Is COVID-19 to Blame? Trends of Incidence and Sex Ratio in Youth-Onset Type 2 Diabetes in Germany

Trends in the incidence of type 1 diabetes and type 2 diabetes in children and adolescents in North Rhine-Westphalia, Germany, from 2002 to 2022

AIMS

The objective of this study was to assess overall and segmented trends in the incidence of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) in children and adolescents younger than 20 years, from 2002 to 2022.

METHODS

This study used registry data on physician-diagnosed T1DM or T2DM from primary and secondary sources, covering the German federal state of North Rhine-Westphalia with 18 million inhabitants. The ages at T1DM and T2DM onset ranged from 0 to 19 and 10-19 years, respectively. The main outcomes were direct age- and/or sex-standardized incidence rates per 100,000 person-years (PYs) and trends estimated as annual percentage changes (APCs), both with 95 % confidence intervals. The segmented trends for subperiods were based on joinpoint regression models.

RESULTS

From 2002-2022, 17,470 and 819 persons had incident T1DM and T2DM, respectively. The total number of PYs was 73,743,982 for T1DM and 39,210,453 for T2DM, with a mean coverage rate of 98 % for T1DM and 90 % for T2DM. The standardized T1DM incidence increased from 17.6 [16.3;18.9} in 2002 to 33.2 [31.3;35.1] in 2022, with an APC of 2.7 % [2.3 %;3.1 %]. The standardized T2DM incidence increased from 1.3 [0.8;1.7] in 2002 to 2.8 [2.0;3.6] in 2022, with an APC of 6.4 % [4.9 %;8.0 %]. There were four different segmented trends for T1DM and T2DM, with the incidence peaking in 2021 and subsequently declining.

CONCLUSIONS

The incidence rates of T1DM and T2DM have increased over the past 20 years, with a wave-like pattern during the Covid-19 pandemic.

Insulin Secretion Defect in Children and Adolescents with Obesity: Clinical and Molecular Genetic Characterization

Introduction

Childhood obesity is increasing worldwide and presents as a global health issue due to multiple metabolic comorbidities. About 1% of adolescents with obesity develop type 2 diabetes (T2D); however, little is known about the genetic and pathophysiological background at young age. The objective of this study was to assess the prevalence of impaired glucose regulation (IGR) in a large cohort of children and adolescents with obesity and to characterize insulin sensitivity and insulin secretion. We also wanted to investigate adolescents with insulin secretion disorder more closely and analyze possible candidate genes of diabetes in a subcohort.

Methods

We included children and adolescents with obesity who completed an oral glucose tolerance test (OGTT, glucose + insulin) in the outpatient clinic. We calculated Matsuda index, the area under the curve (AUC (Ins/Glu)), and an oral disposition index (ISSI-2) to estimate insulin resistance and beta-cell function. We identified patients with IGR and low insulin secretion (maximum insulin during OGTT < 200 mU/l) and tested a subgroup using next generation sequencing to identify possible mutations in 103 candidate genes.

Results

The total group consisted of 903 children and adolescents with obesity. 4.5% showed impaired fasting glucose, 9.4% impaired glucose tolerance, and 1.2% T2D. Matsuda index and Total AUC (Ins/Glu) showed a hyperbolic relationship. Out of 39 patients with low insulin secretion, we performed genetic testing on 12 patients. We found five monogenetic defects (ABCC8 (n = 3), GCK (n = 1), and GLI2/PTF1A (n = 1)).

Conclusion

Using surrogate parameters of beta-cell function and insulin resistance can help identify patients with insulin secretion disorder. A prevalence of 40% mutations of known diabetes genes in the subgroup with low insulin secretion suggests that at least 1.7% of patients with adolescent obesity have monogenic diabetes. A successful molecular genetic diagnosis can help to improve individual therapy.

Incidence of type 2 diabetes before and during the COVID-19 pandemic in Naples, Italy: a longitudinal cohort study

Background

The association of COVID-19 with the development of new-onset diabetes has been recently investigated by several groups, yielding controversial results. Population studies currently available in the literature are mostly focused on type 1 diabetes (T1D), comparing patients with a SARS-CoV-2 positive test to individuals without COVID-19, especially in paediatric populations. In this study, we sought to determine the incidence of type 2 diabetes (T2D) before and during the COVID-19 pandemic.

Methods

In this longitudinal cohort study, we analysed a cohort followed up over a 6-year period using an Interrupted Time Series approach, i.e. 3-years before and 3-years during the COVID-19 pandemic. We analysed data obtained from >200,000 adults in Naples (Italy) from January 1st 2017 to December 31st 2022. In this manner, we had the opportunity to compare the incidence of newly diagnosed T2D before (2017-2019) and during (2020-2022) the COVID-19 pandemic. The key inclusion criteria were age >18-year-old and data availability for the period of observation; patients with a diagnosis of diabetes obtained before 2017 were excluded. The main outcome of the study was the new diagnosis of T2D, as defined by the International Classification of Diseases 10 (ICD-X), including prescription of antidiabetic therapies for more than 30 days.

Findings

A total of 234,956 subjects were followed-up for at least 3-years before or 3-years during the COVID-19 pandemic and were included in the study; among these, 216,498 were analysed in the pre-pandemic years and 216,422 in the pandemic years. The incidence rate of T2D was 4.85 (95% CI, 4.68-5.02) per 1000 person-years in the period 2017-2019, vs 12.21 (95% CI, 11.94-12.48) per 1000 person-years in 2020-2022, with an increase of about twice and a half. Moreover, the doubling time of the number of new diagnoses of T2D estimated by unadjusted Poisson model was 97.12 (95% CI, 40.51-153.75) months in the prepandemic period vs 23.13 (95% CI, 16.02-41.59) months during the COVID-19 pandemic. Interestingly, these findings were also confirmed when examining patients with prediabetes.

Interpretation

Our data from this 6-year study on more than 200,000 adult participants indicate that the incidence of T2D was significantly higher during the pandemic compared to the pre-COVID-19 phase. As a consequence, the epidemiology of the disease may change in terms of rates of outcomes as well as public health costs. COVID-19 survivors, especially patients with prediabetes, may require specific clinical programs to prevent T2D.

Funding

The US National Institutes of Health (NIH: NIDDK, NHLBI, NCATS), Diabetes Action Research and Education Foundation, Weill-Caulier and Hirschl Trusts.

Bioinformatics and system biology analysis revealed the crosstalk between COVID-19 and osteoarthritis

BACKGROUND

The global coronavirus disease 2019 (COVID-19) outbreak has significantly impacted public health. Moreover, there has been an association between the incidence and severity of osteoarthritis (OA) and the onset of COVID-19. However, the optimal diagnosis and treatment strategies for patients with both diseases remain uncertain. Bioinformatics is a novel approach that may help find the common pathology between COVID-19 and OA.

METHODS

Differentially expressed genes (DEGs) were screened by R package "limma." Functional enrichment analyses were performed to find key biological functions. Protein-protein interaction (PPI) network was constructed by STRING database and then Cytoscape was used to select hub genes. External data sets and OA mouse model validated and identified the hub genes in both mRNA and protein levels. Related transcriptional factors (TF) and microRNAs (miRNAs) were predicted with miRTarBase and JASPR database. Candidate drugs were obtained from Drug Signatures database. The immune infiltration levels of COVID-19 and OA were evaluated by CIBERSORT and scRNA-seq.

RESULTS

A total of 74 common DEGs were identified between COVID-19 and OA. Receiver operating characteristic curves validated the effective diagnostic values (area under curve > 0.7) of four hub genes (matrix metalloproteinases 9, ATF3, CCL4, and RELA) in both the training and validation data sets of COVID-19 and OA. Quantitative polymerase chain reaction and Western Blot showed significantly higher hub gene expression in OA mice than in healthy controls. A total of 84 miRNAs and 28 TFs were identified to regulate the process of hub gene expression. The top 10 potential drugs were screened including "Simvastatin," "Hydrocortisone," and "Troglitazone" which have been proven by Food and Drug Administration. Correlated with hub gene expression, Macrophage M0 was highly expressed while Natural killer cells and Mast cells were low in both COVID-19 and OA.

CONCLUSION

Four hub genes, disease-related miRNAs, TFs, drugs, and immune infiltration help to understand the pathogenesis and perform further studies, providing a potential therapy target for COVID-19 and OA.