Methodological considerations

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cancellation the day of interview. We conducted focus group interviews of 3-6 participants that allowed us to collect a variety of information through the group interaction, and reflected our study aims. Other methods such as semi-structured interviews or observation techniques could have provided more in-depth and longitudinal analyses, however, they did not apply due to our study aims, and the time restriction.

In paper II and III, a cross-sectional study design was applied. This method is time and cost efficient when trying to determine the prevalence of a disorder, but has significant limitations when searching for causality, and is prone to systematic errors. We supplemented the cross-sectional data with retrospective data collected from the electronic patient record, and were able to use these data to predict the risk of prediabetes or diabetes 1-3 years after the index pregnancy.

A major strength of our research is the inclusion of South Asian and Nordic participants from the same healthcare setting. Previous studies have commonly centred one ethnicity without comparing them to a South Asian or white population within the same healthcare setting.

Thus, the current study design permitted comparisons between groups. Moreover, in paper I, the inclusion of women with different ethnic backgrounds, the number of participants and the number of focus groups conducted allowed for insights into assorted views and perceptions, in addition to allow sufficient depth in the analyses. To further strengthen the generalisability (or the transferability in qualitative studies), the focus group interviews were undertaken in the first language of the participants, except for Tamil. Hence, the present study does not only mirror women fluent in Norwegian, besides Tamil-speaking women.

The study sample represents women referred to three hospitals in South-Eastern Norway that limits the generalisability of the findings. First, women with GDM diagnosed and treated outside hospitals were not included, and we suppose that the included women had a more advanced GDM than average women diagnosed by the general practitioner or the midwife during ordinary antenatal care. Second, all three hospitals recruited women from the urban Oslo area, and the population may not be representative for rural Norway. Therefore, the results may not be applicable to healthcare services with differences in their GDM structure, or in GDM women managed only by the general practitioner and/or the midwife. Moreover, we cannot exclude selection bias. First, the low participation rate (40% and 14% in the South Asian and Nordic groups, respectively) may have resulted in an over-recruitment of women with high risk of actionable HbA1c. Second, the recruitment process may also have

contributed to selection bias. The Nordic women were invited through a letter only, whereas

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South Asian women were additionally provided a call in their first language. This could have biased the knowledge produced, because the Nordic women invited by letter only may have had a separate interest in the questions discussed, represented by an unexpected high

prevalence of prediabetes or diabetes in the Nordic group. Finally, we did not exclude women with newly diagnosed diabetes in pregnancy (i.e., women with fasting or 2h OGTT values above the diagnostic cut-off levels of diabetes detected during pregnancy) as recommended by the IADPSG or modified Norwegian IADPSG criteria. This, however, seemed acceptable as only 24.1% (n = 7/29) and 45.5% (n = 5/11) of South Asian and Nordic women who classified for the diabetes diagnosis in pregnancy, classified for this diagnosis at the study visit 1-3 years after delivery (unpublished data).

(ii) Confounding bias

A variable that affect or predict the study outcome, in addition to the main variable studied, can be called a covariate. If a covariate is associated with the exposure and the outcome, it (in addition to the exposure) may be the cause for the study outcome – defined as a confounder.

Therefore, to avoid drawing wrong conclusions it is important to include confounders when assessing the relationship between an exposure and a study outcome, i.e., adjust for

confounders (Fig. 11 a). In contrast, a covariate that is the common effect of the exposure and the study outcome, a collider, shall not be included in a regression model to avoid bias (Fig.

11 c). With the outcome variable set as either actionable HbA1c or different indexes for glucometabolic traits, different confounders were added into the logistic and multiple linear regression models to reduce confounding bias. In a majority of the analyses, South Asian ethnicity and central fat accumulation appeared as important risk factors for the outcome variables, after adjusting for the different covariates mentioned in the method section.

However, we did not adjust for physical activity or diet, which both are important

confounders to consider. These analyses are in-progress, and it would be interesting to re-do the analyses with these variables included, as studies have shown that increased fat intake and physical activity are associated with lower and higher insulin clearance, respectively (70).

In paper III we looked at the indirect effect of a covariate on the study outcome, the mediation effect. The total effect of an exposure on a study outcome is divided into a direct and an indirect effect. The indirect effect works through the mediator, while the direct effect works through other mechanisms. The mediator is a covariate that is affected by the exposure, and

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affects the outcome. The mediation analysis can provide valuable information about how much of the exposure difference (e.g., ethnic difference) in an outcome (e.g., insulin sensitivity) is due to a covariate (e.g., waist-to-height ratio). This analysis assumes a

significant association between the exposure and the mediator, between the mediator and the outcome, and between the exposure and the outcome. Additionally, all assumptions for a linear regression model must be filled (Fig. 11 b).

Fig. 11. (a) Confounder (red circle) is a covariate that is associated with the exposure (green circle) and the outcome (blue circle with I), i.e., a common cause of the outcome that should be adjusted for in an analysis to avoid bias. (b) Mediator (blue circle with M) is a covariate that is affected by the exposure, and affect the outcome (i.e., mediate the effect). The total effect of ethnicity on insulin sensitivity (green line). The direct effect of ethnicity on insulin sensitivity (after adjusting for weight-height ratio (WHtR, black dotted line). The indirect effect of ethnicity on insulin sensitivity is calculated as a * b. (c) Collider (grey circle) is a variable that is a common effect of both the exposure and the outcome, and should not be adjusted for in an analysis to avoid bias.

WHtR: waist-to-height ratio. Figure was made with DAGitty Software (118).

(iii) Information bias and precision (reliability)

Some degree of sampling error is impossible to avoid. It can occur before the test is performed (pre-analytical error), during the test analysis (analytical error), or in the

interpretation of the results (post-analytical error). In our study, some women may not have fasted the requested time before the OGTT, which would affect our test results in the direction of a higher prevalence of actionable HbA1c. Glucose is prone to analytical sampling error.

Due to ongoing glycolysis in red and white cells after blood sampling, false low glucose values could be the result if glucose samples are not kept on ice, or collected in specific tubes directed to reduce ongoing glycolysis. We used cooled FluOX tubes, which contained sodium fluorid and potassium oxalat that both inhibit the glycolysis, and strived to centrifuge the samples within 10 min after sampling. Interestingly, a study found lower GDM prevalence (11.6 vs 20.6%) if glucose samples collected in glycolysis-inhibiting tubes were not

centrifuged within 10 min after sampling compared to earlier centrifugation (119). This may also contribute to the high prediabetes and diabetes prevalence in our population compared to studies without strict sample handling.

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Other factors that should be considered are the analytical inter-assay and within-subject biological variation related to the precision of a test. The analytical variation coefficients in our laboratory are 2.5%, 7%, 4-5%, and 1.5-2.5% for glucose, insulin, C-peptide, and HbA1. The biological variation coefficients for fasting glucose, insulin, C-peptide, and HbA1c are 5%, 21.1%, 16.6%, and 1.9%. Fasting glucose and HbA1c have less total variation, and are, hence, more reliable than insulin and C-peptide measurements when interpreting results.

Further, due to lack of a standardised insulin assay it is not possible to compare insulin values directly between laboratories.

The post-analytical errors are also important to consider. The different reporting of HbA1c in International Federation of Clinical Chemistry (IFCC) units (mmol/mol) and/or derived National Glycohemoglobin Standardization Program (NGSP) units (%) result in different absolute HbA1c numbers, and can therefore cause interpretation error. In our studies, we applied dual reporting of HbA1c.

(iv) Other methodological reflections

(a) Comments related to the use of qualitative methods

In qualitative methods the trustworthiness of a study can be divided into credibility (i.e., the validity of a study), transferability (i.e., generalisability) , dependability (i.e., that the knowledge produced is clearly traceable) and confirmability (i.e. that the findings and conclusion are based on the data (120).

In paper I, we applied thematic analysis by Braun and Clarke (96). This method is simple to learn, and easily provides a description of similarities and differences within the data that fitted with our study aims. Further, the method’s flexibility allows for different options of data analyses, although it does not allow for analysis of the language use. We decided to describe women’s real-life constraints, in addition to provide an understanding of their unwanted heath behaviours. This method also allowed us to apply a modified version of Lipsky’s theory to understand mechanisms behind women’s suboptimal follow-up after GDM, which was novel.

Although several studies have used different theoretical models to find a solution to this challenge, these studies commonly use the theory to frame the research question (106, 121).

The focus guide interview or analysis is then more strictly theory-driven (i.e., as in a

deductive analysis) (117). This is in contrast to paper I, where we used the theory only as an inspiration in the analysis (i.e., inductive analysis), and to support our findings. Therefore, the

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focus group interviews were performed without a strict template, and before involving Lipsky’s theory, which may have contributed to a better understanding of the study aims, although increased the needed sample size. To ensure credibility (120) in our study, we used complementary professional backgrounds (moderator with experience in the gestational diabetes field, whilst the last author held competence into health implementation research), defined as observer triangulation. In addition, all the themes and subthemes were discussed according to existing literature in the final manuscript. Due to time restriction, we did not test our findings and interpretations with the participants. Further, to reduce information bias, the moderator and co-moderator shared cultural background with the participants. The shared cultural background strengthened the study’s trustworthiness, hence facilitated the

communication of possibly sensitive themes and contextualizing of the data being generated (122). One limitation, however, is that when the interviewer shares too many of the group’s

‘taken-for granted’ assumptions, it may be difficult to evaluate these critically. In coherence with intersubjectivity and dependability (120), the data analysis was described extensively in the method section to situate the knowledge produced. Considering the reflexivity (i.e., how the interviewer’s position may impact the research process) (122), the proximity to the endocrinologist and diabetes nurse during the pregnancy, may have modified the women`s responses, and hence, represent a limitation. Likewise, the background as healthcare providers could have formed preconceptions, and thus influenced the interpretation. Further, the study sample was purposive, and hence, may not be applicable in contexts outside of the population studied; however, our findings reflect topics discussed in a variety of countries. Finally, to secure a trustworthy thematic analysis, the experienced last author was constantly consulted to consider the credibility, transferability, and dependability of the findings, and thereby secure the confirmability of our study.

(b) Comments related to linear mixed models

In paper III, we evaluated repeated measures of glucose, insulin and C-peptide during the OGTT, and had to account for that values measured at different time points were dependent on each other, i.e., we had clustering within our data. For example, a high fasting glucose value (the intercept) would result in higher values throughout the OGTT (the slope of the glucose curve). Accordingly, we could not say that the data were independent of each other that is an assumption for linear regression models. We, therefore, applied linear mixed effect models to analyse ethnic differences in glucose, insulin and C-peptide levels. This model

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looks at the cluster as a sample of larger population, and thereby circumvent the problem with intercorrelation by including random effects. These models also use all available data in the calculation, although data are missing at some time points. Accordingly, the number of missing values are reduced, and the power increased. This is in contrast to linear regression models that exclude missing values listwise, and thereby exclude the entire case if you miss a variable in a case. We used the Akaike Information Criterion (AIC) to find the model that best fitted our data set. Therefore, in paper II, we went forward with an unstructured covariance matrix.

(c) Comments related to body composition measurements

We used waist, hip, and height measurements to approximate central fat accumulation. Waist and hip measurements are prone to measurement variation, and they cannot discriminate between subcutaneous and intra-abdominal fat. These methods are, however, better than BMI that does not account for fat distribution, and overestimates fat percentage in individuals with high muscle mass (123). Although DIASA 2 and 3 trials will provide more data about ethnic differences in the body composition, it would have been interesting with population level estimates of total body fat by skinfold thickness analyses, or measurements of lean and fat mass through bioelectrical impedance analyses.

(d) Comments related to OGGT and calculations

The OGTT is associated with relatively low precision (124). Factors contributing to this are variable intestinal and splanchnic tissue glucose absorption, variable gastric emptying and variable impact of incretin hormones. Also limited intake of carbohydrates before the test can influence the OGTT results. In general, an intake of 100-150 g carbohydrates are

recommended three days prior to an OGTT (125). We did not require this in particular, and although it is possible that this may have contributed to a higher prediabetes or diabetes prevalence rate, the usual diet in the investigated population is considered to contain sufficient amount of carbohydrates (126). The measurements of insulin and C-peptide are also

associated with a non-negligible analytical and biological variation. Therefore, the early insulin response should not be interpreted without considering this. We used the HOMA computer model that gives values in percentages rather than calculating HOMA values

manually with absolute numbers. The model gives estimates that correlate reasonably with the gold-standard clamp tests for assessing beta cell function and hepatic insulin resistance; r = 0.61 and 0.88, respectively (22). Advantages with the computer model is that it allows

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glucose values up to 25 mmol/L (as renal glucose loss is considered), and accepts different insulin assays (as it incorporate an estimate of proinsulin secretion). It is recommended to use the mean of three samples to reduce between-individual variations when calculating HOMA estimates. In the paper 3, however, we applied only one sample as this is considered

appropriate when evaluating population estimates (22). Further, to reduce bias we used C-peptide to calculate HOMA2-B, and insulin to calculate HOMA2-S. We excluded women with known T2D treated with medication at inclusion. Therefore, we did not need to consider that exogenous insulin use does not fulfil the hepatic insulin clearance assumption used in the model (22). The HOMA model estimates are also shown to be better in women with

overweight and obesity (28), fitting our study population with a mean (SD) BMI of 29.1 (5.9) kg/m².

The disposition index requires that the estimates of insulin secretion and insulin sensitivity are independent of each other. In this dissertation, we considered this by using insulinogenic index and Matsuda-ISI. The gold-standard test for insulin sensitivity is euglycaemic clamp.

We estimated whole-body insulin sensitivity by using Matsuda-ISI during an OGTT, that, however, correlates well with the euglycaemic clamp (r = 0.73) (85). We applied the muscle-ISI calculator (84) to estimate muscle insulin sensitivity. This calculator allows for removal of glucose values that peaks at 120 min (i.e., individuals with T2D), glucose values that are less than 0.5 mmol/L higher than the fasting glucose value (i.e., representing insulin sensitive individuals that are misclassified as individuals with low muscle insulin sensitivity by the calculator), and glucose values with a rebound more than 0.5 mmol/L than the minimum glucose value. After these corrections, the muscle-ISI calculator also provides an acceptable correlation with the euglycaemic clamp test (r = 0.576) (84).

The C-peptide deconvolution technique is, in total, associated with an error of 10-20% in estimating pre-hepatic insulin levels (90). This, however, seems acceptable considering the variation coefficient of insulin, and that pre-hepatic insulin levels are difficult to measure directly in humans. We did not measure ethnic differences in hepatic insulin degradation pathways such as a glycoprotein, CEACAM1, which may be a limitation. A defect in CEACAM1 could result in lower hepatic insulin clearance with secondary hepatic insulin resistance (71). Hence, this may be a key area for further research.

Importantly, we did not combine the OGTT with glucose tracer technique that otherwise would have allowed us to estimate hepatic glucose production, and thereby provide a better estimate of hepatic insulin resistance (23). This could also have provided useful information

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related to our discussion about the relative importance of hepatic insulin clearance vs. hepatic insulin resistance with respect to peripheral insulin levels. Other techniques that would have provided more precise estimates of beta cell function and whole-body insulin sensitivity are:

1) intravenous glucose tolerance test, or the modified minimal model version, that are not impacted by gastrointestinal factors (28). The minimal model estimates ISI as the combined insulin action on muscle and adipose tissue, and glucose effectiveness as glucose’s own ability to increase its uptake and supress hepatic glucose production at fasting insulin levels.

2) Insulin suppression test that provides a direct measurement of insulin sensitivity (28). A somatostatin analogue is given (to supress endogenous insulin and glucagon production) together with simultaneous infusion of insulin and glucose. The steady state plasma glucose levels reflect the insulin sensitivity. Higher values reflect lower insulin sensitivity. 3) Clamp tests such as hyperglycaemic test, which is good to evaluate endogenous insulin secretion (23), or euglycaemic test, which is regarded as the gold-standard method to evaluate insulin sensitivity (28). The euglycaemic clamp test provides direct measurements of glucose metabolised per unit insulin under steady state conditions, and reflects the glucose uptake in skeletal muscle cells. It can also be used to assess insulin-mediated suppression of hepatic glucose production and adipocyte lipolysis. It would be interesting to have directly measured data of ethnic differences in the amount of insulin that is needed to supress lipolysis and hepatic glucose production, and stimulate glucose uptake in muscle. Also ethnic differences in extrahepatic insulin clearance, assessed by intravenous glucose tolerance tests or euglycaemic clamps, deserve further studies. Generally, extra-hepatic insulin is cleared by kidney (~50%) and by insulin-sensitive tissues such as muscle cells, before some recirculate to liver (26, 127).

Although, all the mentioned tests would have provided more precise estimates of insulin secretion and insulin sensitivity, they do not fit for research in a bigger scale as they require frequent blood sampling by trained personnel.

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