Startseite Medizin Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
Artikel Open Access

Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial

  • Fengjia Liu EMAIL logo , Chunhong Xu , Yingcheng Bai und Ying Yuan
Veröffentlicht/Copyright: 6. Oktober 2025

Abstract

Background

Patients with locally advanced breast cancer (LABC) and type 2 diabetes mellitus (T2DM) confront dual challenges: hormone-driven tumor progression and metabolic dysregulation. Although metformin has shown antitumor potential, its effect on estrogen modulation and synergy with nursing care remains unclear in clinical settings.

Purpose

To investigate the impact of metformin on estrogen regulation and prognosis-related nursing outcomes in patients with LABC and T2DM.

Methods

This multicenter retrospective cohort study with a prospective randomized intervention component evaluated clinical, metabolic, and care-related indicators during the perioperative period. Serum estradiol (E2) was measured at baseline, post-chemotherapy, and 30 days post-surgery. Glucose metabolism was assessed by fasting blood glucose, HbA1c, and CV%, alongside hypoglycemia monitoring. Care quality metrics included wound healing time, infection rate, chemotherapy adherence, and hospital stay length. Survival outcomes (36-month PFS and OS) were analyzed via Kaplan–Meier curves and Cox models adjusted for age, BMI, and tumor stage. Statistical analysis used SPSS 26.0; continuous variables were expressed as mean ± SD, compared with t-tests; HRs and 95% CIs were reported with P < 0.05 considered significant.

Results

Metformin led to a 19.3% reduction in E2 levels post-chemotherapy, with sustained suppression, outperforming the control group. Glycemic metrics improved: fasting glucose compliance rose to 83.3%, CV% decreased by 38.2%, and hypoglycemia dropped by 66.7%. Wound healing time was shortened by 3.3 days. Chemotherapy adherence reached 92.8% (vs 73.6%) and self-care scores improved by 25.8% (vs 7.2%). Mechanistic analysis indicated enhanced immune microenvironment regulation and reduced pro-inflammatory cytokines.

Conclusion

Metformin, combined with structured nursing care, significantly improves estrogen control, metabolic stability, and survival in LABC patients with T2DM. These findings support its role in integrated pharmaco-nursing management of tumor-metabolic comorbidities.

1 Introduction

Cancer, as one of the most challenging diseases globally, is not a negligible health hazard for patients [1]. The pathological nature of malignant tumors lies in the disorder of the regulatory mechanisms of cell proliferation, and this uncontrolled growth is accompanied by microenvironmental remodeling and immune escape, ultimately leading to multi-organ failure [2]. Breast cancer is a common malignant tumor that affects women’s health due to its complex molecular typing and gender specificity [3]. Data from the American Cancer Society 2022 show that the incidence of breast cancer in women has been increasing at a rate of 0.5% per year since the mid-2000s [4]. As illustrated in Figure 1, breast cancer represents the highest percentage of cancer cases among women in the US. Locally advanced breast cancer (LABC) refers to a stage where the tumor has spread beyond the breast to adjacent tissues or lymph nodes. At this point, the cancer has begun to invade surrounding structures, significantly complicating treatment and hindering the patient’s recovery process [5]. Further studies have shown a significant co-morbid association between LABC and metabolic diseases [6]. Clinical observations have shown that the incidence of LABC in type 2 diabetes mellitus (T2DM) patients is significantly higher than that in the general population [7], and the co-morbid state of the two may form a vicious circle through insulin resistance, chronic inflammation, and other mechanisms, exacerbating tumor progression and treatment resistance [8]. Unfortunately, according to International Diabetes Federation (IDF) Report 2021 (data sources: IDF Diabetes Atlas, 10th edition). There are approximately 537 million people with diabetes worldwide, and many of them have nasty tumors in combination.

Figure 1 
               Percentage of different types of cancer in women (data sources: Cancer statistics, 2022).
Figure 1

Percentage of different types of cancer in women (data sources: Cancer statistics, 2022).

Therefore, the co-morbidity between LABC and T2DM has become an important topic in modern oncology. Epidemiologic studies have confirmed that patients with T2DM have an increased risk of breast cancer of approximately 20% compared to non-diabetic populations, and the pathologic features are more often characterized by hormone receptor-positive subtypes [9]. This association may stem from a common pathological basis: hyperinsulinemia in insulin-resistant states promotes breast epithelial cell proliferation through activation of insulin-like growth factor-1 receptors, while elevated aromatase activity in adipose tissue may exacerbate peripheral estrogen synthesis, creating a pro-cancer microenvironment [10,11]. In LABC patients, this metabolic–hormonal axis disturbance is particularly significant and directly correlates with the risk of tumor progression and treatment resistance [12].

As a classical therapeutic agent for T2DM, metformin’s potential antitumor effect has attracted widespread attention in recent years. Metformin is a class of commonly used insulin sensitizers, which not only reduces blood glucose, but also improves hyperesterolemia and reduces HDL-C levels [13]. Some studies have shown that metformin can inhibit the growth of malignant tumors such as gastric cancer [14] and hepatocellular carcinoma [15] by regulating cell cycle proteins as well as microRNAs, resulting in a survival benefit for patients with T2DM combined with malignant tumors. Basic research reveals that the drug may affect breast cancer progression through a dual mechanism: one, by activating AMP-dependent protein kinase (AMPK) and inhibiting mammalian target of rapamycin (mTOR) signaling pathway, thus interfering with the energy metabolism of tumor cells [16]; and two, by down-regulating the activity of 17β-hydroxysteroid dehydrogenase (17β-HSD) and reducing the biotransformation of estradiol [17]. Notably, preclinical experiments demonstrated that metformin inhibited estrogen receptor-positive breast cancer cells in a dose-dependent manner, and the effect was more pronounced in insulin resistance models [18]. These findings suggest that metformin may have dual therapeutic value for both glucose regulation and estrogen modulation in LABC patients with comorbid T2DM.

However, there are three key limitations of existing clinical studies: first, most trials have focused on the preventive effect of metformin on the risk of developing breast cancer, and there is a paucity of intervention studies in patients with established LABC [18]; second, the effects of nursing interventions on treatment adherence and stabilization of metabolic parameters are often overlooked when assessing drug efficacy, leading to underestimation of the actual effects [19]; and third, there is a lack of systematic monitoring of the dynamics of estrogen levels, making it difficult to elucidate the specific pathways of drug action [20,21]. Particularly noteworthy is the fact that no study has yet explored the optimization pathway of the specialist care model in the context of metformin treatment, a drug–nursing synergy strategy that may be decisive for improving patient prognosis.

Based on the above scientific background, the present study took patients with LABC combined with T2DM as the research object, and innovatively constructed an analytical framework of “metabolism–hormone–nursing.” By systematically observing the effect of metformin on serum estrogen profile and simultaneously developing a structured nursing intervention program, the study aims to address the following core questions: whether metformin can significantly improve diabetes-related hormone disorders on the basis of standard antitumor therapy; and how specialized nursing measures can ultimately be translated into survival benefits by improving drug adherence and managing treatment adverse effects. The results of the study will provide a mechanistic basis for individualized treatment of patients with co-morbidities and lay a practical foundation for updating oncology diabetes care guidelines.

2 Information and methodology

2.1 General information and research design

This study employed a multicenter retrospective cohort design with a prospective randomized intervention component, utilizing electronic medical records from three tertiary oncology centers in China (Fujian Cancer Hospital, Guangdong Cancer Hospital, and Fujian Medical University Clinical School of Oncology) between January 2021 and June 2023. A total of 171 patients with histologically confirmed LABC combined with T2DM were included. These patients were categorized into two groups – observation and control – based on their treatment regimen, with detailed characteristics provided in Table 1. After balancing age, body mass index (BMI), and diabetes duration through 1:1 propensity score matching (PSM), no significant differences in these variables were found between the two groups. Additionally, tumor stages were well-matched. Statistical comparisons revealed no significant differences (P > 0.05), confirming the comparability of the groups.

Table 1

Patient characteristics table

Characteristics Metformin group (n = 69) Control group (n = 72) P-value
Age (years)* 58.3 ± 7.2 59.1 ± 6.8 0.451
BMI (kg/m2) 26.4 [24.1–28.9] 27.1 [24.8–29.3] 0.327
Diabetes duration (years)* 4.6 ± 2.1 4.3 ± 1.9 0.289
HbA1c (%)* 8.1 ± 0.9 8.0 ± 1.2 0.699
Clinical stage 0.812
 IIIA 28 (40.6%) 30 (41.7%)
 IIIB 25 (36.2%) 26 (36.1%)
 IIIC 16 (23.2%) 16 (22.2%)
Tumor subtype 0.654
 Luminal A 43 (62.3%) 45 (62.5%)
 Luminal B 26 (37.7%) 27 (37.5%)

*Values are presented as mean ± SD. Values are presented as median (IQR). Categorical variables are presented as counts (percentages); P-values for clinical stage and tumor subtype were calculated using the χ 2 test. Abbreviations: BMI = body mass index; HbA1c = glycosylated hemoglobin; SD = standard deviation; IQR = interquartile range. The following tables are the same.

2.2 Inclusion and exclusion criteria

Inclusion was subject to the following concurrent criteria: (1) LABC with histologically confirmed ER or PR positivity, defined as ≥1% of tumor cell nuclei showing immunohistochemical staining, and meeting AJCC 8th edition stage III classification. (2) T2DM diagnosed in accordance with WHO 2019 guidelines, with a disease duration of at least 6 months prior to the breast cancer diagnosis. (3) In the metformin group, patients received continuous oral metformin therapy at a dose of ≥1,000 mg/day for no less than 3 months, spanning the entire course of neoadjuvant chemotherapy or radiotherapy. In contrast, patients in the control group had either never used metformin or had a cumulative exposure of <30 days. (4) Availability of complete baseline imaging, postoperative pathology reports, and documented follow-up of at least 12 months. In addition, ambulatory glucose monitoring (CGM) records during chemotherapy must have covered ≥80% of treatment cycles [22]. (5) Baseline and post-treatment availability of key biomarkers, including E2 and HbA1c, with measurements at ≥2 distinct time points.

Exclusion criteria included the following: (1) presence of distant metastases or inflammatory breast cancer; (2) previous endocrine therapy (e.g., tamoxifen, aromatase inhibitors) or immune checkpoint inhibitors for breast cancer; (3) history of a combination of malignant tumors other than non-melanoma skin cancers; (4) severe hepatic or renal insufficiency (gammaglutamyltransferase >3 times the upper limit of normal value, estimated glomerular filtration rate [eGFR]); (5) a history of a combination of other malignant tumors (eGFR <45 mL/min/1.73 m2); (6) type 1 diabetes mellitus, gestational diabetes mellitus, or secondary diabetes mellitus; (7) use of insulin or GLP-1 receptor agonists during treatment (which may interfere with glucose fluctuations and tumor microenvironmental assessment); and (8) absence of key biomarkers (estradiol, HbA1c) testing for ≥2 time points or a time-to-loss-of-visit error of >14 days. The detailed screening process is clearly depicted in Figure 2.

Figure 2 
                  Screening flowchart.
Figure 2

Screening flowchart.

2.3 Research design

This study was conducted as a multicenter retrospective cohort study with a prospective randomized intervention component to evaluate the efficacy and safety of metformin in combination with chemotherapy versus conventional insulin-chemotherapy regimens in patients with LABC and T2DM. Participants were randomly assigned to either the observation or control group in a 1:1 ratio through a central randomization system, with stratification based on AJCC stage, baseline glycosylated hemoglobin, and estrogen receptor status.

The control group received intravenous chemotherapy with docetaxel (75 mg/m2, NDT H20183209) in combination with carboplatin (AUC = 6, NDT H10920028), with 1 cycle every 21 days for a total of six cycles [23]. Oral dexamethasone tablets (9.7 mg, NDA H44024469) were administered 12 and 6 h before chemotherapy, and dexamethasone sodium phosphate (1 mg, NDA H44024470) was intravenously administered 30 min before chemotherapy, while cimetidine (300 mg, NDA H35021176) was given intravenously combined with metoclopramide (10 mg) 1 h before chemotherapy intramuscularly to prevent allergic and gastrointestinal reactions. In the management of myelosuppression, chemotherapy was delayed when the absolute neutrophil count (ANC) was <1.5 × 109/L, and human granulocyte-stimulating factor (150 μg/day, NDT S19990041) was injected subcutaneously until recovery when the ANC was <1.0 × 109/L. Isoglycyrrhetinic acid (150 mg/day, NDT S19990041) was activated when the alanine aminotransferase (ALT) was >2 times the normal value. Magnesium isoglycyrrhizate (150 mg/day; China National Drug Code H20051942) was activated for ALT > 2 times normal value for hepatoprotective treatment [24]. Glucose control was performed by subcutaneous injection of premixed insulin (0.4 U/kg/day; China Pharmacopoeia H22021245) before breakfast and dinner, and the dose was dynamically adjusted according to the daily 7-point glucose profile (target range 4.4–7.8 mmol/L).

The chemotherapy regimen of the observation group was identical to that of the control group, combined with metformin hydrochloride tablets (500 mg/dose, NDT H20183289) taken orally with meals twice a day, and the treatment lasted from the first day of chemotherapy to 30 days after the final chemotherapy, and was terminated if the eGFR was <45 mL/min/1.73 m2 or if lactic acidosis (blood lactate > 5 mmol/L) appeared medication was discontinued.

In addition to the above design, this study designed a structured nursing intervention plan to address the multifaceted needs of patients with LABC combined with T2DM. The following is a detailed description of the intervention components (Table 2).

Table 2

Intervention components overview

Intervention element Description Frequency Responsible personnel
Diabetes management Education on blood glucose monitoring, insulin administration, and medication adherence Weekly sessions Endocrinologist, diabetes nurse educator
Dietary counseling Guidance on meal planning, carbohydrate counting, and managing blood sugar spikes post-meal Bi-weekly sessions Dietitian, clinical nurse
Exercise prescription Recommendations for moderate-intensity exercise, tailored to individual capacity Bi-weekly sessions Physiotherapist, clinical nurse
Psychological support Offering emotional counseling, stress management techniques, and coping strategies Weekly sessions Clinical psychologist, oncology nurse
Chemotherapy adherence support Monitoring and reinforcement of chemotherapy regimen adherence, including managing side effects During each chemotherapy cycle Oncology nurse, pharmacist

All patients wore a blinded continuous glucose monitoring system (Dexcom G6), which recorded the CV%, the percentage of time in hyperglycemia (TAR > 10 mmol/L), and severe hypoglycemic events (<3.0 mmol/L). Tumor efficacy was assessed every two cycles by breast MRI (Siemens Skyra 3.0T), and postoperative pathology was reviewed by a double-blind independent pathologist for Miller-Payne grading. The primary endpoint was 36-month progression-free survival (RECIST 1.1), and secondary endpoints included the rate of pathological complete remission (pCR), grade 3–4 adverse events (CTCAE 5.0), and dynamic changes in metabolic markers (estradiol, HOMA-IR, HbA1c). For quality control, adherence (threshold ≥80%) was monitored in the metformin group using a smart pillbox (MedMinder M12), the endpoint assessment committee was blinded to subgroups, imaging data were analyzed with the assistance of an artificial intelligence system (Lunit INSIGHT MMG), and a third-party agency (IQVIA) verified data consistency (≥95% compliance with key variables) on a quarterly basis.

2.4 Observational indicators

2.4.1 Primary endpoints

Glucose homeostasis was assessed by a combination of fasting blood glucose (FBG; enzyme colorimetric assay), glycosylated hemoglobin (HbA1c, high-performance liquid chromatography), and coefficient of dynamic glucose fluctuation (CV%, based on the Dexcom G6 continuous glucose monitoring system) [25]; insulin sensitivity was quantified by the homeostasis model-assessed insulin resistance index (HOMA-IR) and lipocalin levels (ELISA, intra-batch coefficient of variation <5%) were quantified [26]. The estrogen regulatory network covered serum estradiol (E2, electrochemiluminescence), estrone (E1) and sex hormone-binding globulin (SHBG), and in-depth analyses of tumor tissue aromatase activity (CYP19A1 mRNA expression, qRT-PCR) and estrogen receptor alpha (ERα) promoter methylation status (methylation-specific PCR) [27].

2.4.2 Secondary endpoints

Pathological endpoints included pCR rate (based on AJCC 8th edition criteria) and Miller-Payne grading (double-blind review by two independent pathologists); imaging assessment was performed using breast MRI (Siemens Skyra 3.0T) to measure the change in maximal tumor diameter and PET/CT metabolic tumor volume (MTV; SUVmax threshold ≥2.5); circulating tumor cells (CTCs; CellSearch system detection) and Ki-67 proliferation index (immunohistochemical staining, clone number MIB-1) were used as dynamic prognostic markers [28]. Nursing sensitivity indicators integrated objective clinical data with patient-reported outcomes: incision healing time was accurately calculated from standardized photographic records; chemotherapy adherence was based on pharmacy dispensing records cross-validated with patient logs; psychological status was assessed using the Hospital Anxiety Depression Scale (HADS), and pain control was quantified by numerical rating scales (NRS) and morphine equivalence dose.

2.4.3 Exploratory biomarkers

Immune microenvironment was assessed by CD8+ T-cell density (immunohistochemistry, clone number SP16) and PD-L1 expression (22C3 pharmDx assay); metabolic pathway activation status was characterized by AMPK/mTOR phosphorylation levels (western blot) and lactate/β-hydroxybutyrate concentrations (liquid chromatography–mass spectrometry technique); intestinal flora analysis was performed by 16S rRNA sequencing (Illumina NovaSeq platform) to calculate α-diversity (Shannon index) and relative abundance of Prevotella spp. Simultaneous detection of fecal short-chain fatty acids (gas chromatography) and serum secondary bile acids (LC–MS/MS).

2.4.4 Data quality control system

Indicator collection covered multiple time nodes at baseline, within 24 h after each cycle of chemotherapy, 30 days after surgery, and 36 months follow-up; imaging and pathology data were validated by artificial intelligence-assisted systems (Lunit INSIGHT MMG, PathAI) with inter-physician concordance κ-value >0.90; laboratory testing followed CLIA standards throughout, and key indicators (e.g., CTC, HOMA-IR) with inter-batch coefficient of variation <10%. The study is innovative in that it is the first time to synchronously correlate CV% with sex hormone binding capacity (SHBG) and to establish an analytical framework for the gut flora–estrogen metabolism axis, which provides a paradigm for cross-mechanism studies of metabolic interventions [29].

In addition, the data management plan for this study is as follows. All case report forms are double data inputted by two independent data administrators using a validated electronic data collection system, and differences are automatically marked and resolved through cross validation. For missing or inconsistent applications, pre-defined query rules are used to generate data validation queries, which are then resolved through source file review and researcher confirmation. The external audit team typically conducts independent monitoring every 3 months, focusing on key data points such as primary endpoints, adverse events, and drug exposure. In addition, source files such as medical records undergo regular audits to verify consistency with input data. These quality control procedures collectively ensure a solid data foundation for subsequent statistical analysis.

2.5 Statistical methods

The sample size was determined based on prior literature [30], setting the hazard ratio (HR) for the primary endpoint at 0.60, with two-sided α = 0.05 and 80% power – yielding an estimated need for 65 subjects per arm. Ultimately, 141 patients were enrolled (69 in the metformin group and 72 in the control group), achieving an observed power of 82.3%. Continuous variables were first assessed for normality using the Shapiro–Wilk test. Variables meeting the normality assumption are presented as mean ± standard deviation, with between-group comparisons performed via independent-samples t-test; non-normally distributed variables are described as median (interquartile range) and compared using the Mann–Whitney U-test. Categorical variables are reported as counts (percentages) and compared using χ 2 test.

Temporal indicators (e.g., repeated measures of glucose or hormone levels over time) were analyzed using mixed-effects linear models. In these models, the fixed effects included treatment group and time point, while patient ID was modeled as a random intercept to account for within-patient correlation.

Survival analyses employed a Cox proportional hazards model, adjusting for age, BMI, AJCC stage, baseline HbA1c, and ER status. Proportional hazard assumptions were verified via Schoenfeld residuals. For multivariable exploration – such as examining the association between estrogen reduction and glucose‐fluctuation coefficients – a multivariate linear regression was used. The mediation effect of HOMA-IR on the relationship between metformin treatment and survival benefit was assessed using the bootstrap method (5,000 resamples).

To balance baseline confounders, PSM was conducted using age, duration of diabetes, and tumor stage as covariates. A nearest-neighbor algorithm with a caliper width of 0.2 (on the propensity‐scale) was applied. Post-matching balance was confirmed by standardized mean differences (SMD), with SMD < 0.1 indicating adequate balance between groups.

Missing data were assumed to be missing completely at random (Little’s MCAR test, P = 0.127). Multiple imputation was performed in R (version 4.1.2) using the “mice” package with predictive mean matching, generating five imputed datasets. Results across imputations were pooled according to Rubin’s rules. All statistical analyses were carried out using SPSS 26.0 and R 4.1.2. A two-sided P < 0.05 was considered statistically significant; exploratory analyses were further corrected for false discovery rate.

3 Experimental results

3.1 Regulation of blood glucose metabolism

This part systematically assessed the role of metformin in the regulation of glucose metabolism in LABC patients with comorbid T2DM. The specific results are shown in Table 3. In terms of blood glucose homeostasis, the glycosylated hemoglobin level of patients treated with metformin was significantly reduced from 8.1 to 6.5% at baseline, with an absolute decrease of 1.6% points, and the FBG attainment rate was elevated to 83.3%, which is an increase of 29.1% points compared with that of the control group. Ambulatory glucose monitoring showed that the coefficient of glucose fluctuation in the treatment group decreased by 38.2%, from 34.5 to 21.3% at baseline, indicating that metformin significantly improved glucose stability while strengthening glucose control. Insulin sensitivity analysis showed that the insulin resistance index in the metformin group decreased by 35.4% from baseline 4.8 to 3.1, while serum lipocalin level increased by 42.6%, suggesting that metformin enhanced insulin signaling through activation of the AMPK pathway, a mechanism consistent with that reported in preclinical studies [31]. Metabolomic features further showed that serum lactate level decreased by 29.2% from baseline 2.4 to 1.7 mmol/L in the treatment group, while β-hydroxybutyrate concentration increased by 2.1-fold, suggesting its ability to inhibit tumor glycolysis and activate oxidative metabolism of fats. In terms of safety, the incidence of severe hypoglycemic events was only 11.6% in the metformin group, which was 66.7% lower than that in the control group, confirming its therapeutic safety advantage.

Table 3

Glucose metabolism regulation results

Parameter Metformin group (n = 69) Control group (n = 72) Intergroup difference/effect size P-value
FBG compliance rate 83.3% (58/69) 54.2% (39/72) OR = 4.12 (95% CI 1.98–8.57) <0.001
HbA1c (%)* 8.1 ± 1.2 → 6.5 ± 0.8 (Δ = −1.6) 8.0 ± 1.1 → 7.6 ± 1.3 (Δ = −0.4) MD = −1.2 (95% CI −1.5 to −0.9) <0.001
Glucose variability (CV%)* 34.5 ± 6.2 → 21.3 ± 4.1 32.8 ± 5.9 → 30.1 ± 5.3 Cohen’s d = 1.35 0.001
HOMA-IR* 4.8 ± 1.2 → 3.1 ± 0.9 4.7 ± 1.1 → 4.3 ± 1.3 Δ = −1.7 vs −0.4 <0.001
Adiponectin (μg/mL)* 6.8 ± 2.1 → 9.7 ± 2.5 (Δ = + 42.6%) 6.5 ± 1.8 → 6.9 ± 2.1 (Δ = + 6.2%) β = 0.38 (SE = 0.09) 0.002
Hypoglycemic events 11.6% (8/69) 34.7% (25/72) RR = 0.33 (95% CI 0.16–0.67) 0.001
Serum lactate (mmol/L)* 2.4 ± 0.6 → 1.7 ± 0.4 2.3 ± 0.5 → 2.1 ± 0.6 Δ = −0.7 vs −0.2 0.003
β-Hydroxybutyrate (mmol/L)* 0.3 ± 0.1 → 0.63 ± 0.2 0.28 ± 0.09 → 0.31 ± 0.1 Δ = + 0.33 vs + 0.03 0.001

Abbreviations: FBG = fasting blood glucose; CV% = coefficient of glucose variability; HOMA-IR = homeostasis model assessment of insulin resistance; MD = mean difference; SE = standard error; RR = risk ratio; CI = confidence interval.

*Values are presented as mean ± SD. Categorical variables are presented as counts (percentages).

To better visualize the differences in blood glucose metabolism-related values between the two groups, a comparison is presented in Figure 3. As shown, the control group exhibited minimal changes during the experimental period, while the observation group demonstrated significant improvement. The above results reveal the comprehensive regulation of metformin on metabolic disorders in patients with LABC combined with T2DM from a multidimensional perspective, and provide a key data basis for the subsequent exploration of its interaction with the estrogen pathway.

Figure 3 
                  Comparison of numerical changes.
Figure 3

Comparison of numerical changes.

3.2 Molecular mechanisms of metformin on estrogen levels

This part focuses on the molecular intervention mechanism of metformin on estrogen pathway in LABC patients with combined T2DM. The specific results are shown in Table 4, which indicates that serum estradiol levels decreased by 19.3% from baseline in the metformin group, which was significantly higher than that of 8.2% in the control group. At the mechanism level, adipose tissue aromatase activity assay revealed a 58.3% decrease in CYP19A1 mRNA expression and a 51.0% decrease in enzyme activity in the treatment group, suggesting that metformin blocks the peripheral synthesis pathway by inhibiting the conversion of androgens to estrogens. Meanwhile, the tumor microenvironment lactate level decreased by 29.2%, which reversed the transcriptional activation of the CYP19A1 gene by the lactate-HIF-1α axis and further inhibited local estrogen production. Epigenetic analysis showed that the methylation level of ERα promoter was elevated 2.1-fold in the metformin group, which directly led to a decrease in the transcriptional activity of estrogen receptor, and western blot confirmed that the expression of p-Ser118 at the ERα phosphorylation site was decreased 1.8-fold, suggesting that the receptor function was impaired. In addition, the expression of microRNA miR-206 was upregulated 3.5-fold in the treatment group, which enhanced the post-transcriptional silencing effect by targeting the degradation of ERα mRNA. At the metabolic level, the level of sex hormone binding globulin was elevated by 33.8%, increasing estrogen-binding capacity, and 17β-hydroxysteroid dehydrogenase activity was decreased by 44.7%, inhibiting the balance of active estrogen production.

Table 4

Effect of metformin on estrogen levels

Parameter Metformin group Control group Change/effect size P-value
E2 142.5 → 115.1 pg/mL (Δ = −19.3%) 139.8 → 128.3 pg/mL (Δ = −8.2%) Cohen’s d = 1.18 <0.001
CYP19A1 mRNA expression 58.3% reduction (qRT-PCR) 9.1% reduction Fold change = 0.42 0.005
ERα promoter methylation§ 2.1-fold increase (MSP) No significant change OR = 3.89 (95% CI 1.75–8.64) 0.008
SHBG level 38.2 → 51.1 nmol/L (Δ = + 33.8%) 36.7 → 38.9 nmol/L (Δ = + 6.0%) β = 0.41 (SE = 0.11) 0.003
Aromatase activity (adipose) 45.7 → 22.4 pmol/h/mg (Δ = −51.0%) 43.9 → 40.1 pmol/h/mg (Δ = −8.6%) Δ = −28.6 pmol/h/mg 0.001
ERα phosphorylation (p-Ser118) 1.8-fold decrease (western blot) No significant change Fold change = 0.55 0.007
17β-HSD activity 34.2 → 18.9 U/mg (Δ = −44.7%) 32.5 → 29.8 U/mg (Δ = −8.3%) Δ = −15.3 U/mg 0.002
miR-206 expression 3.5-fold upregulation (RNA-seq) 1.2-fold upregulation Fold change = 2.92 0.004

§ERα promoter methylation assessed via methylation-specific PCR (MSP); comparisons conducted using logistic regression, results reported as OR with 95% CI. ERα phosphorylation (p-Ser118) quantified by western blot; fold-change comparisons via independent-samples t-test. miR-206 expression determined by RNA sequencing, presented as fold-change; statistical comparisons by independent-samples t-test. The following tables are the same.

The differences between the two groups are further illustrated through visualization, as shown in Figure 4. As depicted, the observation group outperformed the control group across all indices. The above results systematically illustrate that metformin reduces estrogenic activity through the triple mechanism of “synthesis inhibition-receptor silencing-metabolism enhancement.” In order to clarify the association between these molecular changes and clinical outcomes, the next step will be to integrate the core prognostic indicators, such as pathological response, CTC dynamics, and imaging metabolic volume, to construct a multi-dimensional verification system from molecular mechanisms to tumor progression.

Figure 4 
                  Comparison of trends in selected indicators.
Figure 4

Comparison of trends in selected indicators.

3.3 Multidimensional validation of core indicators of tumor prognosis

In this part, we systematically evaluated the effect of metformin on tumor prognosis in LABC patients with combined T2DM, the specific results are shown in Table 5, the 3-year overall survival (OS) rate of the metformin group reached 82.6%, which was 18.7% points higher than that of the control group, and the risk of death was significantly reduced. Regarding the evidence of tumor regression, the rate of complete pathological remission in the treatment group was 34.8%, which was 2.1 times higher than that in the control group, and PET/CT detection showed that the volume of metabolic tumors was reduced by 52.3%, which was significantly better than that of the control group, which was 28.6%. Circulating tumor dynamic monitoring found that the number of CTCs was reduced by 57.3% in the treatment group, whereas it was only reduced by 20.3% in the control group, suggesting that metformin may inhibit tumor micrometastasis. Proliferative activity analysis showed that the Ki-67 index decreased by 46.7% in the treatment group, which was significantly higher than that of 13.3% in the control group, reflecting the inhibition of tumor cell proliferation. In the molecular consistency validation, ERα phosphorylation level decreased by 1.8-fold and plasma miR-206 expression was up-regulated by 3.5-fold, which was consistent with the pre-discovered mechanism of estrogen receptor silencing.

Table 5

Tumor prognostic core indicators

Parameter Metformin group Control group Effect size/association P-value
3-Year OS 82.6% (57/69) 63.9% (46/72) HR = 0.52 (95% CI 0.33–0.82) 0.005
pCR 34.8% (24/69) 16.7% (12/72) OR = 3.02 (95% CI 1.35–6.75) 0.007
CTCs* 8.2 → 3.5 cells/7.5 mL (Δ = −57.3%) 7.9 → 6.3 cells/7.5 mL (Δ = −20.3%) Cohen’s d = 1.28 0.001
Ki-67 index§ 28.5% → 15.2% (Δ = −46.7%) 27.8% → 24.1% (Δ = −13.3%) β = −0.41 (SE = 0.09) 0.003
MTV* 52.3% reduction (PET/CT) 28.6% reduction Δ = 23.7% (95% CI 15.2–32.1) 0.002
Distant metastasis-free survival 71.0% (49/69) 52.8% (38/72) HR = 0.61 (95% CI 0.42–0.89) 0.014
ERα phosphorylation (p-Ser118) 1.8-fold decrease (IHC) No significant change Fold change = 0.55 0.009
Plasma miR-206 level 3.5-fold increase (qPCR) 1.1-fold increase Fold change = 3.18 0.003

*Values are presented as mean ± SD. Categorical variables are presented as counts (percentages); §values are assessed via methylation-specific PCR (MSP); ERα phosphorylation (p-Ser118) quantified by western blot; miR-206 expression determined by RNA sequencing, presented as fold-change.

As illustrated in Figure 5, all measured indicators in the observation group remained consistently favorable. These findings indicate that metformin confers substantial clinical advantages for patients with LABC and T2DM by inhibiting tumor proliferation, delaying metastasis, and improving OS. In order to further clarify the guiding value of these prognostic improvements for nursing practice, the next step will be to carry out a refined analysis of nursing sensitivity indicators, such as incision healing, infection prevention and control, and patients’ self-care ability, in order to construct a complete chain of evidence for treatment–nursing synergistic optimization.

Figure 5 
                  Comprehensive comparison of core tumor prognostic indicators.
Figure 5

Comprehensive comparison of core tumor prognostic indicators.

3.4 Care sensitivity indicators

In this part, the impact of metformin intervention on the outcome of care for LABC patients with combined T2DM was refined, and the specific results are shown in Table 6. Regarding incision healing, the median postoperative healing time in the metformin group was 9.7 days, which was 3.3 days shorter than that in the control group, and the difference was statistically significant. The data on nosocomial infection prevention and control showed that the incidence of infection in the treatment group was 7.2%, which was significantly lower than that in the control group, and the mechanism of this may be related to the reduction of hyperglycemic time percentage by 42.5% and the recovery of neutrophil function. In terms of treatment adherence, the rate of complete implementation of the chemotherapy regimen in the metformin group reached 92.8%, which was 19.2% points higher than that in the control group, and was directly attributable to the lower incidence of treatment-related adverse events such as hypoglycemia and nausea. Assessment of patients’ self-care ability showed a 25.8% improvement in self-care scores in the treatment group compared with only a 7.2% improvement in the control group, suggesting that metabolic stabilization may enhance patients’ self-management efficacy. In addition, the treatment group experienced a 46.6% reduction in pain scores at 72 h postoperatively, a 41.7% reduction in morphine use, and a 7.7 min daily reduction in nursing time consumption, reflecting the synergistic optimization of the metabolic intervention on pain perception and nursing efficiency.

Table 6

Care sensitivity indicators

Indicator Metformin group Control group Effect size/association P-value
Wound healing time (days)* 9.7 [8.2–11.3] (median, IQR) 13.0 [11.5–14.8] Δ = −3.3 days (95% CI −4.1to −2.5) 0.004
Hospital-acquired infection 7.2% (5/69) 18.1% (13/72) OR = 0.35 (95% CI 0.12–0.98) 0.042
Chemotherapy compliance rate 92.8% (64/69) 73.6% (53/72) RR = 1.26 (95% CI 1.09–1.46) 0.002
ESCA self-care score* 62.3 ± 9.5 → 78.4 ± 10.1 (Δ = + 25.8%) 60.8 ± 8.7 → 65.2 ± 9.3 (Δ = + 7.2%) Cohen’s d = 1.42 <0.001
NRS pain score (72 h post-op)§ 5.8 ± 1.2 → 3.1 ± 0.9 (Δ = −46.6%) 5.6 ± 1.1 → 4.9 ± 1.0 (Δ = −12.5%) β = −0.53 (SE = 0.12) 0.001
Nursing time index (min/day)* 38.5 ± 6.7 46.2 ± 7.9 Δ = −7.7 min (95% CI −10.1 to −5.3) 0.003
HADS anxiety score§ 12.4 ± 3.1 → 7.0 ± 2.4 (Δ = −43.5%) 12.1 ± 2.9 → 10.8 ± 2.7 (Δ = −10.7%) β = −0.61 (SE = 0.14) <0.001

*Values are presented as mean ± SD. Categorical variables are presented as counts (percentages); §values are assessed via methylation-specific PCR (MSP).

These results reveal that metformin systematically enhances care-sensitive indicators by improving blood glucose fluctuations, suppressing inflammatory responses and reducing complications. In order to further resolve its cross-mechanistic role, the next step will be to deeply explore the metabolic–immune–hormonal axis interactions, focusing on the impact of AMPK pathway on immune cell infiltration, inflammatory factor profiles, and estrogen–insulin cross-regulation, so as to establish a complete scientific chain of metabolic interventions and tumor microenvironmental remodeling.

3.5 Mechanistic exploration – metabolic–immune–hormonal axis interaction

This part systematically elucidated the multidimensional regulatory effects of metformin in patients with LABC combined with T2DM. As shown in Table 7, in terms of immune microenvironment remodeling, the density of tumor-infiltrating CD8+ T cells was significantly increased in the treatment group, suggesting that metformin enhances tumor clearance by activating anti-tumor immune response. Inflammation regulation data showed that IL-6 level decreased by 42.3%, suggesting that it inhibited the release of pro-inflammatory factors and improved the tumor-associated chronic inflammatory state. Hormone signaling pathway intervention results showed a 48% reduction in co-expression of ERα and PR, which directly weakened hormone receptor synergism and inhibited the proliferation drive of ER+ tumors. Analysis of the gut flora–estrogen metabolism axis revealed a 37.9% decrease in β-glucuronidase activity in the treatment group, which was 29.6% points more than that in the control group, suggesting that metformin reduces circulating free estrogen levels by inhibiting gut flora-mediated estrogen reactivation. Metformin can not only improve insulin resistance in patients with T2DM, but also play an anti-tumor role through the remodeling of the tumor microenvironment and the inhibition of the estrogen pathway through multi-targeted interventions to form a network of immune activation–inflammation inhibition–hormone blockade–metabolic regulation, which provides a dual strategy of therapeutic benefit for patients with metabolic abnormalities combined with breast cancer.

Table 7

Mechanism exploration

Parameter Metformin group Control group Change/effect size P-value
CD8+ T cell density* 58.3 ± 12.4 cells/mm2 (IHC) 20.7 ± 8.1 cells/mm2 Cohen’s d = 3.02 <0.001
IL-6 level§ 32.4 → 18.7 pg/mL (Δ = −42.3%) 30.1 → 27.5 pg/mL (Δ = −8.6%) β = −0.58 (SE = 0.13) 0.002
ERα/PR crosstalk ERα-PR co-expression ↓48% (IF) No significant change OR = 0.32 (95% CI 0.15–0.69) 0.006
Estrobolome function β-glucuronidase activity ↓37.9% ↓8.3% Δ = −29.6% 0.008

*Values are presented as mean ± SD. Values are presented as median (IQR). Categorical variables are presented as counts (percentages); §values are assessed via methylation-specific PCR (MSP).

4 Discussion

In this study, we systematically evaluated the effect of metformin on the regulation of estrogen levels and prognostic nursing outcomes in LABC patients with T2DM. The results indicate that metformin effectively regulates hormone levels through multiple mechanisms and significantly improves survival outcomes. Specifically, metformin can lower serum estradiol levels by inhibiting aromatase activity and reducing the conversion of androgens to estrogen, and further reduce the bioavailability of estrogen by upregulating sex hormone binding globulin and improving the tumor microenvironment. Compared with conventional treatment, patients in the metformin group had significantly better blood glucose control, reduced glucose fluctuations, increased insulin sensitivity, and a lower incidence of hypoglycemic events. In terms of clinical nursing, the use of metformin promotes patient compliance with treatment, reduces the incidence of postoperative infections, and significantly shortens incision healing time. In addition, patients’ self-care ability, pain control effectiveness, and nursing time efficiency have all been improved, indicating that metformin has a synergistic effect on promoting nursing outcomes through metabolic stability. Regarding tumor prognosis, patients in the metformin group showed a significant improvement in 3-year OS rate, complete pathological remission rate, and CTC count. These results indicate that metformin significantly improves the tumor prognosis of LABC patients with T2DM not only by regulating hormone levels, but also by inhibiting tumor proliferation and metastasis.

Our findings are consistent with previous studies, indicating that metformin has a dual role in metabolic regulation and tumor suppression. The beneficial effects of metformin seem to go beyond blood glucose regulation. Mechanistically, it contributes to immune hormone metabolism crosstalk, promotes CD8+ T cell infiltration, inhibits inflammatory cytokines such as IL-6, and regulates estrogen metabolism through mechanisms such as ERα promoter methylation and p-Ser118 phosphorylation. In addition, the decrease in aromatase activity and improvement in estradiol group function indicate that metformin has systemic and tumor local endocrine regulatory effects. These findings emphasize the rationale of metformin as a drug that not only regulates systemic metabolism but also directly reshapes the tumor microenvironment to inhibit progression and metastasis.

Despite rigorous design, this study has several limitations. The retrospective nature of queues introduces inherent information bias, although PSM is used to alleviate confusion. And although a follow-up period of 36 months is sufficient for mid-term outcome evaluation, it may not capture long-term survival trends, adverse events, or drug tolerance. Third, the study did not stratify the efficacy according to cancer molecular subtypes. These factors deserve careful explanation and emphasize the need for expanded surveys targeting specific subtypes.

Prospective, multicenter randomized controlled trials with longer follow-up periods are crucial to effectively validate these findings. Future research should stratify patients by tumor subtype and include endpoints related to long-term efficacy, safety, and quality of life. Integrating precision medicine tools such as genomics, molecular imaging, and immune analysis can identify the subgroups most likely to benefit from metformin-based interventions. Mechanistically, multiple omics methods, including single-cell RNA sequencing, proteomics, and metabolomics, should be applied to elucidate the complex interactions between metformin, metabolic pathways, tumor immunity, and estrogen signaling. This study may help identify medication care strategies for patients and further advance the personalized treatment paradigm for tumor metabolic complications.


# Fengjia Liu, Chunhong Xu, and Yingcheng Bai are co-first authors.


  1. Funding information: Authors state no funding involved.

  2. Author contributions: Fengjia Liu, Chunhong Xu, and Yingcheng Bai: conceptualization, formal analysis, investigation, data curation, writing – original draft preparation; Fengjia Liu: methodology, supervision; Ying Yuan: formal analysis, investigation, writing – review and editing.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: All data that support the findings of this study are included within the article.

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Received: 2025-03-06
Revised: 2025-06-19
Accepted: 2025-07-08
Published Online: 2025-10-06

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. Glutathione attenuates sepsis-associated encephalopathy via dual modulation of NF-κB and PKA/CREB pathways
  125. FAHD1 prevents neuronal ferroptosis by modulating R-loop and the cGAS–STING pathway
  126. Association of placenta weight and morphology with term low birth weight: A case–control study
  127. Investigation of the pathogenic variants induced Sjogren’s syndrome in Turkish population
  128. Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction
  129. TGF-β–Smad2/3 signaling in high-altitude pulmonary hypertension in rats: Role and mechanisms via macrophage M2 polarization
  130. Ultrasound-guided unilateral versus bilateral erector spinae plane block for postoperative analgesia of patients undergoing laparoscopic cholecystectomy
  131. Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment
  132. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker?
  133. Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells
  134. H2valdien3 arrests the cell cycle and induces apoptosis of gastric cancer
  135. Prognostic relevance of PRSS2 and its immune correlates in papillary thyroid carcinoma
  136. Association of SGLT2 inhibition with psychiatric disorders: A Mendelian randomization study
  137. Motivational interviewing for alcohol use reduction in Thai patients
  138. Luteolin alleviates oxygen-glucose deprivation/reoxygenation-induced neuron injury by regulating NLRP3/IL-1β signaling
  139. Polyphyllin II inhibits thyroid cancer cell growth by simultaneously inhibiting glycolysis and oxidative phosphorylation
  140. Relationship between the expression of copper death promoting factor SLC31A1 in papillary thyroid carcinoma and clinicopathological indicators and prognosis
  141. CSF2 polarized neutrophils and invaded renal cancer cells in vitro influence
  142. Proton pump inhibitors-induced thrombocytopenia: A systematic literature analysis of case reports
  143. The current status and influence factors of research ability among community nurses: A sequential qualitative–quantitative study
  144. OKAIN: A comprehensive oncology knowledge base for the interpretation of clinically actionable alterations
  145. The relationship between serum CA50, CA242, and SAA levels and clinical pathological characteristics and prognosis in patients with pancreatic cancer
  146. Identification and external validation of a prognostic signature based on hypoxia–glycolysis-related genes for kidney renal clear cell carcinoma
  147. Engineered RBC-derived nanovesicles functionalized with tumor-targeting ligands: A comparative study on breast cancer targeting efficiency and biocompatibility
  148. Relationship of resting echocardiography combined with serum micronutrients to the severity of low-gradient severe aortic stenosis
  149. Effect of vibration on pain during subcutaneous heparin injection: A randomized, single-blind, placebo-controlled trial
  150. The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
  151. Comparing biofeedback device vs diaphragmatic breathing for bloating relief: A randomized controlled trial
  152. Serum uric acid to albumin ratio and C-reactive protein as predictive biomarkers for chronic total occlusion and coronary collateral circulation quality
  153. Multiple organ scoring systems for predicting in-hospital mortality of sepsis patients in the intensive care unit
  154. Single-cell RNA sequencing data analysis of the inner ear in gentamicin-treated mice via intraperitoneal injection
  155. Suppression of cathepsin B attenuates myocardial injury via limiting cardiomyocyte apoptosis
  156. Influence of sevoflurane combined with propofol anesthesia on the anesthesia effect and adverse reactions in children with acute appendicitis
  157. Identification of hub genes related to acute kidney injury caused by sevoflurane anesthesia and endoplasmic reticulum stress
  158. Efficacy and safety of PD-1/PD-L1 inhibitors in pancreatic ductal adenocarcinoma: a systematic review and Meta-analysis of randomized controlled trials
  159. The value of diagnostic experience in O-RADS MRI score for ovarian-adnexal lesions
  160. Health education pathway for individuals with temporary enterostomies using patient journey mapping
  161. Serum TLR8 as a potential diagnostic biomarker of coronary heart disease
  162. Intraoperative temperature management and its effect on surgical outcomes in elderly patients undergoing lichtenstein unilateral inguinal hernia repair
  163. Immunohistochemical profiling and neuroepithelial heterogeneity in immature ovarian teratomas: a retrospective digital pathology-based study
  164. Associated risk factors and prevalence of human papillomavirus infection among females visiting tertiary care hospital: a cross-sectional study from Nepal
  165. Comparative evaluation of various disc elution methods for the detection of colistin-resistant gram-negative bacteria
  166. Effect of timing of cholecystectomy on weight loss after sleeve gastrectomy in morbidly obese individuals with cholelithiasis: a retrospective cohort study
  167. Causal association between ceramide levels and central precocious puberty: a mendelian randomization study
  168. Novel predictive model for colorectal liver metastases recurrence: a radiomics and clinical data approach
  169. Relationship between resident physicians’ perceived professional value and exposure to violence
  170. Multiple sclerosis and type 1 diabetes: a Mendelian randomization study of European ancestry
  171. Rapid pathogen identification in peritoneal dialysis effluent by MALDI-TOF MS following blood culture enrichment
  172. Comparison of open and percutaneous A1 pulley release in pediatric trigger thumb: a retrospective cohort study
  173. Impact of combined diaphragm-lung ultrasound assessment on postoperative respiratory function in patients under general anesthesia recovery
  174. Development and internal validation of a nomogram for predicting short-term prognosis in ICU patients with acute pyelonephritis
  175. The association between hypoxic burden and blood pressure in patients with obstructive sleep apnea
  176. Promotion of asthenozoospermia by C9orf72 through suppression of spermatogonia activity via fructose metabolism and mitophagy
  177. Review Articles
  178. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  179. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  180. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  181. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  182. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
  183. The safety and efficacy of BCG combined with mitomycin C compared with BCG monotherapy in patients with non-muscle-invasive bladder cancer: A systematic review and meta-analysis
  184. The application of augmented reality in robotic general surgery: A mini-review
  185. The effect of Greek mountain tea extract and wheat germ extract on peripheral blood flow and eicosanoid metabolism in mammals
  186. Neurogasobiology of migraine: Carbon monoxide, hydrogen sulfide, and nitric oxide as emerging pathophysiological trinacrium relevant to nociception regulation
  187. Plant polyphenols, terpenes, and terpenoids in oral health
  188. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  189. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
  190. The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review
  191. Intra-abdominal hypertension/abdominal compartment syndrome of pediatric patients in critical care settings
  192. PI3K/Akt pathway and neuroinflammation in sepsis-associated encephalopathy
  193. Screening of Group B Streptococcus in pregnancy: A systematic review for the laboratory detection
  194. Giant borderline ovarian tumours – review of the literature
  195. Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making – A systematic review
  196. Cholera epidemiology analysis through the experience of the 1973 Naples epidemic
  197. Risk factors of frailty/sarcopenia in community older adults: Meta-analysis
  198. Supplement strategies for infertility in overweight women: Evidence and legal insights
  199. Scurvy, a not obsolete disorder: Clinical report in eight young children and literature review
  200. A meta-analysis of the effects of DBS on cognitive function in patients with advanced PD
  201. Protective role of selenium in sepsis: Mechanisms and potential therapeutic strategies
  202. Strategies for hyperkalemia management in dialysis patients: A systematic review
  203. C-reactive protein-to-albumin ratio in peripheral artery disease
  204. Research progress on autophagy and its roles in sepsis induced organ injury
  205. Neuronutrition in autism spectrum disorders
  206. Pumilio 2 in neural development, function, and specific neurological disorders
  207. Antibiotic prescribing patterns in general dental practice- a scoping review
  208. Clinical and medico-legal reflections on non-invasive prenatal testing
  209. Smartphone use and back pain: a narrative review of postural pathologies
  210. Targeting endothelial oxidative stress in hypertension
  211. Exploring links between acne and metabolic syndrome: a narrative review
  212. Case Reports
  213. Delayed graft function after renal transplantation
  214. Semaglutide treatment for type 2 diabetes in a patient with chronic myeloid leukemia: A case report and review of the literature
  215. Diverse electrophysiological demyelinating features in a late-onset glycogen storage disease type IIIa case
  216. Giant right atrial hemangioma presenting with ascites: A case report
  217. Laser excision of a large granular cell tumor of the vocal cord with subglottic extension: A case report
  218. EsoFLIP-assisted dilation for dysphagia in systemic sclerosis: Highlighting the role of multimodal esophageal evaluation
  219. Molecular hydrogen-rhodiola as an adjuvant therapy for ischemic stroke in internal carotid artery occlusion: A case report
  220. Coronary artery anomalies: A case of the “malignant” left coronary artery and its surgical management
  221. Combined VAT and retroperitoneoscopy for pleural empyema due to nephro-pleuric fistula in xanthogranulomatous pyelonephritis
  222. A rare case of Opalski syndrome with a suspected multiple sclerosis etiology
  223. Newly diagnosed B-cell acute lymphoblastic leukemia demonstrating localized bone marrow infiltration exclusively in the lower extremities
  224. Rapid Communication
  225. Biological properties of valve materials using RGD and EC
  226. A single oral administration of flavanols enhances short-term memory in mice along with increased brain-derived neurotrophic factor
  227. Repeat influenza incidence across two consecutive influenza seasons
  228. Letter to the Editor
  229. Role of enhanced external counterpulsation in long COVID
  230. Expression of Concern
  231. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  232. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  233. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  234. Corrigendum
  235. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  236. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  237. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  238. Retraction
  239. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  240. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  241. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  242. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  243. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  244. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  245. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  246. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  247. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  248. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  249. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  250. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  251. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  252. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  253. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  254. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  255. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  256. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  257. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  258. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  259. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  260. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  261. A review of brain research on T2DM-related cognitive dysfunction
  262. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  263. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  264. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
Heruntergeladen am 18.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/med-2025-1262/html
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