Diabetes and pancreatic cancer risk in a multiracial cohort.

AIMS: To determine the relationship of diabetes with pancreatic cancer incidence among African American and Whites of similar socio-economic status. METHODS: Using the Southern Community Cohort Study, we conducted a follow-up during 2002-2015 of pancreatic cancer incidence of 73,378 mostly low-income participants aged 40-79 years; 15,913 reported diabetes at baseline. Multivariable Cox analysis controlling for sex, family history of pancreatic cancer, BMI, smoking status, alcohol consumption, education, income and other important covariates, and with age as the timescale was used. RESULTS: Totally, 265 incident pancreatic cancer cases were observed. Pancreatic cancer risk was increased among those with diabetes (HR 1.54, CI 1.16-2.05), with similar increases among African Americans (HR 1.51, CI 1.08-2.11) and Whites (HR 1.78, CI 1.00-3.16). No trend in risk was observed for diabetes duration among those with diabetes, with HRs of 1.39 (0.91-2.11), 2.31 (1.51-3.54) and 1.23 (0.80-1.89) for <5, 5-9 and 10+ years duration, respectively. African Americans were at increased risk of pancreatic cancer (HR = 1.40, 95% CI 1.05-1.87), which persisted after adjusting for diabetes (HR 1.36, CI 1.02-1.81). The effect sizes for other pancreatic cancer risk factors with pancreatic cancer were similar by diabetes status, although a stronger association with low BMI was evident among those with diabetes. CONCLUSIONS: Diabetes increases pancreatic cancer risk similarly among African Americans and Whites in this Southern U.S.

Predictors of Change in Skin Intrinsic Fluorescence in Type 1 Diabetes: The Epidemiology of Diabetes Complications Study.

BACKGROUND: Skin intrinsic fluorescent (SIF) scores are indirect measures of advanced glycation end-products (AGEs). SIF scores are cross-sectionally associated with type 1 diabetes (T1D) complications such as increased albumin excretion rate (AER), coronary artery calcification (CAC) and neuropathy. We assessed predictors of SIF score change in those with T1D. METHODS: Data from the 30-year longitudinal Epidemiology of Diabetes Complications (EDC) study of childhood-onset T1D were used to assess AGEs measured with a SIF score produced by the SCOUT DS® device. SIF scores were assessed twice in 83 participants: between 2007-08 and again between 2010-14. Regression analyses were used to assess independent predictors of SIF score change. RESULTS: At baseline, mean age was 47.9 ± 6.9 years, diabetes duration was 36.7 ± 6.4 years, and median glycosylated hemoglobin (HbA1c) was 7.1 (interquartile range: 6.5, 8.5). During a mean follow-up of 5.2 ± 0.9 years, mean change in SIF score was 2.9 ± 2.8 arbitrary units. In multivariable linear regression models, log HbA1c (P < 0.001), log estimated glomerular filtration rate (eGFR) (P < 0.001), overt nephropathy (defined as AER ≥ 200 µg/min, P = 0.06), and multiple daily insulin shots/pump use (MDI) exposure years (P = 0.02) were independent predictors of SIF score change. CONCLUSIONS: Increases in SIF score over 5 years were related to increased glycemic levels and decreased kidney function (eGFR). MDI and glomerular damage were related to a decreased SIF score. This is one of the first studies with repeated SIF assessments in T1D and provides unique, albeit preliminary, insight about these associations.

Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases.

BACKGROUND: Though electronic health record (EHR) data have been linked to national and state death registries, such linkages have rarely been validated for an entire hospital system's EHR. OBJECTIVES: The aim of the study is to validate West Virginia University Medicine's (WVU Medicine) linkage of its EHR to three external death registries: the Social Security Death Masterfile (SSDMF), the national death index (NDI), the West Virginia Department of Health and Human Resources (DHHR). METHODS: Probabilistic matching was used to link patients to NDI and deterministic matching for the SSDMF and DHHR vital statistics records (WVDMF). In subanalysis, we used deaths recorded in Epic (n = 30,217) to further validate a subset of deaths captured by the SSDMF, NDI, and WVDMF. RESULTS: Of the deaths captured by the SSDMF, 59.8 and 68.5% were captured by NDI and WVDMF, respectively; for deaths captured by NDI this co-capture rate was 80 and 78%, respectively, for the SSDMF and WVDMF. Kappa statistics were strongest for NDI and WVDMF (61.2%) and NDI and SSDMF (60.6%) and weakest for SSDMF and WVDMF (27.9%). Of deaths recorded in Epic, 84.3, 85.5, and 84.4% were captured by SSDMF, NDI, and WVDMF, respectively. Less than 2% of patients' deaths recorded in Epic were not found in any of the death registries. Finally, approximately 0.2% of "decedents" in any death registry re-emerged in Epic at least 6 months after their death date, a very small percentage and thus further validating the linkages. CONCLUSION: NDI had greatest validity in capturing deaths in our EHR. As a similar, though slightly less capture and agreement rate in identifying deaths is observed for SSDMF and state vital statistics records, these registries may be reasonable alternatives to NDI for research and quality assurance studies utilizing entire EHRs from large hospital systems. Investigators should also be aware that there will be a very tiny fraction of "dead" patients re-emerging in the EHR.