High-density lipoprotein cholesterol is associated with lowered cognitive recovery among acute ischemic stroke patients with mild cognitive impairment.

High-density lipoprotein (HDL) has been documented to be related to mild cognitive impairment (MCI) and dementia occurrence; however, the underlying basis behind this association remains unclear. We aimed to elucidate this basis by examining the association between HDL levels and cognitive improvements after 6 months, among acute ischemic stroke (AIS) patients with MCI. Five hundred fifty-eight AIS and MCI patients from the NICE study were enrolled, and divided into four groups, according to their baseline HDL quartiles; median HDL was 1.12 mmol/L (interquartile range 0.96-1.34 mmol/L). The primary outcome examined was the extent of cognitive improvement, defined as ΔMoCA (Montreal Cognitive Assessment) ≥ 2, while the secondary outcome was cognitive deterioration, defined as ΔADAS-cog (Alzheimer's Disease Assessment Scale-Cognitive Subscale) ≥ 4 or ΔMMSE (Mini-Mental State Examination) ≤ - 3, at 6-months post-AIS. We found that 314 (56.27%), 49 (8.78%), and 31 (5.56%) patients had ΔMoCA ≥ 2, ΔADAS-cog ≥ 4, and ΔMMSE ≤ - 3, respectively. Furthermore, cognitive improvement negatively correlated to HDL levels, with the lowest being present among patients in quartiles 4 (Q4; adjusted OR = 0.44, 95% CI 0.25-0.78, P = 0.0050) and Q3 (OR = 0.38, CI 0.23-0.65, P = 0.0004), compared to Q2 (OR = 0.57, CI 0.34-0.96, P = 0.0331). Q2 patients also had positive correlations with ΔADAS-cog ≥ 4 (OR = 5.18, CI 1.55-17.29, P = 0.0074). However, no association between HDL and ΔMMSE ≤ - 3 was observed, nor with LDL and any cognitive changes. Additionally, restricted cubic spline analysis found a nonlinear relationship between HDL and cognitive improvements. All these findings suggested that low plasma HDL was positively associated with improved cognitive functioning among AIS patients with MCI after 6 months.

Establishment of comorbidity target pools and prediction of drugs candidate for multiple sclerosis and autoimmune thyroid diseases based on GWAS and transcriptome data.

BACKGROUND: Clinical observation has revealed that multiple sclerosis (MS) and autoimmune thyroid disease (AITD) are strongly correlated. The aim of this study was to explore the shared molecular causes of MS and AITD, and to conduct drug rearrangement on this basis, search for comorbidity drugs and feasible drugs for mutual reference between the two diseases. METHODS: Based on genome-wide association study (GWAS) data and transcriptome data, susceptibility genes and differentially expressed genes related to MS and AITD were identified by bioinformatics analysis. Pathway enrichment, gene ontology (GO), protein-protein interaction analysis, and gene-pathway network analysis of the above genes were performed to identify a common target pool, including common genes, common hub genes, and common pathways, and to explore the specific pathogenesis of the two diseases, respectively. Drugs that target the common pathways/genes were identified through the Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. Common hub genes were compared with the target genes of drugs approved for treating MS/AITD and drugs under investigation identified by DrugBank and ClinicalTrials, respectively. RESULTS: We identified a pool of shared targets containing genes and pathways, including 46 common genetic susceptibility pathways and 9 common differentially expressed pathways, including JAK-STAT signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. In addition, a total of 29 hub genes, including TYK2, JAK1, STAT3, IL2RA, HLA-DRB1, and TLR3, were identified. Drugs approved for treating MS or AITD, such as methylprednisolone, cyclophosphamide, glatiramer, natalizumab, and methimazole, can target the shared genes and pathways, among which methylprednisolone and cyclophosphamide have been shown to be beneficial for the treatment of the two diseases, indicating that these drugs have the potential to become a priority in the treatment of comorbidities. Moreover, drugs targeting multiple common genes and pathways, including tacrolimus, deucravacitinib, and nivolumab, were identified as potential drugs for the treatment of MS, AITD, and their comorbidities. CONCLUSION: We observed that T-cell activation-related genes and pathways play a major role in the pathogenesis of both MS and AITD, which may be the molecular basis of the comorbidity. Moreover, we identified a variety of drugs which may be used as priority or potential treatments for comorbidities.

Increased Two-Hour Post-Load Plasma Glucose Fluctuation Corresponds with Worse Clinical Prognoses among Acute Ischemic Stroke Patients without a History of Diabetes Mellitus.

INTRODUCTION: This study aimed to evaluate the relationship between 2-h post-load minus fasting plasma glucose (2hPG-FPG) and 1-year clinical outcomes, such as death, stroke recurrence, and modified Rankin Scale (mRS) ≥2-3 among acute ischemic stroke (AIS) patients without diabetes mellitus (DM) history. METHODS: 1,214 AIS patients without DM history, obtained from ACROSS-China, were divided into 4 quartiles, based on 2hPG-FPG measurements obtained 14 days post-admission. Four models were constructed using multivariate Cox and logistic regression analyses, based on the inclusion of age, gender, trial of ORG 10172 in acute stroke treatment, NIH Stroke Scale scores (model 1), plus 10 other clinical parameters (model 2), plus newly diagnosed DM (NDDM) post-admission (model 3), plus 2hPG and FPG (model 4). Associations found from those 4 models between 2hPG-FPG and 1-year clinical outcomes were confirmed via stratification, multiplicative interaction, sensitivity, and restricted cubic spline analyses. RESULTS: The highest quartile of 2hPG-FPG, after adjusting for variables, such as stroke severity (model 2), was independently associated with death, stroke recurrence, and mRS ≥2-3 (odds ratio [OR] = 3.95, 2.96, 4.15, and 4.83, respectively, all p < 0.0001). Increased 2hPG-FPG remained independently associated with mRS ≥2-3 in models 3-4, as well as increased mRS ≥2 under stratification analyses among both non-NDDM and NDDM patients. CONCLUSION: 2hPG-FPG is a relatively specific indicator of poorer 1-year clinical prognoses among AIS patients, independent of NDDM, 2hPG, and FPG post-hospital admission. Therefore, the oral glucose tolerance test could be a useful approach for detecting a higher likelihood for developing poorer prognoses among patients without DM history.

Immune cells transcriptome-based drug repositioning for multiple sclerosis.

Objective: Finding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells. Materials and Methods: Based on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results: We obtained 50 hub target genes for CD4+ T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-ß (IFN-ß) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19+ B cells and 15 target pathways for CD4+ T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-ß for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways. Conclusion: We found that applying candidate drugs that target both the "PI3K-Akt signaling pathway" and "Chemokine signaling pathway" (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

The Shared Mechanism and Candidate Drugs of Multiple Sclerosis and Sjögren's Syndrome Analyzed by Bioinformatics Based on GWAS and Transcriptome Data.

Objective: This study aimed to explore the shared mechanism and candidate drugs of multiple sclerosis (MS) and Sjögren's syndrome (SS). Methods: MS- and SS-related susceptibility genes and differentially expressed genes (DEGs) were identified by bioinformatics analysis based on genome-wide association studies (GWAS) and transcriptome data from GWAS catalog and Gene Expression Omnibus (GEO) database. Pathway enrichment, Gene Ontology (GO) analysis, and protein-protein interaction analysis for susceptibility genes and DEGs were performed. The drugs targeting common pathways/genes were obtained through Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. The target genes of approved/investigational drugs for MS and SS were obtained through DrugBank and compared with the common susceptibility genes. Results: Based on GWAS data, we found 14 hub common susceptibility genes (HLA-DRB1, HLA-DRA, STAT3, JAK1, HLA-B, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB5, HLA-DPA1, HLA-DPB1, TYK2, IL2RA, and MAPK1), with 8 drugs targeting two or more than two genes, and 28 common susceptibility pathways, with 15 drugs targeting three or more than three pathways. Based on transcriptome data, we found 3 hub common DEGs (STAT1, GATA3, PIK3CA) with 3 drugs and 10 common risk pathways with 435 drugs. "JAK-STAT signaling pathway" was included in common susceptibility pathways and common risk pathways at the same time. There were 133 overlaps including JAK-STAT inhibitors between agents from GWAS and transcriptome data. Besides, we found that IL2RA and HLA-DRB1, identified as hub common susceptibility genes, were the targets of daclizumab and glatiramer that were used for MS, indicating that daclizumab and glatiramer may be therapeutic for SS. Conclusion: We observed the shared mechanism of MS and SS, in which JAK-STAT signaling pathway played a vital role, which may be the genetic and molecular bases of comorbidity of MS with SS. Moreover, JAK-STAT inhibitors were potential therapies for MS and SS, especially for their comorbidity.