Prediction of 5-year cardiovascular disease risk in people with type 2 diabetes mellitus:derivation in Nanjing, China and external validation in Scotland, UK

BACKGROUND: To use routinely collected data to develop a five-year cardiovascular disease (CVD) risk prediction model for Chinese adults with type 2 diabetes with validation of its performance in a population of European ancestry. METHODS: People with incident type 2 diabetes and no history of CVD at diagnosis of diabetes between 2008 and 2017 were included in derivation and validation cohorts. The derivation cohort was identified from a pseudonymized research extract of data from the First Affiliated Hospital of Nanjing Medical University (NMU). Five-year risk of CVD was estimated using basic and extended Cox proportional hazards regression models including 6 and 11 predictors respectively. The risk prediction models were internally validated and externally validated in a Scottish population–based cohort with CVD events identified from linked hospital records. Discrimination and calibration were assessed using Harrell’s C-statistic and calibration plots, respectively. RESULTS: Mean age of the derivation and validation cohorts were 58.4 and 59.2 years, respectively, with 53.5% and 56.9% men. During a median follow-up time of 4.75 [2.67, 7.42] years, 18,827 (22.25%) of the 84,630 people in the NMU-Diabetes cohort and 8,763 (7.31%) of the Scottish cohort of 119,891 people developed CVD. The extended model had a C-statistic of 0.723 [0.721–0.724] in internal validation and 0.716 [0.713–0.719] in external validation. CONCLUSIONS: It is possible to generate a risk prediction model with moderate discriminative power in internal and external validation derived from routinely collected Chinese hospital data. The proposed risk score could be used to improve CVD prevention in people with diabetes

Making diabetes education interactive : tangible educational toys for children with type-1 diabetes

Younger children (under 9 years) with type-1 diabetes are often very passive in the management of their condition and can face difficulties in accessing and understanding basic diabetes related information. This can make transitioning to self-management in later years very challenging. Previous research has mostly focused on educational interventions for older children. To create an educational tool which can support the diabetes educational process of younger children, we conducted a multiphase and multi-stakeholder user-centred design process. The result is an interactive tool that illustrates diabetes concepts in an age-appropriate way with the use of tangible toys. The tool was evaluated inside a paediatric diabetes clinic with clinicians, children and parents and was found to be engaging, acceptable and effective. In addition to providing implications for the design and adoption of educational tools for children in a clinical setting, we discuss the challenges for conducting user-centred design in such a setting

Cancer incidence in persons with type 1 diabetes : a five-country study of 9,000 cancers in type 1 diabetic individuals

An excess cancer incidence of 20-25% has been identified among persons with diabetes, most of whom have type 2 diabetes. We aimed to describe the association between type 1 diabetes and cancer incidence. Persons with type 1 diabetes were identified from five nationwide diabetes registers: Australia (2000-2008), Denmark (1995-2014), Finland (1972-2012), Scotland (1995-2012) and Sweden (1987-2012). Linkage to national cancer registries provided the numbers of incident cancers in people with type 1 diabetes and in the general population. We used Poisson models with adjustment for age and date of follow up to estimate hazard ratios for total and site-specific cancers. A total of 9,149 cancers occurred among persons with type 1 diabetes in 3.9 million person-years. The median age at cancer diagnosis was 51.1 years (interquartile range 43.5-59.5). The hazard ratios (HRs) (95% CIs) associated with type 1 diabetes for all cancers combined were 1.01 (0.98, 1.04) among men and 1.07 (1.04, 1.10) among women. HRs were increased for cancer of the stomach (men, HR 1.23 [1.04, 1.46]; women, HR 1.78 [1.49, 2.13]), liver (men, HR 2.00 [1.67, 2.40]; women, HR 1.55 [1.14, 2.10]), pancreas (men, HR 1.53 [1.30, 1.79]; women, HR 1.25 [1.02,1.53]), endometrium (HR 1.42 [1.27, 1.58]) and kidney (men, HR 1.30 [1.12, 1.49]; women, HR 1.47 [1.23, 1.77]). Reduced HRs were found for cancer of the prostate (HR 0.56 [0.51, 0.61]) and breast (HR 0.90 [0.85, 0.94]). HRs declined with increasing diabetes duration. Type 1 diabetes was associated with differences in the risk of several common cancers; the strength of these associations varied with the duration of diabetes.Peer reviewe

Cardiovascular disease, cancer and mortality among people with type 2 diabetes and alcoholic or non-alcoholic fatty liver disease hospital admission

OBJECTIVE: To describe associations between alcoholic fatty liver disease (ALD) or non-alcoholic fatty liver disease (NAFLD) hospital admission and cardiovascular disease (CVD), cancer, and mortality in people with T2DM. RESEARCH DESIGN AND METHODS: We performed a retrospective cohort study using linked population-based routine data from the diabetes register, hospital, cancer and death records for people aged 40-89 years, diagnosed with T2DM in Scotland 2004-2013 who had one or more hospital admission records. Liver disease and outcomes were identified using International Classification of Diseases codes. We estimated hazard ratios from Cox proportional hazards models, adjusted for key risk factors (aHRs). RESULTS: There were 134,368 people with T2DM (1707 with ALD and 1452 with NAFLD) with mean follow-up of 4.3 years for CVD and 4.7 years for mortality. Among people with ALD, NAFLD or without liver disease hospital records respectively there were: 378, 320 and 21,873 CVD events, 268, 176 and 15,101 cancers and 724, 221 and 16,203 deaths. For ALD and NAFLD respectively, aHRs (95% CIs) compared to the group with no record of liver disease were: 1.59 (1.43, 1.76) and 1.70 (1.52, 1.90), for CVD; 40.3 (28.8, 56.5) and 19.12(11.71 31.2), for hepatocellular cancer (HCC); 1.28 (1.12, 1.47) and 1.10 (0.94, 1.29) for non-HCC cancer; 4.86 (4.50, 5.24) and 1.60 (1.40, 1.83) for all-cause mortality. CONCLUSIONS: Hospital records of ALD or NAFLD are associated, to varying degrees, with increased risk of CVD, cancer and mortality in people with T2DM

Performance of Cardiovascular Disease Risk Scores in People Diagnosed With Type 2 Diabetes:External Validation Using Data From the National Scottish Diabetes Register

Objective: To evaluate the performance of five cardiovascular disease (CVD) risk scores developed in diabetes populations and compare their performance to QRISK2. Research Design and Methods: A cohort of people diagnosed with type 2 diabetes between 2004 and 2016 was identified from the Scottish national diabetes register. CVD events were identified using linked hospital and death records. Five-year risk of CVD was estimated using each of QRISK2, ADVANCE (Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation), Cardiovascular Health Study (CHS), New Zealand Diabetes Cohort Study (NZ DCS), Fremantle Diabetes Study, and Swedish National Diabetes Register (NDR) risk scores. Discrimination and calibration were assessed using the Harrell C statistic and calibration plots, respectively. Results: The external validation cohort consisted of 181,399 people with type 2 diabetes and no history of CVD. There were 14,081 incident CVD events within 5 years of follow-up. The 5-year observed risk of CVD was 9.7% (95% CI 9.6, 9.9). C statistics varied between 0.66 and 0.67 for all risk scores. QRISK2 overestimated risk, classifying 87% to be at high risk for developing CVD within 5 years; ADVANCE underestimated risk, and the Swedish NDR risk score calibrated well to observed risk. Conclusions: None of the risk scores performed well among people with newly diagnosed type 2 diabetes. Using these risk scores to predict 5-year CVD risk in this population may not be appropriate

Mortality trends in type 1 diabetes:a multicountry analysis of six population-based cohorts

AIMS/HYPOTHESIS: Mortality has declined in people with type 1 diabetes in recent decades. We examined how the pattern of decline differs by country, age and sex, and how mortality trends in type 1 diabetes relate to trends in general population mortality. METHODS: We assembled aggregate data on all-cause mortality during the period 2000–2016 in people with type 1 diabetes aged 0–79 years from Australia, Denmark, Latvia, Scotland, Spain (Catalonia) and the USA (Kaiser Permanente Northwest). Data were obtained from administrative sources, health insurance records and registries. All-cause mortality rates in people with type 1 diabetes, and standardised mortality ratios (SMRs) comparing type 1 diabetes with the non-diabetic population, were modelled using Poisson regression, with age and calendar time as quantitative variables, describing the effects using restricted cubic splines with six knots for age and calendar time. Mortality rates were standardised to the age distribution of the aggregate population with type 1 diabetes. RESULTS: All six data sources showed a decline in age- and sex-standardised all-cause mortality rates in people with type 1 diabetes from 2000 to 2016 (or a subset thereof), with annual changes in mortality rates ranging from −2.1% (95% CI −2.8%, −1.3%) to −5.8% (95% CI −6.5%, −5.1%). All-cause mortality was higher for male individuals and for older individuals, but the rate of decline in mortality was generally unaffected by sex or age. SMR was higher in female individuals than male individuals, and appeared to peak at ages 40–70 years. SMR declined over time in Denmark, Scotland and Spain, while remaining stable in the other three data sources. CONCLUSIONS/INTERPRETATION: All-cause mortality in people with type 1 diabetes has declined in recent years in most included populations, but improvements in mortality relative to the non-diabetic population are less consistent. GRAPHICAL ABSTRACT: [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-022-05659-9) contains peer-reviewed but unedited supplementary material, which is available to authorised users