Association of HDL-cholesterol related inflammatory ratios with reproductive outcomes in women with polycystic ovary syndrome undergoing frozen-thawed embryo transfer

This retrospective cohort study included a total of 5121 infertile women with PCOS at the Reproductive Center of the First Affiliated Hospital of Wenzhou Medical University from 2010 to 2023. The diagnosis of PCOS was based on the Rotterdam consensus criteria [ 24 ], requiring at least two out of three features to be present: (1) oligo- or anovulation; (2) clinical or biochemical signs of hyperandrogenism; and (3) polycystic ovaries on ultrasound. The exclusion criteria included: (1) females aged over 40 years; (2) couples undergoing in vitro maturation, or preimplantation genetic testing; (3) severe male factor (e.g. sperm biopsy); (4) female factors affecting clinical outcomes, including hydrosalpinx, endometriosis or adenomyosis, endocrine dysfunction, thyroid dysfunction, abnormal renal and liver function, chromosome abnormality, uterine malformation, recurrent miscarriage; (5) missing or ongoing pregnancy outcomes; and (6) missing monocyte, lymphocyte, neutrophil, platelets count or HDL-C test values. Ethical approval was obtained from the institutional review board of the First Affiliated Hospital of Wenzhou Medical University (No: KY2024-R117). Written content was waived due to the retrospective nature of this study and all data were deidentified. Controlled ovarian stimulation (COS) was performed according to standardized clinical protocols. Oocyte retrieval was conducted 34–36 h after hCG trigger and the oocytes were fertilized through conventional in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), or IVF+ICSI based on semen condition. Day 3 embryos were graded (A-D) using the ASEBIR criteria [ 25 ], with top-quality embryos (grades A/B) prioritized for fresh transfer or cryopreservation, while remaining embryos cultured to the blastocyst stage. Endometrial preparation for FET followed the routine protocols of our center, and luteal phase support was administered post-transfer until confirmation of pregnancy. As part of the routine baseline evaluation before initiation of IVF/ICSI treatment, blood samples were collected after an overnight fast of at least 8 h. Baseline peripheral monocytes, neutrophils, lymphocytes and platelets count, along with serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), HDL-C and fasting blood glucose (FBG), were quantified by an automatic blood analyser. Sex hormones were measured with an ultrasensitive enzyme-linked immunosorbent assay. HDL-C related inflammation index (NHR, LHR, PHR and MHR) were defined respectively as neutrophil, lymphocyte, platelet and monocyte count (×10 9 /L) divided by HDL-C concentration (mmol/L). Since these inflammation index exhibited skewed distributions, log 2 transformation was applied to approximate normality when used as continuous variables. Our primary outcome was live birth, referring to the delivery of at least one live-born infant. Secondary outcomes were (1) biochemical pregnancy, confirmed by a serum β-hCG level > 10 mIU/mL at 12–15 days post-fertilization; (2) clinical pregnancy, defined as the presence of intrauterine gestational sac with fetal heartbeat at 6–7 weeks; (3) miscarriage, termination of a pregnancy before 22 weeks of gestation; (4) preterm birth, delivery after 22 but before 37 completed weeks of gestation; and (5) low birth weight, defined as birth weight < 2500 g for singletons or at least one neonate < 2500 g in twin deliveries. Laboratory and embryological outcomes were evaluated by the number of oocytes retrieved, oocyte maturity rate (proportion of MII oocytes among all oocytes retrieved), fertilization rate (proportion of normally fertilized 2PN zygotes among mature oocytes), cleavage rate (percentage of cleavage-stage embryos obtained from normally fertilized 2PN zygotes), optimal cleavage embryo rate (proportion of grade A/B embryos among all cleavage-stage embryos), blastocyst formation rate (proportion of embryos reaching blastocyst stage among those cultured beyond day 3), and optimal blastocyst rate (proportion of blastocysts with morphology graded ≥3BB according to the Gardner classification, among all blastocysts). Outliers were identified from the distribution of each covariate, with values above the 99th percentile treated as missing. Baseline characteristics were compared between women with missing and available exposure data in the original pre-imputation dataset to assess potential selection bias. Overall, the two groups were broadly comparable, although some differences were observed (Supplemental Table 1). Missing covariate data were then imputed using 5-fold multiple imputation based on the Markov Chain Monte Carlo approach, whereas variables with more than 25% missingness (e.g., Anti-mullerian hormone, AMH) were excluded from further analysis. Characteristics were similar between complete-case and imputed datasets, and the missing rates ranged from 0.00% to 6.44% (Supplemental Table 2). Continuous variables were presented as median [interquartile range (IQR)] due to non-normal distribution, while categorical variables were expressed as frequencies (percentages). We used the four HDL-C related inflammation indexes, NHR, LHR, PHR and MHR, as independent variables and stratified the study population according to their quartiles. Baseline and clinical characteristics across groups were compared using Kruskal-Wallis H test for continuous variables and Pearson Chi-square test for categorical variables. In addition, embryonic and reproductive outcomes of the groups were compared using the same statistical methods. Feature selection is an important step in model construction. Age and weight status were included a priori because of their established clinical relevance. Additional covariates for multivariable adjustment were selected using group lasso logistic regression with live birth as the outcome (Supplemental Figure 1). This approach was used to reduce overfitting, address potential multicollinearity among candidate predictors and handle categorical variables appropriately. Candidate predictors comprised the baseline and clinical treatment characteristics presented in Table  1 , with the exception of AMH, which was excluded because of substantial missingness. Using 10-fold cross-validation, the group lasso procedure identified following covariates as additional covariates for adjustment at the optimal λ selected by the minimum binomial deviance criterion: follicle-stimulating hormone (FSH), luteinizing hormone (LH), FBG, TG, systolic blood pressure (SBP) and fertilization method (IVF; ICSI; IVF/ICSI). Table 1 Baseline and clinical treatment characteristics according to NHR, LHR, PHR, and MHR quartiles in PCOS NHR levels LHR levels Overall Q1 Q2 Q3 Q4 P Value Q1 Q2 Q3 Q4 P Value N=1,445 n =362 n =361 n =361 n =361 n=362  n =361 n =361 n =361 Age, years 29.0 (27.0, 31.0) 29.0 (27.0, 31.0) 29.0 (27.0, 31.0) 29.0 (27.0, 32.0) 29.0 (27.0, 32.0) 0.259 29.0 (27.0, 31.0) 29.0 (27.0, 31.0) 29.0 (27.0, 32.0) 29.0 (27.0, 32.0) 0.772 Duration of infertility, years 3.0 (2.0, 5.0) 3.0 (2.0, 4.0) 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 0.246 3.0 (2.0, 4.0) 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 0.461 SBP, mmHg 113.0 (105.0, 121.0) 110.0 (101.0, 118.0) 113.0 (104.0, 120.0) 114.0 (106.0, 123.0) 116.0 (107.0, 124.0) <0.001 110.0 (101.0, 118.0) 112.0 (104.0, 120.0) 114.0 (106.0, 122.0) 117.0 (108.0, 126.0) <0.001 DBP, mmHg 74.0 (68.0, 80.0) 72.0 (66.0, 78.0) 74.0 (68.0, 79.0) 75.0 (69.0, 81.0) 77.0 (70.0, 82.0) <0.001 72.0 (66.0, 78.0) 74.0 (69.0, 80.0) 76.0 (69.0, 81.0) 76.0 (70.0, 82.0) <0.001 AMH, ng/mL 9.1 (6.5, 12.0) 9.9 (7.1, 12.6) 9.3 (7.0, 12.9) 9.3 (6.5, 13.1) 8.7 (5.9, 11.2) 0.009 9.3 (7.3, 12.1) 9.2 (6.5, 11.6) 9.6 (6.6, 13.6) 8.7 (6.0, 11.2) 0.041 Basal FSH, IU/L 6.5 (5.5, 7.5) 6.5 (5.7, 7.6) 6.5 (5.5, 7.5) 6.5 (5.4, 7.3) 6.2 (5.4, 7.4) 0.069 6.6 (5.7, 7.6) 6.5 (5.7, 7.6) 6.4 (5.4, 7.4) 6.2 (5.3, 7.4) 0.006 Basal E2, pg/mL 42.2 (31.3, 55.9) 44.1 (31.6, 57.8) 44.1 (31.6, 57.2) 40.3 (30.5, 53.8) 41.7 (31.6, 54.8) 0.124 42.6 (30.5, 56.7) 42.9 (31.6, 59.7) 41.4 (31.3, 54.8) 42.5 (31.6, 53.9) 0.717 Basal LH, IU/L 7.1 (4.9, 9.7) 7.4 (5.1, 9.6) 7.1 (5.1, 10.1) 7.1 (4.9, 10.1) 6.8 (4.4, 9.4) 0.088 7.2 (5.0, 9.3) 7.1 (4.9, 10.3) 7.5 (5.1, 10.2) 6.7 (4.4, 9.3) 0.031 Basal T, ng/ml 0.6 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.5, 0.7) 0.063 0.5 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.5, 0.7) <0.001 AFC in two ovariest, n 30.0 (23.0, 36.0) 28.0 (22.0, 36.0) 30.0 (23.0, 36.0) 30.0 (24.0, 36.0) 30.0 (24.0, 38.0) 0.347 28.5 (23.0, 35.0) 29.0 (23.0, 36.0) 30.0 (23.0, 38.0) 30.0 (24.0, 38.0) 0.083 Weight status, n (%) <0.001 <0.001 Underweight 131 (9.1%) 68 (18.8%) 38 (10.5%) 17 (4.7%) 8 (2.2%) 68 (18.8%) 38 (10.5%) 20 (5.5%) 5 (1.4%)  Normal 740 (51.2%) 240 (66.3%) 200 (55.4%) 170 (47.1%) 130 (36.0%) 248 (68.5%) 209 (57.9%) 172 (47.6%) 111 (30.7%)  Overweight 412 (28.5%) 48 (13.3%) 99 (27.4%) 126 (34.9%) 139 (38.5%) 40 (11.0%) 81 (22.4%) 125 (34.6%) 166 (46.0%)  Obesity 162 (11.2%) 6 (1.7%) 24 (6.6%) 48 (13.3%) 84 (23.3%) 6 (1.7%) 33 (9.1%) 44 (12.2%) 79 (21.9%) FBG, mmol/L 5.2 (4.9, 5.4) 5.1 (4.8, 5.4) 5.1 (4.9, 5.4) 5.2 (4.9, 5.5) 5.2 (4.9, 5.5) <0.001 5.1 (4.9, 5.4) 5.2 (4.9, 5.4) 5.1 (4.9, 5.4) 5.2 (5.0, 5.5) 0.001  TC, mmol/L 4.8 (4.2, 5.4) 4.8 (4.2, 5.5) 4.9 (4.2, 5.4) 4.8 (4.1, 5.4) 4.7 (4.2, 5.5) 0.797 4.9 (4.3, 5.5) 4.7 (4.2, 5.4) 4.7 (4.2, 5.4) 4.7 (4.1, 5.4) 0.154  TG, mmol/L 1.2 (0.9, 1.8) 0.9 (0.7, 1.2) 1.2 (0.8, 1.7) 1.3 (0.9, 1.9) 1.7 (1.2, 2.4) <0.001 0.9 (0.7, 1.2) 1.1 (0.8, 1.5) 1.3 (1.0, 1.9) 1.9 (1.3, 2.6) <0.001  LDL-C, mmol/L 2.8 (2.3, 3.3) 2.6 (2.2, 3.1) 2.9 (2.4, 3.3) 2.9 (2.3, 3.3) 2.9 (2.4, 3.4) <0.001 2.7 (2.2, 3.1) 2.8 (2.3, 3.3) 2.9 (2.3, 3.4) 2.8 (2.3, 3.3) 0.038 Ovarian stimulation protocol, n (%) <0.001 0.054  Agonist 718 (49.7%) 216 (59.7%) 189 (52.4%) 166 (46.0%) 147 (40.7%) 196 (54.1%) 183 (50.7%) 181 (50.1%) 158 (43.8%)  Antagonist 678 (46.9%) 136 (37.6%) 160 (44.3%) 180 (49.9%) 202 (56.0%) 157 (43.4%) 169 (46.8%) 162 (44.9%) 190 (52.6%)  others 49 (3.4%) 10 (2.8%) 12 (3.3%) 15 (4.2%) 12 (3.3%) 9 (2.5%) 9 (2.5%) 18 (5.0%) 13 (3.6%) Fertilization, n (%) 0.815 0.651  ICSI 251 (17.4%) 66 (18.2%) 58 (16.1%) 68 (18.8%) 59 (16.3%) 68 (18.8%) 58 (16.1%) 68 (18.8%) 57 (15.8%)  IVF 927 (64.2%) 233 (64.4%) 236 (65.4%) 220 (60.9%) 238 (65.9%) 226 (62.4%) 232 (64.3%) 224 (62.0%) 245 (67.9%)  IVF/ICSI 267 (18.5%) 63 (17.4%) 67 (18.6%) 73 (20.2%) 64 (17.7%) 68 (18.8%) 71 (19.7%) 69 (19.1%) 59 (16.3%) Transferred embyros, n (%) <0.001 0.341  1 498 (34.5%) 94 (26.0%) 124 (34.3%) 135 (37.4%) 145 (40.2%) 116 (32.0%) 122 (33.8%) 122 (33.8%) 138 (38.2%)  ≥2 947 (65.5%) 268 (74.0%) 237 (65.7%) 226 (62.6%) 216 (59.8%) 246 (68.0%) 239 (66.2%) 239 (66.2%) 223 (61.8%) Stage of embryo transferred, n (%) 0.808 0.497  D3 649 (44.9%) 169 (46.8%) 156 (43.2%) 161 (44.6%) 163 (45.2%) 152 (42.1%) 172 (47.6%) 160 (44.3%) 165 (45.7%)  D5 795 (55.1%) 192 (53.2%) 205 (56.8%) 200 (55.4%) 198 (54.8%) 209 (57.9%) 189 (52.4%) 201 (55.7%) 196 (54.3%) PHR levels MHR levels Q1 Q2 Q3 Q4 P Value Q1 Q2 Q3 Q4 P Value  n=362 n =361 n =361 n =361  n=362 n =361 n =361 n =361 Age, years 29.0 (27.0, 31.0) 29.0 (27.0, 31.0) 29.0 (27.0, 31.0) 29.0 (27.0, 32.0) 0.270 29.0 (27.0, 31.0) 29.0 (27.0, 31.0) 29.0 (27.0, 32.0) 29.0 (27.0, 32.0) 0.772 Duration of infertility, years 3.0 (2.0, 4.0) 3.0 (2.0, 4.0) 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 0.071 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 3.0 (2.0, 5.0) 3.0 (2.0, 4.0) 0.766 SBP, mmHg 110.0 (101.0, 118.0) 111.0 (103.0, 119.0) 114.0 (106.0, 123.0) 117.0 (108.0, 125.0) <0.001 112.0 (101.0, 119.0) 112.0 (105.0, 119.0) 114.0 (105.0, 123.0) 115.0 (106.0, 125.0) <0.001 DBP, mmHg 71.0 (65.0, 78.0) 74.0 (69.0, 79.0) 75.0 (69.0, 81.0) 77.0 (71.0, 83.0) <0.001 72.0 (66.0, 78.0) 74.0 (68.0, 80.0) 76.0 (69.0, 81.0) 76.0 (70.0, 81.0) <0.001 AMH, ng/mL 10.1 (7.3, 12.8) 9.3 (7.0, 11.7) 8.8 (6.0, 11.7) 8.5 (6.1, 11.7) 0.018 10.0 (7.0, 12.7) 9.3 (7.3, 12.9) 9.6 (6.2, 13.8) 8.1 (5.8, 10.5) <0.001 Basal FSH, IU/L 6.7 (5.7, 7.8) 6.5 (5.5, 7.5) 6.4 (5.4, 7.4) 6.2 (5.3, 7.4) 0.003 6.6 (5.8, 7.5) 6.5 (5.5, 7.5) 6.4 (5.4, 7.5) 6.2 (5.3, 7.2) 0.002 Basal E2, pg/mL 45.8 (31.9, 59.9) 42.2 (31.1, 56.7) 41.1 (31.4, 52.3) 41.1 (30.8, 54.8) 0.117 42.5 (32.4, 58.3) 42.0 (30.0, 56.9) 42.5 (30.8, 54.8) 42.2 (32.2, 55.4) 0.796 Basal LH, IU/L 7.6 (5.1, 9.6) 7.0 (5.1, 10.1) 6.7 (4.5, 9.5) 7.1 (4.6, 9.6) 0.114 7.7 (5.4, 9.9) 7.0 (4.9, 10.0) 7.4 (5.0, 9.8) 6.4 (4.1, 9.2) <0.001 Basal T, ng/ml 0.5 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.5, 0.7) <0.001 0.5 (0.4, 0.7) 0.6 (0.4, 0.7) 0.6 (0.5, 0.7) 0.6 (0.4, 0.7) 0.065 AFC in two ovariest, n 28.5 (22.0, 36.0) 30.0 (23.0, 35.0) 30.0 (23.0, 38.0) 30.0 (24.0, 38.0) 0.207 29.0 (23.0, 36.0) 30.0 (23.0, 38.0) 30.0 (23.0, 36.0) 30.0 (24.0, 36.0) 0.658 Weight status, n (%) <0.001 <0.001 Underweight 72 (19.9%) 40 (11.1%) 14 (3.9%) 5 (1.4%) 65 (18.0%) 34 (9.4%) 20 (5.5%) 12 (3.3%)  Normal 241 (66.6%) 222 (61.5%) 165 (45.7%) 112 (31.0%) 238 (65.7%) 209 (57.9%) 174 (48.2%) 119 (33.0%)  Overweight 40 (11.0%) 77 (21.3%) 134 (37.1%) 161 (44.6%) 46 (12.7%) 85 (23.5%) 121 (33.5%) 160 (44.3%)  Obesity 9 (2.5%) 22 (6.1%) 48 (13.3%) 83 (23.0%) 13 (3.6%) 33 (9.1%) 46 (12.7%) 70 (19.4%) FBG, mmol/L 5.1 (4.8, 5.4) 5.1 (4.8, 5.4) 5.2 (4.9, 5.5) 5.3 (5.0, 5.5) <0.001 5.1 (4.9, 5.4) 5.1 (4.9, 5.4) 5.2 (4.9, 5.4) 5.2 (4.9, 5.5) 0.054  TC, mmol/L 4.8 (4.3, 5.5) 4.8 (4.2, 5.3) 4.7 (4.1, 5.4) 4.8 (4.1, 5.4) 0.221 4.9 (4.3, 5.6) 4.9 (4.2, 5.5) 4.7 (4.1, 5.4) 4.7 (4.1, 5.3) 0.007  TG, mmol/L 0.9 (0.7, 1.2) 1.1 (0.8, 1.5) 1.3 (0.9, 1.9) 1.8 (1.3, 2.5) <0.001 0.9 (0.7, 1.3) 1.2 (0.8, 1.6) 1.3 (0.9, 2.0) 1.6 (1.2, 2.4) <0.001  LDL-C, mmol/L 2.7 (2.2, 3.1) 2.8 (2.3, 3.2) 2.9 (2.3, 3.3) 2.9 (2.4, 3.4) <0.001 2.7 (2.3, 3.2) 2.8 (2.3, 3.3) 2.8 (2.3, 3.3) 2.8 (2.3, 3.3) 0.246 Ovarian stimulation protocol, n (%) <0.001 0.004  Agonist 213 (58.8%) 178 (49.3%) 172 (47.6%) 155 (42.9%) 195 (53.9%) 188 (52.1%) 156 (43.2%) 179 (49.6%)  Antagonist 140 (38.7%) 172 (47.6%) 179 (49.6%) 187 (51.8%) 154 (42.5%) 165 (45.7%) 197 (54.6%) 162 (44.9%)  others 9 (2.5%) 11 (3.0%) 10 (2.8%) 19 (5.3%) 13 (3.6%) 8 (2.2%) 8 (2.2%) 20 (5.5%) Fertilization, n (%) 0.674 0.470  ICSI 63 (17.4%) 52 (14.4%) 66 (18.3%) 70 (19.4%) 64 (17.7%) 67 (18.6%) 58 (16.1%) 62 (17.2%)  IVF 235 (64.9%) 242 (67.0%) 225 (62.3%) 225 (62.3%) 237 (65.5%) 217 (60.1%) 232 (64.3%) 241 (66.8%)  IVF/ICSI 64 (17.7%) 67 (18.6%) 70 (19.4%) 66 (18.3%) 61 (16.9%) 77 (21.3%) 71 (19.7%) 58 (16.1%) Transferred embyros, n (%) 0.011 0.666  1 100 (27.6%) 135 (37.4%) 125 (34.6%) 138 (38.2%) 120 (33.1%) 133 (36.8%) 126 (34.9%) 119 (33.0%)  ≥2 262 (72.4%) 226 (62.6%) 236 (65.4%) 223 (61.8%) 242 (66.9%) 228 (63.2%) 235 (65.1%) 242 (67.0%) Stage of embryo transferred, n (%) 0.101 0.014  D3 148 (40.9%) 157 (43.6%) 180 (49.9%) 164 (45.4%) 151 (41.8%) 145 (40.2%) 169 (46.8%) 184 (51.0%)  D5 214 (59.1%) 203 (56.4%) 181 (50.1%) 197 (54.6%) 210 (58.2%) 216 (59.8%) 192 (53.2%) 177 (49.0%) The data was shown as median (interquartile range, IQR) for continuous, n(%) for categorical. P value was obtains by Kruskal Wallis test for continuous, and Chi-square test for categorical The quartiles of each inflammatory ratio were defined as follows: NHR, Q1 (≤2.05), Q2 (2.06–2.85), Q3 (2.86–3.81), Q4 (>3.81); MHR, Q1 (≤0.23), Q2 (0.24–0.30), Q3 (0.31–0.40), Q4 (>0.40); LHR, Q1 (≤1.25), Q2 (1.26–1.70), Q3 (1.71–2.25), Q4 (>2.25); and PHR, Q1 (≤155.91), Q2 (155.92–201.77), Q3 (201.78–255.78), Q4 (>255.78) Sample size of AMH for summary statistics was 788 overall, and imputation was not performed due to the large proportion of missing data (45.5%) Weight status was defined as underweight (<18.5 kg/m²), normal weight (18.5–23.9 kg/m²), overweight (24.0–27.9 kg/m²), and obesity (≥ 28.0 kg/m²), according to Chinese BMI classification Abbreviations: NHR Neutrophil count/high-density lipoprotein cholesterol, LHR Lymphocyte count/high-density lipoprotein cholesterol, PHR Platelet count/high-density lipoprotein cholesterol, MHR Monocyte count/high-density lipoprotein cholesterol, PCOS Polycystic ovary syndrome, SBP Systolic blood pressure, DBP Diastolic blood pressure, AMH Anti-mullerian hormone, FSH Follicle-stimulating hormone, LH Luterining hormone, E2 Estradiol, T Testosterone, AFC Antral follical account, FBG Fasting blood glucose, TC Total cholesterol, TG Triglycerides, LDL - C Low-density lipoprotein cholesterol, ICSI Intracytoplasmic Sperm Injection, IVF In-Vitro Fertilization Baseline and clinical treatment characteristics according to NHR, LHR, PHR, and MHR quartiles in PCOS The data was shown as median (interquartile range, IQR) for continuous, n(%) for categorical. P value was obtains by Kruskal Wallis test for continuous, and Chi-square test for categorical The quartiles of each inflammatory ratio were defined as follows: NHR, Q1 (≤2.05), Q2 (2.06–2.85), Q3 (2.86–3.81), Q4 (>3.81); MHR, Q1 (≤0.23), Q2 (0.24–0.30), Q3 (0.31–0.40), Q4 (>0.40); LHR, Q1 (≤1.25), Q2 (1.26–1.70), Q3 (1.71–2.25), Q4 (>2.25); and PHR, Q1 (≤155.91), Q2 (155.92–201.77), Q3 (201.78–255.78), Q4 (>255.78) Sample size of AMH for summary statistics was 788 overall, and imputation was not performed due to the large proportion of missing data (45.5%) Weight status was defined as underweight (<18.5 kg/m²), normal weight (18.5–23.9 kg/m²), overweight (24.0–27.9 kg/m²), and obesity (≥ 28.0 kg/m²), according to Chinese BMI classification Abbreviations: NHR Neutrophil count/high-density lipoprotein cholesterol, LHR Lymphocyte count/high-density lipoprotein cholesterol, PHR Platelet count/high-density lipoprotein cholesterol, MHR Monocyte count/high-density lipoprotein cholesterol, PCOS Polycystic ovary syndrome, SBP Systolic blood pressure, DBP Diastolic blood pressure, AMH Anti-mullerian hormone, FSH Follicle-stimulating hormone, LH Luterining hormone, E2 Estradiol, T Testosterone, AFC Antral follical account, FBG Fasting blood glucose, TC Total cholesterol, TG Triglycerides, LDL - C Low-density lipoprotein cholesterol, ICSI Intracytoplasmic Sperm Injection, IVF In-Vitro Fertilization Logistic regression models were used to examine the associations between reproductive outcomes and each HDL-C related inflammation index, with odds ratios (OR) and 95% confidence intervals (CI) estimated for each. Model 1 represents a univariate analysis. Model 2 was adjusted for age and weight status (underweight, normal weight; overweight; vs. obesity; according to Chinese body mass index classification) [ 26 ]. Model 3 was further adjusted for covariates as indicated by the group lasso selection. Nonlinear associations of HDL-C related inflammation indexes (log 2 transformed) with reproductive outcomes were explored using the restricted cubic spline (RCS) regression model with three nodes set, adjusting for covariates in Model 3. To assess the potential influence of temporal changes in ART practice over the study period, we performed two sensitivity analyses by additionally adjusting Model 3 for calendar year and by stratifying the analysis of live birth according to calendar period (2010–2014, 2015–2019, and 2020–2023). In addition, since AMH is an important marker of ovarian reserve but had substantial missingness in our study, we conducted a sensitivity analysis restricted to women with available AMH data to assess whether further adjustment for AMH altered the observed associations. All analyses and plotting were performed using R statistical Software (version 4.4.1), with P values < 0.05 considered to be statistically significant.

Of 5121 infertile women with PCOS treated at our center between 2010 and 2023, 1445 women undergoing their first FET cycles were included in the final analysis after applying the predefined exclusion criteria (Fig.  1 ). Their baseline and clinical treatment characteristics, stratified by HDL-C related inflammatory indexes, are shown in Table  1 . The participants had a median age of 29 years and a median infertility duration of 3 years. The characteristics showed approximately the same trend across four indexes, with patients in the higher-quartile group tending to be obese, having higher values for SBP, diastolic blood pressure (DBP), basal testosterone, FBG, TG, and LDL-C but lower values of AMH and basal FSH, compared to those in the lower-quartile group. In addition, use of a GnRH-antagonist protocol during controlled ovarian stimulation was more frequent in higher-quartile groups. Fertilization method, the number of embryos transferred, and transfer stage were generally comparable across quartiles, with the exception of higher proportion of single-embryo transfer in the upper NHR and PHR quartiles and higher proportion of cleavage-stage transfer in the upper MHR quartiles. Fig. 1 Flowchart of participant inclusion and exclusion Flowchart of participant inclusion and exclusion Across quartiles of the HDL-C related indexes, the number of oocytes retrieved was lower in higher quartiles (Table  2 ). Fertilization rate, cleavage rate, optimal cleavage embryo rate, blastocyst formation rate, and optimal blastocyst rate were comparable across quartiles, except that higher-quartile group in MHR had lower rate of optimal blastocyst ( P  = 0.016). Table 2 Laboratory and embryonic outcomes according to NHR, LHR, PHR and MHR quartiles in PCOS Characteristics NHR levels LHR levels Overall Q1 Q2 Q3 Q4 P value Q1 Q2 Q3 Q4 P value N  = 1,445 n  = 362 n  = 361 n  = 361 n  = 361  n = 362 n  = 361 n  = 361 n  = 361 Oocytes retrieved, n 20.0 (15.0, 27.0) 21.0 (16.0, 27.0) 21.0 (15.0, 27.0) 20.0 (15.0, 27.0) 19.0 (15.0, 26.0) 0.064 22.0 (16.0, 28.0) 21.0 (16.0, 28.0) 20.0 (15.0, 27.0) 18.0 (14.0, 25.0) < 0.001 Mature oocyte rate, % 89.5 (81.3, 95.8) 89.1 (81.0, 95.2) 90.5 (81.0, 96.4) 89.5 (81.0, 96.0) 89.5 (81.8, 96.0) 0.670 90.0 (81.3, 95.8) 89.5 (82.4, 95.5) 89.7 (80.0, 96.0) 88.9 (79.2, 96.3) 0.770 Fertilization rate, % 74.1 (63.6, 83.3) 75.0 (61.5, 83.3) 75.0 (66.7, 82.4) 75.0 (64.7, 84.2) 72.7 (61.1, 81.3) 0.073 75.0 (64.7, 83.3) 73.7 (62.5, 82.4) 73.3 (61.9, 82.9) 75.0 (64.7, 84.6) 0.337 Cleavage rate, % 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 0.231 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 0.066 D3 optimum rate, % 53.8 (35.7, 70.8) 55.8 (35.7, 71.4) 53.8 (38.5, 69.2) 52.2 (35.7, 70.0) 52.4 (33.3, 72.7) 0.906 54.5 (36.2, 70.0) 54.5 (36.0, 71.4) 50.0 (33.3, 70.6) 53.8 (36.8, 71.4) 0.616 Blastocyst formation rate, % 35.5 (16.7, 52.6) 33.3 (16.7, 50.0) 35.1 (16.7, 50.0) 38.5 (20.0, 54.5) 36.4 (15.4, 53.3) 0.568 35.7 (18.2, 57.1) 36.4 (18.8, 50.0) 33.3 (16.7, 50.0) 37.5 (16.7, 54.5) 0.595 Blastocyst optimum rate, % 11.1 (0.0, 26.7) 11.1 (0.0, 26.3) 12.5 (0.0, 28.6) 12.5 (0.0, 25.0) 10.0 (0.0, 27.3) 0.626 13.3 (0.0, 30.0) 11.1 (0.0, 26.7) 11.1 (0.0, 26.7) 9.1 (0.0, 25.0) 0.217 Characteristics PHR levels MHR levels Q1 Q2 Q3 Q4 P value Q1 Q2 Q3 Q4 P value n = 362  n  = 361 n  = 361 n  = 361  n = 362 n  = 361 n  = 361 n  = 361 Oocytes retrieved, n 22.0 (16.0, 28.0) 21.0 (16.0, 29.0) 20.0 (14.0, 26.0) 19.0 (15.0, 26.0) < 0.001 22.0 (17.0, 28.0) 22.0 (16.0, 29.0) 19.0 (14.0, 25.0) 19.0 (14.0, 26.0) < 0.001 Mature oocyte rate, % 89.5 (81.5, 95.7) 90.0 (81.3, 95.5) 89.7 (80.5, 96.2) 88.9 (80.0, 96.0) 0.966 89.5 (81.0, 95.5) 90.0 (81.8, 95.8) 90.0 (81.8, 96.6) 88.9 (79.2, 95.7) 0.425 Fertilization rate, % 74.5 (62.5, 83.3) 73.9 (63.9, 82.4) 74.2 (63.6, 83.3) 75.0 (63.6, 83.3) 0.985 75.0 (63.6, 83.3) 75.8 (64.0, 83.3) 73.3 (63.0, 83.3) 73.1 (63.3, 83.3) 0.604 Cleavage rate, % 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 0.413 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 100.0 (100.0, 100.0) 0.741 D3 optimum rate, % 55.1 (35.7, 70.0) 54.2 (35.9, 70.3) 52.2 (33.3, 71.4) 53.8 (36.8, 71.4) 0.722 55.1 (35.3, 71.4) 53.8 (37.0, 69.6) 53.7 (35.7, 71.4) 52.9 (35.0, 71.4) 0.972 Blastocyst formation rate, % 33.3 (16.7, 50.0) 37.5 (20.0, 52.9) 33.3 (16.7, 50.0) 37.5 (17.3, 53.1) 0.630 33.3 (16.7, 52.9) 37.5 (20.0, 54.2) 38.5 (16.7, 54.5) 33.3 (14.3, 50.0) 0.204 Blastocyst optimum rate, % 14.3 (0.0, 28.6) 11.1 (0.0, 26.3) 10.0 (0.0, 25.0) 10.8 (0.0, 27.3) 0.214 14.3 (0.0, 27.3) 12.5 (0.0, 29.4) 11.1 (0.0, 27.3) 8.3 (0.0, 25.0) 0.016 The data was shown as mean (SD) and P value was obtained by Kruskal Wallis test The quartiles of each inflammatory ratio were defined as follows: NHR, Q1 (≤2.05), Q2 (2.06–2.85), Q3 (2.86–3.81), Q4 (>3.81); LHR, Q1 (≤1.25), Q2 (1.26–1.70), Q3 (1.71–2.25), Q4 (>2.25); PHR, Q1 (≤155.91), Q2 (155.92–201.77), Q3 (201.78–255.78), Q4 (>255.78); and MHR, Q1 (≤0.23), Q2 (0.24–0.30), Q3 (0.31–0.40), Q4 (>0.40) Abbreviations: NHR Neutrophil count/high−density lipoprotein cholesterol, LHR Lymphocyte count/high−density lipoprotein cholesterol, PHR Platelet count/high−density lipoprotein cholesterol, MHR Monocyte count/high−density lipoprotein cholesterol, PCOS Polycystic ovary syndrome Laboratory and embryonic outcomes according to NHR, LHR, PHR and MHR quartiles in PCOS The data was shown as mean (SD) and P value was obtained by Kruskal Wallis test The quartiles of each inflammatory ratio were defined as follows: NHR, Q1 (≤2.05), Q2 (2.06–2.85), Q3 (2.86–3.81), Q4 (>3.81); LHR, Q1 (≤1.25), Q2 (1.26–1.70), Q3 (1.71–2.25), Q4 (>2.25); PHR, Q1 (≤155.91), Q2 (155.92–201.77), Q3 (201.78–255.78), Q4 (>255.78); and MHR, Q1 (≤0.23), Q2 (0.24–0.30), Q3 (0.31–0.40), Q4 (>0.40) Abbreviations: NHR Neutrophil count/high−density lipoprotein cholesterol, LHR Lymphocyte count/high−density lipoprotein cholesterol, PHR Platelet count/high−density lipoprotein cholesterol, MHR Monocyte count/high−density lipoprotein cholesterol, PCOS Polycystic ovary syndrome Table  3 shows the percentages of different reproductive outcomes based on quartiles of four inflammatory indexes. Miscarriage rates were generally higher in upper quartiles (NHR: 9.0% vs. 17.0% vs. 14.6% vs. 22.2%, P  < 0.001; LHR: 8.0% vs. 16.3% vs. 19.6% vs. 19.3%, P  = 0.001; PHR: 8.7% vs. 15.1% vs. 18.0% vs. 21.6%, P  < 0.001; MHR: 10.4% vs. 15.6% vs. 20.2% vs. 16.5%, P  = 0.028). Live birth rates decreased across quartiles of NHR (60.5% vs. 55.4% vs. 54.8% vs. 49.9%, P  = 0.040), LHR (62.2% vs. 55.4% vs. 51.8% vs. 51.2%, P  = 0.011), and PHR (63.0% vs. 54.6% vs. 54.3% vs. 48.8%, P  = 0.002) but did not differ across MHR quartiles. Percentages of biochemical pregnancy, clinical pregnancy, premature delivery and low birth weight did not vary by quartiles of any index. Table 3 Reproductive outcomes according to NHR, LHR, PHR and MHR quartiles in PCOS Characteristics NHR levels LHR levels Overall Q1 Q2 Q3 Q4 P Value Q1 Q2 Q3 Q4 P Value N  = 1,445 n  = 362 n  = 361 n  = 361 n  = 361  n = 362 n  = 361 n  = 361 n  = 361 Biochemical pregnancy, n(%) 0.919 0.680  No 346 (23.9%) 91 (25.1%) 85 (23.5%) 87 (24.1%) 83 (23.0%) 86 (23.8%) 86 (23.8%) 94 (26.0%) 80 (22.2%)  Yes 1,099 (76.1%) 271 (74.9%) 276 (76.5%) 274 (75.9%) 278 (77.0%) 276 (76.2%) 275 (76.2%) 267 (74.0%) 281 (77.8%) Clinical pregnancy, n(%) 0.759 0.490  No 481 (33.3%) 118 (32.6%) 114 (31.6%) 122 (33.8%) 127 (35.2%) 112 (30.9%) 115 (31.9%) 126 (34.9%) 128 (35.5%)  Yes 964 (66.7%) 244 (67.4%) 247 (68.4%) 239 (66.2%) 234 (64.8%) 250 (69.1%) 246 (68.1%) 235 (65.1%) 233 (64.5%) Miscarriage, n(%) < 0.001 0.001  No 813 (84.3%) 222 (91.0%) 205 (83.0%) 204 (85.4%) 182 (77.8%) 230 (92.0%) 206 (83.7%) 189 (80.4%) 188 (80.7%)  Yes 151 (15.7%) 22 (9.0%) 42 (17.0%) 35 (14.6%) 52 (22.2%) 20 (8.0%) 40 (16.3%) 46 (19.6%) 45 (19.3%) Live birth, n(%) 0.040 0.011  No 648 (44.8%) 143 (39.5%) 161 (44.6%) 163 (45.2%) 181 (50.1%) 137 (37.8%) 161 (44.6%) 174 (48.2%) 176 (48.8%)  Yes 797 (55.2%) 219 (60.5%) 200 (55.4%) 198 (54.8%) 180 (49.9%) 225 (62.2%) 200 (55.4%) 187 (51.8%) 185 (51.2%) Premature delivery, n(%) 0.968 0.895  No 628 (78.8%) 171 (78.1%) 158 (79.0%) 155 (78.3%) 144 (80.0%) 179 (79.6%) 155 (77.5%) 150 (80.2%) 144 (77.8%)  Yes 169 (21.2%) 48 (21.9%) 42 (21.0%) 43 (21.7%) 36 (20.0%) 46 (20.4%) 45 (22.5%) 37 (19.8%) 41 (22.2%) Low birth weight, n(%) 0.854 0.902  No 620 (77.9%) 168 (76.7%) 157 (78.5%) 152 (76.8%) 143 (79.9%) 175 (77.8%) 153 (76.5%) 149 (79.7%) 143 (77.7%)  Yes 176 (22.1%) 51 (23.3%) 43 (21.5%) 46 (23.2%) 36 (20.1%) 50 (22.2%) 47 (23.5%) 38 (20.3%) 41 (22.3%) Characteristics PHR levels MHR levels Q1 Q2 Q3 Q4 P Value Q1 Q2 Q3 Q4 P Value n = 362  n  = 361 n  = 361 n  = 361  n = 362 n  = 361 n  = 361 n  = 361 Biochemical pregnancy, n(%) 0.530 0.988  No 86 (23.8%) 93 (25.8%) 77 (21.3%) 90 (24.9%) 88 (24.3%) 88 (24.4%) 85 (23.5%) 85 (23.5%)  Yes 276 (76.2%) 268 (74.2%) 284 (78.7%) 271 (75.1%) 274 (75.7%) 273 (75.6%) 276 (76.5%) 276 (76.5%) Clinical pregnancy, n(%) 0.232 0.394  No 109 (30.1%) 122 (33.8%) 116 (32.1%) 134 (37.1%) 113 (31.2%) 124 (34.3%) 113 (31.3%) 131 (36.3%)  Yes 253 (69.9%) 239 (66.2%) 245 (67.9%) 227 (62.9%) 249 (68.8%) 237 (65.7%) 248 (68.7%) 230 (63.7%) Miscarriage, n(%) < 0.001 0.028  No 231 (91.3%) 203 (84.9%) 201 (82.0%) 178 (78.4%) 223 (89.6%) 200 (84.4%) 198 (79.8%) 192 (83.5%)  Yes 22 (8.7%) 36 (15.1%) 44 (18.0%) 49 (21.6%) 26 (10.4%) 37 (15.6%) 50 (20.2%) 38 (16.5%) Live birth, n(%) 0.002 0.119  No 134 (37.0%) 164 (45.4%) 165 (45.7%) 185 (51.2%) 143 (39.5%) 165 (45.7%) 168 (46.5%) 172 (47.6%)  Yes 228 (63.0%) 197 (54.6%) 196 (54.3%) 176 (48.8%) 219 (60.5%) 196 (54.3%) 193 (53.5%) 189 (52.4%) Premature delivery, n(%) 0.869 0.685  No 177 (77.6%) 153 (77.7%) 157 (80.1%) 141 (80.1%) 171 (78.1%) 153 (78.1%) 158 (81.9%) 146 (77.2%)  Yes 51 (22.4%) 44 (22.3%) 39 (19.9%) 35 (19.9%) 48 (21.9%) 43 (21.9%) 35 (18.1%) 43 (22.8%) Low birth weight, n(%) 0.397 0.495  No 169 (74.1%) 159 (80.7%) 155 (79.1%) 137 (78.3%) 168 (76.7%) 151 (77.0%) 158 (81.9%) 143 (76.1%)  Yes 59 (25.9%) 38 (19.3%) 41 (20.9%) 38 (21.7%) 51 (23.3%) 45 (23.0%) 35 (18.1%) 45 (23.9%) The data was shown as n (%), and P value was obtains by Chi−square test The quartiles of each inflammatory ratio were defined as follows: NHR, Q1 (≤2.05), Q2 (2.06–2.85), Q3 (2.86–3.81), Q4 (>3.81); LHR, Q1 (≤1.25), Q2 (1.26–1.70), Q3 (1.71–2.25), Q4 (>2.25); PHR, Q1 (≤155.91), Q2 (155.92–201.77), Q3 (201.78–255.78), Q4 (>255.78); and MHR, Q1 (≤0.23), Q2 (0.24–0.30), Q3 (0.31–0.40), Q4 (>0.40) Abbreviations: NHR Neutrophil count/high−density lipoprotein cholesterol, LHR Lymphocyte count/high−density lipoprotein cholesterol PHR Platelet count/high−density lipoprotein cholesterol, MHR Monocyte count/high−density lipoprotein cholesterol, PCOS Polycystic ovary syndrome, SBP Systolic blood pressure Reproductive outcomes according to NHR, LHR, PHR and MHR quartiles in PCOS The data was shown as n (%), and P value was obtains by Chi−square test The quartiles of each inflammatory ratio were defined as follows: NHR, Q1 (≤2.05), Q2 (2.06–2.85), Q3 (2.86–3.81), Q4 (>3.81); LHR, Q1 (≤1.25), Q2 (1.26–1.70), Q3 (1.71–2.25), Q4 (>2.25); PHR, Q1 (≤155.91), Q2 (155.92–201.77), Q3 (201.78–255.78), Q4 (>255.78); and MHR, Q1 (≤0.23), Q2 (0.24–0.30), Q3 (0.31–0.40), Q4 (>0.40) Abbreviations: NHR Neutrophil count/high−density lipoprotein cholesterol, LHR Lymphocyte count/high−density lipoprotein cholesterol PHR Platelet count/high−density lipoprotein cholesterol, MHR Monocyte count/high−density lipoprotein cholesterol, PCOS Polycystic ovary syndrome, SBP Systolic blood pressure We then fitted three logistic regression models to investigate the independent association between NHR, LHR, PHR, MHR levels and reproductive outcomes (Fig.  2 and Supplemental Table 3). After adjusting for age, weight status, FSH, LH, FBG, TG, SBP and fertilization method in model 3, the risk of miscarriage was higher in the highest quartile (Q4) compared to the lowest quartile (Q1) for NHR (adjusted OR: 2.51, 95% CI: 1.39–4.63), LHR (2.32, 1.23–4.49) and PHR (2.47, 1.34–4.64). For MHR, only Q2 showed 90% higher odds of miscarriage versus Q1, with no does-response pattern ( P for trend = 0.212). In addition, women in the upper quartiles of LHR (Q3: 0.66, 0.48–0.90; Q4: 0.69, 0.48–0.98) and PHR (Q3: 0.71, 0.51–0.98; Q4 0.58, 0.41–0.82) had lower odds of live birth than those in Q1 after fully adjustment. The initially observed inverse association between NHR and live birth in both the unadjusted (model 1) and partially adjusted (model 2) models was attenuated and no longer significant after fully adjustment (model 3). For clinical pregnancy, an inverse association was observed only for PHR Q4 in model 1 and 2, and this association was not retained in model 3. Fig. 2 Forest plot on the association of clinical and birth outcomes with NHR, LHR, PHR and MHR in PCOS. Multivariable logistic regression models were used and adjusted for age, weight status, follicle-stimulating hormone, luteinizing hormone, fasting blood glucose, triglyceride, systolic blood pressure and fertilization method Forest plot on the association of clinical and birth outcomes with NHR, LHR, PHR and MHR in PCOS. Multivariable logistic regression models were used and adjusted for age, weight status, follicle-stimulating hormone, luteinizing hormone, fasting blood glucose, triglyceride, systolic blood pressure and fertilization method The results were consistent in the sensitivity analysis with additional adjustment for calendar year (Supplemental Table 4). In period-stratified analyses for live birth, the overall direction of the associations was similar across calendar periods, although the magnitude and statistical precision varied, with the clearest signals observed during 2015–2019 (Supplemental Table 5). In the sensitivity analysis restricted to women with available AMH data ( n  = 788), precision was reduced potentially because of the smaller sample size; however, the effect estimates were broadly similar before and after additional adjustment for AMH, and inverse associations remained apparent ( Ps  < 0.1) (Supplemental Table 6). To examine potential non-linear associations with miscarriage and live birth, each HDL-C related inflammatory index was modeled as continuous exposure using multivariable restricted cubic spline (Fig.  3 ). Live birth rate showed no statistically significant, but directionally consistent associations with LHR and PHR. Higher NHR, LHR, and PHR were associated with increased odds of miscarriage (overall P  < 0.05 for each) with no evidence of nonlinearity. Fig. 3 Restricted cubic spline (RCS) plots on the association of four HDL-C related inflammation indexes with live birth ( A - D ) and miscarriage ( E - H ) in PCOS. The x-axis shows the continuous log2-transformed exposure values, such that a 1-unit increase corresponds to a doubling of the original value. The red solid lines represent adjusted odds ratios (ORs), the shaded areas indicate 95% confidence intervals (CIs), and the horizontal dashed lines indicate the null value (OR = 1). P for overall trend reflects the overall association, whereas P for non-linear trend tests whether the association deviates from linearity. Models were adjusted for age, weight status, follicle-stimulating hormone, luteinizing hormone, fasting blood glucose, triglyceride, systolic blood pressure and fertilization method Restricted cubic spline (RCS) plots on the association of four HDL-C related inflammation indexes with live birth ( A - D ) and miscarriage ( E - H ) in PCOS. The x-axis shows the continuous log2-transformed exposure values, such that a 1-unit increase corresponds to a doubling of the original value. The red solid lines represent adjusted odds ratios (ORs), the shaded areas indicate 95% confidence intervals (CIs), and the horizontal dashed lines indicate the null value (OR = 1). P for overall trend reflects the overall association, whereas P for non-linear trend tests whether the association deviates from linearity. Models were adjusted for age, weight status, follicle-stimulating hormone, luteinizing hormone, fasting blood glucose, triglyceride, systolic blood pressure and fertilization method

Polycystic ovary syndrome (PCOS) is a prevalent reproductive and endocrine disorders, affecting approximately 4% to 21% of women of reproductive age worldwide [ 1 ]. It is characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology, and is frequently accompanied by metabolic disturbances such as insulin resistance, obesity, and dyslipidemia [ 2 ]. Beyond infertility, women with PCOS face an increased risk of long-term complications including type 2 diabetes, cardiovascular disease, and pregnancy-related morbidity, making it a major health concern [ 3 ]. An increasing proportion of women with PCOS seek assisted reproductive technology (ART), such as in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI), to achieve pregnancy due to impaired oocyte maturation or other infertility factors [ 4 , 5 ]. Although women with PCOS demonstrate a favorable ovarian reserve during controlled ovarian stimulation (COS), they usually experienced an increased risk of ovarian hyperstimulation syndrome (OHSS) during fresh embryo transfer cycles[ 6 , 7 ]. Furthermore, supraphysiological hormonal exposure during fresh transfers may impair endometrial receptivity, contributing to suboptimal reproductive outcomes [ 8 ]. To address these challenges, the “freeze-all” strategy followed by frozen-thawed embryo transfer (FET) has been widely adopted in this population [ 9 , 10 ]. This approach substantially reduces OHSS risk and may enhance endometrial synchronization. Nevertheless, even in FET cycles, women with PCOS remain at increased risk of adverse reproductive outcomes, including miscarriage, preeclampsia, gestational diabetes, and preterm birth [ 11 – 13 ]. To date, the underlying mechanisms remain poorly elucidated. Emerging evidence suggests that chronic low-grade inflammation is a key feature in the pathogenesis of PCOS and may play a pivotal role in its reproductive and metabolic dysfunction [ 14 ]. Elevated levels of systemic inflammatory markers, including C-reactive protein (CRP), complement component 3 (C3), tumor necrosis factor-α (TNF-α), interleukin-18 (IL-18), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1), have been consistently reported in PCOS women [ 15 , 16 ]. Such systemic inflammatory activation has been linked to impaired oocyte quality, disrupted embryo development, and reduced endometrial receptivity, thereby compromising ART outcomes [ 17 , 18 ]. However, the measurement of these markers requires specialized assays and laboratory resources, limiting their applicability in routine clinical practice. This underscores the need for cost-effective and readily accessible biomarkers that can capture systemic inflammation and predict reproductive prognosis in PCOS patients. Recently, several hematological indices derived from routine blood counts have been proposed as simple, inexpensive, and reproducible markers of systemic inflammation. Ratios such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been reported to be significantly elevated in PCOS compared with healthy controls in meta-analyses [ 19 , 20 ]. More recently, novel composite indices that integrate immune cell counts with lipid metabolism—namely, the monocyte-to-high density lipoprotein cholesterol (HDL-C) ratio (MHR), lymphocyte-to-HDL-C ratio (LHR), neutrophil-to-HDL-C ratio (NHR), and platelet-to-HDL-C ratio (PHR)—have attracted increasing attention. These ratios are thought to reflect both inflammation status and HDL-C’s anti-inflammatory, antioxidant capacity, which are often impaired in women with PCOS [ 21 ]. Indeed, Usta et al. reported that MHR was significantly elevated in PCOS women and remained an independent predictor of the syndrome after adjustment for BMI, insulin resistance, and high-sensitivity CRP [ 22 ]. Similarly, Herkiloglu et al. demonstrated that MHR level was particularly increased in obese PCOS women, with a cutoff of 10.1 showing good sensitivity and moderate specificity for diagnosing PCOS [ 23 ]. Despite these insights, the prognostic value of these HDL-C related inflammatory ratios for reproductive outcomes in PCOS, particularly in the context of FET cycles, has not yet been systematically examined. Therefore, the objective of the current study was to investigate the associations between HDL-C related inflammation ratios, including the LHR, NHR, PHR and MHR, and reproductive outcomes in women with PCOS undergoing FET. By integrating systemic inflammatory and lipid-related parameters, this study aimed to identify accessible biomarkers that may support individualized reproductive management in this high-risk population.

In this retrospective cohort study, we observed that several HDL-C related inflammatory indices were associated with reproductive outcomes in women with PCOS undergoing FET. Higher levels of NHR, LHR, and PHR were consistently associated with an increased risk of miscarriage, while LHR and PHR were additionally linked to reduced live-birth rates. In contrast, MHR showed a weaker and no dose-dependent association. Embryological parameters, including fertilization and cleavage rates, were comparable across quartiles. Notably, these associations were observed for pregnancy outcomes but not for early embryological parameters, suggesting that these indices may reflect systemic conditions relevant to implantation and early pregnancy maintenance rather than oocyte or embryo competence. Chronic low-grade inflammation is a well-recognized feature of PCOS and has been implicated in multiple aspects of reproductive dysfunction [ 15 , 27 ]. Elevated levels of proinflammatory cytokines, including TNF-α, IL-6, and CRP, have been reported and are closely associated with metabolic disturbances and subfertility in this population [ 16 , 28 ]. In the context of assisted reproduction, systemic inflammation has been linked to impaired oocyte quality, reduced embryo viability, and compromised implantation [ 29 , 30 ]. Our findings are broadly consistent with previous reports linking composite inflammatory indices to adverse IVF outcomes, and extend this concept to HDL-C related inflammatory indices in FET cycles. Beyond systemic cytokines, HDL particles are increasingly recognized as active regulators of inflammation and oxidative stress. Accumulating evidence indicates that HDL exerts anti-inflammatory and antioxidant effects, contributing to the maintenance of endothelial function [ 31 – 33 ]. However, several studies indicates that HDL-C function is impaired in women with PCOS. For example, obese PCOS patients exhibited reduced paraoxonase 1 (PON1) activity and altered HDL-C subclass distribution, reflecting diminished antioxidative capacity [ 34 ]. Similarly, elevated HDL-C inflammatory index values and oxidative stress markers have been linked to decreased sex hormone-binding globulin levels, highlighting the interplay between HDL-C dysfunction, oxidative stress, and endocrine imbalance [ 35 ]. Even in non-obese PCOS women, proteomic profiling has revealed lower ApoM and increased complement C3 in HDL-C associated proteins, suggesting intrinsic abnormalities in HDL-C composition and function [ 36 ]. These alterations may weaken HDL-C’s capacity to counteract immune cell-mediated inflammation, thereby contributing to impaired reproductive function. To our knowledge, this study is among the first to comprehensively evaluate four HDL-C related inflammatory ratios (LHR, NHR, PHR, and MHR) in relation to reproductive outcomes in PCOS women undergoing FET. The observed association between NHR and miscarriage may reflect neutrophil-driven inflammation, as activated neutrophils release reactive oxygen species and proteolytic enzymes that can disrupt both follicular and endometrial environments [ 37 , 38 ]. Similarly, an elevated PHR may indicate platelet activation, which promotes pro-inflammatory and pro-thrombotic pathways and has been implicated in adverse pregnancy outcomes [ 39 ]. In contrast, LHR integrates lymphocyte counts with HDL-C and may capture impaired immune tolerance at the maternal-fetal interface [ 40 , 41 ]. Reduced lymphocyte activity has been associated with inadequate T-cell-mediated regulation and failure to establish maternal tolerance during implantation [ 40 – 42 ]. Consistent with these observations, our findings revealed that higher PHR and LHR were associated with both increased miscarriage and reduced live-birth rates, suggesting their potential involvement in processes related to pregnancy maintenance. Although MHR is frequently elevated in PCOS and correlates with insulin resistance [ 22 , 23 ], our study revealed only a weak and no dose-dependent association between MHR and reproductive outcomes in PCOS women undergoing FET. This finding suggests that its predictive value in this context may be limited and should be interpreted with caution. One possible explanation is that MHR primarily reflects metabolically driven monocyte activation, which may not directly influence peri-implantation immune processes once metabolic confounders are considered [ 43 ]. In addition, HDL-C, used as the denominator of MHR, reflects circulating cholesterol levels rather than HDL functional capacity. Given that HDL dysfunction is well documented in PCOS, HDL-C levels alone may not adequately capture the lipid-immune interactions relevant to successful implantation [ 44 ]. These considerations may partly explain the limited predictive performance of MHR in our analysis. From a clinical perspective, these HDL-C related inflammatory indices offer several advantages. They can be easily calculated from routine complete blood counts and lipid profiles, making them inexpensive, reproducible, and feasible for clinical implementation [ 45 , 46 ]. Their value is particularly relevant in the context of FET cycles, which have become the preferred strategy for PCOS patients owing to the elevated risk of OHSS in fresh embryo transfer [ 6 , 9 ]. In the setting of FET, simple inflammatory ratios may provide additional information on systemic inflammatory status. While these indices show potential for identifying women at higher risk of adverse pregnancy outcomes, their role in guiding personalized management strategies remains to be established. At present, their use should be considered hypothesis-generating rather than practice-changing. In this study, several limitations should be acknowledged. First, the retrospective design and single-center setting may introduce selection bias and limit generalizability. The long study period also raises the possibility of heterogeneity related to evolving clinical protocols, although adjustment for calendar time did not materially alter the results. Second, inflammatory indices were measured at a single pre-treatment time point, and temporal changes during ovarian stimulation and early pregnancy were not captured. Third, although we adjusted for several covariates, residual confounding from unmeasured factors cannot be excluded. In addition, the lack of detailed cytokine or immune profiling limits mechanistic interpretation. Finally, clinically relevant cut-off values for these indices have not been established in reproductive populations, which restricts their immediate translational value. Future studies should prioritize prospective validation in multicenter cohorts and the establishment of clinically meaningful thresholds for risk stratification. Integration of these indices with established predictors into multivariable models may improve risk assessment. In parallel, mechanistic studies incorporating systemic inflammatory markers and endometrial immune profiling are needed to clarify underlying pathways. Whether interventions targeting systemic inflammation can modify these indices and improve ART outcomes in women with PCOS also warrants further investigation.

In summary, elevated NHR, LHR, and PHR were independently associated with increased miscarriage risk, while higher LHR and PHR were also related to reduced live-birth rates in women with PCOS undergoing FET. These findings highlight HDL-C related inflammatory indices as promising, cost-effective biomarkers for reproductive prognosis in this high-risk population. However, prospective validation and mechanistic studies are essential before their incorporation into routine clinical practice.

Supplementary Material 1. Supplementary Material 1.