Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Background: Posterior reversible encephalopathy syndrome (PRES) is a rare complication of lung transplantation with poorly understood risk factors and clinical characteristics. This study aimed to examine the occurrence, risk factors, and clinical data of patients who developed PRES following lung transplantation. Methods: A retrospective analysis was conducted on 147 patients who underwent lung transplantation between February 2013 and December 2023. The patients were diagnosed with PRES based on the clinical symptoms and radiological findings. We compared the baseline characteristics and clinical information, including primary lung diseases and immunosuppressive therapy related to lung transplantation operations, between the PRES and non-PRES groups. Results: PRES manifested in 7.5% (n=11) of the patients who underwent lung transplantation, with a median onset of 15 days after operation. Seizures were identified as the predominant clinical manifestation (81.8%, n=9) in the group diagnosed with PRES. All patients diagnosed with PRES recovered fully. Patients with PRES were significantly associated with connective tissue disease-associated interstitial lung disease (45.5% vs. 18.4%, P=0.019, odds ratio=9.808; 95% CI, 1.064–90.386; P=0.044). Nonetheless, no significant variance was observed in the type of immunotherapy, such as the use of calcineurin inhibitors, blood pressure, or acute renal failure subsequent to lung transplantation. Conclusions PRES typically manifests shortly after lung transplantation, with seizures being the predominant initial symptom. The presence of preexisting connective tissue disease as the primary lung disease represents a significant risk factor for PRES following lung transplantation.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.