The association of triglyceride-glucose index and uric acid-high-density lipoprotein cholesterol ratio with the risk of uterine fibroids: a real-world analysis based on health examination data.

This study is a real-world-based cross-sectional study. Data were collected from 42,215 adult female participants who did routine health examinations at the Hebei Province Health Examination Center between January 1, 2024, and December 31, 2024. Exclusion criteria: [ 1 ] a history of hysterectomy or oophorectomy; [ 2 ] current use of hormone therapy or oral contraceptives; [ 3 ] presence of malignant tumors, severe hepatic or renal insufficiency, or other diseases that may affect blood pressure, blood glucose, or blood lipid levels; [ 4 ] Patients with cervical lesions, endometrial lesions, and other clearly defined gynecological structural diseases; [ 5 ] incomplete clinical data. 40,529 participants were finally included (Fig. 1 ). All analyses were performed using the full sample ( n = 40,529). Fig. 1 Flowchart of participant screening in this study Flowchart of participant screening in this study The study protocol was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Hebei General Hospital (Ethics Approval No.:2025-LW-0182). All participants provided informed consent. All participants underwent fasting venous blood sampling for biochemical testing, and disease diagnoses were determined based on physical examinations and medical history. UF was diagnosed via transabdominal or transvaginal ultrasound, showing imaging features consistent with UF or based on prior clinical diagnostic records [ 23 ]. Hypertension was classified as either a systolic blood pressure (SBP) reading of 140 mmHg or higher, a diastolic blood pressure (DBP) of 90 mmHg or above, or a previously confirmed diagnosis of hypertension [ 24 , 25 ]. Diabetes was defined as a fasting plasma glucose (FPG) level ≥ 7.0 mmol/L or a prior diagnosis of type 2 diabetes [ 26 ]. The collected baseline variables included age, BMI, SBP, DBP, FPG, uric acid (UA), blood lipid profile [including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and total cholesterol (TC)], hematological parameters [white blood cell count (WBC), monocyte count (MONO), eosinophil count (EO), basophil count (BASO), red blood cell count (RBC), hematocrit (HCT), hemoglobin (HGB), platelet count (PLT), and platelet large cell ratio (P-LCR)], as well as menopausal status and comorbidities (hypertension and diabetes). The main exposure variables were the TyG index and UHR. Based on previous literature [ 27 – 29 ], the TyG index was calculated as TyG = ln[TG (mg/dL) × FPG (mg/dL)/2]. UHR was calculated as UHR (%) = UA (mg/dL)/HDL-C (mg/dL) × 100. Unit conversions were as follows: TG: 1 mmol/L = 88.6 mg/dL; FPG: 1 mmol/L = 18.0 mg/dL; UA: 1 mmol/L = 16.81 mg/dL; HDL-C: 1 mmol/L = 38.67 mg/dL. The outcome variable was UF. Data analysis was done with SPSS 26.0 and R 4.3.2 statistical packages. Categorical variables were summarized as n (%), with group differences examined by χ² tests. Normality of continuous variables was verified by Kolmogorov-Smirnov tests. Normally distributed variables were reported as mean ± SD and compared via independent t-tests, while non-normal distributions were characterized by median [IQR] values and analyzed with Kruskal-Wallis. To facilitate the comparison of the effects of different indicators on UF risk and minimize scale differences, both TyG and UHR were subjected to Z-score standardization. Continuous variables were expressed in standard deviation (SD) units for regression analysis, while quartile analysis was performed using raw values for grouping. To assess the risk of multicollinearity, variance inflation factor (VIF) analysis was conducted for continuous variables (TyG, UHR, and covariates) before their inclusion in the multivariate Logistic regression model. TyG and UHR were each incorporated as separate predictors in multivariate logistic regression models, without including their constituent components (TG/FPG, UA/HDL-C) to prevent redundant adjustment. The constructed models comprised a Crude Model (unadjusted), Model 1 (adjusted for age, BMI, hypertension, and diabetes), Model 2 (further adjusted for menopausal status based on Model 1), and Model 3 (additionally adjusted for inflammatory indicators—WBC, MONO, and EO—on the basis of Model 2). In this context, WBC serves as a marker of systemic inflammation [ 30 ], MONO is associated with chronic inflammation and tissue fibrosis [ 31 ], and EO is actively involved in metabolic abnormalities and hormone‑related inflammation [ 32 , 33 ], collectively reflecting the metabolic‑inflammatory axis status. For continuous variables, the odds ratio (OR) represented the change in UF risk per 1-SD increase. Quartile grouping analysis used the Q1 group as the reference. To investigate the continuous and nonlinear relationships between TyG, UHR, and UF risk, restricted cubic spline (RCS) Logistic models were employed to construct curves for TyG and UHR separately. Three knots were specified for TyG and four knots for UHR. Predicted probabilities and corresponding 95% confidence intervals (CIs) were generated, and peak inflection points were identified. Furthermore, stratified analyses were performed according to age (< 45, 45–60, ≥ 60 years), BMI (< 25, ≥ 25 kg/m²), diabetes status (yes/no), and menopausal status (yes/no). Multivariate Logistic regression was applied within each stratum to calculate odds ratios (OR), 95% CI, and P-values, thereby further evaluating the effects of the main exposure factors across different subgroups. All statistical tests were two-sided. P-value < 0.05 indicated statistical significance.

This study included 40,529 women, with 12,081 cases (29.8%) diagnosed with UF. Table  1 and Table S1 present the baseline characteristics of participants grouped by quartiles of the TyG index and UHR, respectively. The results showed that with increasing levels of TyG and UHR, participants’ age, BMI, blood pressure (SBP/DBP), and the prevalence of hypertension and diabetes all exhibited significant upward trends (all P  < 0.001). With increasing levels of TyG and UHR, the proportion of postmenopausal women also rose significantly ( P  < 0.001). Moreover, the number of patients with UF increased progressively across higher TyG and UHR categories: in the TyG groups, only 4 cases (0.0%) were observed in Q1, which increased markedly to 5,848 cases (57.7%) in Q4; similarly, in the UHR groups, the number of UF cases rose from 1,142 cases (11.3%) in Q1 to 4,352 cases (43.0%) in Q4, with statistically significant between-group differences ( P  < 0.001). Nevertheless, some hematological parameters did not show a consistent linear trend across groups. Table 1 Baseline Characteristics of Participants Grouped by TyG Index Variables Q1 ( n  = 10,136) Q2 ( n  = 10,131) Q3 ( n  = 10,132) Q4 ( n  = 10,130) P -value Age (years) 36.00 [32.00–41.00] 41.00 [35.00–51.00] 48.00 [40.00–57.00] 52.00 [43.00–61.00] < 0.001 BMI (kg/m2) 21.19 [19.61–23.05] 22.89 [20.94–23.77] 23.97 [23.34–24.36] 24.04 [23.63–24.43] < 0.001 SBP (mmHg) 106.00 [100.00-114.00] 111.00 [103.00-122.00] 117.00 [106.00-131.00] 123.00 [111.00-138.00] < 0.001 DBP (mmHg) 72.00 [67.00–77.00] 74.00 [69.00–81.00] 76.00 [70.00–84.00] 79.00 [73.00–87.00] < 0.001 Complication Hypertension 383(3.8%) 1105 (10.9%) 2132 (21.0%) 2989 (29.5%) < 0.001 Diabetes 4 (0.0%) 56 (0.6%) 208 (2.1%) 1446 (14.3%) < 0.001 Menopausal status 742 (7.3%) 2388 (23.6%) 3852 (38.0%) 5221 (51.5%) < 0.001 FPG (mg/dL) 89.82 [85.68–94.32] 93.24 [88.74–98.28] 95.04 [90.00-101.16] 102.24 [95.22-113.76] < 0.001 UA (mg/dL) 4.55 [3.95–5.20] 4.89 [4.21–5.75] 6.03 [4.90–6.13] 6.06 [5.28–6.16] < 0.001 LDL-C (mmol/L) 2.69 [2.36–3.09] 3.06 [2.66–3.51] 3.42 [2.89–3.87] 3.58 [3.05–4.04] < 0.001 HDL-C (mmol/L) 1.53 [1.36–1.73] 1.46 [1.28–1.65] 1.44 [1.26–1.63] 1.36 [1.19–1.56] < 0.001 TG (mmol/L) 0.66 [0.57–0.75] 1.04 [0.93–1.18] 1.53 [1.38–1.70] 2.09 [1.90–2.44] < 0.001 TC (mmol/L) 4.59 [4.11–5.14] 5.03 [4.49–5.60] 5.40 [4.97–5.89] 5.55 [5.12–6.17] < 0.001 White blood cell count (WBC) 5.82 [4.95–6.83] 5.82 [4.95–6.86] 5.78 [4.92–6.78] 5.78 [4.91–6.79] 0.124 Platelet large cell ratio (P-LCR) 24.80 [20.60–29.70] 24.70 [20.50–29.60] 24.70 [20.40–29.30] 24.80 [20.50–29.60] 0.377 Monocyte count (MONO) 4.80 [4.10–5.60] 4.80 [4.10–5.60] 4.80 [4.10–5.50] 4.80 [4.10–5.50] 0.001 Red blood cell count (RBC) 4.47 [4.23–4.74] 4.47 [4.22–4.74] 4.48 [4.24–4.73] 4.47 [4.24–4.72] 0.307 Hematocrit (HCT) 0.41 [0.39–0.43] 0.41 [0.39–0.43] 0.41 [0.39–0.43] 0.41 [0.39–0.43] < 0.001 Lymphocytes (LYMPH) 35.00 [29.90–40.30] 34.90 [29.80–40.30] 35.20 [29.90–40.50] 35.00 [29.90–40.30] 0.264 Basophil count (BASO) 0.50 [0.40–0.70] 0.50 [0.40–0.70] 0.50 [0.40–0.70] 0.50 [0.40–0.70] 0.454 Eosinophil count (EO) 1.70 [1.10–2.70] 1.70 [1.10–2.70] 1.70 [1.10–2.70] 1.70 [1.10–2.70] 0.373 Hemoglobin (HGB) 134.00 [127.00-142.00] 134.00 [126.00-142.00] 134.00 [127.00-141.00] 134.00 [127.00-141.00] 0.006 Platelet count (PLT) 244.00 [209.00-283.00] 245.00 [209.00-283.00] 248.00 [213.00-287.00] 249.00 [213.00-288.00] < 0.001 UF 4 (0.0%) 904 (8.9%) 5325 (52.6%) 5848 (57.7%) < 0.001 UF: Uterine fibroids; BMI: Body Mass Index; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; FPG: Fasting Plasma Glucose; UA: Uric Acid; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; TG: Triglyceride; TC: Total cholesterol Baseline Characteristics of Participants Grouped by TyG Index UF: Uterine fibroids; BMI: Body Mass Index; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; FPG: Fasting Plasma Glucose; UA: Uric Acid; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; TG: Triglyceride; TC: Total cholesterol Participants were randomly divided into a train set ( n  = 28,370, 70%) and a validation set ( n  = 12,159, 30%, Table S1). In the train set, there were 8,563 cases in the UF group and 19,807 cases in the Non-UF group (Table  1 ). Baseline comparisons showed significantly higher values in the UF group than the Non-UF group for age, BMI, DBP, FPG, HDL-C, UA, SBP, LDL-C, TG, TC, TyG, and UHR (all P  < 0.05). Prevalence of hypertension and diabetes was significantly higher in the UF group (both P  < 0.001). Multivariate Logistic regression analysis revealed that both the standardized TyG index and UHR were independent risk factors for UF (Tables  2 and 3 ). In the fully adjusted model (Model 3), each one-SD increase in the standardized TyG index was associated with a 2.068-fold increased prevalence risk of UF (OR = 3.068, 95% CI: 2.944–3.197, P  < 0.001). Owing to the small number of UF cases in Q2, the combined Q1 + Q2 group of the TyG index was used as the reference. The risk of UF was significantly elevated in the Q4 group, with an OR of 14.960 (95% CI: 13.691–16.347, P  < 0.001). Table 2 Association between the Standardized TyG Index and the Risk of UF Variables Crude Model Model 1 Model 2 Model 3 OR (95%CI) P -value OR (95%CI) P -value OR (95%CI) P -value OR (95%CI) P -value TyG_continuous 3.979 (3.851–4.112) < 0.001 3.060 (2.937–3.188) < 0.001 3.065 (2.942–3.194) < 0.001 3.068 (2.944–3.197) < 0.001 TyG_Q1 + Q2 reference reference reference reference TyG_Q3 23.618 (21.865–25.512) < 0.001 12.932 (11.872–14.086) < 0.001 12.890 (11.833–14.041) < 0.001 12.880 (11.822–14.032) < 0.001 TyG_Q4 29.118 (26.950-31.459) < 0.001 14.918 (13.655–16.299) < 0.001 14.950 (13.683–16.333) < 0.001 14.960 (13.691–16.347) < 0.001 Crude Model: Unadjusted. Model 1: Adjusted for basic characteristics (age, BMI, hypertension, and diabetes). Model 2: Further adjusted for menopausal status based on Model 1. Model 3: Further adjusted for inflammatory markers (WBC, MONO, and EO) based on Model 2 The TyG index was standardized using the Z-score. The OR for continuous variables represents the change in risk per one standard deviation increase. 。 Association between the Standardized TyG Index and the Risk of UF Crude Model: Unadjusted. Model 1: Adjusted for basic characteristics (age, BMI, hypertension, and diabetes). Model 2: Further adjusted for menopausal status based on Model 1. Model 3: Further adjusted for inflammatory markers (WBC, MONO, and EO) based on Model 2 The TyG index was standardized using the Z-score. The OR for continuous variables represents the change in risk per one standard deviation increase. 。 Table 3 Association between Standardized UHR and the Risk of UF Variables Crude Model 1 Model 2 Model 3 OR (95%CI) P -value OR (95%CI) P -value OR (95%CI) P -value OR (95%CI) P -value UHR_continuous 1.620 (1.584–1.656) < 0.001 1.115 (1.087–1.145) < 0.001 1.116 (1.087–1.146) < 0.001 1.117 (1.088–1.147) < 0.001 UHR_Q1 reference reference reference reference UHR_Q2 2.542 (2.355–2.744) < 0.001 1.564 (1.430–1.710) < 0.001 1.568 (1.434–1.715) < 0.001 1.566 (1.432–1.713) < 0.001 UHR_Q3 5.382 (5.002–5.791) < 0.001 2.415 (2.217–2.631) < 0.001 2.422 (2.223–2.638) < 0.001 2.421 (2.221–2.638) < 0.001 UHR_Q4 5.928 (5.510–6.377) < 0.001 2.092 (1.921–2.277) < 0.001 2.096 (1.925–2.282) < 0.001 2.100 (1.928–2.287) < 0.001 Crude Model: Unadjusted. Model 1: Adjusted for basic characteristics (age, BMI, hypertension, and diabetes). Model 2: Further adjusted for menopausal status based on Model 1. Model 3: Further adjusted for inflammatory markers (WBC, MONO, and EO) based on Model 2 UHR was standardized using the Z-score. The OR for continuous variables represents the change in risk per one standard deviation increase Association between Standardized UHR and the Risk of UF Crude Model: Unadjusted. Model 1: Adjusted for basic characteristics (age, BMI, hypertension, and diabetes). Model 2: Further adjusted for menopausal status based on Model 1. Model 3: Further adjusted for inflammatory markers (WBC, MONO, and EO) based on Model 2 UHR was standardized using the Z-score. The OR for continuous variables represents the change in risk per one standard deviation increase For the UHR index, in Model 3, each one-SD increase in standardized UHR was associated with an 11.7% increased risk of UF (OR = 1.117, 95% CI: 1.088–1.147, P  < 0.001). Quartile analysis demonstrated that compared with the Q1 group, the Q2, Q3, and Q4 groups were all significantly associated with an elevated risk of UF, with the strongest effect observed in the Q3 group (OR = 2.421, 95% CI: 2.221–2.638, P  < 0.001). Notably, from the Crude Model to Model 3, the positive associations of TyG and UHR with UF remained highly robust despite adjustment for confounding factors, including age, BMI, menopausal status, and inflammatory markers (all P  < 0.001). To assess the risk of multicollinearity, VIF analysis was performed for the main independent variables in the models. The results showed that the VIF values of TyG, UHR, and covariates in Models 1–3 were all < 5, indicating a controllable risk of multicollinearity and robust model results. Detailed VIF values are presented in Table S2. We further explored the continuous relationships between standardized TyG, UHR, and UF risk using RCS analysis. As shown in Fig.  2 , the TyG index and UHR exhibited potential nonlinear associations with UF risk (overall P  < 0.001). The peak values occurred at approximately 0.735 for the TyG Z-score and 0.653 for the UHR Z-score. The 95% confidence intervals for both curves were narrow, indicating favorable stability of the model predictions. Fig. 2 Restricted cubic spline plots of TyG (left) and UHR (right) in relation to UF risk Restricted cubic spline plots of TyG (left) and UHR (right) in relation to UF risk Stratified analyses according to age, BMI, diabetes status, and menopausal status are presented in Table  4 . The association between the TyG index and UF risk was most prominent in young women (< 45 years), those with low BMI (< 25 kg/m²), individuals without diabetes, and premenopausal women (OR = 2.304–3.379, P  < 0.001). Although the effect size was slightly attenuated in middle-aged (45–60 years) and elderly women (≥ 60 years), those with BMI ≥ 25 kg/m², and patients with diabetes, the associations remained significant (OR = 1.306–1.913, P  < 0.001). UHR was significantly associated with UF risk in young and middle-aged individuals, those with low BMI, individuals without diabetes, and premenopausal women (OR = 1.076–1.226, P   0.05). Overall, the TyG index exerted a stronger effect on UF risk and was more dependent on stratification factors, whereas the influence of UHR was attenuated in some high-risk subgroups and the elderly population. Table 4 Results of Stratified Analyses for TyG and UHR Group Strata TyG UHR OR (95%CI) P-value OR (95%CI) P-value Age (years) < 45 3.152 (2.965–3.353) < 0.001 1.220 (1.172–1.271) < 0.001 45–60 2.158 (2.024–2.303) < 0.001 1.170 (1.116–1.227) < 0.001 ≥ 60 1.912 (1.767–2.073) < 0.001 1.060 (1.000-1.124) 0.051 BMI (kg/m 2 ) < 25 2.304 (2.207–2.405) < 0.001 1.076 (1.044–1.109) < 0.001 ≥ 25 1.913 (1.602–2.302) < 0.001 0.975 (0.828–1.145) 0.761 Diabetes No 3.379 (3.233–3.532) < 0.001 1.127 (1.100-1.159) < 0.001 Yes 1.306 (1.169–1.461) < 0.001 1.004 (0.916-1.100) 0.934 Menopause No 2.900 (2.752–3.049) < 0.001 1.226 (1.184–1.270) < 0.001 Yes 2.007 (1.893–2.131) < 0.001 1.086 (1.040–1.134) < 0.001 The TyG and UHR indices were standardized using Z-scores. The OR for continuous variables represents the change in risk per one standard deviation increase Results of Stratified Analyses for TyG and UHR The TyG and UHR indices were standardized using Z-scores. The OR for continuous variables represents the change in risk per one standard deviation increase

Elevated TyG and UHR levels are both significantly associated with the risk of UF in women, and may serve as simple indicators reflecting the relationship between metabolic abnormalities and UF risk. The TyG index exhibits a stronger association with UF, with this effect being more pronounced among younger women, those with low BMI, and premenopausal individuals. Nonlinear analysis suggests the presence of local peaks for TyG and UHR; however, the overall risk shows an increasing trend with rising levels of these indices, indicating a complex relationship between the degree of metabolic abnormalities and UF risk.

This study evaluated associations of TyG and UHR with UF occurrence based on real-world data from over 40,000 healthy women undergoing routine health checkups. To the best of our knowledge, the present study is the first to combine nonlinear dose-response and stratified analyses to reveal distinct effect patterns of TyG and UHR. The main findings are as follows: [ 1 ] Both the TyG index and UHR were independent risk factors for UF, with a significantly stronger effect observed for TyG than for UHR; [ 2 ] Although both indices may exhibit a nonlinear relationship with UF risk, quartile analysis revealed a generally increasing trend in risk across higher index levels, suggesting that the observed peaks merely reflect local features of model fitting; [ 3 ] The association of TyG with UF risk was most prominent among younger individuals, those with low BMI, non-diabetic status, and premenopausal status, whereas the influence of UHR weakened in populations with high BMI, diabetes, and older age. As a comprehensive indicator of glycolipid metabolic disorders, the TyG index reflects insulin resistance status. The present study demonstrated a significant positive correlation between the TyG index and UF. (OR = 3.068, P < 0.001). This result is consistent with previous studies on female reproductive disorders [ 15 , 34 – 36 ], suggesting that abnormal glycolipid metabolism may promote the proliferation of uterine smooth muscle cells through multiple mechanisms. From a potential biological mechanism perspective, elevated TyG may reflect the state of insulin resistance in the body. Insulin resistance can be accompanied by sustained elevated levels of insulin and insulin-like growth factor 1, which activate signaling pathways such as PI3K and MAPK, promoting smooth muscle cell proliferation and extracellular matrix deposition. These changes are associated with the occurrence and development of UF [ 37 – 39 ]. In addition, elevated TyG levels may also reflect increased levels of oxidative stress and chronic inflammation [ 40 ]. Oxidative stress is associated with abnormal proliferation of UF cells, enhanced DNA synthesis, and changes in cell cycle regulation, thereby promoting tumor tissue proliferation [ 41 ]. Chronic inflammation may be associated with the occurrence and development of UF by disrupting the immune balance and hormonal microenvironment. Related studies have shown that sustained inflammatory response can promote the formation and proliferation of fibrotic tissue by upregulating T helper cell-related inflammatory cytokines, weakening the immune regulatory function of target cells. Meanwhile, inflammatory response may also affect estrogen metabolism and signaling pathways in the body, providing a favorable endocrine environment for the growth of UF [ 42 , 43 ]. This study also observed a significant correlation between elevated UHR and increased risk of UF (OR = 1.117, P = 0.001), suggesting that UHR may serve as a comprehensive indicator reflecting metabolic and inflammatory status, and is associated with the risk of UF occurrence. UHR is composed of UA and HDL-C, which can, to some extent, reflect the interaction between the body’s metabolic status and inflammation levels. Elevated levels of UA are closely related not only to metabolic syndrome characteristics such as hypertension, insulin resistance, and obesity, but also to pro-inflammatory responses and oxidative stress states [ 18 , 44 ]. Elevated UA can affect increased production of reactive oxygen species and activation of inflammatory signaling pathways such as NF - κ B, which are associated with cell proliferation and tissue fibrosis. These pathological changes are also reflected in UF [ 45 , 46 ]. HDL-C is generally considered to have anti-inflammatory, antioxidant, and endothelial protective effects [ 47 , 48 ], but its structure and function may change during metabolic abnormalities or chronic inflammation, thereby reducing its protective ability [ 44 , 49 ]. Therefore, under the combined effect of elevated UA and impaired HDL-C function, elevated UHR may reflect an imbalance between metabolic stress and inflammatory activation in the body, providing a potential adverse microenvironment for the formation and progression of UF. RCS analysis revealed potential nonlinear relationships between TyG, UHR, and UF risk, with peaks observed at TyG Z-score of approximately 0.735 and UHR Z-score of approximately 0.653. Of note, quartile analysis demonstrated that UF risk generally exhibited an increasing trend with higher TyG and UHR levels. Therefore, these peaks likely represent local features of model fitting rather than indicating an overall inverted U-shaped trend. These findings suggest that identifying high-risk women for intervention before metabolic indices rise to moderate levels may be more beneficial for UF prevention. This implies that when glycolipid metabolic abnormalities are at moderate levels, they may stimulate uterine smooth muscle cell proliferation and extracellular matrix remodeling, thereby increasing UF risk. Conversely, the slight decline in UF risk at extremely high or low levels of metabolic abnormalities may be attributable to interventions triggered by severe metabolic imbalances or to a state of normal metabolism. Additionally, TyG index showed the strongest significant association in young women (< 45 years), those with low BMI, without diabetes, and premenopausal women, suggesting that insulin resistance plays the greatest driving role in UF development among women with a relatively healthy metabolic profile or unchanged hormonal status. Although the effect of TyG was slightly attenuated with increasing age, elevated BMI, diabetes, or menopause, it remained statistically significant. This phenomenon may be related to the gradual decline in ovarian function and decreased estrogen levels after menopause. Previous studies have indicated that under conditions of long-term estrogen deficiency, UF volume may stabilize or even regress spontaneously [ 50 , 51 ]. UHR was significantly associated with an increased risk of UF in young individuals, those with low BMI, and premenopausal women, whereas its effect was attenuated or nonsignificant in individuals with high BMI, diabetes, or the elderly population. This suggests that the oxidative stress/inflammation axis makes a limited contribution under high-risk conditions. These findings indicate that metabolic indices should be analyzed in combination with age, BMI, diabetes status, and menopausal status for risk stratification to achieve more precise risk identification for UF. This study still has certain limitations. Firstly, this study is a cross-sectional study and cannot determine the causal relationship between metabolic indicators and the occurrence of UF, which may result in information bias. Secondly, the data comes from a single-center health examination population, with limited distribution in the sample area. Due to the self-selection of the examination population, the extrapolation of the results still needs to be further verified in other regions and different populations. Thirdly, medication history information cannot be obtained from real-world physical examination data, making it impossible to control the use of certain drugs that may affect metabolic status. However, this situation is expected to only involve a small portion of the population and has a limited impact on overall results. Finally, the absence of sex hormone levels, parity, and other key gynecological confounding factors in this study constrained a thorough interpretation of the potential mechanisms linking TyG and UHR to UF development. Future studies should adopt a multicenter, prospective cohort design, systematically collect medication history and key biological indicators, and integrate multi-omics and lifestyle data to further clarify the mechanisms by which metabolic disorders contribute to UF initiation and progression, thereby providing more precise evidence for early intervention in high-risk populations.

Uterine fibroids (UF), benign tumors arising from the uterine myometrium, are among the most prevalent pelvic neoplasms in reproductive-aged women, affecting approximately 20–30% of women globally [ 1 – 3 ]. A global epidemiological study shows that the burden of UF continues to exist. In 2021, its age-standardized incidence rate and prevalence rates were 25,093 cases/100,000 people and 2,841.07 cases/100,000 people, respectively [ 4 ]. Although most UF patients are asymptomatic, some patients may present with increased menstrual flow, infertility, pelvic discomfort, irregular vaginal bleeding, or miscarriage [ 5 , 6 ]. About one-third of UF patients require medication or surgical intervention due to the significant impact on their quality of life caused by symptoms [ 7 , 8 ]. However, the current clinical management of UF is mainly symptom-oriented, often starting intervention only after obvious symptoms appear [ 9 ], highlighting the necessity of early identification and precise intervention. UF pathogenesis is linked to estrogen levels, metabolic dysregulation, lipid metabolism abnormalities, and nutritional imbalances [ 1 , 10 – 12 ]. Triglyceride-glucose index (TyG), a simple metric derived from fasting glucose and triglyceride (TG) levels, is widely applied to assess insulin resistance risk [ 13 , 14 ]. Prior studies have established TyG’s association with gynecological conditions. For example, Xu et al. [ 15 ]. found that an increase in TyG index is associated with an increased risk of endometriosis. Wang et al. [ 16 ]. found that a higher TyG index is significantly associated with an increased risk of endometrial cancer, and body mass index (BMI) plays a significant mediating role in the relationship between the two, suggesting that insulin resistance may promote the occurrence of endometrial cancer through the obesity pathway. As a recently identified biomarker, the uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) links metabolic impairment with inflammatory responses. Heightened UHR values signify oxidative stress, endothelial dysfunction, and lipid metabolism disorders [ 17 ]. UHR is associated with the risk of metabolic-related diseases such as cardiovascular disease, non-alcoholic fatty liver disease, diabetes nephropathy, and chronic obstructive pulmonary disease [ 18 – 22 ]. TyG and UHR have shown predictive potential in other gynecological and metabolic diseases. However, the occurrence of UF has a complex endocrine and metabolic background, and there is a lack of systematic evidence to determine whether the glucose and lipid metabolism axis represented by TyG and the oxidative stress axis represented by UHR play independent roles in the development of UF. In addition, BMI, diabetes, and menopause have important regulatory effects on women’s metabolic environment, but how these factors affect the relationship between metabolic indicators and UF is still unclear. Based on this, this study utilizes large-scale real-world health examination data to systematically explore the independent association between TyG and UHR and the risk of UF occurrence, and explore the non-linear dose-response relationship to provide evidence-based support for early risk identification and precise intervention.

Supplementary Material 1 Supplementary Material 1