The combined effect of BMI and age on ART outcomes

Abstract

STUDY QUESTION

For a woman with infertility and overweight/obesity, can infertility treatment be postponed to first promote weight loss?

SUMMARY ANSWER

Advice regarding a delay in IVF treatment to optimize female weight should consider female age, particularly in women over 38 years for whom only substantial weight loss in a short period of time (3 months) seems to provide any benefit.

WHAT IS KNOWN ALREADY

Body weight excess and advanced age are both common findings in infertile patients, creating the dilemma of whether to promote weight loss first or proceed to fertility treatment immediately. Despite their known impact on fertility, studies assessing the combined effect of female age and BMI on cumulative live birth rates (CLBRs) are still scarce and conflicting.

STUDY DESIGN, SIZE, DURATION

We performed a multicentre retrospective cohort study including 14 213 patients undergoing their first IVF/ICSI cycle with autologous oocytes and subsequent embryo transfers, between January 2013 and February 2018 in 18 centres of a multinational private fertility clinic. BMI was subdivided into the following subgroups: underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obesity (≥30.0 kg/m²).

PARTICIPANTS/MATERIALS, SETTING, METHODS

The primary outcome was CLBR. The secondary outcome was time to pregnancy. To assess the influence of female age and BMI on CLBR, two multivariable regression models were developed with BMI being added in the models as either an ordinal categorical variable (Model 1) or a continuous variable (Model 2) using the best-fitting fractional polynomials. CLBR was estimated over 1-year periods (Model 1) and shorter timeframes of 3 months (Model 2). We then compared the predicted CLBRs according to BMI and age.

MAIN RESULTS AND THE ROLE OF CHANCE

When compared to normal weight, CLBRs were lower in women who were overweight (adjusted odds ratio (aOR) 0.86, 95% CI 0.77–0.96) and obese (aOR 0.74, 95% CI 0.62–0.87). A reduction of BMI within 1 year, from obesity to overweight or overweight to normal weight would be potentially beneficial up to 35 years old, while only a substantial reduction (i.e. from obesity to normal BMI) would be potentially beneficial in women aged 36–38 years. Above 38 years of age, even considerable weight loss did not compensate for the effect of age over a 1-year span but may be beneficial in shorter time frames. In a timeframe of 3 months, there is a potential benefit in CLBR if there is a loss of 1 kg/m² in BMI for women up to 33.25 years and 2 kg/m² in women aged 33.50–35.50 years. Older women would require more challenging weight loss to achieve clinical benefit, specifically 3 kg/m² in women aged 35.75–37.25 years old, 4 kg/m² in women aged 37.50–39.00 years old, and 5 kg/m² or more in women over 39.25 years old.

LIMITATIONS, REASONS FOR CAUTION

This study is limited by its retrospective design and lower number of women in the extreme BMI categories. The actual effect of individual weight loss on patient outcomes was also not evaluated, as this was a retrospective interpatient comparison to estimate the combined effect of weight loss and ageing in a fixed period on CLBR.

WIDER IMPLICATIONS OF THE FINDINGS

Our findings suggest that there is potential benefit in weight loss strategies within 1 year prior to ART, particularly in women under 35 years with BMI ≥25 kg/m². For those over 35 years of age, weight loss should be considerable or occur in a shorter timeframe to avoid the negative effect of advancing female age on CLBR. A tailored approach for weight loss, according to age, might be the best course of action.

STUDY FUNDING/COMPETING INTEREST(S)

No specific funding was obtained for this study. All authors have no conflicts to declare.

TRIAL REGISTRATION NUMBER

N/A.

infertility, obesity, age, ART, cumulative live birth rate, time-to-pregnancy, weight loss, clinical decision support systems

Introduction

The rising prevalence of obesity has become critical for women of childbearing age (Vahratian, 2009), with nearly half of women undergoing IVF in the USA having either overweight or obesity (Goldman et al., 2019). Patients with obesity are at an increased risk of obstetric and neonatal complications such as gestational diabetes, hypertensive disorders of pregnancy, caesarean delivery, stillbirth, large for gestational age, congenital anomalies, and neonatal intensive care unit admission (ASRM Practice Committee, 2021).

Regarding ART outcomes, overweight/obesity has been previously associated with poor response after ovarian stimulation (Rothberg et al., 2016), lower quality of oocytes and embryos (Luke et al., 2010; Zhang et al., 2015; Qiuet al., 2019), poor endometrial receptivity (Bellver et al., 2011; Espinós et al., 2017), and lower live birth rates (Kluge et al., 2019; Grzegorczyk-Martin et al., 2020; Shen et al., 2022). The risk of miscarriage is also well recognized (Luke et al., 2010; Sarais et al., 2016), even after the transfer of euploid embryos, demonstrating that aneuploidy is unlikely to be the major cause of pregnancy loss in these women (Cozzolino et al., 2021; del Carmen Nogales et al., 2021). Indeed, while BMI does not seem to be associated with increased rates of maternal embryonic aneuploidy (Hughes et al., 2022), Boots et al. (2014) demonstrated that women with obesity and recurrent pregnancy loss under 10 weeks’ gestational age have a 58% chance of having a euploid loss compared with 37% in women without obesity (risk ratio (RR), 1.63; 95% CI, 2.08–2.47). These findings suggest that higher BMI may be an independent risk factor for miscarriage (Mechanick et al., 2019).

Nevertheless, the relation between female obesity and ART outcomes remains unclear owing to disparity in design of the studies carried out in this area, their small sample sizes, varying BMI classification systems (Shen et al., 2022) and inconsistency in the defined outcome measures (Legge et al., 2014).

When planning ART treatment in women with overweight/obesity and infertility, the best course of action is still unknown. Although modest weight loss in women with obesity may be effective in improving natural conception rates (Sneed et al., 2008; Goldmanet al., 2019; Kluge et al., 2019) and pregnancy outcomes (American College of Obstetricians and Gynecologists, 2015), studies to date have not assessed to the same extent the impact of a decrease in BMI on ART outcomes (Bailey et al., 2014). Furthermore, most studies that analysed the relation between BMI and ART outcomes did not account for the detrimental effect that age or ageing may have during weight loss. Goldman et al. (2019) recently reported on a large registry data study that assessed cumulative live birth rate (CLBR) according to women’s BMI and age, and concluded that age-related decline in fertility has a greater impact than BMI on the CLBR at older ages. However, this study did not account for male age, only spanned 2 years and the potential benefit of weight loss over shorter periods of time was not assessed. Hence, we designed a retrospective cohort study to assess whether women with overweight or obesity could benefit from losing weight prior to ART, in 1-year periods and shorter time frames. We aimed to estimate whether there are thresholds above which delaying treatment to optimize female weight may not be the best approach.

Materials and methods

Study design

This retrospective analysis included all patients (belonging to a multinational group) from 18 private ART clinics undergoing their first IVF/ICSI cycle using autologous gametes between 1 January 2013 and 1 February 2018. Only cycles in which a known live birth outcome was reported after either a fresh or one of the subsequent frozen embryo transfers (FETs) using embryos generated from the same oocyte retrieval procedure and transferred within the study period were included. The decision regarding the preferred stimulation protocol was made according to the patient’s characteristics/preference and physician preference. Culture media used were either single-step LifeGlobal^® (CooperSurgical, Trumbull, CT, USA) or Cook (Cook Medical, Bloomington, IN, USA) sequential media, with the culture conditions being standardized among the included centres. Treatment cycles were excluded whenever no/mild ovarian stimulation was administered or if donated gametes (oocytes or sperm) or embryos were used. Cycles performed for fertility preservation were also excluded.

The age of both the female and male partners, and female preconception BMI, were the independent variables considered. BMI was assessed before the start of ovarian stimulation and was subdivided into the following subgroups: underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obesity (≥30.0 kg/m²), with normal weight used as the reference group.

Main outcome measures

The primary outcome assessed was CLBR, defined as the delivery of at least one live birth resulting from the initial oocyte retrieval (including all fresh embryos transferred and/or eventual frozen embryos subsequently transferred) until the first delivery with a live birth or all embryos are used, whichever occurred first (Zegers-Hochschild et al., 2017). Live birth was defined by the complete expulsion or extraction from a woman of a product of fertilization after 22 completed weeks of gestational age, which shows any evidence of life (Zegers-Hochschild et al., 2017). Our secondary outcome was time to pregnancy (months elapsed between oocyte retrieval and transfer, to a pregnancy leading to a live birth). For our main outcome of interest, CLBR, we estimated that at least 10 000 cycles with a known live born outcome would be available and that the CLBR would be ∼45%. Hence, this size of sample would have >80% power to detect >2% differences in CLBR caused by one of the independent variables.

Exported data from the clinical database was anonymized to protect the patient’s clinical and personal information, in accordance with European legislation regarding Biomedical Research. The anonymized database was only accessible to the statisticians involved.

Statistical analysis

Baseline patient and cycle characteristics were summarized in the text and tables, with categorical data presented using absolute and relative frequencies while continuous values were summarized using mean and SD. Unadjusted between-group comparisons were performed using the Chi-square test for categorical variables and the Student’s t-test for continuous variables. Stata Software version 13.1 (StataCorp, College Station, TX, USA) was used for statistical analysis, with a P-value <0.05 being considered as statistically significant and followed by Bonferroni-adjusted pairwise comparisons.

To estimate whether losing weight prior to ART would be beneficial (instead of undergoing IVF stimulation immediately), we developed two statistical models to attempt to predict the effect on CLBR of a reduction in BMI and an increase in age in a fixed period, by comparing interpatient data. To do so, confounder-adjustment was performed using multivariable logistic regression analysis with confounding factors selected based on their clinical relevance. All models included the age of both the female and male partners, and female BMI. The other variables considered as potential confounders included number of oocytes retrieved, ovulatory factor infertility, tubal factor infertility, uterine factor infertility, infertility diagnosis caused by endometriosis, other female infertility diagnoses, male factor infertility, female smoking habits, date of treatment commencement, and treatment centre (Supplementary Table SI).

BMI was first added to regression models as a categorical variable in attempt to facilitate the application of the results into everyday clinical practice (Model 1). Second, BMI was added as a continuous variable to better appreciate the influence of smaller changes in BMI (Model 2). To avoid bias of assuming that the relation between continuous predictors and BMI was linear, we also used fractional polynomials to assess potential non-linear relationships. The best-fitting fractional polynomial of each of these variables was compared against their linear function to assess which one better described their association with BMI for both models. This statistical approach is widely used to assess which type of relation better explains a certain association between a continuous variable and any given continuous or categorical outcome, including female age and the number of oocytes collected (Templeton et al., 1996; Sauerbrei et al., 2006; Sunkara et al., 2011). Furthermore, it is also important to note that the age of the couple was recalculated prior to extraction in all cases, as the exact subtraction (in days) between the date of oocyte retrieval date and the date of birth, in order to minimize residual confounding that otherwise would have occurred if age were to be rounded to years, as usually performed in the preceding literature.

Finally, competing-risks regression was also performed to assess the mean time to embryo transfer leading to a live birth according to each BMI subgroup, considering the exhaustion of all embryos as a competing risk using the Fine and Gray method.

Ethical approval

Each subject was treated following the approval granted by the respective Ethics Committees for Clinical Research in both Portugal and Spain (study code 2007-LIS-051-SR).

Results

Patient baseline demographics and general characteristics of the treatment cycle

Of the 18 centres originally included for analysis, two did not routinely record BMI and were ultimately excluded from the study. A total of 14 213 couples were included and subdivided in the four BMI groups, with a total of 7040 fresh transfers and 16 927 FETs assessed. The baseline characteristics are shown in Table I. A total of 23.5% of women were overweight or obese. Multiple characteristics differed significantly among the groups, including female and male age, and female infertility diagnoses. Ovulatory factor infertility was more frequent in the obese group (33.0% versus 24.3% in overweight group and 24.8% in reference group, P < 0.01). Moreover, male factor infertility was also less common in the underweight group (35.0% versus 40.2% in reference group, 41.1% in overweight group and 44.6% in obese group, P < 0.01), while women with overweight and obesity were administered higher doses of gonadotrophins.

Table I

Baseline characteristics according to BMI category.

	BMI CATEGORY (kg/m2)
PATIENT CHARACTERISTICS	<18.5	18.5–24.9	25.0–29.9	≥30.0	P-value
	(n = 627)	(n = 10 243)	(n = 2472)	(n = 871)
Female age (years)	35.6 ± 3.9*,†	36.2 ± 4.0*	36.4 ± 4.2†	36.1 ± 4.3	<0.01
≤30	38 (6.1%)	538 (5.3%)	153 (6.2%)	66 (7.6%)
30–34	203 (32.4%)	2808 (27.4%)	603 (24.4%)	222 (25.5%)
35–37	171 (27.3%)	2831 (27.6%)	632 (25.6%)	210 (24.1%)
38–39	115 (18.3%)	1801 (17.6%)	465 (18.8%)	167 (19.2%)
40–41	68 (10.8%)	1357 (13.2%)	381 (15.4%)	140 (16.1%)
≥42	32 (5.1%)	908 (8.9%)	238 (9.6%)	66 (7.6%)
Female smoking habits
No	461 (76.8%)	7459 (76.1%)	1747 (73.9%)	619 (74.2%)	0.23
Current	108 (18.0%)	1889 (19.3%)	490 (20.7%)	168 (20.1%)
Past	31 (5.2%)	450 (4.6%)	126 (5.3%)	47 (5.6%)
Female infertility diagnoses
Ovulatory factor	176 (28.3%)	2514 (24.8%)*	596 (24.3%)†	287 (33.0%)*,†	<0.01
Tubal factor	45 (7.2%)	700 (6.9%)*	219 (8.9%)*	65 (7.5%)	<0.01
Uterine factor	17 (2.7%)	312 (3.1%)*	108 (4.4%)*	31 (3.6%)	<0.01
Endometriosis	62 (10.0%)*	830 (8.2%)†	181 (7.4%)	43 (5.0%)*,†	<0.01
Other female diagnoses	15 (2.4%)	181 (1.8%)	48 (2.0%)	27 (3.1%)	0.04
Male age (years)	38.1 ± 5.1*	38.3 ± 5.4†	38.5 ± 5.6	39.0 ± 6.1*,†	<0.01
<30	19 (3.0%)	262 (2.6%)	70 (2.8%)	32 (3.7%)
30–34	133 (21.2%)	2182 (21.3%)	482 (19.5%)	149 (17.0%)
35–39	262 (41.8%)	4152 (40.5%)	948 (38.3%)	327 (37.5%)
40–44	154 (24.6%)	2533 (24.7%)	696 (28.2%)	250 (28.7%)
45–49	42 (6.7%)	761 (7.4%)	181 (7.3%)	70 (8.0%)
50–54	11 (1.8%)	230 (2.2%)	61 (2.5%)	25 (2.9%)
≥55	6 (1.0%)	123 (1.2%)	34 (1.4%)	18 (2.1%)
Male factor infertility	219 (35.2%)*,†	4074 (40.2%)	1007 (41.1%)*	387 (44.6%)†	<0.01
Total dose of gonadotrophins (IU) (mean)	2174.7 ± 887.2*	2252.9 ± 931.7†	2394.6 ± 905.2*,†	2603.5 ± 939.4*,†	<0.01
Oocytes retrieved (mean)	10.5 ± 7.1	10.8 ± 7.2	10.7 ± 7.2	10.8 ± 7.7	0.78
Embryos transferred/cryopreserved (mean)	2.9 ± 2.9	2.9 ± 3.0*	2.7 ± 2.9*	2.6 ± 3.0	0.01
No viable embryos for transfer/cryopreservation	103 (16.4%)*	1937 (18.9%)	525 (21.2%)*	189 (21.7%)	<0.01
Fresh embryo transfer	324 (66.0%)	5136 (65.5%)	1174 (64.1%)	406 (63.4%)	0.50
Blastocyst stage embryo transfer	174 (53.7%)*	2602 (50.7%)†	568 (48.4%)	179 (44.1%)*,†	0.02
Single embryo transfer	137 (42.3%)	2028 (39.5%)	446 (38.0%)	150 (36.9%)	0.38
Elective frozen embryo transfer	167 (34.0%)	2705 (34.5%)	658 (35.9%)	234 (36.6%)	0.50

*^,†

Numbers with the same superscript were statistically significant (P < 0.05), following Bonferroni-adjusted pairwise comparison. Data are n (%), or mean ± SD.

The number of oocytes retrieved did not vary significantly according to BMI class, while the overweight group presented slightly fewer embryos when compared with the normal weight group. Among the fresh embryo transfers at blastocyst stage, women with obesity (44.1%) had fewer transfers than women with normal and low weight (50.7% and 53.7% in normal and low BMI, respectively, P < 0.01). There were 5.1% of couples with embryos remaining, despite the absence of a live birth.

CLBR

The unadjusted CLBR, stratified by female BMI category, is shown in Fig. 1. Women with overweight and obesity presented lower CLBR (36.8% and 33.1%, respectively) when compared to those of normal weight (41.4%).

Figure 1.

Unadjusted cumulative live birth rates according to BMI category (n = 14 056). *^,†Numbers with the same superscript were statistically significant (P < 0.05), following Bonferroni-adjusted pairwise comparison. ET, embryo transfer; FET, frozen embryo transfer.

To assess the influence of female age and BMI on CLBR, two multivariable regression models were developed. BMI was added to the models either as an ordinal categorical variable (Model 1) or as a continuous variable (Model 2) using the best-fitting fractional polynomial for female age and oocyte number. In Model 1, when compared with the normal weight group, CLBR was lower in the overweight group (adjusted odds ratio (aOR) 0.86, 95% CI 0.77–0.96) and in the obese group (aOR 0.74, 95% CI 0.62–0.87).

The relation between female age and BMI in terms of predicted CLBR (Model 1, presented in detail in Supplementary Table SI) is shown in Table II. CLBR decreased with an increase in BMI for all ages, albeit with a less marked influence as maternal age advanced. For female ages between 30 and 35 years, a reduction from obesity/overweight subgroups to overweight/normal-weight subgroups within the following year would be numerically beneficial in terms of an overall increase in CLBR. Meanwhile, for the females between 36 and 38 years of age only a reduction from obesity to normal weight in the following year would potentially be beneficial. Finally, for women aged 39 years or more, a year-long reduction in BMI group would no longer be sufficient to compensate for age-related decline following a 1-year delay in treatment. This issue becomes even more evident when one accounts also for the mean decrease in CLBRs per year of −0.90% (95% CI −1.78% to −0.02%) owing to male ageing.

Table II

Relation between female age and BMI in terms of predicted cumulative live birth rate (Model 1).

		Female BMI (kg/m2)
	CLBR	<18.5	18.5–24.9	25.0–29.9	≥30.0
Female age (years)	30	56.4%	57.7%	54.7%	51.3%
	31	56.7%	58.0%	55.0%	51.6%
	32	56.4%	57.7%	54.7%	51.3%
	33	55.6%	56.8%	53.8%	50.4%
	34	54.0%	55.3%	52.3%	48.9%
	35	51.8%	53.1%	50.0%	46.6%
	36	48.7%	50.0%	46.9%	43.6%
	37	44.8%	46.1%	43.1%	39.7%
	38	40.1%	41.4%	38.4%	35.2%
	39	34.7%	35.9%	33.0%	30.1%
	40	28.7%	29.8%	27.2%	24.5%
	41	22.6%	23.5%	21.3%	19.0%
	42	16.7%	17.5%	15.7%	13.9%
	43	11.5%	12.1%	10.8%	9.5%

For female ages shadowed in green, a reduction from obesity/overweight subgroups to overweight/normal-weight subgroups within the following year would be numerically beneficial. Meanwhile, for the female ages shadowed in yellow, only a reduction from obesity to normal weight in the following year would be numerically beneficial. Finally, for women 39 years old or older, shadowed in red, a year-long reduction in BMI group would no longer be numerically sufficient to compensate for age-related decline following a 1-year delay in starting treatment. CLBR, cumulative live birth rate.

Model 2 (presented in detail in Supplementary Table SI) assesses the combined effect of BMI and female age on CLBR, using shorter (i.e. 3-month) time intervals, as presented in Fig. 2. This data is also presented in Supplementary Table SII and as an online CLBR interactive calculator (http://clbrcalculator.med.up.pt/). For example, for a 39-year-old female, only a decrease of at least 4 kg/m² in 3 months would be numerically beneficial when accounting for the detrimental effect of ageing during that same time interval. On the contrary, a woman aged 30–33.25 years would only need to lose 1 kg/m² in 3 months to have an improvement in the estimated CLBR. A mean decrease in CLBRs per year of −0.91% (95% CI −1.78% to −0.45%) should also be accounted for by male ageing. The complete regression models are presented in Supplementary Table SI.

Figure 2.

Relation between female age and BMI in terms of predicted cumulative live birth rates (Model 2). For example, it is estimated that, for a 39-year-old female, only a drop in BMI of 4 kg/m² in 3 months would compensate for the detrimental effect of ageing during that time. Conversely, a woman between 30 and 33.25 years of age will have an estimated improvement in cumulative live birth rates (CLBRs) with only a loss of 1 kg/m² in 3 months. For heterosexual couples, a mean decrease in CLBRs per year of −0.91% (95% CI between −1.78% and −0.45%) should also be accounted for owing to male ageing. These data are also presented in table format (Supplementary Table SII) and as an online CBLR interactive calculator (http://clbrcalculator.med.up.pt/). The full regression model is presented in Supplementary Table SI. *BMI decrease required to compensate for age-related decline in cumulative livebirth rates following a 3-month delay in treatment initiation to attempt weight-loss.

Time to pregnancy

A Kaplan–Meier curve presenting the time to embryo transfer (after oocyte retrieval) leading to a live birth is shown in Fig. 3. The mean time to pregnancy was significantly higher for the overweight (3.4 months, 95% CI 3.1–3.8) and obese groups (4.0 months, 95% CI 3.4–4.7) than the reference group (3.0 months, 95% CI 2.9–3.2).

Figure 3.

Time to embryo transfer after oocyte retrieval leading to a live birth.

Discussion

In our study, CLBR was negatively influenced by an increase in BMI and age, as previously described (Goldman et al., 2019; Kluge et al., 2019; Grzegorczyk-Martin et al., 2020; Shen et al., 2022). Our main finding was attesting that reducing BMI within the following year from obesity to overweight, or overweight to normal weight, could be beneficial up to 35 years old. These findings, presented from the 5-year period analysed, are consistent with Goldman et al. (2019), which is reassuring since his study may have failed to capture many FETs owing to the short timeframe for FET cycle inclusion (2 years in total). Indirect evidence of this is that the average number of FET cycles per cycle in our study was 1.20 (16 927/14 123), while in the former study, it was 0.31. Our study also adds to these findings by using age and BMI as a continuous variable, which allows for the analysis of CLBR in a 3-month time frame and changes in BMI as small as 1 kg/m². Furthermore, we included male age in the analysis.

Weight loss is often recommended for infertile women with obesity, even though little is known regarding whether taking the time to achieve a particular BMI is beneficial or harmful to the likelihood of live birth. Moreover, many centres even establish BMI cut-offs to allow ART treatment (Turner-McGrievy and Grant, 2015).

Meaningful weight loss often takes at least a year to achieve (Kim et al., 2020). There is a diversity of therapeutic approaches for losing weight including lifestyle interventions, pharmacotherapy, and bariatric surgery. However, the success of each alternative is heterogeneous, demonstrating a multifactorial and individualized response (Severin et al., 2019). Additionally, there is concern regarding the potential malnutrition/malabsorption caused by bariatric surgery (Caughey, 2015; Luck et al., 2017).

The apprehension for postponing treatment is even greater when significant loss is required (i.e. from obese to overweight or normal weight, as our results proposed). If this loss takes more than 1 year or does not ultimately lead to the target weight, both factors may compound to decrease CLBR further. Considerable thought is needed when refusing ART for patients with obesity based only on BMI, particularly if they are not guided into a well-designed lifestyle programme.

Additionally, a large proportion of women were not willing to lose weight if the delay period for ART was longer than 3 months (Sacha et al., 2018). For this reason, our second model predicting CLBR according to increments of trimesters and units of BMI (kg/m²) may be of best clinical use. For example, a 31-year-old woman with a BMI of 34 kg/m² had an estimated CLBR of 53.25%. In this scenario, even a reduction of just 1 or 2 kg/m² in BMI over the course of 3 months would theoretically improve her CLBR to 53.64% or 54.05%, respectively. To decide between immediate IVF and weight loss, weighing the already high CLBR, the feasibility of weight loss and the potential improvement on perinatal outcomes is crucial, and patient’s preferences should be considered. In contrast, a 37-year-old woman with a BMI of 34 kg/m² was shown to have an estimated CLBR of 41.27%. For an improvement in CLBR rate in a 3-month delay, the decrease in BMI must be ≥3 kg/m² to compensate for the age-related decline. This corresponds to 7–13 kg, depending on height and initial weight, which is challenging in only 3 months and should be advised with caution. Moreover, short-term weight loss of higher magnitudes (≥4 kg/m²) is usually achieved only by bariatric surgery, after which it is recommended to delay pregnancy for 12–18 months (Beard et al., 2008; Mechanick et al., 2019). This delay would defeat the purpose of such a rapid loss and aggravate the impact of ageing on CLBR.

Furthermore, the expected decrease in CLBRs per year owing to male ageing was −0.91% and should be accounted for. In this manner, the information provided enables clearer discussions on weight loss and potential benefit for the individual patient, contributing to a tailored approach to treatment. To aid in the decision-making between patients and their physician, we developed an online CBLR interactive calculator (http://clbrcalculator.med.up.pt/) based on the results above.

Additional strengths of our study were the use of CLBR as a primary outcome and the large sample size, including data from 16 clinics, contributing to the robustness and real-life clinical extrapolation of the results. Regarding the limitations of our study, the main weakness is its retrospective nature. The groups may not be completely comparable and there may be unmeasured confounding. To minimize this limitation, the outcomes studied were adjusted for known confounding factors using multivariable regression; however, this still may have been insufficient. There was a significantly higher number of cases of male factor infertility in the overweight or obese group. Even though the study did not measure the BMI of the males specifically, previous studies have shown a positive correlation of BMI between couples (Cobb et al., 2015) and that a higher male BMI is negatively correlated with semen parameters (Eisenberg et al., 2013). Despite adjusting for male factor infertility in the multivariable regression, it may not have fully addressed the potential confounding effect. We were also not able to adjust for medical co-morbidities. Another relevant limitation is the reduced number of women in the extreme BMI categories, thus limiting the extrapolation of these findings in such cases. Information was also not available on female weight prior to each subsequential embryo transfer, or other measures of obesity for the couple (anthropometric measurements or metabolic parameters), which would have added accuracy on the state of health of the patients.

When interpreting these results caution is also needed as we did not evaluate the actual effect of individual weight loss on individual patient outcomes. Instead, we used retrospective interpatient comparisons to perform an extrapolation of the combined effect of weight loss and ageing in a fixed period on CLBR. Moreover, these results are only valid if the assumption that weight loss alone over a fixed period can, in itself, correct the influence that previous overweight and obesity may have on ovarian response, the quality of oocytes and embryos, endometrial receptivity, miscarriage rate and, ultimately, CLBR, holds true. To that extent, a recent study national register-based case–control study attempted to confirm that assumption by comparing women who underwent IVF after bariatric surgery with women of the same age and BMI at IVF start and found no significant difference in CLBR even though the numbers of retrieved oocytes (7.6 versus 8.9, respectively P = 0.005) and frozen embryos (1.0 versus 1.5, respectively P = 0.041) were significantly fewer in the bariatric surgery group (Nilsson-Condori et al., 2022).

The ideal approach to understanding the impact of weight loss would have been to perform a large randomized controlled trial assessing the effect of multidisciplinary lifestyle intervention programmes on weight reduction, IVF and, subsequently, CLBR. However, thus far, the multiple preceding attempts to perform such studies have been of limited value or even rendered unfeasible owing to the low sample sizes included in some, and the low patient adherence or scarce follow-up in others (Sim et al., 2014; Mutsaerts et al., 2016; Einarsson et al., 2017; Espinós et al., 2017). Until further developments, our data may contribute to the in-office discussion of the impact of a specific age and weight on fertility outcomes.

In conclusion, there may be potential benefit in weight loss strategies prior to ART, particularly in women under 35 years of age with BMI ≥25 kg/m². For those above this age, weight loss should be considerable or occur in shorter timeframes to potentially avoid the hindering effect of advancing female age on CLBR. A tailored approach for weight loss, according to age, may perhaps be the best course of action.

Data availability

The data underlying this article will be shared upon reasonable request to the corresponding author and IVIRMA.

Authors’ roles

F.R., M.D.R., and S.S.-R. developed the concept, designed the study, and analysed the data. J.A.G.-V., N.G., J.B., and S.R.S. assisted in the preparation of the study protocol. F.R., M.C.-P., and S.S.-R. designed the CLBR calculator and M.C.-P. developed the web-application. All authors contributed to the interpretation of the results and to the writing and revising of the article.

Funding

None.

Conflict of interest

None to declare.