Association between unstable diabetes mellitus and risk of pancreatic cancer.

BACKGROUND: Deterioration of glycaemic control in people with long-standing diabetes mellitus (diabetes) may be a possible indicator of pancreatic cancer. However, the magnitude of the association between diabetes deterioration and pancreatic cancer has received little attention. METHODS: We conducted a matched cohort study, nested within a population-based cohort of Australian women with diabetes. Women with unstable diabetes, defined as a change in medication after a 2-year period of stable medication use, were matched by birth year to those with stable diabetes, in a 1:4 ratio. We used flexible parametric survival models to estimate hazard ratios (HRs) and 95% confidence intervals (CI). RESULTS: We included 134,954 and 539,789 women in the unstable and stable diabetes cohorts, respectively (mean age 68 years). In total, 1,315 pancreatic cancers were diagnosed. Deterioration of stable diabetes was associated with a 2.5-fold increased risk of pancreatic cancer (HR 2.55; 95% CI 2.29-2.85). The risk was particularly high within the first year after diabetes deteriorated. HRs at 3 months, 6 months and 1 year were: 5.76 (95% CI 4.72-7.04); 4.56 (95% CI 3.81-5.46); and 3.33 (95% CI 2.86-3.89), respectively. The risk was no longer significantly different after 7 years. CONCLUSIONS: Deterioration in glycaemic control in people with previously stable diabetes may be an indicator of pancreatic cancer, suggesting investigations of the pancreas may be appropriate. The weaker longer-term (3-7 years) association between diabetes deterioration and pancreatic cancer may indicate that poor glycaemic control can be a risk factor for pancreatic cancer.

Predicting the risk of pancreatic cancer in women with new-onset diabetes mellitus.

BACKGROUND AND AIM: People with new-onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new-onset diabetes. METHODS: We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti-diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer. RESULTS: Among 99 687 women aged ≥ 50 years with new-onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta-blockers, acid disorder drugs, and lipid-modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%. CONCLUSIONS: Our model doubled the PPV of pancreatic cancer in women with new-onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge.

The effect of vitamin D supplementation on risk of keratinocyte cancer: an exploratory analysis of the D-Health randomized controlled trial.

BACKGROUND: Vitamin D may play a role in prevention of keratinocyte cancer (KC), but observational studies examining the association between serum 25-hydroxy vitamin D concentration and KC are largely uninformative because sun exposure causes both KC and vitamin D production. There is scant evidence from clinical trials of supplementary vitamin D. OBJECTIVES: To examine the effect of vitamin D supplementation on the risk of developing KC. METHODS: We used data from the D-Health Trial, a randomized placebo-controlled trial of vitamin D supplementation (60 000 international units monthly for 5 years) among Australians aged ≥60 years. KC outcomes were captured through linkage to a national administrative dataset for those who consented (N = 20 334; 95%). We used negative binomial regression to analyse the incidence of KC excisions and the incidence of actinic lesions treated using cryotherapy or serial curettage, and flexible parametric survival models for analysis of time to first KC excision. RESULTS: Randomization to vitamin D supplementation did not reduce the incidence of KC lesions treated by excision [incidence rate ratio (IRR) 1·04; 95% confidence interval (CI) 0·98-1·11], the incidence of actinic lesions treated using other methods (IRR 1·01; 95% CI 0·95-1·08) or time to first histologically confirmed KC excision (hazard ratio 1·02; 95% CI 0·97-1·08). However, in subgroup analysis vitamin D increased the incidence of KC excisions in adults aged ≥ 70 years (IRR 1·13, 95% CI 1·04-1·23; P-value for interaction = 0·01). CONCLUSIONS: Vitamin D supplementation did not reduce the incidence of KC or other actinic lesions. What is already known about this topic? Laboratory studies have suggested possible protective effects of vitamin D on skin cancer. Observational studies investigating the association between vitamin D and risk of keratinocyte cancer are largely uninformative as ultraviolet radiation both causes skin cancer and is the primary source of vitamin D. The evidence from randomized controlled trials of vitamin D is limited and inconclusive. What does this study add? This population-based, randomized controlled trial suggests that supplementing older adults with a high monthly dose of vitamin D for 5 years does not affect the incidence of keratinocyte cancer.

Predicting obesity and smoking using medication data: A machine-learning approach.

PURPOSE: Administrative health datasets are widely used in public health research but often lack information about common confounders. We aimed to develop and validate machine learning (ML)-based models using medication data from Australia's Pharmaceutical Benefits Scheme (PBS) database to predict obesity and smoking. METHODS: We used data from the D-Health Trial (N = 18 000) and the QSkin Study (N = 43 794). Smoking history, and height and weight were self-reported at study entry. Linkage to the PBS dataset captured 5 years of medication data after cohort entry. We used age, sex, and medication use, classified using anatomical therapeutic classification codes, as potential predictors of smoking (current or quit <10 years ago; never or quit ≥10 years ago) and obesity (obese; non-obese). We trained gradient-boosted machine learning models using data for the first 80% of participants enrolled; models were validated using the remaining 20%. We assessed model performance overall and by sex and age, and compared models generated using 3 and 5 years of PBS data. RESULTS: Based on the validation dataset using 3 years of PBS data, the area under the receiver operating characteristic curve was 0.70 (95% confidence interval [CI] 0.68-0.71) for predicting obesity and 0.71 (95% CI 0.70-0.72) for predicting smoking. Models performed better in women than in men. Using 5 years of PBS data resulted in marginal improvement. CONCLUSIONS: Medication data in combination with age and sex can be used to predict obesity and smoking. These models may be of value to researchers using data collected for administrative purposes.

The association between diabetes mellitus of different durations and risk of pancreatic cancer: an Australian national data-linkage study in women.

AIMS: The bidirectional association between diabetes mellitus (DM) and pancreatic cancer (PC) is established; however, the strength of association between duration of DM and risk of PC needs further investigation. METHODS: We conducted a case-control study nested within a population-based cohort of Australian women established using record linkage. Women diagnosed with PC from July 2007 to December 2013, were matched to five controls based on age and state of residence. DM was defined according to prescription of anti-diabetic medication from administrative prescription data. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI), adjusted for area-level socioeconomic status, rurality of residence, weighted comorbidity score, and predicted probability of obesity. RESULTS: The analyses included 7,267 cases and 35,978 controls. The mean age at the time of DM diagnosis was 71 years whereas the mean age at the time of diagnosis of PC was 76 years. A history of DM of any duration was associated with a 2-fold increase in risk of PC (OR=2.12; 95%CI:1.96-2.29) compared to having no history of DM. The risk decreased with increasing duration of DM. The highest risk was in those who had recent-onset DM (OR=8.08; 95%CI:6.88-9.50 for <12 months of DM), but the risk remained elevated with ≥5 years of DM (OR=1.40; 95%CI:1.27-1.55). CONCLUSION: The markedly increased risk of PC in those with recent-onset DM emphasises the need for further research to distinguish patients for whom new-onset DM is a manifestation of PC from those with type-2 DM. The elevated risk associated with long-standing DM suggests that preventing DM may contribute to a reduction in the incidence of PC.