Dietary diversity and Its Association and Metabolic Syndrome: Rafsanjan Cohort Study

doi:10.21203/rs.3.rs-4890947/v1

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Dietary diversity and Its Association and Metabolic Syndrome: Rafsanjan Cohort Study

https://doi.org/10.21203/rs.3.rs-4890947/v1

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The rapid increase in metabolic syndrome (MetS) as a major public health problem may be attributed to changes in population behavior patterns in modern society, including nutritional habits and physical activity. Therefore, this study was conducted to investigate the association between dietary diversity score (DDS) and metabolic syndrome in adults. In this cross-sectional study, 9,990 participants aged 35–70 years from the Rafsanjan Cohort Study (RCS) were included. Demographic, medical, and habitual histories were collected. DDS was calculated using a food frequency questionnaire (FFQ). All analyses were performed using Stata 14, with p-values < 0.05. In total, 9,990 subjects (46.6% men and 53.4% women) with a mean age of 35–70 years were included in this study. Based on the DDS, subjects were divided into four groups (quartiles). The multivariate-adjusted model showed that the risk of MetS increased by 18% in the third quartile and by 25% in the fourth quartile (OR: 1.18; 98% CI: 1.02–1.36 and OR: 1.25; 98% CI: 1.06–1.48, respectively). Additionally, an increased risk of high serum triglyceride levels was observed in the third quartile (OR: 1.19; 98% CI: 1.05–1.35). The results suggest that there is a relationship between DDS and metabolic syndrome, although this relationship changed after adjusting for confounders.

Health sciences/Diseases

Health sciences/Medical research

Dietary diversity

Metabolic Syndrome

Rafsanjan Cohort Study (RCS)

Metabolic syndrome (MetS), a clustering of risk factors (central obesity, insulin resistance dyslipidemia and hypertension),¹ is a well-known condition in the causal pathway of cardiovascular disease (CVD). MetS has also been associated with a higher risk of other chronic diseases, such as cancer² and neurodegenerative diseases³. In recent years, the prevalence of MetS has increased worldwide to the point that presently it is considered as a major public health problem⁴.

The rapid increase in MetS prevalence may be attributed to changes in population behavior patterns in modern society⁵. Previous reports have indicated that lifestyle including nutritional habits and physical activity are considered as the most important independent risk factors for MetS .Among these patterns, diet plays an essential and independent role in the incidence and development of MetS^6,7. The dietary diversity score (DDS) is an indicator for assessing diet by counting the number of major food groups consumed, reflecting overall dietary quality compared with the evaluation of single foods or nutrients^8,9. Epidemiological studies have indicated that a higher DDS is intimately related to a healthier diet with adequate intake of all food groups, which may provide a wide range of macro- and micro nutrients^10,11.

Especially for the Western-style diet high in fat and animal-based foods, a meta-analysis revealed that “Western” dietary patterns were significantly associated with increased MetS risk¹². Over the past few decades, there has been a significant shift in Chinese dietary patterns and behaviors, with a move from a predominantly plant-based diet to a Western-style diet. The positive association between the Western dietary pattern and MetS could be attributed in part to the lower amounts of beneficial foods and nutrients that this pattern contains. Higher consumption of refined grains^13,14. Some studies suggested that dietary diversity helps to consume more energy and increases the incidence of metabolic syndrome, but other studies indicated that dietary diversity is associated with adequate nutrient intake and reduces the incidence of disease¹⁵. Therefore, this study was conducted to investigate the association of dietary diversity score with metabolic syndrome in adults in Rafsanjan.

Study population

Participants aged 35–70 years residing at Rafsanjan, a city in southeast Iran, have been selected to enroll in this cross-sectional study. The sample size was 9990 participants in Rafsanjan Cohort Study (RCS)¹⁶, an arm of the Prospective Epidemiological Research Studies in Iran (PERSIAN) [14]. All of participants had completed questionnaires of demographical, medical, and habitual history. The protocol and questionnaires of this cross-sectional study were designed following the Persian cohort study protocols and under the supervision of the Iranian Ministry of Health and Medical Education (IMHME)¹⁷. Written informed consent was obtained from the participants. In addition, they were approved by the Ethics Committee of Rafsanjan University of Medical Sciences with the Ethical code of IR.RUMS.REC.1400.111. All methods were carried out following the relevant guidelines and regulations.

Data collection

All of selected participants had filled out the questionnaires approved by Persian Cohort study under a standardized ¹⁷. The questionnaires consisted of questions on demographical status (age, gender, education years and socio-economic status), opium and alcohol consumption, smoking, physical activity, blood pressure, body mass index (BMI), nutritional status, laboratory test, and history of disease. Blood pressure was measured twice for each participant from each arm. Anthropometric parameters including height, weight and waist circumference were measured according to a standard protocol¹⁶.

Blood samples were collected after 12 h fasting from all participants. Fasting blood sugar (FBS), triglyceride (TG), and high-density lipoprotein cholesterol (HDL) were measured using Auto Analyzer (BT 1500 Biotechnica, Italy). The CBC assay was also performed using alpha cell counter (Nihon Kohden, Tokyo, Japan). All experiments were performed in Rafsanjan cohort laboratory. Physical activity was measured by 22 questions about total hours reported for activities in 24 h and mentioned as Metabolic Equivalent of Task (MET) calculated for 24 h.¹⁷.

The nutritional status was measured for any subset of variables from the food frequency questioner (FFQ). The socio-economic status (Wealth score index: WSI) was estimated by multiple correspondence analysis (MCA) of the economic variables. The metabolic syndrome prevalence was evaluated in participants according to Adult Treatment Panel III (ATP-III). Based on NCEP-ATP III, metabolic syndrome was defined as the presence of three or more of the following components:

(1) abdominal adiposity (WC ≥ 102 cm in men and ≥ 88 cm in women); (2) low serum HDL-Cholesterol (< 40 mg/dl for men and < 50 mg/dl for women); (3) high serum triglyceride levels (≥ 150 mg/dl); (4) Elevated blood pressure (≥ 130/85 mmHg); (5) abnormal glucose homeostasis (fasting plasma glucose level ≥ 100 mg/dl)¹⁸.

Using the 143-item validated FFQ mentioned above, we calculated an energy-adjusted DD score (DDS)^19,20. It's included five food groups: Vegetables, fruits, cereals, dairy products and protein food groups (legumes, meat, fish, eggs and nuts), based on the food groups. According to Kant’s paper (1995): Foods were grouped into the different groups based on similarities in nutrient composition and uses in the diet. The dairy group was assigned all milk and milk products. The meat group included both animal and plant protein sources. The grain group included all grain products except cakes, pies, cookies and pastries. The fruit group included all fresh, canned, frozen, dried fruits, fruit juices, and fruit drinks. The vegetable group included all raw, cooked, frozen, and canned vegetables. Food mixtures containing foods from various food groups, for example, mixed dishes with meat, grain, dairy and vegetables such as lasagna were assigned to all the relevant food groups. Participants had to consume at least half a unit of each food group in a day to be considered a "consumer". A maximum score of 2 is given to each of the five groups, so that each participant receives a score from 0 to 10. To calculate the score of each group, the number of used subgroups is divided by the total number of subgroups in each main group and then multiplied by 2. The total scores of the five main groups were recorded as the total score

Statistical Analysis

To describe the data, frequency (percentage) and mean ± standard deviation was used for categorical variables and quantitative variables, respectively. The individuals’ baseline characteristics were compared between the groups of our study (quartiles of DDS) using a chi-square test (χ²) and a one-way analysis of variance for categorical and continuous variables, respectively. Cut-points for quartiles of DDS were calculated and subjects were categorized based on quartile cut-points: 1st: ≤1.43; 2nd = 1.44–1.89; 3rd = 1.90–2.39; and 4th = ≥ 2.40. In addition, we used binary logistic analysis to determine the odds ratios (ORs) and the corresponding 95% confidence intervals (CI) for the relationship between MetS and components of metabolic syndrome with quartile categories of dietary diversity score. Besides, we used crude and adjusted models in the regression analysis. Potential confounding parameters were recognized based on subject matter knowledge and relevant epidemiological literature. Next, they were entered into the models sequentially according to their hypothesized strengths of association with MetS and dietary diversity score. To reach this goal, separate models at bivariate level were run to obtain variables associated with MetS and components of metabolic syndrome. Afterwards, variables with a p-value < 0.2 were considered for Multivariate analysis. The baseline (crude) model was stratified based on quartile categories of dietary diversity score. The adjusted models 1 were adjusted for confounding variables, age, gender, education, WSI, smoking, Alcohol and opium consumption, physical activity, total energy intake, and total fat. The adjusted models 2 were adjusted for confounding variables in adjusted model 1 and BMI. All analyses were performed in Stata 14. All p-values were two-sided and p-values < 0.05 and 95% confidence intervals considered statistically significant.

A total of 9715 subjects who completed the nutrition questionnaire were included in this study. Based on the DDS diet, subjects were divided into 4 groups (quartiles). The admission characteristics of the four groups are described in Table 1. The mean and standard deviation age of participants in the first group (51.05 ± 9.74) was higher than the other three groups and this difference between them was significant (P < 0.001). The highest percentage of participants in the first and second groups were men and in the third and fourth groups were women.

The average education, BMI and WSI were significantly lower in the first and second groups than in the other groups. The highest percentage of obese people were in the third and fourth groups, and the highest percentage of normal people was in the first group, and this difference was significant (P < 0.001).

The highest average waist circumference was related to subjects of group 4 (96.60 ± 10.96) and this average had a significant difference between the groups (P < 0.001). Among the participants who consumed alcohol and cigarettes, the highest percentage of subjects were in the third and fourth groups (P < 0.001). The prevalence of metabolic syndrome in individuals who were in the first and second groups was significantly higher than healthy people. On the other hand, the mean energy and total fat intake in people of groups 3 and 4 had increased significantly.

Table 1

Characteristics of the participants of the RCS by quartiles of DDS.
Characteristics	Quartile categories of DDS
Characteristics	1 (n = 2505)	2 (n = 2478)	3 (n = 2410)	4 (n = 2322)	P-value
Age- yr. n (%)					< 0.001
35–45	835(23.25)	896(24.95)	914(25.45)	946(26.34)
46–55	755(25.24)	757(25.31)	776(25.94)	703(25.50)
≥ 56	915(29.21)	825(26.33)	720(22.98)	673(21.48)
Mean ± SD	51.05 ± 9.74	50.15 ± 9.53	49.62 ± 9.45	49.09 ± 9.42	< 0.001
Gender-n (%)					< 0.001
Male	875(19.56)	1057(23.63)	1210(27.05)	1332(29.77)
Female	1630(31.10)	1421(27.11)	1200(22.90)	990(18.89)
Education-n (%)					< 0.001
≤ 5 years	1259(36.75)	932(27.20)	752(21.95)	483(14.10)
6–12 years	1016(21.64)	1196(2547)	1205(25.66)	1279(27.24)
≥ 13 years	2299(14.42)	350(22.04)	451(28.40)	558(35.14)
Mean ± SD	6.73 ± 4.96	8.14 ± 4.93	9.07 ± 4.96	10.20 ± 4.73	< 0.001
BMI-n (%)					< 0.001
< 25	810(28.92)	721(25.74)	683(24.38)	587(20.96)
25-29.99	9709(24.23)	1061(26.51)	999(24.96)	973(24.31)
≥ 30	723(24.89)	695(23.92)	726(24.99)	761(26.20)
Mean ± SD	27.46 ± 5.07	27.75 ± 4.91	27.84 ± 4.94	28.18 ± 4.66	< 0.001
WSI					< 0.001
Mean ± SD	-0.371 ± 1.021	-0.045 ± 0.964	0.115 ± 0.928	0.307 ± 0.948
Physical activity					0.104
Mean ± SD	38.49 ± 6.13	38.87 ± 6.19	38.83 ± 6.63	38.86 ± 6.17
WC (cm)					< 0.001
Mean ± SD	95.19 ± 11.92	95.66 ± 11.19	69.09 ± 11.72	96.60 ± 10.96
Low HDL-cholesterol-n (%)					0.037
No	2205(25.34)	2223(25.55)	2178(25.03)	2095(24.08)
Yes	287(29.47)	246(25.26)	223(22.90)	218(22.38)
Serum triglyceride (mg/dl)-n (%)					0.077
< 150	1371(26.82)	1293(25.29)	1233(24.12)	1215(23.77)
≥ 150	1121(24.57)	1176(25.78)	1167(25.58)	1098(24.17)
High serum triglyceride level-n (%)					0.075
No	1365(26.81)	1287(25.28)	1229(24.14)	1210(23.77)
Yes	1127(24.9)	1182(25.79)	1171(25.55)	1103(24.07)
Fasting blood glucose (mg/dl)-n (%)					0.766
< 100	1063(25.23)	1085(25.75)	1050(24.92)	1016(24.11)
≥ 100	1429(26.17)	1383(25.33)	1351(24.74)	1297(23.75)
Alcohol consumption-n (%)					< 0.001
No	2324(26.65)	2245(25.74)	2144(24.58)	2008(23.02)
Yes	169(18.04)	223(23.80)	252(26.89)	293(31.27)
Opium consumption-n (%)					0.074
No	1955(26.42)	1883(25.44)	1805(24.39)	1758(23.75)
yes	538(23.84)	585(25.92)	591(26.19)	543(24.06)
Cigarette smoking-n (%)					0.001
NO	1917(26.58)	1871(25.95)	1758(24.38)	1665(23.09)
Yes	576(23.54)	597(24.40)	638(26.07)	636(25.99)
Elevated blood pressure -n (%)					0.528
Normal	2105(25.67)	2113(25.77)	2020(24.63)	1962(23.93)
abnormal	391(26.54)	357(24.24)	379(25.73)	346(23.49)
Hypertension-n (%)					0.008
No	1717(25)	1739(25.32)	1728925.16)	1684(24.52)
yes	779(27.77)	731(26.06)	671(23.92)	624(22.25)
Diabetes-n (%)					0.589
No	1014(25.06)	1046(25.85)	1009(24.94)	977(24.15)
yes	1476(26.29)	1420(25.29)	1390(24.76)	1329(23.67)
Metabolic syndrome-n (%)					< 0.001
No	1546(24.52)	1585(25.14)	1593(25.27)	1580(25.06)
yes	938(28.10)	877(26.27)	799(23.94)	724(21.69)
Nutrients
Total energy (kcal/day)					< 0.001
Mean ± SD	1511.16 ± 442.35	1859.70 ± 476.92	2131.23 ± 526.61	25.7.84 ± 562.04
Total lipid Fat (gr/day)					< 0.001
Mean ± SD	38.06 ± 14.28	49.75 ± 16.10	59.10 ± 17.50	71.96 ± 19.96
Quartile cut-points of DDS are as follows: 1st :≤1.43; 2nd = 1.44–1.89; 3rd = 1.90–2.39; and 4th = ≥ 2.40. Abbreviations: Wealth score index (WSI); Body mass index (BMI); Triglyceride (TG); High density lipoprotein (HDL); and Waist circumference (WC).

Table 2

Food consumption according to dietary diversity score. Food consumption according to DDS was shown in Table 2. In all participants, a higher DDS score was associated with higher consumption of grains, meat, fruits, vegetables and dairy (p < 0.001).
Food Consumption (g/1000 kcal)	Quartile categories of DDS
Food Consumption (g/1000 kcal)	1 (n = 2505)	2 (n = 2478)	3 (n = 2410)	4 (n = 2322)	P-Trend
Grains	443.41 ± 3.96	503.50 ± 4.17	561.04 ± 16.19	588.55 ± 5.21	< 0.001
Meat	86.03 ± 0.83	116.53 ± 0.98	140.56 ± 1.13	181.42 ± 1.55	< 0.001
Fruits	174.17 ± 2.94	261.45 ± 3.22	331.19 ± 3.83	454.06 ± 5.05	< 0.001
Vegetables	214.06 ± 2.74	292.41 ± 2.37	353.80 ± 2.85	472.01 ± 4.30	< 0.001
Dairy	152.75 ± 2.07	235.25 ± 7.28	297.32 ± 3.00	421.17 ± 4.96	< 0.001
Values were expressed as the mean ± SD.

Table 3

Multivariate-adjusted odds ratio and 95% confidence intervals for having metabolic syndrome and its features by quartile categories of dietary diversity score (DDS).
Risk factors	Quartile categories of dietary diversity score
Risk factors	1 (n = 2505)	2 (n = 2478)	3 (n = 2410)	4 (n = 2322)
Metabolic syndrome
Crude Model	1	0.91(0.81–1.02)	0.83(0.74–0.93)	0.76(0.67–0.85)
Adjusted Model 1	1	1.11(0.98–1.27)	1.18(1.02–1.36)	1.25(1.06–1.48)
Adjusted Model 2	1	1.06(0.91–1.22)	1.08(0.92–1.26)	1.08(0.90–1.29)
Component of metabolic syndrome
Low serum HDL-cholesterol
Crude Model	1	0.85(0.71–1.02)	0.79(0.65–0.95)	0.80(0.66–0.96)
Adjusted Model 1	1	0.98(0.81–1.19)	1.00(0.81–1.24)	1.17(0.92–1.50)
Adjusted Model 2	1	0.97(0.80–1.18)	0.98(0.79–1.21)	1.14(0.89–1.45)
High-serum triglyceride level
Crude Model	1	1.11(0.99–1.24)	1.16(1.03–1.30)	1.11(0.99–1.24)
Adjusted Model 1	1	1.13(1.00-1.27)	1.19(1.05–1.35)	1.14(0.98–1.32)
Adjusted Model 2	1	1.10(0.97–1.24)	1.13(0.99–1.29)	1.04(0.90–1.21)
Elevated blood pressure
Crude Model	1	0.91(0.78–1.06)	1.01(0.87–1.18)	0.95(0.81–1.11)
Adjusted Model 1	1	0.94(0.79–1.11)	1.07(0.89–1.28)	1.01(0.82.1.24)
Adjusted Model 2	1	0.90(0.76–1.07)	1.00(0.83–1.21)	0.91(0.74–1.13)
Abnormal glucose homeostasis
Crude Model	1	0.95(0.85–1.06)	0.96(0.85–1.07)	0.95(0.85–1.06)
Adjusted Model 1	1	1.03(0.91–1.16)	1.09(0.96–1.25)	1.15(0.99–1.35)
Adjusted Model 2	1	1.00(0.88–1.13)	1.04(0.91–1.19)	1.06(0.91–1.24)
Quartile cut-points of DDS are as follows: 1st :≤1.43; 2nd = 1.44–1.89; 3rd = 1.90–2.39; and 4th = ≥ 2.40

The crude model is stratified on the status of DDS.

The adjusted model 1 is adjusted for confounding variables age, gender, education, WSI, smoking, Alcohol and opium consumption, physical activity, total energy intake, and total fat.

The adjusted model 2 is adjusted for confounding variables in adjusted model 1 and BMI.

P < 0.05 compared to the 4th quartiles.

In adjust model 1, after adjusting confounders such as age, gender, education, WSI, smoking, alcohol and opium consumption, physical activity, total energy intake, and total fat, it has been shown that the risk of metabolic syndrome increased 18% in the third group and 25% in the fourth group (OR: 1.18; 98% CI 1.02–1.36 for quartile 3 and OR: 1.25; 98% CI 1.06–1.48 for quartile 4 respectively). Also, an increase in the risk of high-serum triglyceride level was seen in the third group (OR: 1.19; 98% CI 1.05–1.35).

The adjusted model 2 was adjusted for confounding variables in adjusted model 1 and BMI. The results showed that there was no significant relationship between metabolic syndrome and its components with DDs score.

The present cohort study revealed that DDS was associated with risk of incident MetS inversely but after adjustment for some confounders such as age, gender, education, WSI, smoking, alcohol and opium consumption and physical activity, total energy intake and total fat intake, the risk of MetS increased in third quartile (18%) and fourth quartile (24%), and after adjusting of BMI, there wasn’t any correlation between DDS and MetS.

Some studies found an inverse association between DDS and Mets. In a prospective cohort study among Korean adults aged 40–60 years with a 12 years follow-up, DDS had an inverse association with MetS and its features in men despite women²¹.Also a cross-sectional study was conducted on 581 healthy adults in Tehran, indicated an inverse correlation with MetS and its components²². DDS is a good indicator for assessing nutrients intake adequacy, quality of diet and relationship between diet and diseases ^23–25. Previous studies have revealed that a more DDS is related to adequate intake of food groups and a healthier diet^11,26.

In the present study, after adjusting the confounders, the relationship between DDS and Mets in the third and fourth quartiles was direct. In comparison to other studies, DDS in this study was lower ^21–23, that may be due to differences in DDS determining methods. On the other hand, due to lower DDS, the consumption of useful foods such as fruits, vegetables and fiber has decreased, in addition to the increase of some components of MetS such as triglycerides, blood pressure and fasting blood sugar in subjects cause beneficial changes in the diet and can distort the relationship between DDS and MetS, and therefore the existence of these confounding factors can increase the consumption of healthy food, which is related to the increase of DDS.

This study indicated no association between glucose hemostasis and risk of Mets after adjusting of some confounders. But in adjusted model 1 indicated increased risk of Mets. In contrast, previous studies showed that higher DDS was associated with decreased abnormal glucose hemostasis^22,27. However In the current study, consistent with some studies, there was a direct association between DDS and BMI. Previous studies showed when DDS increased, the intake percentage of most food groups was increased that may be cause excess energy intake and increasing BMI ^28,29. In other study, Mexican men with a more DDS had a higher intake of fat and energy and so overweight³⁰.

In present study, higher DDS associated with excess energy and fat intake. Of course excess energy and total fat may be due to increased consumption of healthy items such as of vegetable oil and whole grain that, there are useful component such as fiber, antioxidants and phytochemicals^31,32. In the other hand, contrast with the current study, some studies have showed an inverse association between DDS and BMI^15,22. A meta-analysis study of observational studies indicated no relationship between BMI and DDS ³³. The contradiction in the studies may be related to the type of study conducted, the method of evaluating and determining food diversity, and the indicators related to its groups and subgroups.

In our results, in line with some studies^34,35, men consume higher varied diets than women. In some studies, there is no relationship between DDS and gender^36,37, and in Cano-Ibáñez et al. study, contrary to our results, the variety of food in women was more than that of men³⁸. Mood and stress, appetite and genetics are included, and even customs and traditions and culture of each region can have a great impact on food diversity³⁵. For example, the environment under our study is located in an area that uses more traditional foods such as stews and refined grain, which is cooked in a traditional way and can affect people's dietary diversity, as well as traditional sweets that contain a lot of saturated fat and Sugar that is a factor in increasing the incidence of overweight and chronic diseases such as metabolic syndrome ^39–42.

In this study, in line with previous studies ^43–45, the relationship between education and DDS was direct, so it can be concluded that as the level of education and awareness increases, attention to food diversity and healthy food consumption increases. Low education can reduce people's ability to learn nutritional knowledge and understanding. Also, studying more increases in people with higher education, so nutritional awareness may increase in people with higher education⁴⁴.

Our study has some limitation. We adjusted the potential confounders but may residue confounders that didn’t adjust and might effect on the Mets risk. Also, this study has some strengths such as large sample size. On the other hand, FFQ was used to assessment of dietary consumption. FFQ may be better for calculating DDS with other tools such as recalls or food records. Analyzing food diversity in each person can help increase the quality of food by increasing the consumption of useful food groups, but according to the results of the studies, it is better to look in the DDS tools and groups and subgroups based on quality, method of processing and the presence of nutrients in them. More studies with higher quality are needed in the future.

Our findings showed that there is a relationship between DDS and metabolic syndrome, although this relationship has changed after adjusting for confounders, due to the existence of contradictions in the results of different studies, there is a need to review the DDS tool and conduct higher quality studies.

Data availability

The data is not available publicly. However, upon a reasonable request, the data can be obtained from the correspondence.

Lida Soltani, [email protected]. 00989133917143

Author contributions

Sh.H. contributed to the design of the study and drafting of the manuscript; P.K. contributed to the design of the study and statistical analysis. F.A collected the data and prepared tables. M.K & M.GH contributed to the drafting of the manuscript. L.S contributed to the design of the study and critical review of the draft manuscript. All authors read and approved the final manuscript.

Funding

The Iranian Ministry of Health and Medical Education has contributed to the funding used in the PERSIAN Cohort through Grant No. 700/534. This study has also been supported by the Vice Chancellery for Research & Technology of Rafsanjan University of Medical Sciences. The context of this article are the views of the authors and the funder had no role in design of the study and collection, analysis, and interpretation of data, decision to publish and writing the manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Dietary diversity and Its Association and Metabolic Syndrome: Rafsanjan Cohort Study

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