Background: Traditional cardiovascular risk indicators only partially explain cardiovascular risks in depressed persons. Depressed persons may exhibit a profile of cardiovascular risk indicators that goes beyond traditional cardiovascular risk indicators, such as symptom severity, insomnia, loneliness and neuroticism, yet research on the added value of these depression-related characteristics in predicting cardiovascular risks of depressed persons is scarce. Methods: Data from N = 1028 depressed Dutch adults without prevalent CVD were derived from two longitudinal depression cohort studies. The outcome was medication-confirmed self-reported CVD. Fifteen depression-related clinical and psychological characteristics were included and tested against traditional cardiovascular risk indicators. Data were analysed using Cox regression models. Incremental values of these characteristics were calculated using c-statistics. Results: After a median follow-up of 65.3 months, 12.7% of the participants developed CVD. Only anxiety and depressive symptom severity were associated with incident CVD beyond traditional cardiovascular risk indicators. The c-statistic of the model with traditional cardiovascular risk indicators was 85.47%. This increased with 0.56 or 0.33 percentage points after inclusion of anxiety or depression severity, respectively. Limitations: Other relevant depression-related characteristics were not available in the datasets used. Conclusion: Anxiety and depressive symptom severity were indicative of an increased cardiovascular risk. Including these as additional risk indicators barely improved the ability to assess cardiovascular risks in depressed persons. Although traditional cardiovascular risk indicators performed well in depressed persons, existing risk prediction algorithms need to be validated in depressed persons.
Original languageEnglish
Pages (from-to)335-342
Number of pages8
JournalJournal of affective disorders
Publication statusPublished - 15 May 2023


  • Cardiovascular disease
  • Depression
  • Risk prediction

Cite this