The relationship between maternal dietary behaviors and pregnancy outcomes among postpartum women in Iran: a cross-sectional study

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Poor nutrition may increase the risk of complications such as gestational diabetes mellitus (GDM), preeclampsia, preterm birth, and low birth weight (LBW). This study aimed to investigate the associations between maternal dietary behaviors and pregnancy outcomes among postpartum women attending healthcare centers in Saveh, Iran in 2024. Methods This cross-sectional study included 404 postpartum women who were selected via two-stage cluster random sampling. Data were collected using a validated 21-item dietary behavior questionnaire, a demographic questionnaire, and a pregnancy outcome checklist. The dietary behaviors were categorized as unhealthy (0–33.3), suboptimal (33.4–66.6), and healthy (> 66.6). Outcomes including GDM, preeclampsia, macrosomia, LBW, preterm birth, and small for gestational age (SGA), were assessed on days 3–5 postpartum. Statistical analyses, including one-way ANOVA, the chi-square test, Pearson’s correlation, and logistic regression modeling, were performed via SPSS 19 (p < 0.05). Results The participants had a mean age of 28.6 ± 5.6 years. Dietary behaviors were distributed as follows: unhealthy (28.5%), suboptimal (40.8%), and healthy (30.7%). Among women with healthy dietary behaviors, 39.5% experienced adverse pregnancy outcomes, whereas among those with unhealthy dietary behaviors, 76.5% experienced adverse pregnancy outcomes. A significant correlation was found between dietary behavior and pregnancy outcomes (p < 0.001). Healthier dietary behaviors were associated with a 3% reduction in the odds of adverse outcomes (OR, 0.97; 95% CI, [0.96–0.98], p < 0.001). Conclusion Poor maternal dietary behavior is associated with negative pregnancy outcomes. Improving nutritional education and integrating dietary counseling into prenatal care could promote maternal and neonatal health outcomes. Maternal nutritional physiological phenomena dietary behavior pregnancy outcomes prenatal care Figures Figure 1 Introduction Proper nutrition during pregnancy is crucial for the health and well-being of both mothers and fetuses [ 1 ]. Inadequate maternal nutrition is a major contributor to maternal and newborn morbidity and mortality, particularly in low-income and middle-income countries [ 2 ]. Fetal growth and development rely on maternal dietary intake before and during pregnancy [ 1 , 3 , 4 ]. Barker’s hypothesis suggests that the intrauterine nutritional environment shapes long-term health outcomes through epigenetic mechanisms [ 5 ]. A balanced maternal diet supports optimal fetal development, whereas malnutrition and obesity are associated with adverse short- and long-term outcomes including low birth weight (LBW), preterm birth, gestational diabetes mellitus (GDM), and macrosomia [ 6 , 7 ]. To meet the increased energy and nutritional demands for fetal tissue development, pregnant women should adhere to a balanced diet [ 8 – 10 ]. However, many pregnant women lack sufficient knowledge of nutritional principles, resulting in dietary practices that lead to nutrient deficiencies or excesses, thereby compromising optimal pregnancy outcomes. [ 11 – 13 ]. For example, Misan et al. reported that 21.2% of pregnant women excluded fish and 8.2% avoided dairy products, often replacing them with an excessive consumption of white bread and sweets. Furthermore, studies indicate that only 44% of women adhere to the recommended minimum meal frequency during pregnancy, with 14% consuming only two meals per day [ 1 , 14 ]. Adverse dietary behaviors increase the risk of complications, including LBW, GDM, preeclampsia, impaired fetal neurodevelopment, cesarean delivery, and prolonged labor [ 2 , 15 , 16 ]. In Iran, where cultural and socioeconomic factors may exacerbate poor dietary practices, there is a paucity of research examining the link between maternal dietary behaviors and pregnancy outcomes [ 17 ]. Since this gap exists, it is important to implement strategies to optimize the maternal diet during the prepregnancy, pregnancy, and breastfeeding periods [ 18 ]. This study investigated the relationship between dietary behaviors and pregnancy outcomes among postpartum women visiting healthcare centers in Saveh, Iran, in 2024 to address this critical gap and inform prenatal care interventions. 2 Method 2.1 Study design and setting This cross-sectional study was conducted in public primary healthcare centers in Saveh, Iran, in 2024 to investigate the associations between maternal dietary behaviors and pregnancy outcomes among 404 postpartum women. 2.2 Participants and sampling The sampling method involves two-stage random clustering. Six health centers, each with an average of three health posts, were listed. The health posts were randomly selected during the first stage. The samples were randomly selected according to the population coverage in the second stage. Pregnant women were listed, randomly sampled and enrolled if eligible and provided consent, until the target sample size was reached. 2.3 Inclusion and exclusion criteria The inclusion criteria were Iranian nationality, being 20–40 years old during pregnancy, and no history of medical conditions such as cardiovascular, renal, diabetes, hypertension, thyroid, gastrointestinal, neurological, or psychiatric disorders. The exclusion criteria included participant dissatisfaction and inaccurate responses to the questionnaires; a documented history of neurological or psychiatric disorders; and the use of tobacco, alcohol, or other substance during pregnancy. 2.4 Data collection tools and procedure Data were collected via three tools to ensure that there were comprehensive measurements of sociodemographic characteristics, maternal dietary behavior, and pregnancy outcomes. The data collection process was systematically conducted on days 3–5 postpartum to ensure accuracy and reliability. 2.4.1 Sociodemographic information The demographic questionnaire included mother’s age, education, income, occupation, number of pregnancies and children, and place of residence. 2.4.2 Maternal dietary behavior assessment A validated 21-item maternal dietary questionnaire assessing five domains: dietary changes (5 items: food, fruit, vegetable, daily fruit, and grain), dairy food consumption (2 items: daily milk/yogurt and dairy), protein consumption (1 item: daily meat and legumes), supplement intake (3 items: folic acid, iron, and multivitamins), and complication management (10 items: behavior for nausea, constipation, heartburn, swelling, anemia, and urinary infection). Each item had 2–6 response options. A fully correct answer was given a score of 100, a completely incorrect answer was given a score of 0, and partially correct responses were given scores proportionately between 1 and 100. The item scores were summed and divided by the number of items to obtain the overall dietary behavior scores which were labeled unhealthy (0–33.3), suboptimal (33.4–66.6), or healthy (> 66.60). The validity of this tool has been confirmed by Charandabi et al. (2012) Cronbach's alpha was 0.87, and reliability was confirmed using the Spearman-Brown coefficient (r = 0.8) [ 19 ]. 2.4.3 Pregnancy outcomes checklist Outcomes such as preeclampsia, GDM, LBW, SGA, macrosomia, and preterm delivery, were assessed through interviews and maternal health histories on days 3–5 postpartum. 2.5 Data analysis Data were analyzed via SPSS 19 with one-way ANOVA for continuous variables, such as prepregnancy body mass index (BMI), and dietary behavior scores, and chi-square tests were used to assess associations between categorical variables (education, income, residence, pregnancy outcomes) and dietary behavior groups. Pearson’s correlation coefficient was used to evaluate the linear relationships between the dietary behavior scores and continuous variables. Binary logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between dietary behavior scores and pregnancy outcomes, adjusted for maternal age, number of pregnancies, number of children, residence, education, occupation, income, and BMI. Statistical significance was set at p < 0.05. 3 Results 3.1 Demographic and nutritional characteristics In total, 404 postpartum women (mean age of 28.64 ± 5.57 years) reported that 28.5% exhibited unhealthy eating behaviors, 40.8% had suboptimal eating behaviors, and 30.7% had healthy eating behaviors. Suboptimal dietary behaviors were observed among women with the highest number of pregnancies and children, body mass index (BMI), and urban residency. By contrast, women with healthy dietary behavior were more likely to be educated (p < 0,001), employed (p = 0.024), and have higher incomes (p < 0.001). The results revealed that maternal dietary behaviors were significantly associated with pregnancy outcomes (p < 0.001). LBW (19.1%,), SGA (8.7%), and preterm birth (3.5%) were significantly more prevalent in the unhealthy dietary behavior group, while the lowest rates were recorded in the healthy dietary behavior group, with only 1.6% reporting both SGA and preterm birth. (p < 0.001). Conversely, GDM, preeclampsia, and macrosomia were the most common suboptimal dietary behaviors (37.6%, 14.5%, and 3.6%, respectively). Overall, adverse pregnancy outcomes were significantly greater in the unhealthy dietary behavior group (76.5%) than in the suboptimal (66.7%) and healthy (39.5%) groups (p < 0.001). Notably, the majority of women with no adverse outcomes reported healthy dietary behavior (60.5%) (Table 1 ). Table 1 Descriptive statistics of demographic, and pregnancy outcomes on the basis of dietary behavior status 1 Characteristic Total (N = 404) Unhealthy dietary behavior 115 (28.5%) Suboptimal dietary behavior 165 (40.8%) Healthy dietary behavior 124 (30.7%) p-value Demographic variables Mother’s age (years) 28.64 ± 5.57 27.16 ± 6.35 29.78 ± 5.37 28.52 ± 4.71 < 0.001* Pregnancies number 1.46 ± 0.69 1.43 ± 0.66 1.51 ± 0.75 1.42 ± 0.63 0.494 Children number 0.44 ± 0.70 0.43 ± 0.70 0.52 ± 0.79 0.36 ± 0.56 0.182 Prepregnancy BMI (kg/m²) 24.44 ± 2.75 23.95 ± 3.80 25.00 ± 2.44 24.16 ± 1.69 0.003* Mother’s education < 0.001* Literature 66 (16.3%) 22 (19.1%) 31 (18.8%) 13 (10.5%) Diploma 215 (53.2%) 71 (61.7%) 91 (55.2%) 53 (42.7%) University Degree 123 (30.4%) 22 (19.1) 43 (26.1%) 58 (46.8) Father’s education 0.455 Illiteracy 96 (23.8%) 34 (29.6%) 34 (20.6%) 28 (22.6%) Diploma 269 (66.6%) 72 (62.6%) 115 (69.7%) 82 (66.1%) University degree 39 (9.7%) 9 (7.8%) 16 (9.7%) 14 (11.3%) Income <0.001* Low 38 (9.4%) 13 (11.3%) 19 (11.5%) 6 (4.8%) Moderate 176 (43.6%) 61 (53.0%) 77 (46.7%) 38 (30.6%) High 190 (47.0%) 41 (35.7%) 69 (41.8%) 80 (64.5%) Mother’s occupation 0.024* Unemployed 292 (72.3%) 84 (73.0%) 129 (78.2%) 79 (63.7%) Employed 112 (27.7%) 31 (27.0%) 36 (21.8%) 45 (36.3%) Father’s occupation 0.286 Self employed 164 (40.6%) 42 (36.5%) 75 (45.5%) 47 (37.9%) Worker 161 (39.9%) 55 (47.8%) 59 (35.8%) 47 (37.9%) Employer 33 (8.2%) 6 (5.2%) 13 (7.9%) 14 (11.3%) Unemployed 46 (11.4%) 12 (10.4%) 18 (10.9%) 16 (12.9%) Place of residence < 0.001* Urban 311 (77.0%) 75 (65.2%) 130 (78.8%) 106 (85.5%) Rural 93 (23.0%) 40 (34.8%) 35 (21.2%) 18 (14.5%) Pregnancy outcomes < 0.001* GDM a 121 (30.0%) 37 (32.2%) 62 (37.6%) 22 (17.7%) Preterm infant 9 (2.2%) 4 (3.5%) 3 (1.8%) 2 (1.6%) LBW b 45 (11.1%) 22 (19.1%) 9 (5.5%) 14 (11.3%) SGA c 18 (4.5%) 10 (8.7%) 6 (3.6%) 2 (1.6%) Preeclampsia 44 (10.9%) 14 (12.2%) 24 (14.5%) 6 (4.8%) Macrosomia 10 (2.5%) 1 (0.9%) 6 (3.6%) 2 (1.6%) Yes 247 (61.1%) 88 (76.5%) 110 (66.7%) 49 (39.5%) No 157 (38.9) 27 (23.5%) 55 (33.3%) 75 (60.5%) 1 Values are presented as means ± SDs for continuous variables (one-way ANOVA) and n (%)for categorical variables (χ² test). a GDM, gestational diabetes mellitus; b LBW, low birth weight; c SGA, small for gestational age. * Significant at 0.05 levels. 3.2 Pregnancy outcomes Among the postpartum women included, 30% had GDM and 11.1% had LBW. Preeclampsia (10.9%), SGA (4.5%), preterm birth (2.2%), and macrosomia (2.5%) were the other reported complications. However, a minority (38.9%) of the mothers, did not experience any complications (Fig. 1 ). 3.3 Analysis of the relationship between dietary behaviors and pregnancy outcome Table 2 indicates significant differences (P < 0.001) between the dietary behavior scores and BMI across the pregnancy outcome groups. Women without complicated outcomes had the highest dietary behavior scores (57.24 ± 20.71) as did those with a healthy BMI (23.99 ± 1.90). While the BMIs of the macrosomia and preeclampsia groups were greater (27.47 ± 1.94 and 26.29 ± 2.31, respectively), the preterm birth and LBW groups reported lower BMIs (20.89 ± 1.90 and 21.63 ± 1.86, respectively). Table 2 Comparison of dietary behavior scores and BMIs across pregnancy outcome groups 1 Pregnancy outcomes Dietary behavior scores Mean ± SD BMI Mean ± SD p-value GDM 46.05 ± 15.84 25.26 ± 2.73 Preterm Birth 37.86 ± 17.05 20.89 ± 1.90 LBW 44.25 ± 16.60 21.63 ± 1.86 < 0.001* SGA 43.30 ± 15.20 25.50 ± 4.29 Preeclampsia 43.94 ± 12.30 26.29 ± 2.31 Macrosomia 52.79 ± 13.33 27.47 ± 1.94 No Complication 57.24 ± 20.71 23.99 ± 1.90 1 The values are presented as means ± SDs based on the one-way ANOVA test. *Significant at 0.05 levels. 3.4 Odds of pregnancy outcomes Table 3 shows that after adjustment, improved dietary behavior scores were associated with a slightly lower risk of pregnancy complications by 3%. (OR, 0.97; 95% CI: [0.96 to 098]; p < 0.001). Table 3 The odds ratios of pregnancy outcomes on the basis of dietary behavior scores Dietary behaviors p-value OR 95% C.I. Lower Upper Model a Dietary Behaviors < 0.001* 0.96 0.95 0.97 Model2 b Dietary Behaviors < 0.001* 0.97 0.96 0.98 a Unadjusted model. b Adjusted for mother’s age, number of pregnancies, number of children, place of residence, education, occupation, income, and BMI. *Significant. 3.5 Relationship between Dietary behavior scores and demographic and nutritional variables The correlations between the dietary behavior scores and demographic and nutritional variables are shown in Table 4 . Dietary behavior scores were inversely correlated with pregnancy outcomes (r = -0.22, p < 0.001). A positive correlation with the mother’s age (r = 0.32, p < 0.001), education (r = 0.21, p < 0.001), and income (r = 0.26, p < 0.001), suggests that higher dietary scores are associated with these factors. Residence showed a weak negative correlation (r = -0.14, p = 0.003). No significant correlations were found with the prepregnancy BMI (r = 0.03, p = 0.515). Table 4 Correlations between dietary behavior scores and demographic and nutritional variables of postpartum women Dietary behavior scores Pregnancy outcomes Mother's age Pre-Pregnancy BMI Mother's education Place of residence Income Correlation -0.22 0.32 0.03 0.21 -0.14 0.26 p-value < 0.001* < 0.001* 0.515 < 0.001* 0.003* < 0.001* *Significant at 0.05 levels. 4 Discussion This study aimed to investigate the associations between maternal dietary behaviors and pregnancy outcomes among 404 postpartum women in Saveh, Iran in 2024. The findings indicated that healthy dietary behaviors were inversely associated with adverse pregnancy outcomes (p < 0.001). Women with healthy dietary behaviors had the lowest prevalence of pregnancy-related complications (39.5%). These data highlight the role of lifestyle and dietary modifications in reducing adverse pregnancy outcomes with long-term benefits to maternal health [ 20 , 21 ]. Similarly, some studies found that adherence to healthy dietary patterns during pregnancy was associated with fewer adverse outcomes [ 22 , 23 ]. The present study revealed that the majority of participants (40.8%) followed suboptimal dietary behavior, whereas 28.5% exhibited unhealthy dietary behavior, which may be influenced by cultural factors.[ 19 , 24 – 26 ]. Notably, gestational diabetes, preeclampsia, and macrosomia were prevalent in the suboptimal dietary behavior group (p < 0.001), which consequently had a higher BMI than the other groups did. This group appeared to follow a mixed diet, including some healthy foods (fruits, vegetables, and dairy), but in lower amounts than the healthy group, alongside high-calorie, high-fat, or high-sodium foods (red meat, cheese, and excessive carbohydrates, as indicated by their moderate scores. According to several studies, a high BMI, driven by calorie-dense diets, contributes to gestational diabetes via insulin resistance [ 27 , 28 ], preeclampsia through oxidative stress and vascular dysfunction [ 29 – 31 ], and macrosomia due to excessive fetal growth [ 32 ]. Diets high in red meat and cheese (which are rich in salt and saturated fat) can cause complications during pregnancy, including hypertension, preeclampsia, and gestational diabetes [ 33 , 34 ]. For example, a meta-analysis in 2014 showed a 33% reduction in preeclampsia occurrence due to dietary interventions during pregnancy [ 35 ]. In contrast, LBW (19.1%), SGA (8.7%), and preterm birth (3.5%) were significantly higher in the unhealthy dietary behavior group (p < 0.001). It is likely that deficiencies in essential nutrients (protein, carbohydrate, iron, folate, dairy, and calcium, contribute to these complications, which are typical of maternal malnutrition [ 36 ]. Several studies have also shown that low dietary diversity is a risk factor for low birth weight [ 37 ]. Similarly, maternal undernutrition has been associated with detrimental effects on birth weight, fetal length, and even fetal death [ 38 , 39 ]. Furthermore, a study in 2024 with 962 participants found that poor maternal nutritional status, especially low mid-upper arm circumference [MUAC], was associated with a higher risk of delivering SGA infants [ 40 ]. Overall, maintaining a normal BMI and adhering to a balanced diet to decrease preterm birth risk should not be underestimated, as demonstrated by a 2016 study that documented the positive effects of a diet high in fruits, vegetables, whole grains, fish, and adequate hydration on preterm birth risk, particularly in women with a history of preterm delivery [ 3 ]. Maternal dietary behaviors were significantly associated with maternal age, education, and family income [ 18 , 41 ]. A study 2019 reported that higher educational levels were linked to better dietary habits, probably due to a better understanding of nutritional guidance [ 13 ]. Studies on nutrition indicate that women with higher education levels have healthier dietary habits than do those with lower education levels. One possible explanation is that mothers with higher education levels are more capable of grasping and implementing nutritional recommendations [ 42 ]. Nevertheless, multiple factors contribute to poor dietary practices among mothers, including heavy workloads for working mothers, limited access to healthy foods such as vegetables, due to economic status, and inadequate knowledge. Studies have indicated that maternal nutrition is an important component of any strategy to reduce maternal and fetal mortality and improve pregnancy outcomes [ 37 , 43 ]. Nutrition during pregnancy is a significant modifiable factor during the prenatal period that influences birth outcomes [ 18 , 37 ]. Although pregnancy complications are multifactorial, personalized nutritional evaluation is necessary to identify deficiencies and mitigate adverse outcomes[ 44 – 47 ]. Thus, pregnant women are recommended personalized nutrition and lifestyle recommendations under the supervision of healthcare providers [ 18 ]. Optimal maternal and fetal health is fundamentally dependent on a woman’s nutritional status before and during pregnancy, highlighting the importance of personalized dietary interventions [ 6 ]. Strengths and limitations of the study A key strength of this study is its large sample size (n = 404), which increased the statistical power and reliability. The use of a validated dietary behavior questionnaire (Cronbach’s α = 0.87) ensured accurate and consistent data collection. Conducting interviews during the early postpartum period (days 3–5 after delivery) minimized recall bias, whereas real-world healthcare settings enhanced the external validity of the findings. However, the cross-sectional design limits the ability to establish causality between dietary behaviors and pregnancy outcomes. This study did not assess specific nutrient quantities, which could provide deeper insights into nutritional status. Although confounding variables were controlled through strict inclusion and exclusion criteria, unmeasured factors, such as physical activity, psychosocial stress, or genetic predispositions, may have influenced the results. Additionally, reliance on self-reported data may be influenced by recall errors or social desirability biases. Finally, the study’s focus on Saveh, Iran may limit the generalizability of the findings to populations with different socioeconomic or cultural contexts. 5 Conclusion This study highlights the significant relationship between maternal dietary behaviors and pregnancy outcomes. These findings suggest that inadequate dietary practices during pregnancy are associated with a greater incidence of complications such as GDM, preeclampsia, macrosomia, LBW, and preterm birth. Conversely, better dietary behavior scores were linked to more favorable outcomes, underscoring the critical role of maternal nutrition in prenatal care. These results emphasize the need for targeted nutritional education and interventions during pregnancy, particularly in at-risk populations. Integrating dietary counseling with routine maternal health services could improve maternal and neonatal health outcomes. Abbreviations GDM Gestational Diabetes Mellitus LBW Low Birth Weight SGA Small for Gestational Age BMI Body Mass Index IRB Institutional Review Board DHS Demographic and Health Surveys MUAC Mid-Upper Arm Circumference OR Odds Ratio CI Confidence Interval SPSS Statistical Package for the Social Sciences Declarations Authors' contributions S.T. contributed to the conceptualization, methodology, manuscript revision and editing, and provided scientific supervision of the study. Z.T. was responsible for data collection, statistical analysis, writing the original draft, and project administration. All authors critically reviewed and approved the final version of the manuscript. Funding No external funding was received for this study. The research was entirely self-funded and conducted independently of any institution or organization. Data availability The data analyzed and/or generated during the present study are available from the corresponding author and will be provided on reasonable request, provided that ethical compliance and participant confidentiality are strictly maintained. Acknowledgement We are highly thankful to the participants (mothers) for their valuable contribution to this study. We also extend our sincere appreciation to the officials of health care in Saveh, Iran, and to Saveh University of Medical Sciences for their kindness and collaboration. Ethical approval and consent to participate This study was approved by the Ethics Committee of Saveh University of Medical Sciences (IR.SAVEHUMS.REC.1401.008). The study was conducted in accordance with the ethical standards of the Declaration of Helsinki. All participants provided informed consent to take part in this research. For all illiterate participants, informed consent was obtained from their legal guardians or husbands. 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High double burden of child stunting and maternal overweight in the Republic of the Marshall Islands. Matern Child Nutr. 2020;16:e12832. Ambreen S, Yazdani N, Alvi AS, Qazi MF, Hoodbhoy Z. Association of maternal nutritional status and small for gestational age neonates in peri-urban communities of Karachi, Pakistan: findings from the PRISMA study. BMC Pregnancy Childbirth. 2024;24[1]:214. Mohammadi A, Effati-Daryani F, Ghelichkhani F, Zarei S, Mirghafourvand M. Effective factors on nutrition behaviors of pregnant women based on the beliefs, attitudes, subjective norms, and enabling factors model: A cross-sectional study. J Educ Health Promotion. 2022;11:12. Ługowska K, Kolanowski W. The nutritional behaviour of pregnant women in Poland. Int J Environ Res Public Health. 2019;16[22]:4357. Christian P. Maternal nutrition, health, and survival. Nutr Rev. 2002;60suppl5:S59–63. Feig DS, Shah BR, Lipscombe LL, Wu CF, Ray JG, Lowe J et al. Preeclampsia as a risk factor for diabetes: a population-based cohort study. PLoS Med. 2013;10[4]:e1001425. Li X, Zhang W, Lin J, Liu H, Yang Z, Teng Y, et al. Preterm birth, low birthweight, and small for gestational age among women with preeclampsia: Does maternal age matter? Pregnancy Hypertens. 2018;13:260–6. Benova L, Tunçalp Ö, Moran AC, Campbell OMR. Not just a number: examining coverage and content of antenatal care in low-income and middle-income countries. BMJ global health. 2018;3[2]:e000779. Miele MJ, Souza RT, Calderon IM, Feitosa FE, Leite DF, Rocha Filho EA et al. Maternal nutrition status associated with pregnancy-related adverse outcomes. Nutrients. 2021;13[7]:2398. Additional Declarations No competing interests reported. Supplementary Files EnglishMaternalNutritionQuestionnaire.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7040765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500490549,"identity":"40e82ef7-6938-4117-adc3-6a0c01b11b17","order_by":0,"name":"Zeinab Taleb","email":"","orcid":"","institution":"SR.C, Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Zeinab","middleName":"","lastName":"Taleb","suffix":""},{"id":500490550,"identity":"82dc6d2f-0ab9-488a-b7cd-f34476e32704","order_by":1,"name":"Sahar Taleb","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYFACHgjFB2QYJFTYAJmMjQeI0GLAwAZkFHw4kwbS0kC8lo8z2w6DxfBqkW8/e/BzBcMfeTb23oObedjO261tPwy0pcYmGpcWgzN5yZJnGAwM23jOJRvz8NxO3nYmEajlWFpuAy4tDDkGkg0MBoxtEjlmxjwSt5PNDgC1MDYcxqlFvv+N8U+gFvs2+Tfmv3kMziWbnX+IXwvDjRwzkC2JbRI8BoYzEg7Ymd0gYIvBjTdmlg0GxsltPHkJBh8OJCeY3QDakoDHL/L9OcY3GyrkbPvZzx4wSPxnZ292Pv3hgw81NrgdBg0EOEgEq0zAqxwN2JOieBSMglEwCkYGAABAPl/d+SAT4AAAAABJRU5ErkJggg==","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Taleb","suffix":""}],"badges":[],"createdAt":"2025-07-03 18:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7040765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7040765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89456085,"identity":"7c376eef-0f0d-4208-a9e7-93050e6aa09c","added_by":"auto","created_at":"2025-08-20 06:58:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86658,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of adverse pregnancy outcomes among postpartum women\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7040765/v1/0535ecdb6b3f87e272bda34d.jpg"},{"id":92089576,"identity":"2b0b8f9d-8725-4df3-992d-829b89360637","added_by":"auto","created_at":"2025-09-24 13:23:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1416960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7040765/v1/231ec2e6-7d8e-459a-9156-31778e96979a.pdf"},{"id":89453597,"identity":"3190130c-fb5a-4a5f-9045-1c4a8a4a4caa","added_by":"auto","created_at":"2025-08-20 06:42:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28580,"visible":true,"origin":"","legend":"","description":"","filename":"EnglishMaternalNutritionQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-7040765/v1/5e90ff9d9f8f339ea996a322.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between maternal dietary behaviors and pregnancy outcomes among postpartum women in Iran: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProper nutrition during pregnancy is crucial for the health and well-being of both mothers and fetuses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Inadequate maternal nutrition is a major contributor to maternal and newborn morbidity and mortality, particularly in low-income and middle-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Fetal growth and development rely on maternal dietary intake before and during pregnancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Barker\u0026rsquo;s hypothesis suggests that the intrauterine nutritional environment shapes long-term health outcomes through epigenetic mechanisms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A balanced maternal diet supports optimal fetal development, whereas malnutrition and obesity are associated with adverse short- and long-term outcomes including low birth weight (LBW), preterm birth, gestational diabetes mellitus (GDM), and macrosomia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To meet the increased energy and nutritional demands for fetal tissue development, pregnant women should adhere to a balanced diet [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, many pregnant women lack sufficient knowledge of nutritional principles, resulting in dietary practices that lead to nutrient deficiencies or excesses, thereby compromising optimal pregnancy outcomes. [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For example, Misan et al. reported that 21.2% of pregnant women excluded fish and 8.2% avoided dairy products, often replacing them with an excessive consumption of white bread and sweets. Furthermore, studies indicate that only 44% of women adhere to the recommended minimum meal frequency during pregnancy, with 14% consuming only two\u0026ensp;meals per day [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Adverse dietary behaviors increase the risk of complications, including LBW, GDM, preeclampsia, impaired fetal neurodevelopment, cesarean delivery, and prolonged labor [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In Iran, where cultural and socioeconomic factors may exacerbate poor dietary practices, there is a paucity of research examining the link between maternal dietary behaviors and pregnancy outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Since this gap exists, it is important to implement strategies to optimize the maternal diet during the prepregnancy, pregnancy, and breastfeeding periods [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study investigated the relationship between dietary behaviors and pregnancy outcomes among postpartum women visiting healthcare centers in Saveh, Iran, in 2024 to address this critical gap and inform prenatal care interventions.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and setting\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted in public primary healthcare centers in Saveh, Iran, in 2024 to investigate the associations between maternal dietary behaviors and pregnancy outcomes among 404 postpartum women.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participants and sampling\u003c/h2\u003e\u003cp\u003eThe sampling method involves two-stage random clustering. Six health centers, each with an average of three health posts, were listed. The health posts were randomly selected during the first stage. The samples were randomly selected according to the population coverage in the second stage. Pregnant women were listed, randomly sampled and enrolled if eligible and provided consent, until the target sample size was reached.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Inclusion and exclusion criteria\u003c/h2\u003e\u003cp\u003eThe inclusion criteria were Iranian nationality, being 20\u0026ndash;40 years old during pregnancy, and no history of medical conditions such as cardiovascular, renal, diabetes, hypertension, thyroid, gastrointestinal, neurological, or psychiatric disorders.\u003c/p\u003e\u003cp\u003eThe exclusion criteria included participant dissatisfaction and inaccurate responses to the questionnaires; a documented history of neurological or psychiatric disorders; and the use of tobacco, alcohol, or other substance during pregnancy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data collection tools and procedure\u003c/h2\u003e\u003cp\u003eData were collected via three tools to ensure that there were comprehensive measurements of sociodemographic characteristics, maternal dietary behavior, and pregnancy outcomes. The data collection process was systematically conducted on days 3\u0026ndash;5 postpartum to ensure accuracy and reliability.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Sociodemographic information\u003c/h2\u003e\u003cp\u003eThe demographic questionnaire included mother\u0026rsquo;s age, education, income, occupation, number of pregnancies and children, and place of residence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Maternal dietary behavior assessment\u003c/h2\u003e\u003cp\u003eA validated 21-item maternal dietary questionnaire assessing five domains: dietary changes (5 items: food, fruit, vegetable, daily fruit, and grain), dairy food consumption (2 items: daily milk/yogurt and dairy), protein consumption (1 item: daily meat and legumes), supplement intake (3 items: folic acid, iron, and multivitamins), and complication management (10 items: behavior for nausea, constipation, heartburn, swelling, anemia, and urinary infection). Each item had 2\u0026ndash;6 response options. A fully correct answer was given a score of 100, a completely incorrect answer was given a score of 0, and partially correct responses were given scores proportionately between 1 and 100. The item scores were summed and divided by the number of items to obtain the overall dietary behavior scores which were labeled unhealthy (0\u0026ndash;33.3), suboptimal (33.4\u0026ndash;66.6), or healthy (\u0026gt;\u0026thinsp;66.60). The validity of this tool has been confirmed by Charandabi et al. (2012) Cronbach's alpha was 0.87, and reliability was confirmed using the Spearman-Brown coefficient (r\u0026thinsp;=\u0026thinsp;0.8) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Pregnancy outcomes checklist\u003c/h2\u003e\u003cp\u003eOutcomes such as preeclampsia, GDM, LBW, SGA, macrosomia, and preterm delivery, were assessed through interviews and maternal health histories on days 3\u0026ndash;5 postpartum.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data analysis\u003c/h2\u003e\u003cp\u003eData were analyzed via SPSS 19 with one-way ANOVA for continuous variables, such as prepregnancy body mass index (BMI), and dietary behavior scores, and chi-square tests were used to assess associations between categorical variables (education, income, residence, pregnancy outcomes) and dietary behavior groups. Pearson\u0026rsquo;s correlation coefficient was used to evaluate the linear relationships between the dietary behavior scores and continuous variables. Binary logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between dietary behavior scores and pregnancy outcomes, adjusted for maternal age, number of pregnancies, number of children, residence, education, occupation, income, and BMI. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic and nutritional characteristics\u003c/h2\u003e\u003cp\u003eIn total, 404 postpartum women (mean age of 28.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57 years) reported that 28.5% exhibited unhealthy eating behaviors, 40.8% had suboptimal eating behaviors, and 30.7% had healthy eating behaviors. Suboptimal dietary behaviors were observed among women with the highest number of pregnancies and children, body mass index (BMI), and urban residency.\u003c/p\u003e\u003cp\u003eBy contrast, women with healthy dietary behavior were more likely to be educated (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), employed (p\u0026thinsp;=\u0026thinsp;0.024), and have higher incomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results revealed that maternal dietary behaviors were significantly associated with pregnancy outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). LBW (19.1%,), SGA (8.7%), and preterm birth (3.5%) were significantly more prevalent in the unhealthy dietary behavior group, while the lowest rates were recorded in the healthy dietary behavior group, with only 1.6% reporting both SGA and preterm birth. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, GDM, preeclampsia, and macrosomia were the most common suboptimal dietary behaviors (37.6%, 14.5%, and 3.6%, respectively).\u003c/p\u003e\u003cp\u003eOverall, adverse pregnancy outcomes were significantly greater in the unhealthy dietary behavior group (76.5%) than in the suboptimal (66.7%) and healthy (39.5%) groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the majority of women with no adverse outcomes reported healthy dietary behavior (60.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of demographic, and pregnancy outcomes on the basis of dietary behavior status\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;404)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnhealthy dietary behavior\u003c/p\u003e\u003cp\u003e115 (28.5%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSuboptimal dietary behavior\u003c/p\u003e\u003cp\u003e165 (40.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealthy dietary\u003c/p\u003e\u003cp\u003ebehavior\u003c/p\u003e\u003cp\u003e124 (30.7%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePregnancies number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrepregnancy BMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMother\u0026rsquo;s education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiterature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215 (53.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (61.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (55.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53 (42.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58 (46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFather\u0026rsquo;s education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliteracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (66.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (62.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115 (69.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 (66.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (43.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (53.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (47.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80 (64.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMother\u0026rsquo;s occupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e292 (72.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (73.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129 (78.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79 (63.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (27.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (36.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFather\u0026rsquo;s occupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (40.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e311 (77.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (65.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (78.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e106 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePregnancy outcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDM \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (32.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm infant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBW \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGA \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreeclampsia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacrosomia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247 (61.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (76.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eValues are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs for continuous variables (one-way ANOVA) and n (%)for categorical variables (χ\u0026sup2; test).\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e GDM, gestational diabetes mellitus; \u003csup\u003eb\u003c/sup\u003e LBW, low birth weight; \u003csup\u003ec\u003c/sup\u003e SGA, small for gestational age.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eSignificant at 0.05 levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Pregnancy outcomes\u003c/h2\u003e\u003cp\u003eAmong the postpartum women included, 30% had GDM and 11.1% had LBW. Preeclampsia (10.9%), SGA (4.5%), preterm birth (2.2%), and macrosomia (2.5%) were the other reported complications. However, a minority (38.9%) of the mothers, did not experience any complications (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analysis of the relationship between dietary behaviors and pregnancy outcome\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the dietary behavior scores and BMI across the pregnancy outcome groups. Women without complicated outcomes had the highest dietary behavior scores (57.24\u0026thinsp;\u0026plusmn;\u0026thinsp;20.71) as did those with a healthy BMI (23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90). While the BMIs of the macrosomia and preeclampsia groups were greater (27.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94 and 26.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31, respectively), the preterm birth and LBW groups reported lower BMIs (20.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90 and 21.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86, respectively).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of dietary behavior scores and BMIs across pregnancy outcome groups\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePregnancy outcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary behavior scores\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e46.05\u0026thinsp;\u0026plusmn;\u0026thinsp;15.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e25.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm Birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e37.86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e20.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;16.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e21.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e43.30\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e25.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreeclampsia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e43.94\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e26.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacrosomia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e52.79\u0026thinsp;\u0026plusmn;\u0026thinsp;13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e27.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Complication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e57.24\u0026thinsp;\u0026plusmn;\u0026thinsp;20.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThe values are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs based on the one-way ANOVA test.\u003c/p\u003e\u003cp\u003e*Significant at 0.05 levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Odds of pregnancy outcomes\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that after adjustment, improved dietary behavior scores were associated with a slightly lower risk of pregnancy complications by 3%. (OR, 0.97; 95% CI: [0.96 to 098]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe odds ratios of pregnancy outcomes on the basis of dietary behavior scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDietary behaviors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95% C.I.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary Behaviors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary Behaviors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eUnadjusted model.\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for mother\u0026rsquo;s age, number of pregnancies, number of children, place of residence, education, occupation, income, and BMI.\u003c/p\u003e\u003cp\u003e*Significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Relationship between Dietary behavior scores and demographic and nutritional variables\u003c/h2\u003e\u003cp\u003eThe correlations between the dietary behavior scores and demographic and nutritional variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Dietary behavior scores were inversely correlated with pregnancy outcomes (r = -0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A positive correlation with the mother\u0026rsquo;s age (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), education (r\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and income (r\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggests that higher dietary scores are associated with these factors. Residence showed a weak negative correlation (r = -0.14, p\u0026thinsp;=\u0026thinsp;0.003). No significant correlations were found with the prepregnancy BMI (r\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.515).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations between dietary behavior scores and demographic and nutritional variables of postpartum women\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDietary behavior scores\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePregnancy outcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMother's age\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePre-Pregnancy\u003c/p\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMother's education\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrelation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Significant at 0.05 levels.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study aimed to investigate the associations between maternal dietary behaviors and pregnancy outcomes among 404 postpartum women in Saveh, Iran in 2024. The findings indicated that healthy dietary behaviors were inversely associated with adverse pregnancy outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women with healthy dietary behaviors had the lowest prevalence of pregnancy-related complications (39.5%). These data highlight the role of lifestyle and dietary modifications in reducing adverse pregnancy outcomes with long-term benefits to maternal health [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, some studies found that adherence to healthy dietary patterns during pregnancy was associated with fewer adverse outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe present study revealed that the majority of participants (40.8%) followed suboptimal dietary behavior, whereas 28.5% exhibited unhealthy dietary behavior, which may be influenced by cultural factors.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, gestational diabetes, preeclampsia, and macrosomia were prevalent in the suboptimal dietary behavior group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which consequently had a higher BMI than the other groups did. This group appeared to follow a mixed diet, including some healthy foods (fruits, vegetables, and dairy), but in lower amounts than the healthy group, alongside high-calorie, high-fat, or high-sodium foods (red meat, cheese, and excessive carbohydrates, as indicated by their moderate scores. According to several studies, a high BMI, driven by calorie-dense diets, contributes to gestational diabetes via insulin resistance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], preeclampsia through oxidative stress and vascular dysfunction [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and macrosomia due to excessive fetal growth [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Diets high in red meat and cheese (which are rich in salt and saturated fat) can cause complications during pregnancy, including hypertension, preeclampsia, and gestational diabetes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. For example, a meta-analysis in 2014 showed a 33% reduction in preeclampsia occurrence due to dietary interventions during pregnancy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, LBW (19.1%), SGA (8.7%), and preterm birth (3.5%) were significantly higher in the unhealthy dietary behavior group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It is likely that deficiencies in essential nutrients (protein, carbohydrate, iron, folate, dairy, and calcium, contribute to these complications, which are typical of maternal malnutrition [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Several studies have also shown that low dietary diversity is a risk factor for low birth weight [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similarly, maternal undernutrition has been associated with detrimental effects on birth weight, fetal length, and even fetal death [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, a study in 2024 with 962 participants found that poor maternal nutritional status, especially low mid-upper arm circumference [MUAC], was associated with a higher risk of delivering SGA infants [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Overall, maintaining a normal BMI and adhering to a balanced diet to decrease preterm birth risk should not be underestimated, as demonstrated by a 2016 study that documented the positive effects of a diet high in fruits, vegetables, whole grains, fish, and adequate hydration on preterm birth risk, particularly in women with a history of preterm delivery [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMaternal dietary behaviors were significantly associated with maternal age, education, and family income [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A study 2019 reported that higher educational levels were linked to better dietary habits, probably due to a better understanding of nutritional guidance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies on nutrition indicate that women with higher education levels have healthier dietary habits than do those with lower education levels. One possible explanation is that mothers with higher education levels are more capable of grasping and implementing nutritional recommendations [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Nevertheless, multiple factors contribute to poor dietary practices among mothers, including heavy workloads for working mothers, limited access to healthy foods such as vegetables, due to economic status, and inadequate knowledge. Studies have indicated that maternal nutrition is an important component of any strategy to reduce maternal and fetal mortality and improve pregnancy outcomes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNutrition during pregnancy is a significant modifiable factor during the prenatal period that influences birth outcomes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although pregnancy complications are multifactorial, personalized nutritional evaluation is necessary to identify deficiencies and mitigate adverse outcomes[\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Thus, pregnant women are recommended personalized nutrition and lifestyle recommendations under the supervision of healthcare providers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Optimal maternal and fetal health is fundamentally dependent on a woman\u0026rsquo;s nutritional status before and during pregnancy, highlighting the importance of\u0026ensp;personalized dietary interventions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA key strength of this study is its large sample size (n\u0026thinsp;=\u0026thinsp;404), which increased the statistical power and reliability. The use of a validated dietary behavior questionnaire (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.87) ensured accurate and consistent data collection. Conducting interviews during the early postpartum period (days 3\u0026ndash;5 after delivery) minimized recall bias, whereas real-world healthcare settings enhanced the external validity of the findings.\u003c/p\u003e\u003cp\u003eHowever, the cross-sectional design limits the ability to establish causality between dietary behaviors and pregnancy outcomes. This study did not assess specific nutrient quantities, which could provide deeper insights into nutritional status. Although confounding variables were controlled through strict inclusion and exclusion criteria, unmeasured factors, such as physical activity, psychosocial stress, or genetic predispositions, may have influenced the results. Additionally, reliance on self-reported data may be influenced by recall errors or social desirability biases. Finally, the study\u0026rsquo;s focus on Saveh, Iran may limit the generalizability of the findings to populations with different socioeconomic or cultural contexts.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study highlights the significant relationship between maternal dietary behaviors and pregnancy outcomes. These findings suggest that inadequate dietary practices during pregnancy are associated with a greater incidence of complications such as GDM, preeclampsia, macrosomia, LBW, and preterm birth. Conversely, better dietary behavior scores were linked to more favorable outcomes, underscoring the critical role of maternal nutrition in prenatal care. These results emphasize the need for targeted nutritional education and interventions during pregnancy, particularly in at-risk populations. Integrating dietary counseling with routine maternal health services could improve maternal and neonatal health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow Birth Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSmall for Gestational Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional Review Board\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDemographic and Health Surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMUAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMid-Upper Arm Circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;S.T. contributed to the conceptualization, methodology, manuscript revision and editing, and provided scientific supervision of the study. Z.T. was responsible for data collection, statistical analysis, writing the original draft, and project administration. All authors critically reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study. The research was entirely self-funded and conducted independently of any institution or organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed and/or generated during the present study are available from the corresponding author and will be provided on reasonable request, provided that ethical compliance and participant confidentiality are strictly maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are highly thankful to the participants (mothers) for their valuable contribution to this study. We also extend our sincere appreciation to the officials of health care in Saveh, Iran, and to Saveh University of Medical Sciences for their kindness and collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Saveh University of Medical Sciences (IR.SAVEHUMS.REC.1401.008). The study was conducted in accordance with the ethical standards of the Declaration of Helsinki. All participants provided informed consent to take part in this research. For all illiterate participants, informed consent was obtained from their legal guardians or husbands.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that this study was carried out without any commercial or financial involvement that could be interpreted as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Author details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eElectronic Health and Statistics Surveillance Research Center, SR.C., Islamic Azad University, Tehran, Iran.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eDepartment of Nutrition, SR.C., Islamic Azad University, Tehran, Iran.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003ePh.D. Student of Reproductive Health, Department of Midwifery and Reproductive Health, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrams B, Altman SL, Pickett KE. 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Maternal nutrition status associated with pregnancy-related adverse outcomes. Nutrients. 2021;13[7]:2398.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Maternal nutritional physiological phenomena, dietary behavior, pregnancy outcomes, prenatal care","lastPublishedDoi":"10.21203/rs.3.rs-7040765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7040765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMaternal dietary behaviors play a critical role in determining pregnancy outcomes. Poor nutrition may increase the risk of complications such as gestational diabetes mellitus (GDM), preeclampsia, preterm birth, and low birth weight (LBW). This study aimed to investigate the associations between maternal dietary behaviors and pregnancy outcomes among postpartum women attending healthcare centers in Saveh, Iran in 2024.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study included 404 postpartum women who were selected via two-stage cluster random sampling. Data were collected using a validated 21-item dietary behavior questionnaire, a demographic questionnaire, and a pregnancy outcome checklist. The dietary behaviors were categorized as unhealthy (0\u0026ndash;33.3), suboptimal (33.4\u0026ndash;66.6), and healthy (\u0026gt;\u0026thinsp;66.6). Outcomes including GDM, preeclampsia, macrosomia, LBW, preterm birth, and small for gestational age (SGA), were assessed on days 3\u0026ndash;5 postpartum. Statistical analyses, including one-way ANOVA, the chi-square test, Pearson\u0026rsquo;s correlation, and logistic regression modeling, were performed via SPSS 19 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe participants had a mean age of 28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u0026ensp;years. Dietary behaviors were distributed as follows: unhealthy (28.5%), suboptimal (40.8%), and healthy (30.7%). Among women with healthy dietary behaviors, 39.5% experienced adverse pregnancy outcomes, whereas among those with unhealthy dietary behaviors, 76.5% experienced adverse pregnancy outcomes. A significant correlation was found between dietary behavior and pregnancy outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Healthier dietary behaviors were associated with a 3% reduction in the odds of adverse outcomes (OR, 0.97; 95% CI, [0.96\u0026ndash;0.98], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePoor maternal dietary behavior is associated with negative pregnancy outcomes. Improving nutritional education and integrating dietary counseling into prenatal care could promote maternal and neonatal health outcomes.\u003c/p\u003e","manuscriptTitle":"The relationship between maternal dietary behaviors and pregnancy outcomes among postpartum women in Iran: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 06:41:57","doi":"10.21203/rs.3.rs-7040765/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"614d2ecd-11b6-41aa-8ade-2663c520ce05","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T13:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 06:41:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7040765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7040765","identity":"rs-7040765","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}