Interaction between a variant of chromosome 9p21.3 locus and diet antioxidant capacity on metabolic syndrome in Tehrani adults

In this cross-sectional study, 363 participants (18–55 years) were enrolled. Sampling was done with advertising in the city. Individuals were included if they met following criteria: namely age 18–55, no smoking and alcohol. Participants with a history of CVDs, diabetes, cancer or stroke were excluded because of possible disease-related changes in diet and who were taking any therapeutic medications. We also excluded subjects whose reported daily energy intakes were 800 kcal/day (3347 kJ/day) or 4200 kcal/day (17 573 kJ/day) [27]. These exclusions left 263 subjects (125 men and 138 women) for the current analysis. Each participant was completely informed about the study protocol and provided a written and informed consent form before taking part in the study. The study protocol has approved by the ethics committee of Endocrinology and Metabolism Research Center of Tehran University of Medical Sciences (TUMS) with the following identification: 93-04-161-27-722-1495-80.

All blood samples were collected at 8:00 to 10:00 A.M. after having 8–12 h fasting state at the EMRC laboratory of Shariatei hospital of TUMS. To collect serums, Serum samples were centrifuged 10 min at 3000 rpm, aliquoted into 1 ml tubes and stored at − 80 until they were analyzed. Fasting plasma glucose (FPG) was measured on the day of blood collection by glucose oxidase phenol 4-aminoantipyrine peroxidase (GOD/PAP) method. Serum triglycerides (TG) concentrations were assayed with triacylglycerol kits (Pars Azmoon Inc, Tehran, Iran) by using glycerol-3-phosphate oxidase phenol 4-aminoantipyrine peroxidase (GPOPAP) method. Total cholesterol levels were measured by the Enzymatic Endpoint method and direct high- and low-density lipoprotein was measured by enzymatic clearance assay. Serum hyper sensitive C-reactive protein (hsCRP), as a sensitive marker of inflammation, was measured by an immunoturbidimetric assay (Randox laboratories kit, Hitachi LTD, Tokyo, Japan). Serum insulin concentrations were analyzed through enzyme-linked immunosorbent assay (ELISA) method (Human insulin ELISA kit, DRG Pharmaceuticals, GmbH, Germany), and the minimum detectable concentration was 1.76 U/ml, Intra CV was 2.19% and Inter CV was 4.4%.

Weight was measured while the subjects were minimally clothed and not wearing shoes. Weight was measured to the nearest 100 g by using digital scales. Height was measured by using a tape measure while the subject was in a standing position and not wearing shoes, and the shoulders were relaxed. Body mass index (BMI) was also calculated using the “weight (kg)/height² (m²) “equation. Blood pressure (BP) was measured using a standardized sphygmomanometer after 5 min of rest. Waist circumference (WC) was measured in the middle point of iliac crest and rib cage.

Participant’s body composition was assessed through Body Composition Analyzer BC-418MA-Tanita (United Kingdom). This Bioelectrical Impedance Analyzer (BIA) is designed send out a very weak electric current to measure the impedance (electrical resistance) of the body. We followed all of the following instructions for an accurate measurement. To prevent a possible discrepancy in measured values, before assessing body composition, Participants were asked not to exercise vigorously, carry out any electric device and intake excessive fluid or food; they were performed in the morning in a fasting condition and urinate just before body composition analysis to get a more accurate result of the measurements.

Dietary data was collected using a validated semi-quantitative food frequency questionnaire (FFQ) with 147 food items. Trained dietitians asked participants to designate their intake frequency for each food item consumed during the past year on a daily, weekly or monthly basis. Portion sizes of consumed foods were reported in household measures, and were then converted to grams [20].

Due to the fact that the Iranian Food composition table (FCT) is incomplete, and has limited data on the nutrient content of raw foods and beverages, the US Department of Agriculture (USDA) FCT [21] was used in order to analyze foods and beverages for their energy and nutrient content. Dietary TAC describes the ability of food antioxidants to scavenge free radicals, and it is measured using ORAC assay [22]. The ORAC is one of the common methods to evaluate the antioxidant capacity [38]. The antioxidant capacity of lipophilic and hydrophilic antioxidants in the samples are evaluated using Lyophilic-oxygen radical absorbance capacity (L-ORAC) and Hydrophilic-oxygen radical absorbance capacity (H-ORAC) methods, respectively, and their total is used as an indicator of the dietary antioxidant capacity of food. Antioxidants can be physically classified by their solubility into two groups: (a) lipophilic antioxidants such as vitamin E and carotenoids and (b) hydrophilic antioxidants such as vitamin C and the majority of polyphenolic compounds [39]. The ORAC values were utilized to develop individual indices for TAC in the following manner: H-ORAC, LORAC, total-oxygen radical absorbance capacity (T-ORAC) and total phenolic (TP). H-ORAC, L-ORAC, and T-ORAC are reported in μmol of Trolox equivalents per 100 grams (μmolTE/100 g), while TP is reported in mg gallic acid equivalents per 100 g (mgGAE/100 g) [23].

Homeostatic model assessment-insulin resistance (HOMA-IR), was calculated according to the following equation: [fasting plasma glucose (mmol/l) × fasting plasma insulin (mIU/l)]/22.5 [24].

Cardio-metabolic risk factors for metabolic syndrome were defined according to the diagnostic criteria proposed by Adult Treatment Panel III (ATP III) [25] and new cutoff points of WC for Iranian adults [26]; the syndrome was characterized as having at least 3 of the following metabolic abnormalities: (1) Hyperglycemia as FPG ≥ 100 mg/dl (5.6 mmol/l); (2) Hypertriglyceridemia as serum TG ≥ 150 mg/dl (1.69 mmol/l); (3) Low HDL-C serum < 40 mg/dl (1.04 mmol/l) for men, and < 50 mg/dl (1.29 mmol/l) for women; (4) Hypertension as BP ≥ 130/85 mmHg; and (5) Abdominal adiposity (defined as waist circumference 88 cm [women] or 102 cm [men]).

Single nucleotide polymorphisms were selected from studies reported to be associated with CVDs or myocardial infarction (MI) [27]. All subjects from whom deoxyribonucleic acid (DNA) samples were available were expected to be genotyped for the rs1333048. The extraction of genomic DNA from blood samples was carried out with the use of the Gene ALL DNA kit (Type G Exgene; Genall; Korea) according to the manufacturer’s protocol. The concentration and purity of extracted DNA was measured using Nano Drop ND-1000 spectrophotometer. DNA was stored at − 20 °C until ready for use. The chromosome 9p21 rs1333048 SNP (major allele: A; minor allele: C) was genotyped by polymerase chain reaction-restricted length polymorphism (PCR–RFLP) technique. PCR was done using the following primers: forward 5′-ACCCGAAGTAGAGCTGCAAA-3′; reverse 5′-CACAAGTTGGAATATGAAGCAGA-3′. PCR reactions were performed in a final volume of 20 µl contains 2 µl extracted DNA, 0.5 µl primers, 10 µl distilled water and 7 µl Taq DNA Polymerase Master Mix (Ampliqon; Denmark) with the following conditions in a DNA thermocycler: The DNA templates were denatured at 94° C for 5 min (min); amplification consisted of 35 cycles at 94 °C, 63 °C and 72 °C (each step for 30 s), with a final extension at 72 °C for 7 min. Amplified DNA (10 µl) was digested with 2 µl of DRI restriction enzyme (Thermo fisher scientific; United states) at 37 °C overnight. All products visualized by electrophoresis in agarose gel. Fragments containing three possible genotypes were then distinguished: uncut homozygous CC (152 bp), cut heterozygous CA (84, 68 and 152 bp) and cut homozygous AA (84 and 68 bp). Ten percent of the samples were directly sequenced for confirmation the PCR–RFLP results.

Normality distribution was tested by applying Kolmogorov–Smirnov’s test. Data on quantitative characteristics were reported as the mean ± SD and data on qualitative characteristics were expressed as a percentage. Qualitative variables were compared with analysis of variance (ANOVA) and independent t test to compare the quantitative variables. Moreover, age, physical activity, sex, BMI and energy intake-adjusted analyses were performed in general linear models (GLM). H-ORAC, L-ORAC, T-ORAC and TP intakes were stratified into two groups: low and high intake based on the median (defined as for H-ORAC, L-ORAC, T-ORAC, TP, respectively 31894.55 μmolTE/100 g, 38992.07 μmolTE/100 g, 70770.86 μmolTE/100 g, 2614.81 mgGAE/100 g). Mean values for the dietary variables were adjusted for total energy intake by using the residual method [27], then the study variables were compared among two groups using an independent T-test. Genotypes of markers were recoded based on risk allele: code 0 for CC, 1 for AC and 2 for AA genotype. In order to examine the interactions between rs1333048 genotype and TAC intakes on odds of MetS, the participants were grouped based on CDKN2B genotypes: group 1 with CC genotype (n = 76), group 2 with AC genotype (n = 127), and group 3 with AA genotype (n = 60). The binary logistic regression model was used to analyze potential interactions between rs1333048 genotype and TAC on odds of MetS. In this model, MetS criteria were entered as the dependent variable and rs1333048 genotypes and categorized intake of H-ORAC, L-ORAC, T-ORAC, and T based on median intakes were entered as covariates included in the crude model, and also adjusted for confunder variable also has been done at the next model. We find confounding factors with best fitted model and adjusted its effect of on exposure group, for interaction between rs 1333048 genotype and H-ORAC intakes on odds of MetS, adjusted for sex, age, physical activity and WC and for interaction between rs1333048 genotype and L-ORAC, T-ORAC and TP intakes on odds of MetS, adjusted for sex, age, physical activity and BMI. Confounding factors were determined from a best-fit model and the change in − 2 log likelihood ratio test. The level of significance was set at a probability of < 0.05 for all tests. Statistical analysis was performed using SPSS version 22.0 (SPSS, Chicago, IL, USA).

This comparative cross-sectional study was conducted on 263 participants (52.5% female). The means (± SD) of age, height, BMI, and weight of individuals were 35.08 ± 8.78 years, 168.23 ± 9.43 cm, 25.93 ± 4.89 kg/m², and 73.51 ± 15.66 kg, respectively (Table 1). The frequencies of A and C alleles of rs1333048 were 52.85% and 47.15% respectively. The overall prevalence of rs1333048 genotypes was 22.6%, 47.9% and 28.7% for AA, AC and CC respectively (Table 2). The distribution frequencies of mentioned SNP in this study followed Hardy–Weinberg equilibrium (P > 0.05). It was found that 12.5% of participants had MetS, and the data demonstrated that MetS across AA, AC, CC genotypes were respectively 13.3%, 11%, and 13% (P = 0.86).

A total of 263 Iranian men and women were categorized based on rs1333048 genotypes and divided into three groups: CC genotype (n = 76), AC genotype (n = 127) and AA genotype (n = 60) (Table 3). Means of WC, hip, BMI, weight, FM, fat percentage and visceral fat rate (VFR) were higher in participants carrying the A allele, compared with individuals in the CC genotype, but that there was no statistically significant difference across three groups, even after adjustment for BMI, age, sex and physical activity. Also, no significant difference was observed regarding TG, T-chol, HDL-C, LDL-C, hs-CRP, systolic and diastolic blood pressure across groups, even after adjustment for confounding factors.

After adjustment for calorie intake across rs1333048 genotypes, there were significant differences in protein (P < 0.001), polyunsaturated fat (P = 0.02), zinc (P = 0.04), phosphorus (P = 0.02), magnesium (P = 0.01), selenium (P = 0.03), vitamin B3 (P = 0.05), L-ORAC (P = 0.02), T-ORAC (P = 0.02) consumption, and marginal significant differences observed in H-ORAC (P = 0.09). The results indicate that mean intake of H-ORAC, L-ORAC, T-ORAC and TP in the AA genotype was lower than in the CC genotype: 25,663.52, 43,613.40, 66,045.60, and 2640.56, respectively. Also, after adjustment for BMI, age, sex, and physical activity, significant differences were observed between genotypes and the intake of H-ORAC, L-ORAC, T-ORAC, protein, zinc, phosphorus, magnesium, and selenium (P < 0.05) (Table 4).

The results of the comparison indicated that means of WC, TG, and T-chol were reduced from the low intake to high intake dietary TAC, but there was no statistically significant difference, as shown in Tables 5 and 6.

The binary logistic regression model analysis was used to examine the interactions between rs1333048 genotypes and H-ORAC, L-ORAC, T-ORAC and TP intake on odds of MetS.

In the crude models, there was no significant interaction between rs1333048 genotypes and TAC on the odds of MetS. After adjusting for age, sex, BMI and physical activity, a significant interaction was observed between rs1333048 genotypes and high L-ORAC intake on reduce odds of MetS (OR = 0.24, 95% CI = 0.06–0.94, P for interaction = 0.04). Moreover, there was no significant interaction between rs1333048 genotypes and T-ORAC and TP intake on the odds of MetS, OR = 0.28, 95% CI = 0.07–1.10, P for interaction = 0.07 and OR = 0.58, 95% CI = 0.16–2.07, P for interaction = 0.40) respiratory. Also, After adjusting for age, sex, WC and physical activity, a significant interaction was observed between rs1333048 genotypes and high H-ORAC intake on reduce odds of MetS (OR = 0.26, 95% CI = 0.06–0.99, P for interaction = 0.04).

Overall, in low L-ORAC intake, percentage of MetS across AA, AC and CC genotypes were 18.9%, 12.7%, and 11.4%, respectively. Whereas, in high L-ORAC intake, were 5%, 10.1%, and 15.8% respectively. Also, in high H-ORAC intake, percentage of MetS, across AA, AC and CC genotypes was 12%, 10.9%, and 17.5% respectively (Fig. 1).

In the general linear model (GLM) analysis, the associations of rs1333048 genotypes and H-ORAC, L-ORAC, T-ORAC and TP were tested on MetS components including: TG, HDL-C, LDL-C, T-chol, WC, and BP but there was no significant difference even after controlling for BMI, sex, age and physical activity.