Disease heterogeneity of adult diabetes based on routine clinical parameters at diagnosis: Results from the German/Austrian DPV registry.

AIMS To cluster adults with diabetes using parameters from real-world clinical care at manifestation. MATERIALS AND METHODS We applied hierarchical clustering using Ward's method to 56,869 adults documented in the Prospective Diabetes Follow-up Registry (DPV). Clustering variables included age, sex, BMI, HbA1c, diabetic ketoacidosis (DKA), components of the metabolic syndrome (hypertension/dyslipidemia/hyperuricemia), and beta-cell antibody status. Time until use of oral antidiabetic drugs (OAD), use of insulin, chronic kidney disease (CKD), cardiovascular disease (CVD), retinopathy, or neuropathy were assessed using Kaplan Meier analysis and Cox regression models. RESULTS We identified eight clusters: Four clusters comprised early diabetes onset (median age between 40 and 50 years), but differed with regard to BMI, HbA1c, DKA and antibody positivity. Two clusters included adults with diabetes onset in their early 60s who met target HbA1c, but differed in BMI and sex distribution. Two clusters were characterized by late diabetes onset (median age 69 and 77 years) and relatively low BMI, but differences in HbA1c. Earlier insulin use was observed in adults with high HbA1c, and earlier OAD use was observed in those with high BMI. Time until CKD or CVD was shorter in those with late onset, whereas retinopathy occurred earlier in adults with late onset and high HbA1c, and in adults with early onset, but high HbA1c and high percentage of antibody positivity. CONCLUSIONS Adult diabetes is heterogeneous beyond classical type 1/type 2 diabetes, based on easily available parameters in clinical practice using an automated clustering algorithm which allows both continuous and binary variables. This article is protected by copyright. All rights reserved

Temporal trends in diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes between 2006 and 2016: results from 13 countries in three continents

Aims/hypothesis The aim of this work was to evaluate geographical variability and trends in the prevalence of diabetic ketoacidosis (DKA), between 2006 and 2016, at the diagnosis of childhood-onset type 1 diabetes in 13 countries over three continents. Methods An international retrospective study on DKA at diagnosis of diabetes was conducted. Data on age, sex, date of diabetes diagnosis, ethnic minority status and presence of DKA at diabetes onset were obtained from Australia, Austria, Czechia, Denmark, Germany, Italy, Luxembourg, New Zealand, Norway, Slovenia, Sweden, USA and the UK (Wales). Mean prevalence was estimated for the entire period, both overall and by country, adjusted for sex and age group. Temporal trends in annual prevalence of DKA were estimated using logistic regression analysis for each country, before and after adjustment for sex, age group and ethnic minority status. Results During the study period, new-onset type 1 diabetes was diagnosed in 59,000 children (median age [interquartile range], 9.0 years [5.5–11.7]; male sex, 52.9%). The overall adjusted DKA prevalence was 29.9%, with the lowest prevalence in Sweden and Denmark and the highest in Luxembourg and Italy. The adjusted DKA prevalence significantly increased over time in Australia, Germany and the USA while it decreased in Italy. Preschool children, adolescents and children from ethnic minority groups were at highest risk of DKA at diabetes diagnosis in most countries. A significantly higher risk was also found for females in Denmark, Germany and Slovenia. Conclusions/interpretation DKA prevalence at type 1 diabetes diagnosis varied considerably across countries, albeit it was generally high and showed a slight increase between 2006 and 2016. Increased awareness of symptoms to prevent delay in diagnosis is warranted, especially in preschool children, adolescents and children from ethnic minority groups

Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm.

AIMS Diabetic ketoacidosis (DKA) is a serious and potentially fatal complication of type 1 diabetes and it is difficult to identify individuals at increased risk. The aim of this study was to identify predictive factors for DKA by retrospective analysis of registry data and use of a subgroup discovery algorithm. MATERIALS AND METHODS Data from adults and children with type 1 diabetes and >2 diabetes-related visits were analyzed from the Diabetes Prospective Follow-up Registry. Q-Finder®, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH <7.3 during a hospitalization event. RESULTS Data for 108,223 adults and children, of whom 5,609 (5.2%) had DKA, were studied. Q-Finder® analysis identified 11 profiles associated with increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; HbA1c ≥8.87 [73 mmol/mol]; no fast-acting insulin intake; age <15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycemia; hypoglycemic coma; and autoimmune thyroiditis. Risk of DKA increased with number of risk profiles matching patients' characteristics. CONCLUSIONS Q-Finder® confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA. This article is protected by copyright. All rights reserved