Abstract

Background: Owing to the increasing prevalence of obesity and diabetes in Asia, and the paucity of studies, we examined the influence of raised blood glucose and diabetes on cancer mortality risk.

Materials and methods: Thirty-six cohort Asian and Australasian studies provided 367 361 participants (74% from Asia); 6% had diabetes at baseline. Associations between diabetes and site-specific cancer mortality were estimated using time-dependent Cox models, stratified by study and sex, and adjusted for age.

Results: During a median follow-up of 4.0 years, there were 5992 deaths due to cancer (74% Asian; 41% female). Participants with diabetes had 23% greater risk of mortality from all-cause cancer compared with those without: hazard ratio (HR) 1.23 [95% confidence interval (CI) 1.12, 1.35]. Diabetes was associated with mortality due to cancer of the liver (HR 1.51; 95% CI 1.19, 1.91), pancreas (HR 1.78; 95% CI 1.20, 2.65), and, less strongly, colorectum (HR 1.32; 95% CI 0.98, 1.78). There was no evidence of sex- or region-specific differences in these associations. The population attributable fractions for cancer mortality due to diabetes were generally higher for Asia compared with non-Asian populations.

Conclusion: Diabetes is associated with increased mortality from selected cancers in Asian and non-Asian populations.

introduction

A substantial proportion of cancer deaths are attributed to unhealthy lifestyles and behaviours including poor diet [1], obesity [2], smoking [3], and alcohol [4]. Mortality rates for specific cancers are known to vary significantly by geographical region and country. Mortality from cancers of the colon and rectum, which are considered to be due in large part to poor diet and lifestyle, is higher for industrialised nations, such as Australia, North America, and Western Europe, as compared with many Asian countries. By comparison, mortality from liver cancer is far more common across Asia, and particularly China, than in the West, due mainly to the high prevalence of chronic hepatitis B and C infections that account for a large majority of all liver cancers worldwide [5].

Previous studies have reported that diabetes status is associated with a 20%–30% increased risk of total cancer mortality [6–10]. A positive association between abnormal glucose tolerance and the risk of cancer mortality has also been demonstrated for Western countries [11–13]. With regard to site-specific cancer mortality, many studies have shown positive associations between diabetes and the risk of mortality from cancers of the pancreas, liver, colorectum, and prostate [12–19]. Most of these studies were conducted in Western populations, with scarce results from Asia. Among the few Asian studies, positive associations between diabetes and cancers of the pancreas, liver, and colorectum, as well as non-Hodgkin lymphoma, have been reported [6, 8, 20–24]. Given increasing life expectancy and urbanisation [25], the increasing prevalence of obesity [26], and the growing prevalence of diabetes in Asian populations [27, 28], more studies are needed to clarify the evidence of the possible effect of diabetes on cancer mortality in Asia.

The Asia Pacific Cohort Studies Collaboration (APCSC) is a large-scale collaborative project with previous reports on the relationship between diabetes and major causes of death [20–22, 29]. A systematic analysis of mortality from specific cancers in relation to diabetes and blood glucose in the APCSC has not yet been reported. The aims of the present study are twofold: first, to examine associations between fasting blood glucose levels, diabetes, and site-specific cancer mortality for which sufficient numbers of fatal events were available for analysis and second, to estimate the population attributable fractions (PAFs) of cancer mortality due to diabetes within countries in the region. These give crude measures of the percentages of deaths that are expected to be due to diabetes, should there be a causal relationship [30].

materials and methods

participating studies

The APCSC is a pooled analysis of individual data from cohort studies conducted in the Asia-Pacific region; details have been published elsewhere [31]. Studies were eligible for inclusion if the following criteria were met: study sample was drawn from the Asia-Pacific region; study was of a prospective cohort design; study had at least 5000 person-years of follow-up. Studies were not eligible if entry was dependent on having a particular medical condition or risk factor. At a minimum, studies must have had data on date of birth or age, sex, blood pressure, and date or age of death. Outcome data included mortality from specific cancers. Cohorts were classified as Asian if study members were recruited from mainland China, Hong Kong, Japan, Korea, Singapore, South Korea, Taiwan, or Thailand and as Australasian if from Australia or New Zealand. This classification largely represents a dichotomy by ethnicity into Asians and non-Asians.

baseline assessment

Study participants' diabetes status were determined on the basis of self-reported history of diabetes or by applying the World Health Organization (WHO) diagnostic criteria to blood glucose levels at baseline [32]. Diabetes status according to glucose levels was positive if fasting whole blood glucose was ≥6.1 mmol/l (110 mg/dl) or plasma glucose ≥7 mmol/l (126 mg/dl) or if non-fasting whole blood glucose was ≥10 mmol/l (180 mg/dl) or plasma glucose ≥11.1 mmol/l (200 mg/dl) (information on glucose-lowering medication not available). All data on cigarette smoking were self-reported as either current smoker or nonsmoker at the time of study entry. Height and weight were ascertained from direct measurements; body mass index (BMI) was calculated as mass in kilograms divided by the square of the height in meters. Systolic blood pressure was measured using a sphygmomanometer. Participants also reported alcohol use habits (current alcohol user/non-alcohol user), exercise habits (‘none or almost none’ as sedentary lifestyle/‘any exercise’ as active exercise), and educational attainment (none/at least primary school).

outcomes

Cancer deaths were classified according to the 9th [33] or 10th [34] revision of the International Classification of Diseases (ICD): bladder (ICD-9; ICD-10: 188; C67), brain and central nervous system (191–192; C70–72), breast (174; C50), colon and rectum (153–154; C18–21), leukaemia (204–208; C91–95), liver (155, 197.7; C22, C78.7), lung (162; C33–34), non-Hodgkin's lymphoma (200, 202; C82, C85), melanoma (172; C43), multiple myeloma (203; C90), ovary and uterus (179–183; C53–56), pancreas (157; C25), prostate (185; C61), kidney (189; C64), and stomach (151; C16). Malignancies of the upper aero-digestive tract were analysed by combining cancers of the oropharynx, oesophagus, and larynx (ICD-9; ICD-10: 140–150, 161; C00-C15, C32).

statistical analyses

Analyses were restricted to participants aged ≥20 years at the time of the baseline survey with complete data on diabetes status and site-specific cancer mortality. Cox proportional hazards regression models, stratified by study cohort and sex, and adjusted for age, were used to compute hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for those with and without diabetes, as well as for those with various fasting serum glucose levels. Further adjustments were made in multivariable models that included a priori potential confounding variables: BMI, height [35], education, smoking status, and alcohol use at baseline. Statistical significance of effect modification across groups defined by geographical area (Asia and Australasia) and sex was tested using the likelihood ratio test [30]. Differences between region and sex were tested for statistical significance using likelihood ratio tests. The PAFs for mortality from site-specific cancer mortality due to diabetes were calculated for each of the countries in the APCSC using previously published prevalence estimates of diabetes that were adjusted to a world standard population [36] by the formula [30]: 

graphic

To explore the possibility of participants with a pre-existing malignancy (e.g. pancreatic cancer) at the time of study entry contributing to the analyses, and potentially attenuating the diabetes–cancer relation (i.e. reverse causality), deaths from cancer in the first 2 years of follow-up were excluded (‘left censored’) in a sensitivity analysis. In doing so, we reasoned that most deaths due to subclinical malignancy at study entry would have occurred during the first 2 years.

Trends were explored through analyses of fasting serum glucose levels according to tertiles (<4.8, 4.8–5.4, and >5.4 mmol/l). Trends were tested for statistical significance using likelihood ratio tests. All statistical analyses were carried out using STATA version 10.1 for Windows (StataCorp, College Station, TX).

results

A total of 44 studies involving 600 443 participants were recruited to the APCSC by the end of 2006 [31]. Figure 1 shows the selection process of the analytical sample for this study. Information on diabetes at baseline and site-specific cancer mortality was available from 36 of the 44 eligible studies involving 367 361 participants, of whom 41% were female, 74% were Asian, and 6.4% had type 2 diabetes mellitus at study entry (Table 1). A summary of the characteristics of the included studies are shown in Table 1. The median follow-up time was 4.0 years and the mean age of participants was 48 years. Participants from the Australasian cohorts were older than those from Asian cohorts. During mortality follow-up, a total of 2 223 958 person-years of follow-up gave rise to 17 413 deaths, of which 5992 were ascribed to cancer (31% female, 51% Asian).

Table 1.

Summary characteristics of participating studiesa from the Asia Pacific Cohort Studies Collaboration (APCSC)

Cohort name Country n Median follow-up (years) Range of follow-up (years) Female (%) Age (years)
 
Diabetes mellitus (%) No. of total cancer deaths 
Mean SD 
Australasia          
    ALSA Australia 1557 4.9 0.04–9.04 47.7 78 8.2 68 
    ANHF Australia 9272 8.3 0.15–8.63 51.0 43 13 1.9 153 
    Busselton Australia 5976 23.5 0.51–35.50 52.2 46 17 3.5 602 
    Canberra Australia 712 9.2 0.04–13.27 45.3 77 6.6 100 
    Fletcher Challenge New Zealand 10 366 5.8 0.02–7.33 28.0 44 15 2.6 135 
    Melbourne Australia 41 286 8.5 0.02–11.39 58.9 55 5.4 1112 
    Newcastle Australia 3462 4.7 0.10–10.25 50.0 53 11 3.5 83 
    Perth Australia 10 222 14.4 0.12–19.56 48.3 45 13 2.1 310 
    WA AAA Screenees Australia 12 203 3.2 0.03–4.71 0.0 72 11.6 400 
Subtotal  95056 8.2 0.02–35.50 42.4 57 14 5.0 2963 
Asia          
    Aito Town Japan 1717 15.2 0.73–16.96 56.7 51 2.7 62 
    Akabane Japan 1828 11.0 0.45–12.92 55.7 54 2.5 57 
    Anzhen China 4122 3.0 0.72–3.00 51.0 47 11.1 
    Beijing Aging China 2092 4.8 0.01–4.99 50.6 70 24.7 48 
    CISCH China 2162 3.3 0.24–3.90 51.0 44 2.4 
    Civil Service Workers Japan 9319 6.7 0.13–7.78 32.7 47 1.7 61 
    CVDFACTS Taiwan 5730 6.0 0.08–9.44 55.3 47 15 2.7 65 
    East Beijing China 1128 17.1 1.02–20.49 51.4 44 15 5.6 20 
    EGAT Thailand 3131 11.4 0.09–12.39 23.3 43 2.4 43 
    Fangshan China 821 2.7 1.75–3.58 67.6 47 7.1 
    Guangzhou Occupational China 5796 7.9 1.17–13.55 34.2 44 10.5 35 
    Hong Kong Hong Kong 2953 2.5 0.04–5.04 57.1 79 8.6 127 
    Huashan China 1649 2.9 0.34–3.71 54.4 53 11 13.0 
    Kinmen Taiwan 2453 2.9 0.09–5.28 48.7 63 10 8.8 41 
    KMIC South Korea 183 581 4.0 0.01–5.00 37.0 44 7.7 1236 
    Konan Japan 1226 6.4 0.02–10.42 55.4 52 16 12.6 26 
    Miyama Japan 1072 6.6 0.15–8.06 55.8 61 10 5.1 36 
    Ohasama Japan 2240 4.1 0.08–5.29 63.8 60 11 10.9 30 
    Saitama Japan 3624 11.0 0.06–12.00 62.2 55 12 1.7 147 
    Seven Cities Cohorts China 10 731 2.7 0.04–11.50 54.5 54 12 1.2 174 
    Shibata Japan 2349 20.0 0.07–20.00 57.7 57 11 1.1 208 
    Shigaraki Town Japan 3757 4.4 0.07–6.44 59.5 57 14 7.2 55 
    Shirakawa Japan 4640 17.5 0.13–20.51 54.3 48 12 0.9 165 
    Singapore Heart Singapore 2325 14.6 0.14–16.31 49.0 41 13 11.4 35 
    Singapore NHS92 Singapore 3305 6.2 0.09–6.32 51.8 39 12 9.7 22 
    Tanno/Soubetsu Japan 1973 16.4 0.42–18.92 53.2 51 7.2 86 
    Yunnan China 6581 4.5 0.02–5.18 3.1 56 0.5 239 
Subtotal  272305 4.0 0.01–20.51 49.9 47 10 6.9 3029 
Total  367361 4.0 0.01–35.50 41.3 48 12 6.4 5992 
Cohort name Country n Median follow-up (years) Range of follow-up (years) Female (%) Age (years)
 
Diabetes mellitus (%) No. of total cancer deaths 
Mean SD 
Australasia          
    ALSA Australia 1557 4.9 0.04–9.04 47.7 78 8.2 68 
    ANHF Australia 9272 8.3 0.15–8.63 51.0 43 13 1.9 153 
    Busselton Australia 5976 23.5 0.51–35.50 52.2 46 17 3.5 602 
    Canberra Australia 712 9.2 0.04–13.27 45.3 77 6.6 100 
    Fletcher Challenge New Zealand 10 366 5.8 0.02–7.33 28.0 44 15 2.6 135 
    Melbourne Australia 41 286 8.5 0.02–11.39 58.9 55 5.4 1112 
    Newcastle Australia 3462 4.7 0.10–10.25 50.0 53 11 3.5 83 
    Perth Australia 10 222 14.4 0.12–19.56 48.3 45 13 2.1 310 
    WA AAA Screenees Australia 12 203 3.2 0.03–4.71 0.0 72 11.6 400 
Subtotal  95056 8.2 0.02–35.50 42.4 57 14 5.0 2963 
Asia          
    Aito Town Japan 1717 15.2 0.73–16.96 56.7 51 2.7 62 
    Akabane Japan 1828 11.0 0.45–12.92 55.7 54 2.5 57 
    Anzhen China 4122 3.0 0.72–3.00 51.0 47 11.1 
    Beijing Aging China 2092 4.8 0.01–4.99 50.6 70 24.7 48 
    CISCH China 2162 3.3 0.24–3.90 51.0 44 2.4 
    Civil Service Workers Japan 9319 6.7 0.13–7.78 32.7 47 1.7 61 
    CVDFACTS Taiwan 5730 6.0 0.08–9.44 55.3 47 15 2.7 65 
    East Beijing China 1128 17.1 1.02–20.49 51.4 44 15 5.6 20 
    EGAT Thailand 3131 11.4 0.09–12.39 23.3 43 2.4 43 
    Fangshan China 821 2.7 1.75–3.58 67.6 47 7.1 
    Guangzhou Occupational China 5796 7.9 1.17–13.55 34.2 44 10.5 35 
    Hong Kong Hong Kong 2953 2.5 0.04–5.04 57.1 79 8.6 127 
    Huashan China 1649 2.9 0.34–3.71 54.4 53 11 13.0 
    Kinmen Taiwan 2453 2.9 0.09–5.28 48.7 63 10 8.8 41 
    KMIC South Korea 183 581 4.0 0.01–5.00 37.0 44 7.7 1236 
    Konan Japan 1226 6.4 0.02–10.42 55.4 52 16 12.6 26 
    Miyama Japan 1072 6.6 0.15–8.06 55.8 61 10 5.1 36 
    Ohasama Japan 2240 4.1 0.08–5.29 63.8 60 11 10.9 30 
    Saitama Japan 3624 11.0 0.06–12.00 62.2 55 12 1.7 147 
    Seven Cities Cohorts China 10 731 2.7 0.04–11.50 54.5 54 12 1.2 174 
    Shibata Japan 2349 20.0 0.07–20.00 57.7 57 11 1.1 208 
    Shigaraki Town Japan 3757 4.4 0.07–6.44 59.5 57 14 7.2 55 
    Shirakawa Japan 4640 17.5 0.13–20.51 54.3 48 12 0.9 165 
    Singapore Heart Singapore 2325 14.6 0.14–16.31 49.0 41 13 11.4 35 
    Singapore NHS92 Singapore 3305 6.2 0.09–6.32 51.8 39 12 9.7 22 
    Tanno/Soubetsu Japan 1973 16.4 0.42–18.92 53.2 51 7.2 86 
    Yunnan China 6581 4.5 0.02–5.18 3.1 56 0.5 239 
Subtotal  272305 4.0 0.01–20.51 49.9 47 10 6.9 3029 
Total  367361 4.0 0.01–35.50 41.3 48 12 6.4 5992 
a

Restricted to studies and participants with information on history of diabetes or blood glucose levels at baseline and site-specific cancer mortality.

SD, standard deviation; ALSA, Australian Longitudinal Study of Aging; ANHF, Australian National Heart Foundation; WA AAA Screenees, Western Australian AAA Screenees; CISCH, Capital Iron and Steel Company Hospital; CVDFACTS, Cardiovascular Disease Risk Factors Two-Township Study; EGAT, Electricity Generating Authority of Thailand; KMIC, Korean Medical Insurance Corporation; NHS92, National Health Study 1992.

Figure 1.

Selection of analytical sample.

Figure 1.

Selection of analytical sample.

study baseline characteristics

Of the 367 361 participants, 23 560 (24% female, 80% Asian) were classified as having diabetes at baseline. Both Asian and Australasian participants with diabetes were older and had higher levels of BMI, systolic blood pressure, total cholesterol, and triglycerides in both Asia and Australasia. Those from either region with diabetes were also more likely to be male, physically inactive, and have lower levels of education.

outcomes

The age-adjusted, sex, and study stratified HR for death from all cancers was 1.23 (95% CI 1.12, 1.35) for individuals with diabetes compared with individuals without diabetes (Table 3). This remained largely unchanged after 2-year left-censoring (HR 1.19; 95% CI 1.06, 1.32). Additional adjustment for BMI, height, education, smoking, and alcohol use had no material effect on the magnitude of the diabetes–cancer association. Analysis of the diabetes–cancer association by sex and region showed no evidence of any difference (P-value for interaction >0.1). For mortality from specific cancers, diabetes was associated with an increased risk of cancers of the liver (HR 1.51; 95% CI: 1.19, 1.91) and pancreas (HR 1.78; 95% CI: 1.20, 2.65), compared with those without diabetes (Table 3). These also persisted after 2-year left-censoring: HR 1.52 (95% CI: 1.15, 2.01) and HR 1.66 (95% CI: 1.04, 2.63), respectively. An increase in the risk of mortality from colorectal cancer was also observed both before and after left-censoring: HR 1.32 (95% CI: 0.98, 1.78) and HR 1.34 (95% CI: 0.96, 1.87), respectively.

Table 2.

Summary characteristics of Asia Pacific Cohort Studies Collaboration (APCSC) participating studiesa with follow-up of ≥8 years

Cohort name Country n Median follow-up (years) Range of follow-up (years) Female (%) Age (years)
 
Diabetes mellitus (%) No. of total cancer deaths 
Mean SD 
Australasia          
    ANHF Australia 9272 8.3 0.15–8.63 51.0 43 13 1.9 153 
    Busselton Australia 5976 23.5 0.51–35.50 52.2 46 17 3.5 602 
    Canberra Australia 712 9.2 0.04–13.27 45.3 77 6.6 100 
    Melbourne Australia 41 286 8.5 0.02–11.39 58.9 55 5.4 1112 
    Perth Australia 10 222 14.4 0.12–19.56 48.3 45 13 2.1 310 
Subtotal  67468 8.5 0.02–35.50 55.5 51 12 4.2 2277 
Asia          
    Aito Town Japan 1717 15.2 0.73–16.96 56.7 51 2.7 62 
    Akabane Japan 1828 11.0 0.45–12.92 55.7 54 2.5 57 
    East Beijing China 1128 17.1 1.02–20.49 51.4 44 15 5.6 20 
    EGAT Thailand 3131 11.4 0.09–12.39 23.3 43 2.4 43 
    Guangzhou Occupational China 5796 7.9 1.17–13.55 34.2 44 10.5 35 
    Saitama Japan 3624 11.0 0.06–12.00 62.2 55 12 1.7 147 
    Shibata Japan 2349 20.0 0.07–20.00 57.7 57 11 1.1 208 
    Shirakawa Japan 4640 17.5 0.13–20.51 54.3 48 12 0.9 165 
    Singapore Heart Singapore 2325 14.6 0.14–16.31 49.0 41 13 11.4 35 
    Tanno/Soubetsu Japan 1973 16.4 0.42–18.92 53.2 51 7.2 86 
Subtotal  28511 11.4 0.06–20.51 47.7 48 11 4.8 858 
Total  95979 9.1 0.02–35.50 53.2 50 12 4.4 3135 
Cohort name Country n Median follow-up (years) Range of follow-up (years) Female (%) Age (years)
 
Diabetes mellitus (%) No. of total cancer deaths 
Mean SD 
Australasia          
    ANHF Australia 9272 8.3 0.15–8.63 51.0 43 13 1.9 153 
    Busselton Australia 5976 23.5 0.51–35.50 52.2 46 17 3.5 602 
    Canberra Australia 712 9.2 0.04–13.27 45.3 77 6.6 100 
    Melbourne Australia 41 286 8.5 0.02–11.39 58.9 55 5.4 1112 
    Perth Australia 10 222 14.4 0.12–19.56 48.3 45 13 2.1 310 
Subtotal  67468 8.5 0.02–35.50 55.5 51 12 4.2 2277 
Asia          
    Aito Town Japan 1717 15.2 0.73–16.96 56.7 51 2.7 62 
    Akabane Japan 1828 11.0 0.45–12.92 55.7 54 2.5 57 
    East Beijing China 1128 17.1 1.02–20.49 51.4 44 15 5.6 20 
    EGAT Thailand 3131 11.4 0.09–12.39 23.3 43 2.4 43 
    Guangzhou Occupational China 5796 7.9 1.17–13.55 34.2 44 10.5 35 
    Saitama Japan 3624 11.0 0.06–12.00 62.2 55 12 1.7 147 
    Shibata Japan 2349 20.0 0.07–20.00 57.7 57 11 1.1 208 
    Shirakawa Japan 4640 17.5 0.13–20.51 54.3 48 12 0.9 165 
    Singapore Heart Singapore 2325 14.6 0.14–16.31 49.0 41 13 11.4 35 
    Tanno/Soubetsu Japan 1973 16.4 0.42–18.92 53.2 51 7.2 86 
Subtotal  28511 11.4 0.06–20.51 47.7 48 11 4.8 858 
Total  95979 9.1 0.02–35.50 53.2 50 12 4.4 3135 
a

Restricted to studies with median follow-up of ≥8 years and participants with information on history of diabetes or blood glucose levels at baseline and site-specific cancer mortality.

SD, standard deviation; ANHF, Australian National Heart Foundation; EGAT, Electricity Generating Authority of Thailand.

Table 3.

Hazard ratio for diabetes in relation to causes of mortality

Site-specific cancer mortality APCSC (n = 367 361)
 
No. of deaths Hazard ratioa (95% CI) 
Bladder 105 1.42 (0.70, 2.86) 
Brain 168 0.96 (0.51, 1.79) 
Breast 299 0.75 (0.39, 1.47) 
Colorectum 596 1.32 (0.98, 1.78)** 
Kidney 75 0.64 (0.23, 1.80) 
Leukaemia 167 1.18 (0.67, 2.06) 
Liver 561 1.51 (1.19, 1.91)* 
Lung 1227 0.88 (0.69, 1.13) 
Melanoma 82 1.60 (0.76, 3.37) 
Multiple myeloma 65 1.89 (0.80, 4.47) 
Non-Hodgkin's lymphoma 161 1.00 (0.55, 1.82) 
Ovarian and uterine 148 0.63 (0.23, 1.71) 
Pancreas 254 1.78 (1.20, 2.65)* 
Prostate 284 1.27 (0.84, 1.93) 
Stomach 662 1.17 (0.89, 1.54) 
Upper aero-digestive tract 266 1.04 (0.67, 1.63) 
All cancers 5992 1.23 (1.12, 1.35)* 
Site-specific cancer mortality APCSC (n = 367 361)
 
No. of deaths Hazard ratioa (95% CI) 
Bladder 105 1.42 (0.70, 2.86) 
Brain 168 0.96 (0.51, 1.79) 
Breast 299 0.75 (0.39, 1.47) 
Colorectum 596 1.32 (0.98, 1.78)** 
Kidney 75 0.64 (0.23, 1.80) 
Leukaemia 167 1.18 (0.67, 2.06) 
Liver 561 1.51 (1.19, 1.91)* 
Lung 1227 0.88 (0.69, 1.13) 
Melanoma 82 1.60 (0.76, 3.37) 
Multiple myeloma 65 1.89 (0.80, 4.47) 
Non-Hodgkin's lymphoma 161 1.00 (0.55, 1.82) 
Ovarian and uterine 148 0.63 (0.23, 1.71) 
Pancreas 254 1.78 (1.20, 2.65)* 
Prostate 284 1.27 (0.84, 1.93) 
Stomach 662 1.17 (0.89, 1.54) 
Upper aero-digestive tract 266 1.04 (0.67, 1.63) 
All cancers 5992 1.23 (1.12, 1.35)* 
a

Hazard ratios are age adjusted, sex, and study stratified.

APCSC, Asia Pacific Cohort Studies Collaboration; CI, confidence interval.

*

P-value < 0.01.

**

P-value = 0.07.

Additional analyses were carried out for diabetes at baseline among individuals with cancer mortality with at least 8 years of follow-up. Information on diabetes at baseline and site-specific cancer mortality was reduced to 15 of the 44 eligible studies involving 95 979 participants, of whom 53% were female, 30% were Asian, and 4.4% had type 2 diabetes mellitus at study entry (Table 2). The new median follow-up time was 9.1 years and the mean age of participants was 50 years. During this longer mortality follow-up, 3135 deaths were ascribed to cancer.

The age-adjusted, sex and study stratified HR for death from all cancers was 1.31 (95% CI: 1.13, 1.51) for individuals with diabetes compared with individuals without diabetes (Table 4). Additional adjustment for BMI, height, education, smoking, and alcohol use had no substantial effect on the magnitude of the diabetes–cancer association. For mortality from specific cancers, diabetes was associated with an increased risk of cancers of the colorectum (HR 1.50; 95% CI: 1.03, 2.18), liver (HR 2.14; 95% CI: 1.08, 4.25), and pancreas (HR 1.85; 95% CI: 1.03, 3.31) compared with those without diabetes (Table 4). In addition, diabetes was associated with an increased risk of cancers of the stomach among those with longer follow-up period (HR 1.90; 95% CI: 1.17, 3.08).

Table 4.

Hazard ratio for diabetes in relation to causes of mortality for studies with follow-up of ≥8 years

Site-specific cancer mortality APCSCa (n = 95 979)
 
No. of deaths Hazard ratiob (95% CI) 
Bladder 63 1.39 (0.55, 3.51) 
Brain 103 0.98 (0.39, 2.43) 
Breast 226 0.69 (0.30, 1.56) 
Colorectum 404 1.50 (1.03, 2.18)* 
Kidney 47 0.78 (0.19, 3.25) 
Leukaemia 98 0.70 (0.25, 1.92) 
Liver 93 2.14 (1.08, 4.25)* 
Lung 493 1.15 (0.80, 1.67) 
Melanoma 65 2.11 (0.94, 4.71) 
Multiple myeloma 47 2.21 (0.85, 5.76) 
Non-Hodgkin's lymphoma 86 0.97 (0.39, 2.44) 
Ovarian and uterine 106 0.75 (0.24, 2.39) 
Pancreas 161 1.85 (1.03, 3.31)* 
Prostate 205 1.32 (0.79, 2.22) 
Stomach 291 1.90 (1.17, 3.08)** 
Upper aero-digestive tract 122 1.13 (0.52, 2.46) 
All cancers 3135 1.31 (1.13, 1.51)*** 
Site-specific cancer mortality APCSCa (n = 95 979)
 
No. of deaths Hazard ratiob (95% CI) 
Bladder 63 1.39 (0.55, 3.51) 
Brain 103 0.98 (0.39, 2.43) 
Breast 226 0.69 (0.30, 1.56) 
Colorectum 404 1.50 (1.03, 2.18)* 
Kidney 47 0.78 (0.19, 3.25) 
Leukaemia 98 0.70 (0.25, 1.92) 
Liver 93 2.14 (1.08, 4.25)* 
Lung 493 1.15 (0.80, 1.67) 
Melanoma 65 2.11 (0.94, 4.71) 
Multiple myeloma 47 2.21 (0.85, 5.76) 
Non-Hodgkin's lymphoma 86 0.97 (0.39, 2.44) 
Ovarian and uterine 106 0.75 (0.24, 2.39) 
Pancreas 161 1.85 (1.03, 3.31)* 
Prostate 205 1.32 (0.79, 2.22) 
Stomach 291 1.90 (1.17, 3.08)** 
Upper aero-digestive tract 122 1.13 (0.52, 2.46) 
All cancers 3135 1.31 (1.13, 1.51)*** 
a

Restricted to studies with median follow-up of ≥8 years and participants with information on history of diabetes or blood glucose levels at baseline and site-specific cancer mortality.

b

Hazard ratios are age adjusted, sex, and study stratified.

APCSC, Asia Pacific Cohort Studies Collaboration; CI, confidence interval.

*

P-value < 0.05.

**P-value = 0.01.

***P-value < 0.0001.

association between fasting serum glucose levels and mortality from cancer

Fasting serum glucose levels were available from 202 681 participants with a total of 1490 cancer deaths, of whom 8% (16 439/202 682) were classified as having diabetes. Based on this smaller subsample, there were no significant linear associations between glucose levels and all-cause cancer mortality (P for trend = 0.39; Table 5) or with any of the site-specific cancers. There was, however, some evidence of a weak positive association with liver cancer (P-value for trend = 0.06) (Table 5). Analysis of trend in those who died from melanoma and multiple myeloma was not possible due to insufficient numbers.

Table 5.

Age-adjusted, sex, and study stratified hazard ratios of cancer mortality with respect to fasting serum glucose levels in Asia Pacific Cohort Studies Collaboration (APCSC)

Site-specific cancer mortality Adjusteda hazard ratio (95% CI)
 
P-value for trendb 
Fasting serum blood glucose levels
 
n First tertile (<4.8 mmol/l) n Second tertile (4.8–5.4 mmol/l) n Third tertile (>5.4 mmol/l) 
Bladder 1.0 (ref.) 0.73 (0.18, 2.97) 0.33 (0.06, 1.86) 0.20 
Brain 13 17 1.25 (0.60, 2.60) 10 0.84 (0.36, 1.96) 0.72 
Breast 22 20 1.02 (0.55, 1.90) 0.67 (0.27, 1.65) 0.47 
Colorectum 26 29 0.89 (0.52, 1.54) 19 0.64 (0.35, 1.20) 0.17 
Kidney 0.46 (0.11, 1.95) 1.02 (0.31, 3.30) 0.86 
Leukaemia 14 11 0.74 (0.33, 1.63) 10 0.78 (0.34, 1.79) 0.53 
Liver 116 120 0.98 (0.76, 1.26) 152 1.26 (0.98, 1.61) 0.06 
Lung 86 63 0.63 (0.45, 0.87) 74 0.75 (0.54, 1.03) 0.08 
Non-Hodgkin's lymphoma 10 0.82 (0.33, 2.04) 0.72 (0.26, 1.98) 0.52 
Ovarian and uterine 11 15 1.58 (0.71, 3.50) 1.39 (0.51, 3.77) 0.42 
Pancreas 21 18 0.72 (0.38, 1.36) 21 0.87 (0.47, 1.63) 0.68 
Prostate 0.45 (0.14, 1.43) 10 0.62 (0.22, 1.73) 0.52 
Stomach 90 94 1.02 (0.76, 1.36) 91 1.06 (0.79, 1.44) 0.68 
Upper aero-digestive tract 25 25 0.87 (0.49, 1.51) 33 1.05 (0.62, 1.80) 0.81 
All cancers 487 473 0.90 (0.94, 1.28) 530 1.06 (0.93, 1.20) 0.39 
Site-specific cancer mortality Adjusteda hazard ratio (95% CI)
 
P-value for trendb 
Fasting serum blood glucose levels
 
n First tertile (<4.8 mmol/l) n Second tertile (4.8–5.4 mmol/l) n Third tertile (>5.4 mmol/l) 
Bladder 1.0 (ref.) 0.73 (0.18, 2.97) 0.33 (0.06, 1.86) 0.20 
Brain 13 17 1.25 (0.60, 2.60) 10 0.84 (0.36, 1.96) 0.72 
Breast 22 20 1.02 (0.55, 1.90) 0.67 (0.27, 1.65) 0.47 
Colorectum 26 29 0.89 (0.52, 1.54) 19 0.64 (0.35, 1.20) 0.17 
Kidney 0.46 (0.11, 1.95) 1.02 (0.31, 3.30) 0.86 
Leukaemia 14 11 0.74 (0.33, 1.63) 10 0.78 (0.34, 1.79) 0.53 
Liver 116 120 0.98 (0.76, 1.26) 152 1.26 (0.98, 1.61) 0.06 
Lung 86 63 0.63 (0.45, 0.87) 74 0.75 (0.54, 1.03) 0.08 
Non-Hodgkin's lymphoma 10 0.82 (0.33, 2.04) 0.72 (0.26, 1.98) 0.52 
Ovarian and uterine 11 15 1.58 (0.71, 3.50) 1.39 (0.51, 3.77) 0.42 
Pancreas 21 18 0.72 (0.38, 1.36) 21 0.87 (0.47, 1.63) 0.68 
Prostate 0.45 (0.14, 1.43) 10 0.62 (0.22, 1.73) 0.52 
Stomach 90 94 1.02 (0.76, 1.36) 91 1.06 (0.79, 1.44) 0.68 
Upper aero-digestive tract 25 25 0.87 (0.49, 1.51) 33 1.05 (0.62, 1.80) 0.81 
All cancers 487 473 0.90 (0.94, 1.28) 530 1.06 (0.93, 1.20) 0.39 
a

Hazard ratios are age adjusted, sex, and study stratified.

b

P-values from likelihood ratio test.

CI, confidence interval.

PAFs of cancer mortality due to diabetes

Figure 2 shows the PAF of mortality from specific cancers that seem to have notable associations with diabetes in the APCSC: pancreas, liver, and colorectum. These PAFs differed substantially across the Asia-Pacific region and were generally higher for Asia than Australasia (Figure 2). Overall, the PAF ranged from 3.1% to 7.3% for pancreatic cancer, 2.0% to 4.9% for liver cancer, and 1.3% to 3.1% for colorectal cancer.

Figure 2.

Estimated population attributable fractions (%) for mortality from pancreatic, liver, and, colorectal cancers due to type 2 diabetes in the Asia-Pacific region.

Figure 2.

Estimated population attributable fractions (%) for mortality from pancreatic, liver, and, colorectal cancers due to type 2 diabetes in the Asia-Pacific region.

discussion

To our knowledge, the present study is the first to systematically examine the associations between diabetes and mortality from specific cancers for the diverse populations of the Asia-Pacific region. The results from our large collaborative study indicate that individuals with diabetes have an approximately 20% greater risk of mortality from all-cause cancer compared with those without the condition. Specifically, diabetes is independently associated with mortality from pancreatic, liver, and, possibly, colorectal cancers. These associations did not vary by region or by sex and were adjusted for BMI, height, education, smoking, and alcohol use. The majority of cases of diabetes in this report are likely to be type 2 diabetes but an undetermined proportion may have diabetes secondary to pancreatic disease and some will have type 1 diabetes especially in the non-Asian cohorts.

We found a nearly 78% increased risk of death from pancreatic cancer for those with diabetes, which is comparable with other reports [6, 13, 14, 16, 20, 37], and a previous meta-analysis [38]. Other studies have also shown abnormal glucose metabolism to be associated with pancreatic cancer mortality [12, 37]. Though our findings suggest diabetes to be a risk factor for pancreatic cancer, the diabetic state is also a potential consequence of pancreatic malignancy [24, 38–41]. The earlier meta-analysis reported a 50% lower excess risk ratio of pancreatic cancer for individuals with >5 year history of diabetes, compared with those with a shorter duration of diabetes [38]. Recent studies have also shown that onset of diabetes mellitus of <2 years of duration was more prevalent for patients with pancreatic cancer and, therefore, more likely to be induced by malignancy [24, 39]. Our finding of a 50% increased risk of liver cancer mortality for those with diabetes, compared with those without, is consistent with previous reports [6, 13–15, 42]. Our results also showed a marginally non-significant trend in risk of liver cancer mortality from fasting serum glucose levels (P-value for trend = 0.06). Previous studies [6, 14, 43–45] as well as a meta-analysis [46] have shown increased risk of colorectal cancer mortality for those with diabetes as suggested in the present study as a possible 30% increased risk of colorectal cancer mortality upon participants with diabetes.

plausible mechanisms

It has been suggested that the increased risk of cancer mortality for those with type 2 diabetes might reflect metabolic and hormonal changes of compensatory hyperinsulinaemia and elevated levels of insulin-like growth factors (IGFs) in response to reduced insulin sensitivity [8, 37, 47–49]. In this regard, IGF-1 has been shown to stimulate cellular proliferation in the pancreas, liver, and colon [48–50]. In addition, increased levels of circulating insulin may activate IGF-1 receptor and thereby promote cellular growth and cell cycle progression [8, 37, 47]. Glucose-lowering therapy, such as exogenous insulin, has been shown to increase cancer risk in a large retrospective cohort study [51]. In these diabetic patients, glucose-lowering therapies with sulphonylurea drugs or insulin were associated with increased cancer risk, as compared with treatment with metformin. In the subjects on insulin, the HR for solid tumour incidence was 1.42 and for pancreatic cancers 4.63, these conditions occurring in <2.0% and <0.2% of cases, respectively. These were a multiplicity of confounders and no causal association can be assumed [51]. The present study, however, did not have information regarding glucose-lowering medications and our reported results may misrepresent the strength of the overall diabetes–cancer association. Similar to the diabetes–pancreatic cancer association, plausible biological mechanisms for the diabetes–liver cancer association may also involve glucose-lowering therapy. Donadon et al. have reported an approximately threefold increased risk of hepatocellular carcinoma for individuals with diabetes on insulin or sulphonylurea treatment [52]. Another putative pathway between diabetes and liver cancer is the occurrence of fatty liver disease. Non-alcoholic fatty liver disease can progress to non-alcoholic steatohepatitis, which may develop subsequently into irreversible cirrhosis, and ultimately hepatocellular carcinoma [53].

strengths and limitations

The key strengths of the APCSC include its prospective design, its capacity to adjust for several possible confounders, and its large sample size, which allows reliable estimates of associations with deaths from rare cancers to be estimated.

One limitation is that information regarding duration of diabetes was not available. Instead, to explore the possibility of reverse causality, the data were 2-year left-censored, with a negligible effect on the original estimates. Given the long latent period between diabetes onset and death from cancer, it is possible that left-censoring the data by 2 years was insufficient to fully eliminate the effects of reverse causality. In this study, the median follow-up time available for analysis was only 4 years duration, so we were unable to explore this issue further. This relatively short median duration of follow-up might not capture mortality from specific cancers that have longer average survival periods. Information regarding cancer incidence, rather than mortality, would be more useful for assessing cancers with low fatality rates (such as prostate cancer) as a smaller proportion of incidence of such cancers would be included in an analysis of cancer mortality alone. There is certainly a distinction between whether diabetes may cause cancer and whether diabetes may increase individual risk of mortality once a particular cancer is acquired—the present study is limited to exploring the latter question as only cancer mortality data were collected. We are, therefore, unable to determine to what extent the increased risk of cancer mortality represents an aetiological role for diabetes or an early manifestation or consequence of cancer.

Another limitation of this study is the diagnostic criteria used for diabetes. While some participants had provided self-reported history of diabetes, approximately two-thirds (65%) of the analytical sample had only laboratory measurements of blood glucose levels to identify their diabetes status. Sole reliance on records of blood glucose levels, without information on self-reported history of diabetes or medication history, might underestimate the true effect of diabetes on cancer mortality as the dataset does not preclude blood glucose levels within normal ranges to be the outcomes of glucose-lowering treatment. The effects of misclassification as such would be conservative and alter the observed effect towards null. However, misclassification of self-reported diabetes may also alter the observed effect away from null as certain participants who reported themselves as ‘non-diabetic’ may have elevated glucose levels if blood samples were not taken from them during the study. Moreover, the effects of glucose-lowering treatment on normalising blood glucose levels, leading to a misclassification of someone with diabetes as non-diabetic, may be more pronounced among those with diabetes of longer duration, which may have a different relation with cancer mortality than diabetes of shorter duration, thereby making prediction of the direction of misclassification bias difficult. Lastly, some significant associations reported in the present study may be due to chance alone, given the large number of statistical tests carried out for 17 specific cancer end points.

Our comparisons of glucose categories also have potential misclassification issues. Individuals in the normoglycaemic group could have had diabetes since, for those analyses, we were not able to identify individuals who were treated with glucose-lowering therapy. Such misclassifications will attenuate the dose–response relationship between fasting serum glucose levels and liver cancer mortality reported in this study. Finally, the possibility of residual confounding remains. We adjusted for BMI but not measures of central obesity that might be more strongly related to some cancers. In particular, heavy alcohol consumption is associated both with diabetes and with cancers of the liver and colorectum, but our measure of alcohol intake was crude and unable to differentiate between amount, type, and duration of alcohol consumed.

conclusions

The present study adds to the growing body of literature concerning the long-term co-morbidities of diabetes and provides insight into future patterns of diabetes-related cancer mortality. Our findings suggest that diabetes is positively associated with cancer mortality for both Asian and Australasian populations. The relative effect of diabetes on the mortality risk from specific cancers for Asian populations is comparable with those for the largely Caucasian populations of Australasia. Given the increasing diabetes epidemic in both regions [54], and if the associations were causal, mortality from pancreatic, liver, and, possibly, colorectal cancers may be expected to rise, given that these cancers had the greatest percentage of deaths explained by diabetes. The large number of people living in Asia, particularly China, suggests that this will be a public health problem of importance. Concerted interventions that target the control and reduction of type 2 diabetes in populations of the Asia-Pacific region may have considerable benefits on reducing mortality from cancer, in addition to that from other chronic illnesses.

funding

Heart Foundation of Australia Career Development Award to RRH; Canadian Institutes of Health Research Fellowship to ALCM; Queen's University Community Medicine and Family Medicine Post-Graduate Medical Residency Program to EL. GDB is a Wellcome Trust Career Development Fellow. The Medical Research Council (MRC) Social and Public Health Sciences Unit receives funding from the UK MRC and the Chief Scientist Office at the Scottish Government Health Directorates.

disclosure

MW has recently received payment for consultancy from AstraZeneca and GlaxoSmithKline, for serving on a steering committee by Roche, for preparation of a paper by Servier, and for speaking at meetings by Servier, AstraZeneca, and Pfizer.

APCSC Executive Committee: M. Woodward (Chair), R. Huxley, X. Fang, D.F. Gu, Y. Imai, T.H. Lam, W.H. Pan, A. Rodgers, I. Suh, H.C. Kim, H. Ueshima.

Participating studies and principal collaborators in APCSC: Aito Town: A. Okayama, H. Ueshima; H. Maegawa; Akabane: M. Nakamura, N. Aoki; Anzhen02: Z.S. Wu; Anzhen: C.H. Yao, Z.S. Wu; Australian Longitudinal Study of Aging: Mary Luszcz; Australian National Heart Foundation: T.A. Welborn and S.S. Dhaliwal; Beijing Aging: Z. Tang; Beijing Steelworkers: L.S. Liu, J.X. Xie; Blood Donors' Health: R. Norton, S. Ameratunga, S. MacMahon, G. Whitlock; Busselton: M.W. Knuiman; Canberra-Queanbeyan: H. Christensen; Capital Iron and Steel Company: X.G. Wu; CISCH: J. Zhou, X.H. Yu; Civil Service Workers: A. Tamakoshi; CVDFACTS: W.H. Pan; East Beijing: Z.L. Wu, L.Q. Chen, G.L. Shan; Electricity Generating Authority of Thailand: P. Sritara; Fangshan: D.F. Gu, X.F. Duan; Fletcher Challenge: S. MacMahon, R. Norton, G. Whitlock, R. Jackson; Guangzhou: Y.H. Li; Guangzhou Occupational: T.H. Lam, C.Q. Jiang; Hisayama: Y. Kiyohara, H. Arima, M. Iida; Hong Kong: J. Woo, S.C. Ho; Huashan: Z. Hong, M.S. Huang, B. Zhou; Kinmen: J.L. Fuh; Konan: H. Ueshima, Y. Kita, S.R. Choudhury; KMIC: I. Suh, S.H. Jee, I.S. Kim; Melbourne: G.G. Giles; Miyama: T. Hashimoto, K. Sakata; Newcastle: A. Dobson; Ohasama: Y. Imai, T. Ohkubo, A. Hozawa; Perth: K. Jamrozik, M. Hobbs, R. Broadhurst; Saitama: K. Nakachi; Seven Cities: X.H. Fang, S.C. Li, Q.D. Yang; Shanghai Factory Workers: Z.M. Chen; Shibata: H. Tanaka; Shigaraki Town: Y. Kita, A. Nozaki, H. Ueshima; Shirakawa: H. Horibe, Y. Matsutani, M. Kagaya; Singapore Heart: K. Hughes, J. Lee; Singapore NHS92: D. Heng, S.K. Chew; Six Cohorts: B.F. Zhou, H.Y. Zhang; Tanno/Soubetsu: K. Shimamoto, S. Saitoh; Tianjin: Z.Z. Li, H.Y. Zhang; Western Australia AAA Screenees: P. Norman, K. Jamrozik; Xi'an: Y. He, T.H. Lam; Yunnan: S.X. Yao.

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