Abstract
Background
This study examines the relationship between overweight and obesity and fertility in the context of China’s fertility. Given the inconsistent results in previous research, which mostly focused on females, our study targets couples in Guangzhou. We investigate the relationship between Body Mass Index (BMI) and time-to-pregnancy (TTP) to provide evidence-based strategies for enhancing reproductive outcomes in China.
Methods
This cohort study, utilizing the National Free Pre-pregnancy Checkups Project (NFPCP), employs a Cox regression model to assess the associations between different BMI categories and TTP. Heatmaps are utilized to investigate the association between various BMI combinations of couples and TTP. Additionally, restricted cubic spline (RCS) curves were used to explore the impact of different ranges of male and female BMI on TTP.
Results
The results showed that females and males classified as overweight and obese (fecundability ratios (FR) 0.78, 95% CI 0.64, 0.95 for females; FR 0.86, 95% CI 0.76, 0.97 for males) had longer TTP compared with those in the normal weight category, regardless of controlled covariates, while those classified as underweight also had longer TTP, but the difference was not statistically significant (P > 0.05). Across all BMI combinations, couples in the overweight and obese groups exhibited the longest TTP, experiencing a 34% increase in TTP compared to couples in the normal BMI combination (FR 0.66; 95% CI 0.50, 0.85). After adjusting for all covariates in the RCS models, a female BMI surpassing 23.65 or a male BMI within the range of 23.40 to 29.44 was significantly associated with an increase in TTP (FR<1).
Conclusions
Increased BMI in both females and males is associated with a certain predictive effect on prolonged TTP. Scientific BMI management is crucial for couples preparing to conceive.
Keywords: Infertility, Body mass index, Time-to-pregnancy, NFPCP, Cohort study
Background
The increasing global challenges of low fertility underscore a growing concern, particularly in China. According to the key metrics of China’s seventh population census in 2021, the total fertility rate was exceptionally low at 1.30 in 2020, revealing a substantial deficit compared to the population replacement level of 2.1 [1]. Recent epidemiological surveys indicate a declining trend in the fertility of women of childbearing age. A survey involving 10,742 Chinese women preparing for pregnancy revealed a 25% incidence of infertility, indicating a youthful and upward trend [2]. Infertility can lead to a cascade of adverse consequences for individuals, families, and society [3], necessitating an urgent exploration of factors contributing to delayed or impaired fertility. Scholars in public health advocate the concept of " time-to-pregnancy (TTP),” denoting the duration for a woman to prepare and successfully conceive, with variations between couples [4]. A shorter TTP correlates with higher fertility [5]. In comparison to other biological indicators, TTP is deemed a more objective and practical measure for evaluating fertility. Moreover, the studies uncovered that a prolonged TTP adversely impacts the psychological well-being of couples, pregnancy complications, and the health of newborns and fetuses [6–8].
Simultaneously, China grapples with growing concerns regarding the rising prevalence of overweight and obesity. Over the last four decades, China has witnessed a swift escalation in its overweight and obese population. Based on Chinese criteria, the most recent prevalence rates of overweight and obesity among adults (≥ 18 years old) in China, recorded from 2015 to 2019, were 34.3% and 16.4%, respectively [9]. Another survey revealed that the prevalence of underweight among Chinese women has surged to 7.8% [10]. This intricate interplay of demographic shifts poses significant challenges to public health, urging a nuanced exploration of factors influencing reproductive outcomes.
The body mass index (BMI) is a measure of body mass (kg)/height2 (m2), used to indirectly assess the body’s fat content [11]. It is a widely used indicator internationally for measuring and diagnosing overweight and obesity, and it has been recognized as a potential contributor to fertility outcomes. Recent research has extensively examined the association between BMI and fertility. Most studies focus on women, consistently finding that higher BMI is linked with decreased fertility [12–15]. Some studies indicate that women with a low BMI have a higher risk of infertility, and at higher BMI levels, there are no significant differences in infertility risk [16]. Moreover, other study suggests no difference in fertility among underweight, normal weight, and overweight/obese women [17].
The research on the relationship between BMI and fertility in males primarily focuses on the association between BMI and semen. There is limited research specifically addressing how male BMI affects TTP. Meta-analysis has have indicated that increased BMI in men can negatively affect fertility outcomes [18]. Additionally, some studies suggest that underweight men may have poorer sperm quality and longer TTP [19]. Furthermore, when studies have included BMI data for both partners, the findings have been inconsistent [20–22].
Understanding the association between the BMI of couples and TTP is crucial for informing public health interventions and improving reproductive planning strategies. However, limited research has specifically focused on the influence of the BMI of couples on TTP. Given the current context of low fertility in China, coupled with the increasingly serious issue of overweight/obesity, studying the impact of BMI on TTP is particularly important. Our study hypothesizes that both high and low BMI in couples are related to TTP and there may be an interaction between the BMI of the partners. The findings of this study will offer evidence-based recommendations for healthcare professionals, policymakers, and individuals seeking to optimize their reproductive health.
Materials and methods
Data source
Guangzhou, as an international mega-city, a national central city, and a core city in the Guangdong-Hong Kong-Macao Greater Bay Area, possesses a highly representative population structure and pattern within the Pearl River Delta region [23]. The National Free Pre-pregnancy Checkups Project (NFPCP) is a national preconception healthcare service in China. It aims to provide free preconception health examinations, counseling, and follow-up of pregnancy outcomes for reproductive-aged couples planning to conceive [24]. In Guangzhou, all residents planning for pregnancy are eligible to participate in the NFPCP, not limited to rural populations. A cohort study was conducted at Guangzhou Baiyun District Maternal and Child Health Hospital. The hospital is the largest designated institution for NFPCP in Guangzhou, conducting more than 10,000 pairs of pre-pregnancy examinations annually, accounting for over 13.5% of Guangzhou. Based on the designed follow-up of the NFPCP [24, 25], we incorporated a follow-up visit 13–15 months after the examination. This focused on inquiring whether the woman continued preparing for pregnancy after participating in the pre-pregnancy examination, including details about her diet, sleep, and exercise during the preparation period. This study received approval from the Medical Ethics Committee at Guangzhou Baiyun District Maternal and Child Health Hospital. Every participant provided written informed consent before enrolling in the study. This study is registered with the China Clinical Trials Registry (www.clinicaltrials.gov), under the registration number ChiCTR2300068809, with the initial trial registration on 01/03/2023.
Population
In this study, we selected couples who participated in the NFPCP from January 2022 to June 2022 as the research subjects. At the 13th to 15th month after the examination, we conducted telephone follow-ups to inquire about pregnancy preparation and subsequent pregnancy. We also tracked the pregnancy outcomes of pregnant women. The inclusion criteria were (1) Couples with a female partner aged between 20 and 49 and a male partner aged 22 or older; (2) Couples who were not pregnant at the time of examination; (3) Both partners self-reported at premarital examination that they were intending to get pregnant and without contraception. The exclusion criteria were (1) Couples with a female partner positive for cytomegalovirus or Toxoplasma gondii IgM antibody, or if one of the couples had syphilis, HIV, or other diseases requiring treatment to delay the pregnancy plan; (2) Those with missing data on height or weight; (3) One of the couples did not agree to cooperate with the survey or to participate in this study; (4) Couples who were pregnant during the month were examined; (5) Couples who were planning to use or had already used assisted reproductive technology (ART). A total of 4,942 couples participated in the NFPCP, 3,742 couples met the inclusion criteria. After applying the exclusion criteria, a total of 1,684 couples were enrolled (Fig. 1).
Fig. 1.
Flow chart of eligible participants’ selection
Exposures and outcome
In this study, BMI was treated as an exposure variable. BMI was calculated as the body mass in kilograms divided by height in meters squared. Under the condition of informed and voluntary choice, the height and weight of the couples would be measured by professionally trained medical personnel using a smart connected height and weight measuring device at NFPCP. Measurement requirements for height and weight include: participants should stand barefoot in a neutral position, with shoes and outer garments removed, and the weight data should be recorded once it has stabilized. According to the guidelines of the Chinese Working Group on Obesity (WGOC), the BMI threshold is defined as follows: underweight < 18.5 kg/m2, normal weight 18.5–23.9 kg/m2, overweight 24–27.9 kg/m2, and obesity ≥ 28 kg/m2. BMI was categorized into three groups: “Underweight,” “Normal weight,” and “Overweight and Obese”.
The primary outcome was TTP.
TTP for pregnant couples = (date of last menstrual before pregnancy - date of last menstrual at examination)/30 + 1;
TTP for unpregnant couples = (date of last menstrual at follow-up - date of last menstrual at examination)/30.
Based on previous research on the calculation methods for TTP [26, 27], if conception is confirmed and occurred within the following cycle, add 1 cycle to the TTP to account for the actual conception time.
If the couple experiences an interrupted pregnancy during pregnancy preparation, the time not preparing for pregnancy will be subtracted when calculating TTP. Additionally, in this study, self-reported pregnancies were all clinically confirmed through testing in the hospital.
Covariates
Variable selection was based on identifying variables with a known or suspected effect on the outcome of interest and/or showing P < 0.05 on univariable analysis. The covariates for this study included age, occupation, tobacco exposure (no, yes), regular menstruation (no, yes), poor sleep (no, yes), sleep time, frequent eating of takeaway (no, yes), regular intake of nutritional supplements (no, yes), and exercise frequency. All covariates pertain to the preconception period. The age of the couples was recorded at the time of their participation in the examination or when they started preparing for pregnancy after the examination. Occupation was categorized as “Business”, “Farmer”, “Housework”, “Services”, “Teacher/Civil servant/Office clerk”, “Worker”, or “Others”. Tobacco exposure was defined as active smoking or exposure to passive smoking for an average of 5 min or more per day. Regular menstruation status was determined through the doctor’s inquiry and judgment during the examination. The study also considered the wife’s sleep situation during pregnancy preparation, and professionals inquired about frequent difficulties falling asleep or poor sleep quality. Additionally, we inquired about the time of falling asleep during the preparation period. Eating takeaway frequently was defined as once or more a day, and taking in nutritional supplements was defined as regularly supplementing with nutrients other than folic acid, such as vitamins, Docosahexaenoic Acid (DHA), bird’s nest, sea cucumber, etc. Exercise frequency referred to the frequency of moderate physical activity (exceeding 30 min each time) per week, categorized as “1–3 times per week”, “>3 times per week”, or “<1 time per week).
Statistical analysis
EpiData (version 3.1) was utilized for data input, and R (version 4.0.0) was employed for statistical analysis. Group differences were compared using the χ2 test (for categorical variables), the Wilcoxon rank-sum test (for non-normally distributed continuous variables), and analysis of variance (for normally distributed continuous variables). Continuous variables were described using the mean and SD (standard deviation), while categorical variables were described as frequency and percentages. To handle missing data, we employed multiple imputation by chained equations (MICE). There were 2.49% of the data on sleep time was missing, and we performed 50 imputations using the MICE algorithm to fill in these missing data. And the imputation model incorporated the following covariates: couples’ BMI, age, occupation, tobacco exposure, and females’ regular menstruation, poor sleep, eating takeaway frequently, taking in nutritional supplements, exercise frequency and TTP. We used Cox regression models to assess the correlation between different BMI and TTP, expressing the relationship with fecundability ratios (FR) values and 95% confidence intervals (95% CI). FR > 1 indicates a shorter TTP and increased fertility; FR < 1 indicates a longer TTP and decreased fertility. In the analysis, we developed four models: Model 1 without any adjustments, Model 2 adjusted for Spouse’s BMI, Model 3 adjusted for Spouse’s BMI and couples’ age, and Model 4 adjusted for all variables. In the Cox regression model, we also explored the multiplicative interactions between the BMI of couples. The assumption of equal proportional hazards for covariates in the regression model was validated using the Schoenfeld residual method. RCS curves were used to explore the impact of different ranges of male and female BMI on TTP. Selecting the number of nodes for RCS using the Akechi information criterion (AIC). In the spline models, adjustments were made for all covariates.
Furthermore, we utilized heat maps to explore the TTP of couples with different BMI combinations while controlling for all variables. Finally, sensitivity analyses were conducted to explore whether live birth, chronic diseases, or polycystic ovary syndrome (PCOS) status impacted the findings. A p-value < 0.05 was considered statistically significant.
Results
Baseline characteristics of study participants
The 1684 couples included in the study contributed to 11,973 cycles and 1127 pregnancies. The average pre-pregnancy BMI of the female and male partners was 20.77 (SD: 2.86) and 23.63 (SD: 3.34), respectively. The distribution of underweight (UW), normal weight (NW), and overweight and obese (OW/OB) in the female partners before pregnancy was 326 (19.36%), 1147 (68.11%), and 211 (12.53%), respectively; while in the male partners, it was 74 (4.39%), 892 (52.97%), and 718 (42.64%), respectively. The pregnancy rates for women with preconception body weights categorized as UW, NW, and OW/OB were 68.4% (223/326), 68.4% (784/1147), and 56.9% (120/211), respectively. For men, the pregnancy rates for UW, NW, and OW/OB were 70.3% (52/74), 69.5% (620/892), and 63.4% (455/718), respectively. The median TTP for female UW, NW, and OW/OB groups were 7.4, 7.3, and 10.5 months, respectively, while for males they were 7.2, 7.0, and 8.4 months, respectively (Fig. 2). Table 1 displays the demographic characteristics of couples included in the BMI stratification. Within diverse BMI groups among females, significant statistical differences emerge in the spouse’s BMI, female age, proportion of regular menstrual cycles, spouse’s age, and the prevalence of tobacco exposure in spouses. Among males in distinct BMI categories, there were significant differences in their spouses’ BMI, male age, and spouses’ age.
Fig. 2.
Pregnancy waiting time curves for different BMI categories in females and males. Note: UW, underweight; UW, normal weight; OW/OB, overweight and obese. The Y-axis represents the proportion of samples that remain in the study (i.e., not yet pregnant) at each time point. (A): Pregnancy waiting time curves for different BMI categories in females. (B): Pregnancy waiting time curves for different BMI categories in males
Table 1.
Demographic characteristics of couples included in this study, stratified by BMI categories
| Characteristics | Female BMI | Male BMI | ||||||
|---|---|---|---|---|---|---|---|---|
| Underweight (n = 326) | Normal weight (n = 1147) | Overweight and obese (n = 211) | P value | Underweight (n = 74) | Normal weight (n = 892) | Overweight and obese (n = 718) | P value | |
| BMI (mean (SD)) | 17.54 (0.73) | 20.66 (1.39) | 26.37 (2.51) | < 0.001 | 17.69 (0.61) | 21.75 (1.46) | 26.56 (2.62) | < 0.001 |
| Spouse’s BMI (mean (SD)) | 23.09(3.28) | 23.58(3.21) | 24.70(3.89) | < 0.001 | 19.75(2.64) | 20.48(2.59) | 21.23(3.11) | < 0.001 |
| Age (mean (SD)) | 27.97 (3.31) | 28.86 (3.49) | 29.38 (4.28) | < 0.001 | 28.74 (3.68) | 30.34 (4.13) | 30.84 (4.75) | < 0.001 |
| Spouse’s Age (mean (SD)) | 29.54(3.82) | 30.59(4.43) | 31.45(4.90) | < 0.001 | 27.32(2.74) | 28.61(3.50) | 29.09(3.72) | < 0.001 |
| Occupation (%) | 0.195 | 0.094 | ||||||
| Business | 12 (3.68) | 66 (5.75) | 18 (8.53) | 9 (12.16) | 108 (12.11) | 100 (13.93) | ||
| Farmer | 2 (0.61) | 12 (1.05) | 6 (2.84) | 2 (2.70) | 14 (1.57) | 7 (0.97) | ||
| Housework | 9 (2.76) | 34 (2.96) | 7 (3.32) | 2 (2.70) | 9 (1.01) | 6 (0.84) | ||
| Services | 52 (15.95) | 207 (18.05) | 38 (18.01) | 22 (29.73) | 154 (17.26) | 114 (15.88) | ||
| Teacher/ Civil servant/Office clerk | 118 (36.20) | 420 (36.62) | 72 (34.12) | 11 (14.86) | 246 (27.58) | 213 (29.67) | ||
| Worker | 34 (10.43) | 88 (7.67) | 16 (7.58) | 10 (13.51) | 129 (14.46) | 95 (13.23) | ||
| Others | 99 (30.37) | 320 (27.90) | 54 (25.59) | 18 (24.32) | 232 (26.01) | 183 (25.49) | ||
| Spouse’s Occupation (%) | 0.048 | 0.137 | ||||||
| Business | 31 (9.51) | 158 (13.78) | 28 (13.27) | 5 (6.76) | 51 (5.72) | 40 (5.57) | ||
| Farmer | 2 (0.61) | 15 (1.31) | 6 (2.84) | 1 (1.35) | 12 (1.35) | 7 (0.97) | ||
| Housework | 2 (0.61) | 11 (0.96) | 4 (1.90) | 1 (1.35) | 30 (3.36) | 19 (2.65) | ||
| Services | 56 (17.18) | 195 (17.00) | 39 (18.48) | 13 (17.57) | 142 (15.92) | 142 (19.78) | ||
| Teacher/ Civil servant/Office clerk | 82 (25.15) | 340 (29.64) | 48 (22.75) | 20 (27.03) | 311 (34.87) | 279 (38.86) | ||
| Worker | 52 (15.95) | 150 (13.08) | 32 (15.17) | 5 (6.76) | 79 (8.86) | 54 (7.52) | ||
| Others | 101 (30.98) | 278 (24.24) | 54 (25.59) | 29 (39.19) | 267 (29.93) | 177 (24.65) | ||
| Tobacco exposure = yes (%) | 15 (4.60) | 51 (4.45) | 10 (4.74) | 0.979 | 31 (41.89) | 268 (30.04) | 243 (33.84) | 0.050 |
| Spouse’s Tobacco exposure = yes (%) | 104 (31.90) | 347 (30.25) | 91 (43.13) | 0.001 | 6 (8.11) | 37 (4.15) | 33 (4.60) | 0.286 |
| Regular menstruation = yes (%) | 281 (86.20) | 1015 (88.49) | 167 (79.15) | 0.001 | ||||
| Poor sleep = yes (%) | 48 (14.72) | 159 (13.86) | 29 (13.74) | 0.918 | ||||
| Sleep time (mean (SD)) | 23.59 (0.92) | 23.64 (0.94) | 23.74 (1.05) | 0.230 | ||||
| Eating takeaway frequently = yes (%) | 88 (27.08) | 346 (30.17) | 69 (32.70) | 0.343 | ||||
| Taking in nutritional supplements = yes (%) | 107 (32.82) | 322 (28.07) | 53 (25.12) | 0.119 | ||||
| Exercise frequency (%) | 0.095 | |||||||
| 1–3 times per week | 110 (33.74) | 424 (36.97) | 66 (31.28) | |||||
| >3 times per week | 41 (12.58) | 189 (16.48) | 37 (17.54) | |||||
| <1 time per week | 175 (53.68) | 534 (46.56) | 108 (51.18) | |||||
The association between pre-pregnancy BMI and TTP
Four Cox logistic regression models were constructed to investigate the potential impact of couples’ BMI on TTP. Table 2 presents the FRs and 95% CIs for the association between different pre-pregnancy BMI and TTP in the four regression models. The results indicated that in models 1, 2, 3 and 4, overweight and obese females (FR 0.71, 95% CI 0.59,0.87; FR 0.74, 95% CI 0.61, 0.90; FR 0.76, 95% CI 0.63, 0.93; FR 0.78, 95% CI 0.64, 0.95) and males (FR 0.80, 95% CI 0.71,0.91; FR 0.82, 95% CI 0.73, 0.93; FR 0.84, 95% CI 0.75, 0.95; FR 0.86, 95% CI 0.76, 0.97) experienced longer TTP compared with normal weight. These results were consistent regardless of the covariates controlled for in the models. Conversely, underweight females and males also exhibited longer TTP, but the difference was not statistically significant (P > 0.05). In Cox regression models 2, 3, and 4, we further examined the interaction between male BMI and female BMI. We included the interaction term (male BMI * female BMI) in our Cox regression models. The results indicated that there was no significant interaction effect between male BMI and female BMI (all p-values > 0.05).
Table 2.
Association between pre-pregnancy BMI and TTP in females and males, Cox regression analysis
| Model 11 | Model 22 | Model 33 | Model 44 | |||||
|---|---|---|---|---|---|---|---|---|
| FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | |
| Female BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 0.98(0.84,1.13) | 0.760 | 0.98(0.84, 1.13) | 0.737 | 0.94(0.81,1.09) | 0.424 | 0.93(0.80,1.08) | 0.357 |
| Overweight and obese | 0.71(0.59,0.87) | <0.001 | 0.74 (0.61,0.90) | 0.002 | 0.76(0.63,0.93) | 0.006 | 0.78(0.64,0.95) | 0.015 |
| Male BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 1.01(0.76,1.33) | 0.967 | 1.00(0.75,1.33) | 0.967 | 0.93(0.70,1.24) | 0.624 | 0.92(0.69,1.23) | 0.584 |
| Overweight and obese | 0.80(0.71,0.91) | <0.001 | 0.82(0.73,0.93) | 0.002 | 0.84(0.75,0.95) | 0.006 | 0.86(0.76,0.97) | 0.015 |
Note: FR, fecundability ratio; 95% Cl, 95% confidence interval
1Model 1: No covariates were adjusted
2Model 2: Adjusted for spouse’s BMI
3Model 3: Adjusted for spouse’s BMI, couples’ age
4Model 4: Adjusted for spouse’s BMI, couples’ age, occupation, tobacco exposure, and females’ regular menstruation, poor sleep, sleep time, eating takeaway frequently, taking in nutritional supplements, exercise frequency
Heat map of association analysis between different BMI combinations and TTP
We combined the BMI of three different categories for females and males into nine different combinations and explored their additive interaction in the fully adjusted Cox regression model, as shown in Fig. 3. Across all BMI combinations, couples in the overweight and obese groups exhibited the longest TTP, experiencing a 34% increase in TTP compared to couples in the normal BMI combination (FR 0.66; 95% CI 0.50, 0.85).
Fig. 3.
Heat map of association analysis between different couples’ BMI combinations and TTP
Note
UW, underweight; UW, normal weight; OW/OB, overweight and obese.
The numbers in the cells of the heatmap represent FR (95% CI), and ** indicates a p-value < 0.01. If the FR value is not followed by an asterisk (*), it indicates that P > 0.05.
In the Cox regression model, we adjusted for covariates including couples’ age, occupation, tobacco exposure, and females’ regular menstruation, poor sleep, sleep time, eating takeaway frequently, taking in nutritional supplements, exercise frequency.
Restricted cubic spline model of the association between BMI and TTP in females and males
We utilized RCS to simulate and model the relationship between BMI and TTP in both female and male participants (Fig. 4). For the female BMI RCS model, three nodes were selected, and for the male BMI RCS model, four nodes were chosen based on the AIC. In the unadjusted covariate RCS model analysis of the association between BMI and TTP, a female BMI exceeding 21.05 and a male BMI ranging from 23.40 to 30.91 were associated with an increase in TTP (FR<1), and these associations were statistically significant. After adjusting for covariates, the BMI threshold for females affecting prolonged TTP (FR<1) increased from 21.05 to 23.65, while the range for males was narrowed from 23.40 to 30.91 to 23.40-29.44. This suggests that, in addition to BMI, covariates such as tobacco exposure, age, and lifestyle behaviors should also be considered in their impact on TTP.
Fig. 4.
Restricted cubic spline model of the association between BMI and TTP in females and males
Note
FR, fecundability ratio.
A: A Restricted Cubic Spline Model for Female BMI without Adjusting for confounding Factors. The P-value for non-linearity was 0.084.
B: A Restricted Cubic Spline Model for Male BMI without Adjusting for confounding Factors. The P-value for non-linearity was 0.250.
C: A Restricted Cubic Spline Model for Female BMI with Adjusting for spouse BMI, couples’ age, occupation, tobacco exposure, and females’ regular menstruation, poor sleep, sleep time, eating takeaway frequently, taking in nutritional supplements, exercise frequency. The P-value for non-linearity was 0.183.
D: A Restricted Cubic Spline Model for Male BMI with Adjusting for spouse BMI, couples’ age, occupation, tobacco exposure, and females’ regular menstruation, poor sleep, sleep time, eating takeaway frequently, taking in nutritional supplements, exercise frequency. The P-value for non-linearity was 0.195.
The black horizontal dashed line indicates the fecundability ratio (FR) = 1.
Sensitivity analyses
Sensitivity analyses were presented in Table 3. The association between BMI and TTP remained almost unchanged after excluding participants who self-reported having chronic diseases, PCOS, and retaining those with live birth outcomes (excluding biochemical pregnancies, abortions or stillbirths).
Table 3.
Association between pre-pregnancy BMI and TTP in sensitivity analyses
| Excluding those who self-reported chronic diseases (n = 1418) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Model 11 | Model 22 | Model 33 | Model44 | |||||
| FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | |
| Female BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 0.98(0.83,1.15) | 0.775 | 0.97(0.83,1.15) | 0.741 | 0.93(0.79,1.10) | 0.404 | 0.95(0.81,1.12) | 0.547 |
| Overweight and obese | 0.67(0.54,0.84) | <0.001 | 0.70(0.56,0.87) | 0.001 | 0.71(0.57,0.88) | 0.002 | 0.73(0.58,0.91) | 0.004 |
| Male BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 1.02(0.75,1.38) | 0.878 | 1.02(0.75,1.38) | 0.897 | 0.94(0.69,1.27) | 0.678 | 0.93(0.68,1.26) | 0.630 |
| Overweight and obese | 0.80(0.70,0.92) | 0.001 | 0.83(0.72,0.95) | 0.005 | 0.85(0.74,0.97) | 0.020 | 0.87(0.76,0.99) | 0.049 |
| Excluding those who self-reported PCOS (n = 1611) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Model 11 | Model 22 | Model 33 | Model 44 | |||||
| FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | |
| Female BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 0.99(0.85,1.15) | 0.900 | 0.99(0.85,1.15) | 0.916 | 0.96(0.82,1.11) | 0.548 | 0.93(0.79,1.08) | 0.315 |
| Overweight and obese | 0.74(0.61,0.91) | 0.004 | 0.77(0.63,0.94) | 0.011 | 0.80(0.66,0.98) | 0.028 | 0.79(0.64,0.97) | 0.021 |
| Male BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 0.97(0.73,1.29) | 0.838 | 0.97(0.72,1.29) | 0.808 | 0.99(0.97,1.00) | 0.448 | 0.89(0.66,1.19) | 0.438 |
| Overweight and obese | 0.81(0.72,0.92) | <0.001 | 0.83(0.73,0.94) | 0.003 | 0.85(0.75,0.96) | 0.011 | 0.87(0.77,0.98) | 0.026 |
| >Retaining those with live birth outcomes (n = 1541) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Model 11 | Model 22 | Model 33 | Model 44 | |||||
| FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | FR (95%CI) | P value | |
| Female BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 0.98(0.84,1.15) | 0.819 | 0.98(0.84,1.15) | 0.813 | 0.94(0.80,1.10) | 0.458 | 0.94(0.80,1.11) | 0.456 |
| Overweight and obese | 0.71(0.57,0.87) | 0.001 | 0.73(0.59,0.90) | 0.003 | 0.75(0.61,0.93) | 0.008 | 0.77(0.62,0.95) | 0.016 |
| Male BMI | ||||||||
| Normal weight | Reference | Reference | Reference | Reference | ||||
| Underweight | 1.03(0.77,1.40) | 0.827 | 1.02(0.76,1.39) | 0.876 | 0.94(0.69,1.27) | 0.682 | 0.93(0.68,1.26) | 0.632 |
| Overweight and obese | 0.81(0.71,0.92) | 0.002 | 0.83(0.73,0.95) | 0.005 | 0.86(0.75,0.98) | 0.023 | 0.86(0.76,0.99) | 0.030 |
Note: FR, fecundability ratio; 95% Cl, 95% confidence interval
1Model 1: No covariates were adjusted
2Model 2: Adjusted for spouse’s BMI
3Model 3: Adjusted for spouse’s BMI, couples’ age
4Model 4: Adjusted for spouse’s BMI, couples’ age, occupation, tobacco exposure, and females’ regular menstruation, poor sleep, sleep time, eating takeaway frequently, taking in nutritional supplements, exercise frequency
Discussion
In this cohort study, 1,684 couples were included, with 1,127 achieving pregnancy, constituting 66.92% of the total couples included. The observed pregnancy rate surpassed that of other NFPCP-related studies [24, 28], potentially attributed to excluding couples discontinuing efforts to conceive after the examination in our inclusion analysis. However, the pregnancy rate still seems relatively low, which may be due to China’s COVID-19 pandemic control measures at the time (including the subsequent widespread infections). Some couples may have interrupted their pregnancy preparations. During telephone follow-ups, we accounted for this interruption, so the actual preconception time for these couples did not reach one year. This factor may be the reason for the observed higher infertility rate. Additionally, when selecting study participants, we excluded 116 couples who became pregnant in the same month as their examination.
In all four different Cox regression models, individuals classified as overweight or obese, regardless of gender, were associated with a prolonged TTP compared to those classified as having a normal BMI. Nonetheless, no statistically significant differences in TTP were observed between individuals classified as underweight and those classified as normal weight. The fourth model underwent meticulous adjustments for covariates, underscoring the robustness of this study. Despite these exclusions, TTP remained prolonged for couples classified as overweight and obese, indicating the independent effect of BMI on TTP. In the sensitivity analysis, we excluded couples with self-reported PCOS, chronic diseases, and non-viable pregnancy outcomes individually. Despite these exclusions, TTP remained prolonged for couples in the overweight and obese BMI categories, affirming the robustness of the results. Simultaneously, we aggregated three BMI categories for female and male into nine combinations. In the Cox regression model adjusted for confounding factors, we found that among all BMI combinations, the TTP was 34% longer in couples with both partners being overweight or obese compared to couples with both partners having a normal weight. This suggests that when managing the weight of couples trying to conceive, special attention should be given to those where both partners are overweight or obese. Apart from couples where both partners are overweight or obese, we did not observe differences in TTP among other BMI combinations compared to normal-weight couples. This may be due to the need for a larger sample size when investigating the impact on TTP across different BMI category combinations (9 groups).
Currently, limited studies have delved into the concurrent examination of marital BMI and fertility. A study from the Danish National Birth Cohort suggested that the risk of low fertility is linked to overweight and obesity for both males and females, particularly in couples where both partners are overweight [29], aligning with our research findings. A retrospective cohort study from China indicates that, in comparison to women with a normal pre-pregnancy BMI, women who were overweight or obese before pregnancy experienced a prolonged TTP and an elevated risk of impaired fertility. However, no association was found between male BMI and TTP [22]. Another study from China focusing on couples experiencing their first pregnancy suggests that underweight, overweight, or obese status in women, and underweight status in men, were associated with prolonged TTP [30]. A cohort study from the United States revealed that, when modeled separately, the BMI of male and female partners exhibited no association with TTP. Nevertheless, in couples where both partners were classified as obese class II, fertility reduction led to a longer TTP compared to couples with a normal BMI [20–22]. A cohort study from Norway, employing logistic regression analysis, supported a J-shaped association between BMI and reduced fertility, indicating that both higher and lower BMIs are linked to a greater risk of reduced fertility [20]. Discrepancies might stem from variations in BMI classification standards, racial diversity among the study population, and differences in sample sizes compared to the aforementioned research.
In the RCS model, for females with a BMI exceeding 23.65 and males with a BMI ranging from 23.40 to 29.44, TTP is significantly prolonged, suggesting that higher BMI negatively impacts fertility for both genders. When adjusting for covariates, the BMI threshold for females affecting prolonged TTP increased from 21.05 to 23.65, while the range for males was narrowed from 23.40 to 30.91 to 23.40–29.44. This suggests that, in addition to BMI, covariates such as tobacco exposure, age, occupation, and lifestyle behaviors should also be considered in their impact on TTP. Additionally, in the RCS model, we observed that the 95% confidence intervals are broad at the extremes (both low and high BMI). This may be due to the small number of participants with underweight and obesity in the study, which made the nonlinear relationship in the RCS curve and the prolongation of TTP in males with high BMI non-significant. This suggests the necessity of increasing the sample size in future studies.
Overweight, obesity, and infertility have always been global concerns, and their interrelationships are worth exploring. In investigating the mechanism of female infertility, some studies suggest that obesity-induced systemic and tissue-specific chronic inflammation and oxidative stress can impair the meiosis and cytoplasmic maturation of oocytes [31–33], thereby reducing their developmental ability for fertilization and pre-implantation embryo development [34]. Additionally, some studies propose that the impact of obesity on female fertility is primarily attributed to alterations in the function of the hypothalamic-pituitary-ovarian (HPO) axis. Obesity is often associated with elevated circulating insulin levels, subsequently leading to increased ovarian androgen production [35]. Excessive adipose tissue is responsible for aromatizing these androgens into estrogens, inducing a negative feedback loop in the HPO axis and affecting the production of gonadotropins [36], thereby causing ovulatory dysfunction and menstrual irregularities. In males, a meta-analysis suggests a significant correlation between increased BMI and decreased seminal volume, sperm count, concentration, and viability [37]. Additionally, in animal studies, obesity is correlated with increased sperm DNA damage, but findings in human studies are inconsistent in this regard [38]. The abnormal lipid profile in obese males may lead to testicular oxidative stress, which is a common pathway for disruption in sperm function [39]. Some studies suggest that in an obese environment, the inflammatory response triggered by excessive accumulation of abdominal fat may lead to hypothalamic inflammation, thereby influencing the release of hormones from the hypothalamus and causing dysregulation of the HPG axis [40]. Furthermore, the mechanisms linking male obesity to infertility may also involve endocrine disruptions, erectile dysfunction, and physical disorders such as high scrotal temperature [41, 42]. Despite the potential existence of these mechanisms, further research is needed to elucidate the underlying molecular pathways linking BMI to infertility. Developing effective interventions for preventing and treating infertility associated with overweight and obesity also requires additional investigation.
At present, correcting obesity is considered a potential way to reverse the impact on the male reproductive system. This is achieved through improving nutritional quality, incorporating appropriate exercise, considering micronutrients, and supplementing with light therapy [43]. Achieving optimal weight or meaningful weight loss/fat reduction before conception may be a targeted intervention to improve female fertility [44]. Lifestyle interventions for obese and infertile women can enhance female reproductive function [45], thereby improving infertility.
In the context of addressing the dual challenges of low fertility and the rising prevalence of obesity, and based on our study results, we recommend incorporating BMI management into pre-pregnancy health check-ups and public health activities targeted at couples of reproductive age. We suggest that healthcare providers integrate BMI management into fertility counseling, such as tailored weight management programs for couples attempting to conceive, and education on the impact of obesity on fertility. In Canada, an interdisciplinary lifestyle intervention program was designed for obese women seeking fertility treatment. This program integrated weight management into fertility clinics and showed promising results in improving fertility, reducing pregnancy complications, and lowering healthcare costs. The program demonstrated that even modest weight loss (5–10%) before conception could significantly enhance reproductive outcomes [46]. Additionally, we advocate for policy development that addresses the broader socio-economic factors contributing to obesity and low fertility, such as urban planning that promotes physical activity, subsidies for healthy foods, or workplace wellness programs. Meanwhile, there is a need for larger-scale and more in-depth research to further clarify the association between BMI and TTP. Future research could also explore the effectiveness of specific interventions aimed at reducing pre-pregnancy BMI and their impact on TTP.
Strengths and limitation
Our research presents several distinct advantages. Firstly, unlike the previous NFPCP, which solely targeted rural populations, our study extended its scope beyond registered residency limitations, encompassing all permanent residents. Secondly, our investigation delved into both male and female BMI, constructing diverse models by adjusting for various variables. Moreover, we employed sensitivity analysis to assess the robustness of our findings. Thirdly, we explored the multiplicative and additive effects of male and female BMI, utilizing restrictive cubic splines to identify specific BMI thresholds associated with reduced fertility in both genders. This nuanced approach enriched our comprehension of the intricate relationship between body mass index and TTP. Lastly, in contrast to conventional NFPCP projects, our study introduced additional variables pertaining to TTP, encompassing aspects such as a woman’s sleep habits, frequency of takeout consumption, nutrient intake, and exercise frequency during pregnancy preparation.
However, there are some limitations to our study. Firstly, potential selection bias may arise from participation in the NFPCP and its subsequent follow-ups, as well as the selection of subjects. Individuals who actively participate in the NFPCP and follow-ups may have a more proactive attitude toward conception. Considering the international definition of infertility (inability to conceive within 12 months of unprotected intercourse) and the possibility of changes in contact information, we conducted follow-up surveys 13 to 15 months after the NFPCP. This might have underestimated the TTP for participants who did not conceive within the follow-up period, as these couples might become pregnant later. However, the results showed that the median TTP for females in the UW, NW, and OW/OB groups were 7.4, 7.3, and 10.5 months, respectively, while for males they were 7.2, 7.0, and 8.4 months, respectively, which align with our follow-up period. Additionally, 19 ART couples were excluded from the cohort, which may also have led to an underestimation of TTP. However, since most ART couples were diagnosed with infertility for various reasons in China, excluding them could provide a more accurate reflection of TTP for natural conception. Additionally, there 1,011 couples were excluded from the cohort due to missing height and weight data. This was because height and weight measurements were voluntary selection items, and some couples refused to accept the measurement for not willing to take off shoes and clothes. These excluded couples might result in selective bias, but we compared the age and occupational characteristics between excluded and included participants, there were no significant differences (P>0.05), which indicated that the occurrences of missing height and weight were random and not enough to invert our conclusion on the association between BMI and TTP of couples.
Secondly, self-reported covariates, such as lifestyle factors, may introduce recall bias, thereby potentially impacting the data’s accuracy. Furthermore, since the follow-up was conducted via phone interviews with the female participants, we did not collect information on the male partners’ lifestyle behaviors during the conception period. This may have resulted in the omission of certain male lifestyle behaviors that could influence TTP, leading to potential confounding factors not being included in the analysis.
Thirdly, it is possible that a small number of individuals did not use contraception before their check-up, which may shorten the TTP. Generally speaking, in China, participating in a free pre-pregnancy health check-up is considered the official start of preparing for pregnancy. In Guangzhou, preconception checkup clinics are adjacent to marriage registration offices, and over 60% of those attending preconception health checks are newly married couples who participate voluntarily. Therefore, we believe that this population represents those who are beginning to prepare for pregnancy. Nevertheless, there is a possibility that a small number of individuals did not use contraception before their check-up. In future research, this should be addressed with more comprehensive data.
Fourthly, our study established a correlation between BMI and fertility without delving into the underlying mechanisms, which presents an avenue for future research. Additionally, the limited geographical focus on Guangzhou may pose challenges to the study’s external validity when extrapolating the findings to a broader Chinese population. Hence, caution is warranted in generalizing these results to a wider demographic.
Conclusions
In conclusion, our study establishes a robust link between couples’ BMI and time-to-pregnancy in the urban setting of Guangzhou, China. Increased BMI in both females and males is associated with a certain predictive effect on prolonged TTP. Scientific BMI management is crucial for couples preparing to conceive. The gender-specific thresholds identified in our study provide valuable guidance for healthcare practitioners and public health initiatives aiming to improve fertility in Chinese populations.
Acknowledgements
Not applicable.
Author contributions
YZ and BL designed the study. YZ collected, analyzed the data and drafted the manuscript. YX and DG collected the data and assisted in literature search. BL and DG gave suggestions, and BL revised the manuscript. All authors contributed to the article and approved the submitted version.
Funding
The study was not funded by any organization.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethical approval and consent to participate
This study received approval from the Medical Ethics Committee at Guangzhou Baiyun District Maternal and Child Health Hospital. Every participant provided written informed consent before enrolling in the study. This study is registered with the China Clinical Trials Registry (www.clinicaltrials.gov) (registration number ChiCTR2300068809).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
This article has been updated to correct several values.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Change history
11/6/2024
A Correction to this paper has been published: 10.1186/s12889-024-20528-3
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.