Relationships between inflammatory indicators, nutritional status, and metabolic status and female infertility

Abstract

Background

Infertility is a growing global challenge that affects millions of people, and various factors influence female reproductive health. This study examined the association between inflammatory indicators, nutritional status, metabolic conditions, and infertility.

Methods

This cross-sectional study was part of the National Health and Nutrition Examination Survey (NHANES) and included women aged 18–45 years between 2013 and 2018. Weighted binary logistic regression was used to investigate independent relationships between inflammatory indicators, nutritional status, metabolic conditions, and infertility. Subsequently, a nomogram risk prediction model was developed along with subgroup analyses.

Results

Among the 1,250 women analyzed, the overall infertility prevalence was 12.3%. Multivariate logistic regression analyses identified marital status, systemic immune inflammation index (SII), body mass index (BMI), nutritional risk index (NRI), and metabolic syndrome (MetS) as independent risk factors for infertility. A nomogram prediction model was constructed based on the independent risk factors, and the area under the ROC curve of the model was 0.703. The calibration and decision curves showed that the model had good calibration and net benefits. Based on the nomogram prediction model, the total risk scores for infertility were calculated and divided into tertiles. The infertility risk was 4.5% in tertile 1, 9.3% in tertile 2, and 22.1% in tertile 3. Both the crude and adjusted models and subgroup analyses confirmed this positive correlation.

Conclusions

Inflammatory, nutritional, and metabolic factors are significantly associated with infertility in women. The reproductive capacity of women of childbearing age can be enhanced by reducing inflammation, optimizing nutrition, and managing metabolic conditions, thereby reducing the risk of infertility.

Background

The global burden of infertility has been steadily increasing, with the incidence of female infertility rising by approximately 15% between 1990 and 2017 [1]. Infertility is defined as the inability to conceive after 12 months of regular unprotected sexual intercourse without the use of contraception [2, 3]. Infertility in women of reproductive age is a complex medical issue influenced by various factors, particularly inflammatory markers, nutritional status, and metabolic syndrome. In recent years, growing evidence has highlighted the close relationship between these factors and infertility.

Inflammatory responses play a crucial role in the reproductive health of women. Chronic low-grade inflammation is believed to impair ovarian function and obstruct the fallopian tubes, thereby affecting fertility [4]. Research indicates that inflammatory markers, such as C-reactive protein and cytokines (e.g., tumor necrosis factor-alpha and interleukin-6), are often elevated in women with infertility [5–7]. These markers not only reflect the body’s inflammatory state but may also directly impact egg quality and the implantation environment, ultimately affecting reproductive capacity [8]. Furthermore, early studies have shown that women diagnosed with polycystic ovary syndrome (PCOS) exhibit significantly increased levels of lymphocytes and elevated proportions of CD4 + T and NK cells, both of which are independent risk factors for PCOS-related infertility [9]. This suggests that peripheral blood inflammatory immune cells may serve as promising predictive factors for infertility in these patients. Therefore, identifying and managing relevant inflammatory factors is crucial for improving reproductive outcomes.

Nutritional status is also a key factor influencing fertility in women of reproductive age. Nutritional deficiencies or imbalances can lead to endocrine disorders that affect follicular development and ovulation [10]. Numerous studies have highlighted the significant correlation between obesity and infertility, demonstrating that women with obesity often exhibit abnormal metabolic markers, including insulin resistance and dyslipidemia [11, 12]. Moreover, the intake of specific nutrients, particularly folic acid, vitamin D, and omega-3 fatty acids, has gained increasing attention owing to its impact on reproductive health [13–15]. Proper nutritional interventions can not only improve women’s reproductive function but also reduce the risk of pregnancy complications. Metabolic syndrome (MetS), characterized by abnormalities in waist circumference, blood glucose, blood pressure, and lipid levels, is commonly associated with infertility [16]. A growing body of research indicates that the prevalence of MetS is higher among patients with infertility and is closely linked to reproductive failure [17]. MetS may disrupt endocrine function, interfere with ovarian hormone secretion, and compromise egg quality, leading to difficulties in conception [18]. Additionally, MetS can lead to complications during pregnancy, such as high-risk pregnancies, hyperglycemia, and an increased risk of obesity in the offspring, creating a vicious cycle. The early identification of MetS and interventions for related risk factors are vital for improving fertility in women of reproductive age.

In summary, infertility in women of reproductive age is associated with multiple factors. However, systematic research on the impact of inflammatory markers, nutritional status, and MetS on the risk of infertility is lacking, and information regarding the influence of individual factors on female infertility remains limited. Therefore, we used data from the National Health and Nutrition Examination Survey (NHANES) to investigate the effects of these factors on female infertility. Based on these analyses, we developed a multivariable risk-prediction model and evaluated its performance using metrics such as the area under the ROC curve, calibration curves, and decision curves. This study integrated multiple indicators to provide a more comprehensive and precise tool for assessing the risk of infertility in clinical settings. It also provides new insights into clinical practice and ultimately improves women’s health management, prevention, and intervention strategies.

Methods

Data source

The NHANES database is a public project that assesses the health and nutritional status of Americans. It employs a complex, multistage, and stratified sampling design to collect health and nutritional information from a representative sample of the non-institutionalized civilian population every two years. We selected data from 2013 to 2018. All NHANES research methods were approved by the NCHS Research Ethics Review Board, and all participants had provided written informed consent. Detailed information about the NHANES study design and data are publicly available at www.cdc.gov/nchs/nhanes/. This cross-sectional study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines.

Participants

Each woman’s self-reported information on infertility from the reproductive health questionnaire (variable name: RHQ074) served as the dependent variable [19–21]. Researchers asked participants questions such as, “Did it take you a year to become pregnant?” If the answer was “yes,” it was classified as “infertility,” whereas a “no” response indicated “fertility.” Additionally, an infertility-related questionnaire was only included in the NHANES cycles from 2013 to 2018. In our analysis, we included participants with comprehensive information on infertility, inflammatory markers, nutritional indicators, and MetS.

All participants were initially considered (n = 29,400). Thereafter, we excluded male participants (n = 14,452), women aged < 18 or > 45 years (n = 10,625), participants without fertility information (N = 654), and those with missing relevant data (n = 2,419). Finally, 1,250 eligible participants were included in the analysis (Fig. 1).

Fig. 1.

NHANES 2013–2018 sample selection flowchart

Study variables

Demographic variables included age, race, marital status, education level, and the ratio of family income to poverty (PIR). Age was categorized into two groups based on the median: < 33 years and ≥ 33 years. Marital status was classified as “married or cohabiting” and “single.” Education levels are divided into three categories: “less than high school,” “high school,” and “more than high school.” PIR was divided into two groups: those with a ratio of < 1 and those with a ratio of ≥ 1.

Inflammatory indicators included systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR). The formulas employed for calculating these ratios were as follows: SII = absolute neutrophil count × absolute platelet count/absolute lymphocyte count; SIRI = absolute neutrophil count × absolute monocyte count/absolute lymphocyte count; NLR = absolute neutrophil count/absolute lymphocyte count; PLR = absolute monocyte count/absolute lymphocyte count; and LMR = absolute lymphocyte count/absolute monocyte count. Continuous variables were categorized as binary variables based on their median values.

Nutritional indicators included controlling nutritional status (CONUT), platelet-to-albumin ratio (PAR), prognostic nutritional index (PNI), body mass index (BMI), and nutritional risk index (NRI). The calculation formulas for these indicators were as follows: CONUT = albumin level [≥ 35 g/dL (0 points), 30–34 g/dL (2 points), 25–29 g/dL (4 points), or < 25 g/dL (6 points)] + lymphocyte count [≥ 1600 count/mm³ (0 point), 1200–1599 count/mm³ (1 point), 800–1199 count/mm³ (2 points), < 800 count/mm³ (3 points)] + total cholesterol level [≥ 180 mg/dL (0 points), 140 −179 mg/dL (1 point), 100–139 mg/dL (2 points), < 100 mg/dL (3 points)]; PAR = absolute platelet count/albumin level; PNI = 5 × lymphocyte count + albumin level; BMI = weight (kg)/height (m)²; NRI = 1.489 × albumin (g/L) + 41.7 × [(current weight/ideal weight. The ideal weight for women was defined as [height (cm) × 100–(height (cm) × 150)/2.5]. The CONUT score was categorized as follows: a score of 0–1 indicated a well-nourished group and a score of ≥ 2 indicated a malnourished group. For BMI, a value < 25 kg/m² was classified as normal weight, whereas a value > 25 kg/m² was classified as overweight. PAR, PNI, and NRI were divided into two groups based on their median values.

Participants with at least three abnormal metabolic components were classified as having MetS. Metabolic components included increased waist circumference, elevated fasting blood glucose, high blood pressure, increased triglyceride levels, and decreased high-density lipoprotein cholesterol levels. MetS was divided into normal and abnormal groups.

Calculation of risk score in the nomogram model

Based on the nomogram prediction model, we calculated the total risk score (the sum of the individual risk scores for each independent risk factor for each participant). Subsequently, the total risk scores (ranging from 0 to 299) were divided into tertiles.

Statistical analysis

According to the recommendations of the Centers for Disease Control and Prevention, all statistical analyses were conducted using the appropriate NHANES sampling weights, accounting for the complex multistage cluster survey design. In the descriptive analysis, categorical variables were assessed using the chi-square test and presented as proportions. The variance inflation factor was used to assess multicollinearity between independent variables. Logistic regression analysis was performed to identify risk factors for infertility in women. A nomogram risk model was established, and its stability was evaluated using calibration, decision, and ROC curves. The total risk score was calculated based on the nomogram scores. Subsequently, the association between the risk scores and infertility was analyzed. All statistical analyses were conducted using R software version 4.2.3. Statistical significance was set at p < 0.05.

Results

Baseline characteristics

A total of 1,250 women aged between 18 and 45 years participated in this study, of whom 154 were diagnosed with infertility. The characteristics of the study participants based on their infertility status are presented in Table 1. Significant differences (p < 0.05) were observed between the infertile and control groups in terms of age, marital status, BMI, NRI, MetS, and SII. However, no significant differences were found between the two groups in terms of race, education level, PIR, CONUT score, PAR, PNI, SIRI, NLR, PLR, or LMR.

Table 1.

Baseline characteristics of the 1250 participants

Variables	Control	Infertility	p-value
	N = 1096	N = 154
Age (years)			0.003
< 33	559(44.7%)	59(4.7%)
≥ 33	537(43.0%)	95(7.6%)
Race			0.641
Mexican American	182(14.6%)	25(2.0%)
Other Hispanic	108(8.6%)	12(1.0%)
Non-Hispanic White	380(30.4%)	62(5.0%)
Non-Hispanic Black	221(17.7%)	31(2.5%)
Other Race—Including Multi-Racial	205(16.4%)	24(1.9%)
Marital			< 0.001
Married or living with partner	610(48.8%)	116(9.3%)
Living alone	486(38.9%)	38(3.0%)
Education			0.937
Below high school	163(13.0%)	24(1.9%)
High school	211(16.9%)	28(2.2%)
Post high school	722(57.8%)	102(8.2%)
PIR			0.176
< 1	306(24.5%)	35(2.8%)
≥ 1	790(63.2%)	119(9.5%)
CONUT			0.902
Normal	842(67.4%)	119(9.5%)
Malnutrition	254(20.3%)	35(2.8%)
PAR			0.389
< 6.19	553(44.2%)	72(5.8%)
≥ 6.19	543(43.4%)	82(6.6%)
PNI			0.532
< 52	549(43.9%)	73(5.8%)
≥ 52	547(43.8%)	81(6.5%)
BMI			< 0.001
< 25	415(33.2%)	19(1.5%)
≥ 25	681(54.5%)	135(10.8%)
NRI			< 0.001
< 115.04	588(44.6%)	37(3.0%)
≥ 115.04	508(40.6%)	117(9.4%)
MET			< 0.001
Normal	924(73.9%)	102(8.2%)
Abnormal	172(13.8%)	52(4.2%)
SII			< 0.001
< 465.94	570(45.6%)	55(4.4%)
≥ 465.94	526(42.1%)	99(7.9%)
SIRI			0.116
< 0.91	558(44.6%)	68(5.4%)
≥ 0.91	538(43.0%)	86(6.9%)
NLR			0.401
< 1.86	552(44.2%)	72(5.8%)
≥ 1.86	544(43.5%)	82(6.6%)
PLR			0.302
< 119.83	542(43.4%)	83(6.6%)
≥ 119.83	554(44.3%)	71(5.7%)
LMR			0.430
< 4.25	525(42.0%)	79(6.3%)
≥ 4.25	571(45.7%)	75(6.0%)

PIR Ratio of family income to poverty, CONUT Controlling nutritional status, PAR Platelet-albumin ratio, PNI Prognostic nutritional index, BMI Body mass index, NRI Nutritional risk index, MetS Metabolic syndrome, SII Systemic immune-inflammation index, SIRI Systemic inflammatory response index, NLR Neutrophil–lymphocyte ratio, PLR Platelet-lymphocyte ratio, LMR Lymphocyte-monocyte ratio

Risk factors for infertility

The variance inflation factor showed an absence of multicollinearity between the independent variables. The results of the univariate and multivariate logistic regression analyses are presented in Table 2. Univariate analysis indicated that age, marital status, body mass index (BMI), NRI, MetS, and SII were potential risk factors. Multivariate analyses showed that marital status [(odds ratio), OR], 0.436; 95% confidence interval (CI) 0.293–0.649]; p < 0.001], BMI (OR, 2.047; 95%CI, 1.096–3.825; p = 0.025), NRI (OR, 2.026; 95%CI, 1.231–3.336; p = 0.005), MetS (OR, 1.546; 95%CI, 1.036–2.309; p = 0.033), and SII (OR, 1.485; 95% CI, 1.029–2.142; p = 0.035) were independent risk factors.

Table 2.

Results of the logistic regression analysis of risk factors associated with female infertility

Variables	Univariate analysis	p-value	Multivariate analysis	p-value
	OR (95% CI)		OR (95% CI)
Age (years)
< 33	Reference		Reference
≥ 33	1.676 (1.186–2.368)	0.003	1.213(0.842–1.748)	0.299
Race
Mexican American	Reference
Other Hispanic	0.809 (0.390–1.676)	0.568
Non-Hispanic White	1.188 (0.723–1.952)	0.497
Non-Hispanic Black	1.021 (0.582–1.792)	0.942
Other Race—Including Multi-Racial	0.852 (0.470–1.545)	0.598
Marital
Married or living with partner	Reference		Reference
Living alone	0.411 (0.280–0.604)	< 0.001	0.436(0.293–0.649)	< 0.001
Education
Below high school	Reference
High school	0.901 (0.503–1.613)	0.726
Post high school	0.959 (0.596–1.544)	0.865
PIR
< 1	Reference
≥ 1	1.317(0.883–1.963)	0.177
CONUT
Normal	Reference
Malnutrition	0.975 (0.652–1.458)	0.902
PAR
< 6.19	Reference
≥ 6.19	1.160(0.827–1.626)	0.390
PNI
< 52	Reference
≥ 52	1.114 (0.794–1.561)	0.532
BMI
< 25	Reference		Reference
≥ 25	4.330 (2.638–7.106)	< 0.001	2.047(1.096–3.825)	0.025
NRI
< 115.04	Reference		Reference
≥ 115.04	3.660 (2.482–5.397)	< 0.001	2.026(1.231–3.336)	0.005
METs
Normal	Reference		Reference
Abnormal	2.739 (1.889–3.971)	< 0.001	1.546(1.036–2.309)	0.033
SII
< 465.94	Reference		Reference
≥ 465.94	1.951 (1.374–2.769)	< 0.001	1.485(1.029–2.142)	0.035
SIRI
< 0.91	Reference
≥ 0.91	1.312 (0.934–1.842)	0.117
NLR
< 1.86	Reference
≥ 1.86	1.156 (0.824–1.620)	0.402
PLR
< 119.83	Reference
≥ 119.83	0.837 (0.597–1.174)	0.302
LMR
< 4.25	Reference
≥ 4.25	0.873 (0.623–1.223)	0.430

PIR Ratio of family income to poverty, CONUT Controlling nutritional status, PAR Platelet-albumin ratio, PNI Prognostic nutritional index, BMI Body mass index, NRI Nutritional risk index, MetS Metabolic syndrome, SII Systemic immune-inflammation index, SIRI Systemic inflammatory response index, NLR Neutrophil–lymphocyte ratio, PLR Platelet-lymphocyte ratio, LMR Lymphocyte-monocyte ratio

Building and validating a nomogram model to predict infertility

Based on inflammatory indicators (SII), nutritional indicators (BMI and NRI), and MetS, a nomogram model was constructed to predict infertility (Fig. 2A). Calibration curve analysis indicated good consistency between the actual observed infertility rates and the predicted outcomes (Fig. 2B). Decision curve analysis demonstrated that the model provided a better net benefit across different risk probability thresholds (Fig. 2C). The area under the ROC curve indicated that the ability of the model to predict infertility far exceeded that of each independent risk factor alone. These results suggest that the model has a strong predictive capability (Fig. 3).

Fig. 2.

A A nomogram model was constructed based on SII, BMI, NRI, and MetS. B The calibration curve indicated a good concordance between the actual observed outcomes and the predicted infertility. C The decision curve analysis demonstrated that the model provides greater net benefits across varying risk thresholds. SII, systemic immune-inflammation index; BMI, body mass index; NRI, nutritional risk index; MetS, metabolic syndrome

Fig. 3.

The area under the receiver operating characteristic curve was used to assess the predictive ability of SII, BMI, NRI, MetS, and the model. A SII; B BMI; C NRI; D MetS; E Complex (the model). SII, systemic immune-inflammation index; BMI, body mass index; NRI, nutritional risk index; MetS, metabolic syndrome

Association between risk score and infertility

The relationship between the risk scores and infertility is presented in Table 3. Our results indicated that the infertility risk for tertiles 1, 2, and 3 was 4.5%, 9.3%, and 22.1%, respectively, suggesting that the risk of infertility increased with increasing risk scores. Similarly, both the crude model and the minimally or fully adjusted models demonstrated a positive correlation between risk scores and infertility. Compared to participants in tertile 1, those in tertile 3 showed a 530% increase in risk (OR = 6.300, 95% CI: 3.680–10.785; P for trend < 0.001).

Table 3.

Associations between risk score and infertility

		OR (95%CI), p-value
		Crude model	Minimally adjusted model	Fully adjusted model
		(Model 1)	(Model 2)	(Model 3)
Categories	N(Infertility)
Tertile 1	19	Reference	Reference
Tertile 2	36	2.195(1.236–3.897),0.007	2.100(1.177–3.749),0.012	2.156(1.205–3.857),0.010
Tertile 3	99	6.036(3.620–10.067), < 0.001	5.716(3.404–9.599), < 0.001	6.300(3.680–10.785), < 0.001
P trend		< 0.001	< 0.001	< 0.001

Model 1, unadjusted; Model 2, adjusted for age, race, marital status, education, and PIR; Model 3, adjusted for age, race, marital status, education, PIR, CONUT score, PAR, PNI, SIRI, NLR, PLR, and LMR

OR Odds ratio, 95% CI 95% Confidence interval, PIR Ratio of family income to poverty, CONUT Controlling nutritional status, PAR Platelet-albumin ratio, PNI Prognostic nutritional index, BMI Body mass index, NRI Nutritional risk index, MetS Metabolic syndrome, SII Systemic immune-inflammation index, SIRI Systemic inflammatory response index, NLR Neutrophil–lymphocyte ratio, PLR Platelet-lymphocyte ratio, LMR Lymphocyte-monocyte ratio

Subsequently, we conducted a subgroup analysis to explore the relationship between risk scores and infertility across different covariates (Fig. 4). The results indicated a positive correlation between the risk scores and infertility in all subgroups except for the Hispanic subgroup (p < 0.05).

Fig. 4.

Subgroup between risk score and infertility

Discussion

This study comprehensively analyzed the risk factors for female infertility from three perspectives: inflammatory indicators, nutritional indicators, and metabolic factors. Our results indicated the following: first, SII, BMI, NRI, and MetS were identified as risk factors for female infertility; second, a nomogram model for predicting infertility was constructed, with calibration, decision, and ROC curves demonstrating strong predictive capabilities; and third, adjusting for various covariates and conducting subgroup analyses revealed that the risk of infertility increased with higher risk scores. These results underscore the need for comprehensive risk assessment models to accurately predict infertility risk.

Infertility is becoming an increasingly global challenge in both medical and social contexts [22]. The World Health Organization (WHO) estimates that approximately 48 million couples worldwide are affected by infertility. Recent evidence from a meta-analysis and systematic review indicated that the overall global prevalence of infertility among women is 46.25%, with primary infertility affecting 51.5% of these women [23]. Female fertility is primarily influenced by gynecological and systemic diseases; lifestyle factors, inflammation, nutrition, and metabolic conditions also play significant roles in reproductive health [24]. Therefore, identifying the risk factors for infertility and developing preventive strategies is essential for mitigating the adverse effects of infertility and its social burden.

Inflammation and immunity are widely believed to play crucial roles in many causes of female infertility, including endometriosis, PCOS, pelvic inflammatory disease, and unexplained infertility. Fortunately, several emerging indicators originating from peripheral blood, such as SII, SIRI, NLR, PLR, and LMR, have been recognized as biomarkers of inflammatory and immune status and have been associated with various pregnancy complications and miscarriage [25–27]. Among these, the significance of SII has been particularly emphasized, as it reflects systemic inflammatory conditions and serves as an effective blood marker. Elevated SII levels have been linked to an increased risk of diseases, such as malignancies, cognitive disorders, depression, cardiopulmonary issues, and metabolic disorders [28–30]. However, the relationship between SII and infertility remains unclear. Previous studies have indicated that high SII levels are associated with miscarriage and adverse neonatal outcomes [31]. The SII comprises neutrophils, platelets, and lymphocytes. Previous studies have confirmed that increased neutrophil counts are one of the reasons for infertility in individuals with PCOS [9]. Additionally, the effect of platelets on the immune system, such as the production of inflammatory cytokines and the release of antimicrobial proteins, is a contributing factor to female infertility [32]. Shao et al. found that a high platelet count was independently associated with an elevated risk of miscarriage [33]. Our study demonstrated that an elevated SII increased the risk of infertility in women of childbearing age, further corroborating previous findings.

In developed countries, there is a significant association between obesity, excess nutrient intake, and female infertility. Our research indicates that BMI ≥ 25 kg/m² and NRI ≥ 115.04 are considered risk factors for infertility among reproductive-aged women in the United States. Nutrient excess often leads to overweight or obesity, which is closely linked to menstrual cycle irregularities and ovulatory disorders [2]. Obesity negatively affects fertility, primarily through alterations in the hypothalamic-pituitary-ovarian (HPO) axis. Women with obesity typically exhibit elevated insulin levels, which are thought to promote increased secretion of ovarian androgens [34]. Excess adipose tissue leads to the rapid conversion of these androgens to estrogen, resulting in negative feedback on the HPO axis and affecting the secretion of gonadotropins [35]. Furthermore, obesity affects the responsiveness of the ovaries to gonadotropins, requiring higher doses of hormones for follicular development and resulting in longer treatment cycles [36, 37]. Additionally, obese women generally produce fewer oocytes and have a higher rate of fertility cycles [37, 38]. Moreover, obese women tend to have elevated leptin levels, whereas the levels of growth hormones and insulin-like growth factor-binding proteins are relatively low. This imbalance can disrupt the neuroregulation of ovarian function and the HPO axis, leading to reduced embryo development prior to implantation and decreased uterine receptivity, thereby increasing the risk of miscarriage and infertility [39, 40]. Several studies have suggested that obese women often have longer pregnancies. Two large cohort studies involving Danish women planning to conceive found that fertilization rates declined as BMI increased [41, 42]. Research has also indicated that obesity hinders assisted reproductive technology. Compared to women with BMI < 25 kg/m², overweight and obese women have lower pregnancy rates following in vitro fertilization [43]. Additionally, excess nutrients are closely associated with mental health in women. Obesity may negatively impact self-image and mental well-being, thereby increasing the risk of anxiety and depression. Psychological factors also influence fertility rates. Therefore, maintaining a balanced diet and healthy weight is crucial for women’s reproductive health.

MetS is a pathological condition characterized by abdominal obesity, hypertension, insulin resistance, and dyslipidemia [44, 45]. Both MetS and obesity have a significant negative impact on female reproductive function, leading to hormonal imbalances and gonadal dysfunction. Both factors are also known contributors to PCOS, which may alter endometrial receptivity [46]. MetS represents a chronic inflammatory state that, together with dyslipidemia, may lead to adverse reproductive outcomes [47, 48]. Abnormal lipid metabolism can result in endothelial injury and reduced placental perfusion, potentially leading to spontaneous preterm birth or preeclampsia [48]. Changes in carbohydrate metabolism associated with insulin resistance and increased carbohydrate intake may impair ovulation and negatively affect endometrial development and implantation [10, 49]. He et al. found that women with MetS experienced longer infertility durations and poorer ovarian stimulation characteristics during in vitro fertilization cycles [50]. Consistent with previous research, we found that MetS was a significant risk factor for female infertility. Overall, MetS negatively affects the reproductive health of women through various physiological mechanisms. Therefore, effective management and intervention of MetS may improve fertility in women.

The results of this study highlight the clinical significance of SII, BMI, NRI, and MetS with respect to fertility in women. Currently, the methods for diagnosing infertility are diverse, complex, and expensive. These indicators can be easily obtained through routine blood tests, biochemical analyses, and simple calculations based on height and weight, making them cost-effective and straightforward. A deeper exploration of these factors will not only enhance our understanding of the pathological mechanisms underlying infertility but also provide new prevention and intervention strategies for clinical practice.

However, this study had several limitations. First, infertility outcomes were obtained through questionnaire surveys, which may have introduced recall bias. Second, the assessment of the causes of female infertility did not account for the health status of male partners, potentially affecting the comprehensiveness of the findings. Third, owing to the cross-sectional design of our study, we could not establish definitive causal relationships between SII, BMI, NRI, and MetS and infertility. Fourth, factors influencing female infertility are complex and multifaceted, including smoking, alcohol consumption, physical activity, and specific reproductive conditions, such as PCOS and endometriosis, which were not fully considered in this study. Finally, the lack of external validation limits the generalizability of our predictive model. Future research should address the aforementioned limitations and adopt a prospective cohort design to validate the model in independent populations and evaluate its robustness and clinical utility.

Conclusions

This study identified the SII, BMI, NRI, and MetS as significant risk factors for female infertility. Despite certain limitations, these findings highlight the importance of managing inflammation, nutrition, and metabolism to improve reproductive health in women.

Abbreviations

BMI

Body mass index

CI

Confidence interval

CONUT

Controlling nutritional status

HPO

Hypothalamic-pituitary-ovarian

LMR

Lymphocyte-monocyte ratio

MetS

Metabolic syndrome

NHANES

National Health and Nutrition Examination Survey

NLR

Neutrophil-lymphocyte ratio

NRI

Nutritional risk index

OR

Odds ratio

PAR

Platelet-to-albumin ratio

PCOS

Polycystic ovary syndrome

PIR

Ratio of family income to poverty

PLR

Platelet-lymphocyte ratio

PNI

Prognostic nutritional index

ROC

Receiver operating characteristic

SII

Systemic immune inflammation index

SIRI

Systemic inflammatory response index

Authors’ contributions

Zhenwen Zhang: Conceptualization, Data curation, Formal analysis, Software, Visualization, Investigation, Writing-original draft, Writing-review & editing. Liangyu Chen: Conceptualization, Data curation, Resources, Investigation, Validation, Writing-original draft, Writing-review & editing. Huihua Chen: Data curation, Resources, Validation, Writing—review & editing. Tingting Lin: Writing-review & editing, Supervision. Chen Gao: Writing-review & editing, Supervision. Lei Yang: Writing-review & editing, Supervision.

Funding

This study received no funding from any agency or individual.

Data availability

The datasets generated and/or analyzed during this study are available in the NHANES database, accessible at https://www.cdc.gov/nchs/nhanes/.

Declarations

Ethics approval and consent to participate

The research involving human participants was reviewed and approved by the NCHS Ethics Review Board. These studies were conducted in accordance with local laws and institutional requirements. All participants provided written informed consent to participate in this study. Written informed consent was obtained from all the participants for the publication of any potentially identifiable images or data included in this article.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.