New research reveals that women with higher levels of hidden visceral fat face greater infertility risk, even if their BMI is normal, spotlighting the need for better screening tools in reproductive health.
Study: Association of relative fat mass with female infertility: a cross-sectional study based on NHANES 2017–2020. Image Credit: Neirfy / Shutterstock
In a recent study published in the journal Scientific Reports, researchers examined the associations between relative fat mass (RFM) and female infertility.
Infertility is the inability to achieve pregnancy after one year of regular, unprotected sex. It affects about 10% to 15% of couples worldwide and significantly impacts mental and physical health. The etiology of infertility is diverse and complex, including reproductive system abnormalities, lifestyle factors, immunological diseases, and endocrine disorders. The relationship between infertility and obesity has attracted substantial interest in recent years.
Female infertility could be due to tubal disease, ovarian dysfunction, polycystic ovary syndrome (PCOS), and endometriosis. PCOS is characterized by hyperandrogenism, insulin resistance, and impaired ovarian follicular function; these abnormalities are particularly more pronounced in females with obesity. Evidence suggests that obesity is positively correlated with infertility risk.
RFM is a more effective measure of visceral fat than body mass index (BMI). RFM is calculated using the formula: RFM = 64 − (20 × height/waist circumference) + 12 (for females). RFM integrates waist circumference (WC), more accurately reflecting visceral fat distribution. Unlike BMI, which may fail to identify women with normal weight but excess visceral fat, RFM offers improved screening for metabolic and reproductive risk.
Besides, visceral fat directly affects fertility and ovarian function by influencing chronic inflammation and insulin resistance, which are better captured by RFM. While RFM correlates with metabolic diseases, how it relates to the female reproductive system, particularly infertility, is poorly defined.
The study also notes that infertility and obesity are both linked to psychosocial impacts such as stress, anxiety, and depression, highlighting the need for a holistic approach to reproductive health.
About the study
In the present study, researchers examined the associations between RFM and infertility in females. The National Health and Nutrition Examination Survey data from 2017 and 2020 were used in the current analyses. Females aged 20–44 were included; those with a history of oophorectomy or hysterectomy, or missing RFM or infertility information were excluded. The primary exposure was RFM, calculated from an individual’s height and WC.
The primary outcome was infertility, ascertained using questionnaire items asking whether participants attempted to achieve pregnancy for a year without success or if they consulted a healthcare provider for being unable to conceive. Covariates included age, ethnicity, marital status, education level, BMI, household income, alcohol intake, menstrual cycle regularity, sleep disorders, smoking status, and prior treatment for pelvic infection or pelvic inflammatory disease.
The association between infertility and RFM was assessed using multivariate logistic regression models. One model was adjusted for sociodemographic variables, while another was adjusted for all covariates. In addition, RFM was stratified into quartiles to test linear trends. The study also employed restricted cubic spline analysis to assess the shape of the association, confirming a linear relationship.
Finally, subgroup analyses were performed to evaluate the stability of the association(s) across various demographic factors, including ethnicity, education, income, BMI, alcohol consumption, smoking, sleep patterns, menstrual cycle regularity, and history of pelvic infection or pelvic inflammatory disease.
Findings
The study included 1,487 females, with a mean age of 31.9 years and RFM of 41.2. Of these, 200 subjects were infertile. Most participants were non-Hispanic White (29%), followed by non-Hispanic Black (28%), and Mexican American (14%). Around 56% of participants were married or cohabiting, and 36% were unmarried. Most participants did not smoke (70%) or have trouble sleeping (77%), and had regular menstrual cycles (93%).
Infertile females were older, married or cohabiting, and had higher RFM than those without infertility. The mean RFM was 42.8 for the infertile group and 40.9 for those without infertility. The researchers noted a significant correlation between RFM and infertility. The crude (unadjusted) model showed that the infertility risk increased by 4% for each unit increment in RFM.
In the fully adjusted model, after accounting for all covariates, each unit increase in RFM was associated with a 6% higher risk of infertility (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.01–1.12, p = 0.019).
The relationship of RFM with infertility remained after adjusting for sociodemographic variables or all covariates. Further, the highest quartile of RFM had a significantly higher risk of infertility than the lowest quartile. Specifically, the risk of infertility in the highest quartile was 2.38 times higher than in the lowest quartile (OR = 2.38, 95% CI: 0.99–5.70), although the confidence interval included 1.00, indicating borderline statistical significance for this finding. T
here was a significant and linear association, with infertility risk increasing continuously with an increase in RFM. Restricted cubic spline analysis confirmed that this association was linear rather than nonlinear.
Results were stable across subgroups. The study found consistent associations across major demographic and clinical subgroups, including ethnicity, education, income, BMI category, alcohol and smoking status, sleep disorders, menstrual cycle regularity, and history of pelvic infection.
Conclusions
Taken together, the findings indicate a significant association between RFM and female infertility, with similar results across different subgroups. The study’s limitations include its cross-sectional design, which precludes causal inference, and poor generalizability due to the sample's limited representation of the United States population.
Additionally, unmeasured confounding factors could not be entirely ruled out. Overall, RFM could be used as a potential infertility screening indicator, particularly in women who may have normal BMI but elevated visceral fat.
Future studies should evaluate its clinical significance, including through prospective and multi-level research addressing genetic, lifestyle, and environmental factors.