Literature DB >> 24569466

Diabetes and prognosis in older persons with colorectal cancer.

J Luo1, H-C Lin2, K He1, M Hendryx2.   

Abstract

BACKGROUND: Epidemiological studies have reported that diabetes significantly increases overall mortality in patients with colorectal cancer. However, it is unclear whether diabetes increases colorectal cancer-specific mortality. We used the US Surveillance Epidemiology and End Results (SEER) database linked with Medicare claims data to assess the influence of pre-existing diabetes on prognosis of patients with colorectal cancer.
METHODS: Data from 61,213 patients aged 67 or older with colorectal cancer diagnosed between 2003 and 2009 were extracted and prospectively followed through the date of death or the end of 2012 if the patient was still alive. Diabetes cases with and without complications were identified based on an algorithm developed for the Chronic Condition Data Warehouse (CCW). Cox models were used to estimate hazard ratios (HRs) for total mortality. The proportional subdistribution hazards model proposed by Fine and Gray was used to estimate HRs for colorectal cancer-specific mortality.
RESULTS: Compared with patients without diabetes, colorectal cancer patients with pre-existing diabetes had significantly higher risk of overall mortality (HR=1.20, 95 % confidence interval (95% CI): 1.17-1.23). The HR for overall mortality was more pronounced for patients who had diabetes with complications (HR=1.50, 95% CI: 1.42-1.58). However, diabetes was not associated with increased colorectal cancer-specific mortality after accounting for non-colorectal cancer outcomes as competing risk.
CONCLUSIONS: Pre-existing diabetes increased risk of total mortality among patients with colorectal cancer, especially among cancer patients who had diabetes with complications. The increased risk of total mortality associated with diabetes was primarily explained by increased cardiovascular-specific mortality, not by increased colorectal cancer-specific mortality.

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Mesh:

Year:  2014        PMID: 24569466      PMCID: PMC3974085          DOI: 10.1038/bjc.2014.68

Source DB:  PubMed          Journal:  Br J Cancer        ISSN: 0007-0920            Impact factor:   7.640


Colorectal cancer is the fourth most common cancer in the United States, and more than two-thirds of patients diagnosed with colorectal cancer are aged ⩾65 years (SEER, 2012a, 2012b). Co-morbidity exerts a strong effect on the probability of survival after a cancer diagnosis (Gross ; Barone ), and there is a critical need for improved understanding of how co-morbid chronic conditions affect outcomes in patients with cancer (Extermann, 2003). Type 2 diabetes is one of the most common chronic diseases (Cowie ) and appears to be an independent risk factor for colorectal cancer incidence (Larsson ); pre-existing diabetes is present in ∼18% of colorectal cancer cases (Gross ). Accumulating epidemiological studies have reported that diabetes significantly increases total mortality in patients with colorectal cancer (Barone ; Stein ). However, it is unclear whether the higher total mortality in colorectal cancer patients with diabetes is driven by a worse colorectal cancer prognosis or by competing risks such as diabetes-related cardiovascular disease or different clinical practices for cancer patients with pre-existing diabetes. In the majority of epidemiological applications, competing risk has been ignored (i.e., patients experiencing competing events were censored at the time of these events), which may substantially overestimate the absolute risk of the event of interest and lead to biased findings (Putter ; Wolbers ). We overcame this limitation through applying an improved analytic approach – proportional subdistribution hazard model proposed by Fine and Gray (Fine and Gray, 1999) to estimate colorectal cancer-specific mortality by accounting for non-colorectal cancer outcomes as competing risk. We used the US Surveillance Epidemiology and End Results (SEER, 2012a, 2012b) database linked with Medicare claims data, a unique population-based source of information, to assess the influence of pre-existing diabetes on prognosis of patients with colorectal cancer. Our study hypothesis was that diabetes would adversely influence colorectal cancer prognosis, including total and cancer-specific mortality. This study aims to address the following questions: (1) Is there a mortality difference between diabetic and non-diabetic patients with colorectal cancer? (2) Is mortality outcome among colorectal cancer patients the result of colorectal cancer prognosis or of risk from non-cancer mortality? (3) Is diabetes associated with unfavourable tumour characteristics? (4) Does the impact of pre-existing diabetes on cancer prognosis differ by patients with and without diabetes complications? Addressing these questions will have great potential to advance the understanding of how pre-existing diabetes influences colorectal cancer prognosis. In particular, we can begin to understand whether diabetes per se may worsen colorectal cancer prognosis, or whether any competing risks may be more important in determining mortality outcomes.

Materials and methods

Data resource: SEER–Medicare data

The Surveillance, Epidemiology and End Results (SEER, 2012a, 2012b)–Medicare-linked database is used in this project. The SEER programme is an epidemiologic surveillance system sponsored by the US National Cancer Institute (NCI), consisting of population-based tumour registries that routinely collect information on all newly diagnosed cancer cases that occur in persons residing in SEER areas (SEER, 2012a, 2012b). Since 2000, the SEER areas capture ∼25% of the US population (SEER, 2012a, 2012b). Cancer registries participating in the SEER programme are required to meet strict standards with respect to case ascertainment and data quality. The information collected about each incident cancer diagnosis includes the patient's demographic characteristics (such as age, sex and race), date of diagnosis, cancer characteristics (e.g., histology, stage and grade), type of surgical treatment and/or radiation therapy recommended or provided within 4 months of diagnosis, follow-up of vital status and cause of death if applicable (Warren ). The Medicare programme, federally funded and administered by the Center for Medicare and Medicaid Services (CMS, 2012), provides health insurance for people aged ⩾65 years, people under age 65 with certain disabilities and people of all ages with end-stage renal disease (permanent kidney failure requiring dialysis or a kidney transplant) (CMS, 2012). Separate claim files can be obtained for inpatient, outpatient, physician and supplier, skilled nursing facility, and hospice services provided to beneficiaries enrolled in fee-for-service plans. Claim files contain diagnosis and procedure codes, dates of services, charges and amount paid. The SEER–Medicare data reflect the linkage of two large population-based sources of data that provide detailed information about Medicare beneficiaries with cancer. The linkage was first completed in 1991 and has been updated biennially. For each of the linkages, 94 per cent of persons aged ⩾65 in the SEER files were matched to the Medicare enrolment file; the deficit reflects the 3% of elderly people who do not enrol in Medicare and another 3% who do not have sufficient or accurate enough information for the linkage (Engels ).

Study population

As of December 2012, the data include all Medicare-eligible persons documented in the SEER data who were diagnosed with cancer through 2009, and their Medicare claims through 2010. Our cohort included patients aged ⩾67 years in the SEER database who had a first primary diagnosis of invasive colorectal cancers between 2003 and 2009. Sixty-seven years was selected as the age cutoff to ensure that each patient would have at least 2 years of Medicare eligibility before their cancer diagnosis. To ensure a complete assessment of pre-existing diabetes (exposure) and cancer treatment received, we only included patients who were continually enrolled in both Medicare Parts A and B and excluded patients who were enrolled in health maintenance organisation plans over the inclusive 2-year period before colorectal cancer diagnosis and 3 months after cancer diagnosis. Patients in health maintenance organisation plans were excluded because these patients do not have complete claim records. In addition, we excluded patients who had end-stage renal disease or disability alone or who were diagnosed exclusively by death certificates or at autopsy. After considering these inclusion and exclusion criteria, our final study cohort consisted of 61 213 patients with colorectal cancer. Of them, 46 483 (75.9%) patients were diagnosed with colon cancer, and 14 730 (24.1%) were diagnosed with rectal cancer.

Measurements

Outcomes

Our primary outcome is colorectal cancer-specific mortality. However, we also examined total mortality as an outcome for comparison with previous findings.

Pre-existing diabetes status

We adapted an algorithm developed for the Chronic Condition Data Warehouse (CCW Chronic Condition Data Warehouse, 2013) by the Centers for Medicare & Medicaid Services (CMS, 2012) (CCW Chronic Condition Data Warehouse, 2013) to identify pre-existing diabetes. Diabetes status was determined on the basis of either a single inpatient claim or at least two outpatient claim diagnoses with the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis code of 250.xx during the interval beginning 2 years before and 3 months after colorectal cancer diagnosis. Extending the time interval to 3 months after diagnosis allowed us to capture previously undiagnosed diabetes as other studies have done (Yang ). To avoid ‘rule out' diagnoses on outpatient claims, a patient's diagnosis must have appeared on at least two different claims that were made >30 days apart. We did not include diabetes medications in the definition since Medicare did not begin covering oral medications without an intravenous equivalent until January 2006. We defined diabetes with complication with ICD-9-CM codes 250.4–250.6 or 250.8–250.9 based on the definition of comorbidities described in the National Cancer Institute SEER–Medicare website.

Co-morbidity

Medicare claims were used to calculate the NCI combined co-morbidity index score proposed by Klabunde and as identified by Charlson . The NCI index (Klabunde ) is composed of two weighted co-morbidity scores derived separately from inpatient and outpatient claims. The NCI combined index uses weights derived from comorbid conditions identified in either Medicare inpatient or outpatient claims into a single co-morbidity index. Study has shown that the new NCI combined index is a more refined, easier to implement co-morbidity measurement algorithm appropriate for investigators using administrative claims databases to study commonly occurring cancers (Klabunde ). Two conditions (diabetes without and with complications) pertaining to diabetes were removed from the NCI Co-morbidity Index to reduce correlation with diabetes. ICD-9-CM diagnostic codes recorded in Medicare claims over 2 years before colorectal cancer diagnosis were searched to create this co-morbidity index. Conditions reported within 1 month of cancer diagnosis were excluded to avoid misclassifying complications or conditions directly resulting from cancer diagnosis or treatment as co-morbidities (Klabunde ).

Cancer stage and tumour characteristics

The colorectal cancer information was extracted from SEER data. The cancer stage was categorised as localised (confined to primary site), regional (spread to regional lymph nodes), distant (cancer has metastasised) or unknown (unstaged). Other tumour characteristics included tumour grade (grade I – well differentiated; grade II – moderately differentiated; grade III – poorly differentiated and grade IV – undifferentiated), and different histological subtypes of colorectal cancer (colon or rectum).

Cancer treatment

The SEER programme routinely collects information regarding certain anti-cancer therapies (i.e. surgery, radiation therapy) occurring within 4 months of diagnosis (first course of therapy). For surgery, we divided patients as two categories: cancer-directed surgery performed or not. For the method of radiation therapy performed as part of the first course of treatment, we collapsed patients who received any radiation (such as bean radiation, radioactive implants, radioisotopes or combination) as yes for radiation. As SEER does not report information pertaining to chemotherapy administration, we searched claims records to identify chemotherapy. Patients who had at least one claims record for chemotherapeutic administration, treatment or agents in any of inpatient and outpatient claims files within 6 months after primary diagnosis were considered chemotherapy recipients. We used codes including ICD-9-CM diagnosis codes (V58.1, V66.2 and V67.2), ICD-9-CM procedure code (99.25) and HCPCS codes (964xx, 965xx, Q0083-Q0085, J9XXX, J8510, J852x, J8530, J856x, J8600, J8610, J870x and J8999) (Warren ; Yang ).

Covariates

In the multivariate model, we adjusted for demographic variables including patient's demographic characteristics (age at diagnosis, sex, race and marital status), and socioeconomic status (median household income). The median income in each patient's census tract was used as a proxy measure for socioeconomic status. It was estimated at census tract level using the 2000 census and stratified into quartiles. Race was categorised as white, black, American Indian/Alaska Native, Asian or Pacific Islander and others. Supplementary Table 1 shows all of the ICD-9 codes listed in the paper.

Statistical analysis

Distribution of baseline patients' characteristics, tumour characteristics and stage at diagnosis were compared between patients with and without diabetes. χ2-tests were used to evaluate differences for categorical covariates, and t-tests were used for continuous variables. Age-adjusted and multivariate-adjusted Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (95% CI) for overall survival. The proportional subdistribution hazard model proposed by Fine and Gray (1999) was used to estimate HRs for colorectal cancer-specific mortality associated with diabetes status by accounting for non-colorectal cancer outcomes as competing risk. In the multivariate models, we adjusted for covariates including age at diagnosis (67–69, 70–74, 75–79, 80–84 and 85+), sex, race (white, black, American Indian/Alaska Native, Asian or Pacific Islander, other), marital status (never married, married, separated, divorced and widowed), grade (grade I – well differentiated; grade II – moderately differentiated; grade III – poorly differentiated and grade IV – undifferentiated), census tract median income (quartiles) and co-morbidity (0, 1, 2+). The underlying time metric in the Cox model was follow-up time since diagnosis of cancer to the date of death or the end of 2012 if the patient was still alive. The date of death from any cause was used for total mortality; the date of death from colorectal cancer was used for cancer-specific mortality for colorectal, colon and rectal cancer patients. The proportionality assumption was confirmed for all exposure variables of interest and for all potential confounding variables, based on graphs of scaled Schoenfeld residuals (Hess, 1995).

Results

Of a total of 61 213 colorectal cancer patients, 14 813 (24.2%) had diabetes including 12 298 without complications and 2515 with complications. Over an average of 38 months of follow-up (median=33 months, range 0–96 months), 28 682 (46.9%) patients died from all causes, and 15 879 (25.9%) patients died from colorectal cancer. Baseline patients' characteristics by diabetes status are shown in Table 1. Compared with patients without diabetes, patients with diabetes were significantly younger, males, members of non-White race groups, unmarried and from low-median income areas. Patients with diabetes were also more likely to have one or more co-morbidities, and were less likely to have radiation therapy performed as part of the first course of cancer treatment. There was no substantial difference between the diabetes group and the non-diabetes group in terms of the stage of diagnosis, tumour grade and whether the patient underwent cancer-direct surgery or chemotherapy, although P-values for statistical tests were significant due to the large sample size. The patterns were similar when comparing patients with complication to patients without complication among patients with diabetes (Table 1).
Table 1

Characteristics of 61 213 colorectal cancer patients by diabetes statusa

  Diabetes
 No diabetesTotal no. (%)Without complicationWith complicationb
All patients46 40014 813 (24.2)12 298 (20.1)2515 (4.1)
Age (year)
65–7416 708 (36)5965 (40)4912 (34)1053 (42)
75–8420 744 (44)6682 (45)5556 (45)1126 (45)
85+8948 (19)2166 (15)1830 (15)336 (13)
Sex (female, %)25 762 (56)7785 (53)6501 (53)1284 (51)
Race
White40 650 (88)12 167 (82)10 206 (83)1961 (78)
Black3411 (7)1779 (12)1383 (11)396 (16)
American Indian/Alaska Native115 (0)61 (0)47 (0)>11 (∼)
Asian or Pacific Islander2025 (4)754 (5)616 (5)138 (6)
Unknown199 (0)52 (0)46 (0)<11 (∼)
Marital status
Single (never married)3623 (8)1169 (8)954 (8)215 ((9)
Married22 715 (49)7151 (48)6010 (49)1141 (45)
Separated270 (1)121 (1)101 (1)20 (1)
Divorced2881 (6)1003 (7)807 (7)196 (8)
Widowed14 881 (32)4711 (32)3871 (32)840 (33)
Unknown2030 (4)658 (4)555 (6)103 (4)
Median Income
Lowest quartile11 092 (24)4210 (28)3436 (28)774 (31)
Second quartile11 483 (25)3823 (26)3193 (27)630 (25)
Third quartile11 666 (25)3644 (25)3056 (25)588 (23)
Highest quartile12 159 (26)3136 (25)2613 (21)523 (21)
No. of comorbidities
037 348 (81)9624 (65)8502 (69)1122 (45)
15323 (12)2313 (16)1855 (15)458 (18)
⩾23729 (8)2876 (19)1941 (16)935 (37)
Cancer site at diagnosis
Colon34 851(75)11 632 (79)9598 (78)2034 (81)
Rectum11 549 (25)3181 (21)2700 (22)481 (19)
Cancer stage
Localised20 088 (43)6478 (44)5336 (43)1142 (45)
Regional17 520 (38)5758 (39)4822 (39)936 (37)
Distant6679 (14)1921 (13)1617 (13)304 (12)
Unknown2113 (5)656 (4)523 (4)133 (5)
Grade
Grade I: well differentiated4132 (9)1335 (9)1097 (9)238 (10)
Grade II: moderately differentiated28 262 (61)9258 (63)7723 (63)1535 (61)
Grade III: poorly differentiated7762 (17)2403 (16)1981 (16)422 (17)
Grade IV: undifferentiated659 (1)205 (1)171 (1)34 (1)
Unknown5585 (12)1612 (11)1326 (11)286 (11)
Cancer-direct surgery
No5099 (11)1533 (10)1237 (10)296 (12)
Yes40 915 (88)13 158 (89)10 967 (89)2191 (87)
Unknown386 (1)122 (1)94 (1)28 (1)
Radiation therapy
No41 187 (89)13 395 (9)11 089 (90)2306 (92)
Yes4628 (10)1214 (8)1043 (9)171 (7)
Unknown585 (1)204 (1)166 (1)38 (2)
Chemotherapy
Yes4055 (9)1338 (9)1161 (9)177 (7)

All tests are significant at P<0.05 between two groups (non-diabetes vs diabetes) or among three groups (non-diabetes, diabetes without complication and diabetes with complication).

There are n=6 cases of Unknown race with diabetes and complications. To comply with the SEER-Medicare rules the cell sizes were suppressed for confidentiality reasons as per the SEER-Medicare data usage agreement.

Figure 1 shows the crude total and colorectal cancer-specific survival curves by diabetes status. Diabetes with complications had the lowest total survival rates and the lowest colorectal cancer-specific mortality. There was no notable difference in colorectal cancer-specific survival rates between patients without diabetes and patients with diabetes but no complications.
Figure 1

Survival curves among colorectal cancer patients by diabetes status (A) for total survival rates (log-rank test P-value <0.0001); (B) colorectal-cancer-specific survival rates (log-rank test P-value=0.001).

Compared with patients without diabetes, we observed that colorectal cancer patients with pre-existing diabetes had significantly higher risk of total mortality (HR=1.20, 95% CI: 1.17–1.23 for patients in all stages, HR=1.25, 95% CI: 1.21–1.29 for patients in localised or regional stage and HR=1.12, 95 CI: 1.06–1.18 for patients in distant stage) after adjusting for potential confounders. The risks of total mortality were more pronounced for patients who had diabetes with complications (HR=1.50, 95% CI: 1.42–1.58 for patients in all stages, HR=1.63, 95% CI: 1.53–1.73 for patients in localised or regional stage and HR=1.21, 95% CI: 1.07–1.37 for patients in distant). Similar findings were observed when we separated colon and rectal cancer patients with an exception that the results for distant-stage rectal cancer patients become non-significant (Table 2). We observed that diabetes was significantly associated with cardiovascular-specific mortality regardless of the site of cancer, especially for diabetes with complications (HR=2.27, 95% CI: 2.06–2.50) (Table 2).
Table 2

Effect of pre-existing diabetes on total mortality in patients with colorectal cancer, by stage

 Overall
Localised or regional stage
Distant stage
CVD-specific mortality
 No. of cases/no. of observationsAge-adjusted HR (95% CI)Multivariate-adjusted HR (95% CI)aNo. of cases/no. of observationsMultivariate-adjusted HR (95% CI)aNo. of Cases/no. of observationsMultivariate-adjusted HR(95% CI)aNo. of Cases/No. of observationsMultivariate-adjusted HR(95% CI)a
Colorectal cancer
No diabetes21 137/46 400ReferentReferent13 768/37 608Referent5753/6679Referent3456/46 400 
Diabetes7545/14 8131.28 (1.25–1.31)1.20 (1.17–1.23)5309/12 2361.25 (1.21–1.29)1710/19211.12 (1.06–1.18)1608/14 8131.38 (1.29–1.46)
Diabetes without complications5983/12 2981.19 (1.16–1.23)1.15 (1.11–1.18)4141/10 1581.17 (1.13–1.22)1430/16171.11 (1.04–1.17)1199/12 2981.28 (1.20–1.37)
Diabetes with complications1562/25151.78 (1.69–1.88)1.50 (1.42–1.58)1168/20781.63 (1.53–1.73)280/3041.21 (1.07–1.37)409/25151.79 (1.61–1.99)
Colon cancer
No diabetes15 560/34 851ReferentReferent10 186/28 483Referent4293/4981Referent2663/34 851 
Diabetes5852/11 6321.29 (1.25–1.33)1.20 (1.17–1.24)4117/96441.26 (1.21–1.31)1351/15081.13 (1.06–1.20)1259/11 6321.35 (1.26–1.45)
Diabetes without complications4581/95981.19 (1.15–1.23)1.14 (1.10–1.18)3163/79561.18 (1.13–1.22)1123/12651.10 (1.03–1.18)938/95981.27 (1.18–1.37)
Diabetes with complications1271/20341.85 (1.75–1.96)1.55 (1.47–1.65)954/16881.70 (1.58–1.82)228/2431.28 (1.11–1.46)321/20341.71 (1.52–1.93)
Rectal cancer
No diabetes5577/11 549ReferentReferent3582/9125Referent1460/1698Referent793/11 549 
Diabetes1693/31811.28 (1.21–1.35)1.22 (1.15–1.29)1192/25921.23 (1.15–1.32)359/4131.10 (0.98–1.24)349/31811.48 (1.30–1.68)
Diabetes without complications1402/27001.23 (1.16–1.30)1.20 (1.13–1.27)978/22021.19 (1.11–1.28)307/3521.13 (0.99–1.28)261/27001.35 (1.17–1.55)
Diabetes with complications291/4811.61 (1.43–1.81)1.35 (1.19–1.52)214/3901.45 (1.26–1.68)52/610.98 (0.74–1.31)88/4812.18 (1.73–2.74)

In the multivariate models, we adjusted for covariates including age at diagnosis (65–69, 70–74, 75–79, 80–84 and 85+), sex (male, female), race (white, black, American Indian/Alaska Native, Asian or pacific Islander, others), marital status (never married, married, separated, divorced and widowed), grade (grade I – well differentiated; grade II – moderately differentiated; grade III – poorly differentiated and grade IV-undifferentiated) and census tract median income (quartiles) and co-morbidity (0, 1, 2+).

In contrast, we did not observe a significantly increased risk for colorectal-specific mortality among patients with colon, rectal or colorectal cancer regardless of stage and diabetes severity (Table 3). There was one exception, as diabetes with complications and advanced-stage colon cancer was significantly associated with colorectal cancer mortality.
Table 3

Effect of pre-existing diabetes on cancer-specific mortality in patients with colorectal cancer, by stage

 Overall
Localised or regional stage
Distant stage
 No. of cases/No. of observationsAge-adjusted HR (95% CI)Multivariate-adjusted HR (95% CI)aNo. of cases/No. of observationsMultivariate-adjusted HR (95% CI)aNo. of cases/No. of observationsMultivariate-adjusted HR (95% CI)a
Colorectal cancer
No diabetes12 214/46 400ReferentReferent6291/37 608Referent4855/6679Referent
Diabetes3665/14 8130.97 (0.94–1.01)0.99 (0.95–1.03)1978/12 2360.98 (0.93–1.03)1386/19211.02 (0.96–1.08)
Diabetes without complications3025/12 2980.96 (0.92–1.001)0.98 (0.94–1.02)1611/10 1580.97 (0.91–1.02)1158/16171.00 (0.94–1.07)
Diabetes with complications640/25151.01 (0.93–1.09)1.04 (0.95–1.12)367/20781.07 (0.96–1.19)228/3041.13 (0.99–1.29)
Colon cancer
No diabetes8806/34 851ReferentReferent4454/28 483Referent3638/4981Referent
Diabetes2768/11 6320.97 (0.93–1.01)0.99 (0.94–1.03)1455/96440.98 (0.93–1.05)1097/15081.02 (0.95–1.09)
Diabetes without complications2253/95980.95 (0.91–1.00)0.97 (0.92–1.01)1166/79560.96 (0.90–1.02)906/12650.98 (0.91–1.06)
Diabetes with complications515/20341.05 (0.96–1.15)1.09 (0.996–1.20)289/16881.12 (0.99–1.26)191/2431.22 (1.06–1.42)
Rectal cancer
No diabetes3408/11 549ReferentReferent1837/9125Referent1217/1698Referent
Diabetes897/31811.00 (0.93–1.07)1.03 (0.95–1.11)523/25921.02 (0.92–1.12)289/4131.02 (0.89–1.16)
Diabetes without complications772/27001.01 (0.94–1.10)1.05 (0.97–1.14)445/22021.02 (0.92–1.13)252/3521.05 (0.92–1.21)
Diabetes with complications125/4810.91 (0.76–1.09)0.90 (0.75–1.08)78/3900.99 (0.79–1.25)37/610.83 (0.59–1.15)

In the multivariate models, we adjusted for covariates including age at diagnosis (65–69, 70–74, 75–79, 80–84 and 85+), sex (males, females), race/ethnicity (white, black, American Indian/Alaska Native, Asian or pacific Islander, others), marital status (never married, married, separated, divorced and widowed), grade (grade I – well differentiated; grade II – moderately differentiated; grade III – poorly differentiated and grade IV – undifferentiated), census tract median income (quartiles) and co-morbidity (0, 1, 2+).

Finally, we performed analyses stratified by sex for colorectal-specific mortality associated with diabetes. No significant difference was found between females and males (data not shown).

Discussion

The present study found that colorectal cancer patients with pre-existing diabetes had significantly higher risk of total mortality than those cancer patients without diabetes. The risk was more pronounced among those who had diabetes with complications. Further performing specific mortality analyses using the competing risk method, diabetes was significantly associated with cardiovascular-specific mortality, but not with colorectal cancer-specific mortality. Our findings were in agreement with the majority of the literature in term of total mortality as an outcome (Barone ; Stein ; Huang ; Dehal ; van de Poll-Franse ; Bella ; Jeon ; Walker ; Yang ), although not all studies have found this relationship (Jullumstro ; Call ; Chen ; Noh ; Huang ). Subgroup analyses of two meta-analysis studies (Barone ; Stein ) based on six studies showed that colorectal cancer patients with diabetes had 32% increased risk of total mortality compared with those without diabetes (95% CI: 1.24–1.41). Among studies (Will ; Polednak, 2006; Siddiqui ; Jullumstro ; Huang , 2012; van de Poll-Franse ; Bella ; Cossor ; Walker ) that examined cancer-specific mortality associated with pre-existing diabetes, the findings are inconsistent. Of them, one study (Huang ) found a significant increased risk for colon cancer-specific mortality. Two (van de Poll-Franse ; Bella ) found a significantly increased risk for only rectal cancer patients but not for colon cancer, and one (Siddiqui ) found an association between poorly controlled pre-existing diabetes and the risk of death attributed to colorectal cancer. Other studies found no significant association between diabetes and subsequent death from colorectal cancer. In addition, an earlier study on this topic (Meyerhardt ) showed diabetes had worse disease-free survival associated with diabetes. However, among all previous studies examining cancer-specific mortality, none of them considered competing risk correctly; rather, they censored patients experiencing competing events at the time of these events, which may substantially overestimate the absolute risk of the event of interest (Putter ; Wolbers ). For the purpose of comparison with previous studies, we used conventional epidemiology methods to analyse colorectal-cancer-specific mortality; the resulting HRs were 1.05 (95% CI: 1.01–1.09) and 1.17 (95% CI: 1.08–1.27) for colorectal cancer-specific mortality associated with pre-existing diabetes without and with complications, respectively. Comparing the two analytic approaches, our analyses suggest that findings for cancer-specific mortality using conventional epidemiological methods were overestimated. The potential influence of diabetes on cancer prognosis is complex. Diabetes may directly influence cancer progression and outcome via physiologic effects of hyperinsulinemia and/or hyperglycaemia (Richardson and Pollack, 2005; Morss and Edelman, 2007). Although our data did not directly assess the association between insulin and colorectal cancer prognosis, experimental and epidemiological evidence suggests that hyperinsulinemia may be an underlying mechanism to explain the association between diabetes and cancer incidence and outcome (Larsson ; Berster and Goke, 2008; Giovannucci ). Second, pre-existing diabetes may also have indirect adverse effects on cancer outcome by influencing patients or providers to make different clinical decisions regarding cancer screening and cancer treatment. Research has documented underuse of colorectal cancer screening among elderly diabetic women compared with those without diabetes (McBean and Yu, 2007), which may lead to detection at a later stage on diagnosis. There may also be decisions to follow less-aggressive cancer treatments among diabetes patients (van de Poll-Franse ). However, our data show that there were no substantial differences between the diabetes group and the non-diabetes group in terms of the stage of diagnosis, tumour grade or whether the patient underwent cancer-direct surgery or chemotherapy, although radiation therapy performed as part of the first course of treatment was slightly lower in diabetes patients. Our study using competing risk methods observed that diabetes was associated with cardiovascular-specific mortality, but not with colorectal-cancer-specific mortality. The findings indicate that diabetes per se may not worsen colorectal cancer prognosis, and that other competing risks such as cardiovascular diseases may be more important in determining mortality outcomes. Thus, besides cancer treatment, preventing patients from developing diabetes and having proper management of diabetes for diabetic patients are also important in improving prognosis for patients with colorectal cancer. The strengths of the present study include using a large, US nationally representative database, the availability of detailed clinical information on cancer and some data regarding severity of diabetes status. The SEER–Medicare data are a unique resource that combines clinical information from population-based cancer registries with claims information from the Medicare programme. Extraction of all of the Medicare claims for each cancer patient makes it possible to longitudinally track persons from their Medicare eligibility until death. There are several limitations in the present study. First, our study typically relied on existing public health surveillance and administrative information that were not designed for this research purpose. Challenges in utilising existing data include lack of other information; for example, some demographic and lifestyle variables such as patients' income, BMI, smoking and alcohol habits are missing. As BMI, smoking or other lifestyle variables may influence survival after a diagnosis of colorectal cancer, if our diabetic colorectal cancer patients were more likely to have high BMI or to have smoked, the worse total survival independently associated with diabetes may be overestimated. Another concern is the completeness and accuracy of Medicare claims. To increase accuracy, we used an algorithm to identify conditions that required two outpatient claims or one in-patient claim. A previous study reported that this algorithm with Medicare claims data identified 69% of pre-existing diabetes cases (Gorina and Kramarow, 2011). The claims-based algorithm we used to identify diabetes has a validated sensitivity of 74.4% and specificity of 97.5% using a 2-year look-back period (Hebert ). Thus, we may have missed some cases of diabetes. In addition, the restricted window available in claims data may be a concern. Specifically, claims data are not available before the age of 65 years, which limits our analysis to patients who are older than 67 years; thus, our findings may only be generalised to older patients enrolled in non-HMO Medicare, although colorectal cancer occurs disproportionately in the elderly with more than two-thirds of all cases occurring in persons aged ⩾65 years (SEER, 2012a, 2012b); further, studies have shown that age, sex and other sociodemographic features of the elderly SEER population are comparable with that of the US elderly population (Warren ). Moreover, we had no information on medication use, because our study cohort predated the advent of Medicare Part D. Metformin, an oral drug widely used as a first-line therapy for type 2 diabetes, has been associated with a lower risk of colorectal incidence compared with other anti-diabetic therapies, such as insulin and sulfonylureas (Zhang ). Studies have also shown that metformin use may lower risk of colorectal cancer-specific and total mortality (Lee ). However, a recent study reported that the use of metformin was not associated with the incidence of colorectal cancer (Smiechowski ). In conclusion, our large population-based study provides additional evidence that pre-existing diabetes increased risk of total mortality among colorectal cancer patients. The increased total mortality associated with diabetes was mainly driven by increased risk of dying from cardiovascular diseases. Preventing diabetes and reducing diabetes complications may improve the survival rate of colorectal cancer patients.

Addendum

To comply with SEER-Medicare data rules on confidentiality, data in the ‘Diabetes/With complication (Race)' cell in Table 1 has been updated since Advance Online Publication and a footnote added to the table.
  42 in total

1.  Graphical methods for assessing violations of the proportional hazards assumption in Cox regression.

Authors:  K R Hess
Journal:  Stat Med       Date:  1995-08-15       Impact factor: 2.373

2.  Diabetes mellitus and risk of colorectal cancer: a meta-analysis.

Authors:  Susanna C Larsson; Nicola Orsini; Alicja Wolk
Journal:  J Natl Cancer Inst       Date:  2005-11-16       Impact factor: 13.506

3.  Cause-specific mortality in Scottish patients with colorectal cancer with and without type 2 diabetes (2000-2007).

Authors:  J J Walker; D H Brewster; H M Colhoun; C M Fischbacher; R S Lindsay; S H Wild
Journal:  Diabetologia       Date:  2013-04-27       Impact factor: 10.122

4.  Use of surveillance, epidemiology, and end results-medicare data to conduct case-control studies of cancer among the US elderly.

Authors:  Eric A Engels; Ruth M Pfeiffer; Winnie Ricker; William Wheeler; Ruth Parsons; Joan L Warren
Journal:  Am J Epidemiol       Date:  2011-08-04       Impact factor: 4.897

5.  Glucose modulates basement membrane fibroblast growth factor-2 via alterations in endothelial cell permeability.

Authors:  Alisa S Morss; Elazer R Edelman
Journal:  J Biol Chem       Date:  2007-02-27       Impact factor: 5.157

6.  The use of metformin and colorectal cancer incidence in patients with type II diabetes mellitus.

Authors:  Brielan Smiechowski; Laurent Azoulay; Hui Yin; Michael N Pollak; Samy Suissa
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-08-21       Impact factor: 4.254

Review 7.  Colorectal cancer outcomes, recurrence, and complications in persons with and without diabetes mellitus: a systematic review and meta-analysis.

Authors:  Kelly B Stein; Claire F Snyder; Bethany B Barone; Hsin-Chieh Yeh; Kimberly S Peairs; Rachel L Derr; Antonio C Wolff; Frederick L Brancati
Journal:  Dig Dis Sci       Date:  2010-07       Impact factor: 3.199

8.  A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients.

Authors:  Carrie N Klabunde; Julie M Legler; Joan L Warren; Laura-Mae Baldwin; Deborah Schrag
Journal:  Ann Epidemiol       Date:  2007-05-25       Impact factor: 3.797

9.  The impact on clinical outcome of high prevalence of diabetes mellitus in Taiwanese patients with colorectal cancer.

Authors:  Ching-Wen Huang; Li-Chu Sun; Ying-Ling Shih; Hsiang-Lin Tsai; Chao-Wen Chen; Yung-Sung Yeh; Cheng-Jen Ma; Che-Jen Huang; Jaw-Yuan Wang
Journal:  World J Surg Oncol       Date:  2012-05-03       Impact factor: 2.754

10.  Full accounting of diabetes and pre-diabetes in the U.S. population in 1988-1994 and 2005-2006.

Authors:  Catherine C Cowie; Keith F Rust; Earl S Ford; Mark S Eberhardt; Danita D Byrd-Holt; Chaoyang Li; Desmond E Williams; Edward W Gregg; Kathleen E Bainbridge; Sharon H Saydah; Linda S Geiss
Journal:  Diabetes Care       Date:  2008-11-18       Impact factor: 17.152

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  18 in total

1.  Hyperglycaemia is associated with cancer-related but not non-cancer-related deaths: evidence from the IPC cohort.

Authors:  Jean-Marc Simon; Frederique Thomas; Sebastien Czernichow; Olivier Hanon; Cedric Lemogne; Tabassome Simon; Bruno Pannier; Nicolas Danchin
Journal:  Diabetologia       Date:  2018-01-05       Impact factor: 10.122

2.  Diabetes, diabetes treatment and breast cancer prognosis.

Authors:  Juhua Luo; Beth Virnig; Michael Hendryx; Sijin Wen; Rowan Chelebowski; Chu Chen; Tomas Rohan; Lesley Tinker; Jean Wactawski-Wende; Lawrence Lessin; Karen Margolis
Journal:  Breast Cancer Res Treat       Date:  2014-09-27       Impact factor: 4.872

3.  Impact of diabetes on colorectal cancer stage and mortality risk: a population-based cohort study.

Authors:  Judy K Qiang; Rinku Sutradhar; Vasily Giannakeas; Dominika Bhatia; Simron Singh; Lorraine L Lipscombe
Journal:  Diabetologia       Date:  2020-01-28       Impact factor: 10.122

4.  Association of the severity of diabetes-related complications with stage of breast cancer at diagnosis among elderly women with pre-existing diabetes.

Authors:  Ebtihag O Alenzi; S Suresh Madhavan; Xi Tan
Journal:  Breast Cancer Res Treat       Date:  2017-09-02       Impact factor: 4.872

5.  Higher Expression of Proteins in IGF/IR Axes in Colorectal Cancer is Associated with Type 2 Diabetes Mellitus.

Authors:  Jing Ding; Cong Li; Jie Tang; Cheng Yi; Ji-Yan Liu; Meng Qiu
Journal:  Pathol Oncol Res       Date:  2016-05-02       Impact factor: 3.201

6.  Fasting Blood Glucose Variability and Unfavorable Trajectory Patterns Are Associated with the Risk of Colorectal Cancer.

Authors:  Hyoju Jun; Jieun Lee; Hye Ah Lee; Seong-Eun Kim; Ki-Nam Shim; Hye-Kyung Jung; Sung-Ae Jung; Chang Mo Moon
Journal:  Gut Liver       Date:  2022-05-15       Impact factor: 4.519

7.  Diabetes, prediabetes and the survival of nasopharyngeal carcinoma: a study of 5,860 patients.

Authors:  Pu-Yun OuYang; Zhen Su; Jie Tang; Xiao-Wen Lan; Yan-Ping Mao; Wuguo Deng; Fang-Yun Xie
Journal:  PLoS One       Date:  2014-10-28       Impact factor: 3.240

Review 8.  Diabetes-induced mechanophysiological changes in the small intestine and colon.

Authors:  Mirabella Zhao; Donghua Liao; Jingbo Zhao
Journal:  World J Diabetes       Date:  2017-06-15

9.  The relationship between diabetes and colorectal cancer prognosis: A meta-analysis based on the cohort studies.

Authors:  Bo Zhu; Xiaomei Wu; Bo Wu; Dan Pei; Lu Zhang; Lixuan Wei
Journal:  PLoS One       Date:  2017-04-19       Impact factor: 3.240

10.  Increased mortality for colorectal cancer patients with preexisting diabetes mellitus: an updated meta-analysis.

Authors:  Jingtao Li; Jixi Liu; Chun Gao; Fang Liu; Hongchuan Zhao
Journal:  Oncotarget       Date:  2017-08-04
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