The Double-Future Dilemma: Dietary Diversity, Pregnancy Outcomes, and Planetary Health

The Double-Future Dilemma: Dietary Diversity, Pregnancy Outcomes, and Planetary Health | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Double-Future Dilemma: Dietary Diversity, Pregnancy Outcomes, and Planetary Health Maria Julia Miele, Jacqueline Tereza da Silva, Manina Norde, Renato Teixeira Souza, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9314030/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Dietary quality has been linked to adequate nutrient intake and women’s health. However, the relationships among dietary diversity, pregnancy outcomes and environmental sustainability remain uncertain. Methods: This study evaluated the associations between adherence to the Minimum Dietary Diversity for Women (MDD-W), pregnancy outcomes, and the environmental footprints of maternal diets. This was a multicentre prospective cohort that included 1,318 low-risk nulliparous women in Brazil and used 24-hour dietary recall (24hR). Maternal diets were classified according to adherence to the Minimum Dietary Diversity for Women (MDD-W) guidelines. Associations between MDD-W adherence and pregnancy outcomes were assessed via logistic regression. The environmentalimpacts of maternal diets were estimated via the Footprints of Foods and Culinary Preparations Consumed in the Brazil database. Results: In the total sample, 904 women achieved MDD-W adequacy (≥5 food groups), whereas 414 were classified in the non-adequacy group (<5 food groups). MDD-W adequacy was not associated with adverse pregnancy outcomes(OR = 0.90, 95% CI: 0.69–1.17). Although the main differencein dietary intake between groups was predominantly plant-based, the adequacy group had greater environmental impacts, even after adjustment per 1,000 kcal, in terms of the ecological footprint(eco-score/day), carbon emissions (g/day) and water use (L/day). The MDD-W is a useful indicator of adequate micronutrient intake. However, dietary intake ofcalcium, iron, folate, and vitamin A was below the recommended levelin approximately 80% of individuals in both groups. Conclusions: Our findings suggest that although MDD-W adherence is a useful tool for dietary micronutrient intake, it is not associated with pregnancy outcomes and that achieving dietary diversity may entail a greaterenvironmental footprint, revealing trade-offs between nutritional adequacy indicators and sustainability goals. This underscores the need for integrated food and nutrition policies that simultaneously address maternal health and environmental sustainability. Pregnant women sit at the nexus of human and planetary futures, highlighting the need for a “double future” perspective linking maternal health and planetary boundaries. Figures Figure 1 Figure 2 BACKGROUND While diets are among the key modifiable factors for preventing adverse pregnancy outcomes [ 1 ], they are also among the main contributors to climate change [ 2 ]. Thus, integrating human and planetary health into dietary guidelines for pregnant women implies the need to act on a double future: for the newborn's life and for the planet. The global community has been actively seeking nutritionally adequate and environmentally sustainable food resources [ 3 ]. Recent studies indicate that food choices significantly influence the environmental impacts of food systems, including greenhouse gas emissions, water use, land degradation, and biodiversity loss [ 2 ]. Food production and consumption patterns simultaneously affect human health, ecosystem health, and environmental sustainability, underscoring the critical need for an integrated “planetary health” approach [ 4 ]. This approach recognizes the interdependence between dietary habits for women's health, particularly during reproductive years, and the broader implications for planetary health, thus highlighting the inseparable relationship between human well-being and environmental sustainability [ 4 , 5 ]. For a long time, however, the human and planetary aspects of diets were assessed separately. The environmental footprint of diets is an essential metric for evaluating the sustainability of dietary intake. Indicators such as the carbon footprint (kg CO₂e), water use (L), and ecological footprint (eco-score) quantify the pressures exerted by food production, processing, and consumption on natural resources on the basis of the life cycle assessment (LCA) framework [ 6 ]. These measures provide a comprehensive understanding of how dietary patterns contribute to the depletion of natural resources and to climate change, opening up an opportunity to complement health-based nutritional assessments. Dietary diversity is a proxy for adequate intake of essential nutrients for maternal health and foetal development [ 7 , 8 ]. Dietary guidelines provide specific approaches to achieve adequate nutritional status by setting different metrics [ 9 – 12 ]. Food groups are practical indicators for assessing diet quality in population settings. Therefore, to assess the diet of women of reproductive age (15–49 years) (WRA), the Food and Agriculture Organization (FAO) developed the minimum dietary diversity for women (MDD-W) as a key indicator that balances the combination of nutrient-rich food groups to achieve micronutrient adequacy for this population [ 13 ]. Previous studies have shown that higher dietary diversity, as measured by the MDD-W, is associated with improved maternal nutritional status, adequate gestational weight gain, a reduced risk of anaemia, and favourable birth outcomes [ 14 – 17 ]. Moreover, increasing evidence highlights the environmental impacts of dietary patterns, and integrated frameworks such as the EAT-Lancet Commission have emphasized the need to align health and sustainability goals [ 18 , 19 ]. However, most studies assessing the health benefits of dietary diversity focus on maternal and child outcomes without examining their environmental implications, whereas sustainability-oriented analyses typically address the general population rather than pregnancy specifically. Thus, the potential trade-offs between achieving dietary diversity during pregnancy and the environmental sustainability of food consumption remain insufficiently explored. Thus, this study investigates whether adherence to MDD-W recommendations during pregnancy is associated with both pregnancy outcomes and environmental impacts by jointly assessing dietary diversity and the environmental footprints of food consumption in the Brazilian context. We examined whether adherence to the MDD-W indicator is associated with better pregnancy outcomes and lower environmental footprints of mothers. METHODS Study design This study is a multicentre prospective cohort that integrates methodological approaches from nutritional epidemiology and environmental sustainability analyses to assess the impact of maternal dietary consumption on both human and planetary health. Associations between MDD-W adherence and pregnancy outcomes were assessed via multiple logistic regression. The environmental impacts of maternal diets were estimated via the Footprints of Foods and Culinary Preparations Consumed in Brazil (FFCP) database, which was developed by the University of São Paulo (USP) and is publicly available on the Open Science Framework. This approach enabled a comparative analytical framework structured into two complementary dimensions. The first dimension, related to maternal health, assessed dietary adequacy according to the MDD-W and its association with adverse pregnancy outcomes derived from the SAMBA and MAES cohorts. The second dimension, related to environmental sustainability, was based on indicators provided by the FFCP database, which provides life cycle assessment–based indicators of the carbon footprint (kg CO₂e/100 g), water footprint (L/100 g), and ecological footprint (eco-score/100 g). Data collection The present study used data from two multicenter cohort studies, the Preterm SAMBA - Preterm Screening and Metabolomics in Brazil and Auckland and the Maternal Actigraphy Exploratory Study (MAES) [ 20 , 21 ]. Both cohort studies followed a standardized protocol for recruitment and individual-level data collection on sociodemographic characteristics, dietary intake, clinical assessment, and maternal outcomes among women with singleton pregnancies. The SAMBA study was conducted from 2015–2018, and the MAES study was conducted from 2018–2020 across six public obstetric referral hospitals located in three geographical regions of Brazil. These centres were selected for their demographic characteristics, which represent the diversity of social and ethnic profiles, as well as regional dietary habits, across the Northeast, South, and Southeast regions of the country [ 22 ]. All nulliparous pregnant women considered to be at low risk and between the 19th and 21st weeks of gestation were invited to participate in this study. Those with a previous history of three or more abortions, cervical alterations, major foetal anomalies, Müllerian anomalies, previous cervical conization, chronic corticosteroid use, or detected or self-reported preexisting diseases, including hypertensive disorders, previous diagnosis of diabetes mellitus, renal disease, systemic lupus erythematosus, antiphospholipid syndrome, sickle-cell anaemia, and HIV-positive serology, were not eligible for study. Women taking medications or supplements that could interfere with outcome assessment, such as aspirin, calcium, fish oil, vitamin C, vitamin E, or heparin, were also excluded. Therefore, the potential confounding effect of dietary supplement use on micronutrient intake was minimized by the study design. Additional exclusions were performed specifically for this study, such as cases missing the dietary assessment, with stillbirth after the first visit, and missing key information such as birthweight. Sociodemographic data were self-reported, and all collected information was entered into an electronic platform (MedSciNet ® AB, Sweden). All income information originally reported in Brazilian national currency was converted into U.S. dollars, using the exchange rate at the time of data collection, to allow international comparability. Women who followed the Brazilian Health System (SUS) protocol for supplements during pregnancy, such as folic acid, iron, or calcium, were not excluded. Since this supplement was used uniformly across the study sample, it was not included as a covariate in the statistical analysis. Sociodemographic characteristics, as well as self-declared skin color/ethnicity, were self-reported [ 23 ]. The overall process of sample selection, exclusion, and final analysis is detailed in Fig. 1 (Flowchart). All participants provided written informed consent before enrollment. The Preterm SAMBA and MAES studies complied with the ethical principles of the Declaration of Helsinki (2013) and were approved by the Research Ethics Committees of all participating centres (protocol No. 20182318.8.0000.5404) as well as by the National Research Ethics Committee (CONEP). Anthropometric assessment For body weight and height assessments, all centers used an electronic scale and a duly calibrated stadiometer, respectively, and measurements were taken at the time of study entry. The study coordinator trained members of the healthcare team to perform the anthropometric measurements of each participant (weight and height) in accordance with the standardized criteria of the Food and Nutritional Surveillance System of the Ministry of Health [ 24 ]. Body mass index (BMI) was automatically calculated via the study’s electronic platform software, which uses weight and height measurements. As weight and height were measured between 19 and 21 weeks of gestation, BMI categories were classified into underweight, adequate, overweight or obese according to the Atalah curve [ 25 ]. These standardized procedures ensure the comparability of measurements across centers. Maternal and neonatal outcomes Maternal and neonatal outcomes were defined according to international criteria. Cases of preeclampsia (PE) and gestational diabetes mellitus (GDM) were defined on the basis of the International Society for the Study of Hypertension in Pregnancy (ISSHP) criteria and the American Diabetes Association (ADA) criteria, respectively [ 26 , 27 ]. Preterm birth (PTB) was considered for all women who gave birth before reaching 37 weeks of pregnancy [ 28 ]. The definition of small for-gestational-age (SGA) newborns, according to the birthweight percentile (< p10) adjusted for maternal characteristics (ethnicity, weight, height and parity, gestational age at birth and infant sex), was performed via the GROW centile calculator: https://www.gestation.net/GROW_documentation.pdf [ 29 ]. The composite variable named adverse pregnancy outcome (APO) was defined as the presence of at least one of the following conditions: PE, GDM, PTB or SGA. Dietary assessment To assess dietary intake, a 24-hour dietary recall (24hR) was applied per woman at the 19th and 21st weeks of gestation, covering all days of the week and all seasons of the year in the total sample. The questionnaire was administered at study entry by healthcare professionals trained by a dietitian via the multiple-pass method, which is a standardized process designed to stimulate the respondent’s memory and increase the accuracy of the information provided [ 30 ]. Serving size was estimated via household measures, which were based on photographs of kitchen utensils and food portions classified as small, medium, or large according to the Brazilian Ministry of Health [ 31 ]. To standardize servings, household measures were converted into grams or millilitres of consumption via Brazilian and international reference manuals [ 32 , 33 ]. This information provides a quantitative basis for assessing dietary adequacy via the MDD-W indicator. Minimum Dietary Diversity for Women The dietary data were analysed in relation to the MDD-W indicator developed by the FAO [ 13 ]. The MDD-W is a validated proxy for micronutrient adequacy among women of reproductive age (WRA). It is based on the consumption of at least five out of ten predefined food groups within the previous 24 hours, representing the minimum threshold of dietary diversity associated with adequate micronutrient intake. The MDD-W reflects a high probability of adequacy across eleven micronutrients that are strongly associated with dietary diversity: vitamin A, thiamine, riboflavin, niacin, vitamin B₆, folate, vitamin B₁₂, vitamin C, calcium, iron, and zinc. In the present study, vitamin D and polyunsaturated fatty acids (PUFAs) were also analysed owing to their clinical relevance during pregnancy and their established associations with maternal–foetal outcomes. To estimate MDD-W, the classification of all food items was performed jointly for both datasets (SAMBA and MAES), and all items were categorized according to the ten MDD-W food groups by a registered dietician: (1) grains, white roots and tubers, and plantains; (2) pulses (beans, peas, and lentils); (3) nuts and seeds; (4) dairy; (5) meat, poultry, and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; and (10) other fruits. To assess the proportion of women whose micronutrient intake met the recommended thresholds, dietary intakes were compared with the dietary reference intakes (DRIs) for pregnant women, using the estimated average requirement (EAR) or, when unavailable, the adequate intake (AI) as reference values [ 9 ]. Nutrient density was calculated to adjust daily micronutrient intake for total energy consumption to account for differences in total food intake between women. Nutrient density was expressed per 1,000 kcal via the following formula: $$\:\text{N}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t}\:\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}=\:\frac{\text{M}\text{i}\text{c}\text{r}\text{o}\text{n}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:(\text{u}\text{n}\text{i}\text{t}\:/\:\text{d}\text{a}\text{y})\:}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{c}\text{a}\text{l}\text{o}\text{r}\text{i}\text{c}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:(\text{k}\text{c}\text{a}\text{l}\:/\:\text{d}\text{a}\text{y})\:}\:\:\times\:\text{1,000}$$ This approach allowed comparison of the mean intakes of the selected micronutrients between women classified as adequate (≥ 5 food groups out of ten) and non-adequate (< 5 food groups out of ten) according to the MDD-W, independent of total caloric intake. For polyunsaturated fatty acids (PUFAs), adequacy was evaluated according to FAO/WHO guidelines [ 34 ] by expressing total PUFA intake as a percentage of total energy intake (% VET) via the following formula: Environmental footprints The “Footprints of Foods and Culinary Preparations Consumed in Brazil” (FFCP), developed by the University of São Paulo (USP) and publicly available on the Open Science Framework ( https://osf.io/gs4cy/ ), was used to estimate the environmental impacts of food production in Brazil [ 6 ]. The FFCP dataset, which is based on the Brazilian Household Budget Survey (IBGE) and Life Cycle Assessment (LCA) studies, provides indicators of the carbon footprint (kg CO₂e/100 g), water footprint (L/100 g), and ecological footprint (eco-score/100 g) of foods consumed in Brazil. All environmental indicators were estimated at the individual level on the basis of total daily intake (g/day) and analysed in relation to dietary adequacy according to the minimum dietary diversity for women (MDD-W) indicator. The FFCP food correspondence to the Preterm SAMBA (2015–2018) and MAES (2018–2020) cohort datasets was manually assigned by a registered dietitian. The maternal intake (from the SAMBA and MAES cohorts), after classification according to the MDD-W food groups, was then matched with its corresponding environmental impact factor, and the total environmental footprint per participant was calculated by summing the footprint values weighted by the amount consumed (100 g). Finally, total dietary intake (g/day) and the corresponding FFCP indicators were used to assess differences in environmental impact between adequate and non-adequate diets according to the MDD-W criteria. Statistical analysis The sociodemographic and health characteristics of the pregnant women are presented as counts and percentages for categorical variables, whereas continuous variables are presented as the means ± standard deviations (SDs), both of which are stratified by their adherence to the MDD-W dietary recommendations. Differences across subgroups were assessed via the Wilcoxon rank sum test for continuous variables and Pearson’s chi-square test for categorical variables. All p values < 0.05 were considered statistically significant. A logistic regression model was used to identify factors associated with non-adequate MDD-W. Variable selection was guided by a priori clinical and epidemiological relevance, including maternal age ( 34 years), body mass index (BMI indicative of underweight, adequate, overweight, and obese), ethnicity (white and non-white), income level (< 12,000 vs ≥ 12,000 USD), and adverse pregnancy outcomes (APO), on the basis of their potential relationship with dietary vulnerability. Multiple logistic regression, within a generalized linear model (GLM) framework with logit links, was conducted with non-adequate MDD-W as the dependent variable and sociodemographic, clinical, and pregnancy-related characteristics, including adverse pregnancy outcomes (APOs), as independent variables. Different combinations of covariates were tested, and model selection was based on clinical plausibility and goodness-of-fit criteria, including the Akaike information criterion (AIC) and Tjur’s R². Coefficients were estimated from the data via the maximum likelihood method, which maximizes the probability of the observed outcomes and provides a measure of predictive accuracy. The effect of each coefficient on the odds of non-adequate MDD-W was assessed according to its positive value (indicating a greater likelihood of occurrence) or negative value (indicating a protective effect). The statistical significance of the coefficients was tested via the Wald statistic. Linear regression models were also used to assess the associations between birth weight (and head circumference) and gestational age according to MDD-W adequacy. The multivariable models were adjusted for maternal age (years, continuous), household income (< 12,000 USD vs. ≥12,000 USD), and total energy intake (kcal/day, continuous). Residual analysis was conducted via least-squares regression and Cook’s distance. To assess multicollinearity, variance inflation factors (VIFs) were examined, and values greater than five were considered indicative of problematic collinearity. For these models, MDD-W adequacy (adequate vs. non-adequate food group diversity) was included as the main exposure variable. The objective of this study was to determine whether dietary diversity adequacy was associated with adverse pregnancy outcomes or birth weight. The results of these analyses are expressed as odds ratios (ORs) and 95% confidence intervals (CIs), whereas linear regression results are reported as beta coefficients (β) with 95% confidence intervals. Statistical analyses were performed via R Core Team software (version 2025) with the packages “pacman,” “sjPlot,” and base regression functions. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, and the completed STROBE checklist is provided in the Supplementary Appendix. Figure 1 . Flow diagram of participant inclusion and exclusion from the preterm SAMBA and MAES cohort studies. RESULTS Among the 1,318 nulliparous pregnant women included in the analysis from both studies (Fig. 1), 904 (68.6%) achieved adequate minimum dietary diversity for women (MDD-W) scores (≥ 5 food groups), whereas 414 (31.4%) did not. Maternal dietary adequacy varied significantly according to age, region, education, income, ethnicity, and occupation (all p < 0.001) but not according to body mass index (p = 0.7) (Table 1 ). Table 1 Maternal sociodemographic characteristics by adequacy to the minimum dietary diversity for women Characteristic Non-adequacy n (%) (n = 414) Adequacy n (%) (n = 904) Overall n (%) (n = 1,318) *p value Age group (years) < 0.001 34 12 (3) 47 (5) 59 (4) BMI (kg/m²) 0.7 Obese 71 (17) 152 (17) 223 (17) Adequate 107 (26) 236 (26) 343 (26) Overweight 156 (38) 360 (40) 516 (40) Underweight 77 (19) 144 (16) 221 (17) Region < 0.001 Southwest/South 155 (38) 426 (48) 581 (45) Northwest 256 (62) 467 (52) 723 (55) Occupation < 0.001 Working 221 (54) 381 (43) 602 (46) Not working 190 (46) 512 (57) 702 (54) Ethnicity < 0.001 White 143 (35) 402 (45) 545 (42) Non-white 268 (65) 491 (55) 759 (58) Education (year) 12 76 (18) 322 (36) 398 (31) Family Annual Income < 0.001 <12,000 (USD) 135 (33) 169 (19) 304 (23) ≥12,000 (USD) 276 (67) 724 (81) 1,000 (77) Adequacy (≥ 5 food groups) and Non-adequacy (< 5 food groups): n/N (%). P value: Pearson’s chi-square test. Overall missing information = 15 (adequacy = 11, non-adequacy = 4). BMI missing information adequacy = 16. Table 1 . Maternal sociodemographic characteristics by adequacy to the minimum dietary diversity for women Among women with adequate MDD-W, a greater proportion were aged 20–34 years (76%), while a lower proportion were adolescents (19%) than the non-adequate group (35% proportion of adolescents). A greater proportion of women residing in the Northeast region was found in the non-adequate group (62%) than in the adequate group (52%). Higher education (above 12 years of study) (36%) and higher annual income (12,000 USD or more) (81%) were more frequently observed among women with adequate dietary diversity. Similarly, the proportions of women not engaged in paid work (57%) and who self-identified as white (45%) were greater among women in the adequacy group than among those in the non-adequacy group. Table 2 shows that women classified as adequate by the MDD-W had higher energy intake, consumed larger quantities of food, and had greater intakes of most micronutrients, standardized by energy intake. The average daily intake of PUFAs, expressed as a percentage of total energy intake, fell within the recommended range of 6–11% in the non-adequate group (6.3%) but not in the adequate group (5.8%). Nevertheless, even among women who achieved MDD-W adequacy, the average daily intake of several micronutrients remained below pregnancy-specific reference values, namely, calcium (AI = 1,000 mg/day), iron (EAR = 22 mg/day), zinc (EAR = 9.5 mg/day), vitamin A (EAR = 550 µg RAE/day), folate (EAR = 520 µg DFE/day), and vitamin B12 (EAR = 2.2 µg/day). For dietary iron and folate intake, 90% and 80% of women were classified as below the recommended thresholds, respectively, independent of MDD-W adequacy. Approximately 70% of women had calcium intake below the reference value, and nearly two-thirds had inadequate dietary vitamin A intake. For dietary zinc and vitamin B12, the proportion of women classified as having insufficient intake was smaller, as this was observed in approximately one quarter and one fifth of women, respectively. Overall, women who achieved MDD-W adequacy tended to have higher micronutrient intakes, suggesting a lower likelihood of inadequate intake than those classified as non-adequate. Table 2 Maternal micronutrient intake by adequacy to minimum dietary diversity for women (MDD-W) Characteristic Non-adequacy (n = 414) Adequacy (n = 904) *p value MDD-W (number) 4.0 (3.0, 4.0) 6.0 (5.0, 7.0) < 0.001 Energy (kcal) 1,829.4 (1,412.3, 2,435.9) 2,032.8 (1,570.2, 2,484.1) < 0.001 Grams (g) 1,111.0 (859.3, 1,414.8) 1,440.0 (1,180.3, 1,785.0) < 0.001 Calcium (mg) 226.0 (126.8, 354.5) 309.0 (219.5, 424.4) < 0.001 Iron (mg) 5.8 (4.5, 6.9) 6.0 (4.9, 7.3) < 0.001 Zinc (mg) 5.6 (4.3, 7.8) 6.3 (4.8, 8.1) < 0.001 Vitamin A (µg RAE) 132.5 (81.7, 206.9) 214.4 (143.1, 343.4) < 0.001 Vitamin B1 (mg) 0.7 (0.5, 0.8) 0.6 (0.5, 0.8) 0.11 Vitamin B2 (mg) 0.8 (0.6, 1.1) 0.9 (0.7, 1.1) < 0.001 Vitamin B3 (mg) 9.6 (7.3, 12.6) 9.6 (7.4, 13.2) 0.4 Vitamin B6 (mg) 0.8 (0.6, 1.0) 0.9 (0.7, 1.1) < 0.001 Vitamin B12 (mg) 1.8 (0.9, 3.1) 2.2 (1.4, 3.3) < 0.001 Folate (µg DFE) 1.2 (0.7, 2.1) 1.6 (1.0, 2.5) < 0.001 Vitamin C (mg) 128.8 (78.2, 195.7) 141.8 (97.3, 209.1) < 0.001 Vitamin D (µg) 12.7 (4.2, 40.3) 44.8 (18.4, 85.9) < 0.001 PUFA (g) 6.3 (4.6, 8.5) 5.8 (4.7, 7.4) 0.006 Adequacy (≥ 5 food groups) and Non-adequacy (< 5 food groups): Medians (IQRs). P value: Wilcoxon rank sum test. Table 2 . Maternal micronutrient intake by adequacy to minimum dietary diversity for women (MDD-W) The comparison of the average intake of MDD-W food groups revealed marked differences between women with non-adequate and adequate MDD-W (Table 3 ). Grains and tubers were consumed universally in both groups, followed by animal-derived foods. However, wide disparities were observed across nine food groups, particularly fruits, vegetables, and vitamin A-rich fruits and vegetables, dark green leafy vegetables, eggs, pulses, and dairy. Overall, nutrient-dense food groups, particularly dairy, fruits, vegetables, and pulses, were the main contributors to distinguishing women who achieved the minimum dietary diversity threshold from those who did not. Table 3 Proportion of pregnant women consuming each food group of the minimum dietary diversity for women (MDD-W) by adequacy status. FOOD GROUPS MDD-W adequacy Non-adequate (n = 414) Adequate (n = 904) Dairy (yes, %) 54.1 88.5 Dark green leafy vegetables (yes, %) 5.3 32.5 Eggs (yes, %) 8.9 23.7 Fruits (yes, %) 41.1 83.7 Grains & Tubers (yes, %) 100.0 99.7 Meat, Poultry, Fish (yes, %) 82.6 94.7 Nuts and seeds (yes, %) 1.0 6.5 Pulses (yes, %) 24.9 47.2 Vegetables (yes, %) 34.1 74.7 Vitamin A-rich fruits and vegetables (yes, %) 10.1 40.6 MDD-W cut-off: adequacy (≥ 5 food groups); non-adequacy (< 5 food groups). The difference between the adequacy groups was significant for all the MDD-W food groups (χ 2 test, all p < 0.001). Table 3 . Proportion of pregnant women consuming each food group of the minimum dietary diversity for women (MDD-W) by adequacy status. Multiple logistic regression identified socioeconomic and demographic predictors associated with the risk of non-adequate MDD-W (Table 4 ). A lower household income and younger maternal age were significantly associated with a greater likelihood of inadequate MDD-W. Compared with those aged 20–34 years, women with an annual income below USD 12,000 had 1.68 (95% CI 1.27–2.23) times greater odds of having inadequate MDD-W. Likewise, adolescents under 20 years of age were twice as likely to have non-adequate MDD-W scores. Table 4 Adjusted odds ratios for non-adequate MDD-W according to maternal characteristics Predictors Non-adequacy MDD-W food groups Odds Ratios 95%CI z value p (Intercept) 0.28 0.21–0.36 -9.42 < 0.001 Higher income (≥ 12,000 USD) Ref. Lower income (< 12,000 USD) 1.68 1.27–2.23 3.59 < 0.001 White Ref. Non-white 1.28 0.99–1.66 1.91 0.056 Obese/Overweight 1.20 0.92–1.57 1.31 0.189 Adequate Ref. Underweight 1.03 0.73–1.46 0.17 0.861 Maternal age (< 20 years) 2.05 1.55–2.72 4.99 34 years) 0.72 0.36–1.35 -0.97 0.333 Adverse outcome 0.90 0.69–1.17 -0.78 0.434 Model fit: R² Tjur = 0.050; AIC = 1576.8. Observation = 1303. Self-declared skin color/ethnicity was not significantly associated with non-adequate MDD-W (OR = 1.28; 95% CI 0.99–1.66; p = 0.056), although the point estimate corresponded to 28% higher odds among non-white women than self-declared white women. No significant associations were observed for maternal nutritional status (obesity, overweight, or underweight) or for adverse pregnancy outcomes. The model showed acceptable fit (Tjur R² = 0.05; AIC = 1576.8). Table 4 . Adjusted odds ratios for non-adequate MDD-W according to maternal characteristics Model fit: R² Tjur = 0.050; AIC = 1576.8. Observation = 1303. Figure 2 shows the relationship between gestational age and birth weight according to MDD-W adequacy. As expected, birth weight increased progressively with gestational age in both groups. The slopes of the regression lines were similar, indicating a consistent positive association between gestational age and foetal growth regardless of dietary adequacy. However, across almost all gestational ages, women with adequate MDD-W (blue line) tended to have infants with slightly higher birth weights than those in the non-adequate group (red line), suggesting a modest nutritional advantage associated with greater dietary diversity. After adjustment for maternal age category, household income, and total energy intake, the results remained unchanged, and the interaction term remained nonsignificant. Figure 2 . Linear association between birth weight and gestational age according to dietary adequacy based on the MDD-W. Multiple linear regression model adjusted for maternal age categories ( 34 years), household income (< 12,000 vs. ≥12,000 USD), and total energy intake (kcal/day, continuous). The P value refers to the interaction term between MDD-W adequacy and gestational age. The associations between gestational age and birth weight according to MDD-W adequacy are shown in Table 5 . Neither dietary adequacy nor its interaction with gestational age reached statistical significance. The model explained approximately 55% of the variance in birth weight (R² = 0.55). Table 5 Associations between birth weight and gestational age according to MDD-W adequacy. Predictors Birth Weight Beta 95%CI z value p (Intercept) -4121.42 -4538.31 – -3704.52 -19.39 < 0.001 Gestational age (A) 187.85 177.06–198.63 34.18 < 0.001 Non-adequacy MDD-W (B) 337.99 -441.97–1117.95 0.85 0.395 A × B -9.04 -29.19–11.11 -0.88 0.379 Multiple linear regression model. Observations: 1301. R2 /R2 adjusted: 0.551/0.550. Table 5 . Associations between birth weight and gestational age according to MDD-W adequacy. Environmental impacts The environmental indicators (carbon, water, and ecological footprints) were analysed as daily averages per individual to compare the sustainability profiles between adequate and non-adequate dietary groups. These results are reported in Table 6 . Table 6 Environmental footprints according to the degree of MDD-W adequacy among pregnant women. INDICATOR (per 1,000kcal) Non-adequate (n = 414) Adequate (n = 904) p value Total intake (g) 599.2 (510.8, 722.6) 714.6 (615.5, 837.7) < 0.001 CO₂e (g) 2,445.7 (1,469.0, 4,480.3) 2,809.6 (1,684.2, 4,620.1) 0.025 Water (L) 2,107.6 (1,419.9, 3,277.4) 2,477.2 (1,668.6, 3,583.9) 0.002 Ecological (pt) 12.3 (8.1, 21.9) 15.7 (10.0, 23.0) < 0.001 Adequacy (≥ 5 food groups) and Non-adequacy (< 5 food groups). The values represent the median (IQR) daily environmental footprint per individual. CO₂e = carbon dioxide equivalent (g/day); Water = litres/day; Ecological = points/day. P values were calculated via the Wilcoxon rank-sum test. Table 6 . Environmental footprints according to the degree of MDD-W adequacy among pregnant women. As shown in Table 2 , women in the adequate group consumed approximately 10% more calories and 30% more food in grams than those in the non-adequate group did. Despite this difference, after adjusting for caloric intake, the ecological footprint was 28% greater in the adequate group, whereas carbon emissions were 15% greater and water usage was 18% greater. DISCUSSION Our main findings suggest that, in addition to being associated with key micronutrient intake, adherence to the MDD-W recommendations was not associated with the evaluated maternal or neonatal outcomes. However, the environmental footprint was significantly greater in women with adequate MDD-W, even after adjustment for 1,000 kcal. Furthermore, although meat-based diets have been mentioned as having the greatest environmental impact, our analysis revealed that the main difference in MDD-W components between groups resulted from higher scores for plant-based food groups. Thus, when “double future” goals are considered, this result suggests a potential misalignment between human and planetary health goals. This study revealed that the MDD-W was a sensitive indicator of socioeconomic and demographic conditions, as women who achieved adequate consumption were more likely to have higher incomes and education levels. The associations between sociodemographic factors and adherence to the MDD-W recommendations among pregnant women have been reported previously in low-income settings, such as the Philippines and African and Asian countries [ 35 ]. However, no other studies have specifically assessed the dietary adequacy of MDD-W in pregnant women in upper-middle-income countries (UMICs), such as Brazil. Although our study was conducted at a UMIC, the mean number of food groups consumed was 3.6 (IQR: 3.0, 4.0) out of the 10 food groups recommended in the MDD-W. A similar result was obtained in an Ethiopian study, which reported an average of 3.8 food groups, which also consumed more grains and fewer FGs of fruits, vegetables, nuts, and animal sources [ 36 ]. Similarly, in Burkina Faso, pregnant women from poverty-stricken areas presented low dietary diversity (3.85 ± 1.46), characterized by lower consumption of animal-sourced foods and higher intake of vegetables and green leafy vegetables [ 37 ]. Similarly, pregnant women from Angola also consumed an average of 3 FGs by the non-adequate group of women [ 38 ]. Recently, the MDD-W has been officially adopted as an indicator to monitor progress towards Sustainable Development Goal 2 (Zero Hunger), particularly target 2.2, for ending all forms of malnutrition [ 39 , 40 ]. Taken together, these findings reinforce that poor dietary diversity, especially among pregnant women, is a concern not only in LMICs, where higher risk is associated with socioeconomic factors such as education and low income but also in UMICs [ 41 , 42 ]. Although dietary diversity is a strategy to improve the probability of micronutrient intake, in this study, there was no association between MDD-W and adequate levels of nutrient intake for gestation, as intakes of calcium, iron, folate, vitamin D, and PUFA remained below daily recommendations [ 9 , 34 ] in MDD-W adequate and inadequate women. A recent review with WRA in LMICs also revealed critical deficiencies in the intake of essential micronutrients, including calcium, iron, and folate [ 43 ]. These nutrients are involved in pregnancy outcomes such as foetal development, low birth weight, neurodevelopment, preeclampsia, preterm birth and bleeding [ 5 , 44 – 47 ]. In Brazil, folic acid, iron, and calcium supplements are provided free of charge to all pregnant women as part of the protocol of the Brazilian Universal Health System (SUS), which likely contributes to reducing adverse outcomes associated with these nutrient deficiencies. Therefore, while food group-based indicators are useful for identifying minimum dietary diversity, pregnancy may require more specific quantitative thresholds to ensure adequate micronutrient intake. With respect to pregnancy outcomes, our findings showed that non-adequate dietary diversity was primarily determined by maternal age and income but not by ethnicity, anthropometric status, or adverse outcomes. Most of the studies evaluating MDD-W focus on dietary intake and micronutrient adequacy, but they are limited in their ability to assess associations with maternal or neonatal health outcomes and anthropometric indicators of nutritional status [ 15 ]. In addition, data from pregnant women in four LMICs revealed that although dietary diversity adequacy was associated with increased intake of several nutrients, a high prevalence of inadequate intake of key micronutrients persisted [ 48 ]. In contrast, in an urban cohort in Tanzania, an association between dietary diversity, gestational weight gain and adverse birth outcomes was found via relative categorization of dietary scores [ 14 ]. Similarly, a study conducted in New Delhi, India, reported positive associations between dietary diversity and anaemia during pregnancy [ 17 ]. However, previous studies have shown inconsistent associations between MDD-W and maternal underweight [ 49 ], which is often associated with low birth weight. In our study, pregnancy outcomes were not associated with maternal dietary diversity, nor did dietary diversity independently predict birth weight or modify the relationship between gestational age and foetal growth. Although studies from LMICs have revealed an association between low maternal dietary diversity and increased risk of low birth weight, they also highlighted substantial methodological heterogeneity, since many studies were conducted in settings with a high baseline prevalence of food insecurity and low birth weight [ 50 ]. We acknowledge the limitations of this study. Although the MDD-W was developed to be applied via a single 24-hour recall, this approach may introduce measurement error through memory bias, and typically, single nutrient intake and the probability of nutrient adequacy could have been better estimated with repeated measurements. However, we assessed more than 1,000 24-hour dietary recalls from six prenatal health centers, which were performed by trained staff and via the multiple-pass method, which is a validated and widely used technique to reduce memory bias. Moreover, although a single 24-hour recall is insufficient to estimate individual usual intake, the distribution of days in our study allows the use of one recall per person to estimate population-level eating habits [ 51 ]. In addition, dietary data were collected during the second trimester, a period with reduced interference from nausea and other conditions affecting food intake. All women were nulliparous and classified as low risk, providing a more homogeneous population. Finally, importantly, this study provides an exploratory profile of dietary intake and its associations with human health and environmental outcomes. The planning and exploration of new approaches are fundamental to shaping human strategies for the near future. In this context, modelling approaches have been developed to assess a wide range of agricultural impacts on nutrition and human health, including future interactions such as mortality [ 52 ]. While previous population-based analyses reported an average footprint of approximately 4,500 g CO₂e per person per day in Brazil [ 53 ], we show that women who achieved MDD-W adequacy presented even greater carbon, water, and ecological footprints (Table 6 ). Although direct comparisons should be interpreted cautiously due to differences in modelling assumptions and system boundaries, the median carbon footprint observed in our sample was closer to estimates reported for the EAT–Lancet reference diet [ 19 ]. A Brazilian study revealed strong linear associations between beef consumption and environmental impacts, with individuals in the highest consumption quintile presenting 48% greater carbon and 31% greater water footprints than those in the lowest quintile [ 54 ]. These findings highlight the need to identify nutritional–environmental hotspots, considering not only the types of foods consumed but also their production systems and supply chain origins. In this context, evidence from China similarly indicates that environmental impacts are largely driven by the quantity of foods consumed, especially cereals, vegetables, and animal-source products [ 55 ]. Moreover, maternal dietary choices influence children's food habits and contribute to the establishment of dietary patterns in the next generation, reinforcing intergenerational eating behaviours and long-term dietary patterns [ 56 , 57 ]. Therefore, integrating life cycle assessment (LCA) approaches that account for production systems and the origin of the food chain is essential to better capture these trade-offs. Conclusion The mixed evidence observed across countries supports our interpretation that the MDD-W is informative for micronutrient adequacy but not a robust predictor of obstetric risk. By evaluating the MDD-W across socioeconomic, nutritional, obstetric, and environmental dimensions, this study extends the state of the art in three key ways. First, it empirically demonstrates that dietary diversity is socially structured and not associated with clinical risk. Second, it clarifies the limited predictive value of the MDD-W for pregnancy outcomes when it is applied beyond its original scope. Third, this study provides new insights into the relationships among dietary diversity, pregnancy outcomes, and environmental footprints. These findings underscore the need for integrated food and nutrition policies that simultaneously address equity, maternal health, and planetary boundaries. Finally, women of reproductive age, particularly during pregnancy, sit at the nexus of human and planetary futures, highlighting the urgency of adopting a “double future” perspective that links maternal health with planetary boundaries. Abbreviations • MDD-W Minimum Dietary Diversity for Women • APO adverse pregnancy outcome • PE Preeclampsia • GDM Gestational diabetes mellitus • PTB Preterm birth • SGA Small for gestational age • LCA Life cycle assessment • FFCP Footprints of Foods and Culinary Preparations Consumed in Brazil • PUFAs Polyunsaturated fatty acids • EAR Estimated average requirement • AI Adequate intake • BMI Body mass index • LMICs Low- and middle-income countries • UMICs Upper-middle-income countries Declarations Consent for publication Not applicable. Funding Maria Julia Miele was supported by the São Paulo Research Foundation (FAPESP), Brazil, through a postdoctoral fellowship (grant #2025/00722-3). The funder had no role in the design of the study; data collection, analysis, or interpretation; or writing of the manuscript. Authors’ contributions MJOM and BJT conceived the study. MJOM performed the analysis and drafted the manuscript. JTS contributed to data interpretation and critically revised the manuscript. MN, RTS and JGC reviewed the manuscript. All authors contributed to the interpretation of the results, read, and approved the final version of the manuscript. Conflict of interest The authors declare that they have no competing interests. Acknowledgements The preterm SAMBA study group also included Renato Passini Jr, Mary A. Parpinelli, Danielly S Santana, School of Medical Sciences (FCM), University of Campinas, Brazil; Iracema P Calderon, Bianca F. Cassettari, School of Medicine of Botucatu, UNESP, Brazil; Janete Vettorazzi, Lucia Pfitscher, Luiza Brust, School of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Brazil; Edilberto Rocha Filho, Debora F Leite, Elias F Melo Junior, Danilo Anacleto, School of Medicine, Federal University of Pernambuco, Recife, Brazil; Francisco E Feitosa, Daisy de Lucena, Benedita Sousa, School of Medicine, Federal University of Ceará, Fortaleza, Brazil. 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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-9314030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628571304,"identity":"20dac357-0e1e-46e9-9716-1ed2ec4192d9","order_by":0,"name":"Maria Julia Miele","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFACxgaGBww2DAzMEG4CcVoSGNJI0gJWdhiJTQjoTjvc+CCh4nzidnbmh59u7mDIM28goMXsdmKzQcKZ24k7m9mMpXPPMBTLHCCspU0ise124obDPAzSuW0MiTMIOQyopf1H4r9zIC3Mv4nVAlTWcACkhY1oW5olEo4lG284zGZmnXtGoliCsJb0hx8+1NjJbjh/+PHt3B02eQS1oALGBhI1gFPCKBgFo2AUjAIMAACceETTq+19UgAAAABJRU5ErkJggg==","orcid":"","institution":"State University of Campinas","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Julia","lastName":"Miele","suffix":""},{"id":628571305,"identity":"468f8e6f-d5d7-4d49-b2c4-c1b169a80fe7","order_by":1,"name":"Jacqueline Tereza da Silva","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Jacqueline","middleName":"Tereza da","lastName":"Silva","suffix":""},{"id":628571309,"identity":"b026dabf-fa15-4318-8b15-653f0924fed9","order_by":2,"name":"Manina Norde","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Manina","middleName":"","lastName":"Norde","suffix":""},{"id":628571311,"identity":"66917287-e408-418b-9ab2-94d32ecc4197","order_by":3,"name":"Renato Teixeira Souza","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Renato","middleName":"Teixeira","lastName":"Souza","suffix":""},{"id":628571314,"identity":"a5c4d680-6490-4955-9dd7-059bc02acde8","order_by":4,"name":"José Guilherme Cecatti","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Guilherme","lastName":"Cecatti","suffix":""},{"id":628571317,"identity":"a18508e6-c045-4295-87a9-5e1949eb7cec","order_by":5,"name":"Barbara Teruel","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Teruel","suffix":""}],"badges":[],"createdAt":"2026-04-03 14:41:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9314030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9314030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107716606,"identity":"3e6e54dd-b83d-4283-bfec-0413b3f591cc","added_by":"auto","created_at":"2026-04-24 10:07:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2346873,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant inclusion and exclusion from the preterm SAMBA and MAES cohort studies.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9314030/v1/3e2a0d5948a6160c8476835d.png"},{"id":107869519,"identity":"cf80cc6c-4122-4ab9-9c82-4e4dc46fb287","added_by":"auto","created_at":"2026-04-27 07:37:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140275,"visible":true,"origin":"","legend":"\u003cp\u003eLinear association between birth weight and gestational age according to dietary adequacy based on the MDD-W. Multiple linear regression model adjusted for maternal age categories (\u0026lt;20 years, 20−34 years, and \u0026gt;34 years), household income (\u0026lt;12,000 vs. ≥12,000 USD), and total energy intake (kcal/day, continuous). The P value refers to the interaction term between MDD-W adequacy and gestational age.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9314030/v1/f027130f67a21482508a78ee.png"},{"id":108803498,"identity":"48cccc0a-7c84-4148-856a-e57388dcac98","added_by":"auto","created_at":"2026-05-08 14:57:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3012979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9314030/v1/6bd71469-7a3c-4f5e-a520-0d1f34887054.pdf"},{"id":107716608,"identity":"a09be939-c173-46d1-9c15-aad834a6d2f9","added_by":"auto","created_at":"2026-04-24 10:07:46","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":448691,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9314030/v1/4ad10af669dcee10267d12b8.jpg"},{"id":108006081,"identity":"d62ac05b-b923-4510-bff0-8f35da6efd6a","added_by":"auto","created_at":"2026-04-28 12:53:04","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":38170,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEnutchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9314030/v1/32d99bc6ba13558f1318b2f4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Double-Future Dilemma: Dietary Diversity, Pregnancy Outcomes, and Planetary Health","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eWhile diets are among the key modifiable factors for preventing adverse pregnancy outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], they are also among the main contributors to climate change [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, integrating human and planetary health into dietary guidelines for pregnant women implies the need to act on a double future: for the newborn's life and for the planet. The global community has been actively seeking nutritionally adequate and environmentally sustainable food resources [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent studies indicate that food choices significantly influence the environmental impacts of food systems, including greenhouse gas emissions, water use, land degradation, and biodiversity loss [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Food production and consumption patterns simultaneously affect human health, ecosystem health, and environmental sustainability, underscoring the critical need for an integrated \u0026ldquo;planetary health\u0026rdquo; approach [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This approach recognizes the interdependence between dietary habits for women's health, particularly during reproductive years, and the broader implications for planetary health, thus highlighting the inseparable relationship between human well-being and environmental sustainability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor a long time, however, the human and planetary aspects of diets were assessed separately. The environmental footprint of diets is an essential metric for evaluating the sustainability of dietary intake. Indicators such as the carbon footprint (kg CO₂e), water use (L), and ecological footprint (eco-score) quantify the pressures exerted by food production, processing, and consumption on natural resources on the basis of the life cycle assessment (LCA) framework [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These measures provide a comprehensive understanding of how dietary patterns contribute to the depletion of natural resources and to climate change, opening up an opportunity to complement health-based nutritional assessments.\u003c/p\u003e \u003cp\u003eDietary diversity is a proxy for adequate intake of essential nutrients for maternal health and foetal development [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Dietary guidelines provide specific approaches to achieve adequate nutritional status by setting different metrics [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Food groups are practical indicators for assessing diet quality in population settings. Therefore, to assess the diet of women of reproductive age (15\u0026ndash;49 years) (WRA), the Food and Agriculture Organization (FAO) developed the minimum dietary diversity for women (MDD-W) as a key indicator that balances the combination of nutrient-rich food groups to achieve micronutrient adequacy for this population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shown that higher dietary diversity, as measured by the MDD-W, is associated with improved maternal nutritional status, adequate gestational weight gain, a reduced risk of anaemia, and favourable birth outcomes [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, increasing evidence highlights the environmental impacts of dietary patterns, and integrated frameworks such as the EAT-Lancet Commission have emphasized the need to align health and sustainability goals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, most studies assessing the health benefits of dietary diversity focus on maternal and child outcomes without examining their environmental implications, whereas sustainability-oriented analyses typically address the general population rather than pregnancy specifically. Thus, the potential trade-offs between achieving dietary diversity during pregnancy and the environmental sustainability of food consumption remain insufficiently explored.\u003c/p\u003e \u003cp\u003eThus, this study investigates whether adherence to MDD-W recommendations during pregnancy is associated with both pregnancy outcomes and environmental impacts by jointly assessing dietary diversity and the environmental footprints of food consumption in the Brazilian context. We examined whether adherence to the MDD-W indicator is associated with better pregnancy outcomes and lower environmental footprints of mothers.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study is a multicentre prospective cohort that integrates methodological approaches from nutritional epidemiology and environmental sustainability analyses to assess the impact of maternal dietary consumption on both human and planetary health. Associations between MDD-W adherence and pregnancy outcomes were assessed via multiple logistic regression. The environmental impacts of maternal diets were estimated via the Footprints of Foods and Culinary Preparations Consumed in Brazil (FFCP) database, which was developed by the University of S\u0026atilde;o Paulo (USP) and is publicly available on the Open Science Framework. This approach enabled a comparative analytical framework structured into two complementary dimensions. The first dimension, related to maternal health, assessed dietary adequacy according to the MDD-W and its association with adverse pregnancy outcomes derived from the SAMBA and MAES cohorts. The second dimension, related to environmental sustainability, was based on indicators provided by the FFCP database, which provides life cycle assessment\u0026ndash;based indicators of the carbon footprint (kg CO₂e/100 g), water footprint (L/100 g), and ecological footprint (eco-score/100 g).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe present study used data from two multicenter cohort studies, the Preterm SAMBA - Preterm Screening and Metabolomics in Brazil and Auckland and the Maternal Actigraphy Exploratory Study (MAES) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Both cohort studies followed a standardized protocol for recruitment and individual-level data collection on sociodemographic characteristics, dietary intake, clinical assessment, and maternal outcomes among women with singleton pregnancies. The SAMBA study was conducted from 2015\u0026ndash;2018, and the MAES study was conducted from 2018\u0026ndash;2020 across six public obstetric referral hospitals located in three geographical regions of Brazil. These centres were selected for their demographic characteristics, which represent the diversity of social and ethnic profiles, as well as regional dietary habits, across the Northeast, South, and Southeast regions of the country [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll nulliparous pregnant women considered to be at low risk and between the 19th and 21st weeks of gestation were invited to participate in this study. Those with a previous history of three or more abortions, cervical alterations, major foetal anomalies, M\u0026uuml;llerian anomalies, previous cervical conization, chronic corticosteroid use, or detected or self-reported preexisting diseases, including hypertensive disorders, previous diagnosis of diabetes mellitus, renal disease, systemic lupus erythematosus, antiphospholipid syndrome, sickle-cell anaemia, and HIV-positive serology, were not eligible for study. Women taking medications or supplements that could interfere with outcome assessment, such as aspirin, calcium, fish oil, vitamin C, vitamin E, or heparin, were also excluded. Therefore, the potential confounding effect of dietary supplement use on micronutrient intake was minimized by the study design. Additional exclusions were performed specifically for this study, such as cases missing the dietary assessment, with stillbirth after the first visit, and missing key information such as birthweight. Sociodemographic data were self-reported, and all collected information was entered into an electronic platform (MedSciNet \u0026reg; AB, Sweden). All income information originally reported in Brazilian national currency was converted into U.S. dollars, using the exchange rate at the time of data collection, to allow international comparability.\u003c/p\u003e \u003cp\u003eWomen who followed the Brazilian Health System (SUS) protocol for supplements during pregnancy, such as folic acid, iron, or calcium, were not excluded. Since this supplement was used uniformly across the study sample, it was not included as a covariate in the statistical analysis. Sociodemographic characteristics, as well as self-declared skin color/ethnicity, were self-reported [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The overall process of sample selection, exclusion, and final analysis is detailed in Fig.\u0026nbsp;1 (Flowchart). All participants provided written informed consent before enrollment. The Preterm SAMBA and MAES studies complied with the ethical principles of the Declaration of Helsinki (2013) and were approved by the Research Ethics Committees of all participating centres (protocol No. 20182318.8.0000.5404) as well as by the National Research Ethics Committee (CONEP).\u003c/p\u003e\n\u003ch3\u003eAnthropometric assessment\u003c/h3\u003e\n\u003cp\u003eFor body weight and height assessments, all centers used an electronic scale and a duly calibrated stadiometer, respectively, and measurements were taken at the time of study entry. The study coordinator trained members of the healthcare team to perform the anthropometric measurements of each participant (weight and height) in accordance with the standardized criteria of the Food and Nutritional Surveillance System of the Ministry of Health [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Body mass index (BMI) was automatically calculated via the study\u0026rsquo;s electronic platform software, which uses weight and height measurements. As weight and height were measured between 19 and 21 weeks of gestation, BMI categories were classified into underweight, adequate, overweight or obese according to the Atalah curve [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These standardized procedures ensure the comparability of measurements across centers.\u003c/p\u003e\n\u003ch3\u003eMaternal and neonatal outcomes\u003c/h3\u003e\n\u003cp\u003eMaternal and neonatal outcomes were defined according to international criteria. Cases of preeclampsia (PE) and gestational diabetes mellitus (GDM) were defined on the basis of the International Society for the Study of Hypertension in Pregnancy (ISSHP) criteria and the American Diabetes Association (ADA) criteria, respectively [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Preterm birth (PTB) was considered for all women who gave birth before reaching 37 weeks of pregnancy [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The definition of small for-gestational-age (SGA) newborns, according to the birthweight percentile (\u0026lt;\u0026thinsp;p10) adjusted for maternal characteristics (ethnicity, weight, height and parity, gestational age at birth and infant sex), was performed via the GROW centile calculator: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gestation.net/GROW_documentation.pdf\u003c/span\u003e\u003cspan address=\"https://www.gestation.net/GROW_documentation.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The composite variable named adverse pregnancy outcome (APO) was defined as the presence of at least one of the following conditions: PE, GDM, PTB or SGA.\u003c/p\u003e\n\u003ch3\u003eDietary assessment\u003c/h3\u003e\n\u003cp\u003eTo assess dietary intake, a 24-hour dietary recall (24hR) was applied per woman at the 19th and 21st weeks of gestation, covering all days of the week and all seasons of the year in the total sample. The questionnaire was administered at study entry by healthcare professionals trained by a dietitian via the multiple-pass method, which is a standardized process designed to stimulate the respondent\u0026rsquo;s memory and increase the accuracy of the information provided [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Serving size was estimated via household measures, which were based on photographs of kitchen utensils and food portions classified as small, medium, or large according to the Brazilian Ministry of Health [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To standardize servings, household measures were converted into grams or millilitres of consumption via Brazilian and international reference manuals [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This information provides a quantitative basis for assessing dietary adequacy via the MDD-W indicator.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMinimum Dietary Diversity for Women\u003c/h2\u003e \u003cp\u003eThe dietary data were analysed in relation to the MDD-W indicator developed by the FAO [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The MDD-W is a validated proxy for micronutrient adequacy among women of reproductive age (WRA). It is based on the consumption of at least five out of ten predefined food groups within the previous 24 hours, representing the minimum threshold of dietary diversity associated with adequate micronutrient intake. The MDD-W reflects a high probability of adequacy across eleven micronutrients that are strongly associated with dietary diversity: vitamin A, thiamine, riboflavin, niacin, vitamin B₆, folate, vitamin B₁₂, vitamin C, calcium, iron, and zinc. In the present study, vitamin D and polyunsaturated fatty acids (PUFAs) were also analysed owing to their clinical relevance during pregnancy and their established associations with maternal\u0026ndash;foetal outcomes.\u003c/p\u003e \u003cp\u003e To estimate MDD-W, the classification of all food items was performed jointly for both datasets (SAMBA and MAES), and all items were categorized according to the ten MDD-W food groups by a registered dietician: (1) grains, white roots and tubers, and plantains; (2) pulses (beans, peas, and lentils); (3) nuts and seeds; (4) dairy; (5) meat, poultry, and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; and (10) other fruits.\u003c/p\u003e \u003cp\u003eTo assess the proportion of women whose micronutrient intake met the recommended thresholds, dietary intakes were compared with the dietary reference intakes (DRIs) for pregnant women, using the estimated average requirement (EAR) or, when unavailable, the adequate intake (AI) as reference values [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nutrient density was calculated to adjust daily micronutrient intake for total energy consumption to account for differences in total food intake between women. Nutrient density was expressed per 1,000 kcal via the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t}\\:\\text{d}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}=\\:\\frac{\\text{M}\\text{i}\\text{c}\\text{r}\\text{o}\\text{n}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:(\\text{u}\\text{n}\\text{i}\\text{t}\\:/\\:\\text{d}\\text{a}\\text{y})\\:}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{c}\\text{a}\\text{l}\\text{o}\\text{r}\\text{i}\\text{c}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:(\\text{k}\\text{c}\\text{a}\\text{l}\\:/\\:\\text{d}\\text{a}\\text{y})\\:}\\:\\:\\times\\:\\text{1,000}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis approach allowed comparison of the mean intakes of the selected micronutrients between women classified as adequate (\u0026ge;\u0026thinsp;5 food groups out of ten) and non-adequate (\u0026lt;\u0026thinsp;5 food groups out of ten) according to the MDD-W, independent of total caloric intake. For polyunsaturated fatty acids (PUFAs), adequacy was evaluated according to FAO/WHO guidelines [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] by expressing total PUFA intake as a percentage of total energy intake (% VET) via the following formula:\u003c/div\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003ch3\u003eEnvironmental footprints\u003c/h3\u003e\n\u003cp\u003eThe \u0026ldquo;Footprints of Foods and Culinary Preparations Consumed in Brazil\u0026rdquo; (FFCP), developed by the University of S\u0026atilde;o Paulo (USP) and publicly available on the Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/gs4cy/\u003c/span\u003e\u003cspan address=\"https://osf.io/gs4cy/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was used to estimate the environmental impacts of food production in Brazil [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The FFCP dataset, which is based on the Brazilian Household Budget Survey (IBGE) and Life Cycle Assessment (LCA) studies, provides indicators of the carbon footprint (kg CO₂e/100 g), water footprint (L/100 g), and ecological footprint (eco-score/100 g) of foods consumed in Brazil.\u003c/p\u003e \u003cp\u003eAll environmental indicators were estimated at the individual level on the basis of total daily intake (g/day) and analysed in relation to dietary adequacy according to the minimum dietary diversity for women (MDD-W) indicator. The FFCP food correspondence to the Preterm SAMBA (2015\u0026ndash;2018) and MAES (2018\u0026ndash;2020) cohort datasets was manually assigned by a registered dietitian. The maternal intake (from the SAMBA and MAES cohorts), after classification according to the MDD-W food groups, was then matched with its corresponding environmental impact factor, and the total environmental footprint per participant was calculated by summing the footprint values weighted by the amount consumed (100 g). Finally, total dietary intake (g/day) and the corresponding FFCP indicators were used to assess differences in environmental impact between adequate and non-adequate diets according to the MDD-W criteria.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe sociodemographic and health characteristics of the pregnant women are presented as counts and percentages for categorical variables, whereas continuous variables are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs), both of which are stratified by their adherence to the MDD-W dietary recommendations. Differences across subgroups were assessed via the Wilcoxon rank sum test for continuous variables and Pearson\u0026rsquo;s chi-square test for categorical variables. All p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003cp\u003eA logistic regression model was used to identify factors associated with non-adequate MDD-W. Variable selection was guided by a priori clinical and epidemiological relevance, including maternal age (\u0026lt;\u0026thinsp;20 years, 20\u0026thinsp;\u0026minus;\u0026thinsp;34 years, and \u0026gt;\u0026thinsp;34 years), body mass index (BMI indicative of underweight, adequate, overweight, and obese), ethnicity (white and non-white), income level (\u0026lt;\u0026thinsp;12,000 vs\u0026thinsp;\u0026ge;\u0026thinsp;12,000 USD), and adverse pregnancy outcomes (APO), on the basis of their potential relationship with dietary vulnerability. Multiple logistic regression, within a generalized linear model (GLM) framework with logit links, was conducted with non-adequate MDD-W as the dependent variable and sociodemographic, clinical, and pregnancy-related characteristics, including adverse pregnancy outcomes (APOs), as independent variables. Different combinations of covariates were tested, and model selection was based on clinical plausibility and goodness-of-fit criteria, including the Akaike information criterion (AIC) and Tjur\u0026rsquo;s R\u0026sup2;. Coefficients were estimated from the data via the maximum likelihood method, which maximizes the probability of the observed outcomes and provides a measure of predictive accuracy. The effect of each coefficient on the odds of non-adequate MDD-W was assessed according to its positive value (indicating a greater likelihood of occurrence) or negative value (indicating a protective effect). The statistical significance of the coefficients was tested via the Wald statistic.\u003c/p\u003e \u003cp\u003eLinear regression models were also used to assess the associations between birth weight (and head circumference) and gestational age according to MDD-W adequacy. The multivariable models were adjusted for maternal age (years, continuous), household income (\u0026lt;\u0026thinsp;12,000 USD vs. \u0026ge;12,000 USD), and total energy intake (kcal/day, continuous). Residual analysis was conducted via least-squares regression and Cook\u0026rsquo;s distance. To assess multicollinearity, variance inflation factors (VIFs) were examined, and values greater than five were considered indicative of problematic collinearity. For these models, MDD-W adequacy (adequate vs. non-adequate food group diversity) was included as the main exposure variable. The objective of this study was to determine whether dietary diversity adequacy was associated with adverse pregnancy outcomes or birth weight.\u003c/p\u003e \u003cp\u003eThe results of these analyses are expressed as odds ratios (ORs) and 95% confidence intervals (CIs), whereas linear regression results are reported as beta coefficients (β) with 95% confidence intervals. Statistical analyses were performed via R Core Team software (version 2025) with the packages \u0026ldquo;pacman,\u0026rdquo; \u0026ldquo;sjPlot,\u0026rdquo; and base regression functions.\u003c/p\u003e \u003cp\u003e This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, and the completed STROBE checklist is provided in the Supplementary Appendix.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e. Flow diagram of participant inclusion and exclusion from the preterm SAMBA and MAES cohort studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAmong the 1,318 nulliparous pregnant women included in the analysis from both studies (Fig.\u0026nbsp;1), 904 (68.6%) achieved adequate minimum dietary diversity for women (MDD-W) scores (\u0026ge;\u0026thinsp;5 food groups), whereas 414 (31.4%) did not. Maternal dietary adequacy varied significantly according to age, region, education, income, ethnicity, and occupation (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not according to body mass index (p\u0026thinsp;=\u0026thinsp;0.7) (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\u003eMaternal sociodemographic characteristics by adequacy to the minimum dietary diversity for women\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eNon-adequacy\u003c/p\u003e \u003cp\u003en (%) (n\u0026thinsp;=\u0026thinsp;414)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequacy\u003c/p\u003e \u003cp\u003en (%) (n\u0026thinsp;=\u0026thinsp;904)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003en (%) (n\u0026thinsp;=\u0026thinsp;1,318)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312 (24)\u003c/p\u003e \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\u003e20\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e933 (72)\u003c/p\u003e \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\u0026gt; 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (4)\u003c/p\u003e \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\u003eBMI (kg/m\u0026sup2;)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223 (17)\u003c/p\u003e \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\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343 (26)\u003c/p\u003e \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\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e516 (40)\u003c/p\u003e \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\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (17)\u003c/p\u003e \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\u003eRegion\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwest/South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e581 (45)\u003c/p\u003e \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\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e467 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e723 (55)\u003c/p\u003e \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\u003eOccupation\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602 (46)\u003c/p\u003e \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\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e702 (54)\u003c/p\u003e \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\u003eEthnicity\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e402 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (42)\u003c/p\u003e \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\u003eNon-white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e759 (58)\u003c/p\u003e \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\u003eEducation (year)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (13)\u003c/p\u003e \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\u003e9\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e738 (57)\u003c/p\u003e \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\u0026gt;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398 (31)\u003c/p\u003e \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\u003eFamily Annual Income\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;12,000 (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304 (23)\u003c/p\u003e \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\u0026ge;12,000 (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e724 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,000 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdequacy (\u0026ge;\u0026thinsp;5 food groups) and Non-adequacy (\u0026lt;\u0026thinsp;5 food groups): n/N (%). P value: Pearson\u0026rsquo;s chi-square test. Overall missing information\u0026thinsp;=\u0026thinsp;15 (adequacy\u0026thinsp;=\u0026thinsp;11, non-adequacy\u0026thinsp;=\u0026thinsp;4). BMI missing information adequacy\u0026thinsp;=\u0026thinsp;16.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Maternal sociodemographic characteristics by adequacy to the minimum dietary diversity for women\u003c/p\u003e \u003cp\u003eAmong women with adequate MDD-W, a greater proportion were aged 20\u0026ndash;34 years (76%), while a lower proportion were adolescents (19%) than the non-adequate group (35% proportion of adolescents). A greater proportion of women residing in the Northeast region was found in the non-adequate group (62%) than in the adequate group (52%). Higher education (above 12 years of study) (36%) and higher annual income (12,000 USD or more) (81%) were more frequently observed among women with adequate dietary diversity. Similarly, the proportions of women not engaged in paid work (57%) and who self-identified as white (45%) were greater among women in the adequacy group than among those in the non-adequacy group.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that women classified as adequate by the MDD-W had higher energy intake, consumed larger quantities of food, and had greater intakes of most micronutrients, standardized by energy intake. The average daily intake of PUFAs, expressed as a percentage of total energy intake, fell within the recommended range of 6\u0026ndash;11% in the non-adequate group (6.3%) but not in the adequate group (5.8%). Nevertheless, even among women who achieved MDD-W adequacy, the average daily intake of several micronutrients remained below pregnancy-specific reference values, namely, calcium (AI\u0026thinsp;=\u0026thinsp;1,000 mg/day), iron (EAR\u0026thinsp;=\u0026thinsp;22 mg/day), zinc (EAR\u0026thinsp;=\u0026thinsp;9.5 mg/day), vitamin A (EAR\u0026thinsp;=\u0026thinsp;550 \u0026micro;g RAE/day), folate (EAR\u0026thinsp;=\u0026thinsp;520 \u0026micro;g DFE/day), and vitamin B12 (EAR\u0026thinsp;=\u0026thinsp;2.2 \u0026micro;g/day). For dietary iron and folate intake, 90% and 80% of women were classified as below the recommended thresholds, respectively, independent of MDD-W adequacy. Approximately 70% of women had calcium intake below the reference value, and nearly two-thirds had inadequate dietary vitamin A intake. For dietary zinc and vitamin B12, the proportion of women classified as having insufficient intake was smaller, as this was observed in approximately one quarter and one fifth of women, respectively. Overall, women who achieved MDD-W adequacy tended to have higher micronutrient intakes, suggesting a lower likelihood of inadequate intake than those classified as non-adequate.\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\u003eMaternal micronutrient intake by adequacy to minimum dietary diversity for women (MDD-W)\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=\".\" 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 \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\u003eNon-adequacy\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;414)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequacy\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;904)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDD-W (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 (3.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,829.4 (1,412.3, 2,435.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,032.8 (1,570.2, 2,484.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrams (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,111.0 (859.3, 1,414.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,440.0 (1,180.3, 1,785.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226.0 (126.8, 354.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309.0 (219.5, 424.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.8 (4.5, 6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0 (4.9, 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6 (4.3, 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3 (4.8, 8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A (\u0026micro;g RAE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132.5 (81.7, 206.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214.4 (143.1, 343.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B1 (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B2 (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.6, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.7, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B3 (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.6 (7.3, 12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6 (7.4, 13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B6 (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.6, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.7, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B12 (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8 (0.9, 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.4, 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolate (\u0026micro;g DFE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.7, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.0, 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.8 (78.2, 195.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.8 (97.3, 209.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.7 (4.2, 40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.8 (18.4, 85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3 (4.6, 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8 (4.7, 7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAdequacy (\u0026ge;\u0026thinsp;5 food groups) and Non-adequacy (\u0026lt;\u0026thinsp;5 food groups): Medians (IQRs). P value: Wilcoxon rank sum test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Maternal micronutrient intake by adequacy to minimum dietary diversity for women (MDD-W)\u003c/p\u003e \u003cp\u003eThe comparison of the average intake of MDD-W food groups revealed marked differences between women with non-adequate and adequate MDD-W (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Grains and tubers were consumed universally in both groups, followed by animal-derived foods. However, wide disparities were observed across nine food groups, particularly fruits, vegetables, and vitamin A-rich fruits and vegetables, dark green leafy vegetables, eggs, pulses, and dairy. Overall, nutrient-dense food groups, particularly dairy, fruits, vegetables, and pulses, were the main contributors to distinguishing women who achieved the minimum dietary diversity threshold from those who did not.\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\u003eProportion of pregnant women consuming each food group of the minimum dietary diversity for women (MDD-W) by adequacy status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFOOD GROUPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMDD-W adequacy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-adequate\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;414)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;904)\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\u003eDairy (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDark green leafy vegetables (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEggs (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFruits (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrains \u0026amp; Tubers (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeat, Poultry, Fish (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNuts and seeds (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulses (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetables (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVitamin A-rich fruits and vegetables (yes, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMDD-W cut-off: adequacy (\u0026ge;\u0026thinsp;5 food groups); non-adequacy (\u0026lt;\u0026thinsp;5 food groups). The difference between the adequacy groups was significant for all the MDD-W food groups (χ\u003csup\u003e2\u003c/sup\u003e test, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Proportion of pregnant women consuming each food group of the minimum dietary diversity for women (MDD-W) by adequacy status.\u003c/p\u003e \u003cp\u003eMultiple logistic regression identified socioeconomic and demographic predictors associated with the risk of non-adequate MDD-W (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A lower household income and younger maternal age were significantly associated with a greater likelihood of inadequate MDD-W. Compared with those aged 20\u0026ndash;34 years, women with an annual income below USD 12,000 had 1.68 (95% CI 1.27\u0026ndash;2.23) times greater odds of having inadequate MDD-W. Likewise, adolescents under 20 years of age were twice as likely to have non-adequate MDD-W scores.\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\u003eAdjusted odds ratios for non-adequate MDD-W according to maternal characteristics\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=\"left\" 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\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eNon-adequacy MDD-W food groups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026ndash;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher income (\u0026ge;\u0026thinsp;12,000 USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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\u003eLower income (\u0026lt;\u0026thinsp;12,000 USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026ndash;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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\u003eNon-white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.056\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese/Overweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age (\u0026lt;\u0026thinsp;20 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u0026ndash;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age (20\u0026ndash;34 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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\u003eMaternal age (\u0026gt;\u0026thinsp;34 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel fit: R\u0026sup2; Tjur\u0026thinsp;=\u0026thinsp;0.050; AIC\u0026thinsp;=\u0026thinsp;1576.8. Observation\u0026thinsp;=\u0026thinsp;1303.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSelf-declared skin color/ethnicity was not significantly associated with non-adequate MDD-W (OR\u0026thinsp;=\u0026thinsp;1.28; 95% CI 0.99\u0026ndash;1.66; p\u0026thinsp;=\u0026thinsp;0.056), although the point estimate corresponded to 28% higher odds among non-white women than self-declared white women. No significant associations were observed for maternal nutritional status (obesity, overweight, or underweight) or for adverse pregnancy outcomes. The model showed acceptable fit (Tjur R\u0026sup2; = 0.05; AIC\u0026thinsp;=\u0026thinsp;1576.8).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Adjusted odds ratios for non-adequate MDD-W according to maternal characteristics\u003c/p\u003e \u003cp\u003eModel fit: R\u0026sup2; Tjur\u0026thinsp;=\u0026thinsp;0.050; AIC\u0026thinsp;=\u0026thinsp;1576.8. Observation\u0026thinsp;=\u0026thinsp;1303.\u003c/p\u003e \u003cp\u003eFigure 2 shows the relationship between gestational age and birth weight according to MDD-W adequacy. As expected, birth weight increased progressively with gestational age in both groups. The slopes of the regression lines were similar, indicating a consistent positive association between gestational age and foetal growth regardless of dietary adequacy. However, across almost all gestational ages, women with adequate MDD-W (blue line) tended to have infants with slightly higher birth weights than those in the non-adequate group (red line), suggesting a modest nutritional advantage associated with greater dietary diversity. After adjustment for maternal age category, household income, and total energy intake, the results remained unchanged, and the interaction term remained nonsignificant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e. Linear association between birth weight and gestational age according to dietary adequacy based on the MDD-W. Multiple linear regression model adjusted for maternal age categories (\u0026lt;\u0026thinsp;20 years, 20\u0026thinsp;\u0026minus;\u0026thinsp;34 years, and \u0026gt;\u0026thinsp;34 years), household income (\u0026lt;\u0026thinsp;12,000 vs. \u0026ge;12,000 USD), and total energy intake (kcal/day, continuous). The P value refers to the interaction term between MDD-W adequacy and gestational age.\u003c/p\u003e \u003cp\u003eThe associations between gestational age and birth weight according to MDD-W adequacy are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Neither dietary adequacy nor its interaction with gestational age reached statistical significance. The model explained approximately 55% of the variance in birth weight (R\u0026sup2; = 0.55).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between birth weight and gestational age according to MDD-W adequacy.\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=\"left\" 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\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBirth Weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4121.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4538.31\u0026nbsp;\u0026ndash;\u0026nbsp;-3704.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-19.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.06\u0026ndash;198.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-adequacy MDD-W (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e337.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-441.97\u0026ndash;1117.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA \u0026times; B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.19\u0026ndash;11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMultiple linear regression model. Observations: 1301. R2 /R2 adjusted: 0.551/0.550.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Associations between birth weight and gestational age according to MDD-W adequacy.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental impacts\u003c/h2\u003e \u003cp\u003eThe environmental indicators (carbon, water, and ecological footprints) were analysed as daily averages per individual to compare the sustainability profiles between adequate and non-adequate dietary groups. These results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental footprints according to the degree of MDD-W adequacy among pregnant women.\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=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINDICATOR (per 1,000kcal)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-adequate\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;414)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;904)\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\u003eTotal intake (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e599.2 (510.8, 722.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e714.6 (615.5, 837.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO₂e (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,445.7 (1,469.0, 4,480.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,809.6 (1,684.2, 4,620.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,107.6 (1,419.9, 3,277.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,477.2 (1,668.6, 3,583.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcological (pt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.3 (8.1, 21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.7 (10.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAdequacy (\u0026ge;\u0026thinsp;5 food groups) and Non-adequacy (\u0026lt;\u0026thinsp;5 food groups). The values represent the median (IQR) daily environmental footprint per individual. CO₂e\u0026thinsp;=\u0026thinsp;carbon dioxide equivalent (g/day); Water\u0026thinsp;=\u0026thinsp;litres/day; Ecological\u0026thinsp;=\u0026thinsp;points/day. P values were calculated via the Wilcoxon rank-sum test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Environmental footprints according to the degree of MDD-W adequacy among pregnant women.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, women in the adequate group consumed approximately 10% more calories and 30% more food in grams than those in the non-adequate group did. Despite this difference, after adjusting for caloric intake, the ecological footprint was 28% greater in the adequate group, whereas carbon emissions were 15% greater and water usage was 18% greater.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur main findings suggest that, in addition to being associated with key micronutrient intake, adherence to the MDD-W recommendations was not associated with the evaluated maternal or neonatal outcomes. However, the environmental footprint was significantly greater in women with adequate MDD-W, even after adjustment for 1,000 kcal. Furthermore, although meat-based diets have been mentioned as having the greatest environmental impact, our analysis revealed that the main difference in MDD-W components between groups resulted from higher scores for plant-based food groups. Thus, when \u0026ldquo;double future\u0026rdquo; goals are considered, this result suggests a potential misalignment between human and planetary health goals.\u003c/p\u003e \u003cp\u003eThis study revealed that the MDD-W was a sensitive indicator of socioeconomic and demographic conditions, as women who achieved adequate consumption were more likely to have higher incomes and education levels. The associations between sociodemographic factors and adherence to the MDD-W recommendations among pregnant women have been reported previously in low-income settings, such as the Philippines and African and Asian countries [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, no other studies have specifically assessed the dietary adequacy of MDD-W in pregnant women in upper-middle-income countries (UMICs), such as Brazil.\u003c/p\u003e \u003cp\u003eAlthough our study was conducted at a UMIC, the mean number of food groups consumed was 3.6 (IQR: 3.0, 4.0) out of the 10 food groups recommended in the MDD-W. A similar result was obtained in an Ethiopian study, which reported an average of 3.8 food groups, which also consumed more grains and fewer FGs of fruits, vegetables, nuts, and animal sources [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similarly, in Burkina Faso, pregnant women from poverty-stricken areas presented low dietary diversity (3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46), characterized by lower consumption of animal-sourced foods and higher intake of vegetables and green leafy vegetables [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similarly, pregnant women from Angola also consumed an average of 3 FGs by the non-adequate group of women [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Recently, the MDD-W has been officially adopted as an indicator to monitor progress towards Sustainable Development Goal 2 (Zero Hunger), particularly target 2.2, for ending all forms of malnutrition [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Taken together, these findings reinforce that poor dietary diversity, especially among pregnant women, is a concern not only in LMICs, where higher risk is associated with socioeconomic factors such as education and low income but also in UMICs [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough dietary diversity is a strategy to improve the probability of micronutrient intake, in this study, there was no association between MDD-W and adequate levels of nutrient intake for gestation, as intakes of calcium, iron, folate, vitamin D, and PUFA remained below daily recommendations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] in MDD-W adequate and inadequate women. A recent review with WRA in LMICs also revealed critical deficiencies in the intake of essential micronutrients, including calcium, iron, and folate [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These nutrients are involved in pregnancy outcomes such as foetal development, low birth weight, neurodevelopment, preeclampsia, preterm birth and bleeding [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In Brazil, folic acid, iron, and calcium supplements are provided free of charge to all pregnant women as part of the protocol of the Brazilian Universal Health System (SUS), which likely contributes to reducing adverse outcomes associated with these nutrient deficiencies. Therefore, while food group-based indicators are useful for identifying minimum dietary diversity, pregnancy may require more specific quantitative thresholds to ensure adequate micronutrient intake.\u003c/p\u003e \u003cp\u003eWith respect to pregnancy outcomes, our findings showed that non-adequate dietary diversity was primarily determined by maternal age and income but not by ethnicity, anthropometric status, or adverse outcomes. Most of the studies evaluating MDD-W focus on dietary intake and micronutrient adequacy, but they are limited in their ability to assess associations with maternal or neonatal health outcomes and anthropometric indicators of nutritional status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, data from pregnant women in four LMICs revealed that although dietary diversity adequacy was associated with increased intake of several nutrients, a high prevalence of inadequate intake of key micronutrients persisted [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In contrast, in an urban cohort in Tanzania, an association between dietary diversity, gestational weight gain and adverse birth outcomes was found via relative categorization of dietary scores [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, a study conducted in New Delhi, India, reported positive associations between dietary diversity and anaemia during pregnancy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, previous studies have shown inconsistent associations between MDD-W and maternal underweight [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which is often associated with low birth weight. In our study, pregnancy outcomes were not associated with maternal dietary diversity, nor did dietary diversity independently predict birth weight or modify the relationship between gestational age and foetal growth. Although studies from LMICs have revealed an association between low maternal dietary diversity and increased risk of low birth weight, they also highlighted substantial methodological heterogeneity, since many studies were conducted in settings with a high baseline prevalence of food insecurity and low birth weight [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe acknowledge the limitations of this study. Although the MDD-W was developed to be applied via a single 24-hour recall, this approach may introduce measurement error through memory bias, and typically, single nutrient intake and the probability of nutrient adequacy could have been better estimated with repeated measurements. However, we assessed more than 1,000 24-hour dietary recalls from six prenatal health centers, which were performed by trained staff and via the multiple-pass method, which is a validated and widely used technique to reduce memory bias. Moreover, although a single 24-hour recall is insufficient to estimate individual usual intake, the distribution of days in our study allows the use of one recall per person to estimate population-level eating habits [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In addition, dietary data were collected during the second trimester, a period with reduced interference from nausea and other conditions affecting food intake. All women were nulliparous and classified as low risk, providing a more homogeneous population. Finally, importantly, this study provides an exploratory profile of dietary intake and its associations with human health and environmental outcomes.\u003c/p\u003e \u003cp\u003eThe planning and exploration of new approaches are fundamental to shaping human strategies for the near future. In this context, modelling approaches have been developed to assess a wide range of agricultural impacts on nutrition and human health, including future interactions such as mortality [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. While previous population-based analyses reported an average footprint of approximately 4,500 g CO₂e per person per day in Brazil [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], we show that women who achieved MDD-W adequacy presented even greater carbon, water, and ecological footprints (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Although direct comparisons should be interpreted cautiously due to differences in modelling assumptions and system boundaries, the median carbon footprint observed in our sample was closer to estimates reported for the EAT\u0026ndash;Lancet reference diet [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A Brazilian study revealed strong linear associations between beef consumption and environmental impacts, with individuals in the highest consumption quintile presenting 48% greater carbon and 31% greater water footprints than those in the lowest quintile [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings highlight the need to identify nutritional\u0026ndash;environmental hotspots, considering not only the types of foods consumed but also their production systems and supply chain origins. In this context, evidence from China similarly indicates that environmental impacts are largely driven by the quantity of foods consumed, especially cereals, vegetables, and animal-source products [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Moreover, maternal dietary choices influence children's food habits and contribute to the establishment of dietary patterns in the next generation, reinforcing intergenerational eating behaviours and long-term dietary patterns [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Therefore, integrating life cycle assessment (LCA) approaches that account for production systems and the origin of the food chain is essential to better capture these trade-offs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe mixed evidence observed across countries supports our interpretation that the MDD-W is informative for micronutrient adequacy but not a robust predictor of obstetric risk. By evaluating the MDD-W across socioeconomic, nutritional, obstetric, and environmental dimensions, this study extends the state of the art in three key ways. First, it empirically demonstrates that dietary diversity is socially structured and not associated with clinical risk. Second, it clarifies the limited predictive value of the MDD-W for pregnancy outcomes when it is applied beyond its original scope. Third, this study provides new insights into the relationships among dietary diversity, pregnancy outcomes, and environmental footprints. These findings underscore the need for integrated food and nutrition policies that simultaneously address equity, maternal health, and planetary boundaries. Finally, women of reproductive age, particularly during pregnancy, sit at the nexus of human and planetary futures, highlighting the urgency of adopting a \u0026ldquo;double future\u0026rdquo; perspective that links maternal health with planetary boundaries.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; MDD-W\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimum Dietary Diversity for Women\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; APO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadverse pregnancy outcome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; GDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreterm birth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall for gestational age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLife cycle assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; FFCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFootprints of Foods and Culinary Preparations Consumed in Brazil\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PUFAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolyunsaturated fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; EAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated average requirement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; AI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdequate intake\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; BMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow- and middle-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; UMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUpper-middle-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaria Julia Miele was supported by the São Paulo Research Foundation (FAPESP), Brazil, through a postdoctoral fellowship (grant #2025/00722-3). The funder had no role in the design of the study; data collection, analysis, or interpretation; or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJOM and BJT conceived the study. MJOM performed the analysis and drafted the manuscript. JTS contributed to data interpretation and critically revised the manuscript. MN, RTS and JGC reviewed the manuscript. All authors contributed to the interpretation of the results, read, and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preterm SAMBA study group also included Renato Passini Jr, Mary A. Parpinelli, Danielly S Santana, School of Medical Sciences (FCM), University of Campinas, Brazil; Iracema P Calderon, Bianca F. Cassettari, School of Medicine of Botucatu, UNESP, Brazil; Janete Vettorazzi, Lucia Pfitscher, Luiza Brust, School of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Brazil; Edilberto Rocha Filho, Debora F Leite, Elias F Melo Junior, Danilo Anacleto, School of Medicine, Federal University of Pernambuco, Recife, Brazil; Francisco E Feitosa, Daisy de Lucena, Benedita Sousa, School of Medicine, Federal University of Ceará, Fortaleza, Brazil.\u003c/p\u003e\n\u003cp\u003eThe author is grateful for the support provided by the São Paulo Centre for the Study of Energy Transition (CPTEN) for its institutional support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRasmussen KM, Yaktine AL et al (2009) Institute of Medicine (US), Weight Gain During Pregnancy: Reexamining the Guidelines. 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J Law Med Ethics 35:22\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1748-720X.2007.00111.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1748-720X.2007.00111.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-9314030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9314030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Dietary quality has been linked to adequate nutrient intake and women’s health. However, the relationships among dietary diversity, pregnancy outcomes and environmental sustainability remain uncertain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: This study evaluated the associations between adherence to the Minimum Dietary Diversity for Women (MDD-W), pregnancy outcomes, and the environmental footprints of maternal diets. This was a multicentre prospective cohort that included 1,318 low-risk nulliparous women in Brazil and used 24-hour dietary recall (24hR). Maternal diets were classified according to adherence to the Minimum Dietary Diversity for Women (MDD-W) guidelines. Associations between MDD-W adherence and pregnancy outcomes were assessed via logistic regression. The environmentalimpacts of maternal diets were estimated via the Footprints of Foods and Culinary Preparations Consumed in the Brazil database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: In the total sample, 904 women achieved MDD-W adequacy (≥5 food groups), whereas 414 were classified in the non-adequacy group (\u0026lt;5 food groups). MDD-W adequacy was not associated with adverse pregnancy outcomes(OR = 0.90, 95% CI: 0.69–1.17). Although the main differencein dietary intake between groups was predominantly plant-based, the adequacy group had greater environmental impacts, even after adjustment per 1,000 kcal, in terms of the ecological footprint(eco-score/day), carbon emissions (g/day) and water use (L/day). The MDD-W is a useful indicator of adequate micronutrient intake. However, dietary intake ofcalcium, iron, folate, and vitamin A was below the recommended levelin approximately 80% of individuals in both groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: Our findings suggest that although MDD-W adherence is a useful tool for dietary micronutrient intake, it is not associated with pregnancy outcomes and that achieving dietary diversity may entail a greaterenvironmental footprint, revealing trade-offs between nutritional adequacy indicators and sustainability goals. This underscores the need for integrated food and nutrition policies that simultaneously address maternal health and environmental sustainability. Pregnant women sit at the nexus of human and planetary futures, highlighting the need for a “double future” perspective linking maternal health and planetary boundaries.\u003c/p\u003e","manuscriptTitle":"The Double-Future Dilemma: Dietary Diversity, Pregnancy Outcomes, and Planetary Health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 10:07:42","doi":"10.21203/rs.3.rs-9314030/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":"17ea56c3-b3ac-4a90-8f2b-8b06a0d738a8","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T10:07:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 10:07:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9314030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9314030","identity":"rs-9314030","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}