Bryant EM, Richardson R, Graham BM. The relationship between salivary Fibroblast Growth Factor-2 and cortisol reactivity and psychological outcomes prior to and during the COVID-19 pandemic.
JOURNAL OF AFFECTIVE DISORDERS REPORTS 2023;
13:100606. [PMID:
37304226 PMCID:
PMC10246939 DOI:
10.1016/j.jadr.2023.100606]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/09/2023] [Accepted: 06/02/2023] [Indexed: 06/13/2023] Open
Abstract
Background
Fibroblast growth factor-2 (FGF2) is a biomarker that is associated with depression, anxiety and stress in rodents. In humans, we have previously demonstrated that salivary FGF2 increased following stress in a similar pattern to cortisol, and FGF2 (but not cortisol) reactivity predicted repetitive negative thinking, a transdiagnostic risk factor for mental illness. The current study assessed the relationship between FGF2, cortisol, and mental health before and during the COVID-19 pandemic.
Methods
We employed a longitudinal correlational design using a convenience sample. We assessed whether FGF2 and cortisol reactivity following the Trier Social Stress Task (TSST) were associated with DASS-21 depression, anxiety and stress, measured at the time of the TSST in 2019-20 (n = 87; time 1), and then again in May 2020 during the first wave of COVID-19 in Sydney (n = 34 of the original sample; time 2).
Results
FGF2 reactivity (but not absolute FGF2 levels) at time 1 predicted depression, anxiety, and stress across timepoints. Cortisol reactivity at time 1 was associated with stress over timepoints, and absolute cortisol levels were associated with depression across timepoints.
Limitations
The sample was comprised of mostly healthy participants from a student population, and there was high attrition between timepoints. The outcomes need to be replicated in larger, more diverse, samples.
Conclusions
FGF2 and cortisol may be uniquely predictive of mental health outcomes in healthy samples, potentially allowing for early identification of at-risk individuals.
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