Subject characteristics

• Sample size of the studies ranged from 12 subjects in a pilot study (21) to 66,597 subjects (5). The median sample size was 1,079.
• Age: Most of the studies included women between 20 and 40 years of age. An exception was the Add Health cohort, which included only adolescent participants (17). Monteagudo et al. reported that their study participants were between ages 21-46 years and the mean age was 31.9 years. Other studies noted that anywhere between 10% and 29% of the study participants were at least 35 years of age. The percentage of participants over 40 years was not specified, except by Knudsen et al. (1% of participants). The rest of the studies presented the mean age, which ranged from ~27 (12) to ~35 (21).
• Pregnancy characteristics: Almost all of the studies included singleton pregnancies, only.
• Health Characteristics:

  • One-third of studies excluded subjects with a previous diagnosis of type 1 and/or 2 diabetes mellitus (1, 5, 6, 10, 11, 14, 19). In addition, Shapiro et al. also excluded subjects with gestational diabetes in their index pregnancy.
  • A few studies included mother-baby dyads only if the baby was born within a specific time period:

    • Hillesund et al. included babies born between 37 and 42 weeks.
    • Poon et al. included children born at or after 35 weeks of gestation, but before 43.5 weeks. In addition, Poon et al. specified that only babies who weighed ≥5 lbs and didn’t stay in the intensive care unit for more than 3 days were included.
    • Shapiro et al. included all children with a gestational age ≥32 weeks.
    • Knudsen et al. included only full-term infants.
    • Lu et al. excluded preterm deliveries.
    • Okubo et al. included term deliveries born between 37 and 41 weeks of gestation.
  • A few studies excluded subjects with the following chronic diseases:

    • Hypertension (1, 8, 14, 19)
    • Thyroid pathology (19)
    • Asthma with active steroid management (10)
  • Timmermans et al. reported that 6% of their subjects had co-morbidities including chronic hypertension, heart disease, diabetes, high cholesterol, thyroid disease and systemic lupus erythematosus. Similarly, Chatzi noted that <3% of their study participants had gestational hypertension and <1% had diabetes before pregnancy.
  • Khoury et al. excluded women with high risk pregnancies caused by endocrine disease, history of thromboembolic disease or significant gastrointestinal, cardiac, pulmonary, or hematologic disease. Women with complications during a previous pregnancy, including neonatal death, still birth, and preterm delivery, were also excluded. Women who experienced ongoing hyperemesis gravidarum or bleeding after gestational week 12 in the current pregnancy were not included.
  • Okubo et al. reported that ~13% of their subjects had one or more of the following conditions: Hyperemesis, hydramnios, oligoamnios, gestosis, abruptio placenta, placenta previa, incompetent cervical os or others diagnosed by a medical doctor.
  • A few studies also excluded subjects with drug-abuse (1, 3) or serious psychiatric illness (10), or who were receiving psychotropic drugs (11).
  • Four studies excluded subjects whose pregnancies were conceived using assisted reproductive technologies (2, 3, 7, 8).
  • Two studies noted that their study participants were healthy or had a low risk pregnancy (12, 21).
• Smoking during pregnancy varied across studies:

  • Daily smoking ranged between ~3% (11) and 32% (20). Lu et al. reported that 30% experienced passive smoking.
  • Khoury et al. excluded current smokers from their study.
  • Pre-pregnancy smoking status (7-13 years prior to becoming pregnant) was reported to be 25% in one study (4).
• Race/ethnicity: About half of the studies (n=10) did not report participants’ race/ethnicity. Among the studies that did report this information:

  • One-third of studies noted that a majority of their subjects were white (Khoury: 100%; Grieger: 89%; Rodriguez-Bernal: 88%; Poon: 87%; Northstone: 82%; Rifas-Shiman: 72%; Shapiro: 55%). Categorized another way, Xie et al. noted that 77% of the study subjects were non-Blacks. Bouwland-Both et al. conducted their study in an all-Dutch population.
  • Only two studies reported including a majority of non-white subjects:

    • Colon-Ramos noted that 62% of their study participants were African American.
    • The GUSTO study participants (based in Singapore) were Chinese, Malayan or Indian. The authors did not specify the overall percentage of each race/ethnicity.
• Parity: There was heterogeneity in terms of study participants’ parity. For example, in some studies, most subjects were nulliparous (88% in Lu et al., 90% in Gresham et al. and 100% in Xie et al.). Other studies (n=11) reported that ~38% to 68% of women were nulliparous.
• Pre-pregnancy BMI:

  • In this body of evidence, Chia et al. reported one of the lowest mean pre-pregnancy BMIs (which ranged between 22.1 and 22.7 kg/m², depending on the dietary pattern quintile). While Okubo et al. noted that the mean pre-pregnancy BMI was 20.2 kg/m², it was based on maternal body weight measured at 20 years of age.
  • Colon-Ramos reported one of the highest mean pre-pregnancy BMI (~27.6 kg/m²), with the African American sub-group noting even higher mean BMI (28.8 kg/m²). The percentage of participants with BMI over 30 kg/m² ranged from 4% (15) to 19% (10).
  • It should be noted that Khoury et al. excluded subjects with a BMI either <19 or >32 kg/m².
  • While Grieger et al. reported on maternal BMI, it is unclear if it was measured pre-pregnancy.
  • Xie et al. reported that pre-pregnancy BMI in adolescents was 22.2 kg/m².
• Maternal education: Among studies that reported maternal education (n=16), participants with more than a high school education ranged from 16% (19) to 91% (9).
• Socioeconomic status (SES) was reported in some of the studies (n=14). Data presented across studies were heterogeneous. Below are a few examples of studies that reported data on SES.

  • Kennedy noted that all of their participants were WIC eligible (income <185% of poverty level).
  • Poon noted that 38% had income <185% of poverty level.
  • Rifas-Shiman reported that 13% of their participants had income below $40,000, whereas Shapiro et al. reported that 29% of participants had income below $40,000.
  • Rodriguez-Bernal noted that 61% of their participants were manual workers. Meanwhile, Monteagudo et al. reported that 37% of participants were employed doing housework and 5% did agricultural work.
  • Grieger et al. reported that 82% of their participants were in the lowest 2/5^th SES strata.
  • Xie et al. study participants were adolescents, so they reported parental education status as a proxy for SES, with 79% of parents having at least a high school education.
  • Although Colon-Ramos did not report the SES of the study participants, the authors mentioned that the study was conducted in a U.S. county that was primarily low income (<200% poverty level).

Interventions/Exposures

Dietary patterns were assessed using 1) index/score analysis; 2) factor analysis and PCA; 3) experimental diet; 4) PCA and reduced rank regression; and 5) another unspecified method. A description of the studies included by each method used to measure dietary patterns is included below.

• Index/score analysis (Table 2: Indices and scores used to assess the relationship between dietary patterns before and during pregnancy and birth weight): Nine studies included in this review used one or more of the following indices/scores:

  • Mediterranean Diet Scale (7)
  • Australian Recommended Food Score (4)
  • New Nordic Diet score (5)
  • Nutritional Risk Score (18)
  • Mediterranean Diet Score for Pregnancy (19)
  • Alternative Mediterranean Diet (aMED) (6)
  • Alternative Healthy Eating Index for Pregnancy (6, 8, 9)
  • Healthy Eating Index-2010 (10)
• Factor analysis and PCA (Table 3: Summary of dietary patterns identified using factor or principal components analysis): Eight studies included in this review assessed dietary patterns using factor analysis or PCA (3, 11–16, 20)
• Experimental diet: Two studies included in this review assigned participants to one of two experimental diets (1, 21). Khoury et al. randomly assigned participants to the following diets:

  • Intervention diet:

    • Dietitians encouraged the intake of fatty fish, vegetable oils, especially olive oil and rapeseed oil, nuts, nut butters, margarine based on olive- or rapeseed oil, and avocado to replace meat, butter, cream, and fatty dairy products; the consumption of fresh fruits and vegetables was advised (at least 6 a day); intake of dairy products in the form of skimmed or low-fat products (skimmed milk, fat-reduced cheese, and yogurt) in place of full fat products was encouraged; subjects were advised to choose meat for a main meal twice a week and use legumes, vegetable main dishes, fatty fish, or poultry with the fat trimmed off on the other days; coffee was limited to 2 cups of filtered coffee a day
    • Compliance data showed that the intervention group included significantly more fish and fish products; fatty fish and fish products; rapeseed-based margarine; oils; olive oil; rapeseed oil; nuts, olives, and seeds; vegetables; and fruits when compared to the control diet³
  • Control diet:

    • Subjects were asked to consume their usual diet based on Norwegian foodstuffs, and not to introduce more oils or low-fat meat and dairy products than usual
    • Included significantly more fatty milk, meat and meat products, fatty minced meat, butter, and hard margarines when compared to the intervention diet⁶
  In a pilot RCT, Clapp randomly assigned participants to one of two experimental diets:

  • Aboriginal carbohydrate diet (Low-GI): used carbohydrates from unprocessed whole grains, fruits, beans, vegetables, and many dairy products; includes most dense whole grain and multigrain breads, bran cereals, pastas, fresh fruits and vegetables, yogurt, ice cream, and nuts
  • Cafeteria carbohydrate diet (High-GI): used carbohydrates from highly processed grains, root vegetables, and simple sugars; includes many highly-refined breads, potatoes, instant rice, most breakfast cereals, desserts, and snack-type foods.
• Logistic Regression and PCA: One study assessed adherence to a Mediterranean diet using logistic regression and PCA (2).
• Unspecified method: One study created dietary patterns by grouping foods into categories defined “on the basis of theoretical expectations” by Dube et al.⁴ and later used by Jeffery et al.⁵ and Agurs-Collins et al⁶ (17). The following three categories were created:

  • High-calorie sweet pattern: includes foods such as doughnuts, ice cream, chocolate candy, regular candy, and cookies
  • High-calorie nonsweet pattern: includes steak, fried chicken, fried fish, pizza, hot dogs, sausage, cheese, whole milk, etc.
  • Low-calorie pattern: includes foods such as low-fat and skim milk, grilled chicken, grilled fish, apples, and breakfast cereal

Time point of exposure

As described below, the time point of exposure and recall periods were heterogeneous across studies (Table 4: Time point of exposure assessment). The following section discusses the time period diet was assessed and the time window it represents.

• Before pregnancy:

  • Grieger et al. (20) collected data at 13 weeks, but the recall period was 12 months prior to conception.
  • Monteagudo et al. (19), Gresham et al. (4) and Xie et al. (17) did not specify a particular time period when the diet was assessed (which could have ranged from before to during pregnancy).
• First trimester: The following studies assessed diet during the first trimester, only:

  • Rodriguez-Bernal et al. (8)
  • Bouwland-Both et al. (3)
  • Timmermans et al. (2)
• Second trimester: The following studies assessed diet during the second trimester, only:

  • Hillesund et al. (5)
  • Chia et al. (11)
  • Colon-Ramos et al. (12)
  • Knudsen et al. (13)
  • Lu et al. (14)
• Third trimester: Northstone et al. (15) assessed diet during the third trimester, only.
• In the following studies, diet measurement spanned multiple trimesters -

  • Rifas-Shiman et al. (9) assessed maternal diet at two distinct time points: first trimester (11.7 ± 3.1 weeks) and second trimester (26 to 28 weeks)
  • Poon et al. (6) assessed diet in a window of 26 to 36 weeks.
  • Chatzi et al. (7) used multiple cohorts, with INMA collecting maternal diet data during the first trimester (~13.8 ± 2 weeks) and Rhea collecting dietary data between 14 and 18 weeks.
  • Shapiro et al. (10) collected dietary data one to eight times between 8 and 24 weeks.
  • Although Okubo et al. (16) collected data at around 18 weeks, the range spanned between 5 and 39 weeks.
  • Clapp (21) assigned diets randomly around 8 weeks and followed them until delivery.
  • Khoury et al. (1) assigned participants to one of two experimental diets between 17 and 20 weeks and followed them until delivery.
• The recall period varied from a few days (10, 11, 17) to a few weeks (13, 14) to a few months (2, 3, 5–9, 12, 16).

Outcomes

Studies assessed several outcomes including birth weight (reported in grams or kilograms), low birth weight, weight-for-length and weight-for-age z scores, macrosomia, SGA, AGA, LGA, body composition, and IUGR. Some studies also assessed fetal growth measures, including crown-rump length, estimated fetal weight, fetal head circumference, abdominal circumference, femur length, and placental resistance measured using pulsatility index umbilical artery and resistance index uterine artery. Many studies did not standardize for gestational age or sex when assessing birth weight (i.e., they used “raw” birth weight as the outcome across a range of preterm and term births). Specifically, one-third of studies (n=7) used both gestational age and sex-specific cut-off values when defining SGA, AGA, LGA and IUGR (1, 5, 6, 11, 13, 14, 16). Table 5: Summary of outcome definitions, summarizes the outcomes and diagnostic criteria grouped by methodology used to create dietary patterns.

Table 2

Indices and scores used to assess the relationship between dietary patterns before and during pregnancy and birth weight.

Table 3

Summary of dietary patterns identified using factor or principal components analysis Study Dietary Patterns.

Table 4

Time point of exposure assessment.

Table 5

Summary of outcome definitions.

Dietary patterns assessed via index/score

Nine studies used indices/scores to assess dietary patterns. Four of these studies found an association between adherence to a healthy dietary pattern and birth weight.

• Chatzi et al. (7) assessed maternal adherence to a Mediterranean Diet (MD) – characterized by vegetables, legumes, fruits and nuts, cereals, fish and seafood, dairy products, and the ratio of mono- to saturated lipids and all types of meat – in the context of fetal growth restriction and birth weight. The study included data from the multicenter INMA study (INMA-Atlantic and INMA-Mediterranean) and the RHEA study. Higher MD adherence in INMA-Mediterranean was associated with a lower risk of fetal growth restriction and higher birth weight. In the other two cohorts, there was no association between MD adherence and the outcomes.
• Gresham et al. (4) examined the association between adherence to the Australian Recommended Food Score (ARFS) before and during pregnancy and self-reported low birth weight. Components included vegetables, fruit, grain, protein (nuts/bean/soya, meat, fish, eggs), dairy, and fat. The exposure captured dietary intake during the previous 12 months. The time point of data collection ranged from before pregnancy to the end of the third trimester of pregnancy. The study found no significant difference in the odds of low birth weight based on adherence to the ARFS.
• Hillesund et al. (5) assessed maternal adherence to the New Nordic Diet (NND), which measured the frequency of eating the following foods: Nordic fruits (apples, pears, plums, strawberries), root vegetables (carrots, rutabaga and various types of onions), cabbages (kale, cauliflower, broccoli and Brussels sprouts), potatoes, whole grain breads, oatmeal porridge, foods from the wild countryside (wild fish, seafood, game and wild berries), milk and water. (The NND also measured the frequency of main meals per week.) High adherence to the NND, compared to low adherence, was associated with reduced odds of having an SGA baby and greater odds of having an LGA baby.
• Kennedy (18) examined the relationship between a nutritional risk score and birth weight. Components of the risk score included meat or alternatives (meats, fish, poultry, liver, eggs, nuts, peanut butter, legumes), milk and cheese, bread and cereal (whole grain, enriched, other), and four categories of fruits and vegetables (citrus and/or vitamin C-rich vegetables, green and yellow vegetables, all other including potato, and total fruits). The study found no association between nutritional risk score and birth weight.
• Monteagudo et al. (19) assessed the relationship between the Mediterranean Diet Score for Pregnancy (MDS-P) and birth weight. The components of MDS-P included vegetables, fruit and nuts, pulses, cereals, fish, ratio of monounsaturated to saturated fats, meat, dairy products and selected micronutrients (folic acid, iron, and calcium). A higher MDS-P score was a predictor of birth weight over 3,500 g.
• Poon et al. (6) assessed adherence to the Alternate Healthy Eating Index for Pregnancy (AHEI-P) and the Alternate Mediterranean Diet (aMED). AHEI-P was characterized by the following components: vegetables, whole fruit, whole grains, nuts and legumes, long-chain (n-3) fats, polyunsaturated fats, folate, calcium, iron, sugar-sweetened beverages, red and processed meat, trans fat, and sodium. aMED was characterized by vegetables, legumes, fruits, nuts, whole grains, fish, and the ratio of monounsaturated to saturated fats and red and processed meats. Adherence to the AHEI-P or aMED was not associated with birth weight or risk of SGA or LGA.
• Rifas-Shiman et al. (9) used an Alternate Healthy Eating Index modified for pregnancy (AHEI-P), which was characterized by intake of vegetables, fruit, ratio of white to red meat, fiber, trans fat, ratio of polyunsaturated to saturated fatty acids, and folate, calcium, and iron from foods. Greater adherence to the AHEI-P during the first or second trimester of pregnancy did not alter the risk of having an SGA or LGA baby.
• Rodriguez-Bernal et al. (8) assessed adherence to the Alternate Healthy Eating Index (AHEI) modified for pregnancy. Components of the AHEI included vegetables, fruit, nuts and soy, ratio of white meat (fish and poultry) to red meat, cereal fiber, trans fat, the ratio of polyunsaturated to saturated fat, and the intake of folate, calcium, and iron from foods. Higher adherence to the AHEI was associated with a lower risk of fetal growth restriction (estimated fetal weight) and improved birth weight.
• Shapiro et al. (10) used the Healthy Eating Index-2010 (HEI-2010) to assess diet quality during pregnancy. The components of HEI-2010 included total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids, refined grains, sodium and empty calories. Better maternal diet quality, defined as greater adherence to the HEI-2010 (score >57), was not associated with birth weight.

Dietary patterns assessed via factor or principal component analysis

Eight studies used data-driven methods (i.e., principal component analysis, exploratory factor analysis, and cluster analysis) to assess dietary patterns. Five of the eight studies found a relationship between diet and birth weight outcomes. A summary of findings from these studies is presented below.

• Bouwland-Both et al. (3) generated three dietary patterns. Higher adherence to the energy-rich dietary pattern (characterized by high intakes of bread/breakfast cereals, margarine, nuts, snacks/sweets and nonsweetened nonalcoholic beverages, and low intakes of sweetened, nonalcoholic beverages) was associated with a greater crown-rump length. However, the energy-rich dietary pattern was not associated with estimated fetal weight or birth weight. The Mediterranean and Western dietary patterns were not associated with crown-rump length and so were not included in further analyses.
• Chia et al. (11) showed that greater adherence to a vegetable, fruit and white rice dietary pattern (characterized by higher intakes of vegetables, fruits, plain white rice, whole-grain bread, fish, and nuts and seeds and lower intakes of fried potatoes, burgers, carbonated and sweetened drinks, and flavored rice) was associated with an increased risk of having LGA babies, but was not associated with having SGA babies. Chia et al. also assessed other dietary patterns (seafood and noodle and pasta, cheese and processed meat), which were not associated with birth weight or having SGA or LGA babies.
• Colon-Ramos et al. (12) generated three dietary patterns:

  • Healthy, characterized by high positive loadings for vegetables, fruits, non-fried fish and chicken, and water
  • Processed, characterized by high positive loadings for processed meat, fast food items, snacks sweets, and soft drinks
  • Southern, characterized by high positive loadings for cooked cereals, peaches, corn fried fish, beans, greens, pig’s feet, neck bones oxtails, tongue, and pork
  The authors also generated healthy-processed, healthy-Southern, Southern-processed, and mixed patterns, reflecting a combination of one or more of the above dietary patterns. None of these patterns were associated with weight-for-length and weight-for-age.
• Grieger et al. (20) measured dietary patterns before pregnancy. Three dietary patterns were generated:

  • High protein/fruit, characterized by a diet rich in fish, meat, chicken, fruit and whole grains
  • High fat, sugar/takeaway, characterized by takeaway foods, potato chips, refined grains, added sugar
  • Vegetarian type, characterized by a diet rich in vegetables, whole grains, legumes
  There was no association between any of these dietary patterns and the risk of having LBW or SGA babies.
• Knudsen et al. (13) used PCA to create three different dietary patterns:

  • Western pattern, including highest intake of high-fat dairy, refined grains, processed and red meat, animal fat (butter and lard), potatoes, sweets, beer, coffee, and high-energy drinks; lowest intake of fruits and vegetables (35% of energy intake from fat)
  • Health conscious pattern, including high intake of fruits, vegetables, fish, poultry, breakfast cereals, vegetable juice, and water; lowest intake of meat and fat of animal origin (25% of energy intake from fat)
  • Intermediate pattern, including high intake of low-fat dairy and fruit juice; consumption of the remaining food groups in between Western and Health conscious DPs (30% of energy intake from fat)
  When compared to a Western dietary pattern, adherence to an intermediate or health conscious dietary pattern was associated with lower odds of having an SGA baby.
• Lu et al. (14) generated the following dietary patterns using cluster analysis:

  • Cereals, eggs, and Cantonese soups pattern, with highest content of staples such as rice, pasta, porridge, eggs, and Cantonese soups; represents a traditional Cantonese diet
  • Dairy pattern, with highest content of dairy
  • Fruits, nuts, and Cantonese desserts pattern, with highest content of fruits, nuts, and Cantonese desserts
  • Meats pattern, with highest content of red meat and processed meat
  • Vegetables pattern, with highest content of leafy and cruciferous vegetables
  • Varied pattern, characterized by relatively high intakes of mixed food, including noodles, bread, root vegetables, melon vegetables, mushrooms, sea vegetables, bean vegetables, processed vegetables, poultry, animal organ meat, fish, other seafood, bean products, yoghourt, sweet beverages, puffed food, confectioneries, and snacks
  When compared to the cereals, eggs, and Cantonese soups pattern (representing a traditional Cantonese diet), none of the dietary patterns were significantly associated with birth weight for gestational age.
• Northstone et al. (15) used PCA to create the following dietary patterns:

  • Health-conscious pattern, with high loadings on salad, fruit, rice, pasta, breakfast cereals, fish, eggs, pulses, fruit juices, poultry and non-white bread
  • Traditional pattern, with high loadings on green vegetables and root vegetables, potatoes, peas and to some extent red meat and poultry
  • Processed pattern, with high loadings on high-fat processed foods, such as meat pies, sausages and burgers, fried foods, pizza, chips and crisps
  • Confectionery pattern, with high intakes of confectionery and other foods with high sugar content such as chocolate, sweets, biscuits, cakes and other puddings
  • Vegetarian pattern, loaded highly on meat substitutes, pulses, nuts and herbal tea and high negative loadings of red meat and poultry
  The health conscious dietary pattern was associated with higher birth weight, while the processed and vegetarian patterns were associated with lower birth weight. There was no association between the traditional and confectionery patterns and birth weight.
• Okubo et al. (16) used cluster analysis to create the following dietary patterns:

  • Meat and eggs pattern, characterized by high intake of beef and pork, processed meat, chicken, eggs, butter and dairy products
  • Wheat products pattern, characterized by high intake of bread, confectioneries, fruit and vegetable juice, and soft drinks
  • Rice, fish, and vegetables pattern, characterized by high intake of rice, potatoes, nuts, pulses, fruits, green and yellow vegetables, white vegetables, mushrooms, seaweeds, Japanese and Chinese tea, fish, shellfish, sea products, miso soup and salt-containing seasoning
  Adherence to the wheat products dietary pattern was associated with lower birth weight and a higher risk of SGA when compared to a rice, fish, and vegetable dietary pattern.

Dietary patterns assessed in the RCT

• Clapp (21) randomly assigned 12 subjects at 8 weeks of gestation to an aboriginal carbohydrate diet (a low glycemic index diet characterized by carbohydrates from unprocessed whole grains, fruits, beans, vegetables, and many dairy products) or a cafeteria carbohydrate diet (a high glycemic index diet characterized by carbohydrates from highly processed grains, root vegetables, and simple sugars). Mothers who were randomized to the cafeteria carbohydrate diet had babies with a greater mean birth weight when compared to those randomized to the aboriginal carbohydrate diet.
• Khoury et al. (1) randomly assigned 290 participants to an intervention or control diet. The intervention diet was characterized by higher amounts of fruits and vegetables, fatty fish, vegetable oils, nuts and low-fat dairy. Participants were asked to restrict meat and replace it with avocado, as well as to limit coffee consumption to 2 cups/day. Subjects who were assigned to the control group were asked to consume their usual diet based on Norwegian foodstuffs and not to introduce more oils or low-fat meat and dairy products than usual (1). There were no significant associations between diet and birth weight or risk of IUGR.

Dietary patterns assessed through other methods

• Timmermans et al. (2) used logistic regression to assess adherence to the Mediterranean dietary pattern, characterized by higher intakes of pasta, rice, vegetable oils, fish, vegetables and alcohol and lower intakes of meat, potatoes and fatty sauces. Compared to high adherence, low adherence was associated with lower fetal abdominal circumference during late pregnancy and lower estimated fetal weight during mid- and late-pregnancy. Similarly, medium and low adherence was associated with lower birth weight when compared to high adherence.
• Xie et al. (17) defined the following dietary patterns on the basis of theoretical expectations:

  • High-calorie, sweet pattern, characterized by foods such as doughnuts, ice cream, chocolate candy, regular candy, and cookies
  • High-calorie, non-sweet pattern, characterized by steak, fried chicken, fried fish, pizza, hot dogs, sausage, cheese, whole milk, etc.
  • Low-calorie pattern, characterized by foods such as low-fat and skim milk, grilled chicken, grilled fish, apples, and breakfast cereal
  The questionnaire was not validated, and the outcome was self-reported. In this adolescent-only cohort, there was no association between reported dietary patterns and birth weight.

Table 6

Results grouped by methodology used for dietary pattern assessment.

Assessment of the body of evidence

A grade was not assignable for this body of evidence. The individual grading elements are discussed below.

Internal validity (determined with the NEL Bias Assessment Tool)

• Study Design: The data were primarily observational in nature, making it difficult to determine causal effect of the dietary patterns. Further, the characteristics of the study participants were also heterogeneous.
• Exposure:

  • Data were primarily self-reported, thus possibly affecting the validity of the data collected.
  • Studies used a variety of tools to measure participants’ dietary intake:

    • Food frequency questionnaires (FFQ) were the primary measurement tool. A few of the FFQ used were neither validated (15, 17, 18) nor assessed for reliability, and the type of FFQ used varied between studies.
    • Some studies used other methods, including diet history questionnaires (6, 10), 24-hr recall (10), a 3-day food diary (11) and dietary recall (17).
    • When the subjects were randomized to a particular diet, compliance was assessed periodically (1, 21).
  • Furthermore, the methods used to create dietary patterns were heterogeneous.
• Timing:

  • Variation in the timing of exposure assessment and the duration of recall periods across studies makes it difficult to draw conclusions for the entire periods before and during pregnancy.

    • Grieger et al. exclusively assessed maternal diet before pregnancy.
    • Monteagudo et al., Gresham et al. and Xie et al. measured diet during a wide window spanning from before pregnancy to the end of pregnancy, and they did not delineate their findings based on timing (before pregnancy vs. during pregnancy).
    • Rodriguez-Bernal et al., Bouwland-Both et al. and Timmermans et al. assessed diet during the first trimester, with the recall period spanning from conception until the point of measurement.
    • Hillesund et al., Chia et al., Colon-Ramos et al., Knudsen et al., and Lu et al. all measured maternal diet during the second trimester (with variable time periods of recall).
    • Northstone et al. measured diet during the third trimester and captured current consumption.
    • Several studies assessed diet across trimesters, which included varied recall periods.
• Confounders:

  • Key confounders (parity, educational attainment, smoking status, race/ethnicity, maternal age, family poverty income ratio, pre-pregnancy BMI, mean total energy intake, and hypertensive disorders of pregnancy) were not consistently accounted for across studies. This limits the internal validity of study findings.

    • Six studies did not adjust for pre-pregnancy BMI, two of which were RCTs. Khoury et al. noted that pre-pregnancy BMI did not differ between intervention and control diet groups, whereas Clapp did not compare pre-pregnancy BMI across groups.
    • Furthermore, only six of 21 studies adjusted for maternal energy intake, a key confounder in the association between dietary patterns and birth weight outcomes.
  • None of the studies included in this review assessed effect measure modification between dietary patterns and maternal pre-pregnancy BMI in the context of birth weight outcomes. Failing to examine these potential interactions could have prevented these studies from observing associations.
• Outcome:

  • Outcomes were assessed in a variety of ways across studies included in this review.

    • Just one-third of studies (n=7) used both gestational age- and sex-specific cut-off values when defining SGA, AGA, LGA and IUGR (1, 5, 6, 11, 13, 14, 16).
    • Twelve studies reported birth weight adjusted for gestational age as an outcome (1, 5–9, 11–14, 16, 20).
    • Nine studies reported birth weight, alone, without standardizing for gestational age or sex or using z-scores (2–4, 10, 15, 17–19, 21).
  • Four studies included subjects born at a specified gestational age:

    • Hillesund (≥ 37 to ≤42 weeks)
    • Poon (≥35 to ≤ 43.5 weeks)
    • Shapiro (≥32 weeks)
    • Okubo (≥37 to ≤41 weeks)
  • The use of birth weight values unadjusted for infant sex or gestational age at delivery in many of these studies makes these data difficult to interpret.

Consistency

• There was heterogeneity in study findings in this body of evidence. Some studies found an association between dietary patterns and birth weight outcomes (n = 11), while others did not (n=10). This varied by the specific measure of size at birth that was used in each study.
• Small- or large-for-gestational age:

  • Five of the 12 studies showed an association between dietary patterns and SGA (5, 7, 8, 13, 16).
  • Two of the six studies noted that a greater adherence to a dietary pattern (specifically, the New Nordic Diet and a vegetable, fruit and white rice pattern) was associated with an increased risk of LGA (5, 11). Monteagudo et al. observed that a higher Mediterranean diet score was predictive of newborn overweight (>3,500 g) when compared to newborn underweight (<2,500g); however, the birth weight was not standardized for gestational age or z-score.

    • Monteagudo et al. findings may be explained in part by their structuring of the outcome as the likelihood of overweight versus underweight as the reference group. This likely contributed to their large observed effect size.
• Birth weight:

  • Birth weight standardized for gestational age was reported in three studies (8, 9, 12), of which only Rodriguez-Bernal et al. reported a positive association with the birth weight.
  • Crude birth weight, without standardizing for gestational age or sex (i.e, using percentiles or z-scores), was reported in 9 studies. Of these, four showed that adherence to a healthy dietary pattern was associated with birth weight (2, 7, 15, 21). Among studies that reported low birth weight as an outcome (n=3), none showed an association with maternal dietary patterns (4, 18, 20).
  • The mean birth weight for included studies was above 3,000 g (range: 3,103 g (16) to ~3,760 g (21)), indicating that babies were born at a healthy weight, overall. This may explain why many studies did not detect an association between healthier dietary patterns and increased birth weight.
• Dietary patterns were associated with other outcomes such as crown-rump length (3), estimated fetal weight during mid- and late-pregnancy and late-pregnancy abdominal circumference (2), but there was no association with other fetal measures (head circumference, femur length) or weight for length z-score.

Impact

• Most of the studies in the body of evidence directly examined the relationship between different dietary patterns or different levels of adherence to a dietary pattern and birth weight. However, there were a few exceptions. First, the RCT by Khoury et al. assessed IUGR and birth weight as secondary outcomes. The primary study was focused on understanding the relationship between a cholesterol-lowering diet during pregnancy and maternal, cord, and neonatal cholesterol levels (1). Second, Shapiro et al. was designed to assess the association between maternal diet and neonatal adiposity, with birth weight as a secondary outcome.
• The lack of consistency across studies limits the practical/clinical significance of this review.

Adequacy

• A substantial number of studies were included in this review. The evidence base included 21 studies comprising 2 RCTs, 18 prospective cohort studies, and 1 retrospective cohort study.
• Most of the studies were conducted by independent research groups. The only exception was Timmermans et al. and Bouwland-Both et al., who both used data from the Generation R study and also had overlapping authorship.
• Sample sizes varied from 12 subjects to 66,597 subjects. The median sample size was 1,079. None of the studies reported power calculations. A few studies (1, 17, 19–21) had lower sample sizes, which might have limited their ability to detect significant differences had such differences been present.

Generalizability

• There are serious limitations to the generalizability of these findings.

  • Only seven out of 21 studies were conducted in the U.S.
  • Adolescent, minority, and lower-SES populations are underrepresented in the body of evidence. Only one study was conducted primarily in adolescent girls (17).
• It is unknown if the findings would apply to more diverse samples before and during pregnancy.

Other limitations/considerations

Many of the studies did not use a standardized outcome measure (such as SGA, LGA, or gestational age- and sex-specific birth weight). Even among those that used a standardized measure, there was heterogeneity in how it was defined. Some studies used observed distributions of fetal size that are standardized for gestational age and sex in a defined population. Meanwhile, others used observed distributions that were customized based on maternal characteristics (e.g. maternal parity, height, ethnicity) or standards derived from a healthy population selected to reflect optimal growth³⁷.

As discussed above, none of the studies assessed effect measure modification between dietary patterns and maternal pre-pregnancy BMI in the context of birth weight outcomes and only a limited number of studies (n=6) adjusted for mean total energy intake. These limitations could have possibly influenced the study findings.

Many different methods (sometimes using the same nomenclature) were used to define and assess dietary patterns, making it difficult to compare or contrast results across studies. Journal editors and peer-reviewers may be less willing to publish studies that replicate others’ findings, which could have resulted in an evidence base with a wide array of dietary patterns. It is important for the editors and peer-reviewers to understand the need for publishing studies that replicate dietary patterns, in addition to publishing studies that assess unique dietary patterns.³⁸

PubMed, US National Library of Medicine

• Date(s) searched: January 1980 to January 2017
• Search Terms:
  pregnancy[mh] OR “Prenatal Exposure Delayed Effects”[mesh] OR “Maternal Exposure”[mesh] OR “pregnant women”[mh] OR pregnan*[tiab] OR prenatal[tiab] OR maternal OR mother* OR postpartum OR newborn*[tiab] OR perinatal OR peri-natal OR pre-conception OR preconception OR peri-conception OR periconception OR “Infant, Newborn”[Mesh] OR neonat*[tiab] OR newly born* OR “Peripartum Period”[Mesh] OR peripartum[tiab] OR peri-partum[tiab] OR gestation* OR natal OR puerperium[tiab] OR “Maternal Nutritional Physiological Phenomena”[Mesh]
  AND
  hypertensi*[tiab] OR “Hypertension”[Mesh:NoExp] OR vomit* OR diabetes*[tiab] OR diabetic*[tiab] OR “Birth Weight”[Mesh] OR “Birth Weight”[tiab] OR “Glucose Intolerance”[Mesh] OR Glucose Intoleran*[tiab] OR glucose toleran* OR “Insulin Resistance”[Mesh] OR Insulin Resistan*[tiab] OR Dysglycemia[tiab] OR fasting blood glucose* OR “Hemoglobin A, Glycosylated”[Mesh] OR “Proteinuria”[Mesh:noexp] OR Albuminuria OR “Blood Pressure”[mh] OR “blood pressure”[tiab]
  OR
  “Diabetes, Gestational”[Mesh] OR (gestation*[tiab] AND (diabetes*[tiab] OR diabetic*[tiab])) OR “Pre-Eclampsia”[Mesh] OR “Pre-Eclampsia”[tiab] OR preeclampsia[tiab] OR “Hypertension, Pregnancy-Induced”[Mesh] OR Eclampsia OR “Gestational Age”[Mesh] OR “Morning Sickness”[Mesh] OR (Hyperemesis Gravidarum) OR “Gestational Age”[tiab] OR “Obstetric Labor, Premature”[Mesh] OR ((prematur*[tiab] OR preterm [tiab]) AND (baby[tiab] OR infant*[tiab] OR birth OR labor OR membrane* OR babies)) OR “Fetal Growth Retardation”[Mesh] OR IUGR[tiab] OR “Intrauterine growth restriction” OR “Fetal Development”[Mesh:noexp] OR “Fetal Weight”[Mesh] OR “Umbilical Arteries”[Mesh] OR “Uterine Artery”[Mesh]
  AND
  (“diet quality” OR dietary pattern* OR diet pattern* OR eating pattern* OR food pattern* OR eating habit* OR dietary habit* OR food habit* OR dietary profile* OR food profile* OR diet profile* OR eating profile* OR dietary guideline* OR dietary recommendation* OR eating style*) OR
  (DASH[ti] OR DASH[tw] OR (“dietary approaches”[ti] AND hypertension[ti]) OR “Diet, Mediterranean”[Mesh] OR Mediterranean[ti] OR vegan* OR vegetarian* OR “Diet, Vegetarian”[Mesh] OR “prudent diet” OR “western diet” OR nordiet OR omni[ti] OR omniheart[tiab] OR (Optimal Macronutrient Intake Trial to Prevent Heart Disease) OR adventist* OR ((Okinawa* OR “Ethnic Groups”[Mesh] OR “plant based” OR Mediterranean[tiab] OR Nordic[tiab] OR “heart healthy”[tiab] OR indo-mediterranean) AND (diet[mh] OR diet[tiab] OR diets[tiab] OR food[mh]))) OR
  (“Guideline Adherence”[Mesh] AND (diet OR food OR eating OR eat OR dietary OR feeding OR nutrition OR nutrient*)) OR (adherence AND (nutrient* OR nutrition OR diet OR dietary OR food OR eat OR eating) AND (guideline* OR guidance OR recommendation*)) OR
  (dietary score* OR adequacy index* OR kidmed OR Diet Quality Index* OR Food Score* OR Diet Score* OR MedDietScore OR Dietary Pattern Score* OR “healthy eating index”) OR
  ((index*[ti] OR score*[ti] OR indexes OR scoring[ti] OR indices[ti]) AND (dietary[ti] OR nutrient*[ti] OR eating[tiab] OR food[ti] OR food[mh] OR diet[ti] OR diet[mh]) AND (pattern* OR habit* OR profile*)) OR meals[mh] OR meals[tiab] OR meal[tiab] OR mealtime*[tiab]
  OR
  diet[mh:noexp] OR diet[ti] OR diets[ti] OR food*[tiab] OR “Food”[mh:noexp] OR “Eating”[mh] OR dietary intake*[tiab] OR food intake*[tiab] OR food habits[mh] OR diet habit*[tiab] OR eating habit*[tiab] OR food choice*[tiab] OR dietary choice*[tiab] OR dietary change*[tiab] NOT (editorial[ptyp] OR comment[ptyp] OR news[ptyp] OR letter[ptyp] OR review[ptyp] OR systematic[sb])

Embase, Elsevier

• Date(s) searched: January 1980 to January 2017
• Search Terms:
  ‘pregnancy’/exp OR ‘pregnant woman’/exp OR ‘prenatal period’/exp OR ‘mother’/exp OR ‘prenatal exposure’/exp OR ‘prenatal growth’/exp OR ‘puerperium’/exp OR ‘newborn’/exp OR prematurity/exp OR pregnan*:ti,ab OR maternal:ti,ab OR mother*:ti,ab OR prenatal:ti,ab OR pre-natal:ti,ab OR ‘puerperium’:ti,ab OR postpartum:ti,ab OR newborn:ti,ab OR neonat*:ti,ab OR “newly born”:ti,ab OR periconception:ti,ab OR peri-conception:ti,ab OR pre-conception:ti,ab OR preconception;ti,ab OR gestation* OR peripartum:ti,ab OR peri-partum:ti,ab OR natal:ti,ab OR gestation* OR ‘perinatal development’/exp OR ‘perinatal care’/de OR perinatal:ti,ab OR peri-natal:ti,ab OR ‘puerperium’/de OR ‘puerperium’:ti,ab OR ‘maternal nutrition’/exp
  AND
  hypertensi* OR hyperemesis:ti,ab OR vomit*:ti,ab OR diabet* OR ‘birth weight’/exp OR birthweight:ti,ab OR ((neonatal OR newborn) NEAR/3 weight)
  OR
  ‘glucose intolerance’/exp OR (Glucose NEAR/2 Intoleran*) OR (glucose NEAR/2 toleran*) OR ‘insulin resistance’/exp OR (Insulin NEAR/1 Resistan*):ti,ab OR Dysglycemia OR “fasting blood glucose” OR ‘hemoglobin A1c’/exp OR ‘hemoglobin A1c’ OR ‘proteinuria’/exp OR albuminuria OR “Blood Pressure”/de
  OR
  ‘pregnancy diabetes mellitus’/exp OR “diabetes mellitus gravidarum”:ti,ab OR ‘eclampsia OR preeclampsia’/exp OR eclampsia:ti,ab OR preeclampsia:ti,ab OR pre-eclampsia:ti,ab OR ‘maternal hypertension’/exp OR ‘gestational age’/exp OR ‘small for date infant’/exp OR ‘gestational age’ OR ‘hyperemesis gravidarum’/exp OR ‘morning sickness’/exp OR (gestation* NEAR/2 diabet*):ti,ab OR (Obstetric NEAR/3 (Labor OR labour)) OR (labor/exp AND obstetric*) OR ‘prematurity’/exp OR ((prematur* OR preterm) NEAR/3 (baby OR infant* OR babies OR birth OR childbirth OR labor OR membrane*)) OR ‘intrauterine growth retardation’/de OR IUGR:ti,ab OR “Intrauterine growth restriction” OR ‘fetus growth’/exp OR ‘fetus development’/exp OR ‘fetus weight’/exp OR ‘umbilical artery’/exp OR ‘uterine artery’/exp OR ((fetal OR fetus OR foetal OR foetus OR embryo*) NEAR/3 (weight OR develop* OR growth)):ti,ab
  AND
  ‘eating habit’/exp OR ‘Mediterranean diet’/exp OR nordiet:ti,ab OR ‘nordic diet’:ti,ab OR DASH:ti,ab OR ‘dietary approaches to stop hypertension’:ti,ab OR vegan*:ab,ti OR vegetarian*:ab,ti OR ‘vegetarian diet’/exp OR ‘vegetarian’/exp OR ‘prudent diet’:ti,ab OR ‘western diet’:ti,ab OR ‘Western diet’/exp OR meal/de OR omniheart:ti,ab OR omni:ti OR ‘plant based diet’ OR ((eating OR food OR diet* OR calori*) NEAR/3 (pattern? OR habit? OR profile? OR recommendation? OR guideline? OR style* OR choice* OR intake OR quality)) OR ((‘ethnic, racial and religious groups’/exp OR Okinawa* OR adventist* OR ‘mediterranean’) AND (diet/exp OR eating/exp OR ‘food intake’/de OR calori* OR diet* OR food OR eating))
  OR
  Diet/de OR ‘dietary intake’/de OR ‘food preference’/de OR ‘food intake’/de OR ‘diet restriction’/exp OR ‘eating habit’/exp OR diet*:ti OR kidmed:ab,ti OR ‘meddietscore’:ab,ti OR ‘healthy eating index’:ab,ti OR ((index OR score OR scoring OR indices) NEAR/3 (diet* OR eating OR food)) OR “food consumption”
  OR
  food*:ti,ab OR “Food”/de OR Eating:ti,ab OR (dietary NEAR/1 change*):ti,ab OR Meal*:ti,ab

Cochrane Central Register of Controlled Trials, John Wiley & Sons

• Date(s) searched: January 1980 to January 2017
• Search Terms:
  [mh pregnancy] OR [mh “Maternal Exposure”] OR [mh “Prenatal Exposure Delayed Effects”] OR [mh “pregnant women”] OR pregnan*:ti,ab OR prenatal OR maternal OR mother* OR postpartum OR newborn*:ti,ab OR perinatal OR peri-natal OR pre-conception OR preconception OR peri-conception OR periconception OR [mh “Infant, Newborn”] OR neonat*:ti,ab OR (newly NEAR/1 born*) OR gestation* OR peripartum OR peri-partum OR natal:ti,ab OR puerperium OR gravidarum OR [mh “Peripartum Period”] OR peripartum:ti,ab OR peri-partum:ti,ab OR natal OR puerperium:ti,ab OR [mh “Maternal Nutritional Physiological Phenomena”]
  AND
  (hypertensi*:ti,ab OR [mh ^Hypertension] OR vomit*:ti,ab OR diabet*:ti,ab OR [mh “Birth Weight”] OR “Birth Weight”:ti,ab OR [mh “Glucose Intolerance”] OR (Glucose NEAR/1 Intoleran*) OR (glucose NEAR/1 toleran*) OR [mh “Insulin Resistance”] OR (Insulin NEAR/1 Resistan*:ti,ab) OR Dysglycemia:ti,ab OR “fasting blood glucose” OR [mh “Hemoglobin A, Glycosylated”] OR [mh ^“Proteinuria”] OR Albuminuria OR [mh “Blood Pressure”] OR “blood pressure”:ti,ab)
  OR
  [mh “Diabetes, Gestational”] OR (gestation* NEAR/1 diabet*) OR [mh “Pre-Eclampsia”] OR “Pre-Eclampsia”:ti,ab OR preeclampsia:ti,ab OR [mh “Hypertension, Pregnancy-Induced”] OR Eclampsia OR [mh “Gestational Age”] OR [mh “Morning Sickness”] OR (Hyperemesis NEAR/3 Gravidarum) OR “Gestational Age”:ti,ab OR [mh “Birth Weight”] OR “Birth Weight”:ti,ab OR ((neonatal OR newborn) NEAR/3 weight) OR [mh “Obstetric Labor, Premature”] OR ((prematur*:ti,ab OR preterm:ti,ab) AND (baby:ti,ab OR infant*:ti,ab OR birth OR labor OR membrane* OR babies)) OR [mh “Fetal Growth Retardation”] OR IUGR:ti,ab OR “Intrauterine growth restriction” OR [mh ^“Fetal Development”] OR [mh “Fetal Weight”] OR [mh “Umbilical Arteries”] OR [mh “Uterine Artery”]
  AND (diet:ti OR diets:ti OR dietary:ti OR meal*:ti,ab OR “prudent diet” OR nordiet:ti,ab OR omniheart OR “Optimal Macronutrient Intake Trial to Prevent Heart Disease” OR ((Index OR score OR indices OR scoring) NEAR/3 (dietary OR diet OR food OR eating)) OR “adequacy index” OR kidmed OR MedDietScore)
  OR ‘dietary approaches to stop hypertension’:ti,ab OR omniheart:ti,ab OR omni:ti OR ‘plant based diet’ OR ((eating OR food OR diet* OR calori*) NEAR/3 (pattern? OR habit? OR profile? OR recommendation? OR guideline? OR style* OR choice* OR intake OR quality))
  OR
  food*:ti,ab OR Eating:ti,ab OR (dietary NEAR/1 change*):ti,ab OR DASH:ti,ab OR vegan*:ab,ti OR vegetarian*:ab,ti OR omni:ti OR ((ethni* OR racial OR religio* OR asia* OR western OR Okinawa* OR adventist* OR ‘mediterranean’ OR Nordic* OR indo-mediterranean) NEAR/3 (calori* OR diet* OR food OR eating))
  OR [mh “Diet, Mediterranean”] OR [mh “Diet, Vegetarian”] OR ([mh “Ethnic Groups”] AND ([mh diet] OR diet*:ti,ab OR [mh ^food] OR eat:ti,ab OR eating:ti,ab OR [mh “Eating”] OR [mh “food habits”])) OR
  ([mh “Guideline Adherence”] AND (diet OR food OR eating OR eat OR dietary)) OR ((adhere* OR adhering) AND (diet OR dietary OR food OR eat OR eating) AND (guideline* OR guidance OR recommendation*)) OR
  [mh meals] OR [mh ^diet] OR diet*:ti,ab OR [mh ^“Food”] OR [mh “Eating”] OR [mh “food habits”]

CINAHL (Plus) with Full Text, EBSCO (Cumulative Index to Nursing and Allied Health Literature)

• Date(s) searched: January 1980 to January 2017
• Search Terms:
  (MH “Food and Beverages”) OR (MH “Food”) OR (MH “Diet”) OR (MH “Eating”) OR (MH “Eating Behavior”) OR (MH “Meals+”) OR (MH “Food Preferences”) OR (MH “Food Habits”) OR (MH “Mediterranean Diet”) OR (MH “Diet, Western”) OR (MH “DASH Diet”) OR (MH “Vegetarianism”)
  OR meal* OR “prudent diet” OR nordiet OR omniheart OR “Optimal Macronutrient Intake Trial to Prevent Heart Disease” OR ((Index OR score OR indices OR scoring) N3 (dietary OR diet OR food OR eating)) OR “adequacy index” OR kidmed OR MedDietScore
  OR “dietary approaches to stop hypertension” OR “plant based diet” OR ((eating OR food* OR diet* OR calori*) N3 (pattern? OR habit? OR profile? OR recommendation? OR guideline? OR style* OR choice* OR intake OR quality))
  OR
  (dietary NEAR/1 change*) OR vegan* OR vegetarian* OR ((ethni* OR racial OR religio* OR asia* OR western OR Okinawa* OR adventist* OR ‘mediterranean’ OR Nordic* OR indo-mediterranean OR omni*) N3 (calori* OR diet* OR food OR eating))
  OR (MH “Ethnic Groups+”) AND ((mh diet) OR diet* OR (MH food) OR eat OR eating OR (MH “Eating”) OR MH “food habits”)) OR
  ((adhere* OR adhering) N3 (diet OR dietary OR food OR eat OR eating)) AND (guideline* OR guidance OR recommendation*))
  (MH “Maternal Nutritional Physiology+”) OR (MH “Maternal Exposure”) OR (MH “Pregnancy+”) OR (MH “Pregnancy in Adolescence+”) OR (MH “Maternal Age 14 and Under”) OR (MH “Pregnancy Outcomes”) OR (MH “Mothers+”) OR (MH “Prenatal Nutritional Physiology”) OR (MH “Infant, Newborn+”) OR (MH “Postnatal Period+”) OR (MH “Periconceptual Period”)
  AND
  (MH “Hypertension+”) OR (MH “Nausea and Vomiting+”) OR (MH “Vomiting+”) OR (MH “Birth Weight”) OR (MH “Glucose Tolerance Test”) OR (MH “Prediabetic State”) OR (MH “Glucose Intolerance”) OR (MH “Insulin Resistance+”) OR (MH “Blood Pressure+”) OR (MH “Proteinuria+”) OR (MH “Hemoglobin A, Glycosylated”)
  OR
  (MH “Diabetes Mellitus, Gestational”) OR (MH “Gestational Age”) OR (MH “Pre-Eclampsia+”) OR (MH “Eclampsia+”) OR (MH “Fetal Growth Retardation”) OR (MH “Fetal Weight”) OR (MH “Umbilical Arteries”) OR (MH “Delivery, Obstetric+”)
  Limiters - Published Date: 19800101-; Peer Reviewed; English Language;
  Exclude MEDLINE records; Pregnancy
  Narrow by SubjectMajor: - energy intake
  Narrow by SubjectMajor: - vegetarianism
  Narrow by SubjectMajor: - women’s health
  Narrow by SubjectMajor: - pregnancy outcomes
  Narrow by SubjectMajor: - pregnancy complications
  Narrow by SubjectMajor: - food habits
  Narrow by SubjectMajor: - diabetes mellitus, gestational

Figure 2

Flow chart of literature search and screening results.

This flow chart illustrates the literature search and screening results for articles examining the relationship between dietary patterns before and during pregnancy and gestational age- and sex-specific birth weight. The results of the electronic database searches were screened independently by two NESR analysts in a step-wise manner by reviewing titles, abstracts, and full text articles to determine which articles met the criteria for inclusion. A manual search was done to ascertain articles not identified through the electronic database search. The systematic review on the relationship between dietary patterns before and during pregnancy and gestational age- and sex-specific birth weight included 21 articles. The literature search was conducted for multiple systematic reviews that addressed the relationships between dietary patterns before and during pregnancy on gestational age- and sex-specific birth weight, hypertensive disorders of pregnancy, gestational diabetes mellitus, and gestational age at birth; the systematic reviews on hypertensive disorders of pregnancy, gestational diabetes mellitus, and gestational age at birth are reported elsewhere.

List of excluded articles

The table below lists the excluded articles with at least one reason for exclusion, but may not reflect all possible reasons.

Table 8

List of excluded articles.