Australasian Diabetes Data Network Research Articles

Variability in Body Mass Index during 2018–2021 for People with Type 1 Diabetes: Real World Data from the USA, Germany, and Australasia

Introduction: The COVID-19 pandemic necessitated worldwide lockdowns in 2020 and 2021, with restrictions on physical activity and changes in eating habits. The aim of this study was to investigate temporal trends in body mass index (BMI) and BMI Standard Deviation Score (SDS) in three international type 1 diabetes (T1D) registries between 2018 and 2021. Methods: Data were extracted from DPV (Germany/Austria/Luxembourg/Switzerland), T1D Exchange Quality Improvement Collaborative (T1DX-QI, USA), and the Australasian Diabetes Data Network (ADDN, Australia/New Zealand). The period affected by the COVID-19 pandemic was defined as March to December 2020 and March to December 2021 and compared with the respective 9-month periods in 2018 and 2019. Estimated mean BMI (adults ≥19 years) and WHO BMI SDS (children and adolescents 5 to <19 years) were calculated, adjusted for sex, age, HbA1c, and diabetes duration. Adjusted mean proportions overweight (BMI ≥25 in adults or BMI SDS >1 in children and adolescents 5 to <19 years) and obese (BMI ≥30 kg/m2 or BMI SDS >2 in children and adolescents 5 to <19 years) were also calculated, adjusted for sex, age, HbA1c, and diabetes duration. Results: The study population comprised: ADDN (n = 14,624, median age 15.7 years, 51% male); DPV (n = 62,732, 16.1 years, 53.3% male); and T1DX-QI (n = 22,942, 17.1 years, 52.1% male). In the DPV registry, BMI SDS in children and adolescents and BMI in adults increased consistently between 2018 and 2021 (p < 0.001). In ADDN and T1DX-QI, variable changes in BMI and BMI SDS were seen in adults and young people. Close to 50% of people in all registries were either overweight or obese. Proportions overweight remained relatively stable across the 4 years. The proportion of obesity increased in children 5 to <10 years. Conclusions: A slight increase in BMI and BMI SDS observed before the pandemic continued during the pandemic years. The proportion of overweight and obesity was overall high. Healthy weight remains a priority for people with T1D.

Demographic, Clinical, Management, and Outcome Characteristics of 8,004 Young Children With Type 1 Diabetes.

To compare demographic, clinical, and therapeutic characteristics of children with type 1 diabetes age <6 years across three international registries: Diabetes Prospective Follow-Up Registry (DPV; Europe), T1D Exchange Quality Improvement Network (T1DX-QI; U.S.), and Australasian Diabetes Data Network (ADDN; Australasia). An analysis was conducted comparing 2019-2021 prospective registry data from 8,004 children. Mean ± SD ages at diabetes diagnosis were 3.2 ± 1.4 (DPV and ADDN) and 3.7 ± 1.8 years (T1DX-QI). Mean ± SD diabetes durations were 1.4 ± 1.3 (DPV), 1.4 ± 1.6 (T1DX-QI), and 1.5 ± 1.3 years (ADDN). BMI z scores were in the overweight range in 36.2% (DPV), 41.8% (T1DX-QI), and 50.0% (ADDN) of participants. Mean ± SD HbA1c varied among registries: DPV 7.3 ± 0.9% (56 ± 10 mmol/mol), T1DX-QI 8.0 ± 1.4% (64 ± 16 mmol/mol), and ADDN 7.7 ± 1.2% (61 ± 13 mmol/mol). Overall, 37.5% of children achieved the target HbA1c of <7.0% (53 mmol/mol): 43.6% in DPV, 25.5% in T1DX-QI, and 27.5% in ADDN. Use of diabetes technologies such as insulin pump (DPV 86.6%, T1DX 46.6%, and ADDN 39.2%) and continuous glucose monitoring (CGM; DPV 85.1%, T1DX-QI 57.6%, and ADDN 70.5%) varied among registries. Use of hybrid closed-loop (HCL) systems was uncommon (from 0.5% [ADDN] to 6.9% [DPV]). Across three major registries, more than half of children age <6 years did not achieve the target HbA1c of <7.0% (53 mmol/mol). CGM was used by most participants, whereas insulin pump use varied across registries, and HCL system use was rare. The differences seen in glycemia and use of diabetes technologies among registries require further investigation to determine potential contributing factors and areas to target to improve the care of this vulnerable group.

Low-density lipoprotein cholesterol in adolescents and young adults with type 1 diabetes: Data from the Australasian Diabetes Data Network registry.

To determine low-density lipoprotein cholesterol (LDL-C) screening frequency and levels, and factors associated with elevated LDL-C, in Australasian youth with type 1 diabetes (T1D). Data were extracted from the Australasian Diabetes Data Network (ADDN), a prospective clinical quality registry, on all T1D healthcare visits attended by young people aged 16-25 years (with T1D duration of >1 year) between January 2011 and December 2020. The primary outcomes were elevated LDL-C > 2.6 mmol/L (100 mg/dL) and threshold for treatment: >3.4 mmol/L (130 mg/dL), according to consensus guidelines. Multivariable Generalised Estimated Equations (GEE) were used to examine factors associated with elevated LDL-C across all visits. A cohort of 6338 young people (52.6% men) were identified, of whom 1603 (25.3%) had ≥1 LDL-C measurement documented. At last measurement, mean age, age at T1D diagnosis and T1D duration were 18.3 ± 2.4, 8.8 ± 4.5 and 8.9 ± 4.8 years, respectively. LDL-C was elevated in 737 (46.0%) and at the treatment threshold in 250 (15.6%). In multivariable GEE elevated LDL-C continuously was associated with older age (OR = 0.07; 0.01-0.13, p = 0.02), female sex (OR = 0.31; 0.18-0.43; p < 0.001), higher HbA1c (OR = 0.04; 0.01-0.08; p = 0.01) and having an elevated BMI (OR = 0.17, 0.06-0.39, p < 0.001). LDL-C screening and levels are suboptimal in this cohort, increasing future cardiovascular complication risk. There is an urgent need to understand how healthcare services can support improved screening and management of dyslipidaemia in this population.

Coexisiting type 1 diabetes and celiac disease is associated with lower Hba1c when compared to type 1 diabetes alone: data from the Australasian Diabetes Data Network (ADDN) registry

AimTo compare HbA1c and clinical outcomes in adolescents and young adults with type 1 diabetes (T1D), with or without celiac disease (CD).MethodsLongitudinal data were extracted from ADDN, a prospective clinical diabetes registry. Inclusion criteria were T1D (with or without CD), ≥ 1 HbA1c measurement, age 16–25 years and diabetes duration ≥ 1 year at last measurement. Multivariable Generalised Estimated Equation models were used for longitudinal analysis of variables associated with HbA1c.ResultsAcross all measurements, those with coexisting T1D and CD had lower HbA1c when compared to those with T1D alone (8.5 ± 1.5% (69.4 ± 16.8 mmol/mol) vs. 8.7 ± 1.8% (71.4 ± 19.8 mmol/mol); p < 0.001); lower HbA1c was associated with shorter diabetes duration (B = − 0.06; 95% CI − 0.07 to − 0.05; p < 0.001), male sex (B = − 0.24; − 0.36 to − 0.11; p < 0.001), insulin pump therapy use (B = − 0.46; − 0.58 to − 0.34; p < 0.001), coexistence of T1D and CD (B = − 0.28; − 0.48 to − 0.07; p = 0.01), blood pressure (B = − 0.16; − 0.23 to − 0.09; p < 0.001) and body mass index (B = -− 0.03; − 0.02 to − 0.04; p = 0.01) in the normal range. At last measurement, 11.7% of the total population had a HbA1c < 7.0% (53.0 mmol/mol).ConclusionsAcross all measurements, coexisting T1D and CD is associated with lower HbA1c when compared to T1D alone. However, HbA1c is above target in both groups.

Blood pressure in adolescents and young adults with type 1 diabetes: data from the Australasian Diabetes Data Network registry

AimHypertension increases complication risk in type 1 diabetes (T1D). We examined blood pressure (BP) in adolescents and young adults with T1D from the Australasian Diabetes Data Network, a prospective clinical diabetes registry in Australia and New Zealand.MethodsThis was a longitudinal study of prospectively collected registry data. Inclusion criteria: T1D (duration ≥ 1 year) and age 16–25 years at last visit (2011–2020). Hypertension was defined as (on ≥ 3 occasions) systolic BP and/or diastolic BP > 95th percentile for age < 18 years, and systolic BP > 130 and/or diastolic BP > 80 mmHg for age ≥ 18 years. Multivariable Generalised Estimating Equations were used to examine demographic and clinical factors associated with BP in the hypertensive range across all visits.ResultsData from 6338 young people (male 52.6%) attending 24 participating centres across 36,655 T1D healthcare visits were included; 2812 (44.4%) had BP recorded at last visit. Across all visits, 19.4% of youth aged < 18 years and 21.7% of those aged ≥ 18 years met criteria for hypertension. In both age groups, BP in the hypertensive range was associated with male sex, injection (vs. pump) therapy, higher HbA1c, and higher body mass index.ConclusionsThere is a high proportion of adolescents and young adults reported with BP persistently in hypertensive ranges. Findings flag the additive contribution of hypertension to the well-established body of evidence indicating a need to review healthcare models for adolescents and young adults with T1D.

A GDPR-Compliant Dynamic Consent Mobile Application for the Australasian Type-1 Diabetes Data Network.

Australia has a high prevalence of diabetes, with approximately 1.2 million Australians diagnosed with the disease. In 2012, the Australasian Diabetes Data Network (ADDN) was established with funding from the Juvenile Diabetes Research Foundation (JDRF). ADDN is a national diabetes registry which captures longitudinal information about patients with type-1 diabetes (T1D). Currently, the ADDN data are directly contributed from 42 paediatric and 17 adult diabetes centres across Australia and New Zealand, i.e., where the data are pre-existing in hospital systems and not manually entered into ADDN. The historical data in ADDN have been de-identified, and patients are initially afforded the opportunity to opt-out of being involved in the registry; however, moving forward, there is an increased demand from the clinical research community to utilise fully identifying data. This raises additional demands on the registry in terms of security, privacy, and the nature of patient consent. General Data Protection Regulation (GDPR) is an increasingly important mechanism allowing individuals to have the right to know about their health data and what those data are being used for. This paper presents a mobile application being designed to support the ADDN data collection and usage processes and aligning them with GDPR. The app utilises Dynamic Consent-an informed specific consent model, which allows participants to view and modify their research-driven consent decisions through an interactive interface. It focuses specifically on supporting dynamic opt-in consent to both the registry and to associated sub-projects requesting access to and use of the patient data for research purposes.

Diabetic Ketoacidosis at Onset of Type 1 Diabetes and Long-term HbA1c in 7,961 Children and Young Adults in the Australasian Diabetes Data Network.

The relationship between diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes and long-term glycemic control varies between studies. We aimed, firstly, to characterize the association of DKA and its severity with long-term HbA1c in a large contemporary cohort, and secondly, to identify other independent determinants of long-term HbA1c. Participants were 7,961 children and young adults diagnosed with type 1 diabetes by age 30 years from 2000 to 2019 and followed prospectively in the Australasian Diabetes Data Network (ADDN) until 31 December 2020. Linear mixed-effect models related variables to HbA1c. DKA at diagnosis was present in 2,647 participants (33.2%). Over a median 5.6 (interquartile range 3.2, 9.4) years of follow-up, participants with severe, but not moderate or mild, DKA at diagnosis had a higher mean HbA1c (+0.23%, 95% CI 0.11,0.28; [+2.5 mmol/mol, 95% CI 1.4,3.6]; P < 0.001) compared with those without DKA. Use of continuous subcutaneous insulin infusion (CSII) was independently associated with a lower HbA1c (-0.28%, 95% CI -0.31, -0.25; [-3.1 mmol/mol, 95% CI -3.4, -2.8]; P < 0.001) than multiple daily injections, and CSII use interacted with severe DKA to lower predicted HbA1c. Indigenous status was associated with higher HbA1c (+1.37%, 95% CI 1.15, 1.59; [+15.0 mmol/mol, 95% CI 12.6, 17.4]; P < 0.001), as was residing in postcodes of lower socioeconomic status (most vs. least disadvantaged quintile +0.43%, 95% CI 0.34, 0.52; [+4.7 mmol/mol, 95% CI 3.4, 5.6]; P < 0.001). Severe, but not mild or moderate, DKA at diagnosis was associated with a marginally higher HbA1c over time, an effect that was modified by use of CSII. Indigenous status and lower socioeconomic status were independently associated with higher long-term HbA1c.

Nationally Subsidized Continuous Glucose Monitoring: A Cost-effectiveness Analysis.

The Continuous Glucose Monitoring (CGM) Initiative recently introduced universal subsidized CGM funding for people with type 1 diabetes under 21 years of age in Australia. We thus aimed to evaluate the cost-effectiveness of this CGM Initiative based on national implementation data and project the economic impact of extending the subsidy to all age-groups. We used a patient-level Markov model to simulate disease progression for young people with type 1 diabetes and compared government-subsidized access to CGM with the previous user-funded system. Three years of real-world clinical input data were sourced from analysis of the Australasian Diabetes Data Network and National Diabetes Services Scheme registries. Costs were considered from the Australian health care system's perspective. An annual discount rate of 5% was applied to future costs and outcomes. Uncertainty was evaluated with probabilistic and deterministic sensitivity analyses. Government-subsidized CGM funding for young people with type 1 diabetes compared with a completely user-funded model resulted in an incremental cost-effectiveness ratio (ICER) of AUD 39,518 per quality-adjusted life-year (QALY) gained. Most simulations (85%) were below the commonly accepted willingness-to-pay threshold of AUD 50,000 per QALY gained in Australia. Sensitivity analyses indicated that base-case results were robust, though strongly impacted by the cost of CGM devices. Extending the CGM Initiative throughout adulthood resulted in an ICER of AUD 34,890 per QALY gained. Providing subsidized access to CGM for people with type 1 diabetes was found to be cost-effective compared with a completely user-funded model in Australia.

Suboptimal glycemic control in adolescents and young adults with type 1 diabetes from 2011 to 2020 across Australia and New Zealand: Data from the Australasian Diabetes Data Network registry.

Competing challenges in adolescence and young adulthood can distract from optimal type 1 diabetes (T1D) self-management, and increase risks of premature morbidity and mortality. There are limited data mapping the glycemic control of people with T1D in this age group, across Australasia. Clinical data were extracted from the Australasian Diabetes Data Network, a prospective clinical diabetes registry. Inclusion criteria were individuals with T1D aged 16-25 years at their last recorded T1D healthcare visit (from 1st January 2011 to 31st December 2020), with T1D duration of at least 1 year. Data were stratified by two last recorded T1D healthcare visit ranges, while generalized estimated equation (GEE) modeling was used to examine factors associated with HbA1c across visits during the 10 year period. Data from 6329 young people (52.6% male) attending 24 diabetes centers across Australasia were included. At the last visit within the most recent 5 years, mean ± SD age was 18.5 ± 2.3 years, T1D duration was 8.8 ± 4.7 years and HbA1c was 8.8 ± 1.8% (72.2 ± 19.9 mmol/mol); only 12.3% had an HbA1c below the international target of <7.0% (53 mmol/mol). Across all T1D healthcare visits, in GEE modeling, higher HbA1c was associated with female sex (B=0.20; 95% CI 0.12 to 0.29, p < 0.001), longer T1D duration (B=0.04, 0.03 to 0.05, p < 0.001). Lower HbA1c was associated with attendance at a pediatric T1D healthcare setting (B=-0.33, -0.45 to -0.21, p < 0.001) and use of CSII versus BD/MDI therapy (B=-0.49, -0.59 to 0.40, p < 0.001). This Australasian study demonstrates widespread and persistent sub-optimal glycemic control in young people with T1D, highlighting the urgent need to better understand how healthcare services can support improved glycemic control in this population.

269-OR: No BMI Increase during the COVID-Pandemic in Children and Adults with T1D in Three Continents: Joint Analysis of ADDN, T1DX, and DPV Registries

The COVID-pandemic necessitated worldwide lockdowns in 2020, with restrictions on physical activity and changes in eating habits. We investigated temporal trends in BMI in three international T1D registries between 2018-2020. Data were extracted from DPV (Germany/Austria/Luxembourg/Switzerland) , T1D Exchange (T1DX, US) , and Australasian Diabetes Data Network (ADDN) . The period affected by the COVID-pandemic was defined as Mar to Dec 2020 and compared with the 9-mth periods in 2018 and 2019. Median age and median BMI were aggregated for each individual per year. Mean BMI was aggregated by year, sex, and age groups (stratified by 2 yearly intervals to age 20 years, and yearly until age 60. Study population: ADDN (n=29,875, mean age 21.9 years, 51% male) ; DPV (n=118,580, 21.2 years, 53% male) ; T1DX (n=47,122, 21.3 years, 51% male) . BMI stratified by age group and sex are shown in the figure. There were no apparent BMI differences across the three years. Females aged 30-40 years and 40-50 years in ADDN only demonstrated a slightly higher BMI in 2020. Conclusion: At a population level, mean BMI was not different before and during the pandemic in people with T1D. In some individuals, a healthier lifestyle or more regulated nutritional intake may have counteracted potential adipogenic effects of lockdown measures. Disclosure K.Narayan: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. R.W.Holl: None. M.E.Craig: None. M.Auzanneau: None. N.Noor: None. S.Rompicherla: None. O.Ebekozien: None. S.Glastras: None. C.E.Smart: None. S.Bachmann: None. R.Welp: None.

440-P: Suboptimal Blood Pressure in Adolescents and Young Adults with Type 1 Diabetes: Data from the Australasian Diabetes Data Network (ADDN) Registry

Hypertension increases complication risk in type 1 diabetes (T1D) . We examined blood pressure (BP) in young people with T1D from ADDN, a prospective clinical diabetes registry in Australia and New Zealand. Inclusion criteria: T1D (duration ≥1 year) and age 16-25 years at last visit (2011-2020) . Hypertension was defined as (on ≥3 occasions) systolic BP (SBP) and/or diastolic BP (DBP) &amp;gt;95th percentile for age &amp;lt;18 years, and SBP &amp;gt;130 and/or DBP &amp;gt;80 mmHg for age ≥18 years. Data from 6,338 young people (male 52.6%) attending 24 participating centres across 36,655 T1D healthcare visits were included; 2,812 (44.4%) had BP measured at last visit (Table 1) . Across all visits, 19.4% of youth aged &amp;lt;18 years and 21.7% of those aged ≥18 years met criteria for hypertension. Using multivariable Generalised Estimating Equations that included all visits, BP in the hypertensive range was associated with: male gender (B=0.36; 95% CI 0.01-0.24, p&amp;lt;0.001) , injection vs. pump therapy (B=0.16; 0.05-0.28, p&amp;lt;0.001) , higher HbA1c (B=0.10; 0.12-0.70, p&amp;lt;0.001) and overweight/obesity (B=0.56; 0.45-0.67, p&amp;lt;0.001) . We demonstrate that hypertension is common in young people with T1D, highlighting the importance of monitoring and treating BP in this at-risk population. Disclosure S.James: None. L.Perry: None. J.Lowe: None. P.G.Colman: None. M.E.Craig: None. Addn study group: n/a.

Glycaemic outcomes in Australasian children and adults with type 1 diabetes: failure to meet targets across the age spectrum.

The goal of therapy in type 1 diabetes (T1D) is to achieve optimal glycaemic targets and reduce complications. Robust data representing glycaemic outcomes across the lifespan are lacking in Australasia. To examine contemporary glycaemic outcomes and rate of use of diabetes technologies in Australasian people with T1D. Cross-sectional analysis of de-identified data from 18 diabetes centres maintained in the Australasian Diabetes Data Network registry during 2019. Glycaemia was measured using glycated haemoglobin (HbA1c). The proportion of people with T1D achieving the international HbA1c target of <53 mmol/mol (7%) was calculated. Rates of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) use were determined. A total of 7988 individuals with T1D with 30 575 visits were recorded in the registry. The median (interquartile range) age was 15.3 (10.0) years and diabetes duration was 5.7 (9.4) years with 49% on multiple daily injections (MDI) and 36% on CSII. The mean HbA1c for the whole cohort was 66 mmol/mol (8.2%). HbA1c increased with age, from 60 mmol/mol (7.6%) in children <10 years, increasing during adolescence and peaking at 73 mmol/mol (8.8%) in the 20-25 years age group. The HbA1c target of <53 mmol/mol (7%) was met in 18% of children and 13% of adults. HbA1c was lower on CSII as compared with those on MDI (P < 0.0001). Only a minority of children and adults achieve the recommended glycaemic goals despite access to specialist care in major diabetes centres. There is a need to identify factors that improve glycaemic outcomes.

A collaborative comparison of international pediatric diabetes registries.

An estimated 1.1 million children and adolescents aged under 20 years have type 1 diabetes worldwide. Principal investigators from seven well-established longitudinal pediatric diabetes registries and the SWEET initiative have come together to provide an international collaborative perspective and comparison of the registries. Information and data including registry characteristics, pediatric participant clinical characteristics, data availability and data completeness from the Australasian Diabetes Data Network (ADDN), Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids), Diabetes prospective follow-up registry (DPV), Norwegian Childhood Diabetes Registry (NCDR), National Paediatric Diabetes Audit (NPDA), Swedish Childhood Diabetes Registry (Swediabkids), T1D Exchange Quality Improvement Collaborative (T1DX-QI), and the SWEET initiative was extracted up until 31 December 2020. The seven diabetes registries and the SWEET initiative collectively show data of more than 900 centers and around 100,000 pediatric patients, the majority with type 1 diabetes. All share the common objectives of monitoring treatment and longitudinal outcomes, promoting quality improvement and equality in diabetes care and enabling clinical research. All generate regular benchmark reports. Main differences were observed in the definition of the pediatric population, the inclusion of adults, documentation of CGM metrics and collection of raw data files as well as linkage to other data sources. The open benchmarking and access to regularly updated data may prove to be the most important contribution from registries. This study describes aspects of the registries to enable future collaborations and to encourage the development of new registries where they do not exist.

Benchmarking care outcomes for young adults with type 1 diabetes in Australia after transition to adult care.

AimTo determine advantages conferred by a youth‐specific transition clinic model for young adults with type 1 diabetes (T1D) at Westmead Hospital (WH) as compared with Australian registry data.MethodsProspectively collected data included age, diabetes duration, visit frequency, post code, BMI, mode of insulin delivery, continuous glucose monitoring, HbA1c, albumin creatinine ratio, BP, retinopathy and diabetic ketoacidosis (DKA) for all WH T1D clinic attendees aged 16–25 between January 2017 and June 2018 (n = 269). Results were compared with data collected during the same time period from 2 separate Australian data registries, one longitudinal (Australasian Diabetes Data Network, ADDN) and one a spot survey (the Australian National Diabetes Audit, ANDA).ResultsAcross the three cohorts, HbA1c was similar (respectively, WH, ADDN, ANDA; 8.7%[72mmol/mol], 8.7%[72mmol/mol], 8.5%[69mmol/mol]) and HbA1c was significantly higher in young adults <21 years (8.7–8.9%[73‐75mmol/mol]) as compared with ≥21 years (8.5%[69mmol/mol], p < .002). In the WH cohort, median interval between visits was shorter than in ADDN (4.5 vs. 9.0 months) and DKA was lower (respectively, 3.6 and 9.2/100 patient years; p < .001).ConclusionsWhile suboptimal HbA1c was recorded in all centres, the WH model of care saw increased attendance and reduced admissions with DKA as compared with other Australian adult centres.

Hemoglobin A1c Patterns of Youth With Type 1 Diabetes 10 Years Post Diagnosis From 3 Continents.

Distinct hemoglobin A1c (HbA1c) trajectories during puberty are identified in youth with established type 1 diabetes (T1D). We used data from 3 international registries to evaluate whether distinct HbA1c trajectories occur from T1D onset. Participants were <18 years old at diagnosis with at least 1 HbA1c measured within 12 months post diagnosis, along with ≥3 duration-year-aggregated HbA1c values over 10 years of follow-up. Participants from the Australasian Diabetes Data Network (n = 7292), the German-Austrian-Luxembourgian-Swiss diabetes prospective follow-up initiative (Diabetes Patienten Verlaufsdokumentation) (n = 39 226) and the US-based Type 1 Diabetes Exchange Clinic Registry (n = 3704) were included. With group-based trajectory modeling, we identified unique HbA1c patterns from the onset of T1D. Five distinct trajectories occurred in all 3 registries, with similar patterns of proportions by group. More than 50% had stable HbA1c categorized as being either low stable or intermediate stable. Conversely, ∼15% in each registry were characterized by stable HbA1c >8.0% (high stable), and ∼11% had values that began at or near the target but then increased (target increase). Only ∼5% of youth were above the target from diagnosis, with an increasing HbA1c trajectory over time (high increase). This group differed from others, with higher rates of minority status and an older age at diagnosis across all 3 registries (P ≤ .001). Similar postdiagnostic HbA1c patterns were observed across 3 international registries. Identifying the youth at the greatest risk for deterioration in HbA1c over time may allow clinicians to intervene early, and more aggressively, to avert increasing HbA1c.

Type 2 diabetes in children and adolescents across Australia and New Zealand: A 6-year audit from The Australasian Diabetes Data Network (ADDN).

To assess the clinical and demographic characteristics of children and adolescents across Australia and New Zealand (NZ) with type 2 diabetes. We performed a descriptive audit of data prospectively reported to the Australasian Diabetes Data Network (ADDN) registry. Data were collected from six tertiary pediatric diabetes centers across Australia (New South Wales, Queensland, South Australia, Western Australia, and Victoria) and NZ (Auckland). Children and adolescents diagnosed with type 2 diabetes aged ≤ 18 years with data reported to ADDN between 2012 and 2017 were included. Age, sex, ethnicity, HbA1c, blood pressure, BMI, waist circumference and lipid profile at first visit were assessed. There were 269 cases of type 2 diabetes in youth reported to ADDN between 2012 and 2017. The most common ethnicities were Indigenous Australian in 56/243 (23%) and NZ Maori or Pacifica in 47 (19%). Median age at diagnosis was 13.7 years and 94% of participants were overweight or obese. Indigenous Australian and Maori/Pacifica children were younger at diagnosis compared with nonindigenous children: median 13.3 years (indigenous Australian); 13.1 years (Maori/Pacifica); 14.1 years (nonindigenous), p = 0.005. HbA1c was higher in indigenous Australian (9.4%) and Maori/Pacifica youth (7.8%) compared with nonindigenous (6.7%) p < 0.001. BMI-SDS was higher in Maori/Pacifica youth (2.3) compared with indigenous Australian (2.1) and nonindigenous (2.2) p = 0.011. Indigenous Australian and Maori/Pacifica youth in ADDN were younger and had worse glycaemic control at diagnosis of type 2 diabetes. Our findings underscore the need to consider targeted and earlier screening in these "high-risk" populations.

Determinants of Cardiovascular Risk in 7000 Youth With Type 1 Diabetes in the Australasian Diabetes Data Network.

Cardiovascular disease occurs prematurely in type 1 diabetes. The additional risk of overweight is not well characterized. The primary aim was to measure the impact of body mass index (BMI) in youth with type 1 diabetes on cardiovascular risk factors. The secondary aim was to identify other determinants of cardiovascular risk. Observational longitudinal study of 7061 youth with type 1 diabetes followed for median 7.3 (interquartile range [IQR] 4-11) years over 41 (IQR 29-56) visits until March 2019. 15 tertiary care diabetes centers in the Australasian Diabetes Data Network.Participants were aged 2 to 25 years at baseline, with at least 2 measurements of BMI and blood pressure. Standardized systolic and diastolic blood pressure scores and non-high-density lipoprotein (HDL) cholesterol were co-primary outcomes. Urinary albumin/creatinine ratio was the secondary outcome. BMI z-score related independently to standardized blood pressure z- scores and non-HDL cholesterol. An increase in 1 BMI z-score related to an average increase in systolic/diastolic blood pressure of 3.8/1.4 mmHg and an increase in non-HDL cholesterol (coefficient + 0.16 mmol/L, 95% confidence interval [CI], 0.13-0.18; P < 0.001) and in low-density lipoprotein (LDL) cholesterol. Females had higher blood pressure z-scores, higher non-HDL and LDL cholesterol, and higher urinary albumin/creatinine than males. Indigenous youth had markedly higher urinary albumin/creatinine (coefficient + 2.15 mg/mmol, 95% CI, 1.27-3.03; P < 0.001) and higher non-HDL cholesterol than non-Indigenous youth. Continuous subcutaneous insulin infusion was associated independently with lower non-HDL cholesterol and lower urinary albumin/creatinine. BMI had a modest independent effect on cardiovascular risk. Females and Indigenous Australians in particular had a more adverse risk profile.

1580-P: Hemoglobin A1c (HbA1c) Trajectories of Youth with Type 1 Diabetes (T1D) over 10 Years Postdiagnosis from Three Continents

Distinct HbA1c trajectories have previously been identified in youth with established T1D. We used data from 3 international registries to evaluate whether distinct HbA1c trajectories occur from T1D onset. Participants were &amp;lt;18 yrs old at diagnosis and had at least 1 HbA1c measured within 12 months post-diagnosis, along with ≥3 duration-year aggregated HbA1c values over 10 yrs of follow-up. 7,292 participants from the Australasian Diabetes Data Network, 39,226 from the German/Austrian Diabetes Prospective Follow-up, and 3,704 from the U.S. T1D Exchange were included. Group-based modelling identified unique HbA1c trajectories from onset of T1D. Five distinct trajectories occurred in all 3 registries (Figure), with similar patterns of proportion by group. Over 50% had stable HbA1c in or near target. Conversely, ∼15% in each registry were characterized by stable HbA1c &amp;gt;8.0%, and ∼11% had values that began in or near target but then increased. Only ∼5% of youth were suboptimal from diagnosis with an increasing HbA1c. This group differed from others with higher rates of minority status and older age at diagnosis across all 3 registries (p≤0.001). We observed similar post-diagnostic HbA1c patterns across 3 international registries. Identifying youth at greatest risk for deterioration in HbA1c over time may allow clinicians to intervene early to avert increasing HbA1c. Disclosure J. Sherr: Advisory Panel; Self; Bigfoot Biomedical, Cecelia Health. Consultant; Self; Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Sanofi, T1D Exchange. A. Schwandt: None. H. Phelan: None. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. R.W. Holl: None. P.Z. Benitez-Aguirre: None. K. Miller: None. J. Woelfle: Advisory Panel; Self; Novo Nordisk A/S. T. Dover: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. E. Fröhlich-Reiterer: None. M.E. Craig: None. Funding German Centre for Diabetes Research (FKZ82DZD01402); German Diabetes Association; European Foundation for the Study of Diabetes; INNODIA; The Leona M. and Harry B. Helmsley Charitable Trust

Five heterogeneous HbA1c trajectories from childhood to adulthood in youth with type 1 diabetes from three different continents: A group-based modeling approach.

Only a fraction of youth meet established targets for glycemic control; many experience deteriorating control over time. We compared trajectories of hemoglobin A1c (HbA1c) among youth from three trans-continental type 1 diabetes (T1D) registries and identified clinical variables associated with the odds of following increasing vs stable trajectories. Analyses included longitudinal data from 15 897 individuals age 8 to 18 with T1D for at least 2 years and HbA1c measurements in at least 5 years during the observation period. Cohorts were selected from Australasian Diabetes Data Network (ADDN; Australia), German/Austrian/Luxembourgian Diabetes-Patienten-Verlaufsdokumentation initiative (DPV; Germany/Austria/Luxembourga), and the T1D Exchange Clinic Network (T1DX; US) clinic registries. Group-based trajectory modeling and multivariable logistic regression identified unique HbA1c trajectories and their predictors. Five heterogeneous trajectories of glycemic control in each registry were identified: low, intermediate, high stable; intermediate and high increasing. The overall HbA1c level for each trajectory group tended to be lowest in the DPV, higher in the ADDN, and highest in the T1DX. The absolute level of HbA1c and the proportion of individuals within each trajectory varied across registries: 17% to 22% of individuals followed an increasing trajectory. Compared with maintaining a stable trajectory, following an increasing trajectory was significantly associated with ethnic minority status, lower height z-score, higher BMI z-score, insulin injection therapy, and the occurrence of severe hypoglycemia; however, these factors were not consistent across the three registries. We report the first multinational registry-based comparison of glycemic control trajectories among youth with T1D from three continents and identify possible targets for intervention in those at risk of an increasing HbA1c trajectory.

BMI Z-Score Trajectories in Youth with T1D—An International Comparison

Obesity in youth with type 1 diabetes (T1D) has emerged as a challenge in care. Data from the Australasian Diabetes Data Network (ADDN), Diabetes Prospective Follow-up (DPV) in Austria and Germany, and T1D Exchange (T1DX) registry in the U.S. were compared to assess international differences in BMI z-score trajectories in youth with T1D. Longitudinal data from 11,513 participants between ages 8-17 years with T1D duration ≥1 year. and ≥5 aggregated body mass index (BMI) values (ADDN: N=1073, 46% female, mean HbA1c 8.3%; DPV: N=8722, 46% female, mean HbA1c 7.8%; T1DX: N=1718, 45% female, mean HbA1c 8.6%) were analyzed. Participants diagnosed with celiac and/or thyroid disease were excluded. Latent class growth modeling by Nagin was used to identify subgroups following similar BMI z-score trajectories. Five distinct trajectories were identified in ADDN and T1DX participants, and six in DPV (Figure). In all registries most participants in ADDN and T1DX maintained weight status over time. However, obese (BMI z-score ≥2) participants from ADDN achieved weight loss over time, compared to obese participants in T1DX, whose BMI SD increased during the teen years. In DPV, there were trajectories indicating dramatic weight gain/loss during puberty. Further analysis by sex and minority status will help to understand the within-registry and between-registry variation and may help target clinical interventions to achieve healthy weight in youth with T1D. Disclosure M. Wu: None. H. Phelan: None. A. Schwandt: None. N.C. Foster: None. J. Couper: None. C. Steigleder-Schweiger: None. S.M. Willi: Advisory Panel; Self; Boehringer Ingelheim GmbH. Other Relationship; Self; Caladrius Biosciences, Inc.. Consultant; Self; GlaxoSmithKline plc., JAEB Center For Health Research. T. Jones: None. P. Kroschwald: None. D.M. Maahs: Advisory Panel; Self; Insulet Corporation. Consultant; Self; Abbott. Research Support; Self; Medtronic, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Roche Diabetes Care Health and Digital Solutions. N. Prinz: None. M.E. Craig: None.