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As one of the most common antipsychotics, olanzapine may cause metabolic‐related adverse effects, but it is still unknown how olanzapine alters lipid metabolism. In this study, we found that olanzapine‐treated mice showed varying degrees of dyslipidemia, which was particularly pronounced in female mice. Based on ultra‐performance liquid chromatography–quadrupole time‐of‐flight–MS (UPLC–Q‐TOF–MS) technology and lipid metabolomics, we mapped the changes in lipid metabolism in olanzapine‐treated mice and then compared the changes in lipid metabolism between male and female mice. There were 98 metabolic differentiators between the olanzapine‐treated and control groups in females and 79 in males. These metabolites were glycerolipids, glycerophospholipids, fatty amides, and sphingolipids, which are involved in glycerolipid metabolism, glycerophospholipid metabolism, and fatty acid metabolism. These results suggest that olanzapine‐induced changes in the levels of lipid metabolites are closely associated with disturbances in lipid metabolic pathways, which may underlie lipemia. This lipidome profiling study not only visualizes changes in lipid metabolism in liver tissue but also provides a foundation for understanding the regulatory pathways and mechanisms involved in olanzapine‐induced lipid metabolism disorders. Furthermore, this study demonstrates differences in lipid metabolism between males and females, providing a reference for clinical treatment regimen selection. [ABSTRACT FROM AUTHOR]

Objective: Alzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence‐associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi‐omics approach and to establish diagnostic assays for AD. Methods: This retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high‐resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K‐nearest neighbors (KNN) algorithm was utilized to construct a multi‐dimensional model for distinguishing AD from CN. Results: Proteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C‐reactive protein (CRP), fibronectin (FN1), and serum amyloid A‐1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN‐based three‐dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD. Interpretation: The multi‐omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging‐associated pathophysiology. [ABSTRACT FROM AUTHOR]