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. 2022 Mar 16;12:4528. doi: 10.1038/s41598-022-08707-x

Association of adverse birth outcomes with in vitro fertilization after controlling infertility factors based on a singleton live birth cohort

Huiting Yu 1,2,#, Zhou Liang 3,#, Renzhi Cai 1, Shan Jin 1, Tian Xia 1,✉, Chunfang Wang 1,✉, Yanping Kuang 3,✉
PMCID: PMC8927480  PMID: 35296798

Abstract

Infants conceived with in vitro fertilization (IVF) are exposed to underlying infertility and the IVF process. High risks of adverse birth outcomes (ABOs) were observed among these infants, including preterm birth, low birth weight, macrosomia, being large/small for gestational age (LGA/SGA). It is unclear whether the specific etiology of the rise of ABOs among IVF infants is IVF technology itself or underlying infertility. A total of 9,480 singletons conceived with IVF and 1,952,419 singletons from the general population were obtained in this study. Multivariable logistic regression model was used to assess variations in risk of ABOs according to causes of infertility. Poisson distributions were applied to calculate standardized risk ratios of IVF infants vs. general population after controlling the causes of infertility. Higher risk of preterm birth and low birth weight were observed among parents with polycystic ovary syndrome, endometriosis, uterine and semen abnormalities. Compared to the general population, after excluding the influence of infertility causes, singletons conceived with IVF were at higher risk of macrosomia (SRR = 1.28, 95% CI 1.14–1.44) and LGA (SRR = 1.25, 95% CI 1.15–1.35). The higher risk of ABOs in IVF was driven by both IVF treatments and infertility, which is important for improving IVF treatments and the managing pregnancies and child development.

Subject terms: Epidemiology, Reproductive disorders, Intrauterine growth, Preterm birth

Introduction

As lifestyles and living environments continue to evolve, infertility is increasing globally, currently affecting more than one-sixth of couples worldwide1–4. Assisted reproductive technology (ART), especially in vitro fertilization (IVF), is widely used for infertility treatment worldwide. Infants conceived by ART have been reported to account for 4–10% of live births in developed countries5,6.

Studies have shown that IVF is associated with an increased risk of adverse birth outcomes (ABOs), such as 1.2–2 times the risk of preterm birth (PTB) and low birth weight (LBW)7, 1.2 times of small for gestational age (SGA)8, and 1.5 times the risk of congenital abnormality9. Infants conceived with IVF are affected by underlying infertility factors in addition to the IVF process. However, it remains unclear whether the association of IVF and ABOs originates from the IVF process or the infertility factors. A systematic review and meta-analysis reported that women with endometriosis had a higher risk of PTB and a similar SGA risk after IVF compared to women without endometriosis10. A cohort study of singletons found a higher risk of PTB and large for gestational age (LGA) after IVF in women with polycystic ovary syndrome (PCOS)11. However, other studies reported that women with unexplained infertility were not at a higher risk of ABOs following IVF12. Infants of subfertile women who conceived naturally had a higher risk of ABOs than infants of fertile women13,14. Taking these studies into consideration, it remains unknown whether IVF increases the risk of ABOs when controlling for the influence of the underlying causes of infertility.

The purpose of this study was to further investigate whether the specific cause of infertility affects the birth outcomes of IVF. In addition, we conducted a large population-based cohort study to assess whether IVF increases the risk of ABOs compared to the general population or whether the increased risks are associated with infertility.

Materials and methods

This population-based retrospective cohort study was approved by the institutional review board of the Shanghai Municipal Centre for Disease Control and Prevention (SCDC), and all methods were performed in accordance with the relevant guidelines and regulations. Following the principles of ethical review in China, it is not necessary to reacquire informed consent from participants for retrospective studies based on population registry information. There, the requirement to obtain written informed consent was waived.

Assisted reproductive technology data

Treatment information and birth outcomes of infants conceived with ART were obtained from the Assisted Reproduction Centre of the Ninth People's Hospital affiliated with Shanghai Jiao Tong University of Medicine, Shanghai, China. The centre provided data for infertility treatments performed from January 2008 through December 2017. Out of 25,698 patients (20–60 years old) who visited the centre for IVF treatment, 12,772 (49.70%) delivered one or more liveborn infants (Fig. 1). After excluding 6793 (41.74%) multiple-birth deliveries, the final sample included 9,480 singleton infants conceived with IVF (694 fresh and 8786 frozen).

Figure 1.

Figure 1

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Flow chart of participants included in the study.

As per the technical standard for Human assisted reproduction under the Ministry of Health of China15–17, details of baseline infertility investigations, ART treatments, and resultant births through ART were recorded for each participant. In addition, data on the treatment period, maternal age, pregestational body mass index (BMI), infertility duration, cause of infertility, fresh or frozen embryo transfer, and live birth occurrence were obtained for this study.

Birth registry data

Birth information from the whole population came from the birth registry system of SCDC, which was established in 2003 and provides all hospitals with authorized delivery services in Shanghai. To ensure comparability with ART data, 2,065,977 live births were collected from the birth registry system during 2008–2017 (Fig. 1). Due to the mother’s age (< 20 or > 60) or insufficient data (no birth weight or gestational age), 61,058 (2.96%) live births were excluded. After excluding 52,500(2.62%) multiple-birth deliveries, 1,952,419 singleton births were set up as the reference group for analysis. The birth information includes the date of birth, infant sex, birth weight, gestational age at birth, embryo number, mode of delivery, gravidity, parity, and socio-demographic characteristics of the parents.

Definitions

To compare the gestational age with that of the general population, the gestational age for an IVF infant was calculated as the interval from the date of embryo transfer to the date of birth plus 14 days and the duration of in vitro embryo culture18. The main ABOs concerned in this study included PTB, LBW, macrosomia, LGA, and SGA. According to the World Health Organization, PTB is defined as delivery before 37 completed weeks of gestation (or 259 days). LBW is defined as a birth weight of less than 2500 g and macrosomia is defined as a birth weight of over 4000 g. According to the China national population-based sex-specific reference curve for normal fetal growth, SGA and LGA are defined as having a birth weight < 10th or > 90th percentile for gestational age, respectively19. In the general population, 20–29 years old is the best childbearing age, so the age group of 20–29 years old was set as the reference group20. The remaining ages were divided into groups of 5 years.

Statistical analysis

Maternal and neonatal characteristics in the IVF group and reference group were described and the chi-square test was used to compare the difference between the IVF group and the general population. We applied a multivariable logistic regression model to evaluate the influence of parental and treatment-related factors on the risk of PTB, LBW, macrosomia, SGA, and LGA among infants conceived with IVF. The factors evaluated in the model included the period of birth, infant sex, maternal age, pregestational BMI, parity, cause of infertility, years of infertility, number of previous procedures involving ART, fresh or frozen embryo transfer cycles, and whether the sperm was from the husband or a donor. Causes of infertility were identified as semen abnormalities (including oligospermia, azoospermia, and teratozoospermia), endometriosis, PCOS, other ovulation failures, tubal factors, and uterine factors (including malformation or pathological uterine or abnormal cervix). Cases where no cause could be found were defined as unexplained infertility. Each diagnosis was set as a binary variable (Yes/No) in the multivariable logistic regression model.

The number of PTB, LBW, macrosomia, SGA, and LGA infants conceived with IVF were compared with expected numbers following a Poisson distribution, which were calculated based on the Shanghai birth registry data and adjusted according to the birth year, maternal age, infant sex, and parity distributions of women conceived with IVF. Standardized risk ratios (SRR) for PTB, LBW, macrosomia, SGA, and LGA were calculated by dividing the observed numbers by the expected numbers and estimated the 95% confidence intervals (CIs).

Additional analyses were performed on the subsamples to distinguish the effect of IVF procedures from subfertility characteristics and the cause of infertility. First, according to the multivariable logistic regression model mentioned above, the subsample was restricted to infants from couples with normal BMI and without infertility factors, including semen abnormalities, endometriosis, polycystic ovary syndrome and uterine factor, which could have affected the birth outcomes. Second, the subsample was limited to infants born to couples with normal BMI and without any apparent cause of infertility (i.e., the unexplained cause group). Third, the subsample was restricted to infants born to couples with a sole diagnosis of tubal disease and with normal BMI; since these infants were considered to be more likely to be conceived with healthy gametes. The statistical and data analysis software package SAS 9.4 was used for data analysis. A value of p < 0.05 was considered to indicate statistical significance.

Results

Maternal and neonatal characteristics

The number of infants conceived with IVF had increased dramatically in recent years, and the utilization rate of frozen embryo transfer had increased from 34% in 2008 to 98% in 2012 and remained above 95% until 2017. (Fig. 2). Maternal and neonatal characteristics showed great differences between the general population and those treated with IVF. The maternal age of women who had conceived with IVF (Mean = 32.80, 95% CI 32.72–32.88) tended to be higher than that in the general population (Mean = 28.15, 95% CI 28.11–28.19) (Table 1). Moreover, women who conceived with IVF were more likely to undergo a caesarean section (76.75% vs. 47.60%, P < 0.0001). The sex ratios at birth of IVF (114.09) and the general population (112.54) were much higher than the United Nations recommendation of 103–107. The average gestational age and birth weight of infants conceived with IVF did not differ significantly from the general population. However, the incidence of PTB (6.59%) and LBW (4.14%) was much higher than that in the general population (4.57 and 2.84%, P < 0.0001). The incidence of macrosomia (8.66%) and LGA (17.82%) was also higher than that in the general population (6.97% and 14.44%, P < 0.0001), while there was no statistically significant difference in the incidence of SGA between IVF (4.98%) and the general population (5.09%, P = 0.6388).

Figure 2.

Figure 2

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The trends of live births by fresh- and frozen-embryo transfer. From 2008 to 2017, there was increased in trend in the proportion of frozen-embryo transfer (Cochran−Armitage trend test χ2 = 41.06, P < 0.0001).

Table 1.

Maternal and neonatal characteristics of IVF infants and general population.

Variable Variable level IVF total Fresh IVF Frozen IVF General population General population VS. IVF
N % N % N % N % P value
Maternal age Mean/Std 32.80 4.07 32.62 3.88 32.82 4.09 28.15 4.49
20–29 years 2507 26.45 187 26.95 2320 26.41 1,269,832 65.04  < 0.0001
30–34 years 4232 44.64 300 43.23 3932 44.75 502,893 25.76
35–39 years 2268 23.92 189 27.23 2079 23.66 155,388 7.96
 ≥ 40 years 473 4.99 18 2.59 455 5.18 24,306 1.24
Missing 0 – 0 – 0 – 0 –
Maternal education level Postgraduate 386 5.8 7 4.64 379 5.83 115,866 5.93  < 0.0001
Graduate 3963 59.59 99 65.56 3864 59.45 899,125 46.05
Middle School 2201 33.09 43 28.48 2158 33.2 383,472 19.64
Primary School 101 1.52 2 1.32 99 1.52 553,943 28.37
Missing 2829 – 543 – 2286 – 13 –
Parity 0 8624 90.97 630 90.78 7994 90.99 1,315,266 67.37  < 0.0001
1 808 8.52 61 8.79 747 8.5 577,600 29.58
 ≥ 2 48 0.51 3 0.43 45 0.51 59,553 3.05
Missing 0 – 0 – 0 – 0 –
Delivery mode Vaginal 2202 23.25 136 19.65 2066 23.54 1,022,996 52.40  < 0.0001
Cesarean 7268 76.75 556 80.35 6712 76.46 929,423 47.60
Missing 10 – 2 – 8 – 0 –
Sex Boy 5052 53.29 355 51.15 4697 53.46 1,033,763 52.95 0.5041
Girl 4428 46.71 339 48.85 4089 46.54 918,656 47.05
Sex ratio – 114.09 – 104.71 – 114.87 – 112.54
Missing 0 – 0 – 0 – 0 –
Gestational age Mean/Std 38.34 1.65 38.30 1.67 38.35 1.65 38.89 1.49
Missing 0 – 0 – 0 – 0 –
PTB 625 6.59 37 5.33 588 6.69 89,242 4.57  < 0.0001
Birth weight Mean/Std 3347.93 502.67 3320.15 495.44 3350.12 503.18 3347.09 453.15
Missing 0 – 0 – 0 – 0 –
LBW 392 4.14 25 3.6 367 4.18 55,500 2.84  < 0.0001
Macrosomia 821 8.66 56 8.07 765 8.71 136,032 6.97  < 0.0001
SGA 472 4.98 28 4.03 444 5.05 99,282 5.09 0.6388
LGA 1689 17.82 119 17.15 1570 17.87 282,006 14.44  < 0.0001
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Risk factors of ABOs in IVF

We found a decreased trend of PTB (OR 0.94, 95% CI 0.90–0.98) and LGA (OR 0.97, 95% CI 0.94–1.00) in IVF in recent years (Table 2). In this study, maternal age was not a significant risk factor for ABOs; however, overweight (24 ≤ BMI < 28) and obesity (BMI ≥ 28) was associated with a higher risk of PTB (OR 1.42, 95% CI 1.15–1.77; OR 1.68, 95% CI 1.16–2.44, respectively).

Table 2.

Risk factors of adverse birth outcomes in singletons conceived with IVF.

Variable Variable level PTB LBW Macrosomia SGA LGA
N Rate (%) OR (95%CI) N Rate (%) OR (95%CI) N Rate (%) OR (95%CI) N Rate (%) OR (95%CI) N Rate (%) OR (95%CI)
Year of birth Change per year – – 0.94(0.90,0.98) – – 0.98(0.93,1.04) – – 0.99(0.95,1.03) – – 1.01(0.96,1.06) – – 0.97(0.94,1.00)
Maternal age 20–29 years 166 6.76 Ref 101 4.11 Ref 226 9.20 Ref 127 5.17 Ref 451 18.36 Ref
30–34 years 263 6.35 0.87(0.71,1.07) 172 4.15 0.95(0.74,1.23) 361 8.72 0.88(0.73,1.05) 192 4.64 0.93(0.73,1.17) 730 17.63 0.90(0.78,1.02)
35–39 years 142 6.42 0.86(0.67,1.11) 87 3.93 0.89(0.65,1.22) 187 8.46 0.80(0.64,0.99) 116 5.25 1.11(0.84,1.47) 395 17.87 0.86(0.73,1.01)
 ≥ 40 years 45 9.78 1.29(0.88,1.88) 21 4.57 1.03(0.61,1.72) 34 7.39 0.62(0.41,0.92) 24 5.22 1.17(0.72,1.87) 81 17.61 0.77(0.58,1.02)
Pregestational BMI  < 18.5 65 6.01 1.02(0.77,1.34) 39 3.60 0.97(0.69,1.37) 45 4.16 0.48(0.35,0.65) 86 7.95 1.84(1.43,2.37) 103 9.52 0.50(0.40,0.62)
18.5–23.9 395 6.08 Ref 247 3.80 Ref 518 7.98 Ref 292 4.50 Ref 1114 17.16 Ref
24–28 120 8.82 1.42(1.15,1.77) 72 5.29 1.41(1.07,1.85) 200 14.70 2.03(1.70,2.43) 68 5.00 1.15(0.88,1.51) 360 26.45 1.75(1.52,2.01)
 ≥ 28 36 10.84 1.68(1.16,2.44) 23 6.93 1.84(1.17,2.90) 45 13.55 1.89(1.35,2.64) 13 3.92 0.92(0.52,1.63) 80 24.10 1.55(1.19,2.03)
Gender Boy 347 7.03 Ref 184 3.73 Ref 516 10.45 Ref 228 4.62 Ref 882 17.86 Ref
Girl 269 6.22 0.89(0.75,1.05) 197 4.55 1.23(1.00,1.52) 292 6.75 0.62(0.53,0.72) 231 5.34 1.17(0.97,1.42) 775 17.91 1.01(0.91,1.12)
Parity 0 542 6.43 Ref 350 4.15 Ref 721 8.55 Ref 427 5.06 Ref 1479 17.54 Ref
1 70 8.89 1.41(1.07,1.85) 29 3.68 0.94(0.63,1.40) 82 10.42 1.18(0.92,1.52) 31 3.94 0.77(0.52,1.13) 166 21.09 1.22(1.01,1.47)
 ≥ 2 4 8.51 1.31(0.46,3.70) 2 4.26 1.09(0.26,4.59) 5 10.64 1.19(0.46,3.06) 1 2.13 0.42(0.06,3.06) 12 25.53 1.58(0.81,3.08)
Year of infertility  < 5 years 424 6.32 Ref 265 3.95 Ref 583 8.69 Ref 339 5.05 Ref 1187 17.69 Ref
5–10 years 162 7.59 1.15(0.95,1.40) 98 4.59 1.11(0.87,1.42) 189 8.86 0.99(0.82,1.18) 101 4.74 0.96(0.76,1.22) 389 18.24 0.99(0.87,1.13)
 ≥ 10 years 30 7.08 1.01(0.68,1.52) 18 4.25 1.03(0.62,1.72) 36 8.49 0.97(0.67,1.40) 19 4.48 0.88(0.54,1.45) 81 19.10 1.03(0.79,1.34)
Number of failure cycle 0 368 6.37 Ref 228 3.95 Ref 485 8.40 Ref 299 5.18 Ref 1005 17.40 Ref
1 99 6.10 0.87(0.69,1.11) 57 3.51 0.86(0.63,1.17) 153 9.43 1.17(0.96,1.43) 69 4.25 0.80(0.60,1.05) 300 18.48 1.07(0.92,1.24)
2 69 8.03 1.20(0.91,1.58) 43 5.01 1.24(0.88,1.75) 78 9.08 1.16(0.90,1.51) 40 4.66 0.88(0.62,1.24) 159 18.51 1.10(0.91,1.34)
 ≥ 3 80 7.94 1.16(0.88,1.51) 53 5.26 1.29(0.93,1.79) 92 9.13 1.17(0.91,1.50) 51 5.06 0.97(0.70,1.33) 193 19.15 1.16(0.96,1.38)
Sperm Husband sperm 608 6.66 Ref 372 4.08 Ref 799 8.76 Ref 449 4.92 Ref 1635 17.92 Ref
Donor sperm 8 5.63 0.84(0.40,1.78) 9 6.34 1.42(0.68,2.93) 9 6.34 0.73(0.36,1.49) 10 7.04 1.45(0.72,2.90) 22 15.49 0.80(0.49,1.29)
Embryo transfer Fresh 37 5.51 Ref 25 3.72 Ref 56 8.33 Ref 28 4.17 Ref 118 17.56 Ref
Frozen 579 6.74 1.51(1.03,2.21) 356 4.14 1.14(0.72,1.81) 752 8.75 1.07(0.77,1.47) 431 5.01 1.20(0.78,1.84) 1539 17.91 1.13(0.90,1.43)
Unexplained No 603 6.63 Ref 373 4.10 Ref 795 8.74 Ref 447 4.91 Ref 1626 17.87 Ref
Yes 13 7.83 1.29(0.69,2.41) 8 4.82 1.51(0.69,3.29) 13 7.83 0.74(0.40,1.36) 12 7.23 1.45(0.75,2.81) 31 18.67 0.92(0.60,1.42)
Semen abnormalities No 515 6.59 Ref 309 3.95 Ref 688 8.81 Ref 385 4.93 Ref 1397 17.88 Ref
Yes 101 6.95 1.08(0.86,1.35) 72 4.95 1.31(1.00,1.71) 120 8.25 0.90(0.74,1.11) 74 5.09 1.04(0.80,1.35) 260 17.88 0.99(0.85,1.15)
Endometriosis No 532 6.52 Ref 325 3.98 Ref 726 8.89 Ref 400 4.90 Ref 1484 18.18 Ref
Yes 84 7.62 1.29(1.01,1.65) 56 5.08 1.37(1.01,1.85) 82 7.43 0.85(0.66,1.08) 59 5.35 1.05(0.78,1.40) 173 15.68 0.88(0.73,1.05)
PCOS No 542 6.35 Ref 342 4.01 Ref 746 8.74 Ref 432 5.06 Ref 1517 17.77 Ref
Yes 74 10.11 1.65(1.25,2.18) 39 5.33 1.30(0.90,1.87) 62 8.47 0.74(0.55,0.99) 27 3.69 0.74(0.49,1.12) 140 19.13 0.90(0.73,1.10)
Other ovulation failure No 588 6.60 Ref 363 4.08 Ref 779 8.75 Ref 436 4.90 Ref 1605 18.02 Ref
Yes 28 7.76 1.28(0.85,1.92) 18 4.99 1.36(0.83,2.24) 29 8.03 0.88(0.59,1.32) 23 6.37 1.33(0.85,2.08) 52 14.40 0.75(0.55,1.02)
Tubal factor No 112 7.87 Ref 67 4.71 Ref 128 9.00 Ref 79 5.55 Ref 265 18.62 Ref
Yes 504 6.43 0.95(0.74,1.21) 314 4.00 1.03(0.76,1.40) 680 8.67 0.88(0.70,1.11) 380 4.84 0.92(0.69,1.23) 1392 17.75 0.89(0.76,1.06)
Uterine factor No 503 6.30 Ref 302 3.78 Ref 702 8.80 Ref 391 4.90 Ref 1438 18.02 Ref
Yes 113 8.78 1.42(1.14,1.77) 79 6.14 1.60(1.23,2.08) 106 8.24 0.89(0.72,1.11) 68 5.28 1.08(0.83,1.42) 219 17.02 0.90(0.77,1.06)
Others* No 599 6.64 Ref 372 4.13 Ref 783 8.69 Ref 449 4.98 Ref 1609 17.85 Ref
Yes 17 6.75 1.05(0.63,1.75) 9 3.57 0.80(0.40,1.59) 25 9.92 1.21(0.78,1.87) 10 3.97 0.68(0.35,1.30) 48 19.05 1.18(0.85,1.65)
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*Others: including chromosomal abnormality, immune factors, pituitary lesions and sexual dysfunction.

Statistical differences are shown in bold.

LBW (OR 1.41, 95% CI 1.07–1.85; OR 1.84, 95% CI 1.17–2.90, respectively), macrosomia (OR 2.03, 95% CI 1.70–2.43; OR 1.89, 95% CI 1.35–2.64, respectively), and LGA (OR 1.75, 95% CI 1.52–2.01; OR 1.55, 95% CI 1.19–2.03, respectively), while lower pregestational BMI (< 18.5) was associated with SGA (OR 1.84, 95% CI 1.43–2.37). In addition, female infants had a higher risk of LBW (OR 1.23, 95% CI 1.00–1.52) and a lower risk of macrosomia (OR 0.62, 95% CI0.53–0.72). Frozen embryo transfer was associated with a higher risk of PTB (OR 1.51, 95% CI 1.03–2.21), but there was no statistically significant difference in LBW, macrosomia, SGA and LGA.

Regarding the causes of infertility, semen abnormalities (OR 1.31, 95% CI 1.00–1.71) were related to a higher risk of LBW; endometriosis was related to a higher risk of PTB (OR 1.29, 95% CI 1.01–1.65) and LBW (OR 1.37, 95% CI 1.01–1.85); PCOS was linked to a higher risk of PTB (OR 1.65, 95% CI 1.25–2.18) and lower risk of macrosomia (OR 0.74, 95% CI 0.55–0.99); and uterine factor infertility was related to a higher risk of PTB (OR 1.42, 95% CI 1.14–1.77) and LBW (OR 1.60, 95% CI 1.23–2.08). There was no statistically significant difference in the incidence of ABOs between women with or without tubal infertility.

Comparison of ABOs risk between IVF and the general population

Compared with the general population, singletons conceived with IVF had a higher risk of PTB (SRR = 1.15, 95% CI 1.06–1.24), LBW (SRR = 1.12, 95% CI 1.02–1.24), macrosomia (SRR = 1.34, 95% CI 1.26–1.44), and LGA (SRR = 1.26, 95% CI 1.20–1.32) (Table 3). We stratified IVF procedures based on fresh and frozen embryo transfer cycles and found that the risks of all ABOs categories were still significantly higher in frozen cycles, whereas only the risks of LGA (SRR = 1.21, 95% CI 1.01–1.45) remained higher in fresh cycles.

Table 3.

Observed and expected cases of adverse birth outcomes among infants conceived with IVF in mothers with normal BMI.

ABOs Group IVF IVF-Fresh IVF-Frozen
Total no No. of events SRR* (95%CI) Total no No. of events SRR* (95%CI) Total no No. of events SRR* (95%CI)
Observed Expected# Observed Expected# Observed Expected#
PTB IVF Total 9480 625 543.2 1.15(1.06,1.24) 694 37 39.8 0.93(0.67,1.28) 8786 588 503.4 1.17(1.08,1.27)
Excluding infertility factors affecting ABOs $ 3787 229 217.0 1.06(0.93,1.20) 336 16 19.3 0.83(0.51,1.36) 3451 213 197.7 1.08(0.94,1.23)
Unexplained infertility cause 111 8 6.4 1.26(0.63,2.52) – – – – 99 8 5.7 1.41(0.71,2.82)
Tubal factor infertility 3333 199 191.0 1.04(0.91,1.20) 286 13 16.4 0.79(0.46,1.37) 3047 186 174.6 1.07(0.92,1.23)
LBW IVF Total 9480 392 348.9 1.12(1.02,1.24) 694 25 25.5 0.98(0.66,1.45) 8786 367 323.3 1.14(1.02,1.26)
Excluding infertility factors affecting ABOs $ 3787 133 139.4 0.95(0.81,1.13) 336 12 12.4 0.97(0.55,1.71) 3451 121 127.0 0.95(0.80,1.14)
Unexplained infertility cause 111 3 4.1 0.73(0.24,2.28) – – – – 99 3 3.6 0.82(0.27,2.55)
Tubal factor infertility 3333 115 122.7 0.94(0.78,1.13) 286 10 10.5 0.95(0.51,1.77) 3047 105 112.1 0.94(0.77,1.13)
Macrosomia IVF total 9480 821 610.5 1.34(1.26,1.44) 694 56 44.7 1.25(0.96,1.63) 8786 765 565.8 1.35(1.26,1.45)
Excluding infertility factors affecting ABOs $ 3787 308 243.9 1.26(1.13,1.41) 336 23 21.6 1.06(0.71,1.60) 3451 285 222.2 1.28(1.14,1.44)
Unexplained Infertility cause 111 10 7.1 1.40(0.75,2.60) 12 1 0.8 1.29(0.18,9.19) 99 9 6.4 1.41(0.73,2.71)
Tubal factor infertility 3333 271 214.6 1.26(1.12,1.42) 286 22 18.4 1.19(0.79,1.81) 3047 249 196.2 1.27(1.12,1.44)
SGA IVF total 9480 472 486.3 0.97(0.89,1.06) 694 28 35.6 0.79(0.54,1.14) 8786 444 450.7 0.99(0.90,1.08)
Excluding infertility factors affecting ABOs $ 3787 174 194.3 0.90(0.77,1.04) 336 16 17.2 0.93(0.57,1.52) 3451 158 177.0 0.89(0.76,1.04)
Unexplained infertility cause 111 7 5.7 1.23(0.59,2.58) 12 1 0.6 1.62(0.23,11.53) 99 6 5.1 1.18(0.53,2.63)
Tubal factor infertility 3333 149 171.0 0.87(0.74,1.02) 286 12 14.7 0.82(0.46,1.44) 3047 137 156.3 0.88(0.74,1.04)
LGA IVF total 9480 1689 1342.4 1.26(1.20,1.32) 694 119 98.3 1.21(1.01,1.45) 8786 1570 1244.1 1.26(1.20,1.33)
Excluding infertility factors affecting ABOs $ 3787 662 536.2 1.23(1.14,1.33) 336 53 47.6 1.11(0.85,1.46) 3451 609 488.7 1.25(1.15,1.35)
Unexplained infertility cause 111 22 15.7 1.40(0.92,2.13) 12 1 1.7 0.59(0.08,4.18) 99 21 14.0 1.50(0.98,2.30)
Tubal factor infertility 3333 589 472.0 1.25(1.15,1.35) 286 47 40.5 1.16(0.87,1.54) 3047 542 431.5 1.26(1.15,1.37)
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#The number of expected cases was calculated by applying the rates of PTB, LBW, macrosomia, SGA and LGA from the birth registry data to the population of infants conceived with assisted reproductive technology. The values were adjusted to account for differences in the distributions of year of birth, maternal age, infant sex and parity between the two populations.

*SRR standardized risk ratio.

$Infertility factors affecting ABOs meant infertility factors that influenced the birth outcomes, including semen abnormalities, endometriosis, polycystic ovary syndrome and uterine factor infertility.

Statistical differences are shown in bold.

In the subgroup analyses, we found that the risks of PTB and LBW no longer increased. However, the risks of macrosomia and LGA remained elevated in analyses restricted to subgroups of the infants conceived by women with a sole diagnosis of tubal factor infertility (macrosomia, SRR = 1.26, 95% CI 1.12–1.42; LGA, SRR = 1.25, 95% CI 1.15–1.35) or by women without infertility factors (semen abnormalities, endometriosis, PCOS, and uterine factor infertility) (macrosomia, SRR = 1.26, 95% CI 1.13–1.41; LGA, SRR = 1.23, 95% CI 1.14–1.33). The risk of LGA (SRR = 1.50, 95% CI 0.98–2.30) was not statistically significant in the group of women diagnosed with unexplained infertility, but the sample size (n = 99) was relatively small.

Discussion

With the wide application of IVF to treat infertility, a large number of ABOs have been observed among infants conceived with IVF8,9,18,21. This large sample study demonstrated that singleton infants conceived with IVF were at a higher risk of ABOs relative to singletons in the general population of Shanghai. These risks could not be explained by the known differences between the two populations in the distribution of sex of infants, maternal age, maternal parity, or maternal BMI. Factors causing infertility, such as semen abnormalities, endometriosis, PCOS, and uterine factors were associated with PTB and abnormal birth weight. However, the increased risks of macrosomia and LGA remained significant when the sample was restricted to (1) parents without infertility factors affecting ABOs wherein the mother has a normal BMI, (2) mothers who have infertility caused by a fallopian tube abnormality. Therefore, this study suggests that the increased risk of birth weight among singletons conceived with IVF may be associated with treatments for infertility. Although the risk of macrosomia and LGA was higher than that in the general population, the incidence of LGA showed a decreasing trend across the entire cohort of IVF cycles, which was also observed in Israel and Sweden22,23.

Consistent with previous studies, our study showed that low pregestational BMI (< 18.5) increased the risk of SGA, and being overweight or obese (BMI > 24) before pregnancy increased the risk of PTB, LBW, macrosomia, and LGA24,25. BMI abnormalities are more common in the infertile population and may be related to ovulation failure, irregular menses, poor oocyte quality, and hormonal imbalances26–28. Additionally, abnormal BMI may be a risk factor for abnormal birth weight in infants conceived with IVF. Therefore, it is necessary to eliminate the influence of abnormal BMI to reveal the real impact of IVF on ABOs.

This study found that PCOS, endometriosis and uterine factors were associated with a higher risk of PTB. Semen abnormalities, endometriosis, and uterine factors were associated with a higher risk of LBW. The association of PCOS with increased risk of PTB but not LBW is similar to previous studies where women with PCOS were found to have a higher risk of gestational diabetes, hypertensive disorders, PTB, and LGA11,29. Moreover, the literature showed that after the correction of hypertensive disorders, the increased risk of PTB was eliminated, and even after the correction of gestational diabetes, the increased risk of LGA remained significant11. Endometriosis, which affects 10–15% of reproductive age women, is associated with inflammation, fibrosis, and aberrant angiogenesis30. Literature shows that endometriosis is associated with a higher risk of PTB and LBW10,31, however, this association is influenced by BMI32. In this study, we also found that the association between endometriosis and risk of PTB and LBW increased after adjusting for maternal BMI. This finding suggests that the higher risk is associated with mechanisms specific to endometriosis33. Additionally, uterine factors such as uterine defects, uterine inflammation, and cervical insufficiency were also associated with a higher risk of PTB and LBW in this study. This finding is supported by literature showing a relationship between uterine factors and many obstetric complications34,35. Some studies have reported that tubal factors increase the risk of PTB and LBW for singletons; the etiological reasons were mainly attributed to inflammation and infections31,36,37. Conversely, other studies showed that a unilateral tubal block did not increase the risk of ABOs38,39. In this study, we did not find any association between tubal factor infertility and PTB or LBW. This difference may be explained by the epidemiological differences in the cause and severity of tubal infertility; hence, further study of the underlying mechanism is warranted.

Another novel finding of this study was that semen abnormality was associated with a higher incidence of LBW but not PTB. This finding contrasts with that of most other studies, which found no significant association between male infertility factors and increased risk of PTB and LBW compared to unexplained infertility31. Moreover, semen parameters did not influence embryo quality or live birth outcomes40,41. Another study showed that male-factor infertility has been associated with LBW and LBW at full term in singletons conceived with ART18; however, it could not be distinguished whether the effect was from the ART or male-factor infertility. This study found that sperm abnormality, oligospermia, and asthenozoospermia were associated with a higher risk of LBW. The increased risk may be due to sperm DNA damage42, but the mechanisms underlying the association remain unclear and warrants further research.

Several additional analyses were performed on the subsamples to distinguish the effects of IVF procedures from underlying characteristics of the patients and the cause of infertility. We found a significant association of macrosomia and LGA with IVF, even when abnormal BMI and infertile causes, such as semen abnormalities, endometriosis, polycystic ovary syndrome and uterine factor infertility for abnormal birth weight were eliminated from the study group. Although increased risk was not observed for fresh embryo transfers, this analysis had greatly reduced sample sizes and should therefore be interpreted with caution. Previous studies found that frozen embryo transfer was associated with increased birth weight and a higher risk of macrosomia and LGA, compared with spontaneous conception and fresh embryo transfer43,44; however, potential confounding factors, such as maternal BMI or cause of infertility had not been adjusted in those studies. In this study, after adjusting for confounding factors, singletons born after frozen embryo transfer had a higher rate of LGA and macrosomia compared with the general population. This finding may partly be explained by aspects related to ART procedure, such as improved endometrial reception, higher quality embryos surviving the freezing–thawing process45, and the effect of cryoprotectants46. The embryos were cultured in cryoprotectants and had undergone freezing and thawing procedures at a critical and vulnerable development stage, which could cause epigenetic changes resulting in a larger gestational size47. Currently, the mechanism remains unclear and epigenetic effects in infants conceived with ART require further investigation. While LGA and macrosomia may seem less threatening to infant survival, they are associated with an increased risk of cardiovascular diseases in adulthood48,49. Thus, more attention should be paid to the association between IVF and increased risk of LGA and macrosomia, and longer-term follow-up of children’s development is also important.

Strengths and limitations

One of the strengths of this study is its large sample size and detailed information available on the treatments from the infertility service centre. Additionally, the availability of outcomes from many frozen embryo transfers is higher compared with many previously reported studies. Confounding factors that affect embryonal development and underlying infertility have prevented researchers from evaluating the association between IVF and ABOs for a long time. This study adjusted for the confounding effects of underlying infertility to compare the fetal growth between IVF infants and the general population. Our research results indicated that the risk of macrosomia and LGA in IVF was increased, especially when frozen embryo transfer was used.

This study has some limitations. The general population was used as the reference group; however, the general population may include 1.7–4.0% of infants conceived with ART as it is not possible to accurately identify and exclude these births. Therefore, the association between ABOs and IVF was likely underestimated. This retrospective cohort study covers a relatively long period and may be affected by inevitable changes to routines for data collection, approach to diagnosis, and improved procedures for IVF. For example, such changes mean that there may be effects from changes to ovarian stimulation and oocyte collection methods that are hard to distinguish. We also did not collect data on pregnancy complications that may have affected the incidence of ABOs; however, we did include most risk factors for pregnancy complications, such as maternal age, pregestational BMI, endometriosis, and PCOS.

Conclusions

This cohort study found that causes of infertility, including endometriosis, PCOS, uterine factors, and semen abnormalities increased the risk of ABOs. After adjusting for these factors, compared with the general population, the risk of LBW and PTB did not increase, but the risk of macrosomia and LGA was still increased. Although we could not exclude the potential effect of pregnancy complications, the findings in this study may nonetheless provide insight for patients seeking ART treatment.

Acknowledgements

This study was supported by the National Natural Science Foundation for Young Scientists of China (82003486), the Fifth Round of the Three-Year Public Health Action Plan of Shanghai (GWV-10.1-XK08), and Cross project of medical engineering in Shanghai Jiaotong University (YG2019QNA14).

Author contributions

Study concept and design: Y.K., H.Y. and Z.L.; Methodology: H.Y., Z.L. and T.X.; Formal Analysis: H.Y. and Z.L.; Data Curation: Z.L., R.C. and S.J.; Writing—Original Draft: H.Y.; Writing—Review & Editing: H.Y., Z.L., T.X. and Y.K.; Project administration: Y.K. and C.W.; Funding acquisition: H.Y. and Z.L.; Critical revision of the manuscript for important intellectual content: All authors.

Competing interests

The authors declare no competing interests.

Footnotes

The original online version of this Article was revised: In the original version of this Article Huiting Yu and Zhou Liang were omitted as equally contributing authors.

Publisher's note

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

These authors contributed equally: Huiting Yu and Zhou Liang.

Change history

5/19/2022

A Correction to this paper has been published: 10.1038/s41598-022-12512-x

Contributor Information

Tian Xia, Email: xiatian@scdc.sh.cn.

Chunfang Wang, Email: 13761019425@163.com.

Yanping Kuang, Email: htychihealth@126.com.

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