Type 2 Diabetes, but Not Insulin (Analog) Treatment, Is Associated With More Advanced Stages of Breast Cancer: A National Linkage of Cancer and Pharmacy Registries

Does diabetes affect breast cancer survival?

OBJECTIVES

The objective of this study is to investigate the influence of diabetes on breast cancer-specific survival among women with breast cancer in Aotearoa/New Zealand.

METHODS

This study included women diagnosed with invasive breast cancer between 2005 and 2020, with their information documented in the Te Rēhita Mate Ūtaetae-Breast Cancer Foundation National Register. Breast cancer survival curves for women with diabetes and those without diabetes were generated using the Kaplan-Meier method. The hazard ratio (HR) of breast cancer-specific mortality for women with diabetes compared to women without diabetes was estimated using the Cox proportional hazards model.

RESULTS

For women with diabetes, the 5-year and 10-year of cancer-specific survival were 87% (95% CI: 85%-88%) and 79% (95% CI: 76%-81%) compared to 89% (95% CI: 89%-90%) and 84% (95% CI: 83%-85%) for women without diabetes. The HR of cancer-specific mortality for patients with diabetes compared to those without diabetes was 0.99 (95% CI: 0.89-1.11) after adjustment for patient demographics, tumor characteristics, and treatments. Age at cancer diagnosis and cancer stage had the biggest impact on the survival difference between the two groups. When stratified by cancer stage, the cancer-specific mortality between the two groups was similar.

CONCLUSIONS

While differences in survival have been identified for women with diabetes when compared to women without diabetes, these are attributable to age and the finding that women with diabetes tend to present with more advanced disease at diagnosis. We did not find any difference in survival between the two groups due to differences in treatment.

Diabetes mellitus and risk of breast cancer: a large-scale, prospective, population-based study

BACKGROUND

The objective of this study was to evaluate associations of diabetes overall, type 1 diabetes (T1D), and type 2 diabetes (T2D) with breast cancer (BCa) risk.

METHODS

We included 250,312 women aged 40-69 years between 2006 and 2010 from the UK Biobank cohort. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated for associations of diabetes and its two major types with the time from enrollment to incident BCa.

RESULTS

We identified 8182 BCa cases during a median follow-up of 11.1 years. We found no overall association between diabetes and BCa risk (aHR = 1.02, 95% CI = 0.92-1.14). When accounting for diabetes subtype, women with T1D had a higher risk of BCa than women without diabetes (aHR = 1.52, 95% CI = 1.03-2.23). T2D was not associated with BCa risk overall (aHR = 1.00, 95% CI = 0.90-1.12). However, there was a significantly increased risk of BCa in the short time window after T2D diagnosis.

CONCLUSIONS

Though we did not find an association between diabetes and BCa risk overall, an increased risk of BCa was observed shortly after T2D diagnosis. In addition, our data suggest that women with T1D may have an increased risk of BCa.

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study

OBJECTIVE

To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches.

DESIGN

Population based cohort study.

SETTING

QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers.

PARTICIPANTS

141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020.

MAIN OUTCOME MEASURES

Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility.

RESULTS

During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches.

CONCLUSION

In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

The Association between Diabetes Medication Use and Tumour Characteristics at Diagnosis in Patients with Urothelial Carcinoma: A Retrospective Registry-Based Study

BACKGROUND

Observational studies indicate a potential association between diabetes medication use and aggressiveness of bladder cancer.

OBJECTIVE

The objective is to exploratively study the association between diabetes medication use, as proxy for diabetes, and cancer characteristics of urothelial carcinoma at diagnosis. Furthermore, differences in associations between specific types of diabetes medication are studied.

METHODS

The association between use of diabetes medication and urothelial carcinoma (UC) characteristics at diagnosis is studied. A retrospective registry-based study among UC patients in the Netherlands was performed for which two large linked registries from PHARMO and IKNL were used. Patients diagnosed with UC between 2000 and 2016 and no previous cancer were included in this study. In this study, 1,168 UC patients who were diabetes medication users were included as well as 3,609 non-users. Conditional logistic regression analysis was performed to determine odds ratios comparing cancer characteristics between different types of diabetes medication users to non-users.

RESULTS

Noninsulin antidiabetic drugs (NIAD) use was associated with a muscle-invasive type of UC compared to non-users (OR = 1.31, 95% CI: 1.10-1.55 for T2+ versus Ta) as well as a poorly differentiated tumour (OR = 1.31, 95% CI: 1.07-1.59 for poorly versus well differentiated tumours).

CONCLUSION

Users of diabetes medication are potentially more likely to be diagnosed with a more aggressive tumour than non-users; however, lifestyle factors could not be adjusted for.

Fasting: From Physiology to Pathology

Overnutrition is a risk factor for various human diseases, including neurodegenerative diseases, metabolic disorders, and cancers. Therefore, targeting overnutrition represents a simple but attractive strategy for the treatment of these increasing public health threats. Fasting as a dietary intervention for combating overnutrition has been extensively studied. Fasting has been practiced for millennia, but only recently have its roles in the molecular clock, gut microbiome, and tissue homeostasis and function emerged. Fasting can slow aging in most species and protect against various human diseases, including neurodegenerative diseases, metabolic disorders, and cancers. These centuried and unfading adventures and explorations suggest that fasting has the potential to delay aging and help prevent and treat diseases while minimizing side effects caused by chronic dietary interventions. In this review, recent animal and human studies concerning the role and underlying mechanism of fasting in physiology and pathology are summarized, the therapeutic potential of fasting is highlighted, and the combination of pharmacological intervention and fasting is discussed as a new treatment regimen for human diseases.

Association of diabetes and breast cancer characteristics at diagnosis

PURPOSE

This study aims to examine the association of diabetes and breast cancer characteristics at diagnosis in Aotearoa/New Zealand.

METHODS

Patients diagnosed with invasive breast cancer between 2005 and 2020 were identified from the National Breast Cancer Register. Logistic regression modeling was used to estimate the adjusted odds ratio (OR) of having stage III-IV cancer and the OR of having stage IV cancer for women with diabetes compared to those without diabetes. The adjusted OR of having screen-detected breast cancers for patients aged 45-69 years with diabetes compared to patients without diabetes was estimated.

RESULTS

26,968 women were diagnosed with breast cancer, with 3,137 (11.6%) patients having diabetes at the time of cancer diagnosis. The probability of co-occurrence of diabetes and breast cancer increased with time. Māori, Pacific and Asian women were more likely to have diabetes than European/Others. The probability of having diabetes also increased with age. For patients with diabetes, the probability of being diagnosed with stage III-IV cancer and stage IV cancer was higher than for patients without diabetes (OR 1.14, 95% CI 1.03-1.27; and 1.17, 95% CI 1.00-1.38). Women aged 45-69 years with diabetes were more likely to have screen-detected cancer than those without diabetes (OR 1.13, 95% CI 1.02-1.26).

CONCLUSIONS

The co-occurrence of diabetes and breast cancer is becoming more common. Overall there is a small but significant adverse impact of having advanced disease for women with diabetes that is found at the time of breast cancer diagnosis, and this may contribute to other inequities that occur in the treatment pathway that may impact on patient outcomes.

The DIAbetes MANagement and Treatment (DIAMANT) Cohort

Introduction

The increasing number of people with diabetes and the unclear long-term safety and effectiveness of newer and older blood-glucose-lowering treatments emphasize the need for more pharmaco-epidemiological studies in this field. A prospective, regularly updated cohort of people with diabetes would provide quick and up-to-date information regarding prevalence, treatment, safety and effectiveness. The current aim was to describe the design of the DIAbetes MANagement and Treatment (DIAMANT) cohort.

Methods

The DIAMANT cohort is a population-based, dynamic, prospective cohort of persons with diabetes. It contains real-world data (RWD) from general practitioners (GP), including diagnoses, symptoms, examinations, communication to/from specialists and medication. Diabetes is defined as a recorded diabetes diagnosis or a prescription of drugs used in diabetes. The cohort is part of the national infrastructure of "Stichting Informatievoorziening voor Zorg en Onderzoek" (STIZON) and is linked to other data sources.

Results

Currently, the cohort enables access to information of 89,883 patients in 2004 to 344,914 in 2020 (6% T1D, 84% T2D and 10% unclassified type of diabetes), with 193,931 participants still registered as being present in the GP practice (active) in 2020. The frequency of follow-up of persons with diabetes is practice dependent. The Dutch guidelines advise 2-4 contacts per year with a more extensive yearly check-up. The DIAMANT cohort is updated several times a year. Anonymised data from the DIAMANT cohort are available to researchers.

Discussion

The DIAMANT cohort provides the opportunity to gain RWD insights into the treatment and outcomes among people with diabetes in daily general practice. The data can be enriched by established linkages to other data sources (eg, hospital data, the Perinatal Registry, the Cancer Registry). The DIAMANT cohort serves as a start of a national infrastructure to study, manage and provide personalised care in order to ultimately improve care and outcomes for people with diabetes.

Diabetes and Cancer - optimising glycaemic control

Diabetes and cancer are both common and increasingly prevalent conditions, but emerging epidemiological evidence confirms that the risk of developing a number of common cancers is increased in those with type 2 diabetes. The risk of cancer in type 1 diabetes is less clearly defined, and therefore this review will focus on type 2 diabetes. Emerging evidence also supports an influence of diabetes on outcomes of cancer treatment. However, this relationship is bi-directional, with cancer and its treatment impacting on glucose control, whilst there is also emerging evidence that diabetes care can deteriorate after a cancer diagnosis (summarised in Figure 1). Despite these clear links there is a lack of evidence to guide clinicians in how to manage patients with diabetes during their cancer treatment. Although recent UK guidelines have started to address this, with the development of guidance for the management of hyperglycaemia in cancer, there is a clear need for wider guidance on the management of multi-morbidity during cancer, including diabetes and obesity, to incorporate nutritional management We have therefore undertaken a narrative review of the evidence of links between type 2 diabetes and cancer incidence and outcomes, and discuss the challenges to diabetes care during cancer treatment. This article is protected by copyright. All rights reserved.

Exosomes as novel biomarkers in metabolic disease and obesity-related cancers

Exosomes include plasma-transported vesicles that are secreted by human tissues and reflect metabolic status. The profile of exosomes (particularly microRNA content) is altered in metabolic disease. In type 2 diabetes mellitus, exosomes circulating in plasma induce transcriptional changes related to tumour progression and pro-metastatic phenotypes in target cancer cells, potentially linking obesity to cancer progression and metastasis.

Survival Risk Analysis of Small Cell Lung Cancer Patients with Pre-Existing Type 2 Diabetes Mellitus: A Single-Center Retrospective Cohort Study

Purpose

We aimed to investigate the clinicopathological characteristics and survival risk factors in small cell lung cancer (SCLC) patients with preexisting type 2 diabetes mellitus (preDM).

Patients and Methods

All patients with SCLC admitted to our hospital between January 2013 and August 2018 were followed up until August 2020 and retrospectively analyzed. Clinical characteristics of SCLC patients with and without preDM were extracted. Cox proportional hazards models were conducted to identify potential independent prognostic factors.

Results

Of 628 eligible individuals, 88 individuals had preDM. preDM was independently significantly associated with distant metastasis in all SCLC patients (p =0.016, OR=1.80, 95% CI 1.11–2.91), while preDM did not affect the outcome of SCLC patients (p=0.803, HR=1.04, 95% CI 0.79–1.36) by multivariate analysis. In the preDM group, the median overall survival (OS) was shorter in the insulin group than in the non insulin group (13.93 months versus 21.77 months, p=0.024). Multivariate analysis identified that insulin treatment was an independent unfavorable factor associated with OS (p =0.009, HR=2.10, 95% CI 1.19–3.64). In addition, poorer performance status (PS) and liver metastasis were also independent unfavorable prognostic factors (all p<0.01), while thoracic therapy significantly improved OS and decreased mortality risk in diabetic patients with SCLC (p<0.05).

Conclusion

preDM may promote distant metastasis of SCLC while it is insulin therapy and not preDM which adversely affects the prognosis of SCLC patients. These findings indicate that enhancing blood glucose control and reducing insulin analog use may be essential to the improvement of the long-term survival of the diabetic population with SCLC.

Associations between comorbidities and advanced stage diagnosis of lung, breast, colorectal, and prostate cancer: A systematic review and meta-analysis

Comorbidities and advanced stage diagnosis (ASD) are both associated with poorer cancer outcomes, but the association between comorbidities and ASD is poorly understood. We summarized epidemiological evidence on the association between comorbidities and ASD of selected cancers in a systematic review and meta-analysis. We searched PubMed and Web of Science databases up to June 3rd, 2021 for studies assessing the association between comorbidities and ASD of lung, breast, colorectal, or prostate cancer. Summary odds ratios (ORs) and 95% confidence intervals (95%CIs) were calculated using random-effects models. Also, potential variations in the associations between comorbidities and ASD by cancer type were investigated using random-effects meta-regression. Thirty-seven studies were included in this review, including 8,069,397 lung, breast, colorectal, and prostate cancer patients overall. The Charlson comorbidity index score was positively associated with ASD (stages III-IV) of breast cancer but was inversely associated with ASD of lung cancer (p_interaction = 0.004). Regarding specific comorbidities, diabetes was positively associated with ASD (OR = 1.17, 95%CI = 1.09-1.26), whereas myocardial infarction was inversely associated with ASD (OR = 0.84, 95%CI = 0.75-0.95). The association between renal disease and ASD differed by cancer type (p_interaction < 0.001). A positive association was found with prostate cancer (OR = 2.02, 95%CI = 1.58-2.59) and an inverse association with colorectal cancer (OR = 0.84, 95%CI = 0.70-1.00). In summary, certain comorbidities (e.g., diabetes) may be positively associated with ASD of several cancer types. It needs to be clarified whether closer monitoring for early cancer signs or screening in these patients is reasonable, considering the problem of over-diagnosis particularly relevant in patients with short remaining life expectancy such as those with comorbidities. Also, evaluation of the cost-benefit relationship of cancer screening according to the type and severity of comorbidity (rather than summary scores) may be beneficial for personalized cancer screening in populations with chronic diseases.

Type 2 Diabetes Mellitus and Clinicopathological Tumor Characteristics in Women Diagnosed with Breast Cancer: A Systematic Review and Meta-Analysis

Poor prognosis caused by type 2 diabetes mellitus (T2DM) in women with breast cancer is conferred, while the association between T2DM and breast tumor aggressiveness is still a matter of debate. This study aimed to clarify the differences in breast cancer characteristics, including stage, size, lymph node status, grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (Her2), between patients with and without pre-existing T2DM. PubMed, Embase, and Web of Science were searched for studies from 1 January 2010 to 2 July 2021. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were pooled by using a random effects model. T2DM was significantly associated with tumor stages III/IV versus cancers in situ and stages I/II (pooled ORs (pOR), 95% CI: 1.19; 1.04-1.36, p = 0.012), tumor size >20 versus ≤20 mm (pOR, 95% CI: 1.18; 1.04-1.35, p = 0.013), and lymph node invasion versus no involvement (pOR, 95% CI: 1.26; 1.05-1.51, p = 0.013). These findings suggest that women with T2DM are at a higher risk of late-stage tumors, large tumor sizes, and invasive lymph nodes at breast cancer diagnosis.

Endocrine Therapy-related Endocrinopathies-Biology, Prevalence and Implications for the Management of Breast Cancer

Nearly 270,000 new breast cancer cases are predicted to be diagnosed in the USA in 2019 with more than 70% being estrogen receptor positive and treated using endocrine therapy. The suppression of estrogen biosynthesis or action via the use of ovarian suppression, aromatase inhibitors and selective estrogen receptor modulators/degraders, respectively, is effective in approximately 70% of women. The systemic inhibition of estrogen during breast cancer treatment is also associated with side effects due to the important endocrine functions of this steroid hormone, including its role in the maintenance of energy homeostasis and bone health. The current work will present perspectives of the impact of endocrine therapy from the point of view of breast medical oncology, endocrinology, and basic science.

Impact of type 2 diabetes on hospitalization and mortality in people with malignancy

AIM

To compare the characteristics of and outcomes for people with malignancies with and without a co-diagnosis of diabetes.

METHODS

Emergency department and hospital discharge data from a single centre for the period between 1 January 2015 and 31 December 2017 were used to identify people with a diagnosis of a malignancy and diabetes. Multivariate Cox regression models were used to estimate the effect of diabetes on all-cause mortality. A truncated negative binomial regression model was used to assess the impact of diabetes on length of hospital inpatient stay. Prentice-Williams-Peterson total time models were used to assess the effect of diabetes on number of emergency department re-presentations and inpatient re-admissions.

RESULTS

Of 7004 people identified with malignancies, 1195 (17.1%) were also diagnosed with diabetes. A diagnosis of diabetes was associated with a greater number of inpatient re-admissions [adjusted hazard ratio 1.13 (95% CI 1.03, 1.24)], a greater number of emergency department re-presentations [adjusted hazard ratio 1.13 (95% CI 1.05, 1.22)] and longer length of stay [adjusted incidence rate ratio 1.14 (95% CI 1.04, 1.25)]. A co-diagnosis of diabetes was also associated with a 48% increased risk of all-cause mortality [adjusted hazard ratio 1.48 (95% CI 1.22-1.76)].

CONCLUSIONS

People with malignancies and diabetes had significantly more emergency department presentations, more inpatient admissions, longer length of hospital stay and higher rates of all-cause mortality compared to people with a malignancy without diabetes.

Hospitalization as an Opportunity to Optimize Glycemic Control in Oncology Patients

PURPOSE OF REVIEW

Many patients experience hyperglycemia during cancer treatment, either as a new-onset condition or as an exacerbation of existing diabetes. This can impact treatment and outcomes, increasing the risk of complications and worsening health-related quality of life (HRQoL). These issues may be particularly significant when patients are hospitalized and/or acutely ill. The purpose of this review is to identify common barriers and strategies specific to the inpatient setting to improve glycemic control and minimize complications both while patients are hospitalized and after discharge.

RECENT FINDINGS

Hyperglycemia in patients who are hospitalized during cancer treatment is common, but there is a lack of consensus on goals and approaches to glycemic management in this setting. Hyperglycemia related to oncology treatment can have unusual causes and challenges in management. Organizational guidelines can help standardize treatment and guide providers in managing hyperglycemia in oncology patients during hospitalization and upon discharge. Hospitalization is a critical period that provides an opportunity to reassess and modify management plans, coordinate follow-up care, and, crucially, educate and empower patients to successfully manage their blood glucose levels once they are discharged. Emerging technology such as patient portals can facilitate hyperglycemia management after discharge. This review discusses evidences and strategies to utilize the period of hospitalization to develop and implement an individualized plan of care for patients with concurrent hyperglycemia and cancer.

Metformin as an adjuvant in breast cancer treatment

Breast cancer is one of the most common malignancies in females. It is an etiologically complex disease driven by a multitude of cellular pathways. The proliferation and spread of breast cancer is intimately linked to cellular glucose metabolism, given that glucose is an essential cellular metabolic substrate and that insulin signalling has mitogenic effects. Growing interest has focused on anti-diabetic agents in the management of breast cancer. Epidemiologic studies show that metformin reduces cancer incidence and mortality among type 2 diabetic patients. Preclinical in vitro and in vivo research provides intriguing insight into the cellular mechanisms behind the oncostatic effects of metformin. This article aims to provide an overview of the mechanisms in which metformin may elicit its anti-cancerous effects and discuss its potential role as an adjuvant in the management of breast cancer.