Effects of Acupuncture on Uterine Hemodynamics and Early-Phase Pain Relief in Primary Dysmenorrhea: A Retrospective Cohort Study.

Primary dysmenorrhea, characterized by recurrent menstrual cramping without pelvic pathology, affects up to 91% of reproductive-age women. 1 , 2 Its pathophysiology involves excessive prostaglandin F2α (PGF2α) synthesis, inducing myometrial hypercontractility and subsequent uterine ischemia. 3 Doppler ultrasound studies indicate that elevated uterine artery resistance indices, specifically the pulsatility index (PI) and resistance index (RI), correlate with reduced tissue perfusion and increased pain severity. 4 , 5 These hemodynamic alterations highlight potential therapeutic targets for improving microcirculation in dysmenorrhea management. While nonsteroidal anti-inflammatory drugs (NSAIDs) and hormonal contraceptives remain first-line treatments, 20–30% of patients experience suboptimal responses or tolerate them poorly due to gastrointestinal or cardiovascular risks. 6 , 7 Moreover, these therapies primarily manage symptoms rather than directly targeting vascular resistance. Acupuncture is increasingly recognized as a potential alternative therapy. Clinical evidence suggests it may modulate pain via endogenous opioids and the suppression of inflammatory mediators. 8–10 Additionally, needling specific acupoints may decrease uterine artery resistance indices, potentially improving endometrial perfusion and reducing ischemia-related pain. 11 , 12 Despite this potential, a critical gap remains: while specialized acupuncture protocols—such as the “Regulating Ren and Unblocking Du” technique—are theorized to harmonize uterine blood flow, empirical evidence is largely restricted to subjective pain outcomes. 13 , 14 There is a lack of quantitative Doppler ultrasound data evaluating how these specific meridian-based protocols affect uterine artery hemodynamics in clinical practice. Therefore, this retrospective cohort study aims to evaluate whether the “Regulating Ren and Unblocking Du” protocol is associated with improvements in uterine artery resistance indices (PI, RI, and systolic/diastolic [S/D] ratio) and early-phase pain relief. By investigating both quantitative Doppler metrics and longitudinal pain scores, this study seeks to further clarify the clinical utility of this adjunctive therapy.

From January 2020 to December 2024, 286 patients were screened. After excluding 38 patients (secondary dysmenorrhea [n=18], coagulation disorders [n=12], incomplete data [n=8]), 248 patients were included in the pre-matched analysis (Intervention: n=85; Control: n=163). In the pre-matched cohort, baseline differences were observed ( Table 1 ). The intervention group exhibited longer disease duration, higher baseline VAS scores, earlier age at menarche, and a higher prevalence of menstrual blood clots compared to the control group (all p < 0.05). Additionally, the proportion of patients receiving hormonal therapy was higher in the intervention group (p = 0.015). Table 1 Demographic, Clinical, and Hemodynamic Characteristics of the Study Population Before and After Propensity Score Matching (PSM) Variable Before PSM After PSM Intervention (n=85) Control (n=163) SMD p-Value Intervention (n=79) Control (n=79) SMD p-Value Demographics Age (years), mean ± SD 26.9 ± 4.3 25.4 ± 4.6 0.334 0.012 26.7 ± 4.2 26.5 ± 4.1 0.048 0.765 BMI (kg/m 2 ), mean ± SD 22.5 ± 3.2 22.9 ± 3.4 0.121 0.354 22.4 ± 3.1 22.6 ± 3.0 0.065 0.682 Gynecological History Age at Menarche (years) 12.8 ± 1.1 13.2 ± 1.3 0.33 0.035 12.9 ± 1.2 13.0 ± 1.2 0.083 0.612 Disease duration (years), median [IQR] 5.0 [3.0–8.0] 3.0 [2.0–6.0] 0.412 0.003 4.0 [2.0–7.0] 4.0 [3.0–7.5] 0.052 0.814 Regular menstrual cycle, n (%) 72 (84.7%) 135 (82.8%) 0.051 0.702 67 (84.8%) 66 (83.5%) 0.036 0.824 Parity (≥1), n (%) 18 (21.2%) 29 (17.8%) 0.085 0.518 17 (21.5%) 15 (19.0%) 0.062 0.698 Uterine Position (Retroverted), n (%) 28 (32.9%) 45 (27.6%) 0.115 0.385 25 (31.6%) 23 (29.1%) 0.054 0.738 Clinical Baseline Baseline VAS score, mean ± SD 7.7 ± 1.2 7.2 ± 1.4 0.384 0.006 7.6 ± 1.3 7.5 ± 1.2 0.08 0.621 Presence of Clots, n (%) 55 (64.7%) 81 (49.7%) 0.306 0.028 50 (63.3%) 48 (60.8%) 0.052 0.745 Baseline Hemodynamics Uterine Artery PI 3.10 ± 0.44 3.05 ± 0.46 0.111 0.402 3.12 ± 0.45 3.08 ± 0.42 0.091 0.578 Uterine Artery RI 0.83 ± 0.06 0.81 ± 0.08 0.281 0.058 0.83 ± 0.06 0.82 ± 0.07 0.076 0.615 Medication Regimen NSAIDs, n (%) 50 (58.8%) 121 (74.2%) 0.331 0.015 49 (62.0%) 48 (60.8%) 0.025 0.873 Hormonal Therapy, n (%) 35 (41.2%) 42 (25.8%) 0.331 0.015 30 (38.0%) 31 (39.2%) 0.025 0.873 Comorbidities & Lifestyle Chronic hypertension, n (%) 3 (3.5%) 4 (2.5%) 0.06 0.645 2 (2.5%) 2 (2.5%) 0 1 Type 2 diabetes, n (%) 2 (2.4%) 3 (1.8%) 0.042 0.76 2 (2.5%) 2 (2.5%) 0 1 Family history, n (%) 23 (27.1%) 40 (24.5%) 0.059 0.662 21 (26.6%) 20 (25.3%) 0.029 0.854 Current smoking, n (%) 6 (7.1%) 10 (6.1%) 0.04 0.768 6 (7.6%) 5 (6.3%) 0.05 0.754 Alcohol use (≥1/week), n (%) 10 (11.8%) 18 (11.0%) 0.025 0.862 9 (11.4%) 8 (10.1%) 0.042 0.795 Notes : Statistical notes: Data presented as mean ± SD, median [IQR], or n (%). p-values calculated via t -test, Mann–Whitney U -test, or Chi-square/Fisher’s exact test. Covariate balance was assessed using SMD (threshold 1 cm in ≥50% of cycles. Hemodynamics: Measured during the follicular phase. Retroverted: Posteriorly tilted uterus on ultrasound. Abbreviations : PSM, propensity score matching; SMD, standardized mean difference; PI, pulsatility index; RI, resistance index; VAS, visual analogue scale. Demographic, Clinical, and Hemodynamic Characteristics of the Study Population Before and After Propensity Score Matching (PSM) Notes : Statistical notes: Data presented as mean ± SD, median [IQR], or n (%). p-values calculated via t -test, Mann–Whitney U -test, or Chi-square/Fisher’s exact test. Covariate balance was assessed using SMD (threshold 1 cm in ≥50% of cycles. Hemodynamics: Measured during the follicular phase. Retroverted: Posteriorly tilted uterus on ultrasound. Abbreviations : PSM, propensity score matching; SMD, standardized mean difference; PI, pulsatility index; RI, resistance index; VAS, visual analogue scale. A 1:1 nearest-neighbor PSM algorithm (caliper 0.02) was applied to adjust for covariates. This process yielded 79 patient pairs (n=158) ( Figure 1 ). Following PSM, baseline demographic, clinical, and hemodynamic characteristics did not differ significantly between the two groups, with all SMDs < 0.10. Regarding treatment adherence, patients in the matched intervention group completed a median of 11 acupuncture sessions (interquartile range: 10–12). Figure 1 Flowchart of patient screening, eligibility assessment, propensity score matching process, and final cohort inclusion. Flowchart of patient screening, eligibility and matching process for intervention and control groups. Initially, 286 patients were screened for eligibility from January 2020 to December 2024. Out of these, 38 were excluded due to secondary dysmenorrhea (18), coagulation disorders/anticoagulants (12) and incomplete baseline data (8). This left 248 eligible patients for pre-matched analysis. These were divided into an Intervention Group (Acupuncture plus Medication) with 85 patients and a Control Group (Medication Alone) with 163 patients. A 1:1 nearest-neighbor propensity score matching algorithm with a caliper of 0.02 was applied. In the Intervention Group, 6 patients were excluded due to no match, resulting in 79 patients included in the final analysis. In the Control Group, 84 patients were excluded due to no match, also resulting in 79 patients included in the final analysis. Flowchart of patient screening, eligibility assessment, propensity score matching process, and final cohort inclusion. At baseline, uterine artery Doppler indices did not differ significantly between the groups ( Table 1 ). Post-treatment evaluations showed that the intervention group had lower vascular resistance compared to the control group. Specifically, post-treatment PI (Mean Difference: −0.54, 95% CI: −0.66 to −0.42, p < 0.001) and RI (Mean Difference: −0.07, 95% CI: −0.09 to −0.05, p < 0.001) were significantly lower in the intervention group. Additionally, the post-treatment S/D ratio was lower in the intervention group (Mean Difference: −1.7, 95% CI: −2.0 to −1.4, p < 0.001), while post-treatment diastolic velocity was significantly higher (10.5 ± 3.0 vs. 9.1 ± 2.4 cm/s; p < 0.001) ( Table 2 and Figure 2 ). Table 2 Comparison of Uterine Artery Doppler Indices Before and After Intervention Parameter Intervention Group (n=79) Control Group (n=79) Group Difference Baseline Post-Treatment Baseline Post-Treatment Mean Diff (95% CI) p-Value Pulsatility Index (PI) 3.12 ± 0.45 2.31 ± 0.39 3.08 ± 0.42 2.85 ± 0.40 −0.54 (−0.66 to −0.42) <0.001 Resistance Index (RI) 0.83 ± 0.06 0.71 ± 0.05 0.82 ± 0.07 0.78 ± 0.06 −0.07 (−0.09 to −0.05) <0.001 S/D Ratio 6.5 ± 1.2 4.1 ± 0.9 6.4 ± 1.1 5.8 ± 1.0 −1.7 (−2.0 to −1.4) <0.001 Diastolic Velocity (cm/s) 8.2 ± 2.1 10.5 ± 3.0 8.4 ± 2.3 9.1 ± 2.4 1.4 (0.6 to 2.2) <0.001 Notes : Statistical notes: Data expressed as mean ± SD. Comparisons: Between-group differences at the post-treatment time point were analyzed using independent t-tests. Baseline values did not differ significantly (p > 0.05) via PSM. Abbreviations : S/D, systolic/diastolic velocity ratio; CI, confidence interval; Diff, difference. Figure 2 Comparison of Uterine Artery Doppler Indices Before and After Intervention. Bar graphs showing changes in ( A ) Pulsatility Index (PI), ( B ) Resistance Index (RI), ( C ) S/D Ratio, and ( D ) Diastolic Velocity (cm/s) comparing baseline and post-treatment values. Blue bars represent the control group and purple bars represent the intervention group. Error bars represent standard deviations. Between-group differences with p-values are shown for each parameter. *** p < 0.001. A grouped bar graph showing uterine artery Doppler indices at baseline and post treatment for two groups. The image A showing a grouped bar graph of Diastolic Velocity left parenthesis centimeters slash seconds right parenthesis. X axis labels: Baseline, Post Treatment. Y axis label: Diastolic Velocity left parenthesis centimeters slash seconds right parenthesis, range 0 to 16. Baseline bars: about 8.3 and about 8.1. Post Treatment bars: about 9.0 and about 10.5. A bracket over Post Treatment marked asterisk asterisk asterisk. The image B showing a grouped bar graph of Pulsatility Index left parenthesis P I right parenthesis. X axis labels: Baseline, Post Treatment. Y axis label: Pulsatility Index left parenthesis P I right parenthesis, range 0 to 4. Baseline bars: about 3.1 and about 3.1. Post Treatment bars: about 2.8 and about 2.3. A bracket over Post Treatment marked asterisk asterisk asterisk. The image C showing a grouped bar graph of Resistance Index left parenthesis R I right parenthesis. X axis labels: Baseline, Post Treatment. Y axis label: Resistance Index left parenthesis R I right parenthesis, range 0.00 to 1.00. Baseline bars: about 0.82 and about 0.83. Post Treatment bars: about 0.78 and about 0.71. A bracket over Post Treatment marked asterisk asterisk asterisk. The image D showing a grouped bar graph of S slash D Ratio. X axis labels: Baseline, Post Treatment. Y axis label: S slash D Ratio, range 0 to 7.5. Baseline bars: about 6.3 and about 6.4. Post Treatment bars: about 5.8 and about 4.1. A bracket over Post Treatment marked asterisk asterisk asterisk. The detailed data points are as follows: - For panel A, diastolic velocity (cm/s) at baseline: group 1 (blue) mean was 8.3 with error plus or minus 2.3 and group 2 (purple) mean was 8.1 with error plus or minus 2.1. - For panel A, diastolic velocity (cm/s) at post-treatment: group 1 (blue) mean was 9.0 with error plus or minus 2.4 and group 2 (purple) mean was 10.5 with error plus or minus 3.0. - For panel B, pulsatility index (PI) at baseline: group 1 (blue) mean was 3.1 with error plus or minus 0.4 and group 2 (purple) mean was 3.1 with error plus or minus 0.4. - For panel B, pulsatility index (PI) at post-treatment: group 1 (blue) mean was 2.8 with error plus or minus 0.4 and group 2 (purple) mean was 2.3 with error plus or minus 0.4. - For panel C, resistance index (RI) at baseline: group 1 (blue) mean was 0.82 with error plus or minus 0.07 and group 2 (purple) mean was 0.83 with error plus or minus 0.06. - For panel C, resistance index (RI) at post-treatment: group 1 (blue) mean was 0.78 with error plus or minus 0.06 and group 2 (purple) mean was 0.70 with error plus or minus 0.05. - For panel D, S over D ratio at baseline: group 1 (blue) mean was 6.4 with error plus or minus 1.1 and group 2 (purple) mean was 6.5 with error plus or minus 1.2. - For panel D, S over D ratio at post-treatment: group 1 (blue) mean was 5.8 with error plus or minus 1.0 and group 2 (purple) mean was 4.1 with error plus or minus 0.9. Comparison of Uterine Artery Doppler Indices Before and After Intervention Notes : Statistical notes: Data expressed as mean ± SD. Comparisons: Between-group differences at the post-treatment time point were analyzed using independent t-tests. Baseline values did not differ significantly (p > 0.05) via PSM. Abbreviations : S/D, systolic/diastolic velocity ratio; CI, confidence interval; Diff, difference. Comparison of Uterine Artery Doppler Indices Before and After Intervention. Bar graphs showing changes in ( A ) Pulsatility Index (PI), ( B ) Resistance Index (RI), ( C ) S/D Ratio, and ( D ) Diastolic Velocity (cm/s) comparing baseline and post-treatment values. Blue bars represent the control group and purple bars represent the intervention group. Error bars represent standard deviations. Between-group differences with p-values are shown for each parameter. *** p < 0.001. Longitudinal analysis of VAS scores demonstrated a significant group-by-time interaction (p < 0.001). At 1 month post-treatment, the intervention group reported significantly lower VAS scores compared to the control group (4.5 ± 1.1 vs. 6.3 ± 1.4), with a mean difference of −1.8 (95% CI: −2.2 to −1.4; p < 0.001). The difference between groups remained significant at 3 months (Mean Difference: −2.3, 95% CI: −2.7 to −1.9; p < 0.001). However, at the 6-month follow-up, VAS scores did not differ significantly between the two groups (4.9 ± 1.2 vs. 5.1 ± 1.3; p = 0.320) ( Table 3 and Figure 3 ). Table 3 Longitudinal Changes in Visual Analogue Scale (VAS) Scores Timepoint Intervention Group (n=79) Control Group (n=79) Mean Difference (95% CI) p-Value Baseline 7.6 ± 1.3 7.5 ± 1.2 0.1 (−0.3 to 0.5) 0.621 1 Month Post-treatment 4.5 ± 1.1 6.3 ± 1.4 −1.8 (−2.2 to −1.4) <0.001 Change from Baseline (Δ) −3.1 ± 1.0 −1.2 ± 0.9 — — 3 Months Post-treatment 3.6 ± 1.0 5.9 ± 1.3 −2.3 (−2.7 to −1.9) <0.001 6 Months Post-treatment 4.9 ± 1.2 5.1 ± 1.3 −0.2 (−0.6 to 0.2) 0.320 Notes : Statistical notes: Data expressed as mean ± SD. Analysis: Group comparisons at each time point utilized independent t-tests. Longitudinal interaction effects (Group × Time) were confirmed significant (p < 0.001) using linear mixed-effects models. A negative mean difference indicates lower pain intensity in the intervention group. Abbreviations : VAS, visual analogue scale (0–10); CI, confidence interval. Figure 3 Changes in Visual Analogue Scale (VAS) Scores Over Time. Line graph showing changes in pain intensity across four time points (baseline, 1 month, 3 months, and 6 months). Blue triangles represent the control group and purple circles represent the intervention group. Error bars represent 95% confidence intervals. Linear mixed-effects models revealed a significant group × time interaction (p < 0.001). *** p < 0.001 vs. control group; ns, not significant. A line graph showing Visual Analogue Scale scores over time after treatment for two groups. Longitudinal Changes in Visual Analogue Scale (VAS) Scores Notes : Statistical notes: Data expressed as mean ± SD. Analysis: Group comparisons at each time point utilized independent t-tests. Longitudinal interaction effects (Group × Time) were confirmed significant (p < 0.001) using linear mixed-effects models. A negative mean difference indicates lower pain intensity in the intervention group. Abbreviations : VAS, visual analogue scale (0–10); CI, confidence interval. Changes in Visual Analogue Scale (VAS) Scores Over Time. Line graph showing changes in pain intensity across four time points (baseline, 1 month, 3 months, and 6 months). Blue triangles represent the control group and purple circles represent the intervention group. Error bars represent 95% confidence intervals. Linear mixed-effects models revealed a significant group × time interaction (p < 0.001). *** p < 0.001 vs. control group; ns, not significant. At baseline, symptom duration and analgesic use did not differ significantly between the two groups ( Table 4 and Figure 4 ). At 1 month post-treatment, the intervention group had a significantly shorter duration of menstrual symptoms compared to the control group (15.2 ± 3.2 vs. 15.8 ± 3.5 hours/cycle; Mean Difference: −0.6, 95% CI: −1.1 to −0.1; p < 0.001). Similarly, analgesic use was significantly lower in the intervention group at 1 month (4.1 ± 1.1 vs. 4.5 ± 1.3 doses/cycle; Mean Difference: −0.4, 95% CI: −0.7 to −0.1; p < 0.001). These differences between groups remained significant at 3 months post-treatment (p < 0.001 for both outcomes). However, at the 6-month follow-up, symptom duration (12.7 ± 2.3 vs. 12.9 ± 2.5 hours/cycle; p = 0.562) and analgesic use (3.8 ± 1.1 vs. 3.8 ± 1.3 doses/cycle; p = 0.953) did not differ significantly between the groups ( Table 4 ). Table 4 Changes in Symptom Duration and Analgesic Use Frequency Outcome Timepoint Intervention Group (n=79) Control Group (n=79) Mean Difference (95% CI) p-Value Symptom Duration Baseline 18.5 ± 4.1 18.2 ± 3.8 0.3 (−0.5 to 1.1) 0.511 (hours/cycle) 1 month post-treatment 15.2 ± 3.2 15.8 ± 3.5 −0.6 (−1.1 to −0.1) <0.001 3 months post-treatment 12.9 ± 2.7 13.9 ± 3.3 −1.0 (−1.6 to −0.4) <0.001 6 months post-treatment 12.7 ± 2.3 12.9 ± 2.5 −0.2 (−0.9 to 0.5) 0.562 Analgesic Use Baseline 5.2 ± 1.8 5.1 ± 1.6 0.1 (−0.3 to 0.5) 0.625 (doses/cycle) 1 month post-treatment 4.1 ± 1.1 4.5 ± 1.3 −0.4 (−0.7 to −0.1) <0.001 3 months post-treatment 3.9 ± 0.9 4.2 ± 1.2 −0.3 (−0.6 to −0.0) <0.001 6 months post-treatment 3.8 ± 1.1 3.8 ± 1.3 0.0 (−0.4 to 0.4) 0.953 Notes : Statistical notes: Data expressed as mean ± SD. Comparisons: Between-group Mean Differences were calculated using independent t-tests at each time point. A negative mean difference indicates shorter symptom duration or fewer analgesic doses in the intervention group. Abbreviation : CI, confidence interval. Figure 4 Changes in secondary outcomes over time. Line graphs showing changes in ( A ) symptom duration (Hours/Cycle) and ( B ) analgesic use (Doses/Cycle) across four time points. Blue triangles represent the control group and purple circles represent the intervention group. Error bars represent 95% confidence intervals. Between-group mean differences were assessed at each time point: *** p < 0.001 vs. control group; ns, not significant. A line graph pair showing symptom duration and analgesic use over time for control and intervention groups. The image A showing a line graph with error bars. Y-axis label: Hours slash Cycle. Y-axis range: 0 to 25. X-axis label: Time After Treatment. X-axis categories: Baseline, 1 Month, 3 Months, 6 Months. Two series are plotted with triangle markers and circle markers. Triangle series coordinate pairs: (Baseline, 18.0), (1 Month, 15.8), (3 Months, 14.0), (6 Months, 12.9). Circle series coordinate pairs: (Baseline, 18.5), (1 Month, 15.2), (3 Months, 13.0), (6 Months, 12.7). Text annotations above time points: at 1 Month, asterisk asterisk asterisk; at 3 Months, asterisk asterisk asterisk; at 6 Months, ns. The image B showing a line graph with error bars. Y-axis label: Doses slash Cycle. Y-axis range: 0 to 8. X-axis label: Time After Treatment. X-axis categories: Baseline, 1 Month, 3 Months, 6 Months. Two series are plotted with triangle markers and circle markers. Triangle series coordinate pairs: (Baseline, 5.0), (1 Month, 4.5), (3 Months, 4.2), (6 Months, 3.8). Circle series coordinate pairs: (Baseline, 5.1), (1 Month, 4.1), (3 Months, 3.9), (6 Months, 3.8). Text annotations above time points: at 1 Month, asterisk asterisk asterisk; at 3 Months, asterisk asterisk asterisk; at 6 Months, ns. The detailed data points are as follows: - In panel A, for the metric Hours over Cycle, the group Blue (triangle) has a baseline of 18.3, 1 Month of 15.8, 3 Months of 14.0 and 6 Months of 13.0. - In panel A, for the metric Hours over Cycle, the group Purple (circle) has a baseline of 18.7, 1 Month of 15.2, 3 Months of 13.0 and 6 Months of 12.8. - In panel B, for the metric Doses over Cycle, the group Blue (triangle) has a baseline of 5.1, 1 Month of 4.5, 3 Months of 4.2 and 6 Months of 3.8. - In panel B, for the metric Doses over Cycle, the group Purple (circle) has a baseline of 5.2, 1 Month of 4.1, 3 Months of 3.9 and 6 Months of 3.8. Changes in Symptom Duration and Analgesic Use Frequency Notes : Statistical notes: Data expressed as mean ± SD. Comparisons: Between-group Mean Differences were calculated using independent t-tests at each time point. A negative mean difference indicates shorter symptom duration or fewer analgesic doses in the intervention group. Abbreviation : CI, confidence interval. Changes in secondary outcomes over time. Line graphs showing changes in ( A ) symptom duration (Hours/Cycle) and ( B ) analgesic use (Doses/Cycle) across four time points. Blue triangles represent the control group and purple circles represent the intervention group. Error bars represent 95% confidence intervals. Between-group mean differences were assessed at each time point: *** p < 0.001 vs. control group; ns, not significant. A correlation analysis was performed to evaluate the relationship between the changes in uterine artery indices and the changes in VAS scores ( Table 5 and Figure 5 ). A positive correlation was observed between the magnitude of PI reduction (ΔPI) and VAS reduction (ΔVAS) (Spearman’s r = 0.52, 95% CI: 0.43–0.61; p < 0.001). Significant positive correlations were also observed between ΔRI and ΔVAS (r = 0.48, 95% CI: 0.38–0.58; p < 0.001), as well as between the ΔS/D ratio and ΔVAS (r = 0.45, 95% CI: 0.35–0.55; p < 0.001). Table 5 Correlation Coefficients Between Hemodynamic Changes and Pain Relief (Δ) Hemodynamic Parameter Correlation with ΔVAS 95% CI p-Value ΔPulsatility Index (PI) r = 0.52 0.43–0.61 <0.001 ΔResistance Index (RI) r = 0.48 0.38–0.58 <0.001 ΔS/D Ratio r = 0.45 0.35–0.55 <0.001 Notes : Spearman’s rank correlation coefficients reported due to non-normal distribution of ΔPI/ΔVAS (Shapiro–Wilk p < 0.05). Abbreviations : Δ, change from baseline to 6 months; CI, confidence interval. Figure 5 Correlation matrix showing relationships between changes in hemodynamic parameters and pain scores. The matrix displays scatterplots with fitted regression lines and 95% confidence bands (lower diagonal), distribution density histograms (diagonal), and Spearman’s rank correlation coefficients (upper diagonal) for changes in Visual Analogue Scale (ΔVAS), Pulsatility Index (ΔPI), Resistance Index (ΔRI), and Systolic/Diastolic ratio (ΔS/D). *** p < 0.001. A correlation matrix of histograms and scatterplots for delta VAS, delta PI, delta RI and delta S over D. A four by four correlation matrix for delta VAS, delta PI, delta RI and delta S over D, with density histograms on the diagonal, scatterplots with fitted regression lines and shaded confidence bands in the lower triangle and correlation text in the upper triangle. Diagonal density histograms: Delta VAS histogram: y-axis tick labels 0.00, 0.05, 0.10, 0.15, 0.20. Delta PI histogram: (axis tick labels unclear). Delta RI histogram: (axis tick labels unclear). Delta S over D histogram: (axis tick labels unclear). Upper triangle correlation text: Delta VAS with delta PI: Corr: 0.520 asterisk asterisk asterisk. Delta VAS with delta RI: Corr: 0.477 asterisk asterisk asterisk. Delta VAS with delta S over D: Corr: 0.452 asterisk asterisk asterisk. Delta PI with delta RI: Corr: 0.674 asterisk asterisk asterisk. Delta PI with delta S over D: Corr: 0.637 asterisk asterisk asterisk. Delta RI with delta S over D: Corr: 0.771 asterisk asterisk asterisk. Scatterplots (lower triangle) use circular markers for points, a solid fitted line and a shaded band around the line. Axis tick labels visible on the bottom row x-axes: Delta VAS: negative 2.5, 0.0, 2.5, 5.0. Delta PI: 0, 1. Delta RI: negative 0.2, 0.0, 0.2. Delta S over D: negative 2, 0, 2, 4. Axis tick labels visible on the left column y-axes: Delta PI: negative 1, 0, 1. Delta RI: negative 0.2, 0.0, 0.2. Delta S over D: negative 2, 0, 2, 4. All fitted lines in the scatterplots slope upward. Correlation Coefficients Between Hemodynamic Changes and Pain Relief (Δ) Notes : Spearman’s rank correlation coefficients reported due to non-normal distribution of ΔPI/ΔVAS (Shapiro–Wilk p < 0.05). Abbreviations : Δ, change from baseline to 6 months; CI, confidence interval. Correlation matrix showing relationships between changes in hemodynamic parameters and pain scores. The matrix displays scatterplots with fitted regression lines and 95% confidence bands (lower diagonal), distribution density histograms (diagonal), and Spearman’s rank correlation coefficients (upper diagonal) for changes in Visual Analogue Scale (ΔVAS), Pulsatility Index (ΔPI), Resistance Index (ΔRI), and Systolic/Diastolic ratio (ΔS/D). *** p < 0.001. Subgroup analyses indicated that age and disease duration had significant interaction effects with the intervention (p = 0.013 and p = 0.038, respectively) ( Table 6 ). For age, the reduction in PI was greater in patients aged <30 years (Mean Difference in ΔPI: −0.34, 95% CI: −0.41 to −0.27) compared to those aged ≥30 years (Mean Difference in ΔPI: −0.09, 95% CI: −0.15 to −0.03). Regarding disease duration, the reduction in VAS scores was greater in patients with a disease duration <5 years (Mean Difference in ΔVAS: −3.3, 95% CI: −3.7 to −2.9) compared to patients with a duration ≥5 years (Mean Difference in ΔVAS: −2.3, 95% CI: −2.8 to −1.8). Table 6 Subgroup Analysis of Treatment Effects by Age and Disease Duration Subgroup Intervention Group Control Group Mean Difference (95% CI) p-Value Interaction p Age 0.013 <30 years (n=128) ΔPI: −0.92 ± 0.21 ΔPI: −0.58 ± 0.19 −0.34 (−0.41 to −0.27) <0.001 ≥30 years (n=70) ΔPI: −0.61 ± 0.18 ΔPI: −0.52 ± 0.16 −0.09 (−0.15 to −0.03) 0.002 Disease Duration 0.038 <5 years (n=202) ΔVAS: −5.1 ± 1.0 ΔVAS: −1.8 ± 0.9 −3.3 (−3.7 to −2.9) <0.001 ≥5 years (n=196) ΔVAS: −3.8 ± 1.2 ΔVAS: −1.5 ± 1.1 −2.3 (−2.8 to −1.8) <0.001 Notes : Statistical notes: Interaction effects tested via generalized linear models with group × subgroup terms. All models adjusted for baseline BMI and menstrual cycle regularity. Abbreviations : ΔPI, change in pulsatility index; ΔVAS, change in visual analogue scale. Subgroup Analysis of Treatment Effects by Age and Disease Duration Notes : Statistical notes: Interaction effects tested via generalized linear models with group × subgroup terms. All models adjusted for baseline BMI and menstrual cycle regularity. Abbreviations : ΔPI, change in pulsatility index; ΔVAS, change in visual analogue scale. In the intervention group, 5 out of 79 participants (6.3%) reported AEs, including localized subcutaneous bruising (n=3) and dizziness (n=2). All events resolved without medical intervention. No severe AEs were recorded. In the control group, 0 out of 79 participants reported AEs. The overall incidence of documented AEs did not differ significantly between the two groups (p = 0.058) ( Table 7 ). Table 7 Adverse Events in the Intervention and Control Groups Event Type Intervention Group (n=79) Control Group (n=79) Statistics p-Value Local Bruising 3 (3.8%) 0 (0.0%) FET 0.245 Dizziness 2 (2.5%) 0 (0.0%) FET 0.497 Total Minor Events 5 (6.3%) 0 (0.0%) FET 0.058 Severe Events 0 (0.0%) 0 (0.0%) — — Notes : Statistical notes: Data expressed as frequency (%). Abbreviation : FET, Fisher’s exact test. Adverse Events in the Intervention and Control Groups Notes : Statistical notes: Data expressed as frequency (%). Abbreviation : FET, Fisher’s exact test. Sensitivity analyses were performed using both CC and MI datasets. In the MI analysis (n=79 per group), VAS scores were significantly lower in the intervention group compared to the control group at 1 month (4.5 vs. 6.3; p < 0.001) and 3 months (3.6 vs. 5.9; p < 0.001). At 6 months, VAS scores did not differ significantly between the groups (p = 0.320). Similar statistical results were observed in the CC analysis (Intervention n=68; Control n=71) ( Table 8 ). Additionally, the E-value sensitivity analysis yielded an E-value of 13.5 for the point estimate of the primary outcome (VAS at 1 month), with a lower confidence limit E-value of 7.2. Table 8 Sensitivity Analysis of VAS Outcomes: Complete-Case vs. Multiple Imputation Timepoint Complete-Case Analysis Multiple Imputation Analysis Int (n=68) Ctrl (n=71) Intervention (n=79) Control (n=79) Baseline VAS 7.6 ± 1.3 7.5 ± 1.2 7.6 ± 1.3 7.5 ± 1.2 1 Month Post-treatment 4.4 ± 1.0 6.2 ± 1.3 4.5 ± 1.1 6.3 ± 1.4 Between-Group Δ (95% CI) −1.8 (−2.2 to −1.4) p < 0.001 −1.8 (−2.2 to −1.4) p < 0.001 3 Months Post-treatment 3.5 ± 1.0 5.8 ± 1.4 3.6 ± 1.0 5.9 ± 1.3 Between-Group Δ (95% CI) −2.3 (−2.7 to −1.9) p < 0.001 −2.3 (−2.7 to −1.9) p < 0.001 6 Months Post-treatment 4.8 ± 1.2 5.0 ± 1.4 4.9 ± 1.2 5.1 ± 1.3 Between-Group Δ (95% CI) −0.2 (−0.6 to 0.2) p = 0.35 −0.2 (−0.6 to 0.2) p = 0.32 Notes : Statistical notes: Complete-case analysis included only participants with VAS data at all timepoints. Multiple imputation used fully conditional specification (FCS) with predictive mean matching (20 imputations). Between-group differences adjusted for baseline VAS and covariates (BMI, disease duration). Abbreviations : VAS, visual analogue scale; CI, confidence interval; Δ, change from baseline. Sensitivity Analysis of VAS Outcomes: Complete-Case vs. Multiple Imputation Notes : Statistical notes: Complete-case analysis included only participants with VAS data at all timepoints. Multiple imputation used fully conditional specification (FCS) with predictive mean matching (20 imputations). Between-group differences adjusted for baseline VAS and covariates (BMI, disease duration). Abbreviations : VAS, visual analogue scale; CI, confidence interval; Δ, change from baseline.

This single-center retrospective cohort study analyzed electronic medical records of patients with primary dysmenorrhea from the gynecology department of The Second Affiliated Hospital of Shaanxi University of Chinese Medicine between January 2020 and December 2024. Data were extracted from the hospital’s standardized electronic database, which included demographics, treatment protocols, ultrasound examinations, and follow-up records. Patients were categorized into two groups: the intervention group received conventional medication combined with “Regulating Ren and Unblocking Du” acupuncture. To ensure adequate therapeutic dosage, the completion of at least 8 acupuncture sessions per treatment course was established as the strictly defined minimum exposure criterion for inclusion in the intervention group, while the control group received conventional medication alone (NSAIDs or hormonal therapy). The study was approved by the Institutional Ethics Committee of The Second Affiliated Hospital of Shaanxi University of Chinese Medicine (Approval No. LW2025007-9), and the requirement for informed consent was waived due to the anonymized retrospective nature of the data. Participants were selected from female patients aged 18–40 years diagnosed with primary dysmenorrhea according to the International Federation of Gynecology and Obstetrics (FIGO) criteria for cyclic pelvic pain unrelated to organic pathology. 15 The strict inclusion criteria were defined as follows: (1) completion of uterine artery Doppler ultrasound within one month before treatment initiation; (2) availability of complete baseline and follow-up records, including visual analogue scale (VAS) scores for pain intensity and menstrual symptom duration; and (3) specifically for the intervention cohort, fulfillment of the minimum exposure criterion of ≥8 acupuncture sessions. The exclusion criteria comprised: (1) secondary dysmenorrhea (eg., endometriosis, uterine fibroids, or pelvic inflammatory disease confirmed by laparoscopy or imaging); (2) comorbid cardiovascular diseases, cerebrovascular disorders, or coagulation abnormalities (eg., thrombocytopenia or anticoagulant therapy); (3) use of other acupuncture therapies or herbal medicine within three months prior to enrollment; and (4) missing ultrasound data or incomplete follow-up evaluations. To ensure diagnostic accuracy, secondary dysmenorrhea cases were rigorously excluded through cross-referencing surgical records, imaging reports, and histopathological findings. Participants in the intervention group received conventional pharmacological therapy combined with the “Regulating Ren and Unblocking Du” acupuncture protocol. Conventional medication included NSAIDs (eg., ibuprofen) or hormonal therapy (eg., combined oral contraceptives) prescribed based on clinical guidelines. The acupuncture protocol targeted specific acupoints based on Chinese edition of the WHO Standard Acupuncture Point Locations in the Western Pacific Region: 16 Ren Mai (Conception Vessel): Guanyuan (CV4, located 3 cun inferior to the umbilicus), Zhongji (CV3, 1 cun inferior to CV4). Du Mai (Governor Vessel): Mingmen (GV4, at the lumbar spine level between L2 and L3), Yaoyangguan (GV3, at the lumbar spine level between L4 and L5). Adjunctive acupoints: Sanyinjiao (SP6, 3 cun superior to the medial malleolus), Xuehai (SP10, on the medial thigh 2 cun above the medial superior border of the patella). Sterile disposable stainless steel needles (0.25 × 40 mm) were inserted perpendicularly to a depth of 10–25 mm, depending on the acupoint location. A balanced reinforcement-reduction technique was applied to achieve the deqi sensation. For the purpose of this study, deqi was operationally defined as a composite subjective sensation comprising soreness, numbness, heaviness, or distension reported by the patient around the acupoint, frequently accompanied by a specific biomechanical sensation of resistance or “needle grasp” perceived by the administering acupuncturist. Needles were retained for 30 minutes per session, administered 3 times weekly over 4 weeks (total ≥8 sessions). All procedures were performed by licensed acupuncturists with ≥5 years of clinical experience, trained in standardized protocols to ensure consistency. The control group received conventional pharmacological therapy alone without acupuncture. Based on patient preference, symptom severity, and clinical contraindications, participants were prescribed either a primary regimen of NSAIDs (predominantly ibuprofen or naproxen) or hormonal therapy (specifically combined oral contraceptives). Patients receiving a concurrent combination of both as their primary maintenance therapy were not included in this cohort. Medication dosages and administration schedules strictly followed institutional guidelines for primary dysmenorrhea management. Endometrial blood flow parameters were assessed via transvaginal Doppler ultrasound (Voluson E10, GE Healthcare) performed by certified sonographers. To minimize cycle-dependent hemodynamic variability, all baseline examinations were strictly scheduled during the follicular phase (days 5–11 of the menstrual cycle). Pulsatility index (PI), resistance index (RI), and systolic/diastolic velocity ratio (S/D) of the uterine arteries were measured bilaterally and averaged for analysis. Baseline ultrasound data were collected within one month before treatment initiation, and follow-up measurements were obtained 1–3 days after the final acupuncture session (intervention group) or 4 weeks after medication initiation (control group). Pain intensity was evaluated using the visual analogue scale (VAS, 0–10 cm). 17 Baseline VAS scores were extracted from standardized patient-reported outcome forms recorded during the initial consultation. Post-treatment VAS assessments were collected at three time points: Short-term: 1 month after treatment completion (covering 1–2 menstrual cycles); Mid-term: 3 months post-treatment; Long-term: 6 months post-treatment. Scores were obtained from follow-up clinic visit records or telehealth consultations. Patients without documented VAS scores at all three time points were excluded to ensure longitudinal data consistency. Menstrual symptom duration (hours per cycle) and analgesic medication use frequency (doses per cycle) were calculated based on self-reported pain diaries and pharmacy dispensing records. To address pharmacological heterogeneity, the primary maintenance medication regimen was categorized as either NSAID-based (eg., ibuprofen, naproxen) or hormonal therapy-based (eg., combined oral contraceptives), based on the dominant prescription filled during the study period. Adverse events (AEs) were defined as any clinical notes indicating localized bruising (subcutaneous hemorrhage ≥5 mm at needle sites) or dizziness (eg., terms like “lightheadedness,” “nausea,” or “diaphoresis” recorded within 24 hours post-treatment). 18 Severe AEs included infections, syncope, or hospitalization directly linked to treatment. For the control group, AEs related to conventional medication (eg., gastrointestinal symptoms, headaches) were extracted from prescription logs and progress notes using standardized ICD-10 codes. 19 Demographic and clinical covariates included age, body mass index (BMI), disease duration, age at menarche (years), and menstrual cycle regularity (21–35 days). Gynecological history was scrutinized for specific menstrual characteristics, specifically the presence of menstrual clots, defined as patient-reported observation of clots >1 cm in diameter during at least 50% of recent cycles, a clinical marker often associated with higher prostaglandin levels and increased pain severity. 20 Additionally, uterine position (anteverted vs. retroverted) was extracted from the baseline ultrasound reports, as uterine retroversion is a recognized anatomical factor that may exacerbate pelvic congestion and dysmenorrhea symptoms. 21 These variables were extracted from electronic health records and baseline assessments. Data analysis was performed using SPSS version 26.0 and R software version 4.3.4. Continuous variables were summarized as mean ± standard deviation or median [interquartile range] based on normality assessed via Shapiro–Wilk tests, while categorical variables were expressed as frequencies and percentages. To rigorously control for selection bias, a 1:1 nearest-neighbor Propensity Score Matching (PSM) algorithm with a caliper of 0.02 was implemented using a multivariate logistic regression model that incorporated age, BMI, disease duration, age at menarche, presence of clots, baseline hemodynamic indices, and medication regimen; covariate balance was validated by Standardized Mean Differences (SMD) < 0.10. In the matched cohort, between-group comparisons employed independent samples t-tests, Mann–Whitney U -tests, or Chi-square/Fisher’s exact tests as appropriate. Longitudinal trajectories of VAS scores and secondary outcomes were evaluated using linear mixed-effects models with random intercepts to account for repeated measures, incorporating group-by-time interaction terms. The robustness of these findings was verified through sensitivity analyses comparing complete-case (CC) data with multiple imputation (MI) (20 datasets). Additionally, relationships between hemodynamic changes and pain relief were quantified using Spearman’s rank correlation coefficients, and subgroup heterogeneity was assessed using generalized linear models with interaction terms, with statistical significance defined as a two-tailed p-value < 0.05. As a retrospective study, the sample size was determined by the availability of eligible electronic medical records rather than an a priori calculation; however, a post-hoc power analysis indicated that the matched cohort of 79 pairs provided >80% statistical power to detect the observed mean difference of −1.8 in VAS scores at 1 month, assuming an alpha error of 0.05. Furthermore, to quantitatively assess the robustness of our findings to unmeasured confounding, we calculated the E-value for the primary clinical outcome (VAS score at 1 month).

In this retrospective cohort study, adding the “Regulating Ren and Unblocking Du” acupuncture protocol to conventional therapy was associated with rapid improvements in endometrial hemodynamics and early-phase pain control in primary dysmenorrhea. Mechanistically, targeting specific lumbosacral (eg., GV3, GV4) and lower abdominal acupoints may induce segmental neuromodulation, which aligns with our observation of reduced uterine vascular resistance. While exploratory analyses suggest this adjunctive approach particularly benefits younger patients, the clinical superiority over medication alone wanes over a six-month period. These findings underscore the value of integrating meridian-based acupuncture with standard care for rapid, hemodynamically supported symptom management, while highlighting the potential need for ongoing maintenance sessions to sustain long-term benefits.

This retrospective cohort study, rigorously adjusted via PSM, suggests that the “Regulating Ren and Unblocking Du” acupuncture protocol, when combined with conventional pharmacological therapy, is associated with improved endometrial blood flow and reduced pain in patients with primary dysmenorrhea. By effectively balancing measured baseline confounders such as medication type and disease severity, our analysis helps to clarify the potential hemodynamic contribution of acupuncture. The intervention group demonstrated pronounced reductions in uterine artery resistance indices, including PI, RI, and S/D ratio, which correlated strongly with decreased VAS scores and shorter menstrual pain duration. These findings align with emerging research highlighting acupuncture’s potential dual role in modulating vascular function and pain pathways, 22 yet they also reveal a distinct temporal pattern characterized by rapid initial relief followed by a convergence of outcomes by six months post-treatment. The observed hemodynamic improvements suggest that acupuncture may enhance endometrial perfusion by potentially reducing vascular resistance. This hypothesized mechanism is consistent with prior studies demonstrating that needling acupoints such as Guanyuan (CV4) and Sanyinjiao (SP6) increases uterine artery diastolic velocity, thereby mitigating ischemia-induced pain. 13 However, our study uniquely emphasizes the “Regulating Ren and Unblocking Du” protocol’s focus on the Conception Vessel (Ren Mai) and Governor Vessel (Du Mai), meridians traditionally associated with reproductive health and Qi circulation. From a neurophysiological perspective, targeting GV3 (Yaoyangguan) and GV4 (Mingmen), located in the lumbosacral region, may induce segmental neuromodulation. This may inhibit excessive sympathetic outflow to the uterine arteries, thereby relieving vasospasm and restoring perfusion, a hypothesis supported by neuroimaging studies showing acupuncture-induced activation of the hypothalamic-pituitary-adrenal axis. 23 Acupuncture has been shown to be effective in primary dysmenorrhea, supported by a large number of studies. 9 , 18 , 24 For instance, a 2018 study by Shetty et al demonstrated that acupuncture at SP6 significantly reduced pain intensity, as measured by the VAS, in patients with primary dysmenorrhea. 25 Our results demonstrated sustained pain relief in the intervention group at 6 months (ΔVAS >3 points), though the effect magnitude was less pronounced than the durable benefits reported in chronic pain populations by MacPherson et al 26 This divergence may reflect differences in pathophysiology (cyclic vs. persistent pain) and control interventions (active pharmacotherapy vs. sham/no treatment). Rather than indicating a failure of acupuncture, the gradual convergence of VAS scores between groups suggests that acupuncture may provide a critical “clinical window of opportunity.” It appears to accelerate symptom control in the early cycles, when pharmacological agents alone may be slow to achieve maximal efficacy, thereby potentially breaking the “pain-ischemia-spasm” cycle rapidly. While the persistent hemodynamic improvements (eg., PI reduction) suggest acupuncture’s biological durability, the clinical parity at six months implies that pharmacological accumulation eventually matches the initial burst of relief provided by acupuncture. Further studies should explore whether extended maintenance sessions enhance long-term efficacy in dysmenorrhea. 27 In our cohort, both groups exhibited comparable pain scores at six months, indicating that the superior additive benefit of acupuncture over conventional medication alone diminished over time. This temporal pattern suggests that while acupuncture provides vital short-term enhancements in pain relief, its effects may not persist without sustained intervention. These findings highlight the potential need for maintenance acupuncture sessions or integrative strategies combining acupuncture with behavioral therapies to prolong therapeutic benefits. Importantly, sensitivity analyses using multiple imputation and complete-case approaches confirmed the consistency of these trends, reinforcing that missing data did not substantially influence the conclusions. The reduction in analgesic use within the intervention group highlights acupuncture’s potential to decrease reliance on NSAIDs, which are associated with gastrointestinal and cardiovascular risks. 6 , 10 A cohort study found that integrating acupuncture with NSAIDs reduced medication use compared to NSAIDs alone, aligning with our findings. 28 Mechanistically, improved uterine perfusion may facilitate the rapid “washout” of local inflammatory metabolites and prostaglandins, thereby reducing the threshold for nociceptor activation. Additionally, acupuncture may suppress prostaglandin synthesis through the downregulation of cyclooxygenase-2 (COX-2) expression, as demonstrated in animal models. 29 Furthermore, the inclusion of Xuehai (SP10), an acupoint linked to blood stasis resolution, may enhance microcirculation and reduce inflammatory mediators, synergizing with pharmacological therapies to amplify pain relief. 30 Exploratory subgroup analyses revealed that younger patients (<30 years) and those with shorter disease duration (<5 years) experienced greater hemodynamic and pain improvements. This age-dependent response may reflect preserved vascular elasticity and neuronal plasticity in younger individuals, allowing for more effective modulation of uterine blood flow. 31 Similarly, patients with shorter disease duration likely have less entrenched central sensitization, a phenomenon observed in chronic pain conditions where prolonged nociceptive input leads to irreversible neural remodeling. 32 These insights advocate for early intervention with acupuncture to maximize therapeutic outcomes, a strategy supported by the WHO’s guidelines recommending acupuncture as a therapy for dysmenorrhea, including adolescent populations. 33 The safety profile of acupuncture further strengthens its clinical utility. Only 6.1% of participants reported minor adverse events, such as localized bruising or transient dizziness, consistent with large-scale registries documenting acupuncture’s favorable safety record. 9 In contrast, the control group’s lack of documented medication-related adverse events may underestimate risks, as retrospective studies often fail to capture subjective symptoms like nausea or headaches. Prospective trials with active AE monitoring are needed to clarify this discrepancy. Despite its strengths, this study has limitations. First, the retrospective design introduces selection bias and limits the control over unmeasured confounders. Although propensity score matching rigorously adjusts for measured covariates, it does not eliminate the possibility of residual confounding from unmeasured variables. However, our E-value sensitivity analysis demonstrated that an unmeasured confounder would need an exceptionally strong association (risk ratio > 7.2) with both the intervention and the outcome to completely nullify the observed analgesic benefits. Since such a magnitude of unmeasured confounding is highly improbable in realistic clinical settings, our core findings remain highly robust despite the observational nature of the study. Second, reliance on electronic health records limited our ability to assess subjective outcomes, such as quality of life or psychological distress, which are critical to comprehensive pain management. Third, the real-world clinical setting inherently precluded the implementation of blinding or sham acupuncture controls; thus, we cannot entirely isolate the specific physiological effects of the acupuncture protocol from potential non-specific placebo responses. Fourth, the single-center setting restricts generalizability, particularly to populations with diverse cultural or socioeconomic backgrounds. Fifth, our follow-up was restricted to six months due to the retrospective design, as real-world patients rarely return for extended routine monitoring once their symptoms improve. Consequently, the sustainability of the hemodynamic effects beyond this time point remains uncharacterized. Future multicenter prospective studies, ideally incorporating sham controls, extended follow-up periods (>6 months), and biomarkers like serum prostaglandins or inflammatory cytokines, could further validate our clinical findings and elucidate the underlying biochemical pathways.