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Purpose: Post-stroke depression (PSD) is a psychiatric complication after stroke that leads to poorer stroke outcomes. Recent observational studies have indicated that lipid profiles were associated with a higher risk of stroke and depression. This study aims to further explore the possible relationship between serum lipid profiles and the development of PSD. Methods: A total of 373 acute ischemic stroke patients were examined. Serum lipid profiles including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), apolipoprotein A1 (Apo A1) and apolipoprotein B (Apo B) were measured within 24 hrs of admission. Depression symptoms were assessed by the 17-item Hamilton Depression Scale (HAMD-17) at the one-month follow-up, and HAMD scores ≥7 indicated a diagnosis of PSD. Results: A total of 114 patients were diagnosed with PSD at the one-month follow-up, for a percentage of 30.6%. There were significant differences in HDL-C levels (P<0.001), LDL-C levels (P=0.002) and the LDL/HDL ratio (P<0.001) between the PSD and non-PSD groups, but no differences were observed in TGs, TC, Apo A1 or Apo B. Low serum HDL-C levels (r = -0.157, P<0.001) and elevated LDL-C levels (r =0.139, P=0.002) and the LDL/HDL ratio (r =0.227, P<0.001) were associated with HAMD scores. After adjusting for the NIHSS score, BI score, mRS score and alcohol consumption in the logistic analysis, low HDL-C levels and the highest quartile (≥3.07) of the LDL/HDL ratio were independently associated with the development of PSD (OR =0.250, 95% CI, 0.077-0.813, P=0.021 and OR =1.874, 95% CI, 1.050-3.347, P=0.034, respectively). Conclusion: Decreased levels of HDL-C and elevated levels of LDL/HDL ratio are associated with PSD. HDL-C and the LDL/HDL ratio are independent predictors of PSD.
Pubmed ID: 31354274
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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