Science and Research

Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study

BACKGROUND: Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations. METHODS: To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data was collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg. RESULTS: Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions. LIMITATIONS: The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometrically collected data. CONCLUSIONS: In summary, our results suggest a strong link between post-COVID depression and fatigue and thus highlighting the need for integrative treatment approaches.

  • Weiß, M.
  • Gutzeit, J.
  • Appel, K. S.
  • Bahmer, T.
  • Beutel, M.
  • Deckert, J.
  • Fricke, J.
  • Hanß, S.
  • Hettich-Damm, N.
  • Heuschmann, P. U.
  • Horn, A.
  • Jauch-Chara, K.
  • Kohls, M.
  • Krist, L.
  • Lorenz-Depiereux, B.
  • Otte, C.
  • Pape, D.
  • Reese, J. P.
  • Schreiber, S.
  • Störk, S.
  • Vehreschild, J. J.
  • Hein, G.

Keywords

  • Elastic net regression
  • Machine learning
  • Post-COVID depression
  • Post-COVID fatigue
Publication details
DOI: 10.1016/j.jad.2024.02.041
Journal: J Affect Disord
Work Type: Original
Location: Assoziierter Partner, ARCN, CPC-M
Disease Area: PALI
Partner / Member: CAU, HMGU, UKSH (Kiel)
Access-Number: 38360365

DZL Engagements

chevron-down