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Updated: Jun 11, 2025

Combined Supine and Standing Imaging for Varicocele: An Improved Diagnostic Approach
Published on: November 22, 2024
Body Mass Index Combined With Inflammatory Factors Can Better Predict Varicocele
Si Yan Miao1, Wen Rui Wu1, Liang Feng1
1Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, CHN.
Insights
Varicocele (VC) is often found during infertility diagnosis. Systemic immune-inflammation index (SII) and neutrophil-lymphocyte ratio (NLR) are risk factors, while higher body mass index (BMI) is protective. Combining BMI with SII and NLR improves VC prediction.
Area of Science:
- Urology
- Andrology
- Inflammation Research
Background:
- Varicocele (VC) is a common cause of male infertility, often asymptomatic and diagnosed late.
- Early identification of VC is crucial for timely clinical intervention.
- Understanding risk and protective factors for VC is essential for improved diagnostic strategies.
Purpose of the Study:
- To identify independent risk and protective factors for varicocele (VC).
- To evaluate the diagnostic value of these factors, individually and in combination, for predicting VC.
- To explore the predictive accuracy of body mass index (BMI) combined with inflammatory markers.
Main Methods:
- Retrospective analysis of clinical data from 423 patients (January 2021-February 2024).
- Propensity score matching (PSM) to create two comparable groups (VC and non-VC).
- Multivariate binary logistic regression and Receiver Operating Characteristic (ROC) curve analysis.
Main Results:
- Systemic immune-inflammation index (SII) and neutrophil-lymphocyte ratio (NLR) were independent risk factors for VC (p<0.05).
- Higher body mass index (BMI) was an independent protective factor against VC (p<0.05).
- Combined BMI with inflammatory factors (SII, NLR) significantly improved VC prediction accuracy (87.5%) compared to individual factors.
Conclusions:
- SII and NLR are significant risk factors for varicocele.
- BMI serves as a protective factor against varicocele.
- Combining BMI with SII and NLR offers a more accurate predictive model for varicocele detection.
Abstract:
Most patients with VC have no symptoms, so they are often discovered due to male infertility. Early identification of them is a matter of concern for clinicians. A retrospective analysis of clinical data from patients between January 1, 2021, and February 1, 2024, was conducted. Patients were divided into VC and non-VC groups. Propensity score matching (PSM) was performed at a ratio of 1:1, and two cohorts with homogeneous baseline status were selected. Multivariate binary logistic regression and receiver operating characteristic (ROC) curve were used to analyze independent risk factors and protective factors and to evaluate their diagnostic value individually and in combination. A p-value <0.05 was considered statistically significant. A total of 256 patients with similar clinical characteristics were further analyzed after PSM in a 1:1 ratio of the 423 patients included in the study. The two groups had statistically significant differences in systemic immune-inflammation index (SII), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and body mass index (BMI) (p<0.05). Multivariate binary logistic regression analysis showed that SII and NLR were independent risk factors for VC, while high BMI could reduce the prevalence of VC. The PLR differences were not significant. The ROC analysis showed that BMI, SII, and NLR could predict VC, with areas under the curve of 68.3% (cut-off value 22.32), 83.4% (cut-off value 357.57), and 83.2% (cut-off value 1.8), respectively. The combination of BMI and inflammatory factors was more accurate for predicting VC than BMI alone (87.5% vs. 68.3%, p=0.0001), SII (87.5% vs. 83.4%, p=0.0106), and NLR (87.5% vs 83.2%, p=0.0058). Both SII and NLR are independent risk factors for VC while BMI is an independent protective factor. The BMI, SII, and NLR values have the potential to predict VC. The BMI combined with these inflammatory factors can improve the accuracy of prediction.
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