Aim
To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm.
Materials and Methods
Data from adults and ...children with type 1 diabetes and more than two diabetes‐related visits were analysed 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 less than 7.3 during a hospitalization event.
Results
Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q‐Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6‐10 years; age 11‐15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast‐acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the 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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 diabetes mellitus (T1DM) that results from absolute insulin deficiency and is marked by acidosis, ketosis, and hyperglycemia ...(1). Therefore, prevention of DKA is one goal in T1DM care, but recent data indicate increased incidence (2). For adult patients, only limited data are available on rates and risk factors for development of DKA, and this complication remains epidemiologically poorly characterized. The Diabetes Prospective Follow-up Registry (DPV) has followed patients with diabetes from 1995. Data for this study were collected from 2000 to 2016. Inclusion criteria were diagnosis of T1DM, age at diabetes onset ≥6 months, patient age at follow-up ≥18 years, and diabetes duration ≥1 year to exclude DKA at manifestation. DKA was defined as serum pH <7.3, and rates of DKA were analyzed based on a Poisson regression model accounting for overdisperson stratified by sex, age, diabetes duration, HbA1c, treatment regimen, size of diabetes center, and migration background, i.e., whether the patient or one or both parents were born outside the countries of the registry. A diabetes center that treated ≥50 adult patients with T1DM in the year 2016 was considered large.
Aim
To examine the time trends and factors associated with the onset of puberty in children with type 1 diabetes (T1D) using data from the German Diabetes Prospective Follow‐up ...(Diabetes‐Patienten‐Verlaufsdokumentation DPV) registry.
Methods
A total of 13 127 children with T1D, aged 6 to 18 years, were included in the analysis. Regression analysis was performed to investigate the relationship between diabetes duration, body mass index (BMI) standard deviation score (SDS), glycated haemoglobin (HbA1c) level, migration background, and the onset of puberty, stratified by sex.
Results
Our findings revealed a significant trend towards earlier puberty in both girls and boys with T1D over the observed period (2000 to 2021). Puberty onset in girls (thelarche Tanner stage B2) decreased from 11.48 (11.35‐11.65) years in 2000 to 10.93 (10.79‐11.08) years in 2021 and gonadarche (Tanner stage G2/testicular volume >3 mL) decreased from 12.62 (12.42‐12.82) years in 2000 to 11.98 (11.79‐12.16) years in 2021 in boys (both P < 0.001). Longer diabetes duration, higher BMI SDS, and lower HbA1c level were associated with earlier puberty in both sexes (P < 0.001).
Conclusions
Our study highlights earlier puberty in children with T1D, influenced by BMI SDS, HbA1c level, and migration background. This has important implications for diabetes management and supporting healthy development. Further research is needed to understand the underlying mechanisms and develop potential interventions for this vulnerable population.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
To assess the prevalence of cardiovascular risk factors (CVRFs) in children and adolescents with type 1 diabetes (T1D) in the International SWEET registry and the possible role of clinical variables ...in modifying the risk of having single or multiple CVRFs.
The study is a cross-sectional study. Cut-off points for CVRFs were fixed according to International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines and WHO parameters: LDL cholesterol (LDL-C) > 100 mg/dL; Systolic Blood Pressure (BP-SDS) > 90th percentile for sex, age, and height; BMI-SDS > 2SD for sex and age. Logistic regression models were applied to evaluate variables associated with at least 1 or 2 CVRFs among registry children and adolescents.
29,649 individuals with T1D (6–18 years, T1D ≥ 2 years) participating in the SWEET prospective multicenter diabetes registry were included. In the cohort, 41 % had one or more CVRFs, and 10 % had two or more CVRFs. Thirty-five percent of enrolled individuals had LDL-C > 100 mg/dL, 26 % had BMI-SDS > 2SD, and 17 % had Systolic BP-SDS > 90th percentile. Females had higher frequency than males of having 1 or 2 CVRFs (45.1 % vs 37.4 %, 11.8 % vs 7.8 %; p < 0.001). Multivariable logistic regression models showed that sex (female), HbA1c category (>7.0 %), and age (>10 years) were associated with a higher chance of having at least 1 or 2 CVRFs (p < 0.001).
In children and adolescents with T1D, female sex, in addition to HbA1c above 7 %, and older age (>10 years) was associated with a higher risk of having at least a CVRF (LDL-C, BMI-SDS, BP) according to internationally defined cut-offs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To evaluate access to screening tools for monogenic diabetes in paediatric diabetes centres across the world and its impact on diagnosis and clinical outcomes of children and youth with genetic forms ...of diabetes.
79 centres from the SWEET diabetes registry including 53,207 children with diabetes participated in a survey on accessibility and use of diabetes related antibodies, c-peptide and genetic testing.
73, 63 and 62 participating centres had access to c-peptide, antibody and genetic testing, respectively. Access to antibody testing was associated with higher proportion of patients with rare forms of diabetes identified with monogenic diabetes (54 % versus 17 %, p = 0.01), lower average whole clinic HbA1c (7.7Q1,Q2: 7.3–8.0%/6156–64mmol/mol versus 9.28.6–10.0%/7770–86mmol/mol, p < 0.001) and younger age at onset (8.3 7.3–8.8 versus 9.7 8.6–12.7 years p < 0.001). Additional access to c-peptide or genetic testing was not related to differences in age at onset or HbA1c outcome.
Clinical suspicion and antibody testing are related to identification of different types of diabetes. Implementing access to comprehensive antibody screening may provide important information for selecting individuals for further genetic evaluation. In addition, worse overall clinical outcomes in centers with limited diagnostic capabilities indicate they may also need support for individualized diabetes management.
Trial Registration: NCT04427189.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Advances in pediatric type 1 diabetes (T1D) management in the last 10 years have led to improvements in glycemic control. Our objective was to compare T1D treatment regimen and glycemic outcomes over ...a 10-year period, from 8 diabetes registries and the SWEET initiative. Registries included ADDN, ČENDA, DanDiabKids, DPV, NCDR, NPDA, Swediabkids, T1DX-QI and SWEET. Investigators compared data from 2013 to 2022. Children aged < 18 years with T1D duration ≥ 3 months were included. For each registry, demographics, HbA1c data, insulin regimen, diabetes ketoacidosis (DKA) and severe hypoglycemia rates were collected. Join point regression analysis was used to study significant breakpoints in temporal trends. Data were available for 109,494 children from the national registries and 35,590 from SWEET. Mean age between registries was similar and stable over time. Mean HbA1c decreased on average from 66.4 mmol/mol in 2013 to 59.3 mmol/mol in 2022, improving in all registries (Fig. 1). Insulin pump use varied widely but increased in all registries (on average 42.9% 2013 to 62.2% in 2022) (Fig. 1). A decreasing trend of DKA and severe hypoglycemia was observed in most registries over time. Glycemic control improved in children with T1D in all registries over the last 10 years. Use of technology has increased dramatically over this time although with significant differences between registries. Disclosure A. Zimmermann: None. S. Lanzinger: None. T. Skrivarhaug: None. J. Svensson: Speaker's Bureau; Novo Nordisk A/S. Stock/Shareholder; Novo Nordisk. Consultant; Medtronic. Research Support; Medtronic. Speaker's Bureau; Sanofi, Rubin Medical. M.E. Craig: None. S. Rompicherla: None. D.M. Maahs: Advisory Panel; Medtronic. Consultant; Abbott, LifeScan Diabetes Institute, Sanofi, Provention Bio, Inc., Bayer Inc., Kriya Therapeutics, BioSpex. O. Ebekozien: Advisory Panel; Sanofi, Medtronic. Research Support; Vertex Pharmaceuticals Incorporated. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; Medtronic, Lilly Diabetes, Abbott, Dexcom, Inc. V. Neuman: None. O. Cinek: None. A.G. Ranjan: None. R.W. Holl: None. H. Robinson: None. J. Warner: None. K. Akesson: Speaker's Bureau; Novo Nordisk. M. Witsch: None. N.A. Lund-Blix: None. H. Bratke: Speaker's Bureau; Novo Nordisk. Z. Sumnik: None. S. Kummernes: None. S.R. Johnson: None. M. Madsen: Other Relationship; Novo Nordisk A/S. K. Eeg-Olofsson: Other Relationship; Abbott, Eli Lilly and Company, Novo Nordisk, Sanofi. S. Pons Perez: None. G.T. Alonso: Advisory Panel; MannKind Corporation. A. Thorén: None.
Objective
To study worldwide differences in childhood diabetes, comparing relevant indicators among five regions within the SWEET initiative.
Subjects
We investigated 26 726 individuals with type 1 ...diabetes (T1D) from 54 centers in the European region; 7768 individuals from 30 centers in the Asia/Middle East/Africa region; 2642 people from five centers in Australia/New Zealand; 10 839 individuals from seven centers in North America, and 1114 patients from five centers in South America.
Methods
The SWEET database was analyzed based on the following inclusion criteria: T1D, time period 2015‐2019, and age < 21 years, with analysis of the most recent documented year of therapy. For the statistical analysis, we used multivariable linear and logistic regression models to adjust for age (<6 years, 6‐ < 12 years, 12‐ < 18 years, 18‐ < 21 years), gender, and duration of diabetes (<2 years, 2‐ < 5 years, 5‐ < 10 years, ≥10 years).
Results
Adjusted HbA1c means ranged from 7.8% (95%‐confidence interval: 7.6‐8.1) in Europe to 9.5% (9.2‐9.8) in Asia/Middle East/Africa. Mean daily insulin dose ranged from 0.8 units/kg in Europe (0.7‐0.8) and Australia/New Zealand (0.6‐0.9) to 1.0 unit/kg 0.9‐1.1) in Asia/Middle East/Africa. Percentage of pump use was highest in North America (80.7% 79.8‐81.6) and lowest in South America (4.2% 3.2‐5.6). Significant differences between the five regions were also observed with regards to body mass index SD scores, frequency of blood glucose monitoring and presence of severe hypoglycaemia.
Conclusions
We found significant heterogeneity in diabetes care and outcomes across the five regions. The aim of optimal care for each child remains a challenge.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
To investigate the psychosocial burden during the COVID-19 pandemic in adolescents with type 1 diabetes and its association with metabolic control.
Prospective multicenter observational cohort study ...based on data from the German Diabetes Prospective Follow-up Registry. Adolescents aged 12–20 years with type 1 diabetes were asked during routine follow-up visits to complete a questionnaire on psychosocial distress and daily use of electronic media during the COVID-19 pandemic from June 2021 to November 2022. Well-being, anxiety, and depression symptoms were assessed using World Health Organization Five Well-Being Index (WHO-5), General Anxiety Disorder scale 7 (GAD-7), and Patient Health Questionnaire-9 questionnaires. The impact of mental health symptoms on metabolic control was analyzed by using multivariable linear regression models adjusted for sex, diabetes duration, treatment, socioeconomic deprivation, and immigrant background.
Six hundred eighty eight adolescents (45.6% females) from 20 diabetes centers participated. Compared with a prepandemic cohort, WHO-5 scores were lower during the COVID-19 pandemic (estimated mean difference −9.6 95% confidence interval -11.6; −7.6, p < .001), but GAD-7 scores were not different (estimated mean difference 0.6 95% confidence interval -0.2; 1.5, p = .14). HbA1c was significantly positively associated with GAD-7 and Patient Health Questionnaire-9 and negatively associated with WHO-5 scores (all p < .001). Daily electronic media use was positively associated with adjusted mental health symptoms (all p < .01).
Although the overall well-being of adolescents with type 1 diabetes was reduced during the later phase of the COVID-19 pandemic, the additional psychological burden was relatively low. However, mental health symptoms were associated with poorer metabolic control and higher use of electronic media.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The COVID-19 pandemic led to significant changes in global care for pediatric type 1 diabetes.•We present diabetes outcomes in >40,000 youths with T1D from the international SWEET registry before ...and during the pandemic.•We evaluated trends in HbA1c and severe adverse events across a 4-year observation period.•Changes in HbA1c and acute diabetes complications were consistent with shifts in provision of diabetes care.•There was a positive impact of transition to telemedicine on glycemic outcomes regardless of the increase in technology use.
This study aimed to provide a global insight into initiatives in type 1 diabetes care driven by the COVID-19 pandemic and associations with glycemic outcomes.
An online questionnaire regarding diabetes care before and during the pandemic was sent to all centers (n = 97, 66,985 youth with type 1 diabetes) active in the SWEET registry. Eighty-two responded, and 70 (42,798 youth with type 1 diabetes) had available data (from individuals with type 1 diabetes duration >3 months, aged ≤21 years) for all 4 years from 2018 to 2021. Statistical models were adjusted, among others, for technology use.
Sixty-five centers provided telemedicine during COVID-19. Among those centers naive to telemedicine before the pandemic (n = 22), four continued only face-to-face visits. Centers that transitioned partially to telemedicine (n = 32) showed a steady increase in HbA1c between 2018 and 2021 (p < 0.001). Those that transitioned mainly to telemedicine (n = 33 %) improved HbA1c in 2021 compared to 2018 (p < 0.001).
Changes to models of care delivery driven by the pandemic showed significant associations with HbA1c shortly after the pandemic outbreak and 2 years of follow-up. The association appeared independent of the concomitant increase in technology use among youth with type 1 diabetes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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