UNASSIGNED: From the introduction of continuous glucose monitoring (CGM) in treatments of type 1 diabetes, particularly its integration with insulin pumps, there has been a quest for new parameters that describe optimal glycemic control. As of the consensus reached in 2019, the ambulatory glucose profile (AGP) has become the standard, with time in range (TIR) emerging as a fundamental parameter for metabolic control assessment. However, with technological advancements, new parameters, such as the glycemia risk index (GRI), have been introduced and clinically utilized. Therefore, exploring the relationships between traditional and novel parameters to understand metabolic control comprehensively is imperative.
UNASSIGNED: This study was conducted at the Pediatric Clinic of the University Hospital of the Republic of Srpska Banja Luka between January and July 2023. The participants were randomly selected, with the inclusion criteria specifying an age greater than eight years and a diabetes type 1 duration exceeding two years. All participants were required to use a sensor-augmented insulin pump for the next three months (90 days), irrespective of prior use, with the suspend-before-low option activated.
UNASSIGNED: Of the 35 participants, 30 completed the study, 14 (46.7%) of whom were male. The mean age of the subjects was 14.90 ± 2.88 years, and the mean duration of diabetes was 7.83 ± 4.76 years. Over the 90-day period, HbA1c increased to an average of 7.31%. The analysis revealed significant effects of TIR (β=-0.771) and GRI (β=0.651) on HbA1c. Furthermore, GRI and TIR strongly correlated (β=-0.953).
UNASSIGNED: New parameters generated from the ambulatory glucose profile (AGP) can help clinicians create a complete picture of a patient\'s metabolic control in relation to HbA1c levels. Additionally, the GRI is a mathematically tailored parameter that incorporates all components of the ambulatory glucose profile and demonstrates strong correlations with laboratory-measured HbA1c and TIR. The GRI potentially can become a valuable statistical parameter for evaluating and managing patients in routine clinical practice.

Diabetic retinopathy (DR) is a serious complication of diabetes featuring abnormal lipid metabolism. However, the specific lipid molecules associated with onset and progression remain unclear. We used a broad-targeted lipidomics approach to assess the lipid changes that occur before the proliferative retinopathy stage and to identify novel lipid biomarkers to distinguish between patients without DR (NDR) and with non-proliferative DR (NPDR). Targeted lipomics analysis was carried out on serum samples from patients with type I diabetes, including 20 NDRs and 20 NPDRs. The results showed that compared with the NDR group, 102 lipids in the NPDR group showed specific expressions. Four lipid metabolites including TAG58:2-FA18:1 were obtained using the Least Absolute Shrink And Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. The four-lipid combination diagnostic models showed good predictive ability in both the discovery and validation sets, and were able to distinguish between NDR patients and NPDR patients. The identified lipid markers significantly improved diagnostic accuracy within the NPDR group. Our findings help to better understand the complexity and individual differences of DR lipid metabolism.

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70-180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future.

This report describes a case of concomitant diabetic ketoacidosis (DKA) and thyroid storm (TS) in a 20-year-old male patient that presented both diagnostic and management challenges owing to their intricate interrelationship in endocrine-metabolic disorders. The patient, previously diagnosed with type 1 diabetes mellitus (T1DM) and hyperthyroidism, was admitted to the emergency department with symptoms of DKA and progressive exacerbation of TS. Initial treatment focused on correcting DKA; as the disease progressed to TS, it was promptly recognized and treated. This case emphasizes the rarity of simultaneous occurrence of DKA and TS, as well as the challenges in clinical diagnosis posed by the interacting pathophysiological processes and overlapping clinical manifestations of DKA and TS. The patient\'s treatment process involved multiple disciplines, and after treatment, the patient\'s critical condition of both endocrine metabolic diseases was alleviated, after which he recovered and was eventually discharged from the hospital. This case report aims to emphasize the need for heightened awareness in patients with complex clinical presentations, stress the possibility of concurrent complications, and underscore the importance of prompt and collaborative treatment strategies.

The available evidence on the impact of specific non-pharmacological interventions on glycaemic control is currently limited. Consequently, there is a need to determine which interventions could provide the most significant benefits for the metabolic health of young individuals with type 1 diabetes mellitus. The aim of this study was to identify optimal nonpharmacological interventions on glycaemic control, measured by glycated haemoglobin (HbA1c), in children and adolescents with type 1 diabetes. Systematic searches were conducted in PubMed, Web of Science, Scopus, and SPORTDiscus from inception to July 1, 2023. Randomised clinical trials (RCT) investigating nonpharmacological interventions (e.g., physical activity, nutrition, and behavioural therapies) were included. Primary outcome was change in HbA1c levels. Secondary outcome was change in daily insulin dose requirement. Seventy-four RCT with 6,815 participants (49.43% girls) involving 20 interventions were analysed using a network meta-analysis. Most interventions showed greater efficacy than standard care. However, multicomponent exercise, which includes aerobic and strength training (n = 214, standardised mean difference [SMD] =- 0.63, 95% credible interval [95% CrI] - 1.09 to - 0.16) and nutritional supplements (n = 146, SMD =- 0.49, - 0 .92 to - 0.07) demonstrated the greatest HbA1c reductions. These interventions also led to the larger decreases in daily insulin needs (n = 119, SMD =- 0.79, 95% CrI -  1.19 to - 0.34) and (n = 57, SMD =- 0.62, 95% CrI -  1.18 to - 0.12, respectively). The current study underscores non-pharmacological options such as multicomponent exercise and nutritional supplements, showcasing their potential to significantly improve HbA1c in youth with type 1 diabetes. Although additional research to confirm their efficacy is required, these approaches could be considered as potential adjuvant therapeutic options in the management of type 1 diabetes among children and adolescents.

BACKGROUND: Children and adolescents with recent-onset type 1 diabetes (T1D) commonly maintain a certain level of insulin production during the remission phase, which can last months to years. Preserving β-cell function can reduce T1D complications and improve glycaemic control. Influenza vaccination has pleiotropic effects and administration of the vaccine during the early phases of T1D may offer β-cell protection. This study aims to assess the effect of influenza vaccination on preserving β-cell function in children and adolescents with recent-onset T1D.
METHODS: The INfluenza VaccInation To mitigate typE 1 Diabetes trial is a randomised, double-blind, placebo-controlled, multicentre trial in paediatric patients with recent-onset T1D aged 7-17 years. 100 participants will be randomised in a 1:1 ratio to receive either a standard inactivated quadrivalent influenza vaccine or a placebo within 14 days of diagnosis. The primary outcome is a difference in mean change (from baseline to 12 months) in C-peptide level between groups during a 2-hour mixed-meal tolerance test. Secondary outcomes include mean change (from baseline to 6 months) in C-peptide levels, haemoglobin A1c, ambulatory glucose profiles and insulin requirements. Exploratory outcomes are diabetes-related autoantibodies, inflammatory markers and serum haemagglutinin inhibition antibody titres against the influenza viruses. The current treatment for T1D is largely symptomatic, relying on insulin administration. There is a pressing need for novel pharmacological approaches aimed at modulating the immune system to preserve residual β-cell function. Existing immunotherapies are cost-prohibitive and associated with multiple side effects, whereas influenza vaccination is inexpensive and generally well tolerated. A positive outcome of this study holds potential for immediate implementation into standard care for children and adolescents with recent-onset T1D and may guide future research on immune modulation in T1D.
BACKGROUND: Ethical approval was obtained from Danish Health Authorities prior to participant enrollment. The trial results will be submitted to a peer-reviewed journal.
BACKGROUND: ClinicalTrials.gov NCT05585983 and EudraCT Number 2022-500906-17-01.

During the coronavirus disease 2019 (COVID-19) pandemic, reports of individuals experiencing new-onset type 1 diabetes (T1D) began to appear in the literature. This spurred subsequent epidemiological studies that demonstrated an increase in new diagnosis of T1D compared to prepandemic. Development of T1D is characterized by the development of an inappropriate T cell response directed against pancreatic beta-cells, leading to eventual loss of insulin secretion. This T cell response occurs in genetically susceptible individuals and may be triggered by viral illnesses. Abnormal cytokine production is another element of the pathogenesis of T1D. Infection with severe acute respiratory syndrome related coronavirus 2 induces a profound increase in the production of inflammatory cytokines and causes significant T-cell dysregulation. These disruptions of the immune system may be linked to the development of T1D following COVID-19. [Pediatr Ann. 2024;53(7):e264-e268.].

Past literature on the development of type 1 diabetes (T1D) and type 2 diabetes (T2D) has emphasized the influence of exogenous factors, including viral infections, in the development of these conditions. The coronavirus disease 2019 (COVID-19) pandemic again highlighted the complicated connection between viral infection and the development of diabetes. The complex interplay of proinflammatory, genetic, and socioeconomic factors can help explain the increased incidence of T1D and T2D during the pandemic. Proposed pathophysiological mechanisms connecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to T1D include the expression of angiotensin enzyme 2 receptors on pancreatic islet cells, resultant proinflammatory states, and potential transient damage caused by viral entry. The intricate web of genetic factors, social determinants of health (including the rise of obesity), and the impact of proinflammatory states during SARS-CoV-2 infection on insulin resistance suggests mechanisms linking SARS-CoV-2 infection to the development of diabetes. [Pediatr Ann. 2024;53(7):e258-e263.].

The coronavirus disease 2019 (COVID-19) pandemic brought about several challenges for pediatric patients that were considerably different than those for adult patients. While adult patients had severe illness with associated respiratory and multiorgan failure and subsequent death, pediatric patients typically had milder disease. There were increases in postinfection complications, including an increased incidence of new-onset type 1 diabetes (T1D) following COVID-19 infection, particularly in adolescent patients. Currently, there is increasing concern that COVID-19 infection may be contributing to the development of T1D. This review will provide an overview of COVID-19, T1D, and the increased incidence noted during the pandemic, and the proposed mechanism of development of T1D in this specific patient demographic. Future studies will be needed to understand the long-term impact of the COVID-19 pandemic on T1D in children and adolescents. [Pediatr Ann. 2024;53(7):e244-e248.].

The SARS-CoV-2 (severe acute respiratory syndrome related coronavirus 2) pandemic revealed many flaws in our health care system. This review aims to explore the significance of loss to follow-up on patients with type 1 diabetes during the pandemic, the morbidity and mortality associated, and strategies to prevent loss to follow-up or to re-engage patients in longitudinal care. [Pediatr Ann. 2024;53(7):e254-e257.].