Correlation between the Systemic Immunoinflammatory Index and Platelet–Lymphocyte Ratio in Patients with Adenomyosis

Dong, Yan; Chen, YaHui; Wang, YaNan; Wang, Lin; Zhou, Yan; Xue, Mei; Sun, Lin

doi:https://doi.org/10.1155/2024/9977750

Mediators of Inflammation

On this page

Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Ethical Approval Consent Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 9977750 | https://doi.org/10.1155/2024/9977750

Correlation between the Systemic Immunoinflammatory Index and Platelet–Lymphocyte Ratio in Patients with Adenomyosis

Yan Dong,¹YaHui Chen,²YaNan Wang,²Lin Wang,²Yan Zhou,²Mei Xue,²and Lin Sun¹

Academic Editor: Helen C. Steel

Received03 Mar 2023

Revised30 Nov 2023

Accepted12 Dec 2023

Published17 Jan 2024

Abstract

Background. The chronic inflammatory immune response is a significant factor in the pathogenesis of benign gynecological diseases. The systemic immunoinflammatory index (SII) and the platelet-to-lymphocyte ratio (PLR) are commonly available biomarkers of inflammation. However, evidence of the relationship between SII and PLR in patients with adenomyosis is limited. This study aimed to investigate the relationship between SII and PLR in patients with adenomyosis. Methods. This cross-sectional study included 483 patients with adenomyosis who were first diagnosed at our institution between January 2019 and December 2021. Basic patient clinical information and inflammatory factors were collected for univariate analysis, smoothed curve fitting, and multivariate segmented linear regression. Results. The results of the univariate analysis showed a significant positive correlation between PLR levels and SII (). In addition, a nonlinear relationship between PLR and SII was tested using a smoothed curve fit after adjusting for potential confounders. Multiple segmented linear regression models showed a significant relationship between SII and PLR in both SII < 1,326.47 (β 0.14, 95% CI: 0.12, 0.16; ) and >1,326.47 (β 0.02, 95% CI: −0.01, 0.05; P = 0.2461). Conclusions. In conclusion, this study showed a nonlinear relationship between SII and PLR in patients with uterine adenomyosis. An increase in serum PLR levels correlates with an increase in SII before SII levels reach an inflection point.

1. Introduction

Adenomyosis is a condition in which the endometrium (including glands and mesenchyme) invades the myometrium and grows into the uterus [1]. The occurrence of adenomyosis is related to childbirth, including abortion, curettage, and other trans-uterine operations or surgeries that may cause damage to the endometrium, making the endometrium depressed or invading the myometrium, thus leading to the occurrence of adenomyosis [2, 3]. Related studies have identified a large number of cytokines, chemokines, growth factors, and prostaglandins as key promoters in the initiation, maintenance, and progression of benign gynecological disease, and adenomyosis may also induce local and systemic inflammation [4].

The systemic immunoinflammatory index (SII) is defined as neutrophil count × platelet count/lymphocyte count [5]. SII is a new indicator of systemic immunoinflammation that has been proposed in recent years and is a prognostic indicator for a variety of cancers [6]. High-SII levels may be associated with shorter overall survival in malignancies such as pancreatic, gynecological, and breast cancers [7]. Platelet-to-lymphocyte ratio (PLR) is defined as platelet count/lymphocyte count. PLR may be associated with poor prognosis in some solid tumors such as oesophageal, colorectal, hepatocellular, cervical, breast, and pancreatic cancers [8, 9]. SII and PLR represent the balance between neutrophils, lymphocytes, and platelets [10, 11]. Recently, SII and PLR have received a lot of attention as new indicators of local and systemic immune status [9]. SII levels are of very high-diagnostic value in differentiating the severity of the disease course caused by the SARS-CoV-2 virus [12]. SII levels are highly correlated with the depth of trophoblastic infiltration in tubal ectopic pregnancy and can also be used to predict tubal ectopic pregnancy rupture [13]. Significantly, higher levels of SII and PLR in patients with preeclampsia compared to healthy pregnant women can be used to predict inflammatory status in patients with preeclampsia [14]. However, the relationship between SII and PLR and patients with adenomyosis is unclear. This study aimed to elucidate the relationship between SII and PLR in patients with adenomyosis.

2. Materials and Methods

2.1. Patients and Clinicopathological Data

This single-center cross-sectional study was approved by the Institutional Review Board of the Jining Medical University Hospital (approval number: 2022C114). Due to the retrospective nature of the cohort study, informed consent was not required.

The study population was patients diagnosed with adenomyosis from January 2019 to December 2021 at the Department of Gynecology, Affiliated Hospital of Jining Medical University (Figure 1). Inclusion criteria were as follows: (1) pathologically diagnosed with adenomyosis; (2) well-documented clinical cases. Exclusion criteria were as follows: (1) patients with hematological disorders, malignancies, autoimmune diseases, metabolic diseases, or existing infections; (2) patients treated with hormone, glucocorticoids, permanent immunomodulatory drugs, antithrombotic drugs, or anti-inflammatory drugs in the 3 months before admission; (3) patients under 18 years of age; (4) women in pregnancy and menstruation; (5) history of previous treatment for adenomyosis.

These data did not include identifiable information to protect patient privacy and were obtained from the hospital’s electronic medical record system. A total of 483 patients were enrolled.

2.2. Evaluation of Clinical Characteristics and Determination of Blood Parameters

Data were collected retrospectively from patients, including age, body mass index (BMI), menstrual status, gravidity, parity, uterine size, and routine blood indicators. Anaemia was defined as hemoglobin below 110, 90–110 g/L as mild anemia, 60–90 g/L as moderate anemia, and less than 60 g/L as severe anemia. The history is taken to obtain the patient’s gravidity, parity, menstrual regularity, menstrual volume, and whether the patient is menopausal. Dysmenorrhea is pain and swelling in the lower abdomen around the time of menstruation or during menstruation, accompanied by back pain. Menopause is the permanent termination of physiological menstruation. The criteria for regularity of menstruation was a menstrual regularity of 21–35 days and a period of 4–6 days. Those who met the criteria were considered regular and those who did not were considered irregular. Menstrual volume was judged to be less than 20, 20–80 mL normal, and more than 80 mL excessive. The volume of the uterus is calculated as long diameter× wide diameter × anterior–posterior diameter × π/6. Fasting blood samples were obtained from all subjects for measurement of laboratory parameters: peripheral venous blood was collected after 8 hr of fasting on admission and routine blood was measured using a Sysmex XN2000 hematocrit analyzer, from which we obtained platelet count, lymphocyte count, and neutrophil count for the calculation of SII and PLR. All measurements were performed by the hospital laboratory technicians and examiners. SII was neutrophil count × platelet count/lymphocyte count and PLR was the ratio of platelet to lymphocyte count, using the same blood samples.

2.3. Statistical Analysis

All statistical analyses were performed with R statistical software (https://www.r-project.org) and Empower Stats (http://www.empowerstats.com, X&Y Solutions, Inc. Boston MA). Normally distributed continuous variables are expressed as mean ± standard deviation and nonnormally distributed continuous variables are expressed as median. Categorical variables are expressed as frequencies or percentages. For further analyses, the SII was equally divided into four groups, ranging from the lowest quartile to the highest quartile, A χ² test (for categorical variables), a one-way ANOVA test (for normal distribution), or a Kruskal–Wallis test (for skewed distribution) were used to test for differences between the different SII groups. The whole data analysis process can be divided into two steps. Step 1: a multivariate linear regression model adjusted for patient characteristics and significant variables from the univariate analysis was created. Step 2: a generalized additive model and a smoothed curve fit (penalized spline method) were developed for the nonlinearity of SII and PLR. If nonlinearity is detected, we first calculate the inflection point using a recursive algorithm and then construct a two-segment linear regression on either side of the inflection point. We determined the best-fit model based on the P-value of the log-likelihood ratio test. To ensure the robustness of the data analysis, we performed a sensitivity analysis. We transformed SII into a categorical variable and calculated the P for trend. The aim was to validate the results for SII as a continuous variable and to observe the possibility of nonlinearity. Statistical significance was accepted at a two-sided P-value < 0.05.

3. Results

3.1. Baseline Characteristics of Included Patients

A total of 483 participants were included in the final analysis. Baseline patient characteristics according to SII quartiles are shown in Table 1. The mean age of the patients was 46.39 ± 5.64 y. There were no statistically significant differences between the SII groups for age, BMI, gravidity, menstrual regularity, dysmenorrhea, hypertension, benign adnexal tumor, endometriosis, uterine leiomyoma, and uterine adenomyoma (). Also, there was a significant difference in menstrual volume, uterine volume, uterine length/width/thickness, diabetes, menopause, and anemia (). Participants in the highest SII quartile (Q4) had higher platelet counts, neutrophil counts, and lower lymphocyte count, and hemoglobin.

3.2. Factors Associated with PLR of the Study Population

The results of the univariate analysis are shown in Table 2. Univariate analysis showed that age, BMI, gravidity, parity, menstrual regularity, dysmenorrhoea, menstrual volume, endometriosis, uterine leiomyoma, adenomyoma, benign adnexal tumors, uterine volume, and uterine length/width/thickness were not associated with PLR. In addition, univariate analysis showed that hypertension, diabetes, menopause, anemia, Hemoglobin, and SII were positively associated with PLR ().

3.3. Results of Adjusted Linear Regression

Models for confounding factors were developed using multiple linear regression to analyze the independent effects of SII on PLR, the results are shown in Table 3. SII and PLR were significantly correlated in unadjusted (Model I) and adjusted (Model II and Model III) models. For example, in the unadjusted model (Model I), when SII increased by 1 unit, PLR increased by 0.11 unit (β 0.11, 95% CI: 0.10, 0.12). For further sensitivity analysis, SII was converted from a continuous variable to a categorical variable (SII quartiles). The P-values for the PLR trend in the minimally adjusted and fully adjusted models were consistent with the P-values when SII was a continuous variable.

Adjust II adjusts for age, BMI, gravidity, parity, menstrual regularity, dysmenorrhoea, menstrual volume, uterine leiomyoma, adenomyoma, hypertension, diabetes, endometriosis, benign adnexal tumors, uterine volume, uterine length/width/thickness, hemoglobin and menopause.

Adjust III adjusts for age, BMI, gravidity, parity, menstrual regularity, dysmenorrhoea, menstrual volume, uterine leiomyoma, adenomyoma, hypertension, diabetes, endometriosis, benign adnexal tumors, uterine volume, uterine length/width/thickness, menopause, anemia, and hemoglobin.

3.4. The Results of Nonlinearity of the SII and PLR

As shown in Figure 2, smoothed curve fits were performed after adjusting for the possible confounders including age, BMI, gravidity, parity, menstrual regularity, dysmenorrhoea, menstrual volume, uterine leiomyoma, adenomyoma, hypertension, diabetes, endometriosis, benign adnexal tumors, uterine volume, uterine length/width/thickness, menopause, anemia, and hemoglobin. PLR has a nonlinear relationship with SII. Specifically, PLR levels increased with increasing SII. As shown in Table 4, the threshold effect was further analyzed based on curve fitting, which showed that the inflection point for SII was 1,326.47. When SII was less than 1,326.47, PLR levels increased with increasing SII (β 0.14, 95% CI: 0.12, 0.16; ). When SII was greater than 1,326.47, PLR levels increased with increasing SII (β 0.02, 95% CI: −0.01, 0.05; ), however, there was no statistical significance. Differential linear regression and segmented linear regression were assessed by a log-likelihood ratio test. P-values less than 0.05 implied that the segmented linear function was better for observation; otherwise, the linear function was better. The results indicate that the P for the log-likelihood ratio test is less than 0.05; therefore, the double-segmented linear regression used to fit the association between SII and PLR accurately represents this relationship.

There was an independent correlation between SII and PLR in patients with combined adenomyoma. The smoothed fitted curves for patients with combined adenomyoma are shown in Figure 3.

4. Discussion

This cross-sectional study observed a nonlinear relationship between SII and PLR in patients with adenomyosis, with an SII inflection point of 1,326.47 mg/dL. When SII was less than 1,326.47 mg/dL, there was a significant positive correlation between SII and PLR.

SII, a novel inflammatory response biomarker based on neutrophil, lymphocyte, and platelet counts, integrates the kinetics of NLR and PLR into a single parameter that provides a comprehensive response to the balance between host immunity and inflammatory status [6, 15, 16]. An increasing amount of research has shown that SII is involved in the prognosis of various malignancies, including colorectal, oesophageal, and pancreatic cancers [17, 18]. High levels of SII are significantly associated with the risk of recurrence and with risk of death in a population of patients with early stage ovarian cancer [19]. PLR is a newly recognized marker of inflammation that can be used as a prognostic indicator for a variety of diseases, including COVID-19, endometrial cancer, ovarian cancer, and endometriosis [20–22]. Neutrophils provide biologically active molecules needed in tumor progression through proangiogenesis, and animal studies have shown that neutrophils in mice with endometriosis can induce new lesion formation [23].

A study found that peripheral blood PLR levels were higher in patients with endometrial cancer and in patients with benign endometrial hyperplasia than in the normal group of patients [24, 25]. Activated platelets promote invasion by cell migration and increased collagen production, leading to increased fibrosis in adenomyosis [26, 27]. Animal studies have shown a positive correlation between the degree of ectopic endometrial platelet aggregation and the medium microvascular density of adenomyosis lesions [26]. A study found that platelet count (PLT) was higher in patients with adenomyosis than in patients with uterine smooth muscle tumors [28]. PLT was positively correlated with pelvic adhesions in patients with adenomyosis and combined with CA125 played an important role in the diagnosis and assessment of adhesions in adenomyosis [29, 30]. One study reported that changes in lymphocytes and their associated factors in patients with adenomyosis were associated with the release of proinflammatory mediators, pelvic pain in adenomyosis, and infertility [31, 32].

Recent studies have shown that immune dysregulation and inflammation occur in patients with adenomyosis and that high expression of inflammatory factors and neurogenic mediators in adenomyotic lesions contributes to the persistence and growth of endometrial implants involved in the development and progression of adenomyosis [33–35]. In this study, we analyzed the relationship between SII and PLR in patients with adenomyosis by smoothing curve fitting and observed a nonlinear relationship between SII and PLR, with PLR levels increasing with increasing SII. This finding suggests that SII and PLR can respond to the inflammatory state of the body and that there is also a positive correlation between SII and PLR in patients with combined adenomyosis.

On the other hand, there are several limitations to the current study. First, the nature of the cross-sectional analysis in this study, without a control group, does not allow us to infer a causal relationship, and further investigation is needed to follow changes in SII and PLR to determine whether this relationship is maintained or changed after adenomyosis treatment. Second, our study was conducted on patients diagnosed with adenomyosis and the results cannot be generalized to other populations. Third, as this study was a single-center study and the participants were mainly Chinese Han adult women, future multicenter studies are planned to analyze the relationship between PLR and SII in patients with adenomyosis, and more studies are needed to explain the potential mechanisms between SII and PLR.

5. Conclusion

In conclusion, the present study describes a nonlinear relationship between SII and PLR in patients with adenomyosis after adjusting for the potential confounding factors. An increase in serum PLR levels correlates with an increase in SII before SII levels reach an inflection point. The results will contribute to understanding the relationship between SII and PLR in patients with adenomyosis. However, a causal relationship between SII and PLR could not be established and further studies are needed to determine the possible mechanism.

Data Availability

Data will be made available on request.

Ethical Approval

By the ethical guidelines of the 1975 Declaration of Helsinki, this study was approved by the Institutional Review Board of the Affiliated Hospital of Jining Medical University (approval number: 2022C231).

Informed consent was not required owing to the traceability of the cohort study.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors thank all the staff members at our institution. This study was supported by the Key Research and Development Program Fund of Jining City (2021YXNS072).

References

K. A. Kho, J. S. Chen, and L. M. Halvorson, “Diagnosis, evaluation, and treatment of adenomyosis,” JAMA, vol. 326, no. 2, pp. 177-178, 2021.
View at: Publisher Site | Google Scholar
S. Schrager, L. Yogendran, C. M. Marquez, and E. A. Sadowski, “Adenomyosis: diagnosis and management,” American Family Physician, vol. 105, no. 1, pp. 33–38, 2022.
View at: Google Scholar
S. E. Bulun, S. Yildiz, M. Adli, and J.-J. Wei, “Adenomyosis pathogenesis: insights from next-generation sequencing,” Human Reproduction Update, vol. 27, no. 6, pp. 1086–1097, 2021.
View at: Publisher Site | Google Scholar
A. AlAshqar, L. Reschke, G. W. Kirschen, and M. A. Borahay, “Role of inflammation in benign gynecologic disorders: from pathogenesis to novel therapies dagger,” Biology of Reproduction, vol. 105, no. 1, pp. 7–31, 2021.
View at: Publisher Site | Google Scholar
Q. Song, S.-X. Xu, J.-Z. Wu et al., “The preoperative platelet to neutrophil ratio and lymphocyte to monocyte ratio are superior prognostic indicators compared with other inflammatory biomarkers in ovarian cancer,” Frontiers in Immunology, vol. 14, Article ID 1177403, 2023.
View at: Publisher Site | Google Scholar
P. Ding, H. Guo, C. Sun et al., “Combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) predicts chemotherapy response and prognosis in locally advanced gastric cancer patients receiving neoadjuvant chemotherapy with PD-1 antibody sintilimab and XELOX: a prospective study,” BMC Gastroenterology, vol. 22, Article ID 121, 2022.
View at: Publisher Site | Google Scholar
Y. Huang, Y. Chen, Y. Zhu et al., “Postoperative systemic immune-inflammation index (SII): a superior prognostic factor of endometrial cancer,” Frontiers in Surgery, vol. 8, Article ID 704235, 2021.
View at: Publisher Site | Google Scholar
S. Matsubara, S. Mabuchi, Y. Takeda, N. Kawahara, and H. Kobayashi, “Prognostic value of pre-treatment systemic immune-inflammation index in patients with endometrial cancer,” PLOS ONE, vol. 16, no. 5, Article ID e248871, 2021.
View at: Publisher Site | Google Scholar
X. Han, S. Liu, G. Yang et al., “Prognostic value of systemic hemato-immunological indices in uterine cervical cancer: a systemic review, meta-analysis, and meta-regression of observational studies,” Gynecologic Oncology, vol. 160, no. 1, pp. 351–360, 2021.
View at: Publisher Site | Google Scholar
X. Meng, Q. Chang, Y. Liu et al., “Determinant roles of gender and age on SII, PLR, NLR, LMR and MLR and their reference intervals defining in Henan, China: a posteriori and big-data-based,” Journal of Clinical Laboratory Analysis, vol. 32, Article ID e22228, 2018.
View at: Publisher Site | Google Scholar
H. Ghobadi, J. Mohammadshahi, N. Javaheri, N. Fouladi, Y. Mirzazadeh, and M. R. Aslani, “Role of leukocytes and systemic inflammation indexes (NLR, PLR, MLP, dNLR, NLPR, AISI, SIR-I, and SII) on admission predicts in-hospital mortality in non-elderly and elderly COVID-19 patients,” Frontiers in Medicine, vol. 9, Article ID 916453, 2022.
View at: Publisher Site | Google Scholar
R. K. Gujar, A. Meena, S. S. Chouhan, and K. S. Likhar, “Hematological profiles of COVID-19 patients at the Ratlam district, Madhya Pradesh State, India,” Bioinformation, vol. 17, no. 7, pp. 686–690, 2021.
View at: Google Scholar
K. Dinc and G. Issın, “Novel marker to predict rupture risk in tubal ectopic pregnancies: the systemic immune-inflammation index,” Ginekologia Polska, vol. 94, no. 4, pp. 320–325, 2023.
View at: Publisher Site | Google Scholar
E. Turgut, M. Yildirim, B. Sakcak, S. G. Ayhan, O. M. Tekin, and D. Sahin, “Predicting miscarriage using systemic immune-inflammation index,” The Journal of Obstetrics and Gynaecology Research, vol. 48, no. 3, pp. 587–592, 2022.
View at: Publisher Site | Google Scholar
M. Yuan, F. Ren, and D. Gao, “The value of SII in predicting the mortality of patients with heart failure,” Disease Markers, vol. 2022, Article ID 3455372, 10 pages, 2022.
View at: Publisher Site | Google Scholar
Ç. Turan and N. Metin, “A novel inflammatory marker in the follow-up of moderate-to-severe acne vulgaris administered isotretinoin: systemic immune-inflammation index (SII),” Current Health Sciences Journal, vol. 48, no. 1, pp. 63–67, 2022.
View at: Publisher Site | Google Scholar
M. Winker, S. Stössel, M. A. Neu et al., “Exercise reduces systemic immune inflammation index (SII) in childhood cancer patients,” Supportive Care in Cancer, vol. 30, no. 4, pp. 2905–2908, 2022.
View at: Publisher Site | Google Scholar
K. He, L. Si, X. Pan et al., “Preoperative systemic immune-inflammation index (SII) as a superior predictor of long-term survival outcome in patients with Stage I–II gastric cancer after radical surgery,” Frontiers in Oncology, vol. 12, Article ID 829689, 2022.
View at: Publisher Site | Google Scholar
N. Bizzarri, M. D’Indinosante, C. Marchetti et al., “The prognostic role of systemic inflammatory markers in apparent early-stage ovarian cancer,” International Journal of Clinical Oncology, vol. 28, no. 2, pp. 314–320, 2023.
View at: Publisher Site | Google Scholar
T. D. Çakırca, A. Torun, G. Çakırca, and R. D. Portakal, “Role of NLR, PLR, ELR and CLR in differentiating COVID-19 patients with and without pneumonia.,” International Journal of Clinical Practice, vol. 75, no. 11, Article ID e14781, 2021.
View at: Publisher Site | Google Scholar
T. Muangto, K. Maireang, Y. Poomtavorn et al., “Study on preoperative neutrophil/lymphocyte (NLR) and platelet/lymphocyte ratio (PLR) as a predictive factor in endometrial cancer,” Asian Pacific Journal of Cancer Prevention, vol. 23, no. 10, pp. 3317–3322, 2022.
View at: Publisher Site | Google Scholar
C.-L. Zhang, X.-C. Jiang, Y. Li et al., “Independent predictive value of blood inflammatory composite markers in ovarian cancer: recent clinical evidence and perspective focusing on NLR and PLR,” Journal of Ovarian Research, vol. 16, no. 1, Article ID 36, 2023.
View at: Publisher Site | Google Scholar
F. Guo, Y. He, Y. Fan et al., “G-CSF and IL-6 may be involved in formation of endometriosis lesions by increasing the expression of angiogenic factors in neutrophils,” Molecular Human Reproduction, vol. 27, no. 11, Article ID 27, 2021.
View at: Publisher Site | Google Scholar
G. Acmaz, H. Aksoy, D. Unal et al., “Are neutrophil/lymphocyte and platelet/lymphocyte ratios associated with endometrial precancerous and cancerous lesions in patients with abnormal uterine bleeding?” Asian Pacific Journal of Cancer Prevention, vol. 15, no. 4, pp. 1689–1692, 2014.
View at: Publisher Site | Google Scholar
O. Ilgen, S. Kurt, R. I. Yuzuguldu, O. Ada, and A. Mankan, “Platelet to lymphocyte and neutrophil to lymphocyte ratios in endometrial pathologies,” Ginekologia Polska, vol. 94, no. 4, pp. 269–274, 2023.
View at: Publisher Site | Google Scholar
B. Zhu, Y. Chen, X. Shen, X. Liu, and S.-W. Guo, “Anti-platelet therapy holds promises in treating adenomyosis: experimental evidence,” Reproductive Biology and Endocrinology, vol. 14, no. 1, Article ID 66, 2016.
View at: Publisher Site | Google Scholar
X. Liu, M. Shen, Q. Qi, H. Zhang, and S.-W. Guo, “Corroborating evidence for platelet-induced epithelial–mesenchymal transition and fibroblast-to-myofibroblast transdifferentiation in the development of adenomyosis,” Human Reproduction, vol. 31, no. 4, pp. 734–749, 2016.
View at: Publisher Site | Google Scholar
B. Coskun, O. Ince, S. Erkilinc et al., “The feasibility of the platelet count and mean platelet volume as markers of endometriosis and adenomyosis: a case control study,” Journal of Gynecology Obstetrics and Human Reproduction, vol. 49, no. 10, Article ID 101626, 2020.
View at: Publisher Site | Google Scholar
Q. Lin, T. Li, S. J. Ding, Q. Yu, and X. Zhang, “Anemia-associated platelets and plasma prothrombin time increase in patients with adenomyosis,” Journal of Clinical Medicine, vol. 11, no. 15, Article ID 4382, 2022.
View at: Publisher Site | Google Scholar
C. Jiang, C. Liu, J. Guo et al., “CA125 modified by PLT and NLR improves the predictive accuracy of adenomyosis-derived pelvic dense adhesion,” Medicine, vol. 96, no. 19, Article ID e6880, 2017.
View at: Publisher Site | Google Scholar
J. Horton, M. Sterrenburg, S. Lane, A. Maheshwari, T. C. Li, and Y. Cheong, “Reproductive, obstetric, and perinatal outcomes of women with adenomyosis and endometriosis: a systematic review and meta-analysis,” Human Reproduction Update, vol. 25, no. 5, pp. 593–633, 2019.
View at: Publisher Site | Google Scholar
T. Gui, C. Chen, Z. Zhang et al., “The disturbance of TH17-treg cell balance in adenomyosis,” Fertility and Sterility, vol. 101, no. 2, pp. 506–514, 2014.
View at: Publisher Site | Google Scholar
D. Liu, X. Yin, X. Guan, and K. Li, “Bioinformatic analysis and machine learning to identify the diagnostic biomarkers and immune infiltration in adenomyosis,” Frontiers in Genetics, vol. 13, Article ID 1082709, 2023.
View at: Publisher Site | Google Scholar
M. Bourdon, P. Santulli, L. Doridot et al., “Immune cells and Notch1 signaling appear to drive the epithelial to mesenchymal transition in the development of adenomyosis in mice,” Molecular Human Reproduction, vol. 27, no. 10, Article ID gaab053, 2021.
View at: Publisher Site | Google Scholar
M. R. Orazov, V. E. Radzinsky, E. N. Nosenko, M. B. Khamoshina, A. O. Dukhin, and M. G. Lebedeva, “Immune-inflammatory predictors of the pelvic pain syndrome associated with adenomyosis,” Gynecological Endocrinology, vol. 33, no. sup1, pp. 44–46, 2017.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2024 Yan Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

214

Downloads

141

Citations