Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18–57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen’s d = 0.25, 95% CI −0.10-0.60) and anxiety patients (+2.91 years, Cohen’s d = 0.27, 95% CI −0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (−2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.
Bibliographical noteFunding Information:
The infrastructure for the Netherlands Study of Depression and Anxiety (https://www. nesda.nl) is funded through the Geestkracht program of the Netherlands Organization for Health Research and Development (Zon-MW, Grant number 10-000-1002) and is supported by participating universities and mental healthcare organizations (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Scientific Institute for Quality of Healthcare [IQ healthcare], and the Netherlands Institute of Mental Health and Addiction [Trimbos Institute]). LKMH and BWJHP are partly funded through Lifebrain, funded by the European Union’s Horizon 2020 research and innovation programme (grant number 732592). We gratefully used the gtsummary package in R to create Table 1 . Data sources are not available due to privacy issues, but we highly value scientific collaboration. Therefore, in principle, NESDA data are available to all scientific researchers working at noncommercial research organizations worldwide. Researchers can request either existing data for data analyses or bioanalysis. Please visit the online data overview for an extensive overview of the available data and NESDA’s current output (www.nesda. nl). The used brain-age prediction model can be found on https://www.photon-ai. com/enigma_brainage/. This article was published as a preprint on medRxiv: https:// doi.org/10.1101/2020.06.16.20132613. BWJHP has received research funding (not related to the current paper) from Boehringer Ingelheim and Jansen Research.
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