Skip to main content
Erschienen in: BMC Endocrine Disorders 1/2021

Open Access 01.12.2021 | Research article

A dietary pattern rich in fruits and dairy products is inversely associated to gestational diabetes: a case-control study in Iran

verfasst von: Abazar Roustazadeh, Hamed Mir, Sima Jafarirad, Farideh Mogharab, Seyed Ahmad Hosseini, Amir Abdoli, Saiedeh Erfanian

Erschienen in: BMC Endocrine Disorders | Ausgabe 1/2021

Abstract

Background

Gestational diabetes mellitus (GDM) causes many problems for mother and her neonate. A healthy diet plays an important role in preventing GDM. This study aimed to investigate the relationship between major dietary patterns and the GDM.

Methods

386 healthy and 306 GDM pregnant women (total 693) completed this case-control study. Basic information and anthropometric indices were recorded, and a food frequency questionnaire was completed. For extracting major dietary patterns, the principal component analysis was performed. Multivariable logistic regression models were used to examine whether specific dietary patterns are associated to the GDM.

Results

Four dietary patterns were identified: “fruits and dairy products”, “red meat and plant-based foods”, “snacks and high-fat foods” and “carbohydrate-rich foods”. Among these major extracted dietary patterns, “fruits and dairy products” showed an inverse association to the GDM (odds ratio adjusted for confounders: 0.50, confidence interval: 0.284–0.882, p-trend = 0.019, for highest vs. lowest quartile).

Conclusions

It seems using a healthy dietary pattern such as “fruits and dairy products” may decrease GDM risk.

Graphical abstract

Hinweise
Abazar Roustazadeh and Hamed Mir are first author and contributed equally to this work.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
BMI
Body mass index
CI
Confidence interval
DASH
Dietary approaches to stop hypertension
FFQ
Food frequency questionnaire
GDM
Gestational diabetes mellitus
KMO test
Kaiser-Meyer-Olkin test
PCA
Principal component analysis
T2DM
Type 2 diabetes mellitus

Background

Gestational diabetes mellitus (GDM) is a disorder during pregnancy, defined as glucose intolerance in women who have never had diabetes so far [1]. The GDM causes many complications during pregnancy or after childbirth for mother and her baby [2]. In 2017, it was reported 1 in 7 births was affected by the GDM worldwide [3]. GDM prevalence in Asia is estimated by 11.5 % [4]. In Iran, a report showed a range from 1.3 to 18.6 % in GDM prevalence [5]. However, GDM incidence in the population who live in urban area is similar to developed countries [6].
Obesity and a family history of diabetes are major risk factors for the GDM [7]. Obesity affects insulin resistance, so it may elevate GDM risk [8]. Having a diet with more energy intake from foods including sweet snacks and fats lead to overweight, obesity [9], or type 2 diabetes mellitus (T2DM) [10].
Previous studies focused on the relationship among nutrients or single foods and GDM [11, 12]. A dietary pattern shows the type of a diet and various foods which people habitually consume [13] which is related to the culture and social factors. It is a better index to assess the relationship between a disease and a diet in epidemiologic studies [14]. Some studies showed rich dietary patterns in the whole grains, fruits, vegetables, and nuts such as prudent and Mediterranean dietary patterns have an inverse relationship with the GDM [15, 16]. Besides, animal-based dietary patterns may increase GDM risk [17]. It seems a diet rich in fruit, vegetables, whole grains, and fish and low in red and processed meat, refined grains, and high-fat dairy can decrease in GDM risk [18].
There are some differences in the type of dietary patterns between Western and Asian countries due to different cultures. A study in China showed the relationship between the traditional Chinese diet and GDM risk [19]. It was an interesting finding, because it showed a traditional diet may lead to an increase in GDM risk. Although conflicting results showed there is no relationship between dietary patterns and GDM risk in the Asian countries [20].
Iran is an Asian country with various cultures and ethnicities. These different cultures may lead to various dietary patterns. There are limited studies about the relationship between dietary patterns and the GDM in Asia and Iran. There are conflicting results due to different cultures [1921]. Therefore, we tried to find a relationship between major dietary patterns and the GDM in Jahrom, a city in the South of Iran.

Methods

Participants

This study is a case-control study conducted in Jahrom, a city in Fars province, South of Iran. Pregnant women who were referred to Motahari and Honari hospitals participated in the study. A simple sampling method was used for selecting subjects. The inclusion criteria for the case and control groups were pregnant women from 24th to 28th weeks of gestation, age ranges of 18–40 years, no history of diabetes or the GDM, having no chronic diseases (such as cardiovascular diseases, chronic renal disease, liver, and gastrointestinal diseases, hypo or hyperthyroidism, and severe anemia), singleton pregnancy and no weight loss program before pregnancy. Abortion, preeclampsia, eclampsia, and incomplete questionnaires were considered exclusion criteria in case and control groups. One-step procedure was used to diagnose the GDM in the case group. In this procedure, the oral glucose tolerance test was done. Blood glucose was measured after 8–12 h of fasting. Then, pregnant women were given 75 g of oral glucose, and blood glucose was measured again after one and two hours. The GDM was diagnosed if any blood glucose values were equal or more than 92 mg/dL, 180 mg/dL, and 153 mg/dL in fasting, 1 and 2 h after oral glucose consumption, respectively [22].
This study was a 1:1, case-control study. The required sample size was calculated assuming the relationship between major dietary patterns and the GDM, due to a study in Asia, which found a traditional dietary pattern was related to the odds of GDM (the highest quartile of traditional dietary pattern compared to the lowest quartile; odds ratio = 2.92) [19]. The sample size was calculated with Fleiss et al.. method [23], using an online OpenEpi calculator (version 3) for case and control studies [24]. By considering 4 % of controls exposed, the sample size was determined by 305 subjects in each case and control group (90 % power of study and a 95 % level of confidence). At first, 565 GDM and 634 healthy pregnant women were invited to study. 435 GDM and 493 healthy pregnant women accepted the invitation. Due to the inclusion criteria, 383 in the case and 454 in control have remained. 67 subjects in the case and 49 subjects in control refused participation in the study, and 721 pregnant women participated (316 in the case and 405 in control). Twenty-eight subjects (10 in the case and 18 in control) were excluded due to incomplete questionnaires. Finally, 693 subjects (306 in the case and 387 in control) were included in the analysis.

General data collection

Subjects completed a general questionnaire about their age, weight before pregnancy, number of children, active smoking before pregnancy (yes, no), level of education, and socioeconomic status (very low, lower medium, medium, high) [25]. The socioeconomic status was determined by asking about income. Then, the participants were divided into very low, lower medium, medium, and high. Level of education was categorized into illiterate or primary school, high school or diploma, college, and university. The level of physical activity was determined by the short form of international physical activity questionnaire [26]. The metabolic equivalent was determined which used to divide participants into sedentary, very low, low, medium, and hard activity levels. In the hospital, a trained technician with an accurate scale measured the weight of pregnant women monthly and recorded the weight and date of health card visit. Therefore, the health card was used to record the trend of weight gain during pregnancy. Pre-pregnancy body mass index (BMI) was calculated by reported pre-pregnancy weight in kilograms divided by the height in squared meters.

Dietary assessment

A food frequency questionnaire (FFQ) was used to estimate dietary intakes with 168 food items. The reliability and validity of FFQ were shown in a study in the Iranian adult population which used twelve 24-hours dietary recalls to compare nutrients intake from FFQ [27]. This questionnaire was completed via a face-to-face interview with the trained nutritionists. Participants reported their intake frequency of an intended food in FFQ during last year, basis on daily, weekly, monthly, or annual frequency of intake. To increase dietary assessment accuracy, interviewers used a booklet which showed them the image of portion size. After completing FFQ, the frequency of each food item was converted into the daily intake. We used household measurements to convert each food to the gram. Besides, food intakes were adjusted for total energy intake by residual method [28]. Data were entered into SPSS (version 17.0, SPSS Inc., Chicago, IL, USA) software to extract dietary patterns.

Dietary pattern extraction

Considering foods similarity, they were categorized into related food groups. Twenty-five food groups were included analysis (Table 1). Principal component analysis (PCA) was used to find major dietary patterns due to energy-adjusted foods intake. This analysis method is widely used which is an adaptive descriptive data analysis tool. Besides, it provides information regarding the maximum number of factors [29]. Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests were used to assess factor analysis suitability. KMO ranges between 0 and 1, and a minimum value for good factor analysis is 0.6. Besides, Bartlett’s test should be significant (p < 0.05) [30]. The sampling sufficiency of components was approved by the KMO test > 0.67. Moreover, inter-correlation of components was confirmed using Bartlett’s test of sphericity < 0.001. We used a factor eigenvalue to decide several factors to retain. Besides, we used the scree plot which involves plotting each value. Dietary patterns were determined due to eigenvalue > 1.2 and scree plot examination (Fig. 1). Orthogonal rotation (varimax) was applied to simplify data interpretation. Factor loading more than 0.3 was chosen to find more relationships between food groups and dietary patterns. The dietary pattern was named using principal food groups in each pattern. A score was assigned to all participants considering adherence to each dietary pattern. Quartiles of factor scores were determined due to total participants and considered in the further analysis [31].
Table 1
Rotated factor loading in four major dietary patternsa
 
Food groups (Items)
Fruits and dairy products
Red meat and plant- based foods
Snacks and high-fat foods
Carbohydrate- rich foods
1
Fresh fruits (cantaloupe, melon, watermelon, pear, apricot, cherry, apple, peach, nectarine, plum, fig, grape, kiwi, grapefruit, orange)
0.685
  
0.330
2
Fruit Juices (grapefruit juice, orange juice, apple juice, cantaloupe juice, canned fruit)
0.608
   
3
High fat dairy products (high fat milk, cocoa milk, strained yogurt, high fat yogurt, traditional ice cream, other ice creams)
0.602
   
4
Low fat dairy products (low fat milk, skim milk, low fat yogurt, doogh (traditional yogurt drink))
0.554
   
5
Creamy dairy products (creamy cheese, creamy yogurt, cream, butter)
0.396
   
6
Olive products (fresh olive, olive oil)
0.370
0.325
  
7
Raw vegetables (lettuce, tomato, cucumber, leek, parsley, scallion, basil, carrot, raw spinach, bell pepper)
 
0.594
  
8
Whole cereals (barley, oatmeal, corn (all types))
 
0.499
  
9
Nuts and seeds (peanuts, walnuts, almonds, pistachios, hazelnuts, sunflower seeds)
 
0.484
  
10
Onion, garlic and cabbage (raw onion, garlic, pepper (red and black), cabbage (all types)
 
0.465
  
11
Dried fruits (dried figs, raisins, dates, dried berries, dried apricots, dried peaches)
 
0.451
  
12
Red meats (beef, Lamb meat, minced lamb, sheep’s tongue, broth)
 
0.406
  
13
Legumes (lentil, pinto bean, pigeon pea, broad bean, mung bean, soybean)
 
0.465
0.332
 
14
Pickles and sauces (ketchup, traditional pickles, sour lemon juice)
 
0.373
0.417
 
15
Salt and salty snacks (salt, salty popcorn, chips)
  
0.590
 
16
Cakes and sweets (biscuit, cracker, tea cake, creamy cake, other cakes, sugar, tablet sugar, honey)
  
0.587
 
17
Organ Meats (tripe, sheep brain, leg of lamb, heart and liver of lamb, sausage)
  
0.487
 
18
Fats and oils (animal fat, mayonnaise, hydrogenated oil, cooking oils, margarine)
  
0.466
 
19
White meats (chicken, fish (all types), shrimp)
  
0.447
 
20
Traditional breads (Lavash, Barbari, Sangak, Taftoon)
   
0.557
21
Cooked vegetables (squash, stewed vegetables, eggplant, zucchini, celery, boiled carrot, fried onion, cooked spinach, mushroom, turnip)
   
0.475
22
Other cereals and starch sources (rice, pasta, potato, French fries, vermicelli, traditional vermicelli (Reshte))
   
0.420
23
White breads (baguette bread, toast, wheat flour)
   
-0.313
24
Cheese and whey (cheese (all types), traditional whey (Kashk))
    
25
Tea and coffee (tea (all types), coffee (all types))
    
 
Variance explained (%)
11.93
6.67
5.81
5.03
aFactor loading < ± 0.3 was not shown

Statistical analysis

Kolmogorov-Smirnov tested the normality of data. Quantitative variables were compared between the case and control groups by independent sample t-test and categorical variables by the Chi-squared test. One-way analysis of variance was used to compare the mean of quantitative variables among quartile groups of the factor scores of dietary patterns. Tukey test was applied as a post-hoc analysis. Non-parametric tests were used in case of non-normal distribution. The logistic regression model was used to find the relation of independent variables and extracted dietary patterns with gestational diabetes. In this analysis, the lowest quartile was considered as a reference. Besides, this test was used to eliminate the effect of confounders. The odds ratio (OR) was determined with a 95 % confidence interval (CI). Stratified analysis was applied to assess a relationship between the dietary pattern and the GDM stratified by pre-pregnancy BMI levels using Mantel-Haenszel test. All analyses were done using SPSS version 17.0 (version 17.0, SPSS Inc., Chicago, IL, USA). A p-value less than 0.05 was considered a significant level.

Results

This study was started from November 2018 which completed in September 2019. Total 721 pregnant women (405 healthy and 316 GDM) participated in the study. Twenty-eight subjects (18 in control and 10 in case) were excluded because of incomplete FFQ. At last, 693 subjects (387 in control and 306 in case) were included in the analysis.
Table 2 shows the comparison between healthy and the GDM pregnant women for demographic, anthropometric, and socioeconomic variables. Subjects age with the GDM was significantly more than healthy (p < 0.001). Besides, pre-pregnancy BMI and weight gain during pregnancy in the GDM subjects were more than healthy ones (p < 0.001). There was no difference between two groups for smoking and job. Chi-square analysis showed a difference in socioeconomic status, physical activity, education, and the number of pregnancies between two case and control groups (p < 0.001).
Table 2
The comparison of baseline characteristics between healthy pregnant women and suffered from gestational diabetes mellitus (GDM)a
variables
GDM
(N = 306)
Healthy
(N = 387)
p-value**
Age
32.7 ± 5.3
28.30 ± 5.4
< 0.001
Weight, pre-pregnancy b
69.7 ± 12.4
63.4 ± 11.6
< 0.001
BMI, pre-pregnancy b
27.3 ± 4.5
24.8 ± 4.2
< 0.001
Weight gain b
9.1 ± 5.2
7.4 ± 4.8
< 0.001
Education
 
< 0.001
 Illiterate or primary school
44.2 %
55.8 %
 High school and diploma
59.5 %
40.5 %
 College and university
37.1 %
62.9 %
Number of pregnancies
 
< 0.001
 First
30.1 %
69.9 %
 Second
46.4 %
53.6 %
 Third and more
53.5 %
46.5 %
Gender of neonate
 Male
46.5 %
53.5 %
0.249
 Female
43.6 %
56.4 %
Job
 Housewife
43.7 %
56.3 %
0.984
 Employee
43.7 %
56.3 %
 Self-employed
46.2 %
53.8 %
Socioeconomic status
 Very low
55.4 %
44.6 %
< 0.001
 Lower medium
27.4 %
72.6 %
 Medium
53.1 %
46.9 %
 High
20.0 %
80.0 %
Active smoker
   
 Yes
51.6 %
48.4 %
0.242
 No
43.6 %
56.4 %
Activity
 None
40.0 %
60.0 %
 
 Very low
72.4 %
27.6 %
< 0.001
 Low
29.0 %
71.0 %
 Medium
54.5 %
45.5 %
 Hard
38.1 %
61.9 %
BMI Body mass index
**Chi-squared test for categorical and Independent t-test for quantitative variables
aData are shown as mean ± standard deviation for quantitative variables and percent for categorical variables 
bNon-normal distribution, Mann-Whitney test
Four major dietary patterns were extracted using PCA. Due to the scree plot, four dietary patterns with eigenvalues higher than 1.2 were selected as major dietary patterns (Fig. 1). These dietary patterns were: “fruits and dairy products”, “red meat and plant-based foods”, “snacks and high-fat foods,” and “carbohydrate-rich foods”. Table 1 shows the factor loading of food groups in each extracted dietary pattern. The cumulative variance of four dietary patterns was 29.45 %. The “fruits and dairy products” pattern consisted of 11.93 % of the variance and contained fresh fruits, fruit juices, olive, and dairy products. The “red meat and plant-based foods” pattern consisted of 6.67 % of the variance and mainly contained vegetables, cereals, nuts, legume and red meats. The “snacks and high-fat foods” pattern consisted of 5.81 % of the variance, and including cake and sweets, salty snacks (such as chips), organ meats, fats and oils, and white meats. The “carbohydrate-rich foods” pattern consisted of 5.03 % of variance and mainly including traditional bread, other cereals, and starch sources (such as potato), and cooked vegetables.
Age, anthropometric indices, number of pregnancies, job, level of education, and socioeconomic status were compared among the quartiles of factor scores of dietary patterns (Table 3). Smoking and physical activity level were not shown because we did not find any significant value. Age of subjects was different among the quartiles of “fruits and dairy products”, also “snacks and high-fat foods” dietary patterns. In the fourth quartile of “snacks and high-fat foods,“ pregnant women had a lower age than the first quartile. However, BMI of pre-pregnancy and weight gain during pregnancy were not different among each dietary pattern quartiles. There was a significant difference in education and the number of pregnancies among the quartiles of “snacks and high-fat foods” dietary pattern. Besides, there was a significant difference and a trend near significance for socioeconomic status and level of education among the quartiles of “fruits and dairy products” dietary pattern (Table 3).
Table 3
Comparison of age, anthropometric indices, number of pregnancies, and socioeconomic factors among quartiles (Q1-Q4) of major extracted dietary patterns
 
Fruits and dairy products
Red meat and plant- based foods
Snacks and high-fat foods
Carbohydrate-rich foods
 
Q1
Q4
Q1
Q4
Q1
Q4
Q1
Q4
Age
30.5 ± 5.7
29.2 ± 5.3
29.5 ± 5.9
30.2 ± 5.6
31.4 ± 5.4
28.3 ± 5.8
29.8 ± 5.4
30.0 ± 5.6
P *
0.010
0.255
< 0.001
0.615
Weight pre- pregnancy
66.4 ± 13.1
66.1 ± 11.6
64.8 ± 11.3
66.9 ± 10.6
67.2 ± 11.8
63.2 ± 13.1
66.9 ± 12.1
66.6.1 ± 13.6
P **
0.915
0.344
0.135
0.815
BMI pre- pregnancy
25.6 ± 4.8
25.8 ± 4.4
25.9 ± 4.4
25.8 ± 4.6
25.8 ± 4.4
26.4 ± 4.5
25.8 ± 4.2
26.4 ± 4.9
P **
0.996
0.844
0.178
0.235
Weight gain
8.6 ± 5.1
8.1 ± 4.9
8.2 ± 5.1
8.0 ± 4.6
8.2 ± 4.6
8.2 ± 5.3
8.2 ± 5.2
8.1 ± 5.1
P **
0.120
0.768
0.997
0.530
Education
 Non or primary
30.6 %
19.8 %
25.4 %
26 %
18 %
35.3 %
26.7 %
31.4 %
 High school and diploma
39.3 %
43 %
46.8 %
38.7 %
34.9 %
42.2 %
45.3 %
37.8 %
 College and university
30.1 %
37.2 %
27.7 %
35.3 %
47.1 %
22.5 %
27.9 %
30.8 %
p ***
0.061
0.086
< 0.001
0.202
Number of pregnancies
 First
33.1 %
33.5 %
29.7 %
28.3 %
24.3 %
39.5 %
32.9 %
27.3 %
 Second
32 %
37.6 %
32 %
34.7 %
37.6 %
34.3 %
30.6 %
39.5 %
 Third and more
34.9 %
28.9 %
38.4 %
37 %
38.2 %
26.2 %
36.4 %
33.1 %
p ***
0.459
0.331
0.004
0.184
Job
 House wife
89 %
87.2 %
89.6 %
88.4 %
82.6 %
91.3 %
92.4 %
88.3 %
 Employee
8.1 %
11.6 %
6.9 %
9.8 %
15.1 %
7.6 %
5.2 %
11.1 %
 Self-employment
2.9 %
1.2 %
3.5 %
1.7 %
2.3 %
1.2 %
2.3 %
0.6 %
p ***
0.533
0.357
0.134
0.003
Socioeconomic status
 Very low
20.2 %
6 %
20.8 %
10.8 %
9.4 %
17.3 %
17.5 %
13.5 %
 Lower medium
27.2 %
45.2 %
27.2 %
37.7 %
38.8 %
32.7 %
33.7 %
30.6 %
 Medium
52.6 %
47 %
50.9 %
49.1 %
48.2 %
48.8 %
46.4 %
54.7 %
 High
0 %
1.8 %
1.2 %
2.4 %
3.5 %
1.2 %
2.4 %
1.2 %
p ***
< 0.001
0.181
0.059
0.456
Data are presented as mean ± standard deviation for quantitative variables and percent for categorical variables
*One-way analysis of variances
**Non-normal distribution, Kruskal–Wallis test
***Chi-squared test
Results showed “fruits and dairy products” pattern could inversely predict GDM (p-trend = 0.003). After adjustment for age, BMI of pre-pregnancy, weight gain during pregnancy, energy intake, socioeconomic status, education, physical activity, and the number of pregnancies, this finding was significant again (p-trend = 0.019). These factors were adjusted, because they were different between the cases and controls. The second quartile of “snacks and high-fat foods” dietary pattern predicted GDM (p = 0.017), and p- trend was near significant (p = 0.068). However, after adjustment for confounders, this significant relation was not seen (Table 4).
Table 4
The odds ratio (95% confidence interval) of gestational diabetes mellitus based on quartile of dietary pattern score
Dietary pattern
 
Model 1
Model 2
Model 3
N
OR
CI
P-trend*
OR
CI
P-trend*
OR
CI
P-trend*
Fruits and dairy products
Q1
175
1
-
0.003
1
-
0.004
1
-
0.019
Q2
176
0.623
0.383-1.013
0.668
0.399-1.120
0.575
0.327-1.011
Q3
176
0.507
0.308-0.833
0.511
0.303-0.862
0.472
0.267-0.835
Q4
172
0.478
0.29-0.785
0.488
0.289-0.825
0.500
0.284-0.882
Red meat and plant-based foods
Q1
177
1
-
0.634
1
-
0.798
1
-
0.950
Q2
169
0.964
0.591-1.571
0.869
0.519-1.455
0.901
0.512-1.585
Q3
173
0.827
0.508-1.346
0.754
0.451-1.263
0.659
0.373-1.163
Q4
174
0.742
0.456-1.209
0.712
0.422-1.200
0.660
0.372-1.173
Snacks and high-fat foods
Q1
175
1
-
0.068
1
-
0.750
1
-
0.672
Q2
184
1.794
1.108-2.905
1.889
1.139-3.135
1.709
0.988-2.957
Q3
162
1.203
0.728-1.988
1.325
0.777-2.262
1.153
0.642-2.068
Q4
172
0.800
0.484-1.321
1.082
0.617-1.897
0.931
0.498-1.741
Carbohydrate-rich foods
Q1
168
1
-
0.569
1
-
0.592
1
-
0.559
Q2
186
0.960
0.588-1.567
0.997
0.592-1.679
1.065
0.606-1.872
Q3
167
1.408
0.852-2.329
1.542
0.905-2.629
1.743
0.970-3.130
Q4
172
1.387
0.843-2.283
1.982
1.129-3.481
1.795
0.984-3.277
Model 1: crude model; Model 2: adjusted for age, the body mass index of pre-pregnancy, weight gain during pregnancy, and total energy intake; Model 3: additionally adjusted for socioeconomic status, level of education, physical activity and number of pregnancies
OR odds ratio, CI confidence interval
*The logistic regression model, the lowest quartile as reference
Because pre-pregnancy BMI may reflect some problems during pregnancy such as the GDM, we adjusted this effect by stratified analysis. Pre-pregnancy BMI was divided into four levels: underweight (< 18.5), normal (18.5–25), overweight (25–30), and obese (> 30). Due to the median of score, the score of factors of “fruits and dairy products” dietary pattern was divided into two levels. By adjusting pre-pregnancy BMI effect, the odds of GDM were lower in the higher median of “fruits and dairy products” dietary pattern (Mentel-Haenszel common OR: 0.580, CI: 0.413–0.810). It seems pre-pregnancy BMI is a confounding factor, but not interacting.

Discussion

The relationship between the GDM and anthropometric indices and social factors was investigated. Besides, PCA was used to identify major dietary patterns and their association with the GDM.
“Fruits and dairy products” showed an inverse relationship with the GDM. Less prediction of GDM was seen in the third and fourth quartile of this pattern. Also, this inverse relationship was significant after adjustment for the effect of confounders. This pattern’s main foods were all kinds of dairy products, fresh fruits and fruit juices and all kinds of olive products. The GDM is characterized as glucose intolerance in pregnancy. It develops when a woman cannot produce enough insulin during pregnancy. Fruits and olives contain phytochemicals such as polyphenols. Polyphenols may affect glucose homeostasis by several mechanisms. These mechanisms include stimulating insulin secretion, controlling the digestion of carbohydrate and absorption of glucose, controlling the release of glucose from the liver and finally activating insulin receptors [32]. Therefore, dietary patterns, which are rich in fruits and olives, such as the Mediterranean diet, may help pregnant women to prevent the GDM. Although “fruits and dairy products” dietary pattern has some differences from Mediterranean diet, it contains Mediterranean diet’s main foods, especially fruits and olives. Some documents showed that dairy products’ consumption is an effective factor in the prevention of T2DM [33]. A study investigated the effect of “dietary approaches to stop hypertension” (DASH) on insulin resistance in the GDM which showed the improvement of fasting blood glucose and serum insulin [34]. A DASH diet is due to whole grains, fruits, vegetables, and low-fat dairy products. Whereas, in “fruits and dairy products” pattern, there are two principal items of DASH diet. All kinds of dairy products were included in this dietary pattern. However, Anue et al. confirmed the protective effect of low-fat dairy products and cheese on T2DM incidence [33]. It seems the positive effect of dairy consumption is due to their potential calcium sources [35]. A study showed high calcium intake from diet is related to less incidence of the GDM [36]. Besides, dairy products contain whey protein as an important nutrient. Whey protein may improve hyperglycemia by several mechanisms including stimulation of insulin secretion and the action of incretin hormones such as glucagon-like polypeptide-1 and gastric inhibitory peptide [37].
“Snacks and high-fat foods” dietary pattern consists of more unhealthy foods. We did not find any relationship between this unhealthy dietary pattern and the GDM. However, the crude model was near to being significantly associated, but no significant value was observed after adjustment for confounders. The main foods in this dietary pattern included fats and oils, organ meats, white meats, legumes, sauces, cakes and sweets, salt and salty snakes which all of them except legumes were unhealthy. Although “snacks and high-fat foods” were similar to Western dietary pattern, legumes might neutralize unhealthy foods’ effect in this dietary pattern. In consistent to our study, two studies in Asia showed no relationship between Western dietary pattern and the GDM [20, 38]. However, two other studies found that using Western dietary pattern increased GDM risk [19, 21]. Besides, we did not find any relationship between “red meat and plant-based foods” and “carbohydrate-rich foods”, with GDM. There are some cultural factors, may affect eating habits which lead to different metabolic events [39]. Besides, diet composition heterogeneity may be another reason for no relationship among these dietary patterns with GDM [40].

Strength and limitation

The present study’s strength is an almost large sample size to extract the dietary patterns and homogeneity of subjects for ethnicity. This study has some limitations. Firstly, FFQ is a semi-quantitative questionnaire and depends on the food list which may cause some errors in food intake estimation, although we used a 168-food item food frequency questionnaire to extract dietary patterns which is a comprehensive questionnaire. Another limitation is using multiple questionnaires which made the mothers bored. Recall bias is also a limitation of study, because mothers were asked to report the food intake during the previous year. Besides, there is not a cause and effect association in a case-control study.
Iran contains different cultures, and it would be suggested for future research to study GDM risk concerning culture and dietary patterns in well-designed prospective studies.

Conclusions

Dietary pattern is an effective factor in the incidence of many chronic diseases. Finding an appropriate dietary pattern such as “fruits and dairy products” could help pregnant women to prevent the GDM. Further prospective studies and clinical trials are needed to confirm the results of this study.

Acknowledgements

The authors greatly appreciate pregnant women who participated in the study and Dr. Amirhossein Ramezani for his useful comments.

Declarations

This study was approved by the Ethics committee of Ahvaz Jundishapur University of Medical Sciences (ID: IR.AJUMS.REC.1397.203) and Jahrom University of Medical Sciences (ID: IR.JUMS.REC.1396.148), based on the declaration of Helsinki. All participants completed a consent form and their information was kept secret.
Not applicable.

Competing interests

The authors declare no competing interests.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Literatur
1.
Zurück zum Zitat Melchior H, Kurch-Bek D, Mund M. The Prevalence of Gestational Diabetes A Population-Based Analysis of a Nationwide Screening Program. Dtsch Arztebl Int. 2017;114(24):412–8.PubMedPubMedCentral Melchior H, Kurch-Bek D, Mund M. The Prevalence of Gestational Diabetes A Population-Based Analysis of a Nationwide Screening Program. Dtsch Arztebl Int. 2017;114(24):412–8.PubMedPubMedCentral
2.
Zurück zum Zitat Ovesen PG, Fuglsang J, Andersen MB, Wolff C, Petersen OB. David McIntyre H. Temporal Trends in Gestational Diabetes Prevalence, Treatment, and Outcomes at Aarhus University Hospital, Skejby, between 2004 and 2016. J Diabetes Res. 2018;2018:5937059.CrossRef Ovesen PG, Fuglsang J, Andersen MB, Wolff C, Petersen OB. David McIntyre H. Temporal Trends in Gestational Diabetes Prevalence, Treatment, and Outcomes at Aarhus University Hospital, Skejby, between 2004 and 2016. J Diabetes Res. 2018;2018:5937059.CrossRef
3.
Zurück zum Zitat International Diabetes Federation. IDF Diabetes Atlas. 8th ed. Brussels: International Diabetes Federation; 2017. International Diabetes Federation. IDF Diabetes Atlas. 8th ed. Brussels: International Diabetes Federation; 2017.
4.
Zurück zum Zitat Lee KW, Ching SM, Ramachandran V, Yee A, Hoo FK, Chia YC, et al. Prevalence and risk factors of gestational diabetes mellitus in Asia: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2018;18(1):494.CrossRef Lee KW, Ching SM, Ramachandran V, Yee A, Hoo FK, Chia YC, et al. Prevalence and risk factors of gestational diabetes mellitus in Asia: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2018;18(1):494.CrossRef
5.
Zurück zum Zitat Jafari-Shobeiri M, Ghojazadeh M, Azami-Aghdash S, Naghavi-Behzad M, Piri R, Pourali-Akbar Y, et al. Prevalence and Risk Factors of Gestational Diabetes in Iran: A Systematic Review and Meta-Analysis. Iran J Public Health. 2015;44(8):1036–44.PubMedPubMedCentral Jafari-Shobeiri M, Ghojazadeh M, Azami-Aghdash S, Naghavi-Behzad M, Piri R, Pourali-Akbar Y, et al. Prevalence and Risk Factors of Gestational Diabetes in Iran: A Systematic Review and Meta-Analysis. Iran J Public Health. 2015;44(8):1036–44.PubMedPubMedCentral
6.
Zurück zum Zitat Keshavarz M, Cheung NW, Babaee GR, Moghadam HK, Ajami ME, Shariati M. Gestational diabetes in Iran: incidence, risk factors and pregnancy outcomes. Diabetes Res Clin Pract. 2005;69(3):279–86.CrossRef Keshavarz M, Cheung NW, Babaee GR, Moghadam HK, Ajami ME, Shariati M. Gestational diabetes in Iran: incidence, risk factors and pregnancy outcomes. Diabetes Res Clin Pract. 2005;69(3):279–86.CrossRef
7.
Zurück zum Zitat Larrabure-Torrealva GT, Martinez S, Luque-Fernandez MA, Sanchez SE, Mascaro PA, Ingar H, et al. Prevalence and risk factors of gestational diabetes mellitus: findings from a universal screening feasibility program in Lima, Peru. BMC Pregnancy Childbirth. 2018;18(1):303. Larrabure-Torrealva GT, Martinez S, Luque-Fernandez MA, Sanchez SE, Mascaro PA, Ingar H, et al. Prevalence and risk factors of gestational diabetes mellitus: findings from a universal screening feasibility program in Lima, Peru. BMC Pregnancy Childbirth. 2018;18(1):303.
8.
Zurück zum Zitat DeSisto CL, Kim SY, Sharma AJ. Prevalence estimates of gestational diabetes mellitus in the United States, Pregnancy Risk Assessment Monitoring System (PRAMS), 2007–2010. Prev Chronic Dis. 2014;11:E104.CrossRef DeSisto CL, Kim SY, Sharma AJ. Prevalence estimates of gestational diabetes mellitus in the United States, Pregnancy Risk Assessment Monitoring System (PRAMS), 2007–2010. Prev Chronic Dis. 2014;11:E104.CrossRef
9.
Zurück zum Zitat Mu M, Xu LF, Hu D, Wu J, Bai MJ. Dietary Patterns and Overweight/Obesity: A Review Article. Iran J Public Health. 2017;46(7):869–76.PubMedPubMedCentral Mu M, Xu LF, Hu D, Wu J, Bai MJ. Dietary Patterns and Overweight/Obesity: A Review Article. Iran J Public Health. 2017;46(7):869–76.PubMedPubMedCentral
10.
Zurück zum Zitat Jannasch F, Kröger J, Schulze MB. Dietary Patterns and Type 2 Diabetes: A Systematic Literature Review and Meta-Analysis of Prospective Studies. J Nutr. 2017;147(6):1174–82.CrossRef Jannasch F, Kröger J, Schulze MB. Dietary Patterns and Type 2 Diabetes: A Systematic Literature Review and Meta-Analysis of Prospective Studies. J Nutr. 2017;147(6):1174–82.CrossRef
11.
Zurück zum Zitat Bowers K, Tobias DK, Yeung E, Hu FB, Zhang C. A prospective study of prepregnancy dietary fat intake and risk of gestational diabetes. Am J Clin Nutr. 2012;95(2):446–53.CrossRef Bowers K, Tobias DK, Yeung E, Hu FB, Zhang C. A prospective study of prepregnancy dietary fat intake and risk of gestational diabetes. Am J Clin Nutr. 2012;95(2):446–53.CrossRef
12.
Zurück zum Zitat Liang Y, Gong Y, Zhang X, Yang D, Zhao D, Quan L, et al. Dietary Protein Intake, Meat Consumption, and Dairy Consumption in the Year Preceding Pregnancy and During Pregnancy and Their Associations With the Risk of Gestational Diabetes Mellitus: A Prospective Cohort Study in Southwest China. Front Endocrinol (Lausanne). 2018;9:596.CrossRef Liang Y, Gong Y, Zhang X, Yang D, Zhao D, Quan L, et al. Dietary Protein Intake, Meat Consumption, and Dairy Consumption in the Year Preceding Pregnancy and During Pregnancy and Their Associations With the Risk of Gestational Diabetes Mellitus: A Prospective Cohort Study in Southwest China. Front Endocrinol (Lausanne). 2018;9:596.CrossRef
13.
Zurück zum Zitat Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3–9.CrossRef Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3–9.CrossRef
14.
Zurück zum Zitat Cespedes EM, Hu FB. Dietary patterns: from nutritional epidemiologic analysis to national guidelines. Am J Clin Nutr. 2015;101(5):899–900.CrossRef Cespedes EM, Hu FB. Dietary patterns: from nutritional epidemiologic analysis to national guidelines. Am J Clin Nutr. 2015;101(5):899–900.CrossRef
15.
Zurück zum Zitat Hassani Zadeh S, Boffetta P, Hosseinzadeh M. Dietary patterns and risk of gestational diabetes mellitus: A systematic review and meta-analysis of cohort studies. Clin Nutr ESPEN. 2020;36:1–9.CrossRef Hassani Zadeh S, Boffetta P, Hosseinzadeh M. Dietary patterns and risk of gestational diabetes mellitus: A systematic review and meta-analysis of cohort studies. Clin Nutr ESPEN. 2020;36:1–9.CrossRef
16.
Zurück zum Zitat Raghavan R, Dreibelbis C, Kingshipp BL, et al. Dietary patterns before and during pregnancy and maternal outcomes: a systematic review. Am J Clin Nutr. 2019;109(Suppl_7):705S–728S.CrossRef Raghavan R, Dreibelbis C, Kingshipp BL, et al. Dietary patterns before and during pregnancy and maternal outcomes: a systematic review. Am J Clin Nutr. 2019;109(Suppl_7):705S–728S.CrossRef
17.
Zurück zum Zitat Mijatovic-Vukas J, Capling L, Cheng S, Stamatakis E, Louie J, Cheung NW, et al. Associations of Diet and Physical Activity with Risk for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Nutrients. 2018;10(6):698.CrossRef Mijatovic-Vukas J, Capling L, Cheng S, Stamatakis E, Louie J, Cheung NW, et al. Associations of Diet and Physical Activity with Risk for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Nutrients. 2018;10(6):698.CrossRef
18.
Zurück zum Zitat Schoenaker DA, Mishra GD, Callaway LK, Soedamah-Muthu SS. The Role of Energy, Nutrients, Foods, and Dietary Patterns in the Development of Gestational Diabetes Mellitus: A Systematic Review of Observational Studies. Diabetes Care. 2016;39(1):16–23.CrossRef Schoenaker DA, Mishra GD, Callaway LK, Soedamah-Muthu SS. The Role of Energy, Nutrients, Foods, and Dietary Patterns in the Development of Gestational Diabetes Mellitus: A Systematic Review of Observational Studies. Diabetes Care. 2016;39(1):16–23.CrossRef
19.
Zurück zum Zitat DU HY, Jiang H, O K, Chen B, Xu LJ, Liu SP, et al. Association of Dietary Pattern during Pregnancy and Gestational Diabetes Mellitus: A Prospective Cohort Study in Northern China. Biomed Environ Sci. 2017;30(12):887–97.PubMed DU HY, Jiang H, O K, Chen B, Xu LJ, Liu SP, et al. Association of Dietary Pattern during Pregnancy and Gestational Diabetes Mellitus: A Prospective Cohort Study in Northern China. Biomed Environ Sci. 2017;30(12):887–97.PubMed
20.
Zurück zum Zitat Mak JKL, Pham NM, Lee AH, Tang L, Pan XF, Binns CW, et al. Dietary patterns during pregnancy and risk of gestational diabetes: a prospective cohort study in Western China. Nutr J. 2018;17(1):107.CrossRef Mak JKL, Pham NM, Lee AH, Tang L, Pan XF, Binns CW, et al. Dietary patterns during pregnancy and risk of gestational diabetes: a prospective cohort study in Western China. Nutr J. 2018;17(1):107.CrossRef
21.
Zurück zum Zitat Sedaghat F, Akhoondan M, Ehteshami M, Aghamohammadi V, Ghanei N, Mirmiran P, et al. Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study. J Diabetes Res. 2017;2017:5173926.CrossRef Sedaghat F, Akhoondan M, Ehteshami M, Aghamohammadi V, Ghanei N, Mirmiran P, et al. Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study. J Diabetes Res. 2017;2017:5173926.CrossRef
22.
Zurück zum Zitat American Diabetes Association. Diabetes management guidelines. Diabetes Care. 2015;38(Suppl 1):1–93. American Diabetes Association. Diabetes management guidelines. Diabetes Care. 2015;38(Suppl 1):1–93.
23.
Zurück zum Zitat Fleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions. 3rd Edition, Hoboken: John Wiley & Sons; 2003. Fleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions. 3rd Edition, Hoboken: John Wiley & Sons; 2003.
24.
Zurück zum Zitat Dean AG, Sullivan KM, Soe MM. OpenEpi. Open Source Epidemiologic Statistics for Public Health, Version. www.OpenEpi.com, [Accessed 15 Jan 2018]. Dean AG, Sullivan KM, Soe MM. OpenEpi. Open Source Epidemiologic Statistics for Public Health, Version. www.OpenEpi.com, [Accessed 15 Jan 2018].
25.
Zurück zum Zitat Shakeri M, Jafarirad S, Amani R, Cheraghian B, Najafian M. A longitudinal study on the relationship between mother’s personality trait and eating behaviors, food intake, maternal weight gain during pregnancy and neonatal birth weight. Nutr J. 2020;19(1):67.CrossRef Shakeri M, Jafarirad S, Amani R, Cheraghian B, Najafian M. A longitudinal study on the relationship between mother’s personality trait and eating behaviors, food intake, maternal weight gain during pregnancy and neonatal birth weight. Nutr J. 2020;19(1):67.CrossRef
26.
Zurück zum Zitat Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.CrossRef Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.CrossRef
27.
Zurück zum Zitat Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13(5):654–62.CrossRef Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13(5):654–62.CrossRef
28.
Zurück zum Zitat Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:1220S–1228S. discussion 1229S–1231S.CrossRef Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:1220S–1228S. discussion 1229S–1231S.CrossRef
29.
Zurück zum Zitat Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150202. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150202.
30.
Zurück zum Zitat Pallant J. SPSS survival manual: McGraw-hill education (UK); 2013. Pallant J. SPSS survival manual: McGraw-hill education (UK); 2013.
31.
Zurück zum Zitat Hsieh CC, Maisonneuve P, Boyle P, Macfarlane GJ, Roberston C. Analysis of quantitative data by quantiles in epidemiologic studies: classification according to cases, noncases, or all subjects? Epidemiology. 1991;2(2):137–40.CrossRef Hsieh CC, Maisonneuve P, Boyle P, Macfarlane GJ, Roberston C. Analysis of quantitative data by quantiles in epidemiologic studies: classification according to cases, noncases, or all subjects? Epidemiology. 1991;2(2):137–40.CrossRef
32.
Zurück zum Zitat Santangelo C, Zicari A, Mandosi E, Scazzocchio B, Mari E, Morano S, et al. Could gestational diabetes mellitus be managed through dietary bioactive compounds? Current knowledge and future perspectives. Br J Nutr. 2016;115(7):1129–44.CrossRef Santangelo C, Zicari A, Mandosi E, Scazzocchio B, Mari E, Morano S, et al. Could gestational diabetes mellitus be managed through dietary bioactive compounds? Current knowledge and future perspectives. Br J Nutr. 2016;115(7):1129–44.CrossRef
33.
Zurück zum Zitat Aune D, Norat T, Romundstad P, Vatten LJ. Dairy products and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. Am J Clin Nutr. 2013;98(4):1066–83.CrossRef Aune D, Norat T, Romundstad P, Vatten LJ. Dairy products and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. Am J Clin Nutr. 2013;98(4):1066–83.CrossRef
34.
Zurück zum Zitat Asemi Z, Samimi M, Tabassi Z, Sabihi SS, Esmaillzadeh A. A randomized controlled clinical trial investigating the effect of DASH diet on insulin resistance, inflammation, and oxidative stress in gestational diabetes. Nutrition. 2013;29(4):619–24.CrossRef Asemi Z, Samimi M, Tabassi Z, Sabihi SS, Esmaillzadeh A. A randomized controlled clinical trial investigating the effect of DASH diet on insulin resistance, inflammation, and oxidative stress in gestational diabetes. Nutrition. 2013;29(4):619–24.CrossRef
35.
Zurück zum Zitat Fumeron F, Lamri A, Abi Khalil C, Jaziri R, Porchay-Baldérelli I, Lantieri O, et al. Dairy consumption and the incidence of hyperglycemia and the metabolic syndrome: results from a french prospective study, Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2011;34(4):813–7.CrossRef Fumeron F, Lamri A, Abi Khalil C, Jaziri R, Porchay-Baldérelli I, Lantieri O, et al. Dairy consumption and the incidence of hyperglycemia and the metabolic syndrome: results from a french prospective study, Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2011;34(4):813–7.CrossRef
36.
Zurück zum Zitat Osorio-Yáñez C, Qiu C, Gelaye B, Enquobahrie DA, Williams MA. Risk of gestational diabetes mellitus in relation to maternal dietary calcium intake. Public Health Nutr. 2017;20(6):1082–9.CrossRef Osorio-Yáñez C, Qiu C, Gelaye B, Enquobahrie DA, Williams MA. Risk of gestational diabetes mellitus in relation to maternal dietary calcium intake. Public Health Nutr. 2017;20(6):1082–9.CrossRef
37.
Zurück zum Zitat Adams RL, Broughton KS. Insulinotropic Effects of Whey: Mechanisms of Action, Recent Clinical Trials, and Clinical Applications. Ann Nutr Metab. 2016;69(1):56–63.CrossRef Adams RL, Broughton KS. Insulinotropic Effects of Whey: Mechanisms of Action, Recent Clinical Trials, and Clinical Applications. Ann Nutr Metab. 2016;69(1):56–63.CrossRef
38.
Zurück zum Zitat Asadi M, Shahzeidi M, Nadjarzadeh A, Hashemi Yusefabad H, Mansoori A. The relationship between pre-pregnancy dietary patterns adherence and risk of gestational diabetes mellitus in Iran: A case–control study. Nutrition Dietetics. 2019;76(5):597–603.CrossRef Asadi M, Shahzeidi M, Nadjarzadeh A, Hashemi Yusefabad H, Mansoori A. The relationship between pre-pregnancy dietary patterns adherence and risk of gestational diabetes mellitus in Iran: A case–control study. Nutrition Dietetics. 2019;76(5):597–603.CrossRef
39.
Zurück zum Zitat Jafarirad S, Mousavi Borazjani A, Fathi M, Hormoznejad R. A Study on Anthropometric Measurements, Blood Pressure, Blood Sugar and Food Intakes Among Different Social Status and Ethnicities. Jundishapur J Chronic Dis Care. 2017;6(1):e38916. Jafarirad S, Mousavi Borazjani A, Fathi M, Hormoznejad R. A Study on Anthropometric Measurements, Blood Pressure, Blood Sugar and Food Intakes Among Different Social Status and Ethnicities. Jundishapur J Chronic Dis Care. 2017;6(1):e38916.
40.
Zurück zum Zitat Imamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, et al. Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment. Lancet Glob Health. 2015;3:e132–42.CrossRef Imamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, et al. Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment. Lancet Glob Health. 2015;3:e132–42.CrossRef
Metadaten
Titel
A dietary pattern rich in fruits and dairy products is inversely associated to gestational diabetes: a case-control study in Iran
verfasst von
Abazar Roustazadeh
Hamed Mir
Sima Jafarirad
Farideh Mogharab
Seyed Ahmad Hosseini
Amir Abdoli
Saiedeh Erfanian
Publikationsdatum
01.12.2021
Verlag
BioMed Central
Erschienen in
BMC Endocrine Disorders / Ausgabe 1/2021
Elektronische ISSN: 1472-6823
DOI
https://doi.org/10.1186/s12902-021-00707-8

Weitere Artikel der Ausgabe 1/2021

BMC Endocrine Disorders 1/2021 Zur Ausgabe

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.