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Abstract
Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers—C-reactive protein, neutrophils, and leukocytes—significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.
Identifying modifiable risk factors that could prevent depression is important. Here, the authors show increased risks of incident depression in pre-frail and frail individuals and highlight the mediating role of brain structure and inflammation.
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1 Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
2 Northeastern University, Department of Psychology, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359); Northeastern University, Department of Bioengineering, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359); Northeastern University, Center for Cognitive and Brain Health, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359)
3 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710)
4 Yale University, Department of Biostatistics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710)
5 Yale University, Interdepartmental Neuroscience Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710)
6 University of Amsterdam, Department of Psychiatry, Amsterdam UMC, Amsterdam, the Netherlands (GRID:grid.7177.6) (ISNI:0000000084992262); Amsterdam Neuroscience, Amsterdam, the Netherlands (GRID:grid.484519.5)
7 Georgia State University, Georgia Institute of Technology, and Emory University, Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, USA (GRID:grid.511426.5)
8 Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China (GRID:grid.20513.35) (ISNI:0000 0004 1789 9964)
9 Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710); Yale University, Interdepartmental Neuroscience Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710); Yale University, Department of Statistics & Data Science, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710); Yale School of Medicine, Child Study Center, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)