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Aim: The objective of this study was to apply quantile regression (QR) methodology to a population from a large representative health insurance plan with known skewed healthcare utilization attributes, co-morbidities, and costs in order to identify predictors of increased healthcare costs. Further, this study provides comparison of the results to those obtained using ordinary least squares (OLS) regression methodology., Methods: Members diagnosed with Type 2 Diabetes and with 24 months of continuous enrollment were included. Baseline patient demographic, clinical, consumer/behavioural, and cost characteristics were quantified. Quantile regression was used to model the relationship between the baseline characteristics and total healthcare costs during the follow-up 12 month period., Results: The sample included 83,705 patients (mean age = 70.6 years, 48% male) residing primarily in the southern US (78.1%); 81.2% of subjects were on oral-only anti-diabetic therapy. Co-morbid conditions included nephropathy (43.5%), peripheral artery disease (26.4%), and retinopathy (18.0%). Variables with the strongest relationship with costs during the follow-up period included outpatient visits, ER visits, inpatient visits, and Diabetes Complications Severity Index score during the baseline period. In the top cost quantiles, each additional glycohemoglobin (HbA1c) test was associated with cost savings ($1400 in the 98th percentile). Stage 4 and Stage 5 chronic kidney disease were associated with an incremental cost increase of $33,131 and $106,975 relative to Stage 1 or no CKD in the 98th percentile ($US)., Conclusions: These results demonstrate that QR provides additional insight compared to traditional OLS regression modeling, and may be more useful for informing resource allocation to patients most likely to benefit from interventions. This study highlights that the impact of clinical and demographic characteristics on the economic burden of the disease vary across the continuum of healthcare costs. Understanding factors that drive costs on an individual patient level provide important insights that will help in ameliorating the clinical, humanistic, and economic burden of diabetes.