Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Boks 1122, Blindern, 0317, Oslo, Norway

Norwegian Resource Centre for Women's Health, Division of Obstetrics and Gynaecology, Oslo University Hospital, Rikshospitalet, Norway

Section of Specialised Endocrinology, Department of Medicine, Oslo University Hospital, Rikshospitalet, Norway

Faculty of Clinical Medicine, University of Oslo, Rikshospitalet, Norway

Division of Obstetrics and Gynaecology, Oslo University Hospital, Rikshospitalet, Norway

Abstract

Background

Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2–3 hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information.

Methods

OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy.

Results

Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as “general level” (FPC1), “time to peak” (FPC2) and “oscillations” (FPC3). FPC1 scores correlated strongly with AUC (

Conclusions

FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy.

Background

Plasma glucose level is one of the most commonly used metabolic measures, both in research and in clinical settings

OGTT values are discrete, ordered measurements from an underlying, continuous process; i.e. an individual’s glucose regulation. Temporal OGTT measurements are often used to illustrate the underlying glucose curves, but the information inherent in the shape of these curves has been the subject of few studies

Functional data analysis (FDA) is a collection of statistical techniques specifically developed to analyse curve data

The main aim was to study the usefulness of FDA in the analysis of OGTT glucose curve trajectories. FDA, and in particular FPCA, was used to analyse OGTT data in a Norwegian prospective cohort study of healthy pregnant women

Methods

Participants and data

The STORK study is a prospective cohort of 1031 healthy pregnant women of Scandinavian heritage who registered for obstetric care at the Oslo University Hospital Rikshospitalet from 2001 to 2008

Venous blood samples were collected for OGTT in tubes containing Ethylenediaminetetraacetic acid (EDTA) between 07:30 and 08:30 after an overnight fast. Fasting glucose was measured immediately in a drop of fresh, whole EDTA blood, and further blood samples were taken every 30 minutes for 2 h, for a total of five OGTT measurements per woman. Glucose measurements were done by the Accu-Chek Sensor glucometer (Roche Diagnostics, Mannheim, Germany). Inter-assay coefficient of variation was <10%. Due to an unexpected increasing trend in fasting glucose values over the 7 years of participant recruitment, all glucose measurements were de-trended prior to the present analyses, as previously described in detail

The study was approved by the Regional Committee for Medical Research Ethics, Southern Norway, Oslo, Norway (reference number S-01191), and performed according to the Declaration of Helsinki. All participating women provided written informed consent.

Data description

Descriptive statistics were mean, standard deviation (SD) and range, or frequency and percentage. The study sample and women with incomplete OGTT data were compared by two-sample ^{2} tests.

Functional data analysis

FDA is a common term for statistical techniques specifically developed for analysing curve data

Curve fitting

The five OGTT measurements for the 974 participating woman were converted into 974 continuous, smooth curves by subject-specific spline smoothing with B-splines basis functions

Functional principal component analysis

FPCA was used to study the temporal variation in the 974 fitted curves. FPCA extracts a limited number of FPC curves that describe the temporal patterns associated with the largest proportions of the variation in the individual, fitted curves

Functional principal component scores vs simple summary measures

The Pearson correlation coefficient (

Functional analysis of variance

The relation between BMI and simple summary measures of glucose values is well-known ^{2}), normal weight (18.5-25 kg/m^{2}, reference category), overweight (25–30 kg/m^{2}) and obese (≥30 kg/m^{2})

FANOVA vs ANOVA of simple summary measures

The simple summary measures described previously were compared across the BMI categories using traditional ANOVA, with Bonferroni corrected post hoc tests.

Curve shape information in regression analyses

There is an on-going discussion about the diagnostic criterion for GDM

To visualise the clinical usefulness of the curve shape information more clearly, and to account for potential non-linear relations between variables, the 2-h values at gestational weeks 30–32 were grouped into seven categories and multinomial logistic regression was performed ^{th} percentile), [3.27, 3.89) (2.5^{th}-10^{th} percentile), [3.89, 6.39) (10^{th}-75^{th} percentile; reference category), [6.39, 6.90) (75^{th}-85^{th} percentile), [6.90, 7.8) (85^{th} percentile to diagnostic cut-off for GDM) [7.8, 8.84) (GDM diagnosis to 98^{th} percentile) and ≥8.84 mmol/l.

Five different models were fitted. Model 1 included BMI and the three independent FPC score variables from gestational weeks 14–16 as covariates, while models 2–5 included BMI and either the fasting value, the 2-h value, the AUC or the shape index, all from gestational weeks 14–16, as covariates. These simple measures were included one at a time in models 2–5, due to colinearity. Other covariates were not included in the models. It is beyond the scope of the article to build an extensive prediction model or to adjust for variables possibly on the causal pathway to the outcome. All covariates were continuous.

Software

FDA, i.e. curve fitting, FPCA and FANOVA, were performed using the fda package in R 2.13.0

**R script for functional data analysis of glucose curves.**

Click here for file

Results

Data description

Characteristics of the study sample at gestational weeks 14–16 are shown in Table

**Characteristic**

**Study sample, **
**
n
**

**Excluded**
^{
b
}
**, **
**
n
**

**Total cohort, **
**
n
**

**Range**

Data are mean (SD) or frequency (%).

^{a} Numbers may not add up to total due to missing data for some variables.

^{b} Women excluded due to incomplete OGTT data.

^{c} ≥1 cigarette/day.

^{d} Birth weight of offspring.

^{e} Gestational diabetes mellitus.

Gestational weeks

15.8 (1.3)

12.1-22.0

16.0 (1.4)

15.8 (1.3)

Age

31 (4)

19-42

31 (4)

31 (4)

Para 0

517 (54%)

28 (50%)

545 (53%)

Daily smoker^{c}

27 (3%)

1 (2%)

28 (3%)

Height (cm)

169 (6)

150-184

169 (6)

169 (6)

Weight (kg)

69.9 (12.0)

44.6-123.1

68.2 (12.5)

69.8 (12.0)

BMI (kg/m^{2})

24.5 (3.9)

17.2-44.0

23.4 (3.8)

24.5 (3.9)

Birth weight^{d} (g)

3588 (570)

600-5420

3554 (671)

3586 (576)

Blood glucose (mmol/l), first trimester

Fasting

4.0 (0.4)

2.6-5.3

4.0 (0.4)

30 min

5.7 (1.2)

2.5-9.7

5.7 (1.2)

60 min

5.0 (1.4)

2.0-10.9

4.9 (1.4)

90 min

4.5 (1.2)

2.0-10.1

4.5 (1.2)

2 h

4.1 (1.1)

1.2-7.8

4.1 (1.1)

GDM^{e}: 2-h value≥7.8 mmol/l

3 (0.3%)

3 (0.3%)

Blood glucose (mmol/l), third trimester

2 h

5.5 (1.3)

1.9-10.3

5.5 (1.3)

GDM^{e}: 2-h value≥7.8 mmol/l

51 (5.5%)

54 (5.5%)

Curve fitting

The individually fitted, smooth OGTT glucose curves at gestational weeks 14–16 showed large variations between the individual curves (Figure

Observed OGTT data and individually fitted curves at gestational weeks 14–16

**Observed OGTT data and individually fitted curves at gestational weeks 14–16. a** shows the observed OGTT data (light grey) and individually fitted curves (dark grey) for the first five women in the study. The straight lines indicate measurements from the same woman. **b** shows the 974 individually fitted curves (grey) and the mean of these curves (black).

Functional principal component analysis

The essential modes of temporal variation between the fitted curves were extracted by FPCA (Figure

Results from the FPCA

**Results from the FPCA. a-c** shows the mean of the fitted curves (solid line) and how the shape of an individual curve differs from the mean curve if a multiplum of the principal component curve (not shown) is added to (+ +) or subtracted from (− −) the mean curve. The multiplums correspond to one SD of the FPC1, FPC2 and FPC3 scores, respectively. **d-f** shows the mean of the fitted curves (black), and the individual curves for the five women with the highest positive scores (dark grey) and the five with the lowest negative scores (light grey) for each of the three FPCs.

For the majority of the women (89%), the entire OGTT glucose curve was between 2.5 and 7.8 mmol/l, while 6% had hypoglycaemic levels (values <2.5 mmol/l

Individual curves

**Individual curves.** The figure shows the 974 individual, fitted curves classified according to the lower (Q1) and upper (Q3) quartiles of the FPC1 and FPC2 scores. The bold black curve is the overall mean of the fitted curves. Higher panels indicate higher FPC1 scores, and panels to the right represent higher FPC2 scores. The magnitudes of the FPC3 scores are represented using shades of grey: the lighter shades indicate higher FPC3 scores. The lower dashed line is 2.5 mmol/l, one possible cut-off for hypoglycaemia

Functional principal component scores vs simple summary measures

The FPCA transformed the five correlated OGTT measurements (0.40≤

**OGTT**

**OGTT**

**FPC scores**

**Fasting**

**30 min**

**60 min**

**90 min**

**2 h**

**FPC1: “General level”**

**FPC2: “Time to peak”**

**FPC3: “Oscillation”**

^{a}

Fasting

1.00

0.44

0.40

0.41

0.42

0.47

−0.12

0.42

30 min

1.00

0.77

0.66

0.55

0.85

−0.47

0.19

60 min

1.00

0.84

0.70

0.96

−0.04

−0.22

90 min

1.00

0.80

0.93

0.31

−0.01

2 h

1.00

0.79

0.40

0.37

AUC

0.50

0.86

0.95

0.92

0.81

0.999

−0.01

0.05

Simple shape index^{a}

−0.10

−0.34

−0.49

−0.41

0.12

−0.42

0.21

0.67

Functional analysis of variance

The means of the fitted curves differed between the four BMI categories (Figure

Results of the FANOVA

**Results of the FANOVA. a** shows the means of the fitted glucose curves for the BMI categories underweight (n=17, light grey curve), normal weight (n=588, bold grey curve), overweight (n=274, dark grey curve) and obese (n=87, black curve). **b** shows the estimated functional regression coefficients with corresponding CIs (shaded) and with normal weight as the reference category.

**curves for pairwise comparisons of BMI categories using functional permutation tests.** The dashed line is the significance level of 0.05.

FANOVA vs ANOVA of simple summary measures

The results from ordinary ANOVA comparing the BMI categories in regard to fasting value, 2-h value or AUC were similar to those of the FANOVA comparisons. However, the shape index was only significantly different between obese and normal weight women (data not shown).

Multinomial regression with FPC scores

The means of the fitted curves at gestational weeks 14–16 for the seven pre-defined categories of 2-h values at gestational weeks 30–32 are shown in Figure

Means of glucose curves in first trimester, for different glucose categories later in pregnancy

**Means of glucose curves in first trimester, for different glucose categories later in pregnancy.** The figure shows the means of the fitted glucose curves at gestational weeks 14–16, for different categories of 2-h values at gestational weeks 30–32. Darker lines indicate higher 2-h values. The 2-h glucose categories are <3.27, [3.27, 3.89), [3.89, 6.39), [6.39, 6.90), [6.90, 7.8), [7.8, 8.84) and ≥8.84 mmol/l.

The results of the multinomial logistic regression analyses are shown in Table

**Model 1: FPC1, FPC2 and FPC3 scores, gestational weeks 14–16***

**2-h value, gestational weeks 30-32**

**
n
**

**FPC1 scores**
^{
a
}

**FPC2 scores**

**FPC3 scores**

**Mean (SD)**

**OR (95% CI)**

**
p
**

**
p
**

**Mean (SD)**

**OR (95% CI)**

**
p
**

**
p
**

**Mean (SD)**

**OR (95% CI)**

**
p
**

**
p
**

^{a} The FPC1 scores in model 1 and the AUC in model 4 yielded nearly identical results and the AUC results are thus not shown.

^{b}

^{c}

* Categories of 2-h values in the third trimester is the response variable and OGTT characteristics in gestational weeks 14–16 are explanatory variables. All models are adjusted for BMI in gestational weeks 14–16.

≥8.84

19

12.6 (13.5)

1.08 (1.04,1.13)

<0.001

0.40

5.7 (5.1)

1.36 (1.20,1.53)

<0.001

0.01

−0.7 (3.0)

0.87 (0.68,1.10)

0.23

0.05

[7.8,8.84)

32

11.1 (13.1)

1.11 (1.07,1.14)

<0.001

0.59

1.8 (4.7)

1.14 (1.04,1.25)

0.01

0.02

0.5 (1.6)

1.14 (0.95,1.37)

0.16

0.41

[6.90,7.8)

83

9.4 (12.8)

1.10 (1.07,1.12)

<0.001

0.02

−0.1 (3.1)

1.01 (0.95,1.08)

0.69

0.60

0.2 (1.9)

1.05 (0.93,1.19)

0.47

0.57

[6.39,6.90)

94

4.9 (9.6)

1.06 (1.04,1.09)

<0.001

−0.5 (3.8)

0.99 (0.93,1.06)

0.79

0.1 (1.8)

1.00 (0.89,1.13)

0.98

[3.89,6.39)

601

−1.8 (9.7)

1

Ref

−0.2 (3.4)

1

Ref

0.1 (1.8)

1

Ref

[3.27,3.89)

70

−6.9 (8.7)

0.94 (0.91,0.98)

<0.001

<0.01

0.0 (3.0)

0.98 (0.90,1.06)

0.62

0.07

−0.5 (1.7)

0.85 (0.74,0.98)

0.03

0.63

<3.27

23

−12.0 (9.8)

0.83 (0.78,0.90)

<0.001

−0.9 (3.8)

0.85 (0.73,0.98)

0.02

−0.8 (2.4)

0.80 (0.62,1.01)

0.07

**Model 2: Fasting value gestational weeks 14-16***

**Model 3: 2-h value gestational weeks 14-16***

**Model 5: Simple shape index**
^{
c
}
**gestational weeks 14-16***

**Mean (SD)**

**OR (95% CI)**

**
p
**

**
p
**

**Mean (SD)**

**OR (95% CI)**

**
p
**

**
p
**

**Mean (SD)**

**OR (95% CI)**

**
p
**

**
p
**

≥8.84

19

4.1 (0.5)

2.00 (0.57,6.86)

0.28

0.55

5.5 (1.4)

3.40 (2.24,5.18)

<0.001

0.71

−0.80 (1.3)

0.53 (0.30,0.92)

0.03

0.36

[7.8,8.84)

32

4.1 (0.3)

3.17 (1.22,8.01)

0.02

0.54

5.3 (1.4)

3.11 (2.24,4.33)

<0.001

0.07

−0.47 (0.8)

0.73 (0.46,1.17)

0.19

0.92

[6.90,7.8)

83

4.2 (0.4)

4.43 (2.26,7.71)

<0.001

0.03

4.9 (1.1)

2.25 (1.79,2.84)

<0.001

0.01

−0.47 (0.8)

0.71 (0.52,0.97)

0.03

0.84

[6.39,6.90)

94

4.1 (0.4)

1.87 (0.99,3.25)

0.04

4.4 (0.9)

1.58 (1.26,1.98)

<0.001

−0.51 (0.7)

0.74 (0.55,1.00)

0.05

[3.89,6.39)

601

4.0 (0.4)

1

Ref

4.0 (0.9)

1

Ref

−0.29 (0.7)

1

Ref

[3.27,3.89)

70

3.8 (0.3)

0.32 (0.15,0.65)

<0.01

0.63

3.5 (0.8)

0.57 (0.42,0.78)

<0.001

<0.01

−0.28 (0.7)

0.95 (0.66,1.37)

0.80

0.18

<3.27

23

3.8 (0.4)

0.23 (0.09,0.93)

0.01

2.9 (0.4)

0.24 (0.14,0.41)

<0.001

−0.33 (1.1)

0.60 (0.34,1.07)

0.09

Discussion

The present study demonstrated how information inherent in the shape of OGTT glucose curves can be extracted. The FDA approach yielded quantifiable shape entities with physiologically interpretable information that was not contained in the traditional simple summary measures. The extracted shape information differed significantly between women who did and did not develop GDM, and between subgroups of women diagnosed with GDM later in pregnancy, while various simple summary measures did not.

The challenge of extracting shape information from glucose curves has been addressed by others

Our results were based on a large and relatively homogenous sample of healthy, pregnant women, but on a small number of glucose measurements per woman, as compared to those of an intravenous glucose tolerance test. One might expect to find even more physiologically interesting details and discriminating features of OGTT glucose curves, e.g. a larger number of FPCs with a substantial percentage of explained variability and more temporal details in the FPCs, in a more heterogeneous population with a more frequent OGTT sampling. For instance, our fitted curves could not reveal more than two peaks, but curves based on more densely sampled measurements over a longer time period than 2 h would likely show decreasingly oscillating curves rather than purely biphasic trajectories

The mean of the fitted curves obtained from FDA (Figures

While a FPCA will decompose the variation between individual curves into a set of uncorrelated, temporal features, the clinical usefulness of this analysis depends on how the FPCs are interpreted. In this study, current insight into metabolism supported the interpretations of the FPCs as plausible and important physiological features. FPC1, which represented the general level and was the most important temporal feature of the curves, was almost perfectly correlated with AUC, and was significantly higher in women with high BMI. The fasting value and the 2-h value were also correlated with FPC1, but not as strongly as AUC. This is to be expected as a single measurement from a temporal phenomenon rarely describes the most essential temporal feature of the corresponding curve satisfactorily. Moreover, AUC is much better than the widely used fasting, or 2-h value in capturing the essential temporal information of OGTT glucose curves, which is consistent with results from previous studies

Glucose tolerance early in pregnancy has been found to predict glucose tolerance later in pregnancy

The alternative to data-driven approaches such as FPCA for analysing full glucose curves is parametric modelling based on differential equation models of physiological mechanisms. Current concepts of blood glucose dynamics have been summarised in such models

Another important issue with parametric models of blood glucose regulation is the “closed loop” assumption, which can be hard to justify when modelling biological processes in the body because such processes are usually also susceptible to external influences. Diet, physical activity, obesity, changes in weight or visceral fat deposits, smoking and stress have all been shown to affect blood glucose levels

Although FDA or parametric modelling are the most natural approaches to glucose data for the study of glucose curves as single entities, there are alternatives to these analyses for the data presented in this article. For instance, the relation between BMI and glucose values could have been examined with a classical longitudinal data analysis with five repeated measurements per woman, with random effect of woman and modelling of the covariance structure. Also, instead of scores from FPCA, ordinary PCA scores based on the five glucose variables could be used as input to the regression analysis of glucose tolerance later in pregnancy. With only five measurements per curve, and measurements taken at the same time points for each woman, such traditional multivariate methods would be expected to extract similar information as the FDA. However, FDA is easier to apply in situations with more frequent sampling, sampling at unequal time points and missing data. In addition, FDA emphasizes the basic assumption about continuity of the underlying process and its derivatives, and opens for analysis of the derivatives of the curves.

Contrary to general statistical advice

The women in the cohort underwent two OGTTs, but only one was considered functional in the present work. We chose the 2-h value in third trimester as the main outcome instead of the entire curve in third trimester, due to the clinical relevance of this value in pregnancy care. As glucose curves are not commonly used, inference about the 2-h value would better illustrate the usefulness of information from FDA for a maternal pregnancy outcome in clinical practice.

Continuous glucose monitoring devices allow for more frequent glucose sampling over longer periods and might increasingly be used in future studies and in individual patient care to obtain OGTT measurements and measurements of glucose profiles in daily life. An increasing use of continuous glucose monitoring advocates the use of statistical tools that can properly analyse the continuous stream of data by providing curves that may be subjected to FDA as illustrated in the current work.

Furthermore, comparison of curve shape information from individuals with insulin resistance or beta cell failure might reveal whether curve features can distinguish between these two main processes that lead to the development of diabetes. Also, the curve shape information as obtained by FPCA in early pregnancy has the potential to predict complications in later pregnancy better than simple summary measures.

Our work shows that the FDA approach worked well, despite the very limited number of measurements for each participant. Dynamic, physiological processes will often be represented by scarcely sampled measurements, especially when repeated blood samples are required. In addition to glucose regulation, other examples where an FDA approach can be valuable include diurnal measurements of hormone regulation, metabolic changes during or after meals, or after physical exercise. The presented techniques should therefore also be explored in studies of metabolic disorders in non-pregnant populations.

Conclusions

In conclusion, the FDA approach was superior to traditional analyses of OGTT data in terms of providing physiologically interpretable and important temporal information, and in terms of differentiating between women who did and did not develop GDM during pregnancy. We recommend the FDA approach for the analysis of glucose data sampled repeatedly during glucose tolerance testing, or continuous glucose monitoring, to capitalise on important information that would otherwise be lost.

Appendix A

**A.1. Curve fitting in functional data analysis**

Let _{
i
}(_{
j
}) be the measurement from individual _{
j
}, _{
i
}(_{
j
}), _{
i
}(_{
i
}(_{
i
}(_{
j
}) is based on the measurement model

where _{
i
}(_{
j
}) is _{
i
} evaluated at time _{
j
} and _{
ij
} ~ N(0, ^{2}) is an error term. It can be shown that a smooth curve is well approximated by a linear combination of a set of smooth basis functions _{
k
}(

where _{
ki
} is the coefficient for the ^{th} basis function, **c**
_{
i
} = (_{1i
}, …, _{
Ki
}), and _{1}(_{
K
}(_{
k
}(_{
j
}) denoting the ^{th} basis function evaluated at time _{
j
}, substituting (2) into (1) yields

which in matrix notation reads

with **Y**, **Φ**, **C** and ** defined from (3). Here Y is the J × n matrix of observed blood glucose measurements; Φ is the J × K matrix of the values of the K basis functions evaluated at times t
_{
j
}, and the J × n matrix of error terms. Finally, C is the K × n matrix of unknown linear coefficients c
_{
ki
}, which we estimate by minimising the penalised least squares expression**

**
**

**
The penalty term, λ
C
^{
T
}
RC, where λ is a smoothing parameter that defines the degree of regularisation, is added to compensate for random error, and is based on the total curvature of the fitted curve,**

**
**

**where D
^{2}
ϕ(s) is the second derivative of the vector of basis functions ϕ(t). The smoothing parameter λ ∈ [0, ∞) is estimated by optimising a generalised cross-validation criterion. For more detail, see publications by Ramsay et al
17
18
.**

Appendix B

**B.1. Functional principal component analysis**

Functional principal component analysis (FPCA) can be viewed as rotating functional data to optimal empirical continuous basis functions, referred to as functional principal component (FPC) curves _{
κ
}(_{
κi
}, _{
i
}(_{
i
}, given by _{
i
} = ∫ _{
i
}(_{1}(_{1i
} subject to the constraint∫ _{1}(^{2}

Appendix C

**C.1. Functional analysis of variance**

Functional analysis of variance (FANOVA) is a method for studying the difference between the functional means of fitted curves in mutually exclusive subgroups of the study sample.Consider a categorisation of the study sample into _{
g
} be the sample size in category ^{th} OGTT glucose curve, _{
g
} in the ^{th} category, _{
lg
}(

Here _{ref}(_{
g
}(^{th} category and the reference category, and _{
ig
}(

Abbreviations

2-h: Two-hour; ANOVA: Analysis of variance; AUC: Area under the curve; BMI: Body mass index (kg/m^{2}); CI: Confidence interval; EDTA: Ethylenediaminetetraacetic acid; FANOVA: Functional analysis of variance; FDA: Functional data analysis; FPC: Functional principal component; FPCA: Functional principal component analysis; GDM: Gestational diabetes mellitus; OGTT: Oral glucose tolerance test; SD: Standard deviation.

Competing interests

The authors declare that they have no competing interests.

Authors’ contribution

TH, JB, NV and KG contributed to the design of the STORK study and acquisition of data. KFF, JR and MBV designed the data analysis and KFF analysed the data. All authors contributed to the statistical or clinical interpretation of the results, writing and revising the manuscript, and approved the final version.

Acknowledgements

We gratefully acknowledge the help of each woman who participated and all the professional personnel at Oslo University Hospital Rikshospitalet who made this study possible. This study was supported by the Norwegian Health Association; the Faculty of Medicine, University of Oslo; and the Department of Obstetrics and Gynaecology and the National Resource Centre for Women's Health, Oslo University Hospital Rikshospitalet. Roche Diagnostics, Norway supplied glucometers and test strips free of charge.

Pre-publication history

The pre-publication history for this paper can be accessed here: