Introduction: The diet during pregnancy has long-term effects on the health outcomes of both mothers and their children. This study aimed to undertake a systematic review to explore the association of high-fiber diet, DASH diet, and Mediterranean diet with metabolic outcomes among mothers and their children. Methods: We searched for relevant articles published from Jan 2012 to Nov 2022 through PubMed, MEDLINE, and Embase. Randomized trials and observational studies that considered high-fiber diet, DASH diet, Mediterranean diet during pregnancy and metabolic outcomes in pregnant women and their children were included in the systematic review. Thirty studies were included involving 41,424 pregnant women and children. Results: A high-fiber diet was associated with reduced risk of gestational diabetes mellitus (GDM) (OR: 0.22; 95% CI: 0.11–0.42), pregnancy hypertensive disorders (OR: 0.45; 95% CI: 0.25–0.81), and lower birth weight (−109.54 g; 95% CI: −204.84 to −14.24). The adherences to the Mediterranean diet and DASH diet were associated with reduced risk of GDM (OR: 0.60; 95% CI: 0.45–0.80; OR: 0.36; 95% CI: 0.26–0.51), and lower risk of excessive gestational weight gain (OR: 0.41; 95% CI: 0.18–0.93; OR: 0.3, 95% CI: 0.16–0.57). Adherence to the Mediterranean diet was associated with a lower risk of small for gestational age, fetal growth restriction, and childhood overweight in the progeny (OR: 0.83, 95% CI: 0.73–0.95; OR: 0.50; 95% CI: 0.28–0.89; OR: 0.85; 95% CI: 0.74–0.97). Conclusions: During pregnancy, the high-fiber diet and adherences to the Mediterranean diet and DASH diet were associated with lower risk of adverse metabolic outcomes in pregnant women and their children.

Pregnancy is a crucial period of transition with important physical and emotional changes for women and their children. At this stage, in pregnant women, the levels of hormones including human chorionic gonadotropin, estrogen, and progesterone; heart capacity; cardiac output; physiologic glycosuria; and urine volume increase, and other changes include physiologic anemia caused by the continuous expansion of blood volume during pregnancy, as well as the increased and delayed gastric emptying and gastroesophageal reflux [1‒3]. Maternal non-communicable diseases during pregnancy can have a significant impact on the health of both the mother and her infant, including abnormalities in the fetal nervous system and an increased risk of cardiometabolic diseases later in life [4‒8].

Diet during pregnancy is one of the important factors affecting maternal health. A balanced diet can reduce the risk of adverse outcomes such as excessive gestational weight gain (GWG), gestational diabetes mellitus (GDM), obesity, and hypertensive disorders of pregnancy (HDPs) [9‒12]. A meta-analysis study showed that dietary fiber supplements significantly improved glucose and lipid metabolism as well as pregnancy outcomes in women with GDM [13]. In addition, the Mediterranean diet and the Dietary Approaches to Stop Hypertension (DASH) diet can improve maternal health during pregnancy. Pregnant women who adhered to the Mediterranean diet had lower weight gain during pregnancy compared to those with poor adherence (p < 0.01) [14]. In a previous study of black, Hispanic, and Caucasian pregnant women, higher adherence to the Mediterranean diet was associated with a lower incidence of pre-eclampsia. However, the effect of the DASH diet on HDPs is controversial. Previous studies have shown that the DASH diet is associated with lower blood pressure in healthy pregnant women without HDPs, but no relationship was found between the DASH diet and blood pressure in pregnant women with HDPs [15]. A prospective cohort study from the USA showed that adherence to the DASH diet in early pregnancy did not prevent HDPs or other adverse pregnancy outcomes [16].

Diet during pregnancy is crucial for fetal growth and development, and long-term health. For example, maternal deficiencies in folic acid, vitamin A, and total energy are associated with kidney defects [17], and maternal vitamin D status affects fetal size, body composition, and bone mineralization measurements, as well as later outcomes in children such as asthma [18]. The ability of a mother to provide nutrition for her baby depends on individual health, which is why perinatal nutrition is important for fetal development.

The findings outlined in the previous paragraphs have led to the hypothesis that healthy eating patterns during pregnancy, including a high-fiber, DASH and/or Mediterranean diet are associated with improved metabolic health in the mother and a reduced risk of adverse metabolic outcomes in the children. However, the data from studies relating these dietary patterns to maternal and infant outcomes have not been systematically reviewed. Therefore, to comprehensively assess the impact of dietary behavior during pregnancy on maternal and children’s health outcomes and summarize existing research findings into valid conclusions, we aimed to conduct a systematic review and meta-analysis of the literature to evaluate the effects of maternal dietary fiber intake, DASH diet, and Mediterranean diet during pregnancy on the improvement of GDM, HDPs, gestational obesity, and excessive GWG in pregnant women, as well as on their children’s health outcomes. These data will help provide relevant advice for health care professionals and individuals to optimize risk management for health benefits on pregnant women and their children.

Search Strategy

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist (online suppl. 2; for all online suppl. material, see https://doi.org/10.1159/000543423). A search strategy was developed to evaluate studies comparing the risk of GDM, HDPs, and excessive GWG in pregnant women adopting higher intake of dietary fiber, the DASH diet and the Mediterranean diet, as well as the impacts on children’s health outcomes. MeSH terms and selection criteria were based on patients, interventions, comparisons, and outcome statements. We searched studies published on PubMed, Medline, and Embase from January 1, 2012, to November 31, 2022, and manually searched previously published systematic reviews and meta-analyses to identify additional studies. Our search strategy involved keywords for pregnancy diet, maternal health, progeny health and metabolic disease (online suppl. 3).

Selection of Studies

Abstract and full-text screening were conducted by three researchers together. In the first round of screening, we screened articles based on their titles and abstracts. The second round of screening involved evaluating the studies based on a set of identical eligibility criteria using the full text. Any conflicts were resolved through consensus or the involvement of a third-party team member (M.P.V.).

We ultimately included studies that compared the risks of GDM, HDPs, obesity, and excessive GWG between pregnant women who followed a typical diet and those who adhered to the DASH diet, the Mediterranean diet, or a higher dietary fiber intake during pregnancy. We also examined the health outcomes of these dietary behaviors during pregnancy in children as secondary outcomes. The exposure group consisted of pregnant women who adhered to the DASH diet, the Mediterranean diet, or had a higher dietary fiber intake.

In the screening stage of the study, we considered intervention studies, prospective cohort studies, cross-sectional studies, and case-control studies as eligible types of research. Reviews, conference abstracts, and case reports were excluded. Studies that included pregnant women with diseases other than those we were interested in GDM, HDPs, or obesity, lacked data on maternal diet, and health indicators during pregnancy (e.g., those did not have orderly classification of the type of diet we adopt and a detailed description of the metabolic indicators such as blood sugar, insulin, blood pressure, body weight, BMI of each group) were excluded after full-text screening.

The outcomes of interest included GDM, HDPs, obesity, excessive GWG. GDM was defined as fasting blood glucose (FBG) ≥5.1 mmol/L, 75 g glucose OGTT 1-h blood glucose ≥10.0 mmol/L, and OGTT 2-h blood glucose ≥8.5 mmol/L. HDPs was defined as any hypertensive effect observed during pregnancy, including pre-existing hypertension, pregnancy-induced hypertension, and pre-eclampsia.

Data Extraction and Quality Assessment

The data were manually extracted and entered into an Excel spreadsheet by three reviewers. The following characteristics of each study were collected: authorship, publication year, country, study design, sample size, follow-up time period, confounding factors, statistical analysis, outcomes of interest, and raw data. The second reviewer reviewed all data extraction and quality assessments. We used the Cochrane Risk of Bias Assessment Tool, and each study was assessed for bias in seven areas: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other bias.

Statistical Analysis

The meta-analysis was conducted using Review Manager (RevMan) version 5.4. The studies were divided into six modules based on the specific dietary patterns of pregnant women. First, subgroup analyses were conducted based on specific maternal adverse outcomes such as GDM, HDPs, excessive GWG and obesity. Second, health indicators in the infants/children were extracted from studies involving the three maternal diet types (DASH, Mediterranean, or high-fiber) and the outcomes of interest (GDM, HDPs, obesity, overweight, etc.). The results were reported as ratio estimates with corresponding 95% confidence intervals (95% CI) based on the random-effects model and the assumption of heterogeneity of the data. The Mantel-Haenszel method was used to calculate the overall odds ratio (OR). Statistical significance was determined by a p value equal to or less than 0.05. The I-squared (I2) test was used to evaluate heterogeneity, with a value greater than 50% considered high heterogeneity. If the meta-analysis included ten or more studies, a funnel plot was used to assess the risk of publication bias.

Study Selection

The screening results of this study are shown in Figure 1. We identified 1,518 studies of dietary fiber intake and maternal metabolic outcomes, the DASH diet and maternal metabolic outcomes, the Mediterranean diet and maternal health outcomes, maternal dietary fiber intake and progeny metabolic health, the DASH diet and progeny metabolic health, and the Mediterranean diet and progeny metabolic health. After removing duplicates, 106 citations met the abstract and title screening criteria. After reading the full text, 76 articles were excluded due to lack of original data or interventions for women who were not pregnant or failure to perform statistical analysis on the target outcome or failure to mention target outcome. Finally, 30 studies met the inclusion criteria and were included in the meta-analysis.

Fig. 1.

Flowchart of the pregnancy diet and maternal/progeny health.

Fig. 1.

Flowchart of the pregnancy diet and maternal/progeny health.

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Characteristics of Included Studies

After screening, we included a total of 30 eligible studies, including 18 prospective cohort studies, 10 intervention or case-control studies, and 2 cross-sectional studies. Characteristics of the studies are listed in the following tables (Tables 1-3).

Table 1.

Descriptive characteristics of prospective cohort studies in pregnant women and progeny

First author, publication yearCountryStudies, nMatching factorExposureExposure durationContinuous outcome (mean±SD)Categorical outcomes (odds ratio, 95% CI)
Maternal study 
 Martin et al. [19] (2016) USA N = 513 Age, race, marital status, parity, household income, education level, pre-pregnancy weight, smoking status, physical activity DASH diet 2001–2005 Glucose, mg/dL 0.42, [0.27, 0.67] 
(1) DASH score tertile 1 (n = 186): 79.1±7.7 
(2) DASH score tertile 2 (n = 182): 78.5±7.4 p = 0.000 
(3) DASH score tertile 3 (n = 145): 78.4±7.4 
 Li et al. [20] (2021) USA N = 1,887 Age, race-ethnicity, marital status, parity, education, weight before pregnancy DASH diet 2009–2013 NA 0.74, [0.40, 1.38] 
p = 0.348 
 Radwan et al. [21] (2022) UAE N = 243 Age, nationality, occupation, education, parity, income Mediterranean diet 2015–2017 NA 0.41, [0.18, 0.93] 
p = 0.0338 
 Flor-Alemany et al. [22] (2021) Spain N = 119 NA Mediterranean diet 2015–2019 Fasting glucose, mg/dL 0.76, [0.33, 1.74] 
(1) MD score tertile 1 (n = 40):77.0±6.7 
(2) MD score tertile 2 (n = 45):78.3±15.4 
(3) MD score tertile 3 (n = 34):76.7±23.1 
(4) Total cohort (n = 119):77.4±15.9 
p = 0.897 p = 0.524 
Progeny study 
 Yisahak et al. [23] (2021) USA N = 1,948 Age, race, socioeconomic status, education, party, BMI DASH diet 2009–2013 NA 0.76, [0.34, 1.69] 
p = 0.330 
 Strohmaier et al. [24] (2020) USA N = 2,729 Ethnicity, maternal age at delivery, BMI before pregnancy, non-drinkers, physical activity, smoking history before pregnancy, education, children gender DASH diet 2011–2013 NA 0.93, [0.77, 1.12] 
p = 0.450 
 Gonzalez-Nahm et al. [25] (2022) USA N = 929 Maternal education level, maternal smoking, gestational age, sex of child Mediterranean diet 2009–2011 NA 1.35, [0.30, 6.05] 
p = 0.700 
 Poon et al. [26] (2013) USA N = 71 Total energy intake, race, education, age, poverty index ratio, smoking, alcohol, pre-pregnancy BMI Mediterranean diet 2005–2007 NA 0.94, [0.48, 1.81] 
p > 0.050 
 Rhee et al. [27] (2021) USA N = 8,507 Maternal age in years, pre-pregnancy BMI, parity, race, education level, smoking status during pregnancy Mediterranean diet 1998–2008 NA 1.06, [0.88, 1.29] 
p > 0.050 
 Peraita-Costa et al. [28] (2020) UK N = 1,446 Mother’s age, country of origin, education, marital status, employment status during pregnancy, maternal physical activity, maternal diseases, parity and mothers’ cigarette exposure, drug use, alcohol use, coffee intake, caffeine drinks, prenatal vitamins use Mediterranean diet 2018 NA 0.92, [0.58, 1.45] 
p > 0.050 
 Makarem et al. [29] (2022) USA N = 7,798 Maternal age, marital status, educational level, self-reported race and ethnicity, smoking, BMI, family history of CVD Mediterranean diet 2010–2013 NA 0.61, [0.47, 0.79] 
p < 0.050 
 Yisahak et al. [23] (2021) USA N = 1,498 Age, race, income, education, employment (or student) status, marital status, insurance coverage, reproductive history, BMI Mediterranean diet 2009–2013 NA 0.60, [0.29, 1.22] 
p < 0.050 
 Fernández-Barrés et al. [30] (2016) Spain N = 1,827 Study region, maternal social class and educational level, maternal pre-pregnancy BMI, total energy intake in pregnancy, weight gain during pregnancy, maternal physical activity during pregnancy, gestational diabetes, smoking during pregnancy, maternal age at delivery, breastfeeding duration, child birth weight, child sex, rapid growth from birth to 6 months and child age at anthropometry measurements Mediterranean diet 2003–2008 NA 0.94, [0.71, 1.24] 
p = 0.596 
 Strohmaier et al. [24] (2020) USA N = 2,729 Ethnicity, maternal age at delivery, BMI before pregnancy, non-drinkers, physical activity, smoking history before pregnancy, education, children gender Mediterranean diet 2011–2013 NA 0.82, [0.70, 0.97] 
p = 0.810 
 Chatzi et al. [31] (2017) USA N = 569 Maternal age at recruitment, education, ethnicity/race, pre-pregnancy body mass index, smoking in pregnancy, parity Mediterranean diet 1999–2003 NA 0.88, [0.79, 0.98] 
p < 0.050 
 Fernández-Barrés et al. [32] (2018) Spain N = 2,195 Cohort, maternal social class and educational level, maternal pre-pregnancy BMI, weight gain during pregnancy, maternal physical activity during pregnancy, gestational diabetes, smoking during pregnancy, parity, maternal age at delivery, predominant breastfeeding duration, child sex, child BMI z score at age 4 Mediterranean diet 2003–2008 NA 0.64, [0.45, 0.92] 
p < 0.050 
 Chatzi et al. [33] (2012) Greece N = 2,461 Gestational age, infant sex, maternal, paternal height, pre-pregnancy weight, parity Mediterranean diet 2004–2008 NA 0.50, [0.28, 0.90] 
p = 0.021 
 Monthé-Drèze et al. [34] (2021) USA N = 732 Child’s sex, birth weight, delivery date, age, height, pre-pregnancy weight, education, race/ethnicity, parity, household income, smoking status Mediterranean diet 1999–2002 Birth weight, g, mean±SD NA 
(1) Experimental group (n = 493): 3,511±509 
(2) Control group (n = 239):3,523±600 p = 0.21 
 Ashwin et al. [35] (2022) Australia N = 458 Birth weight, length, gender, ethnicity, gestational age using participants date of birth Mediterranean diet 2017–2021 Birth weight, g, mean±SD NA 
(1) Experimental group (n = 47): 3,289.9±465 
(2) Control group (n = 184):3,437.6±487.3 p > 0.05 
Body fat percentage, n%, mean±SD 
(1) Experimental group (n = 42): 11.3±3.8 
(2) Control group (n = 154):13.3±4.5 p = 0.004 
 Mahmassani et al. [36] (2022) USA N = 1,008 Maternal age, race/ethnicity, education, household income, marital status, pre-pregnancy weight and height (to calculate BMI), parity, smoking history, breastfeeding duration Mediterranean diet 1999–2002 Birth-weight-for-gestational-age z score, x, mean±SD NA 
(1) Experimental group (n = 585): 0.17±0.99 
(2) Control group (n = 423):0.24±1.03 p = 0.004 
First author, publication yearCountryStudies, nMatching factorExposureExposure durationContinuous outcome (mean±SD)Categorical outcomes (odds ratio, 95% CI)
Maternal study 
 Martin et al. [19] (2016) USA N = 513 Age, race, marital status, parity, household income, education level, pre-pregnancy weight, smoking status, physical activity DASH diet 2001–2005 Glucose, mg/dL 0.42, [0.27, 0.67] 
(1) DASH score tertile 1 (n = 186): 79.1±7.7 
(2) DASH score tertile 2 (n = 182): 78.5±7.4 p = 0.000 
(3) DASH score tertile 3 (n = 145): 78.4±7.4 
 Li et al. [20] (2021) USA N = 1,887 Age, race-ethnicity, marital status, parity, education, weight before pregnancy DASH diet 2009–2013 NA 0.74, [0.40, 1.38] 
p = 0.348 
 Radwan et al. [21] (2022) UAE N = 243 Age, nationality, occupation, education, parity, income Mediterranean diet 2015–2017 NA 0.41, [0.18, 0.93] 
p = 0.0338 
 Flor-Alemany et al. [22] (2021) Spain N = 119 NA Mediterranean diet 2015–2019 Fasting glucose, mg/dL 0.76, [0.33, 1.74] 
(1) MD score tertile 1 (n = 40):77.0±6.7 
(2) MD score tertile 2 (n = 45):78.3±15.4 
(3) MD score tertile 3 (n = 34):76.7±23.1 
(4) Total cohort (n = 119):77.4±15.9 
p = 0.897 p = 0.524 
Progeny study 
 Yisahak et al. [23] (2021) USA N = 1,948 Age, race, socioeconomic status, education, party, BMI DASH diet 2009–2013 NA 0.76, [0.34, 1.69] 
p = 0.330 
 Strohmaier et al. [24] (2020) USA N = 2,729 Ethnicity, maternal age at delivery, BMI before pregnancy, non-drinkers, physical activity, smoking history before pregnancy, education, children gender DASH diet 2011–2013 NA 0.93, [0.77, 1.12] 
p = 0.450 
 Gonzalez-Nahm et al. [25] (2022) USA N = 929 Maternal education level, maternal smoking, gestational age, sex of child Mediterranean diet 2009–2011 NA 1.35, [0.30, 6.05] 
p = 0.700 
 Poon et al. [26] (2013) USA N = 71 Total energy intake, race, education, age, poverty index ratio, smoking, alcohol, pre-pregnancy BMI Mediterranean diet 2005–2007 NA 0.94, [0.48, 1.81] 
p > 0.050 
 Rhee et al. [27] (2021) USA N = 8,507 Maternal age in years, pre-pregnancy BMI, parity, race, education level, smoking status during pregnancy Mediterranean diet 1998–2008 NA 1.06, [0.88, 1.29] 
p > 0.050 
 Peraita-Costa et al. [28] (2020) UK N = 1,446 Mother’s age, country of origin, education, marital status, employment status during pregnancy, maternal physical activity, maternal diseases, parity and mothers’ cigarette exposure, drug use, alcohol use, coffee intake, caffeine drinks, prenatal vitamins use Mediterranean diet 2018 NA 0.92, [0.58, 1.45] 
p > 0.050 
 Makarem et al. [29] (2022) USA N = 7,798 Maternal age, marital status, educational level, self-reported race and ethnicity, smoking, BMI, family history of CVD Mediterranean diet 2010–2013 NA 0.61, [0.47, 0.79] 
p < 0.050 
 Yisahak et al. [23] (2021) USA N = 1,498 Age, race, income, education, employment (or student) status, marital status, insurance coverage, reproductive history, BMI Mediterranean diet 2009–2013 NA 0.60, [0.29, 1.22] 
p < 0.050 
 Fernández-Barrés et al. [30] (2016) Spain N = 1,827 Study region, maternal social class and educational level, maternal pre-pregnancy BMI, total energy intake in pregnancy, weight gain during pregnancy, maternal physical activity during pregnancy, gestational diabetes, smoking during pregnancy, maternal age at delivery, breastfeeding duration, child birth weight, child sex, rapid growth from birth to 6 months and child age at anthropometry measurements Mediterranean diet 2003–2008 NA 0.94, [0.71, 1.24] 
p = 0.596 
 Strohmaier et al. [24] (2020) USA N = 2,729 Ethnicity, maternal age at delivery, BMI before pregnancy, non-drinkers, physical activity, smoking history before pregnancy, education, children gender Mediterranean diet 2011–2013 NA 0.82, [0.70, 0.97] 
p = 0.810 
 Chatzi et al. [31] (2017) USA N = 569 Maternal age at recruitment, education, ethnicity/race, pre-pregnancy body mass index, smoking in pregnancy, parity Mediterranean diet 1999–2003 NA 0.88, [0.79, 0.98] 
p < 0.050 
 Fernández-Barrés et al. [32] (2018) Spain N = 2,195 Cohort, maternal social class and educational level, maternal pre-pregnancy BMI, weight gain during pregnancy, maternal physical activity during pregnancy, gestational diabetes, smoking during pregnancy, parity, maternal age at delivery, predominant breastfeeding duration, child sex, child BMI z score at age 4 Mediterranean diet 2003–2008 NA 0.64, [0.45, 0.92] 
p < 0.050 
 Chatzi et al. [33] (2012) Greece N = 2,461 Gestational age, infant sex, maternal, paternal height, pre-pregnancy weight, parity Mediterranean diet 2004–2008 NA 0.50, [0.28, 0.90] 
p = 0.021 
 Monthé-Drèze et al. [34] (2021) USA N = 732 Child’s sex, birth weight, delivery date, age, height, pre-pregnancy weight, education, race/ethnicity, parity, household income, smoking status Mediterranean diet 1999–2002 Birth weight, g, mean±SD NA 
(1) Experimental group (n = 493): 3,511±509 
(2) Control group (n = 239):3,523±600 p = 0.21 
 Ashwin et al. [35] (2022) Australia N = 458 Birth weight, length, gender, ethnicity, gestational age using participants date of birth Mediterranean diet 2017–2021 Birth weight, g, mean±SD NA 
(1) Experimental group (n = 47): 3,289.9±465 
(2) Control group (n = 184):3,437.6±487.3 p > 0.05 
Body fat percentage, n%, mean±SD 
(1) Experimental group (n = 42): 11.3±3.8 
(2) Control group (n = 154):13.3±4.5 p = 0.004 
 Mahmassani et al. [36] (2022) USA N = 1,008 Maternal age, race/ethnicity, education, household income, marital status, pre-pregnancy weight and height (to calculate BMI), parity, smoking history, breastfeeding duration Mediterranean diet 1999–2002 Birth-weight-for-gestational-age z score, x, mean±SD NA 
(1) Experimental group (n = 585): 0.17±0.99 
(2) Control group (n = 423):0.24±1.03 p = 0.004 

SD, standard deviation; 95% CI, 95% confidence interval; NA, not available; MD, Mediterranean diet; DASH diet, Dietary Approaches to Stop Hypertension diet; BMI, body mass index; CVD, cardiovascular disease.

Table 2.

Descriptive characteristics of intervention studies and case-control studies in pregnant women and progeny

First author, publication yearCountryStudies, nMatching factorExposureExposure timeContinuous outcomeCategorical outcomes (odds ratio, 95% CI)
Maternal study 
 Van Horn et al. [37] (2018) USA N = 281 Age, gestational age, race, education, BMI, family income, activity/sleep DASH diet 2012–2015 Glucose, mg/dL, median (IQR) 0.3, [0.16, 0.57] 
(1) Intervention group (baseline), n = 140: 86 (82–90) 
(2) Intervention group (35 weeks), n = 140: 84 (80–89) p = 0.000 
(3) Control group (baseline), n = 141: 87 (83–91) 
(4) Control group (35 weeks), n = 141: 85 (81–89) 
 Izadi et al. [38] (2016) Iran N = 460 Age, BMI, education, occupation, socioeconomic status DASH diet, Mediterranean diet NA 1. DASH diet 1. DASH diet 
FBG, mg/dL, mean±SD 
(1) DASH score tertile 1 (n = 134): 
114.05±51.58 
(2) DASH score tertile 2 (n = 165): 0.3, [0.18, 0.5] 
114.71±52.06 
(3) DASH score tertile 3 (n = 160): p = 0.001 
96.08±36.75 
2. Mediterranean diet 2. Mediterranean diet 
FBG, mg/dL, mean±SD 
(1) MD score tertile 1 (n = 160): 118.82±54.35 0.2, [0.06, 0.7] 
(2) MD score tertile 2 (n = 134): 112.81±46.70 
(3) MD score tertile 3 (n = 165): 95.21±40.77 p = 0.012 
(4) Total cohort (n = 119): 77.4±15.9 p = 0.0001 
 Mahjoub et al. [39] (2021) Tunisia N = 120 Age, pre- pregnancy BMI, weight gain, family history of diabetes, prior history of GDM, physical activity Mediterranean diet NA FBG, g/L, mean±SD 0.58, [0.28, 1.2] 
(1) Low Mediterranean diet score: 
1.25±0.30 
(2) Moderate Mediterranean diet score: 1.08±0.12 p = 0.142 
(3) High Mediterranean diet score: 
0.91±0.12 p < 0.001 
 H Al Wattar et al. [40] (2019) UK N = 1,194 Maternal age, history of gestational diabetes in a previous pregnancy, stillbirth in a previous pregnancy, family history of hypertensive disorder or diabetes, the center of recruitment Mediterranean diet 2014–2016 Triglycerides, mmol/L, mean±SD 0.65, [0.47, 0.9] 
(1) Intervention group (n = 584): 3.0±1.3 p = 0.009 
(2) Control group (n = 610): 2.9±1.3 p = 0.50 
 Sanjarimoghaddam et al. [41] (2019) Iran N = 202 Age, occupation, number of deliveries, history of disease, physical activity, diet Dietary fiber 2015–2016 NA 0.45, [0.25, 0.81] 
p = 0.008 
 Zhang et al. [42] (2022) China N = 98 Gestational age, age, BMI, systolic blood pressure, diastolic blood pressure, first pregnancy, family history of diabetes, PCOS, adverse pregnancy history Dietary fiber 2021–2022 OGTT 2hPG, mmol/L, mean±SD 0.29, [0.09, 0.97] 
(1) Control group (n = 50): 6.83±1.83 
(2) Fiber supplement group (n = 48): p = 0.044 
6.31±1.12 p = 0.094 
 Zhang et al. [43] (2021) China N = 112 Gestational age, age, GWG Dietary fiber 2018 OGTT 2hPG after 8 weeks intervention, mmol/L, mean±SD 0.19, [0.08, 0.42] 
(1) Fiber supplement group (n = 56): 
7.30±1.30 p = 0.000 
(2) Control group (n = 56): 
8.07±1.60 p < 0.01 
Progeny study 
 Zhang et al. [42] (2022) China N = 144 Age, BMI, systolic blood pressure, diastolic blood pressure, education, Dietary fiber NA Birth weight, g, mean±SD NA 
previous pregnancy, previous PCOS, previous GDM, family history of diabetes, adverse pregnancy history (1) Fiber supplement group (n = 74): 3,260.35±354.97 
 (2) Control group (n = 70): 3,292.30±517.11 p = 0.668 
 Wang et al. [44] (2021) China N = 120 Age, weight before pregnancy, height Dietary fiber 2019–2020 Birth weight, g, mean±SD NA 
(1) Fiber supplement group (n = 60): 3,197.42±417.15 
(2) Control group (n = 60): 3,346.67±330.69 p = 0.030 
 Basu et al. [45] (2021) USA N = 34 Age, race, prenatal vitamin users, history of GDM, family history of diabetes, nulliparous Dietary fiber 2018–2020 Birth weight, g, mean±SD NA 
(1) Fiber supplement group (n = 17): 3,407±552 
(2) Control group (n = 17): 3,740±580 p = 0.11 
 Van Horn et al. [37] (2018) USA N = 251 Age, gestational age, race, education, BMI, family income, activity/sleep DASH diet 2012–2015 Birth weight, g, mean±SD 0.72, [0.41, 1.24] 
(1) Intervention group (n = 130): 3,244±489 p = 0.230 
(2) Control group (n = 121): 3,213±524 p = 0.62 
 Crovetto et al. [46] (2021) Spain N = 793 Age, race and ethnicity, socioeconomic status, BMI before pregnancy, previous medical condition, cigarette smoking, alcohol intake, drug consumption, sports practice Mediterranean diet 2017–2019 NA 0.58, [0.40, 0.84] 
p < 0.050 
First author, publication yearCountryStudies, nMatching factorExposureExposure timeContinuous outcomeCategorical outcomes (odds ratio, 95% CI)
Maternal study 
 Van Horn et al. [37] (2018) USA N = 281 Age, gestational age, race, education, BMI, family income, activity/sleep DASH diet 2012–2015 Glucose, mg/dL, median (IQR) 0.3, [0.16, 0.57] 
(1) Intervention group (baseline), n = 140: 86 (82–90) 
(2) Intervention group (35 weeks), n = 140: 84 (80–89) p = 0.000 
(3) Control group (baseline), n = 141: 87 (83–91) 
(4) Control group (35 weeks), n = 141: 85 (81–89) 
 Izadi et al. [38] (2016) Iran N = 460 Age, BMI, education, occupation, socioeconomic status DASH diet, Mediterranean diet NA 1. DASH diet 1. DASH diet 
FBG, mg/dL, mean±SD 
(1) DASH score tertile 1 (n = 134): 
114.05±51.58 
(2) DASH score tertile 2 (n = 165): 0.3, [0.18, 0.5] 
114.71±52.06 
(3) DASH score tertile 3 (n = 160): p = 0.001 
96.08±36.75 
2. Mediterranean diet 2. Mediterranean diet 
FBG, mg/dL, mean±SD 
(1) MD score tertile 1 (n = 160): 118.82±54.35 0.2, [0.06, 0.7] 
(2) MD score tertile 2 (n = 134): 112.81±46.70 
(3) MD score tertile 3 (n = 165): 95.21±40.77 p = 0.012 
(4) Total cohort (n = 119): 77.4±15.9 p = 0.0001 
 Mahjoub et al. [39] (2021) Tunisia N = 120 Age, pre- pregnancy BMI, weight gain, family history of diabetes, prior history of GDM, physical activity Mediterranean diet NA FBG, g/L, mean±SD 0.58, [0.28, 1.2] 
(1) Low Mediterranean diet score: 
1.25±0.30 
(2) Moderate Mediterranean diet score: 1.08±0.12 p = 0.142 
(3) High Mediterranean diet score: 
0.91±0.12 p < 0.001 
 H Al Wattar et al. [40] (2019) UK N = 1,194 Maternal age, history of gestational diabetes in a previous pregnancy, stillbirth in a previous pregnancy, family history of hypertensive disorder or diabetes, the center of recruitment Mediterranean diet 2014–2016 Triglycerides, mmol/L, mean±SD 0.65, [0.47, 0.9] 
(1) Intervention group (n = 584): 3.0±1.3 p = 0.009 
(2) Control group (n = 610): 2.9±1.3 p = 0.50 
 Sanjarimoghaddam et al. [41] (2019) Iran N = 202 Age, occupation, number of deliveries, history of disease, physical activity, diet Dietary fiber 2015–2016 NA 0.45, [0.25, 0.81] 
p = 0.008 
 Zhang et al. [42] (2022) China N = 98 Gestational age, age, BMI, systolic blood pressure, diastolic blood pressure, first pregnancy, family history of diabetes, PCOS, adverse pregnancy history Dietary fiber 2021–2022 OGTT 2hPG, mmol/L, mean±SD 0.29, [0.09, 0.97] 
(1) Control group (n = 50): 6.83±1.83 
(2) Fiber supplement group (n = 48): p = 0.044 
6.31±1.12 p = 0.094 
 Zhang et al. [43] (2021) China N = 112 Gestational age, age, GWG Dietary fiber 2018 OGTT 2hPG after 8 weeks intervention, mmol/L, mean±SD 0.19, [0.08, 0.42] 
(1) Fiber supplement group (n = 56): 
7.30±1.30 p = 0.000 
(2) Control group (n = 56): 
8.07±1.60 p < 0.01 
Progeny study 
 Zhang et al. [42] (2022) China N = 144 Age, BMI, systolic blood pressure, diastolic blood pressure, education, Dietary fiber NA Birth weight, g, mean±SD NA 
previous pregnancy, previous PCOS, previous GDM, family history of diabetes, adverse pregnancy history (1) Fiber supplement group (n = 74): 3,260.35±354.97 
 (2) Control group (n = 70): 3,292.30±517.11 p = 0.668 
 Wang et al. [44] (2021) China N = 120 Age, weight before pregnancy, height Dietary fiber 2019–2020 Birth weight, g, mean±SD NA 
(1) Fiber supplement group (n = 60): 3,197.42±417.15 
(2) Control group (n = 60): 3,346.67±330.69 p = 0.030 
 Basu et al. [45] (2021) USA N = 34 Age, race, prenatal vitamin users, history of GDM, family history of diabetes, nulliparous Dietary fiber 2018–2020 Birth weight, g, mean±SD NA 
(1) Fiber supplement group (n = 17): 3,407±552 
(2) Control group (n = 17): 3,740±580 p = 0.11 
 Van Horn et al. [37] (2018) USA N = 251 Age, gestational age, race, education, BMI, family income, activity/sleep DASH diet 2012–2015 Birth weight, g, mean±SD 0.72, [0.41, 1.24] 
(1) Intervention group (n = 130): 3,244±489 p = 0.230 
(2) Control group (n = 121): 3,213±524 p = 0.62 
 Crovetto et al. [46] (2021) Spain N = 793 Age, race and ethnicity, socioeconomic status, BMI before pregnancy, previous medical condition, cigarette smoking, alcohol intake, drug consumption, sports practice Mediterranean diet 2017–2019 NA 0.58, [0.40, 0.84] 
p < 0.050 

SD, standard deviation; 95% CI, 95% confidence interval; NA, not available; MD, Mediterranean diet; DASH diet, Dietary Approaches to Stop Hypertension diet; BMI, body mass index; PCOS, polycystic ovary syndrome; GDM, gestational diabetes mellitus; GWG, gestational weight gain; FBG, fasting blood glucose.

Table 3.

Descriptive characteristics of cross-sectional studies in pregnant women and progeny

First author, publication year,CountryStudies, nMatching factorExposureExposure timeContinuous outcomeCategorical outcomes (odds ratio, 95% CI)
Maternal study 
 Comas Rovira et al. [47] (2022) Spain N = 542 Age, BMI, hypertension, nulliparity, foreign origin Mediterranean diet 2019 Weight gain, kg, mean±SD 0.73, [0.43, 1.42] 
(1) Overall population (n = 510): 11.7±5.7 
(2) Non-obese (n = 435): 12.7±5.1 p = 0.242 
(3) Obese (n = 75): 7.2±5.7 p < 0.001 
 Silva-del Valle et al. [48] (2013) Spain N = 170 Age, BMI, education level Mediterranean diet 2010 NA 0.82, [0.23, 2.92] 
p = 0.760 
First author, publication year,CountryStudies, nMatching factorExposureExposure timeContinuous outcomeCategorical outcomes (odds ratio, 95% CI)
Maternal study 
 Comas Rovira et al. [47] (2022) Spain N = 542 Age, BMI, hypertension, nulliparity, foreign origin Mediterranean diet 2019 Weight gain, kg, mean±SD 0.73, [0.43, 1.42] 
(1) Overall population (n = 510): 11.7±5.7 
(2) Non-obese (n = 435): 12.7±5.1 p = 0.242 
(3) Obese (n = 75): 7.2±5.7 p < 0.001 
 Silva-del Valle et al. [48] (2013) Spain N = 170 Age, BMI, education level Mediterranean diet 2010 NA 0.82, [0.23, 2.92] 
p = 0.760 

SD, standard deviation; 95% CI, 95% confidence interval; NA, not available; BMI, body mass index.

Quality Assessment

The articles included in the present study were assessed for bias using the Cochrane risk assessment tool, and no publication bias was found (online suppl. 1).

Findings from Systematic Review

Pregnancy Diet and Maternal Health

In total, three studies were included that assessed effects of dietary fiber during pregnancy on maternal health (shown in Fig. 2a), and the dietary intervention groups and the normal diet groups in these studies showed significant differences, suggesting that dietary fiber intervention can reduce the risk of certain adverse maternal health outcomes during pregnancy (OR: 0.29, 95% CI: 0.09–0.97; OR: 0.19, 95% CI: 0.08–0.42; OR: 0.45, 95% CI: 0.25–0.81). The risk of GDM was assessed in two intervention studies, while the risk of HDPs was assessed in one case-control study. The combined OR value was 0.33 (95% CI: 0.21–0.51), and the results showed statistical differences between the experimental and control groups (p < 0.001). When the outcomes were stratified by specific diseases, heterogeneity disappeared (I2 = 61.8% vs. I2 = 0%). It can be seen in Figure 3a that higher dietary fiber during pregnancy is a protective factor for the development of GDM during pregnancy (OR: 0.22, 95% CI: 0.11–0.42), and for the occurrence of HDPs (OR: 0.45, 95% CI: 0.25–0.81).

Fig. 2.

Forest map of the pregnancy diet on the risk of metabolic diseases in pregnant women. a Forest map of dietary fiber intake during pregnancy on the risk of metabolic diseases in pregnant women. b Forest map of the DASH diet during pregnancy for risk of metabolic diseases in pregnant women. c Forest map of the Mediterranean diet during pregnancy on the risk of metabolic disease in pregnant women. The metabolic disease risk of pregnant women in the experimental group was significantly lower than that of the control group. The forest plot displays the standard mean differences and 95% CI. The diamond indicates the overall estimation and its 95% confidence interval. CI, confidence interval; GDM, gestational diabetes mellitus; HDCP, hypertensive disorders of pregnancy; GWG, gestational weight gain.

Fig. 2.

Forest map of the pregnancy diet on the risk of metabolic diseases in pregnant women. a Forest map of dietary fiber intake during pregnancy on the risk of metabolic diseases in pregnant women. b Forest map of the DASH diet during pregnancy for risk of metabolic diseases in pregnant women. c Forest map of the Mediterranean diet during pregnancy on the risk of metabolic disease in pregnant women. The metabolic disease risk of pregnant women in the experimental group was significantly lower than that of the control group. The forest plot displays the standard mean differences and 95% CI. The diamond indicates the overall estimation and its 95% confidence interval. CI, confidence interval; GDM, gestational diabetes mellitus; HDCP, hypertensive disorders of pregnancy; GWG, gestational weight gain.

Close modal
Fig. 3.

Forest map of the pregnancy diet on the risk of metabolic diseases in progeny. a Forest map of dietary fiber intake during pregnancy on progeny health outcomes. b Forest map of DASH diet during pregnancy on various health outcomes in progeny. c Forest map of studies of Mediterranean diet during pregnancy on health outcomes in progeny. d Forest map of the effects of the Mediterranean diet during pregnancy on progeny growth indices. Compared to the control group, the progeny of the experimental group had a more normal birth weight and a lower metabolic risk. The forest plot displays the standard mean differences and 95% CI. The diamond indicates the overall estimation and its 95% confidence interval. CI, confidence interval; SGA, small for gestational age; FGR, fetal growth restriction; LGA, large for gestational age.

Fig. 3.

Forest map of the pregnancy diet on the risk of metabolic diseases in progeny. a Forest map of dietary fiber intake during pregnancy on progeny health outcomes. b Forest map of DASH diet during pregnancy on various health outcomes in progeny. c Forest map of studies of Mediterranean diet during pregnancy on health outcomes in progeny. d Forest map of the effects of the Mediterranean diet during pregnancy on progeny growth indices. Compared to the control group, the progeny of the experimental group had a more normal birth weight and a lower metabolic risk. The forest plot displays the standard mean differences and 95% CI. The diamond indicates the overall estimation and its 95% confidence interval. CI, confidence interval; SGA, small for gestational age; FGR, fetal growth restriction; LGA, large for gestational age.

Close modal

Four studies were included that investigated the effects of the DASH diet during pregnancy on maternal health (shown in Fig. 2b), in which three studies showed significant differences between the intervention and control groups (OR: 0.42, 95% CI: 0.27–0.67; OR: 0.30, 95% CI: 0.16–0.57; OR: 0.30, 95% CI: 0.18–0.50). The combined OR was 0.40 (95% CI: 0.31–0.53), suggesting a significant negative correlation between the adherence to the DASH diet during pregnancy and pregnant health outcomes (p < 0.001). In addition, stratified analyses showed that adherence to a DASH diet during pregnancy was associated with a reduced risk of GDM (OR: 0.36, 95% CI: 0.26–0.51), and the studies showed no heterogeneity (I2 = 0%). Adherence to a DASH diet during pregnancy was also associated with a lower risk of excessive weight gain during pregnancy (OR: 0.30, 95% CI: 0.16–0.57). However, no statistically significant association was observed between adherence to the DASH diet and the development of HDPs.

Effects of the Mediterranean diet on maternal health are shown in Figure 2c. Three of seven studies showed significant differences between the experimental group and the control group (OR: 0.65, 95% CI: 0.47–0.90; OR: 0.41, 95% CI: 0.18–0.93; OR: 0.20, 95% CI: 0.06–0.70). The combined OR was 0.62 (95% CI: 0.49–0.78), indicating a significant negative correlation between adherence to the Mediterranean diet and the risk of adverse maternal health outcomes during pregnancy (p < 0.001). After stratification, the heterogeneity of the subgroup remained low (obesity I2 = 0%; GDM I2 = 37%). Adherence to the Mediterranean diet was significantly correlated with the lower risk of GDM (OR: 0.60, 95% CI: 0.45–0.80) and excessive GWG (OR: 0.41, 95% CI: 0.18–0.93). There was no significant correlation between adherence to the Mediterranean diet and maternal obesity.

Pregnancy Diet and Progeny’s Health

Figure 3a included 3 studies focusing on children’s birth weight. One study showed a significant negative association between dietary fiber intake during pregnancy and birth weight (mean difference: −149.25, 95% CI: −283.94 to −14.56). The heterogeneity was low (I2 = 28%). In general, the meta-analysis of the three studies found a statistically significant association (Z = 2.25, p = 0.020), indicating that fiber intake during pregnancy was inversely associated with the risk of fetal macrosomia. However, there is no significant relationship between adherence to the DASH diet and overall health outcomes in progeny (Fig. 3b), and the sub-analysis also showed no statistical significance.

The Figure 3c presents the effects of the Mediterranean diet during the pregnancy on children’s health outcomes, based on data analysis from 10 studies. Three subgroups showed significant differences in the children’ health outcomes, including SGA, overweight, and FGR (OR: 0.83, 95% CI: 0.73–0.95; OR: 0.85, 95% CI: 0.74–0.97; OR: 0.50, 95% CI: 0.28–0.89). Among the subgroups, the SGA subgroup had high heterogeneity (I2 = 70%). No heterogeneity was found in the subgroup analysis in terms of children’s overweight (I2 = 0%), and only one study reported a significant result that higher adherence to the Mediterranean diet during pregnancy may reduce the risk of overweight in the children (OR: 0.82, 95% CI: 0.70–0.96). Furthermore, adherence to the Mediterranean diet during pregnancy was negatively associated with FGR (OR: 0.50, 95% CI: 0.28–0.89).

We also conducted a quantitative analysis of three studies (Fig. 3d). Among the three outcome measures, only the trial and control groups in the children’s body fat percentage outcome showed a significant difference (mean difference: −2.00, 95% CI: −3.35 to −0.65).

Compared with pregnant women who consumed a normal diet, pregnant women with higher dietary fiber intake had a lower risk of GDM and HDPs, and those with higher adherence to the Mediterranean diet or the DASH diet had lower risks of GDM and excessive GWG. Among those diet patterns, dietary fiber had the strongest association with GDM, followed by the DASH diet and the Mediterranean diet. Higher intake of dietary fiber and the adherence to the Mediterranean diet were also protective factors for the risk of HDPs during pregnancy. In addition, both the Mediterranean diet and the DASH diet could significantly reduce the risk of excessive GWG in pregnant women. In terms of progeny’s health outcomes, higher dietary fiber intake during pregnancy was negatively associated with birth weight. Furthermore, higher adherence to the Mediterranean diet during pregnancy was associated with a lower risk of FGR, SGA, and children overweight but with a risk of higher body fat.

Consistent with previous meta-analyses [13, 49‒51], higher intake of dietary fiber during pregnancy was associated with a lower incidence of GDM. The intake of dietary fiber from different food groups in mid-pregnancy, especially those rich in total fiber and fruit fiber, has been suggested to reduce risk for the development of GDM [52]. These findings are also in-line with previous studies showing that higher fiber diets are associated with improvements in glucose control in both pregnant and non-pregnant individuals. For instance, an international study reported that adding soluble fiber such as beta-glucan from oat bran to the diet for 4 weeks reduced FBG and glucose levels 2 h after meals (4hpp) [53]. The effects of dietary fiber on glucose metabolism vary depending on the food source, highlighting the potential of specific fiber types to support the prevention of GDM during pregnancy.

Our meta-analysis has revealed that the DASH diet and Mediterranean diet are also protective factors against the development of GDM. This is consistent with findings from previous studies reporting that the Mediterranean diet and the DASH diet are associated with a reduction of GDM incidence by 15–38% [54], and the Mediterranean is widely applied as a first-line treatment for GDM [55, 56]. In an intervention study, Hispanic women who followed the Mediterranean diet had lower incidence of GDM and some other adverse maternal outcomes [55]. Another intervention study reported a negative correlation between adherence to the Mediterranean diet and the FBG and OGTT 2-h blood glucose [39]. This may be attributed to the characteristics of the diet, including its high content of wholegrain cereals, legumes, vegetables, fruit, fish, nuts, extra virgin olive oil, and low amounts of refined cereal products, meat and animal fats, which improves post-meal glucose and insulin responses and increases post-meal insulin sensitivity index [57]. One possible mechanism by which the DASH diet improves GDM is related to insulin regulation. In a randomized controlled trial of the DASH diet in pregnant women, a combination of carbohydrate counting with consumption of the DASH diet was associated with a significant reduction in serum insulin levels and HOMA-IR score [58]. Another possible explanation is that the DASH diet increases the concentration of antioxidant enzymes, which reduces inflammation associated with GDM [59].

High dietary fiber intake was found to be associated with a lower risk of HDPs in pregnant women in our study. In a review summarizing, 290 studies suggested that a high-fiber diet (25–30 g/day) can alleviate lipid abnormalities, reduce blood pressure, inflammation and the risk of pre-eclampsia in pregnant women [60]. This can be explained by changes in the gut microbiota in pregnant women with different metabolic health conditions. Pregnant women with pre-eclampsia have lower species index, such as decreased microbial alpha diversity, compared with normal pregnant women [61], and had a rise in opportunistic pathogens, particularly Fusobacterium and Veillonella, but a reduction in beneficial bacteria, such as Faecalibacterium and Akkermansia [62]. These beneficial bacteria, especially those that belong to the Firmicutes, Actinobacteria, or Bacteroidetes phyla, were positively correlated with short chain fatty acids levels [63], which are breakdown products of soluble dietary fiber by the fermentation of gut bacteria [64]. A previous study reported that the systolic and diastolic blood pressure levels were positively correlated with four short chain fatty acids including acetic acid, propionic acid, isobutyric acid, and valeric acid, which increased in pregnant women with pre-eclampsia [65].

Excessive GWG is a risk factor for adverse pregnancy outcomes, including GDM [13] and pre-eclampsia [66]. Our study shows that both the DASH diet and the Mediterranean diet can reduce the risk of excessive GWG. Consistent with our findings, higher adherence to the Mediterranean diet during pregnancy was associated with a reduced risk of excessive GWG in a previous systematic review [67]. A prospective cohort study from Spain also revealed that a high baseline adherence to the Mediterranean diet may protect against overweight and obesity during pregnancy [48]. Prior studies have found that higher diet quality (HEI) can increase satiety, reduce overeating and glycemic response [68]. Thus, these changes were linked to both an improved metabolic profile, and reduced GWG.

Our study found a negative relationship between higher dietary fiber intake during pregnancy and infant birth weight. Similarly, pregnant women in the intervention group who increased dietary fiber intake by 3.6 g/day, had a smaller number of infants with high birth weight (≥4,000 g), compared to the control group (p = 0.006) [69]. Scholl et al. [70] divided the diets of 1,082 pregnant women in the USA into five groups based on the glycemic index level and found that the higher the glycemic index, the lower the fiber intake. After adjustment for the gestation duration, the higher quintile group was associated with an increase in birth weight. In contrast, a prospective cohort study conducted in Korean women by Bang and Lee [71] found that women with higher intake of dietary fiber during the second trimester of pregnancy (23.0 ± 7.3 g/day) had infants with higher birth weights compared to those with lower fiber intake (19.7 ± 6.6 g/day, p < 0.05). However, this study relied on a single-day 24-h recall method to estimate dietary intake, which may not accurately capture the dietary patterns during pregnancy. Additionally, the analysis did not adjust for any potential confounding variables, which limits the validity of the finding and warrants further investigation. Given the variability in findings, further research is needed to examine the effects of dietary fiber intake during specific trimesters of pregnancy. Our results suggest that maintaining appropriate fiber intake throughout pregnancy may be beneficial in supporting optimal infant birth weight.

Previous meta-analysis study showed that following the DASH diet during pregnancy decreased the risk of LGA [72]. A prospective cohort study from the USA shows that greater maternal adherence to the DASH diet was associated with lower overweight risk in their children [24]. The DASH diet is rich in fruits, vegetables, and low-fat dairy products and can significantly reduce blood pressure, low-density lipoprotein, and high-density lipoprotein cholesterol [73]. However, we did not observe any significant impacts on child health outcomes, such as low birth weight, LGA, SGA, and obesity. We hypothesize that this may be because this meta-analysis only examined studies on DASH diet during pregnancy and metabolic outcomes in children during the 10 years from 2012 to 2022, and few studies were selected so that there was only one study for each subgroup. Therefore, our results are inconsistent with the results of the three studies above: the meta-analysis of RCTs from the inception until October 2019 [72], the cohort study based on the Nurses’ Health Study II and offspring cohort Growing Up Today Study II [24], and the randomized controlled studies based on the general population [73]. There are relatively few studies on the impact of the DASH diet on maternal and child health during pregnancy, and the stratified standards of DASH diet are not uniform. So further investigation focused on maternal adherence to the DASH diet during pregnancy affecting children’s health is needed.

We also found that the Mediterranean diet during pregnancy is a protective factor for SGA and FGR. Similarly, Martínez-Galiano et al. [74] stratified the severity of SGA into moderate (percentiles 6–10) and severe (percentiles ≤5) and found that maternal adherence to the Mediterranean diet during pregnancy was associated with a low SGA risk. Our study also found that mother’s adherence to the Mediterranean diet during pregnancy was associated with lower risks for progeny overweight/obesity. Even though there are relatively few studies on this topic, some consensus and clues can be gleaned from them. In a previous study of 2,695 Dutch mother-child pairs, van den Broek et al. [75] found that higher adherence to vegetable, fish, and oil dietary patterns, as well as nut, soy, and high-fiber cereal dietary patterns during pregnancy was associated with reduced BMI, overweight, fat mass index in their children. Another previous study suggested that pre-pregnancy BMI and weight gain during pregnancy have been identified as independent determinants of late-onset infant obesity [76].

It is suggested that favorable effects of higher adherence to the Mediterranean diet during pregnancy on children’s health may be influenced by the health status of pregnant women. For example, a retrospective study from Italy showed that the Mediterranean diet during pregnancy can reduce the risk of SGA as it has a positive effect on preventing pregnancy-related diseases such as GDM and HDPs [77]. In addition, a prospective cohort study in France found a negative correlation in underweight and normal-weight women between the Mediterranean adherence score and the FGR risk [78]. Thus, we speculate that the influence of maternal diet during pregnancy on children’s health may be explained by its impact on maternal health outcomes.

The main strength of our study is that we summarized the available evidence on the effects of diet during pregnancy on maternal and children’s metabolic health, and published conclusions based on the level and reliability of the evidence. Among them, we innovatively matched three common dietary patterns (High-fiber diet, DASH diet and Mediterranean diet) with relevant health outcomes. For example, adherence to the DASH diet during pregnancy improves GDM and excessive GWG, but there is insufficient evidence for an association with HDPs. Thus, it provides more accurate personalized dietary recommendations for pregnant women with different health conditions. Another strength is that we included child outcomes, highlighting the impact of nutrition during pregnancy on various potential metabolic disease risk indicators in children. Our findings provide a systematic overview of the risk of metabolic diseases for mothers and their children who adopt different dietary patterns during pregnancy, and provide a basis for careful nutritional management and professional care during pregnancy.

Our study has several limitations. We included 32 eligible trials, but some analyses are limited to the small number of studies. For example, articles on the relationship between dietary fiber intake and HDPs, the relationship between the DASH diet and excessive weight gain during pregnancy, and the relationship between the Mediterranean diet and excessive weight gain during pregnancy, are limited to only one study, which may lead to bias as insufficient information was available. Second, our study included RCTS, cohort studies, and cross-sectional studies, which have different levels of evidence to have significant heterogeneity and lack precision, because of lower-quality evidence of observational studies than RCTs. Moreover, the metabolic syndrome, in fact, includes central obesity, hypertension, hyperglycemia, and dyslipidemia [79], but we only assessed the risk of GDM, HDPs and obesity. As a result of these limitations, some uncertainty remains about the true associations between pregnancy diet and the risk for pregnant women and their children’s adverse health.

This systematic review and meta-analysis involving 41,424 women explored that high-fiber diet, higher adherence to the DASH diet, and the Mediterranean diet during pregnancy were associated with reduced risks of maternal and children’s metabolic adverse outcomes. The findings identified which maternal and infant metabolic health outcomes were improved by dietary patterns during pregnancy, thus providing an insight into maternal selection of dietary patterns during pregnancy. This analysis further supports scientific and rational dietary intervention to improve the health of mothers and their children.

An ethics statement is not applicable because this study is based exclusively on published literature.

The authors have no conflicts of interest to declare.

This research was supported by the Chinese Association for Student Nutrition and Health Pro-motion-Mead Johnson Nutritionals (China) Joint Fund [CASNHP-MJN2023-06] and Shanghai Jiao Tong University School of Medicine 16th “Innovative Training Program for College Students” [1622X411].

M.H. designed the research and had primary responsibility for the final content. M.F., Y.C., Y.Z., and Z.Z. conducted the research. M.F. analyzed the data. M.F. and M.H. wrote the manuscript. All authors read and approved the final manuscript.

All data generated or analyzed during this study are included in this article and its online supplementary material files. Further inquiries can be directed to the corresponding author.

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