Original Article

Causal Effects of Body Mass Index and Maternal Age on Oocyte Maturation in Assisted Reproductive Technology: Model-Average Causal Effect and Bayesian LASSO Method


Background: Body Mass Index (BMI) and maternal age are related to various disorders of the female reproductive system. This study aimed to estimate the causal effects of BMI and maternal age on the rate of metaphase II oocytes (MII) using a new statistical method based on Bayesian LASSO and model averaging.

Methods: This investigation was a historical cohort study and data were collected from women who underwent assisted reproductive treatments in Tehran, Iran during 2015 to 2018. Exclusion criteria were gestational surrogacy and donor oocyte. We used a new method based on Bayesian LASSO and model average to capture important confounders.

Results: Overall, 536 cycles of 398 women were evaluated. BMI and Age had inverse relationships with the number of MII based on univariate analysis, but after adjusting the effects of other variables, there was just a significant association between age and the number of MII (adjusted incidence rate ratio (aIRR) of age =0.989, 95% CI: [0.979, 0.998], P=0.02). The results of causal inference based on the new presented method showed that the overall effects of age and BMI of all patients were significantly and inversely associated with the number of MII (both P<0.001). Therefore the expected number of MII decreased by 0.99 for an increase of 1 year (95% CI: [-1.00,-0.97]) and decreased by 0.99 for each 1-unit increase in BMI (95% CI: [-1.01,-0.98]).

Conclusion: Maternal age and BMI have significant adverse casual effects on the rate of MII in patients undergoing ART when the effects of important confounders were adjusted.

1. Lashen H, Ledger W, Bernal AL, et al (1999). Extremes of body mass do not adversely affect the outcome of super-ovulation and in-vitro fertilization. Hum Reprod, 14(3): 712-5.
2. Al-Obaidi MT, Mahdi HB, Alwasiti E (2018). The impact of age and BMI on oocyte maturation and embryo develop-ment. Biomedical Research, 29(9): 1920-1924.
3. Fritz MA, Speroff L. (2012). Clinical gynecologic endocrinology and infertility. Lippincott Wil-liams & wilkins.
4. Grøndahl ML, Christiansen SL, Kesmodel US, et al (2017). Effect of women’s age on embryo morphology, cleavage rate and competence—A multicenter cohort study. PLoS One, 12(4): e0172456.
5. Tatone C, Amicarelli F, Carbone MC, et al (2008). Cellular and molecular aspects of ovarian follicle ageing. Hum Reprod Update, 14(2): 131-42.
6. Broekmans FJ, Knauff EA, te Velde ER, et al (2007). Female reproductive ageing: current knowledge and future trends. Trends Endocrinol Metab, 18(2): 58-65.
7. Hourvitz A, Machtinger R, Maman E, et al (2009). Assisted reproduction in women over 40 years of age: how old is too old? Reprod Biomed Online, 19(4): 599-603.
8. Van Loendersloot L, Van Wely M, Limpens J, et al (2010). Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis. Hum Reprod Update, 16(6): 577-89.
9. Rich-Edwards JW, Goldman MB, Willett WC, et al (1994). Adolescent body mass index and infertility caused by ovulatory disorder. Am J Obstet Gynecol, 171(1): 171-7.
10. Zaadstra BM, Seidell JC, Van Noord P, et al (1993). Fat and female fecundity: pro-spective study of effect of body fat dis-tribution on conception rates. BMJ, 306(6876): 484-487.
11. Crosignani PG, Colombo M, Vegetti W, et al (2003). Overweight and obese anovula-tory patients with polycystic ovaries: par-allel improvements in anthropometric in-dices, ovarian physiology and fertility rate induced by diet. Hum Reprod, 18(9): 1928-32.
12. Green BB, Weiss NS, Daling JR (1988). Risk of ovulatory infertility in relation to body weight. Fertil Steril, 50(5): 721-6.
13. Grodstein F, Goldman MB, Cramer DW (1994). Body mass index and ovulatory infertility. Epidemiology, 5(2): 247-50.
14. Kalem MN, Kalem Z, Sarı T, et al (2016). Effect of body mass index and age on in vitro fertilization in polycystic ovary syn-drome. Journal of the Turkish German Gyne-cology Association, 17(2): 83-90.
15. Wilson A, Reich BJ (2014). Confounder se-lection via penalized credible regions. Bio-metrics, 70(4): 852-61.
16. Tibshirani R (1996). Regression shrinkage and selection via the lasso. J R Stat Soc Se-ries B Stat Methodol, 58(1): 267-288.
17. Park T, Casella G (2008). The Bayesian las-so. J Am Stat Assoc, 103(482): 681-6.
18. Hans C (2010). Model uncertainty and vari-able selection in Bayesian lasso regres-sion. Statistics and Computing, 20(2): 221-229.
19. Rosenbaum PR, Rubin DB (1983). The cen-tral role of the propensity score in obser-vational studies for causal effects. Bio-metrika, 70(1): 41-55.
20. Garrido MM, Kelley AS, Paris J, et al (2014). Methods for constructing and assessing propensity scores. Health Serv Res, 49(5): 1701-1720.
21. McCandless LC, Gustafson P, Austin PC (2009). Bayesian propensity score analysis for observational data. Stat Med, 28(1): 94-112.
22. Team RC (2017). R: A language and envi-ronment for statistical computing. Vien-na, Austria. R Foundation for Statistical Computing. Available from: https://www.R-project.org/
23. Spandorfer SD, Kump L, Goldschlag D, et al (2004). Obesity and in vitro fertilization: negative influences on outcome. J Reprod Med, 49(12): 973-7.
24. van Swieten EC, van der Leeuw-Harmsen L, Badings EA, et al (2005). Obesity and Clomiphene Challenge Test as predictors of outcome of in vitro fertilization and intracytoplasmic sperm injection. Gynecol Obstet Invest, 59(4): 220-4.
25. Maged AM, Fahmy RM, Rashwan H, et al (2019). Effect of body mass index on the outcome of IVF cycles among patients with poor ovarian response. Int J Gynaecol Obstet, 144(2):161-166.
26. Frattarelli JL, Kodama CL (2004). Impact of body mass index on in vitro fertilization outcomes. J Assist Reprod Genet, 21(6): 211-5.
27. Pasquali R, Pelusi C, Genghini S, et al (2003). Obesity and reproductive disorders in women. Hum Reprod Update, 9(4): 359-72.
IssueVol 49 No 11 (2020) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v49i11.4734
Infertility Assisted reproductive technology (ART) Causal effect Age Body mass index (BMI) LASSO regression

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
ALIZADEH A, OMANI-SAMANI R, MANSOURNIA MA, AKBARI SENE A, RAHIMI FOROUSHANI A. Causal Effects of Body Mass Index and Maternal Age on Oocyte Maturation in Assisted Reproductive Technology: Model-Average Causal Effect and Bayesian LASSO Method. Iran J Public Health. 49(11):2161-2169.