Applicability of the low-grade inflammation score in predicting 90-day functional outcomes after acute ischemic stroke

BACKGROUND AND PURPOSE

The low-grade inflammation (LGI) score, a novel indicator of chronic LGI, combines C-reactive protein (CRP), leukocyte counts, the neutrophil/lymphocyte ratio (NLR), and the platelet (PLT) count to predict outcomes of patients with various conditions, such as cardiovascular diseases, cancers, and neurodegenerative diseases. However, few studies have examined the role of the LGI score in predicting functional outcomes of patients with ischemic stroke. The present study aimed to evaluate the association between the LGI score and functional outcomes of patients with ischemic stroke.

METHODS

A total of 1,215 patients were screened in the present study, and 876 patients were finally included in this retrospective observational study based on the inclusion and exclusion criteria. Blood tests were conducted within 24 h of admission. Severity of ischemic stroke was assessed using the NIHSS score with severe stroke denoted by NIHSS > 5. Early neurological deterioration (END) was defined as an increment in the total NIHSS score of ≥ 2 points within 7 days after admission. Patient outcomes were assessed on day 90 after stroke onset using the modified Rankin Scale (mRS).

RESULTS

The LGI score was positively correlated with baseline and the day 7 NIHSS scores (R2 = 0.119, p < 0.001;R2 = 0.123, p < 0.001). Multivariate regression analysis showed that the LGI score was an independent predictor of stroke severity and END. In the crude model, the LGI score in the fourth quartile was associated with a higher risk of poor outcomes on day 90 compared with the LGI score in the first quartile (OR = 5.02, 95% CI: 3.09-8.14, p for trend < 0.001). After adjusting for potential confounders, the LGI score in the fourth quartile was independently associated with poor outcomes on day 90 (OR = 2.65, 95% CI: 1.47-4.76, p for trend = 0.001). Finally, the ROC curve analysis showed an AUC of 0.682 for poor outcomes on day 90 after stroke onset.

CONCLUSION

The LGI score is strongly correlated with the severity of acute ischemic stroke and that the LGI score might be a good predictor for poor outcomes on day 90 in patients with acute ischemic stroke.

Hippocampal gray matter volume alterations in patients with first-episode and recurrent major depressive disorder and their associations with gene profiles

BACKGROUND

Recent studies indicate that patients with first-episode drug-naïve (FEDN) and recurrent major depressive disorder (R-MDD) exhibit distinct atrophy patterns in the hippocampal subregions along the proximal-distal axis. However, it remains unclear whether such differences occur along the long axis and how they may relate to specific genes.

METHODS

In the present study, we analyzed T1-weighted images from 421 patients (FEDN: n = 232; R-MDD: n = 189) and 544 normal controls (NC) as part of the REST-meta-MDD consortium. Additionally, transcriptome maps and structural Magnetic Resonance Imaging (MRI) data of six donated brains were obtained from the Allen Human Brain Atlas (AHBA). We first identified changes in gray matter volume (GMV) within the hippocampus of both FEDN and R-MDD patients and then integrated these findings with AHBA transcriptome data to investigate the genes associated with hippocampal GMV changes.

RESULTS

Compared to NC, FEDN patients displayed reduced GMV in the left hippocampal tail, whereas R-MDD patients exhibited decreased GMV in the bilateral hippocampal body and increased GMV in the bilateral hippocampal tail. Further analysis revealed that expression levels of SYTL2 positively correlated with GMV changes in the hippocampus of FEDN patients, while SORCS3 and SLIT2 positively correlated with those in R-MDD.

CONCLUSIONS

Our results suggest that GMV alterations in hippocampal subfields along the long axis differ between FEDN and R-MDD, reflecting progressive hippocampal deterioration with prolonged depression, potentially supported by the expression of specific genes. These findings offer valuable insights into the distinct neural and genetic mechanisms underlying FEDN and R-MDD, which may aid in the development of more targeted and effective treatment strategies for MDD subtypes.