Introduction: The aim of the study was to assess the interaction between CD36 rs1761667 and the dietary phytochemical index (DPI) on the risk factors related to metabolic syndrome (MetS) among apparently healthy adults. Methods: This cross-sectional study was conducted on 387 apparently healthy adults (aged 20–70 years) of the recruitment phase of the Yazd Health Study (YaHS). The DPI was calculated using data from a validated and reliable food frequency questionnaire. Genotyping of rs1761667 was performed by the polymerase chain reaction-restriction fragment length polymorphism method. All participants were categorized into two categories, based on DPI scores. The interactions were tested using logistic regression in adjusted and unadjusted models. Results: There was no significant association between CD36 gene polymorphism rs1761667 and MetS components, and also, the DPI score was not associated with the components of MetS. Significant interactions were observed between DPI and rs1761667 polymorphism on the odds of MetS (p = 0.05) and odds of abdominal obesity (p = 0.01) so that higher adherence to this index was associated with a low risk of MetS and abdominal obesity in individuals with AG genotype. In contrast, increased adherence to the DPI was associated with higher odds of abdominal obesity among the AA genotype. Conclusion: The AG genotype appears to be a protective factor against the risk of MetS and abdominal obesity with greater adherence to DPI. However, additional research is needed to elucidate these interactions and the mechanisms behind these associations.

Metabolic syndrome (MetS) is a major global public health challenge. It is characterized as a clustering of metabolic abnormalities including central obesity, insulin resistance, hypertension, hypertriglyceridemia, and low levels of high-density lipoprotein cholesterol (HDL-C) [1]. It is the main risk factor for the development of type 2 diabetes and cardiovascular disease in the 21st century [2]. MetS is a complex and multifactorial disorder in which environmental (e.g., dietary intake) and genetic factors are involved in disease pathogenesis [3]. The modification of lifestyles such as healthy eating, calorie restriction, increasing physical activity, and weight loss are considered as prevention and treatment strategies of MetS [4]. Results of the meta-analysis studies have shown that the intake of fruits has a preventive effect on the risk of MetS [5]. Former studies have demonstrated that the components such as dietary fiber, mono- and polyunsaturated fat, vitamins, minerals, and dietary phytochemicals may improve insulin sensitivity, enhance antioxidative defense, and decrease the risk of metabolic disorders [6, 7]. Plant-based foods contain phytochemicals that may help prevent chronic diseases such as MetS via increasing the levels of phytochemicals in the blood. It has been suggested that a higher intake of phytochemicals, especially polyphenols, and flavonoids, from plant-based foods could improve blood pressure (BP), insulin resistance, and fat metabolism [8]. Due to the beneficial health effect of phytochemicals, McCarty proposed a dietary phytochemical index (DPI), which is characterized according to the percent of daily energy intake taken from phytochemical-rich foods [9]. Previous observational studies reported that a higher DPI was associated with reduced odds of MetS [10, 11], although some studies were controversial [8, 12]. One of the reasons for the discrepancy in these results may be related to the combined effects of environmental and genetic factors [13]. Genetic alterations including single nucleotide polymorphisms in the genes related to glycemic and lipid metabolism may play an essential role in metabolic profile changes [14].

The cluster of differentiation 36 (CD36) gene is localized on chromosome 7 in the region of q11.2 (7q11.2) and encompasses 15 exons [15]. This gene plays an important role in regulating lipid metabolism (long-chain fatty acid uptake in a variety of tissues, chylomicron synthesis, etc.), inflammatory reactions, sensing dietary lipids and fat preference, angiogenesis, regulating the metabolic pathways of insulin resistance, and energy metabolism [16]. CD36 rs1761667 (G > A) polymorphism is located in intron of 5′ flanking exon 1A [17]. A meta-analysis of studies reported a significant association between rs1761667 and triglyceride (TG) (allelic, recessive, and homozygous models), HDL-C, and fasting blood glucose (FBG) level (recessive genetic model). Furthermore, it has been reported that the A allele was associated with higher HDL-C and lower total cholesterol and low-density lipoprotein cholesterol levels in Asians [18]. Furthermore, several research studies provided evidence regarding the relationship of this polymorphism with hypertension, dyslipidemia, obesity, metabolic syndrome, type 2 diabetes mellitus, consumption of total fat, and fat orosensory perception [19‒22]. Scarcely, studies have investigated the interactions between CD36 rs1761667 and dietary intake on components of the MetS and other factors. According to a previous study, rs1761667 genotypes and the intake of different types of fat interact with health factors. It has been reported that people with the AA variant carriers had higher cholesterol levels when they intake more monounsaturated (monounsaturated fatty acids [MUFA]) and polyunsaturated fats, which are found in foods like nuts, seeds, and fish. These people also tended to consume more of these fats in their diet [23]. A study reported that fish oil supplementation (6 g/day) could increase HDL-C in subjects with AA and AG genotypes. Furthermore, LDL-C and TG concentrations increased by fish oil supplementation in AA and GG genotypes, respectively [24]. Fujii et al. [25] suggested that the higher intake of total fat and MUFA is associated with decreased risk of hypertension in the AA genotype of rs1761667. So, the genotypes of this polymorphism may change the association between DPI and MetS risk.

To the best of our knowledge, no studies have examined the interaction between DPI and CD36 rs1761667 polymorphism on the risk of MetS and its components. Due to the mentioned points, it has been hypothesized that rs1761667 genotypes might change the association of dietary phytochemicals with risk factors related to MetS. Therefore, the present study aimed to assess the association between DPI and the risk factors related to MetS, as well as DPI and CD36 rs1761667 polymorphism interactions on the risk factors related to MetS in apparently healthy adults living in Yazd city in central Iran.

Study Design and Participants

This cross-sectional study was conducted on 387 apparently healthy adults (aged 20–70 years) who participated in the recruitment phase of the Yazd Health Study (YaHS). The YaHS is a population-based cohort study on adults living in Yazd, which has been conducted since 2014. Detailed information on dietary intake was collected from the Yazd Nutrition Survey (called Taghzieh Mardom Yazd [TAMYZ] in Persian) through a 178-item semiquantitative food frequency questionnaire (FFQ), with which its validity and reliability were previously evaluated [26]. To preserve the biological samples of the YaHS study, including serum, blood, and urine samples, a biobank has been created called Zist Bank-e-Yazd (ZIBA) since 2015. A subset of 387 samples was taken from this biobank and extracted DNA. The detailed description of the YaHS-TAMYZ study has been explained in detail elsewhere [27]. Written informed consent was obtained from all participants. All the experimental protocols were carried out under the guidelines of the Declaration of Helsinki. The inclusion criteria were (i) aged 20–70 years, (ii) having a whole blood sample, and (iii) biochemical and nutritional data in the database. Participants were excluded if they met any of the following characteristics: subjects who had missing or incomplete dietary data, with a history of diabetes, cancer, cardiovascular disease, renal, stroke, liver disease, thyroid, and neurologic disorder at the baseline examination (according to self-reporting); participants with low or high (<800 or >6,000 kcal/day) energy intake [28]; pregnant or lactating women; subjects who used medications that influenced body composition (contraceptives, corticosteroids, etc.); and finally, subjects who consumed alcohol and smoking were excluded. A total of 387 of the remaining subjects were randomly selected to participate in this study.

Calculation of DPI

The DPI was computed based on the method developed by McCarty in 2004: {DPI = (daily energy derived from phytochemical-rich foods [kcal]/total daily energy intake [kcal]) × 100} [9]. Phytochemical-rich foods included fruits, vegetables, whole grains, legumes, nuts, seeds, soy products, and olive oil. Potatoes were not considered as vegetables for their low phytochemical content. Natural fruit and vegetable juices as well as tomato sauces were included in the fruit and vegetable groups because these are also considered for their high phytochemical content [29].

Assessment of Demographic, Physical Activity, and Anthropometric Measurement

Information on demographic (age, sex, marital status, education, diabetes, and other chronic diseases, etc.) was gathered through a self-administered questionnaire. Measurement of the physical activity level was assessed through a short version of the International Physical Activity Questionnaire (IPAQ) and converted to the metabolic equivalent in minutes per week [30]; then the data were categorized into sedentary, moderate, and active levels. Anthropometric parameters including weight (Omron BF511, Omron Inc., Nagoya, Japan), waist circumference (WC) (tape meter with the precision of 0.5 cm), and height (using a tape measure on a straight wall with an accuracy of 0.1) were measured by trained interviewers. Also, the body mass index was calculated as follows: weight (kg) divided by height in meters squared.

Laboratory and BP Measurements

Levels of TGs, HDL-C, and FBG were measured through Pars Azmoon kits (Tehran, Iran) and calibrated with the Ciba Corning (Switzerland) autoanalyzer device. Systolic BP and diastolic BP were measured in a sitting position and repeated 3 times with 5 min between each one. BPs were measured through Reister electronic sphygmomanometers (Model N-Champion, Reister GMBH, Germany).

Diagnosis of MetS

MetS was diagnosed according to the harmonized criteria. Subjects who had at least three out of five of the following criteria were considered as participants with MetS: WC  ≥90 and ≥80 cm for men and women, respectively; levels of serum TGs ≥150 mg/dL; HDL-C <40 and 50 mg/dL for men and women, respectively; FBG ≥100 mg/dL; and BP ≥130/85 mm Hg [1].

DNA Extraction and Genotyping

The whole blood samples were used to extract DNA, and genotyping was performed based on a study by Banerjee et al. [19] using the Mini kit (Favorgen Biotech Corp, Kaohsiung, Taiwan). The CD36 rs1761667 polymorphism was genotyped by the polymerase chain reaction-restriction fragment length polymorphism method. The primer’s sequences were as follows: forward: 5′-CAA​AAT​CAC​AAT​CTA​TTC​AAG​ACC​A-3′; reverse 5′-TTT​TGG​GAG​AAA​TTC​TGA​AGA​G-3′. The volume of the PCR product contained 25 μL, including 3 μL extracted DNA, 1 μL of each primer, 12.5 μL Taq DNA Polymerase 2x Master Mix (Amplicon; Germany), and 7.5 μL distilled water. PCR cycles were run with denaturation at 95°C (10 min); annealing at 95°C, 54°C, and 72°C (each step for 30 s; by 38 cycles), with a final extension at 72°C for 5 min. PCR products were digested using HhaI (Thermo Fisher Scientific, USA; catalog number: ER1851) restriction enzyme and yielded fragments of 52, 138, and 190 bp. The fragments (AA, AG, and GG) were detected by electrophoresis on 3.5% agarose gel. The detailed method of genotyping CD36 rs1761667 polymorphism was described in our previous study [31].

Statistical Analysis

The Kolmogorov-Smirnov test was used for the assessment of the normal distribution of variables. The Hardy-Weinberg equilibrium and qualitative categorical variables were assessed with the χ2 test. Comparison of quantitative variables between genotypes or intake of DPI was performed using ANOVA or independent samples T tests, respectively. The analysis of covariance was applied for adjustment of confounders including age, sex, energy intake, physical activity, and educational and marital status. The interaction between DPI and CD36 rs1761667 polymorphism on the MetS markers was investigated using logistic regression. Three genetic comparison models, namely, additive model (AA vs. GA vs. GG), dominant model (AA + GA vs. GG), and recessive model (AA vs. GA + GG) were used to determine the association and interaction. Statistical analyses were performed using IBM SPSS version 22.0 (IBM Corp, Armonk, NY, USA). p values ≤0.05 were regarded as statistically significant. The sample size was calculated by the Quanto software version 1.2.4 (University of Southern California) [32], according to the statistical power 0.80 (α = 0.05 and β = 0.20). The sample size was estimated to be 301 subjects using the mentioned formula. However, considering the probability of attrition, 387 participants were included in this study (more details are given in the study by Yazdanpanah et al. [31]). Figure 1 illustrates the flow diagram of participants included in this study.

Fig. 1.

Flow diagram of the participants included in this study.

Fig. 1.

Flow diagram of the participants included in this study.

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This cross-sectional study was conducted on 387 apparently healthy individuals. Table 1 illustrates general characteristics according to CD36 rs1761667 genotypes. The percentage of general characteristics such as age, sex, education, marriage status, and physical activity intensity were not different between the three genotypes of this polymorphism. The genotypic distribution of this polymorphism met the Hardy-Weinberg equilibrium (p = 0.52). The total participants were categorized based on rs1761667 genotypes into three groups: AA genotype (n = 44), AG genotype (n = 276), and GG genotype (n = 67). After the categorization of genotypes, the results of the comparison showed no significant differences in the MetS markers between genotypes (Table 2) in both crude and adjusted models. All participants were divided into two groups, based on the DPI score. There was a difference in WC (p = 0.059) and HDL-C (p = 0.06) between the two groups with different DPI intakes; however, it was not significant. The results did not remain significant after adjustment for age, sex, energy intake, physical activity, and educational and marital status (Table 3).

Table 1.

Characteristics of the study participants across CD36 rs1761667 genotypes

VariableAA (n = 44), n (%)AG (n = 276), n (%)GG (n = 67), n (%)p valuea
Age 20–29 13 (29.50) 61 (22.10) 12 (17.90) 0.48 
30–39 8 (18.20) 73 (26.40) 25 (37.30) 
40–49 12 (27.30) 71 (25.70) 12 (17.90) 
50–59 7 (15.90) 49 (17.80) 11 (16.40) 
60–69 4 (9.10) 22 (8.00) 7 (10.40) 
Sex Male 20 (45.50) 137 (49.60) 36 (53.70) 0.68 
Female 24 (54.50) 139 (50.40) 31 (46.30) 
Education Elementary and lower 23 (52.30) 130 (47.10) 25 (37.30) 0.11 
Diploma 13 (29.50) 97 (35.10) 26 (38.80) 
University-educated 8 (18.20) 49 (17.7) 16 (23.90) 
Marriage status Single 4 (9.10) 29 (10.50) 8 (11.90) 0.42 
Married 39 (88.60) 244 (88.40) 56 (83.60) 
Divorced or widowed 1 (2.30) 3 (1.10) 3 (4.50) 
Physical activity Sedentary 8 (19.00) 67 (25.30) 18 (29.50) 0.68 
Moderate 16 (38.10) 109 (41.10) 24 (39.30) 
Active 18 (42.90) 89 (33.60) 19 (31.30) 
VariableAA (n = 44), n (%)AG (n = 276), n (%)GG (n = 67), n (%)p valuea
Age 20–29 13 (29.50) 61 (22.10) 12 (17.90) 0.48 
30–39 8 (18.20) 73 (26.40) 25 (37.30) 
40–49 12 (27.30) 71 (25.70) 12 (17.90) 
50–59 7 (15.90) 49 (17.80) 11 (16.40) 
60–69 4 (9.10) 22 (8.00) 7 (10.40) 
Sex Male 20 (45.50) 137 (49.60) 36 (53.70) 0.68 
Female 24 (54.50) 139 (50.40) 31 (46.30) 
Education Elementary and lower 23 (52.30) 130 (47.10) 25 (37.30) 0.11 
Diploma 13 (29.50) 97 (35.10) 26 (38.80) 
University-educated 8 (18.20) 49 (17.7) 16 (23.90) 
Marriage status Single 4 (9.10) 29 (10.50) 8 (11.90) 0.42 
Married 39 (88.60) 244 (88.40) 56 (83.60) 
Divorced or widowed 1 (2.30) 3 (1.10) 3 (4.50) 
Physical activity Sedentary 8 (19.00) 67 (25.30) 18 (29.50) 0.68 
Moderate 16 (38.10) 109 (41.10) 24 (39.30) 
Active 18 (42.90) 89 (33.60) 19 (31.30) 

Variables are presented as frequency (percentage within genotypes) for categorical variables.

SD, standard deviation.

aχ2 test for categorical variables.

Table 2.

The relation between MetS markers and the genotypes of CD36 rs1761667 polymorphism

VariableAA (n = 44), mean±SDAG (n = 276), mean±SDGG (n = 67), mean±SDp valueap valueb
BMI, kg/m2 25.91±5.23 26.68±4.76 26.18±5.40 0.52 0.43 
WC, cm 89.59±10.91 92.32±12.62 91.05±12.42 0.34 0.57 
FBG, mg/dL 95.38±9.71 97.75±16.31 95.52±10.15 0.38 0.56 
HDL-C, mg/dL 46.38±8.86 47.15±9.85 48.68±10.70 0.42 0.08 
TG, mg/dL 159.00±128.66 146.23±84.98 145.37±74.61 0.66 0.61 
SBP, mm Hg 122.24±14.09 122.45±14.71 123.90±13.60 0.74 0.44 
DBP, mm Hg 78.69±9.61 77.76±10.24 78.41±12.09 0.80 0.62 
VariableAA (n = 44), mean±SDAG (n = 276), mean±SDGG (n = 67), mean±SDp valueap valueb
BMI, kg/m2 25.91±5.23 26.68±4.76 26.18±5.40 0.52 0.43 
WC, cm 89.59±10.91 92.32±12.62 91.05±12.42 0.34 0.57 
FBG, mg/dL 95.38±9.71 97.75±16.31 95.52±10.15 0.38 0.56 
HDL-C, mg/dL 46.38±8.86 47.15±9.85 48.68±10.70 0.42 0.08 
TG, mg/dL 159.00±128.66 146.23±84.98 145.37±74.61 0.66 0.61 
SBP, mm Hg 122.24±14.09 122.45±14.71 123.90±13.60 0.74 0.44 
DBP, mm Hg 78.69±9.61 77.76±10.24 78.41±12.09 0.80 0.62 

Variables are presented as mean ± SD.

SD, standard deviation; BMI, body mass index; WC, waist circumference; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure; ANCOVA, analysis of covariance.

aCrude model (unadjusted), ANOVA test.

bAdjusted for age, sex, energy intake, physical activity, and educational and marital status, ANCOVA.

Table 3.

The relation between MetS markers and DPI

VariableDPI groupsp valueap valueb
low intake (n = 194)high intake (n = 193)
DPI (range) ≤24.20 >24.20   
BMI, kg/m2 26.07±4.68 26.95±5.13 0.07 0.07 
WC, cm 90.60±12.12 92.98±12.61 0.059 0.053 
FBG, mg/dL 98.45±16.05 95.73±13.30 0.07 0.30 
HDL-C, mg/dL 48.26±10.44 46.39±9.25 0.06 0.057 
TG, mg/dL 145.39±84.07 149.69±94.25 0.63 0.52 
SBP, mm Hg 122.92±14.19 122.44±14.70 0.74 0.54 
DBP, mm Hg 78.26±9.65 77.70±11.29 0.60 0.36 
VariableDPI groupsp valueap valueb
low intake (n = 194)high intake (n = 193)
DPI (range) ≤24.20 >24.20   
BMI, kg/m2 26.07±4.68 26.95±5.13 0.07 0.07 
WC, cm 90.60±12.12 92.98±12.61 0.059 0.053 
FBG, mg/dL 98.45±16.05 95.73±13.30 0.07 0.30 
HDL-C, mg/dL 48.26±10.44 46.39±9.25 0.06 0.057 
TG, mg/dL 145.39±84.07 149.69±94.25 0.63 0.52 
SBP, mm Hg 122.92±14.19 122.44±14.70 0.74 0.54 
DBP, mm Hg 78.26±9.65 77.70±11.29 0.60 0.36 

Variables are presented as mean ± SD.

BMI, body mass index; WC, waist circumference; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure; ANCOVA, analysis of covariance; SD, standard deviation.

aCrude model (unadjusted), independent sample t test.

bAdjusted for age, sex, energy intake, physical activity, and educational and marital status, ANCOVA.

The interaction between rs1761667 polymorphism and DPI on the risk of MetS and its components was examined using logistic regression. For this analysis, low intake of DPI was considered as the reference group. After adjustment for potential confounders (age, sex, energy intake, physical activity, and educational and marital status), a significant interaction was observed between DPI and rs1761667 genotypes, on the odds of MetS (Padjusted interaction = 0.05); however, this result did not appear in the unadjusted model (Pcrude interaction = 0.09). In addition, the interaction between rs1761667 genotypes and DPI in terms of abdominal obesity was significant in the additive (Pcrude interaction = 0.01, Padjusted interaction = 0.01) and recessive (Pcrude interaction = 0.01, Padjusted interaction = 0.02) models.

In detail, people with the AG carriers and higher DPI intake had lower odds of having MetS {β coefficient (95% confidence interval [CI]) = −1.50 (0.04–0.99), p = 0.05} than the AA genotype carriers who intake less DPI after adjusting confounders. These significant relationships were observed in the crude model. Furthermore, the logistic regression model indicated that higher adherence to DPI was associated with lower odds of MetS in G-allele carriers as compared to individuals with the AA genotype (β coefficient [95% CI] = −1.36 [0.05; 1.12], p = 0.068), although the result was not significant. AG or G-allele genotype carriers with higher DPI had lower odds of abdominal obesity than AA genotype carriers with a lower score for DPI in the crude and adjusted models. However, higher DPI intake was positively associated with the probability of abdominal obesity in the additive (β coefficient [95% CI] = 1.99 [1.38; 38.78], p = 0.01) and recessive (β coefficient [95 % CI] = 2.03 [1.46; 39.57], p = 0.01) models in those carrying the AA genotype.

Furthermore, lower adherence to the DPI was associated with higher abdominal obesity in AG and G-allele carriers as compared with the AA genotype group in the crude model, whereas no significant associations were detected after adjusting confounders. A higher score in the DPI was negatively associated with the FBG level in the GG genotype in the crude model, but the finding did not remain significant after adjustments for confounding factors. The associations are shown in Table 4 and online supplementary Table S1 (for all online suppl. material, see https://doi.org/10.1159/000535337).

Table 4.

The interaction and association between DPI and CD36 rs1761667 genotypes on the risk factors related to MetS

VariableDPI groupsGenotype
additivePinteractiondominantPinteractionrecessivePinteraction
AAAGGGAA + AGGGAAGG + AG
MetS Low intake Reference 0.25 (0.45–3.71), 0.62 −0.66 (0.13–1.97), 0.33 0.05 Reference −0.87 (0.15, 1.11), 0.08 0.14 Reference 0.15 (0.41, 3.27), 0.76 0.06 
High intake 1.24 (0.85–14.20), 0.08 −1.50 (0.04–0.99), 0.05 −0.34 (0.10–4.62), 0.72 −0.07 (0.55–1.54), 0.77 0.96 (0.68, 10.09), 0.16 1.29 (0.90–14.75), 0.069 −1.36(0.05, 1.12), 0.068 
Abdominal obesity Low intake Reference 0.93 (0.94–6.89), 0.06 0.31 (0.41–4.55), 0.60 0.01 Reference −0.44 (0.26–1.54), 0.32 0.18 Reference 0.88 (0.93–6.28), 0.06 0.02 
High intake 1.99 (1.38–38.78), 0.01 −2.21 (0.01–0.64), 0.01 −0.76 (0.05–3.78), 0.47 0.07 (0.62–1.87), 0.78 1.12 (0.75–12.74), 0.11 2.03 (1.46–39.57), 0.01 −1.99 (0.02–0.76), 0.02 
High FBG Low intake Reference 0.59 (0.61–5.32), 0.28 −0.19 (0.20–3.26), 0.78 0.12 Reference −0.70 (0.18–1.35), 0.17 0.18 Reference 0.78 (0.70–6.80), 0.17 0.056 
High intake 0.96 (0.64–10.85), 0.18 −0.34 (0.05–1.21), 0.08 −0.32 (0.10–4.79), 0.72 −0.17 (0.50–1.40), 0.50 0.79 (0.55–8.91), 0.26 1.26 (0.81–15.27), 0.09 −1.48 (0.04–1.06), 0.059 
Low HDL-C Low intake Reference −0.21 (0.32–2.04), 0.65 −0.56 (0.17–1.79), 0.33 0.40 Reference −0.38 (0.29, 1.59), 0.37 0.81 Reference −0.42 (0.26–1.61), 0.36 0.31 
High intake 1.10 (0.80–11.37), 0.10 −0.94 (0.09–1.60), 0.19 −0.63 (0.09–2.95), 0.46 0.26 (0.81, 2.08), 0.27 0.19 (0.36, 4.04), 0.75 0.98 (0.71–10.00), 0.14 −0.74 (0.11–1.91), 0.29 
High TG Low intake Reference 0.58 (0.64–5.03), 0.26 −0.08 (0.26–3.23), 0.89 0.34 Reference −0.58 (0.23–1.35), 0.19 0.11 Reference 0.45 (0.57–4.33), 0.37 0.96 
High intake −0.007 (0.23–4.23), 0.99 −0.21 (0.17–3.77), 0.78 0.73 (0.32–13.17), 0.43 −0.17 (0.51–1.36), 0.48 0.90 (0.70–8.54), 0.15 −0.009 (0.23–4.20), 0.99 −0.03 (0.21–4.41), 0.96 
High BP Low intake Reference 0.41 (0.29–7.73), 0.61 1.06 (0.48–17.35), 0.24 0.46 Reference 0.69 (0.70–5.72), 0.19 0.97 Reference 0.58 (0.36–8.92), 0.47 0.18 
High intake 1.56 (0.69–32.50), 0.11 −1.25 (0.03–2.21), 0.23 −1.30 (0.02–2.76), 0.27 0.46 (0.81–3.07), 0.17 −0.20 (0.18–3.51), 0.78 1.56 (0.70–32.62), 0.10 −1.29 (0.03–2.04), 0.20 
VariableDPI groupsGenotype
additivePinteractiondominantPinteractionrecessivePinteraction
AAAGGGAA + AGGGAAGG + AG
MetS Low intake Reference 0.25 (0.45–3.71), 0.62 −0.66 (0.13–1.97), 0.33 0.05 Reference −0.87 (0.15, 1.11), 0.08 0.14 Reference 0.15 (0.41, 3.27), 0.76 0.06 
High intake 1.24 (0.85–14.20), 0.08 −1.50 (0.04–0.99), 0.05 −0.34 (0.10–4.62), 0.72 −0.07 (0.55–1.54), 0.77 0.96 (0.68, 10.09), 0.16 1.29 (0.90–14.75), 0.069 −1.36(0.05, 1.12), 0.068 
Abdominal obesity Low intake Reference 0.93 (0.94–6.89), 0.06 0.31 (0.41–4.55), 0.60 0.01 Reference −0.44 (0.26–1.54), 0.32 0.18 Reference 0.88 (0.93–6.28), 0.06 0.02 
High intake 1.99 (1.38–38.78), 0.01 −2.21 (0.01–0.64), 0.01 −0.76 (0.05–3.78), 0.47 0.07 (0.62–1.87), 0.78 1.12 (0.75–12.74), 0.11 2.03 (1.46–39.57), 0.01 −1.99 (0.02–0.76), 0.02 
High FBG Low intake Reference 0.59 (0.61–5.32), 0.28 −0.19 (0.20–3.26), 0.78 0.12 Reference −0.70 (0.18–1.35), 0.17 0.18 Reference 0.78 (0.70–6.80), 0.17 0.056 
High intake 0.96 (0.64–10.85), 0.18 −0.34 (0.05–1.21), 0.08 −0.32 (0.10–4.79), 0.72 −0.17 (0.50–1.40), 0.50 0.79 (0.55–8.91), 0.26 1.26 (0.81–15.27), 0.09 −1.48 (0.04–1.06), 0.059 
Low HDL-C Low intake Reference −0.21 (0.32–2.04), 0.65 −0.56 (0.17–1.79), 0.33 0.40 Reference −0.38 (0.29, 1.59), 0.37 0.81 Reference −0.42 (0.26–1.61), 0.36 0.31 
High intake 1.10 (0.80–11.37), 0.10 −0.94 (0.09–1.60), 0.19 −0.63 (0.09–2.95), 0.46 0.26 (0.81, 2.08), 0.27 0.19 (0.36, 4.04), 0.75 0.98 (0.71–10.00), 0.14 −0.74 (0.11–1.91), 0.29 
High TG Low intake Reference 0.58 (0.64–5.03), 0.26 −0.08 (0.26–3.23), 0.89 0.34 Reference −0.58 (0.23–1.35), 0.19 0.11 Reference 0.45 (0.57–4.33), 0.37 0.96 
High intake −0.007 (0.23–4.23), 0.99 −0.21 (0.17–3.77), 0.78 0.73 (0.32–13.17), 0.43 −0.17 (0.51–1.36), 0.48 0.90 (0.70–8.54), 0.15 −0.009 (0.23–4.20), 0.99 −0.03 (0.21–4.41), 0.96 
High BP Low intake Reference 0.41 (0.29–7.73), 0.61 1.06 (0.48–17.35), 0.24 0.46 Reference 0.69 (0.70–5.72), 0.19 0.97 Reference 0.58 (0.36–8.92), 0.47 0.18 
High intake 1.56 (0.69–32.50), 0.11 −1.25 (0.03–2.21), 0.23 −1.30 (0.02–2.76), 0.27 0.46 (0.81–3.07), 0.17 −0.20 (0.18–3.51), 0.78 1.56 (0.70–32.62), 0.10 −1.29 (0.03–2.04), 0.20 

All values are reported as β coefficient (95% CI), p value.

Significant items with a p value ≤0.05 are bolded.

CI, confidence interval; MetS, metabolic syndrome; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; BP, blood pressure.

aMultiple logistic regression; adjusted model to age, sex, energy intake, physical activity, and educational and marital status as covariates.

The present study investigated the interaction between the CD36 gene variant rs1761667 and the DPI on the risk factors related to MetS. Our results showed that the CD36 gene variant rs1761667 was not associated with the components of MetS. We also did not observe a differential association between the DPI score and the components of MetS. Furthermore, there was a significant interaction between the DPI score and rs1761667 polymorphism on the risk of MetS and abdominal obesity, which is the main finding and novelty of this study.

A previous study by Boghdady et al. [21] showed that the AG genotype of the rs1761667 polymorphism may play a part in coronary artery disease pathogenesis and increase MetS in the Egyptian population. Moreover, Zhang et al. [33] obtained similar findings in a study on individuals with coronary artery heart disease, while another study reported that MetS patients with genotypes AG and GG had a higher degree of dyslipidemia and BP and wider WC than patients with the genotype AA [20]. Contrary to the previous studies, our findings revealed no association between CD36 rs1761667 polymorphism and MetS markers in an Iranian population. In line with our results, a study on nondiabetic subjects reported no relationship between this polymorphism and the components of MetS in Caucasians [17]. This discrepancy in these results might be from several reasons, including different ethnicity, gene-environment and gene-gene interactions, clinical heterogeneity, and variation of the health status in different populations [18].

In this study, there was no significant mean difference in MetS markers across DPI median, likely due to uncontrolled confounding variables or food frequency questionnaire-related measurement error [34]. In agreement with this result, it has previously been revealed that there was no significant relationship between a higher DPI score and some components of MetS[8, 12]. However, in contrast with the current results, some studies indicated that the odds of MetS and its components reduced with a higher intake of DPI [10, 11]. The contradiction of the results may be due to the difference in study design, sample size, genetic characteristics of participants, methods used in dietary assessments, and various criteria used to define MetS.

The findings of this research suggested further insight into the possible linkage between DPI and MetS and its components. Indeed, based on these findings, the AG genotype appears to be a protective factor against the risk of MetS and abdominal obesity with elevated DPI intake. Furthermore, individuals with the AA homozygous genotype may be more vulnerable to the risk of MetS and abdominal obesity, considering adherence to a diet rich in phytochemicals; however, this result should be interpreted with caution, and more studies with a higher sample size are needed to confirm it. It has been reported that gene-diet interaction can be involved in the pathogenesis of MetS and its components [35]. The DPI is a dietary index developed to reflect the entire dietary phytochemical content [36]. Dietary phytochemicals are nonnutritious bioactive compounds consisting of polyphenolics, glucosinolates, carotenoids, organosulfur compounds, terpenoids, and nitrogen-containing compounds [37]. Previous studies have demonstrated the beneficial effects of phytochemicals like phenolic acid and flavonoids in preventing or delaying the components accountable for MetS [38, 39]. Growing evidence supports that the consumption of phytochemical abundant foods such as whole grains, vegetables, fruits, nuts, and legumes may prevent MetS and abdominal obesity [5, 40, 41]. Further investigations suggested that obtained results may be due to the synergistic effects of phytochemicals in combination with their antioxidant and anti-inflammatory properties, antioxidant vitamins such as vitamin C as well as high dietary potassium and folate [42‒44]. It has been demonstrated that polyphenols can reduce TG accumulation, inhibit the proliferation of adipocytes, and decrease adipogenesis, stimulating lipolysis and β-oxidation [45]. In addition, polyphenols can play an important role in decreasing tissue insulin resistance and visceral fat by interfering with the metabolism of energy and changing gene expression such as decreasing peroxisome proliferator-activated receptor gamma (PPARγ) and incrementing uncoupling protein [46, 47]. Also, flavonoid subclasses can reduce lipid absorption at the gastrointestinal level and modulate the activity of various enzymes that participated in lipid metabolism and the expression of transcription factors involved in lipid synthesis such as sterol regulatory element-binding proteins (SREBPs) and CD36 [36, 48]. However, our findings did not show a significant association between the DPI score and MetS components, which may be a result of differences in general and genetic specifications of the individuals.

Previous studies have demonstrated that CD36 polymorphisms are correlated with metabolic dysregulation [49]. Studies about the interaction between CD36 rs1761667 and diet are rare, for example, Navarro-Rios et al. [50] showed that daily consumption of sugary drinks can be a risk factor for hypertriglyceridemic waist phenotype (as a predictor of metabolic disorders) in the AG genotype of the rs1761667 polymorphism, while Lopez-Ramos et al. [23] observed that individuals with the AA genotype of CD36 rs1761667 had a higher intake of total fat, polyunsaturated fatty acids, MUFA, and saturated fatty acids. Furthermore, the serum cholesterol level in this genotype group was significantly higher than AG genotype carriers. In our previous study, we observed that adherence to a healthy dietary pattern that is rich in fish, dairy products, and fiber can be more effective on some cardiometabolic risk factors in the A-allele carriers of rs1761667 polymorphism [31]. On the other hand, some studies have stated possible interactions between dietary factors and CD36 gene variants on some risk factors related to MetS, such as the study by Meng et al. [51] that showed that higher SFA intake in overweight subjects was positively associated with serum TGs in the G-allele carriers of rs1054516 and the study by Madden et al. [24] that indicated a significant interaction of CD36 SNP rs1527483 with fish oil supplements on serum TG levels.

The precise mechanisms of the effects of CD36 on fat metabolism and MetS risk factors are unknown. The evidence has demonstrated that CD36 plays a role in the metabolism of HDL-C and contributes directly to their regulation [52]; in addition, deficiency of it might alter the uptake of long-chain fatty acids, delay plasma fatty acid clearance, and be associated with obesity and insulin resistance. CD36 deletion increases the cyclic adenosine monophosphate level, which leads to TG hydrolysis and an increment of free fatty acid in the plasma [53]. On the other hand, it was observed that upregulated CD36 can cause inflammation, endothelial apoptosis, macrophage trapping, and thrombosis. The CD36 increase might be a result of elevated PPARγ and pro-inflammatory cytokines at the post-transcriptional level [54]. A recent investigation reported that the function of CD36 (rs1761667) could directly/indirectly regulate the synthesis/release of peptide YY from taste bud cells [55]. Additionally, studies have shown that polymorphism rs1761667 in the CD36 gene is associated with decreasing CD36 expression in monocytes and platelets, lower sensitivity for fat perception, and increasing preference for high-fat food [56, 57]. Thus, it seems that CD36 gene polymorphisms and multiple factors could cause changing the expression of this gene, and environmental factors including diet probably could interact with them in altering this gene expression.

This study has a number of limitations. The sample size of the study was not enough to perform subgroup analysis according to sex. The population of the study was restricted to one of the central cities of Iran with special eating habits; therefore, findings could not be generalized to other ethnic backgrounds. Thus, it needs to be replicated in other populations. The study design was cross-sectional, so causality cannot be inferred. FFQ and factor analysis have some limitations including measurement errors. Moreover, data on participants’ past medical history was obtained by self-reporting, which may have introduced bias. Another limitation of the present study was the width of 95% CI in some models which may be due to the rarity of the AA genotype. However, further studies with a larger sample size are needed to identify these interactions. Despite the limitations discussed above, the strengths of this study include the following: a reliable and validated FFQ was used to collect dietary information by trained interviewers; several potential confounders including age, sex, energy intake, physical activity, and educational and marital status were adjusted for. Based on a database search, this is the first study on the interaction between CD36 rs1761667 polymorphism and DPI on the risk factors related to MetS.

In conclusion, the present study demonstrated a significant interaction between DPI and the rs1761667 CD36 gene in relation to odds of MetS and abdominal obesity; higher adherence to DPI in AG homozygote carriers was associated with a lower probability of MetS and abdominal obesity. On the contrary, a higher intake of DPI among the AA genotype of rs1761667 was associated with higher odds of abdominal obesity. Further investigations in different populations with varied health status are warranted to confirm these findings.

The authors are grateful to the participants of YaHS-TaMYZ studies as well as its chief investigators for sharing the data.

The current study was approved by the Ethics Committee of the Shahid Sadoughi University of Medical Sciences under the code 17/1181/73941 and IR.SSU.SPH.REC.1398.136. Written informed consent was obtained from all individuals for participation in this study.

The authors declare no competing interests.

This study was funded by the Shahid Sadoughi University of Medical Sciences, Yazd, Iran (Grant ID: 7173).

All the authors contributed to the design of the study. Z.Y., M.M., and H.R.A. conducted the research. Z.Y., A.S.-A., and H.M.-K. were involved in the statistical analysis and result interpretation. Z.Y. and Z.D. wrote the first draft of the manuscript which was written and reviewed for intellectual content by A.S.-A., M.H.S., M.M., and H.M.-K. All the authors read and approved the final version manuscript.

The known genetic polymorphism (rs1761667) analyzed during the current study is available in the dbSNP repository [https://www.ncbi.nlm.nih.gov/snp/rs1761667]. The additional datasets can be obtained from the corresponding author on a reasonable request.

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