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.
Abstract
Background
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 and obesity. 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 (AUC) 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.
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.
Summary
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.
Laboratory studies have suggested possible protective effects of vitamin D on skin cancer; however, the evidence from randomized controlled trials is limited and inconclusive. Our analysis of the large population‐based D‐Health Trial did not find a benefit of monthly doses of vitamin D on the incidence of keratinocyte cancer or other actinic lesions.
Linked Comment: M.N. Passarelli and M.R. Karagas. Br J Dermatol 2022; 187:635–636.
Plain language summary available online
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.
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.
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).
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.
•There is limited data about the association between pancreatic cancer (PC) and diabetes mellitus (DM) of different durations, particularly DM of very short duration (<3 months).•We found a marked difference in risk of PC according to duration of DM with extremely high risk for DM of <12 months duration.•The associations between PC and DM of duration <3 months and 3 to <6 months were particularly strong; these could not be explained by detection bias.•The elevated risk of PC was evident even with DM of 10 or more years.•Differentiating high-risk new-onset DM secondary to PC from those with type 2 DM will provide a potential avenue for early detection of PC.
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.
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).
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.
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.
Abstract
Worsening of hyperglycemia in people with pre-existing diabetes mellitus may be an indicator of undiagnosed pancreatic cancer. However, there is limited evidence from longitudinal studies ...regarding the association between deterioration of diabetes and pancreatic cancer in people with previously stable diabetes. The size of the association between deterioration of diabetes and pancreatic cancer, and how the association changes over time, needs further investigation. A population-based matched cohort study was conducted within Australian women with stable diabetes mellitus. Unstable diabetes, the exposure of interest, was defined as change in the type of antidiabetic medication or addition of another antidiabetic medication after at least 24 months of stable antidiabetic medication use. The date of exposure was defined as the index date. Each woman with unstable diabetes was matched to 4 women with stable diabetes by birth year (±1 year) at the index date. Flexible parametric survival models were used to estimate hazard ratios (HR) (overall and at different times) and 95% confidence intervals (CI), and absolute risk in both groups at different time points. We identified 134,954 women with unstable diabetes; these were matched to women who had not developed unstable diabetes at the index date. The mean age of the cohort was 68 years. Over a median follow-up period of 2.8 years, 1,315 pancreatic cancer cases were diagnosed. Overall, women who experienced deterioration in glycemic control had a 2.5-fold increased risk of developing pancreatic cancer (HR 2.55; 95% CI 2.29 – 2.85). The risk was very high during the initial few months HRs at 3 months (5.76; 95% CI 4.72 – 7.04), 6 months (4.56; 95% CI 3.81 – 5.46), and 12 months (3.33; 95% CI 2.86 – 3.89). The risk then progressively declined with time. Worsening of glycemic control in those with previously stable diabetes may be an early clinical manifestation of pancreatic cancer. Further research is warranted to differentiate this pancreatic cancer-associated deterioration of glycemic control from other causes. The longer-term (3-7 years) weaker association between pancreatic cancer and deterioration of glycemic control suggests that poorly controlled hyperglycemia is a predisposing factor for pancreatic cancer.
Citation Format: Sitwat Ali, Michael Coory, Peter Donovan, Renhua Na, Nirmala Pandeya, Sallie-Anne Pearson, Katrina Spilsbury, Louise Stewart, Bridie Thompson, Karen Tuesley, Mary Waterhouse, Penelope Webb, Susan Jordan, Rachel Neale. Association between unstable diabetes mellitus and risk of pancreatic cancer abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A009.
Abstract The present study aimed to find out differences of social support, perceived emotion invalidation, psychological needs, and use of adaptive and maladaptive cognitive emotion regulation ...strategies in maritally adjusted and maladjusted after controlling for age, education, employment status, and depressive symptomatology. The cross‐sectional study uses a matched pairs design. The sample was divided into two groups; maritally adjusted and maladjusted women ( n = 40 pairs) on basis of scores obtained on revised‐dyadic adjustment scale. Forty maritally adjusted women were matched with 40 maritally maladjusted women according to age, education, and employment status. Social support questionnaire, perceived invalidation of emotion scale, basic psychological need satisfaction frustration scale, cognitive emotion regulation questionnaire, and center for epidemiologic studies depression scale were administered. One‐way ANCOVA revealed that maritally maladjusted women had lower level of social support mean difference; −5.65(−9.97, −1.33), p < 0.05, partial η 2 = 0.08 and more emotional invalidation mean difference; 15.36(13.08, 17.65), p < 0.001, partial η 2 = 0.71 compared to maritally adjusted women after controlling for the effect of depressive symptomatology. Maritally maladjusted women had more need frustration mean difference; 10.75(7.59, 13.92), p < 0.001, partial η 2 = 0.38 compared to maritally adjusted women. However, maritally adjusted women had more need satisfaction mean difference; 13.36(9.67, 17.05), p < 0.001, partial η 2 = 0.41 compared to maritally maladjusted women. Maritally adjusted women used more adaptive CER strategies (acceptance, refocus on planning and putting into perspective) mean difference; 4.66(2.36, 6.95), p < 0.001, partial η 2 = 0.18 compared to maritally maladjusted women whereas, maritally maladjusted women used more maladaptive strategies (self‐blame, catastrophizing and blaming others) mean difference; 4.66(2.77, 6.54), p < 0.001, partial η 2 = 0.25 compared to maritally adjusted women. Maladjusted women had less social support and more emotional invalidation of emotions and psychological needs frustration. They used more maladaptive strategies to manage their negative emotions in comparison to maritally adjusted women. Identification of these cognitive emotion regulation strategies will help clinicians and counselors to devise psychological intervention targeting the use of adaptive strategies to minimize the negative mental health consequences.
Layered semiconductors of the V-VI group have attracted considerable attention in optoelectronic applications owing to their atomically thin structures. They offer thickness-dependent optical and ...electronic properties, promising ultrafast response time, and high sensitivity. Compared to the bulk, 2D bismuth selenide (Bi
Se
) is recently considered a highly promising material. In this study, 2D nanosheets are synthesized by prolonged sonication in two different solvents, such as
-methyl-2-pyrrolidone (NMP) and chitosan-acetic acid solution (CS-HAc), using the liquid-phase exfoliation (LPE) method. X-ray diffraction confirms the amorphous nature of exfoliated 2D nanosheets with maximum peak intensity at the same position (015) crystal plane as that obtained in its bulk counterpart. SEM confirms the thin 2D nanosheet-like morphology. Successful exfoliation of Bi
Se
nanosheets up to five layers is achieved using CS-HAc solvent. The as-synthesized 2D nanosheets in different solvents are employed to fabricate the photodetector. At minimum selected power density, the photodetector fabricated using exfoliated ultrathin 2D nanosheets exhibits the highest range of responsivity, varying from 15 to 2.5 mA/W, and detectivity ranging from 2.83 × 10
to 6.37 × 10
. Ultrathin 2D Bi
Se
nanosheets have fast rise and fall times, ranging from 0.01 to 0.12 and 0.01 to 0.06 s, respectively, at different wavelengths. Ultrathin Bi
Se
nanosheets have improved photodetection parameters as compared to multilayered nanosheets due to the high surface to volume ratio, reduced recombination and trapping of charge carrier, improved carrier confinement, and faster carrier transport due to the thin layer.
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