The effect of a diabetes prevention program on dietary quality in women with previous gestational diabetes

Women with recent GDM were randomized to a multicentre, prospective, open randomized controlled trial in the Australian states of Victoria and South Australia. The study protocol was previously published, as well as the primary outcomes of the study [25‐27]. In brief, women 18 years and older with GDM diagnosed using the Australasian Diabetes in Pregnancy Society (ADIPS) criteria in their most recent pregnancy were eligible for inclusion. The diagnosis of GDM at any time in pregnancy was based on any one of the following values: fasting plasma glucose 5.1–6.9 mmol/L; 1-h post 75 g oral glucose load ≥10 mmol/L; 2-h 75 g oral glucose load 8.5–11.0 mmol/L. All women were within 12 months postpartum at commencement of the study. Any women with pre-existing diabetes, cancer, severe mental illness, substance abuse, myocardial infarction in the preceding three months, difficulty with English; involvement in another post-natal intervention trial; and pregnancy at post-natal baseline testing or at any point during the 12 months of study involvement were excluded.

Recruitment occurred in antenatal clinics after the diagnosis of GDM was made at approximately 28 weeks. Eligible women were provided with a pre-paid envelope containing the patient information and consent form to return within 4 weeks. If they were not returned within that timeframe follow-up contact was made with the women. Additional women were recruited through the Australian National Gestational Diabetes Register (NGDR) using relevant postcodes in Victoria and South Australia. Hospital records were searched for women with recent GDM and referrals from a private consultant obstetrician were also conducted in South Australia to increase recruitment. After written informed consent was obtained, baseline testing occurred. Eligible women were randomised following baseline diabetes screening. Permuted block randomisation was used and it was stratified by recruitment location and method using the MADGA-DPP management database. A sequence number and assignment code were revealed to the randomisation office at Deakin University and eligible women were allocated to intervention or usual care. Deakin University’s Human Research Ethics Committee (2010–005) maintained the primary ethical review, partner organisations maintained other ethical review procedures and these are listed in table one of the MAGDA-DPP protocol paper [27]. The study was approved by the relevant ethics committees and registered as an RCT (Australian New Zealand Clinical Trials Registry ANZCTRN 12610000338066). The study adhered to CONSORT guidelines.

The intervention consisted of an individual session completed in the woman’s home and five group sessions conducted at community settings close to the woman’s home. The individual session included personalised risk identification, goal setting and diabetes prevention through lifestyle modification. The group sessions covered healthy eating, physical activity and overall wellbeing. The Finnish Diabetes Prevention Study goals were used for this intervention, which were ≤ 30% total energy from total fat intake, ≤10% total energy from saturated fat, ≥15 g dietary fibre per 1000 kcals, ≥30 min moderate to vigorous physical activity per day and ≥ 5% weight loss [28]. The first group session covered diabetes and diabetes risk factors, reducing saturated fat and family-focused activities to achieve this, and the setting and review of personalised goals. The second session covered reduction of total fat, postpartum weight management, reducing the fat content in whole family’s diet, and the setting and review of personalised goals. The third session covered increasing fibre, healthier food shopping, getting more fibre into the whole family’s diet, and the setting and review of personalised goals. The fourth session covered healthier meal planning, negotiating stressful situations around food choice with family members, mindful eating, good sleep hygiene, and the setting and review of personalised goals. The final session covered postnatal depression awareness, stress management, maintenance of dietary and physical activity behaviour change, review of personalised goals and longer-term goal setting. Sessions lasted approximately 120 min and were held at fortnightly intervals. Two follow-up phone calls occurred at 3 months and 6 months after the active intervention period to reinforce behaviour change. The control group received usual care within their local setting. All women were followed up at 12 months after the baseline measurements.

Blood samples were collected by the study nurse/phlebotomist and analyzed by a private pathology company. Height, weight, waist circumference and blood pressure were measured using standard protocols. Fasting venous blood samples was analyzed for triglycerides, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol, HbA1c, and fasting glucose and 2-h glucose tolerance as previously described [25]. Baseline and 12 months survey data included: demographics (breastfeeding, parity, education, employment status, cultural background; baseline only); Food Frequency Questionnaire (FFQ) [29]; Active Australia Questionnaire [30]; diet and physical activity self-regulation and self-efficacy; Multi-dimensional Scale of Perceived Social Support; Assessment of Quality of Life 8D; Patient Health Questionnaire; and health status (smoking status and history of diabetes, myocardial infarction, cancer, and mental disorders; baseline only).

Dietary analysis data were obtained using the Cancer Council of Victoria FFQ at baseline and 12 months. The dietary intakes were recoded using the Australian Recommended Food Score (ARFS) [31], which provides a qualitative assessment of the dietary intake and higher scores indicate a greater adherence to the Dietary Guidelines for Australians [32]. The ARFS in nine categories were summed to provide a maximum total ARFS of 74 for the best diet quality. Within the vegetables category, a maximum score of twenty-two could be achieved. There were twenty-one named types of vegetables and they each received a score of one if one or more servings were consumed in a week, and zero if less than that. An additional score of one is awarded for frequency of vegetable intake four or more servings daily. Within the fruit category a maximum score was fourteen could be achieved. There were thirteen types of fruit and they each received a score of one if one or more servings were consumed in a week, and zero if less than that. An additional score of one is awarded for frequency of fruit intake two or more servings daily. The grains category had a maximum score of fourteen. Consumption of high fibre white bread, wholemeal bread, rye bread, multigrain bread, all-bran, sultana bran/fibre plus/branflakes, weet-bix/vitabrits/weeties, rice, pasta/noodles, vegemite/marmite/promite, porridge, muesli, cornflakes/nutrigrain/special K each scored a one if consumed at least once a week, zero if not consumed. An additional score of one is awarded for consuming four or more slices of bread per day. The protein category had a maximum score of fourteen, which was split into the following sub-categories: nuts and legumes (total score of seven); meats, eggs and poultry (total score of five); and fish (total score of two). The nuts and legumes sub-category scored one for the consumption of one or more servings of nuts, peanut butter, baked beans, soy beans/tofu, soya milk, chickpeas, lentils per week, and zero if less than that. The meat and poultry sub-category was scored one if one to four servings of each of the following were consumed in a week: beef, veal, lamb, pork, chicken, up to two eggs and zero for any amount above or below this. The fish subcategory was scored one if one to four servings of each of the following were consumed in a week: fish (steamed/grilled/baked) and canned fish (salmon/tuna/sardines) and zero for any amount above or below this. The dairy category had a maximum score of seven. Consuming more than 500 ml milk per day received a score of one, zero if not. The consumption of reduced fat or skim milk, yoghurt, ricotta/cottage cheese, low-fat cheese received a score of one if one or more servings each were consumed in a week, and zero if less than that. Consuming cheese and ice-cream less than once per week received a score of one, zero if more than that. The fats category awarded a score of one if polyunsaturated spread, monounsaturated spread, or no fat spread were used, zero if any other used. The alcohol category was scored one for the consumption of less than once per month and up to four days/week of beer/wine/spirits and zero if outside those limits. Consuming more than two standard drinks per day received a score of zero and one if inside those limits.

Statistical analysis was performed using SPSS version 22. Baseline participant characteristics are shown as summary measures. All participants randomized to the study (‘intention to treat’; ITT, n = 573) and those meeting the ‘per protocol set’ (PPS, n = 331) were analysed. The definition of the PPS was decided a priori. 14% of usual care (40/289) and 12% of intervention (35/284) women became pregnant during the trial and were excluded from ITT and PPS analyses. Women who were lost to contact (n = 52) or withdrew (n = 19) were also excluded from ITT and PPS analyses. Protocol deviations (hence exclusion from PPS) included assessments outside of the specified time window (n = 2); control group allocation but received intervention (n = 1); other postnatal intervention participation (n = 3); exclusion criteria met during intervention (T2DM (n = 12)). Women who did not attend a minimum of one group and the individual session (n = 78, 49% intervention participants) were also excluded from the PPS as they did not meet the minimum intervention exposure.

Linear mixed model analyses was used for all ordinal scale endpoints implementing the residual maximum likelihood (REML) estimation method to cope with missing values. The models include fix effect of intervention allocation and measurement time as nominal factors and the two-way interaction between treatment allocation and measurement time. In this setting the two-way interaction estimates the between-group differential change from baseline at 12-month follow-up (i.e. intervention effect). Within-participant autocorrelation was taken into account by assuming an exchangeable covariance pattern for repeated measures. Significance of the F-test for the treatment allocation and measurement time interaction is reported as well as t-tests for the within-group changes over time and the between-group differences at each time. Effect size (Cohen’s d) was calculated based on observed data with effects being interpreted as small (.20–.49), medium (.50–.79), or large (≥.80) [33]. Alcohol and fat scores were dichotomised endpoints and generalised linear mixed models with binomial distribution and logit link were used to analyse them. The model structure was similar to the linear mixed models analysis and odds ratio (OR) and 95% CI for the treatment allocation and measurement time was reported as intervention effect. All statistical tests were conducted at the two-sided 5% significance level with no adjustments for multiplicity of either endpoints or comparisons. Significance was achieved where P-value was ≤0.05. The sample size required to achieve an effect size of ≥0.27 in FPG over 12 months based on the GGT-DPP study, which was a six session, group-based Australian diabetes prevention program intervention [34], (assuming mean difference between intervention and control groups of 0.14 mmol/L and within group standard deviation of 0.5 mmol/L), using a two-sided 5% significance level and 80% power, was 574 (287 in each arm). A 25% attrition rate was added to the sample size calculated. A post hoc power analysis using the available sample size and observed standard deviation (i.e. SD = 7) for AFRS total score showed that the study has more 80% power to detect effect size (defined as standardised mean difference) 0.25 or larger.

573 women were randomised into intervention (n = 289) and control (n = 284). At baseline (see Table 1), the participants were well matched but had higher BMIs, larger waist circumferences, less physical activity than the average for Australian women within this age group [35]. These differences were not reflected in their metabolic health as the mean HbA1c was 34 mmol/mol (5.34%) and only 10% had impaired glucose tolerance and 2% impaired fasting glucose.

Baseline dietary intakes data showed the average energy intake was 7868 kJ/day (1881 kcal/d). Detailed intake data are shown in Table 2. Total fat intakes were 38% and saturated fat intakes were 16% of total energy intake. Dietary fibre was 10.9 g/1000 kcal. Total AFRS scores were 31.8 out of 74 pointing to a relatively low dietary quality (see Table 2). There were no significant differences seen at baseline for any dietary intake data.

The metabolic outcomes are described in detail in O’Reilly et al. [25] but in summary for the co-primary endpoints, the ITT analysis showed the intervention group’s mean weight loss was 0.23 kg (95% CI -0.89, 0.43) compared with weight gain of 0.72 kg (95% CI 0.09, 1.35) in the usual care group (change difference 0.95 kg, 95% CI -1.87, − 0.04; group by treatment ITT interaction P = 0.04) over 12 months. The intervention group’s mean waist circumference reduction was − 2.24 cm (95% CI -3.01, − 1.42) compared with − 1.74 cm (95% CI -2.52, − 0.96) in the usual care group (change difference − 0.50 cm, 95% CI -1.63, 0.63; group by treatment ITT interaction P = 0.389) over 12 months. The intervention group’s mean increase in fasting blood glucose was 0.18 mmol/L (95% CI 0.11, 0.24) compared with an increase of 0.22 mmol/L (95% CI 0.16, 0.29) in the usual care group (change difference − 0.05 mmol/L, 95% CI -0.14, 0.05; group by treatment ITT interaction P = 0.331) over 12 months. No other statistically significant results were identified across the primary and secondary endpoints when using the F-test of the group by time interaction – a result that was consistent for both ITT and PPS.

The ITT analysis did not find any significant difference with intervention over time in the dietary quality as measured using AFRS total score (see Table 3). The nuts and legumes sub-category was significantly decreased (p = 0.04) driven by change within the intervention group over time (p = 0.01, Cohen’s D = − 0.19). Other small changes, some positive (improved dairy sub-score in intervention group over time, p <  0.01, Cohen’s D = 0.14) and some negative (decreased grain sub-score in both groups over time, p <  0.01, Cohen’s D = − 0.07), were seen over time or by treatment group but not in the overall model analysis. There was a significant 2% reduction in alcohol for the intervention compared with − 1% in usual care group (p = 0.04).

The PPS analysis defined a priori the major protocol violations, which included insufficient intervention exposure (minimum of one individual and one group session). There was no significant change in total ARFS between intervention and usual care over time (Table 4). However, the dairy intake sub-category achieved significance for improvement in the intervention group over time compared with usual care (p = 0.05). The difference for the dairy sub-category score between groups at 12 months was 0.39 (p <  0.01).

The identification of the dairy sub-category as significantly improved related to the most attended intervention sessions (Table 5). It was a key area covered in both sessions (1 and 2) as it is one of the easiest changes to make to reduce saturated fat and total energy in Australian diets. Session 2 covered alcohol as a major contributor to empty calories and discussion focused on lower energy alternatives for situations where alcohol is consumed.