Pregnancy adverse outcomes related to pregravid body mass index
and gestational weight gain, according to the presence or not of
gestational diabetes mellitus: A retrospective observational
study
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Diabetes & Metabolism xxx (2015) xxxxxx
Original article
Pregnancy adverse outcomes related to pregravid body mass index
and gestational weight gain, according to the presence or not of
gestational diabetes mellitus: A retrospective observational
study
E. Cosson a,b,, C. Cussac-Pillegand a, A. Benbara c, I.
Pharisien c, M.T. Nguyen a, S. Chiheb a, P. Valensi a, L. Carbillon
c
a Department of Endocrinology-Diabetology-Nutrition, CRNH-IdF,
CINFO, Paris 13 University, Sorbonne Paris Cit, Jean-Verdier
Hospital, APHP, Bondy, France
b Sorbonne Paris Cit, UMR U1153 Inserm, U1125 Inra, Cnam,
Universit Paris 13, Bobigny, France
c Department of Obstetrics and Gynecology, Paris 13 University,
Sorbonne Paris Cit, Jean-Verdier Hospital, APHP, Bondy, France
Received 6 February 2015; received in revised form 1st June
2015; accepted 2 June 2015
Abstract
Aim. This study retrospectively evaluated the complications
associated with prepregnancy overweight (OW) or obesity (OB) and
gestational weight gain (GWG) in women with or without universally
screened and treated gestational diabetes mellitus (GDM).
Methods. A total of 15,551 non-Asian women without pregravid
diabetes or hypertension who delivered singleton babies (20022010)
were classified according to GDM (13.5%), pregestational body mass
index (BMI; normal range: 18.524.9 kg/m2 ), OW (26.2%), OB (13.9%;
BMI 30 kg/m2 ) and GWG (< 7 kg: 32%; 711.5 kg: 37%; 11.616 kg:
23%; > 16 kg: 8%). Main outcome measures were large/small for
gestational age (LGA/SGA), caesarean section, preeclampsia, preterm
delivery and shoulder dystocia.
Results. GDM was associated with more LGA babies [Odds Ratio
(OR): 2.12, 95% confidence interval (CI): 1.852.43], caesarean
section (OR: 1.49, 95% CI: 1.341.65) and preeclampsia (OR: 1.59,
95% CI: 1.212.09). OW/OB and GWG were associated with LGA infants
whatever the GDM status, and with SGA babies only in women without
GDM. LGA status was independently associated with GWG in women with
GDM (11.616 kg: OR: 1.74, 95% CI: 1.492.03 and > 16 kg OR: 3.42,
95% CI: 2.834.13 vs 711.5 kg) and in women without GDM (OR: 2.14,
95% CI: 1.542.97 or OR: 2.65, 95% CI: 1.684.17, respectively), and
with BMI only in women without GDM (OR: 1.12, 95% CI: 1.001.24,
per
10 kg/m2 ). SGA status was independently associated with OW (OR:
0.86, 95% CI: 0.770.98), OB (OR: 0.84, 95% CI: 0.720.98) and GWG
< 7 kg
(1.14, 95% CI: 1.011.29) only in women without GDM.
Conclusion. In our European cohort and considering the
triumvirate of GDM, BMI and GWG, GDM was the main contributor to
caesarean section and preeclampsia. OW/OB and GWG contributed to
LGA and SGA infants mainly in women without GDM.
2015 Elsevier Masson SAS. All rights reserved.
Keywords: Gestational diabetes mellitus; Gestational weight
gain; Obesity; Pregnancy; Prognosis
1. Introduction
+Model ARTICLE IN PRESS
DIABET-702;
No. of Pages 9
+Model ARTICLE IN PRESS
DIABET-702;
No. of Pages 9
Please cite this article in press as: Cosson E, et al. Pregnancy
adverse outcomes related to pregravid body mass index and
gestational
weight gain, according to the presence or not of gestational
diabetes mellitus: A retrospective observational study. Diabetes
Metab (2015), http://dx.doi.org/10.1016/j.diabet.2015.06.001
Please cite this article in press as: Cosson E, et al. Pregnancy
adverse outcomes related to pregravid body mass index and
gestational
weight gain, according to the presence or not of gestational
diabetes mellitus: A retrospective observational study. Diabetes
Metab (2015), http://dx.doi.org/10.1016/j.diabet.2015.06.001
Abbreviations: GWG, Gestational weight gain; IOM, Institute of
Medicine; HAPO, Hyperglycaemia and Adverse Pregnancy Outcomes;
IADPSG, Interna- tional Association of Diabetes and Pregnancy Study
Groups; LGA, large for gestational age; SGA, small for gestational
age.
Corresponding author at: Department of
Endocrinology-Diabetology-
Nutrition, hpital Jean-Verdier, avenue du 14-juillet, 93143
Bondy cedex, France. Tel.: +33 148 02 65 80; fax: +33 148 02 65
79.
E-mail address: [email protected] (E. Cosson).
Gestational diabetes mellitus (GDM) is defined as any degree of
glucose intolerance with onset or first recognition during
pregnancy, and is associated with adverse outcomes during pregnancy
[1]. Obesity has a growing prevalence in women of childbearing age
[2] and is a confounding factor. First, it is a risk factor for GDM
[2,3]. Second, it shares complications with GDM, such as
large-for-gestational-age (LGA) infants
http://dx.doi.org/10.1016/j.diabet.2015.06.001
1262-3636/ 2015 Elsevier Masson SAS. All rights reserved.
2E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx
[411], caesarean section [4,5,7,8,11,12], hypertensive disor-
ders [4,5,7,8] and, in certain studies, shoulder dystocia [5].
Also, gestational weight gain (GWG) appears to be crucial
[5,810,13,14].
To date, only five recent studies, four from the United States
[5,10,15,16] and only one from Europe [9], have explored the impact
of GDM, obesity and GWG together. Some limitations may affect these
observational studies. First, the prevalence of GDM is sometimes
very low [9,16] with screening which might not have been universal
[5,10,15,16]. Second, women with pregravid diabetes and
hypertension were not excluded [5,9,10,15,16], whereas these
conditions are often associated with overweight and obesity.
Therefore, considering women with isolated obesity might better
evaluate the role of obesity per se [12]. Regarding body mass index
(BMI), underweight women are not always considered separately
[5,16] nor is the lower BMI cutoff point in Asian women [17] taken
into account to define overweight and obesity [5,9]. Finally,
excessive GWG [9,10], determined according to pregravid BMI status
as pro- posed by the Institute of Medicine (IOM) [18] rather than
GWG per se, has often been considered and is an additional
confound- ing factor.
Dietary advice and drugs are generally provided only to women
with GDM, as GWG [5,810,13,14], treatment modal- ities and
glycaemic levels achieved can modify the outcomes [19]. Only the
Hyperglycaemia and Adverse Pregnancy Out- comes (HAPO) study
reported obesity-related adverse events independently of glycaemic
status and its treatment [4,7]. How- ever, in that study, BMI was
measured at the time of oral glucose tolerance tests at between 24
and 32 weeks of gestation, and not before pregnancy. Therefore, GWG
could not be assessed.
Given this context, a large multiethnic European cohort of
non-Asian women who delivered singleton babies and were without
pregravid diabetes or hypertension was selected for our present
retrospective observational study. In this cohort, the adverse
outcomes related to isolated overweight, obesity and GWG were
investigated in women with and without universally screened and
treated GDM.
2. Methods
2.1. Participants, GDM screening and care
A total of 20,653 women delivered at our hospital between
January 2002 and December 2010. Data are routinely entered at birth
for all women (no exceptions) giving birth at our university
hospital by the midwife assisting at the delivery, then checked and
collected during the maternity stay by a midwife quali- fied in
data management and storage (I.P.), with no interactions with the
women themselves. The authors did not have access to identification
of patients information prior to anonymization. The purposes of the
database are to assess the overall quality of obstetric care and to
regularly update medical management protocols. The data are
retrospective and observational, with no need for either approval
by an ethics committee/institutional review board or patients
written informed consent. The patients records/information are
anonymous, and the database is declared
to the French data protection authority (Commission nationale de
linformatique et des liberts [CNIL]).
In the present study, women with known diabetes (n = 204),
previous hypertensive disorders (n = 448) and multiple pregnan-
cies (n = 378) were not included. Furthermore, women whose
prepregnancy BMI (n = 1669) and GWG (n = 2) were unknown were also
not included. Finally, those also excluded were women from Asia (n
= 628) or India/Pakistan/Sri Lanka (n = 1076), and those with a BMI
< 18.5 kg/m2 (n = 687).
Thus, 15,551 pregnancies were analyzed. Definitions of our
parameters did not change over the 9 years of the study. BMI was
calculated from self-reported pregravid weight and measured height
during pregnancy, using the following formula: weight (in kg)
divided by the height (in m) squared. Women were clas- sified as
normal weight, overweight and obese when their BMI
was 18.524.9 kg/m2 , 25.029.9 kg/m2 and 30 kg/m2 , respec-
tively [17]. GWG categories (< 7 kg, 7.011.5 kg, 11.616 kg
and > 16 kg) were defined according to the usual thresholds
proposed by IOM guidelines for overweight women (optimal GWG: 711.5
kg) and normal-weight women (optimal GWG:
11.616 kg) [18].
GDM was assessed using a one-step screening and diagnos- tic
test, which always comprised a 75-g oral glucose tolerance test
[2,20,21]. GDM was defined as a fasting plasma glucose value 5.3
mmol/L (the same fasting plasma glucose target as in previous
French recommendations) and/or a 2-h blood glu- cose value 7.8
mmol/L (World Health Organization criteria) [2,20,21]. One-step
screening was chosen to limit the number of participants lost to
follow-up, as our study population was cha- racterized by
widespread geographical origins [21]. Screening was specifically
prescribed during the hospital routine follow-up visit and then
performed out of hospital. As is usual for epidemi- ological
studies, the women without screening were considered to be without
GDM [2,9,10,15].
Women who were overweight or obese had no specific follow-up
unless they were diagnosed with GDM. All women with such a
diagnosis were referred to a multidisciplinary team, which included
a diabetologist, obstetrician, midwife, dietitian and nurse
educator. These women received individ- ualized dietary advice,
were instructed on how to perform self-monitoring of blood glucose
levels six times a day, and visited the diabetologist every two to
four weeks. Insulin ther- apy was started if fasting and 2-h
postprandial glucose levels were > 5.3 mmol/L and > 6.8
mmol/L, respectively. Antenatal visits were scheduled for every two
to four weeks up to 34 weeks, and weekly thereafter, with
cardiotocography and assessment of amniotic fluid volume
[2,21].
2.2. Prognosis
The following outcomes were considered: LGA or SGA (birth weight
> 90th percentile or < 10th percentile, respectively, of the
general French population) [22]; caesarean section; preeclampsia
(blood pressure 140/90 mmHg on two measure- ments taken 4 h apart
and proteinuria 300 mg/24 h or 3+ or more on dipstick testing of a
random urine sample); preterm delivery (before 37 full weeks); and
shoulder dystocia, defined
E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx3
Table 1
Maternal characteristics and complications by gestational
diabetes mellitus (GDM) status and pregravid body mass index
(BMI).
Total cohort
(n = 15,551)
No GDM
(n = 13,436)
GDM
(n = 2097)
ANOVA,
P
Normal weight
(n = 9317)
Overweight
(n = 4075)
Obesity
(n = 2159)
ANOVA,
P
Characteristics
Pregravid BMI (kg/m2 ) BMI classification
24.6 4.7
24.6 4.7
24.9 4.8
< 0.001
< 0.01
21.6 1.6
26.6 1.4a
33.6 4.0a , b
< 0.001
Normal weight (%)
9317 (59.9)
8127 (60.4)
1190 (56.7)
Overweight (%)
4075 (26.2)
3489 (25.9)
586 (27.9)
Obesity (%)
2159 (13.9)
1838 (13.7)
321 (15.3)
Age (years)
29.7 5.8
29.6 5.8
30.6 5.8
< 000.1
29.7 5.9
29.9 5.8
29.7 5.9
NS
Parity (n)
2.1 1.3
2.0 1.3
2.2 1.4
< 000.1
2.0 1.2
2.1 1.3a
2.2 1.4a
< 0.001
Multiparity (%)
9045 (58.2)
7728 (57.4)
1317 (62.8)
< 000.1
5315 (57.0)
2426 (59.5)a
1304 (60.4)a
< 0.01
Maternal smoking
Before pregnancy (%)
2243 (14.4)
1994 (14.8)
249 (11.9)
< 000.1
1421 (15.3)
526 (12.9)a
296 (13.7)
< 0.001
During pregnancy (%)
1503 (9.7)
1352 (10.0)
151 (7.2)
< 000.1
946 (10.2)
344 (8.4)a
213 (9.9)
< 0.01
Ethnicity
< 000.1
< 0.01
Caucasian (%)
9881 (63.5)
8434 (62.7)
1447 (69.0)
5998 (64.4)
2530 (62.1)
1353 (62.7)
Sub-Saharan African (%)
3566 (22.9)
3181 (23.6)
385 (18.4)
2058 (22.1)
1020 (25.0)
488 (22.6)
Caribbean (%)
1365 (8.8)
1196 (8.9)
169 (8.1)
817 (8.8)
324 (8.0)
224 (10.4)
Other (%)
739 (4.8)
643 (4.8)
96 (4.6)
444 (4.8)
201 (4.9)
94 (4.4)
Family history of diabetes (%)
3227 (20.8)
2718 (20.2)
509 (24.3)
< 000.1
1950 (20.9)
833 (20.4)
444 (20.6)
NS
Previous pregnancy with macrosomia (%)
457 (2.9)
366 (2.7)
91 (4.3)
< 000.1
266 (2.9)
124 (3.0)
27 (3.1)
NS
Previous pregnancy with GDM (%)
498 (3.2)
268 (2.0)
230 (11.0)
< 000.1
276 (3.0)
136 (3.3)
86 (4.0)a
< 0.05
Events
GDM (%)
2097 (13.5)
1190 (12.8)
586 (14.4)a
321 (14.9)a
< 0.01
Gestational weight gain (kg) Gestational weight gain
classification
8.9 5.7
9.0 5.7
8.5 5.5
< 000.1
< 0.01
9.1 5.6
8.8 5.6
8.5 5.8a
< 0.001
< 0.001
< 7 kg (%)
5041 (32.4)
4272 (31.8)
769 (36.7)
2902 (31.1)
1362 (33.4)
777 (32.4)
711.5 kg (%)
5695 (36.6)
4945 (36.8)
750 (35.8)
3481 (37.4)
1471 (36.1)
743 (29.6)
11.616 kg (%)
3586 (23.1)
3149 (23.4)
437 (20.8)
2180 (23.4)
929 (22.8)
477 (22.1)
> 16 kg (%)
1229 (7.9)
1088 (8.1)
141 (6.7)
754 (8.1)
313 (7.7)
162 (7.5)
Large-for-gestational-age babies (%)
1341 (8.6)
1028 (7.6)
313 (14.9)
< 000.1
778 (8.4)
361 (8.9)
202 (9.4)
NS
Small-for-gestational-age babies (%)
2114 (13.6)
1837 (13.7)
277 (13.1)
NS
1332 (14.3)
517 (12.7)a
123 (13.9)a
< 0.01
Caesarean section (%)
3265 (21.0)
2696 (20.0)
569 (27.1)
< 000.1
1944 (20.9)
863 (21.2)
458 (21.2)
NS
Preeclampsia (%)
335 (2.2)
269 (2.0)
66 (3.1)
< 000.1
185 (2.0)
88 (2.2)
62 (2.9)a
< 0.05
Preterm delivery (%)
1207 (7.8)
1032 (7.7)
175 (8.3)
NS
733 (7.9)
296 (7.3)
178 (8.2)
NS
Shoulder dystocia (%)
231 (1.5)
193 (1.4)
38 (1.8)
NS
137 (1.5)
59 (1.4)
35 (1.6)
NS
Data are presented as means SD or as n (%); NS: not
significant.
a P < 0.05 vs women with BMI 18.524.9 kg/m2 .
b P < 0.05 vs women with BMI 2529.9 kg/m2 .
as the use of obstetric manoeuvres (such as a McRoberts epi-
siotomy after delivery of the fetal head, suprapubic pressure,
posterior arm rotation to an oblique angle, rotation of the infant
by 180 degrees and delivery of the posterior arm) [23].
2.3. Statistical analyses
Continuous variables were expressed as means SD, and compared by
one-way analysis of variance (ANOVA) or the MannWhitney U test as
adequate. The significance of differ- ences in proportions was
tested with the Chi2 test. Logistic regression was used for
analyses of BMI and GWG effects on LGA and SGA infants, caesarean
section and preeclampsia in women with and without GDM. Logistic
regression was also used for multivariate analyses based on a model
including factors associated with LGA and then SGA, with a P value
< 0.10 on uni- variate analyses. All statistical analyses were
carried out using
SPSS software (SPSS, Chicago, IL, USA). The 0.05 probability
level was considered statistically significant.
3. Results
3.1. Characteristics of the study population
Maternal characteristics are shown in Table 1. In summary, the
women were 29.7 5.8 years old, and their mean parity was 2.1 1.3.
GDM was diagnosed in 13.5%. The mean pre- gravid BMI was 24.6 4.7
kg/m2 , with overweight and obesity observed in 26.2% and 13.9%,
respectively. Mean GWG was
8.9 5.7 kg. Of note, the cohort was multiethnic, with most of
the subjects being Caucasian (from Europe or North Africa;
63.5%) or from sub-Saharan Africa (22.9%).
Pregravid parameters associated with GDM were BMI, age, parity,
maternal smoking, ethnicity, family history of diabetes
4E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx
and previous pregnancy with macrosomia or GDM (Table 1). Classes
of increasing BMI (normal weight, overweight and obe- sity) were
associated with higher parity, less smoking before and during
pregnancy, ethnicity and a more frequent personal history of GDM.
Mean GWG was lower in obese women (Table 1).
3.2. Pregnancy-related events associated with GDM and
overweight/obesity
Table 1 also shows that GDM was associated with less GWG, and
more LGA infants (OR: 2.12, 95% CI: 1.852.43), cae- sarean section
(OR: 1.49, 95% CI: 1.341.65) and preeclampsia (OR: 1.59, 95% CI:
1.212.09). An increased BMI classification was associated with
lower GWG, more GDM, preeclampsia and fewer SGA infants (Table
1).
3.3. Complications associated with BMI and GWG
according to GDM status
On analyzing the contribution of overweight/obesity and GWG
classification to the incidence of LGA infants (Fig. 1A), SGA
infants (Fig. 1B), caesarean sections (Fig. 1C) and preeclampsia
(Fig. 1D) by GDM status, the GWG and BMI class were associated with
LGA infants regardless of GDM status (Fig. 1A, P < 0.0001) and
with SGA infants only in women with- out GDM (Fig. 1B, P <
0.01). Of note, the mean rate of SGA was
13.7% in women without GDM whereas, when GWG was < 7 kg with
normal weight, overweight and obesity, the rates were
16.3%, 12.8% and 13.6%, respectively. There were no asso-
ciations between GWG, BMI class, caesarean section (Fig. 1C) and
preeclampsia (Fig. 1D) in women with and without GDM.
3.4. Factors independently associated with LGA and SGA
infants
The probability of delivering an LGA infant was associ- ated
with the following maternal and pregnancy characteristics: positive
association with increasing age (29.7 5.9 years in women without an
LGA infant vs 30.2 5.7 years in those with an LGA infant; P <
0.01), BMI (24.6 4.7 kg/m2 vs
24.9 4.8 kg/m2 , respectively; P < 0.05), multiparity (58.4%
vs 73.2%, respectively; P < 0.0001), family history of diabetes
(20.4% vs 24.4%, respectively; P < 0.05), previous pregnancy
with macrosomia (2.1% vs 11.3%, respectively; P < 0.0001), GDM
(12.6% vs 23.3%, respectively; P < 0.0001) and GWG
classification (P < 0.001); negative association with smoking
before and during pregnancy (9.9% vs 5.6%, respectively; P <
0.0001), sub-Saharan African (24.0% vs 19.8%, respec- tively; P
< 0.0001) and Caribbean (9.1% vs 6.6%, respectively; P <
0.0001) origins, and preeclampsia (2.3% vs 11.0%, respec- tively; P
< 0.01). Multivariate analyses taking into account these
parameters to explain LGA infants showed that all of these factors,
except age, were independently associated with LGA infants,
including BMI and GWG (Table 2); this was also true when only women
without GDM were considered. Factors inde- pendently associated
with LGA in women with GDM were age, multiparity, previous
pregnancy with macrosomia, and GWG
11.516 kg and > 16 kg, but with none of the other parameters,
including BMI (Table 2).
The probability of delivering an SGA infant was associated with
BMI classification, multiparity (60.0% in women with- out an SGA
infant vs 57.7% in those with; P < 0.05), smoking before and
during pregnancy (9.3% vs 10.7%, respectively; P < 0.05), family
history of diabetes (21.2% vs 17.8%, respec- tively; P <
0.0001), previous pregnancy with macrosomia (3.1% vs 1.9%,
respectively; P < 0.01), preeclampsia (1.9% vs 3.5%,
respectively; P < 0.001) and GWG class (P < 0.01). On mul-
tivariate analyses, all of these parameters, except multiparity,
were associated with SGA infants, including BMI (negative
association) and GWG < 7 kg (positive association; Table 3). On
multivariate analyses performed according to GDM status,
overweight/obese and GWG classes remained independently associated
with SGA infants in women without GDM, but no longer for women with
GDM (Table 3).
4. Discussion
Our present findings confirm and extend previous reports linking
GDM, high maternal BMI and GWG with pregnancy outcomes. In our
large European, non-Asian cohort of women without pregestational
diabetes or hypertension, both pregravid BMI and GWG were
associated with LGA and SGA infants in women without GDM. In
contrast, in women with treated GDM, overweight/obesity was not
independently associated with LGA and SGA infants, and GWG was only
associated with LGA infants. Considered altogether, these results
suggest that both overweight/obesity and GWG are crucial for fetal
growth in women without GDM, whereas GWG is the main additional
contributor in GDM to fetal overgrowth, with the role of BMI
blunted in women treated for GDM.
4.1. GDM prevalence, BMI excess and GWG in our
European cohort
As previously reported, GDM prevalence was high in our
followed-up women [2]. This was due to our diagnostic criteria,
which used low thresholds even before our adoption of Inter-
national Association of Diabetes and Pregnancy Study Groups
(IADPSG) criteria for defining GDM in 2011. In addition, many of
our patients have risk factors for GDM: 20% of our women are from
North Africa [21] and are particularly vulnerable. Indeed, it was
recently reported that more than half the women with GDM in the
four largest maternity units in our area had psychosocial
insecurity [24,25]. A large proportion of women in our cohort began
their pregnancies while overweight (26.2%) or obese (13.9%); these
are among the highest rates in France and most likely due to the
large proportion of deprived populations living in our area. In
addition, the proportion of women of childbearing age with obesity
is increasing in France [26,27]; it was recently reported that the
proportion of pregnant women with overweight or obesity had
increased from 30.8% in 2002 to 37.6% in 2010 at our centre [2].
The prevalence of obesity was higher than that of FriuliVenezia
Giulia, a region in northeastern Italy [9], and almost the same as
that reported by the international HAPO study
E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx5
Fig. 1. Crude prevalences in women with and without gestational
diabetes mellitus (GDM) of (A) large-for-gestational-age (LGA) and
(B) small-for-gestational-age
(SGA) infants, (C) caesarean section and (D) preeclampsia,
according to body mass index (obesity, overweight and normal
weight) and gestational weight gain.
6E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx
Table 2
Parameters associated with large-for-gestational-age (LGA)
infants in the total cohort and in women without and with
gestational diabetes mellitus (GDM).
Total cohort
Women without GDM
Women with GDM
Multivariate analysis
Multivariate analysis
Multivariate analysis
OR [95 CI]
P
OR [95 CI]
P
OR [95 CI]
P
Age (10 years)
NS
NS
1.29 [1.071.51]
< 0.01
Maternal BMI (10 kg/m2 )
1.12 [1.001.24]
< 0.05
1.17 [1.031.31]
< 0.001
NS
Multiparity (%)
1.87 [1.642.14]
< 0.001
1.90 [1.642.21]
< 0.001
1.74 [1.302.35]
< 0.001
Maternal smoking
No (%)
REF
REF
REF
Before (%)
Before and during (%)
0.48 [0.370.61]
NS
< 0.001
0.42 [0.320.56]
NS
< 0.001
NS
NS
Ethnicity
Caucasian (%)
Sub-Saharan African (%)
0.74 [0.640.89]
REF
< 0.001
0.70 [0.590.83]
REF
< 0.001
REF NS
Caribbean (%)
Other (%)
0.63 [0.500.80]
< 0.001
NS
0.64 [0.490.83]
< 0.001
NS
NS
NS
Family history of diabetes (%)
1.16 [1.011.33]
< 0.05
1.18 [1.011.38]
< 0.05
NS
Previous pregnancy with macrosomia (%)
4.52 [3.656.00]
< 0.001
4.86 [3.826.19]
< 0.001
3.78 [2.395.96]
< 0.001
GDM (%)
2.01 [1.752.32]
< 0.001
Preeclampsia (%)
0.49 [0.290.83]
< 0.01
0.42 [0.210.84]
< 0.05
NS
GWG classication
< 7 kg (%)
NS
NS
NS
711.5 kg (%)
11.616 kg (%)
1.74 [1.492.03]
REF
< 0.001
1.64 [1.381.95]
REF
< 0.001
2.14 [1.542.97]
REF
< 0.001
> 16 kg (%)
3.42 [2.834.13]
< 0.001
3.58 [2.914.40]
< 0.001
2.65 [1.684.17]
< 0.001
Mutivariate analyses used a logistic-regression model including
the above factors associated with LGA infants and P < 0.10 on
univariate analyses; NS: not significant; REF: reference group.
Table 3
Parameters associated with small-for-gestational-age (SMA)
infants in the total cohort, and in women without and with
gestational diabetes mellitus (GDM).
Total cohort
Women without GDM
Women with GDM
Multivariate analysis
Multivariate analysis
Multivariate analysis
OR [95 CI]
P
OR [95 CI]
P
OR [95 CI]
P
BMI classication
Normal weight (%)
REF
REF
REF
Overweight (%)
0.87 [0.780.97]
< 0.05
0.86 [0.770.98]
< 0.05
NS
Obesity (%)
Multiparity (%)
0.83 [0.720.96]
< 0.05
NS
0.84 [0.720.98]
0.90 [0.821.00]
< 0.05
0.05
NS
NS
Maternal smoking
No (%) Before (%)
Before and during (%)
1.2 [1.031.4]
REF NS
< 0.05
1.2 [1.031.4]
REF NS
< 0.05
REF NS NS
Family history of diabetes (%)
0.81 [0.720.91]
< 0.001
0.84 [0.740.95]
< 0.01
0.65 [0.470.91]
< 0.05
Preeclampsia (%)
1.87 [1.442.44]
< 0.001
1.90 [1.422.54]
< 0.001
1.85 [1.013.40]
0.05
GWG classication
< 7 kg (%)
1.13 [1.011.26]
< 0.05
1.14 [1.011.29]
< 0.05
NS
711.5 kg (%)
11.616 kg (%)
> 16 kg (%)
REF
NS NS
REF
NS NS
REF
NS NS
Multivariate analyses used a logistic-regression model including
the above factors, which were associated with SGA and P < 0.10
on univariate analyses, plus previous pregnancy with macrosomia;
REF: reference group; NS: not significant.
E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx7
[7], in which 13.7% had a BMI 33 kg/m2 at inclusion. How- ever,
obesity is still far less than reported in recent US reports, which
ranged from 20.0% [28] to 31.9% [29]. Indeed, as in the US [15],
our study shows that race/ethnicity is a determinant of
overweight/obesity.
As previously reported, women with overweight and obe- sity had
less GWG than those with a normal BMI [5,8,13,14]. As women
diagnosed with GDM are followed-up with dietary counselling, this
probably explains why this is so [30]. For this reason, our study
analyzed the incidence of pregnancy events according to mutually
exclusive BMI/GWG groups in women without GDM and then in those
treated for GDM.
4.2. Roles of BMI and GWG on fetal growth in women with and
without GDM
As in the five studies looking at the triumvirate of GDM, BMI
and GWG, multivariate analyses of our total cohort showed that
these three factors were independently associated with LGA infants.
Interestingly, Black et al. [5] recently reported on the
contributions of BMI, mild untreated GDM and GWG to fetal
overgrowth: 21.6% of LGA infants were attributable to maternal
overweight or obesity and 23.3% to overweight or obesity with GDM,
with an increasing GWG associated with a greater preva- lence of
LGA in all groups. Similarly, Kim et al. [10] showed, in another US
cohort, that for all racial or ethnic groups, GDM contributed the
least, whereas GWG contributed the most, to LGA. This result was
partly due to a notably high prevalence of overweight/obesity in
that cohort. Indeed, their calculation of the partial population
attributable fraction took into account both the prevalence and OR
of a given risk factor, and was interpreted as the proportion of
cases that would be prevented if it were pos- sible to eliminate
that risk factor from the population [10]. As the partial
population attributable fraction of GDM, BMI and GWG was < 50%,
other factors may be important contributors to LGA infants. Similar
to the findings of others, our data indi- cate that, while
ethnicity [15,16], a familial history of diabetes and personal
history of a child with macrosomia [15,29] suggest genetic causes
of macrosomia, smoking habits [6], multiparity and preeclampsia
[31] are also possible, non-genetic causes of macrosomia. All these
factors may play a role through epigenetic mechanisms, with fetal
epigenetic programming of adipokines involved when considering BMI
and GWG [32].
The effects of BMI and GWG may vary according to GDM status. Our
present study found that the effects of BMI and GWG were greatest
in patients without GDM, who received no specific care in our
maternity unit at the time. This has been consistently reported in
women without GDM [5,15,16,28]. For example, Di Benedetto et al.
[8] showed, in an Italian cohort, that the effect of overweight and
obesity was present only in glucose-tolerant women who had excess
weight gain during pregnancy. This suggests that the risk of
macrosomia associated with overweight/obesity might be limited by
well-controlled GWG. The counterpart of a low GWG would be a higher
inci- dence of SGA infants [14,18]. In our cohort, however, this
effect was blunted in overweight/obese women, who were unlikely
to
deliver SGA infants. The increase in SGA infants associated with
GWG < 7 kg was balanced by a decreased incidence of
overweight/obese women. To illustrate this point, GWG < 7 kg was
associated with the highest prevalence of SGA infants only in lean
women whereas, in the overweight/obese women, SGA rates were
similar to the mean rate of the overall cohort. These findings
suggest that closer monitoring of GWG in women with pregravid BMIs
25 kg/m2 may be warranted to prevent LGA infants with no negative
impact on SGA births, as rec- ommended by the IOM [18].
Nevertheless, meta-analyses also show that, although antenatal
interventions for overweight or obesity can limit GWG [33], the
outcomes are often unchanged [33,34].
Our present results differed in women with GDM, who were
followed-up to control both their diets and blood glucose levels.
In fact, as found by others [30], these women had less GWG than
women without GDM. Also, in these women, BMI no longer had any
effect on LGA births. Indeed, the effect of BMI might be driven by
GDM, which is more frequent when BMI is increased [3,15].
Otherwise, GWG remained an important contributor to fetal
overgrowth in this subpopulation, with the same result as found in
an Italian cohort. When only women with GDM were considered, GWG,
but neither overweight nor obesity, were associated with macrosomia
[9]. However, an inde- pendent effect of GWG and obesity on LGA
infants was found in three American studies [16,28,29]. This was
also the case of the study by Black et al. [5] although, in that
study, GDM was treated by neither diet nor medication.
4.3. Other outcomes
As in previous reports, GDM [1,5,9,10,15] and obesity [4,5,7,8]
were associated in our cohort with more preeclamp- sia, and GDM
with more caesarean section, after examining the contributions of
BMI and GWG to these outcomes according to GDM status. Regarding
preeclampsia, there was no influence of overweight/obesity and GWG
classification, thus stressing the role of GDM per se. Also, no
association was identified between BMI and GWG classes for
caesarean section regard- less of GDM status, whereas such an
association has often been reported [4,5,7,8,12]. Several
population-based studies found that planned and especially
emergency caesarean delivery was significantly increased with
increasing BMI [35]. However, these studies did not stratify
patients according to GDM status. Caregivers were inclined to
perform caesarean sections in obese patients because of concerns
about obesity-related macrosomia, and perinatal complications such
as shoulder dystocia [5] and perinatal mortality [36].
4.4. Strengths and weaknesses of the study
Our study involved a large sample size, thereby giving power to
its significant differences, and allowing multivariate analyses and
strict selection criteria, including no known hypertensive
disorders or diabetes before pregnancy, no underweight women, no
women of Asian origin and no twin pregnancies. This removed the
major sources of potential confounders. The data
8E. Cosson et al. / Diabetes & Metabolism xxx (2015)
xxxxxx
came from a single institution with a comprehensive and consis-
tent perinatal care programme, and the multiethnic population
included a large proportion of women living with psychoso- cial
vulnerability [24,25]. All information was collected at a
university hospital, precluding generalization of the results.
Regarding the parameters of interest, although GDM screen- ing
was universal, some women were not screened. Therefore, our study
considered the presence of GDM in the intention- to-screen
population, as done in other studies [10,15]. The proportion of
unscreened women was 12.5% in 2011 at our hospital and is likely to
have remained stable over the past decade. GDM was not defined
according to IADPSG criteria. However, the association between BMI
and pregnancy out- comes is reportedly not influenced by the
definition of GDM [37,38], and our cohort had a GDM prevalence rate
similar to that of the IADPSG criteria. Our results were not
adjusted for glucose control, as these data were not available. BMI
was cal- culated from measured height, but weight before pregnancy
was self-reported.
One strength of our study is that GWG was not considered
according to BMI, as that would have led to consideration of the
BMI effect twicefirst per se and then through excessive GWG,
defined according to normal weight, overweight and obesity sta-
tus. However, GWG was not adjusted for gestational age at delivery,
which is difficult as GWG is not linear during preg- nancy, but
increases during the lattermost weeks of pregnancy. Finally, LGA
and SGA infants were defined by comparison to the general French
population [22] with similar thresholds across ethnicities.
However, their determinants were adjusted for ethnicity.
5. Conclusion
In the context of the current escalation of obesity [26,27] and
GDM [39], our present study confirms that GDM, even when treated,
is associated with adverse pregnancy outcomes. In our European
cohort of women with GDM, GWG is additionally and independently
associated with more LGA infants, whereas overweight/obesity is
not. This suggests that, even after rein- forcing GWG control in
women treated for GDM, the most this would achieve does not appear
to include more SGA infants in this pregnant population. However,
weight loss subsequent to a diagnosis of GDM has recently been
reported to be associated with a 1.69-fold increased rate of SGA
infants [40].
In women without GDM, our data show that considering pre- gravid
BMI and GWG is crucial for estimating the risk for LGA infants. Our
observational results suggest that dietary advice could be offered
to overweight/obese women before pregnancy to reduce the risk of
preeclampsia and LGA as well as the risk of GDM. In women without
GDM, controlling weight gain during pregnancy might limit the
incidence of LGA without inducing fetal restriction in those who
are overweight/obese. It was recently shown that a low-intensity
lifestyle intervention in women at high risk for GDM optimalized
healthy GWG, while limiting weight gain was more effective in
overweight women [41].
Disclosure of interest
The authors declare that they have no conflicts of interest
concerning this article.
Acknowledgements
We thank Prof Eric Vicaut (APHP, Unit of Clinical Research,
Lariboisire Hospital, Paris 7 University, Paris, France) for his
help with the statistical analyses.
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