BackgroundMounting evidence strongly uncovered that peripheral immuno-inflammatory response induced by acute stroke is associated with the appearance of post-stroke depression (PSD), but the mechanism remains unclear.Methods103 stroke patients were assessed at 2 weeks after onset using Diagnostic and Statistical Manual of Mental Disorders, 5th edition and then divided into PSD and non-PSD groups. Polymorphisms of inflammatory molecules (interleukin [IL]-1β, IL-6, IL-10, IL-18, tumor necrosis factor-α [TNF-α], interferon-γ [IFN-γ] and C-reactive protein [CRP]), complete blood count parameters, splenic attenuation (SA) and splenic volume (SV) on unenhanced chest computed tomography, demographic and other clinical characteristics were obtained. Binary logistic regression model was used to analyze the associations between inflammation-related factors and the occurrence of PSD at 2 weeks after stroke.Results49 patients were diagnosed with PSD at 2 weeks after onset (early-onset PSD). The C/T genotypes of CRP rs2794520 and rs1205 were less in PSD group than non-PSD group (both adjusted odds ratio = 3.364; 95%CI: 1.039-10.898; p = 0.043). For CRP rs3091244, the frequency of G allele was higher (80.61% vs. 13.89%) while the frequency of A allele was lower (6.12% vs. 71.30%) in PSD patients than non-PSD patients (χ2 = 104.380; p<0.001). SA of PSD patients was lower than that of non-PSD patients in the presence of CRP rs2794520 C/T genotype and rs1205 C/T genotype (both t = 2.122; p = 0.039). Peripheral monocyte count was less in PSD group than non-PSD group (adjusted odds ratio = 0.057; 95%CI: 0.005-0.686; p = 0.024).ConclusionsCRP polymorphisms, SA based on CRP genotype, and peripheral monocytes are associated with the risk of early-onset PSD, suggesting peripheral immuno-inflammatory activities elicited by stroke in its aetiology.

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Objective The purpose of this study was to establish a nomogram predictive model of clinical risk factors for post-stroke depression (PSD). Patients and Methods We used the data of 202 stroke patients collected from Xuanwu Hospital from October 2018 to September 2020 as training data to develop a predictive model. Nineteen clinical factors were selected to evaluate their risk. Minimum absolute contraction and selection operator (LASSO, least absolute shrinkage and selection operator) regression were used to select the best patient attributes, and seven predictive factors with predictive ability were selected, and then multi-factor logistic regression analysis was carried out to determine six predictive factors and establish a nomogram prediction model. The C-index, calibration chart, and decision curve analyses were used to evaluate the predictive ability, accuracy, and clinical practicability of the prediction model. We then used the data of 156 stroke patients collected by Xiangya Hospital from June 2019 to September 2020 for external verification. Results The selected predictors including work style, number of children, time from onset to hospitalization, history of hyperlipidemia, stroke area, and the National Institutes of Health Stroke Scale (NIHSS) score. The model showed good prediction ability and a C index of 0.773 (95% confidence interval: [0.696–0.850]). It reached a high C-index value of 0.71 in bootstrap verification, and its C index was observed to be as high as 0.702 (95% confidence interval: [0.616–0.788]) in external verification. Decision curve analyses further showed that the nomogram of post-stroke depression has high clinical usefulness when the threshold probability was 6%. Conclusion This novel nomogram, which combines patients’ work style, number of children, time from onset to hospitalization, history of hyperlipidemia, stroke area, and NIHSS score, can help clinicians to assess the risk of depression in patients with acute stroke much earlier in the timeline of the disease, and to implement early intervention treatment so as to reduce the incidence of PSD.

Recurrent ischemic stroke (IS) is one of the leading causes of disability and death worldwide. Patients with recurrent IS, in comparison with survivors of the initial non-cardiogenic IS, have more serious neurological deficit and longer average hospital stay as well as heavier family and socio-economic burden. Therefore, recurrent IS is a major challenge that we urgently need to address. The recurrence rate of non-cardiogenic IS is not zero, and even shows an increasing trend over a long period of time, despite receiving evidence-based management in accordance with guideline, indicating that patients suffering from non-cardiogenic IS and who are receiving the optimal management remain at considerable residual risks (RRs) responsible for the recurrence of cerebrovascular events. In addition to low-density lipoprotein cholesterol (LDL-C) and platelets, some new non-traditional parameters such as high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), lipoprotein(a) [Lp(a)], peripheral circulating platelet-derived microvesicles, white blood cells-platelet complexes, NOD-like receptor protein 3 (NLRP3) inflammasome, monomeric C-reactive protein, neutrophils and their products (neutrophil extracellular traps, NETs), may also be potential sources of RRs for recurrent IS. On the basis of the three pillars of secondary stroke prevention, namely, blood pressure reduction, lipid-lowering and antiplatelet therapy, the reduction in RRs may provide additional protection against recurrent IS. With this background, the identification and quantification of RRs associated with disease heterogeneity and individualized treatment strategies based on risk stratification are favorable in the mitigation of huge stroke burden people unceasingly face.