The WHO-5 well-being questionnaire in type 1 diabetes: screening for depression in pediatric and young adult subjects.

OBJECTIVES: To evaluate the WHO-5 tool in pediatric and young adult subjects with type 1 diabetes, and to analyse associations with demographic/psychological characteristics. METHODS: We included 944 patients with type 1 diabetes 9-25 years of age, documented in the Diabetes Patient Follow-up Registry between 2018 and 2021. We used ROC curve analysis to determine optimal cut-off values for the WHO-5 scores to predict psychiatric comorbidity (ICD-10-diagnoses) and analysed associations with obesity, HbA1c, therapy regimen, and lifestyle via logistic regression. All models were adjusted for age, sex, and diabetes duration. RESULTS: In the total cohort (54.8% male), the median score was 17 [Q1-Q3: 13-20]. Adjusted for age, sex, and diabetes duration, the WHO-5 scores<13 were associated with psychiatric comorbidity, especially depression and ADHD, poor metabolic control, obesity, smoking, and less physical activity. There were no significant associations with therapy regimen, hypertension, dyslipidemia, or social deprivation. In subjects with any diagnosed psychiatric disorder (prevalence 12.2%), the odds ratio for conspicuous scores was 3.28 [2.16-4.97] compared to patients without mental disorders. Using ROC analysis, the optimal cut-off to anticipate any psychiatric comorbidity in our cohort was 15, and 14 for depression. CONCLUSIONS: The WHO-5 questionnaire is a useful tool to predict depression in adolescents with type 1 diabetes. ROC analysis suggests a slightly higher cut-off for conspicuous questionnaire results compared to previous reports. Due to the high rate of deviant results, adolescents and young adults with type-1 diabetes should be screened regularly for signs of psychiatric comorbidity.

Type 1 diabetes mellitus and SARS-CoV-2 in pediatric and adult patients - Data from the DPV network.

BACKGROUND: Data on patients with type 1 diabetes mellitus (T1DM) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are sparse. This study aimed to investigate the association between SARS-CoV-2 infection and T1DM. METHODS: Data from the Prospective Diabetes Follow-up (DPV) Registry were analyzed for diabetes patients tested for SARS-CoV-2 by polymerase chain reaction (PCR) in Germany, Austria, Switzerland, and Luxembourg during January 2020-June 2021, using Wilcoxon rank-sum and chi-square tests for continuous and dichotomous variables, adjusted for multiple testing. RESULTS: Data analysis of 1855 pediatric T1DM patients revealed no differences between asymptomatic/symptomatic infected and SARS-CoV-2 negative/positive patients regarding age, new-onset diabetes, diabetes duration, and body mass index. Glycated hemoglobin A1c (HbA1c) and diabetic ketoacidosis (DKA) rate were not elevated in SARS-CoV-2-positive vs. -negative patients. The COVID-19 manifestation index was 37.5% in individuals with known T1DM, but 57.1% in individuals with new-onset diabetes. 68.8% of positively tested patients were managed as outpatients/telemedically. Data analysis of 240 adult T1MD patients revealed no differences between positively and negatively tested patients except lower HbA1c. Of these patients, 83.3% had symptomatic infections; 35.7% of positively tested patients were hospitalized. CONCLUSIONS: Our results indicate low morbidity in SARS-CoV-2-infected pediatric T1DM patients. Most patients with known T1DM and SARS-CoV-2 infections could be managed as outpatients. However, SARS-CoV-2 infection was usually symptomatic if it coincided with new-onset diabetes. In adult patients, symptomatic SARS-CoV-2 infection and hospitalization were associated with age.

Coeliac disease is associated with depression in children and young adults with type 1 diabetes: results from a multicentre diabetes registry.

AIMS: To analyse the association between coeliac disease (CD) and depression in children, adolescents, and young adults with type 1 diabetes (T1D). METHODS: We included 79,067 T1D patients aged 6-20 years, with at least six months of diabetes duration, and treatment data between 1995 and 2019 were documented in the diabetes patient follow-up registry. We categorized patients into four groups: T1D only (n = 73,699), T1 + CD (n = 3379), T1D + depression (n = 1877), or T1D + CD + depression (n = 112). RESULTS: CD and depression were significantly associated (adjusted OR: 1.25 [1.03-1.53]). Females were more frequent in both the depression and the CD group compared with the T1D only group. Insulin pumps were used more frequently in T1D + CD and T1D + depression compared with T1D only (both p < .001). HbA1c was higher in T1D + depression (9.0% [8.9-9.0]), T1D + CD + depression (8.9% [8.6-9.2]), both compared with T1D only (8.2% [8.2-8.2], all p < .001). We found comorbid autism, attention deficit hyperactivity disorder, anxiety, schizophrenia, and eating disorders more frequently in the T1D + CD + depression group compared with T1D only (all p < .001). CONCLUSIONS: CD and depression are associated in young T1D patients. The double load of T1D and CD may lead to an increased risk for depression. Depression was associated with additional psychological and neurological comorbidities. Aside from imperative CD screening after T1D diagnosis and regular intervals, depression screening might be helpful in routine care, especially in patients with diagnosed CD.

Type 1 diabetes and epilepsy in childhood and adolescence: Do glutamic acid decarboxylase autoantibodies play a role? Data from the German/Austrian/Swiss/Luxembourgian DPV Registry.

AIMS: We aimed to analyze the relationship between epilepsy and glutamic acid decarboxylase autoantibodies (GADA) in patients with type 1 diabetes mellitus (T1DM) and the impact of GADA on demographic, clinical, and metabolic data in T1DM patients with epilepsy. METHODS: We searched for patients with T1DM ≤20 years and GADA measurements, and within this group for patients with epilepsy. We formed groups: T1DM + Epilepsy + GADA positive; T1DM + Epilepsy + GADA negative; T1DM + GADA positive; T1DM + GADA negative. We used logistic regression to analyze the relationship between epilepsy and GADA with odds ratio adjusted for sex, duration of diabetes (DOD), and age at diabetes onset (ADO). We used logistic regression with odds ratio adjusted for DOD and ADO onset using epilepsy as a dependent variable and GADA, HbA1c, ketoacidosis, severe hypoglycemia (SH), sex, celiac disease, and autoimmune thyroiditis as independent variables. We conducted regression analyses adjusted for sex, DOD, and ADO to analyze differences in clinical/metabolic parameters between the groups. RESULTS: Epilepsy was not more frequent in GADA-positive patients (GPP). Logistic regression including all patients with GADA measurements showed that hypoglycemia with coma (HC) correlated with epilepsy when compared to no SH. We found no differences in clinical and metabolic data between GPP and GADA-negative patients (GNP) with epilepsy. SH occurred more often in GPP with epilepsy in comparison to GPP without epilepsy. GNP with epilepsy had a higher rate of HC than GPP without epilepsy. CONCLUSION: We found no relationship between epilepsy and GADA. A relationship between T1DM and epilepsy might be explainable by SH.