https://orcid.org/0009-0009-2806-7788
Figures
Abstract
Background
One of the risk indicators of infertility is obesity. The cardiometabolic index (CMI) comprises obesity and blood lipids and is regarded as a novel indicator for evaluating obesity. Nevertheless, it is unclear whether it has any connection to infertility. This study set out to investigate the association between infertility and CMI.
Methods
Based on cross-sectional data from the 2013–2018 National Health and Nutrition Examination Survey (NHANES), infertility and CMI statistics with complete information were selected. This study investigated the correlation between CMI and infertility using multivariate logistic regression analyses and subgroups. Use fitted smooth curves and threshold effect analysis to describe the nonlinear association between CMI and infertility.
Results
202 (13.31%) among the 1720 participants that got involved in the investigation were female infertile. Among the three models, the outcomes confirmed a positive correlation between CMI levels and the incidence of infertility (OR = 1.12, 95% CI: 1.01–1.24). Additionally, significant relationships were maintained in subgroup analysis (p > 0.05). Smooth curve fitting indicated a nonlinear positive connection between CMI and infertility, and an inflection point of 0.93 (log-likelihood ratio P < 0.05) was shown by threshold effect analysis.
Citation: Kong L, Ding X, Wang Q, Xie R, Sun F, Zhou N, et al. (2024) Association between cardiometabolic index and female infertility: A population-based study. PLoS ONE 19(12): e0313576. https://doi.org/10.1371/journal.pone.0313576
Editor: Akingbolabo Daniel Ogunlakin, Bowen University, NIGERIA
Received: July 4, 2024; Accepted: October 26, 2024; Published: December 4, 2024
Copyright: © 2024 Kong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The National Health and Nutrition Examination (NHANES) database at https://wwwn.cdc.gov/nchs/nhanes provides access to the study's data for the general public.
Funding: This work was supported by the Wuxi Taihu Lake Talent Plan, Supports for Leading Talents in Medical and Health Profession. The funding of this manuscript does not involve any financial or other contractual arrangements that could create or be perceived to create a conflict of interest. No financial relationship exists between any author and company whose product is prominently featured in the submitted manuscript. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The typical description of infertility is being unable to conceive after 12 months or more of regular, unprotected sexual behavior [1]. Infertility impacts over 1.86 million individuals globally, and in the United States, 8.8% of women between the age range of 15 and 49 suffer medical or psychological problems that render it harder for them to conceive naturally [2, 3]. Infertility encompasses much more than merely the loss of fertility; it also involves societal prejudice towards the patient and their family, gloom, pain, and low marital satisfaction [4]. Although the precise cause of infertility is unclear, there is mounting evidence that, in addition to pelvic tubal abnormalities and ovulation obstacles, the reasons for infertility are intimately linked to unhealthy lifestyles (such as obesity).
The obesity percentage among American women of reproductive age has risen to 37%. Obesity has emerged as a significant global public health concern [5]. Obese women who are of reproductive age are more inclined to encounter menstruation disorders, ovulation problems, decreased follicular growth and development, and infertility [6–8]. Due to aberrant lipid metabolism, obesity, a hallmark of metabolic diseases, could affect female fertility [7, 9]. The setting up precise metrics for lipid metabolism monitoring facilitates the timely detection of obesity. The body mass index (BMI), currently commonly employed to quantify generalized obesity, is less sensitive when assessing the degree of obesity [10]. The weight-adjusted waist index (WWI), which gauges the proportion of fat and muscle, is used to evaluate central obesity [11]. In 2015, Wakabayashi et al. introduced the cardiometabolic index (CMI), an innovative index that assesses the extent of obesity and lipid levels in individuals. The CMI may be used to determine the likelihood of metabolic syndrome in obese women [12–14]. Numerous studies have indicated that the risk of infertility may be identified by waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHI), and body size index (ABSI) [15–17]. Due to the limitations of BMI in reflecting the obesity phenotype [18–20], other obesity indicators have improved, but they lack the ability to reflect blood lipids. At present, the link between blood lipid levels and infertility remains uncertain. Studies have linked elevated LDL-C to an increased risk of female infertility, but the lack of studies comparing lipid-related characteristics with female infertility has limited the interpretation of the findings [21]. CMI can not only evaluate the degree of obesity, but also reflect the level of blood lipid metabolism, so CMI may be a better marker of the relationship between obesity, blood lipids and infertility.
Consequently, we investigated the relationship between CMI and infertility utilizing NHANES data.
Methods
Survey description and study population
The National Center for Health Statistics (NCHS) utilized the National Health and Nutrition Examination (NHANES) to compile a sample for this research to evaluate the nutritional status of American households. The public can view comprehensive NHANES demographic, nutritional, laboratory tests, physical examination, and questionnaire data. The public may access all NHANES study data at www.cdc.gov/nchs/nhanes/. It has an effectively representative sample, performing a nationwide stratified multistage probability sampling every two years. The NCHS Research Ethics Review Board approved all NHANES, and each participant completed informed consent forms. The NHANES database may be accessed without additional institutional review board approval.
This research examined data from three cyclical NHANES from 2013 to 2018 (Fig 1), excluding minor (age <18) participants, and people with full CMI and infertility data were picked for inclusion. A total of 29,400 respondents were initially recruited. After eliminating those with missing self-reported infertility (n = 24,860), missing CMI data (n = 2611), and missing data on any of the factors in this research (n = 209), 1,720 suitable participants were subsequently included, ranging in age from 20 to 59.
Cardiometabolic index and infertility definition
The outcome variable was infertility, and the Reproductive Health Questionnaire (RHQ) item RHQ074 supplied the data. "Have you ever attempted to get pregnant and been unsuccessful for at least a year?" Participants who answered "yes" were deemed infertile, whereas those who selected "no" were judged fertile. Therefore, the diagnosis of infertility is based on the participants’ self-reports.
CMI was intended to be used as an indicator of exposure. The data used for calculating CMI were obtained from measurements of waist circumference (WC), height, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C). The formula for computation is as follows: WHtR = WC(cm)/height(cm), CMI = WHtR ×[TG (mg/dL)/HDL-C (mg/dL)] [12].
Covariates of interest
A variety of potential factors that might reinforce the link between CMI and infertility were also taken into account in this investigation. Demographics include, for instance, age (years), race (Mexican American/Other Hispanic/Non-Hispanic White/Non-Hispanic Black/Other Race), education level (below high school/high school/above high school), and marital status (married or living with a partner/living alone). The questionnaire obtained data on the following topics: age when first menstrual period occurred (<10/10 ≤ age <15/15 ≤ age ≤20), had regular periods in past 12 months (yes/no), ever taken birth control pills (yes/no), diabetes (yes/no), ever treated for a pelvic infection/PID(yes/no), smoked at least 100 cigarettes (yes/no), and cardiovascular disease (CVD). Self-reported questionnaires were utilized to assess cardiovascular disease. Any of the five criteria for coronary heart disease, angina, congestive heart failure, heart attack, or stroke indicates that participant have cardiovascular disease [22].
Statistical analysis
Since NHANES follows the recommendations of the Centers for Disease Control and Prevention (CDC), it adopts a complex multi-stage sampling design. We compared two groups based on infertility status using t-tests for continuous variables and chi-square tests for categorical data. Continuous variables are expressed as standard deviation ± mean, whereas categorical data is displayed as frequencies and percentages. The association between CMI and infertility was explored using multivariable regression models, and three different logistic regression models were constructed for confounding factors. No variables were adjusted in Model 1. Model 2 was adjusted for age and race. Model 3 was adjusted for age, race, educational attainment, marital status, PIR, CVD, smoking, diabetes, age at first menstruation, regularity of menstruation during the previous 12 months, ever treated for a pelvic infection/PID, and ever taken birth control pills. We also converted these CMI into quartile categorical variables for sensitivity analysis to assess the stability of the results. Smoothed curve fitting was utilized to investigate the nonlinear relationship between CMI and infertility in this research. For the analysis of threshold effects, we compared the piecewise regression model with the singleton model by using a log-likelihood ratio test. Moreover, to testing the stability of the association between CMI and infertility, subgroup analyses of the relationship between CMI and infertility were performed using stratified multifactorial regression analysis. We stratified analysis for age, race (Mexican American/Other Hispanic/Non-Hispanic White/Non-Hispanic Black/Other Race), education level (below high school/high school/above high school), marital status (married or living with a partner/living alone), diabetes (yes/no), age when first menstrual period occurred (<10/10 ≤ age <15/15 ≤ age ≤20), had regular periods in past 12 months (yes/no), ever treated for a pelvic infection/PID (yes/no), ever taken birth control pills(yes/no), smoked at least 100 cigarettes (yes/no). In order to better reflect the distribution of the data and enhance the significance of differences between groups, we chose to use three-digit analysis. For the exact stratification of other covariates, please refer to the covariate selection mentioned above. PackageR (http://www.r-project.org) and EmpowerStats (http://www.enpowerstats.com) for all analyses. It was deemed statistically significant when P < 0.05.
Results
Baseline characteristics of participants
The demographic characteristics and other variables for each subject are arranged by infertility in Table 1. This research comprised 1720 individuals in total, with 202 women claiming self-reported infertility, making up 11.74% of the sample, with a mean age of 41.27 ± 10.90 years. Compared to fertile participants, women with infertility had higher family income and education levels, a higher prevalence of diabetes, a higher rate of receiving pelvic infection/PID treatment, and a lower prevalence of cardiovascular disease. Infertile women additionally possessed higher CMI (1.51 ± 2.00).
Association of cardiometabolic index with infertility
The association between CMI and infertility is demonstrated in Table 2. According to the outcome of the logistic regression study, there was a positive correlation between CMI and infertility in both model 1 and model 2. The positive relationship was also steady in the fully rectified model 3 (OR = 1.12, 95% CI:1.01–1.24, P = 0.0330). We observed that for every unit, an increase in CMI was associated with a 12% increased risk of infertility. Our further analyses, adjusting the continuous variable of CMI into categorical variables (quartiles), still showed statistically significant results. For each unit increase in CMI, participants in the highest quartile of CMI had an 88% higher prevalence of infertility than participants in the lowest quartile of CMI (OR = 1.88, 95% CI: 1.12–3.15, p for trend = 0.0593).
We analyzed further the nonlinear correlation between CMI and infertility using smooth curve fitting. The results indicated a positive nonlinear relationship between CMI and infertility (Fig 2). We applied breakpoint analysis to determine the inflection point of the nonlinear relationship. The analysis revealed that the inflection point was 0.93 (log-likelihood ratio P < 0.05). When the CMI was under 0.93, the odds of infertility prevalence increased by 162% for every unit increase in CMI (OR = 2.62,95% CI:1.27–5.44, p = 0.0094). When CMI was more than 0.93 (OR = 1.06,95% CI: 0.94–1.18, p = 0.3468), there was no correlation with the odds of infertility prevalence (Table 3). We further analyzed the potential relationship between CMI and infertility in non-obese women. The results showed no association between CMI and infertility in people with a BMI < 30 (S1 Table). In addition, we also performed multiple logistic regression on the relationship between BMI and infertility. (S2 Table)
Subgroup analysis
To assess if the link between CMI and infertility was stable in the presence of the effect of other variables, we conducted subgroup analyses according to age, race, education level, marital status, diabetes, smoking, age at first menstruation, regularity of menstruation in the past 12 months, ever treated for a pelvic infection/PID, and ever taken birth control pills. None of these factors impacted the positive association between CMI and infertility, as shown in Table 4. Our study observed that individuals in the lowest age tertile had 33% higher odds of infertility prevalence for every unit rise in CMI (OR = 1.33,95% CI:1.06–1.68). When diabetes is absent, there is a 16% higher odds of infertility prevalence for every unit increase in CMI (OR = 1.16, 95% CI: 1.02–1.32). The onset menstruation age was 10 to 15 years old; with every unit rise in CMI, the prevalence of infertility rose by 14% (OR = 1.14,95% CI: 1.02–1.28). For every unit increase in CMI, the odds of infertility prevalence rose by 17% in patients who smoked (OR = 1.17,95% CI:1.02–1.33). In addition, we performed a subgroup analysis of BMI, but the results were not significant (S3 Table).
Discussion
We observed a positive correlation between CMI and infertility in this cross-sectional research with 1720 participants, indicating that a higher CMI may raise the odds of infertility prevalence. And the subgroup analysis showed that this connection did not change. Moreover, there was a positive correlation between CMI levels and infertility when the CMI was less than 0.93. The relationship between CMI and infertility was not statistically significant when CMI was higher than 0.93. According to the research, controlling CMI levels may help manage infertility prevalence. CMI is a novel metric for assessing visceral fat distribution and dysfunction, and it has applications in predicting diabetes, hypertension, related cardiovascular metabolic conditions, and other diseases [23–25]. As far as we know, this is the first research to examine the relationship between CMI and the probability of being infertile.
While numerous studies have investigated the relationship between obesity and infertility, results have been inconsistent. A recent systematic evaluation and meta-analysis of the influence of lifestyle interventions before pregnancy on reproductive outcomes revealed that these interventions resulted in improved fat reduction and increased rates of successful pregnancies and live births [26]. Separate research showed that lifestyle modifications had a beneficial effect on the sexual functioning, cardiovascular well-being, and fertility of obese women who were experiencing infertility [27]. Interestingly, Legro et al. investigated a 16-week preconception lifestyle intervention on 379 obese infertile women. The findings of this randomized controlled trial indicate that women in the intervention group went through a 7 percent reduction in body weight on average and showed improvements in cardiometabolic markers compared to the control group. However, the intensive preconception lifestyle intervention did not enhance pregnancy rates, live births, length of pregnancy, or infant birth weight [28]. Similarly, a randomized controlled research study in Sweden had comparable findings. The trial included a 12-week weight-reduction intervention before conception. However, the trial did not demonstrate a substantial effect on live birth rates in women undergoing IVF compared to the group that did not undergo weight loss [29].
Presently, the prevailing method for evaluating the extent of obesity involves measuring BMI and waist circumference. Recently, several researchers have investigated the use of WWI as a measure of obesity. Ying et al. discovered a positive correlation between rising waist circumference and a higher probability of suffering infertility [15]. Furthermore, Wen & Zhong et al. observed that WWI was significantly and positively associated with infertility risk [17, 30]. Similarly, our research shows that an elevation in CMI leads to a more significant prevalence of infertility. The degree of obesity and lipid levels tend to affect fertility. CMI reflects the distribution of visceral adipose tissue and blood lipid levels [31]. CMI may become a new obesity indicator to predict the incidence of infertility. Apart from early recognition of infertility, CMI has also been linked to several other illnesses. Song et al. found a positive correlation between CMI and higher levels of insulin resistance (IR: OR = 3.46), impaired fasting glucose (IFG: OR = 1.27), and type 2 diabetes mellitus (T2DM: OR = 2.01). They also suggested that CMI has the potential to be used as a simple and reliable metabolic assessment [32]. Lazzer et al. demonstrated that CMI and visceral obesity index (VAI) exhibited higher sensitivity and specificity in evaluating metabolic syndrome in obese women, as compared to other obesity indices such as waist-to-hip ratio (WHR), body fat index (BMFI), and WHtR [33].
Multiple studies have shown a strong correlation between obesity and fertility. Typically, Generally, obese women are more likely to experience irregular menstrual cycles, ovulatory disorders, and infertility [34]. The regular functioning of women’s menstrual cycle and ovulation is regulated by the hypothalamic-pituitary-ovarian (HPO) axis. Obesity affects the balance of the HPO axis primarily by increasing leptin levels [35, 36]. Increased levels of leptin interfere with the release of gonadotropin-releasing hormone (GnRH) in the hypothalamus, causing irregular production of luteinizing hormone (LH) and follicle-stimulating hormone (FSH). This disruption affects the menstrual cycle and ovulation capacity [37]. Obesity decreases the production of adiponectin, a hormone that has anti-inflammatory and insulin-sensitizing characteristics [38]. Reduced adiponectin levels may result in insulin resistance, leading ovarian cells to overproduce androgens. This excessive production of androgens affects the secretion of GnRH, FSH, and LH, disrupting the HPO axis [39, 40]. Furthermore, excess fatty tissue promotes inflammation and oxidative stress, decreasing the quality of oocytes and uterine tolerance [41, 42]. CMI is the product of WHtR and TG/HDL-C. WHtR reflects the amount of fat accumulated under the skin. Research has shown that higher WHtR values are associated with decreased fertility [16]. TG/HDL-C serves as an indicator of metabolic condition. Lipid metabolism disorders affect cardiovascular well-being and raise levels of reactive oxygen species (ROS) due to excessive lipid accumulation, resulting in oxidative stress and ovarian granulosa cell stress that can damage the reproductive system [43–45].
Additionally, studies have shown that women who are obese are predisposed to developing polycystic ovarian syndrome (PCOS), which may be attributed to the fact that obesity induces obstruction of ovulation and excessive levels of male hormones in those with PCOS, causing metabolic irregularities and decreased fertility [46, 47]. To enhance their fertility, most women unable to conceive may make lifestyle alterations, including changes to their food, engaging in fitness programs, and making psychological modifications. Also, there is a significant number of individuals who actively use Assisted Reproductive Technology (ART) as a means of achieving pregnancy [48]. Nevertheless, the disruption of oocyte maturation and impaired spindle morphology in obese women significantly contribute to the higher rates of failure in ART procedures for this group [49, 50].
In conclusion, our study presents several advantages. The data in this research were derived from the NHANES, making the results applicable to the U.S. As far as we are aware, this is the first research to look at the relationship between CMI and the occurrence of infertility while considering the effects of obesity and metabolic profiles on infertility. Moreover, we accounted for pertinent confounders to more precisely evaluate the dependability of the correlation between CMI levels and infertility. Nevertheless, it is essential to admit certain limitations of our research. First, this cross-sectional research only examined the correlation between CMI and the likelihood of infertility prevalence. The aim was to provide some reference values for the early identification of infertility occurrence. However, the study was unable to demonstrate a causal link between CMI and infertility. Second, our findings are limited to specific country regions, and it is yet to be determined if they are to populations in different nations. In addition, despite the inclusion of some potential covariates in this research, other variables may still impact the likelihood of experiencing infertility, such as the shortage of diagnostic information about PCOS. Finally, the diagnosis of infertility relies on self-reported data, which unavoidably involves some recall bias caused by the potential for inaccurate recollection.
Conclusion
Our findings suggest a significant relationship between CMI and infertility in American females. Levels of CMI might be a meaningful indicator of infertility. However, we still need further extensive prospective research to authenticate the findings of this research. This helps identify high-risk groups for infertility, informing clinical practice and public health policy to improve metabolic and reproductive health.
Supporting information
S1 Table. Association between CMI and infertility stratified by BMI < 30.
https://doi.org/10.1371/journal.pone.0313576.s001
(DOCX)
S2 Table. Associations between BMI and infertility.
https://doi.org/10.1371/journal.pone.0313576.s002
(DOCX)
S3 Table. Association between CMI and infertility stratified by BMI.
https://doi.org/10.1371/journal.pone.0313576.s003
(DOCX)
References
- 1. Definitions of infertility and recurrent pregnancy loss: a committee opinion. Fertil Steril. 2020;113(3):533–5. Epub 20200227. pmid:32115183.
- 2. Carson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. Jama. 2021;326(1):65–76. pmid:34228062; PubMed Central PMCID: PMC9302705.
- 3. Inhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update. 2015;21(4):411–26. Epub 20150322. pmid:25801630.
- 4. Pasha H, Basirat Z, Faramarzi M, Kheirkhah F. Comparative Effectiveness of Antidepressant Medication versus Psychological Intervention on Depression Symptoms in Women with Infertility and Sexual Dysfunction. Int J Fertil Steril. 2018;12(1):6–12. Epub 20180107. pmid:29334200; PubMed Central PMCID: PMC5767934.
- 5. Barbour LA, Hernandez TL. Maternal Non-glycemic Contributors to Fetal Growth in Obesity and Gestational Diabetes: Spotlight on Lipids. Curr Diab Rep. 2018;18(6):37. Epub 20180509. pmid:29744612.
- 6. Sundaram R, Mumford SL, Buck Louis GM. Couples’ body composition and time-to-pregnancy. Hum Reprod. 2017;32(3):662–8. pmid:28158570; PubMed Central PMCID: PMC5400044.
- 7. Broughton DE, Moley KH. Obesity and female infertility: potential mediators of obesity’s impact. Fertil Steril. 2017;107(4):840–7. Epub 20170311. pmid:28292619.
- 8. Shah DK, Missmer SA, Berry KF, Racowsky C, Ginsburg ES. Effect of obesity on oocyte and embryo quality in women undergoing in vitro fertilization. Obstet Gynecol. 2011;118(1):63–70. pmid:21691164.
- 9. Lei R, Chen S, Li W. Advances in the study of the correlation between insulin resistance and infertility. Front Endocrinol (Lausanne). 2024;15:1288326. Epub 20240126. pmid:38348417; PubMed Central PMCID: PMC10860338.
- 10. Farhangiyan Z, Latifi SM, Rashidi H, Shahbazian H. The most appropriate cut-off point of anthropometric indices in predicting the incidence of metabolic syndrome and its components. Diabetes Metab Syndr. 2019;13(4):2739–45. Epub 20190716. pmid:31405702.
- 11. Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep. 2018;8(1):16753. Epub 20181113. pmid:30425288; PubMed Central PMCID: PMC6233180.
- 12. Wakabayashi I, Daimon T. The "cardiometabolic index" as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274–8. Epub 20140906. pmid:25199852.
- 13. Wang H, Sun Y, Wang S, Qian H, Jia P, Chen Y, et al. Body adiposity index, lipid accumulation product, and cardiometabolic index reveal the contribution of adiposity phenotypes in the risk of hyperuricemia among Chinese rural population. Clin Rheumatol. 2018;37(8):2221–31. Epub 20180516. pmid:29770928.
- 14. Venkatesh SS, Ferreira T, Benonisdottir S, Rahmioglu N, Becker CM, Granne I, et al. Obesity and risk of female reproductive conditions: A Mendelian randomisation study. PLoS Med. 2022;19(2):e1003679. Epub 20220201. pmid:35104295; PubMed Central PMCID: PMC8806071.
- 15. Yin YH, Zhou SY, Lu DF, Chen XP, Liu B, Lu S, et al. Higher waist circumference is associated with increased likelihood of female infertility: NHANES 2017–2020 results. Front Endocrinol (Lausanne). 2023;14:1216413. Epub 20231020. pmid:37937052; PubMed Central PMCID: PMC10627239.
- 16. Sun F, Liu M, Hu S, Xie R, Chen H, Sun Z, et al. Associations of weight-adjusted-waist index and depression with secondary infertility. Front Endocrinol (Lausanne). 2024;15:1330206. Epub 20240307. pmid:38516413; PubMed Central PMCID: PMC10956697.
- 17. Zhong H, Yu B, Zhao F, Cui H, You L, Feng D, et al. Associations between weight-adjusted-waist index and infertility: Results from NHANES 2013 to 2020. Medicine (Baltimore). 2023;102(48):e36388. pmid:38050258; PubMed Central PMCID: PMC10695495.
- 18. Marquard KL, Stephens SM, Jungheim ES, Ratts VS, Odem RR, Lanzendorf S, et al. Polycystic ovary syndrome and maternal obesity affect oocyte size in in vitro fertilization/intracytoplasmic sperm injection cycles. Fertil Steril. 2011;95(6):2146–9, 9.e1. Epub 20101111. pmid:21071018; PubMed Central PMCID: PMC3684964.
- 19. Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med. 2012;9(12):e1001356. Epub 20121218. pmid:23271957; PubMed Central PMCID: PMC3525527.
- 20. Walls HL, Wolfe R, Haby MM, Magliano DJ, de Courten M, Reid CM, et al. Trends in BMI of urban Australian adults, 1980–2000. Public Health Nutr. 2010;13(5):631–8. Epub 20090922. pmid:19772687.
- 21. Zhu X, Hong X, Wu J, Zhao F, Wang W, Huang L, et al. The Association between Circulating Lipids and Female Infertility Risk: A Univariable and Multivariable Mendelian Randomization Analysis. Nutrients. 2023;15(14). Epub 20230713. pmid:37513548; PubMed Central PMCID: PMC10384410.
- 22. Joseph P, Leong D, McKee M, Anand SS, Schwalm JD, Teo K, et al. Reducing the Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk Factors. Circ Res. 2017;121(6):677–94. pmid:28860318.
- 23. Shi WR, Wang HY, Chen S, Guo XF, Li Z, Sun YX. Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study. Lipids Health Dis. 2018;17(1):236. Epub 20181012. pmid:30314516; PubMed Central PMCID: PMC6186046.
- 24. Wang H, Chen Y, Sun G, Jia P, Qian H, Sun Y. Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in Chinese population. Postgrad Med. 2018;130(3):325–33. Epub 20180301. pmid:29478365.
- 25. Abolnezhadian F, Hosseini SA, Alipour M, Zakerkish M, Cheraghian B, Ghandil P, et al. Association Metabolic Obesity Phenotypes with Cardiometabolic Index, Atherogenic Index of Plasma and Novel Anthropometric Indices: A Link of FTO-rs9939609 Polymorphism. Vasc Health Risk Manag. 2020;16:249–56. Epub 20200624. pmid:32612360; PubMed Central PMCID: PMC7322142.
- 26. Hoek A, Wang Z, van Oers AM, Groen H, Cantineau AEP. Effects of preconception weight loss after lifestyle intervention on fertility outcomes and pregnancy complications. Fertil Steril. 2022;118(3):456–62. pmid:36116799.
- 27. Wekker V, Karsten MDA, Painter RC, van de Beek C, Groen H, Mol BWJ, et al. A lifestyle intervention improves sexual function of women with obesity and infertility: A 5 year follow-up of a RCT. PLoS One. 2018;13(10):e0205934. Epub 20181023. pmid:30352059; PubMed Central PMCID: PMC6198949.
- 28. Legro RS, Hansen KR, Diamond MP, Steiner AZ, Coutifaris C, Cedars MI, et al. Effects of preconception lifestyle intervention in infertile women with obesity: The FIT-PLESE randomized controlled trial. PLoS Med. 2022;19(1):e1003883. Epub 20220118. pmid:35041662; PubMed Central PMCID: PMC8765626.
- 29. Einarsson S, Bergh C, Friberg B, Pinborg A, Klajnbard A, Karlström PO, et al. Weight reduction intervention for obese infertile women prior to IVF: a randomized controlled trial. Hum Reprod. 2017;32(8):1621–30. pmid:28854592.
- 30. Wen Z, Li X. Association between weight-adjusted-waist index and female infertility: a population-based study. Front Endocrinol (Lausanne). 2023;14:1175394. Epub 20230808. pmid:37614708; PubMed Central PMCID: PMC10442810.
- 31. Cheng L, Wu Q, Wang S. Cardiometabolic index is associated with increased depression: A population-based study. J Affect Disord. 2024;348:259–64. Epub 20240102. pmid:38171182.
- 32. Song J, Li Y, Zhu J, Liang J, Xue S, Zhu Z. Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol (Lausanne). 2024;15:1341828. Epub 20240212. pmid:38410697; PubMed Central PMCID: PMC10894973.
- 33. Lazzer S, D’Alleva M, Isola M, De Martino M, Caroli D, Bondesan A, et al. Cardiometabolic Index (CMI) and Visceral Adiposity Index (VAI) Highlight a Higher Risk of Metabolic Syndrome in Women with Severe Obesity. J Clin Med. 2023;12(9). Epub 20230423. pmid:37176497; PubMed Central PMCID: PMC10179486.
- 34. Silvestris E, de Pergola G, Rosania R, Loverro G. Obesity as disruptor of the female fertility. Reprod Biol Endocrinol. 2018;16(1):22. Epub 20180309. pmid:29523133; PubMed Central PMCID: PMC5845358.
- 35. Doufas AG, Mastorakos G. The hypothalamic-pituitary-thyroid axis and the female reproductive system. Ann N Y Acad Sci. 2000;900:65–76. pmid:10818393.
- 36. Zheng H, Lenard NR, Shin AC, Berthoud HR. Appetite control and energy balance regulation in the modern world: reward-driven brain overrides repletion signals. Int J Obes (Lond). 2009;33 Suppl 2(Suppl 2):S8-13. pmid:19528982; PubMed Central PMCID: PMC2838178.
- 37. Krentz AJ, von Mühlen D, Barrett-Connor E. Adipocytokine profiles in a putative novel postmenopausal polycystic ovary syndrome (PCOS) phenotype parallel those in premenopausal PCOS: the Rancho Bernardo Study. Metabolism. 2012;61(9):1238–41. Epub 20120505. pmid:22560129.
- 38. Messinis IE, Messini CI, Anifandis G, Dafopoulos K. Polycystic ovaries and obesity. Best Pract Res Clin Obstet Gynaecol. 2015;29(4):479–88. Epub 20141112. pmid:25487256.
- 39. Zheng L, Yang L, Guo Z, Yao N, Zhang S, Pu P. Obesity and its impact on female reproductive health: unraveling the connections. Front Endocrinol (Lausanne). 2023;14:1326546. Epub 20240109. pmid:38264286; PubMed Central PMCID: PMC10803652.
- 40. Diamanti-Kandarakis E, Dunaif A. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr Rev. 2012;33(6):981–1030. Epub 20121012. pmid:23065822; PubMed Central PMCID: PMC5393155.
- 41. Snider AP, Wood JR. Obesity induces ovarian inflammation and reduces oocyte quality. Reproduction. 2019;158(3):R79–r90. pmid:30999278.
- 42. Monteiro R, Azevedo I. Chronic inflammation in obesity and the metabolic syndrome. Mediators Inflamm. 2010;2010. Epub 20100714. pmid:20706689; PubMed Central PMCID: PMC2913796.
- 43. Bitzur R, Cohen H, Kamari Y, Shaish A, Harats D. Triglycerides and HDL cholesterol: stars or second leads in diabetes? Diabetes Care. 2009;32 Suppl 2(Suppl 2):S373-7. pmid:19875584; PubMed Central PMCID: PMC2811435.
- 44. Brookheart RT, Michel CI, Listenberger LL, Ory DS, Schaffer JE. The non-coding RNA gadd7 is a regulator of lipid-induced oxidative and endoplasmic reticulum stress. J Biol Chem. 2009;284(12):7446–54. Epub 20090115. pmid:19150982; PubMed Central PMCID: PMC2658040.
- 45. Wu LL, Dunning KR, Yang X, Russell DL, Lane M, Norman RJ, et al. High-fat diet causes lipotoxicity responses in cumulus-oocyte complexes and decreased fertilization rates. Endocrinology. 2010;151(11):5438–45. Epub 20100922. pmid:20861227.
- 46. Wei S, Schmidt MD, Dwyer T, Norman RJ, Venn AJ. Obesity and menstrual irregularity: associations with SHBG, testosterone, and insulin. Obesity (Silver Spring). 2009;17(5):1070–6. Epub 20090129. pmid:19180069.
- 47. Lim SS, Norman RJ, Davies MJ, Moran LJ. The effect of obesity on polycystic ovary syndrome: a systematic review and meta-analysis. Obes Rev. 2013;14(2):95–109. Epub 20121031. pmid:23114091.
- 48. Boyle BR, Ablett AD, Ochi C, Hudson J, Watson L, Rauh D, et al. The effect of weight loss interventions for obesity on fertility and pregnancy outcomes: A systematic review and meta-analysis. Int J Gynaecol Obstet. 2023;161(2):335–42. Epub 20221209. pmid:36440496.
- 49. Brewer CJ, Balen AH. The adverse effects of obesity on conception and implantation. Reproduction. 2010;140(3):347–64. Epub 20100415. pmid:20395425.
- 50. Linné Y. Effects of obesity on women’s reproduction and complications during pregnancy. Obes Rev. 2004;5(3):137–43. pmid:15245382.