Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes.

Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007-2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999-1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82-0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

Cardiovascular risk factors, micro and macrovascular complications at diagnosis in patients with young onset type 2 diabetes in India: CINDI 2.

CONTEXT: Type 2 diabetes mellitus (T2DM) in young adults is increasing in India. Data on the prevalence of cardiovascular (CV) risk factors and complications associated with young-onset T2DM (YOD) at the time of diagnosis of diabetes are limited. This data can aid in aggressive diabetes management, CV risk reduction, and prevention of complications. AIM: To determine the prevalence of CV risk factors, micro and macrovascular complications in patients with newly diagnosed YOD. To assess the percentage of patients who require statin therapy based on current American Diabetes Association (ADA) guidelines. SETTINGS AND DESIGN: This was a retrospective cross-sectional study of 1500 patients with newly detected YOD across seven centers from 2013 to 2015. DESIGNS AND METHODS: Patients were evaluated for complications of diabetes and CV risk factors such as body mass index (BMI), hypertension, dyslipidemia, and smoking. STATISTICAL ANALYSIS: Measurements have been presented as mean ± standard deviation; results on categorical measurements have been presented in percentages. RESULTS: The mean age, glycated hemoglobin and BMI were 34.7 ± 4.2 years, 9.9 ± 2.4%, and 26.8 ± 4.7 kg/m(2). Hypertension, dyslipidemia, BMI >23 kg/m(2), and smoking were presented in 27.6%, 62.4%, 84.2%, and 24%. Diabetic retinopathy, neuropathy, and nephropathy were seen in 5.1%, 13.2%, and 0.9%. Ischemic heart disease, peripheral vascular disease, and stroke were presented in 0.7%, 2%, and 0.1%. As per current guidelines, 95.33% needed statin therapy. CONCLUSION: This study demonstrates that patients with YOD have micro and macrovascular complications at diagnosis. Nearly, every patient required a statin to reduce CV risk. This highlights the importance of screening patients with YOD for CV risk factors and complications of diabetes at the time of diagnosis.