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Different types of dietary advice for women with gestational diabetes mellitus

  1. Shanshan Han1,*,
  2. Caroline A Crowther2,
  3. Philippa Middleton1,
  4. Emer Heatley2

Editorial Group: Cochrane Pregnancy and Childbirth Group

Published Online: 28 MAR 2013

Assessed as up-to-date: 16 OCT 2012

DOI: 10.1002/14651858.CD009275.pub2


How to Cite

Han S, Crowther CA, Middleton P, Heatley E. Different types of dietary advice for women with gestational diabetes mellitus. Cochrane Database of Systematic Reviews 2013, Issue 3. Art. No.: CD009275. DOI: 10.1002/14651858.CD009275.pub2.

Author Information

  1. 1

    The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robinson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia

  2. 2

    The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia

*Shanshan Han, ARCH: Australian Research Centre for Health of Women and Babies, The Robinson Institute, Discipline of Obstetrics and Gynaecology, The University of Adelaide, Women's and Children's Hospital, 72 King William Road, Adelaide, South Australia, 5006, Australia. shan.han@adelaide.edu.au.

Publication History

  1. Publication Status: New
  2. Published Online: 28 MAR 2013

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Background

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
 

Description of the condition

 

Introduction and definition of gestational diabetes mellitus

Although there is no universally accepted diagnostic criteria (Coustan 2010), gestational diabetes mellitus (GDM) can be defined as 'glucose intolerance or hyperglycaemia (high blood glucose concentration) with onset or first recognition during pregnancy' (ACOG 2001; Hoffman 1998; Metzger 1998; NICE 2008). It is one of the most common pregnancy complications, with about 1% to 14% of pregnancies affected every year around the world (Mulla 2010). The prevalence of GDM continues to increase in line with the increasing prevalence of maternal obesity and type 2 diabetes mellitus (T2DM) (Bottalico 2007; Dabelea 2005; Mulla 2010).

 

Pathophysiology of gestational diabetes mellitus

In pregnancy, insulin resistance increases with advancing gestation (Clapp 2006). Hormones secreted from the placenta, including tumour necrosis factor-alpha (TNF-α), placental lactogen, placental growth hormone, cortisol and progesterone are thought to be the likely triggers of these physiological changes (Clapp 2006; Devlieger 2008). Increasing insulin resistance in pregnancy, especially during the third trimester, helps to meet the increased nutrient requirement for fetal development and promotes fetal growth by increasing maternal glucose supply (Devlieger 2008). GDM results when the insulin secretion is inadequate for the degree of insulin resistance (Clapp 2006).

 

Risk factors for gestational diabetes mellitus

A range of factors have been found to increase the risk of developing GDM (Morisset 2010). Advancing maternal age and maternal overweight (body mass index (BMI) equal to or greater than 25 kg/m²) or obesity (equal to or greater than 30 kg/m²) are the two most common risk factors (Morisset 2010). It is important to note that the prevalence of overweight or obesity is increasing worldwide, which is associated with increasing prevalence of GDM (Petry 2010).

High parity, non-white race/ethnicity, family history of diabetes mellitus, maternal high or low birthweight and polycystic ovarian syndrome are the known non-modifiable risk factors for GDM (Cypryk 2008; Petry 2010; Solomon 1997). The modifiable risk factors include history of having a macrosomic (birthweight 4000 grams or more) infant and history of GDM (Petry 2010). Other modifiable risk factors are lifestyle related, which include physical inactivity (Chasan-Taber 2008), having a low-fibre and high-glycaemic load diet (Zhang 2006), and excessive weight gain during pregnancy, especially for those who are overweight or obese (Hedderson 2010).

 

Health risks for gestational diabetes mellitus

Negative impacts of GDM on the health of women and their babies have been consistently reported (Crowther 2005; Landon 2009; Metzger 2008; Reece 2009).

Short-term risks for women with GDM include developing pre-eclampsia and increased need for induction of labour (Anderberg 2010; Crowther 2005; Dodd 2007; Ju 2008; Landon 2009; Metzger 2008) and caesarean section (Dodd 2007; Landon 2009; Metzger 2008). The incidence of cephalopelvic disproportion, uterine rupture, shoulder dystocia and perineal lacerations is increased in women with GDM due to the increased likelihood of having a large-for-gestational age (LGA) or macrosomic baby (Jastrow 2010). In the longer term, women who have a history of GDM have at least a seven-fold risk of developing T2DM in the future when compared with women who have had a normoglycaemic pregnancy (Bellamy 2009), and up to 50% of women with GDM will develop T2DM within 10 years of the index pregnancy (Kim 2002).

One of the most significant health risks for babies born to mothers with GDM is being LGA or macrosomic (Crowther 2005; Landon 2009; Metzger 2008; Reece 2009). Being a LGA fetus or macrosomic infant is a surrogate for many of the complications associated with GDM (Esakoff 2009). LGA or macrosomic infants are at increased risk of birth injury, such as shoulder dystocia, perinatal asphyxia, bone fractures and nerve palsies (Henriksen 2008; Langer 2005; Metzger 2008). Babies LGA at birth are more likely to be heavier at every age (adjusted for height) and to develop early overweight or obesity and T2DM (Pettitt 1993; Whincup 2008). In addition, babies born LGA are at increased risk of developing metabolic syndrome (a cluster of risk factors defined by the occurrence of three of the following: obesity, hypertension, hypertriglyceridaemia and low HDL cholesterol concentration) in childhood, adolescence or adulthood (Baker 1994; Guerrero-Romero 2010; Harder 2009). Development of the metabolic syndrome during childhood predicts adult T2DM at 25 to 30 years of age (Morrison 2008). These health problems repeat across generations (Mulla 2010; Petitt 1985).

Besides the risks relating to LGA or macrosomia, other perinatal risk factors for babies born to women with GDM include respiratory distress syndrome, hypoglycaemia, hyperbilirubinaemia (increased levels of bilirubin in the blood), cardiomyopathy (the deterioration of the function of the heart muscle layer), hypocalcaemia, hypomagnesaemia, polycythaemia (increase in the number of circulating red blood cells, hyperviscosity and admission to the neonatal nursery (Metzger 2008; Reece 2009; Soler 1978). Other longer-term risks for these babies include developing type 1 diabetes mellitus (Harder 2009) and having impaired neurobehavioural development (Rizzo 1997).

 

Management of gestational diabetes mellitus

The primary aims of management for GDM are to optimise glycaemic control and improve pregnancy outcomes (Alwan 2009; Kim 2010a). Providing dietary and lifestyle advice is usually recommended as the primary therapeutic strategy for women with GDM (ACOG 2001; Hoffman 1998; NICE 2008). If diet and lifestyle management alone are not enough to achieve good maternal glycaemic control, insulin therapy or oral hypoglycaemics such as glyburide and metformin may be indicated (ACOG 2001; Hoffman 1998; NICE 2008; Silva 2010; Simmons 2004). As a part of GDM management, maternal glucose monitoring and ultrasonography are advised to monitor treatment and to guide care for birth (ACOG 2001; Hoffman 1998; NICE 2008).

 

Description of the intervention

 

Dietary advice for managing gestational diabetes mellitus

Although it is widely accepted that diet therapy is the primary strategy for managing GDM, there is very little evidence on specific nutritional approaches such as total energy intake and nutrient distribution in GDM management (Cheung 2009; Kim 2010a; Metzger 2007). Elevated blood glucose concentrations, especially postprandial glucose elevations are associated with adverse pregnancy outcomes in GDM (De Veciana 1995). Dietary advice provided for women with GDM should ensure adequate nutrients for normal fetal growth and maternal health, but not induce weight loss or excessive weight gain during pregnancy; it also aims to assist optimal glycaemic control (ACOG 2001; Hoffman 1998; Metzger 2007; NICE 2008).

 

Total energy intake and weight gain during pregnancy

Given the high prevalence of overweight and obesity in women with GDM, dietary advice for appropriate pregnancy weight gain is always included as a part of nutritional management of GDM (Kim 2010a). It is estimated that the prevalence of GDM for women with a BMI within the range of 35 kg/m² to 64.9 kg/m² (extremely obese) is 15.4%, and decreases to 5.5%, 4.8% and 2.3% for women having a BMI within the ranges of 30 kg/m² to 34.9 kg/m² (obese), 25 kg/m² to 29.9 kg/m² (overweight) and 18.5 kg/m² to 24.9 kg/m² (normal weight), respectively (Kim 2010b). Small reductions in weight improve glycaemic control (ACOG 2005). However, severe calorie restriction and pregnancy weight loss are discouraged due to the risks of ketonaemia and small-for-gestational-age (SGA) infants (ACOG 2001; Hoffman 1998; NICE 2008).

In 2009, the Institute of Medicine released its new guidelines for weight gain during pregnancy, which are stratified by pre-pregnancy BMI, i.e. women with a pre-pregnancy BMI between 25 and 29.9 kg/m² should aim for 6.8 to 11.4 kg weight gain and those with pre-pregnancy BMI of 30 kg/m² or more should aim for 5 to 9 kg weight gain (IOM 2009). However, the degree of energy restriction for pre-pregnancy overweight and obese women to achieve these weight gain goals is unknown (Kim 2010a).

The optional proportion of the total energy derived from each of the macronutrients in GDM management is still controversial (Kim 2010a). In Australia, the principles of dietary management of diabetes are also recommended for GDM management (i.e. carbohydrate contributes up to 50% total energy intake, fat accounts for less than 30% total energy and protein accounts for 10% to 20% total energy intake) (Colagiuri 2009; Hoffman 1998).

 

Carbohydrate and glycaemic index (GI)

Carbohydrate is an important source of energy, vitamins, minerals and fibre and is the main nutrient that affects blood glucose values (Reader 2007). Its impact on blood glucose concentrations can be affected by the total amount and type of carbohydrate (Reader 2007).

Evidence on the proportion in carbohydrate in diet therapy for GDM management is also controversial (Kim 2010a). Both low-carbohydrate diets (i.e. carbohydrate accounts for less than 42% total energy intake) and high-carbohydrate diets (i.e. carbohydrate accounts for 55% total energy intake) have been found beneficial in improving pregnancy outcomes in non-randomised studies (Clapp 2002; Major 1998; Romon 2001). These inconsistent findings triggered the hypothesis that in addition to the total amount of carbohydrate, the type of carbohydrate may also be an important factor that affects postprandial blood glucose levels (Kim 2010a). GI is a ranking of the effects of carbohydrates on blood glucose concentrations (Jenkins 1981). Foods with a low GI (less than 55) produce a lower postprandial glucose elevation and area under the curve; foods with a high GI (more than 70) produce a rapid increase in postprandial blood glucose concentrations (Jenkins 1981). In non-pregnant people with diabetes, evidence shows that using low-GI diets helps lower HbA1C and gives better glycaemic control (Thomas 2010). During pregnancy, the concept of GI is still valid (Cheung 2009). Some evidence has suggested benefits of using low-GI diets in GDM management (Moses 2009).

 

Fat and other nutrients

Polyunsaturated fatty acids may be protective against impaired glucose tolerance, while saturated fatty acids can increase glucose and insulin concentrations in women with GDM (Ilic 1999). However, the specific amount and sources of fat that are beneficial for GDM management are not clear (Kim 2010a). Therefore, recommendations on the fat intake for women with GDM have not yet been promulgated (ACOG 2001; Hoffman 1998; Metzger 2007; NICE 2008).

Recommendations on the intake of other nutrients for women with GDM are usually based on the general recommendations for diabetes mellitus (Cheung 2009).

 

Why it is important to do this review

GDM affects a significant proportion of pregnant women each year and the prevalence is increasing worldwide (Bottalico 2007; Dabelea 2005; Mulla 2010). It is associated with a range of adverse pregnancy outcomes and these adverse health outcomes can repeat across generations (Metzger 2008; Mulla 2010). Dietary counselling is the primary therapeutic strategy in GDM management (Hoffman 1998; Metzger 2007; NICE 2008). However, there is much inconsistency and uncertainty around the best dietary therapies for women with GDM (Dornhorst 2002; Kim 2010a). This review will provide reliable evidence on the effects of different dietary advice for women with GDM for improving pregnancy outcomes. One Cochrane review has addressed the effects of different diets, including low-GI diet and high-fibre diet, in pregnancy for preventing GDM (Tieu 2008). Another Cochrane review has addressed the effects of different treatments for women with GDM (Alwan 2009). In Alwan 2009, diet and exercise advice for women with GDM was compared with pharmacological treatment, additional diet and exercise advice was compared with standard antenatal care and standard diet advice was compared with individualised diet advice for women with GDM, but they did not compare different types of dietary advice, as is done in this review.

 

Objectives

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms

To assess the effects of different types of dietary advice given to women with gestational diabetes mellitus (GDM) in pregnancy outcomes.

 

Methods

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
 

Criteria for considering studies for this review

 

Types of studies

All published randomised controlled trials (RCTs) and cluster-randomised trials assessing the effects of different dietary advice for gestational diabetes mellitus (GDM) management. We intended to include published abstracts for RCTs and cluster-randomised trials if relevant outcome data were available. We planned to exclude quasi-RCTs and cross-over trials.

We planned to include trials assessing the effects of lifestyle interventions (e.g. include both providing dietary advice and physical exercise advice) in GDM management if data for the effects of dietary advice could be extracted separately.

 

Types of participants

Pregnant women with GDM.

Diagnostic criteria for GDM based on oral glucose tolerance test (OGTT) results were defined variously by individual trials according to the policies of local health authorities and professional organisations.

We planned to include trials recruiting pregnant women with normal glycaemia, GDM or pre-existing diabetes mellitus if subgroup data for women with GDM could be extracted separately.

Women were eligible regardless of gestation, age, parity or plurality.

 

Types of interventions

We planned to include any type of dietary advice for women with GDM in the review.

We planned to compare different types of dietary advice with each other. We intended to compare two or more forms of the same type of dietary advice against each other, i.e. standard dietary advice compared with individualised dietary advice, individual dietary education sessions compared with group dietary education sessions. We intended to compare different intensities of dietary intervention with each other, i.e. single dietary counselling session compared with multiple dietary counselling sessions.

 

Types of outcome measures

 

Primary outcomes

 
Fetal/neonatal outcomes

  1. Fetal, neonatal or perinatal mortality.
  2. Large-for-gestational age (LGA) (birthweight greater than or equal to 90th percentile for gestational age).
  3. Macrosomia (birthweight greater than 4000 g or greater than 4500 g as defined by authors).

 
Maternal outcomes

  1. Mode of birth (normal vaginal birth, operative vaginal birth, caesarean section).

 

Secondary outcomes

 
Fetal/neonatal outcomes

  1. Neonatal hypoglycaemia requiring treatment (variously defined by authors of individual trials).
  2. Gestational age at birth.
  3. Preterm birth (less than 37 weeks' gestation).
  4. Birthweight.
  5. Small-for-gestational age (SGA).
  6. Shoulder dystocia.
  7. Bone fracture.
  8. Nerve palsy.
  9. Respiratory distress syndrome.
  10. Use of assisted ventilation.
  11. Hyperbilirubinaemia requiring treatment.
  12. Apgar scores (less than seven at five minutes).
  13. Apgar scores (less than four at five minutes).
  14. Ponderal index*.
  15. Skinfold thickness measurements.

* A measure of leanness of a person calculated as a relationship between mass and height (can provide valid results even for very short and very tall persons).

 
Childhood outcomes

  1. Weight.
  2. Height.
  3. Body mass index (BMI).
  4. Fat mass/fat-free mass.
  5. Skinfold thickness measurements.
  6. Blood pressure.
  7. Impaired glucose tolerance (as defined by author(s)).
  8. Type 1 diabetes.
  9. Type 2  diabetes.
  10. Insulin sensitivity (as defined by author(s)).
  11. Dyslipidaemia or metabolic syndrome.
  12. Childhood neurodisability.
  13. Educational achievement.

 
Adulthood

  1. Weight.
  2. Height.
  3. BMI.
  4. Fat mass/fat-free mass.
  5. Skinfold thickness measurements.
  6. Blood pressure.
  7. Impaired glucose tolerance (as defined by author(s)).
  8. Dyslipidaemia or metabolic syndrome.
  9. Development of type 1 diabetes.
  10. Development of type 2 diabetes.
  11. Insulin sensitivity (as defined by author(s)).
  12. Educational achievement.

 
Maternal outcomes
 
Perinatal

  1. Pre-eclampsia.
  2. Insulin or oral hypoglycaemic agent required for hyperglycaemia.
  3. Weight gain during pregnancy (according to IOM 2009 pregnancy weight gain guidelines).
  4. Induction of labour.
  5. Augmentation of labour.
  6. Perineal trauma.
  7. Postpartum haemorrhage.
  8. Postpartum infection.
  9. Adherence to dietary advice.
  10. Women’s sense of well-being and quality of life (as defined by author(s)).
  11. Women’s view of dietary intervention.

 
Long term

  1. Postnatal weight retention.
  2. BMI.
  3. Gestational diabetes in subsequent pregnancy.
  4. Development of type 2 diabetes mellitus.
  5. Development of type 1 diabetes mellitus.
  6. Impaired glucose tolerance (as defined by author(s)).
  7. Insulin sensitivity (as defined by author(s)).

 
 Health services cost

  1. Number of hospital visits/antenatal visits for mother.
  2. Dietitian visits.
  3. Medical physician visits.
  4. Costs to families in relation to the dietary advice provided.
  5. Length of postnatal stay (mother).
  6. Cost of maternal care.
  7. Admission to neonatal nursery/neonatal intensive care unit.
  8. Length of postnatal stay (baby).
  9. Cost of offspring care.

 

Search methods for identification of studies

 

Electronic searches

We contacted the Trials Search Co-ordinator to search the Cochrane Pregnancy and Childbirth Group’s Trials Register (17 May 2012). 

The Cochrane Pregnancy and Childbirth Group’s Trials Register is maintained by the Trials Search Co-ordinator and contains trials identified from:

  1. monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);
  2. weekly searches of MEDLINE;
  3. weekly searches of EMBASE;
  4. handsearches of 30 journals and the proceedings of major conferences;
  5. weekly current awareness alerts for a further 44 journals plus monthly BioMed Central email alerts.

Details of the search strategies for CENTRAL, MEDLINE and EMBASE, the list of handsearched journals and conference proceedings, and the list of journals reviewed via the current awareness service can be found in the ‘Specialized Register’ section within the editorial information about the Cochrane Pregnancy and Childbirth Group.

Trials identified through the searching activities described above are each assigned to a review topic (or topics). The Trials Search Co-ordinator searches the register for each review using the topic list rather than keywords. 

In addition, we searched the Women and Babies Health and Wellbeing: Action through Trials (WOMBAT) Perinatal Trials Registry (last searched 17 April 2012) using the search terms detailed in Appendix 1.

We did not apply any language restrictions.

 

Data collection and analysis

 

Selection of studies

Two review authors independently assessed for inclusion all the potential studies we identified as a result of the search strategy. Disagreements were resolved through discussion and a third review author was consulted. We resolved disagreements through discussion and consulted a third review author as necessary.

 

Data extraction and management

We designed a form to extract data. For eligible studies, at least two review authors extracted the data using the agreed form. We resolved discrepancies through discussion. We entered data into Review Manager software (RevMan 2011) and checked for accuracy.

When information regarding any of the above was unclear, we attempted to contact authors of the original reports to provide further details.

 

Assessment of risk of bias in included studies

Two review authors independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We resolved any disagreement by discussion or by involving a third assessor.

 

(1) Sequence generation (checking for possible selection bias)

We described for each included study the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.

We assessed the method as:

  • low risk of bias (any truly random process, e.g. random number table; computer random number generator);
  • high risk of bias (any non-random process, e.g. odd or even date of birth; hospital or clinic record number);
  • unclear risk of bias.   

 

 (2) Allocation concealment (checking for possible selection bias)

We described for each included study the method used to conceal the allocation sequence and determine whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment.

We assessed the methods as:

  • low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);
  • high risk of bias (open random allocation; unsealed or non-opaque envelopes, alternation; date of birth);
  • unclear risk of bias.   

 

(3.1) Blinding of participants and personnel (checking for possible performance bias)

We described for each included study the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We considered that studies were at low risk of bias if they were blinded, or if we judged that the lack of blinding would be unlikely to affect results. We assessed blinding separately for different outcomes or classes of outcomes.

We assessed the methods as:

  • low, high or unclear risk of bias for participants;
  • low, high or unclear risk of bias for personnel.

 

(3.2) Blinding of outcome assessment (checking for possible detection bias)

We described for each included study the methods used, if any, to blind outcome assessors from knowledge of which intervention a participant received. We assessed blinding separately for different outcomes or classes of outcomes.

We assessed methods used to blind outcome assessment as:

  • low, high or unclear risk of bias.

 

(4) Incomplete outcome data (checking for possible attrition bias through withdrawals, dropouts, protocol deviations)

We described for each included study, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We stated whether attrition and exclusions were reported, the numbers included in the analysis at each stage (compared with the total randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes. Where sufficient information was reported, or could be supplied by the trial authors, we re-included missing data in the analyses which we undertook.

We assessed methods as:

  • low risk of bias (e.g. no missing outcome data; missing outcome data balanced across groups);
  • high risk of bias (e.g. numbers or reasons for missing data imbalanced across groups; ‘as treated’ analysis done with substantial departure of intervention received from that assigned at randomisation);
  • unclear risk of bias.

 

(5) Selective bias (checking for reporting bias)

We described for each included study how we investigated the possibility of selective outcome reporting bias and what we found.

We assessed the methods as:

  • low risk of bias (where it was clear that all of the study’s pre-specified outcomes and all expected outcomes of interest to the review had been reported);
  • high risk of bias (where not all the study’s pre-specified outcomes had been reported; one or more reported primary outcomes were not pre-specified; outcomes of interest were reported incompletely and so could not be used; study fails to include results of a key outcome that would have been expected to have been reported);
  • unclear risk of bias.

 

(6) Other sources of bias

We described for each included study any important concerns we had about other possible sources of bias.

We assessed whether each study was free of other problems that could put it at risk of bias:

  • low risk of other bias;
  • high risk of other bias;
  • unclear whether there is risk of other bias.

 

(7) Overall risk of bias

We made explicit judgements about whether studies were at high risk of bias, according to the criteria given in the Handbook (Higgins 2011). With reference to (1) to (6) above, we assessed the likely magnitude and direction of the bias and whether we considered it was likely to impact on the findings. We planned to explore the impact of the level of bias through undertaking sensitivity analyses - see Sensitivity analysis

 

Measures of treatment effect

 

Dichotomous data

For dichotomous data, we presented results as risk ratio with 95% confidence intervals. 

 

Continuous data

For continuous data, we used the mean difference and 95% confidence intervals if outcomes were measured in the same way between trials. We used the standardised mean difference to combine trials that measured the same outcome, but used different methods.  

 

Unit of analysis issues

 

Cluster-randomised trials

We did not identify any cluster-randomised trials for inclusion. However, if we identify cluster-randomised trial in future updates of this review, we will include them in the analyses along with individually-randomised trials. We will adjust their sample sizes using the methods described in the Handbook using an estimate of the intracluster correlation co-efficient (ICC) derived from the trial (if possible), from a similar trial or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. If we identify both cluster-randomised trials and individually-randomised trials, we plan to synthesise the relevant information. We will consider it reasonable to combine the results from both if there is little heterogeneity between the study designs and the interaction between the effect of intervention and the choice of randomisation unit is considered to be unlikely.

We will also acknowledge heterogeneity in the randomisation unit and perform a sensitivity analysis to investigate the effects of the randomisation unit.

 

Dealing with missing data

For included studies, we noted levels of attrition. If we had identified studies with high levels of missing data, we would have used sensitivity analysis to explored the impact of including studies with high levels of missing data in the overall assessment of treatment effect.

For all outcomes, we carried out analyses, as far as possible, on an intention-to-treat basis, i.e. we attempted to include all participants randomised to each group in the analyses, and all participants were analysed in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial was the number randomised minus any participants whose outcomes were known to be missing.

 

Assessment of heterogeneity

We assessed statistical heterogeneity in each meta-analysis using the T², I² and Chi² statistics. We regarded heterogeneity as substantial if the I² was greater than 30% and either the T² was greater than zero, or there was a low P value (less than 0.10) in the Chi² test for heterogeneity. 

 

Assessment of reporting biases

In future updates of this review, if there are 10 or more studies in the meta-analysis we will investigate reporting biases (such as publication bias) using funnel plots. We will assess funnel plot asymmetry visually. If we detect asymmetry, we will perform exploratory analyses to investigate it.

 

Data synthesis

We carried out statistical analysis using the Review Manager software (RevMan 2011). We used fixed-effect meta-analysis for combining data where it was reasonable to assume that studies were estimating the same underlying treatment effect: i.e. where trials were examining the same intervention, and the trials’ populations and methods were judged sufficiently similar. If there was clinical heterogeneity sufficient to expect that the underlying treatment effects differed between trials, or if substantial statistical heterogeneity was detected, we planned to use random-effects meta-analysis to produce an overall summary if an average treatment effect across trials was considered clinically meaningful. The random-effects summary would have been treated as the average range of possible treatment effects and we would have discussed the clinical implications of treatment effects differing between trials. If the average treatment effect was not clinically meaningful, we did not combine trials.

When we used random-effects analyses, we have presented the results as the average treatment effect with its 95% confidence interval, and the estimates of  T² and I².

 

Subgroup analysis and investigation of heterogeneity

If we had identified substantial heterogeneity, we would have investigated it using subgroup analyses and sensitivity analyses. We planned to consider whether an overall summary was meaningful, and if it was, use random-effects analysis to produce it.

Maternal characteristics, ways of delivering dietary advice and intensities of the dietary intervention might have significant effects on pregnancy outcomes. We planned to carry out the following subgroup analyses, however, there were insufficient data to do so.

1. Maternal characteristics

  • Maternal age:
    • greater than or equal to 35 years of age will be compared with below 35 years of age.
  • Ethnicity:

    • ethnic groups of Hispanic, African-American, Asian-American, native American, African, Asian and Pacific islanders and indigenous Australian compared with white ethnicity.
  • Parity:
    • parity of 0 compared with 1-2;
    • parity of 0 compared with 3 or more.

  • Maternal education level:

    • less than 12 years compared with 12 years or more.

  • Maternal body mass index (BMI) at or before trial entry:

    • BMI ranges of 18.5 to 24.9 kg/m² compared with less than 18.5 kg/m²;
    • BMI ranges of 18.5 to 24.9 kg/m² compared with 25 to 29.9 kg/m²;
    • BMI ranges of 18.5 to 24.9 kg/m² compared with 30 kg/m² to 39.9 kg/m²;
    • BMI ranges of 18.5 to 24.9 kg/m² compared with 40 kg/m² or more.

2. Ways of delivering dietary advice

  • Standard dietary advice compared with individualised dietary advice.
  • Individual dietary counselling session compared with group dietary education session.
  • Face-to-face dietary intervention compared with non-face-to-face dietary intervention (e.g. phone counselling, information package, etc.).

3. Intensities of dietary intervention

  • Single dietary counselling session compared with multiple dietary counselling sessions.

We planned to use primary outcomes in subgroup analyses.

We planned to assess differences between subgroups by interaction tests where possible.

 

Sensitivity analysis

We did not conduct any sensitivity analysis in this review. In future updates, we plan to carry out sensitivity analysis to explore the effects of trial quality assessed by allocation concealment and other risk of bias components, by omitting studies rated as inadequate for these components. Sensitivity analysis will be restricted to the primary outcomes.

 

Results

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
 

Description of studies

See: Characteristics of included studies; Characteristics of excluded studies.

 

Results of the search

We identified a total of 16 trials. Fifteen trials were identified through the search conducted by the Cochrane Pregnancy and Childbirth Group and one additional trial was identified through the search of the WOMBAT (Women and Babies health and wellbeing: Action through Trials) perinatal trial registry (WOMBAT 2011) by review authors. Following the application of eligibility criteria, we included nine trials (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Louie 2011; Magee 1990; Moses 2009; Rae 2000; Reece 1995) and excluded seven trials (Gillen 2004; Gillmer 1986; Ilic 1999; Knopp 1991; Ma 2011; Nolan 1984; Reader 2006). See Characteristics of included studies and Characteristics of excluded studies.

 

Included studies

Of the nine included trials, three were conducted in Australia (Louie 2011; Moses 2009; Rae 2000), two were conducted in the United States (Magee 1990; Reece 1995); one trial each was from Canada (Grant 2011), Denmark (Lauszus 2001), Mexico (Balas-Nakash 2010) and Poland (Cypryk 2007).

 

Participants

A total of 437 women and 444 babies were recruited to the nine trials, with outcome data available for 429 women and 436 babies.

 
Maternal trial entry weight and BMI

In Louie 2011, 68% of 92 study participants had a pre-pregnancy body mass index (BMI) of less than 25 kg/m²; the pre-pregnancy mean BMI [SD] was 23.9 [4.4] kg/m² for women in the low-GI group and 24.1 [5.7] kg/m² for those in the high-fibre moderate glycaemic index (GI) diet group. In Moses 2009, the mean trial entry BMI [SD] was 32.0 [1.2] kg/m² for the 31 women in the low-GI diet group and 32.8 [1.4] kg/m² for the 32 women in the high-GI diet group. Magee 1990 recruited 12 women who were obese at trial entry; obesity in this trial was defined as greater than 120% of their ideal body weight (according to the corrected 1959 Metropolitan Life Insurance tables) (Magee 1990). Rae 2000 included 117 women whose respective weights were greater than 110% of their ideal weight (100% ideal body weight was defined as BMI of 25 kg/m²).

Three trials did not report trial entry weight for the subgroup of women with GDM in their studies (Balas-Nakash 2010; Grant 2011; Reece 1995). Women with both GDM and T2DM were included in Balas-Nakash 2010; women with GDM and insulin-dependent diabetes were included in Reece 1995 and women with GDM and impaired glucose tolerance without meeting GDM diagnostic criteria were included in Grant 2011.

In Lauszus 2001, women were recruited after their diagnosis of GDM and were then instructed to follow a high-carbohydrate diet until the 34th week of pregnancy where women were randomised into two groups. No information was reported on women's weight and BMI at recruitment, but a baseline weight was reported for women at randomisation (33 weeks of gestation) (Lauszus 2001). The mean BMI [SD] at 33 weeks' gestation were 35 [2.4] kg/m² and 32.2 [1.5] kg/m² for women in the high-monounsaturated fatty acids diet group and the high-carbohydrate diet group, respectively (Lauszus 2001),

No data were reported for women's BMI at trial entry in Cypryk 2007.

 
Diagnosis of GDM

Different GDM diagnostic criteria were used in the nine included trials. The Australian Diabetes in Pregnancy Society (ADIPS) criteria were used in two trials (Louie 2011; Moses 2009). One trial each used the American Diabetes Association (ADA) criteria (Magee 1990), Canadian Diabetes Association (CDA) criteria (Grant 2011) and World Health Organization (WHO) criteria (Cypryk 2007). Lauszus 2001 used three-hour 75 grams oral glucose tolerance test (OGTT) for GDM diagnosis, and GDM was defined as two or more plasma glucose samples above three standard deviations of the mean. Rae 2000 used criteria as fasting blood glucose level (BGL) greater than 5.4 mmol/L and/or two-hour BGL greater than 7.9 mmol/L in 75 grams OGTT.

There is no information on GDM diagnostic criteria in Balas-Nakash 2010 and Reece 1995.

See Characteristics of included studies for details.

 

Intervention and comparison

Four trials assessed the effect of a low-GI food or diet in GDM management (Balas-Nakash 2010; Grant 2011; Louie 2011; Moses 2009). In Balas-Nakash 2010, women in the low-GI diet group were advised to select low-to-moderate GI carbohydrate food, while women in the control group were allowed any type of carbohydrate food. There was no information reported on the definitions for low-GI carbohydrate, moderate-GI carbohydrate or high-GI carbohydrate in this study (Balas-Nakash 2010). Grant 2011 advised women in the low-GI diet group to select their starch food from an exchange list of low- and intermediate- GI choices, while women in the comparison group were asked to select their starch choices from an exchange list of intermediate- and high-GI food (Grant 2011). Food exchange lists for study diets were provided in the published report for Grant 2011, which indicated that the carbohydrate food recommended for women in low-GI diet group having a GI range of 26-66 and for women in the control group having a GI range of 58 to 87. In Moses 2009, women in the low-GI diet group were advised to select low-GI food (55 or less) based on the international tables of GI and glycaemic load values (Atkinson 2008) and women in the comparison group were advised to follow a high-fibre, low-sugar diet, with no specific mention of the GI.

In Louie 2011, a low-GI diet aiming for a GI target of no higher than 50, was compared with a moderate-GI diet (GI around 60).

Two trials compared an energy-restricted diet with a no energy restriction diet (Magee 1990; Rae 2000). Women in Magee 1990 were hospitalised during the intervention period. In the first week of hospitalisation, women in both groups had a 2400 kcal/day diet, with 50% total energy derived from carbohydrate, 30% from fat and 20% from protein (Magee 1990). During the second week of hospitalisation, one group of women continued the diet consumed in the first week, while women in the other group restricted their daily energy intake to 1200 kcal, which was achieved by reducing serving size without changing diet content (Magee 1990). In Rae 2000, a 6800 kJ to 7600 kJ per day diet was compared with a diet providing 8600 kJ to 9500 kJ energy.

Two trials focused on specific nutrients in the diet (Lauszus 2001; Reece 1995). Lauszus 2001 compared a high-carbohydrate diet with a high-monounsaturated fat diet, without specifying the proportion of daily energy sources for a high carbohydrate or a high monounsaturated fat. In Reece 1995, a diet containing 80 grams of fibre per day was compared with a standard ADA diet providing 20 grams fibre per day.

One trial assessed different proportions of energy derived from carbohydrate, protein and fat (Cypryk 2007). Women in the low-carbohydrate group had 45% of their daily energy from carbohydrate, 25% from protein and 30% from fat; women in the high-carbohydrate group derived 60% daily energy from carbohydrate, 25% from protein and 15% from fat (Cypryk 2007).

Therefore, we structured the comparisons as:

See Characteristics of included studies for further details.

 

Outcome

Seven included studies focused on perinatal outcomes for women and their babies without any longer-term outcomes reported (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Louie 2011; Moses 2009; Rae 2000; Reece 1995). One trial reported maternal and infant perinatal outcomes as well as maternal early postnatal outcomes of postnatal BMI and development of glucose intolerance or type 2 diabetes (up to nine months postpartum) (Lauszus 2001). One trial reported biochemical outcomes only (Magee 1990).

See Characteristics of included studies for more details.

 

Excluded studies

A total of six trials were excluded. We excluded three trials as they compared different types of care for women with GDM, where dietary advice was included as part of the care (Gillen 2004; Gillmer 1986; Reader 2006). Another two trials were excluded as they were cross-over studies (Ilic 1999; Nolan 1984). Knopp 1991 was excluded as it was a systematic review, not a clinical trial.

See Characteristics of excluded studies for further details.

 

Risk of bias in included studies

The nine included studies had various levels of risk of bias. See Figure 1 and Figure 2 for details.

 FigureFigure 1. 'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
 FigureFigure 2. 'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.

 

Allocation

Four of the nine trials were at low risk of bias for sequence generation (Lauszus 2001; Louie 2011; Moses 2009; Reece 1995) with the remainder of trials unclear or not reporting this component.

Four trials reported adequate allocation concealment methods (Grant 2011; Lauszus 2001; Louie 2011; Moses 2009) with the remainder not reporting their method or not reporting it clearly.

In Grant 2011, numbered, sealed, opaque envelopes were used and the randomisation order was generated by one of the investigators who was not involved in recruitment. Randomisation in Lauszus 2001 was performed by a third person at an independent centre. In Louie 2011, women were centrally randomised to study groups by computer-generated random numbers and the recruiter was not able to predict the allocation sequence. In Moses 2009, the allocation method was not reported in detail but was judged as likely to have been adequately concealed (women were allocated to the study groups using permuted blocks of unequal size with the list generated using STATA).

 

Blinding

In six trials women were not blinded (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Moses 2009; Reece 1995). Two trials reported that participants were blinded to group allocation (Louie 2011; Rae 2000). One trial did not provide information on whether or not the participants were blinded (Magee 1990).

Eight trials did not report whether the outcome assessors were blinded to group allocation (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Magee 1990; Moses 2009; Rae 2000; Reece 1995). One trial reported that the unblinded research dietitian was involved in outcome data collection (Louie 2011).

 

Incomplete outcome data

Seven included trials were judged as being at low risk for attrition bias (Cypryk 2007; Grant 2011; Lauszus 2001; Louie 2011; Magee 1990; Moses 2009; Rae 2000). One trial was rated as high risk of attrition bias (Balas-Nakash 2010) and another having an unclear risk of attrition bias (Reece 1995).

Three trials reported no losses to follow-up or post randomisation exclusions (Cypryk 2007; Magee 1990; Moses 2009). In Lauszus 2001, Louie 2011 and Rae 2000, small numbers of women were lost to follow up or withdrew after randomisation with reasons reported and were unlikely to affect the results. In Grant 2011, three (10.3%) women in the low-GI group withdrew after randomisation, reasons were given and data analyses were based on an intent-to-treat basis.

In Balas-Nakash 2010, of a total of 108 women potentially eligible women who were involved in another clinical trial, 20 declined (15.8%) with no reason reported and another 19 women (17.5%) were excluded due to incomplete dietary information. No information was available on the characteristics of these declined and excluded women (Balas-Nakash 2010). With the remaining 69 women in Balas-Nakash 2010, 37 were diagnosed with GDM and provided outcome data in this review. In Reece 1995, 61 women with insulin-dependent diabetes or GDM were included, 11/61 (18%) women were excluded after randomisation without specifying the numbers of women with insulin-dependent diabetes and GDM. Reasons for exclusion were reported as: spontaneous abortion (one women), moved away (two women), and noncompliant (four women in each of the study groups) (Reece 1995).

 

Selective reporting

All nine included trials were at high risk of reporting bias (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Louie 2011; Magee 1990; Moses 2009; Rae 2000; Reece 1995). Most of the prespecified health outcomes for women and their babies were not reported in included trials (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Louie 2011; Moses 2009; Rae 2000; Reece 1995). One trial only reported biochemical outcomes without any information provided on the health outcomes for women and their babies (Magee 1990).

 

Other potential sources of bias

There was no obvious risk of other potential sources of bias in seven trials (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Magee 1990; Moses 2009; Rae 2000; Reece 1995).

In Lauszus 2001, women in the high-monounsaturated fat diet group had a higher trial entry BMI (mean [SD]: 35 [2.4] kg/m²) when compared with women in the high-carbohydrate group (mean [SD]: 32.2 [1.5] kg/m² ). In Louie 2011, baseline blood glucose concentrations at two hours post 75 grams glucose load were significantly different between the study groups (mean [SD]: 8.6 [1.2] mmol/L for women in the low-GI group, 8.0 [1.3] mmol/L for women in the high-fibre group, P = 0.024).

 

Effects of interventions

Eleven different types of dietary advice for women with GDM were assessed under six different comparisons (see Data and analyses).

 

Primary outcomes

 

Low-moderate GI food versus moderate-high GI food

Three trials involving 126 women and their babies contributed data to this comparison (Balas-Nakash 2010; Grant 2011; Moses 2009). Authors from Grant 2011 and Moses 2009 provided additional unpublished outcome data.

Fetal or neonatal mortality was not reported in any of these trials (Balas-Nakash 2010; Grant 2011; Moses 2009). No significant differences were seen in the rates of macrosomia (two trials, 89 infants, risk ratio (RR) 0.45, 95% confidence interval (CI) 0.10 to 2.08) ( Analysis 1.1) and large-for-gestational age (LGA) (two trials, 89 infants, RR 0.95, 95% CI 0.27 to 3.36) between the two study groups ( Analysis 1.2). No significant differences were seen in the rates of caesarean section (one trial, 63 women, RR 0.66, 95% CI 0.29 to 1.47) ( Analysis 1.3), operative vaginal birth (one trial, 63 women, RR 0.62, 95% CI 0.16 to 2.37) ( Analysis 1.4) and normal vaginal birth (one trial, 63 women, RR 1.35, 95% CI 0.89 to 2.07) ( Analysis 1.5) between women in the two study groups.

 

Low-GI diet versus high-fibre moderate-GI diet

One trial (Louie 2011) involving 92 women and their babies contributed data for this comparison. Authors were contacted for full report before the publication of this trial (Louie 2011).

No information was provided on fetal or neonatal mortality. No significant differences were seen in the rates of macrosomia (birthweight greater than 4000 g) (one trial, 92 infants, RR 0.32, 95% CI 0.03 to 2.96) ( Analysis 2.1) and LGA (one trial, 92 infants, RR 2.87, 95% CI 0.61 to 13.50) ( Analysis 2.2) between babies born to women in the low-GI diet group and high-fibre moderate-GI diet group. No significant difference in caesarean section rate was seen between women in the two study groups (one trial, 88 women, RR 1.80, 95% CI 0.66 to 4.94) ( Analysis 2.3).

No data reported on operative vaginal birth or normal birth.

 

Energy-restricted diet versus no energy restriction diet

Two trials (Rae 2000 (117 women and 124 babies); Magee 1990 (12 women and their babies)) assessed the effects of energy-restricted diet for women with GDM. Magee 1990 did not report any birth outcome data.

There were no fetal deaths reported (one trial, 124 infants) ( Analysis 3.1) and neonatal mortality was not reported (Rae 2000). Macrosomia was defined as birthweight at least 4000 grams or at least 90th centile weight for gender, gestational age and maternal height classified according to the Peinatal Statistics in Western Australia for macrosomia in Rae 2000. No significant differences were seen in the rates of macrosomia (one trial, 122 infants, RR 1.56, 95% CI 0.61 to 3.94) ( Analysis 3.2) and LGA (one trial, 123 infants, RR 1.17, 95% CI 0.65 to 2.12) ( Analysis 3.3) between babies born to women in the energy-restricted diet and no energy restriction diet.

For maternal primary outcomes, there were no significant differences seen for the rates of caesarean section (one trial, 121 infants, RR 1.18, 95% CI 0.74 to 1.89) ( Analysis 3.4), operative vaginal birth (one trial, 121 infants, RR 0.98, 95% CI 0.38 to 2.54) ( Analysis 3.5) and normal vaginal birth (one trial, 121 infants, RR 0.89, 95% CI 0.63 to 1.27) ( Analysis 3.6) between the two study groups.

 

Low-carbohydrate diet (≤ 45% daily total energy intake from carbohydrate) versus high-carbohydrate diet (≥ 50% daily total energy intake from carbohydrate)

One trial (Cypryk 2007) involving 30 women and their babies contributed data for this comparison.

No data were reported on fetal or neonatal mortality. None of the infants were macrosomic ( Analysis 4.1). There were no significant differences between the two study groups in the rates of caesarean section (one trial, 30 women, RR 1.40, 95% CI 0.57 to 3.43) ( Analysis 4.2), operative vaginal birth (one trial, 30 women, RR 1.00, 95% CI 0.07 to 14.55) ( Analysis 4.3) and normal vaginal birth (one trial, 30 women, RR 0.78, 95% CI 0.39 to 1.54) ( Analysis 4.4).

 

High-monounsaturated fat diet (at least 20% total energy from monounsaturated fat) versus high-carbohydrate diet (at least 50% total energy from carbohydrate)

One trial (Lauszus 2001) involving 27 women and their babies contributed data for this comparison. The author was contacted and contributed additional unpublished outcome data.

No information was provided on fetal or neonatal mortality. No significant differences were seen in the rates of macrosomia (birthweight greater than 4000 grams) (one trial, 27 infants, RR 0.65, 95% CI 0.19 to 2.18) ( Analysis 5.1) and LGA (one trial, 27 infants, RR 0.54, 95% CI 0.21 to 1.37) ( Analysis 5.2) between babies born to women in the high-monounsaturated fat diet group and to women in the high-carbohydrate diet group.

No data on maternal primary outcomes was reported.

 

Standard-fibre (20 grams fibre/day) diet versus high-fibre (80 grams fibre/day) diet

One trial involving 22 women with GDM and their babies contributed data to this comparison (Reece 1995).

No data were available on maternal and child primary outcomes under this comparison.

 

Secondary outcomes

 

Low-moderate GI food versus moderate-high GI food

No significant differences were seen between the two study groups for the outcomes of birthweight, gestational age at birth, small-for-gestational age (SGA), induction of labour and preterm birth ( Analysis 1.6;  Analysis 1.7;  Analysis 1.8;  Analysis 1.9;  Analysis 1.10). A random-effects analysis was used for the outcome of insulin or oral hypoglycaemic agent required for hyperglycaemia as significant heterogeneity was noted (Tau2= 0.41; I² = 78%). This may due to the different criteria used in different trials for hypoglycaemic agents (Balas-Nakash 2010; Grant 2011; Moses 2009). On average, there was no significant difference in the use of insulin or hypoglycaemic agent between women in the two study groups, but the treatment effects of two trials were strongly in opposite directions, so it is possible that both positive and negative effects will be found in different trials and populations ( Analysis 1.11).

In Balas-Nakash 2010, women's adherence to dietary advice was assessed by three different methods including adaption of calorie intake, adherence questionnaire and self-perception of adherence. There were no subset data reported on the adherence to dietary advice for women with GDM (Balas-Nakash 2010). Grant 2011 reported all participants in the low-GI group rated the foods as being "good" and indicated they would consider continuing these low-GI foods postpartum; no separate data were reported on adherence to diet advice for the women with GDM. No information was reported on women's adherence to dietary advice in Moses 2009.

There were no data reported on other prespecified secondary review outcomes.

 

Low-GI diet versus high-fibre moderate-GI diet

There were no significant differences in birthweight (one trial, 92 infants, mean difference (MD) 0.00 g, 95% CI -277.18 to 277.18) ( Analysis 2.4). Gestational age at birth (one trial, 92 infants, MD -0.10 weeks, 95% CI -0.39 to 0.19) ( Analysis 2.5); preterm birth (one trial, 92 infants, RR 0.96, 95% CI 0.14 to 6.51) ( Analysis 2.6) and SGA (one trial, 92 infants, RR 1.20, 95% CI 0.34 to 4.18) ( Analysis 2.7) showed no differences between groups. No significant differences were seen in the ponderal index for infants born to women in the two study groups (one trial, 92 infants, MD 0.20 kg/m³, 95% CI -0.79 to 1.19) ( Analysis 2.8).

Gestational weight gain for women in the low-GI diet group was not significantly different when compared with the high-fibre moderate-GI diet group (one trial, 87 women, MD -1.20 kg, 95% CI -3.43 to 1.03) ( Analysis 2.9). There was no significant difference seen in the number of women required insulin treatment for hyperglycaemia between the two study groups (one trial, 92 women, RR 0.83, 95% CI 0.58 to 1.17) ( Analysis 2.11). Adherence to dietary intervention was assessed by a 24-hour recall when women were attending their dietitian appointments (Louie 2011); there was no significant difference seen in the number of women who were adherent to the study diets (one trial, 92 women, RR 0.84, 95 %CI 0.64 to 1.11) ( Analysis 2.10).

There were no data available on other prespecified secondary review outcomes.

 

Energy-restricted diet versus no energy restriction diet

There were no significant differences in the outcomes of induction of labour (one trial, 114 women, RR 1.02, 95% CI 0.68 to 1.53) ( Analysis 3.7), pre-eclampsia (one trial, 117 women, RR 1.00, 95% CI 0.51 to 1.97) ( Analysis 3.8) and insulin required for maternal hyperglycaemia (one trial, 117 women, RR 1.05, 95% CI 0.47 to 2.34) ( Analysis 3.9). Fasting plasma glucose and fasting plasma insulin were reported in a single, small randomised trial involving 12 women; no significant differences were see in fasting plasma glucose (standardised mean difference (SMD -0.35 mmol, 95% CI -1.51 to 0.81) and fasting plasma insulin (SMD -0.17 pM, 95% CI -1.32 to 0.98) between the two study groups ( Analysis 3.10). Outcomes including shoulder dystocia, birthweight, gestational age at birth were reported in one trial (Rae 2000) but since numbers in each group were unclear or these outcomes, no data were able to be included in the review.

Adherence to dietary intervention in Rae 2000 was assessed by three-day food diaries. Women in the energy-restricted diabetic diet group consumed slightly less (97%) energy than the diet goal of 6800 kJ/day to 7600 kJ/day; whereas women in the no energy restriction diabetic diet consumed considerably less energy (77%) than the dietary goal of 8600 Kj/day to 9500 Kj/day. As a result, there was no significant difference between average daily energy intake between women in the two groups (Rae 2000).

No data were reported on other secondary review outcomes.

 

Low-carbohydrate diet (≤ 45% daily total energy intake from carbohydrate) versus high-carbohydrate diet (at least 50% daily total energy intake from carbohydrate)

There were no significant differences seen in birthweight (one trial, 30 infants, MD 22.00 g, 95% CI -241.06 to 285.06) ( Analysis 4.5) or gestational age at birth (one trial, 30 infants, MD 0.10 week, 95% CI -0.83 to 1.03) ( Analysis 4.6) between the two study groups. Adherence to dietary advice was reported as 12/15 (80%) in the low-carbohydrate group and 11/15 (73.3%) in the high-carbohydrate diet group (full adherence to recommended menu) (Cypryk 2007). Twenty (66.7%) women reported no symptoms of hunger, seven women reported hunger after breakfast on the first few days of the study period and three women reported most intense hunger before breakfast (Cypryk 2007). None of the women experienced any symptoms of intolerance towards the recommended diets (Cypryk 2007).

There were no data reported on other prespecified secondary review outcomes.

 

High-monounsaturated fat diet (at least 20% total energy from monounsaturated fat) versus high-carbohydrate diet (at least 50% total energy from carbohydrate)

There were no significant differences in birthweight (one trial, 27 infants, MD 1.00 g, 95% CI -112.85 to 114.85) ( Analysis 5.3) and gestational age at birth (one trial, 27 infants, MD 0.10 week, 95% CI -0.13 to 0.33) ( Analysis 5.4).

None of the women developed pre-eclampsia ( Analysis 5.5) or needed insulin for hyperglycaemia ( Analysis 5.6) (Lauszus 2001). Women in the high-monounsaturated fat diet group were significantly heavier in late pregnancy (third trimester) ( Analysis 5.7) and had a significantly higher BMI during late pregnancy (third trimester) ( Analysis 5.8) and at six to nine months postpartum (one trial, 27 women, MD 4.10 kg/m², 95% CI 2.34 to 5.86) ( Analysis 5.9), when compared with women in the high-carbohydrate diet group. However, it is important to note a significant BMI difference existed at trial entry between women in the two study groups, and in the original analysis carried out by the authors was adjusted for BMI (Lauszus 2001). There were no significant differences seen in the incidence of developing type 2 diabetes within six weeks postpartum (one trial, 24 women, RR 2.00, 95% CI 0.45 to 8.94) ( Analysis 5.10) and four months or more postpartum (one trial, six women, RR 1.00, 95% CI 0.10 to 9.61) ( Analysis 5.10) between women in the two study groups. No significant differences were seen in the incidence of developing glucose intolerance without meeting type 2 diabetes diagnostic criteria within six weeks postpartum (one trial, 24 women, RR 1.50, 95% CI 0.30 to 7.43) ( Analysis 5.11) and four months or more postpartum (one trial, seven women, RR 0.27, 95% CI 0.01 to 4.93) ( Analysis 5.11). Insulin sensitivity was assessed by using an intravenous glucose tolerance test (Galvin 1992), with no significant difference seen in maternal fasting glucose, fasting insulin and insulin sensitivity during pregnancy between the two study groups.

There were no data available on other prespecified secondary review outcomes.

 

Standard-fibre (20 grams fibre/day) diet versus high-fibre (80 grams fibre/day) diet

When compared with infants born to women having a standard ADA diet (with 20 grams fibre per day), there were no significant difference seen in birthweight (one trial, 22 infants, MD -94.00 g, 95% CI -446.71 to 258.71) ( Analysis 6.1) and gestational age at birth (one trial, 22 infants, MD 0.00 week, 95% CI -1.30 to 1.30) ( Analysis 6.2). No women required insulin treatment for hyperglycaemia during pregnancy (Reece 1995) ( Analysis 6.3). No significant difference was seen in gestational weight gain between women in the two study groups (one trial, 22 women, MD 2.40 kg, 95% CI -2.20 to 7.00) ( Analysis 6.4). No information was given on the start and end point weights used for calculating weight change in the trial provided outcome data (Reece 1995).

Women were instructed to keep daily food records for assessing their compliance to the study dietary intervention (Reece 1995). Dietary compliance was rated as good in 60% and acceptable in 40% of the whole sample size, which included both women with type 2 diabetes and GDM (Reece 1995). No subset data were reported on the adherence to dietary advice for women with GDM (Reece 1995).

No data were available on other prespecified secondary review outcomes.

 

Subgroup analyses and sensitivity analyses

Due to the small number of studies included and limited data available, no subgroup analyses and sensitivity analyses were conducted.

 

Discussion

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
 

Summary of main results

Diet comparisons including low-moderate GI food versus high-moderate GI food; low-GI diet versus high-fibre moderate-GI diet; energy-restricted diet versus no energy restriction diet; low-carbohydrate diet versus high-carbohydrate diet; high-monounsaturated fat diet versus high-carbohydrate diet; standard-fibre diet providing 20 grams fibre per day versus fibre-enriched diet providing 80 grams fibre per day were investigated in this review.

No significant differences were seen in any of the reported primary outcomes. Based on the current very limited data, it remains unclear which diet is the most suitable diet for women with GDM in improving the health for women and their babies in the short or long term.

 

Overall completeness and applicability of evidence

The evidence on different dietary advice for women with GDM is incomplete. Although a wide range of dietary advice was investigated, very limited outcome data were reported for each of the dietary comparisons and most reported outcomes were based on data from individual studies.

In the comparison of standard ADA diet (20 grams fibre/day) versus fibre-enriched diet (80 grams fibre/day), none of the review's primary outcomes were reported (Reece 1995). One trial reported biochemical outcomes only, without any information provided on clinical outcomes for the women and their babies under the comparison of energy-restricted diet versus no energy restriction diet (Magee 1990). For longer term outcomes, only very limited data from one small randomised trial were available on postpartum BMI, future development of type 2 diabetes and future development of glucose intolerance without meeting type 2 diabetes diagnostic criteria (Lauszus 2001). No data were available on any other maternal or child longer term outcomes and health service cost.

Due to the small number of studies involved in each of the dietary comparisons, various levels of risk of bias of the included studies and small numbers of participants, the applicability of the current available evidence was very limited.

 

Quality of the evidence

A total of nine small trials were included (437 women, 444 babies); outcome data were available from 429 women and 436 babies .

The majority of the comparisons in this review were based on data from one or two small trials. The quality of the evidence for each of the dietary comparisons greatly depends on the quality of the one or two trial(s) which provided outcome data.

Risk of bias varied across the nine included trials in this review. Four included studies (Grant 2011; Lauszus 2001; Moses 2009; Rae 2000) had low-to-moderate risk of bias. Three included trials (Cypryk 2007; Louie 2011; Reece 1995) were of moderate-to-high risk of bias. Published reports for Balas-Nakash 2010 and Magee 1990 did not allow a detailed assessment of their risk of bias. Due to the nature of behavioural interventions, blinding of participants was not implemented in six out of the nine trials (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Moses 2009; Reece 1995). Blinding of outcome assessor(s) was unclear in all nine included trials (Balas-Nakash 2010; Cypryk 2007; Grant 2011; Lauszus 2001; Louie 2011 Magee 1990; Moses 2009; Moses 2009; Rae 2000; Reece 1995).

Women's adherence to dietary interventions may have had an impact on outcomes of interventions, with some suggested dietary changes easier to adopt than others. Furthermore, in Rae 2000, there was no significant difference between average daily energy intake between women in the energy-restricted diabetic diet group and group with no energy restriction, which may have contributed to the research findings of no significant difference on pregnancy outcomes between women in the two groups. In Lauszus 2001, authors referred to study groups as high-carbohydrate diet group and high-monounsaturated fatty acids diet without giving a clear definition about this in terms of foods, making it hard to judge women's adherence. In Balas-Nakash 2010, Grant 2011 and Reece 1995, a mixed population including women with type 2 diabetes and women with GDM were included. None of the three trials reported dietary adherence for the subgroup of women with GDM; or the GI values of their actual food intake (Balas-Nakash 2010; Grant 2011; Reece 1995).

The remaining four trials did not report information about women's adherence to dietary interventions (Cypryk 2007; Louie 2011; Magee 1990; Moses 2009 ).

 

Potential biases in the review process

Systematic searches of all potential eligible trials were carried out by the Trials Search Co-ordinator of the Cochrane Pregnancy and Childbirth Group. We also searched the WOMBAT (Women and Babies health and wellbeing: Action through Trials) perinatal trial database (WOMBAT 2011) (Last Search: 17 April 2012) and reference list of the identified potential trials. Authors of the included trials were also contacted via email for additional data where possible during the review process.

One potential bias introduced during the review process may be the definition of eligible women for this review. We used the definition of GDM according to the diagnostic criteria selected by trial authors. Due to the inconsistencies existing in GDM diagnostic methods around the world, we may have included women with various degrees of pregnancy hyperglycaemia.

Another possible bias may have been introduced when we grouped trials under different comparisons for data synthesis. For the comparison of low-carbohydrate diet versus high-carbohydrate diet, there was only one trial that provided outcome data (Cypryk 2007). In this trial, women in the low-carbohydrate diet group had a 30% energy from fat while those in the high-carbohydrate diet group had only 15% energy from fat (Cypryk 2007). A 50% difference in diet fat content may have introduced bias to a comparison that focused on carbohydrate only. In addition, in the comparison of energy-restricted diet versus no energy restriction diet, the two trials included under this comparison actually had different levels of energy restrictions; one trial had 50% energy restriction and the other had 20% to 30% energy restriction (Magee 1990; Rae 2000). Combining evidence from two trials with different levels of energy restriction may have introduced bias, although in this current review, only one of the two trials contributed outcome data.

 

Agreements and disagreements with other studies or reviews

In this review, we did not find enough evidence to suggest the most suitable diet compositions for women with GDM in improving pregnancy outcomes.

There is a systematic review assessing the effect of low-GI diet for women with GDM (Louie 2010). Only one randomised clinical trial (Moses 2009) involving 63 women, was included in this review (Louie 2010). Louie 2010 found a significantly lower proportion of women in the low-GI group met the criteria to commence insulin than women in the high-GI group and there were no significant differences in any other reported pregnancy outcomes. In our review, three included trials involving 126 women contributed data on the outcome of requirement of insulin for pregnancy hyperglycaemia. One trial was the same trial included in Louie 2010. We did not find a significant difference in the requirement of insulin for pregnancy hyperglycaemia for women in the low-GI group when compared with women in the high-GI group; we also did not find any significant differences in other primary and secondary outcomes under this comparison.

Modest calorie restriction, defined as 1600 to 1800 kcal/day or a 33% energy reduction was reviewed in two review papers (Knopp 1991; Reader 2007). Both review papers reported the benefits of optimising glycaemic control and regulating pregnancy weight gain, without increasing the risk of ketosis by having a modest calorie restriction diet for obese women with GDM (Knopp 1991; Reader 2007). Neither reviews report data on pregnancy outcomes (Knopp 1991; Reader 2007). In our review, two trials investigated the effects of energy restriction for women with GDM (Magee 1990; Rae 2000). Pregnancy outcome data were only available from one of the two trials (Rae 2000), which assessed the effects of diet with 20% to 30% energy restriction for women with GDM. We did not find any significant differences in the reported primary outcomes of fetal mortality, macrosomia, large-for-gestational age and mode of birth or the reported secondary outcomes. However, it is important to note that the actual daily energy intake for women in Rae 2000 was not significantly different between the two study groups.

 

Authors' conclusions

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms

 

Implications for practice

Overall, results were inconclusive due to the very limited number of trials, participants and data available for each of the six dietary comparisons. Women having a high-monounsaturated fat diet (contributing at least 20% total daily energy intake) are more likely to be heavier during the third trimester and have a higher late pregnancy BMI and postnatal BMI when compared with women having a high-carbohydrate diet (contributing at least 50% total daily energy intake). However, it is important to note that these findings are based on very limited data often from single, small randomised trials with data unadjusted for trial entry imbalance in maternal BMI. In our review, we did not find any other significant benefits or harms of the diets investigated under the six comparisons.

No conclusive suggestions on the most appropriate diets for women with GDM can be made based on currently available evidence from randomised controlled trials.

 
Implications for research

Further larger trials with sufficient power to assess the effects of different diets for women with GDM on maternal and infant health outcomes are needed. Participants' adherence to dietary interventions and methods about improving intervention adherence need to be addressed and reported. Multi-faceted dietary interventions (i.e. a dietary intervention targeting total energy, proportion of energy from different macronutrients and glycaemic index) may be worth considering when designing future trials. Outcomes such as longer-term health outcomes for women and their babies, women's quality of life and health service cost should be included.

 

Acknowledgements

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms

Thanks to Professor Thomas Wolever, Professor Robert Moses, Ms Anne Rae, Mr Jimmy Louie, Professor Jennie Brand-Miller, Associate Professor Finn Friis Lauszus for providing additional information for their trials. Thanks to Rosie Helmore for her help in translation of Balas-Nakash 2010's report from Spanish to English.

As part of the pre-publication editorial process, this review has been commented on by three peers (an editor and two referees who are external to the editorial team), a member of the Pregnancy and Childbirth Group's international panel of consumers and the Group's Statistical Adviser.

 

Data and analyses

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
Download statistical data

 
Comparison 1. Low-moderate GI food versus high-moderate food

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Macrosomia (birthweight greater than 4000 g)289Risk Ratio (M-H, Fixed, 95% CI)0.45 [0.10, 2.08]

 2 Large-for-gestational age (birthweight ≥ 90th percentile for gestational age)289Risk Ratio (M-H, Fixed, 95% CI)0.95 [0.27, 3.36]

 3 Caesarean section163Risk Ratio (M-H, Fixed, 95% CI)0.66 [0.29, 1.47]

 4 Operative vaginal birth163Risk Ratio (M-H, Fixed, 95% CI)0.62 [0.16, 2.37]

 5 Normal vaginal birth163Risk Ratio (M-H, Fixed, 95% CI)1.35 [0.89, 2.07]

 6 Birthweight (g)162Mean Difference (IV, Fixed, 95% CI)-50.70 [-272.56, 171.16]

 7 Gestational age at birth162Mean Difference (IV, Fixed, 95% CI)0.30 [-0.30, 0.90]

 8 Small-for-gestational age163Risk Ratio (M-H, Fixed, 95% CI)5.16 [0.26, 103.27]

 9 Induction of labour163Risk Ratio (M-H, Fixed, 95% CI)0.88 [0.33, 2.34]

 10 Preterm birth (< 37 weeks' gestation)163Risk Ratio (M-H, Fixed, 95% CI)0.52 [0.05, 5.41]

 11 Insulin or oral hypoglycaemic agent required for hyperglycaemia3126Risk Ratio (M-H, Random, 95% CI)0.85 [0.37, 1.93]

 
Comparison 2. Low-GI diet versus high-fibre moderate-GI diet

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Macrosomia (birthweight greater than 4000 g)192Risk Ratio (M-H, Fixed, 95% CI)0.32 [0.03, 2.96]

 2 Large-for-gestational age (birthweight ≥ 90th percentile for gestational age)192Risk Ratio (M-H, Fixed, 95% CI)2.87 [0.61, 13.50]

 3 Caesarean section188Risk Ratio (M-H, Fixed, 95% CI)1.8 [0.66, 4.94]

 4 Birthweight (g)192Mean Difference (IV, Fixed, 95% CI)0.0 [-277.18, 277.18]

 5 Gestational age at birth (weeks)192Mean Difference (IV, Fixed, 95% CI)-0.10 [-0.39, 0.19]

 6 Preterm birth192Risk Ratio (M-H, Fixed, 95% CI)0.96 [0.14, 6.51]

 7 Small-for-gestational age192Risk Ratio (M-H, Fixed, 95% CI)1.20 [0.34, 4.18]

 8 Ponderal index (kg/m3)192Mean Difference (IV, Fixed, 95% CI)0.20 [-0.79, 1.19]

 9 Weight gain during pregnancy (kg)187Mean Difference (IV, Fixed, 95% CI)-1.20 [-3.43, 1.03]

 10 Adherence to dietary intervention192Risk Ratio (M-H, Fixed, 95% CI)0.84 [0.64, 1.11]

 11 Insulin required for hyperglycaemia192Risk Ratio (M-H, Fixed, 95% CI)0.83 [0.58, 1.17]

 
Comparison 3. Energy-restricted diet versus no energy restriction diet

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Fetal mortality1124Risk Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 2 Macrosomia1122Risk Ratio (M-H, Fixed, 95% CI)1.56 [0.61, 3.94]

 3 Large-for-gestational age1123Risk Ratio (M-H, Fixed, 95% CI)1.17 [0.65, 2.12]

 4 Caesarean section1121Risk Ratio (M-H, Fixed, 95% CI)1.18 [0.74, 1.89]

 5 Operative vaginal birth1121Risk Ratio (M-H, Fixed, 95% CI)0.98 [0.38, 2.54]

 6 Normal vaginal birth1121Risk Ratio (M-H, Fixed, 95% CI)0.89 [0.63, 1.27]

 7 Induction of labour1114Risk Ratio (M-H, Fixed, 95% CI)1.02 [0.68, 1.53]

 8 Pre-eclampsia1117Risk Ratio (M-H, Fixed, 95% CI)1.0 [0.51, 1.97]

 9 Insulin or oral hypoglycaemic agent required for hyperglycaemia1117Risk Ratio (M-H, Fixed, 95% CI)1.05 [0.47, 2.34]

 10 Insulin sensitivity1Std. Mean Difference (IV, Fixed, 95% CI)Subtotals only

    10.1 Fasting plasma glucose (mmol)
112Std. Mean Difference (IV, Fixed, 95% CI)-0.35 [-1.51, 0.81]

    10.2 Fasting plasma insulin (pM)
112Std. Mean Difference (IV, Fixed, 95% CI)-0.17 [-1.32, 0.98]

 
Comparison 4. Low-carbohydrate (CHO) diet (≤ 45% total energy from CHO) versus high-CHO diet (≥ 50% total energy from CHO)

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Macrosomia (birthweight greater than 4000 g)130Risk Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 2 Caesarean section130Risk Ratio (M-H, Fixed, 95% CI)1.4 [0.57, 3.43]

 3 Operative vaginal birth130Risk Ratio (M-H, Fixed, 95% CI)1.0 [0.07, 14.55]

 4 Normal vaginal birth130Risk Ratio (M-H, Fixed, 95% CI)0.78 [0.39, 1.54]

 5 Birthweight (g)130Mean Difference (IV, Fixed, 95% CI)22.0 [-241.06, 285.06]

 6 Gestational age at birth (weeks)130Mean Difference (IV, Fixed, 95% CI)0.10 [-0.83, 1.03]

 
Comparison 5. High-monounsaturated fat (MUFA) diet (≥ 20% total energy from MUFA) versus high-CHO diet (≥ 50% total energy from CHO)

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Macrosomia (birthweight greater than 4000 g)127Risk Ratio (M-H, Fixed, 95% CI)0.65 [0.19, 2.18]

 2 Large-for-gestational age127Risk Ratio (M-H, Fixed, 95% CI)0.54 [0.21, 1.37]

 3 Birthweight (g)127Mean Difference (IV, Fixed, 95% CI)1.0 [-112.85, 114.85]

 4 Gestational age at birth (weeks)127Mean Difference (IV, Fixed, 95% CI)0.10 [-0.13, 0.33]

 5 Pre-eclampsia127Risk Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 6 Insulin or oral hypoglycaemic agent required for hyperglycaemia127Risk Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 7 Maternal weight at late pregnancy (third trimester) (kg)1Mean Difference (IV, Fixed, 95% CI)Subtotals only

    7.1 Maternal weight at 36 weeks' gestation
127Mean Difference (IV, Fixed, 95% CI)12.60 [7.93, 17.27]

    7.2 Maternal weight at 38 weeks' gestation
127Mean Difference (IV, Fixed, 95% CI)11.80 [7.23, 16.37]

    7.3 Maternal weight at delivery
127Mean Difference (IV, Fixed, 95% CI)11.90 [7.47, 16.33]

 8 Maternal BMI at late pregnancy (third trimester) (kg/m2)1Mean Difference (IV, Fixed, 95% CI)Subtotals only

    8.1 Maternal BMI at 36 weeks' gestation (kg/m2)
127Mean Difference (IV, Fixed, 95% CI)4.70 [3.18, 6.22]

    8.2 Maternal BMI at 38 weeks' gestation (kg/m2)
127Mean Difference (IV, Fixed, 95% CI)3.80 [2.22, 5.38]

    8.3 Maternal BMI at delivery (kg/m2)
127Mean Difference (IV, Fixed, 95% CI)3.90 [2.41, 5.39]

 9 Maternal postpartum BMI (> 4 months postpartum) (kg/m2)127Mean Difference (IV, Fixed, 95% CI)4.10 [2.34, 5.86]

 10 Development of type 2 diabetes1Risk Ratio (M-H, Fixed, 95% CI)Subtotals only

    10.1 Diagnosed by OGTT at early postnatal period (within 6 weeks postpartum)
124Risk Ratio (M-H, Fixed, 95% CI)2.0 [0.45, 8.94]

    10.2 Diagnosed by OGTT at ≥ 4 months postpartum
16Risk Ratio (M-H, Fixed, 95% CI)1.0 [0.10, 9.61]

 11 Development of glucose intolerance without meeting type 2 diabetes diagnostic criteria1Risk Ratio (M-H, Fixed, 95% CI)Subtotals only

    11.1 Diagnosed by OGTT at early postnatal period (within 6 weeks postpartum)
124Risk Ratio (M-H, Fixed, 95% CI)1.5 [0.30, 7.43]

    11.2 Diagnosed by OGTT at ≥ 4 months postpartum
17Risk Ratio (M-H, Fixed, 95% CI)0.27 [0.01, 4.93]

 
Comparison 6. Standard ADA diet (20 g fibre/day) versus fibre-enriched diet (80 g fibre/ day)

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Birthweight (g)122Mean Difference (IV, Fixed, 95% CI)-94.0 [-446.71, 258.71]

 2 Gestational age at birth (weeks)122Mean Difference (IV, Fixed, 95% CI)0.0 [-1.30, 1.30]

 3 Insulin or oral hypoglycaemic agent required for hyperglycaemia122Risk Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 4 Gestational weight gain (kg)122Mean Difference (IV, Fixed, 95% CI)2.40 [-2.20, 7.00]

 

Appendices

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
 

Appendix 1. WOMBAT Perinatal Trials Registry search strategy

We searched trials in the Women and Babies Health and Wellbeing: Action through Trials (WOMBAT) Perinatal Trials Registry using the terms of "gestational diabetes mellitus", "pregnancy", "pregnant", "glucose intolerance", "diet", "dietary advice", "nutrition". We reviewed all relevant trials listed under the search results.

 

Contributions of authors

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms

Shanshan Han wrote drafts of the protocol and review, with Caroline Crowther and Philippa Middleton contributing to data extraction, and commenting on and editing to all drafts. Emer Heatley was involved in data extraction and assessment of risk of bias of the included studies.

 

Declarations of interest

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms

None known.

 

Sources of support

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms
 

Internal sources

  • ARCH, Robinson Institute, The University of Adelaide, Australia.

 

External sources

  • Australian Department of Health and Ageing, Australia.
  • NHMRC: National Health and Medical Research Council, Australia.

 

Differences between protocol and review

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Results
  6. Discussion
  7. Authors' conclusions
  8. Acknowledgements
  9. Data and analyses
  10. Appendices
  11. Contributions of authors
  12. Declarations of interest
  13. Sources of support
  14. Differences between protocol and review
  15. Index terms

Maternal secondary outcomes of maternal weight at late pregnancy (third trimester) and maternal BMI at late pregnancy (third trimester) were added.

References

References to studies included in this review

  1. Top of page
  2. AbstractRésumé
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. Contributions of authors
  13. Declarations of interest
  14. Sources of support
  15. Differences between protocol and review
  16. Characteristics of studies
  17. References to studies included in this review
  18. References to studies excluded from this review
  19. Additional references
Balas-Nakash 2010 {published data only}
  • Balas-Nakash M, Rodriguez-Cano A, Munoz-Manrique C, Vasquez-Pena P, Perichart-Perera O. Adherence to a medical nutrition therapy program in pregnant women with diabetes, measured by three methods, and its association with glycemic control [Tres metodos para medir la adherencia a un programa de terapia medica y nutricia en mujeres embarazadas con diabetes y su asociacion con el control glucemico]. Revista De Investigacion Clinica 2010;62(3):235-43.
Cypryk 2007 {published data only}
  • Cypryk K, Kaminska P, Kosinski M, Pertynska-Marczewska M, Lewinski A. A comparison of the effectiveness, tolerability and safety of high and low carbohydrate diets in women with gestational diabetes. Endokrynologia Polska 2007;58(4):314-9.
Grant 2011 {published and unpublished data}
  • Grant SM, Wolever TMS, O'Connor DL, Nisenbaum R, Josse RG. Effect of a low glycaemic index diet on blood glucose in women with gestational hyperglycaemia. Diabetes Research and Clinical Practice 2011;91(1):15-22.
Lauszus 2001 {published and unpublished data}
  • Lauszus FF, Rasmussen OW, Henriksen JE, Klebe JG, Jensen L, Lauszus KS, et al. Effect of a high monounsaturated fatty acid diet on blood pressure and glucose metabolism in women with gestational diabetes mellitus. European Journal of Clinical Nutrition 2001;55(6):436-43.
Louie 2011 {published data only}
  • Louie JC, Markovic TP, Perera N, Foote D, Petocz P, Ross GP, et al. Randomized controlled trial investigating the effects of a low-glycemic index diet on pregnancy outcomes in gestational diabetes mellitus. Diabetes Care 2011;34(11):2341-6.
Magee 1990 {published data only}
Moses 2009 {published and unpublished data}
  • Moses RG, Barker M, Winter M, Petocz P, Brand-Miller JC. Can a low-glycemic index diet reduce the need for insulin in gestational diabetes mellitus? A randomized trial. Diabetes Care 2009;32(6):996-1000.
Rae 2000 {published data only}
Reece 1995 {published data only}
  • Reece EA, Hagay Z, Gay LJ, O'Connor T, DeGennaro N, Homko CJ, et al. A randomized clinical trial of a fiber-enriched diabetic diet vs. the standard American Diabetes Association-recommended diet in the management of diabetes mellitus in pregnancy. Journal of Maternal-Fetal Investigation 1995;5:8-12.

References to studies excluded from this review

  1. Top of page
  2. AbstractRésumé
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. Contributions of authors
  13. Declarations of interest
  14. Sources of support
  15. Differences between protocol and review
  16. Characteristics of studies
  17. References to studies included in this review
  18. References to studies excluded from this review
  19. Additional references
Gillen 2004 {published data only}
  • Gillen LJ, Tapsell LC. Advice that includes food sources of unsaturated fat supports future risk management of gestational diabetes mellitus. Journal of the American Dietetic Association 2004;104:1863-7.
Gillmer 1986 {published data only}
  • Gillmer MDG, Maresh M, Beard RW, Elkeles RS, Alderson C, Bloxham B. Low energy diets in the treatment of gestational diabetes. Acta Endocrinologica. Supplementum 1986;277:44-9.
Ilic 1999 {published data only}
  • Ilic S, Jovanovic L, Pettitt DJ. Comparison of the effect of saturated and monounsaturated fat on postprandial plasma glucose and insulin concentration in women with gestational diabetes mellitus. American Journal of Perinatology 1999;16(9):489-95.
Knopp 1991 {published data only}
  • Knopp RH, Magee MS, Raisys V, Benedetti T, Bonet B. Hypocaloric diets and ketogenesis in the management of obese gestational diabetic women. Journal of the American College of Nutrition 1991;10(6):649-67.
Ma 2011 {published data only}
  • Ma WJ, Qi BH, Zhang YJ, Huang ZH, Xiao BX, Li YH, et al. Application of different nutrition therapies in pregnancy with abnormal glucose metabolism. Zhonghua yu fang yi xue za zhi [Chinese Journal of Preventive Medicine] 2011;45(5):426-9.
Nolan 1984 {published data only}
  • Nolan CJ. Improved glucose tolerance in gestational diabetic women on a low fat, high unrefined carbohydrate diet. Australian and New Zealand Journal of Obstetrics and Gynaecology 1984;24(3):174-7.
Reader 2006 {published data only}
  • Reader D, Splett P, Gunderson EP, for the Diabetes Care and Education Dietetic Practice Group. Impact of gestational diabetes mellitus nutrition practice guidelines implemented by registered dietitians on pregnancy outcomes. Journal of the American Dietetic Association 2006;106(9):1426-33.

Additional references

  1. Top of page
  2. AbstractRésumé
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. Contributions of authors
  13. Declarations of interest
  14. Sources of support
  15. Differences between protocol and review
  16. Characteristics of studies
  17. References to studies included in this review
  18. References to studies excluded from this review
  19. Additional references
ACOG 2001
  • American College of Obstetricians and Gynecologists Committee on Practice Bulletins--Obstetrics. ACOG Practice Bulletin. Clinical management guidelines for obstetrician-gynecologists. Number 30, September 2001 (replaces Technical Bulletin Number 200, December 1994). Gestational diabetes. Obstetrics & Gynecology 2001;98(3):525-38.
ACOG 2005
  • ACOG. ACOG Committee Opinion number 315, September 2005. Obesity in pregnancy. Obstetrics & Gynecology 2005;106(3):671-5.
Alwan 2009
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Anderberg 2010
Atkinson 2008
Baker 1994
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Bellamy 2009
Bottalico 2007
Chasan-Taber 2008
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Cheung 2009
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Clapp 2002
Clapp 2006
  • Clapp JF. Effects of diet and exercise on insulin resistance during pregnancy. Metabolic Syndrome and Related Disorders 2006;4(2):84-90.
Colagiuri 2009
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Coustan 2010
  • Coustan DR, Lowe LP, Metzger BE, Dyer AR, International Association of Diabetes and Pregnancy Study Groups. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study: paving the way for new diagnostic criteria for gestational diabetes mellitus. American Journal of Obstetrics & Gynecology 2010;202(6):654.e1-6.
Crowther 2005
  • Crowther CA, Hiller JE, Moss JR, McPhee AJ, Jeffries WS, Robinson JS, et al. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. New England Journal of Medicine 2005;352(24):2477-86.
Cypryk 2008
  • Cypryk K, Szymczak W, Czupryniak L, Sobczak M, Lewinski A. Gestational diabetes mellitus - an analysis of risk factors. Endokrynologia Polska (Warszawa) 2008;59(5):393-7.
Dabelea 2005
  • Dabelea D, Snell-Bergeon JK, Hartsfield CL, Bischoff KJ, Hamman RF, McDuffie RS, et al. Increasing prevalence of gestational diabetes mellitus (GDM) over time and by birth cohort: Kaiser Permanente of Colorado GDM Screening Program. Diabetes Care 2005;28(3):579-84.
De Veciana 1995
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Devlieger 2008
Dodd 2007
Dornhorst 2002
Esakoff 2009
  • Esakoff TF, Cheng YW, Sparks TN, Caughey AB. The association between birthweight 4000 g or greater and perinatal outcomes in patients with and without gestational diabetes mellitus. American Journal of Obstetrics and Gynecology 2009;200(6):672.e1-672.e4.
Galvin 1992
  • Galvin P, Ward G, Walters J, Pestell R, Koschmann M, Vaag A, et al. A simple method for quantitation of insulin sensitivity and insulin release from an intravenous glucose tolerance test. Diabetic Medicine 1992;9(10):921-8.
Guerrero-Romero 2010
  • Guerrero-Romero F, Aradillas-García C, Simental-Mendia LE, Monreal-Escalante E, De la Cruz Mendoza E, Rodríguez-Moran M. Birth weight, family history of diabetes, and metabolic syndrome in children and adolescents. Journal of Pediatrics 2010;156(5):719-23.
Harder 2009
  • Harder T, Roepke K, Diller N, Stechling Y, Dudenhausen JW, Plagemann A. Birth weight, early weight gain, and subsequent risk of type 1 diabetes: systematic review and meta-analysis. American Journal of Epidemiology 2009;169(12):1428-36.
Hedderson 2010
Henriksen 2008
Higgins 2011
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