Pediatric Diabetes最新文献

Objective: To investigate whether human leukocyte antigens (HLAs) influence gut microbiota composition and contributes to delayed type 1 diabetes mellitus (T1DM) onset in children.

Methods: This multicenter cross-sectional study included 106 newly diagnosed pediatric T1DM patients (age <18 years) and 69 healthy controls from nine Chinese cities. Gut microbiota was profiled via whole-metagenome shotgun sequencing, and HLA alleles were genotyped by PCR sequence-based typing. Participants were stratified by HLA-risk scores. Statistical analyses included α/β-diversity metrics, linear discriminant analysis effect size analysis (LEfSe), and Spearman correlation adjusted for confounders.

Results: Principal coordinates analysis (PCoA) exposed discernible disparities in gut microbiota structures within the high-HLA-risk T1DM cohort relative to both high- and low-HLA-risk control groups (R ² = 0.0562, p=0.003 and R ² = 0.0343, p=0.003). HLA-C ^∗ 0304 carriers exhibited delayed T1DM onset compared to noncarriers (adjusted R ² = 0.225, p=0.017). High-HLA-risk T1DM patients showed distinct microbiota divergence from controls (R ² = 0.0562, p=0.003), driven by reduced Lachnospiraceae and Blautia (butyrate producers) in noncarriers. Conversely, HLA-C ^∗ 0304-positive T1DM patients had enriched Blautia (p=0.005) and Lachnospiraceae (p=0.039), alongside lower opportunistic pathogens (Citrobacter; p < 0.05). High-HLA-risk patients also displayed lower fasting C-peptide levels than low-risk counterparts (0.19 ± 0.14 vs. 0.26 ± 0.19 µg/mL, p=0.029).

Conclusions: Our study demonstrates that specific HLA class I subtypes (e.g., C ^∗ 0304) may modulate T1DM onset through selective enrichment of beneficial gut microbiota. Elucidating the mechanisms by which HLA variants regulate mucosal immunity and coordinate HLA-microbiota-immune interactions holds significant potential for developing targeted interventions against T1DM pathogenesis.

Aim: This study aimed to explore the lived experiences and support needs of parents caring for children with type 1 diabetes mellitus (T1DM) in Zabol, Iran, a rural and resource-scarce region.

Methods: A qualitative phenomenological design was employed with 36 parents (25 mothers and 11 fathers) of children aged 4-17 years who had lived with T1DM for at least 6 months. Semistructured interviews were conducted in Persian, transcribed, translated, and thematically analyzed using Braun and Clarke's six-step framework, in accordance with Consolidated Criteria for Reporting Qualitative Research (COREQ) reporting guidelines.

Results: Three overarching themes emerged. Parents reported emotional and practical support gaps, including caregiver exhaustion, lack of respite opportunities, and limited guidance. They described social isolation and stigma driven by cultural misconceptions such as viewing diabetes as a curse, which led to exclusion of both parents and children. Families also faced healthcare system challenges, including limited specialist access, insufficient diabetes education, financial strain (2-15 million IRR monthly), and inadequate resources, all exacerbated by rural isolation.

Conclusions: Parents of children with T1DM in Zabol experience substantial unmet emotional, social, and systemic needs. Addressing these challenges requires structured peer support, culturally sensitive community education to reduce stigma, and expanded access to affordable healthcare. These findings provide a foundation for developing targeted interventions to strengthen resilience and improve outcomes among families in underserved rural regions.

Objective: This study aimed to assess the association between depressive symptoms and glycemic control among children and adolescents with diabetes and to determine if their self-care behaviors mediate this association.

Methods: A total of 207 patients of children and adolescents with diabetes were included in a cross-sectional survey study. The Chinese version of the Children's Depression Inventory (CDI) was used to evaluate the depressive symptoms of the patients. The Chinese version of the Summary of Diabetes of Self-Care Activities (SDSCA) was used to evaluate the level of diabetes self-care behaviors. The values of HbA1c of children and adolescents with diabetes were obtained from patients' medical history cases or self-reporting. Structural equation modeling (SEM) was used to examine the mediation effect of self-care behaviors between depressive symptoms and glycemic control.

Results: In 207 children and adolescents with diabetes, the total score of depressive symptoms was 12.71 ± 6.73 and the total score of self-care behaviors was 42.31 ± 14.09. The HbA1c of the patients was 9.14 ± 2.55%. High depressive symptoms and low self-care behaviors are related to high levels of HbA1c (all p  < 0.001). The results revealed that the effect of depressive symptoms on glycemic control was partly mediated by self-care behaviors and the mediation effect accounts for 30.65% of the total effect.

Conclusions: Depressive symptoms show a significant association with glycemic control among children and adolescents with diabetes, with self-care behaviors serving as a partial mediator in this relationship. Depressive severity may influence glycemic control partly by affecting self-care behaviors.

Since COVID-19 onset, pediatric endocrinologists have been making an assumption that there was a shift in diagnosis age of type 1 diabetes mellitus (T1DM) to younger children. Younger children are more likely to present in DKA, are more difficult to diagnose and treat, and age at diagnosis can affect prognosis. We performed a retrospective chart review of patients diagnosed with T1DM for 3 years before COVID-19 and the 3 years during COVID-19. Demographics were evaluated using the Chi-squared test for categorical data and Student's t-test or ANOVA for continuous data. During this time, 698 patients were diagnosed with T1DM, with more patients during COVID-19. The average age of diagnosis significantly increased by 0.7 years (p=0.025). There was a significant difference in the distribution of age groups between the two time periods (p=0.0065). There was a significant decrease in new cases among patients between the ages of 2-5 years from 2017 to 2020, a transient finding as they reverted back to previous rates by 2022. New diagnoses between 13 and 18 years were increasing prior to 2020 (7%-23%), subsequently leveling out. Patients were 1.6 times more likely to present in DKA during COVID-19; however, there was no significant change in hemoglobin A1c (HbA1c). There was no significant change in thyroperoxidase (TPO) antibody positivity. There was a significant decrease (p=0.018) in patients with elevated tissue transglutaminase (TTG)-IgA from pre-COVID to post-COVID. Average age at diagnosis in our cohort increased since the start of COVID-19, contradicting previous studies and our hypothesis. The number of new cases increased, and the age distribution changed. There was a significant decrease in number of younger patients in 2020, followed by a normalization of new cases in those 2-5 years old, which may have led to the belief that more toddlers were being diagnosed. The rate of other antibodies did not increase. These results illustrate that changes in demographics may have been short-lived post-COVID-19.

Objective: This study aimed to identify novel proteomic and lipidomic biomarkers of insulin resistance (IR) in young children with obesity and to assess the ability of hub lipids and proteins in the diagnosis of IR.

Methods: The discovery cohort consisted of 50 prepubertal children, including 30 children with obesity and 20 lean. The validation cohort included 25 children with obesity and IR (obese-IR) and 25 children with obesity without IR (obese-NIR). Fasting plasma was collected from all participants for Olink proteomics and untargeted lipidomics. Pearson correlation analysis was used to identify proteins and lipids associated with IR, and area under the receiver operating characteristic (AUROC) was applied to compare the ability of the identified proteins and lipids with traditional indices in the diagnosis of IR.

Results: In the discovery cohort, a total of 15 lipids and 10 proteins had significant correlation with IR. In the validation cohort, protein fatty acid binding protein 4 (FABP4) and gene serpin family E member 1 (PAI) were overexpressed in obese-IR children compared to obese-NIR children, while insulin like growth factor binding protein 1 (IGFBP-1) and paraoxonase 3 (PON3) were lower in the IR group than in the obese-NIR group; five lipids including sphingosine (d16:0), coenzyme (Q8), ceramides phosphate (d42:2), phosphatidylethanolamine (37:2e), and phosphatidylcholine (18:1e_16:0), showed significant (p < 0.05) change in obese-IR children compared to obese-NIR children. In addition, the AUC-ROC was 0.89 for IGFBP-1, 0.81 for PON3, and 0.65 for PAI. The ability of IGFBP-1, PON3, and PAI to diagnose IR was better than that of adiponectin and leptin. The AUROC of phosphatidylcholine (18:1e_16:0) and coenzyme (Q8) were 0.80 and 0.73, respectively, which was significantly higher than the AUROC of triglycerides(TGs), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C).

Conclusion: Proteomic and lipidomic analysis can allow for the identification of potential new candidate biomarkers for IR. The ability of novel biomarkers to diagnose IR was better than traditional indicators.

Trial registration: Chinese Clinical Trial Registry: ChiCTR2300072179.

Objective: Wolfram Syndrome Type 1 (WS1) is a rare neurodegenerative disorder characterized by diabetes insipidus (DI), diabetes mellitus (DM), optic atrophy (OA), and deafness (D) due to biallelic mutations in the WFS1 gene. As the cardinal symptoms of DI, polyuria and polydipsia, overlap with those of DM, DI might be underdiagnosed or delayed in the early stages of WS1. In the present study, we assessed whether DI could be an early sign of WS1 and analyzed genotype-phenotype correlations in a group of Turkish patients with Type 1 DM. Patients and Methods: We applied a polyuria/polydipsia questionnaire to 1278 children with Type 1 DM. Patients with suggestive symptoms of DI were further evaluated for other clinical features of WS1 and molecular genetic analysis of the WFS1 gene. Clinical, laboratory, and genetic characteristics of cases identified using questionnaires were compared with a historical case series of seven children with WS1 and previously published literature data. Results: Eighteen patients were considered to have a diagnosis of DI, thereby being eligible for genetic analysis of WFS1 variants. Of those, six had biallelic variations (four missense variants, one in-frame duplication, and three frameshift variants) in the WFS1 gene, and a diagnosis of WS1 was confirmed. The age of admission for DM was younger in the historical cases (5.1 ± 2.0 vs. 8.7 ± 3.4; p=0.04). There was no statistically significant difference between the ages for the diagnosis of WS1 (12.9 ± 5.0 vs. 9.6 ± 2.7; p=0.191), though the diagnostic delay from DM onset to WS1 diagnosis was significantly shorter in the screened group (median 1.8 vs. 6.9 years; p ≈ 0.015). Conclusion: Our findings suggest that DI may present before OA in WS1. Enriching the diagnosis of DI using a simple polyuria/polydipsia questionnaire may provide an earlier diagnosis of WS1 in patients followed with Type 1 DM. Screening and early genetic testing of these patients enhances the diagnosis, follow-up, and management strategies of patients with WS1.

Introduction: Type 1 diabetes is commonly associated with microvascular complications. Sublingual microcirculation examination using sidestream dark-field (SDF) imaging can reflect the situation in visceral microcirculation. The main goals of this observational study were to assess the feasibility of SDF imaging in children with compensated type 1 diabetes, determine selected sublingual microcirculation parameters, and compare them with parameters obtained in healthy children.

Methods: In total 30 children with stable type 1 diabetes without clinical or laboratory signs of microvascular complications were included in the study, 15 males and 15 females in three age categories. Three video clips were recorded using an SDF probe from different parts of the sublingual area and analyzed by software-aided offline analysis.

Results: Videoclips were successfully recorded in all children. Compared with healthy children, the De Backer score (DeBS) in females and total vessel density (TVD), small vessel density (SVD), perfused vessel density (PVD), and perfused SVD (PSVD) in both genders were significantly lower in children with T1D. There were no differences in TVD, SVD, PVD, PSVD, and DeBS between age or gender categories. DeBS correlated with ketonemia; otherwise, no significant relationship was observed between microcirculatory and other recorded parameters.

Conslusions: Sublingual microcirculation examination using SDF imaging is feasible in children with type 1 diabetes. Alteration of sublingual microcirculatory parameters is detectable in children with type 1 diabetes before they show clinical or laboratory signs of any microvascular complication.

Trial registration: ClinicalTrials.gov identifier: NCT05958264.

Aim: To examine the impact of adding an intensive, integrated telehealth intervention on glycemic control in children and adolescents with type 1 diabetes using continuous glucose monitoring (CGM) and multiple daily injections (MDIs) of insulin. Materials and Methods: In this randomized, two-period crossover trial conducted between May 2023 and June 2024, 105 children and adolescents with type 1 diabetes using FreeStyle Libre 2 CGM were randomized to receive intensive telehealth weekly over 12 weeks first followed by routine care (n = 50) or routine care over 12 weeks first followed by intensive telehealth weekly (n = 55), with a 2-week washout. Intensive telehealth was intensified follow-up with weekly teleconsultation (20 min, by telephone) and digital support from a trained diabetes educator delivering structured support, including review of the latest ambulatory glucose profile. The primary outcome measures were HbA1c and GCM metrics. Results: The average (SD) age of the study cohort (n = 105) was 11.8 (4.2) years, 48.6% were female, with an average diabetes duration of 3.5 (3.0) years and suboptimally controlled diabetes in terms of HbA1c levels (9.4 (1.6) %, target < 6.5%), and other 14-day CGM metrics. Compared with routine care, intensified follow-up with weekly intensive telehealth was associated with a decrease in HbA1c (-0.29 (0.60) %, 95%CIs -0.41 to -0.17, p  < 0.001), significantly increased time in range (TIR), and decreased time above range (TAR), average glucose level, glucose variability, glucose management indicator (GMI), and frequency of low glucose events. Teleconsultation did not affect time below range (TBR), which was already within target. Conclusion: This randomized, controlled, and crossover study shows that intensified follow-up with a weekly telehealth intervention results in small but significant improvements in glycemic control metrics in children and adolescents. The clinical impact of these changes requires prospective study.

Aims: This study aimed to identify age-related peripheral immune endotypes in pediatric patients with type 1 diabetes (T1D) at disease onset and assess their metabolic control 1 year post-diagnosis. Methods: Immune cell subpopulations (T and B lymphocytes, myeloid cells, and natural killer [NK] cells) were analyzed via multicolor flow cytometry in pediatric T1D patients and age- and sex-matched controls, grouped as <7 years, 7-12 years, and >12 years. Sociodemographic, clinical, and metabolic data were collected, including autoantibodies, bicarbonate (HCO₃), C-peptide, HbA1c, and time in range (TIR), with follow-up for 1 year to evaluate partial remission (PR) likelihood and metabolic control. Results: Patients <7 years showed reduced regulatory immune cells (memory/activated regulatory T lymphocytes (Tregs), regulatory B cells, and Th17) and more severe disease onset (shorter symptoms, greater acidosis, and lower C-peptide). Ages 7-12 exhibited increased memory B cells, particularly the unswitched ones. Myeloid cells showed no significant variation in T1D, despite age trends in controls. Anti-insulinoma-antigen 2 (IA2) titers were lower in patients >12 years, while anti-glutamic acid decarboxylase 65 (GAD65) positivity remained constant. Younger patients had lower PR rates and poorer glycemic control at 1 year. Conclusions: Younger patients face greater immune dysregulation and β-cell loss, while older patients show better immune maturity and metabolic outcomes. These differences underline the need for age-specific T1D therapies.

Objective: To design a dynamic prediction model for estimating the time of progression from a single glutamic acid decarboxylase autoantibody (GADA) to multiple islet autoantibodies and type 1 diabetes in children, exploring different longitudinally measured risk variables. Research Design and Methods: GADA-positive children (n = 379) participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study were followed for the appearance of additional autoantibodies against either insulin autoantibody (IAA), insulinoma-like 2 autoantibody (IA-2A), or zinc transporter 8 antibody (ZnT8A) and type 1 diabetes. A dynamic prediction model was designed, including trajectories of longitudinal risk variables, autoantibody titers, and metabolic variables (C-peptide, glucose, and HbA1c) together with time-invariant variables (gender, age at GADA positivity, and high-risk HLA genotypes). Results: Transition risk from GADA to multiple autoantibodies was increased by lower age (p < 0.001) and by increased GADA titers during follow-up (p < 0.001), and was less likely in children with HLA DQ2/X but not DQ2/8 (p=0.004). The transition risk from multiple autoantibodies without IA-2A to IA-2A positivity was associated with increased levels of 2 h glucose following oral glucose tolerance test (OGTT) (p < 0.001) and increased ZnT8A titers (p < 0.001). Increasing HbA1c (p < 0.001) and GADA titers (p < 0.001) were associated with an increased risk of transition from GADA only to type 1 diabetes; while increasing HbA1c (p < 0.001) was associated with the transition from multiple autoantibodies to type 1 diabetes. Risk of transition from multiple autoantibodies, including IA-2A to type 1 diabetes was also associated with 2 h glucose level (p < 0.001). Conclusion: The dynamic prediction model presented an individual time-specific risk of transition from a single GADA to multiple autoantibodies and type 1 diabetes.