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BMC Pregnancy and Childbirth

, 19:386 | Cite as

Effect of interpregnancy weight change on perinatal outcomes: systematic review and meta-analysis

  • Noor E. W. D. TeulingsEmail author
  • Katya L. Masconi
  • Susan E. Ozanne
  • Catherine E. Aiken
  • Angela M. Wood
Open Access
Research article
Part of the following topical collections:
  1. Labor, delivery, and postpartum health

Abstract

Background

Although obesity is a well-known risk factor for adverse pregnancy outcomes, evidence is sparse about the effects of interpregnancy weight change on the risk of adverse perinatal complications in a subsequent pregnancy. The current study aims to assess the effect of interpregnancy weight change on the risk of developing gestational diabetes, pre-eclampsia, pregnancy induced hypertension, preterm birth, or delivering a large- or small-for-gestational age neonate.

Methods

Pubmed, Ovid Embase, ClinicalTrial.gov and the Cochrane library were systematically searched up until July 24th, 2019. Interpregnancy weight change was defined as the difference between pre-pregnancy weight of an index pregnancy and a consecutive pregnancy. Inclusion criteria included full text original articles reporting quantitative data about interpregnancy weight change in multiparous women with any time interval between consecutive births and the risk of any perinatal complication of interest. Studies reporting adjusted odds ratios and a reference group of − 1 to + 1 BMI unit change between pregnancies were harmonised by meta-analysis.

Results

Twenty-three cohort studies identified a total of 671,906 women with two or more consecutive pregnancies. Seven of these studies were included in the meta-analysis (280,672 women). Interpregnancy weight gain was consistently associated with a higher risk of gestational diabetes, pre-eclampsia, pregnancy induced hypertension and large-for-gestational age births. In contrast, interpregnancy weight loss was associated with a lower risk of delivering a large-for-gestational age neonate. The effect magnitude (relative risk) of interpregnancy weight gain on pregnancy induced hypertension or delivering a large-for-gestational age neonate was greater among women with a normal BMI in the index pregnancy compared to women with a starting BMI ≥25 kg/m2.

Conclusion

These findings confirm that interpregnancy weight change impacts the risk of developing perinatal complications in a subsequent pregnancy. This provides evidence in support of guidelines encouraging women to achieve post-partum weight loss, as their risk of perinatal complications might be minimised if they return to their pre-pregnancy weight before conceiving again.

Prospectively registered with PROSPERO (CRD42017067326).

Keywords

BMI Hypertensive disorders of pregnancy Interpregnancy weight change Meta-analysis Obesity Perinatal complications Systematic review 

Abbreviations

BMI

Body mass index

CI

Confidence interval

CS

Caesarean section

GDM

Gestational diabetes mellitus

PE

Preeclampsia

PIH

Pregnancy induced hypertension

NOS

Newcastle-Ottawa scale

aOR

adjusted odds ratio

cOR

crude odds ratio

PRISMA

Preferred reporting items for systematic reviews and meta-analysis PTB

Preterm birth

LGA

Large-for-gestational age

SGA

Small-for-gestational age

Background

Obesity is an increasing global health concern, with more than 1.9 billion adults worldwide being overweight [1] and approximately one in two US women of childbearing age now being considered overweight or obese [2]. Considerable evidence exists showing serious perinatal complications associated with obesity in pregnancy including gestational diabetes (GDM), pre-eclampsia (PE) and neonatal death [3]. There is also an increased risk of complications such as fetal growth restriction and preterm birth amongst underweight women [4]. However, evidence is sparse about the effect of interpregnancy weight change on the risk of adverse outcomes in subsequent pregnancies. Current NICE guidelines in the UK recommend that overweight or obese women are referred for weight loss support at the 6–8 week postnatal check-up [5] despite limited evidence to support widespread implementation of such health promotion strategies and of benefit for future pregnancy outcomes [6].

The current study aimed to systematically synthesise the published evidence on the associations between interpregnancy weight change and common perinatal complications for both mother and child including GDM, PE, pregnancy induced hypertension (PIH), preterm birth (PTB), and delivery of a large- and small-for-gestational age neonate (LGA and SGA). Additionally, we compared the risk of these complications after interpregnancy weight change in women with a normal BMI and overweight or obese women, and where possible, we investigated the dose-response relationships.

Methods

Eligibility criteria, information sources, search strategy

The electronic databases PubMed, Ovid EMBASE, ClinicalTrials.gov and Cochrane Central were systematically searched until July 24th, 2019. The search strategy included terms relating to ‘interpregnancy’, ‘between pregnancy’, ‘weight change’ or ‘BMI’. These search terms were combined with the outcomes of interest (‘gestational diabetes’, ‘pre-eclampsia’, ‘pregnancy-induced hypertension’, ‘preterm birth’, ‘small-for-gestational age’ and ‘large-for-gestational age’) and synonyms of these outcomes (for full search string see Additional file 3: Table S1). Furthermore, we cross-referenced selected papers for additional articles to include. The studies identified were uploaded onto Covidence, an online tool for screening of papers for systematic reviews (www.covidence.org). The review protocol was designed a priori and registered with PROSPERO under registration number CRD42017067326.

Study selection

Studies were selected using the following predetermined inclusion criteria: [i] interpregnancy weight change reported in kilogram (kg), BMI units (kg/m2) or percentage body weight change in multiparous women with any time interval between the consecutive births, [ii] any of the perinatal outcomes of interest in the subsequent pregnancy, and [iii] observational, cohort or case-controlled human study design with a sample size ≥50, that were reported in English. When studies reported data from overlapping study populations, the study with the largest sample size was selected for inclusion. Information extracted from each study included country of research, study cohort name (if applicable), study period, sample size, study inclusion criteria, methods of weight reporting, definition of reference group, diagnostic criteria for perinatal outcomes and demographics that studies adjusted for. All study selection, full text screening, and data extraction was undertaken independently by two researchers (NEWDT and KLM), following PRISMA guidelines [7]. Disagreements were decided through a third opinion (AMW).

Data synthesis

Interpregnancy weight change was defined as the difference between pre-pregnancy weight in the index pregnancy, defined as the earliest recorded pregnancy, and pre-pregnancy weight in the subsequent pregnancy. Interpregnancy weight gain and loss were defined on two categorical scales; (i) for the meta-analysis we utilised categories of > 1 BMI unit interpregnancy weight loss, BMI gain between 1 and 2 units, BMI gain between 2 and 3 units or BMI gain of more than 3 units and (ii) for the dose-response analysis we utilised a BMI change of 0, 1, 2 or 3+ units. Crude odds ratios (calculated from studies providing relevant counts) and adjusted odds ratios for each outcome of interest were extracted from the selected publications.

Random effects meta-analysis was used to synthesize the odds ratios for weight change categories. To ensure a consistent reference group, only studies that employed a reference group of interpregnancy weight change between 1-unit weight loss and 1-unit weight gain were included. Heterogeneity was assessed using the I2 statistic.

We conducted a separate analysis comparing interpregnancy weight change and the risk of developing adverse perinatal outcomes in women with a normal BMI (< 25 kg/m2) versus women with an overweight BMI (≥25 kg/m2), at the start of their index pregnancy. To do so, adjusted odds ratios for both BMI categories were extracted from the publications and summarised by random effects meta-analysis.

Dose-response relationships were assessed by plotting association measurements from studies providing multiple weight gain categories. Where ranges of BMI changes were reported, the midpoint of the category was used (e.g. 1.5 BMI units change for the category weight change between 1 and 2 BMI units).

Statistical analysis and graphical presentation were performed using the metafor package in R for Windows, version 3.4.2.

Assessment of risk of bias

A sensitivity analysis was undertaken to assess potential impact of bias in individual studies by excluding studies that scored below 5 out of 9 points in the Newcastle-Ottawa Scale (NOS [8]) quality scoring assessment (Additional file 4: Table S2). Furthermore, leave-one-study-out analyses were conducted to identify whether one study leveraged the overall effect size estimate.

Results

Study selection

We identified and screened 4500 unique publications and included 194 articles for full text review (Fig. 1). A total of 27 studies were eligible for inclusion. Three studies were excluded due to overlapping study populations [9, 10, 11] and one was excluded because of a sample size < 50 women [12]. From the remaining 23 studies selected to take forward, a total of 671,906 women were identified for inclusion in the review (Table 1). Eighteen studies included only nulliparous women at the index pregnancy. The proportion of women older than 35 years varied between studies from 3 to 33%. All studies were conducted in Western populations, although this was not an inclusion criterion. Seven studies, comprising of 280,672 women, were included in the meta-analysis.
Fig. 1

Flow diagram of study inclusion and exclusion

Table 1

Characteristics of studies investigating the association between interpregnancy weight change and adverse perinatal outcomes

Author & publication date

Included in meta-analysis?a

Country

Study cohort (if applicable)

Study period

Sample size

Inclusion criteria

Reported weight

Reference group

Diagnostic criteria

Confounders adjusted for

Bogaerts et al. 2013 [25]

Yes

Belgium

Study Centre for Perinatal Epidemiology database

2009–2011

7897

First two consecutive births

Self-reported weight and height

±1 BMI unit

GDM: not clarified

PIH: not clarified

Prepregnancy BMI at first pregnancy, interpregnancy interval, gestational age at first delivery, maternal age, gestational weight gain, complications at first pregnancy (GDM, PIH, induction of labour, CS, malformations and mortality)

Bender et al. 2018 [46]

No

USA

Hospital of Pennsylvania retrospective cohort

2005–2010

537

Singleton livebirth followed by consecutive pregnancy

Weight measured at first antenatal visit, self-reported height

Stable BMI category

GDM: Carpenter–Coustan criteria for the 3-h glucose tolerance test

PIH: Task Force on Hypertension in Pregnancy

PTB: < 37 weeks

Maternal age, GDM in prior pregnancy, prepregnancy BMI category

Benjamin et al. 2019 [39]

No

USA

Texas linked siblings pair

2005–2012

2481

Birth certificates linked with older live birth, singleton sibling

Self-reported weight and height

0 to < 1 BMI units weight gain

SGA: Not reported

LGA: Not reported

PTB: Not reported

Prepregnancy BMI at sibling pregnancy, ethnicity, smoking status, gestational weight gain, height, maternal age and education

Chen et al. 2009 [26]

No

USA

Collaborative Perinatal Project

1959–1966

1892

Singleton livebirth followed by consecutive singleton pregnancy

Self-reported weight and height

− 0.32 to 1.48 BMI units

PTB: < 37 weeks

Maternal age, research centre, race, smoking status, socio-economic index, marital status and interpregnancy interval

Cheng et al. 2003 [27]

No

USA

Missouri maternally linked cohort

1989–1997

14,114

Second-born SGA infants

Self-reported weight and height

No change in BMI

SGA: <10th percentile

Not reported

Crosby et al. 2017 [47]

No

Ireland

Follow up of ROLO study

2007–2015

280

Secundigravida who previously gave birth to macrosomic (> 4.0 kg) baby

Weight and height measured at first antenatal visit

No interpregnancy weight gain (not further specified)

GDM: Not specified

No adjusted model available

Ehrlich et al. 2011 [24]

Yes

USA

Kaiser Permanente Northern California

1996–2006

22,351

Women without recognised diabetes before pregnancy, first and second live born singletons

Measured by clinician at time of alpha fetoprotein test (mean GA 16.9 weeks)

± 1.0 BMI unit

GDM: According ADA criteria

Maternal age, race, ethnicity, place of birth, GDM status in first pregnancy, prepregnancy BMI in first pregnancy, gestational age, interpregnancy interval

Getahun et al. 2007 [28]

No

USA

Missouri vital record system

1989–1997

136,884

No history of pre-eclampsia in index pregnancy, delivering second baby.

Self-reported weight and height

Normal BMI (18.5–24.9 kg/m2) in both pregnancies

PE: hypertension and proteinuria beyond 20th week gestation in women normotensive before pregnancy

Maternal age, race, education, marital status, prenatal care, smoking status and interpregnancy interval

Getahun et al. 2007 [29]

No

USA

Missouri vital record system

1989–1997

146,227

First two consecutive singleton pregnancies

Self-reported weight and height

Normal BMI (18.5–24.9 kg/m2) in both pregnancies

LGA: ≥90th percentile

Maternal age, race, education, marital status, prenatal care, smoking status, alcohol during pregnancy, marital status and interpregnancy interval

Glazer et al. 2004 [36]

No

USA

Washington State Longitudinal Births Database

1992–1998

4102

Non-diabetic women with weight ≥ 200lbs. with ≥2 singleton births.

Pre-pregnancy weight from birth certificate, unspecified how measured

± 10 lb

GDM: not clarified

Maternal age, gestational weight gain in index pregnancy and gestational weight gain during subsequent pregnancy

Hoff et al. 2009 [30]

No

USA

Missouri birth certificates

1995–2004

1035

First two consecutive singleton pregnancies in overweight women

Pre-pregnancy weight from birth certificate, unspecified how measured

overweight BMI (25.0–29.9 kg/m2) in both pregnancies

PIH: not clarified

PTB: < 37 weeks

No adjusted model available

Jain et al. 2013 [31]

No

USA

Missouri vital record system

1998–2005

10,444

First two consecutive singleton pregnancies with a BMI ≥ 30 at index pregnancy.

Self-reported weight and height

± 2 BMI units

SGA: <10th percentile

LGA:< 90th percentile

Maternal ae, race, martial status, education, socioeconomic status, obesity status in first pregnancy, gestational weight gain, smoking, PE, prenatal care, previous SGA or LGA birth, DM, hypertension, renal or cardiac disease

Knight-Agarwal et al. 2016 [32]

Yes

Australia

Birthing Outcome System

2008–2013

14,875

Women of all parity with subsequent pregnancies.

Weight and height recorded at first antenatal visit (mean GA not reported)

± 1 BMI unit

GDM: not clarified

Maternal age, parity, country of birth, smoking status

Kruse et al. 2015 [33]

No

Denmark

2009–2013

72

Primiparas with a history of GDM

Unspecified how weight was recorded

No change in BMI units

GDM: ≥9.0 mmol/L blood glucose 2 h after OGTT.

No adjusted model available

Lynes et al. 2017 [22]

Yes

USA

NICHD Consecutive Pregnancy Study

2002–2010

46,521

First two consecutive singleton births

Unspecified how weight was recorded

± 1 BMI unit

GDM: not clarified

PIH: ≥140 mmHg systolic and ≥ 90 mmHg diastolic without proteinuria

PE: ≥140 mmHg systolic and ≥ 90 mmHg diastolic with proteinuria

Maternal race, interpregnancy interval, maternal age, marital status, smoking status, alcohol use during second pregnancy, prepregnancy BMI, complication in first pregnancy (GDM, PE, PIH)

McBain et al. 2016 [34]

No

Australia

Women’s and Childeren’s Health Network

2000–2012

5371

First and second consecutive deliveries.

BMI units recorded at first antenatal visit (before GA 15 weeks)

±2 BMI units

GDM: not clarified

PTB: not clarified

SGA: <10th centile

LGA: ≥90th centile

Maternal age, socioeconomic status, prepregnancy BMI in first pregnancy, smoking status, race, interpregnancy interval, first pregnancy outcome (GDM, PIH, birth method, LGA and SGA)

Pole et al. 1999 [37]

No

Canada

Nova Scotia Atlee Perinatal Database

1988–1996

19,932

Two or more singletons

Not stated

± 3% weight

GDM: two abnormal glucose values on a GTT according to Joslin Clinic or O’Sullivan criteria.

PIH: BP ≥90 mmHg diastolic, twice in 24 h

Prepregnancy weight (in lbs) of index pregnancy, gestational age, marital status, previous CS, maternal age, gestational weight gain, GDM in previous pregnancy

Simonsen et al. 2013 [38]

No

USA

Maternally linked Utah birth and fetal records

1989–2007

8468

First three singleton

live births.

Pre-pregnancy BMI from birth certificate (mean GA not reported)

BMI category unchanged

PTB: ≥20 and < 37 weeks

Maternal age, ethnicity, gestational weight gain, father on birth record, interpregnancy interval, subtype of previous PTB, gestational age at previous PTB, fetal death or anomaly in history

Sorbye et al. 2017 [23]

Yes

Norway

Medical Birth Registry of Norway

2006–2014

24,198

First and second delivery without GDM in index pregnancy

Unspecified how weight was recorded

± 1 BMI units

GDM: fasting glucose < 7.0 mmol/l and serum glucose after OGTT ≥7.8 mmol/l

Maternal age, country of birth, maternal education, smoking status, interpregnancy interval and year of delivery

Villamor et al. 2006 [20]

Yes

Sweden

Swedish Birth Register

1992–2001

151,025

First and second consecutive singleton births.

BMI units recorded at first antenatal visit (mean GA not reported)

± 1 BMI units

GDM: ICD-9648 W, ICD-10 O244.

PE: ICD-9642E-642H, ICD-10 O11 and O14.

PIH: ICD-9642D and 642X, ICD-10 O13

LGA: ≥2 SD above mean birthweight

Prepregnancy BMI in first pregnancy, height, interpregnancy interval, maternal age, country of birth, education, year of delivery, smoking status

Wallace et al. 2014 [21]

Yes

Scotland

Aberdeen Maternity and Neonatal Databank

1986–2007

12,740

First two consecutive births.

Weight and height recorded at first antenatal visit (mean GA not reported)

± 1 BMI units

PE: ISSHP definition

PIH: ISSHP definition

PTB: < 37 weeks

SGA: <10th percentile

LGA: ≥90th percentile

Prepregnancy BMI in first pregnancy, height inter-delivery interval, maternal age, year of delivery, smoking status, gestational age and fetal gender at second pregnancy.

Wallace et al. 216 [35]

No

Scotland

Aberdeen Maternity and Neonatal Databank

1986–2013

24,520

First two consecutive births and the same perinatal complication in both pregnancies

Weight and height recorded at first antenatal visit (mean GA not reported)

± 2 BMI units

PTB: < 37 weeks

PE: ISSHP definition

PIH: ISSHP definition

SGA: <10th percentile

LGA: ≥90th percentile

Prepregnancy BMI in first pregnancy, year of delivery, height, inter-delivery interval, maternal age, smoking status, gestational age and fetal sex at first and second pregnancy

Ziauddeen et al. 2019 [19]

Yes

England

Birth registry at Southampton Hospital

2003–2017

15,940

First two consecutive singleton live-birth pregnancies

Weight recorded at first antenatal visit; height self-reported

± 1 BMI unit

LGA: >90th percentile for GA

Baseline BMI, maternal age, education level, infertility treatment, smoking status, employment status, GDM in current pregnancy and interpregnancy interval

aTo ensure a consistent reference group, only studies that employed a reference group of interpregnancy weight change between 1-unit weight loss and 1-unit weight gain were included. GDM gestational diabetes, PE pre-eclampsia, PIH pregnancy induced hypertension, PTB preterm birth, SGA small-for-gestational age, LGA large-for gestational age, BMI body mass index, GA gestational age, CS caesarean section, DM diabetes mellitus

Synthesis of results

Interpregnancy weight gain of between 1 and 2 BMI units was associated with a 51% higher risk of developing GDM (aOR 1.51 [1.22–1.80], I2 = 70.1%), whereas an increase of 2–3 or more than 3 BMI units was associated with an 81 and 137% higher risk (aOR 1.81 [1.20–2.41], I2 = 88.4% and aOR 2.37 [1.50–3.34], I2 = 91.0% respectively) (Fig. 2). Furthermore, interpregnancy weight gain of more than 3 BMI units was associated with a higher risk of PE or PIH (aOR 1.70 [1.50–1.91], I2 = 0.0% and aOR 1.71 [1.51–1.91] I2 = 0.0% respectively) (Figs. 3 and 4). The association between interpregnancy weight change and the risk of delivering an LGA neonate could only be estimated for a weight gain > 3 BMI units, and showed a 63% higher risk (aOR 1.63 [1.30–1.97], I2 = 85.6%) (Fig. 5). In contrast, interpregnancy weight loss of > 1 BMI unit was associated with a lower risk of delivering an LGA neonate, (aOR 0.79 [0.58–0.99], I2 = 86.1%) (Fig. 5), but there was no conclusive evidence of association of interpregnancy weight loss with the risk of developing GDM, PE or PIH (Figs. 2, 3 and 4). There was an insufficient number of studies to conduct a meta-analysis on adjusted odds ratios for the outcomes of SGA and PTB. A meta-analysis combining the crude odds ratios rather than adjusted ratios showed a significant higher risk of developing PE (cOR 1.31 [1.09–1.53], I2 = 75.1%), but showed similar results for the association between interpregnancy weight gain and the risk of developing GDM, PE or PIH (Additional file 1: Figure S1 for interpregnancy weight loss and Additional file 2: Figure S2 for weight gain). For the outcomes of SGA and PTB, meta-analyses of crude odds ratios showed interpregnancy weight loss of > 1 BMI unit was associated with a higher risk of delivering a SGA neonate or delivering preterm (cOR 1.53 [1.35–1.71], I2 = 0.0% and cOR 1.45 [1.21–1.69], I2 = 26.7%] respectively), but there was no evidence of association with interpregnancy weight gain (Additional file 1: Figure S1 and Additional file 2: Figure S2).
Fig. 2

Forest plot from random effects meta-analysis showing the association between interpregnancy weight change and the risk of developing gestational diabetes in subsequent pregnancy. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

Fig. 3

Forest plot from random effects meta-analysis showing the association between interpregnancy weight change and the risk of developing pre-eclampsia in subsequent pregnancy. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

Fig. 4

Forest plot from random effects meta-analysis showing the association between interpregnancy weight change and the risk of developing pregnancy induced hypertension in subsequent pregnancy. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

Fig. 5

Forest plot from random effects meta-analysis showing the association between interpregnancy weight change and the risk of delivering a large-for-gestational age neonate in subsequent pregnancy. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

Figures 6, 7 and 8 show the odds ratios for the risk of developing an adverse perinatal outcome after interpregnancy weight gain, stratified by BMI category in the index pregnancy (normal weight; BMI < 25 kg/m2 versus overweight; BMI ≥25 kg/m2). Women with a normal weight at the start of the index pregnancy had a higher risk of developing GDM after interpregnancy weight gain > 3 BMI units (aOR 4.36 [2.29–6.44], I2 = 81.6%) compared to women with an overweight BMI (aOR 2.26 [1.40–3.12], I2 = 74.4%) (Fig. 6a and b). Similarly, women with a BMI < 25 kg/m2 were at higher risk of delivering a LGA neonate after interpregnancy weight gain > 3 BMI units than women with BMI ≥25 kg/m2 (aOR 1.80 [1.24–2.35], I2 = 87.2% versus aOR 1.50 [1.35–1.66], I2 = 0.0% respectively) (Fig. 7a and b). Women with a normal BMI at the start of their index pregnancy were at higher risk of developing PIH in a subsequent pregnancy after interpregnancy weight gain of 2–3 BMI (aOR 1.60 [1.04–2.16, I2 = 54.6%) and > 3 BMI units (aOR 2.21 [1.81–2.60], I2 = 0.0%), compared to women with an overweight BMI (2–3 units gain; aOR 0.95 [0.73–1.17], I2 = 0.0%, > 3 units gain; aOR 1.37 [1.16–1.59], I2 = 0.0%) (Fig. 8a and b). We did not find differential effects of interpregnancy weight loss between women with a normal BMI and women with an overweight BMI on the risk of developing GDM, PIH or delivering an LGA neonate.
Fig. 6

Forest plot from random effects meta-analysis showing association between interpregnancy weight change and the risk of developing gestational diabetes, stratified by BMI category at the start of index pregnancy. a Normal weight classified as BMI < 25 kg/m2; b Overweight classified as BMI ≥25 kg/m2. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

Fig. 7

Forest plot from random effects meta-analysis showing association between interpregnancy weight change and the risk of delivering a large-for-gestational age neonate, stratified by BMI category at the start of index pregnancy. a Normal weight classified as BMI < 25 kg/m2; b Overweight classified as BMI ≥25 kg/m2. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

Fig. 8

Forest plot from random effects meta-analysis showing association between interpregnancy weight change and the risk of developing pregnancy induced hypertension, stratified by BMI category at the start of index pregnancy. a Normal weight classified as BMI < 25 kg/m2; b Overweight classified as BMI ≥25 kg/m2. All adjusted odds ratios are relative to the reference category of interpregnancy weight change between − 1 and + 1 BMI units. BMI, body mass index (in kg/m2); aOR, adjusted odds ratio; CI, confidence interval

There was an approximate log-linear association between interpregnancy weight gain and the risk of developing GDM (Fig. 9a), PE (Fig. 9b) or PIH (Fig. 9c) and delivering a LGA neonate (Fig. 9d).
Fig. 9

Dose-response curve with line of best fit for the increase in odds ratio of developing perinatal complications after interpregnancy weight gain. a Gestational Diabetes. b Pre-eclampsia. c Pregnancy Induced Hypertension d Large-for-gestational age. Where ranges of BMI changes were reported, the midpoint category was utilised (e.g. 1.5 BMI units change for the category weight change between 1 and 2 BMI units). aOR, adjusted odds ratio. BMI, body mass index (in kg/m2)

Risk of bias of included studies

After assessing the study selection criteria, comparability of cases and controls and outcome assessments through the NOS, we identified four studies of poor quality (NOS score < 5, Additional file 4: Table S2). However, as these studies did not employ a reference group of ±1 BMI unit, they were already excluded from the meta-analyses. Leave-one-out-analyses showed that removing the study by Villamor et al. made the association between GDM and interpregnancy weight change of 2–3 or > 3 BMI units not significant. We did not find evidence that the outcomes for PE or PIH were driven by one study. For the outcome of delivering an LGA neonate, leave-one-out analyses could not be conducted due to only two studies being included in the meta-analysis.

Discussion

Main findings

This study systematically summarises and examines the published literature on the associations between interpregnancy weight change and several common perinatal outcomes. Our main findings are that interpregnancy weight gain is associated with a higher risk of developing GDM, PE, PIH and delivering an LGA neonate, while interpregnancy weight loss is associated with a lower risk of delivering an LGA neonate. BMI at the start of the index pregnancy possibly modifies the risk of developing GDM, PIH or delivering an LGA neonate after interpregnancy weight gain. Furthermore, we identify a positive approximately log-linear relationship between interpregnancy weight gain and the risk of developing GDM, PE, PIH or delivering a LGA neonate.

Comparison with existing literature

Our study confirms the associations between interpregnancy weight gain and the risk of developing GDM and LGA, as also shown in a recent meta-analysis [13]. Our research additionally summarises the effect of interpregnancy weight change on the risk of developing hypertensive disorders in pregnancy. However, our meta-analysis is to the authors knowledge the first study to show that gaining weight between pregnancies increases the risk of developing hypertensive disorders in the subsequent pregnancy. The observation that starting BMI possibly modifies this association is important for women with a healthy BMI at the start of their index pregnancy, as research often emphasises the risk associated with being overweight or obese, and women with a healthy BMI might not be aware of the risk that comes with (small) interpregnancy weight gain. Although the risks of (excessive) gestational weight gain [14] and high pre-pregnancy BMI [15, 16] on perinatal outcomes are well understood, the effects of interpregnancy weight gain are relatively unknown and are essential to understand in order to guide women in periconception and perinatal weight management.

Our study shows an approximate log-linear association between BMI gain and the risk of developing GDM or hypertensive disorders in pregnancy. This result contributes towards understanding the association between maternal weight and pregnancy complications. Linear dose-response associations are established between obesity and the incidence of GDM, PE and PIH [17], between adiposity and pre-eclampsia [18], as well as maternal weight and pre-eclampsia [15] and GDM [16]. Our identified associations emphasise the detrimental effects of (small amounts of) weight gain, additional to the influence of absolute BMI. This can contribute towards understanding the importance of postpartum weight management and highlights the need for the development of clinical guidelines.

Strengths and limitations

A strength of our study is we ensured a homogenous reference group (i.e. a BMI change ≤1 kg/m2) for our meta-analysis rather than including studies with very different reference groups [13]. Furthermore, we only harmonised studies reporting adjusted odds ratios, which all considered maternal age, country of origin, social economic status and smoking status as potential confounders. Nevertheless, our study has several limitations. First, between-study heterogeneity remained, arising from differences in outcome definitions and demographics, such as parity and age, and potentially differences in length of interpregnancy intervals and prevalence of perinatal complications. Of the studies selected for meta-analysis, only Lynes et al. did not restrict to nulliparous women, although removing this study had little impact on the results. Second, GDM, PE and PIH were either not defined in publications or the definitions of these adverse outcomes differed between studies, hence caution is needed when comparing effect estimates between studies. Third, it was not possible to consistently assess the impact of previous pregnancy complications, which may lead to excessive interpregnancy weight changes and a higher risk of subsequent pregnancy complications. Fourth, studies varied in the way they measured pre-pregnancy weight, with the majority of studies using self-reported weight (and height) to calculate BMI and interpregnancy weight change. Although evidence suggests that maternal reports of pre-pregnancy weight are in general consistent with clinical records [42], bias due to systematic over- or underreporting cannot be excluded. We can also not exclude the possibility of publication bias, as this could not be assessed due to the small number of studies available per adverse outcome and funnel plot assessment is generally not recommended with less than 10 studies [43]. Lastly, we were unable to make the distinction between spontaneous preterm birth and medically induced preterm birth. We hypothesise that an increased risk of preterm birth is at least partly related to the increased risk of carrying an SGA neonate, as (suspected) growth restriction is one of the main causes of medically induced premature birth [44]. However, inadequate nutrition in the context of severe maternal weight loss could also contribute to a higher risk of both SGA and preterm birth [45].

Conclusions

Our study highlights the importance of postpartum weight management, but also identifies opportunities for future research. There is a need to capture the typical weight change profiles of women in various BMI classes, to further elucidate risk groups. This will support further research into weight management strategies, eventually aiming to implement evidence-based weight control interventions to benefit maternal and offspring health. It is particularly important to elucidate strategies for postpartum weight loss in normal weight women, as this group might not be the focus of current research and interventions, yet may be at highest risk of adverse outcomes from interpregnancy weight gain.

In conclusion, we show that interpregnancy weight gain impacts on the risk of developing perinatal complications in a subsequent pregnancy and it is possible that BMI at the index pregnancy modifies these associations. These findings highlight the need to encourage women to return to their pre-pregnancy weight before conceiving again in an effort to reduce the risk of perinatal complications. Future work should focus on defining the most effective strategies to achieve this outcome.

Notes

Acknowledgements

This work was supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*].

*The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Authors’ contributions

NEWDT, CEA, SEO and AMW made substantial contributions to the conception of the project. NEWDT and KLM developed the protocol, AMW advised on the protocol design. NEWDT and KLM managed the systematic search and screened papers for inclusion with consultation from AMW. NEWDT and KLM extracted the data and assessed the risk of bias in included studies. NEWDT preformed the statistical analysis and wrote the first draft of the manuscript. All authors contributed to interpretation of the data, substantively revised the manuscript and approved the final version.

Funding

NEWDT is sponsored by a British Heart Foundation PhD Scholarship under the reference FS/16/53/32729. KLM is funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). SEO is supported by the British Heart Foundation (RG/17/12/33167) and Medical Research Council (MC_UU_12012/4). CEA is supported by an Isaac Newton Trust/Wellcome Trust ISSF/ University of Cambridge Joint Research Grant. The funding bodies did not play any role in the design of the study, collection, analysis and interpretation of the data, nor in the writing of the manuscript.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12884_2019_2566_MOESM1_ESM.tiff (9 mb)
Additional file 1: Figure S1. Forest plot from random effects meta-analysis showing the crude odds ratios for the association between interpregnancy weight loss and the risk for perinatal outcomes of interest. Black, solid dots represent studies with reference group of interpregnancy weight change between − 1 and + 1 BMI unit and are therefore included in the meta-analyses. White, open dots represent studies not using a reference group of interpregnancy weight change between 1-unit weight loss and 1-unit weight gain and are visually displayed but not included in the meta-analysis. cOR, crude odds ratio; CI, confidence interval.
12884_2019_2566_MOESM2_ESM.tiff (9 mb)
Additional file 2: Figure S2. Forest plot from random effects meta-analysis showing the crude odds ratios for the association between interpregnancy weight gain and the risk for perinatal outcomes of interest. Black, solid dots represent studies with reference group of interpregnancy weight change between − 1 and + 1 BMI unit and are therefore included in the meta-analyses. White, open dots represent studies not using a reference group of interpregnancy weight change between 1-unit weight loss and 1-unit weight gain and are visually displayed but not included in the meta-analysis. cOR, crude odds ratio; CI, confidence interval.
12884_2019_2566_MOESM3_ESM.docx (15 kb)
Additional file 3: Table S1. Search string.
12884_2019_2566_MOESM4_ESM.docx (19 kb)
Additional file 4: Table S2. Assessment of study quality through the Newcastle–Ottawa scale.

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© The Author(s). 2019

Open AccessThis 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, and reproduction 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.

Authors and Affiliations

  1. 1.Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
  2. 2.University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases UnitWellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s HospitalCambridgeUK
  3. 3.Department of Obstetrics and GynaecologyUniversity of Cambridge, Box 223, The Rosie Hospital and NIHR Cambridge Comprehensive Biomedical Research CentreCambridgeUK

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