What are the clinico-demographic profiles of children with type 1 diabetes in a tertiary institution?

Research Proposal: Clinico-Demographic Profiles of Children with Type 1 Diabetes in a Tertiary Institution

Background and Rationale

Type 1 diabetes represents the most common form of diabetes in the pediatric population, with increasing global incidence rates documented across diverse geographic regions. 1 Understanding the clinico-demographic characteristics of affected children is essential for optimizing care delivery, identifying high-risk populations, and tailoring interventions to reduce morbidity and mortality. 1

Recent evidence demonstrates significant heterogeneity in presentation patterns, with variations based on:

• Age at diagnosis: Children under 3 years face substantially higher risk of diabetic ketoacidosis (DKA) at presentation (42% overall DKA rate, with younger children at greatest risk). 2
• Ethnic and racial background: Hispanic and African-American children present with higher rates of DKA (51.8% and 56.7% respectively vs 38.2% in non-Hispanic white children) and elevated BMI at diagnosis. 3
• Socioeconomic factors: Lower maternal education levels and socioeconomic status correlate with poor metabolic control (HbA1c ≥7.5%). 4

The American Diabetes Association emphasizes that 30-40% of children present with ketoacidosis at diagnosis, representing a critical window for intervention. 1 Furthermore, 24% of children with type 1 diabetes are overweight and 15% are obese at diagnosis, complicating the clinical picture and potentially delaying recognition. 1

Research Objectives

Primary Objective

To comprehensively characterize the clinico-demographic profiles of children diagnosed with type 1 diabetes at [Institution Name], including age distribution, anthropometric measurements, clinical presentation patterns, and metabolic parameters at diagnosis.

Secondary Objectives

• Determine the prevalence of DKA at presentation and identify associated demographic risk factors. 2
• Assess the distribution of autoimmune markers (GAD65, IA-2A, ZnT8, insulin autoantibodies) and their correlation with clinical severity. 2
• Evaluate socioeconomic determinants including parental education levels, family structure, and their association with presentation characteristics. 4
• Document the proportion of overweight/obese children at diagnosis and examine ethnic/racial variations. 1, 3
• Analyze family history patterns, particularly first-degree relatives with type 1 diabetes. 1, 5

Methodology

Study Design

Retrospective cross-sectional study with prospective data collection component.

Study Population

Inclusion Criteria:

• Children aged 0-18 years diagnosed with type 1 diabetes at [Institution Name]
• Diagnosis confirmed by presence of hyperglycemia (random glucose ≥200 mg/dL or fasting glucose ≥126 mg/dL) plus clinical symptoms OR presence of ≥2 islet autoantibodies. 1
• Study period: [Specify timeframe, recommend minimum 3-5 years for adequate sample size]

Exclusion Criteria:

• Monogenic diabetes (MODY, neonatal diabetes) confirmed by genetic testing. 1
• Type 2 diabetes (based on obesity, acanthosis nigricans, absence of autoantibodies, preserved C-peptide >0.6 ng/mL after 2 years). 1
• Secondary diabetes due to medications, cystic fibrosis, or other conditions. 1

Data Collection Variables

Demographic Parameters:

• Age at diagnosis (categorized: <3 years, 3-6 years, 7-12 years, 13-18 years). 2
• Sex distribution. 3
• Ethnicity/race (non-Hispanic white, Hispanic, African-American, Asian, other). 3
• Parental education levels (elementary, high school, bachelor's degree or higher). 4
• Family income bracket and insurance status. 4
• Family history of type 1 diabetes in first- and second-degree relatives. 1, 5

Clinical Presentation:

• Duration of symptoms prior to diagnosis (polyuria, polydipsia, polyphagia, weight loss, fatigue). 1, 6
• Presence and severity of DKA at diagnosis (pH <7.3, bicarbonate <15 mEq/L, presence of ketones). 1, 2
• Anthropometric measurements: weight, height, BMI, BMI percentile for age/sex. 1, 3
• Pubertal status (Tanner staging). 3

Laboratory Parameters:

• Initial glucose level at presentation. 3
• HbA1c at diagnosis. 6
• C-peptide levels (fasting or stimulated). 3
• Autoantibody panel: GAD65, IA-2A, ZnT8, insulin autoantibodies. 2
• Thyroid function tests and celiac screening (as per standard care). 1

Genetic Risk Assessment (if available):

• HLA genotyping (DR3-DQ2, DR4-DQ8, or neutral genotypes). 2

Statistical Analysis

Descriptive Statistics:

• Continuous variables: mean ± SD or median (interquartile range) depending on distribution. 2, 4
• Categorical variables: frequencies and percentages. 7, 6

Comparative Analysis:

• Chi-square tests for categorical variables (DKA presence across age groups, ethnic differences). 2, 3
• ANOVA or Kruskal-Wallis tests for continuous variables across multiple groups. 4
• Logistic regression to identify independent predictors of DKA at presentation, adjusting for age, ethnicity, BMI, and socioeconomic factors. 2, 4
• Multiple correspondence analysis to evaluate associations between socioeconomic characteristics and metabolic control. 4

Predictive Modeling:

• Develop a risk prediction model for DKA at diagnosis incorporating demographic, clinical, and immunological markers (target: 70% prediction accuracy as demonstrated in Belgian cohort). 2

Ethical Considerations

• Institutional Review Board approval required prior to data collection. 1
• Waiver of informed consent for retrospective chart review component. 7
• Written informed consent for prospective enrollment and additional testing. 1
• Data de-identification and secure storage per HIPAA regulations. 1

Expected Outcomes and Clinical Implications

This study will provide institution-specific data to:

• Identify high-risk populations requiring enhanced surveillance and earlier intervention (particularly children <3 years, ethnic minorities, and those from lower socioeconomic backgrounds). 2, 4, 3
• Establish baseline metabolic control parameters to guide quality improvement initiatives targeting the American Diabetes Association goal of HbA1c <7.5%. 1, 8
• Inform development of culturally sensitive educational programs tailored to the demographic composition of the patient population. 1
• Guide resource allocation for multidisciplinary diabetes team services (pediatric endocrinologist, nurse educator, dietitian, mental health professional). 1
• Contribute to regional epidemiological understanding of pediatric type 1 diabetes trends. 7, 6

Critical pitfalls to avoid:

• Misclassification of diabetes type: Ensure thorough autoantibody testing in overweight/obese children, as 10% of youth with type 2 phenotype have islet autoimmunity. 1
• Incomplete socioeconomic data: Maternal education level is a stronger predictor than paternal education; ensure complete maternal data collection. 4
• Overlooking neutral HLA genotypes: Paradoxically associated with 1.5-fold increased DKA risk despite lower overall diabetes susceptibility. 2

Timeline and Resources

Phase 1 (Months 1-2): IRB approval, database development, staff training Phase 2 (Months 3-8): Retrospective chart review and data extraction Phase 3 (Months 9-12): Prospective enrollment and additional testing Phase 4 (Months 13-15): Statistical analysis and manuscript preparation

Required Resources:

• Access to electronic medical records system. 7
• Laboratory support for autoantibody and HLA testing (if not routinely performed). 2
• Statistical software (SPSS, SAS, or R) for multivariate analysis. 4
• Research coordinator time (0.5 FTE minimum). 1