Abstract
Background
It is unclear whether diabetes and glycemic control affects the outcomes of breast cancer, especially among those with metastatic disease. This study aims to determine the impact of diabetes and hyperglycemia on cancer progression and mortality in individuals with metastatic breast cancer (MBC).
Methods
Patients with a diagnosis of MBC between 2010 and 2021 were identified using the MBC database at 2 academic institutions. We evaluated the effects of diabetes and glycemic control on overall survival (OS) and time to next treatment (TTNT).
Results
We compared 244 patients with diabetes (median age 57.6 years) to 244 patients without diabetes (matched for age, sex, ethnicity, and receptor subtype). OS at 5 years [diabetes: 54% (95% CI 47-62%) vs controls: 56% (95% CI 49-63%), P = 0.65] and TTNT at 1 year [diabetes: 43% (95% CI 36-50%) vs controls: 44% (95% CI 36-51%), P = 0.33] were similar between groups. A subgroup analysis comparing those with good glycemic control and those with poor glycemic control among patients with specific receptor subtype profiles showed no differences in OS at 5 years or TTNT at 1 year. In an 8-year landmark subgroup analysis, there was worse OS among individuals with diabetes compared to controls, and OS was found to be better among those with good glycemic control compared to those with poor control.
Conclusions
Diabetes was not associated with increased mortality in individuals with MBC at 5 years. However, diabetes and hyperglycemia were associated with worse OS among a cohort of longer-term survivors. These findings suggest that individualized diabetes and glycemic goals should be considered in patients with MBC.
Keywords: diabetes, hyperglycemia, metastatic breast cancer, overall survival, cancer outcomes
It is well established that a complex relationship between breast cancer and diabetes exists. Approximately 10% to 20% of all postmenopausal women with breast cancer of any stage or receptor subtype have coexisting type 2 diabetes mellitus (T2DM) (1-3). Both conditions share similar risk factors including older age and obesity (4), and many antineoplastic agents used to treat breast cancer (particularly in the metastatic setting), such as immune checkpoint inhibitors and phosphoinositide 3-kinase/Akt/mammalian target of rapamycin inhibitors, have also been associated with the development of diabetes and hyperglycemia (5, 6).
Epidemiological studies have consistently shown an increased rate of mortality in individuals with both T2DM and breast cancer when compared to individuals without diabetes (3, 7-11). The reasons behind the increased mortality in patients with T2DM is not well understood and is likely multifactorial (10). Postulated contributing factors include competing causes of mortality such as cardiovascular death (12), a greater number of comorbidities in women with diabetes (which may limit suitability for more aggressive cancer therapies) (13, 14), associated behaviors such as lower breast cancer screening rates (15, 16), and chronic hyperinsulinemia and hyperglycemia (17, 18).
While insulin resistance and hyperinsulinemia are thought to be the main malefactors associated with tumorigenesis due to overactivation of the insulin and insulin-like growth factor pathways (19), hyperglycemia is likely to play a contributory role. In contrast to normal cells, cancer cells rely predominantly on anaerobic glycolysis to generate energy due to their altered metabolism (Warburg effect) (20). The Warburg effect leads to increased glucose uptake and metabolism by cancer cells (20). Therefore, a high glucose environment—such as that often present in patients with poorly controlled diabetes—is thought to promote tumorigenesis.
To date, there are limited data investigating the effects of diabetes and, in particular, the impact of poor glycemic control on breast cancer outcomes. Existing studies also primarily focus on early rather than metastatic breast cancer (MBC) populations. MBC populations are often heterogenous, and individuals with metastatic disease can experience variable disease courses and mortality rates. Given the impact of MBC on life expectancy, tight glycemic control is not always aggressively pursued. The question therefore remains as to whether diabetes and glycemic control exerts an effect on clinical outcomes within this heterogeneous patient population. To our knowledge, this represents the first study to examine the effects of diabetes and hyperglycemia on cancer progression and mortality in individuals with MBC.
We hypothesized that in individuals with MBC those with diabetes would have higher mortality and a higher likelihood of disease progression than aged-matched MBC controls (individuals without diabetes). We also hypothesized that in individuals with MBC, those with poorly controlled diabetes would have higher mortality and a greater likelihood of disease progression than MBC individuals with well-controlled diabetes. An association between poor glycemic control and worse cancer outcomes may modify the way clinicians and patients perceive and approach diabetes and hyperglycemia in the MBC setting.
Methods
This was a retrospective study conducted at the Brigham and Women’s Hospital and the Dana-Farber Cancer Institute (Boston, MA, USA). The study was approved by the Brigham and Women’s Hospital and the DFCI institutional review boards.
Participants
We identified all patients diagnosed with MBC between January 2010 and January 2021 using the Ending Metastatic Breast Cancer for Everyone (EMBRACE) database. This database includes all patients seen at least once at the DFCI for a diagnosis of MBC within the past decade. It provides detailed data regarding patients’ oncologic history and treatment regimens but does not provide information on diabetes status or glycemic control.
To identify our diabetes and control cohorts, we searched the Research Patient Data Registry (RPDR), a warehouse of populated data from several sources associated with the Mass General Brigham Health System. Unlike EMBRACE, this database provides detailed information about diabetes history, glucose-lowering treatments, and laboratory results but provides limited data on oncology history and treatments.
Individuals with diabetes and MBC were identified in the RPDR using the following search criteria:
International Classification of Diseases (ICD) 9 or 10 code for diabetes, or
Hemoglobin A1c (HbA1c) ≥ 6.5%, or
Random blood glucose (RBG) ≥ 200 mg/dL and documented use of glucose lowering agents, and
ICD 9 or 10 code for MBC
Individuals with MBC but no diagnosis of diabetes (controls) were identified in the RPDR using the following search criteria:
Exclusion of patients with any of the following: ICD 9 or 10 code for diabetes, HbA1c ≥ 6.5%, or RBG ≥ 200 mg/dL and documented use of glucose lowering agents, and
ICD 9 or 10 code for MBC
As we were interested in the individuals who had both oncology and glycemic data, only the individuals identified using the described RPDR search criteria and who were also registered in the EMBRACE database were considered for inclusion into the study (Fig. 1).
Figure 1.
Study flow diagram. Abbreviations: DFCI, Dana-Farber Cancer Institute; EMBRACE, Ending Metastatic Breast Cancer for Everyone; HbA1c, hemoglobin A1c; ICD, International Classification of Diseases; RPDR, Research Patient Data Registry.
Data Collection
Detailed review of the medical records was performed to determine the date of diabetes diagnosis, type of diabetes, glucose-lowering agents, date of MBC diagnosis, receptor subtype, antineoplastic therapies, duration of first-line cancer treatment, progression of disease, and date of death.
Definition of Glycemic Control
We utilized HbA1c and RBG levels to determine glycemic control. To account for variations in HbA1c and RBG, we calculated the median HbA1c and RBG levels for each participant over time from metastatic disease onset. Patients with no recorded measurements or those with only a single glucose or HbA1c measurement at or after the time of MBC diagnosis were excluded from glycemia-related analyses.
Using median RBG measurements, glycemic control was defined as good if the median RBG was ≤180 mg/dL, poor if the median RBG was between 180 and 200 mg/dL, and very poor if median RBG was >200 mg/dL. Using median HbA1c measurements, glycemic control was considered good if the median HbA1c was ≤7% and poor if the median HbA1c was >7%. The median RBG and HbA1c cut off of 180 mg/dL and 7%, respectively, were used as it has been demonstrated that an increasing period of time exposed to these thresholds have been associated with worse outcomes [ie, increased incidence of micro- (21-24) and macrovascular complications (25, 26)] in individuals with diabetes.
Outcome Measures
Our primary outcome measures of interest were (1) overall survival (OS) and (2) time to next treatment (TTNT). As Response Evaluation Criteria in Solid Tumor (RECIST) data were not available, the TTNT (the time interval for a patient to transition from a first-line metastatic therapy to a second-line regimen due to documented progression of disease) was used as a surrogate measure of progression-free survival.
We chose to report OS at 5 years since this represents a standard timepoint for reporting in the breast cancer literature (27). We reported TTNT at 1 year, as this timeframe is clinically pertinent for patients with metastatic cancer.
Statistical Analysis
Statistical analyses were performed using R statistical software (version 4.0.5) (28) and IBM SPSS Statistics for Windows, version 25 (IBM Corp., Armonk, N.Y., USA). Data were reported as median (interquartile range). The Wilcoxon test was used to compare continuous baseline variables between groups, while the Chi-square and Fisher’s exact tests were used to compare categorical baseline variables between groups.
Propensity-score matching was performed to create 2 equally sized groups (diabetes and controls) with similar clinical characteristics. To generate the propensity score, a logistic regression was performed using diabetes as the dependent variable. Variables used to generate the propensity score were age, sex, and ethnicity. Individuals with diabetes and controls were then matched 1:1 (without replacement), using the “greedy” matching method whereby patients with diabetes were selected and paired with a patient without diabetes utilizing a propensity score calliper width of 0.02 (29). Among propensity score–matched pairs, standardized differences were used to determine the degree of balance between the 2 groups with respect to their baseline clinical characteristics. The test proposed by Klein and Moeschberger (30), which accounts for the matched nature of the sample, was used to compare OS and TTNT between the diabetes and control groups.
When comparing OS and TTNT between subgroups of glycemic control, the Kaplan-Meier method and log-rank test were used, given the number of participants in each group were no longer equal and matched (not all patients had complete data concerning glycemic control, hence the unequal numbers on subgroup analysis).
Results
Participants
From January 2010 to January 2021, 2517 patients with newly diagnosed MBC were identified through the EMBRACE database. Over the same time period, 684 patients with MBC and diabetes, and 2637 patients with MBC but no history of diabetes were identified in the RPDR (Fig. 1).
Of the 1028 individuals who were common to both the EMBRACE and RPDR databases, 250 had diabetes and 778 did not. Propensity score matching yielded 244 patient-pairs matched for age, sex, and ethnicity (Fig. 1).
Clinical Characteristics
Baseline characteristics by group are shown in Table 1. The median duration of follow-up was 3.3 (1.8-5.6) years. The 2 groups were well balanced with respect to their clinical characteristics, including receptor subtype at baseline. There was also a high degree of balance with regards to first-line therapy regimens. There was a greater absolute number of participants in the diabetes group with de novo MBC (standardized difference 18%), but this difference was not statistically significant (P = 0.13).
Table 1.
Baseline characteristics
| Diabetes Group (n = 244) | Control Group (n = 244) | P-value | Standardized differencea | |
|---|---|---|---|---|
| Age, years | 57.6 (49.4-65.7) | 56.6 (48.6-66.0) | 0.79 | 0.8 |
| Female | 243 (99.6) | 243 (99.6) | 1.00 | 0.0 |
| Ethnicity | 0.94 | |||
| Asian | 5 (2) | 3 (1.2) | −6.5 | |
| African American | 16 (6.6) | 17 (7) | 1.6 | |
| Hispanic | 2 (0.8) | 4 (1.6) | 7.4 | |
| White | 206 (84.4) | 204 (83.6) | −2.2 | |
| Other | 8 (3.3) | 8 (3.3) | 0.0 | |
| Unknown | 7 (2.9) | 8 (3.3) | 2.4 | |
| De novo MBC | 62 (25.4) | 44 (18.0) | 0.13 | −18.0 |
| Receptor subtype | 0.59 | |||
| HER2−/HR+ | 150 (61.5) | 155 (63.5) | −4.2 | |
| HER2+/HR− | 21 (8.6) | 16 (6.6) | 7.7 | |
| HER2+/HR+ | 32 (13.1) | 25 (10.2) | 8.9 | |
| TNBC | 38 (15.6) | 42 (17.2) | −4.4 | |
| Unknown | 3 (1.2) | 6 (2.5) | −9.1 | |
| First-line therapy | 0.11 | |||
| Endocrine therapy (± targeted therapy) | 123 (50.4) | 127 (52.0) | 3.3 | |
| Cytotoxic chemotherapy alone Anti-HER2 therapy (± concurrent chemotherapy |
45 (18.4) 50 (20.4) |
45 (18.4) 39 (16.0) |
0.0 −11.7 |
|
| Other | 26 (10.7) | 27 (11.1) | 1.3 |
Data are given as median (interquartile range) or n (%).
Abbreviations: HR, hormone receptor; HER-2, human epidermal receptor 2; MBC, metastatic breast cancer; NA, not applicable; TNBC, triple negative breast cancer.
aA high degree of balance is reflected by a standardized difference of ≤10%.
The type of diabetes for individuals in the diabetes cohort are listed in Table 2. One hundred and twenty (49.2%) participants in the diabetes group had T2DM. Fifty-nine (24.2%) participants had a diagnosis of diabetes prior to their MBC diagnosis [median time interval from diabetes diagnosis to MBC diagnosis was 4.5 (1.8-7.7) years], while 130 (53.3%) participants reported a diagnosis of diabetes after being diagnosed with MBC [median time interval from MBC diagnosis to diagnosis of diabetes was 2.8 (0.9-4.4) years]. The date of diabetes diagnosis was unknown for 55 (22.5%) participants. Of the 130 participants who developed diabetes after the diagnosis of MBC, 39 (30.0%) had a HbA1c measurement performed prior to their diagnosis of diabetes, 64 (49.2%) only had HbA1c measurements performed after their diagnosis of diabetes, and 27 (20.8%) did not have any recorded measurements of HbA1c. Of the 39 patients with HbA1c measurements performed prior to their diabetes diagnosis, 24 (62%) had preexisting prediabetes (defined as an HbA1c of 5.7% to 6.4%). The median time of progression from prediabetes to diabetes among this cohort was 0.6 (0.2-2.9) years.
Table 2.
Baseline diabetes type and glucose-lowering agents among individual with diabetes
| n (%) | |
|---|---|
| Diabetes type | |
| T1DM | 3 (1.2) |
| T2DM | 120 (49.2) |
| Glucocorticoid-associated | 18 (7.4) |
| PI3K inhibitor-associated | 16 (6.6) |
| CPI-associated | 3 (1.2) |
| Other | 2 (0.8) |
| Unknown | 81 (33.2) |
| Glucose-lowering agent | |
| No treatment | 58 (23.8) |
| Metformin | 79 (32.4) |
| Monotherapy | 53 (21.7) |
| Insulin | 59 (24.2) |
| Regular monotherapy | 21 (8.6) |
| Sliding scale monotherapy | 31 (12.7) |
| Sulfonylurea | 16 (6.6) |
| DPP-4 inhibitor | 4 (1.6) |
| GLP-1 agonist | 3 (1.2) |
| SGLT 2 inhibitor | 2 (0.8) |
| Unknown | 43 (17.6) |
Abbreviations: CPI, immune checkpoint inhibitor; DPP-4, dipeptidyl peptidase-4; GLP-1, glucagon-like peptide-1; T1DM, type 1 diabetes; T2DM, type 2 diabetes; SGLT-2, sodium-glucose contransporter-2.
Glucose-Lowering Agents
Of the 244 participants in the diabetes group, a recent and valid record of their glucose-lowering medication regimen was available in 82.4%. At baseline, the most common glucose-lowering agents utilized by participants were metformin, insulin, and sulfonylureas (Table 2). By design, no patients in the control group were taking any form of glucose-lowering agents at the time of MBC diagnosis.
Glycemic Control
Seventeen individuals were excluded from further analyses due to having either no measurement or only a single RBG or HbA1c measurement at or after the time of MBC diagnosis. The median HbA1c for the diabetes and control groups were 6.6% (5.9-7.5%) and 5.5% (5.1-5.7%; P < 0.001), respectively, while the median RBG for diabetes and control groups were 123 (108-149) mg/dL and 100 (94-107) mg/dL (P < 0.001), respectively. The number of participants with good vs poor glycemic control during the study period (as defined by median HbA1c and RBG levels) is reported in Table 3.
Table 3.
Proportion of participants with good vs poor glycemic control, as defined by median hemoglobin A1c and random blood glucose levels
| Glycemic measure | n (%) |
|---|---|
| HbA1c | |
| Good (median HbA1c ≤ 7%) | 19 (4.0) |
| Poor (median HbA1c > 7%) | 39 (8.3) |
| RBG | |
| Good (median RBG ≤ 180 mg/dL) | 440 (98.4) |
| Poor (median RBG 180-200 mg/dL) | 17 (3.6) |
| Very poor(median RBG > 200 mg/dL) | 13 (2.8) |
Abbreviations: HbA1c, hemoglobin A1c; RBG, random blood glucose.
Overall Survival
There was no statistically significant difference in OS between diabetes and control groups at 5 years, [diabetes: 54% (95% CI 47-62%) vs controls: 56% (95% CI 49-63%), P = 0.65] (Fig. 2). At 5 years, there was comparable OS between the 3 glycemic subgroups based on median RBG levels [good control: 55% (95% CI 50-60%) vs poor control: 48% (95% CI 20-77%) vs very poor control: 23% (95% CI 0-61%), P = 0.27] (Fig. 3A). Similarly, at 5 years, there was comparable OS between the subgroups based on HbA1c [median HbA1c ≤ 7%: 69% (95% CI 58-79%) vs median HbA1c > 7%: 69% (95% CI 50-88%), P = 0.24] (Fig. 3B).
Figure 2.
Overall survival stratified by diabetes status.
Figure 3.
Overall survival, stratified by glycemic status. (A) Survival stratified by median random blood glucose level. (B) Survival stratified by hemoglobin A1c level.
Time to Next Treatment
There was no statistically significant difference in TTNT between diabetes and control groups at 1 year [diabetes: 56% (95% CI 49-62%) vs controls: 56% (95% CI 49-63%), P = 0.33] (Fig. 4). At 1 year, there was comparable TTNT between the 3 glycemic subgroups based on median RBG levels [good control: 57% (95% CI 52-62%) vs poor control: 47% (95% CI 20-74%) vs very poor control: 27% (95% CI 1-54%), P = 0.25] (Fig. 5A). Similarly, at 1 year, there was comparable TTNT between the HbA1c subgroups [median HbA1c ≤ 7%: 58% (95% CI 47-70%) vs median HbA1c > 7%: 53% (95% CI 35-71%), P = 0.95] (Fig. 5B).
Figure 4.
Freedom from next treatment (second-line regimen) over time, stratified by diabetes status.
Figure 5.
(A) Freedom from next treatment (second-line regimen) over time, stratified by median random blood glucose level. (B) Freedom from next treatment (second-line regimen), stratified by hemoglobin A1c level.
Post Hoc Analysis
Given the small number of participants in our cohort who recorded a median RBG > 180 mg/dL (n = 30) or a HbA1c > 7% (n = 39), we conducted additional analyses using alternative definitions for glycemic control. Patients were categorized as having poor control if they recorded a RBG > 180 mg/dL on at least 2 separate occasions within a month (n = 156). Patients who did not fulfill these criteria were otherwise categorized as having good control (n = 330). There was comparable OS at 5 years between the good and poor control subgroups [good control: 56% (95% CI; 50-62%) vs poor control: 54% (95% CI 45-63%), P = 0.32). At 1 year, there was a trend toward those in the good control group having better TTNT compared to the poor control group [good control: 57% (95% CI 51-63%) vs poor control: 53% (95% CI 44-62%), P = 0.07]. Of note, no differences in OS at 5 years or TTNT at 1 year between the 2 glycemic groups (good vs poor control) were observed even after performing subgroup analyses for each of the receptor subtypes (Table 4).
Table 4.
Overall survival at 5 years and TTNT at 1 year based on receptor subtype
| Good glycemic control vs poor glycemic control | ||||
|---|---|---|---|---|
| Overall survival, % (95% CI) | P-value | Progression-free survival, % (95% CI) | P-value | |
| HER2−/HR+ | 57 (49-65) vs 58 (46-70) | 0.53 | 60 (53-68) vs 61 (50-72) | 0.49 |
| HER2+/HR− | 76 (57-95) vs 44 (14-75) | 0.26 | 59 (37-81) vs 40 (10-70) | 0.32 |
| HER2+/HR+ | 65 (46-84) vs 62 (40-85) | 0.85 | 69 (52-85) vs 69 (46-92) | 0.37 |
| TNBC | 29 (14-43) vs 30 (7-52) | 0.40 | 30 (15-45) vs 22 (5-40) | 0.37 |
Abbreviations: HR, hormone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple negative breast cancer.
Poor glycemic control was then redefined as a RBG > 200 mg/dL on at least 2 separate occasions within a month (n = 138). At 5 years, there was comparable OS between the 2 groups [good control: 58% (95% CI 52-64%) vs poor control: 51% (95% CI 41-61%), P = 0.40]. At 1 year, there was again, a trend toward a better TTNT in the good control group when compared to the poor control group [good control: 58% (95% CI 52-64%) vs poor control: 54% (95% CI 45-63%), P = 0.08], although the absolute differences were small.
Metformin
Given metformin is believed to carry antitumoral properties, we examined OS and TTNT between those who received metformin monotherapy (MM) group vs those who received a glucose-lowering regimen other than MM (non-MM group). The latter group included patients who received metformin in addition to other glucose-lowering agents, as well as those who did not receive metformin at all. At 5 years, there was a trend toward a better OS among the MM group compared to the non-MM group [MM: 61% (95% CI 44-79%) vs non-MM: 49% (95% CI 39-59%), P = 0.06]. At 1 year, TTNT was better in the MM group compared to the non-MM group [MM: 60% (95% CI 45-75%) vs non-MM: 52% (95% CI 42-61%), P = 0.03].
Landmark analysis
While no statistically significant difference was observed in OS between the diabetes and control groups, there appeared to be separation of the curves at approximately 8 years (Fig. 2). To investigate whether there was a survival difference among those with a relatively longer survival period, we performed a subgroup landmark analysis for those who survived >8 years from the time of MBC diagnosis. There were 27 patients with diabetes and 28 controls alive beyond 8 years available for this analysis. Within this subgroup, the 10-year OS among individuals with diabetes was worse when compared to controls [diabetes: 67% (95% CI 49-86%) vs controls: 87% (95% CI 73-100%,) P = 0.047]. The 10-year OS was better among those with good glycemic control (using the post hoc glycemic definitions—good control: did not fulfill the criteria for poor control and poor control: RBG > 180 mg/dL on at least 2 separate occasions within a month) compared to those with poor glycemic control [good control (n = 23): 83% (95% CI 71-96%) vs poor control (n = 8): 63% (95% CI 37-89%), P = 0.018].
Discussion
In this retrospective study, patients with diabetes and MBC did not have worse OS or TTNT when compared to controls. Poor control, as defined by a median RBG > 180 mg/dL or a HbA1c > 7%, was also not associated with increased mortality at 5 years or worse TTNT at 1 year when compared to individuals with good control. These data provide some reassurance that within the first 5 years, hyperglycemia may not be a major contributor toward increased mortality or progression of disease in individuals with MBC.
Despite the high prevalence of diabetes in breast cancer patients, only 1 study to date has evaluated the effects of hyperglycemia on breast cancer progression (31), and few studies have investigated the associations between hyperglycemia and OS in breast cancer patients. OS data are limited to results from retrospective and observational studies with relatively small cohorts of individuals with diabetes (32-34). Many of these studies did not adjust for confounders such as receptor subtype or medication regimen and usually relied on a single HbA1c measurement to define glycemic control. Patients with MBC were also commonly excluded (32-34). As individuals with metastatic disease experience a different disease course and mortality rate than those with early breast cancer, data from studies on the latter cannot be extrapolated to individuals with MBC. To our knowledge, this is the first study to examine the effects of diabetes and hyperglycemia on cancer outcomes in a MBC population.
The Association Between Diabetes, Hyperglycemia and Overall Survival
Diabetes has been shown to be associated with a greater all-cause mortality in breast cancer patients (7, 8, 10, 11, 35). In a meta-analysis including 6 studies (n = 108 223), preexisting diabetes was associated with a 49% increased risk for all-cause mortality [hazard ratio (HR) 1.49 (95% CI 1.35-1.65)] (10). However, only 3 of the studies included in this meta-analysis adjusted for breast cancer stage (3, 9, 35). Of these 3 studies, 1 excluded all individuals with MBC (35) while another only included 11 participants with MBC (9). Therefore, while a relationship between diabetes and mortality in breast cancer likely exists, the data specific to MBC populations remain limited.
Studies investigating the effects of glycemic control on OS in individuals with early breast cancer have yielded mixed results. A substudy of the prospective Women’s Healthy Eating and Living Study (WHEL), utilized archived blood samples (taken at their WHEL baseline visit) of 3003 participants with early breast cancer to measure HbA1c levels (33). Participants were then categorized based on their HbA1c (<6.5%, 6.5-6.9%, and ≥7%), and outcome data were collected over 7 years of follow-up. The risk of all-cause mortality was twice as high in individuals with a HbA1c ≥ 7% (n = 91) when compared to those with an HbA1c < 6.5% (n = 2818), suggesting that good glycemic control may be associated with better breast cancer prognosis. However, in contrast to our study, the WHEL study did not include individuals with MBC, and participants who were insulin-dependent were also excluded. As individuals who require treatment with exogenous insulin often have greater insulin resistance, more advanced diabetes, and a greater comorbidity burden (36), the higher survival rate in this noninsulin-dependent population may be the result of potential confounding.
One retrospective study (n = 2812) reported that poorly controlled diabetes (mean HbA1c > 9%, n = 16) was associated with an increased risk of all-cause and breast cancer–specific mortalities in individuals with early breast cancer, while patients with well-controlled diabetes (HbA1c < 7%, n = 77) had comparable survival to individuals without diabetes (32). Again, patients with MBC were excluded. Another retrospective cohort study (n = 82, mean age 62 years) categorized participants into 2 groups (HbA1c < 6.5% and ≥6.5%) and found that OS was not statistically different [HR for HbA1c ≥ 6.5%: 2.6 (95% CI 0.26-25), P = 0.4] (34). Here, adjustment for prognostic factors such as cancer stage and receptor subtype was not performed, which may have confounded the effect of glycemic control on mortality.
A larger retrospective study involving 1530 breast cancer patients with a diagnosis of diabetes (mean age 71 years) observed that HbA1c level (as a continuous variable) at the time of cancer diagnosis was not associated with all-cause mortality in breast cancer patients (37). This study included individuals with all stages of breast cancer. However, since adjustment was not performed for cancer stage, it is possible that the high mortality rate in the participants with MBC may have masked the effects of glycemic control on survival.
Our findings suggest that diabetes may not increase overall mortality in individuals with MBC at 5 years. However, as the median survival for patients with MBC varies between 3 to 5 years (depending on cancer subtype, sites of metastatic involvement, and burden of metastatic disease) (38), we postulated that the effects of diabetes and hyperglycemia may only become apparent among those with a relatively longer survival period. Indeed, no study to date has evaluated this premise.
The results of our landmark analysis suggest that in individuals with MBC, having a concurrent diagnosis of diabetes may increase overall mortality but only among patients who live >8 years after their MBC diagnosis. There are several possible explanations as to why this difference in survival becomes apparent only in the latter period of follow-up. First, this observed difference in mortality may be due to the associated consequences of long-standing diabetes (ie, micro- and macrovascular diabetic complications) including cardiovascular disease, exerting their prognostic impact on survival. As the risk of developing diabetic complications increases with either a longer duration of diabetes or a longer exposure to poor glycemic control, the patients who died early from MBC may not have been exposed long enough to the effects of diabetes or poor glycemic control for an impact on OS to be observed. It is also possible that poor glycemic control may be a surrogate marker of illness or comorbidity (ie, where glycemic management is no longer a primary focus of care), which in itself is associated with worse OS. However, our landmark analysis demonstrated a worse OS in individuals with poor glycemic control among participants who survived >8 years. Given individuals who are ill and comorbid are unlikely to survive beyond this time interval, our results suggest that glycemic control is more than just a surrogate marker of health.
The Association Between Diabetes, Hyperglycemia, and Progression-free Survival
Our post hoc analyses did detect a trend toward better TTNT at 1 year between good and poor control subgroups. It is possible that with a larger sample size, a stronger association would have been detected. There are scant data in the literature specifically investigating the effects of diabetes on breast cancer progression (35). One study found that preexisting diabetes had an adverse effect on disease-free survival in a cohort of African American (n = 331) and Caucasian women (n = 257) diagnosed with early stage breast cancer [HR 1.81 (95% CI 1.03-3.18)] (35). In contrast, the previously described WHEL study (31) found that hyperglycemia (HbA1c ≥ 7%) was not significantly associated with disease-free survival [HR 1.26 (95% CI 0.78-2.02)] in individuals with early breast cancer. Indeed, further studies evaluating the effects of diabetes and glycemic control on progression-free survival are required.
Glucose Treatment Targets
There are no studies that have investigated the effects of lowering glucose to a specific glycemic target and the impact this has on breast cancer outcomes. In our cohort, individuals who had poor (median RBG > 180 mg/dL, median HbA1c > 7%, or RBG > 180 mg/dL on ≥2 occasions within a month) or very poor glycemic control (median RBG > 200 mg/dL or a RBG > 200 mg/dL on ≥2 occasions within a month) did not have a higher mortality at 5 years when compared to individuals with good glycemic control. This provides some reassurance that intermittent hyperglycemia, even to levels > 200 mg/dL, within the first 5 years of MBC diagnosis does not impact OS. Again, the impact of poor glycemic control may lie beyond the lifespan for a significant proportion of these patients.
Based on these findings, it would be reasonable to consider a less stringent approach to glycemic control in individuals with MBC, where prognosis is poor and quality of life is the primary focus. Emphasis should be on preventing the acute signs and subsequent complications caused by sustained hyperglycemia and hypoglycemia, rather than achieving a specific glycemic target. Nevertheless, as our landmark analysis demonstrated improved OS among those who survived at least 8 years, a more proactive approach to glycemic control may be justified in patients who have a more favorable prognosis. We acknowledge that it may be difficult for clinicians to identify the patients who will have a relatively longer survival period. Features that have been associated with a more favorable prognosis include a relapse-free interval > 2 years (39-42), absence of hepatic/lymphangitic pulmonary disease (39, 40, 43), estrogen receptor–positive status (41, 44, 45), no evidence of significant weight loss, and good performance status at the time of diagnosis (39, 46). Understanding these prognostic indicators can therefore permit identification of patients in whom greater attention to glycemic control may be appropriate.
Choice of Glucose-Lowering Agents
Metformin is thought to decrease breast cancer cell growth either by indirectly reducing circulating insulin and insulin-like growth factor (47) or by directly activating adenosine 5′-monophosphates-kinase, which in turn inhibits mammalian target of rapamycin and therefore cellular proliferation (48, 49). However, the results of observational and clinical studies investigating the effects of metformin on breast cancer outcomes have yielded mixed results (50, 51). Meta-analyses of large cohorts have demonstrated that metformin use is associated with improved survival and decreased all-cause mortality in patients with diabetes and breast cancer (50-52). However, the results of a large, randomized controlled trial involving 3649 patients (mean age 52.7 years) with early breast cancer found that adjuvant metformin therapy (850 mg twice daily) for 5 years was not associated with improved cancer outcomes when compared to placebo (52). In our study, there was a trend toward better OS at 5 years in those prescribed MM compared to those prescribed non-MM therapy. TTNT at 1 year was also better among those prescribed MM compared to those prescribed non-MM therapy. Given these potential cancer-specific benefits, along with its safety and tolerability as a glucose-lowering agent, metformin should be utilized in most individuals with diabetes and breast cancer.
Also, given the well-described cardiovascular-related benefits associated with sodium-glucose cotransporter-2 inhibitors (53, 54) as well as glucagon-like peptide 1 agonists (55, 56), the use of these agents should be prioritized if possible.
Limitations
This study has several limitations, in part due to its retrospective nature. First, as RECIST data were not uniformly available, we identified patients as having progressed if they changed from a first- to second-line metastatic treatment regimen in the setting of documented progression of disease as assessed by their treating physician. It is possible that a subgroup of patients whose progress fell outside of these criteria (ie, patients who changed treatments due to drug intolerance and then subsequently progressed), resulting in overall underreporting of progression in our study. Also, the number of participants who reported a median RBG > 180 mg/dL or a median HbA1c > 7% were relatively small, which may have reduced the statistical power associated with our analyses. We however, did attempt to account for this by altering our definitions of good and poor glycemic control in a post hoc analysis. Our cohort was heavily skewed toward a White, female population; thus, our findings may not be generalizable to all patients with MBC. Also, while we did report on the glucose-lowering agents each participant was taking at the time of MBC diagnosis, data regarding changes in these medications over the course of their disease were not available. Therefore, many of the patients who started off with MM may have had subsequent modifications to their regimens that could have confounded our findings. We acknowledge that the better cancer outcomes observed in the individuals treated with MM at the time of MBC diagnosis may reflect an overall better health status (ie, less insulin resistance, less severe diabetes and diabetes-related complications) rather than a true metformin effect. Finally, the size of the cohort was relatively modest, and specific causes of death were not available.
Conclusions
Among patients with MBC, diabetes was not associated with increased mortality at 5 years or worse TTNT at 1 year. Poor glycemic control was not associated with increased mortality within the first 5 years but may be associated with worse TTNT at 1 year, when compared to individuals with good glycemic control. Both diabetes and poor glycemic control were associated with greater mortality in longer-term survivors. A strategy of individualizing glycemic goals—taking into account patient prognosis—should be utilized in patients with MBC and diabetes.
Contributor Information
Yee-Ming M Cheung, Division of Endocrinology, Diabetes, and Hypertension, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA; Department of Medicine, Endocrine Unit, Austin Hospital, University of Melbourne, Victoria, Australia.
Melissa Hughes, Department of Medical Oncology, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Julia Harrod, Division of Endocrinology, Diabetes, and Hypertension, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA.
Janet Files, Department of Medical Oncology, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Greg Kirkner, Department of Medical Oncology, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Lauren Buckley, Department of Medical Oncology, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Nancy U Lin, Department of Medical Oncology, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Sara M Tolaney, Department of Medical Oncology, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Marie E McDonnell, Division of Endocrinology, Diabetes, and Hypertension, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA.
Le Min, Division of Endocrinology, Diabetes, and Hypertension, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA.
Funding
National Comprehensive Cancer Network Oncology Research Program (in collaboration with Pfizer Independent Grants for Learning and Change) - Pfizer Enhancing Academic Community-Patient Partnerships in Metastatic Breast Cancer Care. Dana-Farber/Harvard Cancer Center Breast Specialized Program of Research Excellence (SPORE) National Cancer Institute funded program, (Grant 1P50CA168504). Lilly USA, LLC Oncology Grant: Transform Cancer Care Initiative.
Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Y.C., M.H., J.H., J.F., G.K., and L.B. The first draft of the manuscript was written by Y.C. L.M., M.M., S.T., and N.L. had substantial contribution in the conception and design of the manuscript, along with its methodology. All authors have contributed to the drafting and revisions of the manuscript. All authors have given approval for this version of the manuscript to be published.
Conflicts of Interest
L.M. and M.M. received institutional research funding from Lilly. S.T. receives institutional research funding from AstraZeneca, Lilly, Merck, Nektar, Novartis, Pfizer, Genentech/Roche, Immunomedics, Exelixis, Bristol-Myers Squibb, Eisai, Nanostring, Cyclacel, Odonate, and Seattle Genetics; has served as an advisor/consultant to AstraZeneca, Lilly, Merck, Nektar, Novartis, Pfizer, Genentech/Roche, Immunomedics, Bristol-Myers Squibb, Eisai, Nanostring, Puma, Sanofi, Celldex, Paxman, Puma, Silverback Therapeutics, G1 Therapeutics, Gilead, AbbVie, Athenex, OncoPep, Outcomes4Me, Kyowa Kirin Pharmaceuticals, Daiichi-Sankyo, and Samsung Bioepsis Inc. All other authors have no funding or conflicts of interest to disclose.
Data Availability
The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
