Partial Clinical Remission of Type 1 Diabetes in Swedish Children: A Longitudinal Study from the Swedish National Quality Register (SWEDIABKIDS) and the Better Diabetes Diagnosis (BDD) Study.

Aims/Hypotheses: To investigate the frequency and characteristics of partial remission in Swedish children with type 1 diabetes and whether the insulin delivery method, that is, continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDIs), affects incidence and duration of this period, 2007-2011. Factors that increase the proportion of subjects who enter partial remission and extend this period can improve long-term metabolic control and reduce the risk of severe hypoglycemia, improve quality of life, and, in the long run, reduce late complications. Methods: Longitudinal data from 2007 to 2020 were extracted from the Swedish National Quality Register (SWEDIABKIDS) with all reported newly diagnosed children. Data on C-peptide from the participants in the Better Diabetes Diagnosis study from 2007 to 2010 were used. The definition of partial remission was insulin dose-adjusted HbA1c: HbA1c (%) + [4 × total daily insulin dose (U/kg/day)] ≤9. Results: Of the 3887 patients, 56% were boys. More boys than girls were in partial remission throughout the follow-up period until 24 months after diabetes onset. Fewer children 0-6 years old had partial remission at 3 and 12 months but not at 24 months compared with older age-groups. A larger proportion of patients using CSII at 12 and 24 months remained in partial remission compared with those with MDI (37% vs. 33%, P = 0.02 and 31% vs. 27%, P = 0.01, respectively). The level of C-peptide was higher in the group with partial remission and mean HbA1c was lower (both P < 0.001). Partial remission at 12 months after diabetes onset was associated with CSII (odds ratio [OR]: 1.39, confidence interval [CI]:1.13, 1.71), shorter diabetes duration (OR: 0.80, CI: 0.76, 0.84), and male sex (OR: 1.23, CI: 1.04, 1.46). Conclusions/Interpretation: Insulin through MDI, longer duration of diabetes, and female sex were associated with lower frequency of partial remission. Use of CSII seems to contribute to longer partial remission among Swedish children with type 1 diabetes.

Development of a clinical calculator to aid the identification of MODY in pediatric patients at the time of diabetes diagnosis.

Maturity Onset Diabetes of the Young (MODY) is a young-onset, monogenic form of diabetes without needing insulin treatment. Diagnostic testing is expensive. To aid decisions on who to test, we aimed to develop a MODY probability calculator for paediatric cases at the time of diabetes diagnosis, when the existing "MODY calculator" cannot be used. Firth logistic regression models were developed on data from 3541 paediatric patients from the Swedish 'Better Diabetes Diagnosis' (BDD) population study (n = 46 (1.3%) MODY (HNF1A, HNF4A, GCK)). Model performance was compared to using islet autoantibody testing. HbA1c, parent with diabetes, and absence of polyuria were significant independent predictors of MODY. The model showed excellent discrimination (c-statistic = 0.963) and calibrated well (Brier score = 0.01). MODY probability > 1.3% (ie. above background prevalence) had similar performance to being negative for all 3 antibodies (positive predictive value (PPV) = 10% v 11% respectively i.e. ~ 1 in 10 positive test rate). Probability > 1.3% and negative for 3 islet autoantibodies narrowed down to 4% of the cohort, and detected 96% of MODY cases (PPV = 31%). This MODY calculator for paediatric patients at time of diabetes diagnosis will help target genetic testing to those most likely to benefit, to get the right diagnosis.

Prevalence and Predictive Factors for Celiac Disease in Children With Type 1 Diabetes: Whom and When to Screen? A Nationwide Longitudinal Cohort Study of Swedish Children.

OBJECTIVE: To examine the prevalence and predictive factors for celiac disease (CD) after a diagnosis of type 1 diabetes (T1D) in children and adolescents, to improve the current screening guidelines. RESEARCH DESIGN AND METHODS: The association between sex, age at T1D diagnosis, HLA, and diabetes autoantibodies, and a diagnosis of CD was examined in 5,295 children with T1D from the Better Diabetes Diagnosis study in Sweden. RESULTS: The prevalence of biopsy-proven CD was 9.8%, of which 58.2% already had a CD diagnosis before or at T1D onset. Almost all, 95.9%, were diagnosed with CD within 5 years after the T1D diagnosis. Younger age at the T1D diagnosis and being homozygote for DQ2 increased the risk of CD after T1D, but neither sex nor diabetes-related autoantibodies were associated with the risk. CONCLUSIONS: Age at and time after diabetes diagnosis should be considered in screening guidelines for CD in children with T1D.

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

Relative poverty is associated with increased risk of diabetic ketoacidosis at onset of type 1 diabetes in children. A Swedish national population-based study in 2014-2019.

AIMS: The aim of the study was to estimate the effect of household relative poverty on the risk of diabetic ketoacidosis at diagnosis of children with type 1 diabetes using an international standard measurement of relative poverty. METHODS: A national population-based retrospective study was conducted. The Swedish National Diabetes Register (NDR) was linked with data from Sweden's public statistical agency (Statistics Sweden). Children who were diagnosed with new-onset type 1 diabetes in the period of 2014-2019 were common identifiers. The definition of diabetic ketoacidosis was venous pH <7.30 or a serum bicarbonate level <18 mmol/L. The exposure variable was defined according to the standard definition of the persistent at-risk-of-poverty rate used by the statistical office of the European Union (Eurostat) and several other European public statistical agencies. Univariate and multi-variable analyses were used to calculate the effect of relative poverty on the risk of diabetic ketoacidosis. RESULTS: Children from households with relative poverty had a 41% higher risk of diabetic ketoacidosis (1.41, CI 1.12-1.77, p = 0.004) and more than double the risk of severe diabetic ketoacidosis (pH <7.10) (RR 2.10, CI 1.35-3.25, p = 0.001), as compared to children from households without relative poverty. CONCLUSIONS: Relative poverty significantly increases the risk of diabetic ketoacidosis at onset of type 1 diabetes in children, even in a high-income country with publicly reimbursed health care.