Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-25T21:11:55.592Z Has data issue: false hasContentIssue false

The association between low-carbohydrate diet score and metabolic syndrome among Iranian adults

Published online by Cambridge University Press:  23 July 2021

Zohreh Sadat Sangsefidi
Affiliation:
Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Department of Nutrition and Public Health, School of Public Health, North Khorasan University of Medical Sciences, Bojnurd, Iran
Elnaz Lorzadeh
Affiliation:
Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Azadeh Nadjarzadeh
Affiliation:
Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Masoud Mirzaei
Affiliation:
Yazd Cardiovascular Research Centre, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Mahdieh Hosseinzadeh*
Affiliation:
Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Objective:

Assessing the relationship between low-carbohydrate diet (LCD) score and metabolic syndrome (Mets) in Iranian adults.

Design:

Cross-sectional study

Setting:

Yazd Health Study and Taghzieh Mardom-e-Yazd study.

Participants:

Data of 2074 participants were used. Dietary intakes were assessed by a validated semi-quantitative FFQ. LCD score was calculated for each person by summing up the assigned scores to deciles of energy percentages from macronutrients. Mets was evaluated using National Cholesterol Education Program Adult Treatment Panel III. Eventually, association between LCD score and Mets was examined using logistic regression.

Results:

Total Mets prevalence was approximately 40·5 %. After adjustment for confounders, subjects in the higher quartile of LCD score had a significant lower chance of Mets than lower quartile among all participants (Q4 v. Q1: OR: 0·68, 95 % CI (0·50, 0·92)) and separately in men (Q4 v. Q1: OR: 0·54, 95 % CI (0·34, 0·86)) and women (Q2 v. Q1: OR: 0·53, 95 % CI (0·34, 0·82)). Furthermore, more LCD adherence in men reduced abdominal obesity by 47 % (Q3 v. Q1: OR: 0·53, 95 % CI (0·28, 0·99)). A significant inverse relation was also observed between low HDL cholesterol and LCD score in all participants (Q4 versus Q1 OR: 0·74, 95% CI: 0·56–0·99) and separately in men (Q4 versus Q1 OR: 0·63, 95% CI: 0·40–0·98).

Conclusions:

More adherence to LCD might be related to lower chance of Mets and some of its components such as low HDL-cholesterol and abdominal obesity specially in men. Further studies are required to confirm the findings.

Type
Research paper
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

Metabolic syndrome (Mets) clusters a number of metabolic complications which is characterised by obesity, hypertension, dyslipidemia, glucose intolerance and insulin resistance(Reference Shirani, Esmaillzadeh and Keshteli1,Reference Dandona, Aljada and Chaudhuri2) . The prevalence of Mets in the developed and developing countries is growing. It is reported that Mets is present in 40 % of the adult population in the USA(Reference Ford, Li and Zhao3) and less than 25 % in European nations(Reference Shirani and Azadbakhat4). Various modifiable factors such as diet can be effective in preventing and management of Mets(Reference Kimokoti and Brown5,Reference Reaven6) .

Carbohydrate is the main source of energy intake in the middle-eastern region including Iran and is being consumed through foods such as rice, potato and grains. Increasing in carbohydrate consumption is associated with the risks of CVD or other components of Mets by high intakes of energy and glycemic load(Reference Shirani and Azadbakhat4,Reference Mirmiran, Asghari and Farhadnejad7) . However, most of these outcomes are controversial, for instance in a study, sugar and sweetened beverage consumption did not have any significant effect on Mets(Reference Lorzadeh, Sangsefidi and Mirzaei8). Therefore, investigating the effect of macronutrients intake within a dietary pattern can help evaluate the association between diet and diseases(Reference Jafari-Maram, Daneshzad and Brett9,Reference Hite, Berkowitz and Berkowitz10) .

Dietary pattern assessment takes both the complexity and synergistic effect of the foods and nutrients that make up a diet into account(Reference Kant11Reference Millen, Quatromoni and Copenhafer13). Low-carbohydrate diet (LCD) is a diet with lower intakes of carbohydrate and higher consumption of fat and protein(Reference Jafari-Maram, Daneshzad and Brett9,Reference Hite, Berkowitz and Berkowitz10,Reference Sangsefidi, Salehi-Abarghouei and Sangsefidi14) . Studies suggest that higher carbohydrate intake is followed by lower HDL, hypertriglyceridemia and hyperinsulinemia(Reference Shirani and Azadbakhat4,Reference Hu, Mills and Yao15,Reference Volek, Sharman and Forsythe16) . Therefore, LCD may have protective effect against chronic diseases including MetS(Reference Mirmiran, Asghari and Farhadnejad7). Limited studies have been conducted assessing the relation between LCD and Mets, or its components and the outcomes are inconsistent(Reference Shirani, Esmaillzadeh and Keshteli1,Reference Mirmiran, Asghari and Farhadnejad7,Reference Ainsworth, Haskell and Whitt17Reference Hu, Mills and Yao21) . For example, it has been suggested in Shirani et al. study that LCD was not significantly associated with Mets in Iranian women(Reference Shirani, Esmaillzadeh and Keshteli1) which is similar to the results of a recent Korean study that observed no significant relationship between this dietary pattern and Mets(Reference Ha, Joung and Song22). Furthermore, another study among Tehranian adults by Mirmiran et al. reported that LCD may in fact be associated with decreased risk of Mets and its components(Reference Mirmiran, Asghari and Farhadnejad7). According to several studies, Iranians traditionally consume high portions of carbohydrate foods, especially refined grains (which have a high glycemic load and increase the energy intake) such as rice and potatoes or foods that consist of a lot of simple sugars; this high-carbohydrate diet could be the possible reason for the incidence or development of many cardiovascular risk factors(Reference Mirmiran, Asghari and Farhadnejad7,Reference Liu, Willett and Stampfer23Reference Mirmiran, Bahadoran and Delshad27) .

As a conclusion, considering the growth in Mets prevalence, limited investigations assessing the association between LCD and Mets and inconsistencies in the achieved outcomes, this study aims to investigate the association between LCD and Mets in a sample of adult population in Yazd city, Iran.

Materials and methods

Study population and data collection

We used Yazd Health (YaHS) and Taghzieh Mardom-e-Yazd (TAMIZ) studies data in the current cross-sectional survey. These population-based cohort studies have been conducted in a large sample of adults (20–69 years old) in Yazd city, Iran. Adults (n 10 000) from 200 clusters were randomly selected from Yazd population based on residential postal codes via cluster sampling method. Yazd Nutrition Survey (locally known as TAMYZ in Persian) has evaluated dietary and supplements intakes of participants of YaHS by a validated FFQ. More details of these studies have been previously published(Reference Mirzaei, Salehi-Abargouei and Mirzaei28). Information on socio-demographic characteristics, smoking status, history of chronic disease, biochemical and physical activity evaluations and dietary assessment was collected using a validated questionnaire. Furthermore, anthropometric examinations were performed. In the current study, out of 3443 available cases with data on dietary intakes, biochemical assessment and Mets, some individuals were excluded according to the following exclusion criteria: having under- or over-reporting (total daily energy intake less than 800 or higher than 6500 kcal)(29), pregnancy, following a special diet, having a history of chronic disease such as CVD, diabetes and cancer. Eventually, 2074 participants were entered in the present research. This study was approved by the logical Ethics Committee. Written informed consents were also taken from all subjects.

Dietary assessment

Dietary intakes were evaluated via a validated FFQ consisting of 178 food items which was a modified version of a previously validated 168-item FFQ(Reference Mirzaei, Salehi-Abargouei and Mirzaei28,Reference Esfahani, Asghari and Mirmiran30) . Ten additional questions relating to the consumption of Yazd-specific food items were added to the original 168-item FFQ(Reference Mirzaei, Salehi-Abargouei and Mirzaei28,Reference Esfahani, Asghari and Mirmiran30) . Frequency and usual amount of food items intake were answered by participants, and eventually amounts of consumptions were converted to grams based on the guidelines of household scales(Reference Ghaffarpour, Houshiar-Rad and Kianfar31).

Calculation of the low-carbohydrate diet score

For computing LCD score, first the participants were classified based on deciles of percentages of energy from carbohydrates, proteins and fats. For carbohydrate consumption, individuals in the lowest decile received 9 points, adults in the second decile received 8 points, and so on down to the subjects in the highest decile received 0 point. For fats and proteins intake, the assigned points to deciles were reversed so that individuals in the highest decile received 9 points and those in the lowest decile received 0 point. Finally, the assigned points to all macronutrients were summed up and LCD score was obtained. After calculation, LCD score ranged from 0 to 27 and the higher score showed more adherence to LCD dietary pattern (the lower carbohydrate intake and the higher protein and fat intake)(Reference Shirani, Esmaillzadeh and Keshteli1,Reference Jafari-Maram, Daneshzad and Brett9) . Eventually, participants were classified according to quartiles of LCD score.

Metabolic syndrome definition

Mets definition was based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III)(Reference Alberti, Eckel and Grundy32). Subjects who had at least three of following criteria were considered as those with Mets: serum TAG ≥150 mg/dl, serum HDL-cholesterol <40 mg/dl for men and HDL-cholesterol < 50 mg/dl for women, fasting blood glucose ≥ 100 mg/dl, blood pressure ≥130/85 mmHg, and waist circumference ≥102 cm for men and >88 cm for women.

Anthropometric measurements

Weight was measured by Omron BF511 portable digital scale with the accuracy of 0·1 kg. Height was measured in a standing position by a tape measure on a straight wall to the nearest centimetre according to the standard method. BMI was also calculated by dividing the body weight (kg) by the square of height (m).

Physical activity assessment

Physical activity was examined via the Persian translated short form of the International Physical Activity Questionnaire (IPAQ). Eventually, the physical activity levels presented as metabolic equivalent (MET)(33,Reference Moghaddam, Aghdam and Jafarabadi34) .

Statistical analysis

Statistical analysis was performed by Statistical Package for Social Sciences (SPSS Corp., version 18). KolmogorovSmirnov test was used for assessing the normality of data. For the description of data, frequency and percent or median and interquartile range were performed. Comparing categorical and continuous variables were conducted via χ 2 or KruskalWallis tests, respectively, based on the categories of LCD score.

Examination of relationship between adherence to LCD with Mets and its components among all participants and separately in men and women was performed by logistic regression analysis in various models. In the first model, we carried out the adjustments for history of chronic disease (hypercholesterolemia, brain disease, asthma, thyroid disorders, depression, Alzheimer’s disease, blood coagulation disorders, arthritis and osteoporosis; yes/no); age (20–29, 30–39, 40–49, 50–59 and 60–69 years); marital status (single, married, widowed or divorced); education level (illiterate, high school, diploma and associated diploma, bachelors, masters and PhD); smoking status (never smoker, current smoker and ex-smoker); total energy intake (continues, kcal/d) and physical activity level (continues, MET/min/week). Further adjustment was conducted for BMI (continues, kg/m2). P-value less than 0·05 was considered as statistical significant level.

Results

As demonstrated in Table 1, the general characteristics of participants across the quartiles of LCD scores are provided. Mets prevalence was estimated as 40·5 % among total population, and the prevalence across the quartiles of LCD was reported in Table 1. Those with higher LCD score were less likely to have Mets and more likely to be more active (P < 0·05). No other significant difference has been observed across the quartiles of LCD scores.

Table 1 General characteristics of participants according to quartiles of LCD score

LCD, low-carbohydrate diet; IQR, interquartile range.

* Comparisons were performed using χ 2 or KruskalWallis tests for categorical and continues variables, respectively.

P < 0·05 was considered as a significance level.

Data were presented as n (%) for categorical variables or median and IQR for continues variables.

Table 2 provides selected food groups and nutrient intake across the quartiles of LCD scores. Participants in the highest quartile of the LCD score had increased intakes of vegetables, legumes, dairy products, red meat, poultry, fish, eggs, nuts and nutrients such as protein and fat in comparison to the lowest quartiles (P < 0·05). A significant difference was found across the quartiles of LCD scores for consumption of fruit, whole and refined grains and carbohydrate (P < 0·05), while no significant difference was observed in terms of total energy intake.

Table 2 Dietary intakes of participants according to quartiles of LCD score

LCD, low-carbohydrate diet; IQR, interquartile range.

* Data were presented as median and IQR.

** Comparisons were performed using KruskalWallis test.

P < 0·05 was considered as a significance level.

LCD score and Mets and its components

The OR for developing Mets and its components according to quartiles of the LCD score is presented in Table 3. After adjustment for various confounders such as history of chronic disease, age, marital status, education level, smoking status, total energy intake and physical activity level, it was observed that individuals in the highest quartile of LCD score had 31 % significant lower chances of developing Mets than lowest quartile (OR: 0·69, 95 % CI (0·52, 0·92)). It remained significant even after further adjustment for BMI (OR: 0·68, 95 % CI (0·50, 0·92)). Similarly, the results based on the gender showed a significant decrease in odds of Mets in men in the highest quartiles of LCD score than lowest quartile (OR: 0·57, 95 % CI (0·37, 0·90)) after adjusting for potential confounders. Additional adjustment for BMI remained significant (OR: 0·54, 95 % CI (0·34, 0·86)). Moreover, in women, it was observed that the chance of Mets in the second quartile of LCD score was 41 % lower than those in the first quartile (OR: 0·59, 95 % CI (0·38, 0·90)) after adjustment for confounding variables. This association did not change after additional adjustment for BMI (OR: 0·53, 95 % CI (0·34, 0·86)). No other significant association has been found.

Table 3 Multivariable-adjusted OR for metabolic syndrome and its components across quartiles low-carbohydrate diet (LCD) score

Model 1: Adjusted for history of chronic disease, age, marital status, education level, smoking status, total energy intake and physical activity level (MET/min/week).

Model 2: Model 1 + BMI.

*P < 0·05 was considered as a significance level.

Higher LCD score in the third quartile in men reduced abdominal obesity significantly by 47 % after adjustment for all the confounders including BMI (OR: 0·53, 95 % CI (0·28, 0·99)). Furthermore, significant reduction in low HDL-cholesterol was observed in the highest quartile of LCD adherence compared to the first quartile in all participants (OR: 0·74, 95 % CI (0·56, 0·99)) and in men (OR: 0·63, 95 % CI (0·40, 0·98)) after adjusting for all the potential confounder variables.

Discussion

The present research showed a significant protective association between LCD score and odds of Mets after adjustment for potential confounding variables. Higher LCD score was also associated with lower chance of HDL-cholesterol in all participants and specifically men. Moreover, these health benefit effects were observed for abdominal obesity only in the male population.

Previous researches on the association of LCD score and the risk of Mets are limited(Reference Brunner, Wunsch and Marmot35Reference Kim and Jo37). Studies suggest that consumption of diets with high carbohydrate and less fat in patients with non-alcoholic fatty liver disease is associated with higher risk of Mets, obesity and type 2 diabetes in men(Reference Kang, Greenson and Omo36,Reference De Koning, Malik and Rimm38,Reference Liu, Manson and Stampfer39) . Furthermore, observational studies in Korean(Reference Park, Lee and Park40,Reference Song, Lee and Song41) , Japanese(Reference Nanri, Mizoue and Noda42) and Chinese populations(Reference Villegas, Liu and Gao43) show that high carbohydrate intake is associated with high levels of Mets and type 2 diabetes. In line with our results, a recent study among Iranian adults reported a significant association between LCD and Mets risk(Reference Mirmiran, Asghari and Farhadnejad7). However, in contrast to our study, a study by Eslamian et al. did not report any significant relation between LCD score and Mets(Reference Eslamian, Mirmiran and Asghari44), and also Shirani et al. did not find any significant association between scores of LCD and Mets among Iranian women(Reference Shirani, Esmaillzadeh and Keshteli1).

Our study found a significant inverse relation between LCD and abdominal obesity only in men. Similar to our results, one cross-sectional study by Jafari-Maram et al. in Iranian women showed no significant relationship between LCD and obesity(Reference Jafari-Maram, Daneshzad and Brett9). However, contrary to our finding, a meta-analysis reported that carbohydrate intake did not have any effect on the risk of obesity(Reference Sartorius, Sartorius and Madiba45). Mixed results regarding the association between LCD and obesity are also reported(Reference Hu and Bazzano46Reference Lagiou, Sandin and Weiderpass49) A limited number of studies have assessed the effects of LCD on abdominal fat loss and, therefore, the improvement in cardiovascular factors in both obese and none obese individuals(Reference Miyashita, Koide and Ohtsuka50). As suggested by Brinkworth et al., LCD can decrease abdominal fat mass by approximately 30 %(Reference Brinkworth, Noakes and Buckley51).

This study found a significant relationship between LCD score and OR of low HDL-cholesterol in all the participants and in men. Just like what has been mentioned above, previous investigations have shown controversial results. Several studies reported that despite a low-fat intake, a high-carbohydrate diet can positively associate with a low HDL-cholesterol level and an increased risk of Mets(Reference Choi, Song and Kim20,Reference Song, Lee and Song41) . These findings similar to our results are also in accordance with previous Randomized clinical trial that indicated an improvement in levels of HDL-cholesterol after the administration a LCD(Reference Hu, Mills and Yao15,Reference Mansoor, Vinknes and Veierød52) . Additionally, the recent Japanese(Reference Nakamura, Ueshima and Okuda53) and Korean studies(Reference Ha, Joung and Song22) have found that higher LCD scores had positive effects on HDL-cholesterol and dyslipidemia level, regardless of the food source. However, in contrast to our findings, in some studies that were conducted among Iranian adults, no significant association was reported between HDL-cholesterol and LCD score(Reference Shirani, Esmaillzadeh and Keshteli1,Reference Mirmiran, Asghari and Farhadnejad7) .

In the light of what has been mentioned above, multiple systematic review studies have shown that LCD score is negatively related to hypertension and low HDL-cholesterol(Reference Santos, Esteves and da Costa Pereira54,Reference Hu and Bazzano55) . Even though we did not observe any significant association between hypertension or any of the other components of Mets with LCD score in the present study which might be due to different study design (randomised clinical trial v. cross-sectional study), carbohydrate proportion and population characteristics, most of the studies included in these systematic reviews were conducted in Western countries in which a LCD is defined differently(Reference Hu, Mills and Yao15,Reference Mansoor, Vinknes and Veierød52) .

In Iranian population, consuming a large portion of food with carbohydrate base such as grains, rice and potatoes accompanied by food containing simple sugar is very common which contribute to the incident or development of the cardiovascular risk factors(Reference Liu, Willett and Stampfer23Reference Mirmiran, Bahadoran and Delshad27). In this study, LCD is defined as a relatively lower percentage of carbohydrate intake, accompanied by higher percentage of protein and fat intake, in which each macronutrient may affect the risk of Mets differently(33,Reference Moghaddam, Aghdam and Jafarabadi34) . Subjects with high scores of LCD consume more food groups such as vegetables, legumes, dairy products, red and white meat (poultry and fish), and eggs and generally have higher intakes of protein and fat. This could be another possible explanation for the null association outcomes, since the high protein portion which consists of healthy foods such as poultry and fish inversely associates with Mets. On the other hand, the high amount of red and processed meat consumption has direct effects on the incident and development of Mets(Reference Kim and Jo37,Reference Radhika, Van Dam and Sudha56,Reference Micha, Wallace and Mozaffarian57) and this can lead to neutral results. Therefore, our results are similar to Mediterranean dietary pattern which is rich in vegetables, fish, nuts and legumes and is indicated to reduce the risk of Mets(Reference Esposito, Kastorini and Panagiotakos58). In contrast, Western dietary pattern is characterised by the high consumption of carbohydrate such as sweet beverage and sugar that is majorly associated with greater odds of increased Mets components(Reference Amini, Esmaillzadeh and Shafaeizadeh59). Moreover, the present study reports that participants in high quartiles of LCD score consume less amount of carbohydrate (which consist of whole and refined grains and fruit) in general. This outcome is similar to the findings of multiple studies that suggest individuals with lower intake of simple sugars such as fructose are less susceptible to cardiometabolic disorders including dyslipidemia, insulin resistance, high BP and obesity(Reference Tappy and Lê60,Reference Sackner-Bernstein, Kanter and Kaul61) . We also observed that higher scores of LCD were associated with increased intakes of dairy products as well which has further been suggested to be associated with lower risk of Mets(Reference Azadbakht, Kimiagar and Mehrabi62). Overall, it seems that participants with higher scores of LCD are less prone to various chronic diseases such as Mets.

With what has been mentioned above, it is reported that Mets is highly carbohydrate-intolerant(Reference Volek and Feinman63). Over consumption of carbohydrate is related to high plasma levels of glucose and insulin which is followed by insulin resistance and symptoms of Mets. Since dietary carbohydrate not only serves as a source of energy but also serves as a control element whether directly through glucose or fructose or indirectly as an insulin signalling agent, its decrease can ameliorate markers of Mets more efficiently than a low-fat diet(Reference Feinman and Volek64). Low-fat and high-carbohydrate diet are reported to exacerbate Mets(Reference Paoli, Rubini and Volek65). Dietary fat has also a passive role in insulin resistance that contributes to down-regulation due to hyperinsulinemia(Reference Sears and Perry66). Low intake of dietary carbohydrates resulted in decreased carbohydrates-induced insulin and causes the impaired regulation goback to normal levels(Reference Feinman and Volek64). Furthermore, LCD diet may be generally associated with lower fructose intake. Fructose consumption has a major role in epidemics of obesity, Mets and type 2 diabetes and is known to be the cause of hypertension, de novo lipogenesis, hepatic insulin resistance and adiposity(Reference Lê and Tappy67Reference Barclay, Petocz and McMillan-Price70). Additionally, it is worth mentioning that high-carbohydrate diets and consumptions of foods with high glycemic index, especially fructose, can conduct rapid stimulation of lipogenesis, TAG accumulation, adipocyte hypertrophy and macrophage accumulation in adipose tissues that is associated with obesity(Reference Yang, Chung and Kim18,Reference Blades and Garg71) . High blood glucose, a result of high glycemic index foods consumption, is followed by an enhanced need to insulin secretion and consequently impairment in beta cells function and glucose metabolism which accompanied by counter-regulatory insulin secretion and then a number of metabolic disorders and chronic diseases such as Mets(Reference Barclay, Petocz and McMillan-Price70).

Several key strengths are needed to be taken into consideration. To the best of our knowledge, this is the first study that evaluated the association between LCD and Mets in a larger sample of Iranian adults with various confounders being controlled in different models. In the present study, a validated FFQ was also used which was based on a list of food items and list of specific foods that are commonly consumed in the study population. Moreover, dietary assessments were achieved by professional interviewers. Our study has several limitations: (i) the cross-sectional design does not show a causal relationship between LCD and Mets; therefore, more prospective studies are needed to be done to truly examine the effect of LCD on Mets in the Iranian population. Moreover, the subjects with Mets may have modified their diet towards improvement for reduction of disease complications due to cross-sectional nature of the study. However, we excluded the individuals with chronic disease history to decrease this issue; (ii) despite controlling for various confounders in our analyses, other confounding factors due to unknown or unmeasured confounders cannot be ignored; (iii) since dietary assessments in this study were based on FFQ, misclassification of study participants might be occurred; (iv) although socio-demographic characteristics of the population including age, gender, education, marital status and smoking status were evaluated, we did not assess economic status; and (v) it should be considered that the study participants were selected from municipal areas of Yazd city. Thus, the findings generalisation may be done with caution.

In conclusion, our findings suggest that adherence to LCD may be associated with lower chances of Mets and some of its component such as HDL-cholesterol levels and abdominal obesity in men. Further large-scale researches, especially cohort studies, are highly recommended to clarify the nature of the observed associations.

Acknowledgements

Acknowledgements: The authors thank Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran, to support this study. Financial support: This study supported by Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran (Number: 7943). Conflict of interest: The authors declare that they have no conflict of interest. Authorship: M.H., A.N. and M.M. designed the study. Z.S.S. and E.L. conducted the statistical analysis. Z.S.S. and E.L. wrote the draft of manuscript. M.H., A.N. and M.M. critically revised the manuscript and confirmed the final version of it to submit. All authors read the final version of the manuscript and approved it. Ethics of human subject participation: The research was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (Ethical approval code: IR.SSU.SPH.REC.1399.159, Date: 16 September 2020). Written informed consents were also obtained from all subjects.

References

Shirani, F, Esmaillzadeh, A, Keshteli, AH et al. (2015) Low-carbohydrate-diet score and metabolic syndrome: an epidemiologic study among Iranian women. Nutrition 31, 11241130.CrossRefGoogle ScholarPubMed
Dandona, P, Aljada, A, Chaudhuri, A et al. (2005) Metabolic syndrome: a comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation 111, 14481454.CrossRefGoogle ScholarPubMed
Ford, ES, Li, C & Zhao, G (2010) Prevalence and correlates of metabolic syndrome based on a harmonious definition among adults in the US. J Diabetes 2, 180193.CrossRefGoogle ScholarPubMed
Shirani, F & Azadbakhat, L (2011) The association between carbohydrate intake and metabolic syndrome. J Isfahan Med Sch 29, 111.Google Scholar
Kimokoti, R & Brown, L (2011) Dietary management of the metabolic syndrome. Clin Pharmacol Ther 90, 184187.CrossRefGoogle ScholarPubMed
Reaven, GM (2000) Diet and syndrome X. Curr Atheroscler Rep 2, 503507.CrossRefGoogle ScholarPubMed
Mirmiran, P, Asghari, G, Farhadnejad, H et al. (2017) Low carbohydrate diet is associated with reduced risk of metabolic syndrome in Tehranian adults. Int J Food Sci Nutr 68, 358365.CrossRefGoogle ScholarPubMed
Lorzadeh, E, Sangsefidi, ZS, Mirzaei, M et al. (2020) Dietary habits and their association with metabolic syndrome in a sample of Iranian adults: a population-based study. Food Sci Nutr 8, 62176225.CrossRefGoogle Scholar
Jafari-Maram, S, Daneshzad, E, Brett, NR et al. (2019) Association of low-carbohydrate diet score with overweight, obesity and cardiovascular disease risk factors: a cross-sectional study in Iranian women. J Cardiovasc Thorac Res 11, 216.CrossRefGoogle ScholarPubMed
Hite, AH, Berkowitz, VG & Berkowitz, K (2011) Low-carbohydrate diet review: shifting the paradigm. Nutr Clin Pract 26, 300308.CrossRefGoogle ScholarPubMed
Kant, AK (1996) Indexes of overall diet quality: a review. J Am Diet Assoc 96, 785791.CrossRefGoogle ScholarPubMed
Millen, BE, Quatromoni, PA, Pencina, M et al. (2005) Unique dietary patterns and chronic disease risk profiles of adult men: the Framingham nutrition studies. J Am Diet Assoc 105, 17231734.CrossRefGoogle ScholarPubMed
Millen, BE, Quatromoni, PA, Copenhafer, DL et al. (2001) Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham nutrition studies. J Am Diet Assoc 101, 187194.CrossRefGoogle ScholarPubMed
Sangsefidi, ZS, Salehi-Abarghouei, A, Sangsefidi, ZS et al. (2021) The relation between low carbohydrate diet score and psychological disorders among Iranian adults. Nutr Metabol 18, 19.CrossRefGoogle ScholarPubMed
Hu, T, Mills, KT, Yao, L et al. (2012) Effects of low-carbohydrate diets v. low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials. Am J Epidemiol 176, S44S54.CrossRefGoogle Scholar
Volek, JS, Sharman, MJ & Forsythe, CE (2005) Modification of lipoproteins by very low-carbohydrate diets. J Nutr 135, 13391342.CrossRefGoogle ScholarPubMed
Ainsworth, BE, Haskell, WL, Whitt, MC et al. (2000) Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 32, S498S504.CrossRefGoogle ScholarPubMed
Yang, EJ, Chung, HK, Kim, WY et al. (2003) Carbohydrate intake is associated with diet quality and risk factors for cardiovascular disease in US adults: NHANES III. J Am Coll Nutr 22, 7179.CrossRefGoogle ScholarPubMed
Silva, FM, Steemburgo, T, de Mello, VD et al. (2011) High dietary glycemic index and low fiber content are associated with metabolic syndrome in patients with type 2 diabetes. J Am Coll Nutr 30, 141148.CrossRefGoogle ScholarPubMed
Choi, H, Song, S, Kim, J et al. (2012) High carbohydrate intake was inversely associated with high-density lipoprotein cholesterol among Korean adults. Nutr Res 32, 100106.CrossRefGoogle ScholarPubMed
Hu, T, Mills, KT, Yao, L et al. (2012) Effects of low-carbohydrate diets v. low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials. Am J Epidemiol 176, S44S54.CrossRefGoogle Scholar
Ha, K, Joung, H & Song, Y (2018) Low-carbohydrate diet and the risk of metabolic syndrome in Korean adults. Nutr Metabol Cardiovasc Dis 28, 11221132.CrossRefGoogle ScholarPubMed
Liu, S, Willett, WC, Stampfer, MJ et al. (2000) A prospective study of dietary glycemic load, carbohydrate intake, and risk of coronary heart disease in US women. Am J Clin Nutr 71, 14551461.CrossRefGoogle ScholarPubMed
Khosravi-Boroujeni, H, Mohammadifard, N, Sarrafzadegan, N et al. (2012) Potato consumption and cardiovascular disease risk factors among Iranian population. Int J Food Sci Nutr 63, 913920.CrossRefGoogle ScholarPubMed
Hosseinpour-Niazi, S, Sohrab, G, Asghari, G et al. (2013) Dietary glycemic index, glycemic load, and cardiovascular disease risk factors: Tehran lipid and glucose study. Arch Iran Med 16, 401407.Google ScholarPubMed
Bahadoran, Z, Mirmiran, P, Delshad, H et al. (2014) White rice consumption is a risk factor for metabolic syndrome in Tehrani adults: a prospective approach in Tehran lipid and glucose study. Arch Iran Med 17, 435440.Google ScholarPubMed
Mirmiran, P, Bahadoran, Z, Delshad, H et al. (2014) Effects of energy-dense nutrient-poor snacks on the incidence of metabolic syndrome: a prospective approach in Tehran lipid and glucose study. Nutrition 30, 538543.CrossRefGoogle ScholarPubMed
Mirzaei, M, Salehi-Abargouei, A, Mirzaei, M et al. (2018) Cohort Profile: the Yazd Health Study (YaHS): a population-based study of adults aged 20–70 years (study design and baseline population data). Int J Epidemiol 47, 697h698h.CrossRefGoogle ScholarPubMed
Food & Nutrition Board (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington, DC: National Academy Press.Google Scholar
Esfahani, FH, Asghari, G, Mirmiran, P et al. (2010) Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran lipid and glucose study. J Epidemiol 20, 150158.CrossRefGoogle Scholar
Ghaffarpour, M, Houshiar-Rad, A & Kianfar, H (1999) The manual for household measures, cooking yields factors and edible portion of foods. Tehran Nashre Olume Keshavarzy 7, 213.Google Scholar
Alberti, KGMM, Eckel, RH, Grundy, SM et al. (2009) Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation 120, 16401645.CrossRefGoogle Scholar
Committee IR (2005) Guidelines for data processing and analysis of the International physical activity questionnaire (IPAQ)-short and long forms. http://wwwipaqkise/scoringpdf (accessed April 2004).Google Scholar
Moghaddam, MB, Aghdam, FB, Jafarabadi, MA et al. (2012) The Iranian version of international physical activity questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World Appl Sci J 18, 10731080.Google Scholar
Brunner, E, Wunsch, H & Marmot, M (2001) What is an optimal diet? Relationship of macronutrient intake to obesity, glucose tolerance, lipoprotein cholesterol levels and the metabolic syndrome in the Whitehall II study. Int J Obes 25, 4553.CrossRefGoogle ScholarPubMed
Kang, H, Greenson, JK, Omo, JT et al. (2006) Metabolic syndrome is associated with greater histologic severity, higher carbohydrate, and lower fat diet in patients with NAFLD. Am J Gastroenterol 101, 22472253.CrossRefGoogle ScholarPubMed
Kim, J & Jo, I (2011) Grains, vegetables, and fish dietary pattern is inversely associated with the risk of metabolic syndrome in South Korean adults. J Am Diet Assoc 111, 11411149.CrossRefGoogle ScholarPubMed
De Koning, L, Malik, VS, Rimm, EB et al. (2011) Sugar-sweetened and artificially sweetened beverage consumption and risk of type 2 diabetes in men. Am J Clin Nutr 93, 13211327.CrossRefGoogle ScholarPubMed
Liu, S, Manson, J, Stampfer, MJ et al. (2000) A prospective study of whole-grain intake and risk of type 2 diabetes mellitus in US women. Am J Public Health 90, 1409.Google ScholarPubMed
Park, S-H, Lee, K-S & Park, H-Y (2010) Dietary carbohydrate intake is associated with cardiovascular disease risk in Korean: analysis of the third Korea National Health and Nutrition Examination Survey (KNHANES III). Int J Cardiol 139, 234240.CrossRefGoogle ScholarPubMed
Song, S, Lee, JE, Song, WO et al. (2014) Carbohydrate intake and refined-grain consumption are associated with metabolic syndrome in the Korean adult population. J Acad Nutr Diet 114, 5462.CrossRefGoogle ScholarPubMed
Nanri, A, Mizoue, T, Noda, M et al. (2010) Rice intake and type 2 diabetes in Japanese men and women: the Japan public health center–based prospective study. Am J Clin Nutr 92, 14681477.CrossRefGoogle ScholarPubMed
Villegas, R, Liu, S, Gao, Y-T et al. (2007) Prospective study of dietary carbohydrates, glycemic index, glycemic load, and incidence of type 2 diabetes mellitus in middle-aged Chinese women. Arch Intern Med 167, 23102316.CrossRefGoogle ScholarPubMed
Eslamian, G, Mirmiran, P, Asghari, G et al. (2014) Low carbohydrate diet score does not predict metabolic syndrome in children and adolescents: Tehran lipid and glucose study. Arch Iran Med 17, 417422.Google Scholar
Sartorius, K, Sartorius, B, Madiba, TE et al. (2018) Does high-carbohydrate intake lead to increased risk of obesity? A systematic review and meta-analysis. BMJ Open 8, e018449.CrossRefGoogle ScholarPubMed
Hu, T & Bazzano, L (2014) The low-carbohydrate diet and cardiovascular risk factors: evidence from epidemiologic studies. Nutr Metabol Cardiovasc Dis 24, 337343.CrossRefGoogle ScholarPubMed
Foster, GD, Wyatt, HR, Hill, JO et al. (2003) A randomized trial of a low-carbohydrate diet for obesity. N Engl J Med 348, 20822090.CrossRefGoogle ScholarPubMed
Trichopoulou, A, Psaltopoulou, T, Orfanos, P et al. (2007) Low-carbohydrate–high-protein diet and long-term survival in a general population cohort. Eur J Clin Nutr 61, 575581.CrossRefGoogle Scholar
Lagiou, P, Sandin, S, Weiderpass, E et al. (2007) Low carbohydrate–high protein diet and mortality in a cohort of Swedish women. J Intern Med 261, 366374.CrossRefGoogle Scholar
Miyashita, Y, Koide, N, Ohtsuka, M et al. (2004) Beneficial effect of low carbohydrate in low calorie diets on visceral fat reduction in type 2 diabetic patients with obesity. Diabetes Res Clin Pract 65, 235241.CrossRefGoogle ScholarPubMed
Brinkworth, GD, Noakes, M, Buckley, JD et al. (2009) Long-term effects of a very-low-carbohydrate weight loss diet compared with an isocaloric low-fat diet after 12 mo. Am J Clin Nutr 90, 2332.CrossRefGoogle ScholarPubMed
Mansoor, N, Vinknes, KJ, Veierød, MB et al. (2016) Effects of low-carbohydrate diets v. low-fat diets on body weight and cardiovascular risk factors: a meta-analysis of randomised controlled trials. Br J Nutr 115, 466479.CrossRefGoogle ScholarPubMed
Nakamura, Y, Ueshima, H, Okuda, N et al. (2016) Relationship of three different types of low-carbohydrate diet to cardiometabolic risk factors in a Japanese population: the INTERMAP/INTERLIPID study. Eur J Nutr 55, 15151524.CrossRefGoogle Scholar
Santos, FL, Esteves, SS, da Costa Pereira, A et al. (2012) Systematic review and meta-analysis of clinical trials of the effects of low carbohydrate diets on cardiovascular risk factors. Obes Rev 13, 10481066.CrossRefGoogle ScholarPubMed
Hu, T & Bazzano, LA (2014) The low-carbohydrate diet and cardiovascular risk factors: evidence from epidemiologic studies. Nutr Metabol Cardiovasc Dis 24, 337343.CrossRefGoogle ScholarPubMed
Radhika, G, Van Dam, RM, Sudha, V et al. (2009) Refined grain consumption and the metabolic syndrome in urban Asian Indians (Chennai urban rural epidemiology study 57). Metabolism 58, 675681.CrossRefGoogle ScholarPubMed
Micha, R, Wallace, SK & Mozaffarian, D (2010) Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. Circulation 121, 22712283.CrossRefGoogle ScholarPubMed
Esposito, K, Kastorini, C-M, Panagiotakos, DB et al. (2013) Mediterranean diet and metabolic syndrome: an updated systematic review. Rev Endocr Metabol Disord 14, 255263.CrossRefGoogle ScholarPubMed
Amini, M, Esmaillzadeh, A, Shafaeizadeh, S et al. (2010) Relationship between major dietary patterns and metabolic syndrome among individuals with impaired glucose tolerance. Nutrition 26, 986992.CrossRefGoogle ScholarPubMed
Tappy, L & , K-A (2010) Metabolic effects of fructose and the worldwide increase in obesity. Physiol Rev 90, 2346.CrossRefGoogle ScholarPubMed
Sackner-Bernstein, J, Kanter, D & Kaul, S (2015) Dietary intervention for overweight and obese adults: comparison of low-carbohydrate and low-fat diets. A meta-analysis. PloS One 10, e0139817.CrossRefGoogle ScholarPubMed
Azadbakht, L, Kimiagar, M, Mehrabi, Y et al. (2007) Soy inclusion in the diet improves features of the metabolic syndrome: a randomized crossover study in postmenopausal women. Am J Clin Nutr 85, 735741.CrossRefGoogle ScholarPubMed
Volek, JS & Feinman, RD (2005) Carbohydrate restriction improves the features of metabolic syndrome. Metabolic syndrome may be defined by the response to carbohydrate restriction. Nutr Metabol 2, 31.CrossRefGoogle ScholarPubMed
Feinman, RD & Volek, JS (2008) Carbohydrate restriction as the default treatment for type 2 diabetes and metabolic syndrome. Scand Cardiovasc J 42, 256263.CrossRefGoogle ScholarPubMed
Paoli, A, Rubini, A, Volek, JS et al. (2013) Beyond weight loss: a review of the therapeutic uses of very-low-carbohydrate (ketogenic) diets. Eur J Clin Nutr 67, 789796.CrossRefGoogle ScholarPubMed
Sears, B & Perry, M (2015) The role of fatty acids in insulin resistance. Lipids Health Dis 14, 121121.CrossRefGoogle ScholarPubMed
, K-A & Tappy, L (2006) Metabolic effects of fructose. Curr Opin Clin Nutr Metabol Care 9, 469475.CrossRefGoogle ScholarPubMed
Rutledge, AC & Adeli, K (2007) Fructose and the metabolic syndrome: pathophysiology and molecular mechanisms. Nutr Rev 65, S13S23.CrossRefGoogle ScholarPubMed
Zammit, VA (2002) Insulin stimulation of hepatic triacylglycerol secretion in the insulin-replete state: implications for the etiology of peripheral insulin resistance. Ann N Y Acad Sci 967, 5265.CrossRefGoogle ScholarPubMed
Barclay, AW, Petocz, P, McMillan-Price, J et al. (2008) Glycemic index, glycemic load, and chronic disease risk—a meta-analysis of observational studies. Am J Clin Nutr 87, 627637.CrossRefGoogle ScholarPubMed
Blades, B & Garg, A (1995) Mechanisms of increase in plasma triacylglycerol concentrations as a result of high carbohydrate intakes in patients with non-insulin-dependent diabetes mellitus. Am J Clin Nutr 62, 9961002.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 General characteristics of participants according to quartiles of LCD score

Figure 1

Table 2 Dietary intakes of participants according to quartiles of LCD score

Figure 2

Table 3 Multivariable-adjusted OR for metabolic syndrome and its components across quartiles low-carbohydrate diet (LCD) score