Background

We hypothesize that depression in type 2 diabetes might be associated with poor glycemic control, in part due to suboptimal self-care. We tested this hypothesis by examining the associations of depression with clinical and laboratory findings in a multicenter survey of Chinese type 2 diabetic patients.

Method

2538 patients aged 18-75 years attending hospital-based clinics in four cities in China underwent detailed clinical-psychological-behavioral assessment during a 12-month period between 2011 and 2012. Depression was diagnosed if Patient Health Questionnaire-9 (PHQ-9) score ≥10. Diabetes self-care and medication adherence were assessed using the Summary of Diabetes Self-care Activities and the 4-item Morisky medication adherence scale respectively.

Results

In this cross-sectional study (mean age: 56.4 ± 10.5[SD] years, 53% men), 6.1% (n = 155) had depression. After controlling for study sites, patients with depression had higher HbA(1c) (7.9 ± 2.0 vs. 7.7 ± 2.0%, P = 0.008) and were less likely to achieve HbA(1c) goal of <7.0% (36.2% vs 45.6%, P = 0.004) than those without depression. They were more likely to report hypoglycemia and to have fewer days of being adherent to their recommended diet, exercise, foot care and medication. In logistic regression, apart from young age, poor education, long disease duration, tobacco use, high body mass index, use of insulin, depression was independently associated with failure to attain HbA(1c) target (Odds Ratio [OR] = 1.56, 95%CI:1.05-2.32, P = 0.028). The association between depression and glycemic control became non-significant after inclusion of adherence to diet, exercise and medication (OR = 1.48, 95% CI 0.99-2.21, P = 0.058).

Conclusion

Depression in type 2 diabetes was closely associated with hyperglycemia and hypoglycemia, which might be partly mediated through poor treatment adherence.

Background

A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.

Methods

We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.

Results

The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.

Conclusion

The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.