Aims

To compare cardiovascular risk factor control in adults with diabetes participating in a national diabetes registry to those in the general population and to ascertain regional differences in diabetes care.

Methods

Adults with diagnosed diabetes in the Diabetes Collaborative Registry (DCR) were compared with those in the National Health and Nutrition Examination Survey (NHANES) from 2015 to 2016; standardized mean difference (SMD) > 0.2 defined significance. Regional differences were assessed in the DCR cohort; p < .05 defined significance.

Results

The DCR cohort was older (61 vs. 57 years, SMD = 0.38), more insured (99.7% vs. 91.0%, SMD = 0.42), and less ethnically diverse (83% non-Hispanic white vs. 76%, SMD = 0.30) compared with NHANES. The proportion of overweight/obesity, A1c < 7% (<53 mmol/mol), and BP < 140/90 were similar, but DCR participants had higher proportion with LDL < 2.59 mmol/L (61% vs. 41%, SMD = 0.39) and fewer tobacco users (17% vs. 32%, SMD = 0.35). Regionally, obesity, lack of glycaemic control, and tobacco use were highest in the Midwest, BP control was the lowest in the South, and LDL control was lowest in the Northeast.

Conclusions

Significant regional differences in diabetes care delivery and outcomes were identified using a national diabetes registry. Serial analyses of the DCR may supplement national evaluations to deepen our understanding of diabetes care in the US.

Objectives

Optimizing glycemic control in pediatric type 1 diabetes (T1D) is essential to minimizing long-term risk of complications. We used the T1D Exchange database from 58 US diabetes clinics to identify differences in diabetes management characteristics among children categorized as having excellent vs. poor glycemic control.

Methods

Among registry participants 6-17 yr old with diabetes duration ≥ 2 yr, those with excellent control [(A1c <7%)(53 mmol/mol) (N = 588)] were compared with those with poor control [(A1c ≥ 9% )(75 mmol/mol) (N = 2684)] using logistic regression.

Results

The excellent and poor control groups differed substantially in diabetes management (p < 0.001 for all) with more of the excellent control group using insulin pumps, performing blood glucose monitoring ≥ 5 ×/d, missing fewer boluses, bolusing before meals rather than at the time of or after a meal, using meal-specific insulin:carbohydrate ratios, checking their blood glucose prior to giving meal time insulin, giving insulin for daytime snacks, giving more bolus insulin, and using a lower mean total daily insulin dose than those in poor control. After adjusting for demographic and socioeconomic factors, diabetes management characteristics were still strongly associated with good vs. poor control. Notably, frequency of severe hypoglycemia was similar between the groups while DKA was more common in the poorly controlled group.

Conclusions

Children with excellent glycemic control tend to exhibit markedly different diabetes self-management techniques than those with poor control. This knowledge may further inform diabetes care providers and patients about specific characteristics and behaviors that can be augmented to potentially improve glycemic control.

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.