Con respecto al proceso de autoevaluación.

There are several study design aspects that need to be discussed in order to be able to place the emerging body of evidence regarding the association of diet with the development of GDM, including results presented in this thesis (chapters 5-7) into context. These include study population, time frame, effect size and type of study (i.e. observational vs. intervention) and will be discussed below.

Study population

In this thesis, two different study populations have been used to investigate the role of diet in the development of GDM. In chapters 5 and 6 data from the ALSWH study, a large population-based prospective cohort study was used. The advantage of a population-based cohort study is the high external validity. However, as prevalence of GDM is around 5% in Caucasian populations [2], the number of cases is still rather low. An alternative to ensure enough cases and thus enough statistical

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GLIMP2 study, which oversampled women with a history of GDM, a previous macrosomic infant, polycystic ovarian syndrome (PCOS), and overweight. Oversampling of women at high risk of GDM in the GLIMP2 study resulted in a GDM incidence of 10%. A consequence of selecting a high-risk study population is that the degree of gradually developed, underlying, subclinical insulin resistance in the years before pregnancy (see figure 9.1) might be too severe for dietary factors to have any beneficial effect [51, 86, 87], thus potentially limiting the ability to detect an effect of diet. Furthermore, BMI can also act as a mediator in the association between diet and GDM [88], and should be considered when selecting only obese women, as is done in some other studies [89-93]. In addition to having a higher risk of GDM, the GLIMP2 study population consisted of women who were mostly highly educated and with an interest in health, and thus more likely to be more health conscious than the general population. This may not only have reduced external generalizability, but potentially may also have limited variation in dietary intake and thus the ability to detect an association with GDM.

Figure 9.1: A schematic overview of how dietary intake might vary over time and can be influenced by specific

events in relation to insulin resistance/GDM development.

Time frame

Diet is a lifelong exposure and it is important to take the timing of dietary assessment into account when studying diet-disease associations, especially since chronic disease development does not happen overnight, but develops over time. To avoid reverse causation, dietary assessment should be done before disease diagnosis, in this case before GDM diagnosis, as is done in the ALSWH and GLIMP2 studies (chapters 5-7). Furthermore, depending on the method used, dietary intake may reflect intake at a certain point in time, which is not necessarily representative of habitual intake. For example, seasonal variation might influence dietary intake. Pregnancy is a major life event that can influence

General discussion dietary intake. For example, food safety recommendations are given to ensure avoidance of contaminants, but also pregnancy-induced food aversions, nausea and vomiting can affect dietary intake. Therefore, diet measured several years before pregnancy (e.g. ALSWH) may not reflect the diet consumed during pregnancy, and vice versa, diet assessed during pregnancy might not reflect habitual pre-pregnancy diet, whereas both can have an effect on the development of insulin resistance (see figure 9.1). In the ALSWH study, two FFQs were administered six years apart and sensitivity analyses in which data from both were combined as long-term dietary intake showed similar associations as those based on the FFQ closest to the pregnancy (chapter 5, 6). In theory, multiple measurements both in the preconception and pregnancy period are necessary to accurately capture dietary intake and changes over time. In chapter 7, we assessed diet quality and micronutrient intake in the preconception period, on average 13 weeks before conception, and twice during pregnancy. We observed that dietary intake remained stable over this time frame. Combined with results obtained from others [94-96], it is likely that, although total intake and intake of specific foods may change in the year before the pregnancy, the effect on diet quality and micronutrient intake is limited. Furthermore, in the GLIMP2 study, we did not observe a significant effect of nausea and vomiting on dietary intake estimates.

Observational versus intervention studies

Results presented in this thesis with respect to the association between diet and GDM development (chapters 5-7) are based on observational data, as are the majority of results of other studies. However, randomized controlled trials (RCTs) are needed to prove causality. A recent systematic review and meta-analysis summarized results from 23 RCTs investigating combined diet and exercise interventions to prevent GDM compared to no intervention and concluded that there was a possible reduced risk of GDM (average risk ratio 0.85, 95% confidence interval 0.71 to 1.01; 6633 women; 19 RCTs), but that the quality of the evidence was moderate [97]. Furthermore, there was large heterogeneity observed in the type of dietary intervention given, so effective dietary components are difficult to disentangle.

Another limitation of RCTs is that they often start during pregnancy, due to feasibility reasons, and thus have only a short time window (e.g. from 12 weeks of gestation at inclusion to 24-28 weeks of gestation for GDM testing) to change dietary intake and for this change in diet to have an effect on insulin resistance and glucose homeostasis. Therefore, new trials aim to start already in the preconception period. One of these trials is the recently started large multicentre, multi-ethnic randomized “Nutritional intervention Preconception and during Pregnancy to maintain healthy glucose metabolism and offspring health (NiPPeR)” trial investigating the effect of an optimized

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the intervention in the preconception phase [98]. A major challenge of this study, however, is the large number of women needed, n=1800, to be able to study the aimed 600 pregnancies, as not all women will conceive and anticipating dropout.