Conectar
by Satomi Mezuki, Ryu Matsuo, Fumi Irie, Yuji Shono, Takahiro Kuwashiro, Hiroshi Sugimori, Yoshinobu Wakisaka, Tetsuro Ago, Masahiro Kamouchi, Takanari Kitazono, on behalf of the Fukuoka Stroke Registry Investigators
BackgroundThis study aimed to examine whether post-stroke early body temperature is associated with neurological damage in the acute phase and functional outcomes at three months.
MethodsWe included 7,177 patients with acute ischemic stroke within 24 h of onset. Axillary temperature was measured daily in the morning for seven days. Mean body temperature was grouped into five quintiles (Q1: 35.1‒36.5°C, Q2: 36.5‒36.7°C, Q3: 36.7‒36.8°C, Q4: 36.8‒37.1°C, and Q5: 37.1‒39.1°C). Clinical outcomes included neurological improvement during hospitalization and poor functional outcome (modified Rankin scale score, 3–6) at three months. A logistic regression analysis was performed to evaluate the association between body temperature and clinical outcomes.
ResultsThe patient’s mean (SD) age was 70.6 (12.3) years, and 35.7% of patients were women. Mean body temperature was significantly associated with less neurological improvement from Q2 (odds ratios [95% confidence interval], 0.77 [0.65–0.99] vs. Q1) to Q5 (0.33 [0.28–0.40], P for trend 37.0°C.
ConclusionsPost-stroke early high body temperature is independently associated with unfavorable outcomes following acute ischemic stroke.
by Kayo Wakisaka, Ryu Matsuo, Fumi Irie, Yoshinobu Wakisaka, Tetsuro Ago, Masahiro Kamouchi, Takanari Kitazono, on behalf of the Fukuoka Stroke Registry Investigators
BackgroundIt is unclear whether abdominal adiposity has an additional effect on post-stroke outcomes. This study aimed to determine whether waist circumference (WC) is independently associated with clinical outcomes after acute ischemic stroke.
MethodsWe enrolled patients with acute ischemic stroke from a multicenter hospital-based stroke registry in Fukuoka, Japan. We measured WC on admission and categorized patients into four groups (Q1–Q4) according to the quartiles in females and males. The clinical outcomes were poor functional outcome (modified Rankin scale score 2–6) and death from any cause. Logistic regression analysis was performed to estimate the odds ratio and 95% confidence interval of the outcomes of interest after adjusting for potential confounding factors, including body mass index (BMI).
ResultsA total of 11,989 patients (70.3±12.2 years, females: 36.1%) were included in the analysis. The risk of poor functional outcome significantly decreased for Q2–Q4 (vs. Q1) at discharge and Q2–Q3 (vs. Q1) at 3 months, even after adjusting for potential confounders, including BMI. In contrast, adjustment of BMI eliminated the significant association between WC and all-cause death at discharge and 3 months. The association between high WC and favorable functional outcome was not affected by fasting insulin levels or homeostatic model assessment for insulin resistance and was only found in patients without diabetes (P = 0.02 for heterogeneity).
ConclusionsThese findings suggest that abdominal adiposity has an additional impact on post-stroke functional outcome, independent of body weight and insulin action.
by Guanghao Xin, Jingyan Niu, Qinghua Tian, Yanchi Fu, Lixia Chen, Tingting Yi, Kuo Tian, Xuesong Sun, Na Wang, Jianjian Wang, Huixue Zhang, Lihua Wang
BackgroundParkinson’s disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson’s disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model.
MethodsWe downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples).
ResultsUltimately, we screened for four immune-related Parkinson’s disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson’s disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson’s disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes.
ConclusionIn this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.
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