The aim of the study was to explore the metabolic characteristics and outcome parameters in youth with type 1 diabetes and anxiety disorders. HbA1c levels, rates of severe hypoglycemia, diabetic ketoacidosis (DKA), and hospital admission in children, adolescents, and young adults with type 1 diabetes and an anxiety disorder from 431 diabetes‐care‐centers participating in the nationwide German/Austrian/Swiss/Luxembourgian diabetes survey DPV were analyzed and compared with youth without anxiety disorders. Children, adolescents, and young adults with type 1 diabetes and anxiety disorders (n = 1325) had significantly higher HbA1c (8.5% vs. 8.2%), higher rates of DKA (4.2 vs. 2.5 per 100 patient‐years), and higher hospital admission rates (63.6 vs. 40.0 per 100 patient‐years) than youth without anxiety disorders (all p < 0.001). Rates of severe hypoglycemia did not differ. Individuals with anxiety disorders other than needle phobia (n = 771) had higher rates of DKA compared to those without anxiety disorders (4.2 vs. 2.5 per 100 patient‐years, p = 0.003) whereas the rate of DKA in individuals with needle phobia (n = 555) was not significantly different compared to those without anxiety disorders. Children, adolescents, and young adults with anxiety disorders other than needle phobia had higher hospitalization rates (73.7 vs. 51.4 per 100 patient‐years) and more inpatient days (13.2 vs. 10.1 days) compared to those with needle phobia (all p < 0.001). Children, adolescents, and young adults with type 1 diabetes and anxiety disorders had worse glycemic control, higher rates of DKA, and more hospitalizations compared to those without anxiety disorders. Because of the considerable consequences, clinicians should screen for comorbid anxiety disorders in youth with type 1 diabetes.

Background: To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry. Methods: Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test. Results: Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70–180 mg/dL] 54% vs. 49%) and hypoglycemic range 180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged 250 mg/dL, CV and daytime–night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days. Conclusions: In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.

Youths with chronic medical conditions (CMCs) represent a high-risk group for comorbid anxiety, depression, or behavioral problems, with a reported prevalence of 10% to 42%.^1-6 Concomitant psychological concerns compromise therapy for somatic disorders, health-related behavior, medication adherence, and increase long-term health risks into adulthood.^7-11 Early detection of mental health problems and timely initiation of evidence-based treatment is important.^12-17 However, physicians can encounter substantial individual barriers, and referring youths who lack intrinsic motivation to psychological counseling typically fails. Youths tend to underutilize services, fearing stigmatization and preferring autonomy,^18-21 and only a fraction receives adequate treatment.^22-25 Growing evidence exists for the effectiveness of integrated mental health care for pediatric in- and outpatient care. Identification of psychological disorders and referral to adequate treatment options are important factors for therapy uptake.²⁶ Validated tools to detect comorbid mental health problems and brief psychological interventions, such as motivational interviewing (MI) are applicable.²⁷ MI is an evidence-based, collaborative counseling technique, exploring intrinsic motivation and ambivalence.^28,29 It has been shown to facilitate uptake of therapy in suicidal patients³⁰ and the uptake of cognitive behavioral therapy or Internet-based interventions for depression in adolescents.^31-34

Background: The daily demands of type 1 diabetes management may jeopardize adolescents’ mental health. We aimed to assess anxiety and depression symptoms by broad-scale, tablet-based outpatient screening in adolescents with type 1 diabetes in Germany. Methods: Adolescent patients with type 1 diabetes mellitus (n = 2,394; mean age 15.4 y [SD 2.0]; 50.7% male) were screened for anxiety (GAD-7) and depression symptoms (PHQ-9) by self-report questionnaires and linked to clinical data from the DPV patient registry. Logistic regression was used to estimate the contribution of clinical parameters to positive screening results. Results: Altogether, 30.2% showed a positive screening (score ≥ 7 in either test), and 11.3% reported suicidal ideations or self-harm. Patients with anxiety and depression symptoms were older (15.7 y [CI 15.5–15.8] vs. 15.3 y [CI 15.2–15.4]; p < 0.0001), had higher HbA1c levels (7.9% [CI 7.8-8.0] (63 mmol/mol) vs. 7.5% [CI 7.4–7.5] (58 mmol/mol); p < 0.0001), and had higher hospitalization rates. Females (adjusted odds ratio (aOR) 2.66 [CI 2.21–3.19]; p < 0.0001), patients > 15 years (aOR 1.40 [1.16–1.68]; p < 0.001), who were overweight (aOR 1.40 [CI 1.14–1.71]; p = 0.001), with HbA1c > 9% (> 75 mmol/mol; aOR 2.58 [1.83–3.64]; each p < 0.0001), with a migration background (aOR 1.46 [CI 1.17–1.81]; p < 0.001), or smoking (aOR 2.72 [CI 1.41–5.23]; p = 0.003) had a higher risk. Regular exercise was a significant protective factor (aOR 0.65 [CI 0.51–0.82]; p < 0.001). Advanced diabetes technologies did not influence screening outcomes. Conclusions: Electronic mental health screening was implemented in 42 centers in parallel, and outcomes showed an association with clinical parameters from sociodemographic, lifestyle, and diabetes-related data. It should be integrated into holistic patient counseling, enabling early recognition of mild mental health symptoms for preventive measures. Females were disproportionally adversely affected. The use of advanced diabetes technologies did not yet reduce the odds of anxiety and depression symptoms in this cross-sectional assessment.

This cluster-randomised monocentric controlled trial focuses on improving the uptake symptoms of mental health care in adolescents with chronic medical conditions who have been identified by screening to have depression or anxiety. The study aims to determine the efficacy of motivational interviewing (MI) delivered by trained physicians to increase 12- to 20-year-old adolescents’ utilisation of psychological health care for symptoms of anxiety or depression. In this single-centre approach, n = 1,000 adolescents will be screened (using PHQ-9 and GAD-7), and adolescents with results indicative of anxiety or depressive symptoms (n = 162) will be advised to seek psychological health care in clusters from treating physicians in specialised outpatient departments. Participants who screen positive will receive either two sessions of MI or treatment as usual (TAU; regarded as the typical daily clinical practice), which is focused on recommending them to seek psychological health care for further evaluation. MI efficacy will be compared to the current TAU as the control condition. The primary outcome is the utilisation rate of psychological health care after counselling by an MI-trained physician vs. an untrained physician. Additionally, reasons for not claiming psychological support and changes in disease-related parameters will be evaluated in a 6-month follow-up session. This trial will evaluate the feasibility of MI as a way to improve the utilisation of mental health-care services by adolescents who need further support other than that provided by standard care for chronic diseases. Physicians offering MI to adolescents may serve as a model for optimising health-care management in daily clinical practice, which may improve adolescents’ long-term well-being by improving adherence to medical treatment and preventing negative lifelong consequences into adulthood. German Trials Register (DRKS), DRKS00014043 . Registered on 26 April 2018. Düsseldorf University study ID: 2017114504.

The highest genetic type 1 diabetes risk is conferred by HLA class II haplotypes defined by alleles at the HLA-DR and -DQ loci. The combination of HLA-DQA1*03:01 and DQB1*03:02 alleles (summarized as ‘HLA-DQ8′) is reported to be among the two most prevalent HLA class II haplotypes in Caucasian type 1 diabetes patients. This classification is based on conventional genotyping of exon 2 of the DQ gene locus and excludes exon 3. In this study, HLA genotyping on the type 1 diabetes susceptibility loci HLA-DRB1, DQA1 and DQB1 was performed using a high-resolution next generation sequencing method. In addition to the routinely examined exon 2, exon 3 was also sequenced. Samples from 229 children with type 1 diabetes were included and compared to a cohort of 9,786 controls. In addition to previously described HLA-DQ haplotypes in type 1 diabetes patients, we found that as well as HLA-DQA1*03:01,HLA-DQA1*03:03 also contributed to HLA-DQ8. HLA-DQA1*03:03 differs from HLA-DQA1*03:01 by one nucleotide substitution in exon 3 at position 160, leading to a single amino acid replacement. DRB1*04:05 was exclusively associated with DQA1*03:03 whereas the DRB1*04:01 haplotype comprised either DQA1*03:01 or DQA1*03:03. Significantly increased type 1 diabetes risk was confirmed for all these haplotypes with only minor differences between DQA1*03:01 and DQA1*03:03 alleles. This study identified the HLA-DQA1*03:03 allele as an addition to the already known type 1 diabetes risk haplotypes, and can contribute to more precise HLA genotyping approaches.