ArticleNutrients2022
Biomedical Indicators of Patients with Non-Puerperal Mastitis: A Retrospective Study.
Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Risk factors for non-puerperal mastitis: a meta-analysis.BMC women's health · 2025Pooled it
- Efficacy of red ointment in wound cavity repair following non-puerperal mastitis debridement.Hereditas · 2025Trial
- Local Triamcinolone Injection versus Anti-Tuberculosis Triple Therapy for Non-Puerperal Mastitis: A Comparative Study.International journal of women's health · 2026Article
- Associations of obesity-related mammary intraductal secretions with inflammatory severity, immune dysregulation, and adverse prognosis in non-puerperal mastitis: a retrospective cohort study.Lipids in health and disease · 2025Article
- Mediation effect of peripheral blood inflammatory factors in the association between severity of nonlactating mastitis and glycemic abnormalities.Journal of medical biochemistry · 2025Article
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- Clinical Characteristics of Non-Lactational Mastitis of Accessory Breast: A Case-Series Study.International journal of women's health · 2025Article
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- Article
- Diagnosis of non-puerperal mastitis based on "whole tongue" features: non-invasive biomarker mining and diagnostic model construction.Frontiers in cellular and infection microbiology · 2025Article
- Mechanisms of EMT in the immune microenvironment of plasma cell mastitis.Frontiers in immunology · 2025Review
- Factors influencing therapeutic efficacy of non-lactational mastitis based on hematological inflammatory markers and establishment of a predictive model.Frontiers in medicine · 2025Article
- Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.BMC medical informatics and decision making · 2024Article
- Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators.Journal of inflammation research · 2024Article
- Novel insights: crosstalk with non-puerperal mastitis and immunity.Frontiers in immunology · 2024Review
- Clinical Characteristics of Non-Puerperal Mastitis: A Retrospective Analysis of 724 Patients.International journal of women's health · 2024Article
- Mendelian randomization suggests causal correlations between inflammatory cytokines and immune cells with mastitis.Frontiers in immunology · 2024Article
- Nutrition, Metabolites, and Human Health.Nutrients · 2023Article
- A hematological parameter-based model for distinguishing non-puerperal mastitis from invasive ductal carcinoma.Frontiers in oncology · 2023Article
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Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundNon-puerperal mastitis (NPM), a recurrent chronic inflammation of non-lactating breast, often proves tremendous difficulty in treatment, and it may give rise to its complicated symptoms and unclear etiology. Furthermore, the clinical morbidity rate of NPM has been increasing in recent years.
methodsOverall, 284 patients diagnosed with NPM were consecutively recruited as cases in this study, and patients with benign breast disease (n = 1128) were enrolled as control. The clinical, biomedical, and pathological indicators were analyzed. Univariate and multivariate logistic analysis were used to distinguish risks between NPM and benign breast mass patients. Furthermore, according to the pathological characteristics, the patients of NPM were classified into two subgroups: mammary duct ectasia (MDE) and granulomatous lobular mastitis (GLM). The differences of biomedical indicators between MDE and GLM groups were also analyzed.
resultsCompared with benign breast mass group, the level of high-density lipoprotein (HDL-C) significantly decreased, while lipoprotein(a) (Lp(a)) and blood glucose (GLU) both increased in NPM group. According to univariate and multivariate logistic analysis, the onset age and HDL-C were generally decreased, while Lp(a) and GLU were increased in NPM group. The onset age, HDL-C, Lp(a), and GLU were modeled to distinguish NPM and benign breast mass. Significant differences were also observed between MDE and GLM patients in biomedical indicators, such as lipoprotein(a) (Lp(a)), lactate dehydrogenase (LDH), creatine kinase (CK), total cholesterol (TC), and so on.
conclusionsOur results indicated for the first time that biomarkers were associated with NPM. The biomedical indicators involved in lipid metabolism might be important factors in the development and treatment of NPM. In addition, MDE and GLM are two diseases with different inflammatory states of NPM. These findings would be helpful for a better understanding of NPM and give us some insights to develop new diagnostic and therapeutic strategies.
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