Impact of serum lipid on recurrence of uterine fibroids: a single center retrospective study.

This retrospective cohort study was conducted in patients undergoing first myomectomy (aged ≥ 18 years) who came from Li Huili Hospital, Ningbo Medical Center between December 2019 and January 2023 ( n  = 474). Patients with the following conditions were excluded from the study: (1) having other cervical diseases ( n  = 1); (2) preoperative administration of lipid-regulating drugs, glucocorticoids, or long-term use of immunomodulatory medications ( n  = 0); (3) undergoing hysterectomy for other gynecological diseases during the follow-up period ( n  = 0); (4) having metabolic disease, hematological disease, malignancies, or any immune system disease (rheumatoid arthritis, ankylosing spondylitis, systemic lupus erythematosus, and thrombocytopenic purpura) ( n  = 0); (5) congenital abnormal uterus ( n  = 0); (6) residual UF after surgery ( n  = 137); (7) missing data on variables ( n  = 13). Flow chart of patient selection was shown in Fig.  1 . The study was approved by the Ethical Committee of the Ningbo Medical Center Lihuili Hospital (No. KY2023SL275-01). Fig. 1 Flow chart of patient selection Flow chart of patient selection The primary endpoint of the study was the recurrence of UF within 12 months following surgery. Recurrence of UF was defined as a normal postoperative examination at 3 months and the presence of a new UF measuring > 1 cm in the surgical area, as detected by ultrasound examination at both 6 months and one year after surgery. Study exposure variables included total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). All exposure indexes were divided into high and low levels groups based on the optimal cutoff value with the maximum Youden index by the receiver operator characteristic (ROC) curve analysis (Supplementary Fig. 1); TC: < 4.405 mmol/L (low level), ≥ 4.405 mmol/L (high level); TG: < 1.125 mmol/L (low level), ≥ 1.125 mmol/L (high level); LDL-C: < 2.485 mmol/L (low level), ≥ 2.485 mmol/L (high level); HDL-C: < 1.315 mmol/L (low level), ≥ 1.315 mmol/L (high level). The follow-up period commenced upon discharge and concluded one year thereafter. Preoperative ultrasonography was conducted to assess the location, morphology, and size of the UF, as well as its relationship with surrounding tissue. The patient underwent general anesthesia, electrocardiogram (ECG) monitoring, and the operative area was routinely disinfected. 1 cm transverse or longitudinal incision was made above or below the umbilicus, and a pneumoperitoneum needle was inserted to establish carbon dioxide (CO 2 ) pneumoperitoneum. The intra-abdominal pressure was maintained at 12–14 mmHg, and a trocar was inserted through the umbilicus for visualization of the location and size of UF. The myometrium was injected with a dilution of 12U pituitrin and 30 mL saline, followed by making a longitudinal incision at the serosal muscle layer of the protruding myoma using a unipolar electric hook. After completing the myoma resection, the uterine wound was meticulously sutured in a continuous manner using No. 1 absorbable thread. Infection prevention measures were implemented for 24 h after surgery along with uterine contraction hemostasis and fluid rehydration treatment for three days. Preoperative preparation was the same as laparoscopic myomectomy. Uterine cavity was irrigated with normal saline to achieve a distension fluid pressure of 80 ~ 110 mmHg, while misoprostol tablets (400ug) were administered vaginally 2–6 h prior to surgery for cervical dilation. The hysteroscope was utilized to precisely locate the junction between the uterine cavity and UF. Following the opening of the endometrium on the surface of UF using a ring electrode, oxytocin was administered. The curved electrode ring gradually electrocuted and subsequently excised and removed the uterine fibroids with clamp assistance. Some variables of participants were collected: age (years), height (m), weight (kg), body mass index (BMI, kg/m 2 ), age of menarche (years), menstrual cycle (days), menstrual duration (days), menstrual colic, number of pregnancies, number of births, vaginal delivery, caesarean section, endometriosis, adenomyosis, endometrioma, adnexal benign mass, combined with other pelvic diseases, course of UF, clinical symptoms (dysmenorrhea, heavy menstrual bleeding, abnormal uterine bleeding, lower abdominal pain, infertility, miscarriage, dyspareunia, or other), hemoglobin (g/L), red blood cell count (RBC, 10 12 /L), white blood cell (WBC, 10 9 /L), platelet (PLT, 10 9 /L), neutrophil (NEUT, 10 9 /L), lymphocyte count (LYM, 10 9 /L), monocyte (MONO, 10 9 /L), alanine transaminase (ALT, U/L), aspartate transaminase (AST, U/L), total bilirubin (TBIL, μmol/L), direct bilirubin (DBIL, μmol/L), albumin (ALB, g/L), globulin (GLB, g/L), lactate dehydrogenase (LDH, U/L), blood urea nitrogen (BUN, mmol/L), creatinine (Cr, μmol/L), uric acid (μmol/L), fasting blood-glucose (FBG, mmol/L), prothrombin time (PT, second), activated partial thromboplastin time (APTT, second), thrombin time (second), fibrinogen (FIB, g/L), D-dimer (ng/mL), estradiol (pmol/L), progesterone (nmol/L), testosterone (nmol/L), follicle stimulating hormone (FSH, IU/L), luteinizing hormone (LH, IU/L), prolactin (PRL, mIU/L), carbohydrate antigen 125 (CA125, U/mL), carbohydrate antigen 199 (CA199, U/mL), manifestations of UF (solitary fibroid and multiple fibroids), maximum diameter of UF (cm), excised site of the largest UF, removed pathological type of largest fibroids during the operation, surgical method [ 14 , 15 ] (laparoscopic myomectomy, or hysteroscopic myomectomy), operation duration, postoperative medication, postoperative bleeding, infection, subcutaneous emphysema, and abdominal adhesion. Within 1 week before surgery, blood tests were performed. All included patients were randomly assigned to the training group for nomogram development and the testing group for nomogram validation, with a ratio of 7:3. In the training group, predictors were screened. Then these predictors were used to develop prediction nomograms (TC-model, LDL-C-model and HDL-C-model) in predicting the risk of recurrence of UF among patients undergoing first myomectomy. ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the predicting performance of constructed nomogram. In the present study, skewness and kurtosis methods were used to test the normality of continuous variables, and Levene test was used to test the homogeneity of variance. The continuous variables of a normal distribution were described using the Mean and standard deviation [Mean (± SD)]. A t-test was employed to compare groups with homogeneity of variance, while a t' test was used for non-homogeneity of variance. Non-normal data were represented by the median and quartile intervals [M (Q₁, Q₃)], and inter-group comparisons were conducted using Wilcoxon rank-sum test. The frequency and composition ratio n (%) are used to describe categorical variables, while the comparison between groups was conducted using either Chi-square test or Fisher exact test. The characteristic of the population between training group and testing group was compared. In the training group, we used least absolute shrinkage and selection operator (LASSO) regression to screen covariates for this study. Variance inflation factor (VIF) analysis was utilized to assess the collinearity of covariates, with a VIF threshold of less than 10 indicating no significant collinearity. Univariate and multivariate logistic regression models were adopted to evaluate the association between four serum lipid parameters and the risk of recurrence of UF separately. Odds ratio (OR) with 95% confidence interval (CI) were calculated. P  < 0.05 is considered statistical significance. In addition, we developed and validated the prediction nomograms related to serum lipid parameters for predicting the risk of recurrence of UF. All statistical analyses were conducted using software R version 4.2.3.

A total of 323 patients were included in this analysis, with 98 (30.34%) experiencing the recurrence of UF within 12 months following surgery (Fig.  1 ). These included patients were randomly divided into training group ( n  = 226) and testing group ( n  = 97). As shown in Supplement Table 1, the results of the difference analysis between the two groups showed that the division of the data was balanced and comparable. Table 1 shows the characteristic of the population in the training group. The mean (± SD) age was 41.13 (± 6.36) years. Compared with non-recurrence individuals, recurrence of UF patients have higher mean TC and LDL-C level. In the training group, we observed significant variables ( P  < 0.05) between recurrence ( n  = 75) and non-recurrence ( n  = 151) of UF groups. These significant variables underwent further screening using LASSO regression, resulting in the selection of seven covariates for this study: PT, FIB, progesterone, CA199, manifestations of UF, subcutaneous emphysema and abdominal adhesion (Supplementary Fig. 2). The VIF analysis revealed no evidence of multicollinearity among covariates (Supplementary Table 2). In addition, we also assessed the collinearity of four serum lipid parameters, and found that their VIFs were all below 10, indicating the absence of strong multicollinearity among variables (Supplementary Table 3). Table 1 Study population characteristics Variables Training group ( n  = 190) No-recurrence of UF group ( n  = 136) Recurrence of UF group ( n  = 54) P TC, mmol/L, Mean ± SD 4.65 (± 0.87) 4.43 (± 0.86) 5.09 (± 0.72) < 0.001 TG, mmol/L, M (Q₁, Q₃) 1.06 (0.78, 1.46) 1.00 (0.75, 1.38) 1.18 (0.82, 1.59) 0.058 LDL-C, mmol/L, Mean ± SD 2.71 (± 0.62) 2.52 (± 0.57) 3.08 (± 0.56) < 0.001 HDL-C, mmol/L, Mean ± SD 1.49 (± 0.37) 1.47 (± 0.38) 1.51 (± 0.35) 0.428 TC, n (%) < 0.001  Low-Level 91 (40.27) 83 (54.97) 8 (10.67)  High-Level 135 (59.73) 68 (45.03) 67 (89.33) TG, n (%) 0.037  Low-Level 129 (57.08) 94 (62.25) 35 (46.67)  High-Level 97 (42.92) 57 (37.75) 40 (53.33) LDL-C, n (%) < 0.001  Low-Level 91 (40.27) 84 (55.63) 7 (9.33)  High-Level 135 (59.73) 67 (44.37) 68 (90.67) HDL-C, n (%) 0.017  Low-Level 80 (35.40) 62 (41.06) 18 (24.00)  High-Level 146 (64.60) 89 (58.94) 57 (76.00) Age, years, Mean ± SD 41.13 (± 6.36) 40.77 (± 6.81) 41.85 (± 5.30) 0.190 Height, m, Mean ± SD 1.60 (± 0.05) 1.60 (± 0.05) 1.60 (± 0.05) 0.756 Weight, kg, Mean ± SD 58.81 (± 8.83) 58.22 (± 9.06) 60.01 (± 8.28) 0.150 BMI, kg/m 2 , Mean ± SD 22.90 (± 3.16) 22.69 (± 3.29) 23.32 (± 2.85) 0.159 BMI, kg/m 2 , n (%) 0.187   13 64 (28.32) 47 (31.13) 17 (22.67) Menstrual cycle, days, n (%) 0.306  ≤ 35 216 (95.58) 146 (96.69) 70 (93.33)  > 35 10 (4.42) 5 (3.31) 5 (6.67) Menstrual duration, days, n (%) 0.258  2–7 202 (89.38) 132 (87.42) 70 (93.33)  > 7 24 (10.62) 19 (12.58) 5 (6.67) Menstrual colic, yes, n (%) 20 (8.85) 13 (8.61) 7 (9.33) 1.000 Number of pregnancies, Mean ± SD 2.31 (± 1.46) 2.39 (± 1.59) 2.16 (± 1.14) 0.212 Number of births, Mean ± SD 1.17 (± 0.64) 1.18 (± 0.69) 1.15 (± 0.51) 0.695 Vaginal delivery, yes, n (%) 127 (56.19) 88 (58.28) 39 (52.00) 0.451 Caesarean section, yes, n (%) 81 (35.84) 49 (32.45) 32 (42.67) 0.174 Endometriosis, yes, n (%) 40 (17.70) 25 (16.56) 15 (20.00) 0.650 Adenomyosis, yes, n (%) 22 (9.73) 12 (7.95) 10 (13.33) 0.295 Endometrioma, yes, n (%) 9 (3.98) 3 (1.99) 6 (8.00) 0.063 Adnexal benign mass, yes, n (%) 30 (13.27) 20 (13.25) 10 (13.33) 1.000 Combined with other pelvic diseases, yes, n (%) 8 (3.54) 6 (3.97) 2 (2.67) 1.000 Course of UF, n (%) 0.319  ≤ 1 95 (42.04) 70 (46.36) 25 (33.33)  1–3 35 (15.49) 22 (14.57) 13 (17.33)  3–6 52 (23.01) 32 (21.19) 20 (26.67)  > 6 44 (19.47) 27 (17.88) 17 (22.67) Clinical symptoms, yes, n (%) 98 (43.36) 66 (43.71) 32 (42.67) 0.995 Hemoglobin, g/L, Mean ± SD 116.28 (± 22.32) 114.41 (± 23.47) 120.04 (± 19.39) 0.057 RBC, 10 12 /L, Mean ± SD 4.35 (± 0.51) 4.33 (± 0.54) 4.40 (± 0.42) 0.248 WBC, 10 9 /L, Mean ± SD 5.44 (± 1.64) 5.38 (± 1.67) 5.57 (± 1.58) 0.390 PLT, 10 9 /L, Mean ± SD 274.03 (± 78.96) 271.20 (± 74.73) 279.72 (± 87.11) 0.446 NEUT, 10 9 /L, Mean ± SD 3.28 (± 1.34) 3.24 (± 1.37) 3.35 (± 1.29) 0.550 LYM, 10 9 /L, Mean ± SD 1.65 (± 0.52) 1.65 (± 0.55) 1.65 (± 0.45) 0.981 MONO, 10 9 /L, Mean ± SD 0.38 (± 0.12) 0.37 (± 0.13) 0.38 (± 0.12) 0.799 ALT, U/L, M (Q₁, Q₃) 15.00 (12.00, 19.00) 15.00 (12.00, 19.00) 16.00 (12.50, 20.00) 0.259 AST, U/L, M (Q₁, Q₃) 19.00 (17.00, 22.00) 19.00 (17.00, 22.00) 20.00 (17.00, 23.00) 0.311 TBIL, μmol/L, Mean ± SD 9.08 (± 4.30) 8.78 (± 4.25) 9.67 (± 4.37) 0.143 DBIL, μmol/L, Mean ± SD 2.25 (± 0.94) 2.28 (± 0.99) 2.20 (± 0.84) 0.539 ALB, g/L, Mean ± SD 45.08 (± 3.47) 45.08 (± 3.67) 45.07 (± 3.05) 0.988 GLB, g/L, Mean ± SD 27.00 (± 3.98) 27.17 (± 4.15) 26.67 (± 3.62) 0.380 LDH, U/L, Mean ± SD 168.25 (± 31.67) 167.79 (± 31.61) 169.19 (± 31.98) 0.755 BUN, mmol/L, Mean ± SD 4.27 (± 1.02) 4.26 (± 1.11) 4.29 (± 0.83) 0.807 Cr, μmol/L, Mean ± SD 52.29 (± 7.50) 52.41 (± 7.51) 52.03 (± 7.53) 0.723 Uric acid, μmol/L, Mean ± SD 272.02 (± 61.35) 268.13 (± 59.45) 279.86 (± 64.71) 0.177 FBG, mmol/L, Mean ± SD 5.16 (± 0.51) 5.14 (± 0.45) 5.20 (± 0.61) 0.431 PT, second, Mean ± SD 11.03 (± 0.72) 11.11 (± 0.78) 10.87 (± 0.58) 0.012 APTT, second, Mean ± SD 30.47 (± 2.86) 30.39 (± 2.70) 30.63 (± 3.17) 0.544 Thrombin time, second, Mean ± SD 15.51 (± 1.13) 15.55 (± 1.11) 15.44 (± 1.17) 0.512 FIB, g/L, Mean ± SD 2.57 (± 0.76) 2.50 (± 0.79) 2.72 (± 0.66) 0.037 D-dimer, ng/mL, Mean ± SD 80.66 (± 50.35) 82.15 (± 52.37) 77.68 (± 46.20) 0.531 Estradiol, pmol/L, Mean ± SD 526.06 (± 471.47) 530.50 (± 468.12) 517.14 (± 481.20) 0.842 Progesterone, nmol/L, Mean ± SD 8.59 (± 15.58) 9.90 (± 17.14) 5.97 (± 11.51) 0.042 Testosterone, nmol/L, M (Q₁, Q₃) 0.74 (0.51, 0.90) 0.71 (0.52, 0.88) 0.77 (0.49, 0.94) 0.420 FSH, IU/L, M (Q₁, Q₃) 6.12 (4.13, 9.39) 5.93 (4.07, 9.15) 6.70 (4.53, 9.45) 0.337 LH, IU/L, Mean ± SD 8.50 (± 8.79) 8.42 (± 9.18) 8.65 (± 8.03) 0.856 PRL, mIU/L, M (Q₁, Q₃) 245.00 (178.00, 382.08) 243.70 (175.90, 385.80) 246.30 (186.60, 337.55) 0.830 CA125, U/mL, M (Q₁, Q₃) 11.80 (8.60, 18.60) 11.80 (8.70, 18.30) 11.80 (8.25, 18.80) 0.633 CA199, U/mL, M (Q₁, Q₃) 12.35 (7.80, 19.42) 13.00 (8.10, 20.05) 9.80 (6.15, 17.15) 0.042 Manifestations of UF, n (%) 0.001  Solitary fibroid 134 (59.29) 102 (67.55) 32 (42.67)  Multiple fibroids 92 (40.71) 49 (32.45) 43 (57.33) Maximum diameter of UF, cm, n (%) 0.177  < 5 69 (30.53) 51 (33.77) 18 (24.00)  ≥ 5 157 (69.47) 100 (66.23) 57 (76.00) Excised site of the largest UF, n (%) 0.611  Submucous myoma 41 (18.14) 29 (19.21) 12 (16.00)  Intramural myoma 158 (69.91) 106 (70.20) 52 (69.33)  Subserous myoma 27 (11.95) 16 (10.60) 11 (14.67) Removed pathological type of largest fibroids during the operation, n (%) 0.721  Classic type 217 (96.02) 144 (95.36) 73 (97.33)  Highly cellular leiomyoma 9 (3.98) 7 (4.64) 2 (2.67) Surgical method, n (%) 0.590  Laparoscopic myomectomy 187 (82.74) 123 (81.46) 64 (85.33)  Hysteroscopic myomectomy 39 (17.26) 28 (18.54) 11 (14.67) Operation duration, Mean (± SD) 117.65 (± 60.33) 112.75 (± 52.62) 127.53 (± 72.85) 0.120 Postoperative medication, yes, n (%) 12 (5.31) 7 (4.64) 5 (6.67) 0.539 Postoperative bleeding, yes, n (%) 80 (35.40) 50 (33.11) 30 (40.00) 0.383 Infection, yes, n (%) 26 (11.50) 14 (9.27) 12 (16.00) 0.204 Subcutaneous emphysema, yes, n (%) 29 (12.83) 14 (9.27) 15 (20.00) 0.039 Abdominal adhesion, yes, n (%) 33 (14.60) 14 (9.27) 19 (25.33) 0.003 UF Uterine fibroids, TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, BMI Body mass index, RBC Red blood cell count, WBC White blood cell, PLT Platelet, NEUT Neutrophil, LYM Lymphocyte count, MONO Monocyte, ALT Alanine transaminase, AST Aspartate transaminase, TBIL Total bilirubin, DBIL Direct bilirubin, ALB Albumin, GLB Globulin, LDH Lactate dehydrogenase, BUN Blood urea nitrogen, Cr Creatinine, FBG Fasting blood-glucose, PT Prothrombin time, FIB Fibrinogen, APTT Activated partial thromboplastin time, FSH Follicle stimulating hormone, LH Luteinizing hormone, PRL Prolactin, CA125 Carbohydrate antigen 125, CA199 Carbohydrate antigen 199, SD Standard deviation Study population characteristics UF Uterine fibroids, TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, BMI Body mass index, RBC Red blood cell count, WBC White blood cell, PLT Platelet, NEUT Neutrophil, LYM Lymphocyte count, MONO Monocyte, ALT Alanine transaminase, AST Aspartate transaminase, TBIL Total bilirubin, DBIL Direct bilirubin, ALB Albumin, GLB Globulin, LDH Lactate dehydrogenase, BUN Blood urea nitrogen, Cr Creatinine, FBG Fasting blood-glucose, PT Prothrombin time, FIB Fibrinogen, APTT Activated partial thromboplastin time, FSH Follicle stimulating hormone, LH Luteinizing hormone, PRL Prolactin, CA125 Carbohydrate antigen 125, CA199 Carbohydrate antigen 199, SD Standard deviation We used the univariate and multivariate logistic regression models to assess the relationship of four serum lipid parameters with recurrence of UF risk in Table  2 . Model 1: univariate logistic regression model (not adjust covariates); Model 2: adjusted all covariates, including PT, FIB, progesterone, CA199, manifestation of UF, subcutaneous emphysema and abdominal adhesion. In the Model 1, using low TC level as the reference, we found significantly positive association between high TC level and recurrence of UF risk (Model 1: OR = 10.22, 95%CI: 4.59–22.75, P  < 0.001). Similar result was observed in Model 2 (OR = 9.98, 95%CI: 4.28–23.30, P  < 0.001). Taking low LDL-C level as the reference, high LDL-C level was associated with increased recurrence of UF risk in two models (Model 1: OR = 12.18, 95%CI: 5.25–28.26, P  < 0.001; Model 2: OR = 11.31, 95%CI: 4.66–27.47, P  < 0.001). Likewise, the result of logistic regression models also indicated that high HDL-C level was related to increased recurrence of UF risk (Model 1: OR = 2.21, 95%CI: 1.19–4.11, P  = 0.013; Model 2: OR = 2.37, 95%CI: 1.21–4.64, P  = 0.011). In addition, the association between TG level and UF recurrence risk did not statistical significance, adjusting for covariates ( P  > 0.05). Table 2 Association of four serum lipid parameters with recurrence of UF risk Variables Model 1 Model 2 OR (95% CI) P OR (95% CI) P TC  Low-Level Ref Ref  High-Level 10.22 (4.59–22.75) < 0.001 9.98 (4.28–23.30) < 0.001 TG  Low-Level Ref Ref  High-Level 1.88 (1.08–3.30) 0.027 1.57 (0.85–2.88) 0.147 LDL-C  Low-Level Ref Ref  High-Level 12.18 (5.25–28.26) < 0.001 11.31 (4.66–27.47) < 0.001 HDL-C  Low-Level Ref Ref  High-Level 2.21 (1.19–4.11) 0.013 2.37 (1.21–4.64) 0.011 Model 1: univariate logistic regression model (not adjust covariates); Model 2: adjusted prothrombin time, fibrinogen, progesterone, Carbohydrate antigen 199, manifestations of UF, subcutaneous emphysema, and abdominal adhesion UF Uterine fibroids, TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, OR Odds ratio, CI Confidence interval Association of four serum lipid parameters with recurrence of UF risk Model 1: univariate logistic regression model (not adjust covariates); Model 2: adjusted prothrombin time, fibrinogen, progesterone, Carbohydrate antigen 199, manifestations of UF, subcutaneous emphysema, and abdominal adhesion UF Uterine fibroids, TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, OR Odds ratio, CI Confidence interval In the training group (Table  3 ), we developed prediction nomogram (TC-model) by integrating TC and clinical features (including PT, FIB, progesterone, CA199, manifestation of UF, subcutaneous emphysema and abdominal adhesion). Similarly, other three predictive nomograms were also developed: TG-model (integrating TG and PT, FIB, progesterone, CA199, manifestation of UF, subcutaneous emphysema and abdominal adhesion); LDL-C-model (integrating LDL-C and PT, FIB, progesterone, CA199, manifestation of UF, subcutaneous emphysema and abdominal adhesion); HDL-C-model (integrating HDL-C and PT, FIB, progesterone, CA199, manifestation of UF, subcutaneous emphysema and abdominal adhesion). Table 3 Prediction models by integrating serum lipid levels (TC or TG or LDL-C or HDL-C) and clinical features in the training group Variables TC-model TG-model LDL-C-model HDL-C-model OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P TC  Low-Level Ref / / / / / /  High-Level 9.98 (4.28–23.30) < 0.001 / / / / / / TG  Low-Level / / Ref / / //  High-Level / / 1.57 (0.85–2.88) 0.147 / / / / LDL-C  Low-Level / / / / Ref / /  High-Level / / / / 11.31 (4.66–27.47) < 0.001 / / HDL-C  Low-Level / / / / / / Ref  High-Level / / / / / / 2.37 (1.21–4.64) 0.011 PT 0.84 (0.51–1.39) 0.495 0.81 (0.51–1.28) 0.364 0.88 (0.54–1.45) 0.622 0.79 (0.50–1.24) 0.308 FIB 1.21 (0.76–1.94) 0.424 1.27 (0.81–2.01) 0.302 1.20 (0.76–1.88) 0.436 1.37 (0.87–2.17) 0.175 Progesterone 0.98 (0.95–1.00) 0.088 0.98 (0.96–1.01) 0.132 0.98 (0.96–1.01) 0.188 0.98 (0.95–1.00) 0.081 CA 199 0.99 (0.97–1.01) 0.379 0.99 (0.97–1.01) 0.360 1.00 (0.97–1.02) 0.760 0.99 (0.96–1.01) 0.260 Manifestations of UF  Solitary fibroid Ref Ref Ref Ref  Multiple fibroids 2.40 (1.25–4.64) 0.009 2.47 (1.35–4.50) 0.003 2.52 (1.30–4.90) 0.006 2.40 (1.31–4.39) 0.005 Subcutaneous emphysema  No Ref Ref Ref Ref  Yes 1.84 (0.71–4.74) 0.210 1.72 (0.68–4.31) 0.251 1.61 (0.62–4.21) 0.331 1.75 (0.71–4.36) 0.226 Abdominal adhesion  No Ref Ref Ref Ref  Yes 2.23 (0.87–5.70) 0.094 2.25 (0.94–5.39) 0.069 2.35 (0.91–6.09) 0.077 2.14 (0.89–5.14) 0.090 TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, PT Prothrombin time, FIB Fibrinogen, CA199 Carbohydrate antigen 199, UF Uterine fibroids, OR Odds ratio, CI Confidence interval Prediction models by integrating serum lipid levels (TC or TG or LDL-C or HDL-C) and clinical features in the training group TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, PT Prothrombin time, FIB Fibrinogen, CA199 Carbohydrate antigen 199, UF Uterine fibroids, OR Odds ratio, CI Confidence interval Four online individualized predictive tools: https://mayimin.shinyapps.io/TC_DynamicNomogram/ (TC-model); https://mayimin.shinyapps.io/TG_DynamicNomogram/ (TG-model) https://mayimin.shinyapps.io/TC_DynamicNomogram/ (LDL-C-model); https://mayimin.shinyapps.io/HDL-C_DynamicNomogram/ (HDL-C-model) https://mayimin.shinyapps.io/TC_DynamicNomogram/ (TC-model); https://mayimin.shinyapps.io/TG_DynamicNomogram/ (TG-model) https://mayimin.shinyapps.io/TC_DynamicNomogram/ (LDL-C-model); https://mayimin.shinyapps.io/HDL-C_DynamicNomogram/ (HDL-C-model) ROC curves were used to evaluate the discriminability of four prediction nomograms on recurrence of UF. As shown in Table  4 and Fig.  2 , the area under the curve (AUC) of the TC-model was 0.820 (95% CI: 0.763–0.877) in the training group, with an accuracy of 0.735 (95% CI: 0.672–0.791), a specificity of 0.682 (95% CI: 0.608–0.756), a sensitivity of 0.840 (95% CI: 0.757–0.923), a positive predictive value (PPV) of 0.568 (95% CI: 0.475–0.660), and a negative predictive value (NPV) of 0.896 (95% CI: 0.840–0.952). The AUC of the TC-model was 0.706 (95% CI: 0.568–0.843) in the testing group, with an accuracy of 0.784 (95% CI: 0.688–0.861), a specificity of 0.851 (95% CI: 0.770–0.932), a sensitivity of 0.565 (95% CI: 0.363–0.768), a PPV of 0.542 (95% CI: 0.342–0.741), and a NPV of 0.863 (95% CI: 0.784–0.942). The AUC of the TG-model were 0.729 (95% CI: 0.659–0.799) in the training group and 0.690 (95% CI: 0.553–0.827) in the testing group. The AUC of the LDL-C-model and HDL-C-model in the training group were 0.823 (95% CI: 0.767–0.879) and 0.743 (95% CI: 0.674–0.811). In the testing group, the AUC of the LDL-C-model and HDL-C-model were 0.770 (95% CI: 0.642–0.899) and 0.753 (95% CI: 0.640–0.865). These results indicated that these developed prediction nomograms have good stability and prediction accuracy for predicting recurrence of UF risk. Figure  3 shows calibration curves of developed prediction nomograms in the training group and the testing group. and the results showed that the predictions are close to the observed results, which further demonstrates the reliability of the nomograms in predicting risk of recurrence of UF. In addition, the DCA analysis showed that patients had a good net benefit, indicating the good clinical value of developed prediction models (Fig.  4 ). Table 4 Predictive performance of prediction nomograms on recurrence of UF Variables Training group Testing group TC-model  AUC (95% CI) 0.820 (0.763–0.877) 0.706 (0.568–0.843)  Accuracy (95% CI) 0.735 (0.672–0.791) 0.784 (0.688–0.861)  Specificity (95% CI) 0.682 (0.608–0.756) 0.851 (0.770–0.932)  Sensitivity (95% CI) 0.840 (0.757–0.923) 0.565 (0.363–0.768)  PPV (95% CI) 0.568 (0.475–0.660) 0.542 (0.342–0.741)  NPV (95% CI) 0.896 (0.840–0.952) 0.863 (0.784–0.942) TG-model  AUC (95% CI) 0.729 (0.659–0.799) 0.690 (0.553–0.827)  Accuracy (95% CI) 0.730 (0.667–0.787) 0.804 (0.711–0.878)  Specificity (95% CI) 0.762 (0.694–0.830) 0.892 (0.821–0.963)  Sensitivity (95% CI) 0.667 (0.560–0.773) 0.522 (0.318–0.726)  PPV (95% CI) 0.581 (0.477–0.686) 0.600 (0.385–0.815)  NPV (95% CI) 0.821 (0.758–0.885) 0.857 (0.779–0.935) LDL-C -model  AUC (95% CI) 0.823 (0.767–0.879) 0.770 (0.642–0.899)  Accuracy (95% CI) 0.721 (0.658–0.779) 0.825 (0.734–0.894)  Specificity (95% CI) 0.675 (0.601–0.750) 0.905 (0.839–0.972)  Sensitivity (95% CI) 0.813 (0.725–0.902) 0.565 (0.363–0.768)  PPV (95% CI) 0.555 (0.462–0.647) 0.650 (0.441–0.859)  NPV (95% CI) 0.879 (0.820–0.939) 0.870 (0.795–0.945) HDL-C-model  AUC (95% CI) 0.743 (0.674–0.811) 0.753 (0.640–0.865)  Accuracy (95% CI) 0.712 (0.649–0.770) 0.784 (0.688–0.861)  Specificity (95% CI) 0.742 (0.672–0.812) 0.838 (0.754–0.922)  Sensitivity (95% CI) 0.653 (0.546–0.761) 0.609 (0.409–0.808)  PPV (95% CI) 0.557 (0.453–0.661) 0.538 (0.347–0.730)  NPV (95% CI) 0.812 (0.746–0.877) 0.873 (0.796–0.951) UF Uterine fibroids, TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, AUC Area under the curve, CI Confidence interval Fig. 2 ROC curves for evaluating the discriminability of ( A ) TC-prediction nomogram; ( B ) TG-prediction nomogram; ( C ) LDL-C-prediction nomogram; ( D ) HDL-C-prediction nomogram Fig. 3 Calibration curves for evaluating the discriminability of ( A ) TC-prediction nomogram; ( B ) TG-prediction nomogram; ( C ) LDL-C-prediction nomogram; ( D ) HDL-C-prediction nomogram Fig. 4 Decision curve analysis (DCA) for evaluating the discriminability of ( A ) TC-prediction nomogram; ( B ) TG-prediction nomogram; ( C ) LDL-C-prediction nomogram; ( D ) HDL-C-prediction nomogram Predictive performance of prediction nomograms on recurrence of UF UF Uterine fibroids, TC Total cholesterol, TG Triglyceride, LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, AUC Area under the curve, CI Confidence interval ROC curves for evaluating the discriminability of ( A ) TC-prediction nomogram; ( B ) TG-prediction nomogram; ( C ) LDL-C-prediction nomogram; ( D ) HDL-C-prediction nomogram Calibration curves for evaluating the discriminability of ( A ) TC-prediction nomogram; ( B ) TG-prediction nomogram; ( C ) LDL-C-prediction nomogram; ( D ) HDL-C-prediction nomogram Decision curve analysis (DCA) for evaluating the discriminability of ( A ) TC-prediction nomogram; ( B ) TG-prediction nomogram; ( C ) LDL-C-prediction nomogram; ( D ) HDL-C-prediction nomogram

Uterine fibroids (UF), the most common benign gynecologic tumors, occur in approximately 70% women of reproductive age, with around 25% requiring surgical intervention [ 1 ]. Hysteroscopic/laparoscopic myomectomy is currently the most commonly employed surgical method for UF in clinical practice [ 2 ]. It is reported that the postoperative recurrence rate ranges from 11 to 40% [ 3 ]. This may be attributed to the activation of mitotic and angiogenic growth factors expression caused by surgical damage to the myometrium, promoting the proliferation of uterine leiomyoma cells, thereby contributing to the recurrence of UF [ 4 ]. Studies have confirmed that the surgical method, and the number and size of fibroids removed during operation are related to UF recurrence which may lead to more severe clinical symptoms [ 5 , 6 ]. Currently, gynecologists still rely on their own experience to assess an individual's risk of UF recurrence [ 7 ]. Therefore, it is crucial to explore easily measurable parameters related to UF recurrence for the early identification and appropriate intervention of high-risk UF recurrence patients, which holds significant clinical implications in reducing the recurrence of UF and improving surgical outcomes. A growing body of evidence substantiates the potential role of metabolic syndrome and its determinants (such as dyslipidemia), in the pathogenesis of UF [ 8 – 10 ]. The change in lipid cholesterol content can be attributed to increased deposition of visceral fat, with chronic inflammation triggered by visceral fat playing a pivotal role in the process of cellular differentiation and proliferation, serving as a necessary condition for the occurrence of UF [ 10 ]. Additionally, a recent study investigated lipid profiles in individuals initially diagnosed with UF and experiencing recurrent UF, and the result has substantiated the involvement of lipids in the pathogenesis of UF [ 3 ]. Serum lipid provides a timely reflection of the body's lipid metabolism status and offers the advantages of convenience and easy accessibility, which is used for monitoring dyslipidemia [ 11 ]. Studies have found that serum lipid levels were related to the prognosis of various diseases and are an independent risk predictor for patients with relapse [ 12 , 13 ]. Therefore, this study hypothesis that serum lipid levels were associated with the risk of recurrence after UF resection.

High TC, LDL-C and HDL-C levels exhibit a positive correlation with the recurrence of UF at 12 months, indicating their potential predictive value. In addition, we have developed online prediction nomograms by integrating serum lipid levels and clinical features for predicting the risk of recurrence of UF among patients undergoing first myomectomy. These prediction nomograms demonstrate good prediction performance and can provide valuable information for clinical decision-making.

Considering the high recurrence rates observed following myomectomy for UF [ 16 ], we investigated the relationship between serum lipid levels and recurrence of UF risk, with the optimization of enhancing clinical decision-making. In this retrospective cohort study based on Chinese population, we observed that high TC level, high LDL-C level, and high HDL-C level were associated with increased recurrence of UF risk, respectively. Subsequently, the prediction nomograms for predicting the risk of recurrence of UF among patients undergoing first myomectomy were developed (TC-model, TG-model, LDL-C-model and HDL-C-model). Through verification, these models may have good prediction performance for predicting the recurrence of UF risk. Several observational studies have evaluated the impact of serum lipid levels on the development of other human cancers [ 17 , 18 ]. A meta-analysis reported a modest but statistically significant inverse association between TC, more specifically HDL-C, and the risk of breast cancer [ 19 ]. In the study of Cheng S et al., the serum lipid levels in patients undergoing radical prostatectomy showed no significant correlation with the recurrence of prostate cancer [ 20 ]. A two-sample Mendelian randomization study confirmed a causal effect between LDL-C levels and hepatocellular carcinoma risk [ 21 ]. The development of uterine leiomyoma is characterized by calcium-dependent apoptosis, decreased proliferation, and reduced extracellular matrix deposition [ 22 ]. Various evidence has suggested the impact of cholesterol level on UF development. Sharami SH et al. found that the probability of developing UF increases with higher levels of LDL-C [ 23 ]. Vignini A et al. reported that dyslipidemia play an important role in UF pathogenesis, and women with UF showed a significantly lower levels of HDL-C, higher levels of LDL-C, and oxidized LDL [ 24 ]. Tonoyan NM et al., characterized alterations in the lipid profile of tissues associated with the first-time diagnosed UF and recurrent UF, and pointed out the involvement of lipids in the pathogenesis of UF [ 3 ]. For the present study, high TC level, high LDL-C level, and high HDL-C level were found to be associated with increased recurrence of UF risk, respectively. The associations may be attributed to the abnormal lipid metabolism caused by elevated levels of multiple lipids in the serum of UF patients after operation, which further inhibits fibroid cell apoptosis and promotes cell proliferation of uterine smooth muscle cells, ultimately leading to UF recurrence [ 25 , 26 ]. In the study of Tonoyan NM et al., they revealed a significant association between elevated TG levels and the development of UF and recurrent UF, potentially linked to obesity [ 3 ]. However, the association between TG level and risk of UF recurrence in this study were not significant. This could potentially be attributed to the limited sample size. Further research is necessary to assess the relationship of TG and recurrence of UF in patients undergoing first myomectomy. At present, nomogram has been demonstrated their efficacy as a valuable tool for predicting the likelihood of clinical events in individuals, offering the advantages of simplicity, intuitiveness, and convenience for clinicians in prognosticating diseases [ 27 , 28 ]. To the best of our knowledge, few studies have developed prediction nomogram to identify the risk of recurrence of UF among patients undergoing first myomectomy. In the present study, we have also developed online prediction nomograms by integrating serum lipid levels and clinical features for predicting the risk of recurrence of UF among patients undergoing first myomectomy (TC-model, TG-model, LDL-C-model and HDL-C-model), which may be more convenient for clinical applications. Seven clinical features (PT, FIB, progesterone, CA199, manifestations of UF, subcutaneous emphysema and abdominal adhesion) were identified as potential predictors for the risk of UF recurrence, and were further included in the prediction nomograms. PT has been shown to influence recurrence after myomectomy [ 29 ]. FIB has a pro-inflammatory function, making a link between FIB levels and the risk of UF recurrence [ 30 ]. Since fibroids are more influenced by hormones, progesterone was an independent risk factor for UF recurrence. Manifestations of UF may influence recurrence after myomectomy. Our study revealed a significant association between the presence of multiple fibroids and an elevated risk of UF recurrence, suggesting that the likelihood of recurrence escalates with an increasing number of fibroids [ 31 ]. Subcutaneous emphysema represents a severe life-threatening complication after myomectomy [ 32 ]. Several UF patients developed adhesions after laparoscopic myomectomy, and these abdominal adhesions may lead to the development of fibrotic conditions [ 33 ]. In addition, after verification, our prediction nomograms also showed favorable prediction efficiency. In short, this study fills the gap in the prediction of UF recurrence risk among patients undergoing first myomectomy. The developed predictive nomograms may also be a potential tool to guide clinicians in predicting the risk of recurrence of UF among patients undergoing first myomectomy, which help take early interventions to prevent UF recurrence and improved clinical outcomes of patients. The strength of our study is that it is the first study on the association of serum lipid levels and recurrence of UF risk, which contributes to enhancing the management of UF patients through risk stratification and facilitating clinical decision-making processes. However, this study has some limitations. First, this study was limited by its single-center design and small sample size, potentially introducing bias. Therefore, it is imperative to conduct multicenter studies with larger sample sizes in order to enhance the robustness of our findings. Second, the present study only included a single preoperative lipid measurement, which may not fully capture the dynamic changes in lipid levels throughout the entire follow-up period. Moreover, no significant association between these dynamic changes and postoperative recurrence was observed, thereby limiting the generalizability of our findings. Third, the duration of follow-up in this study was relatively short, which may weaken the generalizability of findings. The clinical value of the prediction nomograms in predicting the risk of UF recurrence could be further evaluated by extending the follow-up period in future studies. Lastly, the diagnostic methods commonly employed for fibroids are magnetic resonance imaging and ultrasonography. However, in this study, only ultrasonography was utilized to diagnose the recurrence of UF. It should be noted that different diagnostic methods may yield varying outcomes. Future prospective, multicenter studies with larger sample sizes are warranted to validate our findings.

Supplementary Material 1. Supplementary Material 2. Supplementary Material 3. Supplementary Material 4. Supplementary Material 5. Supplementary Material 1. Supplementary Material 2. Supplementary Material 3. Supplementary Material 4. Supplementary Material 5.