Background: Although research suggests that type II diabetes mellitus (DM-2) is associated with overall and breast cancer–specific decreased survival, most prior studies of breast cancer survival investigated the effect of preexisting DM-2 without assessing the effect of DM-2 diagnosed at or after breast cancer diagnosis. This study examined the relationship between DM-2 diagnosed before and after breast cancer diagnosis and overall survival.

Methods: This study uses linked Department of Defense cancer registry and medical claims data from 9,398 women diagnosed with breast cancer between 1998 and 2007. Cox proportional hazards models were used to assess the association between DM-2 and overall survival.

Results: Our analyses showed that women with DM-2 diagnosed before breast cancer diagnosis tended to have a higher risk of mortality compared with women without diabetes [HR = 1.17; 95% confidence interval (CI), 0.95–1.44] after adjustment for potential confounders. Similarly, patients diagnosed with DM-2 at or after breast cancer diagnosis had increased mortality compared with women without DM-2 (HR = 1.39; 95% CI, 1.16–1.66). The similar tendency was also observed among most subgroups when results were stratified by race, menopausal status, obesity, tumor hormone receptor status, and stage.

Conclusions: Using data from a health system that provides universal health care to its beneficiaries, this study showed an increased risk of death associated with DM-2, regardless of whether it was diagnosed before or at/after breast cancer diagnosis.

Impact: These results suggest the potential effects of factors independent of the timing of DM-2 clinical diagnosis on the association of DM-2 with overall survival. Cancer Epidemiol Biomarkers Prev; 27(1); 50–57. ©2017 AACR.

In the United States, breast cancer remains the second most common cause of cancer-specific mortality among women (1). Although the 5-year relative survival rate for women with breast cancer was approximately 91% during 2005 to 2011 (1), some studies suggest that certain comorbid conditions, like diabetes, may negatively impact prognosis (2, 3).

As of 2012, approximately 9.3% of the U.S. population was diagnosed with diabetes (4). There are two main types of diabetes (type I and type II), with type II accounting for 95% of all clinical diagnoses (4). The association between diabetes (primarily type II) and breast cancer survival has been inconsistent in the literature (5), but most studies have found that it is associated with decreased overall survival (6–14) and breast cancer–specific survival (11, 14–17). A prospective cohort of one million U.S. adults reported a 2-fold increased risk of all-cause mortality and a 16% increased risk of breast cancer–specific mortality among persons with diabetes (10). In a large case–control study, diabetes was significantly associated with an elevated risk of all-cause mortality, with greater risk among those who were obese (6). However, some studies have reported no association between diabetes and overall survival (5) or breast cancer–specific survival (8, 12).

Most previous studies examined the association between breast cancer survival and diabetes diagnosed prior to breast cancer (5, 6, 8, 9, 11, 14, 18–20). In a large cohort study of stage I–III breast cancer patients from MD Anderson Cancer Center (Houston, TX), those with preexisting diabetes had a higher risk of all-cause mortality [HR = 1.39; 95% confidence interval (CI), 1.10–1.77; ref. 8]. In another study based on SEER-Medicare data, women diagnosed with diabetes prior to breast cancer diagnosis had a higher all-cause mortality compared with women without diabetes (20). In a meta-analysis of 17 studies, preexisting diabetics were shown to have a 50% higher all-cause mortality than those without diabetes (21). To the best of our knowledge, the association between diabetes diagnosed at or after breast cancer diagnosis and survival has not been studied. Diabetes detected at or following breast cancer diagnosis may differ from diabetes diagnosed prior to breast cancer diagnosis in the severity of the disease, potential effects on breast cancer treatment, and therefore, survival. As shown in a study based on the data from seven state cancer registries in the United States (22), diabetes severity is related to lower guideline-concordant chemotherapy and lower guideline concordance for locoregional treatment, although the association may vary by age. Diabetes can increase the risk of wound infection after surgery (23), which may delay the initiation of adjuvant chemotherapy and thereby potentially result in worse outcomes (24). Such effects may be more likely for more severe diabetes. In addition, antidiabetic treatment of preexisting diabetes itself may affect breast cancer survival (25), and it is possible that diabetes may result from breast cancer treatment (26). Thus, an examination of the relationship between diabetes and survival by timing of diabetes may help better understand the basis of the association. For example, if breast cancer–specific mortality is increased among patients with diabetes reported before breast cancer diagnosis but not at/after, it may suggest that influences of diabetes on breast cancer treatment may be more possible because the former tends to have more severe form of the disease. On the other hand, if all-cause mortality is increased irrespective of timing of the diagnosis of diabetes, it may suggest that factors related to diabetes itself may be more influential.

Previous studies have been conducted in populations in which accessibility to health care may vary. Access to health care has been found to influence the detection and diagnosis of diabetes, its management, treatment, and overall survival (27–30). Access to health care could potentially not only increase the likelihood of diabetes detection and diagnosis (due to better blood glucose monitoring), but also possibly decrease the risk of diabetes (due to the implementation of better preventive measures, such as exercise and improved diet). Thus, when diabetics and nondiabetics are compared in the general population, the identified difference in mortality may partially result from differential detection or diagnosis of diabetes and other socioeconomic factors related to access to care. Research in an equal access system can reduce these potential effects and provide information to assess the relationship between diabetes and cancer. The Military Health System (MHS) of the U.S. Department of Defense (DoD) is an equal access system, providing its beneficiaries with health care at little or no charge. The objective of this study was to examine the relationship between timing of type II diabetes (DM-2) and overall survival among breast cancer patients in the MHS.

Data source

This study was based on the linked data from the DoD's Central Cancer Registry (CCR) and the MHS Data Repository (MDR). The linked data include cancer patients diagnosed between 1998 and 2007 who received health care through the DoD health care program, including active duty members, retirees, National Guard and Reserve members, and dependents. The CCR contains demographic, tumor characteristic, cancer treatment, and vital status data that are abstracted from the records of patients diagnosed and/or treated at military treatment facilities (MTF). The data are reviewed and edited according to North America Association of Central Cancer Registries guidelines. The MDR data contain administrative and medical care records for inpatient and outpatient services that are either provided directly at MTFs (direct care) or paid for by the DoD at civilian facilities (indirect care). The MDR data include clinical diagnoses, diagnostic procedures, prescription medications, treatment, and vital status.

The data linkage project was reviewed and approved by the Institutional Review Boards of the Walter Reed National Military Medical Center, Tricare Management Activity, and the National Institutes of Health Office of Human Subjects Research.

Study subjects

Women with histologically confirmed, first primary, malignant breast cancer (ICD-0-3 codes C500-C506 and C508-C509) diagnosed between 1998 and 2007 were eligible for the study (n = 9,944). They were followed up until December 31, 2008. Women with type I diabetes (n = 536), under 20 years of age (n = 1), and/or with missing survival information (n = 9) were excluded.

Study variables

Medical conditions were identified in the MDR. Women were classified as having type II diabetes (DM-2) if they had ICD-9 diagnoses codes 250.x0, 250.x2, 357.2, 362.0, and 366.41 in at least one inpatient record, or at least three outpatient records (31, 32). Other medical conditions included in the analyses were obesity, heart disease, kidney disease, chronic obstructive pulmonary disease (COPD), stroke, and hypertension. These conditions were considered to be present if codes were identified in one or more inpatient or three or more outpatient records.

Pathologic breast cancer features obtained from CCR included AJCC tumor stage (I through IV, unknown), histologic grade, and hormone receptor status. Histologic grade was classified on the basis of level of differentiation (well differentiated, moderately differentiated, poorly differentiated, and unknown). Estrogen receptor (ER) and progesterone receptor (PR) statuses were categorized into five groups (ER+/PR+, ER/PR, ER+/PR, or ER/PR+, unknown).

Medical care and recurrence variables included breast cancer treatment (surgery, chemotherapy, radiotherapy, and hormone therapy), diabetes treatment (insulin, insulin secretagogue, and metformin), surveillance mammography, and recurrence. Receipt of breast cancer surgery, chemotherapy, and radiotherapy was determined by combining data from CCR and MDR and was considered “yes” if therapy occurred within 3 months postdiagnosis. Hormone therapy information was obtained from CCR. Information on diabetes-specific treatment and surveillance mammography was obtained from MDR. Use of diabetes medications, which may be related to cancer survival (33, 34), was considered “yes” if one or more prescriptions were documented and filled. Surveillance mammography was a variable for women with a breast or breasts left after surgery, including those who underwent breast conserving surgery and received a bilateral mammogram, or those who underwent a unilateral mastectomy and received a unilateral mammogram. It was categorized as “yes” if it was identified 6 months or more after a previous mammogram among women. If a mammogram was received 2 months prior to a diagnosis of a breast mass or other breast symptom, it might be diagnostic and thus was not considered as surveillance procedures (35). Recurrence was defined on the basis of the CCR definition and supplemented with information obtained from MDR (36).

Demographic characteristics were obtained from CCR with missing values supplemented from MDR. Demographic variables included age at diagnosis, race/ethnicity, marital status, active duty status, service branch of the active duty member/sponsor, rank of the active duty member/sponsor, and insurance plan (TRICARE Prime, an HMO-like component, TRICARE Standard, and TRICARE Extra).

Statistical analysis

We first described the distributions of demographics, comorbid conditions, tumor features, surveillance, and treatment by timing of DM-2 diagnosis. We then used Cox proportional hazards models to assess the association between DM-2 (before or at/after breast cancer diagnosis) and overall survival with adjustment for potential confounding factors. Survival time was calculated as the time interval beginning at the date of breast cancer diagnosis through the date of death, date of last contact, or study end (truncated at December 31, 2008). DM-2 status before breast cancer diagnosis was defined as a dichotomized variable (no/yes) based on whether DM-2 was identified prior to breast cancer diagnosis. In regard to DM-2 diagnosed at/after breast cancer, we define it as a time-dependent variable to minimize the potential impact of immortal time bias (37), in which only those who survive to the point of exposure (diabetes at/after breast cancer for this study) could have the chance to be exposed, therefore biasing the survival results in favor of the exposed group. A potential confounder was defined if it was associated with both DM-2 and survival and was not an intervening variable between DM-2 and survival. The identified potential confounding variables included age at diagnosis, race, marital status, rank of active duty member/sponsor, benefit type, tumor grade, obesity, coronary heart disease, chronic obstructive pulmonary disease, stroke, kidney disease, ER/PR status, and chemotherapy. Although tumor stage did not meet the confounder criteria, this variable was adjusted for due to its complex effects and association with prognosis. We repeated Cox model analyses with stratification by race, menopausal status, obesity, ER/PR status, and tumor stage. Information on menopausal status was not available; therefore, age at diagnosis (<50 and ≥50 years) was used as a proxy variable based on the average age of natural menopause (38). The proportional hazards assumption was tested for Cox model analyses, and no violations of model assumptions were found.

All analyses were conducted using SAS version 9.3 (SAS Institute, Cary, NC). All tests of significance were two-tailed and performed at α = 0.05.

Among 9,398 study women, 1,594 had DM-2 (17%). Table 1 shows the distributions of demographic factors and other medical conditions by timing of DM-2 diagnosis. Compared with women without DM-2, those with DM-2 diagnosed both before and at/after breast cancer diagnosis were less likely to be white, active-duty members, and holders of nonprime TRICARE insurance (P < 0.01, all variables). Although both DM-2 groups were older than the nondiabetic group (P < 0.01), women diagnosed before breast cancer tended to be oldest. Women with DM-2 diagnosed before breast cancer were also more likely to be enlisted service members than the other two groups (P < 0.01). Despite timing of diagnosis, women with DM-2 tended to have a higher frequency of obesity, coronary heart disease, kidney disease, COPD, stroke, and hypertension than nondiabetics (P < 0.01, all variables).

Table 2 shows the distributions of treatment, surveillance, and tumor characteristics by timing of DM-2 diagnosis. Women with DM-2 diagnosed before breast cancer diagnosis were more likely to have well-differentiated tumors (P = 0.03) and less likely to have radiation treatment (P = 0.04), chemotherapy (P < 0.01), and recurrence of the disease (P = 0.01) than the other two groups. Although the proportions of women who received metformin, insulin, or insulin secretagogue were negligible in the nondiabetic group, they were higher in women with DM-2 diagnosed before breast cancer than those diagnosed at/after the disease (P < 0.01, all variables). There were no significant differences in tumor stage between the groups (P = 0.59).

The median follow-up time after breast cancer diagnosis was 5.7 years. During follow-up, 16.9% of women with DM-2 and 13.9% of women without DM-2 died. After adjusting for potential confounders, women diagnosed with DM-2 before breast cancer tended to have an increased risk of mortality compared with women without DM-2 (HR = 1.17; 95% CI, 0.95–1.44; Table 3). Women diagnosed with DM-2 at/after breast cancer had higher mortality than women without the disease (HR = 1.39; 95% CI, 1.16–1.66).

Table 4 shows the results of multivariate analyses stratified by demographic, tumor, or obesity variables. Among women diagnosed with DM-2 both before and at/after breast cancer, the tendency of increased mortality was seen across most subgroups stratified by race, menopausal status, or obesity, although not significant for most of them due to smaller numbers of women within those subgroups. Similar findings were observed when data were stratified by tumor hormone receptor status or stage.

This study showed a modest increased risk of death associated with DM-2, regardless of whether it was diagnosed before or at/after breast cancer diagnosis. This association tended to exist in most subgroups when results were stratified by race, age, obesity status, tumor stage, or tumor hormone receptor status, although it was not significant in many strata due to smaller sample sizes.

Most previous studies examined diagnosis of DM-2 prior to breast cancer. Although the results of these studies were not consistent (5), most found an increase in all-cause mortality with preexisting DM-2 (6, 8, 9, 18–20). Our results on DM-2 diagnosed before breast cancer diagnosis, although marginally significant, support these findings. This may be attributable to differences in the receipt of cancer treatment and the effects of diabetes on overall survival.

As a result of diabetes and its complications, breast cancer patients with DM-2 may be less likely to receive breast cancer treatments such as adjuvant chemotherapy or radiotherapy (20, 39, 40). Research has reported that physicians may use chemotherapy less frequently or aggressively to treat breast cancer patients with DM-2 due to an increase in hospitalization for chemotherapy toxicity as well as an increase in breast cancer–specific mortality (20). Less receipt of radiation treatment among diabetics compared with nondiabetics has been reported in the literature (40). These were also demonstrated in our study, in which women with DM-2 diagnosed before breast cancer were less likely to receive chemotherapy and radiotherapy than women without DM-2. However, chemotherapy as a potential confounder was adjusted for in the analysis, and thus, the potential effects that treatment may have on survival were reduced. Nevertheless, we cannot exclude the possibility that treatment regimen (intensity, type, timing, frequency, or duration of chemotherapy as well as other cancer treatments) may have differed between women with and without DM-2. Such differences could be associated with increased mortality risk.

As all-cause mortality was the study outcome, DM-2 and other related medical conditions may have affected the outcome. Hyperglycemia, a serious problem for many individuals with DM-2, may play a role in the decreased survival among women diagnosed with DM-2. Increased blood glucose levels have been found to increase cancer mortality (41–43), with survival decreasing as glycemic control decreases. In addition, patients with DM-2 in the current study were about twice as likely to have obesity, coronary heart disease, kidney disease, COPD, stroke, and hypertension, which can increase the risk of death. These medical conditions were adjusted for in the models. Nonetheless, repeated analysis without these conditions did not change the HR estimates substantially, suggesting the possible effects of factors other than these medical conditions on the identified association between diabetes and overall survival.

We assessed the independent effects of DM-2 diagnosed at or after breast cancer diagnosis with the hypothesis that DM-2 diagnosed at or after breast cancer may be a more indolent or less severe form of the disease than DM-2 diagnosed before breast cancer diagnosis. This hypothesis is based on the fact that DM-2 diagnosed at or after breast cancer diagnosis is detected as a result of medical examination for cancer diagnosis or medical surveillance for cancer care and has shorter duration of disease. Therefore, it may be less likely to affect breast cancer treatment. Lower severity of DM-2 diagnosed after breast cancer diagnosis was demonstrated in our study: Patients with DM-2 diagnosed at or after breast cancer diagnosis were less likely to receive diabetes treatment than those diagnosed before breast cancer. It is possible that DM-2 diagnosed at/after breast cancer diagnosis might not influence breast cancer treatment decisions as heavily. Our data showed that patients with DM-2 diagnosed at/after breast cancer diagnosis were more likely to receive radiation treatment and chemotherapy than those with DM-2 diagnosed before breast cancer. The proportions of breast cancer treatment among the former were close to those among patients without DM-2. Although the hypothesis on DM-2 severity and possible effects on breast cancer treatment is tenable as shown in our data, our analysis did not display significant differences in survival between patients with DM-2 diagnosed at/after and before breast cancer diagnosis. The overall mortality was also higher among DM-2 patients diagnosed at/after breast cancer diagnosis, being similar to those with DM-2 diagnosed before breast cancer. Therefore, factors other than diabetes severity and breast cancer treatment might account for a higher mortality among DM-2 patients despite timing at diagnosis.

Limitations in this study included possible misclassification of DM-2 status because of lack of data on diabetes diagnoses prior to 1998. However, such misclassification was likely limited as individuals with diabetes would continue to receive antidiabetic therapy, which would be captured in their records after 1998. In addition, such misclassification was likely to occur only for DM-2 diagnosed before breast cancer because the initial year for breast cancer diagnosis was 1998. This misclassification might dilute rather than overestimate the association between DM-2 and survival. Second, we cannot exclude residual confounding by the frequency, intensity, timing, and duration of breast cancer treatment, as mentioned above. Third, overadjustment or underadjustment for confounders may be possible. We adjusted for obesity and other comorbidities in our multivariate models. The relationship between diabetes and obesity and other comorbidities is complex. We do not exclude the possibility that a comorbid condition is on the causal pathway between diabetes and mortality, and thus, our multivariate results were overadjusted. However, the HR estimates remained similar after we removed these comorbidities from the multivariate models. On the other hand, there is a possibility that we underadjusted for potential confounders as we did not have data on variables such as physical activity, dietary intake, and smoking, which may be associated with both diabetes and survival (44–46). Finally, there might be incomplete information and inaccurate coding in our data that are typical in medical records data. Nonetheless, the extent of these limitations might not be substantial enough to change the results.

In summary, our findings indicate that breast cancer patients with DM-2 are at a higher risk of mortality despite timing at diagnosis, compared with those without DM-2. Further research into these findings is warranted.

No potential conflicts of interest were disclosed.

The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Army, Department of Defense, National Cancer Institute, nor the U.S. government. Nothing in the presentation implies any federal/Department of Defense endorsement.

Conception and design: A.A. Gill, C.D. Shriver, K. Zhu

Development of methodology: A.A. Gill, K. Zhu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): K. Zhu

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S. Shao, S.H. Zahm, K.A. McGlynn, K. Zhu

Writing, review, and/or revision of the manuscript: S. Shao, A.A. Gill, S.H. Zahm, I. Jatoi, K.A. McGlynn, K. Zhu

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.A. Gill, C.D. Shriver

Study supervision: C.D. Shriver, K. Zhu

The authors thank the following individuals and institutes for their contributions to or support for the original data linkage project: Guy J. Garnett, David E. Radune, and Dr. Aliza Fink of ICF Macro; Wendy Funk, Julie Anne Mutersbaugh, Linda Cottrell, and Laura Hopkins of Kennel and Associates, Inc.; Kim Frazier, Dr. Elder Granger, and Dr. Thomas V. Williams of TMA; Annette Anderson, Dr. Patrice Robinson, and Dr. Chris Owner of the Armed Forces Institute of Pathology; Dr. Joseph F. Fraumeni Jr, Dr. Robert N. Hoover, Dr. Susan S. Devesa, and Gloria Gridley of the NCI; Dr. Lindsey Enewold, Dr. John Potter, Raul Parra, Anna Smith, Fiona Renalds, William Mahr, Hongyu Wu, Dr. Larry Maxwell, Miguel Buddle, and Virginia Van Horn of the United States Military Cancer Institute. The authors also thank Dr. Jie Lin for her help in conducting supplementary statistical analysis. This project was supported by the John P. Murtha Cancer Center, Walter Reed National Military Medical Center via the Uniformed Services University of the Health Sciences under the auspices of the Henry M. Jackson Foundation for the Advancement of Military Medicine and by the intramural research program of the NCI. The original data linkage was supported by the United States Military Cancer Institute and Division of Cancer Epidemiology and Genetics, NCI.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Siegel
RL
,
Miller
KD
,
Jemal
A
. 
Cancer statistics, 2016
.
CA Cancer J Clin
2016
;
66
:
7
30
.
2.
Louwman
WJ
,
Janssen-Heijnen
ML
,
Houterman
S
,
Voogd
AC
,
van der Sangen
MJ
,
Nieuwenhuijzen
GA
, et al
Less extensive treatment and inferior prognosis for breast cancer patient with comorbidity: a population-based study
.
Eur J Cancer
2005
;
41
:
779
85
.
3.
Yancik
R
,
Wesley
MN
,
Ries
LA
,
Havlik
RJ
,
Edwards
BK
,
Yates
JW
. 
Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older
.
JAMA
2001
;
285
:
885
92
.
4.
Centers for Disease Control and Prevention
.
National diabetes statistics report, 2014
.
Atlanta, GA
:
Centers for Disease Control and Prevention
; 
2014
.
5.
Du
W
,
Simon
MS
. 
Racial disparities in treatment and survival of women with stage I-III breast cancer at a large academic medical center in metropolitan Detroit
.
Breast Cancer Res Treat
2005
;
91
:
243
8
.
6.
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
:
1193
203
.
7.
Griffiths
RI
,
Danese
MD
,
Gleeson
ML
,
Valderas
JM
. 
Epidemiology and outcomes of previously undiagnosed diabetes in older women with breast cancer: an observational cohort study based on SEER-Medicare
.
BMC Cancer
2012
;
12
:
613
.
8.
Jiralerspong
S
,
Kim
ES
,
Dong
W
,
Feng
L
,
Hortobagyi
GN
,
Giordano
SH
. 
Obesity, diabetes, and survival outcomes in a large cohort of early-stage breast cancer patients
.
Ann Oncol
2013
;
24
:
2506
14
.
9.
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
:
389
95
.
10.
Campbell
PT
,
Newton
CC
,
Patel
AV
,
Jacobs
EJ
,
Gapstur
SM
. 
Diabetes and cause-specific mortality in a prospective cohort of one million U.S. adults
.
Diabetes Care
2012
;
35
:
1835
44
.
11.
Gold
HT
,
Makarem
N
,
Nicholson
JM
,
Parekh
N
. 
Treatment and outcomes in diabetic breast cancer patients
.
Breast Cancer Res Treat
2014
;
143
:
551
70
.
12.
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
:
153
62
.
13.
Yeh
HC
,
Platz
EA
,
Wang
NY
,
Visvanathan
K
,
Helzlsouer
KJ
,
Brancati
FL
. 
A prospective study of the associations between treated diabetes and cancer outcomes
.
Diabetes Care
2012
;
35
:
113
8
.
14.
Wu
AH
,
Kurian
AW
,
Kwan
ML
,
John
EM
,
Lu
Y
,
Keegan
TH
, et al
Diabetes and other comorbidities in breast cancer survival by race/ethnicity: the California Breast Cancer Survivorship Consortium (CBCSC)
.
Cancer Epidemiol Biomarkers Prev
2015
;
24
:
361
8
.
15.
Minicozzi
P
,
Berrino
F
,
Sebastiani
F
,
Falcini
F
,
Vattiato
R
,
Cioccoloni
F
, et al
High fasting blood glucose and obesity significantly and independently increase risk of breast cancer death in hormone receptor-positive disease
.
Eur J Cancer
2013
;
49
:
3881
8
.
16.
Coughlin
SS
,
Calle
EE
,
Teras
LR
,
Petrelli
J
,
Thun
MJ
. 
Diabetes mellitus as a predictor of cancer mortality in a large cohort of US adults
.
Am J Epidemiol
2004
;
159
:
1160
7
.
17.
De Bruijn
KM
,
Arends
LR
,
Hansen
BE
,
Leeflang
S
,
Ruiter
R
,
van Eijck
CH
. 
Systematic review and meta-analysis of the association between diabetes mellitus and incidence and mortality in breast and colorectal cancer
.
Br J Surg
2013
;
100
:
1421
9
.
18.
Peairs
KS
,
Barone
BB
,
Snyder
CF
,
Yeh
HC
,
Stein
KB
,
Derr
RL
, et al
Diabetes mellitus and breast cancer outcomes: a systematic review and meta-analysis
.
J Clin Oncol
2011
;
29
:
40
6
.
19.
Yerrabothala
S
,
Shaaban
H
,
Capo
G
,
Maroules
M
,
Debari
VA
. 
The impact of diabetes mellitus on breast cancer outcomes: a single center retrospective study
.
Pathol Oncol Res
2014
;
20
:
209
14
.
20.
Srokowski
TP
,
Fang
S
,
Hortobagyi
GN
,
Giordano
SH
. 
Impact of diabetes mellitus on complications and outcomes of adjuvant chemotherapy in older patients with breast cancer
.
J Clin Oncol
2009
;
27
:
2170
6
.
21.
Zhao
XB
,
Ren
GS
. 
Diabetes mellitus and prognosis in women with breast cancer: a systematic review and meta-analysis
.
Medicine
2016
;
95
:
e5602
.
22.
Sabatino
SA
,
Thompson
TD
,
Wu
XC
,
Fleming
ST
,
Kimmick
GG
,
Trentham-Dietz
A
, et al
The influence of diabetes severity on receipt of guideline-concordant treatment for breast cancer
.
Breast Cancer Res Treat
2014
;
146
:
199
209
.
23.
Martin
ET
,
Kaye
KS
,
Knott
C
,
Nguyen
H
,
Santarossa
M
,
Evans
R
, et al
Diabetes and risk of surgical site infection: a systematic review and meta-analysis
.
Infect Control Hosp Epidemiol
2016
;
37
:
88
99
.
24.
Chavez-MacGregor
M
,
Clarke
CA
,
Lichtensztajn
DY
,
Giordano
SH
. 
Delayed initiation of adjuvant chemotherapy among patients with breast cancer
.
JAMA Oncol
2016
;
2
:
322
9
.
25.
Ferroni
P
,
Riondino
S
,
Buonomo
O
,
Palmirotta
R
,
Guadagni
F
,
Roselli
M
. 
Type 2 diabetes and breast cancer: the interplay between impaired glucose metabolism and oxidant stress
.
Oxid Med Cell Longev
2015
;
2015
:
183928
.
26.
Lipscombe
LL
,
Chan
WW
,
Yun
L
,
Austin
PC
,
Anderson
GM
,
Rochon
PA
. 
Incidence of diabetes among postmenopausal breast cancer survivors
.
Diabetologia
2013
;
56
:
476
83
.
27.
Zhang
X
,
Geiss
LS
,
Cheng
YJ
,
Beckles
GL
,
Gregg
EW
,
Kahn
HS
. 
The missed patient with diabetes: how access to health care affects the detection of diabetes
.
Diabetes Care
2008
;
31
:
1748
53
.
28.
Nelson
KM
,
Chapko
MK
,
Reiber
G
,
Boyko
EJ
. 
The association between health insurance coverage and diabetes care; data from the 2000 Behavioral Risk Factor Surveillance System
.
Health Serv Res
2005
;
40
:
361
72
.
29.
Vest
BM
,
Kahn
LS
,
Danzo
A
,
Tumiel-Berhalter
L
,
Schuster
RC
,
Karl
R
, et al
Diabetes self-management in a low-income population: impacts of social support and relationships with the health care system
.
Chronic Illn
2013
;
9
:
145
55
.
30.
Ward
E
,
Halpern
M
,
Schrag
N
,
Cokkinides
V
,
DeSantis
C
,
Bandi
P
, et al
Association of insurance with cancer care utilization and outcomes
.
CA Cancer J Clin
2008
;
58
:
9
31
.
31.
Chen
WW
,
Shao
YY
,
Shau
WY
,
Lin
ZZ
,
Lu
YS
,
Chen
HM
, et al
The impact of diabetes mellitus on prognosis of early breast cancer in Asia
.
Oncologist
2012
;
17
:
485
91
.
32.
Gill
KS
,
Muntner
P
,
Lafayette
RA
,
Petersen
J
,
Fink
JC
,
Gilbertson
DT
, et al
Red blood cell transfusion use in patients with chronic kidney disease
.
Nephrol Dial Transplant
2013
;
28
:
1504
15
.
33.
Col
NF
,
Ochs
L
,
Springmann
V
,
Aragaki
AK
,
Chlebowski
RT
. 
Metformin and breast cancer risk: a meta-analysis and critical literature review
.
Breast Cancer Res Treat
2012
;
135
:
639
46
.
34.
Currie
CJ
,
Poole
CD
,
Gale
EA
. 
The influence of glucose-lowering therapies on cancer risk in type 2 diabetes
.
Diabetologia
2009
;
52
:
1766
77
.
35.
Enewold
L
,
McGlynn
KA
,
Zahm
SH
,
Jatoi
I
,
Anderson
WF
,
Gill
AA
, et al
Surveillance mammography among female Department of Defense beneficiaries: a study by race and ethnicity
.
Cancer
2013
;
119
:
3531
8
.
36.
Cheng
L
,
Swartz
MD
,
Zhao
H
,
Kapadia
AS
,
Lai
D
,
Rowan
PJ
, et al
Hazard of recurrence among women after primary breast cancer treatment–a 10-year follow-up using data from SEER-Medicare
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
800
9
.
37.
Shariff
SZ
,
Cuerden
MS
,
Jain
AK
,
Garg
AX
. 
The secret of immortal time bias in epidemiologic studies
.
J Am Soc Nephrol
2008
;
19
:
841
3
.
38.
Thomas
F
,
Renaud
F
,
Benefice
E
,
de Meeus
T
,
Guegan
JF
. 
International variability of ages at menarche and menopause: patterns and main determinants
.
Hum Biol
2001
;
73
:
271
90
.
39.
van de Poll-Franse
LV
,
Houterman
S
,
Janssen-Heijnen
ML
,
Dercksen
MW
,
Coebergh
JW
,
Haak
HR
. 
Less aggressive treatment and worse overall survival in cancer patients with diabetes: a large population based analysis
.
Int J Cancer
2007
;
120
:
1986
92
.
40.
Luo
J
,
Hendryx
M
,
Virnig
B
,
Wen
S
,
Chlebowski
R
,
Chen
C
, et al
Pre-existing diabetes and breast cancer prognosis among elderly women
.
Br J Cancer
2015
;
113
:
827
32
.
41.
Seshasai
SR
,
Kaptoge
S
,
Thompson
A
,
Di Angelantonio
E
,
Gao
P
,
Sarwar
N
, et al
Diabetes mellitus, fasting glucose, and risk of cause-specific death
.
N Engl J Med
2011
;
364
:
829
41
.
42.
Saydah
SH
,
Loria
CM
,
Eberhardt
MS
,
Brancati
FL
. 
Abnormal glucose tolerance and the risk of cancer death in the United States
.
Am J Epidemiol
2003
;
157
:
1092
100
.
43.
Erickson
K
,
Patterson
RE
,
Flatt
SW
,
Natarajan
L
,
Parker
BA
,
Heath
DD
, et al
Clinically defined type 2 diabetes mellitus and prognosis in early-stage breast cancer
.
J Clin Oncol
2011
;
29
:
54
60
.
44.
Goodwin
PJ
,
Ambrosone
CB
,
Hong
CC
. 
Modifiable lifestyle factors and breast cancer outcomes: current controversies and research recommendations
.
Adv Exp Med Biol
2015
;
862
:
177
92
.
45.
Berube
S
,
Lemieux
J
,
Moore
L
,
Maunsell
E
,
Brisson
J
. 
Smoking at time of diagnosis and breast cancer-specific survival: new findings and systematic review with meta-analysis
.
Breast Cancer Res
2014
;
16
:
R42
.
46.
Zhu
B
,
Wu
X
,
Wang
X
,
Zheng
Q
,
Sun
G
. 
The association between passive smoking and type 2 diabetes: a meta-analysis
.
Asia Pac J Public Health
2014
;
26
:
226
37
.