Skip to main content
  • More from ADA
    • Diabetes
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care
  • Subscribe
  • Log in
  • My Cart
  • Follow ada on Twitter
  • RSS
  • Visit ada on Facebook
Diabetes Care

Advanced Search

Main menu

  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • Special Article Collections
    • ADA Standards of Medical Care
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • Special Article Collections
    • ADA Standards of Medical Care
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcasts
    • Diabetes Core Update
    • Special Podcast Series: Therapeutic Inertia
    • Special Podcast Series: Influenza Podcasts
    • Special Podcast Series: SGLT2 Inhibitors
    • Special Podcast Series: COVID-19
  • Submit
    • Submit a Manuscript
    • Journal Policies
    • Instructions for Authors
    • ADA Peer Review
  • More from ADA
    • Diabetes
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care

User menu

  • Subscribe
  • Log in
  • My Cart

Search

  • Advanced search
Diabetes Care
  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • Special Article Collections
    • ADA Standards of Medical Care
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • Special Article Collections
    • ADA Standards of Medical Care
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcasts
    • Diabetes Core Update
    • Special Podcast Series: Therapeutic Inertia
    • Special Podcast Series: Influenza Podcasts
    • Special Podcast Series: SGLT2 Inhibitors
    • Special Podcast Series: COVID-19
  • Submit
    • Submit a Manuscript
    • Journal Policies
    • Instructions for Authors
    • ADA Peer Review
Considerations in the Management of Gestational Diabetes Mellitus

Randomized Controlled Trial Investigating the Effects of a Low–Glycemic Index Diet on Pregnancy Outcomes in Women at High Risk of Gestational Diabetes Mellitus: The GI Baby 3 Study

  1. Tania P. Markovic1,2⇑,
  2. Ros Muirhead1,3,4,
  3. Shannon Overs1,3,4,
  4. Glynis P. Ross2,5,
  5. Jimmy Chun Yu Louie1,3,4,
  6. Nathalie Kizirian1,3,4,
  7. Gareth Denyer3,4,
  8. Peter Petocz6,
  9. Jon Hyett7 and
  10. Jennie C. Brand-Miller1,3,4
  1. 1The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
  2. 2Department of Endocrinology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
  3. 3Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
  4. 4School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
  5. 5Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
  6. 6Department of Statistics, Macquarie University, Sydney, NSW, Australia
  7. 7RPA Women and Babies, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
  1. Corresponding author: Tania P. Markovic, tania.markovic{at}sydney.edu.au.
Diabetes Care 2016 Jan; 39(1): 31-38. https://doi.org/10.2337/dc15-0572
PreviousNext
  • Article
  • Figures & Tables
  • Info & Metrics
  • PDF
Loading

Abstract

OBJECTIVE Dietary interventions can improve pregnancy outcomes in women with gestational diabetes mellitus (GDM). We compared the effect of a low–glycemic index (GI) versus a conventional high-fiber (HF) diet on pregnancy outcomes, birth weight z score, and maternal metabolic profile in women at high risk of GDM.

RESEARCH DESIGN AND METHODS One hundred thirty-nine women [mean (SD) age 34.7 (0.4) years and prepregnancy BMI 25.2 (0.5) kg/m2] were randomly assigned to a low-GI (LGI) diet (n = 72; target GI ∼50) or a high-fiber, moderate-GI (HF) diet (n = 67; target GI ∼60) at 14–20 weeks’ gestation. Diet was assessed by 3-day food records and infant body composition by air-displacement plethysmography, and pregnancy outcomes were assessed from medical records.

RESULTS The LGI group achieved a lower GI than the HF group [mean (SD) 50 (5) vs. 58 (5); P < 0.001]. There were no differences in glycosylated hemoglobin, fructosamine, or lipids at 36 weeks or differences in birth weight [LGI 3.4 (0.4) kg vs. HF 3.4 (0.5) kg; P = 0.514], birth weight z score [LGI 0.31 (0.90) vs. HF 0.24 (1.07); P = 0.697], ponderal index [LGI 2.71 (0.22) vs. HF 2.69 (0.23) kg/m3; P = 0.672], birth weight centile [LGI 46.2 (25.4) vs. HF 41.8 (25.6); P = 0.330], % fat mass [LGI 10 (4) vs. HF 10 (4); P = 0.789], or incidence of GDM.

CONCLUSIONS In intensively monitored women at risk for GDM, a low-GI diet and a healthy diet produce similar pregnancy outcomes.

Introduction

Gestational diabetes mellitus (GDM) is defined as carbohydrate intolerance that is diagnosed for the first time in pregnancy (1). Pregnancy-related hormonal changes that reduce insulin sensitivity result in glucose intolerance in women with reduced β-cell reserve or with more marked underlying insulin resistance. Glucose intolerance in pregnancy has implications for both mother and child, including higher rates of preeclampsia, operative deliveries, macrosomia, and birth injury (2). Hyperglycemia in pregnancy and neonatal adiposity have also been linked to increased subsequent childhood obesity and type 2 diabetes mellitus (T2DM) in early adulthood (3,4). High maternal glycemia results in increased fetal insulin production, which is considered to be the main driver of macrosomia. How the intrauterine environment confers higher risk has not been established, but data from animal studies suggest that epigenetic processes modulate gene transcription in utero (5).

Current best practice for GDM management consists of maintaining maternal blood glucose levels within the normal pregnancy range by dietary intervention either alone or combined with insulin therapy. Dietary intervention often includes a reduction in carbohydrate quantity or in dietary glycemic index (GI). A low-GI diet produces lower postmeal blood glucose levels in healthy individuals (6,7) and has been shown to reduce the incidence of large-for-gestational-age (LGA) babies in nondiabetic pregnancy (8). Some studies suggest that a low-GI diet improves glucose tolerance in women with GDM (9,10) or with a history of macrosomia (11). A low-GI diet may also reduce maternal weight gain in women with normal glucose tolerance, GDM, or T2DM (11,12) but potentially increases the risk of prematurity (12). In women with GDM, we found no additional beneficial effect of a low-GI diet on neonatal outcomes (13), although the relatively late institution of the intervention at 29 weeks’ gestation (the time at which GDM is usually diagnosed) may preclude a clinically important effect on fetal growth rate. A greater effect might be achieved in high-risk patients who adopt the diet earlier in pregnancy.

Our aim therefore was to compare the effect of a low-GI diet with a conventional healthy diet on birth weight z score in women at high risk of GDM. Secondary aims were to compare the effects on pregnancy outcomes and maternal metabolic profile. Our hypothesis was that infants born to pregnant women at high risk of GDM who receive an early intervention of low-GI dietary advice will have a lower birth weight z score and lower body fat mass than those born to mothers who received advice on a macronutrient-matched high-fiber diet with a moderate GI.

Research Design and Methods

The GI Baby 3 study was a two-arm randomized controlled trial based at the antenatal clinic at the Royal Prince Alfred Hospital, Camperdown, NSW, Australia. Apart from the study dietitians (R.M. and S.O.) who provided dietary education, all study personnel were blinded to dietary assignment. The study was conducted according to the Declaration of Helsinki, and all procedures were approved by the Human Research Ethics Committee of the Sydney South West Area Health Service (Royal Prince Alfred Hospital zone; reference no. HREC/10/RPAH/453).

Subject Recruitment, Randomization, and Stratification

Women >18 years of age between 12 and 20 weeks of gestation and at high risk of GDM with an otherwise healthy single pregnancy were eligible for the study. Women were considered to be at high risk if they had at least one of the following risk factors: age >35 years, first-degree relative with T2DM, prepregnancy BMI ≥30 kg/m2, past history of GDM or glucose intolerance, history of a previous baby >4,000 g, or belonging to a high-risk ethnic group (Aboriginal or Torres Strait Islander, Polynesian, Middle Eastern, Indian, or Asian). Women who had preexisting diabetes or special dietary requirements (including vegetarianism/veganism) were excluded. Subjects were recruited from the fetal medicine clinic at the time of nuchal scanning. A total of 706 women were approached between January 2011 and October 2012, of whom 304 expressed an interest in participating. Of these, 157 subsequently declined, leaving 147 women who formally consented to commence the study. At study entry, all of the women had a routine early (between 14 and 20 weeks’ gestation) 75-g oral glucose tolerance test (OGTT). GDM diagnosis was based on modified Australasian Diabetes in Pregnancy Society 1998 criteria (14): fasting glucose level (BGL) ≥5.5 mmol/L, 1 h ≥10.0 mmol/L, and 2 h ≥8.0 mmol/L. However, if fasting BGL was ≥5.8 mmol/L and 2-h reading was ≥11.1 mmol/L in the initial (<20 week) OGTT, subjects were excluded, as these values were considered likely to represent more significant hyperglycemia requiring more intensive management and monitoring. After the OGTT, computer-generated random numbers, which were unpredictable and concealed from the recruiter, were used to allocate subjects to a low- or moderate-GI diet (1:1 allocation), stratified by BMI (<30 vs. ≥30 kg/m2). Apart from the additional dietary instruction, participants received routine antenatal and GDM care, as applicable, regardless of their dietary assignment. Subjects who had a normal OGTT at study entry had a second OGTT at 26–28 weeks. In subjects with GDM, insulin treatment was commenced if mean fasting or 1-h postprandial BGL in the preceding week exceeded 5.2 and 7.5 mmol/L, respectively, after eliminating abnormal readings clearly attributable to atypical intake.

At enrollment, demographic information, family history of diabetes, ethnicity, and medical history were recorded. Subjects were weighed at study entry and at every antenatal visit wearing light indoor clothes and without shoes.

Dietary Assessment

Subjects were asked to complete a 3-day food record (including 2 weekdays and 1 weekend day) at baseline and again at 34–36 weeks’ gestation. Both 3-day food records were entered into an Australian nutrition analysis software (FoodWorks 7 Professional; Xyris Software, Brisbane, QLD, Australia) based on the Australian food composition database AUSNUT2007 (15). The GI of individual food items was assigned according to a published method (16). Dietary glycemic load (GL) was calculated as sum of (GI × available carbohydrate of each food portion)/100 per day. Dietary GI was calculated as (dietary GL/total daily available carbohydrate) × 100.

Dietary Interventions

Subjects attended a total of five individual dietary consultations with a dietitian at 14–20, 18–24, 22–28, 26–32, and 34–36 weeks of gestation. At study visit 1 (14–20 weeks), subjects were randomized to one of two healthy diets of similar macronutrient composition: protein (15–25% total energy intake [E]), fat (25–30%E), and carbohydrate content (40–45%E). One group was asked to follow a low-GI diet (LGI) (target GI ≤50) and the other group a high-fiber, moderate-GI diet (HF), similar to the Australian population average (target GI 60). Both study diets provided all the essential nutrients for pregnancy other than iron and iodine, which were supplemented as appropriate.

The baseline 3-day food record provided information on usual dietary intake. At visit 1, this served as the basis of dietary counseling where written information regarding suitable LGI/HF foods and pregnancy nutrition was provided. At mid-study visits (visits 2, 3, and 4), four-stage multiple-pass 24-h recalls were performed to check dietary compliance. Subjects were deemed compliant if their final dietary GI was ≤50 in the LGI group and >50 in the HF group. In the case of noncompliance, suitable alternative foods were encouraged. A selection of recipes was also provided. For improvement of diet adherence and product recognition, subjects were provided with food samples containing key foods for the assigned diet at all five consultations. The content of supplementary baskets has previously been described (13).

Blood Biochemistry and Pregnancy Outcomes

Subjects provided fasting blood samples at study entry [mean (SD) 17.4 (2.0) weeks) and at visit 5 (36 weeks’ gestation). Last recorded maternal weight before delivery was obtained from the medical record.

Gestational age was estimated from the date of the last menstrual period and early pregnancy ultrasound. Birth weight, length, head circumference, mode of delivery, neonatal complications, and length of stay in neonatal intensive care unit were obtained from the electronic medical record. The infant’s body composition was determined within 48 h of the baby’s birth using a Pea Pod (COSMED Asia-Pacific Pty Ltd, Artarmon, NSW, Australia), an air-displacement plethysmograph that uses whole-body densitometry to measure percent fat and fat-free mass in infants. Birth weight z score was determined using the World Health Organization anthropometry for personal computers software (version 3.2.2, 2011, World Health Organization, Geneva, Switzerland). Birth weight centile was calculated using a macro program for Microsoft Excel (available from http://www.gestation.net) and was used to categorize the infant as small for gestational age (SGA) (birth weight <10th centile), normal, or LGA (birth weight >90th centile). Ponderal index, an estimate of neonatal adiposity, was calculated as birth weight in kilograms × infant length (m−3). Macrosomia was defined as birth weight >4,000 g.

Power Calculation and Statistical Analysis

Data from an Australian study (8) was used to generate the sample size. We aimed to have 60 women in each group (120 in total) so that we had a statistical power of 80% (two-sided α value of 0.05) to detect a difference in birth weight z score of 0.5 (∼260 g). To allow loss to follow-up of ∼20%, we aimed to enroll ∼150 women. A biostatistician blinded to the dietary allocation performed the statistical analysis. The primary analysis included all women who attended at least one dietary education session but excluded eight women as described below. All statistical analyses were performed using SPSS (version 22; IBM Australia, St Leonards, NSW, Australia). Results for continuous data are reported as mean (SD) and categorical data (e.g., emergency cesarean delivery) as a percentage. Pearson χ2 test was used to test for differences between groups for categorical data, while continuous data were tested using one-way ANOVA. A paired t test was used to assess within-group changes in outcomes from baseline to end of intervention.

Results

The flow of participants through the study is shown in the CONSORT diagram (Fig. 1). Of the 147 women recruited, 139 were included in the primary intention-to-treat analysis. Among the eight excluded, one was diagnosed with “overt” diabetes after the early OGTT, two underwent pregnancy termination, one had a twin pregnancy, one had a premature delivery (<37 weeks), and three moved interstate. A further 14 participants withdrew after commencing the study (4 were too busy, 2 were lost to follow-up, 4 did not wish to follow their allocated diet, and 4 gave no reason), leaving 125 participants who completed the study (completers analysis). Baseline characteristics are shown in Table 1. There were no differences between the two diet groups with respect to age, BMI, ethnicity, or level of education. There were no differences in risk factors for GDM, apart from family history of T2DM, which was more common in the LGI group. Most subjects had completed tertiary education (68% in LGI and 80% in HF group). Similar numbers of women were diagnosed with GDM at study entry: 10 in the LGI group and 11 in the HF group.

Figure 1
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1

Study flow (CONSORT diagram).

View this table:
  • View inline
  • View popup
Table 1

Subject characteristics

Table 2 shows the diet analysis. At baseline, the LGI group had a slightly lower dietary GI, but the total carbohydrate intake and GL were not significantly different. By the end of the intervention, there was a significant difference in dietary GI and GL between the two groups (P < 0.001). The protein and total fat intake did not change in either group, but energy, saturated fat, and total sugars intake were significantly reduced only in the LGI group.

View this table:
  • View inline
  • View popup
Table 2

Baseline and end-of-intervention dietary analysis for completers only

The biochemical parameters are presented in Table 3. Outcomes were similar in both groups at baseline and at the end of the intervention. HbA1c increased significantly with an increase in plasma C-peptide concentration. There was a tendency for C-reactive protein to decline in the LGI group (P = 0.051). While triglycerides and LDL cholesterol increased during pregnancy in both groups, free fatty acids did not change. As expected in pregnancy, iron and vitamin B12 levels fell but 25-hydroxyvitamin D increased, as this vitamin was routinely supplemented.

View this table:
  • View inline
  • View popup
Table 3

Biochemical parameters at baseline and end of intervention for completers only

Primary and secondary pregnancy outcomes were similar in both groups (Table 4), including birth weight, birth weight centile, birth weight z score, % body fat at birth, and maternal weight gain. There was no difference in SGA, LGA, or macrosomia in the offspring, whether analyzed as intention to treat or among completers only, nor was there any difference in mode of delivery or maternal complications (data not presented). Stratification based on family history of T2DM and the actual GI of the mother’s diet, regardless of the group to which they were assigned, did not alter the primary outcomes (data not presented). Admission to neonatal intensive care was higher in the HF (n = 10, 15%) compared with the LGI (n = 4, 6%) group, but the difference was not statistically significant (P = 0.067). In the LGI group, one neonate was admitted with jaundice, one with low oxygen saturation and meconium aspiration, one with bilious vomit, and another with respiratory distress. In the HF group, one neonate was admitted with jaundice; one with jaundice and neutropenia (premature); one with pneumonia and respiratory distress; one with hypothermia, low birth weight, and intrauterine growth retardation; one with hypoglycemia, hypothermia, jaundice, and leuco- and neutrophilia; one with hypoglycemia; one with transient tachypnea; and three were admitted with respiratory distress (one of which was premature).

View this table:
  • View inline
  • View popup
Table 4

Pregnancy outcomes

Ten women (14%) developed GDM before 20 weeks in the LGI group and 11 (16%) in the HF group. Another 14% of women developed GDM by 26–28 weeks in both diet groups (10 in the LGI group and 9 in the HF group) (Table 4). There were no significant differences in GDM diagnosis whether results were examined as intention to treat or completers only. Non-Caucasian women developed GDM earlier, and relatively more women of non-Caucasian ethnicity developed GDM overall. Twenty-one of the 40 women who developed GDM were non-Caucasian (i.e., 53%), whereas this group represented only 40% of the study population. Of the 21 women who had GDM by 20 weeks, 67% were non-Caucasian, and of the further 19 who developed GDM by 28 weeks, 53% were non-Caucasian. Sixteen subjects required insulin treatment in the LGI group and 9 in the HF group (P = 0.178). Insulin treatment was commenced at mean (SD) 29 (6) weeks in the LGI group and 25 (5) weeks in the HF group (P = 0.129). The maximum insulin dose used was 15.6 (9.9) units in the LGI and 37.9 (27.3) units in the HF group (P = 0.007).

Conclusions

Contrary to our hypothesis, we found no evidence of a difference in pregnancy and neonatal outcomes in women at high risk of GDM who were randomly allocated to follow either a low-GI diet or a conventional healthy diet in the second trimester of pregnancy. The average infant birth weight, birth weight centile, and neonatal percent body fat were in the healthy range in both dietary groups. In particular, the rates of SGA, LGA, and macrosomia were the same in each dietary group and were relatively low for this high-risk cohort.

At study entry, 15% met the diagnostic criteria for GDM and a further 14% developed GDM at the start of the 3rd trimester, with no differences between diet groups. Among those diagnosed with GDM, there was no difference in requirement for insulin therapy commencement, although the women on the LGI diet required a significantly lower dose, and there was a tendency to start insulin later in this group. These findings suggest that the low-GI diet may have had a favorable effect on glycemia. A previous study showed that women with GDM who followed a low-GI diet had a lower need for insulin compared with women on a conventional diet (9), although the baseline BMI of its participants was markedly higher than in our cohort (BMI 32 vs. 25 kg/m2).

In women with GDM, the rate of macrosomia has been reported to be as high as 22% (2,17), whereas in our study it was 12% overall (11% in those who did not develop GDM, 19% in those who developed GDM before 20 weeks, and 11% in women who developed GDM by 26–28 weeks). Our LGA rates were slightly lower than those reported in a study of women with well-controlled GDM and normal glucose tolerance (18), i.e., 7% in the GDM group and 9% in normal glucose tolerant subjects compared with 5.6% in the LGI group and 6.0 in the HF group in our study. Similarly, the mean SGA rate was only 6.5%, which is lower than expected, and importantly, neither diet increased the rate of SGA (18). The rate of cesarean delivery (28% for our entire cohort) was comparable to the usual rate that has been reported (30% overall and 38% in women with GDM) (19).

There are a number of possible reasons for the lack of an effect of the low-GI diet. These women were predominantly highly educated, with prepregnancy body weight just above the normal range (BMI 25.2 kg/m2), and from a lower-risk Caucasian population. They may therefore represent a group of well-motivated women who already followed a healthy lifestyle, reflected in their prepregnancy body weight, baseline dietary intake, and willingness to join the study. The antenatal management of all subjects is likely to have been improved by the intensity of the intervention, including an increase in professional contact time. In addition, the conventional healthy diet was high in fiber and had a lower GI than in the control arm of similar studies. Another interpretation of our findings is that a low-GI diet might not convey any benefits to women at high risk of GDM in pregnancy over and above that of a conventional healthy diet. However, a recent meta-analysis of existing dietary studies suggests that a low-GI diet results in less insulin use and lower birth weight and concluded that it is currently the most appropriate dietary intervention in GDM (20). The lack of a difference in HbA1c between the interventions is not unexpected, as HbA1c does not provide a good indication of postprandial glycemia. A low-GI diet may elicit other benefits including a reduction in glucose fluctuations and lower inflammatory markers, as evidenced by lower C-reactive protein levels in a study by Wolever et al. (21) and a tendency for this in our study. On the other hand, the fructosamine fell in both groups. While there are few data on fructosamine levels in pregnancy, this parameter represents changes in glycemia over the preceding weeks, rather than months, outside pregnancy. Thus, the fall in fructosamine might reflect the beneficial effects of the intervention more effectively than the HbA1c. It is also possible that dietary intervention initiated in the second trimester is still too late to substantially influence outcomes. In nondiabetic pregnant women, a higher-GI diet that was similar to that of our study (8) was associated with significantly more macrosomia. However, the intervention started earlier, between 12 and 16 weeks.

It is conceivable that the dietary pattern around conception is more important for any effects on metabolic programming than later in the pregnancy. There is increasing evidence that nutrition in the prenatal environment affects epigenetic processes, such as DNA methylation and histone modifications, resulting in modulation of gene transcription, thereby altering the phenotype of the offspring (22). Animal studies, in which dietary manipulations occur from conception, have induced DNA methylation and covalent histone modifications in genes that affect offspring body composition and metabolic phenotype (23,24). In a study in which maternal GI and GL were calculated in early (11 weeks) and late (34 weeks) pregnancy, it was found that only maternal GI and GL in early pregnancy were associated with fat mass at 4 and 6 years of age but not with fat mass at birth (25). A Danish study showed that dietary GI at 30 weeks’ gestation was associated with insulin sensitivity and insulin and leptin levels in offspring aged 20 years (26). Thus, the effects of any dietary intervention in pregnancy may not be apparent until sometime after the perinatal period.

The strengths of our study include the randomized design, the diverse ethnicity of the participants, concealment of diet allocation, blinding of investigators, intensity of intervention, measurement of neonatal body composition (not just birth weight), use of medical records for primary and secondary outcomes, and apparent maternal compliance with dietary instruction. Australia is a culturally diverse country, with about one-quarter of the population born overseas and a further 20% of residents having at least one parent born overseas (27). Furthermore, in Australia minority ethnic groups show a higher prevalence of GDM than women of Caucasian origin (28). This is reflected in the current study participants, where ∼40% were of non-Caucasian origin, yet this group represented 67% of the women who had GDM by 20 weeks and 53% who were diagnosed with GDM by 28 weeks. However, the study limitations include the fact that those assigned to the LGI diet group not only reduced the GI but also energy, saturated fat, and total sugars. Not all carbohydrate foods were provided (only samples), and there is the possibility that compliance may have been optimized during recording periods. The high level of education and unrepresentative nature of the cohort limit the generalizability of the findings.

In conclusion, we found that a low-GI diet commenced early in the second trimester in women at high risk of GDM resulted in no apparent advantage over a conventional healthy diet with respect to risk of development of GDM, glycemia, adverse pregnancy outcomes, and neonatal anthropometry. The offspring of both groups had mean birth weight, birth weight centile, and neonatal adiposity within the normal range. Importantly, a low-GI diet did not result in any increased risk of adverse outcomes, and the insulin dose requirement was lower in women who developed GDM. Whether differences may have been seen in a group of more obese or less intensively managed women remains to be determined. Further studies examining the effect of a low-GI diet in women planning pregnancy and of the effects of a low-GI diet in pregnancy on the longer-term metabolic health of the offspring are also recommended.

Article Information

Acknowledgments. The authors thank the study participants for their contribution to the research and the Diabetes Centre, Antenatal Clinic, and midwives at Royal Prince Alfred Hospital for assistance with study implementation.

Funding. This study was funded by the Australian National Health and Medical Research Council (grant 632889).

Duality of Interest. J.C.Y.L. consults for the Glycemic Index Foundation. J.C.B.-M. is the President of the Glycemic Index Foundation, Director of the University of Sydney Glycemic Index Research Service, and author of popular books about the glycemic index of foods. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. T.P.M., G.P.R., and J.C.B.-M. conceived and conducted the study, interpreted data, and wrote the manuscript. R.M. and S.O. implemented the protocol, instructed the participants, and generated the final data. J.C.Y.L. and P.P. analyzed and interpreted data. G.D. designed and managed the research database. T.P.M., J.H., and G.P.R. oversaw clinical aspects. All authors contributed to the discussion of the manuscript. J.C.B.-M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the Australian Diabetes Society Annual Scientific Meeting, Sydney, Australia, 28–30 August 2013.

Footnotes

  • Clinical trial reg. no. ACTRN12610000681055, anzctr.org.au.

  • See accompanying articles, pp. 13, 16, 24, 39, 43, 50, 53, 55, 61, and 65.

  • Received March 23, 2015.
  • Accepted June 22, 2015.
  • © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

References

  1. ↵
    1. Buyken AE,
    2. Goletzke J,
    3. Joslowski G, et al
    . Association between carbohydrate quality and inflammatory markers: systematic review of observational and interventional studies. Am J Clin Nutr 2014;99:813–833pmid:24552752
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Crowther CA,
    2. Hiller JE,
    3. Moss JR,
    4. McPhee AJ,
    5. Jeffries WS,
    6. Robinson JS; Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) Trial Group
    . Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N Engl J Med 2005;352:2477–2486pmid:15951574
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    1. Adamo KB,
    2. Ferraro ZM,
    3. Brett KE
    . Can we modify the intrauterine environment to halt the intergenerational cycle of obesity? Int J Environ Res Public Health 2012;9:1263–1307pmid:22690193
    OpenUrlCrossRefPubMed
  4. ↵
    1. Hillier TA,
    2. Pedula KL,
    3. Schmidt MM,
    4. Mullen JA,
    5. Charles MA,
    6. Pettitt DJ
    . Childhood obesity and metabolic imprinting: the ongoing effects of maternal hyperglycemia. Diabetes Care 2007;30:2287–2292pmid:17519427
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Godfrey KM,
    2. Barker DJP,
    3. Robinson S,
    4. Osmond C
    . Maternal birthweight and diet in pregnancy in relation to the infant’s thinness at birth. Br J Obstet Gynaecol 1997;104:663–667pmid:9197868
    OpenUrlCrossRefPubMedWeb of Science
  6. ↵
    1. Brand-Miller J,
    2. Hayne S,
    3. Petocz P,
    4. Colagiuri S
    . Low-glycemic index diets in the management of diabetes: a meta-analysis of randomized controlled trials. Diabetes Care 2003;26:2261–2267pmid:12882846
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Buyken AE,
    2. Toeller M,
    3. Heitkamp G, et al, .; EURODIAB IDDM Complications Study Group
    . Glycemic index in the diet of European outpatients with type 1 diabetes: relations to glycated hemoglobin and serum lipids. Am J Clin Nutr 2001;73:574–581pmid:11237934
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Moses RG,
    2. Luebcke M,
    3. Davis WS, et al
    . Effect of a low-glycemic-index diet during pregnancy on obstetric outcomes. Am J Clin Nutr 2006;84:807–812pmid:17023707
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Moses RG,
    2. Barker M,
    3. Winter M,
    4. Petocz P,
    5. Brand-Miller JC
    . Can a low-glycemic index diet reduce the need for insulin in gestational diabetes mellitus? A randomized trial. Diabetes Care 2009;32:996–1000pmid:19279301
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Grant SM,
    2. Wolever TM,
    3. O’Connor DL,
    4. Nisenbaum R,
    5. Josse RG
    . Effect of a low glycaemic index diet on blood glucose in women with gestational hyperglycaemia. Diabetes Res Clin Pract 2011;91:15–22pmid:21094553
    OpenUrlCrossRefPubMed
  11. ↵
    1. Walsh JM,
    2. McGowan CA,
    3. Mahony R,
    4. Foley ME,
    5. McAuliffe FM
    . Low glycaemic index diet in pregnancy to prevent macrosomia (ROLO study): randomised control trial. BMJ 2012;345:e5605pmid:22936795
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Perichart-Perera O,
    2. Balas-Nakash M,
    3. Rodriguez-Cano A,
    4. Legorreta-Legorreta J,
    5. Parra-Covarrubias A,
    6. Vadillo-Ortega F.
    Low Glycemic Index Carbohydrates versus All Types of Carbohydrates for Treating Diabetes in Pregnancy: A Randomized Clinical Trial to Evaluate the Effect of Glycemic Control. Int J Endocrinol 2012;2012:296017
  13. ↵
    1. Louie JC,
    2. Markovic TP,
    3. Perera N, et al
    . A randomized controlled trial investigating the effects of a low-glycemic index diet on pregnancy outcomes in gestational diabetes mellitus. Diabetes Care 2011;34:2341–2346pmid:21900148
    OpenUrlAbstract/FREE Full Text
  14. ↵
    1. Hoffman L,
    2. Nolan C,
    3. Wilson JD,
    4. Oats JJN,
    5. Simmons D; The Australasian Diabetes in Pregnancy Society
    . Gestational diabetes mellitus--management guidelines. Med J Aust 1998;169:93–97pmid:9700346
    OpenUrlPubMedWeb of Science
  15. ↵
    AUSNUT 2007. Canberra, ACT, Australia, Food Standards Australia New Zealand, 2008
  16. ↵
    1. Louie JC,
    2. Flood VM,
    3. Atkinson FS,
    4. Barclay AW,
    5. Brand-Miller JC
    . Methodology for assigning appropriate glycaemic index values to an Australian food composition database. J Food Comp Anal 2015;38:1–6
    OpenUrlCrossRef
  17. ↵
    1. Louie JC,
    2. Brand-Miller JC,
    3. Markovic TP,
    4. Ross GP,
    5. Moses RG
    . Glycemic index and pregnancy: a systematic literature review. J Nutr Metab 2010;2010:282464
    OpenUrlPubMed
  18. ↵
    1. Au CP,
    2. Raynes-Greenow CH,
    3. Turner RM,
    4. Carberry AE,
    5. Jeffery HE
    . Body composition is normal in term infants born to mothers with well-controlled gestational diabetes mellitus. Diabetes Care 2013;36:562–564pmid:23223404
    OpenUrlAbstract/FREE Full Text
  19. ↵
    AIHW. Diabetes in pregnancy: its impact on Australian women and their babies. Diabetes series no. 14. Cat. no. CVD 52 [Internet], 2010. Canberra, ACT, Australia, AIHW. Available from http://www.aihw.gov.au/publication-detail/?id=6442472448. Accessed 1 July 2014
  20. ↵
    1. Viana LV,
    2. Gross JL,
    3. Azevedo MJ
    . Dietary intervention in patients with gestational diabetes mellitus: a systematic review and meta-analysis of randomized clinical trials on maternal and newborn outcomes. Diabetes Care 2014;37:3345–3355pmid:25414390
    OpenUrlAbstract/FREE Full Text
  21. ↵
    1. Wolever TM,
    2. Gibbs AL,
    3. Mehling C, et al
    . The Canadian Trial of Carbohydrates in Diabetes (CCD), a 1-y controlled trial of low-glycemic-index dietary carbohydrate in type 2 diabetes: no effect on glycated hemoglobin but reduction in C-reactive protein. Am J Clin Nutr 2008;87:114–125pmid:18175744
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Godfrey KM,
    2. Sheppard A,
    3. Gluckman PD, et al
    . Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes 2011;60:1528–1534pmid:21471513
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Bogdarina I,
    2. Welham S,
    3. King PJ,
    4. Burns SP,
    5. Clark AJ
    . Epigenetic modification of the renin-angiotensin system in the fetal programming of hypertension. Circ Res 2007;100:520–526pmid:17255528
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Lillycrop KA,
    2. Slater-Jefferies JL,
    3. Hanson MA,
    4. Godfrey KM,
    5. Jackson AA,
    6. Burdge GC
    . Induction of altered epigenetic regulation of the hepatic glucocorticoid receptor in the offspring of rats fed a protein-restricted diet during pregnancy suggests that reduced DNA methyltransferase-1 expression is involved in impaired DNA methylation and changes in histone modifications. Br J Nutr 2007;97:1064–1073pmid:17433129
    OpenUrlCrossRefPubMed
  25. ↵
    1. Okubo H,
    2. Crozier SR,
    3. Harvey NC, et al
    . Maternal dietary glycemic index and glycemic load in early pregnancy are associated with offspring adiposity in childhood: the Southampton Women’s Survey. Am J Clin Nutr 2014;100:676–683pmid:24944056
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. Danielsen I,
    2. Granström C,
    3. Haldorsson T, et al
    . Dietary glycemic index during pregnancy is associated with biomarkers of the metabolic syndrome in offspring at age 20 years. PLoS One 2013;8:e64887pmid:23741411
    OpenUrlCrossRefPubMed
  27. ↵
    Australian Bureau of Statistics. 3416.0 - perspectives on migrants - Mar 2013 [Internet], 2013. Available from http://www.abs.gov.au/ausstats/abs@.nsf/latestproducts/3416.0main+features2mar+2013. Accessed 15 June 2015
  28. ↵
    1. Abouzeid M,
    2. Versace VL,
    3. Janus ED, et al
    . A population-based observational study of diabetes during pregnancy in Victoria, Australia, 1999-2008. BMJ Open 2014;4:e005394pmid:25398676
    OpenUrlAbstract/FREE Full Text
View Abstract
PreviousNext
Back to top
Diabetes Care: 39 (1)

In this Issue

January 2016, 39(1)
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by Author
  • Masthead (PDF)
Sign up to receive current issue alerts
View Selected Citations (0)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about Diabetes Care.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Randomized Controlled Trial Investigating the Effects of a Low–Glycemic Index Diet on Pregnancy Outcomes in Women at High Risk of Gestational Diabetes Mellitus: The GI Baby 3 Study
(Your Name) has forwarded a page to you from Diabetes Care
(Your Name) thought you would like to see this page from the Diabetes Care web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Randomized Controlled Trial Investigating the Effects of a Low–Glycemic Index Diet on Pregnancy Outcomes in Women at High Risk of Gestational Diabetes Mellitus: The GI Baby 3 Study
Tania P. Markovic, Ros Muirhead, Shannon Overs, Glynis P. Ross, Jimmy Chun Yu Louie, Nathalie Kizirian, Gareth Denyer, Peter Petocz, Jon Hyett, Jennie C. Brand-Miller
Diabetes Care Jan 2016, 39 (1) 31-38; DOI: 10.2337/dc15-0572

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Add to Selected Citations
Share

Randomized Controlled Trial Investigating the Effects of a Low–Glycemic Index Diet on Pregnancy Outcomes in Women at High Risk of Gestational Diabetes Mellitus: The GI Baby 3 Study
Tania P. Markovic, Ros Muirhead, Shannon Overs, Glynis P. Ross, Jimmy Chun Yu Louie, Nathalie Kizirian, Gareth Denyer, Peter Petocz, Jon Hyett, Jennie C. Brand-Miller
Diabetes Care Jan 2016, 39 (1) 31-38; DOI: 10.2337/dc15-0572
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Research Design and Methods
    • Results
    • Conclusions
    • Article Information
    • Footnotes
    • References
  • Figures & Tables
  • Info & Metrics
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • Considerations in the Management of Gestational Diabetes Mellitus: “You Are What Your Mother Ate!”
  • The Role of Energy, Nutrients, Foods, and Dietary Patterns in the Development of Gestational Diabetes Mellitus: A Systematic Review of Observational Studies
  • Low Carbohydrate–Diet Scores and Long-term Risk of Type 2 Diabetes Among Women With a History of Gestational Diabetes Mellitus: A Prospective Cohort Study
Show more Considerations in the Management of Gestational Diabetes Mellitus

Similar Articles

Navigate

  • Current Issue
  • Standards of Care Guidelines
  • Online Ahead of Print
  • Archives
  • Submit
  • Subscribe
  • Email Alerts
  • RSS Feeds

More Information

  • About the Journal
  • Instructions for Authors
  • Journal Policies
  • Reprints and Permissions
  • Advertising
  • Privacy Policy: ADA Journals
  • Copyright Notice/Public Access Policy
  • Contact Us

Other ADA Resources

  • Diabetes
  • Clinical Diabetes
  • Diabetes Spectrum
  • Scientific Sessions Abstracts
  • Standards of Medical Care in Diabetes
  • BMJ Open - Diabetes Research & Care
  • Professional Books
  • Diabetes Forecast

 

  • DiabetesJournals.org
  • Diabetes Core Update
  • ADA's DiabetesPro
  • ADA Member Directory
  • Diabetes.org

© 2021 by the American Diabetes Association. Diabetes Care Print ISSN: 0149-5992, Online ISSN: 1935-5548.