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Receptor soluble de productos finales de glicación avanzada circulante como biomarcador novedoso que vincula la
Xueying Zhang1, Chao Sun1, Shiting Yuan1
1Suzhou Guangji Hospital, Suzhou, Jiangsu Province, China; Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China.
Objective:
The receptor for advanced glycation end products (RAGE) activates NF-κB and MAPK signaling pathways, which are critically implicated in biological rhythm regulation. Importantly, altered serum soluble RAGE (sRAGE) levels are documented in somatic and neuropsychiatric disorders, including major depressive disorder (MDD). However, the association between sRAGE and biological rhythm disruptions in MDD remains unclear, so this study aimed to elucidate the mechanisms and find potential biomarkers.
Methods:
We conducted a case-control study involving 73 MDD patients and 48 healthy controls (HCs). Assessments included the self-designed demographic questionnaire, Insomnia Severity Index, Self-Rating Idea of Suicide Scale, Social Support Rating Scale, Hamilton Depression Rating Scale-24, Hamilton Anxiety Rating Scale-14, and Biological Rhythms Interview of Assessment in Neuropsychiatry. Serum biomarkers (S100A8, S100A9, IL-10, and sRAGE) were quantified using Luminex multiplex bead-based immunoassays.
Results:
MDD patients exhibited severe biological rhythm disruptions versus HCs (t = 9.651), alongside significant insomnia (t = 9.688), suicidal ideation (t = 11.127), reduced social support (t = -3.307), and anxiety (t = 17.383). Stepwise linear regression analyses showed that depression (β = 0.347), insomnia (β = 0.367), and sRAGE (β = -0.223) were predictive factors for biological rhythm disruptions. Furthermore, mediation analysis identified insomnia and sRAGE as mediators of biological rhythm disruption in MDD patients, with sRAGE demonstrating a beneficial role (β = -0.21) (p < 0.05 for all).
Conclusion:
sRAGE may be favorably associated with MDD by mitigating disruptions in biological rhythms and may serve as a potential biomarker for predicting and managing depressive episodes.
