To describe the diet quality of a national sample of Australian women with a recent history of gestational diabetes mellitus (GDM) and determine factors associated with adherence to national dietary recommendations.
A postpartum lifestyle survey with 1499 Australian women diagnosed with GDM ⩽3 years previously. Diet quality was measured using the Australian recommended food score (ARFS) and weighted by demographic and diabetes management characteristics. Multinominal logistic regression analysis was used to determine the association between diet quality and demographic characteristics, health seeking behaviours and diabetes-related risk factors.
Mean (±s.d.) ARFS was 30.9±8.1 from a possible maximum score of 74. Subscale component scores demonstrated that the nuts/legumes, grains and fruits were the most poorly scored. Factors associated with being in the highest compared with the lowest ARFS quintile included age (odds ratio (OR) 5-year increase=1.40; 95% (confidence interval) CI:1.16–1.68), tertiary education (OR=2.19; 95% CI:1.52–3.17), speaking only English (OR=1.92; 95% CI:1.19–3.08), being sufficiently physically active (OR=2.11; 95% CI:1.46–3.05), returning for postpartum blood glucose testing (OR=1.75; 95% CI:1.23–2.50) and receiving risk reduction advice from a health professional (OR=1.80; 95% CI:1.24–2.60).
Despite an increased risk of type 2 diabetes, women in this study had an overall poor diet quality as measured by the ARFS. Women with GDM should be targeted for interventions aimed at achieving a postpartum diet consistent with the guidelines for chronic disease prevention. Encouraging women to return for follow-up and providing risk reduction advice may be positive initial steps to improve diet quality, but additional strategies need to be identified.
Gestational diabetes mellitus (GDM) is a form of glucose intolerance diagnosed during pregnancy.1 It affects an estimated 5% of Australian women, increasing up to 14% in some high-risk groups.2 GDM is associated with increased perinatal risks, while long-term consequences include development of type 2 diabetes and increased cardiovascular risk.3 Although research to date has varied in estimates of future type 2 diabetes risk, one recent Australian study reported a 9.6 times greater risk of type 2 diabetes in women with previous GDM and a cumulative risk of 25% after 15 years.4
Research demonstrates that intensive lifestyle interventions are effective in the prevention of type 2 diabetes,5 hence, the diagnosis of GDM provides an opportunity for early intervention in an at-risk group. Despite this, there is some evidence to suggest that women diagnosed with GDM have postpartum lifestyle behaviours that are not consistent with guidelines for the prevention of type 2 diabetes, including suboptimal physical activity levels,6, 7 poor intake of fruit and vegetables and high-fat diets.8, 9, 10 However, to date there has been little published data on the postpartum dietary intakes of Australian women with prior GDM.
Recent studies examining whole diets, as opposed to single nutrients or dietary components, have highlighted the important role of dietary patterns and overall diet quality in the prevention of type 2 diabetes.11, 12, 13, 14 Healthful dietary patterns characterised by high consumption of fruit and vegetables, whole grains, fish and poultry may delay the progression to type 2 diabetes,13, 15 whereas Western dietary patterns have been demonstrated to increase risk.16 Likewise, a variety of diet quality tools that measure adherence to dietary guidelines have demonstrated that a high-diet quality, representing alignment with national dietary guidelines, is inversely associated with obesity, blood lipids, hyperglycaemia and hyperinsulinaemia, as well as all-cause mortality and indices of self-rated health.17, 18 In prospective studies, overall diet quality has also been inversely associated with type 2 diabetes risk in women, independent of body mass index (BMI).19 Diet quality may therefore have an important role in mediating the development of chronic disease in a group known to be at high risk of type 2 diabetes.
The aim of this study was to describe the diet quality of a national sample of Australian women with a recent history of GDM and determine factors associated with adherence to national dietary recommendations.
Materials and methods
This was a cross-sectional study of Australian women with a recent history of GDM. Participants were recruited from the National Diabetes Service Scheme (NDSS) database. The NDSS is an initiative of the Australian Government that provides subsidised blood glucose testing strips and free syringes to residents diagnosed with diabetes. Registrants also have the option of nominating whether or not they consent to being contacted for research purposes. Study inclusion criteria were: diagnosed with GDM ⩽3 years previously, registered with the NDSS and consented to be contacted for research purposes. Women were excluded if they were aged <18 years at the time of registration. Eligible women were invited to participate by mail. Additional women were recruited from two major maternity clinics in Brisbane, Australia. Women from the clinics were pregnant at the time of recruitment, but surveyed 6 months postpartum. This additional sampling was to recruit women with very recent GDM, who may be missed in the NDSS database due to status update delay. The University of Newcastle Human Research Ethics Committee, the University of Queensland, the Royal Brisbane Women’s Hospital and the Mater Health Services approved the study, and Diabetes Australia Ltd approved the NDSS database search.
The survey was administered by two methods. First, a self-administered written questionnaire and second, a telephone interview conducted in parallel by trained interviewers using Computer-Assisted-Telephone-Interviewing for Windows (WinCati, Version 4.2; Sawtooth Technologies, Northbrook, IL, USA); full details of which have been described elsewhere.6, 20 Briefly, the survey questions addressed demographics, educational attainment, language spoken at home and occupation using standard items from the 2001 Australian census.21 Information regarding GDM management, lifestyle-related risk factors, family and medical history, and postpartum follow-up were collected by self-report. Data on respondent’s height and pre and postpartum weight were self-reported and used to calculate BMI as weight (kg)/height (m)2. Physical activity was assessed using the validated Active Australia Questionnaire (AAQ), which involves recall of frequency and duration of physical activity in the past week. The AAQ is a widely used reliable and valid measure of physical activity.22, 23 Physical activity levels were defined according to the AAQ criteria,24 whereby ‘sufficient’ physical activity was defined as the accumulation of at least 150 min of moderate or equivalent weighted vigorous activity over at least five sessions in the past week. Physical activity overreporters were recoded according to the AAQ guidelines.24 The self-administered questionnaire was pilot tested with a convenience sample of women (n=23) from the Diabetes Australia-NSW membership database. The telephone questionnaire was pilot tested with six women who had a recent GDM (<3 years) pregnancy using a snowball sampling method.
Australian recommended food score (ARFS)
Diet quality was assessed using the ARFS. The ARFS is a diet quality score modelled on the Recommended Food Score developed by Kant and Thompson25 and derived from the Victorian Cancer Council’s Dietary Questionnaire for Epidemiological Studies (DQES) food frequency questionnaire (FFQ).26 The DQES was originally developed for use in an ethnically diverse cohort,26 and has been validated against 7-day weighed food records in young Australian women and found to an accurate estimate of usual dietary intake.27 The ARFS is an index of dietary variety and nutritional quality with higher scores reflecting greater adherence to the Dietary Guidelines for Australians28 and food variety within core food groups of the Australian Guide to Healthy Eating.29 It has been validated in a nationally representative sample of Australian women,18 with a higher ARFS associated with a lower percentage of energy from total and saturated fat, a higher percentage of energy from carbohydrates and protein, and higher intake of micronutrients.18
The ARFS requires respondents to report their usual consumption of foods over the preceding 12 months. It includes nine questions regarding frequency of consumption of core foods and details of usual food choices within each group. These questions are closed ended with multiple response categories. This is followed by a 48-item FFQ with dichotomised response categories. The FFQ includes only foods from the original DQES FFQ that make a healthful contribution to dietary intake. The ARFS scoring is mostly independent of reported quantities of food, rather is based on frequency of consumption of core food items. Items from the 48-questions FFQ consumed less than once a week scored zero and those consumed once a week or more scored one. An additional score of one was allocated for each of the following: consuming two or more fruit serves per day, four or more vegetables per day, the use of reduced fat or skim milk or soy milk, consuming at least 500 ml of milk per day, using high fibre, wholemeal, rye or multigrain breads, consuming at least four slices of bread per day, using polyunsaturated or monounsaturated spreads or no fat spread, having one or two eggs per week, using ricotta or cottage cheese and using low fat cheese, consuming ice cream and cheese each less than once a week, yoghurt more than once a week. Frequency of alcohol consumption between 1–2 days/month and 4 days/week was allocated one point, and one point was allocated for quantity of one or two standard drinks. Zero points were added for alcohol consumed outside these ranges. Further details are provided in Table 1. The maximum ARFS that indicates greater adherence to the recommendations in both the Dietary Guidelines for Australians and AGHE is 74.
For analysis, ARFS was divided into quintiles to create a categorical variable, with quintile one representing the lowest category of dietary quality and quintile five the highest dietary quality. Those with more than four missing items were excluded from the analysis and missing values were recoded as zero for those with up to four items missing.
To correct for potential sampling bias, descriptive statistics, ARFS and component scores were adjusted for age, country of birth, state of residence and insulin usage using weights from 15 880 women with complete data in the NDSS data set. Unweighted analyses were used to examine the predictors of ARFS. Univariate χ2 analyses were performed to determine variables associated with ARFS quintiles. Statistically significant variables (P⩽0.05), as well as age and BMI, were included in a multiple variable multinominal logistic regression analysis. Likelihood ratio tests were used to assess significance of effects in the logistic regression model and used as the basis for retaining a variable in the model. The Pearson χ2 was used to check the goodness of fit of the model. The multiple variable model provides odds ratio (OR) estimates adjusted for other variables in the model. ORs for quintiles 2–5 were referenced to quintile 1, and 95% confidence intervals were calculated for each of these quintiles. Analyses were completed using SPSS version 18.0 (IBM Corp., Somers, NY, USA).
Of the 15 893 women registered on the NDSS with gestational diabetes, invitations were sent to 5147 women who met the inclusion criteria, with 302 women unable to be contacted. Of those invited, 1736 women consented to participate (36% response rate). Ineligible respondents who were currently pregnant (n=189), diagnosed with other forms of diabetes (n=9) or those with missing demographic data required for sample weighting (n=39), were excluded from analyses. Final data were available for 1499 respondents.
Using weighted data, the mean age±s.d. was 34.2±5.1. Approximately two thirds were Australian born (64.5%) or currently employed (67.4%). Less than half (40.1%) were tertiary educated, 22.6% spoke a language other than English and 1.7% were from an Aboriginal or Torres Strait Islander background. A previous diagnosis of GDM (before the index pregnancy) was reported by 13.1% of respondents, 25.7% used insulin during the index pregnancy, 29.0% were overweight and 26.3% were obese with a mean (±s.d.) self-reported BMI of 27.1±6.5.
The ARFS was calculated for 1447 women (52 women had more than four missing items, so were excluded from the analyses). Mean (±s.d.) diet quality score was 30.9±8.1 from a possible maximum score of 74. Subscale component scores are reported in Table 1 and demonstrate that the meat, alcohol and vegetable components were the most highly scored groups relative to the other components with nuts/legumes, grains and fruits the most poorly scored.
Table 2 reports the demographic characteristics, health seeking behaviours and diabetes-related risk factors of women with GDM by ARFS quintile. Independent variables found to be significant (P⩽0.05) in univariate analyses included region of birth, speaking only English, being tertiary educated, returning for postpartum follow-up blood glucose testing, being sufficiently physically active and receiving risk reduction advice from a health professional. When these variables (as well as age and BMI) were included in multinominal logistic regression analyses, they remained significant, with the exception of region of birth that was excluded from the final model, see Table 3. The Pearson χ2 was not significant (ChiSq (5116)=5116, P=0.499), indicating a satisfactory fit of the model to the data.
Table 3 contains all the significant effects in the multiple variable multinomial logistic regression model expressed as OR and 95% CIs for ARFS quintiles 2–5, using the lowest quintile as the reference group for each OR. The reference groups for the categorical explanatory variables are indicated by OR=1. Interpretation of the effects is similar for all variables in the model as they have a positive relationship with dietary score. The relative impact of the six significant factors can be assessed by comparing the OR’s for ARFS quintile 5. Factors associated with being in the highest compared with the lowest ARFS quintile included age (OR 5-year increase=1.40; 95% CI:1.16–1.68), tertiary education (OR=2.19; 95% CI:1.52–3.17), speaking only English (OR=1.92; 95% CI:1.19–3.08), being sufficiently physically active (OR=2.11; 95% CI:1.46–3.05), returning for postpartum blood glucose testing (OR=1.75; 95% CI:1.23–2.50) and receiving risk reduction advice from a health professional (OR=1.80; 95% CI:1.24–2.60). There was a trend such that as BMI increased women were less likely to be in the highest compared with the lowest ARFS quintile (reference group). However, this failed to reach significance in the likelihood ratio test (P=0.078) and was excluded from the final model. Table 3 also provides OR estimates for the other three quintiles of diet quality to show the overall pattern across quintiles.
This is the first Australian study to date investigating diet quality in a national sample of women with a history of GDM. Despite their increased risk of developing type 2 diabetes, women in this study had an overall poor diet quality as measured by the ARFS, indicating suboptimal intake of key food groups and eating patterns not aligned with national guidelines.28 These findings are consistent with research done with representative samples of young and mid-aged Australian women whereby poor diet quality and disparities between national food group recommendations and dietary intakes have been reported.18, 30, 31
Analysis by component subscores indicated that nuts/legumes, fruit and grains were the food groups most poorly scored by women with previous GDM. To achieve a higher score in these food categories, women would need to consume a variety of high fibre and wholegrain breads and cereals, legumes and increase the amount and variety of fruit consumed each week. Despite an already elevated risk of type 2 diabetes in this group, it is plausible that poor diet quality, as found in this study, may further increase their risk for long-term chronic disease risk including both type 2 diabetes14, 32, 33 and cardiovascular disease.34 This highlights a need to target specific dietary changes for women with previous GDM to prevent subsequent chronic disease.
Consistent with other studies, we found that tertiary educated35 and older women had better diet quality. These results are consistent with the findings of Collins et al.,18 who found the same relationship in a nationally representative sample of mid-aged Australian women.18 In the current study, we also found that those who spoke only English were almost twice as likely to have an ARFS in the upper quintile after adjustment for education and other significant variables, indicating that language or cultural barriers influence an individual’s ability to achieve a high quality diet. Considering that the risk of developing GDM in Australia is greater among women from non-English speaking backgrounds,2, 36 this is an important finding and indicates that this group may require additional support and/or targeted interventions.
As may be expected, the current study confirms that women who practise other preventative health behaviours are more likely to report better quality dietary intakes. In the present study, women who met the guidelines for physical activity were more than twice as likely to be in the upper compared with the lower quintile for diet quality. Women who sought postpartum testing for diabetes also reported better diet quality. Although previous studies have shown low rates of postpartum testing for diabetes following a GDM pregnancy,37, 38, 39 this finding suggests that either they are the more motivated group to improve their lifestyle following GDM or that being advised to return for follow-up acts as a motivating factor for improved diet quality.
The finding that women who received risk reduction advice from a health professional were more likely to have better diet quality highlights the importance of providing lifestyle interventions targeting postpartum risk reduction. Despite this, we have previously demonstrated poor follow-up and limited provision of postpartum dietary advice for this high-risk group.40 With diabetes prevention studies providing evidence of the benefit of intensive lifestyle interventions for reducing the incidence of type 2 diabetes in those at highest risk,41, 42, 43 these results support the need for additional resources to address postpartum lifestyle management.
The association between BMI and diet quality has been reported in previous studies.44, 45 Although we found a trend towards women with a lower BMI having better diet quality, these results did not reach statistical significance in logistic regression analysis. Postpartum weight retention may have confounded this relationship between weight and diet quality. The use of self-reported weight may also have biased BMI calculations. Studies using postal survey methodology have demonstrated that self-report underestimates weight in women by an average of 0.95 kg, with those in overweight and obese categories underestimating by up to 2.5 kg.46 With both body weight and dietary patterns being important determinants of type 2 diabetes risk,19 this trend warrants further investigation in particular with women with a longer postpartum duration.
This study has several limitations; most notable is the low (36%) response rate. It is also possible that a response bias towards potentially more health conscious women may present an optimistic assessment of postpartum diet quality. As with any tool used to measure dietary intake, the ARFS has a number of limitations. Respondents are asked to report their usual consumption of foods over the preceding 12 months, therefore results may be influenced by the season in which the questionnaire was administered or be more likely to emphasise recently consumed foods. It is possible that our findings are also influenced by under or overreporting. However, as the ARFS focuses on frequency of consumption of core foods and the variety of food choices within those groups, the scoring is independent of reported amounts of food items that would have limited the associated measurement error. Further, we did not collect longitudinal data to determine associations between diet quality and long-term chronic disease risk. Despite these limitations, our study did have a large sample size drawn from a population-based registry as opposed to a hospital or insurance-based data set, strengthening the applicability of the study to a larger population of women with prior GDM.
Women with previous GDM should be targeted for dietary interventions aimed at improving overall diet quality in the postpartum period. In particular, barriers to healthy eating may need to be addressed in those at highest risk of poor diet quality including younger women, those with a lower level of education, women who speak a language other than English and those who do not seek postpartum follow-up. Our study suggests that health professionals could have an important role in providing postpartum risk reduction advice that may improve overall diet quality, and further research is needed to assess the impact of health professional advice on preventative behaviours and subsequent chronic disease risk among women with GDM. A systematic approach to follow-up is urgently needed to ensure that all women diagnosed with GDM receive adequate information and support to achieve a diet consistent with the guidelines for chronic disease prevention.
Hoffman L, Nolan C, Wilson JD, Oats JN, Simmons D . Gestational diabetes mellitus—management guidelines. Med J Aust 1998; 169: 93–97.
Templeton M, Pieris-Caldwell I Gestational Diabetes Mellitus in Australia, 2005–06. Australian Institute of Health and Welfare: Canberra, 2008.
Kitzmiller J, Dang-Kilduff L, Taslimi MM . Gestational diabetes after delivery: short-term management and long-term risks. Diabetes Care 2007; 30: S225–S235.
Lee A, Hiscock RJ, Wein P, Walker S, Permezel M . Gestational diabetes mellitus: clinical predictors and long-term risk of developing type 2 diabetes. Diabetes Care 2007; 30: 878–883.
The Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393–403.
Koh D, Miller YD, Marshall AL, Brown WJ, McIntyre D . Health-enhancing physical activity behavior and related factors in postpartum women with recent gestational diabetes mellitus. J Sci Med Sport 2010; 13: 42–45.
Smith BJ, Cheung NW, Bauman AE, Zehle K, McLean M . Postpartum physical activity and related psychosocial factors among women with recent gestational diabetes mellitus. Diabetes Care 2005; 28: 2650–2654.
Zehle K, Smith B.J, Chey T, McLean M, Bauman A.E, Cheung N.W . Psychosocial factors related to diet among women with recent gestational diabetes: opportunities for intervention. Diabetes Educator 2008; 34: 807–814.
Kieffer EC, Sinco B, Kim C . Health behaviors among women of reproductive age with and without a history of gestational diabetes mellitus. Diabetes Care 2006; 29: 1788–1793.
Stage E, Ronneby H, Damm P . Lifestyle change after gestational diabetes. Diabetes Res Clin Pract 2004; 63: 67–72.
Fung TT, McCullough M, van Dam RM, Hu FB . A prospective study of overall diet quality and risk of type 2 diabetes in women. Diabetes Care 2007; 30: 1753–1757.
Hodge AM, English DR, O'Dea K, Giles GG . Dietary patterns and diabetes incidence in the Melbourne Collaborative Cohort Study. Am J Epidemiol 2007; 165: 603–610.
Montonen J, Järvinen R, Heliövaara M, Reunanen A, Aromaa A, Knekt P . Food consumption and the incidence of type II diabetes mellitus. Eur J Clin Nutr 2005; 59: 441–448.
Nettleton JA, Steffen LM, Ni H, Liu K, Jacobs DR . Dietary patterns and risk of incident type 2 diabetes in the multi-ethnic study of atherosclerosis (MESA). Diabetes Care 2008; 31: 1777–1782.
Esposito K, Kastorini CM, Panagiotakos DB, Giugliano D . Prevention of type 2 diabetes by dietary patterns: a systematic review of prospective studies and meta-analysis. Metab Syndr Relat Disord 2010; 8: 471–476.
Fung TT, Schulze M, Manson JE, Willett WC, Hu FB . Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med 2004; 164: 2235–2240.
Wirt A, Collins CE . Diet Quality - what is it and does it matter? Public Health Nutr 2009; 12: 2473–2492.
Collins CE, Young AF, Hodge A . Diet quality is associated with higher nutrient intake and self-rated health in mid-aged women. J Am Coll Nutr 2008; 27: 146–157.
Hu FB, Manson JE, Stampfer MJ, Colditz GA, Liu S, Solomon CG, Willett WC . Diet and lifestyle and risk of type 2 diabetes mellitus in women. New Engl J Med 2001; 345: 790–797.
Morrison MK, Lowe JM, Collins CE . Perceived risk of type 2 diabetes in Australian women with a recent history of gestational diabetes mellitus. Diabet Med 2010; 27: 882–886.
Trewin D Census Dictionary. Australian Bureau of Statistics, Commonwealth of Australia. Australian Bureau of Statistics: Canberra, 2001.
Armstrong T, Bauman A, Davies J Physical Activity Patterns of Australian Adults. Results of the 1999 National Physical Activity Survey. Australian Institute of Health and Welfare: Canberra, 2000.
Brown WJ, Trost SG, Bauman A, Mummery K, Owen N . Test-retest reliability of four physical activity measures used in population surveys. J Sci Med Sport 2004; 7: 205–215.
Australian Institute of Health and Welfare. The Active Australia Survey: a Guide and Manual for Implementation, Analysis and Reporting. Australian Institute of Health and Welfare: Canberra, 2003.
Kant AK, Thompson FE . Measures of overall diet quality from a food frequency Questionnaire: National health interview survey 1992. Nutr Res 1997; 17: 1443–1456.
Ireland P, Jolley D, Giles G, O’Dea K, Powles J, Rutishauser I, Wahlqvist ML, Williams J . Development of the Melbourne FFQ: a food frequency questionnaire for use in an Australian prospective study involving an ethnically diverse cohort. Asia Pac Clin Nutr 1994; 3: 19–31.
Hodge A, Patterson A, Brown W, Ireland P, Giles G . The anti cancer council of Victoria FFQ. Relative validity of nutrient intakes compared with diet diaries in young to middle-aged women in a study of iron supplementation. Aust NZ J Public Health 2000; 24: 576–583.
Commonwealth of Australia, National Health and Medical Research Council. Dietary Guidelines for Australian Adults: A Guide to Healthy Eating. Australian Government Publishing Service: Canberra, Australia, 2003.
Smith A, Kellett E, Schmerlaib Y The Australian Guide to Healthy Eating. Australian Government Publishing Service: Canberra, 1998.
Hure A, Young A, Smith R, Collins C . Diet and pregnancy status in Australian women. Public Health Nutr 2009; 12: 853–861.
Blumfield ML, Hure AJ, Macdonald-Wicks LK, Patterson AJ, Smith R, Collins CE . Disparities exist between National food group recommendations and the dietary intakes of women. BMC Women’s Health 2011; 8: 11–37.
de Munter JS, Hu FB, Spiegelman D, Franz M, van Dam RM . Whole grain, bran, and germ intake and risk of type 2 diabetes: a prospective cohort study and systematic review. PLoS Med 2007; 4: e261.
Venn BJ, Mann JI . Cereal grains, legumes and diabetes. Eur J Clin Nutr 2004; 58: 1443–1461.
Belin RJ, Greenland P, Allison M, Martin L, Shikany JM, Larson J, Tinker L, Howard BV, Lloyd-Jones D, Van Horn L . Diet quality and the risk of cardiovascular disease: the Women's Health Initiative (WHI). Am J Clin Nutr 2011; 94: 49–57.
Darmon N, Drewnowski A . Does social class predict diet quality? Am J Clin Nutr 2008; 87: 1107–1117.
Holdenson Z, Catanzariti L, Phillips G, Waters AM . A picture of diabetes in overseas-born Australians. Australian Institute of Health and Welfare 2003.
Blatt AJ, Nakamoto JM, Kaufman HW . Gaps in diabetes screening during pregnancy and postpartum. Obstet Gynecol 2011; 117: 61–68.
Shah BR, Lipscombe LL, Feig DS, Lowe JM . Missed opportunities for type 2 diabetes testing following gestational diabetes: a population-based cohort study. BJOG 2011; 118: 1484–1490.
Morrison MK, Collins CE, Lowe JM . Postnatal testing for diabetes in Australian women following gestational diabetes mellitus. Aust NZ J Obstet Gynaecol 2009; 49: 494–498.
Morrison MK, Collins CE, Lowe JM . Dietetic practice in the management of gestational diabetes mellitus: A survey of Australian dietitians. Nutr Dietetics 2011; 68: 189–194.
Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM . Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393–403.
Louheranta J, Mannelin A, Rastas M, Salminen M, Eriksson V, Uusitupa M, Tuomilehto J . The Finnish Diabetes Prevention Study (DPS): lifestyle intervention and 3-year results on diet and physical activity. Diabetes Care 2003; 26: 3230–3236.
Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucosetolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 1997; 20: 537–544.
Shah BS, Freeland-Graves JH, Cahill JM, Lu H, Graves GR . Diet quality as measured by the healthy eating index and the association with lipid profile in low-income women in early postpartum. JADA 2010; 110: 274–279.
Laraia BA, Bodnar LM, Siega-Riz AM . Pregravid body mass index is negatively associated with diet quality during pregnancy. Public Health Nutr 2007; 10: 920–926.
Burton NW, Brown W, Dobson A . Accuracy of body mass index estimated from self-reported height and weight in mid-aged Australian women. Aust NZ J Public Health 2010; 34: 620–623.
We are very grateful to the women with GDM whose participation made this study possible. This study was funded by the Diabetes Australia Research Trust, the University of Queensland’s Enabling Grants Scheme, the National University of Malaysia PhD Scholarship, the Dietitians Association of Australia Unilever Post-Graduate Research Scholarship, the Lions District 201N3 Diabetes Foundation and the Neville Samson Diabetes Grants-In-Aid. CE Collins if funded by a National Health and Medical Research Council Career Development Fellowship. We acknowledge David McIntyre and Wendy Brown for input into the sampling strategies and reviewing the measures used, and the National Diabetes Services Scheme and Diabetes Australia-NSW for their support.
The authors declare no conflict of interest.
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Cite this article
Morrison, M., Koh, D., Lowe, J. et al. Postpartum diet quality in Australian women following a gestational diabetes pregnancy. Eur J Clin Nutr 66, 1160–1165 (2012). https://doi.org/10.1038/ejcn.2012.84
- gestational diabetes
- diet quality