and the Health Professionals Follow-up Study, by which a high AHEI was associated with 36%, 23%, and 39% lower risk of type 2 diabetes, respectively [5, 7]. Similarly, a multiethnic cohort study reported that a higher adherence to the AHEI-2010 was associated with 13–28% lower risk of type 2 diabetes [11]. A meta-analysis of cohort study that assessed diet quality with health outcomes showed that the diets of the highest quality, as assessed by the HEI, AHEI and DASH score, resulted in a significant risk reduction of type 2 diabetes by 22% [29]. On the other hand, consistent with our findings, a prospective cohort study of 3,818 women reported, AHEI-2010 score was significantly inversely associated with the risk of high blood pressure [14]. This protective effect of the healthier score of AHEI-2010 was due to its high content of fiber-rich foods. Therefore, the higher intake of fruits, vegetables, whole grains, nuts, and lower intake of red meat could reduce the risk of type 2 diabetes.

Persistent exposure to chronic inflammation could initiate the development of low-grade inflammatory response in the body which leads to an increase in the risk of obesity and obesity-related metabolic risks [4, 27]. Dietary factors are possibly one of the key determinants of the balance that influences the overall inflammatory process in chronic conditions. Several lines of evidence suggest that dietary components can influence the inflammatory process at various sites and thus modulate the balance within the process [19, 35]. The EDIP has been found to be associated with a variety of chronic inflammation related outcomes. Therefore, it is recommended that using EDIP score could enhance the ability to predict the concentration of circulating inflammatory biomarkers [33].

In the present study, we found a significant positive association between higher EDIP score and the odds of high DBP and high serum concentration of TG. Although metabolic abnormalities are highly prevalent worldwide [23], scarce data are available regarding the association between EDIP score and metabolic risks. In a recent cross-sectional study of 403 overweight/obese Iranian, a higher EDIP score was associated with higher odds of high FBS, and low- HDL-c [31]. In a cross-sectional analysis of the Polish-Norwegian study, no significant association was found between dietary inflammatory index (DII) score, the literature-derived, and high serum TG and high WC except for an inverse association between HDL-c and the DII score [30]. However, the possible reasons for the discrepancies of results could be partly explained by the methods used to assess the inflammatory potential of a diet (EDIP vs DII), by which EDIP considered food groups instead of food parameters, variability in the type and numbers of participants in each individual study, and also due to the discrepancy on metabolic risks definition.

Several studies have explored the association between healthful dietary score and metabolic risk factors but evidence of associations has been mixed. In our study, the AHEI-2010 score was associated with lower serum concentrations of TG and higher BMI. Whereas the EDIP score was associated with higher serum concentrations of TG. Other studies were also revealed that highest scores of AHEI-2010 had a significantly higher BMI, lower DBP, lower HDL-c, and lower TG compared with subjects with the lowest AHEI-2010 scores after confounding adjustment [18]. However, in studies from China and Iran, the AHEI-2010 score was not associated with any of the metabolic risk factors, including HDL-c, TGs, SBP, and DBP [9, 22]. Moreover, in a cross-sectional analysis of 775 healthy women, no significant association was found between the AHEI and serum concentration of TG and HDL after adjustment for potential covariates [1]. These discrepancies accounted for the variability on the sample size and study population included, and differences on the tool used for gathering dietary information. These results suggest that a high diet quality incorporating more anti-inflammatory diet may have potential benefit on reducing metabolic risks. In addition, diet quality measured by AHEI-2010 and EDIP score may be more useful in predicting metabolic health outcomes among obese Iranian adults.

There are several strengths to our study. First, the study was a firsthand study by which the sample were selected systematically and representative of the Tehran. Second, the laboratory methods used are standardized for each specimen, contributing to the objectivity and the validity of the measurements. The current study has also some limitations which should be considered when interpreting the results. First, because of the cross-sectional design of the present study, we were not able to extract the causal inferences. Second, unequal distribution of sex among participants, 15.3% male and 84.7% female, which restricted not to perform stratification based on sex, which could have an influence on the result of this study. Third, in both diet quality indices, we did not consider alcohol component (beer and wine) which is not usual in Iranian population. Thus, this might have an effect on the associations.

In conclusion, we found that greater adherence to AHEI-2010 score was negatively associated with the odds of high FBG, while the pro-inflammatory diet (higher EDIP) score was positively associated with the odds of high DBP. These findings suggest that greater adherence to dietary recommendations as reflected in the AHEI-2010 and the intake of more anti-inflammatory diet was associated with a substantially lower risk of type 2 diabetes. Further studies analyzing the link between healthier dietary indices and inflammatory potential of a diet are warranted to deepen our understanding of the role of diet in developing metabolic risks and its consequences.

Footnotes

Acknowledgments

The authors are very grateful to all the participants of the study for their enthusiastic collaboration and to Tehran University of Medical Sciences International Campus (TUMS-IC) for the support it provided to conduct this study.

Conflict of interest

The authors have no conflict of interest.

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The association between diet quality and obesity-related metabolic risks

Abstract

BACKGROUND:

METHOD:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Anthropometric and biochemical assessment

2.2 Dietary intake assessment

2.3 Diet quality indices

2.3.1 The Alternate Healthy Eating Index (AHEI-2010)

2.3.2 An empirically derived dietary inflammatory pattern (EDIP) score

2.4 Assessment of covariates

Ethical approval

Table 1 Descriptive characteristics of participants ( n = 300) based on lower and upper quartiles of the AHEI-2010 and EDIP scores

3. Results

Table 2 Metabolic risk factors based on lower and upper quartiles of the AHEI-2010 and EDIP score among study participants

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Descriptive characteristics of participants ( $n=$ 300) based on lower and upper quartiles of the AHEI-2010 and EDIP scores

Table 2
Metabolic risk factors based on lower and upper quartiles of the AHEI-2010 and EDIP score among study participants