Goldstein et al. BMC Medicine (2018) 16:153
https://doi.org/10.1186/s12916-018-1128-1

RESEARCH ARTICLE

Open Access

Gestational weight gain across continents
and ethnicity: systematic review andmeta-analysis of maternal and infant
outcomes in more than one million women
Rebecca F. Goldstein1,2, Sally K. Abell1,2, Sanjeeva Ranasinha1, Marie L. Misso1, Jacqueline A. Boyle1,
Cheryce L. Harrison1, Mary Helen Black3, Nan Li4, Gang Hu5, Francesco Corrado6, Hanne Hegaard7, Young Ju Kim8,
Margaretha Haugen9, Won O. Song10, Min Hyoung Kim11, Annick Bogaerts12,13,14, Roland Devlieger15,16,
Judith H. Chung17 and Helena J. Teede1,2*Abstract
Background: The association between Institute of Medicine (IOM) guidelines and pregnancy outcomes acrossethnicities is uncertain. We evaluated the associations of gestational weight gain (GWG) outside 2009 IOM
guidelines, with maternal and infant outcomes across the USA, western Europe and east Asia, with subgroup
analyses in Asia. The aim was to explore ethnic differences in maternal prepregnancy body mass index (BMI), GWG
and health outcomes across these regions.
Methods: Systematic review, meta-analysis and meta-regression of observational studies were used for the study.
MEDLINE, MEDLINE In-Process, Embase and all Evidence-Based Medicine (EBM) Reviews were searched from 1999 to2017. Studies were stratified by prepregnancy BMI category and total pregnancy GWG. Odds ratio (ORs) 95%
confidence intervals (CI) applied recommended GWG within each BMI category as the reference. Primary outcomes
were small for gestational age (SGA), preterm birth and large for gestational age (LGA). Secondary outcomes were
macrosomia, caesarean section and gestational diabetes.
Results: Overall, 5874 studies were identified and 23 were included (n = 1,309,136). Prepregnancy overweight/
obesity in the USA, Europe and Asia was measured at 42%, 30% and 10% respectively, with underweight 5%, 3%
and 17%. GWG below guidelines in the USA, Europe and Asia was 21%, 18% and 31%, and above was 51%, 51%
and 37% respectively. Applying regional BMI categories in Asia showed GWG above guidelines (51%) was similar tothat in the USA and Europe.
GWG below guidelines was associated with a higher risk of SGA (USA/Europe [OR 1.51; CI 1.39, 1.63]; Asia [1.63; 1.45, 1.82])
and preterm birth (USA/Europe [1.35; 1.17, 1.56]; Asia [1.06; 0.78, 1.44]) than GWG within guidelines. GWG above guidelines
was associated with a higher risk of LGA (USA/Europe [1.93; 1.81, 2.06]; Asia [1.68; 1.51 , 1.87]), macrosomia (USA/Europe[1.87; 1.70, 2.06]; Asia [2.18; 1.91, 2.49]) and caesarean (USA/Europe [1.26; 1.21, 1.33]; Asia [1.37; 1.30, 1.45]). Risks remained
elevated when regional BMI categories were applied for GWG recommendations. More women in Asia were categorised(Continued on next page)* Correspondence: helena.teede@monash.edu
1Monash Centre for Health Research and Implementation, Monash University,
Clayton, VIC, Australia
2
Monash Diabetes and Endocrine Units, Monash Health, Locked Bag 29,
Clayton Rd, Clayton, VIC 3168, Australia
Full list of author information is available at the end of the article© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Goldstein et al. BMC Medicine (2018) 16:153

Page 2 of 14(Continued from previous page)as having GWG below guidelines using World Health Organization (WHO) (60%) compared to regional BMI categories(16%), yet WHO BMI was not accompanied by increased risks of adverse outcomes.
Conclusions: Women in the USA and western Europe have higher prepregnancy BMI and higher rates of GWG above
guidelines than women in east Asia. However, when using regional BMI categories in east Asia, rates of GWG above
guidelines are similar across the three continents. GWG outside guidelines is associated with adverse outcomes acrossall regions. If regional BMI categories are used in east Asia, IOM guidelines are applicable in the USA, western Europe
and east Asia.
Keywords: Pregnancy, Ggestational weight gain, Maternal and infant outcomes, Obesity, Small for gestational age,
Large for gestational age, Gestational diabetes, Caesarean section, Macrosomia, Preterm birth

Background
Gestational weight gain (GWG) is influenced by manyfactors including the obesogenic environment, prepregnancy body mass index (BMI), age, parity, smoking,socioeconomic status and comorbid medical conditions[1, 2]. Excess or insufficient GWG is associated withhigher risks of adverse pregnancy outcomes, including
preterm birth, macrosomia and caesarean delivery [3].
The US Institute of Medicine (IOM) developed GWG
guidelines in 1990 and updated them in 2009 (Table 1),
yet nearly three quarters of women now gain weight outside these guidelines [4, 5]. Given that lifestyle intervention improves outcomes, meeting GWG guidelines is an
important target [6]. However, the IOM guidelines are
based on data from primarily USA-dwelling, Caucasianand Black women, with limited ethnic diversity that may
not be applicable to women from Europe and Asia.
Given that Asia is the most populous continent, inhabited by 60% of the world’s population, applicability of
GWG guidelines to Asian populations is an international
public health priority.
At lower BMI, people from Asia have a greater risk for
cardiovascular disease and diabetes [7, 8] than Caucasians,
with a higher body fat percentage and greater central
obesity [9]. During pregnancy, women from Asian countries have different risk profiles than Caucasian women.
Asian-American women have a higher risk of gestational
diabetes mellitus (GDM), caesarean section and low birthweight babies, and a lower risk of gestational hypertension
and macrosomia compared to non-Hispanic white women[10]. Amongst Asian women, Korean and Taiwanesewomen have greater GWG and postpartum weight retention than women from other Asian countries [11]. In thiscontext, GWG guidelines in Asian women may need to beconsidered differently; however, there is insufficient comparative research to date.
The 2009 IOM guidelines, although based on limited
data, showed no ethnic differences in associations between
GWG and pregnancy outcomes, whilst calling for further
research [4]. Currently, there are no specific GWG guidelines for women from Asia. Most Asian studies use
Caucasian-derived IOM GWG guidelines, and some use
their own regional guidelines [12]. This creates heterogeneity and limits comparisons across regions, underpinningcalls for new ethnic-specific regional GWG guidelines in
China [13], highlighting gaps in current guidelines.
In this systematic review, meta-analysis andmeta-regression, we aimed to explore ethnic differences
in maternal prepregnancy BMI, GWG and health outcomes across the USA, Europe and Asia. In Asia, we
also aimed to explore GWG and health outcomes using
ethnic-specific regional BMI and World HealthOrganization (WHO) BMI categories.

Methods
This systematic review and meta-analysis was conductedaccording to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.
This protocol was registered with the PROSPERO International Prospective Register of Systematic reviews(registration number CRD42015023325). An analysis of
all pooled data is published [5]. This study focused on
ethnic differences in maternal BMI, GWG and maternal
and neonatal outcomes.
The methods used for study eligibility, data extraction
and risk of bias have been detailed previously [5] (search

Table 1 2009 IOM Recommendations for gestational weight gain during pregnancy
Recommendations

Underweight

Normal weight

Overweight

Obese

Prepregnancy BMI (kg/m2)< 18.518.5–24.9

25.0–29.9≥30

Total weight gain range (kg)

12.5–18

11.5–16

7–11.5

5–9

Total weight gain range (lbs)

28–40

25–35

15–24

11–20

Adapted from 2009 IOM guidelines

Goldstein et al. BMC Medicine (2018) 16:153

terms and search strategy are discussed in Additional
files 1 and 2). Briefly, observational studies published in
the English language between January 1999 and February
2017, with a sample size of more than 500 women were
included. Studies assessing multiple pregnancies andpregnancies in women < 18 years were excluded. Inclusion required that studies present data examining thewomen by prepregnancy BMI category (underweight,
normal weight, overweight, obese), stratified by the total
pregnancy GWG (studies using weekly GWG were excluded). The odds ratio (OR) for each outcome had to
be stratified by maternal BMI and GWG. Papers thatmutually adjusted for BMI and GWG were excluded.
After identifying wide variations in prepregnancy BMI
and GWG categories, meaningful interpretation andmeta-analysis were not possible. Relevant authors were
contacted to reanalyse and present data using consistent categories. Chinese and Korean studies usedethnic-specific BMI categories (China: underweight
BMI < 18.5 kg/m2, normal weight 18.5–23.9 kg/m2,
overweight 24–28 kg/m2 and obese ≥ 28 kg/m2;
Korea: underweight BMI < 18.5 kg/m2, normal weight
18.5–22.9 kg/m2, overweight 23–25 kg/m2 and obese≥ 25 kg/m2) whilst Japanese and Taiwanese studies
used WHO BMI categories (underweight < 18.5 kg/m2,
normal weight 18.5–24.9 kg/m2, overweight 25–
29.9 kg/m2 and obese ≥30 kg/m2).
Primary outcomes were (1) small for gestational age(SGA): < 10th percentile of birthweight for sex and gestational age, (2) pre-term birth: spontaneous birth < 37 weeks
gestation, (3) large for gestational age (LGA): > 90th percentile of birthweight for sex and gestational age. Secondary outcomes were (1) macrosomia: birthweight > 4000 g,(2) caesarean section and (3) GDM.
Strategy for data synthesis

Study findings were synthesised based on target population characteristics, type of study and outcome. Proportions were calculated using the pooled number in a groupdivided by the total number (%). The chi-squared test wasused to assess difference in proportion of women within
BMI categories and GWG categories between regions.
The two-sample test of proportions was used to assess differences between two particular regions.
Summaries of outcomes associated with GWG were
produced for each study by calculating the ORs and 95%
confidence intervals (CIs), using the recommended
GWG within each BMI category as the reference. Where
two or more studies assessed the same outcome, the results were pooled using random-effects meta-analysis,
calculating the OR and 95% CI for each outcome. Extracted pooled ORs for each outcome were combined toconstruct a summary pooled OR for all outcomes. Crude
data was used where possible given the variation inPage 3 of 14

control for confounding factors. However, some papers
presented adjusted ORs only [14–21]. US and European
studies were combined as one group in themeta-analysis of pregnancy outcomes (to allow for two
or more studies to assess each outcome) and comparedto Asian studies. We were unable to demonstrate statistical significance for comparison of ORs for SGA, preterm birth, LGA, macrosomia and caesarean section
between the US/Europe and Asian studies due to similar
ORs and overlap in CIs.
Heterogeneity was assessed using the I2 statistic. An I2
value greater than 50% was indicative of substantial heterogeneity [22] . Where there was sufficient data available, a meta-regression analysis was performed toinvestigate sources of heterogeneity, including percentage of smokers in pregnancy, mean age and percentage
nulliparity. Sufficient data on race/ethnicity was notavailable for inclusion in the meta-regression. Studies
from Europe and Asia did not provide information regarding race or ethnicity. Studies from the USA provided
race/ethnicity data; however, this varied with reporting
methods (some report percentage of total population,others report percentage stratified by GWG).
A further analysis of women living in Asian countries
was performed comparing studies using regional BMI categories (Chinese and Korean studies) and WHO BMI categories (Japanese and Taiwanese) assessing alignment
with 2009 IOM GWG guidelines and maternal and infant
adverse outcomes. Statistical analysis used Stata software
v.14 and was supported by a biostatistician (SR).

Results
From 5874 studies identified by the initial search, 302
studies were selected for full text review (Fig. 1) and 261
studies were excluded, using a priori selection criteria.
Forty papers grouped women by prepregnancy BMI category (underweight, normal weight, overweight, obese),stratified by the total GWG for the pregnancy. One
study [23] did not initially meet inclusion criteria because ORs were not stratified by both BMI and GWG.
However, through collaborations, this data was available
in the required format. Where required, authors were
contacted for data reanalysis, and 13 collaborated(Additional file 2).
In total, 23 cohort studies [12, 14–21, 23–36] were included in this systematic review and meta-analysis, reporting data on more than 1 million women (n = 1,309,136).
Study characteristics

Table 2 describes the study design and size, eligibility criteria and outcomes (descriptive characteristics are shown
in Additional file 3: Table S1). Eighteen studies were retrospective, five were prospective [14, 25, 28, 31, 36]. Tenstudies were from the USA [14, 17, 18, 20, 23, 27, 29, 30,

Goldstein et al. BMC Medicine (2018) 16:153

Page 4 of 14

Fig. 1 Flow Diagram of study selection process

32, 33], five from western Europe (one each from Norway[25], Belgium [35], Italy [24], Denmark [28] and Sweden[15]) and eight from east Asia (four from China [16, 26,
31, 36], two from Korea [12, 34], one each from Taiwan[21] and Japan [19]). The sample size ranged from 1034 to
570,672.
Overall, 66% (n = 865,790) of women were from theUSA, 10% (n = 125,203) from Western Europe and 24%(n = 318,143) from east Asia.
Analysis by region: USA, Europe and Asia

In the descriptive analysis of maternal BMI only, it was
required to exclude two European studies [15, 35] (52%of European women) and four US studies [18, 20, 30,
32] (3% of US women) which studied obese women
only, and one Asian study [16] (4% of Asian women)
which studied normal weight women only. In theremaining studies, overweight and obesity were present
in 43% of women in the USA, 31% in Europe and 10%
in Asia (Table 3). Underweight BMI was present in 5%
in the USA, 3% in Europe and 17% in Asia. The proportion of women within each BMI category was different
between the regions (p < 0.0001) (using the chi-squaredtest).
Overall, underweight women had the greatest prevalence of GWG below guidelines (43%), whereas overweight women, followed by obese women, had thegreatest prevalence of GWG above guidelines (64% and
60% respectively) (Table 4).
For GWG below guidelines, prevalence was 21%, 18%
and 31% in the USA, Europe and Asia respectively, including all Asian data (Table 5). The proportion of women
gaining below guidelines was different between the three
regions (p < 0.0001) (using the chi-squared test).

Goldstein et al. BMC Medicine (2018) 16:153

Page 5 of 14

Table 2 Characteristics of 23 included studies
Study

Country

Study period Study design Sample size Setting

Outcomes

Durst,
2016 [20]USA

2000–2014

Retrospective 5651

University of Alabama,
Birmingham

SGA, LGA, macrosomia,
caesarean section

Enomoto,2016 [19]

Japan

2013

Retrospective 97,157

Japan Society of Obstetrics andGynaecology Registry systemwith 280 participating hospitals

SGA, preterm birth, LGA,
macrosomia, caesarean

Hung,
2016 [21]Taiwan2009–2015

Retrospective 10,973

Taipei Chang Gung
Memorial Hospital

SGA, LGA, macrosomia, caesarean section

Xionge,2016 [36]China

2012–2013

Prospective

Hospitals and community centres Caesarean section

Bogaerts,
2015 [35]Belgium

2009–2011

Retrospective 18,053

Flemish study center for perinatal Caesarean section, macrosomia, LGA

Shin,
2015 [33]USA

2004–2011

Retrospective 219,868

Pregnancy risk assessment
monitoring system (PRAMS)

Preterm birth, SGA, LGA

Wene,
2015 [16]China

2009–2013

Retrospective 13,776

Jishuitan Hospital

Preterm birth

Yange,
2015 [31]China

2011–2013

Prospective

85,765Wuhan Women and Children
Health Care Center

Macrosomia

Badon,
2014 [14]USA

2000–2006

Prospective

5297

North American Field Centers,
HAPO

LGA

Chihara,2014 [17]USA

2003–2005

Retrospective 19,130

Hawaii’s special supplemental
program for women, infants
and children (WIC)

Macrosomia

Haugenc,
2014 [25]Norway1999–2008

Prospective

Norwegian Mother andChild cohort study

Macrosomia, caesarean section

Leed,2014 [34]Korea

2010–2012

Retrospective 16,297

Single medical centre

LGA

Swank,
2014 [32]

USA

2007

Retrospective 1034

Californian birth certificate data

Caesarean, macrosomia

Black,
2013 [23]USA

2005–2010

Retrospective 9835

Kaiser Permanente
Southern California

LGA (provided additional outcomesin reanalysis incl. SGA, preterm,
macrosomia and caesarean section

Kominiarekb,
2013 [18]USA

2002–2008

Retrospective 21,020

12 institutions (19 hospitals)

Caesarean section, SGA,
LGA, macrosomia

Lia, 2013 [26]China

2009–2011

Retrospective 33,973

Tianjin Women’s andChildren’s Health Centre

Caesarean, preterm delivery, LGA,
SGA macrosomia

Di Benedetto,
2012 [24]Italy

2004–2009

Retrospective 2225

University Hospital

Macrosomia, caesarean section

Moore Simas,
2012 [29]USA

2006–2010

Retrospective 11,203

University Hospital

SGA, LGA

Blomberg,
2011 [15]Sweden1993–2008

Retrospective 46,595

Swedish Medical birth registry

Caesarean, LGA, SGA

J Parkd,
2011 [12]Korea

2005–2007

Retrospective 2311

University Hospital

SGA, LGA, macrosomia,
caesarean section, preterm birth

S Park,2011 [27]USA

2004–2007

Retrospective 570,672

Florida birth certificate data

SGA, LGA

Vesco,
2011 [30]USA

2000–2005

Retrospective 2080

Kaiser Permanente group practice Macrosomia, LGA, SGA

Prospective

University HospitalRode, 2007 [28] Denmark 1996–1998
a57,891

56,082

2248

Data according to both Chinese and WHO BMI categories (Chinese used here)b
Sample size changed when provided additional data, OR not recalculatedcSample size changed when provided additional datadData according to both Korean and WHO BMI categories (Korean used here)eData according to Chinese BMI categories
HAPO Hyperglycaemia and Adverse Pregnancy Outcomes, NR not reported

Macrosomia

Goldstein et al. BMC Medicine (2018) 16:153

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Table 3 Body mass index prepregnancy by regions (%)
RegionUnderweight

Normal weight

Overweight

Obese

5

51

23

21

Including all studies
USA
Europe

1

33

10

6

Asia

16

74

8

2

Excluding studies that assessed obese women [15, 18, 20, 30, 32, 35] or normal weight women only [16]
USA

5

53

24

18

Europe

3

67

21

9

Asia (overall)

17

73

8

2

Asia (regional BMI)

16

73

9

2

Asia (WHO BMI)

18

71

8

3

For GWG above guidelines, prevalence was 51%, 51%
and 37% in the USA, Europe and Asia respectively, including all Asian data. The proportion of women above
guidelines was different between the three regions (p <0·0001) (using the chi-squared test). GWG above guidelines was higher in the USA than Asia (p < 0·0001) andhigher in Europe than Asia (p < 0·0001), but this was nottrue between the USA and Europe (p = 1·0) (using thetwo-sample test of proportions).
However, when Asian studies applying regional BMI categories only were analysed, GWG above guidelines (51%)
was no longer significantly different from GWG above
guidelines in the USA and Europe (p = 0.28). There was asubstantial difference between GWG below guidelines in
Asia, using regional BMI (16%), compared to WHO BMI
categories when applying IOM guidelines (60%).
A summary of pooled ORs for primary and secondary
outcomes is given in Fig. 2a and b and Table 6. Pooled
ORs for individual analyses for outcomes are presented
in Additional file 4.
Primary outcomes

SGA: eleven studies (seven USA/Europe; four Asia)
Eleven studies assessed SGA. This was defined as birthweight < 10th percentile for gestational age in five studies [12, 19, 26, 27, 33]; four additionally accounted forsex [21, 23, 25, 29], one for sex and race/ethnicity [30]and another for sex, race and parity [20].
Table 4 Proportions of women gaining below, within andabove guidelines, stratified by prepregnancy BMI (%)
BMI group

Below IOM

Within IOM

Above IOM

Underweight

43

36

21

Normal weight

28

36

36

Overweight

13

23

64

Obese

19

21

60

Data from 20/23 studies: n = 1,146,350 (88% of total population). Excluding
studies that did not stratify GWG by BMI category [17, 31, 36]

GWG below guidelines was associated with a higher
risk for SGA than GWG within guidelines; for USA/Europe OR 1.51 (1.39–1.63), I2 = 88% and for Asia OR 1.63
(1.45–1.82), I2 = 63. The association of SGA risk was
highest with underweight women for both USA/Europe(1.95; 1.83–2.07) and Asia (1.90; 1.34–2.70).
GWG above guidelines was associated with lower risk for
SGA than GWG within guidelines: USA/Europe (OR 0.65;
0.62–0.69) I2 = 65% and Asia (OR 0.69; 0.63–0.76) I2 = 20%.
Preterm birth: five studies (two USA/Europe; threeAsia) Five studies assessed preterm birth (< 37 weeks
gestation); four did not specify whether this was spontaneous or induced [16, 23, 26, 33] and one specified
spontaneous and induced combined [19].
GWG below guidelines was associated with a higher
risk for preterm birth than GWG within guidelines:
USA/Europe (OR 1.35; 1.17–1.56) I2 = 81% and Asia
(OR 1.06; 0.78–1.44) I2 = 86%.
GWG above guidelines was associated with a lower
risk for preterm birth than GWG within guidelines:
USA/Europe (0.83; 0.74–0.94) I2 = 79% and Asia (OR0.71; 0.58–0.87) I2 = 68%.
LGA: thirteen studies (eight USA/Europe; five Asia)
Thirteen studies assessed LGA. This was defined as
birthweight > 90th percentile for gestational age in six
studies [12, 19, 26, 27, 33, 34]. Four defined LGA byadditionally accounting for infant sex [21, 23, 25, 29],
one for sex and race/ethnicity [30], one for sex, race andparity [20] and one for sex, parity and study centre [14].
GWG below guidelines was associated with a lower
risk for LGA than GWG within guidelines: USA/Europe(OR 0.62; 0.57–0.68) I2 = 72% and Asia (OR 0.55; 0.48–
0.63) I2 = 78%. The risk was lowest in the underweight
women: (USA/Europe [OR 0.42; 0.30–0.60] and Asia
[OR 0.42; 0.30–0.59]).
GWG above guidelines was associated with a higher
risk for LGA: USA/Europe (OR 1.93; 1.81–2.06) I2 =Goldstein et al. BMC Medicine (2018) 16:153

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Table 5 Gestational weight gain during pregnancy by regions (%)
RegionBelow guidelines

Within guidelines

Above guidelines

USA

21

28

51

Europe18

31

51

Asia (overall)

31

32

37

Asia (regional BMI)

16

33

51

Asia (WHO BMI)

60

31

9

Including all studies

Including all studies

80% and Asia (OR 1.68; 1.51–1.87) I2 = 69%. For bothgroups, the risk was greatest in underweight women,with risk decreasing as BMI increased.
Secondary outcomes

Macrosomia: twelve studies (seven USA/Europe; fiveAsia) Macrosomia was defined as birthweight > 4000 g
in the majority [12, 17, 19–21, 23–26, 28, 31]; one study
used birthweight > 4500 g [30].
GWG below guidelines was associated with a lower
risk for macrosomia than GWG within guidelines: USA/
Europe (OR 0.62; 0.54–0.70) I2 = 39% and Asia (OR0.60; 0.47–0.77) I2 = 79%.
GWG above guidelines was associated with a higher
risk for macrosomia: USA/Europe (OR 1.87; 1.70–2.06)
I2 = 56% and Asia (OR 2.18; 1.91–2.49) I2 = 66%. In Asia,
the risk decreased as the BMI increased.
Caesarean section: nine studies (four USA/Europe;
five Asia) Nine studies assessed caesarean section.
Seven included emergency and elective deliveries [12,19, 23–26, 36] and two did not specify [20, 21]. Two [20,23] included repeat caesarean (total caesarean section),
one primary caesarean only [21] and six did not distinguish these.
GWG below guidelines was associated with a lower
risk for caesarean: USA/Europe (OR 0.92; 0.87–0.98)
I2 = 0%, with no statistically significant result for Asia
(OR 0.98; 0.89–1.06) I2 = 83%.
GWG above guidelines was associated with a higher
risk for caesarean: USA/Europe (OR 1.26; 1.21–1.33)
I2 = 0% and Asia (OR 1.37; 1.30–1.45) I2 = 59%. In Asia,
the risk was greatest in underweight women (OR 1.51;
1.30–1.45).
Gestational diabetes: Six studies Six studies assessed
GDM, but did not use consistent definitions, and had
different findings for GWG above guidelines and GDMrisk, preventing the intended meta-analysis of GDM andits relationship to GWG.
We were unable to demonstrate statistical significancefor comparison of ORs for SGA, preterm birth, LGA,

macrosomia and caesarean section between the USA/
Europe and Asian studies due to similar ORs and overlap in CIs.
Subgroup analysis: Asian studies

Of the eight studies from Asia, four were from China[16, 26, 31, 36], two from Korea [12, 34], with one eachfrom Japan [19] and Taiwan [21].
Results are stratified by country in Additional files 5
and 6 (Table S2: BMI at onset of pregnancy and Table
S3: GWG during pregnancy).
Comparison between studies using ethnic-specific regional
BMI categories and WHO BMI categories

A further analysis comparing studies using regional BMI
categories (Chinese and Korean studies) and WHO BMI
categories (Japanese and Taiwanese studies) was performed to assess for differences in adherence to 2009
IOM GWG guidelines and differences in maternal andinfant adverse outcomes.
Asian studies using ethnic-specific regional BMI categories showed 16% of women with GWG below guidelines, 33% within and 51% above, whereas studies using
WHO BMI categories had 60% with GWG below, 31%
within and 9% above (Table 5).
An additional meta-analysis was performed in Asianstudies, where studies using regional BMI categories(Chinese and Korean studies) were compared to thosestudies using WHO BMI categories (Japanese and Taiwanese studies) (Table 6). Pooled ORs for individual
analyses for outcomes are presented in Additional file 7.
SGA, LGA, macrosomia and caesarean section could
be examined in a meta-analysis (Table 6).
Wen et al. only included normal weight women, andYang et al. had women in all weight categories except
obese. Yang defined underweight as < 18 kg/m2.
For OR calculation, Hung, Xiong and Yang combined
overweight and obese into one group. The OR was used
for the overweight group here. Although Enomoto created separate ORs for overweight and obese, only overweight was used in the meta-analysis as there were nocomparison groups for obese.

Goldstein et al. BMC Medicine (2018) 16:153

Page 8 of 14abFig. 2 a Pooled odds ratio for individual outcomes for USA and Europe combined vs Asia, for the association between GWG below guidelines
with adverse outcomes. b Pooled odds ratio for individual outcomes for USA and Europe combined vs Asia, for the association between GWG
above guidelines with adverse outcomesGoldstein et al. BMC Medicine (2018) 16:153

Page 9 of 14

Table 6 Odds ratios for pregnancy outcomes by regions
Outcome

USA and Europe

Asia overall

Regional BMI studies(Chinese and Korean)

WHO BMI studies(Japanese and Taiwanese)

GWG< guidelines

GWG> guidelines

GWG< guidelines

GWG> guidelines

GWG< guidelines

GWG> guidelines

GWG< guidelines

GWG> guidelines

SGA

1.51
(1.39, 1.63)

0.65(0.62, 0.69)

1.63(1.45, 1.82)

0.69(0.63, 0.76)

1.43(1.2, 1.7)

0.65
(0.57, 0.75)

1.77(1.56, 2.01)

0.70(0.63, 0.79)

Preterm birth

1.35(1.17, 1.56)

0.83(0.74, 0.94)

1.06(0.78, 1.44)

0.71(0.58, 0.87)

N/A

N/A

N/A

N/A

LGA

0.62
(0.57, 0.68)

1.93(1.81, 2.06)

0.55(0.48, 0.63)

1.68(1.51, 1.87)

0.61(0.48, 0.76)

1.86(1.66, 2.09)

0.49(0.39, 0.62)

1.49(1.18, 1.87)

Macrosomia

0.62(0.54, 0.70)

1.87(1.70, 2.06)

0.60(0.47, 0.77)

2.18(1.91, 2.49)

0.75(0.68, 0.83)

2.0(1.71, 2.34)

0.52(0.31, 0.88)

2.76(2.25, 3.38)

Caesarean section

0.92(0.87, 0.98)

1.26(1.21, 1.33)

0.98(0.89, 1.06)

1.37(1.30, 1.45)

0.94(0.85, 1.04)

1.43(1.34, 1.52)

1.02(0.92, 1.14)

1.32(1.19, 1.46)

N/A Unable to perform meta-analysis for preterm birth because less than 2 studies within each region

Meta-regression

Substantial heterogeneity (I2 > 50%) was present for
GWG below guidelines for SGA (USA/Europe and Asia),
preterm birth (USA/Europe and Asia), LGA (USA/Europe and Asia), macrosomia (Asia) and caesarean section(Asia), and for GWG above guidelines for SGA (USA/Europe), preterm birth (USA/Europe and Asia), LGA(USA/Europe and Asia), macrosomia (USA/Europe andAsia) and caesarean section (Asia).
Where there was sufficient data available, we performed a meta-regression analysis to investigate possible
sources of heterogeneity, including percentage ofsmokers in pregnancy, mean age and percentage nulliparity (Additional file 8) in studies from the USA/Europe
and Asia.
The effect of GWG below guidelines on SGA (p <0.0001) for USA/Europe was associated with mean maternal age (p < 0.0005) and nulliparity (p < 0.0005) andmarginally associated with smoking (p = 0.056). The
GWG below guidelines effect on LGA (p = 0.002) for
USA/Europe was associated with mean maternal age(p = 0.021) and nulliparity (p < 0.005). The effect of
GWG above guidelines on LGA was significantly associated with nulliparity (p = 0.025) and marginally associated with mean age (p = 0.084) for the USA/
Europe. Heterogeneity was unexplained for theremaining outcomes.
Publication bias

There was no evidence of publication bias for SGA, LGA,
macrosomia or caesarean section (Additional file 9).
Assessment for publication bias was not assessed forpreterm birth (less than five studies).
Risk of bias

Participants were selected from maternity clinics or fromlarge datasets (Additional file 10). Apart from twostudies [17, 31], there was adequate description of inclusion and exclusion criteria. Studies were mostly retrospective, with three prospective studies [14, 25, 28] andone unspecified [31]. Given the nature of observational
studies, attrition bias was not considered relevant. Performance bias was difficult to assess. Very few studies
provided information regarding diet/exercise advice
given and whether this differed between groups. The
overweight and obese women may have been treatedmore intensively, and this could be a source of bias.
However, we postulate this difference would be similar
across studies and therefore propose that studies carry a
low risk of performance bias overall.
There were three studies with moderate risk of bias
and 16 studies with low risk of bias. Main reasons formoderate risk of bias included self-reported final weight(detection bias), self-reported outcome measures (detection bias), failure to report all outcomes (report bias)
and insufficient adjustment for confounding variables(confounding bias). Authors on 15 studies reported noconflict of interest.

Discussion
In this study of 1,309,136 pregnancies, we present a systematic review, meta-analysis and meta-regression incorporating women from diverse ethnicities across threecontinents, contemporary cohorts and from across the
BMI range. We explore ethnic differences in prepregnancy BMI, prevalence of GWG outside IOM guidelines
and maternal and neonatal health outcomes between
women living in the USA, western Europe and east Asia.
Within Asia, we compare studies applying regional and
WHO BMI categories. Women in the USA and Europe
have higher prepregnancy BMI, higher prevalence of
GWG above guidelines and lower rates of GWG below
guidelines than women in Asia. However, when applying
regional BMI categories, women in Asia have similar

Goldstein et al. BMC Medicine (2018) 16:153

GWG above guidelines to the other continents, but retain lower prevalence of GWG below guidelines. GWG
outside guidelines is associated with adverse health outcomes across all regions. A greater percentage of womenin Asia had GWG below guidelines, using WHO BMI(60%) compared to regional BMI categories (16%), yet
WHO BMI was not accompanied by increased risks ofadverse outcomes.
Given that Asian women have greater risks of healthcomplications at a lower BMI, Asian countries often use
lower BMI cut-offs for overweight and obese categories.
However in 2004, a WHO review of relevant evidence
concluded there was no clear cut-off for overweight andobesity for those of Asian ethnicity, and thus WHO did
not change their current BMI guidelines [37]. They
did, however, identify trigger points of > 23 kg/m2
and > 27.5 kg/m2, representing increased and highrisks respectively for public health action. In practice,
BMI categories commonly used in China [16, 26, 31]
are underweight BMI < 18.5 kg/m2, normal weight
18.5–23.9 kg/m2, overweight 24–28 kg/m2 and obese≥28 kg/m2. In Korea, the classifications are underweight
BMI < 18.5 kg/m2, normal weight 18.5–22.9 kg/m2, overweight 23–25 kg/m2 and obese ≥25 kg/m2 [12, 34]. Studies from Taiwan [38, 39] and Japan used WHO BMI
categories [40] despite Japanese Society of Obesity guidelines that define obesity at a BMI ≥ 25 kg/m2 [41]. The
European Board and College of Obstetrics and Gynaecology (EBCOG) [42] notes difficulties in accurately
comparing prevalence of prepregnancy BMI groups internationally with heterogeneity of data sets. However, comparison is important across regions to inform ourunderstanding of relationships between GWG and pregnancy outcomes. To the best of our knowledge, this is the
only systematic review comparing prepregnancy BMI and
exploring relationships to GWG and health outcomes
across international settings. We have compared Asianstudies using regional and WHO BMI categories in assessment of prepregnancy BMI, GWG and pregnancy healthoutcomes to explore applicability of regional and WHO
BMI categories in applying IOM GWG guidelines.
Applying WHO prepregnancy BMI categories, theUSA had the greatest prevalence of overweight andobesity at 43%, consistent with trends from the 2013–2014 National Health and Nutrition Examination
Survey (NHANES), with 37% of reproductive-aged
women obese [43]. This is significant as, preconception, a higher BMI independently increases pregnancy
complications including GDM, preeclampsia, caesarean section and LGA [44, 45]. In contrast, Asia had
the greatest prevalence of women in normal weight
and underweight categories. A lower BMI preconception is associated with increased risks including SGA[46]. The high prevalence of prepregnancy BMI outside

Page 10 of 14

of the healthful range shown here highlights the critical need to focus on achieving healthy preconception
weight, especially in the USA, but also across Europe
and Asia.
Women in the USA and Europe had higher GWG
above guidelines than women in Asia. However, in studies applying ethnic-specific regional BMI categories,
women in Asia had similar rates of GWG above guidelines. The prevalence of GWG above guidelines is consistent with observational studies [47–50]. Excess GWG
increases adverse pregnancy outcomes, independent of
BMI, as demonstrated here, and also increases postpartum weight retention and obesity [45, 51]. A systematic
review of postpartum weight retention in Asian womenfound that whilst prepregnancy BMI had an impact,
GWG was the most important predictor [11], supportingthe clinical relevance of our findings on long-term contribution to obesity. Here we have advanced the literature to highlight the high prevalence of GWG above
guidelines across the USA, Europe and Asia and show
the impact of using regional BMI categories on the application of IOM guidelines.
Exploring health outcomes by GWG, we combined
USA and Europe to ensure adequate numbers formeta-analysis and compared USA/Europe to Asia.
Across regions, GWG below guidelines was associatedwith a higher risk of SGA and preterm birth, compared
to GWG within guidelines. Likewise across regions,
GWG above guidelines was associated with a greater risk
for LGA, macrosomia and caesarean section. For womenin Asia, adverse outcomes were noted applying both regional and WHO BMI categories. We were generally unable to compare differences in adverse health outcomes
because ORs between regions were similar with overlapping CIs. Further research using both regional and
WHO BMI categories in all studies of GWG and healthoutcomes may be useful. We also support the recommendations for standardisation of GWG categories andcore outcome parameters to enable more accurate comparisons for future studies [42, 52].
With high prepregnancy BMI, high rates of GWG
above guidelines and clear adverse health outcomesshown here across the USA, Europe and Asia, and in
our pooled data analysis [5], intervention is clearly vital.
The Journal of the American Medical Association editorial accompanying our recent data analysis on GWG
discussed barriers to healthful lifestyle intervention during pregnancy in addressing GWG and improving healthoutcomes [53]. Barriers included inadequate evidence ofimprovement of adverse pregnancy outcomes and modest changes in GWG. Yet, the largest individual patient
data (IPD) analysis of 36 randomised controlled trials in
pregnancy (~ 12,000 women) [6], recently published in
The BMJ, demonstrates that even modest reduction inGoldstein et al. BMC Medicine (2018) 16:153

excessive GWG improves outcomes, reducing caesarean
section, preterm birth and GDM, the latter being particularly modifiable with physical activity intervention.
Reported results were independent of maternal characteristics including age, BMI, parity and ethnicity, enhancing generalisability of the findings. It appears that evenmodest changes to lifestyle and GWG effectively reduce
adverse health outcomes, affirming the need for implementation of healthful lifestyle in routine antenatal carefor public health impact [54].
There may also be differences to consider within Asia.
Comparing Asian studies, prepregnancy BMI was similar. Overall, 16% of Chinese women were underweight,
74% normal weight and 9% overweight and obese. These
values are lower than those of recent cohort studies,
where 15–28% of reproductive-aged women in China
are above healthy weight [13, 47]. This contrasted withJapan, with 18%, 71% and 11% respectively. In China
53% of women gained above GWG guidelines consistent
with the USA and Europe. In Japan GWG below guidelines was 64%, with only 7% above. These differences arguably occur because WHO BMI categories wereapplied in Japan. Differences may also relate to ethnic
variation. In Singapore, difference in GWG between ethnicities was postulated to be due to difference in dietquality and psychosocial factors [55]. However we postulate that the degree of observed difference primarily related to application of BMI categories. Asian studies
have already suggested the need for specific guidelines[56]. In 2000, Chinese-specific guidelines for GWG [57]were developed, but have not been commonly adopted,
with most Asian studies using mainly Caucasian-derived
IOM GWG guidelines [55]. A call has been made formulti-centre collaboration to create optimal GWG
guidelines for Asian women using modified BMI categories [58]. Here however, we demonstrate that applying regional BMI categories generated GWG patterns
and health outcomes similar to those in the USA/Europe. With regional BMI categories, apparent higher risks
of macrosomia and caesarean section were demonstrated. Overall our data are reassuring for clinicians andpolicy makers that IOM GWG guidelines are applicablein women of Asian background, provided regional BMI
categories are used, to avoid overestimation of GWG
below recommendations that are not accompanied byincreased risks of adverse outcomes.
Limitations of our study include the lack of cohorts
from developing countries and the exclusion of
non-English language articles. It did not include studies
from eastern Europe and south Asian countries, whichhave historical and ethnic differences from adjacent western European and east Asian countries respectively, yet
this is the broadest systematic review and meta-analysis
performed to date. For the meta-analysis, we combined

Page 11 of 14

the USA and Europe into one group, due to inadequate
reported outcomes. Within each study there may be heterogeneity regarding race/ethnicity, and results should not
be interpreted that the sample represented the country oforigin. The European and Asian studies did not provide
demographic data, and we have assumed the populationsin these studies to be largely homogeneous. Studies fromthe USA do include some women from Asia, and wherereported proportions are small, reporting is inconsistent,
limiting capacity to interpret the overall prevalence of
Asian women in US and European studies. Preterm birth
was not adjusted for gestational age, potentially resulting
in less total gestational weight gain than would have beenotherwise attained. Meta-analyses for GDM could not be
performed due to deficiencies in the primary data sets.
Heterogeneity among studies may affect the reliability of
the results, although this was only relevant for the effectof GWG below guidelines in SGA and LGA in USA/Europe. Lastly, we included studies published before 2009
IOM guidelines, so treating physicians and midwives may
have had different GWG targets and guidelines compared
to studies from after that time.
Strengths are the inclusion of common maternal andinfant risks associated with GWG below and above theIOM 2009 guidelines across the entire prepregnancy BMI
spectrum, with an analysis across three continents.
Notably, a quarter of the women in these studies werefrom Asia. This is the only systematic review that hascompared Asian studies applying regional compared to
WHO BMI categories. We searched four databases, performed a thorough risk of bias appraisal and sought international collaboration to facilitate reanalysis, enabling
broad inclusion of data in excess of 1.3 million pregnant
women. The collaboration with authors has enabled data
in a more homogeneous format for meta-analysis, with
unprecedented data integration and meta-analysis.

Conclusions
In this study of 1,309,136 pregnancies, incorporating
women from diverse ethnicities, contemporary cohorts
and from across the BMI range, we show that womenfrom the USA and Europe have higher prepregnancy
BMI than those from Asia (even when applying regional
BMI categories). In the USA and Europe, GWG above
guidelines appeared higher than in Asia and GWG
below guidelines was highest in Asia. However in Asianstudies applying regional BMI categories, GWG above
guidelines was similar across the USA, Europe and Asia.
In Asia, regional BMI categories may be more applicablethan WHO BMI categories when applying IOM GWG
guidelines. Across all prepregnancy BMI categories andin different ethnicities, insufficient GWG is associatedwith increased risk of SGA and preterm birth and excess
GWG with increased risk of LGA, macrosomia andGoldstein et al. BMC Medicine (2018) 16:153

caesarean section. Risks associated with excess GWG
may be higher in women from Asia. These findings havepractice and policy implications. This work attests to thebroad applicability of the 2009 IOM guidelines, when
Asian regional BMI categories are applied. As lifestyleinterventions in pregnancy increase attainment of recommended GWG and show health benefits, IOM implementation of GWG guidelines and pregnancy lifestyleinterventions should be considered broadly across maternity care [59, 60].

Additional files
Additional file 1: Search terms. (DOCX 17 kb)
Additional file 2: Additional methods. (DOCX 14 kb)
Additional file 3: Table S1. Descriptive characteristics of 23 included
studies. (DOCX 27 kb)
Additional file 4: Figure S1. Summary of pooled OR for the association
between gestational weight gain below and above guidelines foradverse outcomes. (DOCX 220 kb)
Additional file 5: Table S2. Body mass index at onset of pregnancy for
Asian studies. (DOCX 17 kb)
Additional file 6: Table S3. Gestational weight gain during pregnancy
for Asian studies. (DOCX 14 kb)
Additional file 7: Figure S2. Asian subgroup analysis: studies using local
BMI categories (China, Korea) vs WHO BMI categories (Japan, Taiwan).
Summary of pooled OR for the association between gestational weight gain
below and above guidelines for adverse outcomes. (DOCX 106 kb)
Additional file 8: Table S4. Meta-regression. (DOCX 21 kb)
Additional file 9: Figure S3. Publication bias. (DOCX 53 kb)
Additional file 10: Table S5. Summary of risk of bias assessment.
(DOCX 20 kb)

Abbreviations
BMI: Body mass index; GDM: Gestational diabetes mellitus; GWG: Gestational
weight gain; IOM: Institute of Medicine; LGA: Large for gestational age;
SGA: Small for gestational age; WHO: World Health Organization
Acknowledgements
We would like to thank L Rode (Copenhagen University Hospital,
Righospitalet, Denmark), T Moore Simas (University of Massachusetts Medical
School, Massachusetts, USA), S Park (Centers for Chronic Disease Preventionand Health Promotion (NCCDPHP), Centers for Disease Control andPrevention and M Kominiarek (University of Illinois, Chicago) for theirassistance in providing additional data.
Funding
RFG is supported by an RTS scholarship and SKA by a National Health andMedical Research Council (NHMRC) scholarship. JB and HJT are NHMRC
council fellows. The funders had no role in the study design or preparationof the manuscript.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
Authors’ contributions
RFG, SKA, MM, SR, JB and HJT participated in the conception, design andimplementation of the study. RFG and SKA extracted data. RFG and HJT
wrote the first manuscript. RFG and SR performed the statistical analysis.
MHB, NL, GH, FC, HH, YJK, MH, WS, MHK, AB, RD and JHC provided datareanalysis. All authors reviewed and approved the final manuscript.

Page 12 of 14

Ethics approval and consent to participate
This systematic review and meta-analysis includes previously publishedobservational studies. We could not influence the design of the prior studies
upon which this work is based and cannot comment on individual ethics
approval or consent. Patients were not involved in the conduct of ourepidemiological study, and there is no identifiable data.
Competing interests
Author/Professor Boyle reports personal fees from Pfizer, outside thesubmitted work. The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Monash Centre for Health Research and Implementation, Monash University,
Clayton, VIC, Australia. 2Monash Diabetes and Endocrine Units, Monash
Health, Locked Bag 29, Clayton Rd, Clayton, VIC 3168, Australia. 3Kaiser
Permanente, Southern California, Los Angeles, USA. 4Tianjin Women’s andChildren’s Health Center, Tianjin, China. 5Pennington Biomedical ResearchCenter, Baton Rouge, LA, USA. 6University Hospital, Messina, Italy.
7
Copenhagen University Hospital, Righospitalet, Copenhagen, Denmark.
8
Department of Obstetrics and Gynecology, School of Medicine, Ewha
Womans University, Seoul, Republic of Korea. 9Norwegian Institute of Public
Health, Oslo, Norway. 10Michigan State University, East Lansing, MI, USA.
11
Cheil Genetal Hospital and Women’s Healthcare Centre Dankook University
College of Medicine, Seoul, South Korea. 12Department of Development andRegeneration KU Leuven, University of Leuven, Leuven, Belgium. 13Faculty of
Medicine and Health Sciences, Centre for Research and Innovation in Care
(CRIC), University of Antwerp, Antwerp, Belgium. 14Faculty of Health andSocial Work, Research unit Healthy Living, UC Leuven-Limburg, Leuven,
Belgium. 15Department of Obstetrics and Gynaecology, University Hospitals
KU Leuven, Leuven, Belgium. 16Department of Obstetrics, Gynaecology andFertility, GZA Campus Sint-Augustinus, Wilrijk, Belgium. 17University ofCalifornia, Irvine, CA, USA.
Received: 27 February 2018 Accepted: 13 July 2018

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