Prolactin And Non-Puerperal Mastitis: A Cohort Study Using Real-World Data

Prolactin And Non-Puerperal Mastitis: A Cohort Study Using Real-World Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prolactin And Non-Puerperal Mastitis: A Cohort Study Using Real-World Data yulian yin, Haoxin Le, Yifan Cheng, Yuanyuan Zhong, Yiqin Cheng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3919363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Objective Non-puerperal mastitis (NPM) is an umbrella term for non-specific inflammatory mastitis inflammation with unclear etiology. The objective of the current study is to characterize NPM patients and examine the determinants associated with NPM severity. Method This study analyzed the NPM inpatients admitted to the Department of Breast Surgery, Longhua Hospital Affiliated with Shanghai University of Traditional Chinese Medicine from 2016 to 2020. We explored NPM patient characteristics through demographics, physical risks, lab tests, and medical history indicators. Multivariable logistic regression was conducted to identify the relationship between the prolactin (PRL) level and NPM severity stratified by breast structure. Result The majority of NPM inpatients had normal breast structures and were of lower average age than those with congenital nipple deformity (p = 0.002). Significant positive risk (p < 0.001) association between PRL level and NPM severity was observed among NPM inpatients with normal breast structure in both crude and adjusted model(adjusted OR: 2.91; 95%CI:1.88–4.52), with age as a protective factor (OR:0.94; 95%CI:0.91–0.97) and smoking history as a risk factor(OR:2.22; 95%CI:1.22–4.05). For NPM patients with nipple deformity, increasing odds of NPM severity regarding higher PRL level was observed while the result is not statistic significant at 0.05 level. (OR: 2.17; 95%CI: 0.94–5.03; p = 0.076). Conclusion The risk of NPM severe episodes is higher among patients with higher PRL levels, of which the association is stronger for NPM patients with normal breast structure, implying different pathogenesis between NPM patients with varied breast anatomy. Disagreement of the interaction effect testification indicates an improvement window for current study. Trial registration ChiCTR2000035929. Registered 20 August 2020(retrospectively registered). Biological sciences/Immunology/Immunological disorders Biological sciences/Immunology/Inflammation Biological sciences/Biological techniques/Biophysical methods Health sciences/Diseases/Metabolic disorders Prolactin Non-puerperal Mastitis Real-world Data Risk Factors Binomial Logistic Regression Figures Figure 1 Introduction Non-puerperal mastitis (NPM), defined as a general term for non-specific inflammatory mastitis diseases, mostly involves granulomatous lobular mastitis, ductal dilation, and periductal mastitis [ 1 ] . As the largest subgroup in chronic mastitis, NPM usually occurs in young and middle-aged women who are not lactating or pregnant, and its etiology remains unclear [ 2 , 3 ] . The typical clinical symptom of NPM is breast lumps presenting with pain and erythema. Most patients have pus and abscess successively rupturing in different areas of the breast, which could eventually result in mammary fistulas, sinus tracts, and ulcers [ 1 ] . Some patients could be misdiagnosed as breast cancer, because of the concomitant symptoms such as persistent fever, erythema nodosum, arthritis, and paroxysmal cough [ 4 ] . It is also difficult to define NPM in radiology diagnosis. The ultrasonography image of NPM is analogous to breast cancer, indicating lobulated irregular masses, with duct ectasia and interstitial septa between abscess cavities. Presentations of enhanced MRI among NPM patients with inflammation and abscess are also similar to breast cancer, including regional enhancement in patchy foci of the areola area and circumferential abscess [ 4 ] . Mammography is unrecommended for the clinical diagnosis of NPM not only because of the limitation in diagnostic significance concerning the image with dense and substantial mass [ 5 ] but also because of the particular procedure of mammography which may add an external force stimulus [ 6 ] to the inflammatory breast. Although there is no agreement on the risk factors of NPM so far, it is acknowledged that several factors could increase the risk of the onset and the recurrence of NPM, including obesity [ 7 ] , nipple deformity [ 8 , 9 ] , lifestyle, specific medications utilization, dietary pattern, and occupation [ 10 , 11 ] . Other potential factors include histories of ductal dilatation, nipple discharge, breast and cranial trauma [ 12 ] . In addition, blockage of milk ducts and the onset of acute mastitis during pregnancy and lactation, are relevant to the higher risk of NPM as well [ 13 ] . However, no previous study demonstrated solid evidence of the causal association between these factors and NPM. Current treatments of NPM include oral treatment and surgery. For oral treatment, the main pharmacological treatments are antibiotics, steroids [ 14 , 15 ] , methotrexate, and traditional Chinese medicine [ 16 ] . However, as the pathogen of NPM is not clear, most oral treatments lack valid evidence of efficacy. Although there are varieties of surgical treatments, such as abscess drainage, extensive local excision, and mastectomy, no recommendation on the best treatment for NPM [ 17 ] because of the long healing time of surgery incisions [ 8 , 18 , 19 ] . Previous studies reported a wide range of the recurrence rates of NPM, which were from 5–50% after the surgery procedure [ 20 , 21 ] , without a clear conclusion about the risk factors related to recurrence [ 22 , 23 ] . Although previous studies showed the long-lasting condition of NPM and the potential repeated breast surgeries could negatively affect the physical and mental health of patients with higher medical costs [ 24 ] , few studies have provided a comprehensive description of severe NPM episodes. Most of the previous studies are case reports and are limited in sample size, data quality, valid study design, and analysis [ 3 ] , and only described the characteristics of NPM [ 25 , 26 ] . Some evidence illustrated that increasing prolactin (PRL) level is highly related to NPM reoccurrence [ 27 , 28 ] , but few studies focused on the association between PRL level and NPM severity of first time onset, or further investigated risk factors among NPM patients who need surgical treatment. This observational study intends to explore an inclusive understanding of NPM by applying electronic medical record (EMR) data. The primary objective is to map the characteristics of NPM patients who are first time onset and need surgical treatment. The secondary objective is to investigate the association between PRL level and NPM severity and clarify if there were other potential determinants related to NPM severity. We hypothesize that higher PRL level increases the risk of NPM severity compared to normal to low PRL level. Methods Data source and sample This observational study included the diagnosis information and medical history data from EMRs of NPM patients admitted to the Department of Breast Surgery, Longhua Hospital Affiliated with Shanghai University of Traditional Chinese Medicine for operation from Jan.2016 to Dec.2020. All included NPM patients received surgery after they were diagnosed as N61.x01(mammary inflammatory granuloma), N61.x02 (mammary duct fistula), N61.x06 (plasma cell mastitis), and N60.400 (mammary duct ectasia) using GB/T14396-2016 Classification and Code of Diseases (ICD-10 of China Version), and additionally with following specific texture in pathology diagnosis report: granuloma formation, duct dilatation, periductal inflammation, and fistula. All data are collected at the time of admission. For those inpatients with multiple admissions during the period, this study only counted them one time and used the data at the first admission. 7 patients who have two-side onset are excluded. To ensure the validity of missing data imputation, 8 patients who had missing values in glucose (GLU), triglycerides (TG), total cholesterol (tCHOL), testosterone (TE), and follicle-stimulating hormone at the same time are excluded since these values are violate to random missing assumption. This study was approved by the Medical Ethics Committee of Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine(approval no.2021LCSY047). Written informed consent of personal data collection and utilization was obtained from all individual participants included in the study.All information collected was kept private and confidential.Everyone who participated had the option to withdraw at any moment. This study adhered to the declaration of Helsinki. All methods were performed in accordance with relevant guidelines and regulations.The study registration number is ChiCTR2000035929. Measures Exposure: Prolactin (PRL) level We used the blood test lab result of PRL at the admission of each patient as primary exposure. The clinical normal level of PRL is from 108.8～557.1 mIU/ L. We define patients with PRL higher than 557.1 mIU/ L as the high-level PRL group, and the rest of them as the low-normal PRL group. Outcome: NPM severity assessment score Currently, there is no official diagnostic standard for the severity of NPM in China. Hence, we created a unified scoring measurement for daily clinical practice to assess NPM severity through clinical manifestations of patients, the imaging results of the ultrasound, and breast enhancement MRI. The lesion of a quadrant of the breast and main mammary accumulate to the total score for each patient, with a scale of 1 to 5 points (Attached file: Scoring scale of NPM severity). Each patient was assessed back-to-back by two attending physicians engaged in clinical work for more than five years. If the scores of the two attending physicians are different, a chief physician with more than 10 years of working experience will conduct the re-assessment to reach an agreeable conclusion. Covariates/Confounders This study included demographics, physical risks, lab tests, and medical history indicators in the multivariable logistic regression model based on the conclusions from previous studies [1,2,8-10] . In this study, demographics included age and smoking status (have a smoking history or not). Physical risks include two variables: nipple deformity and external force.The definition of nipple deformity is congenital nipple invagination (NI), in that the nipple is born with not protruding from the areola plane, or even contracting and rolling into a crater shape, which is also known as a crater nipple. The information about external forces is collected through the inquiry. The questions include “Was there an accidental impact on the breast”, “Is the patient used to wearing a tight bra”, and “Is there an external pressure check (i.e. mammography)”. We further asked the patient to investigate whether the external force was relevant to the latest episode or worsening the disease. Lab test and medical history indicators include four variables: GLU level (High level if GLU higher than 6.1 mmol/L), Alanine aminotransferase (ALT) level(High level if ALT higher than 45 IU/L), TE level(High level if TE higher than 1.67 nmol/L), and hyperlipidemia(HLP) status (Yes if TG>= 1.7 mmol/L or tCHOL>=5.72 mmol/L or positive in the previous history of hyperlipidemia or fatty liver disease). Statistical analysis Missing data imputation This study assumed that the missing completely at random (MCAR) and applied Multiple Imputation by Chained Equations (MICE) for missing data imputation. We conducted predictive mean matching (pmm) in MICE since variables need imputation are quantitative and with missing data of less than 5% in total. The algorithm was set to run for 50 iterations and presented us with 5 imputations for each missing datum. GLU is the variable that has the highest missing proportion (31 missing in 674 patients). Figure 1 shows the distribution of the imputed dataset in red is similar to the observed data in blue. Data Analysis The current study used the t-test for numerical variables and the ANOVA tests for categorical variables and reported the distribution of demographics, physical risks, lab tests, medical history indicators, and other characteristics stratifying patients into with and without nipple deformity. This study conducted logistic regression among NPM patients without pituitary tumor history. We defined the NPM severity assessment score into reference level (1-3 points) and higher lever (4-5 points). The binomial regression model was applied separately in patients with and without nipple deformity. For the patient with nipple deformity, the models excluded TE level as a covariant because of the limited sample size. The current study defined the significant level at a p-value less than 0.05 and reported all the results in both regression models. We used R studio version 4.0.3 (2020-10-10) for data analysis and conducted missing data imputation with the mice package [11] . Results This study included 674 NPM inpatients admitted from 2016 to 2020 in demographic analysis and 653 of them without pituitary tumor history for logistic regression. Table 1 presents showed the characteristics of NPM patients by PRL level between nipple deformity subgroups. The majority of NPM inpatients are not born with nipple inversion. Among NPM inpatients without normal breasts, those who have high PRL levels are more likely to have a pituitary tumor history (p= 0.016). Table 1. Comparison of NPM patient characteristics between PRL level stratified by breast deformity With NI Without NI P 3 PRL Low-Normal PRL High P 1 PRL Low-Normal PRL High P 2 Total 140 46 342 146 Demograpiic age (mean (SD)) 34.28 (9.80) 32.30 (7.30) 0.211 31.86 (5.58) 32.14 (5.32) 0.614 0.002 smoking_flg (n(%)) no 127 ( 90.7) 37 ( 80.4) 0.107 305 (89.2) 125 ( 85.6) 0.336 1 yes 13 ( 9.3) 9 ( 19.6) 37 (10.8) 21 ( 14.4) Physical risk crash_flg (n(%)) no 58 ( 41.4) 15 ( 32.6) 0.374 162 (47.4) 67 ( 45.9) 0.841 0.088 yes 82 ( 58.6) 31 ( 67.4) 180 (52.6) 79 ( 54.1) Lab test and medical history glycimia_grp (n(%)) Low-Normal 134 ( 95.7) 45 ( 97.8) 0.836 326 (95.3) 133 ( 91.1) 0.11 0.351 High 6 ( 4.3) 1 ( 2.2) 16 ( 4.7) 13 ( 8.9) ALT_grp (n(%)) Low-Normal 132 ( 94.3) 45 ( 97.8) 0.565 319 (93.3) 131 ( 89.7) 0.248 0.24 High 8 ( 5.7) 1 ( 2.2) 23 ( 6.7) 15 ( 10.3) TEabnormal_flg (n(%)) no 133 ( 95.0) 42 ( 91.3) 0.574 326 (95.3) 142 ( 97.3) 0.459 0.424 yes 7 ( 5.0) 4 ( 8.7) 16 ( 4.7) 4 ( 2.7) HLP_flg_fin (n(%)) no 77 ( 55.0) 29 ( 63.0) 0.433 216 (63.2) 83 ( 56.8) 0.227 0.354 yes 63 ( 45.0) 17 ( 37.0) 126 (36.8) 63 ( 43.2) Other tumor_flg (n(%)) no 133 ( 95.0) 40 ( 87.0) 0.128 340 (99.4) 140 ( 95.9) 0.016 0.001 yes 7 ( 5.0) 6 ( 13.0) 2 ( 0.6) 6 ( 4.1) relapse_flg (n(%)) no 138 ( 98.6) 44 ( 95.7) 0.55 331 (96.8) 146 (100.0) 0.063 1 yes 2 ( 1.4) 2 ( 4.3) 11 ( 3.2) 0 ( 0.0) abortion_flg (n(%)) no 139 ( 99.3) 46 (100.0) 1 338 (98.8) 144 ( 98.6) 1 0.714 yes 1 ( 0.7) 0 ( 0.0) 4 ( 1.2) 2 ( 1.4) HMsupply_flg (n(%)) no 140 (100.0) 45 ( 97.8) 0.557 339 (99.1) 143 ( 97.9) 0.527 0.714 yes 0 ( 0.0) 1 ( 2.2) 3 ( 0.9) 3 ( 2.1) THabnormal_flg (n(%)) no 138 ( 98.6) 46 (100.0) 1 335 (98.0) 144 ( 98.6) 0.887 0.716 yes 2 ( 1.4) 0 ( 0.0) 7 ( 2.0) 2 ( 1.4) mental_flg (n(%)) no 139 ( 99.3) 46 (100.0) 1 340 (99.4) 143 ( 97.9) 0.324 0.886 yes 1 ( 0.7) 0 ( 0.0) 2 ( 0.6) 3 ( 2.1) 1. P-value of the chi-squared test and ANOVA test between different PRL level among patient with nipple inversion 2. P-value of the chi-squared test and ANOVA test between different PRL level among patient with normal breast structure 3. P-value of the chi-squared test and ANOVA test between different breast structure condition 4. Notes: g lycimia_grp : High level if GLU higher than 6.1 mmol/L, and others belong to Low-Normal level. ALT_grp : High level if ALT higher than 45 IU/L. HLP_flg_fin : Yes if TG>= 1.7 mmol/L or tCHOL>=5.72 mmol/L or positive in the previous history of hyperlipidemia or fatty liver disease. tumor_flg : Patients diagnosed as pituitary microadenomas were Yes. relapse : Yes if the patient recurs during the follow-up period. abortion : Patients who have had a history of abortion within six months before onset were judged as Yes. HMsupply_flg : Patients had hormone supplements within six months before onset were judged as Yes. THabnormal_flg : Patients diagnosed with a history of thyroid disease were judged as Yes. mental_flg : Patients diagnosed with a history of mental illness were judged as Yes. Table 2 implied that the severity degree and the characteristics are different between NPM patients with and without nipple deformity. The distribution of patients in severity level varied between NPM patients with normal nipple structure and those are nipple deformity (p<0.001). NPM patients with normal breasts are less likely to have a pituitary tumor (p= 0.001). In addition, NPM patients with normal breast structure are more likely to be younger than those with congenital nipple deformity in average (p= 0.002 in Table 2), which is not significantly within the corresponding nipple structure subgroups (p=0.211 among with NI and p=0.614 among without NI in Table1). However, the interaction effect between PRL level and nipple deformity in logistic regression model is not statistically significant at 0.05 level (Table S1). Table 2. Comparison of NPM patients with and without breast deformity (only show the covariant with p<0.05) Nipple inversion NO Nipple Inversion P n 186 488 PRL_flg (%) Low-Normal 140 (75.3) 342 (70.1) 0.216 High 46 (24.7) 146 (29.9) ass_outcome (%) level1 41 (22.0) 40 ( 8.2) <0.001 level2 60 (32.3) 100 (20.5) level3 46 (24.7) 211 (43.2) level4 29 (15.6) 100 (20.5) level5 10 ( 5.4) 37 ( 7.6) age (mean (SD)) 33.79 (9.27) 31.94 (5.50) 0.002 tumor_flg (%) no 173 (93.0) 480 (98.4) 0.001 yes 13 ( 7.0) 8 ( 1.6) Notes: ass_outcome(%) : Based on the severity scoring system adopted in this study, the proportion of patients with different scores. tumor_flg : Patients diagnosed as pituitary microadenomas were Yes. The odds of NPM severity for patients with higher PRL levels varied in unadjusted and adjusted OR (Table 3) between the stratified nipple deformity subgroups. Among NPM patients without congenital nipple inversion, those with higher PRL levels had significantly higher odds of more severe NPM episodes both in unadjusted (OR: 2.93; 95%CI:1.91-4.47; p< 0.001) and adjusted (OR: 2.91; 95%CI:1.88-4.52; p< 0.001). Besides, age is a protective factor for NPM severity (OR:0.95; 95%CI: 0.91-0.99; p=0.012) while smoking history increased the odds of more severe NPM symptoms (OR: 2.22; 95%CI:1.22-4.05; p=0.011) in patients with normal breast structure. For NPM patients with congenital nipple inversion, there is no statistical difference in the risk of NPM severity between the patient who had higher PRL or other potential risk factors. Table 3. Crude and adjusted OR stratified by breast structure Nipple Inversion NO Nipple Inversion OR.95.CI. table.P.LR.test. OR.95.CI. table.P.LR.test. PRL_flg..High.vs.Low.Normal(crude OR) 2.16 (0.96,4.89) 0.07 2.93 (1.91,4.47) < 0.001 PRL_flg..High.vs.Low.Normal(adjusted OR) 2.17 (0.94,5.03) 0.076 2.91 (1.88,4.52) < 0.001 age..cont..var.. 0.97 (0.92,1.02) 0.176 0.95 (0.91,0.99) 0.012 glycimia_grp..High.vs.Low.Normal 0.93 (0.09,9.24) 0.95 2.13 (0.94,4.82) 0.076 ALT_grp..High.vs.Low.Normal 1.52 (0.27,8.44) 0.642 0.56 (0.23,1.36) 0.184 HLP_flg_fin..yes.vs.no 1.41 (0.61,3.26) 0.419 1.17 (0.75,1.84) 0.486 smoking_flg..yes.vs.no 0.85 (0.25,2.85) 0.784 2.22 (1.22,4.05) 0.011 crash_flg..yes.vs.no 1.63 (0.71,3.7) 0.24 1.11 (0.73,1.7) 0.621 TEabnormal_flg..yes.vs.no / / 1.24 (0.44,3.45) 0.685 Discussion This study discovered that the risk of NPM severity is higher among patients with higher PRL levels. NPM severity distribution and the effects of risk factors varied between NI and non-NI patients, implying the potential interaction effect between PRL level and nipple deformity. This is the first study that investigated the relationship between PRL level and NPM severity considering confounders including nipple deformity, age, smoking, external force, and autoimmune with feasible sample size. The conclusion is agreeable to previous studies that the effect of these factors differed from the breast structure [ 13 , 30 – 32 ] . The results illustrate that NPM patients with nipple deformity are more likely to receive milder assessment results (p < 0.001), which is consistent with previous studies assuming that the reason for restricted lesions among these patients is associated with squamous metaplasia of lactiferous ducts that link to subareolar abscesses [ 33 ] . However, since this is an observational cohort study, the sample size of the non-NI subgroup is about 2.5 times of the NI subgroup. The restriction and imbalance in patient size in analysis could lead to several potential bias. First, we failed to include other potential risk factors in the regression model because of the restricted patient number. Previous evidence demonstrated that incidence of relapse condition is higher among patients who received simple segmental mastectomy [ 34 ] . Other evidence showed that hormonal contraceptive utilization, breastfeeding, abortion, hormone supplements, thyroid abnormalities, and mental health conditions [ 10 , 11 , 23 , 35 ] are also potential confounders that related to NPM. This study did not include these factors in the analysis because patient number of corresponding potential confounders were too small to conduct valid analysis. For example, there are only 2 patients have hormonal contraceptive use history and 13 patients with breastfeeding history. Other confounders that were excluded in analysis were listed in Table 1 as in “Other” category. Second, this study was not able to clarify the interaction effect between PRL and nipple deformity associated to NPM severity because of the imbalance sample size between nipple deformity subgroups. Although we found the difference in OR after stratification which implied that nipple deformity may be an effect measure modification (EMM), the result was not statistically significant in OR when directly using interaction term in logistic model. Because of the disagreement between two approaches of EMM testification, in addition to that the causal association between breast structure and NPM severity is not clear, further study is needed to verify the interaction effect between PRL and nipple deformity, and whether the interaction is additive or multiplicative. Current study only observed the effects of age and smoking among NPM patients without NI, both of which agree with NPM clinical features and previous studies although those studies did not stratify the NPM patients by breast duct structure [ 30 , 35 ] . Age is protective in both nipple deformity groups, despite that the result of NPM patients with nipple deformity group was not statistically significant at 0.05 level. We inferred that the protective effect could relate to the lower PRL level since PRL level will decrease as the age increases, which further influenced the NPM severity. The reason of different statistic significancy could associate with imbalance in patient size between nipple deformity subgroups. Another concern of current study is the validity of NPM severity assessment scoring system. Although the measurement of NPM severity is based on imaging results, clinical manifestations and physician judgement [ 36 , 37 ] , it is still limited in potential inter-observer variability and scoring system reliability, and the measurement form is not widely used as guideline [ 1 , 38 ] . Although current study re-categorized the severity level into two group for comparison to increase the internal validity, further investigation is needed to assess the validity of scoring system and how the limitation of scoring system influences the result. Finally, this cohort study only demonstrated a snapshot of the un-causal relationship between PRL level and NPM severity among patient with severe case and need surgical treatment. Future research that emphasizes on longitudinal studies and randomized clinical studies are needed, to clarify causal relationship between relevant factors regarding different NPM outcome. Conclusion As an umbrella term for non-specific inflammatory mastitis diseases without a certain conclusion of etiology, it is critical to investigate risk factors of NPM severity that may be relevant to the pathogenesis. This study found an association between NPM severity and the interaction of breast structure and PRL level. Further discussion focusing on the improvement of the current NPM severity assessment is necessary. Declarations Data availability statement The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the first author Yulian Yin ( [email protected] ). Ethics, consent and permissions statement The Medical Ethics Committee of Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine approved this research(2021LCSY047). Consent to publish We have obtained consent to publish from the participant (or legal parent or guardian for children) to report individual patient data. Author contributions Haoxin Le and Yulian Yin: conceptualization,formal analysis, visualization and writing-review & editing. Haoxin Le: methodology and software. Yulian Yin and Yifan Cheng: writing—original draft. Yulian Yin and Yuanyuan Zhong:data curation and investigation. Yinqin Cheng,Bing Wang,Jingjing Wu: resources. Meina Ye and Hongfeng Chen:project administration and supervision. Yulian Yin and Hongfeng Chen: funding acquisition. All authors contributed to the article and approved the submitted version. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding National Natural Science Foundation of China (No.82104854); The second major clinical research project of "Three-year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals (2020-2022)" (SHDC2020CR2051B);Shanghai Sailing Program (20YF1449800) ; References Zhou F, Shang XC, Tian XS, Yu ZG. Clinical practice guidelines for diagnosis and treatment of patients with non-puerperal mastitis: Chinese Society of Breast Surgery (CSBrS) practice guideline 2021. Chin Med J (Engl). 2021 May 19;134(15):1765-7. doi: 10.1097/cm9.0000000000001532. Kamal RM, Hamed ST, Salem DS. Classification of inflammatory breast disorders and step by step diagnosis. Breast J. 2009 Jul-Aug;15(4):367-80. doi: 10.1111/j.1524-4741.2009.00740.x. Tan H, Li R, Peng W, Liu H, Gu Y, Shen X. Radiological and clinical features of adult non-puerperal mastitis. Br J Radiol. 2013 Apr;86(1024):20120657. doi: 10.1259/bjr.20120657. Zhang L, Hu J, Guys N, Meng J, Chu J, Zhang W, et al. 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Treatments for Periductal Mastitis: Systematic Review and Meta-Analysis. Breast Care (Basel). 2022 Feb;17(1):55-62. doi: 10.1159/000514419. Chirappapha P, Thaweepworadej P, Supsamutchai C, Biadul N, Lertsithichai P. Idiopathic granulomatous mastitis: A retrospective cohort study between 44 patients with different treatment modalities. Ann Med Surg (Lond). 2018 Dec;36:162-7. doi: 10.1016/j.amsu.2018.11.001. Shin YD, Park SS, Song YJ, Son SM, Choi YJ. Is surgical excision necessary for the treatment of Granulomatous lobular mastitis? BMC Womens Health. 2017 Jul 24;17(1):49. doi: 10.1186/s12905-017-0412-0. Lei X, Chen K, Zhu L, Song E, Su F, Li S. Treatments for Idiopathic Granulomatous Mastitis: Systematic Review and Meta-Analysis. Breastfeed Med. 2017 Sep;12(7):415-21. doi: 10.1089/bfm.2017.0030. Patel RA, Strickland P, Sankara IR, Pinkston G, Many W, Jr., Rodriguez M. Idiopathic granulomatous mastitis: case reports and review of literature. J Gen Intern Med. 2010 Mar;25(3):270-3. doi: 10.1007/s11606-009-1207-2. Co M, Cheng VCC, Wei J, Wong SCY, Chan SMS, Shek T, et al. Idiopathic granulomatous mastitis: a 10-year study from a multicentre clinical database. Pathology. 2018 Dec;50(7):742-7. doi: 10.1016/j.pathol.2018.08.010. Uysal E, Soran A, Sezgin E. Factors related to recurrence of idiopathic granulomatous mastitis: what do we learn from a multicentre study? ANZ J Surg. 2018 Jun;88(6):635-9. doi: 10.1111/ans.14115. Qiu Q, Shen X. Application of multidisciplinary comprehensive diagnosis and treatment model in the treatment of patients with complex and refractory non-lactation mastitis. Guangxi Medical Journal. 2019 ;41(21). doi: 10.11675/j.issn.0253-4304.2019.21.25. Smith E, Moore DA, Jordan SG. You'll see it when you know it: granulomatous mastitis. Emerg Radiol. 2021 Dec;28(6):1213-23. doi: 10.1007/s10140-021-01931-4. Al-Khaffaf B, Knox F, Bundred NJ. Idiopathic granulomatous mastitis: a 25-year experience. J Am Coll Surg. 2008 Feb;206(2):269-73. doi: 10.1016/j.jamcollsurg.2007.07.041. Huang Y, Wu H. A retrospective analysis of recurrence risk factors for granulomatous lobular mastitis in 130 patients: more attention should be paied to prolactin level. Ann Palliat Med. 2021 Mar;10(3):2824-31. doi: 10.21037/apm-20-1972. Tian C, Wang H, Liu Z, Han X, Ning P. Characteristics and Management of Granulomatous Lobular Mastitis Associated with Antipsychotics-Induced Hyperprolactinemia. Breastfeed Med. 2022 Jul;17(7):599-604. doi: 10.1089/bfm.2021.0341. https://datascienceplus.com/imputing-missing-data-with-r-mice-package/ Gollapalli V, Liao J, Dudakovic A, Sugg SL, Scott-Conner CE, Weigel RJ. Risk factors for development and recurrence of primary breast abscesses. J Am Coll Surg. 2010 Jul;211(1):41-8. doi: 10.1016/j.jamcollsurg.2010.04.007. Sheybani F, Naderi HR, Gharib M, Sarvghad M, Mirfeizi Z. Idiopathic granulomatous mastitis: Long-discussed but yet-to-be-known. Autoimmunity. 2016 Jun;49(4):236-9. doi: 10.3109/08916934.2016.1138221. Ciftci AB, Bük Ö F, Yemez K, Polat S, Yazıcıoğlu İ M. Risk Factors and the Role of the Albumin-to-Globulin Ratio in Predicting Recurrence Among Patients with Idiopathic Granulomatous Mastitis. J Inflamm Res. 2022 Sep;15:5401-12. doi: 10.2147/jir.S377804. Serrano LF, Rojas-Rojas MM, Machado FA. Zuska's breast disease: Breast imaging findings and histopathologic overview. Indian J Radiol Imaging. 2020 Jul-Sep;30(3):327-33. doi: 10.4103/ijri.IJRI_207_20. Hur SM, Cho DH, Lee SK, Choi MY, Bae SY, Koo MY, et al. Experience of treatment of patients with granulomatous lobular mastitis. J Korean Surg Soc. 2013 Jul;85(1):1-6. doi: 10.4174/jkss.2013.85.1.1. Al-Chalabi M, Bass AN, Alsalman I. Physiology, Prolactin. 2023 Jul 24. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan–. PMID: 29939606. Chu AN, Seiler SJ, Hayes JC, Wooldridge R, Porembka JH. Magnetic resonance imaging characteristics of granulomatous mastitis. Clin Imaging. 2017 May-Jun;43:199-201. doi: 10.1016/j.clinimag.2017.03.012. Kayadibi Y, Ucar N, Akan YN, Kaya MF, Yildirim E, Kurt SA, et al. Magnetic resonance imaging findings associated with recurrence in idiopathic granulomatous mastitis. Clin Imaging. 2022 Apr;84:47-53. doi: 10.1016/j.clinimag.2022.01.010. Yu HJ, Wang Q, Yang JM, Lian ZQ, Zhang AQ, Li WP, et al. [Anti-mycobacteria drugs therapy for periductal mastitis with fistula]. Zhonghua Wai Ke Za Zhi. 2012 Nov;50(11):971-4. doi: 10.3760/cma.j.issn.0529-5815.2012.11.003. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Appendix1ScoringscaleofNPMseverity.docx Figure1technicalsupply.docx Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Jan, 2025 Reviews received at journal 24 Oct, 2024 Reviews received at journal 17 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviews received at journal 13 Sep, 2024 Reviewers agreed at journal 13 Sep, 2024 Reviews received at journal 26 May, 2024 Reviewers agreed at journal 24 May, 2024 Reviews received at journal 28 Feb, 2024 Reviewers agreed at journal 28 Feb, 2024 Reviewers invited by journal 27 Feb, 2024 Editor assigned by journal 27 Feb, 2024 Editor invited by journal 17 Feb, 2024 Submission checks completed at journal 17 Feb, 2024 First submitted to journal 01 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Red and blue density lines are similar, indicating plausible blood glucose estimates.)\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3919363/v1/d40c884b15532252073cfd45.png"},{"id":78689041,"identity":"7f6801e8-9f23-44e0-b6b2-4c8d3023527d","added_by":"auto","created_at":"2025-03-17 16:10:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3919363/v1/2b7788f6-06d9-4df0-8b33-880699c0b7d1.pdf"},{"id":51397781,"identity":"f0241e80-1226-43c6-8234-5e36f3302496","added_by":"auto","created_at":"2024-02-20 20:44:40","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13312,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3919363/v1/4fa78263d9a12e7a2d9061dd.docx"},{"id":51397780,"identity":"06579f2b-9400-4afe-a4f4-bc69eb525c91","added_by":"auto","created_at":"2024-02-20 20:44:40","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11082,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1ScoringscaleofNPMseverity.docx","url":"https://assets-eu.researchsquare.com/files/rs-3919363/v1/ed3b3f0d1c6140a8efd7eb01.docx"},{"id":51397784,"identity":"a9d04565-95ae-435a-89ef-657f14109af1","added_by":"auto","created_at":"2024-02-20 20:44:41","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4248757,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1technicalsupply.docx","url":"https://assets-eu.researchsquare.com/files/rs-3919363/v1/8a94708b5db8758021fbac0b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prolactin And Non-Puerperal Mastitis: A Cohort Study Using Real-World Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-puerperal mastitis (NPM), defined as a general term for non-specific inflammatory mastitis diseases, mostly involves granulomatous lobular mastitis, ductal dilation, and periductal mastitis \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As the largest subgroup in chronic mastitis, NPM usually occurs in young and middle-aged women who are not lactating or pregnant, and its etiology remains unclear \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe typical clinical symptom of NPM is breast lumps presenting with pain and erythema. Most patients have pus and abscess successively rupturing in different areas of the breast, which could eventually result in mammary fistulas, sinus tracts, and ulcers \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Some patients could be misdiagnosed as breast cancer, because of the concomitant symptoms such as persistent fever, erythema nodosum, arthritis, and paroxysmal cough \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is also difficult to define NPM in radiology diagnosis. The ultrasonography image of NPM is analogous to breast cancer, indicating lobulated irregular masses, with duct ectasia and interstitial septa between abscess cavities. Presentations of enhanced MRI among NPM patients with inflammation and abscess are also similar to breast cancer, including regional enhancement in patchy foci of the areola area and circumferential abscess \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Mammography is unrecommended for the clinical diagnosis of NPM not only because of the limitation in diagnostic significance concerning the image with dense and substantial mass \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e but also because of the particular procedure of mammography which may add an external force stimulus\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e to the inflammatory breast.\u003c/p\u003e \u003cp\u003eAlthough there is no agreement on the risk factors of NPM so far, it is acknowledged that several factors could increase the risk of the onset and the recurrence of NPM, including obesity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, nipple deformity \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, lifestyle, specific medications utilization, dietary pattern, and occupation \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Other potential factors include histories of ductal dilatation, nipple discharge, breast and cranial trauma \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In addition, blockage of milk ducts and the onset of acute mastitis during pregnancy and lactation, are relevant to the higher risk of NPM as well \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, no previous study demonstrated solid evidence of the causal association between these factors and NPM.\u003c/p\u003e \u003cp\u003eCurrent treatments of NPM include oral treatment and surgery. For oral treatment, the main pharmacological treatments are antibiotics, steroids\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, methotrexate, and traditional Chinese medicine\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, as the pathogen of NPM is not clear, most oral treatments lack valid evidence of efficacy. Although there are varieties of surgical treatments, such as abscess drainage, extensive local excision, and mastectomy, no recommendation on the best treatment for NPM\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e because of the long healing time of surgery incisions\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Previous studies reported a wide range of the recurrence rates of NPM, which were from 5\u0026ndash;50% after the surgery procedure \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, without a clear conclusion about the risk factors related to recurrence \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough previous studies showed the long-lasting condition of NPM and the potential repeated breast surgeries could negatively affect the physical and mental health of patients with higher medical costs \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, few studies have provided a comprehensive description of severe NPM episodes. Most of the previous studies are case reports and are limited in sample size, data quality, valid study design, and analysis \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, and only described the characteristics of NPM \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Some evidence illustrated that increasing prolactin (PRL) level is highly related to NPM reoccurrence\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, but few studies focused on the association between PRL level and NPM severity of first time onset, or further investigated risk factors among NPM patients who need surgical treatment.\u003c/p\u003e \u003cp\u003eThis observational study intends to explore an inclusive understanding of NPM by applying electronic medical record (EMR) data. The primary objective is to map the characteristics of NPM patients who are first time onset and need surgical treatment. The secondary objective is to investigate the association between PRL level and NPM severity and clarify if there were other potential determinants related to NPM severity. We hypothesize that higher PRL level increases the risk of NPM severity compared to normal to low PRL level.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData source and sample\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis observational study included the diagnosis information and medical history data from EMRs of NPM patients admitted to the Department of Breast Surgery, Longhua Hospital Affiliated with Shanghai University of Traditional Chinese Medicine for operation from Jan.2016 to Dec.2020. All included NPM patients received surgery after they were diagnosed as N61.x01(mammary inflammatory granuloma), N61.x02 (mammary duct fistula), N61.x06 (plasma cell mastitis), and N60.400 (mammary duct ectasia) using GB/T14396-2016 Classification and Code of Diseases (ICD-10 of China Version), and additionally with following specific texture in pathology diagnosis report: granuloma formation, duct dilatation, periductal inflammation, and fistula. All data are collected at the time of admission. For those inpatients with multiple admissions during the period, this study only counted them one time and used the data at the first admission. 7 patients who have two-side onset are excluded. To ensure the validity of missing data imputation, 8 patients who had missing values in glucose (GLU), triglycerides (TG), total cholesterol (tCHOL), testosterone (TE), and follicle-stimulating hormone at the same time are excluded since these values are violate to random missing assumption. This study was approved by the Medical Ethics Committee of Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine(approval no.2021LCSY047). Written informed consent of personal data collection and utilization was obtained from all individual participants included in the study.All information collected was kept private and confidential.Everyone who participated had the option to withdraw at any moment. This study adhered to the declaration of Helsinki. All methods were performed in accordance with relevant guidelines and regulations.The study registration number is ChiCTR2000035929.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eExposure:\u003c/u\u003e\u003c/em\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;Prolactin (PRL) level\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used the blood test lab result of PRL at the admission of each patient as primary exposure. The clinical normal level of PRL is from 108.8～557.1 mIU/ L. We define patients with PRL higher than 557.1 mIU/ L as the high-level PRL group, and the rest of them as the low-normal PRL group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eOutcome: NPM severity assessment score\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, there is no official diagnostic standard for the severity of NPM in China. Hence, we created a unified scoring measurement for daily clinical practice to assess NPM severity through clinical manifestations of patients, the imaging results of the ultrasound, and breast enhancement MRI. The lesion of a quadrant of the breast and main mammary accumulate to the total score for each patient, with a scale of 1 to 5 points (Attached file: Scoring scale of NPM severity). Each patient was assessed back-to-back by two attending physicians engaged in clinical work for more than five years. If the scores of the two attending physicians are different, a chief physician with more than 10 years of working experience will conduct the re-assessment to reach an agreeable conclusion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eCovariates/Confounders\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study included \u003cem\u003edemographics, physical risks, lab tests, and medical history indicators\u0026nbsp;\u003c/em\u003ein the multivariable logistic regression model based on the conclusions from previous studies \u003csup\u003e[1,2,8-10]\u003c/sup\u003e. In this study, \u003cem\u003edemographics\u003c/em\u003e included age and smoking status (have a smoking history or not).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhysical risks\u003c/em\u003e include two variables: nipple deformity and external force.The definition of nipple deformity is congenital nipple invagination (NI), in that the nipple is born with not protruding from the areola plane, or even contracting and rolling into a crater shape, which is also known as a crater nipple. The information about external forces is collected through the inquiry. The questions include “Was there an accidental impact on the breast”, “Is the patient used to wearing a tight bra”, and “Is there an external pressure check (i.e. mammography)”. We further asked the patient to investigate whether the external force was relevant to the latest episode or worsening the disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLab test and medical history indicators\u003c/em\u003e include four variables: GLU level (High level if GLU higher than 6.1\u0026nbsp;mmol/L), Alanine aminotransferase (ALT) level(High level if ALT higher than 45\u0026nbsp;IU/L), TE level(High level if TE higher than\u0026nbsp;1.67\u0026nbsp;nmol/L), and hyperlipidemia(HLP) status (Yes if TG\u0026gt;=\u0026nbsp;1.7 mmol/L or tCHOL\u0026gt;=5.72 mmol/L or positive in the previous history of hyperlipidemia or fatty liver disease).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eMissing data imputation\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study assumed that the missing completely at random (MCAR) and applied Multiple Imputation by Chained Equations (MICE) for missing data imputation. We conducted predictive mean matching (pmm) in MICE since variables need imputation are quantitative and with missing data of less than 5% in total. The algorithm was set to run for 50 iterations and presented us with 5 imputations for each missing datum. GLU is the variable that has the highest missing proportion (31 missing in 674 patients). Figure 1 shows the distribution of the imputed dataset in red is similar to the observed data in blue.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eData Analysis\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe current study used the t-test for numerical variables and the ANOVA tests for categorical variables and reported the distribution of\u0026nbsp;\u003cem\u003edemographics, physical risks, lab tests, medical history indicators,\u003c/em\u003e and other characteristics stratifying patients into with and without\u0026nbsp;nipple deformity.\u003c/p\u003e\n\u003cp\u003eThis study conducted logistic regression among NPM patients without pituitary tumor history. We defined the NPM severity assessment score into reference level (1-3 points) and higher lever (4-5 points). The binomial regression model was applied separately in patients with and without nipple\u0026nbsp;deformity.\u0026nbsp;For the patient with nipple deformity, the models excluded TE level as a covariant because of the limited sample size.\u003c/p\u003e\n\u003cp\u003eThe current study defined the significant level at a p-value less than 0.05 and reported all the results in both regression models. We used R studio version 4.0.3 (2020-10-10) for data analysis and conducted missing data imputation with the \u003cem\u003emice\u0026nbsp;\u003c/em\u003epackage\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 674 NPM inpatients admitted from 2016 to 2020 in demographic analysis and 653 of them without pituitary tumor history for logistic regression. Table 1 presents showed the characteristics of NPM patients by PRL level between nipple deformity subgroups. The majority of NPM inpatients are not born with nipple inversion. Among NPM inpatients without normal breasts, those who have high PRL levels are more likely to have a pituitary tumor history (p= 0.016).\u003c/p\u003e\n\u003cp\u003eTable 1.\u0026nbsp;Comparison of\u0026nbsp;NPM patient\u0026nbsp;characteristics\u0026nbsp;between\u0026nbsp;PRL level stratified by breast\u0026nbsp;deformity\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003eWith NI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003eWithout NI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRL\u0026nbsp;Low-Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRL\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRL\u0026nbsp;Low-Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRL\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemograpiic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eage (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e34.28 (9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.30 (7.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.86 (5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.14 (5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003esmoking_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 127 ( 90.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;37 ( 80.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 305 (89.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 125 ( 85.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;13 ( \u0026nbsp;9.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 9 ( 19.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;37 (10.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;21 ( 14.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003ePhysical risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003ecrash_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;58 ( 41.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;15 ( 32.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 162 (47.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;67 ( 45.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e0.088\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;82 ( 58.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;31 ( 67.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 180 (52.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;79 ( 54.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.230590961761298%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLab test and medical history\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eglycimia_grp\u0026nbsp;(n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow-Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 134 ( 95.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;45 ( 97.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 326 (95.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 133 ( 91.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e0.351\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6 ( \u0026nbsp;4.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1 ( \u0026nbsp;2.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;16 ( 4.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;13 ( \u0026nbsp;8.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eALT_grp (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow-Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 132 ( 94.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;45 ( 97.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 319 (93.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 131 ( 89.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 8 ( \u0026nbsp;5.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1 ( \u0026nbsp;2.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;23 ( 6.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;15 ( 10.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eTEabnormal_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 133 ( 95.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;42 ( 91.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 326 (95.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 142 ( 97.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 7 ( \u0026nbsp;5.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 4 ( \u0026nbsp;8.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;16 ( 4.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 4 ( \u0026nbsp;2.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eHLP_flg_fin (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;77 ( 55.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;29 ( 63.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 216 (63.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;83 ( 56.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;63 ( 45.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;17 ( 37.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 126 (36.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;63 ( 43.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003etumor_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 133 ( 95.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;40 ( 87.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 340 (99.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 140 ( 95.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 7 ( \u0026nbsp;5.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6 ( 13.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( 0.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6 ( \u0026nbsp;4.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003erelapse_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 138 ( 98.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;44 ( 95.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 331 (96.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 146 (100.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( \u0026nbsp;1.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( \u0026nbsp;4.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;11 ( 3.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0 ( \u0026nbsp;0.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eabortion_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 139 ( 99.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;46 (100.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 338 (98.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 144 ( 98.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1 ( \u0026nbsp;0.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0 ( \u0026nbsp;0.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 4 ( 1.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( \u0026nbsp;1.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eHMsupply_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 140 (100.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;45 ( 97.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 339 (99.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 143 ( 97.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0 ( \u0026nbsp;0.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1 ( \u0026nbsp;2.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3 ( 0.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3 ( \u0026nbsp;2.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003eTHabnormal_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 138 ( 98.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;46 (100.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 335 (98.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 144 ( 98.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( \u0026nbsp;1.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0 ( \u0026nbsp;0.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 7 ( 2.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( \u0026nbsp;1.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003emental_flg (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 139 ( 99.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;46 (100.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 340 (99.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 143 ( 97.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.844727694090384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385863267670915%\" valign=\"bottom\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1 ( \u0026nbsp;0.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0 ( \u0026nbsp;0.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6048667439165705%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.295480880648899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2 ( 0.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.89223638470452%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3 ( \u0026nbsp;2.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.836616454229432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.952491309385863%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1. P-value of the chi-squared test and ANOVA test between different PRL level among patient with nipple inversion\u003c/p\u003e\n\u003cp\u003e2. P-value of the chi-squared test and ANOVA test between different PRL level among patient with normal breast structure\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp;P-value of the chi-squared test and ANOVA test between different breast structure condition\u003c/p\u003e\n\u003cp\u003e4. Notes: \u003cstrong\u003eg\u003c/strong\u003e\u003cstrong\u003elycimia_grp\u003c/strong\u003e:\u0026nbsp;High level if GLU higher than 6.1\u0026nbsp;mmol/L, and others belong to Low-Normal level.\u003cstrong\u003eALT_grp\u003c/strong\u003e: High level if ALT higher than 45 IU/L. \u003cstrong\u003eHLP_flg_fin\u003c/strong\u003e: Yes if \u0026nbsp;TG\u0026gt;= 1.7 mmol/L or tCHOL\u0026gt;=5.72 mmol/L or positive in the previous history of hyperlipidemia or fatty liver disease.\u003cstrong\u003etumor_flg\u003c/strong\u003e: Patients diagnosed as pituitary microadenomas were\u0026nbsp;Yes.\u003cstrong\u003erelapse\u003c/strong\u003e: Yes if the patient recurs during the follow-up period.\u003cstrong\u003eabortion\u003c/strong\u003e: Patients who have had a history of abortion within six months before onset were judged as Yes.\u003cstrong\u003eHMsupply_flg\u003c/strong\u003e: Patients had\u0026nbsp;hormone supplements\u0026nbsp;within six months before onset were judged as Yes.\u003cstrong\u003eTHabnormal_flg\u003c/strong\u003e:\u0026nbsp;Patients diagnosed with a history of thyroid disease were judged as\u0026nbsp;Yes.\u003cstrong\u003emental_flg\u003c/strong\u003e: Patients diagnosed with a history of mental illness were judged as Yes.\u003c/p\u003e\n\u003cp\u003eTable 2 implied that the severity degree and the characteristics are different between NPM patients with and without nipple deformity. The distribution of patients in severity level varied between NPM patients with normal nipple structure and those are nipple deformity (p\u0026lt;0.001). NPM patients with normal breasts are less likely to have a pituitary tumor (p= 0.001). In addition, NPM patients with normal breast structure are more likely to be younger than those with congenital nipple deformity in average (p= 0.002 in Table 2), which is not significantly within the corresponding nipple structure subgroups (p=0.211 among with NI and p=0.614 among without NI in Table1). However, the interaction effect between PRL level and nipple deformity in logistic regression model is not statistically significant at 0.05 level (Table S1).\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;Comparison of\u0026nbsp;NPM patients with and without breast\u0026nbsp;deformity\u0026nbsp;(only show the covariant with p\u0026lt;0.05)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003eNipple inversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003eNO Nipple Inversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003ePRL_flg (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003eLow-Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e140 (75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e342 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e46 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e146 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003eass_outcome (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003elevel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e41 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e40 ( 8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003elevel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e60 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e100 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003elevel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e46 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e211 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003elevel4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e29 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e100 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003elevel5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e10 ( 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e37 ( 7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003eage (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e33.79 (9.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e31.94 (5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003etumor_flg (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e173 (93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e480 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.180722891566266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" valign=\"top\"\u003e\n \u003cp\u003e13 ( 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.05421686746988%\" valign=\"top\"\u003e\n \u003cp\u003e8 ( 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.198795180722891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes:\u003cstrong\u003eass_outcome(%)\u003c/strong\u003e: Based on the severity scoring system adopted in this study, the proportion of patients with different scores. \u003cstrong\u003etumor_flg\u003c/strong\u003e: Patients diagnosed as pituitary microadenomas were Yes.\u003c/p\u003e\n\u003cp\u003eThe odds of NPM severity for patients with higher PRL levels varied in unadjusted and adjusted OR (Table 3) between the stratified nipple deformity subgroups. Among NPM patients without\u0026nbsp;congenital\u0026nbsp;nipple inversion, those with higher PRL levels had significantly higher odds of more severe NPM episodes both in unadjusted (OR:\u0026nbsp;2.93; 95%CI:1.91-4.47; p\u0026lt;\u0026nbsp;0.001) and adjusted (OR:\u0026nbsp;2.91; 95%CI:1.88-4.52; p\u0026lt;\u0026nbsp;0.001). Besides, age is a protective factor for NPM severity (OR:0.95; 95%CI: 0.91-0.99;\u0026nbsp;p=0.012) while smoking history increased the odds of more severe NPM symptoms (OR: 2.22; 95%CI:1.22-4.05;\u0026nbsp;p=0.011) in patients with normal breast structure. For NPM patients with\u0026nbsp;congenital\u0026nbsp;nipple inversion, there is no statistical difference in the risk of NPM severity between the patient who had higher PRL or other potential risk factors.\u003c/p\u003e\n\u003cp\u003eTable 3. Crude and\u0026nbsp;adjusted OR stratified by breast structure\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003eNipple Inversion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"top\"\u003e\n \u003cp\u003eNO Nipple Inversion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003eOR.95.CI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003etable.P.LR.test.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003eOR.95.CI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003etable.P.LR.test.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRL_flg..High.vs.Low.Normal(crude OR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e2.16 (0.96,4.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.93 (1.91,4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRL_flg..High.vs.Low.Normal(adjusted OR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e2.17 (0.94,5.03)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.91 (1.88,4.52)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003eage..cont..var..\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.92,1.02)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95 (0.91,0.99)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003eglycimia_grp..High.vs.Low.Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.09,9.24)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.13 (0.94,4.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003eALT_grp..High.vs.Low.Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e1.52 (0.27,8.44)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.56 (0.23,1.36)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003eHLP_flg_fin..yes.vs.no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e1.41 (0.61,3.26)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.17 (0.75,1.84)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003esmoking_flg..yes.vs.no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.25,2.85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.22 (1.22,4.05)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003ecrash_flg..yes.vs.no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e1.63 (0.71,3.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.11 (0.73,1.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.920489296636084%\" valign=\"bottom\"\u003e\n \u003cp\u003eTEabnormal_flg..yes.vs.no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.290519877675841%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.314984709480122%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.406727828746178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.24 (0.44,3.45)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.067278287461773%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study discovered that the risk of NPM severity is higher among patients with higher PRL levels. NPM severity distribution and the effects of risk factors varied between NI and non-NI patients, implying the potential interaction effect between PRL level and nipple deformity.\u003c/p\u003e \u003cp\u003eThis is the first study that investigated the relationship between PRL level and NPM severity considering confounders including nipple deformity, age, smoking, external force, and autoimmune with feasible sample size. The conclusion is agreeable to previous studies that the effect of these factors differed from the breast structure \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The results illustrate that NPM patients with nipple deformity are more likely to receive milder assessment results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is consistent with previous studies assuming that the reason for restricted lesions among these patients is associated with squamous metaplasia of lactiferous ducts that link to subareolar abscesses\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, since this is an observational cohort study, the sample size of the non-NI subgroup is about 2.5 times of the NI subgroup. The restriction and imbalance in patient size in analysis could lead to several potential bias.\u003c/p\u003e \u003cp\u003eFirst, we failed to include other potential risk factors in the regression model because of the restricted patient number. Previous evidence demonstrated that incidence of relapse condition is higher among patients who received simple segmental mastectomy\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Other evidence showed that hormonal contraceptive utilization, breastfeeding, abortion, hormone supplements, thyroid abnormalities, and mental health conditions \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e are also potential confounders that related to NPM. This study did not include these factors in the analysis because patient number of corresponding potential confounders were too small to conduct valid analysis. For example, there are only 2 patients have hormonal contraceptive use history and 13 patients with breastfeeding history. Other confounders that were excluded in analysis were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e as in \u0026ldquo;Other\u0026rdquo; category.\u003c/p\u003e \u003cp\u003eSecond, this study was not able to clarify the interaction effect between PRL and nipple deformity associated to NPM severity because of the imbalance sample size between nipple deformity subgroups. Although we found the difference in OR after stratification which implied that nipple deformity may be an effect measure modification (EMM), the result was not statistically significant in OR when directly using interaction term in logistic model. Because of the disagreement between two approaches of EMM testification, in addition to that the causal association between breast structure and NPM severity is not clear, further study is needed to verify the interaction effect between PRL and nipple deformity, and whether the interaction is additive or multiplicative.\u003c/p\u003e \u003cp\u003eCurrent study only observed the effects of age and smoking among NPM patients without NI, both of which agree with NPM clinical features and previous studies although those studies did not stratify the NPM patients by breast duct structure \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAge is protective in both nipple deformity groups, despite that the result of NPM patients with nipple deformity group was not statistically significant at 0.05 level. We inferred that the protective effect could relate to the lower PRL level since PRL level will decrease as the age increases, which further influenced the NPM severity. The reason of different statistic significancy could associate with imbalance in patient size between nipple deformity subgroups.\u003c/p\u003e \u003cp\u003eAnother concern of current study is the validity of NPM severity assessment scoring system. Although the measurement of NPM severity is based on imaging results, clinical manifestations and physician judgement \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, it is still limited in potential inter-observer variability and scoring system reliability, and the measurement form is not widely used as guideline \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Although current study re-categorized the severity level into two group for comparison to increase the internal validity, further investigation is needed to assess the validity of scoring system and how the limitation of scoring system influences the result.\u003c/p\u003e \u003cp\u003eFinally, this cohort study only demonstrated a snapshot of the un-causal relationship between PRL level and NPM severity among patient with severe case and need surgical treatment. Future research that emphasizes on longitudinal studies and randomized clinical studies are needed, to clarify causal relationship between relevant factors regarding different NPM outcome.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs an umbrella term for non-specific inflammatory mastitis diseases without a certain conclusion of etiology, it is critical to investigate risk factors of NPM severity that may be relevant to the pathogenesis. This study found an association between NPM severity and the interaction of breast structure and PRL level. Further discussion focusing on the improvement of the current NPM severity assessment is necessary.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the first author Yulian Yin ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, consent and permissions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Medical Ethics Committee of Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine approved this research(2021LCSY047).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have obtained consent to publish from the participant (or legal parent or guardian for children) to report individual patient data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaoxin Le and Yulian Yin: conceptualization,formal analysis, visualization and writing-review \u0026amp; editing. Haoxin Le: methodology and software. Yulian Yin and Yifan Cheng: writing—original draft. Yulian Yin and Yuanyuan Zhong:data curation and investigation. Yinqin Cheng,Bing Wang,Jingjing Wu: resources. Meina Ye and Hongfeng Chen:project administration and supervision. Yulian Yin and Hongfeng Chen: funding acquisition. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (No.82104854); The second major clinical research project of \"Three-year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals (2020-2022)\" (SHDC2020CR2051B);Shanghai Sailing Program (20YF1449800) ;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhou F, Shang XC, Tian XS, Yu ZG. Clinical practice guidelines for diagnosis and treatment of patients with non-puerperal mastitis: Chinese Society of Breast Surgery (CSBrS) practice guideline 2021. Chin Med J (Engl). 2021 May 19;134(15):1765-7. doi: 10.1097/cm9.0000000000001532.\u003c/li\u003e\n\u003cli\u003eKamal RM, Hamed ST, Salem DS. Classification of inflammatory breast disorders and step by step diagnosis. Breast J. 2009 Jul-Aug;15(4):367-80. doi: 10.1111/j.1524-4741.2009.00740.x.\u003c/li\u003e\n\u003cli\u003eTan H, Li R, Peng W, Liu H, Gu Y, Shen X. Radiological and clinical features of adult non-puerperal mastitis. Br J Radiol. 2013 Apr;86(1024):20120657. doi: 10.1259/bjr.20120657.\u003c/li\u003e\n\u003cli\u003eZhang L, Hu J, Guys N, Meng J, Chu J, Zhang W, et al. Diffusion-weighted imaging in relation to morphology on dynamic contrast enhancement MRI: the diagnostic value of characterizing non-puerperal mastitis. 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Am Surg. 2013 Jan;79(1):54-60. doi: 10.1177/000313481307900130.\u003c/li\u003e\n\u003cli\u003eMahoney MC, Ingram AD. Breast emergencies: types, imaging features, and management. AJR Am J Roentgenol. 2014 Apr;202(4):W390-9. doi: 10.2214/ajr.13.11758.\u003c/li\u003e\n\u003cli\u003eYin Y, Liu X, Meng Q, Han X, Zhang H, Lv Y. Idiopathic Granulomatous Mastitis: Etiology, Clinical Manifestation, Diagnosis and Treatment. J Invest Surg. 2022 Mar;35(3):709-20. doi: 10.1080/08941939.2021.1894516.\u003c/li\u003e\n\u003cli\u003eAltintoprak F, Kivilcim T, Ozkan OV. Aetiology of idiopathic granulomatous mastitis. World J Clin Cases. 2014 Dec 16;2(12):852-8. doi: 10.12998/wjcc.v2.i12.852.\u003c/li\u003e\n\u003cli\u003eCheng J, Ding HY, Du YT. [Granulomatous lobular mastitis associated with mammary duct ectasia: a clinicopathologic study of 32 cases with review of literature]. Zhonghua Bing Li Xue Za Zhi. 2013 Oct;42(10):665-8. doi: 10.3760/cma.j.issn.0529-5807.2013.10.005.\u003c/li\u003e\n\u003cli\u003eXie S, Yu H, Lian Z, Wang Q. Commentary on \u0026quot;Idiopathic granulomatous mastitis with normal prolactin level caused by risperidone\u0026quot;. Asian J Surg. 2022 Jul;45(7):1515. doi: 10.1016/j.asjsur.2022.03.019.\u003c/li\u003e\n\u003cli\u003eAltintoprak F, Kivilcim T, Yalkin O, Uzunoglu Y, Kahyaoglu Z, Dilek ON. Topical Steroids Are Effective in the Treatment of Idiopathic Granulomatous Mastitis. World J Surg. 2015 Nov;39(11):2718-23. doi: 10.1007/s00268-015-3147-9.\u003c/li\u003e\n\u003cli\u003eMizrakli T, Velidedeoglu M, Yemisen M, Mete B, Kilic F, Yilmaz H, et al. Corticosteroid treatment in the management of idiopathic granulomatous mastitis to avoid unnecessary surgery. Surg Today. 2015 Apr;45(4):457-65. doi: 10.1007/s00595-014-0966-5.\u003c/li\u003e\n\u003cli\u003eXue JX, Ye B, Liu S, Cao SH, Bian WH, Yao C. Treatment Efficacy of Chuang Ling Ye, a Traditional Chinese Herbal Medicine Compound, on Idiopathic Granulomatous Mastitis: A Randomized Controlled Trial. Evid Based Complement Alternat Med. 2020 ;2020:6964801. doi: 10.1155/2020/6964801.\u003c/li\u003e\n\u003cli\u003eXu H, Liu R, Lv Y, Fan Z, Mu W, Yang Q, et al. Treatments for Periductal Mastitis: Systematic Review and Meta-Analysis. Breast Care (Basel). 2022 Feb;17(1):55-62. doi: 10.1159/000514419.\u003c/li\u003e\n\u003cli\u003eChirappapha P, Thaweepworadej P, Supsamutchai C, Biadul N, Lertsithichai P. Idiopathic granulomatous mastitis: A retrospective cohort study between 44 patients with different treatment modalities. Ann Med Surg (Lond). 2018 Dec;36:162-7. doi: 10.1016/j.amsu.2018.11.001.\u003c/li\u003e\n\u003cli\u003eShin YD, Park SS, Song YJ, Son SM, Choi YJ. Is surgical excision necessary for the treatment of Granulomatous lobular mastitis? BMC Womens Health. 2017 Jul 24;17(1):49. doi: 10.1186/s12905-017-0412-0.\u003c/li\u003e\n\u003cli\u003eLei X, Chen K, Zhu L, Song E, Su F, Li S. Treatments for Idiopathic Granulomatous Mastitis: Systematic Review and Meta-Analysis. Breastfeed Med. 2017 Sep;12(7):415-21. doi: 10.1089/bfm.2017.0030.\u003c/li\u003e\n\u003cli\u003ePatel RA, Strickland P, Sankara IR, Pinkston G, Many W, Jr., Rodriguez M. Idiopathic granulomatous mastitis: case reports and review of literature. J Gen Intern Med. 2010 Mar;25(3):270-3. doi: 10.1007/s11606-009-1207-2.\u003c/li\u003e\n\u003cli\u003eCo M, Cheng VCC, Wei J, Wong SCY, Chan SMS, Shek T, et al. Idiopathic granulomatous mastitis: a 10-year study from a multicentre clinical database. Pathology. 2018 Dec;50(7):742-7. doi: 10.1016/j.pathol.2018.08.010.\u003c/li\u003e\n\u003cli\u003eUysal E, Soran A, Sezgin E. Factors related to recurrence of idiopathic granulomatous mastitis: what do we learn from a multicentre study? ANZ J Surg. 2018 Jun;88(6):635-9. doi: 10.1111/ans.14115.\u003c/li\u003e\n\u003cli\u003eQiu Q, Shen X. Application of multidisciplinary comprehensive diagnosis and treatment model in the treatment of patients with complex and refractory non-lactation mastitis. Guangxi Medical Journal. 2019 ;41(21). doi: 10.11675/j.issn.0253-4304.2019.21.25.\u003c/li\u003e\n\u003cli\u003eSmith E, Moore DA, Jordan SG. You\u0026apos;ll see it when you know it: granulomatous mastitis. Emerg Radiol. 2021 Dec;28(6):1213-23. doi: 10.1007/s10140-021-01931-4.\u003c/li\u003e\n\u003cli\u003eAl-Khaffaf B, Knox F, Bundred NJ. Idiopathic granulomatous mastitis: a 25-year experience. J Am Coll Surg. 2008 Feb;206(2):269-73. doi: 10.1016/j.jamcollsurg.2007.07.041.\u003c/li\u003e\n\u003cli\u003eHuang Y, Wu H. A retrospective analysis of recurrence risk factors for granulomatous lobular mastitis in 130 patients: more attention should be paied to prolactin level. Ann Palliat Med. 2021 Mar;10(3):2824-31. doi: 10.21037/apm-20-1972.\u003c/li\u003e\n\u003cli\u003eTian C, Wang H, Liu Z, Han X, Ning P. Characteristics and Management of Granulomatous Lobular Mastitis Associated with Antipsychotics-Induced Hyperprolactinemia. Breastfeed Med. 2022 Jul;17(7):599-604. doi: 10.1089/bfm.2021.0341.\u003c/li\u003e\n\u003cli\u003ehttps://datascienceplus.com/imputing-missing-data-with-r-mice-package/\u003c/li\u003e\n\u003cli\u003eGollapalli V, Liao J, Dudakovic A, Sugg SL, Scott-Conner CE, Weigel RJ. Risk factors for development and recurrence of primary breast abscesses. J Am Coll Surg. 2010 Jul;211(1):41-8. doi: 10.1016/j.jamcollsurg.2010.04.007.\u003c/li\u003e\n\u003cli\u003eSheybani F, Naderi HR, Gharib M, Sarvghad M, Mirfeizi Z. Idiopathic granulomatous mastitis: Long-discussed but yet-to-be-known. Autoimmunity. 2016 Jun;49(4):236-9. doi: 10.3109/08916934.2016.1138221.\u003c/li\u003e\n\u003cli\u003eCiftci AB, B\u0026uuml;k \u0026Ouml; F, Yemez K, Polat S, Yazıcıoğlu İ M. Risk Factors and the Role of the Albumin-to-Globulin Ratio in Predicting Recurrence Among Patients with Idiopathic Granulomatous Mastitis. J Inflamm Res. 2022 Sep;15:5401-12. doi: 10.2147/jir.S377804.\u003c/li\u003e\n\u003cli\u003eSerrano LF, Rojas-Rojas MM, Machado FA. Zuska\u0026apos;s breast disease: Breast imaging findings and histopathologic overview. Indian J Radiol Imaging. 2020 Jul-Sep;30(3):327-33. doi: 10.4103/ijri.IJRI_207_20.\u003c/li\u003e\n\u003cli\u003eHur SM, Cho DH, Lee SK, Choi MY, Bae SY, Koo MY, et al. Experience of treatment of patients with granulomatous lobular mastitis. J Korean Surg Soc. 2013 Jul;85(1):1-6. doi: 10.4174/jkss.2013.85.1.1.\u003c/li\u003e\n\u003cli\u003eAl-Chalabi M, Bass AN, Alsalman I. Physiology, Prolactin. 2023 Jul 24. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan\u0026ndash;. PMID: 29939606.\u003c/li\u003e\n\u003cli\u003eChu AN, Seiler SJ, Hayes JC, Wooldridge R, Porembka JH. Magnetic resonance imaging characteristics of granulomatous mastitis. Clin Imaging. 2017 May-Jun;43:199-201. doi: 10.1016/j.clinimag.2017.03.012.\u003c/li\u003e\n\u003cli\u003eKayadibi Y, Ucar N, Akan YN, Kaya MF, Yildirim E, Kurt SA, et al. Magnetic resonance imaging findings associated with recurrence in idiopathic granulomatous mastitis. Clin Imaging. 2022 Apr;84:47-53. doi: 10.1016/j.clinimag.2022.01.010.\u003c/li\u003e\n\u003cli\u003eYu HJ, Wang Q, Yang JM, Lian ZQ, Zhang AQ, Li WP, et al. [Anti-mycobacteria drugs therapy for periductal mastitis with fistula]. Zhonghua Wai Ke Za Zhi. 2012 Nov;50(11):971-4. doi: 10.3760/cma.j.issn.0529-5815.2012.11.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prolactin, Non-puerperal Mastitis, Real-world Data, Risk Factors, Binomial Logistic Regression","lastPublishedDoi":"10.21203/rs.3.rs-3919363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3919363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eNon-puerperal mastitis (NPM) is an umbrella term for non-specific inflammatory mastitis inflammation with unclear etiology. The objective of the current study is to characterize NPM patients and examine the determinants associated with NPM severity.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study analyzed the NPM inpatients admitted to the Department of Breast Surgery, Longhua Hospital Affiliated with Shanghai University of Traditional Chinese Medicine from 2016 to 2020. We explored NPM patient characteristics through demographics, physical risks, lab tests, and medical history indicators. Multivariable logistic regression was conducted to identify the relationship between the prolactin (PRL) level and NPM severity stratified by breast structure.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe majority of NPM inpatients had normal breast structures and were of lower average age than those with congenital nipple deformity (p\u0026thinsp;=\u0026thinsp;0.002). Significant positive risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) association between PRL level and NPM severity was observed among NPM inpatients with normal breast structure in both crude and adjusted model(adjusted OR: 2.91; 95%CI:1.88\u0026ndash;4.52), with age as a protective factor (OR:0.94; 95%CI:0.91\u0026ndash;0.97) and smoking history as a risk factor(OR:2.22; 95%CI:1.22\u0026ndash;4.05). For NPM patients with nipple deformity, increasing odds of NPM severity regarding higher PRL level was observed while the result is not statistic significant at 0.05 level. (OR: 2.17; 95%CI: 0.94\u0026ndash;5.03; p\u0026thinsp;=\u0026thinsp;0.076).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe risk of NPM severe episodes is higher among patients with higher PRL levels, of which the association is stronger for NPM patients with normal breast structure, implying different pathogenesis between NPM patients with varied breast anatomy. Disagreement of the interaction effect testification indicates an improvement window for current study.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eChiCTR2000035929. Registered 20 August 2020(retrospectively registered).\u003c/p\u003e","manuscriptTitle":"Prolactin And Non-Puerperal Mastitis: A Cohort Study Using Real-World Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 20:44:35","doi":"10.21203/rs.3.rs-3919363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-08T17:14:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-24T09:04:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-17T20:00:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312337304904678586308706471775651680439","date":"2024-10-08T22:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231415640278922926620992831200501269434","date":"2024-10-08T19:09:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-13T16:51:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190548383271852901998020288459726772849","date":"2024-09-13T16:44:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-26T09:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217891381640688576376083735098046814312","date":"2024-05-24T06:37:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-28T17:21:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a685f2dc-33cd-4a2e-9be7-3216197361c8","date":"2024-02-28T12:47:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-27T21:19:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-27T21:18:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-17T06:06:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-17T06:03:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-02T02:20:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d563570-e2e1-41d8-b4e8-b816d51231a4","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28819390,"name":"Biological sciences/Immunology/Immunological disorders"},{"id":28819391,"name":"Biological sciences/Immunology/Inflammation"},{"id":28819392,"name":"Biological sciences/Biological techniques/Biophysical methods"},{"id":28819393,"name":"Health sciences/Diseases/Metabolic disorders"}],"tags":[],"updatedAt":"2025-03-17T16:04:23+00:00","versionOfRecord":{"articleIdentity":"rs-3919363","link":"https://doi.org/10.1038/s41598-025-92504-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-12 15:58:20","publishedOnDateReadable":"March 12th, 2025"},"versionCreatedAt":"2024-02-20 20:44:35","video":"","vorDoi":"10.1038/s41598-025-92504-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-92504-9","workflowStages":[]},"version":"v1","identity":"rs-3919363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3919363","identity":"rs-3919363","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}