Abstract
Obesity is a significant risk factor for female infertility. The conicity index (C-index) is an important measure for assessing body fat distribution, but its relationship with female infertility is not well understood. This study aims to investigate the correlation between the C-index and female infertility. The research data is sourced from the National Health and Nutrition Examination Survey conducted between 2013 and 2018. Female infertility is evaluated using a reproductive health questionnaire, and the C-index is calculated based on waist circumference, body mass index, and height. A multiple factor logistic regression model is utilized to analyze the correlation between the C-index and the incidence of infertility. Additionally, the restricted cubic spline method is applied to examine the dose–response relationship between the C-index, treated as a continuous variable, and female infertility. Subgroup analyses are performed to investigate the consistency of associations across various demographic and health-related factors. A total of 3496 female patients were included in this study, with 412 diagnosed with infertility. The results of the multiple logistic regression analysis indicated that the C-index is associated with female infertility. As the C-index grouping level increased, the odds of female infertility prevalence also increased (odds ratio: 1.80, 95% confidence interval [95% CI]: 1.25–2.59, P = .002). This association was consistent across all subgroups. Ultimately, 3 multiple regression models were retained. The results from the linear relationship test and restricted cubic spline analysis demonstrated that as the C-index level continued to rise, the odds of female infertility prevalence increased gradually (P for nonlinear = .834, P for overall < .001). There is a positive relationship between the C-index and infertility in American women. Utilizing C-index measurements can aid in the early identification of infertile women, and managing obesity based on C-index results may help decrease the incidence of infertility.
Keywords: conicity index, cross-sectional study, infertility, linear relationship, NHANES
1. Introduction
Infertility is defined as the inability to conceive after 12 months of regular unprotected intercourse.[1] It affects 17.8% of the population in high-income countries and 16.5% in low- and middle-income countries, indicating that the prevalence of infertility is relatively consistent across different income levels.[2] According to the World Health Organization, infertility may soon become the third most common health issue, following tumors and cardiovascular diseases. This presents a significant challenge for reproductive medicine and is also an important social concern worldwide.
The causes of infertility are complex and can be influenced by age, lifestyle, and environmental factors. Multiple pieces of evidence[1,3-6 indicate that obesity is an independent risk factor for female infertility. While obesity is commonly defined as an excessive accumulation of body fat, it is important to recognize that the location of this fat in the body has different metabolic effects, which can be more significant than the overall amount of body fat.[6] In recent years, there has been a growing number of studies examining the relationship between the distribution of adipose tissue, especially abdominal obesity, and reproductive diseases.[7-10] While body mass index (BMI) is traditionally considered a standard measure for assessing obesity, it does not differentiate between adipose and muscle tissue, nor does it provide information about the distribution of body fat. Therefore, relying solely on BMI to determine abdominal obesity can lead to significant inaccuracies.[11] Several specific indicators sensitive to abdominal fat deposition have been developed. One such measure is the taper index, which is based on the observation that a person with significant fat accumulation in the abdominal region tends to have a biconical body shape. In contrast, individuals with less abdominal fat typically have a cylindrical body shape. The conicity index (C-index) helps determine the degree of obesity by analyzing factors such as weight, height, and waist circumference (WC). As abdominal fat increases, the value of the C-index also rises, making it an important indicator of body fat distribution.[12,13] Additionally, the C-index is a significant cardiovascular risk factor linked to insulin resistance, diabetes, hypertension, and dyslipidemia[14-17] However, it is still unclear whether a correlation exists between the C-index and female infertility.
We utilized the National Health and Nutrition Examination Survey (NHANES) database from 2013 to 2018 to investigate, for the first time, the relationship between C-index and female infertility in the United States. We hypothesized that higher C-index levels are positively associated with an increased prevalence of female infertility.
2. Materials and methods
2.1. Survey description
The present study utilized data from the NHANES, a national population-based cross-sectional study conducted by the National Center for Health Statistics. This study was approved by the Research Ethics Review Board of the Centers for Disease Control and Prevention, and all participants provided written informed consent, eliminating the need for further ethical review. Additionally, our research adhered to the guidelines outlined in the statement on epidemiology to improve the reporting of observational studies. All detailed data sources from the NHANES study are publicly available at www.cdc.gov/nchs/nhanes/.
2.2. Study population
We screened all participants (n = 35,706) from the NHANES database for the years 2013 to 2018. First, we excluded male participants (n = 17,616) as well as those aged 19 or younger (n = 7354) and those aged 45 or older (n = 6265). Next, we removed participants who were missing essential data for C-index calculation, including height (n = 252), WC (n = 201), and weight (n = 3). Additionally, we excluded participants with missing pregnancy history (n = 376), those who had undergone hysterectomy (n = 141) or ovariectomy (n = 2). In total, 3496 subjects were eligible for analysis. For further details, please refer to Figure 1.
Figure 1.
Inclusion and exclusion criteria flowchart. NHANES = National Health and Nutrition Examination Survey, WC = waist circumference.
2.3. Infertility and C-index assessment
In this study, infertility was the outcome variable and C-index was the exposure variable. Infertility was assessed during NHANES interviews based on the reproductive health question RHQ074: “Have you ever tried to get pregnant for a year or longer without success?” participants who answered “yes” were classified as having infertility.
C-index is calculated using BMI, WC, and height. BMI is derived by dividing weight (kg) by height squared (m2). The formula for calculating C-index is as follows[12]:
2.4. Variates assessment
The covariates examined in this study include age, BMI, poverty-to-income ratio (PIR), age of first menses, education (<9th grade, 9–11th grade, high school graduate/GED or equivalent, some college or AA degree, college graduate or above), smoking history, regular menstruation, pelvic infection/PID, female hormones, race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, Non-Hispanic Black, non-Hispanic Asian, other race), marital status (married, widowed, divorced, separated, never married, living with partner). These data were collected using standardized questionnaires and interview procedures conducted by NHANES. To calculate the PIR, household income is divided by the poverty line and adjusted according to household size. For more information on the program used to obtain these covariates, please visit www.cdc.gov/nchs/hanes/.
2.5. Statistical analysis
All statistical analyses were performed using SPSS 25.0 and R 4.2.2 statistical software. Quantitative data with normal distribution were represented as mean ± SD, and comparison between groups was analyzed using the t test or analysis of variance. Quantitative data with skewed distribution were represented as M (P25, P75), and comparison between groups was analyzed using the rank sum test. Categorical data were presented as n (%), and chi-square test was used for comparison between groups. A multivariate logistic regression model was constructed to analyze the correlation between C-index and the incidence of infertility in the female population after adjusting for related confounding factors, and the odds ratio (OR) and 95% CI were calculated. Finally, 3 multivariate regression models were retained: model 1 to model 3. (Model 1 did not adjust for confounders. Model 2 was adjusted for age, PIR level, BMI, smoking history, and education. Model 3 adjusted for age, PIR level, BMI, smoking history, education, menstrual regularity, pelvic infection, hormone levels, and age at first menstrual period.) Restricted cubic spline was used to analyze the dose–response relationship between C-index as a continuous variable and female infertility. Subgroup analysis was performed according to the general characteristics of patients such as age, smoking history, regular menstruation, pelvic infection/PID, hormone levels, and marital status, and a forest plot of subgroup analysis was drawn. For all statistical analyses, P < .05 was considered statistically significant.
3. Results
3.1. Characteristics of the study population
A total of 3496 female patients were included in this study, with an average age of 31.91 ± 7.2 years, and 412 (11.78%) of them were infertile. The age, BMI index and PIR level of the female population with infertility were higher than those of the normal female population (P < .05). The C-index was used as a continuous variable and a graded variable to analyze the difference between the 2 groups. The results showed that C-index was higher in infertile women (Z = −6.45, P < .001). In addition, among the 4 groups, a high C-index level (Q4) accounted for a higher proportion of infertile women (35.68% vs 23.57%, P < .001; Table 1).
Table 1.
Baseline characteristics of study population according to infertility.
| Variables | Total population (n = 3496) | Normal (n = 3084) | Infertility (n = 412) | Statistic | P |
|---|---|---|---|---|---|
| Age, Mean ± SD | 31.91 ± 7.20 | 31.63 ± 7.23 | 33.96 ± 6.58 | t = -6.67 | <.001 |
| BMI, Mean ± SD | 29.61 ± 8.39 | 29.33 ± 8.26 | 31.74 ± 9.09 | t = -5.12 | <.001 |
| PIR, M (Q1, Q3) | 1.89 (1.03, 3.45) | 1.89 (1.02, 3.40) | 1.98 (1.18, 3.94) | Z = -2.83 | .005 |
| Age of first menses, M (Q1, Q3) | 12.00 (11.75, 13.00) | 12.00 (12.00, 13.00) | 12.00 (11.00, 13.00) | Z = -1.06 | .289 |
| C-index, M (Q1, Q3) | 0.72 (0.69, 0.76) | 0.72 (0.69, 0.76) | 0.74 (0.70, 0.78) | Z = -6.45 | <.001 |
| C-index Quantile, n (%) | χ2 = 37.75 | <.001 | |||
| Q1 (C-index ≤ 0.69) | 874 (25.00) | 805 (26.10) | 69 (16.75) | ||
| Q2 (0.69 < C-index ≤ 0.72) | 874 (25.00) | 788 (25.55) | 86 (20.87) | ||
| Q3 (0.72 < C-index ≤ 0.76) | 874 (25.00) | 764 (24.77) | 110 (26.70) | ||
| Q4 (C-index > 0.76) | 874 (25.00) | 727 (23.57) | 147 (35.68) | ||
| Education, n (%) | χ2 = 1.95 | .744 | |||
| <9th grade | 166 (4.75) | 150 (4.86) | 16 (3.88) | ||
| 9–11th grade (includes 12th grade with nodiploma) | 353 (10.10) | 306 (9.92) | 47 (11.41) | ||
| High school graduate/GED or equivalent | 688 (19.68) | 605 (19.62) | 83 (20.15) | ||
| Some college or AA degree | 1297 (37.10) | 1142 (37.03) | 155 (37.62) | ||
| College graduate or above | 992 (28.38) | 881 (28.57) | 111 (26.94) | ||
| Smoking, n (%) | 1000 (28.60) | 858 (27.82) | 142 (34.47) | χ2 = 7.86 | .005 |
| Regular menstruation, n (%) | 3257 (93.16) | 2870 (93.06) | 387 (93.93) | χ2 = 0.43 | .511 |
| Pelvic infection/PID, n (%) | 165 (4.72) | 130 (4.22) | 35 (8.50) | χ2 = 14.80 | <.001 |
| Female hormones, n (%) | 89 (2.55) | 73 (2.37) | 16 (3.88) | χ2 = 3.37 | .066 |
| Race, n (%) | χ2 = 1.78 | .878 | |||
| Mexican American | 569 (16.28) | 500 (16.21) | 69 (16.75) | ||
| Other Hispanic | 374 (10.70) | 331 (10.73) | 43 (10.44) | ||
| Non-Hispanic White | 1103 (31.55) | 965 (31.29) | 138 (33.50) | ||
| Non-Hispanic Black | 836 (23.91) | 738 (23.93) | 98 (23.79) | ||
| Non-Hispanic Asian | 438 (12.53) | 393 (12.74) | 45 (10.92) | ||
| Other race-including multiracial | 176 (5.03) | 157 (5.09) | 19 (4.61) | ||
| Marital status, n (%) | χ2 = 30.60 | <.001 | |||
| Married | 2354 (67.33) | 2038 (66.08) | 316 (76.70) | ||
| Widowed | 9 (0.26) | 7 (0.23) | 2 (0.49) | ||
| Divorced | 130 (3.72) | 111 (3.60) | 19 (4.61) | ||
| Separated | 70 (2.00) | 62 (2.01) | 8 (1.94) | ||
| Never married | 648 (18.54) | 610 (19.78) | 38 (9.22) | ||
| Living with partner | 285 (8.15) | 256 (8.30) | 29 (7.04) |
BMI = body mass index, PIR = poverty-to-income ratio.
3.2. The differences of menstruation, hormones and pregnancy status in different C-index groups
The C-index was divided into 4 quartiles: group Q1 (C-index ≤ 0.69), group Q2 (0.69 < C-index ≤ 0.72), group Q3 (0.72 < C-index ≤ 0.76), and group Q4 (C-index > 0.76). The differences of menstruation, hormone and pregnancy status among the 4 groups with different C-index levels were compared. The results showed that the higher the C-index, the higher the odds of infertility prevalence (16.82% vs 7.89%, Q4 vs Q1, P < .001). There was no significant difference in C-index with respect to menstrual status and hormone levels (Table 2).
Table 2.
Comparison of menstrual, hormonal and pregnancy status among different C-index groups.
| Variables | Total (n = 3496) | Q1 (n = 874) | Q2 (n = 874) | Q3 (n = 874) | Q4 (n = 874) | Statistic | P |
|---|---|---|---|---|---|---|---|
| Regular menstruation, n (%) | χ2 = 4.58 | .206 | |||||
| No | 239 (6.84) | 47 (5.38) | 59 (6.75) | 66 (7.55) | 67 (7.67) | ||
| Yes | 3257 (93.16) | 827 (94.62) | 815 (93.25) | 808 (92.45) | 807 (92.33) | ||
| Status of pregnancy, n (%) | χ2 = 37.75 | <.001 | |||||
| No | 3084 (88.22) | 805 (92.11) | 788 (90.16) | 764 (87.41) | 727 (83.18) | ||
| Yes | 412 (11.78) | 69 (7.89) | 86 (9.84) | 110 (12.59) | 147 (16.82) | ||
| Pelvic infection/PID, n (%) | χ2 = 4.19 | .241 | |||||
| No | 3331 (95.28) | 838 (95.88) | 839 (96.00) | 831 (95.08) | 823 (94.16) | ||
| Yes | 165 (4.72) | 36 (4.12) | 35 (4.00) | 43 (4.92) | 51 (5.84) | ||
| Female hormones, n (%) | χ2 = 3.72 | .293 | |||||
| No | 3407 (97.45) | 859 (98.28) | 848 (97.03) | 852 (97.48) | 848 (97.03) | ||
| Yes | 89 (2.55) | 15 (1.72) | 26 (2.97) | 22 (2.52) | 26 (2.97) |
Q1 (C-index ≤ 0.69); Q2 (0.69 < C-index ≤ 0.72); Q3 (0.72 < C-index ≤ 0.76); Q4 (C-index > 0.76).
3.3. Association between C-index and female infertility
Multivariate logistic regression analysis showed that there was a significant correlation between different C-index groups and female pregnancy status. In the model 1 without adjusting any covariates, compared with Q1 group, the OR value of Q4 group was 2.36 (95% CI: 1.74–3.19, P < .001). After further adjustment for age, PIR, BMI, smoking history, education level, menstruation, hormones, pelvic infection/PID, age at first menstrual period and other confounding factors (model 3), the results showed that as the level of C-index grouping increased. The higher odds of infertility prevalence in women (OR: 1.80, 95% CI: 1.25–2.59, P = .002, Q4 vs Q1) indicates that the association between C-index level and pregnancy status is independent of the confounding factors described above (Table 3).
Table 3.
Multivariate logistic regression analysis of the correlation between different C-index groups and pregnancy status.
| Variables | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | |
| Q1 (C-index ≤ 0.69) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Q2 (0.69 < C-index ≤ 0.72) | 1.27 (0.91–1.77) | .153 | 1.13 (0.81–1.59) | .478 | 1.14 (0.81–1.60) | .440 |
| Q3 (0.72 < C-index ≤ 0.76) | 1.68 (1.22–2.31) | .001 | 1.40 (1.01–1.96) | .049 | 1.41 (1.01–1.98) | .044 |
| Q4 (C-index > 0.76) | 2.36 (1.74–3.19) | <.001 | 1.78 (1.24–2.56) | .002 | 1.80 (1.25–2.59) | .002 |
Model 1: No adjust.
Model 2: Adjust: age, PIR, BMI, smoking, education.
Model 3: Adjust: Model 2 + regular menstruation, pelvic infection/PID, Female hormones, age of first menses.
BMI = body mass index, CI = confidence interval, C-index = conicity index, OR = odds ratio, PIR = poverty-to-income ratio.
To examine the dose–response relationship between C-index and female pregnancy status, we performed linear relationship tests and restricted cubic spline analyses. The results showed that with the continuous increase of C-index level, the odds of female infertility prevalence (P for nonlinear = .834, P for overall < .001) gradually increased (Fig. 2).
Figure 2.
Results of restricted cubic spline analysis of the association between C-index and pregnancy status. (The red line shows the relationship between BMI and the hazard ratio for the endpoint event, and the red area shows the 95% confidence interval [95% CI].) BMI = body mass index, CI = confidence interval.
3.4. Subgroup analyses
Subgroup analysis was conducted according to the general clinical characteristics of the female population (age, regular menstruation, pelvic infection/PID, hormone levels, and marital status). With the occurrence of infertility as the outcome, multivariate logistic regression analysis was performed. The results showed that the interaction test between C-index and infertility was significant among age subgroups (P = .003). A high C-index level was positively correlated with infertility status in both the appropriate pregnant age group (20–31 years old) and the advanced pregnant age group (32–44 years old; P < .05), and the correlation was higher in the appropriate pregnant age group (OR: 1.54, 95% CI: 1.32–1.80, P < .001). However, there were no significant differences between the 2 in other subgroups such as menstruation, pelvic infection/PID, hormone levels, and marital status (P > .05; Fig. 3).
Figure 3.
Forest plot of subgroup analysis of women with or without infertility as the endpoint event. CI = confidence interval, OR = odds ratio.
4. Discussion
This cross-sectional study observed that as the C-index level continues to rise, the odds of female infertility prevalence gradually increases. Even after adjusting for various covariates, a positive correlation still exists, indicating that higher levels of C-index are a risk factor for infertility. This association is consistent across all subgroups. The linear relationship test and restrictive cubic spline analysis further emphasized the significant positive nonlinear correlation between the C-index and infertility.
Numerous studies indicate that obese women experience longer times to conceive and have a threefold increased risk of infertility compared to nonobese women. Additionally, obesity is linked to several negative outcomes during pregnancy, including preeclampsia, gestational diabetes, spontaneous preterm labor, placental issues, and low birth weight. Research on the link between obesity and infertility often relies on BMI to categorize overweight individuals, yet the findings are inconsistent. A multicenter cohort study in the United States analyzed the prepregnancy BMI of over 7000 women and found that obesity is associated with decreased fertility across all subgroups. Interestingly, even among women with normal menstrual cycles, those who are overweight or obese experience reduced fertility.[18] Similarly, research by van der Steeg et al.[19] that included a large Dutch cohort of over 3000 women with normal menstrual cycles revealed that as BMI exceeded 29 kg/m2, the likelihood of achieving a natural pregnancy decreased linearly. Specifically, for women with a high BMI, the pregnancy rate declined by 4% for every 1 kg/m2 increase in BMI. Regardless of metabolic health status, obesity itself is associated with a higher risk of infertility, and a higher BMI can to some extent predict poorer fertility.[20] A study involving 20 women undergoing in vitro fertilization found that the waist to hip ratio > 0.8 was associated with a significant reduction in the pregnancy rate (OR: 0.42, 95% CI: 0.2–0.9), but did not find BMI to be related to the outcome of in vitro fertilization.[21]
Obesity raises infertility risk, but it is not an absolute barrier. Not all obese individuals are infertile. Thresholds for obesity-induced infertility vary. On the one hand, although some obese women have problems such as insulin resistance and ovulation abnormalities, they maintain basic ovulation function through compensatory hyperinsulinemia. For example, about 30% of patients with polycystic ovary syndrome can still ovulate naturally.[22] On the other hand, the distribution of fat also significantly impacts fertility.[23] The relationship between central obesity (abdominal obesity), insulin resistance, and reproductive endocrine disorders is closer. In summary, there are significant individual differences in the weight threshold and degree of metabolic abnormalities that trigger infertility risk among different obese women, avoiding an absolute interpretation of the association between obesity and infertility.
BMI primarily measures overall obesity in an individual, but it may not provide a comprehensive assessment of variations in body fat and muscle composition. This limitation could help explain the ongoing controversy mentioned earlier. Therefore, using visceral obesity as a measure of female reproductive health appears to be more reasonable and accurate. The C-index is a new measure of body fat distribution that considers not only WC, but also height and weight. By combining the strengths of both BMI and WC, the C-index offers a more comprehensive view of body size. This feature enables a better assessment of an individual’s fat distribution, particularly the ratio of abdominal fat to the rest of the body. Higher C-index values indicate a greater accumulation of abdominal fat, making it more suitable for evaluating diseases closely associated with abdominal obesity. Currently, research has found a positive correlation between C-index and cardiovascular risk factors.[14] Our study, on the other hand, investigated the association between C-index and female infertility and showed that infertility rates increased dramatically with increasing C-index levels, revealing a positive association between C-index and infertility prevalence. The C-index may provide a more accurate reflection of the relationship between body fat distribution and infertility. Thus, managing obesity as defined by the C-index could potentially help reduce the odds of female infertility prevalence.
Nonetheless, the precise mechanism by which an elevated C-index is linked to a higher prevalence of infertility in women remains unclear. One potential factor is the impact of obesity on the function of the hypothalamic-pituitary-ovarian axis.[24,25] Excess adipose tissue enables androgens to convert to estrogens at a high rate in the peripheral tissues. This process creates negative feedback on the hypothalamic-pituitary-ovarian axis, which affects the production of gonadotropins. As a result, this can lead to menstrual irregularities and ovulatory dysfunction. Elevated androgen levels also contribute to the accumulation of visceral fat, leading to insulin resistance and hyperinsulinemia. This condition, in turn, stimulates further androgen production by the ovaries and adrenal glands, creating a vicious cycle. Excess free fatty acids can have toxic effects on reproductive tissues, potentially leading to damage in oocytes and contributing to a chronic low-grade inflammatory state.[26] In obese individuals, altered levels of adipokines can affect steroid hormone production and directly impact endometrial receptivity and embryo development[27-30] This is supported by the higher rates of miscarriage, stillbirth, and preeclampsia observed in the obese population. Additionally, a complex interaction of psychosocial, sociobiological, and physiological factors may contribute to lower fertility in obese women.[31] In conclusion, the C-index may play a role in mediating the relationship between obesity and infertility through multiple pathways.
This study represents the first effort to investigate the relationship between C-index and infertility. It analyzed a substantial sample of 3496 women aged 20 to 44 years from the NHANES database in the United States, which contributes to the reliability of the conclusions. Additionally, the NHANES database was adjusted for confounding factors, including serum estradiol, total testosterone, and sex hormone-binding globulin levels, to further enhance the reliability of the findings. The study also explored the strength of the association between C-index and infertility across various populations by conducting detailed stratified analyses of different subgroups. In conclusion, this research serves as a preliminary exploration of the connection between fat distribution markers and infertility, providing valuable insights for future investigations into the relationship and mechanisms linking abdominal obesity and female infertility.
This study does have some limitations. First, due to the cross-sectional design, we were unable to determine whether there is a causal relationship between the C-index and infertility risk. Second, due to data accessibility limitations, this study has not yet been validated on an independent external dataset, we plan to validate the C-index and the relationship with infertility using clinical data to enhance reliability and generalizability. Furthermore, while we adjusted for several relevant confounding factors, we could not completely account for other important variables, such as anti-Müllerian hormone and basal sex hormones. These are significant indicators for diagnosing and treating infertility, but they were not available in the NHANES 2013–2018 dataset and thus could not be included as covariates in this study. Finally, the outcome indicators used to assess infertility were primarily based on participants’ self-reported questionnaires, which can be influenced by their subjective perceptions and memory biases. This may lead to reporting bias in the results.
5. Conclusion
The study’s results indicated a positive association between C-index and female infertility. This underscores the importance of monitoring fat distribution indicators to identify patients who may be at risk of infertility. Future prospective studies with larger sample sizes are necessary to further validate these findings and establish causality.
Author contributions
Conceptualization: Dong-Mei Tan, Ping-Ping Cai.
Funding acquisition: Ping-Ping Cai.
Visualization: Ping-Ping Cai, Yi-Min Shi, Xin-Liang Kong, Zhao-Qing Meng.
Writing – original draft: Dong-Mei Tan.
Writing – review & editing: Dong-Mei Tan.
Abbreviations:
- BMI
- body mass index
- CI
- confidence interval
- C-index
- conicity index
- NHANES
- National Health and Nutrition Examination Survey
- OR
- odds ratio
- PIR
- poverty-to-income ratio
- WC
- waist circumference
2023 Qilu Biancang Traditional Chinese Medicine talent training project (Lu health Letter No. [2024] 78); the fifth batch of National Traditional Chinese Medicine clinical outstanding talents training project (National Administration of Traditional Chinese Medicine talent education letter No. [2022] 1).
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Tan D-M, Cai P-P, Shi Y-M, Kong X-L, Meng Z-Q. Association between conicity index and female infertility: Insights from NHANES. Medicine 2026;105:7(e47568).
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