Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

RETRACTED: Investigating the relationship between insulin use and all-cause mortality, breast cancer mortality, and recurrence risk in diabetic patients with breast cancer: A comprehensive systematic review and meta-analysis

  • Marina V. Loktionova,

    Roles Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation

    ⨯
  • Mahdi Mohammadian,

    Roles Conceptualization, Writing – original draft, Writing – review & editing

    Affiliation MSc in Epidemiology, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran

    ⨯
  • Roya Choopani,

    Roles Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Assistant Professor of Neonatal-Perinatal Medicine, Department of Pediatrics, School of Medicine, Shahrekord University of Medical Sciences, Shahrekord, Iran

    ⨯
  • Soleiman Kheiri,

    Roles Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Professor of Biostatistics, Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran

    ⨯
  • Abdollah Mohammadian-Hafshejani

    Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    amohamadii1361@gmail.com

    Affiliation Assistant Professor of Epidemiology, Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran

    ⨯

Retraction

The PLOS One Editors retract this article [1] due to concerns about potential manipulation of the publication process. These concerns call into question the validity and provenance of the reported results and compliance with PLOS policies. We regret that the issues were not identified prior to the article’s publication.

MVL, MM, SK, and AMH did not agree with the retraction. RC either did not respond directly or could not be reached.

18 Dec 2025: The PLOS One Editors (2025) Retraction: Investigating the relationship between insulin use and all-cause mortality, breast cancer mortality, and recurrence risk in diabetic patients with breast cancer: A comprehensive systematic review and meta-analysis. PLOS ONE 20(12): e0339149. https://doi.org/10.1371/journal.pone.0339149 View retraction

Abstract

Background

The co-occurrence of breast cancer and diabetes presents complex clinical challenges, as each condition may influence the progression and management of the other, potentially worsening patient outcomes. This study aims to examine the association between insulin use and the risks of all-cause mortality, breast cancer-specific mortality, and recurrence in diabetic patients with breast cancer.

Methods

A systematic review and meta-analysis were conducted using studies identified from multiple databases, including Web of Science, Scopus, PubMed, Cochrane, Google Scholar, and Embase. The meta-analysis approach was used to estimate the relative risk (RR) of the relationship between insulin use and the risks of all-cause mortality, breast cancer-specific mortality, and recurrence in diabetic patients with breast cancer. Heterogeneity among studies was assessed using statistical tests such as the Chi-square test, I2, and forest plots. Meta-regression and sensitivity analyses were performed to explore sources of heterogeneity. The quality of the included studies was assessed using the Newcastle-Ottawa Scale checklist. Data were analyzed using Stata version 17 (Stata Corp, College Station, Texas).

Results

Data from 22 studies conducted between 2002 and 2023, with a total of 159,674 participants, were analyzed. Nineteen studies were rated as high quality, and three as moderate quality. Diabetic patients with breast cancer who received insulin had a 1.65 (95% CI: 1.36–2.02; P < 0.001; I2 = 89.7%) times higher risk of overall mortality compared to those who did not use insulin. Meta-regression revealed that sample size and study quality were significant contributors to heterogeneity (P ≤ 0.10). Furthermore, insulin use was associated with a 1.22 (95% CI: 1.05–1.42; P = 0.009; I2 = 37.9%) times higher risk of breast cancer-specific mortality. For breast cancer recurrence, insulin use was associated with a 1.45 (95% CI: 1.19–1.77; P < 0.001; I2 = 3.4%) times higher risk. Sensitivity analysis confirmed the stability of the results across all outcomes.

Conclusion

This meta-analysis provides strong evidence that insulin use in diabetic patients with breast cancer is associated with increased risks of overall mortality, breast cancer-specific mortality, and recurrence. These findings underscore the need for careful consideration of insulin therapy in this patient population.

Citation: Loktionova MV, Mohammadian M, Choopani R, Kheiri S, Mohammadian-Hafshejani A (2024) RETRACTED: Investigating the relationship between insulin use and all-cause mortality, breast cancer mortality, and recurrence risk in diabetic patients with breast cancer: A comprehensive systematic review and meta-analysis. PLoS ONE 19(12): e0314565. https://doi.org/10.1371/journal.pone.0314565

Editor: Kathleen Bennett, Royal College of Surgeons in Ireland, IRELAND

Received: April 23, 2024; Accepted: November 12, 2024; Published: December 5, 2024

Copyright: © 2024 Loktionova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Breast cancer is a significant global public health issue, being the most commonly diagnosed malignancy among women worldwide [1, 2]. Simultaneously, diabetes mellitus is a prevalent chronic disease affecting a significant percentage of populations globally [3, 4]. The co-occurrence of breast cancer and diabetes presents unique clinical challenges as each condition can influence the course and management of the other [5]. Breast cancer is the most common cancer among women with diabetes, while women with breast cancer have an increased risk of developing diabetes, potentially affecting one in three individuals [6–8]. This bidirectional relationship between the two diseases can complicate treatment approaches and impact patient outcomes [9]. Chemotherapy regimens used for breast cancer treatment can occasionally induce diabetes or worsen existing hyperglycemia [10, 11]. Conversely, poorly controlled blood glucose levels in diabetic breast cancer patients may hinder response to chemotherapy and radiation [12].

Managing post-treatment survivorship can be particularly challenging for individuals with breast cancer and diabetes [13, 14]. Research indicates that diabetes is associated with a poorer prognosis and higher mortality following a breast cancer diagnosis [15]. Managing diabetes becomes even more complex when considering factors such as lymphedema and the impact of hormonal or targeted therapies used in breast cancer treatment, which can affect insulin resistance and weight [11].

In addition to the inherent difficulties of managing diabetes, breast cancer treatment-related issues further complicate the management of this chronic condition [16, 17]. Lymphedema, a common side effect of breast cancer treatment, can limit physical activity and increase the risk of infection, disrupting glycemic control and complicating insulin management [18].

Given the substantial number of women living with both breast cancer and diabetes, it is crucial to conduct further research on optimal multidisciplinary care models, strategies to mitigate treatment side effects, and approaches to enhance long-term survivorship for this high-risk patient group. Coordinated global efforts are necessary to address the significant public health challenges posed by the intertwined epidemics of breast cancer and diabetes [19, 20].

Understanding the effects of insulin therapy on mortality outcomes is vital for the clinical management and glycemic control of diabetes, especially for those with type 1 or advanced type 2 diabetes who require exogenous insulin replacement [21–23]. However, the impact of insulin use on mortality risk in women with coexisting breast cancer and diabetes needs further investigation [24]. Clarifying this relationship holds significant implications for personalized treatment approaches aiming to maximize survival and quality of life for these high-risk patients [25]. Several mechanisms could potentially influence this relationship, such as insulin’s potential promotion of tumor growth and progression through mitogenic signaling pathways. Conversely, optimal glycemic control achieved with insulin could improve response to breast cancer therapies and reduce complications [11, 21, 22].

Existing large epidemiological studies on insulin use and mortality among diabetic breast cancer patients have reported inconsistent findings [26–29]. Some retrospective analyses found higher mortality with insulin therapy [26, 28, 30], while others observed no significant difference or even a possible survival benefit [28, 29, 31]. However, methodological limitations and confounding factors prevent definitive conclusions.

To address this knowledge gap, we conducted a rigorous systematic review and meta-analysis to investigate the relationship between insulin use and mortality and recurrence risk among diabetic patients diagnosed with breast cancer. Our objective was to provide a robust synthesis of the current epidemiological evidence, offering valuable insights into the potential impact of insulin therapy on survival outcomes in this vulnerable patient subgroup. The findings of our comprehensive systematic review and meta-analysis have the potential to inform clinical decision-making and guide the optimal management of diabetic patients with breast cancer. By elucidating the impact of insulin therapy on mortality risk, we aim to contribute to the development of personalized treatment strategies that can improve patient outcomes and ultimately reduce mortality rates in this specific patient population.

Materials and methods

Type of study and search strategies

This systematic review and meta-analysis study on the relationship between insulin use and mortality and recurrence risk in diabetic patients with breast cancer strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To ensure transparency, a thorough literature search was conducted across multiple databases, including PubMed, Web of science, Cochrane Central Register of Controlled Trials, Embase, Google Scholar, and Scopus. The search covered articles from the inception of these databases to January 1st, 2024, to include the most up-to-date research. The study protocol was approved by the Ethics Committee of Shahrekord University of Medical Sciences (IR.SKUMS.REC.1401.019).

Medical Subject Headings (MeSH) terms were employed to identify relevant keywords and synonyms related to insulin use, mortality, and recurrence risk in diabetic patients with breast cancer. Only full-text, original research studies involving human participants were included. These studies quantitatively assessed the association between insulin use and both mortality and recurrence risk in diabetic breast cancer patients, utilizing statistical measures such as odds ratios, hazard ratios, and relative risk.

The database searches involved combining keywords using Boolean operators (AND, OR) to retrieve articles containing all specified keywords. Additionally, the reference lists of included studies were manually reviewed to identify any other relevant articles published before 2024 that may have been missed in the initial searches.

Inclusion and exclusion criteria of study

The systematic review and meta-analysis focused on investigating the relationship between insulin use and mortality and recurrence risk in diabetic patients with breast cancer.

Only original research studies published in peer-reviewed journals were considered eligible. The chosen study designs included case-control, cross-sectional, Randomized Control Trial (RCTs), and retrospective/prospective cohort designs, which are well-suited for examining associations between insulin use and mortality and recurrence risk in diabetic patients with breast cancer. To ensure robust findings, studies were required to report quantitative effect size estimates and their corresponding 95% confidence intervals (CI). This information allowed for objective assessment of the strength and precision of the relationship between insulin use and study outcomes in diabetic patients with breast cancer. Studies that did not provide this data, such as letters, editorials, case reports, and reviews, were excluded.

Additional exclusions were made for studies that did not involve human subjects, failed to adequately measure exposure and outcome variables, or had incomplete or unclear data reporting. These stringent criteria ensured the inclusion of only high-quality studies, minimizing bias and allowing for a meaningful synthesis of evidence.

The study selection process involved two independent reviewers who systematically screened titles, abstracts, and full texts. Any discrepancies in study selection were resolved through discussion with a third reviewer, maintaining objectivity and reproducibility.

Study selection process

Following the comprehensive literature searches across multiple databases, all records were diligently checked for duplicates using Endnote reference management software. The software automatically identified duplicate records based on matching titles, author names, and publication years. However, to maintain objectivity, the titles of all records were also manually cross-checked in an Excel spreadsheet by two independent reviewers.

The first stage of study selection involved carefully screening the titles to exclude articles that were clearly irrelevant to study aims. For the remaining articles that appeared relevant based on title alone, the abstracts were examined in the second screening stage. Articles were excluded at this stage if the abstract provided enough information to determine that the study did not measure the key exposure and outcome variables of interest or if it utilized ineligible study designs, such as case reports, without reporting quantitative effect sizes.

The full texts of the remaining articles after title and abstract screening underwent the most rigorous screening stage. Both independent reviewers thoroughly examined each study against the pre-defined inclusion and exclusion criteria.

In cases where disagreements arose between the reviewers during any of the screening stages, a third independent reviewer was consulted. This third reviewer objectively examined the full texts in question and facilitated open discussion between all reviewers until a consensus decision was reached on final study selection.

This systematic screening methodology, conducted by independent reviewers and with consensus decision-making, minimized potential bias or errors. It ensured a robust, credible, and reproducible process for conducting the literature searches and selecting the most suitable studies to comprehensively address the research question.

Data extraction

To maintain the integrity and consistency of the data, a detailed standardized data extraction form was developed prior to conducting the extractions. This form underwent thorough pilot testing by the research team to ensure it effectively captured all essential study details in an organized and reproducible manner.

Two independent reviewers utilized this well-defined form to systematically extract relevant information from each included study. To ensure accuracy, the extracted data from each study underwent rigorous cross-checking by both reviewers. Any discrepancies were resolved through open discussion and consensus decision-making, involving a third reviewer when necessary.

The comprehensive data extraction form enabled the collection of important bibliographic details, study characteristics, population descriptors, exposure and outcome assessment methods, reported effect estimates, adjusted covariates, and quality assessment ratings. In addition, specifically, information about the way of reviewing patients’ files and documents in each study, the method of collecting information about insulin consumption from the patients under study, the duration of insulin use, estrogen receptor (ER) status, Progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, the inclusion and exclusion criteria of each research, along with a complete list of information about the studied subjects, was collected specifically for each study (Table 2). By capturing this information in a standardized manner from each study, consistency and completeness were ensured.

The meticulously extracted data facilitated a thorough synthesis of the evidence, critical evaluation of study methodologies, identification of potential biases, and exploration of heterogeneity. Studies lacking sufficient effect estimates or raw data for quantitative meta-analysis were excluded. Moreover, in cases where multiple publications were based on the same data, only the most recent report with the latest findings was included to avoid duplication bias.

Diagnosis and patient identification in original studies

In all the studies included in this systematic review and meta-analysis, the diagnosis and identification of patients with cancer and diabetes were centrally managed by specialized teams within each study. These teams adhered to standardized protocols to ensure consistency and accuracy across all cases. Patient identification was based on thorough medical and hospital records reviews, supplemented by structured assessments within the studies. These centralized procedures not only ensured the accurate identification of patients with comorbid cancer and diabetes but also facilitated the precise evaluation of key outcomes, such as overall mortality, cancer-specific mortality, and recurrence. The methods for patient identification and outcome assessment were systematically extracted from the materials and methods or results sections of the studies included in the analysis.

Evaluation of the quality of the studies

The methodological quality of the included studies was rigorously assessed using the widely recognized Newcastle-Ottawa Scale (NOS), which is specifically designed for evaluating non-randomized studies. This scale assesses studies across three key domains: the selection of study groups, comparability of groups, and the ascertainment of exposure and outcome variables. Each study was assigned a quality score ranging from 0 to 9 points, with higher scores indicating stronger methodological rigor and more reliable reporting. The NOS incorporates essential criteria for case-control, cross-sectional, and cohort studies, such as the representativeness of case selection, the definition of control groups, controlling for key confounders, and the accuracy of exposure status measurement.

For the assessment of randomized controlled trials (RCTs), the Jadad checklist was employed. This tool consists of eight questions that evaluate critical aspects of study design and reporting. The Jadad scale assigns scores ranging from 0 to 8, with scores below 4 indicating low-quality studies, scores between 4 and 6 reflecting moderate quality, and scores of 7 or above representing high-quality studies [32, 33].

Two independent reviewers applied these standardized criteria to each study. Any discrepancies in scoring were resolved through discussion, with a third reviewer consulted if consensus could not be reached. Based on the total scores, studies were classified into categories of high, moderate, or low methodological quality.

This thorough and standardized quality assessment process provided an objective means of identifying potential biases within and across studies. Additionally, it allowed for meaningful subgroup analyses, enabling us to explore whether the strength of associations differed according to study quality ratings.

Statistical method

To ensure the validity and reliability of our systematic review and meta-analysis findings, we employed rigorous statistical and graphical techniques to assess heterogeneity comprehensively. For studies that reported effect estimates separately for different time periods of exposure, we conducted meta-analyses methods to synthesize the stratified estimates into overall effects within each study. This approach maximized the inclusion of data without duplicating participant populations. Similarly, if studies provided results stratified by important covariates, but did not report an overall estimate, we performed meta-analyses to combine the stratified effects. In cases where studies presented raw exposure and outcome group data without a calculated effect size, we used RevMan software to generate risk ratio estimates with 95% confidence intervals.

To assess between-study heterogeneity, we utilized both statistical tests and visual inspection of forest plots. The Chi-square test was used to assess whether the observed differences could be attributed to chance, with a significance level of P < 0.10 indicating the presence of statistically significant heterogeneity. Additionally, we calculated the I2 statistic to quantify the percentage of total variation attributed to heterogeneity rather than sampling error. If significant heterogeneity was detected, we selected random-effects models for meta-analyses. We carefully examined forest plots to visually assess the overlap and distribution of confidence intervals across studies. Any potential outliers were further investigated through meta-regression model, subgroup analyses, and sensitivity analyses to identify potential sources of heterogeneity.

To explore the impact of covariates on heterogeneity, we conducted univariate and multivariate meta-regression using Stata software. Covariates such as study year, sample size, study quality score, geographical region, average age of participants, estrogen receptor (ER) status, Progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, follow-up duration, and average insulin use duration were examined. Sensitivity analyses were performed by excluding each study individually to evaluate the influence of any single study. Additionally, subgroup analyses were conducted based on the variables examined in the meta-regression. We also assessed publication bias through inspection of funnel plots for asymmetry and conducted Egger’s and Begg’s tests to statistically evaluate potential bias. To address the potential bias from missing unpublished studies, we imputed theoretical missing studies using Metatarium (trim and fill method) and recalculated pooled estimates. In cases of significant heterogeneity, we reported the most conservative of fixed and random-effects models based on the highest significance level. Variables with missing data were excluded from analyses such as meta-regression and subgroup analysis. Data analysis was conducted using Stata 17 software.

Results

Characteristics of included studies

A comprehensive electronic search was conducted using specific keywords, resulting in a total of 2,775 articles. After removing duplicates (963 articles), 1,812 articles remained for further evaluation. These articles underwent screening based on predefined criteria, leading to the exclusion of 1,759 articles. Ultimately, 53 relevant articles were identified. Among these, 19 articles were excluded due to not reporting effect size or inability to calculate it, 13 articles included non-diabetic patients. This selection process yielded 21 articles for the study. An additional study was identified through reference checking, resulting in a total of 22 articles for the systematic review and meta-analysis [24, 26–31, 34–48] (Fig 1).

thumbnail
Download:
Fig 1. Flowchart depicting the selected studies for meta-analysis.

https://doi.org/10.1371/journal.pone.0314565.g001

In this research, data from 22 studies conducted between 2002 and 2023, involving a total of 159,674 participants, were analyzed. Of the 22 studies included in the analysis, 19 had a cohort design, one was cross-sectional, one was a clinical trial, and one was a case-control study. The average age of the participants was 57.3 years (ranging from 49.4 to 77.4 years). On average, patients were followed for 77.4 months, with a minimum follow-up of 6 months and a maximum of 180 months (Table 1).

thumbnail
Download:
Table 1. Characteristics of the studies included in the meta-analysis to investigate the relationship between insulin intake and overall mortality, breast cancer mortality, and breast cancer recurrence.

https://doi.org/10.1371/journal.pone.0314565.t001

Of the 22 studies reviewed, 21 studies (95.45%) documented insulin use through patient registries, medical records, or patient files, while only one relied on a questionnaire for this information. All the studies confirmed that breast cancer patient records were centrally reviewed and assessed. The studies reported that patients had been on insulin therapy for an average of 69.38 months, with durations ranging from 6 to 216 months. To calculate the effect size for the association between insulin use and the outcomes studied, adjustments were made for ER status in 12 studies, PR status in 13 studies, and HER2 status in 12 studies (Table 2).

thumbnail
Download:
Table 2. Summary of data collection and adjustment factors in included studies in the meta-analysis.

https://doi.org/10.1371/journal.pone.0314565.t002

Insulin intake and overall mortality.

A total of 16 studies were analyzed to examine the association between insulin intake and overall mortality risk in diabetic patients with breast cancer. These studies were conducted between 2002 and 2023 in various countries including the United States of America, Canada, Denmark, Taiwan, China, Finland, and South Korea. The studies included a total of 119,564 participants [26, 28–31, 34–36, 39–45, 48] (Tables 1–4).

thumbnail
Download:
Table 3. Relative risk of insulin intake on overall mortality, breast cancer mortality, and breast cancer recurrence in included studies.

https://doi.org/10.1371/journal.pone.0314565.t003

thumbnail
Download:
Table 4. Adjusted variables in included studies in the meta-analysis.

https://doi.org/10.1371/journal.pone.0314565.t004

The study found that diabetic patients with breast cancer who received insulin had a 1.65 (95% CI: 1.36–2.02; P-value <0.001) times higher risk of overall mortality compared to those who did not receive insulin (Fig 2). There was no evidence of publication bias for the association between insulin intake and overall mortality in diabetic patients with breast cancer, as shown by Begg’s test (p-value = 0.392). However, Egger’s test (p-value = 0.036) did indicate publication bias (Fig 3). Additionally, the modified effect size estimated using the metatrim command (trim and fill method) did not differ from the effect size estimated using the usual meta-analysis method.

thumbnail
Download:
Fig 2. Relationship between insulin intake and overall mortality, breast cancer mortality, and breast cancer recurrence.

https://doi.org/10.1371/journal.pone.0314565.g002

thumbnail
Download:
Fig 3. Evaluation of publication bias in meta-analysis studies of the relationship between insulin intake and overall mortality.

https://doi.org/10.1371/journal.pone.0314565.g003

In the meta-regression analysis, we evaluated several factors, including study year, sample size, study quality score, geographical region, average age of participants, ER status, PR status, HER2 status, follow-up duration, and average insulin use duration. In this analysis, sample size, and study quality score showed a significant relationship with heterogeneity (P-value ≤ 0.10) (S11 Table in S1 File).

Sensitivity analysis

Sensitivity analysis was carried out by removing each study from the analysis one by one during each run. However, the estimated RR did not vary considerably, indicating that the meta-analysis results were robust (Fig 4).

thumbnail
Download:
Fig 4. Results of sensitivity analysis for the relationship between insulin intake and overall mortality.

https://doi.org/10.1371/journal.pone.0314565.g004

The subgroup analysis revealed a significant increase in the risk of overall mortality in studies with a sample size of less than 1000 participants, conducted in 2016 or later, carried out in Asian countries, received good-quality scores, had a follow-up period of five years or longer, and involved participants with an average age below 60 years. The results of subgroup analysis for other study variables can be seen in Table 5.

thumbnail
Download:
Table 5. Subgroup analysis of the association between insulin intake and overall mortality, breast cancer mortality, and breast cancer recurrence.

https://doi.org/10.1371/journal.pone.0314565.t005

Insulin intake and breast cancer mortality.

A total of 14 studies were analyzed to examine the association between insulin intake and breast cancer mortality risk in diabetic patients with breast cancer. These studies were conducted between 2005 and 2021 in various countries including the United States of America, Canada, Taiwan, China, and Finland. The studies included a total of 96,932 participants [24, 27–29, 35–41, 45–47] (Tables 1–4).

In the study on the relationship between insulin intake and breast cancer mortality in diabetic patients, it was found that the group receiving insulin had a 1.22 (95% CI: 1.05–1.42; P-value = 0.009) times higher risk of breast cancer mortality compared to those not receiving insulin (Fig 2). There was no evidence of publication bias, as indicated by Begg’s test (p-value = 0.381) and Egger’s test (p-value = 0.587) (Fig 5).

thumbnail
Download:
Fig 5. Evaluation of publication bias in meta-analysis studies of the relationship between insulin intake and breast cancer mortality.

https://doi.org/10.1371/journal.pone.0314565.g005

During the meta-regression analysis, none of the variables showed a significant association with heterogeneity (P-value >0.10) (S12 Table in S1 File).

Sensitivity analysis

Sensitivity analysis did not reveal any significant changes in the study results (Fig 6).

thumbnail
Download:
Fig 6. Results of sensitivity analysis for the relationship between insulin intake and breast cancer mortality.

https://doi.org/10.1371/journal.pone.0314565.g006

The subgroup analysis indicated a significant increase in the risk of breast cancer mortality in studies with a sample size of 1000 participants or more, conducted in 2016 or later, conducted in USA and Canada, received high-quality scores, had a follow-up period of five years or longer, and involved participants with an average age of 60 years or more. The results of subgroup analysis for other study variables can be seen in Table 5.

Insulin intake and breast cancer recurrence.

A total of 6 studies were analyzed to examine the association between insulin intake and breast cancer recurrence risk in diabetic patients with breast cancer. These studies were conducted between 2002 and 2021 in various countries including the United States of America, Canada, and China. The studies included a total of 5,632 participants [24, 26, 30, 37, 38, 44] (Tables 1–4).

In the study examining the relationship between insulin intake and breast cancer recurrence in diabetic patients, it was observed that the group receiving insulin had a 1.45 (95% CI: 1.19–1.77; P-value <0.001) times higher risk of breast cancer recurrence compared to those not receiving insulin (Fig 2). There was no evidence of publication bias as indicated by Begg’s test (p-value = 0.452) and Egger’s test (p-value = 0.078) (Fig 7).

thumbnail
Download:
Fig 7. Evaluation of publication bias in meta-analysis studies of the relationship between insulin intake and breast cancer recurrence.

https://doi.org/10.1371/journal.pone.0314565.g007

Considering that six studies were investigated regarding the relationship between insulin intake and breast cancer recurrence in diabetic patients, it is not possible to perform a meta-regression model due to insufficient observations.

Sensitivity analysis

Sensitivity analysis did not reveal any significant changes in the study results (Fig 8).

thumbnail
Download:
Fig 8. Results of sensitivity analysis for the relationship between insulin intake and breast cancer recurrence.

https://doi.org/10.1371/journal.pone.0314565.g008

The subgroup analysis indicated a notable increase in the risk of breast cancer recurrence in studies with a sample size of 500 participants or more, conducted in 2015 or earlier, conducted in USA or Canada, received high-quality scores, and had a follow-up period of five years or longer. The results of subgroup analysis for other study variables can be seen in Table 5.

Discussion

This systematic review and meta-analysis aimed to investigate the relationship between insulin use and various outcomes in diabetic patients with breast cancer. Across the studies included in this analysis, patients had been on insulin therapy for an average of 69.38 months. Insulin use was documented in 95.45% of the studies, primarily through patient registration systems, medical records, or patient files. Notably, all studies confirmed that breast cancer patient records were centrally reviewed and assessed. Our findings revealed significant associations between insulin use and increased risks of overall mortality, breast cancer-specific mortality, and breast cancer recurrence.

In recent years, extensive research has explored the association between diabetes and the risk of cancer incidence and mortality [7, 49–53]. Studies consistently demonstrate that individuals with diabetes have a higher risk of developing certain types of cancer, including pancreatic, breast, and liver cancer [7, 51, 54, 55]. Several factors may contribute to this increased risk, such as the activation of insulin and insulin-like growth factor pathways, dysregulation of sex hormones, elevated blood glucose levels, and chronic inflammation commonly observed in diabetes mellitus patients [56, 57]. Since antidiabetic drugs affect some of these pathophysiological processes, researchers are interested in investigating the relationship between these treatments and cancer incidence and mortality. Exploring the impact of these drugs on overall mortality, breast cancer-specific mortality, and breast cancer recurrence in diabetic patients is highly relevant.

In this study, we examine the association between insulin use and the risk of recurrence and mortality in diabetic patients with breast cancer. Our findings reveal that diabetic patients with breast cancer who received insulin had a higher risk of overall mortality compared to those who did not receive insulin. Specifically, these patients had a 1.65 times higher risk of mortality. These findings align with previous studies. A systematic review and meta-analysis by Wang L et al. found that the risk of mortality in individuals receiving insulin was 52% higher compared to those who did not receive insulin [58]. Similar results have been observed in other studies [26, 29, 30, 42, 43].

Our study also identified a link between prolonged insulin use and increased overall mortality. This may be due to several factors: prolonged insulin therapy could influence cancer progression by affecting insulin and insulin-like growth factor pathways, which are involved in cell proliferation and survival. Additionally, extended insulin use may indicate poorly controlled diabetes, a known risk factor for adverse health outcomes. Patients requiring long-term insulin therapy may experience more complications from both diabetes and cancer, contributing to higher mortality rates [56, 57]. These findings emphasize the importance of carefully managing insulin therapy in diabetic breast cancer patients and highlight the need for individualized treatment plans to optimize both glycemic control and cancer outcomes. Further research is needed to explore the underlying mechanisms and develop strategies to mitigate these risks.

Regarding breast cancer mortality, the study found that insulin intake was associated with a 1.22 times higher risk compared to those not receiving insulin. This is consistent with findings from Wang L et al., who reported a 33% higher risk of breast cancer mortality in individuals receiving insulin [58]. Other studies have shown similar results [37, 38].

The overall mortality risk is higher than the specific mortality risk from breast cancer (65% vs. 22%). Mortality from all causes includes deaths from cardiovascular diseases, which account for the majority of deaths in diabetic and cancer patients [59–62]. Unfortunately, the inability to report the effect size of cardiovascular mortality in primary studies prevented the inclusion of this information. Typically, diabetic patients who cannot control their disease with oral drugs receive insulin [63, 64]. These patients are often older with other underlying diseases, increasing their risk of death due to cardiovascular diseases [59, 63]. Studies suggest that exogenous insulin may lead to an increased risk of death from all causes and cardiovascular diseases in diabetic patients [65, 66].

In terms of breast cancer recurrence, the study revealed that insulin intake was associated with a 1.45 times higher risk compared to those not receiving insulin. This finding is consistent with Wang L et al., who found an 80% higher risk of recurrence in individuals receiving insulin [58]. This result is in line with the results of other studies conducted in this field [24, 26].

The increased risk of total mortality, breast cancer-specific mortality, and recurrence in diabetic patients with breast cancer receiving insulin compared to non-insulin users can be attributed to several factors. Firstly, insulin is a hormone that plays a crucial role in regulating glucose metabolism [67]. However, it also has potent mitogenic effects, which means it can promote cell growth and proliferation [68]. In the context of breast cancer, high insulin levels may stimulate cancer cell growth and progression, leading to higher mortality rates and increased recurrence risk [68, 69]. Insulin resistance, common in type 2 diabetes, is associated with elevated insulin levels and can create an unfavorable tumor microenvironment characterized by increased inflammation and impaired immune response, facilitating tumor growth and treatment resistance [11, 70].

Moreover, diabetic patients requiring insulin therapy often have more advanced or poorly controlled diabetes. Poorly controlled blood sugar levels and other diabetes-related complications, such as cardiovascular disease, may contribute to increased mortality and recurrence rates. The underlying mechanisms of diabetes, including chronic inflammation and dysregulation of growth factors, can exacerbate the cancer-promoting effects of insulin, increasing the risk of adverse outcomes [65–67, 69, 70].

It is important to note that individual variations, such as the specific characteristics of the breast cancer, the overall health status, and other comorbidities, can also influence the differences in mortality and recurrence rates between the insulin and non-insulin groups. Further research is needed to fully understand the complex interplay between insulin, diabetes, and breast cancer, and to develop targeted strategies for improving outcomes in these patients.

Conclusion

Overall, the meta-analysis review study provides significant evidence linking insulin intake to increased overall mortality, breast cancer mortality, and breast cancer recurrence in diabetic patients with breast cancer. The findings shed light on the potential risks associated with insulin use in this specific population and underscore the importance of further research and careful consideration of treatment options for these patients.

Supporting information

S1 File. This supplementary file contains all the data extracted from the studies included in the meta-analysis.

It comprises several tables that provide comprehensive information on study characteristics, inclusion and exclusion criteria, prevalence of underlying diseases, data collection methods, adjustment factors, and the results of statistical analyses. Tables also include the quality assessment of the studies using the Newcastle-Ottawa Scale (NOS), effect size calculations, and meta-regression analyses. Additionally, the PRISMA 2020 checklist and a list of all identified studies are provided.

https://doi.org/10.1371/journal.pone.0314565.s001

(PDF)

References

  1. 1. Arnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast. 2022;66:15–23. Epub 20220902. pmid:36084384; PubMed Central PMCID: PMC9465273.
  2. 2. Coughlin SS, Ekwueme DU. Breast cancer as a global health concern. Cancer Epidemiol. 2009;33(5):315–8. Epub 20091107. pmid:19896917.
  3. 3. Lovic D, Piperidou A, Zografou I, Grassos H, Pittaras A, Manolis A. The Growing Epidemic of Diabetes Mellitus. Curr Vasc Pharmacol. 2020;18(2):104–9. pmid:30961501.
  4. 4. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. Epub 20171208. pmid:29219149.
  5. 5. Gurney J, Stanley J, Teng A, Krebs J, Koea J, Lao C, et al. Cancer and diabetes co-occurrence: A national study with 44 million person-years of follow-up. PloS one. 2022;17(11):e0276913. Epub 20221128. pmid:36441693; PubMed Central PMCID: PMC9704677.
  6. 6. Liao S, Li J, Wei W, Wang L, Zhang Y, Li J, et al. Association between diabetes mellitus and breast cancer risk: a meta-analysis of the literature. Asian Pac J Cancer Prev. 2011;12(4):1061–5. pmid:21790252.
  7. 7. García-Esquinas E, Guinó E, Castaño-Vinyals G, Pérez-Gómez B, Llorca J, Altzibar JM, et al. Association of diabetes and diabetes treatment with incidence of breast cancer. Acta Diabetol. 2016;53(1):99–107. Epub 20150429. pmid:25916213; PubMed Central PMCID: PMC4749645.
  8. 8. Eketunde AO. Diabetes as a Risk Factor for Breast Cancer. Cureus. 2020;12(5):e8010. Epub 20200507. pmid:32528752; PubMed Central PMCID: PMC7279688.
  9. 9. Kang C, LeRoith D, Gallagher EJ. Diabetes, Obesity, and Breast Cancer. Endocrinology. 2018;159(11):3801–12. pmid:30215698; PubMed Central PMCID: PMC6202853.
  10. 10. Gallo M, Muscogiuri G, Felicetti F, Faggiano A, Trimarchi F, Arvat E, et al. Adverse glycaemic effects of cancer therapy: indications for a rational approach to cancer patients with diabetes. Metabolism. 2018;78:141–54. Epub 20171007. pmid:28993227.
  11. 11. Samuel SM, Varghese E, Varghese S, Büsselberg D. Challenges and perspectives in the treatment of diabetes associated breast cancer. Cancer Treat Rev. 2018;70:98–111. Epub 20180810. pmid:30130687.
  12. 12. Shahid RK, Ahmed S, Le D, Yadav S. Diabetes and Cancer: Risk, Challenges, Management and Outcomes. Cancers (Basel). 2021;13(22). Epub 20211116. pmid:34830886; PubMed Central PMCID: PMC8616213.
  13. 13. Aziz NM. Cancer survivorship research: state of knowledge, challenges and opportunities. Acta Oncol. 2007;46(4):417–32. pmid:17497308.
  14. 14. Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, et al. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016;66(4):271–89. Epub 20160602. pmid:27253694.
  15. 15. Lipscombe LL, Goodwin PJ, Zinman B, McLaughlin JR, Hux JE. The impact of diabetes on survival following breast cancer. Breast Cancer Res Treat. 2008;109(2):389–95. Epub 20070721. pmid:17659440.
  16. 16. Cho J, Jung SY, Lee JE, Shim EJ, Kim NH, Kim Z, et al. A review of breast cancer survivorship issues from survivors’ perspectives. J Breast Cancer. 2014;17(3):189–99. Epub 20140930. pmid:25320616; PubMed Central PMCID: PMC4197348.
  17. 17. Radice D, Redaelli A. Breast cancer management: quality-of-life and cost considerations. Pharmacoeconomics. 2003;21(6):383–96. pmid:12678566.
  18. 18. Kenyon M, Mayer DK, Owens AK. Late and long-term effects of breast cancer treatment and surveillance management for the general practitioner. J Obstet Gynecol Neonatal Nurs. 2014;43(3):382–98. Epub 20140401. pmid:24690072.
  19. 19. Lipscombe LL, Fischer HD, Austin PC, Fu L, Jaakkimainen RL, Ginsburg O, et al. The association between diabetes and breast cancer stage at diagnosis: a population-based study. Breast Cancer Res Treat. 2015;150(3):613–20. Epub 20150317. pmid:25779100.
  20. 20. Xue F, Michels KB. Diabetes, metabolic syndrome, and breast cancer: a review of the current evidence. Am J Clin Nutr. 2007;86(3):s823–35. pmid:18265476.
  21. 21. Treggiari MM, Karir V, Yanez ND, Weiss NS, Daniel S, Deem SA. Intensive insulin therapy and mortality in critically ill patients. Crit Care. 2008;12(1):R29. Epub 20080229. pmid:18312617; PubMed Central PMCID: PMC2374630.
  22. 22. Sims EK, Carr ALJ, Oram RA, DiMeglio LA, Evans-Molina C. 100 years of insulin: celebrating the past, present and future of diabetes therapy. Nat Med. 2021;27(7):1154–64. Epub 20210715. pmid:34267380; PubMed Central PMCID: PMC8802620.
  23. 23. Wilson LM, Castle JR. Recent Advances in Insulin Therapy. Diabetes Technol Ther. 2020;22(12):929–36. Epub 20200512. pmid:32310681; PubMed Central PMCID: PMC7864088.
  24. 24. Mu L, Zhu N, Zhang J, Xing F, Li D, Wang X. Type 2 diabetes, insulin treatment and prognosis of breast cancer. Diabetes Metab Res Rev. 2017;33(1). Epub 20160620. pmid:27184049.
  25. 25. Novosyadlyy R, Lann DE, Vijayakumar A, Rowzee A, Lazzarino DA, Fierz Y, et al. Insulin-mediated acceleration of breast cancer development and progression in a nonobese model of type 2 diabetes. Cancer Res. 2010;70(2):741–51. Epub 20100112. pmid:20068149; PubMed Central PMCID: PMC2946167.
  26. 26. Goodwin PJ, Ennis M, Pritchard KI, Trudeau ME, Koo J, Madarnas Y, et al. Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study. J Clin Oncol. 2002;20(1):42–51. pmid:11773152.
  27. 27. Swerdlow AJ, Laing SP, Qiao Z, Slater SD, Burden AC, Botha JL, et al. Cancer incidence and mortality in patients with insulin-treated diabetes: a UK cohort study. Br J Cancer. 2005;92(11):2070–5. pmid:15886700; PubMed Central PMCID: PMC2361792.
  28. 28. Lega IC, Austin PC, Gruneir A, Goodwin PJ, Rochon PA, Lipscombe LL. Association between metformin therapy and mortality after breast cancer: a population-based study. Diabetes Care. 2013;36(10):3018–26. Epub 20130430. pmid:23633525; PubMed Central PMCID: PMC3781496.
  29. 29. Luo J, Virnig B, Hendryx M, Wen S, Chelebowski R, Chen C, et al. Diabetes, diabetes treatment and breast cancer prognosis. Breast Cancer Res Treat. 2014;148(1):153–62. Epub 20140927. pmid:25261292; PubMed Central PMCID: PMC4393950.
  30. 30. Calip GS, Yu O, Hoskins KF, Boudreau DM. Associations between diabetes medication use and risk of second breast cancer events and mortality. Cancer Causes Control. 2015;26(8):1065–77. Epub 20150509. pmid:25956271; PubMed Central PMCID: PMC4501774.
  31. 31. Cleveland RJ, North KE, Stevens J, Teitelbaum SL, Neugut AI, Gammon MD. The association of diabetes with breast cancer incidence and mortality in the Long Island Breast Cancer Study Project. Cancer Causes Control. 2012;23(7):1193–203. Epub 20120522. pmid:22674293; PubMed Central PMCID: PMC3383781.
  32. 32. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, McQuay HJ. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Controlled clinical trials. 1996;17(1):1–12. Epub 1996/02/01. pmid:8721797.
  33. 33. Pakfetrat A DZ, Shakeri MT, sarraf shirazi A, Mosannen Mozaffari P, Moeintaghavi A, Karimi Mobarekeh B. Critical Appraisal of Iranian Dentistry Clinical Trials Published in English from 1999 to 2012. Journal of Mashhad Dental School. 2017;42(1):19–30.
  34. 34. Ranc K, Jørgensen ME, Friis S, Carstensen B. Mortality after cancer among patients with diabetes mellitus: effect of diabetes duration and treatment. Diabetologia. 2014;57(5):927–34. Epub 20140315. pmid:24633676.
  35. 35. Tseng CH. Use of Insulin and Mortality from Breast Cancer among Taiwanese Women with Diabetes. J Diabetes Res. 2015;2015:678756. Epub 20150611. pmid:26171401; PubMed Central PMCID: PMC4480938.
  36. 36. Vissers PA, Cardwell CR, van de Poll-Franse LV, Young IS, Pouwer F, Murray LJ. The association between glucose-lowering drug use and mortality among breast cancer patients with type 2 diabetes. Breast Cancer Res Treat. 2015;150(2):427–37. Epub 20150312. pmid:25762476.
  37. 37. Chen L, Chubak J, Boudreau DM, Barlow WE, Weiss NS, Li CI. Diabetes Treatments and Risks of Adverse Breast Cancer Outcomes among Early-Stage Breast Cancer Patients: A SEER-Medicare Analysis. Cancer Res. 2017;77(21):6033–41. Epub 20170921. pmid:28935814; PubMed Central PMCID: PMC5668156.
  38. 38. Sonnenblick A, Agbor-Tarh D, Bradbury I, Di Cosimo S, Azim HA Jr., Fumagalli D, et al. Impact of Diabetes, Insulin, and Metformin Use on the Outcome of Patients With Human Epidermal Growth Factor Receptor 2-Positive Primary Breast Cancer: Analysis From the ALTTO Phase III Randomized Trial. J Clin Oncol. 2017;35(13):1421–9. Epub 20170313. pmid:28375706; PubMed Central PMCID: PMC5455460.
  39. 39. Baglia ML, Cui Y, Zheng T, Yang G, Li H, You M, et al. Diabetes Medication Use in Association with Survival among Patients of Breast, Colorectal, Lung, or Gastric Cancer. Cancer Res Treat. 2019;51(2):538–46. Epub 20180702. pmid:29986576; PubMed Central PMCID: PMC6473299.
  40. 40. Hosio M, Urpilainen E, Hautakoski A, Marttila M, Arffman M, Sund R, et al. Survival after breast cancer in women with type 2 diabetes using antidiabetic medication and statins: a retrospective cohort study. Acta Oncol. 2020;59(9):1110–7. Epub 20200601. pmid:32478629.
  41. 41. Lawrence WR, Hosler AS, Gates Kuliszewski M, Leinung MC, Zhang X, Schymura MJ, et al. Impact of preexisting type 2 diabetes mellitus and antidiabetic drugs on all-cause and cause-specific mortality among Medicaid-insured women diagnosed with breast cancer. Cancer Epidemiol. 2020;66:101710. Epub 20200401. pmid:32247208; PubMed Central PMCID: PMC9920233.
  42. 42. Choi M, Han J, Yang BR, Jang MJ, Kim M, Lee DW, et al. Association of Insulin, Metformin, and Statin with Mortality in Breast Cancer Patients. Cancer Res Treat. 2021;53(1):65–76. Epub 20200923. pmid:32972040; PubMed Central PMCID: PMC7812023.
  43. 43. Kim BH, Cho MJ, Kwon J. Potential intrinsic subtype dependence on the association between metformin use and survival in surgically resected breast cancer: a Korean national population-based study. Int J Clin Oncol. 2021;26(11):2004–16. Epub 20210810. pmid:34374879.
  44. 44. Hui T, Shang C, Yang L, Wang M, Li R, Song Z. Metformin improves the outcomes in Chinese invasive breast cancer patients with type 2 diabetes mellitus. Sci Rep. 2021;11(1):10034. Epub 20210511. pmid:33976288; PubMed Central PMCID: PMC8113316.
  45. 45. Hosio M, Urpilainen E, Hautakoski A, Marttila M, Arffman M, Sund R, et al. Association of antidiabetic medication and statins with survival from ductal and lobular breast carcinoma in women with type 2 diabetes. Sci Rep. 2021;11(1):10445. Epub 20210517. pmid:34001921; PubMed Central PMCID: PMC8129135.
  46. 46. He X, Esteva FJ, Ensor J, Hortobagyi GN, Lee MH, Yeung SC. Metformin and thiazolidinediones are associated with improved breast cancer-specific survival of diabetic women with HER2+ breast cancer. Ann Oncol. 2012;23(7):1771–80. Epub 20111122. pmid:22112968; PubMed Central PMCID: PMC3387820.
  47. 47. Xu H, Aldrich MC, Chen Q, Liu H, Peterson NB, Dai Q, et al. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc. 2015;22(1):179–91. Epub 20140722. pmid:25053577; PubMed Central PMCID: PMC4433365.
  48. 48. Chen Y, Fang M, Ke Y. Association of high insulin receptor expression with poor prognosis in patients with breast cancer. Gland Surg. 2023;12(2):252–62. Epub 20230215. pmid:36915808; PubMed Central PMCID: PMC10005982.
  49. 49. Ali S, Coory M, Donovan P, Na R, Pandeya N, Pearson SA, et al. Association between unstable diabetes mellitus and risk of pancreatic cancer. Pancreatology. 2024;24(1):66–72. Epub 20231117. pmid:38000983.
  50. 50. Kashfi K, Rosen CL, Aslan M. Obesity, Type-2 Diabetes and Cancer: Mechanistic Insights. Crit Rev Oncog. 2019;24(3):285–305. pmid:32422026.
  51. 51. Ramos-Garcia P, Roca-Rodriguez MDM, Aguilar-Diosdado M, Gonzalez-Moles MA. Diabetes mellitus and oral cancer/oral potentially malignant disorders: A systematic review and meta-analysis. Oral Dis. 2021;27(3):404–21. Epub 20200218. pmid:31994293.
  52. 52. Suh S, Kim KW. Diabetes and Cancer: Cancer Should Be Screened in Routine Diabetes Assessment. Diabetes Metab J. 2019;43(6):733–43. pmid:31902143; PubMed Central PMCID: PMC6943263.
  53. 53. Yilmaz Kavcar SR, Acikgoz A, Ergor G, Unal B. Evaluating the Relationship between Diabetes and Cancer in a Cohort. Asian Pac J Cancer Prev. 2022;23(4):1223–9. Epub 20220401. pmid:35485679; PubMed Central PMCID: PMC9375631.
  54. 54. Zhao XB, Ren GS. Diabetes mellitus and prognosis in women with breast cancer: A systematic review and meta-analysis. Medicine (Baltimore). 2016;95(49):e5602. pmid:27930583; PubMed Central PMCID: PMC5266055.
  55. 55. Ling S, Brown K, Miksza JK, Howells L, Morrison A, Issa E, et al. Association of Type 2 Diabetes With Cancer: A Meta-analysis With Bias Analysis for Unmeasured Confounding in 151 Cohorts Comprising 32 Million People. Diabetes Care. 2020;43(9):2313–22. pmid:32910779.
  56. 56. Cohen DH, LeRoith D. Obesity, type 2 diabetes, and cancer: the insulin and IGF connection. Endocr Relat Cancer. 2012;19(5):F27–45. Epub 20120905. pmid:22593429.
  57. 57. Giovannucci E, Harlan DM, Archer MC, Bergenstal RM, Gapstur SM, Habel LA, et al. Diabetes and cancer: a consensus report. Diabetes Care. 2010;33(7):1674–85. pmid:20587728; PubMed Central PMCID: PMC2890380.
  58. 58. Wang L, Zhang HJ, Liu YF, Chen GY. Impact of insulin use on outcomes of diabetic breast cancer patients: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2021;25(9):3507–18. pmid:34002825.
  59. 59. Ali Raza J, Movahed A. Current concepts of cardiovascular diseases in diabetes mellitus. Int J Cardiol. 2003;89(2–3):123–34. pmid:12767534.
  60. 60. Bethel MA, Patel RA, Merrill P, Lokhnygina Y, Buse JB, Mentz RJ, et al. Cardiovascular outcomes with glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes: a meta-analysis. Lancet Diabetes Endocrinol. 2018;6(2):105–13. Epub 20171206. pmid:29221659.
  61. 61. Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson AM, Miftaraj M, et al. Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes. N Engl J Med. 2017;376(15):1407–18. pmid:28402770.
  62. 62. Ricci L, Scatena A, Tacconi D, Ventoruzzo G, Liistro F, Bolognese L, et al. All-cause and cardiovascular mortality in a consecutive series of patients with diabetic foot osteomyelitis. Diabetes Res Clin Pract. 2017;131:12–7. Epub 20170615. pmid:28668718.
  63. 63. Korytkowski M. When oral agents fail: practical barriers to starting insulin. Int J Obes Relat Metab Disord. 2002;26 Suppl 3:S18–24. pmid:12174319.
  64. 64. Karter AJ, Subramanian U, Saha C, Crosson JC, Parker MM, Swain BE, et al. Barriers to insulin initiation: the translating research into action for diabetes insulin starts project. Diabetes Care. 2010;33(4):733–5. Epub 20100119. pmid:20086256; PubMed Central PMCID: PMC2845015.
  65. 65. Herman MEO’Keefe JH, Bell DSH, Schwartz SS. Insulin Therapy Increases Cardiovascular Risk in Type 2 Diabetes. Prog Cardiovasc Dis. 2017;60(3):422–34. Epub 20170925. pmid:28958751.
  66. 66. Gamble JM, Simpson SH, Eurich DT, Majumdar SR, Johnson JA. Insulin use and increased risk of mortality in type 2 diabetes: a cohort study. Diabetes Obes Metab. 2010;12(1):47–53. Epub 20090924. pmid:19788429.
  67. 67. Rahman MS, Hossain KS, Das S, Kundu S, Adegoke EO, Rahman MA, et al. Role of Insulin in Health and Disease: An Update. Int J Mol Sci. 2021;22(12). Epub 20210615. pmid:34203830; PubMed Central PMCID: PMC8232639.
  68. 68. Khandwala HM, McCutcheon IE, Flyvbjerg A, Friend KE. The effects of insulin-like growth factors on tumorigenesis and neoplastic growth. Endocr Rev. 2000;21(3):215–44. pmid:10857553.
  69. 69. Pan K, Chlebowski RT, Mortimer JE, Gunter MJ, Rohan T, Vitolins MZ, et al. Insulin resistance and breast cancer incidence and mortality in postmenopausal women in the Women’s Health Initiative. Cancer. 2020;126(16):3638–47. Epub 20200612. pmid:32530506.
  70. 70. Yee LD, Mortimer JE, Natarajan R, Dietze EC, Seewaldt VL. Metabolic Health, Insulin, and Breast Cancer: Why Oncologists Should Care About Insulin. Front Endocrinol (Lausanne). 2020;11:58. Epub 20200220. pmid:32153503; PubMed Central PMCID: PMC7045050.