Association of diet quality and fiber intake with carotid intima-media thickness in a young adult population of the Tehran lipid and Glucose study

Abstract

Background

Previous studies showed a probable association of dietary variables with carotid intima media thickness (CIMT), as a surrogate marker of subclinical atherosclerosis. This study aimed to evaluate the association of the global diet quality score (GDQS) and macronutrient intakes with CIMT values in adults.

Methods

This cross-sectional study was performed on 941 young adults that were selected from the Tehran Lipid and Glucose Study. Dietary intake was evaluated using a valid and reliable food frequency questionnaire. CIMT was measured using an ultrasound examination. Multivariate-adjusted linear and logistic regression was used to determine the association of the GDQS and macronutrient intakes with CIMT. High CIMT was defined as CIMT ≥ 90th percentile values for age and sex derived from this study.

Results

The mean age of participants was 28.2 ± 4.25 years (51% men). The GDQS and its categories did not have a significant inverse association with CIMT (β = 0.001, P = 0.23), while we observed a significant inverse association between fiber intake and CIMT (β=-0.003, P = 0.02). Adults with higher quartiles of fiber intake had a lower odds ratio of high CIMT in comparison to the first quartile (OR: Q1: Ref., Q2: 0.61, Q3: 0.74, Q4: 0.53; P trend = 0.04), independent of confounding factors. There were no significant associations between other macronutrient intakes and CIMT value.

Conclusion

Among macronutrients, dietary fiber intake, which often contains phytochemicals, was found to be inversely associated with CIMT value in a young adult Tehranian population. There were no associations between diet quality, as measured by the GDQS, healthy and unhealthy subgroups, and CIMT values.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-026-01100-9.

Background

Cardiovascular disease (CVD) is the leading cause of early death and disability worldwide, accounting for over 30% (23 million) of all deaths predicted to occur by 2030 [1]. In Iran, cardiovascular disease is the first leading cause of death (46%), which may be due to rapid economic, nutritional and cultural change [2]. Atherosclerosis, the main cause of CVD, can be detected early in life. Carotid intima media thickness (CIMT) as a surrogate marker of subclinical atherosclerosis is measured through an ultrasound examination [3]. Previous studies showed a probable association between CIMT and lifestyle factors such as smoking, BMI, physical activity, and diet [4–7]. Two studies reported that in healthy adults, subjects with a higher ideal cardiovascular health (ICVH) metric had lower CIMT, compared to participants with a lower ICVH score [5, 8]. Previous studies reported that consuming healthy foods, including nuts [9], total vegetables [10, 11], garlic [6], or adhering to a posteriori-healthy dietary pattern [12] were inversely associated with CIMT value in adults, children, and adolescents. Diet quality indices (priori-dietary patterns) represent the synergistic effect of healthy and unhealthy food group components based on dietary guidelines. The global diet quality score (GDQS) is a food-based metric and is designed for assessing nutritional quality. It covers healthy and unhealthy food groups as a whole. This score was comprehensively validated against health outcomes by using analysis of existing datasets from multiple countries representing a wide range of income levels and cultures [13]. Previous studies observed the association of GDQS with diet quality [13] and diet-related non-communicable disease risk, including type 2 diabetes (T2D) and metabolic syndrome, the in Iranian population [14, 15]. Adherence to a higher GDQS was associated with lower odds of dyslipidemia and inversely related to lower risk of non-fatal CVD and stroke [16, 17].

However, there are rare population-based studies about the association of diet quality index, including GDQS and macronutrient intakes, with CIMT among young adult individuals in a Middle-Eastern population. Finding the association between consuming a high-quality diet and subclinical atherosclerosis may provide a useful approach for future studies that contribute to a healthy diet for early prevention of CVD. The present cross-sectional study aimed to evaluate the association of GDQS and macronutrient intakes with CIMT value among adult participants.

Methods

Subjects

The Tehran lipid and glucose study (TLGS) was performed to determine risk factors of non-communicable diseases (NCDs) in district No.13 of Tehran, Iran. A total of 15,005 subjects aged ≥ 3 years were enrolled in the baseline cross-sectional survey (1999–2001) using a multistage random sampling method. Data collection, including lifestyle and demographic data, clinical biochemical, and anthropometric measurements, was designed to be performed every three years. The description of this population-based study had been documented elsewhere [18, 19]. For this cross-sectional secondary analysis, we used data available from individuals participating in the sixth phase of the TLGS. Of the 4,100 subjects aged 3–18 years who participated in the baseline of TLGS, 2,641 individuals had follow-up data at the 6th phase. These subjects were invited for CIMT measurement via a telephone call. Of them, 1,455 participants accepted participation in the CIMT measurement. The measurements were carried out between Feb. 2017 and Oct. 2019 [4, 20]. Participants with cancer (n = 4), chronic use of corticosteroids (n = 38), pregnancy (n = 13) and missing dietary data in the sixth phase (n = 449) were excluded, also subjects with under- or over-reporting of energy intake were excluded (n = 10) as the range of energy intake (EI) to energy estimated requirement (EER) ratio were outside the ±3SD [21, 22]. The formula for EER calculation (dietary reference intake equation) is based on age, weight, height, gender, and level of physical activity of participants [23]. Finally, the data of 941 subjects remained for analysis (Fig. 1).

Fig. 1.

Flowchart of study participants

The characteristics of participants who were entered this study were similar to total population aged 20–40 years in the 6th phase of TLGS (n = 2641); the mean ± SD age of subjects who entered this study was 27.7 ± 4.2, and the percentage of male individuals was 51.0%, compared to 28.4 ± 4.5 and 47.6% in the total population. Also, in our study participants, the mean ± SD of waist circumference (WC), BMI, and FBG were 88.4 ± 12.5, 26.0 ± 4.76, and 88.5 ± 10.1, respectively, compared to 88.1 ± 12.3, 26.1 ± 4.94, and 89.4 ± 11.6 in the total population.

Measurements

Dietary assessment

Dietary data were collected by trained nutritionists via a valid and reliable semi-quantitative food frequency questionnaire (FFQ) over personal face-to-face interviews [24, 25]. The FFQ includes 147 items of food with specific serving sizes. The usual frequency of dietary food items were asked during the last year as portion amounts per day, week, month, or year basis; the portion amounts of eaten food items were changed from household measurements to grams per day in such a way that participants’ frequency of consumption in each period was converted to days. Then, it was multiplied by the number of grams per serving. Due to imperfections in the Iranian Food Composition Table (FCT), the United States Department of Agriculture (USDA) FCT was applied for the nutrient content of cooked food items (e.g., bread, legumes, nuts, white or red meat). The Iranian FCT was used for national foods (like doogh and Kashk) that could not be incorporated into the USDA FCT.

The GDQS is a food-based metric, so there were no modifications for local Iranian dietary patterns [13]. This quantitative score categorizes subjects into three clusters based on consumed amounts of specific food groups (gr/day), except the high-fat dairy group, which is classified into four classes. It comprises 25 food groups, including 16 healthy, 7 unhealthy, and 2 unhealthy in excessive amounts. The score of each food group is based on the amount of individuals’ food group intake ranging from 0 to 49. Healthy food groups include kinds of fruits and vegetables, legumes, nuts and seeds, whole grains, fish, poultry, liquid oil, low-fat dairy, and eggs. The highest consumption amount of each healthy food group can have 0.5, 2, or 4 points, which makes a total of 32 points. Unhealthy food groups consist of refined grains, processed meats, sweets, sugar-sweetened beverages, potato or cassava flour, juice, and deep-fried foods. The lowest consumption amount of each food group can have 2 points, which makes a total of 14 points; two unhealthy food groups, when consumed in excessive amounts including, high-fat dairy and red meat, in which higher scores were assigned until certain amounts had been consumed, after which no points were obtained. The sum of points in these food groups was three (Supplementary Table 1).

Physical activity measurements

Physical activity level was estimated using the Persian-translated modifiable activity questionnaire (MAQ) by an expert interviewer. The moderate validity and high reliability of this questionnaire were reported in the previous study [26]. The intensity, frequency, and time of usual daily activities and exercise were detailed over the last year. These activity data were converted into metabolic equivalent/minutes/week (MET/min/week).

Anthropometric and blood pressure measurements

Weight (kg) was measured with a digital scale (Seca 707, Seca Corporation, Hanover, Maryland; range, 0.1–150) (accuracy 100 g) with no shoes and light clothing. Height (cm) was measured in the standing position with a non-flexible tape measure (model 208 Portable Body Meter Measuring Device; Seca) (accuracy 0.5 cm). We measured WC with the light clothing at the umbilicus after exhaling, without pressure on the body’s surface (accuracy 0.1 cm). The anthropometric assessments were done by one appraiser for women and one for men to avert subjective errors.

A standardized mercury sphygmomanometer was used to determine the systolic and diastolic blood pressure (BP) (mmHg) with the cuff placed on the right arm. The participants stayed seated for 15 min, and then a clinician measured BP twice, and the mean values were confirmed [18, 19].

Biochemical analysis

From 7 to 9 a.m., after 12 to 14 h of fasting, the technician took blood samples. They analyzed the blood samples on the day of blood collection with the assistance of the Selectra 2 automatic analyzer in the TLGS research laboratory. They calculated fasting blood glucose (FBG) concentrations by means of the enzymatic colorimetric method and the glucose oxidase method (Vital Scientific, Spankeren, Netherlands). The concentrations of triglycerides (TG) and total cholesterol (TC) were estimated by enzymatic colorimetric analysis by glycerol phosphate oxidase, cholesterol esterase, and cholesterol oxidase, respectively, from the product by Pars Azmoon Inc (Tehran, Iran). The concentration of high-density lipoprotein cholesterol (HDL-C) was assessed after precipitation of lipoproteins containing apolipoprotein B and phosphotungstic acid. The inter- and intra-assay FBG-difference coefficients were 2.2 and 3.0, respectively. The inter- and intra-assay coefficients for TG variation were 1.6 and 0.6%, respectively.

Assessment of CIMT

Ultrasound evaluation of the subjects was performed with a linear 7.5–10 MHz transducer (Samsung Medison SonoAceR3 ultrasound machine). Two radiologists checked the individuals while blinded to the study participants’ details. Subjects were positioned supine with the neck being extended and slightly rotated to the examiner’s opposite side. Both left and right carotid arteries were assessed, but we used the left based on the literature [27–29]. Initially, carotid scans in the transverse plane of the arteries were done to determine the artery’s anatomy. Then, longitudinal scans of the arteries were done from various angles. The measurements were made on plaque-free arterial segments that also met the criteria for optimal B-mode imaging considering clear visualization of arterial walls in the far wall with a fully anechoic luminal volume, depth optimization by scanning the arterial lumen in the center of the image, and setting the focus zone at the arterial lumen level. The scan’s depth was then adjusted so that the arterial lumen was in the center of the picture and the focal zone was at the arterial lumen’s level. A hypoechoic band between the arterial wall’s adventitial and echogenic intimal surfaces was used for manual CIMT. Three measurements were made of the distance between the leading edges of the first and second echogenic lines of the far walls of the distal segment of the common carotid artery on each side, with the average serving as the side’s final measurement. At the time of scanning, an electrocardiography (ECG) was not carried out. Consequently, it was not possible to identify at which cardiac cycle the measurements were taken. In participants who met the requirements for the best technique and image, the CIMT of the carotid bulb and the internal carotid artery on both sides were occasionally measured. To test the reliability of the agreement, CIMT was measured by both radiologists in a subsample of 30 individuals. An intra-class correlation coefficient (ICC) with a 2-way mixed-effects model was reported as good reliability, 0.79 (95% confidence interval: 0.55–0.90). Additionally, there was a mean difference (SD) of 0.08 (0.12) mm in CIMT between the readers.

Definition of CVD risk score and high CIMT value

To reduce the number of confounding factors, the variables, including age, systolic BP, hypertension treatment, TC, HDL-C, T2D, and smoking, were all combined to calculate the CVD risk score using a sex-specific “general CVD” algorithm [30]. Systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or being on antihypertensive medication, were considered indicators of hypertension. FBG of at least 126 mg/dl, 2-hour post-challenge plasma glucose of at least 200 mg/dl, or the use of antidiabetic drugs were considered indicators of T2D. HDL-C levels below 40 mg/dl in men and less than 50 mg/dl in women, or those under medication, were considered as low HDL-C.

First-degree female relatives under 65 years old or first-degree male relatives under 55 years old who had previously been diagnosed with CVD were considered to have a family history of CVD.

Due to the continuous nature of CIMT value [31], high CIMT was defined as CIMT≥90th percentile values for age and sex derived from 941 Tehranian adults in the TLGS study. The 90th percentile for men and women in the age group 20–30 was 0.61 and 0.67 mm, respectively, and 0.70 mm for both men and women in the age group 30–40.

Statistical methods

For data analysis, IBM SPSS software, version 20, was used. It was determined that a two-sided P < 0.05 was statistically significant. Kolmogorov–Smirnov test and histogram were used to determine the normal distribution of the data. To compare the mean and frequency of participants’ baseline characteristics across quartiles of the GDQS, a χ² test and one-way ANOVA were used for categorical and continuous variables, respectively. TG concentration (as non-normal distribution data) was log-transformed for statistical analysis, and geometric means were reported. Linear regression analysis was used to investigate the association of GDQS and its categories (healthy and unhealthy food groups), macronutrients, including total carbohydrate, protein, total fat, saturated fatty acid (SFA), mono-unsaturated fatty acid (MUFA), and poly-unsaturated fatty acid (PUFA) intakes as a percentage of energy and fiber intake (gr/1000 kcal), with CIMT. Binary logistic regression was used to examine the high CIMT across quartiles of the GDQS and its categories, macro-nutrients, and fiber intake. We also considered potential confounding factors in three models. Model 1adjusted for sex, model 2 adjusted for sex, physical activity, education level, family history of CVD, total energy intake, and SFA; model 3 adjusted according to model 2 plus CVD risk score and BMI. Test for P trend was performed based on the GDQS, its categories, and fiber quartiles as a continuous variable in the adjusted logistic regression model. The Hosmer and Lemeshow test was applied to check fitting the data for the models. Each confounder was examined in the univariable regression model; a two-tailed p-value < 0.2 was used for describing admission in the model. Variance inflation factor (VIF) between 1.00 and 1.81 indicated no potential multicollinearity between these variables.

For sensitivity analysis, we categorized the CIMT value into quartiles based on age and sex; the first three quartiles were considered as low CIMT, and the last one as high CIMT (CIMT≥75th percentile).

Results

A total of 941 subjects were enrolled in this cross-sectional analysis, of them 479 and 462 were male and female, respectively. The mean ages of men and women were 28.1 ± 4.26 and 28.3 ± 4.24, respectively. There were 172 participants with high CIMT rates based on the 90th percentile by age and gender of our population. The GDQS ranged from 14.25 to 39.75 in our participants. The mean ± SD of GDQS was 27.5 ± 4.3.

Table 1 shows the characteristics of participants beyond the GDQS quartiles. Subjects in the higher GDQS quartiles were more physically active than those in the lower quartiles. The percentage of current smokers was not different between the GDQS quartiles. There was no significant association between GDQS quartiles and anthropometric or biochemical values.

Table 1.

Characteristics of adult participants per quartiles of the global diet quality score (GDQS) (n = 941)

Quartiles of the GDQS
Variables	Q1 14.25–24.50 n = 243	Q2 24.75–27.25 n = 211	Q3 27.50–30.50 n = 257	Q4 30.75–39.75 n = 230	P value
Age (years)	28.1 ± 4.47	28.0 ± 4.17	28.4 ± 4.12	28.3 ± 4.22	0.64
Sex (% Male)	54.3	53.6	49.0	47.0	0.07
Current smoker (%)	43.0	35.5	39.1	37.9	0.29
Education > 12 years (%)	55.6	57.8	68.5	67.8	0.003
Physical activity (MET/min/week)	553 ± 806	620 ± 760	687 ± 923	781 ± 955	0.03
BMI (kg/m2)	25.9 ± 4.95	25.9 ± 5.43	25.8 ± 4.31	25.9 ± 4.41	0.96
WC (cm)	88.5 ± 12.7	88.0 ± 13.6	87.6 ± 11.7	88.0 ± 11.2	0.88
SBP (mmHg)	108 ± 11.7	107 ± 12.0	107 ± 11.7	106 ± 12.2	0.44
DBP (mmHg)	73.1 ± 9.59	72.5 ± 9.10	73.3 ± 8.86	72.0 ± 9.00	0.41
Total cholesterol (mg/dl)	175 ± 36.5	172 ± 34.7	172 ± 33.6	173 ± 34.7	0.68
HDL-C (mg/dl)	47.3 ± 11.1	46.8 ± 10.8	49.3 ± 11.6	48.0 ± 10.7	0.07
FBG (mg/dl)	88.9 ± 8.31	88.4 ± 10.7	88.0 ± 12.4	88.5 ± 8.50	0.78
TG (mg/dl)*	105 ± 1.66	103 ± 1.79	94.6 ± 1.65	100 ± 1.65	0.14
Family history of CVD (%)	2.9	0.5	0.8	1.7	0.33
CIMT (mm)	0.55 ± 0.10	0.55 ± 0.08	0.55 ± 0.09	0.55 ± 0.08	0.94

Values are mean ± SD unless otherwise listed

P values were derived from analysis of variance and Chi square test for continuous and dichotomous variables, respectively. *Geometric means

MET: Metabolic equivalent, WC: waist circumference, SBP: systolic blood pressure, DBP: diastolic blood pressure, HDL-C: high density lipoprotein cholesterol, FBG: fasting blood glucose, TG: triglyceride, CIMT: carotid intima media thickness

Table 2.

Dietary macronutrient intakes per quartiles of the global diet quality score (n = 941)

Dietary Variables	Quartiles of the GDQS				P value
	Q1 14.25–24.50 n = 243	Q2 24.75–27.25 n = 211	Q3 27.50–30.50 n = 257	Q4 30.75–39.75 n = 230
Energy intake, kcal/d	1987 ± 669	2279 ± 719	2614 ± 902	2855 ± 816	< 0.001
Carbohydrate (% of energy)	58.2 ± 6.66	58.6 ± 5.81	58.3 ± 6.84	58.4 ± 5.09	0.93
Protein (% of energy)	15.0 ± 6.54	15.0 ± 4.29	15.8 ± 5.09	15.1 ± 3.70	0.04
Total fat (% of energy)	29.9 ± 6.65	29.8 ± 5.48	29.9 ± 5.72	29.7 ± 4.31	0.94
SFA (% of energy)	9.65 ± 3.25	9.54 ± 2.60	9.50 ± 2.47	9.34 ± 2.28	0.64
PUFA (% of energy)	6.13 ± 2.33	6.15 ± 1.86	6.14 ± 1.86	6.02 ± 1.56	0.88
MUFA (% of energy)	10.3 ± 2.81	10.1 ± 2.20	10.2 ± 2.52	9.93 ± 1.79	0.34
Fiber (gr/1000 Kcal)	7.75 ± 2.32	9.07 ± 2.41	9.93 ± 2.99	11.5 ± 2.77	< 0.001
GDQS	22.1 ± 2.05	26.0 ± 0.76	28.9 ± 0.99	33.1 ± 1.88	< 0.001
Unhealthy component of the GDQS	9.42 ± 1.96	9.56 ± 2.12	9.61 ± 2.01	9.95 ± 1.99	0.03
Healthy component of the GDQS	12.6 ± 2.84	16.4 ± 2.28	19.3 ± 2.18	23.1 ± 2.46	< 0.001
Total vegetables (serving/day)	1.94 ± 1.32	2.86 ± 1.65	3.46 ± 1.96	4.78 ± 2.45	< 0.001
Fruits (serving/day)	2.01 ± 1.69	3.47 ± 3.88	4.66 ± 3.99	6.03 ± 3.96	< 0.001
Legumes and nuts (serving/day)	1.09 ± 1.16	1.73 ± 1.63	3.33 ± 1.16	4.41 ± 4.64	< 0.001
Whole grain (serving/day)	1.93 ± 2.25	1.75 ± 2.11	2.25 ± 3.30	2.57 ± 3.23	0.01
Refined grains (serving/day)	6.35 ± 3.62	6.91 ± 3.59	7.19 ± 3.94	6.89 ± 4.23	0.10
Red and processed meat (serving/day)	0.98 ± 1.61	0.98 ± 1.69	0.97 ± 1.19	1.14 ± 2.01	0.60
Low fat dairy (serving/day)	2.61 ± 2.51	3.68 ± 3.80	4.13 ± 3.74	4.61 ± 3.18	< 0.001
High fat dairy (serving/day)	0.82 ± 1.39	0.91 ± 1.11	0.96 ± 1.13	0.94 ± 0.91	0.54
Sodium intake (mg/day)	3033 ± 1199	3419 ± 1167	3888 ± 1440	4130 ± 1450	< 0.001

Values are mean ± SD. P values were derived from analysis of variance

GDQS: Global diet quality score, SFA: Saturated fatty acids, PUFA: Poly unsaturated fatty acid, MUFA: Mono unsaturated fatty acid

In addition, the higher quartiles of the GDQS had a higher intake of energy (Q1: 1987 ± 669, Q4: 2855 ± 816 kcal/day), fiber (Q1: 7.75 ± 2.32, Q4: 11.5 ± 2.77 gr/1000 kcal), and sodium (Q1: 3033 ± 1199, Q4: 4130 ± 1450 mg/day) than the lower quartiles. Mean intakes of the healthy food groups, including total vegetables, fruit, legumes and nuts, whole grains and low-fat dairy, were higher in the higher than the lower GDQS quartiles.

Our findings showed that the GDQS and its categories did not have a significant inverse association with CIMT, while we observed a statistically significant converse association between fiber intake and CIMT (β=-0.003, P = 0.02) after adjusting for confounding variables (Table 3). Also, there were no significant associations between other macronutrient intakes and CIMT values (data not shown).

Table 3.

Linear regression of CIMT and the GDQS, its categories^a, and fiber in adult participants of the Tehran lipid and glucose study (n = 941)

	Model 1	P value	Model 2	P value	Model 3	P value
	Beta (SE)		Beta (SE)		Beta (SE)
GDQS	-0.02(0.001)	0.61	0.001(0.001)	0.87	0.001(0.001)	0.23
Healthy food group score	-0.001(0.001)	0.43	0.001(0.001)	0.85	0.001(0.001)	0.14
Unhealthy food group score	0.01(0.001)	0.47	0.001(0.002)	0.98	0.001(0.002)	0.83
Fiber	-0.002(0.001)	0.09	-0.002(0.001)	0.08	-0.003(0.001)	0.02

^a GDQS score categories: The healthy food group and the unhealthy food group scores

GDQS: Global diet quality score, CIMT: Carotid intima-media thickness

Model 1: adjusted for sex

Model 2: model 1 + physical activity, Education level, family history of cardiovascular disease, total energy intake, saturated fat intake

Model 3: model 2 + cardiovascular disease risk score, BMI

Multivariable odds ratio for the association of high CIMT per quartiles of the GDQS, its categories, and fiber intake in adult participants is shown in Table 4. We observed that adults with higher quartiles of fiber intake had lower odds ratios of high CIMT in comparison to the first quartile after adjusting for confounding variables (OR: Ref., Q₂: 0.61, Q₃: 0.74, Q₄: 0.53; P trend = 0.04).

Table 4.

Multivariable odds ratio (95% CI) for the high CIMT per quartiles of the global diet quality score, its categories^a and fiber intake in adult participants of the Tehran lipid and glucose study (n = 941)

Variables	Quartiles
	Q1	Q2	Q3	Q4	P trend
Case/event	243/40	211/42	257/50	230/40
GDQS, median	22.5	26.0	28.7	32.7
Model 1	1	1.27(0.78–2.06)	1.28(0.80–2.03)	1.13(0.69–1.83)	0.61
Model 2	1	1.24(0.76–2.02)	1.23(0.75–2.02)	1.06(0.62–1.82)	0.83
Model 3	1	1.24(0.76–2.03)	1.27(0.77–2.09)	1.07(0.62–1.84)	0.75
Case/event	233/38	248/42	227/47	233/45
Healthy food group score, median	12.5	16.5	19.5	23.5
Model 1	1	1.06(0.65–1.72)	1.35(0.84–1.17)	1.18(0.73–1.90)	0.34
Model 2	1	1.02(0.62–1.69)	1.29(0.77–2.16)	1.09(0.59–2.01)	0.57
Model 3	1	1.08(0.65–1.78)	1.40(0.83–2.36)	1.14(0.61–2.10)	0.47
Case/event	260/58	151/24	362/67	168/23
Unhealthy food group score (Median)	7.0	9.0	10.0	12.0
Model 1	1	0.71(0.42–1.20)	0.91(0.61–1.36)	0.67(0.39–1.16)	0.29
Model 2	1	0.72(0.42–1.25)	0.95(0.61–1.49)	0.71(0.39–1.29)	0.48
Model 3	1	0.68(0.39–1.19)	0.93(0.59–1.46)	0.71(0.39–1.28)	0.45
Case/event	235/58	235/37	236/45	235/32
Fiber intake (gr/1000 Kcal), median	6.38	8.29	10.1	12.8
Model 1	1	0.59(0.32–0.94)	0.79(0.51–1.23)	0.57(0.35–0.93)	0.06
Model 2	1	0.57(0.36–0.91)	0.74(0.46–1.18)	0.52(0.31–0.88)	0.04
Model 3	1	0.61(0.38–0.98)	0.74(0.49–1.24)	0.53(0.31–0.89)	0.04

^a GDQS score categories: The healthy food group and the unhealthy food group scores

GDQS: Global diet quality score, CIMT: Carotid intima-media thickness

Model 1: adjusted for sex

Model 2: model 1 + physical activity, Education level, family history of cardiovascular disease, total energy intake, saturated fat intake

Model 3: model 2 + cardiovascular disease risk score, BMI

Test for P trend was performed based on the GDQS or its categories and fiber quartiles as a continuous variable in the adjusted logistic regression model

CIMT≥90th percentile was considered as high CIMT

We found that adults with higher quartiles of GDQS and its categories did not have a significant association with the odds ratio of high CIMT in comparison with the first quartile. Also, there were no significant associations between quartiles of other macronutrient intakes and the odds ratio of high CIMT (data not shown). The non-significant goodness-of-fit test for all models indicated that the data fit the model.

As a sensitivity analysis, the multivariable odds ratio for the prediction of high CIMT (CIMT≥75th percentile) per quartiles of the GDQS, its categories, and fiber intake in adult participants is shown in Supplementary Table 2. After adjusting for covariates, fiber intake was inversely associated with high CIMT (OR: Ref., Q₂: 0.55, Q₃: 0.68, Q₄: 0.55; P trend = 0.06). The results were not changed in model 3 (after adjusting CVD risk score). Other results were similar to the main analysis.

Discussion

This cross-sectional study investigated the association of the GDQS and macronutrient intake with CIMT value in Tehranian adults aged 20–40 years old. We found that there was no significant association between CIMT value and the GDQS and its categories (healthy and unhealthy food groups); however, we found that fiber intake was inversely associated with CIMT values independent of confounding factors. Our results showed that a 1 gram per 1000 kcal increase in fiber intake was associated with CIMT reductions of 0.003 mm. Also, compared with subjects in the first quartile of fiber intake, those who were in the fourth quartile had a 47% lower risk of high CIMT. Moreover, no significant associations were observed with consumption of other macronutrient intakes, including carbohydrate, protein, total fat, SFA, MUFA, and, PUFA with CIMT.

The mean value of GDQS is close to previous studies on the adult Iranian population (12.2-41.25) [15]. The average of GDQS for the adult Chinese population (19.8) was lower than that in our study [32]. Like our study, the GDQS was higher in highly educated people. Moreover, it is important to consider food groups (healthy or unhealthy) with the highest value in total GDQS, which were not mentioned in most studies.

Our results indicate that the GDQS was not associated with a surrogate atherosclerosis marker; it is possible that only high intakes of particular foods or nutrients are linked to CIMT value, and our scoring was unable to distinguish these high intakes. While there is evidence that some nutritional components have a positive relationship with CIMT and, consequently, subclinical atherosclerosis, this relationship is unclear when the same components are taken with others, either in a particular food group or in the context of dietary patterns deemed healthy. The potential negative relationship of the unhealthy component could offset the potential positive relationship of the healthy component due to the lack of conclusive evidence regarding the association between CIMT and both elements deemed healthy or unhealthy [33]; In agreement with our findings, earlier cross-sectional studies reported no association between CIMT and different diet quality indices including diet quality score in middle aged Spanish adults [33] and dietary diversity score in Belgian individuals aged 35–55 years [34].

Contrary to our findings, a previous cohort study in Brazilian middle-aged individuals found that a higher diet quality score through the cardiovascular health diet index, and food groups including whole grain and nuts were associated with a slight decrease in CIMT value [35]. Moreover, higher adherence to the dietary approach to stop hypertension (DASH) diet score in 24–28 year old individuals [36] and the adapted healthy eating index in diabetic subjects were inversely associated with CIMT value [37]. In a population with high cardiovascular risk, the Mediterranean diet score was inversely associated with CIMT in US individuals [38]. However, the GDQS, despite being designed for global use, did not perform similarly in our population. It appears that a diet quality score specifically designed to target cardiovascular health may offer more valuable insights for detecting subclinical atherosclerosis.

Also, 1 gram of dietary fiber per 1000 Kcal was inversely associated with CIMT value with a small magnitude effect size (β = 0.003). The recommended dietary fiber intake is 25–30 g per day which is generally used for adults [39]; food groups including grains, in particular whole grains, vegetables, fruits, legumes, nuts and seeds, respectively contribute to dietary fiber intake. This small effect size may be offset if the person consumes the recommended amount of dietary fiber. The magnitude of the effect size is 0.07–0.09 mm for a person who consumes 25–30 gram of dietary fiber per day. Clinically, the magnitude of the small effect size may be important, as increasing CIMT by 0.1 mm may increase the risk of CVD by 18% [3].

In addition, an inverse relationship between dietary fiber intake and high CIMT (≥ 90th percentile) was observed independently of CVD risk score. These results indicate that dietary fiber intake in individuals consuming approximately 25 g per day (4th quartile of dietary fiber intake in our population) is inversely associated with a high CIMT (OR: 0.53). These regression analyses have adjusted for energy intake as a confounding factor to minimize the effect of high total food consumption associated with GDQS or high fiber intake. In the sensitivity analysis, although high CIMT was considered to be ≥ 75th percentile, the results were consistent with the previous findings.

An earlier longitudinal study by Fu and colleagues reported a strong inverse association between a high-fiber dietary pattern and CIMT [7]. The results of a meta-analysis showed that in healthy subjects, a vegetarian diet was associated with a reduction of atherosclerosis as measured by CIMT, compared to omnivorous diet [40]. The mean CIMT decreased by 0.8% for each 10 g of cruciferous vegetables consumed in elderly women; sulforaphane, as a phytochemical of cruciferous vegetables, may prevent vascular damage by inhibiting advanced glycation end products or by blocking oxidative stress [11]. The intake of more dietary fiber appears to be associated with a reduced risk of cardiovascular disease by reducing inflammation.

High-fiber diets contain high concentrations of phytochemicals, including polyphenols, which may inhibit transcriptional activity of pro-inflammatory factors and scavenging free radicals. Increased fiber intake may also reduce the risk of cardiovascular disease by improving serum lipid levels and lowering BP [41].

However, our findings are subject to several limitations. A narrow age range improves internal validity but limits generalizability. Our results may not be nationally representative as the study was conducted with data from the population of the capital of Iran. It should also be noted that a cause-effect analysis was not possible as the inverse association between GDQS, macronutrient intake and CIMT was based on cross-sectional data. To confirm the association, prospective studies should be conducted. The process of selecting and excluding participants may introduce selection bias; however, some demographic and biochemical variables were similar. The sample size for linear regression analysis was adequate due to power calculation > 80%, but in logistic regression analysis, the sample size was inadequate and the power of study was < 80%. However, we are fortunate for observing a significant association between fiber intake and CIMT in our study population. Due to low study power, non-significant associations in logistic regression analyses cannot be reported with certainty. If the number of samples is sufficient, non-significant findings are certain to be obtained. The use of FFQ for the collection of dietary data can also be affected by recall bias; the use of trained nutritionists for the collection of dietary data through face-to-face interviews can reduce this bias. Another limitation of the study was the use of two FCTs to analyze nutrient data. The analysis has taken into account the classical potential confounders; however, our regression analysis may represent over-adjustments. To minimize this limitation, we performed a regression analysis in three models (model 2 without CVD risk score).

ECG was not carried out at the time of scanning, so it was not feasible to identify at which cardiac cycle the measurements were taken, as it may affect measurement precision. CIMT has limited ability to detect early vascular changes in young adults.

To our knowledge, this is the first population-based study on the association between CIMT, GDQS and macronutrient intakes in the Middle-Eastern young adult population. In addition to the measurement of anthropometric and biochemical variables in the TLGS, all questionnaires were completed by qualified staff.

Conclusion

Among macronutrients, dietary fiber intake as a source of phytochemicals was found to be inversely associated with CIMT in the young adult Tehranian population. There was no association between diet quality, as measured by the GDQS, the subgroups of healthy and unhealthy food groups and CIMT. The relationship between subclinical atherosclerosis markers and diet quality requires further prospective research with long-term designs.

Abbreviations

BP

Blood pressure

CIMT

Carotid intima media thickness

CVD

Cardiovascular disease

DASH

Dietary approach to stop hypertension

FBG

Fasting blood glucose

FCT

Food Composition Table

GDQS

Global diet quality score

HDL-C

High-density lipoprotein cholesterol

ICVH

ideal cardiovascular health

MAQ

Modifiable activity questionnaire

MET

Metabolic equivalent

MUFA

Mono-unsaturated fatty acid

NCD

Non-communicable disease

PUFA

Poly-unsaturated fatty acid

SFA

Saturated fatty acid TC: Total cholesterol

TG

Triglycerides

USDA

United States Department of Agriculture

Author contributions

F.H-E, M.B., M.V, PD and, S.H-N collected the data and wrote the first draft of the manuscript, M.M, and S.S, performed statistical analysis, FH-E, PM, FA and MB revised the manuscript critically. PM, M.V and FA supervised the study. All the authors have given the final approval of the version to be published.

Funding

This study was supported by the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Grant NO. 43013252).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

All participants signed an informed consent form before participating in this study. The study was performed according to the Declaration of Helsinki; the study proposal was approved by the Research Institute for Endocrine Sciences ethics committee, Shahid Beheshti University of Medical Sciences (Tehran, Iran) IR.SBMU.ENDOCRINE.REC.1404.003.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.