Outcomes of adding second hypoglycemic drug after metformin monotherapy failure among type 2 diabetes in Hungary

<p>Abstract</p> <p>Aim</p> <p>The objective of this observational study was to assess the status of glycemic control and associated patient-reported outcomes in ambulatory Hungarian patients with type 2 diabetes mellitus (T2DM) who were prescribed either a sulfonylurea (SU) or a thiazolidinedione (TZD) in addition to the prior metformin (MF) monotherapy.</p> <p>Methods</p> <p>Type 2 diabetics aged ≥ 30 years and who had added an SU or TZD to previous MF monotherapy at least 1 year prior to the visit date were identified during January 2006 to March 2007. Information on HbA1c (A1C), medication use and co-morbid conditions was extracted from the medical record up to 6 months prior to the addition of SU or TZD to MF (baseline), and a minimum of one year after the initiation of either SU or TZD. Glycemic control (A1C < 6.5%) was assessed using the last available A1C value in the medical record. Self-reported hypoglycemia, health-related quality of life (HRQoL) and treatment satisfaction were also assessed.</p> <p>Results</p> <p>A total of 414 patients (82% SU+MF and 18% TZD+MF) with a mean age of 60.5 years (SD = 9.4 years) participated in the study. About 27% of patients reported hypoglycemic episodes, with about one-third reporting episodes that resulted into interruption of activities or required medical/non-medical assistance. Three quarters of patients were not at glycemic goal and BMI was the only factor significantly associated with failure to have an A1C level < 6.5%. Patients' HRQoL was significantly associated with self-reported hypoglycemic episodes (p = 0.017), and duration of diabetes (p = 0.045).</p> <p>Conclusion</p> <p>Nearly 75% of patients were not at A1C goal of < 6.5% despite using two oral anti-hyperglycemic medications. Approximately 9% of patients reporting hypoglycemia required some kind of medical/non-medical assistance. Greater BMI at baseline was associated with an A1C level ≥ 6.5%. Finally, self- reports of hypoglycemia and duration of diabetes were associated with low HRQoL.</p

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis

A dual covariant biomarker approach to Kawasaki disease, implications for coronary pathogenesis using vascular endothelial growth factor A and B gene expression; systematic secondary analysis of clinical datasets

Introduction Kawasaki disease (KD) is the most common vasculitis in young children, with coronary artery lesions (CALs) and coronary aneurysms (CAAs) being responsible for most KD-related deaths. Objective We hypothesized that Vascular Endothelial Growth Factors (VEGFs) are pivotal in KD inflammation and coronary artery lesions. This study assessed VEGF-A and VEGF-B gene expression (GE) as potential biomarkers in KD inflammation. Study design We analyzed NCBI-GEO datasets, categorizing gene expression patterns as inflammatory or non-inflammatory . We focused on TNF-, NFKB1, VEGF-A, and VEGF-B GEs. Datasets were filtered based on differential changes in TNF and NFKB1 levels to isolate those with inflammatory shifts. Results Inflammatory datasets (GSE63881, GSE73464, and GSE68004) displayed elevated TNF, NFKB1, and VEGF-A GE levels during acute KD. VEGF-B GE exhibited a distinctive trend: an initial drop and subsequent rise during recovery, a pattern that was missing in the non-inflammatory group. The treatment response was also studied, with intravenous immunoglobulin (IVIG) responders showing significant downregulation of NFKB1 GE after treatment: GSE16797 [IVIG ± methylprednisolone; p = 8.6443-03], GSE48498 [IVIG; p = 6.618e-02, infliximab; p = 3.240e-03], and GSE18606 [IVIG; p = 3.518e-02]. Considering the similar binding of VEGF-A and VEGF-B to the VEGFR1 receptor, a co-variate and inverse relationship is suggested. Conclusion Temporal VEGF-A, VEGF-B, and GE changes show promise as new post-inflammatory biomarkers for KD. Novelty results with the biomarker approach, with the potential for a dual temporal relationship between VEGF-A and VEGF-A. A comprehensive exploration of VEGF-A and VEGF-B genes and protein analysis in KD is warranted to understand the functional aspects of these changes and how best to utilize the pattern of changes for therapeutic benefit