Preterm Birth and Breast Cancer Risk: A Systematic Review and Meta-Analysis

Background

According to GLOBOCAN's 2018 report, more than 18 million new cases of cancer, as well as 9.6 million deaths from cancer have been reported worldwide [1]. Meanwhile, breast cancer (BC) is the most common cancer among women in most countries of the world, accounting for 25% of cases of cancer and 12% of deaths due to cancer among women [2–6]. In 2016, BC is known as the most common cancer with 1.68 million cases (and 535,000 deaths and 15.1 million DALYs) [7].

A number of studies in different countries have shown that numerous factors, such as genetics, lifestyle, obesity, reproductive factors and anthropometric factors, have an impact on BC and have contradictory results in this regard [1, 5, 8–15]. The results of some studies have shown that the number of previous pregnancies, as well as the age at first pregnancy and delivery, is associated with the risk of BC in women. Changes observed in estrogen levels during pregnancy [16, 17] as well as histologic changes in breast tissue can justify this relationship [18]. On the other hand, the characteristics of pregnancy, such as gestational age and birth weight are related to the level of maternal hormones, which levels of hormones can be associated with the risk of BC [19–22].

Swerdlow et al. [23] have shown in their study that gestational age is inversely associated with BC (especially before menopause), and the risk in women with gestational age of 26–31 weeks was 2.4 times compared to 40–41 weeks. However, M Kaijser et al. [24] suggested that PB and low birth weight are not associated with an increased risk of BC. Another study concluded that preterm labor cannot increase the risk of premenopausal BC, except for women with gestational age less than 32 weeks [25]. Therefore, there is no general consensus on this relationship.

In several other studies, conflicting results have been reported, which may be due to low sample size, methodological problems (in the design, analysis, and reporting of results) or the difference in risk factors in diverse populations. On the other hand, the primary studies in compared to meta-analysis study were hampered by low statistical power because of low sample size. Therefore, the aim of this study was to conduct a systematic review and meta-analysis of the relationship between the PB and BC risk in women.

Methods

Study design

This was a systematic review and meta-analysis. To design, run and report the findings, we followed the PRISMA Statement [26] (Preferred reporting items for systematic reviews and meta-analyses).

Data sources, search strategy, and selection criteria

Published studies were located back to the earliest available publication date (1983), using the Medline, Embase, Scopus, Web of Science bibliographic databases, and by hand-searching of reference lists of identified studies and pertaining review papers. We also checked the citation lists of relevant publications to find additional pertinent studies. As recommended in the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guideline [27], we searched unpublished studies using gray literature databases. The literature search was made with no language or publication date restrictions. Search details are available in Appendix s1. The search results of different sources were combined, and duplicates were removed. The search results were evaluated by two independent reviewers (M.S and A.A-H) by screening titles and abstracts, followed by a full-text review. Consensus was reached by discussion or third party opinion (S.V).

Eligibility criteria

In the present systematic review and meta-analysis, studies were included if they met the following inclusion criteria: (1) observational studies (cohort, case-control, nested case-control or case-cohort studies) that evaluated the associations of PB with the risk of developing BC, (2) and they reported any form of effect size estimate (odds ratio (OR), hazard ratio (HR), standardized incidence ratio (SIR) or rate ratio (RR)).We excluded studies if: (1) they had cross-sectional design, (2) we were not able to extract the exact details about research method or results; (3) and presented only as abstracts, conference paper, letters to the editor and editorials.

Data extraction and risk of bias assessment:

All eligible studies were reviewed, and the following data was extracted by two investigators independently: (1) authors; (2) year of publication; (3) location; (4) study population; (5) duration of follow-up; (6) the number of cancer cases; (7) risk estimates with CIs; (8) confounders adjusted for in multivariate analysis. Methodological quality assessment for the included studies was done independently by two reviewers (M.S and J.H) using criteria as outlined in the Newcastle–Ottawa scaling for case-control and cohort studies. Explicit judgment regarding the following items was done: selection of the study groups, comparability of the groups and ascertainment of exposure and outcome. Any disagreement was resolved by discussion or third party opinion (S.V).

Statistical analyses

All effect size estimates (HRs, ORs and RRs) were treated as equivalent measures of risk. We calculated the pooled risk of BC associated with PB stratified by parity and menopausal status. Extracted risk estimates from primary studies were pooled using inverse-variance weighted DerSimonian-Laird random-effect models which incorporates between-study variability into the calculations. To investigate whether the results of the meta-analysis were depend on a particular trial or group of trials, we recomputed the meta-analysis statistic after omitting one study at a time(sensitivity analysis). Additionally, we assessed the probability of publication bias with Egger’s test, with P-value < 0.10 considered representative of statistically significant publication bias. All comparisons were two-tailed, and 95% confidence intervals (CI) were described where applicable. The Stata software (Version 13.0) (Stata Corp, College Station, Texas) was used for Meta-analysis.

Results

The Literature search

A flow diagram of the systematic review showing the study selection is presented in Fig. 1. The initial search identified 3,426 potentially relevant articles (315 from PubMed, 153 from Embase, 949 from Scopus, 499 from Web of Science and 25 from other sources), of which 606 articles were excluded because they were duplicates. Briefly, we identified 13 potentially relevant studies for meta-analysis.

Study characteristics

Table 1 outlines the main characteristics of the included studies. These six studies were conducted between 1998 and 2018, of which six studies were conducted after 2010. The studies were conducted in the United States (6 studies), Sweden (3 studies), United Kingdom (1 study), Denmark (1 study), Norway (1 study) and one in Iraq. In terms of study design, five of the thirteen studies were designed as a cohort, seven were case-control and one nested case-control study. Sample size ranged from 22,758 to 694,657 participants in cohort studies and 300 to 41,255 in case-control studies. The summary of methodological quality appraisal of the included studies is shown in additional file 2. All studies classified as good quality. All studies adopted appropriate approach to account for potential confounders. All cohort studies selected their exposed and nonexposed participants from the same community sample. Two of seven included case-control studies used hospital control and classified as moderate risk of selection bias. All studies provided adequate criteria for diagnosis of the outcomes of interest and provided a proper description of how the outcomes were measured.

Table 1

The characteristics of included primary studies
Author	Location	Design	Sample size	Breast cancer ascertainment	Exposure source	Factors adjusted for in analyses
Troisi, 1998	USA	Case-control	Cases = 1,669 Controls = 1,505	Hospital records	Hospital records	Race, education, parity, age, previous spontaneous and induced abortion, site of tumor
Hsieh, 1999	Sweden	Nested case-control	Cases = 2318 Control = 10,199	National Cancer Registry	Swedish National Board	Age, age at first birth
Melbye, 1999	Denmark	Cohort	474,156	Danish Cancer Registry	Danish Civil Registration System	Age, age at first birth, parity, stillbirths, preterm and term deliveries, history of spontaneous and induced abortion.
Innes, 2000	USA	Case-control	Cases = 484 Controls = 2,904	Computerized Cancer registry	Birth registry data	Woman’s social security number, full maiden, date of birth, race, county of residence
Vatten, 2002	Norway	Cohort	694,657	Norwegian Cancer Registry	Medical Birth Registry	Age, calendar period of diagnoses, total number of births
Innes, 2004	USA	Case-control	Cases = 2,522 Controls = 10,052	Computerized Cancer registry	Birth registry data	Maternal race (black, non-Hispanic white, Hispanic and other), marital status, maternal education
Cnattingius, 2005	Sweden	Cohort	314,019	Cancer Register	Swedish National Board of Health And Welfare and Statistics Sweden	Age, placental weight, birth weight, gestational age, infant sex, family situation, smoking habits, mother’s country of birth, height, BMI, pregnancy induced hypertensive diseases, vaginal bleeding in late pregnancy, diabetes mellitus, and parity
Nechuta, 2010	USA	Case-control	Cases = 8,251 Controls = 33,004	Michigan Cancer Surveillance Program’s statewide cancer registry	Michigan Birth Registry	Age at first and last birth, education at first birth, infant’s gender at first and last birth, parity, maternal birth year race, gestational age
Sanderson, 2012	USA	Case–control	Cases = 979 Controls = 974	Rio Grande Valley located At the southern tip of Texas	Rio Grande Valley located At the southern tip of Texas	Age, family history of breast cancer, age at menarche, menopausal status, parity, BMI, use of oral contraceptives, use of hormone replacement therapy, alcohol intake, number of mammograms in past 6 years, physical activity
Altaha, 2013	Iraq	Case control	Cases = 100 Controls = 200	Oncology clinic in AL-Ramadi General Hospital	Al-Anbar governorate	Age of the women in years, residence of women whether urban or rural, marital status, education level, occupation of women, age at menarche, age at first full term delivery, number of live births, number of stillbirths, number of previous abortions before the 24th week of pregnancy, whether it is spontaneous or induced
Troisi, 2013	USA	Case-control	22,758	Cancer Surveillance System (CSS) of western Washington	Washington State Cancer Registry (WSCR)	Parity, calendar year of delivery, age at delivery, race, ethnicity
Hajiebrahimi, 2016	Sweden	Cohort	Cases = 8,327 Controls = 8,327	Swedish Cancer Register	Medical Birth Register	Age at latest pregnancy, parity, educational level and calendar time of offspring birth, age at latest pregnancy, parity, educational level, calendar year, gestational age
Swerdlow, 2018	UK	Cohort	83,451	National Health Service Central Registers	National Health Service Central Registers	Age, time since recruitment to cohort, benign breast disease, family history of breast cancer in first-degree relatives, socio-economic score, own birth weight, age at menarche, parity, age at first pregnancy, cumulative duration of breast feeding, current oral contraceptive use before menopause height at age 20, alcohol consumption, age started smoking, pre or post-menopausal BMI and hormones

Overall association between PB and BC

Thirteen studies including a total of 2,845,553 women were included in this meta-analysis. Pooled results suggested that PB could increase the risk of BC (RR = 1.03, 95% CI: 1.00, 1.07; I² = 62.5%, Fig. 2). There was an evidence for publication bias (Egger’s regression intercept: 0.68, 95%CI: 0.01 to 1.35, P = 0.045, Fig. 3). Sensitivity analysis showed that the estimates of the pooled SMD range from 1.02 (95% CI: 0.98 to 1.06) to 1.04 (95% CI: 1.00 to 1.07), suggesting that no one study is substantially influencing the pooled estimate.

Subgroup analysis

PB < 37 compared to > 37 gestational weeks

Three (one cohort and two case-control) studies including a total of 487,835 women evaluated the association between PB and BC. When the studies were combined, there was no difference in the risk of BC between women with a PB at < 37 gestational weeks and who with a PB > 37 gestational weeks (RR = 1.13, 95% CI: 0.86, 1.39; I² = 54.7%, Fig. 2).

PB 32–36 compared to > 37 gestational weeks

Six studies comprising 2,036,812 participants investigated the risk of BC in women with a PB at 32–36 compared to women with a PB at > 37 gestational weeks. The summary RR of BC for the 32–36 compared with > 37 gestational weeks categories was 1.01 (95% CI, 0.98, 1.04) with low heterogeneity (I² = 27.7%, P for heterogeneity = 0.19) (Fig. 2).

PB < 32 compared to > 37 gestational weeks

A total of seven studies (including 2,037,112 participants and 150,902 cases) were included in the meta-analysis. When the studies were combined, there was no difference in the risk of BC between women with a PB at < 32 gestational weeks and those with > 37 gestational weeks (RR = 0.99, 95% CI: 0.81, 1.17; I² = 83.2%, Fig. 2).

PB 37–39 compared to 40–41 gestational weeks

Three studies including 794,762 women assessed the risk of BC. Risk was, significantly increased in women with a PB at > 37 gestational weeks compared to those with 40–41 gestational weeks (RR = 1.03, 95% CI: 1.01, 1.06; I² = 0%, Fig. 2).

PB 32–36 compared to 40–41 gestational weeks

There were three studies with 794,762 participants (two cohort and one case-control) that compared the risk of BC in women with a PB at 32–36 compared to women with 40–41 gestational weeks. The pooled analysis revealed that the PB was not associated with BC risk (RR = 1.04, 95% CI: 0.89, 1.19) using a random-effects model, and significant heterogeneity was observed among individual studies (I² = 67.1%, P for heterogeneity = 0.01).

PB 26–31 compared to 40–41 gestational weeks

Three studies including 794,762 women assessed the risk of BC. A significant increment in the risk of BC (RR = 1.03, 95% CI: 1.01, 1.06) was observed in women with a PB at 26–31 gestational weeks compared to those with 40–41 gestational weeks, with non-significant heterogeneity (I² = 0%, P heterogeneity = 0.42) (Fig. 2).

Uniparous compared to multiparous

Four studies including 2,444,775 participants assessed the risk of BC in uniparous women. The overall summary RRs of the uniparity versus the multiparity category show that parity modify the association between PB and BC. A significant increment in the risk of BC in uniparous women with a PB (RR = 1.05, 95% CI = 1.01, 1.08) was observed, while this relationship in multiparous women was not significant (RR = 1.03, 95% CI = 1.00, 1.07).

BC diagnosis age

All studies were included in the meta-analysis of PB and BC risk by age status. The relationship between PB and BC risk showed a significant increment (RR = 1.12, 95% CI = 1.01, 1.24) in women with > 45 years category but in women with < 45 years category (RR = 0.95, 95% CI = 0.83, 1.07).

Discussion

To assess the relationship of gestational age and BC risk, we carried out a systematic review and meta-analysis in which, out of 3,426 potentially relevant articles, 13 relevant studies were included in the meta-analysis. The main result of this study revealed that there is mild evidence that support the relationship between PB and women BC risk, since the risk in women with PB were on average at a 3% greater risk of BC. And also, this meta-analysis provided some mild evidence of higher BC risk in women with a birth at 26–31 and 37–39 weeks compared to 40–41 weeks. The main findings of our study recognized that the PB increases the risk of BC in women with > 45 years. And also the results showed that PB in primiparous women could lead to an increased risk of BC, while in multiparous women, this relationship was not observed. However, it should be noted that in these analyzes, the effect of other effective variables on the relationship between PB and the risk of BC has not been adjusted, therefore, the interpretation and generalization of the findings must be done with caution.

The present systematic review and meta-analysis suggested an increase in the BC risk (RR = 1.03) in women with a PB 26–31 gestational weeks compared to 40–41 gestational weeks. Similar to our study, Swerdlow et al. [23] have shown in their study that the risk in women with gestational age of 26–31 weeks was 2.4 times compared to 40–41 weeks. Previous studies as well as the results of this study showed that early delivery may increase the risk of BC. Swerdlow et al. [23] have suggested that hormonal stimulation and breast proliferation at the beginning of pregnancy and the lack of enough opportunity for differentiation that occurs at the end of pregnancy are the cause of this relationship. Mammary cells in human and animals differentiate in the third trimester [28–30] and therefore a full term pregnancy is considered as a protective factor for BC [31]. Therefore, term or post-term pregnancy may be expected to increase the degree of differentiation, which will reduce the risk of BC.

Oestrogens are one of the effective factors in BC etiology [32], and increased concentrations of oestrogens during pregnancy may affect the risk of BC in daughters. Babies born before the 28th week of pregnancy have high levels of estrogen after birth and the previous studies have shown that birth before 32 weeks of pregnancy is a major risk factor for BC [24, 33, 34]. Therefore, the relationship between preterm delivery and the risk of BC can be explained by changes observed in levels of these hormones.

In our study, the risk of BC in women with preterm delivery at 26–31 weeks compared with compared with delivery at 41–41 weeks did not have a significant difference in risk of breast cancer. As same as our results, Innes KE and Byers TE in their study [35] concluded that very or extreme PB is related to higher risk of maternal BC risk. In another similar study by Melbye M et al. [25], the results showed a higher risk of BC in women with gestational age less than 32 weeks. Some studies have contradicted our findings. M Kaijser et al. [24] reported that PB are not associated with an increased risk of breast cancer.

Some studies have reported the relationship between the induced abortion and BC risk [36, 37]. Probably a part of the relationship between abortion and BC risk can be attributed to the duration of pregnancy. On the other hand, “Collaborative Group on Hormonal Factors in Breast Cancer” in a meta-analysis of 53 studies suggested that abortion is not associated with increased risk of BC [38]. Therefore, this relationship is still ambiguous and further studies are needed.

The findings of this meta-analysis were in line with this, with the higher BC risk for PB, significantly for post-menopausal BC and primiparous women and also borderline significantly for BC overall. Some studies evaluated the association of PB and BC risk in parous and nulliparous women. Melbye M et al. [25] reported a higher risk of BC in parous women with preterm delivery below 32 weeks compared with women with term delivery. Yongchun Deng et al. [36] in a meta-analysis study revealed that in parous women, induced abortion can increase the BC risk, but it was not significant in the nulliparous women.

Our study documented that the PB increases the risk of BC in women with > 45 years, but not in women with < 45 years. As accord to our results, Melbye M et al. [25] concluded that preterm labor cannot increase the risk of premenopausal breast cancer.

In terms of generalizability of our results, it seems the results are generalizable to various populations because it was a systematic review and meta-analysis and pooled the different results from different countries. It should also be mentioned that there was no significant heterogeneity between primary studies.

There are some limitations in this study. The most important limitation of this study was that the gestational age had different categories in primary studies, making it difficult to extract needed data and analysis, and led to different subgroups analyzes. Another limitation of this study was that there are some potential confounder variables in the relationship between PB and BC risk, which in this study was not possible to adjust their effect.

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