gestational diabetes melitus

Glycemia and Its Relationship to Outcomesin the Metformin in Gestational DiabetesTrialJANET A. ROWAN, MBCHB, FRACP1

WANZHEN GAO, PHD2WILLIAM M. HAGUE, MD, FRCP, FRCOG3

HAROLD DAVID MCINTYRE, MB, BS, FRACP4

OBJECTIVE To determine how glucose control in women with GDM treated with met-formin and/or insulin influenced pregnancy outcomes.

RESEARCHDESIGNANDMETHODS Women randomly assigned to metformin orinsulin treatment in the Metformin in Gestational Diabetes (MiG) trial had baseline glucosetolerance test (OGTT) results and A1C documented, together with all capillary glucose mea-surements during treatment. In the 724 women who had glucose data for analysis, tertiles ofbaseline glucose values and A1C and of mean capillary glucose values during treatment werecalculated. The relationships between maternal factors, glucose values, and outcomes (includinga composite of neonatal complications, preeclampsia, and large-for-gestational-age [LGA] andsmall-for-gestational-age infants) were examined with bivariable and multivariate models.

RESULTS Baseline OGTT did not predict outcomes, but A1C predicted LGA infants (P0.003). During treatment, fasting capillary glucose predicted neonatal complications (P 0.001) and postprandial glucose predicted preeclampsia (P 0.016) and LGA infants (P 0.001). Obesity did not influence outcomes, and there was no interaction between glycemiccontrol, randomized treatment, or maternal BMI in predicting outcomes. The lowest risk ofcomplications was seen when fasting capillary glucose was4.9 mmol/l (mean SD 4.6 0.3mmol/l) compared with 4.95.3 mmol/l or higher and when 2-h postprandial glucose was5.96.4 mmol/l (6.2 0.2 mmol/l) or lower.

CONCLUSIONS Glucose control in women with gestational diabetes mellitus treatedwith metformin and/or insulin is strongly related to outcomes. Obesity is not related to outcomesin this group. Targets for fasting and postprandial capillary glucose may need to be lower thancurrently recommended.

Diabetes Care 33:916, 2010

A lthough treatment of gestational di-abetes mellitus (GDM) has beenshown to improve perinatal out-comes (1,2), there is lack of consensusabout ideal glucose targets and how otherfactors, such as fetal abdominal circum-ference, should influence these targets(3,4). The Fifth International Workshop-Conference on Gestational Diabetes en-dorsed targets of capillary fasting glucose5.3 mmol/l, 1-h postprandial 7.8mmol/l, and/or 2-h postprandial 6.7

mmol/l until further data addressing op-timal goals become available (5). Im-proved pregnancy outcomes have beenreported in women achieving these tar-gets compared with those in women whodid not (6); the latter group had higherbaseline mean BMI and A1C, which mayhave influenced outcomes. Obesity hasbeen reported as an independent factorinfluencing outcome in women withGDM treated with diet but not in thosetreated with insulin (7,8).

Published studies have comparedwomen who aim for or achieve predeter-mined glucose targets with those who donot: it is not clear whether such aims areoptimal and whether glycemia influencesdifferent outcomes equally. Several stud-ies suggested that treatment intensity canbe usefully stratified according to fetal ab-dominal circumference measured by ul-trasound (3): intensive treatment ofwomen carrying fetuses with an abdomi-nal circumference above the 70 75thpercentile lowered the frequency of large-for-gestational-age (LGA) infants withoutincreasing rates of small-for-gestational-age (SGA) infants. However, lowering ofmean maternal glucose to4.8 mmol/l isassociated with increased frequency ofSGA infants (9).

Data showing relationships betweendifferent fasting and postprandial glucosevalues and a range of outcomes would as-sist clinicians in setting target ranges foroptimal glucose control more objec-tively. In the Metformin in Gestational Di-abetes (MiG) trial, women with GDM,who had one or more home capillaryblood glucose measures 5.5 mmol/lfasting or 6.7 mmol/l 2-h postprandialafter lifestyle intervention, were randomlyassigned to either insulin or metformintreatment (10). The primary objective ofthe trial was to compare metformin withinsulin treatment, but prespecified sec-ondary objectives were to determine theimpact of glycemia on outcomes and todetermine whether treatment with met-formin or insulin was more effective atdifferent levels of glycemia. Baseline gly-cemia measures and capillary glucosemeasures throughout treatment were re-corded in the trial database. The specificaims of the present analysis were 1) todetermine how glucose control influ-enced trial outcomes, including the pri-mary outcome (a composite of neonatalcomplications), maternal preeclampsia,and rates of LGA and SGA infants; 2) toidentify additional baseline factors influ-encing outcomes, including baseline gly-cemia and obesity; and 3) to examine anydifferences between treatment arms at dif-ferent levels of glycemia.

From the 1National Womens Hospital, Auckland City Hospital, Auckland, New Zealand; the 2Center forAsian Health, College of Health Professions, Temple University, Philadelphia, Pennsylvania; the 3Depart-ment of Obstetrics, Womens and Childrens Hospital, University of Adelaide, Adelaide, South Australia,Australia; and the 4Department of Obstetric Medicine, University of Queensland and Mater HealthServices, South Brisbane, Queensland, Australia.

Corresponding author: Janet A. Rowan, [email protected] 30 July 2009 and accepted 12 October 2009. Published ahead of print at http://care.

diabetesjournals.org on 21 October 2009. DOI: 10.2337/dc09-1407. 2010 by the American Diabetes Association. Readers may use this article as long as the work is properly

cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be herebymarked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

C l i n i c a l C a r e / E d u c a t i o n / N u t r i t i o n / P s y c h o s o c i a l R e s e a r c hO R I G I N A L A R T I C L E

care.diabetesjournals.org DIABETES CARE, VOLUME 33, NUMBER 1, JANUARY 2010 9

RESEARCH DESIGN ANDMETHODS The MiG trial was aprospective, randomized, multicentertrial comparing metformin with insulintreatment in women with GDM. Themethodology and outcomes have beenpublished previously (10). The mean ges-tation at recruitment was 30 3 weeks.All women gave written informed con-sent. The trial was approved by ethicscommittees at each participating site.

Assessment of glycemiaBaseline glycemia measures included thediagnostic 75-g oral glucose tolerance test(OGTT) results and A1C at random as-signment to treatment with insulin ormetformin. Treatment glycemia measuresincluded capillary glucose measurementsthat were documented four times daily:fasting and 2 h from the start of each meal.Medisense (now Optium) meters (AbbottDiabetes Care, Alameda, CA) and occa-sional Accu-Chek Advantage meters(Roche Diagnostics, Mannheim, Ger-many) were used, and the stored resultswere downloaded. Relevant results weretranscribed into the trial database fromthe day after medication was started untildelivery. Of 733 women who had datacollected, 7 women did not have fastingglucose, 8 did not have postprandial glu-cose, and 9 did not have any glucose re-cordings documented, leaving 724women for assessment. The median num-ber (interquartile range) of capillary glu-cose values documented for each womanincluded 46 (34 60) fasting and 115(83161) postprandial measures. Themeans of fasting and postprandial glucosemeasures were calculated separately foreach patient.

Outcomes and definitionsSeveral prespecified outcomes from thetrial were selected to examine the impactof glycemia measures: the primary out-come composite (neonatal complica-tions), preeclampsia, and rates of LGA(customized birth weight 90th percen-tile) and SGA (customized birth weight10th percentile) infants. The primaryoutcome composite included one or moreof the following neonatal complications:recurrent hypoglycemia (two or moreblood glucose measurements 2.6mmol/l), respiratory distress (need for4 h of respiratory support with supple-mental oxygen, continuous positive air-way pressure, or intermittent positive-pressure ventilation during first 24 h),need for phototherapy, birth trauma,

5-min Apgar score7, and preterm birth(37 weeks of gestation). The birthweight percentile was calculated using cus-tomized calculators (http://www.gestation.net), which adjust for sex and gestationalage of the infant as well as for maternalheight, weight in early pregnancy, ethnicgroup, and parity (11). In this calculator,birth weight is adjusted if a womans BMIis between 20 and 30 kg/m2 but not fur-ther adjusted for BMI above this range;European, Maori, Chinese, Indian, andother specific Asian and Pacific Island eth-nicities can be selected. In the MiG data-base, the specific Pacific Island ethnicitywas not documented, so Samoan ethnic-ity was used to represent this group in thecalculations. Of note, customized chartsapplied to a general nondiabetic obstetricpopulation with ethnicities similar tothose of the MiG population report ratesof SGA infants between 12.1 and 12.8%and of LGA infants between 8.4 and 8.8%(http://www.adhb.govt.nz/nwhealthinfo).

Statistical analysisMean capillary glucose values were calcu-lated from the daily records. They areshown as mean fasting, mean postpran-dial, and/or overall mean glucose (meanfasting plus mean postprandial divided by2). Fasting, postprandial, and overallmean glucose measures and their rela-tionship to outcomes were explored usingcontinuous measures and categorizedquartile and tertile groupings; all methodsyielded similar relationships. Tertilegroups were chosen for this report be-cause, with larger numbers in each group,they provided a simple demonstration ofrelationships between glucose levels andoutcomes. A bivariable analysis of base-line characteristics was performed to ex-plore associations with outcomes.Interactions with glycemic control, bothof randomized treatment (metformin orinsulin) and of obesity, were explored us-ing the Breslow-Day test through strati-fied analysis and logistic regressionanalysis. Multivariable binary logistic re-gression was performed to identify inde-pendent risk factors associated with boththe primary composite outcome measureand preeclampsia. For the customizedbirth weight percentiles, multinomial lo-gistic regression was used, given that theoutcome measure was categorized intothree groups: appropriate-for-gestational-age, SGA, and LGA infants. In these anal-yses, the backwards elimination methodwas used. Specifically, any potential riskfactors identified through bivariable anal-

ysis and stratified analysis (P 0.25), aswell as others considered potentially rel-evant for clinical reasons or from the lit-erature, were included in the initialmodels; variables were then eliminatedstepwise from the models if their contri-butions were not significant, until allvariables retained were significantly asso-ciated with the outcome measure. Twomultivariable analysis models were exam-ined. In the first, the glycemic measuresincluded the baseline OGTT results, A1Cvalues, and mean fasting, postprandial,and overall capillary glucose concentra-tions in addition to other potential base-line risk factors (total available n 582for this model for the primary compositeoutcome). In the second model, the base-line glycemia measures were excluded,giving a total n 724 for the analysis ofprimary composite measures. Interpreta-tion of the results relating to risk factorsfor SGA and LGA infants took into ac-count the fact that the customized clas-sification of SGA and LGA infants hadalready been adjusted for infant sex, ges-tation, maternal ethnicity, parity, andBMI between 20 and 30 kg/m2.

Maternal weight at the earliest book-ing was missing in 147 women, so thesedata were imputed using a regressionmethod. Specifically, the weight was firstimputed using a formula based on thepostpregnancy weight. If this variable wasmissing, then a formula based on the ran-domization weight was used instead. Theimputed mean of weight at the earliestbooking was 85.98 kg, very close to themean of 85.25 kg before imputation, sug-gesting that the imputation is robust.

All analyses were performed usingSAS (version 9.1; SAS Institute, Cary,NC), using 0.05 to determinesignificance.

RESULTS Table 1 shows tertiles ofbaseline glycemia measures and capillaryglucose levels during treatment. Supple-mentary Table 1A (available at http://care.diabetesjournals.org/cgi/content/full/dc09-1407/DC1) shows baseline characteristicsof the women according to tertiles of cap-illary glucose during treatment.

The relationships between tertiles ofmean fasting, postprandial, and overallcapillary glucose during treatment andoutcomes are shown in Table 2. Examin-ing the components of the primary out-come composite, we note that thefrequencies of recurrent neonatal hypo-glycemia and preterm birth increasedacross tertiles of control, and there was a

Glycemia and MiG trial outcomes

10 DIABETES CARE, VOLUME 33, NUMBER 1, JANUARY 2010 care.diabetesjournals.org

trend to higher rates of respiratory dis-tress and a need for phototherapy. Mater-nal preeclampsia and frequency of LGAinfants also increased across tertiles ofachieved glycemia. The frequency of SGAinfants fell across the tertiles of fasting andmean glycemia but not across the post-prandial tertiles. The frequency of SGAinfants in the lowest fasting tertile was notincreased compared with that of the back-ground population.

The bivariable relationships betweenmaternal baseline characteristics, glyce-mia measures, and outcomes are shownin Table 3. Baseline factors that were re-lated to the primary outcome compositeincluded maternal ethnicity, nulliparity,previous preeclampsia, and previous de-livery of a baby weighing4,000 g. Base-line glycemia measures were not relatedto the primary outcome composite, butmean capillary glucose measures on treat-ment were strongly related. Preeclampsiawas associated with Polynesian ethnicity,

chronic hypertension, previous hyperten-sive complications during pregnancy, andmaternal weight gain from early preg-nancy to recruitment. It was also associ-ated with baseline A1C and all measuresof glucose control on treatment. Addi-tional factors (not all shown) that werenot related to either the primary outcomecomposite or preeclampsia included ges-tation at recruitment (2027 vs. 2833weeks), tertiary education level, smokingin pregnancy, past history of GDM, pre-vious recurrent miscarriage/termination(three or more), and maternal first-degreefamily history of diabetes, hypertension,or preeclampsia.

In Table 3, the frequency of SGA in-fants was associated with Asian ethnicityand with the woman having a first-degreerelative with diabetes. It was inversely re-lated to maternal weight gain from earlypregnancy to recruitment. Baseline glyce-mia measures were not predictive of SGAinfants. During treatment, risk of SGA in-

fants was lower in women in the highesttertile of capillary fasting glucose, butthere was no relationship with postpran-dial glucose. The frequency of LGA in-fants was associated with Polynesianethnicity, previous delivery of a babyweighing4,000 g, maternal weight gainfrom early pregnancy to recruitment, andA1C at recruitment. The rate of LGA in-fants was increased in women whose cap-illary glucose during treatment was in thehighest tertile. Other factors (not shown)that did not relate to SGA or LGA infantsincluded chronic hypertension, gestationat recruitment, smoking in pregnancy,tertiary education, previous history of re-current miscarriages/terminations, GDMor hypertensive complications, and a fam-ily history of preeclampsia. The Breslow-Day test and logistic regression analysisshowed no interactions of glycemic con-trol with randomized treatment or withmaternal BMI in predicting the primary

Table 1Baseline and treatment glycemia tertiles

Tertile 1upper limit Tertile 1

Tertile 2limits Tertile 2

Tertile 3lower limit Tertile 3

Baseline measuresOGTT fasting glucose (mmol/l) 5.2 4.7 0.3 5.25.8 5.5 0.2 5.8 6.8 1.1OGTT 2-h glucose (mmol/l) 8.8 7.5 1.0 8.89.9 9.4 0.3 9.9 11.6 1.8A1C (%) 5.4 5.1 0.3 5.45.8 5.7 0.1 5.8 6.5 0.6

Treatment measuresFasting capillary glucose (mmol/l) 4.9 4.6 0.3 4.95.3 5.1 0.1 5.3 5.9 0.6Postprandial capillary glucose (mmol/l) 5.9 5.6 0.2 5.96.4 6.2 0.2 6.4 7.2 0.7Mean capillary glucose (mmol/l) 5.4 5.2 0.2 5.45.8 5.7 0.1 5.8 6.5 0.6

Data are tertile boundaries and mean SD glycemia measures within each tertile. Glucose and A1C were recorded to 1 decimal place.

Table 2Outcomes by fasting, postprandial tertiles, and overall mean glucose tertiles

Fasting tertiles Postprandial tertiles Mean glucose tertiles

1 2 3 P* 1 2 3 P* 1 2 3 P*

Primary outcome components 22.9 32.5 39.6 0.001 25.6 29.4 40.3 0.001 23.0 31.1 41.4 0.001Recurrent glucose 2.6 mmol/l 10.4 18.3 21.3 0.002 13.0 17.7 19.3 0.07 11.7 15.3 23.2 0.001Respiratory distress 2.9 2.9 5.4 0.15 2.5 1.6 7.1 0.01 2.5 3.2 5.5 0.09Phototherapy 5.4 8.1 10.8 0.03 7.1 6.9 10.5 0.18 5.4 9.7 9.3 0.13Birth trauma 4.2 4.1 5.0 0.66 3.8 3.2 6.3 0.18 5.0 2.4 5.9 0.64Apgar score 7 at 5 min 0.4 0 0.8 0.48 0 0 1.3 0.03 0 0 1.3 0.03Prematurity: 37 weeks gestation 5.8 10.2 13.3 0.006 5.5 9.3 14.7 0.001 5.9 10.5 13.1 0.008Maternal preeclampsia 4.2 4.9 9.6 0.01 3.4 4.4 10.9 0.001 3.4 5.2 10.1 0.002Birth weight 0.001 0.001 0.00110th (customized) 13.8 11.4 6.7 0.01 11.8 10.5 9.7 0.46 13.8 9.7 8.4 0.0690th (customized) 12.1 11.4 24.6 0.001 10.1 12.9 24.8 0.001 10.5 13.3 24.1 0.0012,500 g 5.0 6.5 5.0 1.0 2.9 6.5 7.1 0.045 4.6 6.5 5.5 0.6724,000 g 8.3 10.2 19.6 0.001 6.7 13.3 17.7 0.001 6.7 12.9 18.1 0.001

Data are frequency of outcome (%). *Cochrane-Armitage trend test. Customized charts in general obstetric population with ethnicities similar to those of the MiGpopulation reported rates of SGA infants of 12.112.8% and of LGA infants of 8.48.8% (http://www.adhb.govt.nz/nwhealthinfo). 2 test of three levels ofcustomized birth weight.

Rowan and Associates


Table3

Bivariablean

alysis:b

aselinecharacteristics,glucosemea

suresat

baseline

andon

trea

tmen

t,an

dtheirrelation

shipsto

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es,including

theprim

aryou

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e(for

acompo

site

ofne

onatal

outcom

es,see

text),preeclam

psia,a

ndcustom

ized

SGAan

dLGA

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ary

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ome

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clam

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tom

ized

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and

LGA

%O

R(9

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I)P

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SGA

(%)

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(95%

CI)

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line

char

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rist

ics

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nici

tyE

urop

ean

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casi

an/m

ixed

373

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48.

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001

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an15

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42.

46)

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68)

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an/o

ther

204

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20.

90)

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7)16

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77(0

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93.

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mal

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ory

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79.

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onal

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sion

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440

28.9

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3.9

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10.2

117

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Yes

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338

32.3

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0.

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Yes

162

24.1

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(0.4

41.

02)

7.4

1.72

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93.

77)

5.6

0.57

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31.5

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233

37.3

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390

32.3

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886.

41

0.87

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Yes

343

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33)

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)



Table3

Con

tinu

ed

n

Prim

ary

outc

ome

Pree

clam

psia

Cus

tom

ized

SGA

and

LGA

%O

R(9

5%C

I)P

%O

R(9

5%C

I)P

SGA

(%)

OR

(95%

CI)

*LG

A(%

)O

R(9

5%C

I)*

Gly

cem

iam

easu

res

At

base

line

(ven

ous

plas

ma)

75-g

OG

TT

resu

ltat

diag

nosi

sFa

stin

gte

rtile

120

829

.81.

000.

393.

41.

000.

0612

.51.

009.

61.

00

0.00

1Fa

stin

gte

rtile

221

832

.61.

14(0

.75

1.72

)6.

41.

97(0

.78

4.98

)11

.50.

94(0

.52

1.69

)12

.41.

32(0

.71

2.44

)Fa

stin

gte

rtile

322

836

.01.

32(0

.89

1.98

)9.

22.

91(1

.21

7.00

)9.

70.

91(0

.49

1.67

)24

.12.

95(1

.69

5.15

)2-

hte

rtile

120

630

.11.

000.

094.

41.

000.

197.

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outcome composite, preeclampsia orLGA/SGA infants.

The first multivariable analysis modelincluded baseline glycemia measures toexamine whether they related to the cho-

sen outcome measures (model 1). Afterbackward elimination, none of the base-line glycemia measures predicted the pri-mary outcome or preeclampsia, buttreatment capillary glucose was strongly

related. None of the glucose measurespredicted SGA infants. For LGA infants,A1C at recruitment was the only predic-tive glycemia measure (P 0.003). Com-pared with the lowest A1C tertile, the

Table 4Multiple logistic regression model 2: significant factors relating to outcomes

Adjusted OR (95% CI) P

Primary outcome compositeFasting capillary glucose on treatment

Tertile 1 1.00 0.001Tertile 2 1.75 (1.172.63)Tertile 3 2.66 (1.764.01)

NulliparityNo 1.00 0.001Yes 1.85 (1.302.65)

Previous baby 4,000 gNo 1.00 0.001Yes 0.58 (0.370.90)

PreeclampsiaPostprandial capillary glucose on

treatmentTertile 1 1.00 0.016Tertile 2 1.38 (0.533.49)Tertile 3 3.14 (1.317.32)

BMI categoryNormal 1.00 0.036Overweight 8.48 (1.0966.22)Obese 4.31 (0.5533.50)

Weight gain (kg) from earlypregnancy to recruitment 1.06 (1.001.11) 0.034

Ethnicity categoryEuropean/Caucasian/mixed 1.00 0.041Polynesian 2.40 (1.125.12)Asian/other 0.86 (0.352.11)

Chronic hypertensionNo 1.00 0.034Yes 2.69 (1.086.73)

Previous gestational hypertensionNo 1.00 0.008Yes 3.26 (1.268.40)Nulliparity 2.74 (1.325.70)

SGA or LGA SGA infants LGA infants

Postprandial capillary glucose ontreatment

Tertile 1 1.00 1.00 0.001Tertile 2 0.89 (0.501.58) 1.30 (0.732.33)Tertile 3 0.98 (0.541.78) 2.82 (1.654.84)

Previous baby 4,000 gNo 1.00 1.00 0.001Yes 0.60 (0.281.28) 3.00 (1.844.89)Nulliparity 1.20 (0.712.04) 0.84 (0.481.45)

Weight gain (kg) from earlypregnancy to recruitment 0.94 (0.900.99) 1.05 (1.021.09) 0.001

Maternal first-degree relative: diabetesNo 1.00 1.00 0.01Yes 2.04 (1.243.34) 0.84 (0.541.28)

Data are adjusted ORs (95% CI). BMI range was included in the model to determine whether there was any effect of BMI 30 kg/m2. (Customized birth weightsalready adjust for maternal BMI between 20 and 30 kg/m2.)



second tertile had an odds ratio (OR) forLGA infants of 2.64 (95% CI 1.335.24)and the third tertile had an OR for LGAinfants of 4.0 (2.037.87).

The results of the second multivari-able analysis model (excluding baselineglycemia measures and thus includinglarger numbers) are summarized in Table4. Variables that were eliminated for alloutcomes are not shown. In this analysis,fasting capillary glucose on treatment wasassociated with the primary outcomecomposite, and postprandial capillaryglucose was related to LGA infants andpreeclampsia. Overweight women weremore likely to develop preeclampsia thannormal-weight women, but the rate ofpreeclampsia in obese women was not in-creased. Numbers of women taking aspi-rin (n 45) were too small to determinewhether this was a confounding factor.Polynesian ethnicity, chronic hyperten-sion, and previous gestational hyperten-sion remained factors for preeclampsia.Having a previous baby weighing4,000g reduced the risk of neonatal complica-tions but increased the risk of LGA in-fants. Weight gain from early pregnancyto recruitment increased the risk of pre-eclampsia and LGA infants.

CONCLUSIONS The key findingfrom this study is that capillary glucosevalues during treatment for GDM withmetformin and/or insulin related stronglyand independently to the primary out-come composite of neonatal outcomes, tomaternal preeclampsia, and to frequencyof LGA infants. These data are novel inthat the outcomes examined seem to besensitive to different levels of glycemia:rates of neonatal hypoglycemia increasedbetween the lowest and middle tertiles ofglycemia control, whereas risk of LGA in-fants and maternal preeclampsia in-creased between the middle and highesttertiles. These differential effects may beimportant to consider when one is deter-mining treatment goals and which out-comes to report in assessing treatment ofGDM.

The diagnostic OGTT values were notpredictive of outcomes, but A1C at re-cruitment was predictive of LGA infants.It may be that intervention in the trial wastoo late to modify this outcome. The fetalultrasound measurements at recruitmentand at 37 weeks of gestation have not yetbeen analyzed to see whether growth ve-locity was modified differentially accord-ing to glycemic control, as others haveshown (3).

During treatment, fasting capillaryglucose predicted neonatal complica-tions, whereas postprandial capillary glu-cose was related to risks of preeclampsiaand LGA infants. It is therefore importantto focus on both fasting and postprandialglucose control to optimize outcomes.The relationship between postprandialglucose levels and complications may berelevant for obese women without GDM,who have higher postprandial glucoselevels and increased rates of preeclampsiaand LGA infants compared with leanwomen (12). In addition, the Hyperglyce-mia and Adverse Pregnancy Outcome(HAPO) study demonstrated that mater-nal glycemia, as measured by a 75-gOGTT at 28 weeks of gestation, is an im-portant risk factor for preeclampsia andLGA infants at values below those usedcurrently to diagnose GDM (13). A fur-ther study has shown that fluctuating glu-cose levels have a stronger effect onendothelial function (important in thepathogenesis of preeclampsia [14]) thansustained hyperglycemia (15). Fromthese observations, it can be speculatedthat reducing postprandial glucose fluc-tuations in obese women could reducerates of preeclampsia and/or LGA infants.

There was no independent effect ofobesity on the primary outcome compos-ite, preeclampsia or LGA infants, consis-tent with previous findings in womentreated with insulin (7). It may be thatpharmacotherapy more strongly modifiesfetal nutrient supply than dietary inter-vention alone, overriding the effects ofobesity per se.

There was no increase in SGA infantsin the lowest glucose tertile, but meanglucose values were above the thresholdpreviously shown to increase risk of SGAinfants (9). Customized centiles wereused because, particularly in heteroge-neous populations, they perform betterthan does a population centile for identi-fying SGA infants, both in the general ob-stetric population and in women withtype 2 diabetes (11,16). It is recognizedthat these calculators require further de-velopment in relation to the adjustmentfor ethnicity and maternal BMI (16).

The rate of SGA infants was increasedin women with a first-degree relative withdiabetes. The reason for this may relate togenetic associations between low birthweight and type 2 diabetes (17).

There were no differences seen be-tween metformin and insulin treatmentgroups at different levels of glucose con-trol. This finding suggests that, if met-

formin is used, supplemental insulinshould be used readily if glucose targetsare not achieved.

These data do not provide definitiveanswers regarding optimal glucose tar-gets. However, women with mean post-treatment fasting capillary glucose 4.9mmol/l had significantly better outcomesthan women with posttreatment fastingcapillary glucose between 4.9 and 5.3mmol/l. At 2 h postprandial, mean capil-lary glucose 6.4 mmol/l was associatedwith improved outcomes, and a further,but small, improvement was seen withmean postprandial capillary glucose5.9mmol/l. In an earlier study, women in awell-controlled group had a mean fast-ing capillary glucose of 4.7 mmol/l andeither a mean 1-h capillary glucose of 6.5mmol/l or a mean 2-h capillary glucoseof 5.7 mmol/l, whereas a less well-controlled group had a mean fasting cap-illary glucose of 5.3 mmol/l and either amean 1-h capillary glucose of 7.2 mmol/lor a mean 2-h capillary glucose of 6.8mmol/l (6). The glucose means in thewell-controlled group were very similarto the means seen in women in the lowesttertile in the current study. These data sug-gest that clinicians should aim for lower tar-gets than currently recommended.

In summary, these data demonstratethat glucose control in women with GDMis strongly related to pregnancy out-comes. Targets for fasting and postpran-dial capillary glucose may need to be lowerthan current guidelines recommend.

Acknowledgments The MiG trial was sup-ported by grants from the following bodies:Auckland Medical Research Foundation, Na-tional Womens Evelyn Bond Charitable Trust,Health Research Council of New Zealand, andNational Health and Medical Research Coun-cil of Australia.

No potential conflicts of interest relevant tothis article were reported.

Parts of this study were presented in ab-stract form at the annual meeting of the So-ciety of Obstetric Medicine of Australia andNew Zealand, Auckland, New Zealand, 30October1 November 2009.

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