{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T03:02:18Z","timestamp":1791428538606,"version":"4.3.3"},"reference-count":46,"publisher":"Royal College of Psychiatrists","issue":"1","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"unspecified","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Eur. Psychiatr."],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S0924933826101539_sec1\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Major depressive disorder is a prevalent and debilitating mental health condition contributing to a growing global burden. Late-life depression (LLD), affecting individuals over 60 years of age, is further associated with elevated risks for cardiovascular diseases, cognitive decline, and dementia. Treatment responses vary widely, potentially due to underlying neurodegeneration and cellular senescence. We aimed to explore blood-based biomarkers related to Alzheimer\u2019s disease and senescence-associated secretory phenotype (SASP) proteins, seeking to identify biological underpinnings of LLD and their association with response to psychotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924933826101539_sec2\" sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a secondary analysis of the Cognitive Behavioral Therapy for Late-Life Depression (CBTlate) trial in 228 participants aged 60\u00a0years and older with a diagnosis of LLD. Depression trajectories were compared using clustering. In participants with available plasma samples, biomarker data were generated post hoc. We assessed associations between biomarkers and depression trajectories, biomarker dynamics, and their ability to predict treatment response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924933826101539_sec3\" sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Two depression trajectories were identified: persistently high stable Geriatric Depression Scale (GDS) scores (hsGDS) and decreasing scores over time (dGDS). The hsGDS group had more severe baseline depression (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a02.88 \u00d7 10\n                      <jats:sup>\u22126<\/jats:sup>\n                      ), anxiety (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a04.39 \u00d7 10\n                      <jats:sup>\u22124<\/jats:sup>\n                      ), and sleep disorders (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a01.09 \u00d7 10\n                      <jats:sup>\u22123<\/jats:sup>\n                      ), and was more likely to have a history of major depression (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.01) and mild cognitive impairment (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.01). Biomarker analysis revealed elevated baseline plasma neurofilament light chain (NfL,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a02.51 \u00d7 10\n                      <jats:sup>\u22122<\/jats:sup>\n                      ) and reduced C-X-C Motif Chemokine Ligand 5 (CXCL5,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a02.83 \u00d7 10\n                      <jats:sup>\u22122<\/jats:sup>\n                      ) in the hsGDS group. Including CXCL5 in predictive models improved trajectory differentiation (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a03.94 \u00d7 10\n                      <jats:sup>\u22123<\/jats:sup>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924933826101539_sec4\" sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Cellular aging biomarkers like CXCL5 may improve understanding of LLD and guide personalized therapeutic interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1192\/j.eurpsy.2026.10153","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:41:17Z","timestamp":1769150477000},"source":"Crossref","is-referenced-by-count":3,"title":["Exploring blood-based biomarkers in late-life depression: Correlates of psychotherapeutic treatment 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