Metabolic syndrome (MetS) is a complex metabolic disorder that is associated with an increased risk of CVD, stroke and mortality(Reference Li, Li and Lin1–Reference Obunai, Jani and Dangas3). This syndrome imposes a large burden and cost to healthcare system(Reference Goolam Mahyoodeen, Crowther and Snyman4). The prevalence of MetS has been increasing at an alarming rate. In the USA, 40 % of adults are affected by this syndrome(Reference Ford, Li and Zhao5). National estimates in Iran showed a prevalence of 30 % among Iranian adults(Reference Mansouri, Abasi and Nasiri6). Therefore, finding the appropriate strategies to prevent MetS is of great importance.
A complicated combination between genetic, metabolic and environmental factors, including diet, contributes to the aetiology of MetS and its components(Reference Abou Ziki and Mani7–Reference Sadeghi, Sadeghian and Rahmani12). Prior studies reported that lower intakes of hydrogenated vegetable oil, refined grains and red meat, and greater intakes of vegetables, antioxidants and Ca are associated with decreased odds of MetS(Reference Suriyaprom, Kaewprasert and Putpadungwipon13–Reference Parohan, Anjom-Shoae and Nasiri18). Traditionally, published studies have mainly focused on individual nutrients, foods and food groups, and little attention has been laid on the patterns of dietary intakes, particularly patterns of nutrient intake(Reference Zhang and Zhang19,Reference Hosseinpour-Niazi, Mirmiran and Amiri20) . As we know, people do not consume nutrients separately and in fact, they consume several nutrients together, which might interact with each other(Reference Willett21). Nutrients can synergistically affect the absorption and function of the others. Furthermore, considering the patterns of nutrient intake has several priorities compared with dietary patterns. Nutrients are consumed by people worldwide, while the consumption of foods is dependent on geographical region and culture(Reference Malmir, Shayanfar and Mohammad-Shirazi22). Therefore, nutrient pattern (NP) analysis provides a more realistic view of diet–disease relationships than dietary pattern analysis, particularly when comparing dietary intakes across different nations. In addition, compared with foods, nutrients functionally cannot be exchanged. In NP analysis, unlike food patterns, the underlying mechanisms on the diet–disease associations can be explained by nutrient content of patterns. Also, NP analysis can be an interface between food patterns and food metabolome integrating the measurements of both diet and metabolism(Reference Malmir, Shayanfar and Mohammad-Shirazi22–Reference McNaughton24).
Based on our literature search, we only found two studies examining the association between patterns of nutrient intake and MetS(Reference Bian, Gao and Zhang25,Reference Khayyatzadeh, Moohebati and Mazidi26) . In the study of Bian et al.(Reference Bian, Gao and Zhang25), ‘B-vitamins’ pattern was inversely associated with MetS in Chinese adults, while in another cross-sectional study, adherence to a pattern rich in riboflavin and vitamin B12 was positively associated with MetS(Reference Khayyatzadeh, Moohebati and Mazidi26). Therefore, data in this regard are conflicting. In addition, few studies are available from the understudied region of the Middle East, where the prevalence of MetS is estimated to be high(Reference Mansouri, Abasi and Nasiri6,Reference Ghorabi, Salari-Moghaddam and Daneshzad27) . In this region, a specific type of abdominal obesity, which is characterised by abdominal fat accumulation and enlarged waist circumference, is prevalent, particularly among women(Reference Sadeghi, Keshteli and Doostan28,Reference Esmaillzadeh and Azadbakht29) . This type of abdominal obesity might be a reason for the high prevalence of hyperlipidaemia among Middle Eastern women. Therefore, assessing the contributing factors to MetS is important in this region. Hence, the current study aimed to assess the relationship between patterns of nutrient intake and MetS among a large sample of Iranian adults.
Materials and methods
Participants and study design
The current study was a cross-sectional analysis of the Shahedieh cohort study which was a part of the PERSIAN (Prospective Epidemiological Research Studies in Iran) cohort study that is carrying out in different cities of Iran. The purpose of the PERSIAN cohort was to identify the potential risk factors of non-communicable diseases. This cohort was done by investigators at local universities in cooperation with the Ministry of Health and Medical Education. Detailed information about the study design, participants and data collection methods of the PERSIAN cohort has been published previously(Reference Poustchi, Eghtesad and Kamangar30). In brief, the Shahedieh cohort study was started in the Shahedieh region of Yazd city, Iran, in 2014. At baseline, all adults (n 10 194) with an age range of 35–70 years from this region were invited to participate in this cohort. Inclusion criteria were being of Iranian descent and living in the Shahedieh region for at least 9 months of the year. However, we excluded individuals with physical or psychological disabilities that render them unable to complete the enrolment process. In order to collect data, participants were invited to the health centre of Shahedieh. They were asked to arrive in a fasting state for the collection of biological samples. Among 10 194 adults who were invited, 9977 participated in the current study. After taking the blood sample from each participant, required information about socio-demographic characteristics, physical activity and dietary intakes was gathered by trained interviewers. After merging data and excluding participants with incomplete data on exposure and outcome variables as well as those who had under- and over-reporting for energetic intake according to the ±3 sd of mean energy intake, 7325 remained for the final analysis. No significant difference was seen in terms of demographical variables between individuals included and excluded from the analysis. All participants provided written informed consent.
Dietary assessment
We used a validated block-format 120-item semi-quantitative FFQ which was online for gathering data on dietary intakes. This questionnaire was specifically designed for Iranian adults. Details on the design, foods included as well as the validity of this questionnaire were published previously(Reference Esfahani, Asghari and Mirmiran31). Trained interviewers recorded dietary intake of each food item in three steps based on participants’ responses. In the first step, interviewer recorded the frequency of consumption for each food item and then the amount of consumption at any time was recorded (step 2). In the third step, the number of months that seasonal foods were consumed during the past year was recorded. To determine the amount of consumption, interviewers showed household measures to individuals for increasing the accuracy of estimations. Finally, based on the consumption frequency and amount of consumption at each time as well as the number of months that each food was consumed in the past year, we calculated the intake of each food item as g/d. In addition, we calculated the daily nutrients intake for each participant according to the nutrient contents of all foods. The US Department of Agriculture’s national nutrient databank was used to obtain nutrient contents of foods(32). Findings from previous studies revealed that the FFQ could provide reasonably valid and reliable measures of long-term dietary intakes in the Iranian population(Reference Dabbagh-Moghadam, Mozaffari-Khosravi and Nasiri17,Reference Esfahani, Asghari and Mirmiran31) .
Biochemical assessment
Twenty-five millilitres of blood was collected from each individual in a fasting state using Vacutainers (Greiner Bio-One International GmbH). Then, blood was centrifuged and fractioned into different aliquots which were labelled and stored in freezers (–70°C). In addition to storing the samples, a small amount of blood was used to measure fasting blood glucose, TAG and HDL-cholesterol concentrations. We used an enzymatic colorimetric method to measure fasting blood glucose. TAG levels were measured via enzymatic colorimetric tests with glycerol phosphate. To measure HDL-cholesterol concentrations, the precipitation of the apoB-containing lipoproteins with phosphotungstic acid was determined. We applied commercially available enzymatic reagents (Pars Azmoon), adopted to an auto-analyzer system (Selectra E; Vitalab) for all measurements.
Anthropometric measures
All anthropometric indicators were measured according to the US National Institutes of Health protocols. Weight was measured using a digital scale at the state of minimum clothing without shoes to the nearest 100 g. Standing height was measured using a standard stadiometer, without shoes, to the nearest 0·5 cm. BMI was calculated as weight (kg)/height (m2). Waist circumference was measured using a strip metre at mid-distance interval between the super elliptic bone and the last gear, to the nearest 0·5 cm. To reduce measurement error/bias to the lowest levels, anthropometric data were acquired in the morning and after sample collection, while the participant was still fasting.
Assessment of other variables
To measure blood pressure, first, subjects were asked to rest for 10 min. Then, blood pressure was measured by the use of a standard mercury sphygmomanometer at the right arm, three times with a 5-min interval, when subjects were sitting. Subjects’ blood pressure was considered as the average of three measurements. We used the International Physical Activity Questionnaire to measure the physical activity of participants through face-to-face interviews. All results of the International Physical Activity Questionnaire were expressed as metabolic equivalents per week (MET-h/week). International Physical Activity Questionnaire was translated to Farsi and validated for the Iranian population previously(Reference Moghaddam, Aghdam and Jafarabadi33). Further information on age, gender (male/female), education (university graduated/non-university education), marital status (married/single/divorced), house possession (owner/non-owner), smoking (non-smoker/ex-smoker/current smoker), family size (≥4/<4 people), supplement use (yes/no), having frequent night work shift (≥1 shift per month) and history of depression and thyroid disorders was collected using pretested questionnaires via face-to-face interview.
Definition of terms
We defined the MetS according to the National Cholesterol Education Program Adult Treatment Panel III, modified for Iranian adults(Reference Grundy, Cleeman and Daniels34). The presence of at least three components of the following abnormalities was considered as MetS: (i) abnormal glucose homoeostasis (fasting blood glucose of ≥5·55 mmol/l), (ii) low concentrations of HDL-cholesterol (<1·28 mmol/l for women and <1·02 mmol/l for men), (iii) high serum TAG (≥1·69 mmol/l), (iv) elevated blood pressure (systolic blood pressure ≥130 mmHg and diastolic blood pressure ≥85 mmHg) and (v) abdominal obesity (waist circumference ≥ 95 cm for both genders). Iranian National Committee of Obesity proposed a single cut-point of waist circumference ≥ 95 cm to define abdominal obesity for Iranian men and women(Reference Azizi, Hadaegh and Khalili35).
Statistical analysis
First, we obtained energy-adjusted intakes of thirty-four nutrients and bioactive compounds by the residual method(Reference Willett21) and then factor analysis with the orthogonal transformation (varimax procedure) was applied to determine major NP. To examine if the distribution of the different nutrients allows the use of principal components, the Kaiser–Meyer–Olkin test was used. Obtained factors were retained for further analysis based on Eigen values on the Scree plot. In the current study, we retained factors with Eigen values of ≥2·5 to extract major NP.
In the current study, participants were categorized based on quintiles of NP’ scores. Differences in quantitative variables across quintiles of NP’ scores were determined using one-way ANOVA. To assess the distribution of categorical variables across quintiles of NP’ scores, the χ 2 test was used. We applied the binary logistic regression in different models to determine OR and 95 % CI for MetS across quintiles of NP’ scores. In the first model, we adjusted for age, gender and energy intake. Further adjustment was made for education (university graduated/non-university education), marital status (married/single/divorced), house possession (owner/non-owner), family size (≥4/<4 people), smoking (none or ex-smoker/current smoker), supplement use (yes/no), having frequent night work shift (≥1 shift per month), physical activity (continuous) and history of depression and thyroid disorders in the second model. BMI was additionally adjusted in the final model to see if the association is independent of obesity. In all models, participants in the first quintile of NP’ scores were considered as the reference group. To obtain the trend of OR across increasing quintiles of NP’ scores, we considered these categories as an ordinal variable. The statistical analyses were conducted using SPSS version 20. P values were considered significant at <0·05.
Results
Mean age of study participants was 48·51 ± 9·69 years, and 54·9 % were female. The MetS was prevalent among 26·1 % of individuals participated in the current study.
Based on factor analysis, we identified three NP (Table 1). The Kaiser–Meyer–Olkin value was 0·77 indicating good sampling adequacy. The first nutrient pattern (NP1) was rich in P; vitamins B1, B3, B6 and B5; Se; Mg; Fe; protein; Cr; Cu; dietary fibre; biotin; Mn; Zn and Na. The second one (NP2) was greatly loaded with Ca; K; vitamins B2, B12, A, D, K and C; SFA; dietary cholesterol and trans-fatty acid (TFA). The third nutrient pattern (NP3) was characterised by the high consumption of carbohydrate and refined sugar, and lower intakes of vitamin E, PUFA, MUFA and SFA. Overall, these NP explained 62·4 % of the variance.
Table 1 Factor-loading matrix of nutrients used in the nutrient pattern (NP) analysis
Factor loadings <0·20 are not shown for simplicity.
General characteristics of participants across quintiles of NP’ scores are indicated in Table 2. Compared with participants in the bottom quintile of NP1, those in the top quintile were more likely to be female, house owner, have thyroid disorders, abnormal glucose homoeostasis, abdominal obesity and MetS, and less likely to be of large family sizes, current smoker, university educated and have frequent night work shift. In addition, greater adherence to NP1 was associated with higher age, BMI, fasting blood glucose, HDL-cholesterol and lower physical activity. Within the NP2, individuals in the highest quintile were more likely to be male, physically active, married, current smoker, university graduated, have frequent night work shift and elevated concentrations of serum TAG, and less likely to have depression, thyroid disorders, low serum HDL-cholesterol concentrations and abdominal obesity compared with those in the lowest quintile. In terms of NP3, participants in the fifth quintile were more likely to be older, female, house owner, current smoker, have abnormal glucose homoeostasis, abdominal obesity and MetS, and less likely to be married, university graduated, of large family sizes and to have frequent night work shift compared with those in the first quintile.
Table 2 General characteristics of participants across quintiles of nutrient patterns’ (NP) scores
WC, waist circumference, PA, physical activity, FBG, fasting blood glucose, MetS, metabolic syndrome, BP, blood pressure.
Data are presented as mean and se or percentages.
* Obtained from one-way ANOVA or χ 2 test, where appropriate.
Dietary intakes of participants across quintiles of NP’ scores are shown in Table 3. Greater adherence to the NP1 was associated with higher intake energy, protein, carbohydrate, vitamin B6, folate, Fe, Ca, Na, dietary fibre and refined grains, and lower intake of fat, SFA, vitamin E, vitamin C, vegetables, dairy, fruits, whole grains, legumes, nuts and red meat. Within the NP2, participants in the top quintile had greater intake of fat; protein; SFA; vitamins B6 and C; Ca; vegetables; dairy; fruits; whole grains; legumes; nuts and red meat, and lower intake of energy, carbohydrate, vitamin E, folate, Fe, Na, dietary fibre and refined grains than those in the bottom quintile. Greater adherence to NP3 was associated with higher intake of energy, carbohydrate, vitamin B6, folate, vitamin C, Fe, dietary fibre, vegetables, fruits and refined grains, and lower intake of protein, fat, SFA, vitamin E, Ca, Na, dairy, whole grains, legumes, nuts and red meat.
Table 3 Dietary intakes of participants across quintiles of nutrient patterns’ (NP) scores
All values are presented as mean and se and adjusted for energy except for dietary energy intake.
* Obtained from one-way ANOVA.
Multivariable-adjusted OR and 95 % CI for MetS across quintiles of NP’ scores are shown in Table 4. A significant positive association was found between adherence to the NP1 and MetS (OR 1·68, 95 % CI 1·43, 1·98). This association was significant even after controlling for age, gender and energy intake (OR 1·22, 95 % CI 1·03, 1·45). However, after adjusting for socio-demographic variables and BMI, this association became non-significant. In terms of NP2, no significant association was seen with MetS. However, after taking potential confounders into account, individuals in the highest quintile of NP2 were 26 % more likely to have MetS compared with those in the lowest quintile (OR 1·26, 95 % CI 1·05, 1·51). Moreover, a significant positive association was found between adherence to NP3 and odds of MetS (OR 1·41, 95 % CI 1·19, 1·66); however, in the fully adjusted model, this association became non-significant.
Table 4 OR and 95 % CI for the metabolic syndrome across quintiles of nutrient patterns’ (NP) scores
* Model 1: adjusted for energy, age and gender.
† Model 2: additionally adjusted for marital status, education, smoking, family size, house possession, night working shift, physical activity, supplement use, history of depression and thyroid diseases.
‡ Model 3: further adjustment for BMI.
Discussion
In the current study, we found that adherence to NP2 was positively associated with MetS. NP2 was rich in Ca; K; vitamins B2, B12, A, D, K and C; SFA; dietary cholesterol and TFA. This association was obtained after taking potential confounders into account. However, in the fully adjusted model, no significant association was seen between adherence to NP1 and NP3 and MetS.
Earlier studies on the link between nutrients and MetS have been mostly limited to individual nutrient intake(Reference Moore-Schiltz, Albert and Singer36–Reference Lee, Lee and Kim38). NP analysis is a new approach in nutrition epidemiology that covers the intake of all nutrients in addition to their interactions(Reference Willett21). Previous studies on the NP named the patterns based on the nutrients loaded in each pattern, for example, B vitamins-rich NP, vitamin and fibre NP, carbohydrate-rich NP, animal product NP. In the current study, we found three NP with a very complex set of nutrients that made our interpretation somewhat difficult. In NP1, sixteen nutrients including P; vitamins B1, B3, B6 and B5; Se; Mg; Fe; protein; Cr; Cu; dietary fibre; biotin; Mn; Zn and Na were loaded. We can label it as a semi-plant NP because plant-based foods such as whole grains, vegetables, legumes and nuts contain a high amount of these nutrients. However, this pattern may have a high amount of animal protein because of phosphorous and protein loaded. Overall, the interactions between these nutrients with neutral, MetS-inducing(Reference Bian, Gao and Zhang25,Reference Lim, Shin and Yeom39) and MetS-protecting effects(Reference Chen, Chen and Wang40,Reference Wei, Liu and Lin41) resulted in a non-significant association between NP1 and MetS. This was the case for NP3 that was not significantly associated with MetS. NP3 was rich in carbohydrate and refined sugar, and poor in vitamin E, MUFA, PUFA and SFA resulting in, we labelled it as, a high-carbohydrate/low-fat NP.
In the current study, we found a significant positive association between NP2 and odds of MetS. This pattern was characterised by the high intakes of Ca; K; vitamins B2, B12, A, D, K and C; SFA; dietary cholesterol and TFA. We labelled it as a semi-animal NP because the majority of nutrients in this pattern are present in animal-based foods. However, K and vitamins C and K are mostly available in plant-based foods. Dietary intakes of SFA, TFA and dietary cholesterol have been positively related to MetS in the previous studies(Reference Nybacka, Hellstrom and Hirschberg42,Reference Guo, Miura and Turin43) . In contrast, there is evidence indicating an inverse association between Ca; K and vitamins B12, B2, C and D with MetS(Reference Mansouri, Abasi and Nasiri6,Reference Wieder-Huszla, Jurczak and Szkup44–Reference Li, Gueant-Rodriguez and Quilliot47) . In regard to Ca and MetS, there is a controversy; some studies presented a protective association, while others failed to find a significant association between dietary Ca intake and MetS(Reference Gomes, Costa and Alfenas48,Reference Shin, Choi and Kim49) . The combination of nutrients with neutral (vitamins A and K), MetS-protecting (Ca, K and vitamins B12, B2, C and D) and MetS-inducing (SFA, TFA and dietary cholesterol) effects in NP2 made our interpretation complicated. However, the overall effect of this pattern, by considering the interactions between nutrients, increased the odds of MetS. It should be kept in mind that patients with MetS might change their diet to alleviate the components of MetS, and this issue might be a reason for the presence of MetS-protecting nutrients in this pattern.
Several mechanisms have been reported for the role of nutrients in MetS. Saturated fat intake increases the NEFA concentration in serum and induces insulin resistance as the main risk factor for MetS(Reference Yang, Chen and Hsu50). Dietary intake of TFA contributes to increased risk of dyslipidaemia, hypertension and consequently MetS(Reference Ooi, Watts and Ng51,Reference Mazidi, Wong and Katsiki52) . Findings from experimental studies revealed that animal protein intake was positively associated with the low-grade inflammation which is a known risk factor for all components of MetS including abdominal obesity, hypertension, dyslipidaemia and hyperglycaemia(Reference Mu, VanHoosier and Elks53,Reference Tak, Ahn and Kim54) .
The current study has several strengths. The current study was the first comprehensive study with a large sample size to examine the association between NP and MetS in the Middle East. We controlled for a wide range of potential confounders. However, the potential effects from residual confounders cannot be excluded. We collected questionnaire-based information via face-to-face interviews for increasing the accuracy of data. However, some limitations should be considered when interpreting our findings. The nature of our study was cross-sectional, and therefore we cannot confer a causal link between NP and MetS because of the transposition of exposure and outcome. Further studies with prospective designs are required to confirm our findings. In the current study, patients with MetS might have changed their diet to manage lipid profile and blood pressure or to lose weight. However, this confounding effect would tend to attenuate the effect sizes; therefore, the true estimates may be even stronger than those obtained. In addition, like other epidemiological studies, the existence of measurement error and misclassification of study participants cannot be excluded in the current study as well. However, we used a validated FFQ for the assessment of dietary intakes.
In conclusion, we found that adherence to a semi-animal NP characterised by the high intakes of Ca; K; vitamins B2, B12, A, D, K and C; SFA; dietary cholesterol and TFA was positively associated with MetS. Further studies, particularly those with a prospective design, are required to confirm our findings.
Acknowledgements
Acknowledgements. The authors wish to thank all participants and staffs of the PERSIAN cohort and the Shahedieh cohort study, for their great collaboration. The authors also acknowledge the Shahid Sadoughi University of Medical Sciences, Yazd, Iran, for its support. Financial support: The current study was supported by the Shahid Sadoughi University of Medical Sciences, Yazd, Iran. A.S.A. was supported by the Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran (code: 41193). Conflict of interest: The authors declare that there is no conflict of interest in the current study. Authorship: H.M. contributed to the concept and study design. O.S. and A.S.A. contributed to the manuscript drafting. O.S. performed the statistical analyses. H.M. contributed to the manuscript editing. A.S.H. contributed to the English language editing and manuscript drafting. All authors read and approved the final manuscript. All authors declare that the content has not been published elsewhere. Ethical standards disclosure: The Shahedieh cohort study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Bioethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Written informed consent was obtained from all subjects. Transparency declaration: The lead author affirms that this manuscript is an honest, accurate and transparent account of the study being reported. The reporting of the current work is compliant with STROBE (Strengthening the reporting of observational studies in epidemiology) guidelines. The lead author affirms that no important aspects of the study have been omitted and any discrepancies from the study have been explained.
