To assess the prevalence and risk factors for early and severe diabetic retinopathy and macular edema in a large cohort of patients with type 2 diabetes Retinopathy grading (any retinopathy, severe ...retinopathy, diabetic macular edema) and risk factors of 64784 were prospectively recorded between January 2000 and March 2013 and analyzed by Kaplan-Meier analysis and logistic regression. Retinopathy was present in 20.12% of subjects, maculopathy was found in 0.77%. HbA1c > 8%, microalbuminuria, hypertension, BMI > 35 kg/m2 and male sex were significantly associated with any retinopathy, while HbA1c and micro- and macroalbuminuria were the strongest risk predictors for severe retinopathy. Presence of macroalbuminuria increased the risk for DME by 177%. Retinopathy remains a significant clinical problem in patients with type 2 diabetes. Metabolic control and blood pressure are relevant factors amenable to treatment. Concomitant kidney disease identifies high risk patients and should be emphasized in interdisciplinary communication.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study uses diabetes registry data to compare the frequency of diabetic ketoacidosis (DKA) in children and adolescents at time of type 1 diabetes diagnosis in Germany during the first 2 months of ...the coronavirus disease 2019 (COVID-19) pandemic vs the same time period in 2018 and 2019.
OBJECTIVE
As diabetes technology use in youth increases worldwide, inequalities in access may exacerbate disparities in hemoglobin A1c (HbA1c). We hypothesized that an increasing gap in diabetes ...technology use by socioeconomic status (SES) would be associated with increased HbA1c disparities.
RESEARCH DESIGN AND METHODS
Participants aged <18 years with diabetes duration ≥1 year in the Type 1 Diabetes Exchange (T1DX, U.S., n = 16,457) and Diabetes Prospective Follow-up (DPV, Germany, n = 39,836) registries were categorized into lowest (Q1) to highest (Q5) SES quintiles. Multiple regression analyses compared the relationship of SES quintiles with diabetes technology use and HbA1c from 2010–2012 to 2016–2018.
RESULTS
HbA1c was higher in participants with lower SES (in 2010–2012 and 2016–2018, respectively: 8.0% and 7.8% in Q1 and 7.6% and 7.5% in Q5 for DPV; 9.0% and 9.3% in Q1 and 7.8% and 8.0% in Q5 for T1DX). For DPV, the association between SES and HbA1c did not change between the two time periods, whereas for T1DX, disparities in HbA1c by SES increased significantly (P < 0.001). After adjusting for technology use, results for DPV did not change, whereas the increase in T1DX was no longer significant.
CONCLUSIONS
Although causal conclusions cannot be drawn, diabetes technology use is lowest and HbA1c is highest in those of the lowest SES quintile in the T1DX, and this difference for HbA1c broadened in the past decade. Associations of SES with technology use and HbA1c were weaker in the DPV registry.
IMPORTANCE: Insulin pump therapy may improve metabolic control in young patients with type 1 diabetes, but the association with short-term diabetes complications is unclear. OBJECTIVE: To determine ...whether rates of severe hypoglycemia and diabetic ketoacidosis are lower with insulin pump therapy compared with insulin injection therapy in children, adolescents, and young adults with type 1 diabetes. DESIGN, SETTING, AND PARTICIPANTS: Population-based cohort study conducted between January 2011 and December 2015 in 446 diabetes centers participating in the Diabetes Prospective Follow-up Initiative in Germany, Austria, and Luxembourg. Patients with type 1 diabetes younger than 20 years and diabetes duration of more than 1 year were identified. Propensity score matching and inverse probability of treatment weighting analyses with age, sex, diabetes duration, migration background (defined as place of birth outside of Germany or Austria), body mass index, and glycated hemoglobin as covariates were used to account for relevant confounders. EXPOSURES: Type 1 diabetes treated with insulin pump therapy or with multiple (≥4) daily insulin injections. MAIN OUTCOMES AND MEASURES: Primary outcomes were rates of severe hypoglycemia and diabetic ketoacidosis during the most recent treatment year. Secondary outcomes included glycated hemoglobin levels, insulin dose, and body mass index. RESULTS: Of 30 579 patients (mean age, 14.1 years SD, 4.0; 53% male), 14 119 used pump therapy (median duration, 3.7 years) and 16 460 used insulin injections (median duration, 3.6 years). Patients using pump therapy (n = 9814) were matched with 9814 patients using injection therapy. Pump therapy, compared with injection therapy, was associated with lower rates of severe hypoglycemia (9.55 vs 13.97 per 100 patient-years; difference, −4.42 95% CI, −6.15 to −2.69; P < .001) and diabetic ketoacidosis (3.64 vs 4.26 per 100 patient-years; difference, −0.63 95% CI, −1.24 to −0.02; P = .04). Glycated hemoglobin levels were lower with pump therapy than with injection therapy (8.04% vs 8.22%; difference, −0.18 95% CI, −0.22 to −0.13, P < .001). Total daily insulin doses were lower for pump therapy compared with injection therapy (0.84 U/kg vs 0.98 U/kg; difference, −0.14 −0.15 to −0.13, P < .001). There was no significant difference in body mass index between both treatment regimens. Similar results were obtained after propensity score inverse probability of treatment weighting analyses in the entire cohort. CONCLUSIONS AND RELEVANCE: Among young patients with type 1 diabetes, insulin pump therapy, compared with insulin injection therapy, was associated with lower risks of severe hypoglycemia and diabetic ketoacidosis and with better glycemic control during the most recent year of therapy. These findings provide evidence for improved clinical outcomes associated with insulin pump therapy compared with injection therapy in children, adolescents, and young adults with type 1 diabetes.
The aim of this study was to investigate the incidence of type 1 diabetes in children and adolescents during the coronavirus disease 2019 (COVID-19) pandemic in Germany compared with previous years.
...Based on data from the multicenter German Diabetes Prospective Follow-up Registry, we analyzed the incidence of type 1 diabetes per 100,000 patient-years in children and adolescents from 1 January 2020 through 30 June 2021. Using Poisson regression models, expected incidences for 2020/21 were estimated based on the data from 2011 to 2019 and compared with observed incidences in 2020/21 by estimating incidence rate ratios (IRRs) with 95% CIs.
From 1 January 2020 to 30 June 2021, 5,162 children and adolescents with new-onset type 1 diabetes in Germany were registered. The observed incidence in 2020/21 was significantly higher than the expected incidence (24.4 95% CI 23.6-25.2 vs. 21.2 20.5-21.9; IRR 1.15 1.10-1.20; P < 0.001). IRRs were significantly elevated in June 2020 (IRR 1.43 1.07-1.90; P = 0.003), July 2020 (IRR 1.48 1.12-1.96; P < 0.001), March 2021 (IRR 1.29 1.01-1.65; P = 0.028), and June 2021 (IRR 1.39 1.04-1.85; P = 0.010).
A significant increase in the incidence of type 1 diabetes in children was observed during the COVID-19 pandemic, with a delay in the peak incidence of type 1 diabetes by ∼3 months after the peak COVID-19 incidence and also after pandemic containment measures. The underlying causes are yet unknown. However, indirect rather than direct effects of the pandemic are more likely to be the cause.
Context:
Knowing the changes of cardiovascular risk factors (CRFs) in relation to weight loss would be helpful to advise overweight children and their parents and to decide whether drugs should be ...prescribed in addition to lifestyle intervention.
Objective:
The objective of the study was to determine the body mass index (BMI)-SD score (SDS) reduction to improve CRFs in overweight children.
Design:
This was a prospective observation study.
Setting:
The study was conducted at a specialized outpatient obesity clinic.
Patients:
A total of 1388 overweight children (mean BMI 27.9 ± 0.1 kg/m2, mean age 11.4 ± 0.1 y, 43.8% male, 45.5% prepubertal) participated in the study.
Intervention:
The study included a 1-year lifestyle intervention.
Main Outcome Measures:
We studied changes of blood pressure (BP), fasting high-density lipoprotein- and low-density lipoprotein-cholesterol, triglycerides, glucose, and homeostasis model assessment (HOMA) of insulin resistance index. Change of weight status was determined by δBMI-SDS based on the recommended percentiles of the International Task Force of Obesity.
Results:
BMI-SDS change was associated with a significant improvement of all CRFs except fasting glucose and low-density lipoprotein-cholesterol after adjusting for multiple confounders such as baseline CRFs, age, gender, BMI, pubertal stage, and its changes. BMI-SDS reduction of 0.25–0.5 was related to a decrease of systolic blood pressure (BP) (−3.2 ± 1.4 mm Hg), diastolic BP (−2.2 ± 1.1 mm Hg), triglycerides (−6.9 ± 5.8 mg/dL), HOMA (−0.5 ± 0.3), and triglyceride/high-density lipoprotein)-cholesterol (−0.3 ± 0.2), whereas high-density lipoprotein (HDL)-cholesterol increased (+1.3 ± 1.2 mg/dL). A reduction of greater than 0.5 BMI-SDS led to more pronounced improvement (systolic BP −6.0± 1.3 mm Hg, diastolic BP −5.1 ± 1.3 mm Hg, triglycerides −16.4 ± 7.1 mg/dL, HDL-cholesterol +1.6 ± 1.5 mg/dL, HOMA −0.9 ± 0.3). Per 0.1 BMI-SDS reduction in systolic BP (−1.0 mm Hg), diastolic BP (−0.8 mm Hg), triglycerides (−2.3 mg/dL), HOMA (−0.2), and triglyceride/HDL-cholesterol (−0.5) decreased significantly, whereas HDL-cholesterol (0.2 mg/dL) increased significantly in linear regression analyses and accounted for multiple confounders.
Conclusions:
A BMI-SDS reduction of 0.25 or greater significantly improved hypertension, hypertriglyceridemia, and low HDL-cholesterol, whereas a BMI-SDS greater than 0.5 doubled the effect.
A BMI-SDS reduction of 0.25 which is approximately a stable BMI over a 1y period improves the cardiovascular risk profile in obese children.
Severe hypoglycemia is a major complication of insulin treatment in patients with type 1 diabetes, limiting full realization of glycemic control. It has been shown in the past that low levels of ...hemoglobin A1c (HbA1c), a marker of average plasma glucose, predict a high risk of severe hypoglycemia, but it is uncertain whether this association still exists. Based on advances in diabetes technology and pharmacotherapy, we hypothesized that the inverse association between severe hypoglycemia and HbA1c has decreased in recent years.
We analyzed data of 37,539 patients with type 1 diabetes (mean age ± standard deviation 14.4 ± 3.8 y, range 1-20 y) from the DPV (Diabetes Patienten Verlaufsdokumentation) Initiative diabetes cohort prospectively documented between January 1, 1995, and December 31, 2012. The DPV cohort covers an estimated proportion of >80% of all pediatric diabetes patients in Germany and Austria. Associations of severe hypoglycemia, hypoglycemic coma, and HbA1c levels were assessed by multivariable regression analysis. From 1995 to 2012, the relative risk (RR) for severe hypoglycemia and coma per 1% HbA1c decrease declined from 1.28 (95% CI 1.19-1.37) to 1.05 (1.00-1.09) and from 1.39 (1.23-1.56) to 1.01 (0.93-1.10), respectively, corresponding to a risk reduction of 1.2% (95% CI 0.6-1.7, p<0.001) and 1.9% (0.8-2.9, p<0.001) each year, respectively. Risk reduction of severe hypoglycemia and coma was strongest in patients with HbA1c levels of 6.0%-6.9% (RR 0.96 and 0.90 each year) and 7.0%-7.9% (RR 0.96 and 0.89 each year). From 1995 to 2012, glucose monitoring frequency and the use of insulin analogs and insulin pumps increased (p<0.001). Our study was not designed to investigate the effects of different treatment modalities on hypoglycemia risk. Limitations are that associations between diabetes education and physical activity and severe hypoglycemia were not addressed in this study.
The previously strong association of low HbA1c with severe hypoglycemia and coma in young individuals with type 1 diabetes has substantially decreased in the last decade, allowing achievement of near-normal glycemic control in these patients. Please see later in the article for the Editors' Summary.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Digital health technology, especially digital and health applications ("apps"), have been developing rapidly to help people manage their diabetes. Numerous health-related apps provided on smartphones ...and other wireless devices are available to support people with diabetes who need to adopt either lifestyle interventions or medication adjustments in response to glucose-monitoring data. However, regulations and guidelines have not caught up with the burgeoning field to standardize how mobile health apps are reviewed and monitored for patient safety and clinical validity. The available evidence on the safety and effectiveness of mobile health apps, especially for diabetes, remains limited. The European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) have therefore conducted a joint review of the current landscape of available diabetes digital health technology (only stand-alone diabetes apps, as opposed to those that are integral to a regulated medical device, such as insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems) and practices of regulatory authorities and organizations. We found that, across the U.S. and Europe, mobile apps intended to manage health and wellness are largely unregulated unless they meet the definition of medical devices for therapeutic and/or diagnostic purposes. International organizations, including the International Medical Device Regulators Forum and the World Health Organization, have made strides in classifying different types of digital health technology and integrating digital health technology into the field of medical devices. As the diabetes digital health field continues to develop and become more fully integrated into everyday life, we wish to ensure that it is based on the best evidence for safety and efficacy. As a result, we bring to light several issues that the diabetes community, including regulatory authorities, policy makers, professional organizations, researchers, people with diabetes, and health care professionals, needs to address to ensure that diabetes health technology can meet its full potential. These issues range from inadequate evidence on app accuracy and clinical validity to lack of training provision, poor interoperability and standardization, and insufficient data security. We conclude with a series of recommended actions to resolve some of these shortcomings.
Context:
The concept of metabolic healthy obese (MHO) status has been proposed also for children. However, it is unclear whether this is a stable status in childhood.
Objective:
The aim was to ...analyze the changes of MHO status over time.
Design and Setting:
This is 1-year longitudinal analysis of our obesity cohort.
Participants:
All obese children of our outpatient obesity clinic with 1-year follow-up were included.
Interventions:
Standard care intervention was used.
Main outcome measures:
We examined body mass index (BMI), waist circumference, pubertal stage, blood pressure, fasting lipids, glucose, and insulin resistance index homeostasis model assessment (HOMA). MHO status was defined by absence of cardiovascular risk factors.
Results:
A total of 2017 obese children (mean age, 11.6 ± 2.8 y; 45% male; BMI, 28.5 ± 5.3 kg/m2; BMI-z score, 2.4 ±0.5) were enrolled onto the study, and 49.3% of the children were MHO at baseline. After 1 year, the majority of the MHO remained MHO (68.0%). MHO children were significantly younger, more frequently prepubertal, and less overweight compared with metabolic unhealthy obese (MUO) children (all P < .05). In the longitudinal analyses, entering into puberty (OR, 1.9; 95% confidence interval, 1.3–2.8; P = .004) doubled the risk for switching from MHO to MUO, whereas changing from mid to late puberty nearly tripled the likelihood for switching from MUO to MHO (OR 3.1 2.1–4.5, P < .001) in multiple logistic regression analyses adjusted for age, sex, and changes of body mass index standard deviation score (BMI-SDS).
Conclusions:
MHO is a stable status in childhood obesity as long as pubertal status remains stable. Due to the strong association between puberty and MUO status, the concept of MHO is questionable, at least in pubertal children.
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