Type 2 & other diabetes

Emotional distress and cardiovascular disease risk among participants enrolled in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) study

Diabetes Res Clin PractPublished Cross-sectional analysis of the GRADE study

Authors' conclusion

Depressive symptoms, particularly cognitive-affective symptoms, are associated with increased 10-year predicted ASCVD risk among adults with early T2DM. Diabetes distress is not significantly associated with the predicted ASCVD risk when accounting for covariates.

Study context

Population
Adults with T2D in GRADE; 54% non-Latino White, 18% Latino, 19% non-Latino Black, 66% male, mean age 57.5 (SD 10.25) years, diabetes duration 4.2 (SD 2.8) years, HbA1c 7.5% (SD 0.5%)
Participants
1,605 adults
Exposure
Depressive symptoms and diabetes distress vs estimated 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk
Outcome
Estimated 10-year cardiovascular disease (CVD) risk (ASCVD risk score)
Follow-up
Baseline
Methodological detailsGRADE Emotional Distress Substudy, linear regression, adjusted for age, sex, race/ethnicity, education, income, diabetes duration, complications and HbA1c
  • GRADE Emotional Distress Substudy
  • Linear regression
  • Adjusted for age, sex, race/ethnicity, education, income, diabetes duration, complications and HbA1c

Main finding

As reported in the abstract

Association of depressive symptoms with ASCVD risk

Depressive symptoms (especially cognitive-affective) associated with ASCVD risk, adjusted: estimate (95% CI 0.04, 0.025 as printed), p = 0.006

0.15

Higher depressive symptoms associated with higher ASCVD risk, adjusted incl. diabetes distress: estimate (95% CI 0.07, 0.30), p = 0.002

0.19

Observational data: associations, not effects.

Original publication

Emotional distress and cardiovascular disease risk among participants enrolled in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) study. Diabetes Res Clin Pract. Published 30 June 2023. doi:10.1016/j.diabres.2023.110808

Composed by AI from the published abstract and checked against it. How Briefs are made →

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