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Bigdeli J, Jarahi L, Ranjbar G, Khadem Ghaebi N, Safarian M, Soleimani D. Food Insecurity and Preeclampsia: A Case-Control Study. JNFS 2021; 6 (1) :87-97
URL: http://jnfs.ssu.ac.ir/article-1-319-en.html
URL: http://jnfs.ssu.ac.ir/article-1-319-en.html
Junaid Bigdeli 
, Lida Jarahi , Golnaz Ranjbar , Nayereh Khadem Ghaebi , Mohammad Safarian * , Davood Soleimani
, Lida Jarahi , Golnaz Ranjbar , Nayereh Khadem Ghaebi , Mohammad Safarian * , Davood Soleimani
Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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2 Department of Community Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
3 Women’s Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
4 Department of Nutrition, School of Nutrition Sciences and Food Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Introduction
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Food Insecurity and Preeclampsia: A Case-Control Study
Junaid Bigdeli; MSc1, Golnaz Ranjbar; PhD1, Lida Jarahi; PhD2 , Nayereh Khadem Ghaebi; OB, GYN3,
Davood Soleimani; PhD4 & Mohammad Safarian; MD, PhD*1
1 Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.Davood Soleimani; PhD4 & Mohammad Safarian; MD, PhD*1
2 Department of Community Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
3 Women’s Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
4 Department of Nutrition, School of Nutrition Sciences and Food Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
| ARTICLE INFO | ABSTRACT | |
| ORIGINAL ARTICLE | Background: Household food insecurity is defined as the limited or uncertain access to adequate and safe food or limited ability to obtain food in a socially acceptable manner. Preeclampsia is a severe case of high-risk pregnancy, which endangers the health of women across the world, especially in developing countries. The current study aimed to use the nutritional deficiencies theory in pathogenesis of preeclampsia and determine the correlation between food insecurity and preeclampsia. Methods: This case-control study was conducted on 100 women with preeclampsia and 200 normal pregnant women with the gestational age of 20 weeks. Participants were selected via purposive sampling according to the eligibility criteria. Data were collected using the USDA questionnaire for food insecurity, socioeconomic data, and demographic data through interviews. Results: The findings indicated that the frequency of food insecurity was significantly higher in the preeclampsia women than the healthy women (71% vs. 21%; P < 0.001). Logistic regression indicated that the risk of preeclampsia was six times higher among pregnant women in the unsafe food status group than those in safe food status group [odds = 6.4; 95%CI: 3.3-12.4; P < 0.001]. Among the studied variables, socioeconomic status, history of stillbirth, history of preterm delivery, and ethnicity were significantly associated with preeclampsia during pregnancy (P < 0.05). In addition, women with low socioeconomic status were twice at the higher risk of preeclampsia compared to those with favorable socioeconomic status [odds = 2.7; 95%CI: 1.1-6.2; P = 0.01]. Conclusion: The current study indicated that the prevalence of food insecurity was high in Iranian women with preeclampsia, especially those with a history of preterm labor, history of stillbirth, low socioeconomic status, or non-Persian Keywords: Food insecurity; Preeclampsia; Pregnancy; Nutrition |
|
| Article history: Received: 3 May 2020 Revised: 9 Aug 2020 Accepted: 9 Aug 2020 |
||
| *Corresponding author: safarianm@mums.ac.ir Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Postal code: 9177948564 Tel: +9851-38002259 |
Introduction
Attention to maternal health plays a pivotal role in the progress of every society since maintaining the mothers’ health is associated with the health of other family members (Yazdanpanahi et al., 2008). Preeclampsia is a severe case of high-risk pregnancy, which endangers women’s health across the world, especially in the developing countries (Forouhari et al., 2009). Preeclampsia is a specific multi-systemic disorder of pregnancy characterized by proteinuria and high blood pressure during pregnancy (Cheng and Wang, 2009, Cudihy and Lee, 2009). It is a common pregnancy complication and a major cause of mortality and morbidity in pregnant women and their offsprings (Lain and Roberts, 2002).
Preeclampsia affects nearly 5-10% of all pregnancies worldwide. In the developing countries, the prevalence of preeclampsia was estimated up to 16.7%, which is expected to account for 40-60% of maternal mortality in the developing countries (Osungbade and Ige, 2011). The World Health Organization (WHO) estimated that the incidence of preeclampsia is seven times (2.8% of live births) higher in the developing countries compared to the developed countries (Trogstad et al., 2008).
Although several studies were conducted in this regard, the etiology and pathology of the disease remain unanswered (Jain et al., 2010, Pathak et al., 2004). Several mechanisms have also been proposed to describe the causes of diversity in the pathophysiology of preeclampsia (Ilekis et al., 2007, Lyall and Belfort, 2007, von Dadelszen and Magee, 2008). Maternal diet plays a key role in the etiology of preeclampsia and many nutritional factors were suggested as the potential causes of this syndrome (Xu et al., 2009).
Among the nutritional deficiencies presented in this regard (Sibai et al., 2008), food insecurity is defined as limited or uncertain access to adequate and safe food or limited ability to obtain food in a socially acceptable manner (Bickel et al., 2000). According to literature, the prevalence of food insecurity is nearly 5-8% in the developing countries, while it has been reported as 4-16% in the developed countries (Ronsmans et al., 2006, Villar et al., 2003). Food insecure individuals are at the risk of lower food diversity, lower quality diets, reduced intake of micronutrients, and lower intake of fruits and vegetables (Vozoris and Tarasuk, 2003, Weigel et al., 2007).
Evaluation of food safety is of paramount importance in women since food insecurity in the family is associated with the decreased consumption of fruits and vegetables in women as well as reduced intake of micronutrients in reproductive ages (Kendall et al., 1996, Rose and Oliveira, 1997). Dlamini et al. (Dlamini, 1996) reported that poor socioeconomic status was associated with the risk of preeclampsia in pregnant women in the developing countries. Moreover, Haelterman et al. reported that the risk of preeclampsia was twice higher in pregnant women with poor socioeconomic status compared to those with favorable socioeconomic status (Haelterman et al., 2003). A study conducted in Yazd, Iran, indicated that more than half of the pregnant women were in the mild to severe range of food insecurity and the prevalence of this situation was higher in women with low social economic status and non-native groups (Rajizadeh et al., 2019). Since pregnant women are considered as one of the most vulnerable groups in the community and lack of food safety may cause various complications during pregnancy, prevention of preeclampsia and its complications in pregnant women and their fetus is of particular importance.
Therefore, the current study aimed to determine the correlation between food insecurity and preeclampsia in pregnant women.
Materials and Methods
Participants and study design: This case-control study was conducted on pregnant women with preeclampsia who referred to the department of obstetrics and gynecology at Ghaem Hospital and Imam Reza Hospital in Mashhad, Iran, between winter and summer 2016. The participants were diagnosed with preeclampsia by a gynecologist as follows: systolic blood pressure > 140 mmHg or diastolic blood pressure ≥ 90 mmHg and 24-hour proteinuria ≥ 0.3 g. The ccontrol group consisted of healthy women (after six months of pregnancy) referring to the health centers who were matched with the case group members in a 2:1 ratio in terms of place of residence and age.
Regarding the case group, inclusion criteria were consent to participate in the study, diagnosis with preeclampsia, residence in Mashhad, and age of 18 to 45 years. The exclusion criteria of both groups were presence of pre-pregnancy systemic chronic diseases and use of drugs affecting preeclampsia.
Sample size was estimated using the two proportions formula related to a qualitative attribute from two independent populations and references (Mozayeni et al., 2014) based on the level of food insecurity without starvation. Considering the error of α = 0.01 and β = 90, the sample size of each group was estimated as 100. In order to increase the accuracy and considering availability of the control group, sample size of the control group increased to twice the number of case group members.
In order to access the samples, the required coordination was made with the department of obstetrics and gynecology at Ghaem Hospital and Imam Reza Hospital as well as the health department of Mashhad. The women with preeclampsia who were diagnosed by a gynecologist and referred to the mentioned hospitals enrolled in the study. To select the control group members and match their place of residence, the municipality area, where the case group members resided, was determined. Later, health centers in the determined areas were identified and a list of the pregnant women who were in their third trimester of pregnancy was provided from each health care center. Age grouping was performed to match women in terms of age and from each age group. The required samples were randomly selected from the list of eligible pregnant women provided by the health centers.
Measurements: Pre-pregnancy body mass index (BMI) was obtained from the medical records of the participants. Accordingly, the BMI ≤ 18.5 kg/m2, BMI 18.5-24.9 kg/m2, BMI 25-29.9 kg/m2, and BMI ≥ 30 kg/m2 were defined as low weight, normal weight, overweight, and obese, respectively (Barba et al., 2004). All participants signed written informed consents to enter the study. Data were collected using a general information questionnaire containing demographic and socioeconomic information. Moreover, food security status was measured using the USDA 18-item food security questionnaire.
Data on the socioeconomic status of the participants were collected via interviews and the socioeconomic status questionnaire. In addition, the results of a valid and reliable questionnaire, applied in a previous study (Garmaroudi and Moradi, 2010), were used to assess the socioeconomic status and its association with various health outcomes. The variables determining the socioeconomic status of the households in this questionnaire included household education level, education level of the spouse, residential property, residential housing infrastructure, and social welfare (e.g., possession of a car and personal computer), which were rated based on the participants’ responses. The maximum score of the questionnaire was 48. Based on the respondent’s mean score as well as the first and third quadrants, participants were divided into four levels of poor, moderate, favorable, and excellent according to the socioeconomic status of the household.
Food Insecurity: In the current research, the USDA questionnaire was administered to assess food security, which consisted of 18 items to evaluate the food security status of the households during 12 months. The questionnaire was validated for the Iranian population in a previous study (Ramakrishnan, 2004). To evaluate the responses, the positive responses (Often True, Sometimes True, Almost Every Month, and Number of Months) were interpreted as Yes and coded as one, while the negative responses (Only One/two months) were interpreted as No. In addition, the unanswered questions were coded as zero. Table 1 shows the assigned codes used to assess the status of food security (Ramesh et al., 2010).
The questionnaires were evaluated based on food security and food insecurity (3 ≥ Yes) in terms of marginal food security (Yes: 1-2). In its classification, the USDA has categorized marginal food security in the food security group. However, Laraia demonstrated that economic variables were correlated with social marginal food security and food insecurity (Laraia et al., 2006). In the present study, we examined the food security of the borde. According to the 18-item USDA food security status questionnaire, food security was classified into four groups of food secure, food insecure without starvation, food insecure with moderate starvation, and food insecure with severe starvation. Regarding food insecurity with severe starvation, all households with children had low food intake to the extent that their children experienced hunger. In some other households with children, this occurs at an earlier stage of severity. In the households with and without children, adults repeatedly experience more extensive reduction in their food intake. In our research, the participants were divided into two groups of food secure and food insecure. The scoring range of food security was 0-18 based on the number of positive responses (food secure: 0-2, food insecure: 3-18) (Bickel et al., 2000).
Ethical considerations: This study was derived from a master’s thesis approved by the Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.fm.REC.1395.359). Before data collection, written informed consents were obtained from all participants.
Data analysis: Data analysis was performed in SPSS version 16. Median and quadruple range were reported to describe the quantitative variables and Mann-Whitney U test was used to compare the quantitative variables between the two groups. As for the qualitative variables, frequency tables were drawn and Chi-square test was used. In order to evaluate the correlation between food insecurity and preeclampsia, multivariate logistic regression analysis was carried out by adjusting the confounding variables. The food security status of each household was determined based on their score. In all statistical analyses, P-value of less than 0.05 was significant.
Results
According to the information in Table 1 and considering that the two groups were matched, the median and quadruple range of age had no significant differences between the patients with preeclampsia and healthy participants (P = 0.32). Moreover, no significant differences were observed between groups in terms of their number of successful pregnancies, age at first pregnancy, number of children, number of family members, and number of children aged less than 18 years. However, the study groups were significantly different in terms of pre-pregnancy BMI and total number of pregnancies (P < 0.05) since the rate of these parameters was higher in the preeclampsia group compared to the control group. No significant differences were found between the food secure and insecure groups in terms of the median and quadruple range of age and pre-pregnancy BMI. However, the number of successful pregnancies, age at first pregnancy, total number of pregnancies, number of children, number of family members, and number of children aged less than 18 years were significantly different (P < 0.05).
Table 2 shows the results of tests for the qualitative variables between the preeclampsia and healthy participants. Accordingly, pre-pregnancy BMI, history of stillbirth, history of preterm labor, history of preeclampsia, family history of preeclampsia in the first-degree relatives (mother and sister), pre-pregnancy smoking habits, performing pre-pregnancy physical exercise, ethnicity, socioeconomic status, and food security were significantly different between the study groups (P < 0.05). Mann-Whitney U test was used to compare ranking of the qualitative variables. The results indicated that the frequency of food insecurity was 71% in the pregnant women with preeclampsia and 21% in the control group members.
As it can be seen from Table 3, the variables that affected preeclampsia and food insecurity (P < 0.01) were considered as the quantitative and qualitative confounding variables. However, the history of preeclampsia and pre-pregnancy smoking habits were considered as the influential factors in this regard; they were eliminated due to the lack of data in the control group. Multivariate logistic regression was applied using the significant variables regarding preeclampsia and food security (total number of pregnancies, history of stillbirth, history of preterm labor, ethnicity, and socioeconomic status). Moreover, the study variables had a significant correlation with preeclampsia and food insecurity (P < 0.001; odds ratio = 6.4).
Preeclampsia affects nearly 5-10% of all pregnancies worldwide. In the developing countries, the prevalence of preeclampsia was estimated up to 16.7%, which is expected to account for 40-60% of maternal mortality in the developing countries (Osungbade and Ige, 2011). The World Health Organization (WHO) estimated that the incidence of preeclampsia is seven times (2.8% of live births) higher in the developing countries compared to the developed countries (Trogstad et al., 2008).
Although several studies were conducted in this regard, the etiology and pathology of the disease remain unanswered (Jain et al., 2010, Pathak et al., 2004). Several mechanisms have also been proposed to describe the causes of diversity in the pathophysiology of preeclampsia (Ilekis et al., 2007, Lyall and Belfort, 2007, von Dadelszen and Magee, 2008). Maternal diet plays a key role in the etiology of preeclampsia and many nutritional factors were suggested as the potential causes of this syndrome (Xu et al., 2009).
Among the nutritional deficiencies presented in this regard (Sibai et al., 2008), food insecurity is defined as limited or uncertain access to adequate and safe food or limited ability to obtain food in a socially acceptable manner (Bickel et al., 2000). According to literature, the prevalence of food insecurity is nearly 5-8% in the developing countries, while it has been reported as 4-16% in the developed countries (Ronsmans et al., 2006, Villar et al., 2003). Food insecure individuals are at the risk of lower food diversity, lower quality diets, reduced intake of micronutrients, and lower intake of fruits and vegetables (Vozoris and Tarasuk, 2003, Weigel et al., 2007).
Evaluation of food safety is of paramount importance in women since food insecurity in the family is associated with the decreased consumption of fruits and vegetables in women as well as reduced intake of micronutrients in reproductive ages (Kendall et al., 1996, Rose and Oliveira, 1997). Dlamini et al. (Dlamini, 1996) reported that poor socioeconomic status was associated with the risk of preeclampsia in pregnant women in the developing countries. Moreover, Haelterman et al. reported that the risk of preeclampsia was twice higher in pregnant women with poor socioeconomic status compared to those with favorable socioeconomic status (Haelterman et al., 2003). A study conducted in Yazd, Iran, indicated that more than half of the pregnant women were in the mild to severe range of food insecurity and the prevalence of this situation was higher in women with low social economic status and non-native groups (Rajizadeh et al., 2019). Since pregnant women are considered as one of the most vulnerable groups in the community and lack of food safety may cause various complications during pregnancy, prevention of preeclampsia and its complications in pregnant women and their fetus is of particular importance.
Therefore, the current study aimed to determine the correlation between food insecurity and preeclampsia in pregnant women.
Materials and Methods
Participants and study design: This case-control study was conducted on pregnant women with preeclampsia who referred to the department of obstetrics and gynecology at Ghaem Hospital and Imam Reza Hospital in Mashhad, Iran, between winter and summer 2016. The participants were diagnosed with preeclampsia by a gynecologist as follows: systolic blood pressure > 140 mmHg or diastolic blood pressure ≥ 90 mmHg and 24-hour proteinuria ≥ 0.3 g. The ccontrol group consisted of healthy women (after six months of pregnancy) referring to the health centers who were matched with the case group members in a 2:1 ratio in terms of place of residence and age.
Regarding the case group, inclusion criteria were consent to participate in the study, diagnosis with preeclampsia, residence in Mashhad, and age of 18 to 45 years. The exclusion criteria of both groups were presence of pre-pregnancy systemic chronic diseases and use of drugs affecting preeclampsia.
Sample size was estimated using the two proportions formula related to a qualitative attribute from two independent populations and references (Mozayeni et al., 2014) based on the level of food insecurity without starvation. Considering the error of α = 0.01 and β = 90, the sample size of each group was estimated as 100. In order to increase the accuracy and considering availability of the control group, sample size of the control group increased to twice the number of case group members.
In order to access the samples, the required coordination was made with the department of obstetrics and gynecology at Ghaem Hospital and Imam Reza Hospital as well as the health department of Mashhad. The women with preeclampsia who were diagnosed by a gynecologist and referred to the mentioned hospitals enrolled in the study. To select the control group members and match their place of residence, the municipality area, where the case group members resided, was determined. Later, health centers in the determined areas were identified and a list of the pregnant women who were in their third trimester of pregnancy was provided from each health care center. Age grouping was performed to match women in terms of age and from each age group. The required samples were randomly selected from the list of eligible pregnant women provided by the health centers.
Measurements: Pre-pregnancy body mass index (BMI) was obtained from the medical records of the participants. Accordingly, the BMI ≤ 18.5 kg/m2, BMI 18.5-24.9 kg/m2, BMI 25-29.9 kg/m2, and BMI ≥ 30 kg/m2 were defined as low weight, normal weight, overweight, and obese, respectively (Barba et al., 2004). All participants signed written informed consents to enter the study. Data were collected using a general information questionnaire containing demographic and socioeconomic information. Moreover, food security status was measured using the USDA 18-item food security questionnaire.
Data on the socioeconomic status of the participants were collected via interviews and the socioeconomic status questionnaire. In addition, the results of a valid and reliable questionnaire, applied in a previous study (Garmaroudi and Moradi, 2010), were used to assess the socioeconomic status and its association with various health outcomes. The variables determining the socioeconomic status of the households in this questionnaire included household education level, education level of the spouse, residential property, residential housing infrastructure, and social welfare (e.g., possession of a car and personal computer), which were rated based on the participants’ responses. The maximum score of the questionnaire was 48. Based on the respondent’s mean score as well as the first and third quadrants, participants were divided into four levels of poor, moderate, favorable, and excellent according to the socioeconomic status of the household.
Food Insecurity: In the current research, the USDA questionnaire was administered to assess food security, which consisted of 18 items to evaluate the food security status of the households during 12 months. The questionnaire was validated for the Iranian population in a previous study (Ramakrishnan, 2004). To evaluate the responses, the positive responses (Often True, Sometimes True, Almost Every Month, and Number of Months) were interpreted as Yes and coded as one, while the negative responses (Only One/two months) were interpreted as No. In addition, the unanswered questions were coded as zero. Table 1 shows the assigned codes used to assess the status of food security (Ramesh et al., 2010).
The questionnaires were evaluated based on food security and food insecurity (3 ≥ Yes) in terms of marginal food security (Yes: 1-2). In its classification, the USDA has categorized marginal food security in the food security group. However, Laraia demonstrated that economic variables were correlated with social marginal food security and food insecurity (Laraia et al., 2006). In the present study, we examined the food security of the borde. According to the 18-item USDA food security status questionnaire, food security was classified into four groups of food secure, food insecure without starvation, food insecure with moderate starvation, and food insecure with severe starvation. Regarding food insecurity with severe starvation, all households with children had low food intake to the extent that their children experienced hunger. In some other households with children, this occurs at an earlier stage of severity. In the households with and without children, adults repeatedly experience more extensive reduction in their food intake. In our research, the participants were divided into two groups of food secure and food insecure. The scoring range of food security was 0-18 based on the number of positive responses (food secure: 0-2, food insecure: 3-18) (Bickel et al., 2000).
Ethical considerations: This study was derived from a master’s thesis approved by the Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.fm.REC.1395.359). Before data collection, written informed consents were obtained from all participants.
Data analysis: Data analysis was performed in SPSS version 16. Median and quadruple range were reported to describe the quantitative variables and Mann-Whitney U test was used to compare the quantitative variables between the two groups. As for the qualitative variables, frequency tables were drawn and Chi-square test was used. In order to evaluate the correlation between food insecurity and preeclampsia, multivariate logistic regression analysis was carried out by adjusting the confounding variables. The food security status of each household was determined based on their score. In all statistical analyses, P-value of less than 0.05 was significant.
Results
According to the information in Table 1 and considering that the two groups were matched, the median and quadruple range of age had no significant differences between the patients with preeclampsia and healthy participants (P = 0.32). Moreover, no significant differences were observed between groups in terms of their number of successful pregnancies, age at first pregnancy, number of children, number of family members, and number of children aged less than 18 years. However, the study groups were significantly different in terms of pre-pregnancy BMI and total number of pregnancies (P < 0.05) since the rate of these parameters was higher in the preeclampsia group compared to the control group. No significant differences were found between the food secure and insecure groups in terms of the median and quadruple range of age and pre-pregnancy BMI. However, the number of successful pregnancies, age at first pregnancy, total number of pregnancies, number of children, number of family members, and number of children aged less than 18 years were significantly different (P < 0.05).
Table 2 shows the results of tests for the qualitative variables between the preeclampsia and healthy participants. Accordingly, pre-pregnancy BMI, history of stillbirth, history of preterm labor, history of preeclampsia, family history of preeclampsia in the first-degree relatives (mother and sister), pre-pregnancy smoking habits, performing pre-pregnancy physical exercise, ethnicity, socioeconomic status, and food security were significantly different between the study groups (P < 0.05). Mann-Whitney U test was used to compare ranking of the qualitative variables. The results indicated that the frequency of food insecurity was 71% in the pregnant women with preeclampsia and 21% in the control group members.
As it can be seen from Table 3, the variables that affected preeclampsia and food insecurity (P < 0.01) were considered as the quantitative and qualitative confounding variables. However, the history of preeclampsia and pre-pregnancy smoking habits were considered as the influential factors in this regard; they were eliminated due to the lack of data in the control group. Multivariate logistic regression was applied using the significant variables regarding preeclampsia and food security (total number of pregnancies, history of stillbirth, history of preterm labor, ethnicity, and socioeconomic status). Moreover, the study variables had a significant correlation with preeclampsia and food insecurity (P < 0.001; odds ratio = 6.4).
| Table 1. Median and interquartile range of some demographic characteristics ::in quantitative variables in study groups | ||||||
| Variables | Median (Interquartile range) |
P-value a | Median (Interquartile range) |
P-value a | ||
| Case | Control | Safe | Unsafe | |||
| Age (y) | 32 (11) | 30 (10) | 0.32 | 32 (10) | 32 (10) | 0.18 |
| Pre-pregnancy body mass index (kg/m2( | 26 (7.6) | 24 (6.0) | 0.06 | 25.2 (6.2) | 24.7 (8.2) | 0.88 |
| Successful pregnancy | 1 (2) | 1 (2) | 0.19 | 1 (1) | 1 (2) | < 0.001 |
| Age at first pregnancy (y) | 23 (8.0) | 24 (6.0) | 0.11 | 24 (6.0) | 21.5 (7.5) | 0.03 |
| Total number of pregnancies | 3 (3) | 2 (2) | 0.03 | 2 (2.0) | 3 (2.2) | 0.003 |
| Number of children | 1 (2) | 1 (2) | 0.28 | 1 (1) | 1 (2) | < 0.001 |
| Number of family members | 3 (2) | 3 (2) | 0.26 | 3 (1) | 3 (2) | < 0.001 |
| Number of family member food board | 3 (2) | 3 (2) | 0.32 | 3 (1) | 3 (2) | < 0.001 |
| Number of children < 18 years | 1 (2) | 1 (2) | 0.27 | 1 (1) | 3 (2) | < 0.001 |
| A: Mann-Whiteny U test | ||||||
| Table 2. Correlation of qualitative variables in study groups | ||||||
|---|---|---|---|---|---|---|
| Variables | Case | Control | P-value | Security | Insecurity | P-value |
| N (%) | N (%) | N (%) | N (%) | |||
| Age (y) >20 20-24 25-29 30-34 ≥35 |
2 (2.0) 16 (16.0) 23 (23.0) 24 (24.0) 35 (35.0) |
4 (2.0) 29 (14.5) 49 (24.5) 49 (24.5) 69 (34.5) |
0.99a |
4 (2.1) 28 (15.0) 50 (26.7) 44 (23.5) 61 (32.6) |
2 (1.8) 17 (15.0) 22 (19.5) 29 (25.7) 43 (38.1) |
0.28a |
| Pre-pregnancy weight status Underweight Normal Overweight Obese |
2 (2.2) 40 (44.0) 21 (23.1) 28 (30.8) |
6 (3.2) 90 (48.4) 61 (32.8) 29 (15.6) |
0.07a |
6 (3.4) 79 (44.9) 61 (34.7) 30 (17.0) |
2 (2.0) 51 (50.5) 21 (20.8) 27 (26.7) |
0.72a |
| Successful pregnancies None One Two/more |
36 (36.0) 24 (24.0) 40 (40.0) |
76 (38.0) 67 (33.5) 57 (28.5) |
0.19a |
82 (43.9) 59 (31.6) 46 (24.6) |
30 (26.5) 32 (28.3) 51 (45.1) |
< 0.001a |
| Total number of pregnancies One Two Three/more |
25 (25.0) 23 (23.0) 52 (52.0) |
63 (31.5) 57 (28.5) 80 (40.0) |
0.06a |
62 (33.2) 54 (28.9) 71 (38.0) |
26 (23.0) 26 (23.0) 61 (54.0) |
0.008a |
| History of stillbirth | 17 (17.0) | 4 (2.0) | < 0.001b | 9 (4.8) | 12 (10.6) | 0.05b |
| History of abortion | 33 (33.0) | 55 (27.5) | 0.32b | 60 (32.1) | 28 (24.8) | 0.17b |
| History of preterm labor | 16 (16.0) | 1 (0.5) | < 0.001b | 6 (3.2) | 11 (9.7) | 0.01b |
| History of preeclampsia | 21 (21.0) | 0 (0) | < 0.001b | 7 (3.7) | 14 (12.4) | 0.004b |
| Family history of preeclampsia | 5 (5;0) | 0 (0) | < 0.001b | 3 (1.6) | 2 (1.8) | 0.91b |
| Pre- pregnancy smoking habits | 11 (11.0) | 0 (0) | < 0.001b | 4 (2.1) | 7 (6.2) | 0.07b |
| Pre-pregnancy physical exercise | 11 (11.0) | 0 (0) | 0.02b | 10 (5.3) | 7 (6.2) | 0.758b |
| Number of children None One More |
36 (36.0) 26 (26.0) 38 (38.0) |
75 (37.5) 67 (33.5) 58 (29.0) |
0.31a |
79 (42.2) 62 (33.2) 46 (24.6) |
32 (28.3) 31 (27.4) 50 (44.2) |
<0.001a |
| Number of family members Two More |
37 (37.0) 63 (63.0) |
78 (39.0) 122 (60.5) |
0.73b |
84 (44.9) 103 (55.1) |
31 (27.4) 82 (72.6) |
0.003b |
| Number of family member food board Two More |
37 (37.0) 63 (63.0) |
78 (39.0) 122 (61.0) |
0.73b |
84 (44.9) 103 (55.1) |
31 (27.4) 82 (72.6) |
0.003b |
| Number of children < 18 Years None One More |
36 (36.0) 28 (28.0) 36 (36.0) |
78 (39.0) 66 (33.0) 56 (28.0) |
0.29a |
83 (44.4) 59 (31.6) 45 (24.1) |
31 (27.4) 35 (31.0) 47 (41.6) |
< 0.001a |
| Ethnicity Persian Non-Persian |
74 (74.0) 26 (26.0) |
192 (96.0) 8 (4.0) |
< 0.001b |
179 (95.7) 8 (4.3) |
87 (77.0) 26 (23.0) |
< 0.001b |
| Socioeconomic status Poor Average Good Excellent |
47 (47.0) 30 (30.0) 15 (15.0) 8 (8.0) |
30 (15.0) 60 (30.0) 57 (28.5) 53 (26.5) |
< 0.001a |
23 (12.3) 51 (27.3) 60 (32.1) 53 (28.3) |
54 (47.8) 39 (34.5) 12 (10.6) 8 (7.1) |
< 0.001a |
| Food security status Security Insecurity |
29 (29.0) 71 (71.0) |
158 (79.0) 42 (21.0) |
< 0.001b | - |
- |
- |
| a: Mann-Whitney U test; b: Chi-square test | ||||||
| Table 3. Multivariate logistic regression analysis to control quantitative and qualitative confounding variables | ||||||||
| Variables | B | SEa | P-value. | Odds | 95% CI | |||
| Lower | Upper | |||||||
| Food security status |
Security | Reference | ||||||
| Insecurity | 1.86 | 0.33 | < 0.001 | 6.4 | 3.3 | 12.4 | ||
| Total number of pregnancies |
Three/more | Reference | ||||||
| Two | 0.28 | 0.40 | 0.87 | 1.3 | 0.6 | 2.8 | ||
| One | 0.54 | 0.39 | 0.16 | 1.7 | 0.8 | 3.7 | ||
| History of stillbirth |
No | Reference | ||||||
| Yes | 1.58 | 0.75 | 0.03 | 4.8 | 1.1 | 21.1 | ||
| History of preterm labor |
No | Reference | ||||||
| Yes | 3.36 | 1.17 | 0.004 | 29.1 | 2.8 | 292.3 | ||
| Ethnicity |
Persian | Reference | ||||||
| Non-Persian | 1.56 | 0.50 | 0.002 | 4.7 | 1.7 | 12.7 | ||
| Socioeconomic status |
Good/Excellent | Reference | ||||||
| Moderate | 0.32 | 0.39 | 0.41 | 1.3 | 0.6 | 2.9 | ||
| Low | 1.00 | 0.42 | 0.01 | 2.7 | 1.1 | 6.2 | ||
| a: standard error | ||||||||
Discussion
The findings of this research indicated a significant correlation between food insecurity and preeclampsia as an influential factor. Moreover, the risk of preeclampsia in the individuals with food insecurity was six times higher compared to the food secure participants.
In a study, Hajji et al. evaluated family food insecurity and its association with some pregnancy complications, reporting significant correlations between food insecurity, diabetes, and hypertension during pregnancy (Hojaji et al., 2015). Furthermore, Seligman et al. investigated the association of food insecurity with chronic diseases in low-income adults, denoting that food insecurity was associated with hypertension and diabetes (Seligman et al., 2010).
In another research, Laraia et al. determined the correlation between household food insecurity
and pregnancy complications, observing that among various pregnancy complications, only diabetes was associated with household insecurity, while it had no correlation with pregnancy and anemia (Laraia et al., 2010).
Background reasons such as low socioeconomic status and the subsequent food insecurity may lead to lower levels of food intake, reduced dietary diversity, and intake of high-calorie foods with low nutritious contents (Brantsæter et al., 2009, Longo-Mbenza et al., 2008, Ødegård et al., 2000, Torjusen et al., 2014). Discrepancy among the findings of various studies in this regard can be due to different prevalence of pregnancy complications and socioeconomic levels in various populations, which may influence family food insecurity and complications during pregnancy.
Adults living in food insecure households eat less than half a serving of fruits, vegetables, and dairy weekly and have lower intake levels of micronutrients (e.g., group B vitamins, magnesium, iron, zinc, and calcium) (Dixon et al., 2001, Lee and Frongillo, 2001, Tarasuk and Beaton, 1999). This food pattern is likely to increase the risk of chronic diseases and complications during pregnancy (Klesges et al., 2001, Vozoris and Tarasuk, 2003).
The correlation between dietary nutrition components and preeclampsia was investigated in several case-control and cohort studies and the risk of preeclampsia was associated with the intake of high-calorie foods, sugary drinks, polyunsaturated fatty acids, and trans fatty acids. However, the high intake of milk and appropriate intake of vitamin D were associated with the reduced risk of preeclampsia (Bodnar et al., 2007, Chavarro et al., 2011, Clausen et al., 2001, Haugen et al., 2009, Oken et al., 2007, Olafsdottir et al., 2006).
Several epidemiological studies also indicated that consumption of fruits, vegetables, and dietary fiber was associated with the reduced risk of preeclampsia (Brantsæter et al., 2009, Longo-Mbenza et al., 2008, Qiu et al., 2008, Torjusen et al., 2014), which affect preeclampsia through anti-inflammatory mechanisms (North et al., 2009). Moreover, recent findings denoted that deficiency in magnesium, vitamins A, vitamin C, folic acid, and calcium may increase the risk of preeclampsia (Bodnar et al., 2006, Hofmeyr et al., 2014, Jain et al., 2010, Wen et al., 2008).
Vegetables are rich in micronutrients such as phytochemicals, antioxidants, vitamins and minerals, and fiber, while processed foods contain sugars, salt, and saturated fatty acids. None of these dietary components is responsible for the positive results in the cases mentioned earlier. Overall, the interactions between many high-quality dietary components play a key role in disease prevention (Brantsæter et al., 2009).
To date, clinical trials extensively investigated the correlation between dietary supplements and preeclampsia, while further research is urgently required on the diet of pregnant women during pregnancy. The findings in this regard indicated that absorption of nutrients through diet may have more beneficial health effects compared to supplementation (Lichtenstein and Russell, 2005).
Findings indicated the significant correlation of poor socioeconomic status as a risk factor with the prediction of preeclampsia. Furthermore, the probability of developing preeclampsia was twice higher in individuals with low socioeconomic levels compared to those with favorable socioeconomic status. It is also notable that socioeconomic status was determined as a major risk factor for obesity, hypertension, metabolic syndrome, and cardiovascular diseases (Langenberg et al., 2003, Loucks et al., 2007, Mackenbach et al., 2000, Vargas et al., 2000). In addition, it may be associated with the increased risk of preeclampsia.
In this regard, Dlamini et al. reported that poor socioeconomic status was associated with the risk of preeclampsia in pregnant women in the developing countries (Dlamini, 1996). Moreover, Haelterman et al. reported that the risk of preeclampsia was twice higher in pregnant women with poor socioeconomic status compared to those with favorable socioeconomic status (Haelterman et al., 2003). Another study in Mexico also proposed similar results (Cerón-Mireles et al., 2001). Our findings are inconsistent with the research by Gudmundsson et al., who reported that the risk of preeclampsia was not significantly correlated with the low socioeconomic level in women; it is also notable that the mentioned study was conducted in the developing countries (Gudmundsson et al., 1997). Low socioeconomic status is considered to be a major risk factor for preeclampsia and poor socioeconomic status is associated with various nutritional issues, reduced prenatal care, and inappropriate health status (Ramesh et al., 2014).
The main strength of the current research was that all data were obtained by conducting interviews with the participants. In the cases where no appropriate place or time could be set with the participant for the interviews, data collection was performed via phone calls. Since questions in the food security questionnaire could not be answered properly through phone calls, they might have been affected by the unfavorable conditions while answering the questions. Another strength of the study was its case-control design; although several studies investigated the correlation between diet and preeclampsia, this was the first study to evaluate diet from the perspective of food insecurity.
One of the limitations of this study was that its sampling was limited to two hospitals in Mashhad city over a limited period of time. Furthermore, the non-blindness of the study procedure and possible effects of the confounding variables (e.g., employment status of the pregnant women, stress during pregnancy) could have affected the study outcomes.
Conclusion
After adjusting the confounding factors, findings of the study indicated that living in food insecure households was associated with the risk of preeclampsia during pregnancy. Considering the influential factors in this regard, preventive measures must be taken into account to provide practical solutions to the relevant organizations and prevent food insecurity in households. Since socioeconomic status was observed as the most important determinant of food insecurity, appropriate strategies should be adopted to improve the socioeconomic status of households and provide the required facilities to reduce the prevalence of food insecurity in high-risk households.
Acknowledgments
The authors wish to thank all participants of this study for their cooperation. This study was financially supported by Mashhad University of Medical Sciences (MUMS).
Authors' contributions
Bigdeli J and Safarian M initially conceptualized and designed the study. Rangbar G, Jarahi L, Khadem Ghaebi N, and Soleimani D upgraded the protocol design and contributed to obtaining initial funding. The manuscript was written by Bigdeli J and reviewed by all members.Jarahi L was responsible for design optimizing and statistical analysis. All authors read and approved the final manuscript.
Conflicts of interest
None declared.
References
The findings of this research indicated a significant correlation between food insecurity and preeclampsia as an influential factor. Moreover, the risk of preeclampsia in the individuals with food insecurity was six times higher compared to the food secure participants.
In a study, Hajji et al. evaluated family food insecurity and its association with some pregnancy complications, reporting significant correlations between food insecurity, diabetes, and hypertension during pregnancy (Hojaji et al., 2015). Furthermore, Seligman et al. investigated the association of food insecurity with chronic diseases in low-income adults, denoting that food insecurity was associated with hypertension and diabetes (Seligman et al., 2010).
In another research, Laraia et al. determined the correlation between household food insecurity
and pregnancy complications, observing that among various pregnancy complications, only diabetes was associated with household insecurity, while it had no correlation with pregnancy and anemia (Laraia et al., 2010).
Background reasons such as low socioeconomic status and the subsequent food insecurity may lead to lower levels of food intake, reduced dietary diversity, and intake of high-calorie foods with low nutritious contents (Brantsæter et al., 2009, Longo-Mbenza et al., 2008, Ødegård et al., 2000, Torjusen et al., 2014). Discrepancy among the findings of various studies in this regard can be due to different prevalence of pregnancy complications and socioeconomic levels in various populations, which may influence family food insecurity and complications during pregnancy.
Adults living in food insecure households eat less than half a serving of fruits, vegetables, and dairy weekly and have lower intake levels of micronutrients (e.g., group B vitamins, magnesium, iron, zinc, and calcium) (Dixon et al., 2001, Lee and Frongillo, 2001, Tarasuk and Beaton, 1999). This food pattern is likely to increase the risk of chronic diseases and complications during pregnancy (Klesges et al., 2001, Vozoris and Tarasuk, 2003).
The correlation between dietary nutrition components and preeclampsia was investigated in several case-control and cohort studies and the risk of preeclampsia was associated with the intake of high-calorie foods, sugary drinks, polyunsaturated fatty acids, and trans fatty acids. However, the high intake of milk and appropriate intake of vitamin D were associated with the reduced risk of preeclampsia (Bodnar et al., 2007, Chavarro et al., 2011, Clausen et al., 2001, Haugen et al., 2009, Oken et al., 2007, Olafsdottir et al., 2006).
Several epidemiological studies also indicated that consumption of fruits, vegetables, and dietary fiber was associated with the reduced risk of preeclampsia (Brantsæter et al., 2009, Longo-Mbenza et al., 2008, Qiu et al., 2008, Torjusen et al., 2014), which affect preeclampsia through anti-inflammatory mechanisms (North et al., 2009). Moreover, recent findings denoted that deficiency in magnesium, vitamins A, vitamin C, folic acid, and calcium may increase the risk of preeclampsia (Bodnar et al., 2006, Hofmeyr et al., 2014, Jain et al., 2010, Wen et al., 2008).
Vegetables are rich in micronutrients such as phytochemicals, antioxidants, vitamins and minerals, and fiber, while processed foods contain sugars, salt, and saturated fatty acids. None of these dietary components is responsible for the positive results in the cases mentioned earlier. Overall, the interactions between many high-quality dietary components play a key role in disease prevention (Brantsæter et al., 2009).
To date, clinical trials extensively investigated the correlation between dietary supplements and preeclampsia, while further research is urgently required on the diet of pregnant women during pregnancy. The findings in this regard indicated that absorption of nutrients through diet may have more beneficial health effects compared to supplementation (Lichtenstein and Russell, 2005).
Findings indicated the significant correlation of poor socioeconomic status as a risk factor with the prediction of preeclampsia. Furthermore, the probability of developing preeclampsia was twice higher in individuals with low socioeconomic levels compared to those with favorable socioeconomic status. It is also notable that socioeconomic status was determined as a major risk factor for obesity, hypertension, metabolic syndrome, and cardiovascular diseases (Langenberg et al., 2003, Loucks et al., 2007, Mackenbach et al., 2000, Vargas et al., 2000). In addition, it may be associated with the increased risk of preeclampsia.
In this regard, Dlamini et al. reported that poor socioeconomic status was associated with the risk of preeclampsia in pregnant women in the developing countries (Dlamini, 1996). Moreover, Haelterman et al. reported that the risk of preeclampsia was twice higher in pregnant women with poor socioeconomic status compared to those with favorable socioeconomic status (Haelterman et al., 2003). Another study in Mexico also proposed similar results (Cerón-Mireles et al., 2001). Our findings are inconsistent with the research by Gudmundsson et al., who reported that the risk of preeclampsia was not significantly correlated with the low socioeconomic level in women; it is also notable that the mentioned study was conducted in the developing countries (Gudmundsson et al., 1997). Low socioeconomic status is considered to be a major risk factor for preeclampsia and poor socioeconomic status is associated with various nutritional issues, reduced prenatal care, and inappropriate health status (Ramesh et al., 2014).
The main strength of the current research was that all data were obtained by conducting interviews with the participants. In the cases where no appropriate place or time could be set with the participant for the interviews, data collection was performed via phone calls. Since questions in the food security questionnaire could not be answered properly through phone calls, they might have been affected by the unfavorable conditions while answering the questions. Another strength of the study was its case-control design; although several studies investigated the correlation between diet and preeclampsia, this was the first study to evaluate diet from the perspective of food insecurity.
One of the limitations of this study was that its sampling was limited to two hospitals in Mashhad city over a limited period of time. Furthermore, the non-blindness of the study procedure and possible effects of the confounding variables (e.g., employment status of the pregnant women, stress during pregnancy) could have affected the study outcomes.
Conclusion
After adjusting the confounding factors, findings of the study indicated that living in food insecure households was associated with the risk of preeclampsia during pregnancy. Considering the influential factors in this regard, preventive measures must be taken into account to provide practical solutions to the relevant organizations and prevent food insecurity in households. Since socioeconomic status was observed as the most important determinant of food insecurity, appropriate strategies should be adopted to improve the socioeconomic status of households and provide the required facilities to reduce the prevalence of food insecurity in high-risk households.
Acknowledgments
The authors wish to thank all participants of this study for their cooperation. This study was financially supported by Mashhad University of Medical Sciences (MUMS).
Authors' contributions
Bigdeli J and Safarian M initially conceptualized and designed the study. Rangbar G, Jarahi L, Khadem Ghaebi N, and Soleimani D upgraded the protocol design and contributed to obtaining initial funding. The manuscript was written by Bigdeli J and reviewed by all members.Jarahi L was responsible for design optimizing and statistical analysis. All authors read and approved the final manuscript.
Conflicts of interest
None declared.
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Bodnar LM, Tang G, Ness RB, Harger G & Roberts JM 2006. Periconceptional multivitamin use reduces the risk of preeclampsia. American Journal of Epidemiology. 164 (5): 470-477.
Brantsæter AL, et al. 2009. A dietary pattern characterized by high intake of vegetables, fruits, and vegetable oils is associated with reduced risk of preeclampsia in nulliparous pregnant Norwegian women. Journal of Nutrition. 139 (6): 1162-1168.
Cerón-Mireles P, Harlow SD, Sanchez-Carrillo C & Nunez RM 2001. Risk factors for pre-eclampsia/eclampsia among working women in Mexico City. Paediatric and Perinatal Epidemiology. 15 (1): 40-46.
Chavarro JE, Halldorsson TI, Leth T, Bysted A & Olsen SF 2011. A prospective study of trans fat intake and risk of preeclampsia in Denmark. European Journal of Clinical Nutrition. 65 (8): 944.
Cheng M-H & Wang P-H 2009. Placentation abnormalities in the pathophysiology of preeclampsia. Expert Review of Molecular Diagnostics. 9 (1): 37-49.
Clausen T, et al. 2001. High intake of energy, sucrose, and polyunsaturated fatty acids is associated with increased risk of preeclampsia. American Journal of Obstetrics and Gynecology. 185 (2): 451-458.
Cudihy D & Lee R 2009. The pathophysiology of pre-eclampsia: current clinical concepts. Journal of Obstetrics and Gynaecology. 29 (7): 576-582.
Dixon LB, Winkleby MA & Radimer KL 2001. Dietary intakes and serum nutrients differ between adults from food-insufficient and food-sufficient families: Third National Health and Nutrition Examination Survey, 1988–1994. Journal of Nutrition. 131 (4): 1232-1246.
Dlamini NJ 1996. Factors associated with pre-eclampsia and quality care of affected teenagers during labour within health region H. in KwaZulu-Natal Province. In Nursing Science Department. University of Zululand.
Forouhari S, Zahra Y, Parsanezhad M & Raigan-Shirazi M 2009. The effects of regular exercise on pregnancy outcome. Iranian Red Crescent Medical Journal. 2009 (1): 57-60.
Garmaroudi GR & Moradi A 2010. Socio-economic status in Iran: a study of measurement index. Payesh (Health Monitor) 9(2): 137-144.
Gudmundsson S, Björgvinsdóttir L, Molin J, Gunnarsson G & Marsal K 1997. Socioeconomic status and perinatal outcome according to residence area in the city of Malmö. Acta Obstetricia et Gynecologica Scandinavica. 76 (4): 318-323.
Haelterman E, Qvist R, Barlow P & Alexander S 2003. Social deprivation and poor access to care as risk factors for severe pre-eclampsia. European Journal of Obstetrics & Gynecology and Reproductive Biology. 111 (1): 25-32.
Haugen M, et al. 2009. Vitamin D supplementation and reduced risk of preeclampsia in nulliparous women. Epidemiology. 720-726.
Hofmeyr GJ, Lawrie TA, Atallah ÁN, Duley L & Torloni MR 2014. Calcium supplementation during pregnancy for preventing hypertensive disorders and related problems. The Cochrane Library.
Hojaji E, Zavoshy R, Noroozi M & Jahanihashemi H 2015. Assessment of household food security and its relationship with some pregnancy complications. Journal of Mazandaran University of Medical Sciences. 25 (123): 87-98 [in Persian].
Ilekis JV, Reddy UM & Roberts JM 2007. Preeclampsia—a pressing problem: an executive summary of a National Institute of Child Health and Human Development workshop. Reproductive Sciences. 14 (6): 508-523.
Jain S, Sharma P, Kulshreshtha S, Mohan G & Singh S 2010. The role of calcium, magnesium, and zinc in pre-eclampsia. Biological Trace Element Research. 133 (2): 162-170.
Kendall A, Olson CM & Frongillo EA 1996. Relationship of hunger and food insecurity to food availability and consumption. Journal of the American Dietetic Association. 96 (10): 1019-1024.
Klesges LM, et al. 2001. Financial difficulty in acquiring food among elderly disabled women: results from the Women's Health and Aging Study. American Journal of Public Health. 91 (1): 68.
Lain KY & Roberts JM 2002. Contemporary concepts of the pathogenesis and management of preeclampsia. Journal of the American Medical Association. 287 (24): 3183-3186.
Langenberg C, Hardy R, Kuh D, Brunner E & Wadsworth M 2003. Central and total obesity in middle aged men and women in relation to lifetime socioeconomic status: evidence from a national birth cohort. Journal of Epidemiology & Community Health. 57 (10): 816-822.
Laraia BA, Siega-Riz AM & Gundersen C 2010. Household food insecurity is associated with self-reported pregravid weight status, gestational weight gain, and pregnancy complications. Journal of the American Dietetic Association. 110 (5): 692-701.
Laraia BA, Siega-Riz AM, Gundersen C & Dole N 2006. Psychosocial factors and socioeconomic indicators are associated with household food insecurity among pregnant women. Journal of Nutrition. 136 (1): 177-182.
Lee JS & Frongillo EA 2001. Nutritional and health consequences are associated with food insecurity among US elderly persons. Journal of Nutrition. 131 (5): 1503-1509.
Lichtenstein AH & Russell RM 2005. Essential Nutrients: Food or Supplements?: Where Should the Emphasis Be? Journal of the American Medical Association. 294 (3): 351-358.
Longo-Mbenza B, Tshimanga BK, Buassa-bu-Tsumbu B & Kabangu J 2008. Diets rich in vegetables and physical activity are associated with a decreased risk of pregnancy induced hypertension among rural women from Kimpese, DR Congo. Nigerian Journal of Medicine. 17 (3): 265-269.
Loucks EB, Rehkopf DH, Thurston RC & Kawachi I 2007. Socioeconomic disparities in metabolic syndrome differ by gender: evidence from NHANES III. Annals of Epidemiology. 17 (1): 19-26.
Lyall F & Belfort M 2007. Pre-eclampsia: etiology and clinical practice. Cambridge University Press.
Mackenbach JP, Cavelaars A, Kunst AE & Groenhof F 2000. Socioeconomic inequalities in cardiovascular disease mortality. An international study. European Heart Journal. 21 (14): 1141-1151.
Mozayeni M, Dorosty-Motlagh A, Eshraghian M & Davaei M 2014. Relationship between food sequrity and stress in pregnant mothers and low birth weight infant in child birth conducted in 2020 in tehran akbar abadi hospital. International Journal of current life science. 4: 2915-2921.
North C, Venter C & Jerling J 2009. The effects of dietary fibre on C-reactive protein, an inflammation marker predicting cardiovascular disease. European Journal of Clinical Nutrition. 63 (8): 921.
Ødegård RA, Vatten LJ, Nilsen ST, Salvesen KÅ & Austgulen R 2000. Risk factors and clinical manifestations of pre‐ eclampsia. BJOG: An International Journal of Obstetrics & Gynaecology. 107 (11): 1410-1416.
Oken E, et al. 2007. Diet during pregnancy and risk of preeclampsia or gestational hypertension. Annals of Epidemiology. 17 (9): 663-668.
Olafsdottir AS, et al. 2006. Relationship between high consumption of marine fatty acids in early pregnancy and hypertensive disorders in pregnancy. International Journal of Obstetrics & Gynaecology. 113 (3): 301-309.
Osungbade KO & Ige OK 2011. Public health perspectives of preeclampsia in developing countries: implication for health system strengthening. Journal of Pregnancy. 2011.
Pathak P, et al. 2004. Prevalence of multiple micronutrient deficiencies amongst pregnant women in a rural area of Haryana. Indian Journal of Pediatrics. 71 (11): 1007-1014.
Qiu C, Coughlin KB, Frederick IO, Sorensen TK & Williams MA 2008. Dietary fiber intake in early pregnancy and risk of subsequent preeclampsia. American Journal of Hypertension. 21 (8): 903-909.
Rajizadeh A, et al. 2019. Food Security Status of Pregnant Women in Yazd, Iran, 2014-2015. Journal of Nutrition and Food Security. 4 (3): 152-160.
Ramakrishnan U 2004. Nutrition and low birth weight: from research to practice. American Jjournal of Clinical Nutrition. 79 (1): 17-21.
Ramesh K, Gandhi S & Rao V 2014. Socio-demographic and other risk factors of pre eclampsia at a tertiary care hospital, karnataka: case control study. Journal of Clinical and Diagnostic Rresearch. 8 (9): JC01.
Ramesh T, Dorosty Motlagh A & Abdollahi M 2010. Prevalence of household food insecurity in the City of Shiraz and its association with socio-economic and demographic factors, 2008. Iranian Journal of Nutrition Sciences & Food Technology. 4 (4): 53-64.
Ronsmans C, Graham WJ & group LMSSs 2006. Maternal mortality: who, when, where, and why. Lancet. 368 (9542): 1189-1200.
Rose D & Oliveira V 1997. Nutrient intakes of individuals from food-insufficient households in the United States. American Journal of Public Health. 87 (12): 1956-1961.
Seligman HK, Laraia BA & Kushel MB 2010. Food insecurity is associated with chronic disease among low-income NHANES participants. Journal of Nutrition. 140 (2): 304-310.
Sibai BM, et al. 2008. Serum inhibin A and angiogenic factor levels in pregnancies with previous preeclampsia and/or chronic hypertension: are they useful markers for prediction of subsequent preeclampsia? American Journal of Obstetrics and Gynecology. 199 (3): 268. e261-268. e269.
Tarasuk VS & Beaton GH 1999. Women’s dietary intakes in the context of household food insecurity. Journal of Nutrition. 129 (3): 672-679.
Torjusen H, et al. 2014. Reduced risk of pre-eclampsia with organic vegetable consumption: results from the prospective Norwegian Mother and Child Cohort Study. BMJ open. 4 (9): e006143.
Trogstad L, Magnus P, Skjærven R & Stoltenberg C 2008. Previous abortions and risk of pre-eclampsia. International Journal of Epidemiology. 37 (6): 1333-1340.
Vargas CM, Ingram DD & Gillum RF 2000. Incidence of Hypertension and Educational Attainment The NHANES I Epidemiologic Followup Study. American Journal of Epidemiology. 152 (3): 272-278.
Villar K, et al. 2003. Eclampsia and pre-eclampsia: a health problem for 2000 years. Preeclampsia. 189: 207.
von Dadelszen P & Magee L 2008. What matters in preeclampsia are the associated adverse outcomes: the view from Canada. Current Opinion in Obstetrics and Gynecology. 20 (2): 110-115.
Vozoris NT & Tarasuk VS 2003. Household food insufficiency is associated with poorer health. Journal of Nutrition. 133 (1): 120-126.
Weigel MM, Armijos RX, Hall YP, Ramirez Y & Orozco R 2007. The household food insecurity and health outcomes of US–Mexico border migrant and seasonal farmworkers. Journal of Immigrant and Minority Health. 9 (3): 157-169.
Wen SW, et al. 2008. Folic acid supplementation in early second trimester and the risk of preeclampsia. American Journal of Obstetrics and Gynecology. 198 (1): 45. e41-45. e47.
Xu H, Shatenstein B, Luo Z-C, Wei S & Fraser W 2009. Role of nutrition in the risk of preeclampsia. Nutrition Reviews. 67 (11): 639-657.
Yazdanpanahi Z, Forouhari S & Parsanezhad M 2008. Prepregnancy body mass index and gestational weight gain and their association with some pregnancy outcomes. Iranian Red Crescent Medical Journal. 2008 (4):326-331.
Type of article: orginal article |
Subject:
public specific
Received: 2020/05/3 | Published: 2021/01/13 | ePublished: 2021/01/13
Received: 2020/05/3 | Published: 2021/01/13 | ePublished: 2021/01/13
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29. Lee JS & Frongillo EA 2001. Nutritional and health consequences are associated with food insecurity among US elderly persons. Journal of Nutrition. 131 (5): 1503-1509.
30. Lichtenstein AH & Russell RM 2005. Essential Nutrients: Food or Supplements?: Where Should the Emphasis Be? Journal of the American Medical Association. 294 (3): 351-358.
31. Longo-Mbenza B, Tshimanga BK, Buassa-bu-Tsumbu B & Kabangu J 2008. Diets rich in vegetables and physical activity are associated with a decreased risk of pregnancy induced hypertension among rural women from Kimpese, DR Congo. Nigerian Journal of Medicine. 17 (3): 265-269.
32. Loucks EB, Rehkopf DH, Thurston RC & Kawachi I 2007. Socioeconomic disparities in metabolic syndrome differ by gender: evidence from NHANES III. Annals of Epidemiology. 17 (1): 19-26.
33. Lyall F & Belfort M 2007. Pre-eclampsia: etiology and clinical practice. Cambridge University Press.
34. Mackenbach JP, Cavelaars A, Kunst AE & Groenhof F 2000. Socioeconomic inequalities in cardiovascular disease mortality. An international study. European Heart Journal. 21 (14): 1141-1151.
36. Mozayeni M, Dorosty-Motlagh A, Eshraghian M & Davaei M 2014. Relationship between food sequrity and stress in pregnant mothers and low birth weight infant in child birth conducted in 2020 in tehran akbar abadi hospital. International Journal of current life science. 4: 2915-2921.
37. North C, Venter C & Jerling J 2009. The effects of dietary fibre on C-reactive protein, an inflammation marker predicting cardiovascular disease. European Journal of Clinical Nutrition. 63 (8): 921.
38. Ødegård RA, Vatten LJ, Nilsen ST, Salvesen KÅ & Austgulen R 2000. Risk factors and clinical manifestations of pre‐eclampsia. BJOG: An International Journal of Obstetrics & Gynaecology. 107 (11): 1410-1416.
40. Oken E, et al. 2007. Diet during pregnancy and risk of preeclampsia or gestational hypertension. Annals of Epidemiology. 17 (9): 663-668.
41. Olafsdottir AS, et al. 2006. Relationship between high consumption of marine fatty acids in early pregnancy and hypertensive disorders in pregnancy. International Journal of Obstetrics & Gynaecology. 113 (3): 301-309.
42. Osungbade KO & Ige OK 2011. Public health perspectives of preeclampsia in developing countries: implication for health system strengthening. Journal of Pregnancy. 2011.
43. Pathak P, et al. 2004. Prevalence of multiple micronutrient deficiencies amongst pregnant women in a rural area of Haryana. Indian Journal of Pediatrics. 71 (11): 1007-1014.
44. Qiu C, Coughlin KB, Frederick IO, Sorensen TK & Williams MA 2008. Dietary fiber intake in early pregnancy and risk of subsequent preeclampsia. American Journal of Hypertension. 21 (8): 903-909.
45. Rajizadeh A, et al. 2019. Food Security Status of Pregnant Women in Yazd, Iran, 2014-2015. Journal of Nutrition and Food Security. 4 (3): 152-160.
46. Ramakrishnan U 2004. Nutrition and low birth weight: from research to practice. American Jjournal of Clinical Nutrition. 79 (1): 17-21.
47. Ramesh K, Gandhi S & Rao V 2014. Socio-demographic and other risk factors of pre eclampsia at a tertiary care hospital, karnataka: case control study. Journal of Clinical and Diagnostic Rresearch. 8 (9): JC01.
48. Ramesh T, Dorosty Motlagh A & Abdollahi M 2010. Prevalence of household food insecurity in the City of Shiraz and its association with socio-economic and demographic factors, 2008. Iranian Journal of Nutrition Sciences & Food Technology. 4 (4): 53-64.
49. Ronsmans C, Graham WJ & group LMSSs 2006. Maternal mortality: who, when, where, and why. Lancet. 368 (9542): 1189-1200.
50. Rose D & Oliveira V 1997. Nutrient intakes of individuals from food-insufficient households in the United States. American Journal of Public Health. 87 (12): 1956-1961.
51. Seligman HK, Laraia BA & Kushel MB 2010. Food insecurity is associated with chronic disease among low-income NHANES participants. Journal of Nutrition. 140 (2): 304-310.
52. Sibai BM, et al. 2008. Serum inhibin A and angiogenic factor levels in pregnancies with previous preeclampsia and/or chronic hypertension: are they useful markers for prediction of subsequent preeclampsia? American Journal of Obstetrics and Gynecology. 199 (3): 268. e261-268. e269.
53. Tarasuk VS & Beaton GH 1999. Women’s dietary intakes in the context of household food insecurity. Journal of Nutrition. 129 (3): 672-679.
54. Torjusen H, et al. 2014. Reduced risk of pre-eclampsia with organic vegetable consumption: results from the prospective Norwegian Mother and Child Cohort Study. BMJ open. 4 (9): e006143.
55. Trogstad L, Magnus P, Skjærven R & Stoltenberg C 2008. Previous abortions and risk of pre-eclampsia. International Journal of Epidemiology. 37 (6): 1333-1340.
56. Vargas CM, Ingram DD & Gillum RF 2000. Incidence of Hypertension and Educational Attainment The NHANES I Epidemiologic Followup Study. American Journal of Epidemiology. 152 (3): 272-278.
57. Villar K, et al. 2003. Eclampsia and pre-eclampsia: a health problem for 2000 years. Preeclampsia. 189: 207.
58. von Dadelszen P & Magee L 2008. What matters in preeclampsia are the associated adverse outcomes: the view from Canada. Current Opinion in Obstetrics and Gynecology. 20 (2): 110-115.
59. Vozoris NT & Tarasuk VS 2003. Household food insufficiency is associated with poorer health. Journal of Nutrition. 133 (1): 120-126.
60. Weigel MM, Armijos RX, Hall YP, Ramirez Y & Orozco R 2007. The household food insecurity and health outcomes of US–Mexico border migrant and seasonal farmworkers. Journal of Immigrant and Minority Health. 9 (3): 157-169.
61. Wen SW, et al. 2008. Folic acid supplementation in early second trimester and the risk of preeclampsia. American Journal of Obstetrics and Gynecology. 198 (1): 45. e41-45. e47.
62. Xu H, Shatenstein B, Luo Z-C, Wei S & Fraser W 2009. Role of nutrition in the risk of preeclampsia. Nutrition Reviews. 67 (11): 639-657.
63. Yazdanpanahi Z, Forouhari S & Parsanezhad M 2008. Prepregnancy body mass index and gestational weight gain and their association with some pregnancy outcomes. Iranian Red Crescent Medical Journal. 2008 (4): 326-331.
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