Skip to main content

Association between pre-gravid body mass index and clinical outcomes in in vitro fertilization: a multicentered retrospective cohort study

Abstract

Background

With the increasing incidence of obesity and the childbearing-age delay among women, a debate over obesity’s impacts on pregnancy and neonatal outcomes becomes hot. The potential negative effects of obesity and aging on fertility lead to an idea, whether an obese female pursuing IVF treatment can benefit from an ideal BMI achieved over a long-time weight loss process at the cost of aging? We aimed to assess the association between body mass index (BMI) and clinical or neonatal outcomes in patients undergoing in vitro fertilization (IVF) treatment, for answering whether it is necessary to lose weight first for obese patients, particularly those at advanced age.

Methods

A retrospective cohort study was performed using multicentered data from China. The women were stratified into 5 groups in terms of pre-gravid BMI (kg/m2) with the WHO obesity standard (group 1: BMI < 18.5; group 2: 18.5 ≤ BMI < 23.0; group 3: 23.0 ≤ BMI < 25.0; group 4: 25.0 ≤ BMI < 30.0; group 5: BMI ≥ 30.0). The primary outcome was cumulative live birth rate (CLBR), and other clinical and neonatal outcomes were weighed as secondary outcomes. Multivariate logistic regression analyses were carried to evaluate the association between BMI and the CLBR, or between BMI and some neonatal outcomes. Furthermore, we implemented a machine-learning algorithm to predict the CLBR based on age and BMI.

Results

A total of 115,287 women who underwent first IVF cycles with autologous oocytes from January 2013 to December 2017 were included in our study. The difference in the CLBR among the five groups was statistically significant (P < 0.001). The multivariate logistic regression analysis showed that BMI had no significant impact on the CLBR, while women’s age associated with the CLBR negatively. Further, the calculation of the CLBR in different age stratifications among the five groups revealed that the CLBR lowered with age increasing, quantitatively, it decreased by approximately 2% for each one-year increment after 35 years old, while little difference observed in the CLBR corresponding to the five groups at the same age stratification. The machine-learning algorithm derived model showed that BMI’s effect on the CLBR in each age stratification was negligible, but age’s impact on the CLBR was overwhelming. The multivariate logistic regression analysis showed that BMI did not affect preterm birth, low birth weight infant, small for gestational age (SGA) and large for gestational age (LGA), while BMI was an independent risk factor for fetal macrosomia, which was positively associated with BMI.

Conclusions

Maternal pre-gravid BMI had no association with the CLBR and neonatal outcomes, except for fetal macrosomia. While the CLBR was lowered with age increasing. For the IVF-pursuing women with obesity plus advanced age, rather than losing weight first, the sooner the treatment starts, the better. A multicentered prospective study with a large size of samples is needed to confirm this conclusion in the future.

Peer Review reports

Background

With its worldwide prevalence in past decades, obesity, associated with metabolic, cardiovascular complications, menstrual disorders, obstetric complications and women’s infertility [1], has also become a serious public health problem in China. With China’s rapid economic progress, obesity in Chinese adults has also been soaring in the past 30 years, and, the percentage of people with BMI ≥ 24 kg/m² might reach an unprecedented 65.3% by 2030 with the absolute number approaching 800 million [2]. Simultaneously, the controversy about the impacts of obesity or overweight on pregnancy and neonatal outcomes is becoming hot in the background of the delay of first-born among modern women, which demarcates international professionals into two lines of yea or nay, the former indicating obesity’s disadvantage over clinical pregnancy rate and live birth rate [3, 4] while the latter presenting opposite data [5,6,7]. Similarly, no consistent conclusion can be made about the effects of obesity or overweight on the neonate or fetus [8,9,10,11].

The childbearing-age delay plus the fact that childbearing-age women gain weight with aging perplexes the clinical situation when these women refer to an IVF treatment, because the advanced age can cause adverse effects on pregnancy outcomes, and the role of obesity in women’s reproduction remains an open question. The potential negative effects of obesity and aging on fertility lead to an idea, whether an obese female pursuing IVF treatment can benefit from an ideal BMI achieved over a long-time weight loss process at the cost of aging? Recently, several prospective randomized controlled studies showed that lifestyle interventions and guidance for weight loss did not improve fertility outcomes but might increase the financial burden on patients and delay their pregnancy [12,13,14]. The present study aimed to assess BMI’s association with clinical and neonatal outcomes through analyses on multicentered data. It is worth noting that our BMI groups is based on the WHO obesity standard. Furthermore, based on the analyses, we tried to optimize the ART protocol for obese patients, particularly those at advanced age (≥ 35 years).

Methods

Study design and population

This retrospective cohort study used data from five China’s large academic reproductive medicine centers, including The Sixth Affiliated Hospital, Sun Yat-sen University, Tongji Hospital of Huazhong University of Science and Technology, Northwest Women’s and Children’s Hospital, First Affiliated Hospital of Nanjing Medical University, and Henan Provincial People’s Hospital.

Ethical considerations

The original data collection and analysis for this study received approval from the institutional review board at each participating center, including The Sixth Affiliated Hospital, Sun Yat-sen University (2020ZSLYEC-295), Tongji Hospital of Huazhong University of Science and Technology (TJ-IRB20210320), Northwest Women’s and Children’s Hospital (2019013), First Affiliated Hospital of Nanjing Medical University (2020-SR-046), and Henan Provincial People’s Hospital (SYSZ-LL-2019110401). The five review boards waived the requirement for written informed consent from the patients because of the retrospective nature of the study.

Data collection and extraction

The patients’ demographic, clinical, and laboratory characteristics were recorded in a digitalized, standardized recording system, particularly, their heights and weights were measured right before they started an IVF cycle. The clinical outcomes data, such as pregnancy, ectopic pregnancy, and early pregnancy loss, were collected upon the patient on-site visit, while the clinical outcomes after the 12 weeks of pregnancy as well as neonatal outcomes collected by site nurses via telephone follow-up. Throughout the present study, all the primary data from the five centers were extracted, cleaned, and standardized by a specialized data company to veil the patients’ privacy, including names and ID card numbers.

The women who underwent first IVF/ICSI cycles with autologous oocytes from January 2013 to December 2017 were subjected, and the data from fresh and all subsequent frozen-thawed embryo transfer (TET) cycles were collected. Participants were followed up until a live birth was achieved or all embryos from the same retrieval cycle had been thawed and transferred. The exclusion criteria were: (1) one of the couples had chromosomal abnormality or a genetic mutation that causes infertility; (2) sperms were of the donated or obtained surgically; (3) oocytes were of the donated or in vitro maturation; (4) multiple fertilization methods coincided in the same cycle; (5) couples underwent preimplantation genetic testing. Finally, a total of 115,287 women were included, and stratified into five groups in terms of their BMIs (kg/m2) with the WHO obesity standard (group 1: BMI < 18.5; group 2: 18.5 ≤ BMI < 23.0; group 3: 23.0 ≤ BMI < 25.0; group 4: 25.0 ≤ BMI < 30.0; group 5: BMI ≥ 30.0).

Outcomes

The primary outcome was cumulative live birth rate (CLBR), which is defined as the proportion of patients who delivered at least one live birth in the fresh embryo transfer (FET) or subsequent TET cycles. The secondary outcomes included the rates of clinical pregnancy, miscarriage, live birth, twins, and neonatal outcomes including preterm birth, small for gestational age (SGA), large for gestational age (LGA), low birth weight infant and fetal macrosomia. Clinical pregnancy was defined as the presence of a gestational sac by ultrasound. Miscarriage was defined as the pregnancy loss up to the 28 weeks of gestation. Live birth was defined as the birth of at least one live infant (at least 28 weeks of gestation). Based on the Chinese birthweight reference [15, 16], SGA and LGA were defined as birthweight lower than the 10th percentile and higher than the 90th percentile of the referential birthweight, respectively.

Statistical analyses

Continuous data are expressed as “mean (SD)” for parametric data or “median (IQR)” for non-parametric data and were compared using Student’s t test or Mann-Whitney U test as appropriate. Categorical variables are presented as numbers with percentages and were compared using Chi-square test or Fisher’s exact test. Logistic regression analyses were performed for clinical and neonatal outcomes, and multivariate logistic regression analyses performed to evaluate the association between BMI and the CLBR, or between BMI and some neonatal outcomes, with adjustments being made for co-variables and potential confounding factors. A machine-learning algorithm was implemented to predict the CLBR based on age and BMI. A sensitivity analyses was performed to check the robustness of our results. The results are presented as the odds ratio (OR) and 95% confidence interval (CI). Statistical analysis was done using R software (https://www.r-project.org/) and Python software (https://www.python.org/). All calculated P values were two sided, and a P value lower than 0.05 was considered statistically significant.

Results

Patients’ baseline and cycle characteristics

Figure 1 represents the flow chart through which 115,287 women were recruited for the study, and Table 1 lists patients’ baseline characteristics. Specifically, the baseline characteristics featured two tendencies: (1) BMI’s increase accompanied the upward proportion of PCOS patients significantly; (2) with the increasing of BMI, the proportion of young women shrank while the advanced women (> 35 years) swelled except for group 5 (BMI ≥ 30), and this trend persisted even when PCOS patients were excluded (Table SI). Cycle characteristics are listed in Table 2, showing that the differences in all the parameters were of significance among the five groups (P < 0.001) except the number of transferred embryos in the TET cycle.

Table 1 Patients’ baseline characteristics in the five groups
Table 2 Patients’ cycle characteristics in the five groups
Fig. 1
figure 1

Flow chart of patient recruitment

IVF = in vitro fertilization; ICSI = intracytoplasmic sperm injection; COS = controlled ovarian stimulation; IVM = in vitro maturation; PGT = preimplantation genetic testing; BMI = body mass index (kg/m2)

Clinical outcomes

We calculated and compared the clinical outcomes among the five groups (Table 3): (1) in FET cycles: the difference in the miscarriage rate among the five groups was statistically significant (P < 0.001), but no statistical difference in the clinical pregnancy rate, live birth rate, and the proportions of singleton and twin births; (2) in complete cycles: the differences in the CLBR (P < 0.001) and in the ratio of twins (P = 0.0434) among the 5 groups were statistically significant.

Table 3 Clinical outcomes in the five groups

Logistic analysis was applied to evaluate the effect of BMI on the CLBR (Table 4). After a univariate logistic regression analysis on the variables for initial screening (Table 5), the significant variables (P < 0.1) were included in the next step, a multivariate logistic regression analysis, which was performed under the consideration that the variables described in Table 5 can act as potential confounding factors. It was found that BMI had no statistically significant association with the CLBR, while women’s age associated with the CLBR negatively (Table 4). When compared with the women younger than 35 years, the CLBR in the women aged from 35 to 37 years old decreased by about 46% (OR 0.5428, 95% CI 0.4459–0.6615, P < 0.001), aged from 38 to 40 decreased by about 68% (OR 0.3175, 95% CI 0.2426–0.4145, P < 0.001), aged from 41 to 42 decreased by about 89% (OR 0.1084, 95% CI 0.0595–0.1864, P < 0.001), and aged ≥ 43 years old decreased by about 96% (OR 0.0386, 95% CI 0.0093–0.1082, P < 0.001).

Table 4 Multivariate logistics regression for clinical and neonatal outcome
Table 5 Univariate logistics regression for CLBR

Furthermore, Fig. 2 refines the spectrum of the above results. In Fig. 2A, looking vertically, the CLBR lowers with the increasing of age, while, looking horizontally, little difference observed in the CLBR corresponding to the 5 groups at the same age. In Fig. 2B, evidently, the CLBR remains on a downswing with the increasing of age, quantitatively, after 35 years old, the CLBR decreased by approximately 2% for each one-year increment in age. Figure 2A and B suggest that it is age that negatively associates with the CLBR rather than BMI. The machine-learning algorithm derived predictive model showed, from different perspectives, that BMI’s effect on the CLBR in each age stratification was negligible, but age’s impact on the CLBR was overwhelming in different BMI levels (Fig. 2C and D). In our study, sensitivity analysis was conducted, and each of the five centers performed multivariate logistic regression analyses with cumulative live birth rate as the outcome. The results consistently aligned with our research findings (Table SIII).

Fig. 2
figure 2

Impacts of age and BMI on CLBR. A: the CLBR (%) based on maternal age and BMI among 115,287 women. B: Line Chart of CLBR at different ages. C: Logistics regression model of BMI and the CLBR in different age groups. The model diagram is shaped like a straight line. In different age groups, the CLBR decreased very little as BMI increased. D: Logistics regression model of age and the CLBR in different BMI groups. The model diagram is shaped like adverse “S”. In different BMI groups, from the age of 20, CLBR slides down with age

BMI = body mass index (kg/m2); CLBR = cumulative live birth rate

Neonatal outcomes

Given that twin pregnancy is an influence factor in neonatal outcomes, we subdivided neonates into two: the singleton and twin groups. In the singleton group, the differences were statistically significant (P < 0.001) in the occurrences of preterm birth, SGA, LGA, low birth weight infant and fetal macrosomia among the 5 groups. In the twin group, the increasing of BMI was associated with a significantly lower rate of SGA (P < 0.001) (Fig. 3). Similarly, we performed univariate logistic regression analyses of neonatal outcomes including preterm birth, low birth weight infant, SGA, LGA and fetal macrosomia, then significant variables (P < 0.1) were included in the subsequent multivariate logistic regression analysis (Table 4), which showed that BMI did not affect preterm birth, low birth weight infant, SGA and LGA, and BMI was an independent risk factor for fetal macrosomia, which was positively associated with BMI (Table 4).

Fig. 3
figure 3

Comparison of neonatal outcomes among the five groups. A: Neonatal outcomes of singletons. B: Neonatal outcomes of twins. Blue bars = group 1, red bars = group 2, green bars = group 3, purple bars = group 4, orange bars = group 5. NS represents no significance; * P < 0.05; *** P < 0.001

Discussion

Our study included 5-centered large sample data geographically covering large areas of the south, east, north, west, and central of China, avoiding the bias from geographic deviation and applying multivariate logistic regression to adjust for confounding factors. One of the findings is that the female BMI had no association with the CLBR, but age is an independent risk factor for clinical outcomes, which is consistent with the previous literature indicating that advanced age decreases the likelihood of pregnancy [17, 18]. Noteworthily, upon the finding of no association between BMI and the CLBR, our further analyses also revealed that the CLBR decreased with the increasing of age in the five groups (Fig. 2), suggesting that for women with a high BMI, especially those at advanced age, IVF treatment should be rendered as soon as possible, instead of losing weight first.

With ART’s rapid advancement and widespread practice, mounting women are seeking IVF treatment, and the composition of patients appears to be of diversity, which make the management of the obese women who refer to IVF treatment not only a challenge but also a controversy. Several studies investigated the impact of obesity or overweight on pregnancy outcomes, some showing that obesity negatively impacted live birth rate [19, 20], while some concluding that BMI did not influence live birth rate [21, 22]. Anyway, all these studies, whether in favor of obesity’s impacts or not, only analyzed clinical outcomes after FET. Considering that TET cycles’ number has greatly increased, it is more comprehensive to assess the CLBR when evaluating obesity’s effects because the CLBR represents the clinical outcome in the entire cycle, including both FET and subsequent TET cycles. Indeed, in 2019 and 2020, two studies investigated the association between BMI and the CLBR, one showing that the CLBR was negatively impacted by increased BMI [23], and another describing an “inverted U shape” association between the two [24]. Nevertheless, distinct from the two studies, the present study found no association between BMI and the CLBR. Such discrepancy probably resulted from the following reasons. (1) Different BMI-based grouping criteria were applied. (2) Both studies only involved a single-centered small size of samples (14,782 and 15,972, respectively), but we used a multicentered large size of samples. (3) Both studies did not include the number of transferrable embryos, a very pertinent confounder, which has a significant effect on the CLBR [25]. Concretely, Xue et al. ‘s study [24] reported a lower number of retrieved oocytes in the obese group, which is consistent with our results, and it is a reasonable deduction that the true reason for the lower CLBR in their obese group was the lower number of transferrable embryos resulted from fewer retrieved oocytes, rather than BMI’s impact. In addition, they included smoking as a confounding factor, but with merely 15 (smoking) out of 14215 women, which may diverge the analysis to a biased result. The study by Zhao et al. [23] included too few confounding factors without reporting the numbers of retrieved oocytes and transferrable embryos, which, to some extent, compromises its reference value.

In addition, many studies have investigated the impact of maternal BMI on neonatal outcomes, and their results are, however, not consistent [8, 26, 27]. Therefore, we also observed obesity’s effect on neonatal outcomes in the obese women receiving IVF treatment, and the results showed that the rates of preterm birth, LGA, low birth weight infant, and fetal macrosomia in singletons increased significantly with BMI’s increasing (Fig. 2A). However, further multivariate logistics analysis by adjusting for confounding factors revealed that BMI had no association with neonatal outcomes except for fetal macrosomia (Table 4). Different from our results, a literature reported that, in FET cycles, pre-pregnancy BMI ≥ 25 kg/m2 was associated with a higher incidence of LGA in IVF/ICSI singletons [28], and Yang et al. reported that pre-pregnancy overweight and obesity were associated with significant increases in preterm birth, macrosomia, and LGA in TET cycles [8]. This inconsistence can be attributed to (1) the two studies were of a single-centered data research while we were a multicentered one with a large size of samples, (2) the two studies collected data of either FET or TET cycles while ours included both with more comprehensive information, (3) BMI grouping methods were different, with the WHO criteria being used by us while the couple’s BMI or Asian criteria being respectively used by them.

Obesity, an emerging global health issue affecting 603.7 million people over the past four decades [29], can impose adverse effects on reproduction, including ovulatory function, natural fecundity, and obstetric outcomes [30,31,32]. Parallel to the weight-increasing population, the delay of marriage and childbirth is also becoming a tendency these years [33]. Because age is a well-accepted factor relevant to pregnancy and an individual becomes fat naturally with aging, it seems that obesity and the delay of marriage and childbirth are together transforming the IVF posture, which necessitates revisiting the impact of obesity on IVF’s outcomes.

Having reviewed the pioneering research above-mentioned and bearing in mind that age is a well-accepted critical factor affecting female fertility, for finding a definitive ART treatment strategy in the obese patients at advanced age, we designed this study, which, to our knowledge, is among the only two studies to investigate the effects of BMI and age on the CLBR [34]. In contrast to our finding, reducing body weight is often recommended so far for obese women prior to infertility treatment although its effectiveness and rationality remain open to clinicians, because many guess that weight loss can bring benefits for patients based on obesity’s general adverse effects. Nevertheless, the result of a randomized controlled research argued against this doing [35], and some late clinical studies were swinging from the previous assumption that weight loss interventions improve reproductive outcomes [12, 36]. Collectively, we are here proposing readers to reconsider the decision of weight loss interventions for obese patients. Future research may determine the direct impact of weight loss in advanced women on the CLBR; however, a large and meaningful study would be challenging given the large sample size required and its prospective nature, which many subjects would not voluntarily defer long-term.

Strengths of this study include large sample size that captures data from 5 large IVF centers covering large areas of the south, east, north, west, and central of China, increasing generalizability. Our main outcome was cumulative live birth rate, which is arguably the most important outcome to patients, and was obtained using linked fresh plus frozen cycles. This study evaluated the joint impact of age and BMI. Meanwhile, this study investigated the impact of maternal BMI on neonatal outcomes.

Limitations and countermeasures are as follows. (1) This is a retrospective study in nature, and to guarantee a reliable conclusion, a large size of samples from five centers was involved, and reasonable statistical methods were applied for controlling confounding factors. (2) BMI’s variance during IVF treatment and pregnancy, which was not involved in the study, may also be a potential confounding impact on the live birth. (3) The proportion of PCOS patients was different among the five groups, and apparently there is a positive correlation between PCOS and obesity, thus introducing bias. Therefore, a multivariate regression analysis was also performed in the PCOS population, and the result showed that BMI had no effect on the CLBR in PCOS patients and the age in PCOS population was still an independent risk factor for the CLBR (Table SII). (4) BMI was the only index to define obesity without testing patients’ body fat, insulin and other indicators of lipid and glucose metabolism. (5) The outcome indicators for newborns were relatively simple, and there were no data related to maternal pregnancy complications and neonatal diseases, therefore, considering the association between obesity and other systemic diseases, maternal and neonatal safety should be investigated thoroughly in future studies.

Conclusions

In contrast to age, an independent risk factor for the CLBR, the BMI of adult females had no association with the CLBR and neonatal outcomes, except for fetal macrosomia. For the IVF-pursuing women with obesity plus advanced age, rather than losing weight first, the sooner the treatment starts, the better. A multicentered prospective study with a large size of samples is needed to confirm this conclusion in the future.

Data availability

For study transparency and reproducibility, research data (i.e., de-identified participant data and other additional documents (i.e., statistical analysis plan) will be made available at publication upon request to the corresponding author. Interested researchers should send data access request to huangr57@mail.sysu.edu.cn. The corresponding author will review the requests with other authors for consideration. Data sharing will only be available for academic research, instead of other objectives (e.g., commercial use). A data use agreement will be required before the release of participant data and institutional review board approval as appropriate.

Abbreviations

ART:

Assisted reproductive technology

BMI:

Body mass index

CLBR:

Cumulative live birth rate

FET:

Fresh embryo transfer

ICSI:

Intracytoplasmic sperm injection

IVF:

In vitro fertilization

LGA:

Large for gestational age

SGA:

Small for gestational age

TET:

Frozen-thawed embryo transfer

References

  1. Norman RJ, Noakes M, Wu R, Davies MJ, Moran L, Wang JX. Improving reproductive performance in overweight/obese women with effective weight management. Hum Reprod Update. 2004;10(3):267–80.

    Article  PubMed  Google Scholar 

  2. Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol. 2021;9(7):446–61.

    Article  PubMed  Google Scholar 

  3. Rittenberg V, Seshadri S, Sunkara SK, Sobaleva S, Oteng-Ntim E, El-Toukhy T. Effect of body mass index on IVF treatment outcome: an updated systematic review and meta-analysis. Reprod Biomed Online. 2011;23(4):421–39.

    Article  PubMed  Google Scholar 

  4. Sermondade N, Huberlant S, Bourhis-Lefebvre V, Arbo E, Gallot V, Colombani M, et al. Female obesity is negatively associated with live birth rate following IVF: a systematic review and meta-analysis. Hum Reprod Update. 2019;25(4):439–51.

    Article  CAS  PubMed  Google Scholar 

  5. Dechaud H, Anahory T, Reyftmann L, Loup V, Hamamah S, Hedon B. Obesity does not adversely affect results in patients who are undergoing in vitro fertilization and embryo transfer. Eur J Obstet Gynecol Reproductive Biology. 2006;127(1):88–93.

    Article  Google Scholar 

  6. Insogna IG, Lee MS, Reimers RM, Toth TL. Neutral effect of body mass index on implantation rate after frozen-thawed blastocyst transfer. Fertil Steril. 2017;108(5):770–e61.

    Article  PubMed  Google Scholar 

  7. Prost E, Reignier A, Leperlier F, Caillet P, Barriere P, Freour T, et al. Female obesity does not impact live birth rate after frozen-thawed blastocyst transfer. Hum Reprod. 2020;35(4):859–65.

    Article  CAS  PubMed  Google Scholar 

  8. Yang X, Zheng B, Wang Y. Effect of pre-pregnancy body mass index on neonatal outcomes in women undergoing autologous frozen-thawed embryo transfer. Fertil Steril. 2021;116(4):1010–9.

    Article  CAS  PubMed  Google Scholar 

  9. Catalano PM, Shankar K. Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child. BMJ. 2017;356:j1.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PLoS ONE. 2013;8(4):e61627.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. McDonald SD, Han Z, Mulla S, Beyene J, Knowledge Synthesis G. Overweight and obesity in mothers and risk of preterm birth and low birth weight infants: systematic review and meta-analyses. BMJ. 2010;341:c3428.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mutsaerts MAQ, van Oers AM, Groen H, Burggraaff JM, Kuchenbecker WKH, Perquin DAM, et al. Randomized trial of a lifestyle program in obese infertile women. N Engl J Med. 2016;374(20):1942–53.

    Article  CAS  PubMed  Google Scholar 

  13. Legro RS, Hansen KR, Diamond MP, Steiner AZ, Coutifaris C, Cedars MI, et al. Effects of preconception lifestyle intervention in infertile women with obesity: the FIT-PLESE randomized controlled trial. PLoS Med. 2022;19(1):e1003883.

    Article  PubMed  PubMed Central  Google Scholar 

  14. van Oers AM, Mutsaerts MAQ, Burggraaff JM, Kuchenbecker WKH, Perquin DAM, Koks CAM, et al. Cost-effectiveness analysis of lifestyle intervention in obese infertile women. Hum Reprod. 2017;32(7):1418–26.

    Article  PubMed  Google Scholar 

  15. Dai L, Deng C, Li Y, Zhu J, Mu Y, Deng Y, et al. Birth weight reference percentiles for Chinese. PLoS ONE. 2014;9(8):e104779.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Dai L, Deng C, Li Y, Yi L, Li X, Mu Y, et al. Population-based birth weight reference percentiles for Chinese twins. Ann Med. 2017;49(6):470–8.

    Article  PubMed  Google Scholar 

  17. Lean SC, Derricott H, Jones RL, Heazell AEP. Advanced maternal age and adverse pregnancy outcomes: a systematic review and meta-analysis. PLoS ONE. 2017;12(10):e0186287.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Koning AM, Mutsaerts MA, Kuchenbecker WK, Broekmans FJ, Land JA, Mol BW, et al. Complications and outcome of assisted reproduction technologies in overweight and obese women. Hum Reprod. 2012;27(2):457–67.

    Article  CAS  PubMed  Google Scholar 

  19. Supramaniam PR, Mittal M, McVeigh E, Lim LN. The correlation between raised body mass index and assisted reproductive treatment outcomes: a systematic review and meta-analysis of the evidence. Reprod Health. 2018;15(1):34.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kawwass JF, Kulkarni AD, Hipp HS, Crawford S, Kissin DM, Jamieson DJ. Extremities of body mass index and their association with pregnancy outcomes in women undergoing in vitro fertilization in the United States. Fertil Steril. 2016;106(7):1742–50.

    Article  PubMed  PubMed Central  Google Scholar 

  21. MacKenna A, Schwarze JE, Crosby JA, Zegers-Hochschild F. Outcome of assisted reproductive technology in overweight and obese women. JBRA Assist Reprod. 2017;21(2):79–83.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chen H, Li J, Cai S, Zeng S, Yin C, Kuang W, et al. Impact of body mass index (BMI) on the success rate of fresh embryo transfer in women undergoing first in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment. Int J Obes (Lond). 2022;46(1):202–10.

    Article  PubMed  Google Scholar 

  23. Zhao Z, Jiang X, Li J, Zhang M, Liu J, Dai S, et al. The combined impact of female and male body mass index on cumulative pregnancy outcomes after the first ovarian stimulation. Front Endocrinol (Lausanne). 2021;12:735783.

    Article  PubMed  Google Scholar 

  24. Xue X, Shi W, Zhou H, Tian L, Zhao Z, Zhou D, et al. Cumulative live birth rates according to maternal body mass index after first ovarian stimulation for in vitro fertilization: a single center analysis of 14,782 patients. Front Endocrinol (Lausanne). 2020;11:149.

    Article  PubMed  Google Scholar 

  25. Yang R, Niu ZR, Chen LX, Liu P, Li R, Qiao J. Analysis of related factors affecting cumulative live birth rates of the first ovarian hyperstimulation in vitro fertilization or intracytoplasmic sperm injection cycle: a population-based study from 17,978 women in China. Chin Med J (Engl). 2021;134(12):1405–15.

    Article  PubMed  Google Scholar 

  26. Sebire NJ, Jolly M, Harris JP, Wadsworth J, Joffe M, Beard RW, et al. Maternal obesity and pregnancy outcome: a study of 287,213 pregnancies in London. Int J Obes Relat Metab Disord. 2001;25(8):1175–82.

    Article  CAS  PubMed  Google Scholar 

  27. Takagi K, Iwama N, Metoki H, Uchikura Y, Matsubara Y, Matsubara K, et al. Paternal height has an impact on birth weight of their offspring in a Japanese population: the Japan environment and children’s study. J Dev Orig Health Dis. 2019;10(5):542–54.

    Article  CAS  PubMed  Google Scholar 

  28. Chen R, Chen L, Liu Y, Wang F, Wang S, Huang Y, et al. Association of parental prepregnancy BMI with neonatal outcomes and birth defect in fresh embryo transfer cycles: a retrospective cohort study. BMC Pregnancy Childbirth. 2021;21(1):793.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Collaborators GBDO, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27.

    Article  Google Scholar 

  30. Metwally M, Ong KJ, Ledger WL, Li TC. Does high body mass index increase the risk of miscarriage after spontaneous and assisted conception? A meta-analysis of the evidence. Fertil Steril. 2008;90(3):714–26.

    Article  PubMed  Google Scholar 

  31. Kalliala I, Markozannes G, Gunter MJ, Paraskevaidis E, Gabra H, Mitra A, et al. Obesity and gynaecological and obstetric conditions: umbrella review of the literature. BMJ. 2017;359:j4511.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Poston L, Caleyachetty R, Cnattingius S, Corvalan C, Uauy R, Herring S, et al. Preconceptional and maternal obesity: epidemiology and health consequences. Lancet Diabetes Endocrinol. 2016;4(12):1025–36.

    Article  PubMed  Google Scholar 

  33. Yang S, Jiang Q, Sanchez-Barricarte JJ. China’s fertility change: an analysis with multiple measures. Popul Health Metr. 2022;20(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Goldman RH, Farland LV, Thomas AM, Zera CA, Ginsburg ES. The combined impact of maternal age and body mass index on cumulative live birth following in vitro fertilization. Am J Obstet Gynecol. 2019;221(6):617.e1-e13.

    Article  Google Scholar 

  35. Einarsson S, Bergh C, Friberg B, Pinborg A, Klajnbard A, Karlstrom PO, et al. Weight reduction intervention for obese infertile women prior to IVF: a randomized controlled trial. Hum Reprod. 2017;32(8):1621–30.

    Article  PubMed  Google Scholar 

  36. Legro RS, Dodson WC, Kris-Etherton PM, Kunselman AR, Stetter CM, Williams NI, et al. Randomized controlled trial of preconception interventions in infertile women with polycystic ovary syndrome. J Clin Endocrinol Metab. 2015;100(11):4048–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all participants in this study, all research staff in study sites. We also thank Dr. Shen Chen for his writing assistance.

Funding

This study was supported by the National Natural Science Foundation of China (grant numbers 82171604, 81971759 and 82271651), National Key R&D Program of China (grant number 2016YFC1000302), Natural Science Foundation of Guangdong Province (grant number 2019A1515011764), Guangzhou Science and Technology Program Key R&D Program (grant number 202206010089) and Excellent Talents Training Project of The Sixth Affiliated Hospital of Sun Yat-sen University (grant number R20210217202601970).

Author information

Authors and Affiliations

Authors

Contributions

RH and XYL designed the study and developed the conceptual ideas. RH, LJ, JZS, YDM, and CLZ enrolled participants and collected the data. XPL, PYC, MW and WEZ analyzed the data. XPL, PYC and RH interpreted the results. XPL drafted the manuscript. RH had a primary responsibility for final content.

Corresponding author

Correspondence to Rui Huang.

Ethics declarations

Ethics approval and consent to participate

The original data collection and analysis for this study received approval from the institutional review board at each participating center, including The Sixth Affiliated Hospital, Sun Yat-sen University (2020ZSLYEC-295), Tongji Hospital of Huazhong University of Science and Technology (TJ-IRB20210320), Northwest Women’s and Children’s Hospital (2019013), First Affiliated Hospital of Nanjing Medical University (2020-SR-046), and Henan Provincial People’s Hospital (SYSZ-LL-2019110401). The five review boards waived the requirement for written informed consent from the patients because of the retrospective nature of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Chen, P., Wang, M. et al. Association between pre-gravid body mass index and clinical outcomes in in vitro fertilization: a multicentered retrospective cohort study. BMC Pregnancy Childbirth 24, 469 (2024). https://doi.org/10.1186/s12884-024-06661-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12884-024-06661-2

Keywords