Associations between weight-adjusted-waist index and infertility: Results from NHANES 2013 to 2020.

In conclusion, our study found that elevated levels of WWI were strongly associated with the risk of developing infertility, as we found a striking positive correlation between infertility and WWI among U.S. adults. It might eventually be utilized as a straightforward anthropometric indicator to forecast infertility.

Infertility is the inability to become pregnant after a year of regular, unprotected sexual activity. [ 1 ] An identifiable reason can be found in 85% of infertile couples. The remaining 15% of infertile couples experience “unexplained infertility.” Smoking and obesity are 2 lifestyle and environmental variables that might harm fertility. [ 2 , 3 ] Infertility has become a global public health issue. The physical and mental health of a woman is affected, and infertility has substantial public health repercussions. [ 4 ] A worldwide epidemic of obesity has emerged. According to the World Health Organization, 60% of women in Europe and the U.S are obese. In comparison, obesity now affects more than 20% of American women of childbearing age and affects 30% of women overall. [ 5 , 6 ] The adverse effects of obesity on reproductive physiology are known, as obese women often experience irregular menstruation, impaired ovulation, and endometriosis. [ 7 , 8 ] In spite of its popularity as a measure of obesity, [ 9 ] body mass index (BMI) is limited in its ability to distinguish between lean and fat tissues. [ 10 – 12 ] Waist circumference (WC) as a predictor of obesity is associated with visceral fat and abdominal obesity. [ 13 ] In 2018, Park et al introduced Weight-adjusted waist circumference index (WWI) (cm/√kg), a novel measure for measuring obesity based on WC divided by the square root of body weight, [ 12 ] which reflects both the components of muscle and fat mass, irrespective of BMI category. [ 14 , 15 ] A previous study showed that a positive association between WWI and fat mass, while an inverse association was observed with muscle mass. [ 16 ] Thus, WWI indicates central obesity, however, no research has ever investigated the connection between infertility and WWI. In contrast, WWI may be an essential tool for assessing health risks. [ 17 ] To increase understanding of obesity negative impact on infertility, it is essential to explore the relationship between infertility and WWI. Therefore, we investigate the relationship between WWI and infertility using the 2013 to 2020 National Health and Nutrition Examination Survey (NHANES), and we suppose that WWI levels would be positively correlated with the prevalence of infertility.

Data curation: Huanxin Zhong. Formal analysis: Huanxin Zhong, Hongyin Cui, Yi Lu. Funding acquisition: Yi Lu. Investigation: Hongyin Cui, Lifang You, Dao Feng, Yi Lu. Methodology: Bin Yu, Lifang You, Dao Feng. Project administration: Bin Yu, Lifang You, Dao Feng. Resources: Fen Zhao. Software: Bin Yu, Fen Zhao. Supervision: Bin Yu, Fen Zhao. Validation: Fen Zhao.

The studied data were taken from NHANES database, a significant nationwide initiative run by the National Center for Health Statistics of the Centers for Disease Control and Prevention to evaluate the level of nutrition and health in the American population. The survey unique aspect is its seamless blending of physical checks and interviews. Concerns about demographics, socioeconomics, diet, and health are covered in the survey interview. In addition, the examination components also include laboratory analysis conducted by highly qualified medical professionals, along with medical, dental, and physiological evaluations. All participants in the NHANES study protocols that were authorized by the Research Ethics Review Board for National Center for Health Statistics signed the informed consent form. The public can access more in-depth information about the NHANES survey at https://www.cdc.gov/nchs/nhanes/index.htm . The data from 4 survey cycles (2013–2020) were used in this cross-sectional analysis. A total of 35,706 research participants were screened. We excluded men (n = 16,384), missing WWI data (n = 1914), and without completing the infertility questionnaire (n = 8612) were excluded. In the end, 6333 people were included in our research (Fig. 1 ). Flow chart of participants selection. NHANES = National Health and Nutrition Examination Survey. WWI which gauges obesity by normalizing the WC of body weight, is computed as the square root of WC (cm) divided by body weight (kg). Health technicians in the MEC with the appropriate training measured participants’ weight and WC. For analysis, the participants were categorized into 4 groups (Q1–Q4) based on the quartiles of the WWI. In our research, WWI was considered as an exposure and was thought to be a continuous variable. Infertility was the study outcome variable (questionnaire variable name: RHQ074). Infertility was assumed to exist in those who responded positively to the survey question “Have you ever tried to become pregnant for at least a year without becoming pregnant?.” [ 18 ] Covariates included age, race, BMI, family income-to-poverty ratio, Triglycerides, low-density lipoprotein cholesterol, Alcohol intaking, Education level, Smoking, Diabetes, and Marital Status. EmpowerStats (version 5.0) and R (version 4.2) software were used to conduct the statistical analysis. To evaluate participant demographic characteristics by WWI quartiles, we performed chi-square tests and t tests. We investigated the linear link between WWI and infertility using weighted multiple logistic regression analysis. To investigate trends in the linear relationship between female infertility and WWI, we used a trend test after dividing WWI into quartiles. In addition, we conducted subgroup analyses based on age, race, marital status, education, and diabetes status to examine the relationship between female infertility and WWI in different subpopulations. To evaluate the consistency of the association between the other groupings, we also performed interaction tests. We also used smoothed curve fitting to investigate the nonlinear relationship between female infertility and WWI. A 2-sided P  < .05 was utilized as statistical significance. [ 19 , 20 ]

Based on the inclusion and exclusion criteria, 6333 individuals were included in the study, with 708 infertility patients (11.8%) and a mean (SD) age of 38.29 ± 11.69 years. Mexican Americans (15.68%), non-Hispanic Blacks (24.04%), non-Hispanic Whites (32.81%), and Other Races (27.47%). The mean (SD) WWI for all individuals is 11.01 (0.85). The clinical features of the study subjects are presented in Table 1 , and the classification based on WWI stratified the participants into 4 groups of equal size. Participants in the highest WWI quartile had a higher likelihood of Mexican Americans and older than those in the lowest quartile. Participants with a high level of WWI had lower levels of income and education. Along with having a higher BMI, triglycerides, and low-density lipoprotein cholesterol, they also had a higher frequency of diabetes and a higher chance of infertility. Basic characteristics of participants by weight-adjusted-waist index quartile. Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model. (%) for categorical variables: the P value was calculated by the weighted chi-square test. BMI = body mass index, LDL-C = low-density lipoprotein cholesterol, PIR = Ratio of family income to poverty, Q = quartile. Table 2 displays the outcomes of the multiple regression analysis. In the model without adjustment [1.30 (1.19, 1.43)], WWI is positively associated with infertility. Additionally, even after controlling for the corresponding factors in Models 2 [1.25 (1.13, 1.38)] and Model 3 [1.33 (1.17, 1.50)], this connection remained significant. In the completely revised model (Model 3), each unit increase in WWI was related to a 33% greater risk of infertility [1.33 (1.17, 1.50)]. In model 3, those in the highest quartile of the survey (Q4) had a 92% greater probability of experiencing infertility than those in the lowest quartile (Q1) (OR = 1,92, 95% CI:1.42, 2.59). The associations between weight-adjusted-waist index and infertility. Model 1: no covariates were adjusted. Model 2: age and race were adjusted. Model 3: age, race, BMI, Family PIR, Triglycerides, LDL-C, Alcohol intaking, Education level, Smoking, Diabetes, Marital Status. WWI = weight-adjusted waist circumference index. We performed subgroup analyses to determine whether the WWI-infertility association was constant across strata. WWI indicated a substantial positive connection with infertility in all subgroups. Table 3 shows that no subgroup, including race, education, marital status, or diabetes, had an impact on the independently positive connection between infertility and WWI (all P  > .05 for interaction). Subgroup analysis of the association between weight-adjusted-waist index and infertility. Race, age, BMI, smoking, Education level, Marital Status, PIR, LDL-C, and triglycerides were adjusted. The nonlinear positive connection between infertility and WWI has been confirmed at all 4 sites by smoothed curve fitting results (Fig. 2 ). Non-linear relationship between weight-adjusted waist circumference index and infertility. The solid red line represents a smooth curve fit between the variables. The blue bars represent the 95% confidence intervals of the fitted results. WWI = weight-adjusted waist circumference index.

In this cross-sectional study, which included 6333 participants, we explored the association between infertility and WWI in US adults and found a favorable correlation between infertility and WWI. Subgroup analysis showed that all stratification variables had no impact on the stability of the connection between WWI and infertility and the positive association persisted even after WWI was converted into a categorical variable by quartiles (Q1–Q4). Additionally, we discovered proof of a substantial link between infertility and WWI in participants between 28 and 36 years. Finally, we used a smoothing curve to further establish the nonlinear relationship between female infertility and WWI. As far as we know, this is the first research to explore the association between female infertility and WWI, and it emphasizes the positive association of WWI levels with a higher risk of infertility. Previous research has shown that obesity negatively impacts infertility. [ 21 , 22 ] According to a large U.S. cohort of more than 7000 women, menstruating obese women had lower fertility, according to Gesink Law et al In a large cohort study from the Netherlands by Van der Steeg et al’s, a cohort of more than 3000 women with regular menstrual cycles showed that every BMI above 29 kg/m 2 linearly decreased the likelihood of spontaneous conception. [ 23 , 24 ] After adjustment for BMI, another US prospective cohort found attenuated negative correlations between WC and waist-to-hip ratio and fertility. [ 25 ] These results imply that the association between BMI and females” infertility is more complex than a simple linear positive correlation can capture. Numerous research has discovered saturation effects and nonlinear correlations between BMI and infertility among people of different ages, genders and various ethnic groups. [ 19 , 26 ] Currently, most studies use BMI and abdominal circumference to assess the degree of obesity; however, obese patients, due to lack of physical activity, tend to exhibit fat accumulation, which further raises the possibility of infertility development in non-obese patients. [ 18 , 20 ] Additionally, several research have investigated the connection between fertility rates and central obesity, specifically deep subcutaneous tissue fat buildup, which has been suggested as a possible trigger for the onset of infertility. [ 27 – 29 ] The status of obese people may thus not be fully reflected by BMI computed using total weight. While central obesity is largely represented by WC, which is computed by normalizing it to body weight, WWI allows for the assessment of high-fat mass and low muscle mass, [ 14 ] which has been investigated in several fields and predicts total body fat percentage more accurately. [ 30 , 31 ] For instance, Ding et al reported no association between BMI and WC and a nonlinear positive association between WWI levels and the risk of cardiovascular disease and all-cause mortality. [ 30 ] Additionally, Qin et al found a higher correlation with the prevalence of proteinuria than additional fat-related indicators. This shows that WWI, as opposed to other indicators of fat, such BMI, is a stronger predictor of albuminuria. [ 31 ] Infertility is widespread, and the underlying causes of this unfavorable relationship between females’ infertility and WWI are uncertain. Body fat in the abdomen differs metabolically from subcutaneous fat, and central obesity is characterized by the excessive buildup of adipose tissue in the abdominal region, particularly in the visceral area. Visceral adipose tissue exhibits a consistent ability to secrete pro-inflammatory stimulants, which subsequently accumulate in various tissues and give rise to detrimental effects known as lipotoxicity. [ 32 ] Adipotoxicity plays a role in the increased systemic inflammatory response and the development of insulin resistance in obese women as a potential mechanism for obesity-induced organelle damage in oocytes. [ 33 ] Other studies have shown that the predisposition to infertility in obese patients may be associated with functional changes in the hypothalamic-pituitary-ovarian axis. Tortoriello et al designed an experimental animal study to show that obese (diet-induced obesity) mice have a 60% lower pregnancy rate when induced by diet, however, this defect can be overcome using exogenous gonadotropins. [ 34 , 35 ] This study has several limitations that need to be mentioned. First, since the study was cross-sectional, a causal relationship between WWI and female infertility could not be established. Second, we should proceed with caution because several infertility-related factors, like menarche and medication use, could not be studied due to database constraints. There are many advantages to our research despite these limitations. The 2 strengths of our study are the use of a sophisticated multi-stage random sampling strategy and a sufficiently large sample size. After adjusting for confounders, we used subgroup analyses to determine the reliability of the regression analyses.

I would like to thank all participants in this study.