Antiphospholipid antibodies predict post-stroke depression after acute ischemic stroke.

J Affect Disord

Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province 215123, China. Electronic address:

Published: October 2019

Background: Antiphospholipid activity was reported to be increased in depressive patients, while the impact of antiphospholipid antibodies (aPLs) on post-stroke depression (PSD) is unclear. We aimed to investigate the associations of aPLs, including antiphosphatidylserine (aPS) and anticardiolipin (aCL) antibodies with depression after acute ischemic stroke.

Methods: aPS and aCL were measured in 497 ischemic stroke patients recruited from 7 of 26 participating hospitals of China Antihypertensive Trial in Acute Ischemic Stroke. 24-item Hamilton Depression Rating Scale was used to evaluate PSD status at 3 months after stroke.

Results: Compared with aPS-negative or aCL-negative, the adjusted odds ratios (ORs) [95% confidence intervals (CIs)] associated with aPS-positive or aCL-positive were 1.77 (1.07-2.92) or 2.06 (1.11-3.80) for risk of PSD. On continuous analyses, per 1-SD increment of aPS and aCL were associated with 29% (OR 1.29, 95% CI 1.06-1.58) and 30% (OR 1.30, 95% CI 1.06-1.60) increased risks for PSD, respectively. Adding aPLs to conventional risk factors models significantly improved risk reclassification for PSD (net reclassification improvement index = 21.87%, P = 0.016 for aPS; net reclassification improvement index = 32.24%, P = 0.0004 for aCL).

Limitations: aPLs levels were tested only at baseline without serial measurements, and we were unable to detect the association between aPLs changes and PSD.

Conclusions: Higher aPS and aCL levels in the acute phase of ischemic stroke were associated with increased risk of 3-month PSD, suggesting that aPLs may play an important role in post-stroke depression prediction.

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http://dx.doi.org/10.1016/j.jad.2019.07.013DOI Listing

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