4.6 Article

Serum biomarkers predictive of depressive episodes in panic disorder

Journal

JOURNAL OF PSYCHIATRIC RESEARCH
Volume 73, Issue -, Pages 53-62

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jpsychires.2015.11.012

Keywords

Panic disorder; Major depressive episode; Prediction; Risk factor; Prognosis; Secondary depression

Categories

Funding

  1. Stanley Medical Research Institute (SMRI)
  2. Netherlands Organisation for Health Research and Development (Zon-Mw) [10-000-1002]
  3. VU University Medical Center
  4. GGZ inGeest
  5. Arkin
  6. Leiden University Medical Center
  7. GGZ Rivierduinen
  8. University Medical Center Groningen
  9. Lentis
  10. GGZ Friesland
  11. GGZ Drenthe
  12. Scientific Institute for Quality of Healthcare (IQ healthcare)
  13. Netherlands Institute for Health Services Research (NIVEL)
  14. Netherlands Institute of Mental Health and Addiction (Trimbos Institute)
  15. Engineering and Physical Sciences Research Council [1351685] Funding Source: researchfish

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Panic disorder with or without comorbid agoraphobia (PD/PDA) has been linked to an increased risk to develop subsequent depressive episodes, yet the underlying pathophysiology of these disorders remains poorly understood. We aimed to identify a biomarker panel predictive for the development of a depressive disorder (major depressive disorder and/or dysthymia) within a 2-year-follow-up period. Blood serum concentrations of 165 analytes were evaluated in 120 PD/PDA patients without depressive disorder baseline diagnosis (6-month-recency) in the Netherlands Study of Depression and Anxiety (NESDA). We assessed the predictive performance of serum biomarkers, clinical, and self-report variables using receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC). False discovery-rate corrected logistic regression model selection of serum analytes and covariates identified an optimal predictive panel comprised of tetranectin and creatine kinase MB along with patient gender and scores from the Inventory of Depressive Symptomatology (IDS) rating scale. Combined, an AUC of 0.87 was reached for identifying the PD/PDA patients who developed a depressive disorder within 2 years (n = 44). The addition of biomarkers represented a significant (p = 0.010) improvement over using gender and IDS alone as predictors (AUC = 0.78). For the first time, we report on a combination of biological serum markers, clinical variables and self-report inventories that can detect PD/PDA patients at increased risk of developing subsequent depressive disorders with good predictive performance in a naturalistic cohort design. After an independent validation our proposed biomarkers could prove useful in the detection of at-risk PD/PDA patients, allowing for early therapeutic interventions and improving clinical outcome. (C) 2015 Elsevier Ltd. All rights reserved.

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