Study design and participants
This study is a secondary analysis of the randomised controlled intervention study RADIEL, which was performed during the years 2008–2013 in Helsinki University Hospital in Helsinki, Finland, and South Karelia Central Hospital, Lappeenranta, Finland. Previous publications have presented the details of the study [8, 15, 16].
The study participants were 18 years of age or older, and had a BMI ≥30 kg/m2 and/or a history of GDM. Multiple pregnancy, communication problems (based on language skills, for example), physical disabilities, medications or diagnoses affecting glucose metabolism, severe psychiatric diagnoses and current substance abuse led to exclusion. Women were recruited either before or in early pregnancy before 20 weeks of gestation, and allocated to an active lifestyle intervention group or a control group.
The study complied with the Declaration of Helsinki. All participants entered the study voluntarily, signed an informed consent form and were free to discontinue the study at any stage. The study was approved by the ethics committees of Helsinki University Hospital (14 September 2006, Dnro 300/E9/06) and South Karelia Central Hospital (11 September 2008, Dnro M06/08), and it was registered at ClinicalTrials.gov (clinical trial registration number NCT01698385).
The combined lifestyle intervention, provided by trained study nurses, aimed at prevention of GDM among women at high diabetes risk. The study visits took place every 3 months before pregnancy, once in each trimester, and at 6 weeks and 6 and 12 months postpartum. The intervention group received individualised advice on increasing moderate-intensity PA to 150 min/week and limiting gestational weight gain. The dietary advice followed the Nordic dietary recommendations , and focused mainly on increasing the intake of fibre, vegetables, fruits and berries, as well as decreasing the intake of saturated fats. The control group attended the study visits for measurements but received only standard care such as general leaflets usually provided by antenatal clinics.
At each study visit, anthropometric measurements were obtained and venous blood samples were taken. The biochemical analyses at each visit included assessments of glucose metabolism (HbA1c, fasting glucose, fasting insulin) and lipid metabolism (cholesterol, LDL-cholesterol, HDL-cholesterol, triacylglycerols), and inflammatory markers (high-sensitivity C-reactive protein). A 2 h 75 g OGTT was performed at enrolment, in the first and second trimester of pregnancy (unless GDM was diagnosed earlier), and at 6 weeks and 12 months postpartum. Participants with prior bariatric surgery or those with known diabetes diagnosis did not receive an OGTT. Background questionnaires covered socioeconomic status, lifestyle (e.g. smoking and medications), previous pregnancies, and family history of diabetes and cardiovascular diseases. Smoking in any trimester of pregnancy was recorded as ‘smoking during pregnancy’.
PA was self-reported as minutes of at least moderate-intensity PA per week. Based on the data from food frequency questionnaires, we calculated a healthy food intake index describing the quality of the diet overall. The maximum score was 18, with points for each nutritional goal: intake of high-energy/low-nutrient snacks (0, 1 or 2 points), sugar-sweetened beverages (0 or 1 points), fast food (0 or 1 points), high-fibre grains (0, 1 or 2 points), fat spread (0, 1 or 2 points), low-fat cheese (0 or 1 points), low-fat milk (0, 1 or 2 points), fish (0, 1 or 2 points), red and processed meat (0, 1 or 2 points), vegetables (0, 1 or 2 points), and fruits and berries (0 or 1 points). A higher score indicated a healthier diet.
Genotyping and calculating the type 2 diabetes polygenic risk score
DNA was extracted from whole blood samples from 537 participants using a Maxiprep kit (Qiagen, Valencia, CA, USA). We genotyped 336 SNPs associated with type 2 diabetes, obesity or hyperlipidaemia using a Sequenom iPLEX platform (Sequenom, San Diego, CA, USA) in the year 2014.
We used PLINK 1.9 software (http://pngu.mgh.harvard.edu/~purcell/plink/) for genotype quality control and clumping . We used the following parameters for clumping of the genotype data: p value threshold 1, linkage disequilibrium threshold (r2) 0.5, clumping window width 250 kb. Prior to clumping, we excluded all SNPs with a minor allele frequency <0.05, genotyping rate <0.9 and Hardy–Weinberg equilibrium p value <1 × 10−4. We also excluded samples if data on >10% of SNPs were missing. After quality control, there were 537 samples with genotype data on 195 SNPs. We used PRSice 2.1  to calculate the PRS, using the genotype quality control settings recommended by the software developers . For the SNP weights, we used the effect-size estimates obtained from Xue et al . We applied a p value threshold of 5 × 10−8 for including type 2 diabetes-associated SNPs in the PRS. This resulted in inclusion of 50 SNPs in the PRS.
The Finnish Current Care Guidelines provided the thresholds for diagnosing GDM based on 2 h 75g OGTT: 0 h ≥ 5.3 mmol/l, 1 h ≥10.0 mmol/l and 2 h ≥8.6 mmol/l . One value exceeding any of the cut-offs led to a GDM diagnosis, and exceeding these thresholds in the first trimester led to a diagnosis of early GDM. ‘Booking GDM’ refers to a GDM diagnosis at enrolment (mean 13 gestational weeks) for the women recruited in early pregnancy.
Abnormal glucose metabolism 12 months postpartum refers to a diagnosis of either impaired fasting glucose (fasting glucose 6.1–6.9 mmol/l), impaired glucose tolerance (2 h glucose 7.8–11.0 mmol/l) or type 2 diabetes (fasting glucose ≥7.0 mmol/l or 2 h glucose ≥11.1 mmol/l). Alternatively, prior physician-diagnosed diabetes led to registration of a glycaemic abnormality. The indices for insulin resistance (HOMA-IR) and insulin secretion (HOMA-B) used the equations according to Matthews et al .
We also calculated a success score based on the example in the Finnish Diabetes Prevention Study , and modified the components of the score on achievement of the predefined lifestyle goals specific to this study. The maximum score was 5, consisting of the following: increasing fibre intake to 30 g or more (0 or 1 points), consumption of five or more portions of fruits, berries and vegetables per day (0 or 1 points), intake of saturated fats less than 10% of daily energy intake (0 or 1 points), gestational weight gain adequate or less than adequate according to the US Institute of Medicine’s guidelines  (0 or 1 points), and self-reported duration of moderate-intensity PA per week of 150 min or more (0 or 1 points). The definition of a successful intervention was three or more points.
The data are presented as mean values with SD, medians with IQR, or as frequencies with percentages. We used the Shapiro–Wilk test to examine the normal distribution of the variables. The χ2 test, Fisher’s exact test, Mann–Whitney U test, Kruskal–Wallis test, ANOVA, or independent-samples t test were used for between-group comparisons as appropriate.
Associations between the PRS and glycaemic markers were assessed using linear regression, and logistic regression was used when analysing the association with glycaemic diagnoses. Additionally, we included an interaction term in the regression analyses to detect the possible effect of an interaction between a type 2 diabetes PRS and lifestyle intervention on GDM incidence or the incidence of glycaemic abnormalities 12 months postpartum. In the case of any significant PRS × intervention interaction effects, we also assessed the SNP-level interactions. These analyses were adjusted for age.
To compare the effects of the intervention according to genetic risk, we divided the participants into tertiles based on their type 2 diabetes PRS: low risk (n = 176), medium risk (n = 176) and high risk (n = 177). The adjusted means for the occurrence of GDM and glycaemic abnormalities at 12 months postpartum were calculated using ANCOVA. All statistical tests were two-tailed.
All analyses were performed using the SPSS 24.0 software program (IBM SPSS, Chicago, IL, USA), and we considered a p value ≤0.05 as statistically significant.