Biomedical Indicators of Patients with Non-Puerperal Mastitis: A Retrospective Study

Novel insights: crosstalk with non-puerperal mastitis and immunity

The two primary types of non-puerperal mastitis (NPM) are granulomatous lobular mastitis (GLM) and plasma cell mastitis (PCM). Existing research indicates that immune inflammatory response is considered to be the core of the pathogenesis of GLM and PCM, and both innate and adaptive immune responses play an important role in the pathophysiology of PCM and GLM. However, the regulatory balance between various immune cells in these diseases is still unclear. Consequently, we present a comprehensive summary of the immune-related variables and recent advances in GLM and PCM.

Evaluation of significantly changed chemokine factors of idiopathic granulomatous mastitis in non-puerperal patients

Idiopathic granulomatous mastitis (IGM), a recurrent inflammation disease of the non-lactating breast, has had an increasing clinical morbidity rate in recent years, and its complicated symptoms and unclear etiology make it challenging to treat. This rare benign inflammatory breast disease, centered on the lobules, represents the most challenging type of non-puerperal mastitis (NPM), also known as non-lactating mastitis. In this study, patients diagnosed with IGM (M, n = 23) were recruited as cases, and patients with benign control breast disease (C, n = 17) were enrolled as controls. Cytokine microarray detection measured and analyzed the differentially expressed cytokine factors between IGM and control patients. Then, we verified the mRNA and protein expression levels of the significantly changed cytokine factors using Q-RT-PCR, ELISA, western blot, and IHC experiments. The cytokine factor expression levels significantly changed compared to the control group. We observed a significant increase between IGM and control patients in cytokine factors expression, such as interleukin-1β (IL-1β), monokine induced by gamma interferon (MIG), macrophage inflammatory protein (MIP)-1α, MIP-1β, tumor necrosis factor receptor 2 (TNF RII). Then, we verified the expression of these top five dysregulated factors in both mRNA and protein levels. Our results demonstrated the cytokine map in IGM and indicated that several cytokines, especially chemokines, were associated with and significantly dysregulated in IGM tissues compared to the control group. The chemokine factors involved might be essential in developing and treating IGM. These findings would be helpful for a better understanding of IGM and offer valuable insights for devising novel diagnostic and therapeutic strategies.

Non-lactational mastitis with multiple sinus wounds treated by integrated traditional Chinese and Western medicine

OBJECTIVE

Non-lactational mastitis (NLM) is a benign inflammatory disease of the mammary gland, with pain, swelling and redness as the main clinical manifestations. There is no unified and effective standard treatment plan for this disease at present. In addition to breast cancer, non-lactational mastitis is also becoming a presenting complaint in an increasing number of outpatients at the authors' clinic. This case report summarises the treatment and management of a 35-year-old female patient with NLM complicated with multiple sinus wounds after surgery.

METHOD

The patient was treated as follows, with: timely debridement according to the local condition of the wound, with manual compression to drain exudate from the sinus wound; selected wound dressings according to their performance and characteristics to fill the sinus tract for drainage and infection control; psychological care of the patient and their family to ensure that patients actively participate in the treatment; family support to the patient to deal with negative emotions; integrated traditional Chinese and Western medicine to prevent/manage infection; dietary care and control; posture management and health education to facilitate the patient's wound healing process.

RESULTS

After local management with systemic treatment and management using integrated traditional Chinese and Western medicine, the wound healed after 46 days, with no recurrence during a follow-up period of one year.

CONCLUSION

As shown in this case report, the wound should be cut and drained as soon as possible in order to prevent obstruction of the sinus drainage. Modern wound dressings are selected for the 'external' treatment of local wounds. Integrated traditional Chinese and Western medicine may help in systemic therapy of the whole patient.

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning

OBJECTIVES

This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan.

METHODS

This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model.

RESULTS

There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features' clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence.

CONCLUSIONS

The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance.

Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators

Background

Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast disease, and abscess formation is a common complication of GLM. The process of abscess formation is accompanied by changes in multiple inflammatory markers. The present study aimed to construct a diagnosis model for the early of GLM abscess formation based on multiple inflammatory parameters.

Methods

Based on the presence or absence of abscess formation on breast magnetic resonance imaging (MRI), 126 patients with GLM were categorised into an abscess group (85 patients) and a non-abscess group (41 patients). Demographic characteristics and the related laboratory results for the 9 inflammatory markers were collected. Logistics univariate analysis and collinearity test were used for selecting independent variables. A regression model to predict abscess formation was constructed using Logistics multivariate analysis.

Results

The univariate and multivariate analysis showed that the N, ESR, IL-4, IL-10 and INF-α were independent diagnostic factors of abscess formation in GLM (P<0. 05). The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.890, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (81.2% and 85.40%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The results of this calibration curve indicated that the model had a good value and stability in predicting abscess formation in GLM. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model.

Conclusion

A predictive model for abscess formation in GLM based on inflammatory markers was constructed in our study, which may provide a new strategy for early diagnosis and treatment of the abscess stage of GLM.

A hematological parameter-based model for distinguishing non-puerperal mastitis from invasive ductal carcinoma

Purpose

Non-puerperal mastitis (NPM) accounts for approximately 4-5% of all benign breast lesions. Ultrasound is the preferred method for screening breast diseases; however, similarities in imaging results can make it challenging to distinguish NPM from invasive ductal carcinoma (IDC). Our objective was to identify convenient and objective hematological markers to distinguish NPM from IDC.

Methods

We recruited 89 patients with NPM, 88 with IDC, and 86 with fibroadenoma (FA), and compared their laboratory data at the time of admission. LASSO regression, univariate logistic regression, and multivariate logistic regression were used to screen the parameters for construction of diagnostic models. Receiver operating characteristic curves, calibration curves, and decision curves were constructed to evaluate the accuracy of this model.

Results

We found significant differences in routine laboratory data between patients with NPM and IDC, and these indicators were candidate biomarkers for distinguishing between the two diseases. Additionally, we evaluated the ability of some classic hematological markers reported in previous studies to differentiate between NPM and IDC, and the results showed that these indicators are not ideal biomarkers. Furthermore, through rigorous LASSO and logistic regression, we selected age, white blood cell count, and thrombin time to construct a differential diagnostic model that exhibited a high level of discrimination, with an area under the curve of 0.912 in the training set and with 0.851 in the validation set. Furthermore, using the same selection method, we constructed a differential diagnostic model for NPM and FA, which also demonstrated good performance with an area under the curve of 0.862 in the training set and with 0.854 in the validation set. Both of these two models achieved AUCs higher than the AUCs of models built using machine learning methods such as random forest, decision tree, and SVM in both the training and validation sets.

Conclusion

Certain laboratory parameters on admission differed significantly between the NPM and IDC groups, and the constructed model was designated as a differential diagnostic marker. Our analysis showed that it has acceptable efficiency in distinguishing NPM from IDC and may be employed as an auxiliary diagnostic tool.

Nutrition, Metabolites, and Human Health

The field of metabolomics and related "omics" techniques allows for the identification of a vast array of molecules within biospecimens [...].

The cause and effect of gut microbiota in development of inflammatory disorders of the breast

BACKGROUND

Inflammatory disorders of the breast (IDB) damages the interests of women and children and hinders the progress of global health seriously. Several studies had offered clues between gut microbiota (GM) and inflammatory disorders of the breast (IDB). The gut-mammary gland axis also implied a possible contribution of the GM to IDB. However, the causality between them is still elusive.

METHODS

The data of two-sample Mendelian randomization (MR) study related to the composition of GM (n = 18,340) and IDB (n = 177,446) were accessed from openly available genome-wide association studies (GWAS) database. As the major analytical method, inverse variance weighted (IVW) was introduced and several sensitive analytical methods were conducted to verify results.

RESULTS

Inverse variance weighted revealed Eubacterium rectale group (OR = 1.87, 95% CI: 1.02-3.43, p = 4.20E-02), Olsenella (OR = 1.29, 95% CI: 1.02-1.64, p = 3.30E-02), Ruminiclostridium-6 (OR = 1.53, 95% CI: 1.08-2.14, p = 1.60E-02) had an anti-protective effect on IDB. Peptococcus (OR = 0.75, 95% CI: 0.60-0.94, p = 1.30E-02) had a protective effect on IDB. The results were credible through a series of test.

CONCLUSIONS

We revealed causality between IDB and GM taxa, exactly including Ruminiclostridium-6, Eubacterium rectale group, Olsenella and Peptococcus. These genera may become novel biomarkers and supply new viewpoint for probiotic treatment. However, these findings warrant further test owing to the insufficient evidences.

LC/MS-based untargeted lipidomics reveals lipid signatures of nonpuerperal mastitis

BACKGROUND

Nonpuerperal mastitis (NPM) is a disease that presents with redness, swelling, heat, and pain during nonlactation and can often be confused with breast cancer. The etiology of NPM remains elusive; however, emerging clinical evidence suggests a potential involvement of lipid metabolism.

METHOD

Liquid chromatography‒mass spectrometry (LC/MS)-based untargeted lipidomics analysis combined with multivariate statistics was performed to investigate the NPM lipid change in breast tissue. Twenty patients with NPM and 10 controls were enrolled in this study.

RESULTS

The results revealed significant differences in lipidomics profiles, and a total of 16 subclasses with 14,012 different lipids were identified in positive and negative ion modes. Among these lipids, triglycerides (TGs), phosphatidylethanolamines (PEs) and cardiolipins (CLs) were the top three lipid components between the NPM and control groups. Subsequently, a total of 35 lipids were subjected to screening as potential biomarkers, and the chosen lipid biomarkers exhibited enhanced discriminatory capability between the two groups. Furthermore, pathway analysis elucidated that the aforementioned alterations in lipids were primarily associated with the arachidonic acid metabolic pathway. The correlation between distinct lipid populations and clinical phenotypes was assessed through weighted gene coexpression network analysis (WGCNA).

CONCLUSIONS

This study demonstrates that untargeted lipidomics assays conducted on breast tissue samples from patients with NPM exhibit noteworthy alterations in lipidomes. The findings of this study highlight the substantial involvement of arachidonic acid metabolism in lipid metabolism within the context of NPM. Consequently, this study offers valuable insights that can contribute to a more comprehensive comprehension of NPM in subsequent investigations.

TRIAL REGISTRATION

Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (Number: 2019-702-57; Date: July 2019).