Month: May 2024

Endotrophin as an Early Marker of Kidney Outcomes in Type 2 Diabetes

Posted on by Claire Hornung

Endotrophin as an early marker of kidney outcomes in persons with type 2 diabetes: findings from the PROVALID study

Introduction

Diabetic kidney disease (DKD) is driven by pathophysiological processes, including fibrosis. Endotrophin (ETP), a pro-fibrotic fragment generated during collagen type VI formation, has previously been shown to be a biomarker of DKD progression1,2,3,4,5. The aim of this study was to investigate, for the first time, circulating ETP as a risk marker for kidney outcomes in persons with type 2 diabetes (T2D) being taken care of at the primary level of healthcare.

Poster

ERA2024 Endotrophin in Type 2 Diabetes_Nordic BioscienceDownload

Conclusion

Plasma ETP (endotrophin) has been identified as an independent risk marker for kidney outcomes in individuals with type 2 diabetes (T2D) with early-stage kidney disease. Higher levels of ETP were associated with a significantly increased risk of developing the kidney endpoint in persons with eGFR >90 ml/min/1.73 m2. These findings demonstrate that markers of fibrosis, such as ETP, may serve as early markers for kidney disease progression or kidney failure in persons with T2D and apparently normal kidney function.

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    LG1M is Prognostic for Long-Term Readmission After AKI

    Posted on by Claire Hornung

    The novel fibrosis biomarker LG1M is prognostic for long-term readmission after AKI

    Introduction

    Acute Kidney Injury (AKI), survivors are at increased risk of long-term adverse outcomes, including readmission to hospital. Pathophysiological consequences of AKI, include extracellular matrix (ECM) remodeling. Tools to monitor the long-term health risk of patients after AKI are needed to improve
    patient outcome.

    Poster

    ERA2024 LG1M in AKI_Nordic BioscienceDownload

    Conclusion

    Circulating levels of LG1M were elevated in AKI patients 1 year after the AKI episode and correlated with markers of kidney function in the AKI group and to a lower extent in the CKD control group. In the AKI group, LG1M was associated with the risk of readmission, even though the significance of the association was lost in adjusted analyses. This biomarker, quantifying circulating levels of a laminin fragment, may reflect injury to the basement membrane (of which laminin is a major component) after AKI, which was associated with an increased risk of worse outcome in patients that experienced an episode of AKI.

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      Advancing Diagnostic Precision of Emphysema COPD

      Posted on by Claire Hornung

      Machine-learning classification integrating non-invasive biomarkers, clinical characteristics and pulmonary function

      Introduction

      Computed tomography (CT) is used for evaluating phenotypic abnormalities in chronic obstructive
      pulmonary disease (COPD), yet its cost and time-intensive nature limit routine use. Developing an
      easily implementable technique for classifying emphysema extent is thus essential.

      This study aimed to develop a diagnostic model to classify emphysema extent in COPD
      patients relying solely on easily obtained measures such as clinical characteristics and
      non-invasive biomarkers.

      Poster

      ATS2024_Emphysema_Model Nordic BioscienceDownload

      Conclusion

      Diagnostic models incorporating easily obtainable measures effectively
      distinguished COPD patients with high emphysema extent from those with
      low extent. Such models for classifying emphysema patterns have the
      potential for clinical implementation, aiding in diagnosis or serving as a
      decision-making tool to determine the necessity of further CT scans.

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        Data-Driven Comorbidity Profiles in COPD

        Posted on by Claire Hornung

        Data-driven identification and investigation of comorbidity profiles in patients with chronic obstructive pulmonary disease: a multicohort study

        Introduction

        Comorbidities are common in chronic obstructive pulmonary disease (COPD), adversely affecting
        patients’ quality of life and their disease trajectories. While previous studies have predominantly
        examined individual comorbidities, there has been limited exploration of their coexistence.

        This study aimed to identify comorbidity clusters among real-world cohorts of COPD
        patients using machine learning techniques, and to investigate clinical characteristics and
        mortality within these clusters.

        Poster

        ATS2024_ Comorbidity_Clusters_Nordic BioscienceDownload

        Conclusion

        This study confirms distinct comorbidity clusters in two well-characterized
        cohorts of patients with COPD which can be linked to different patient
        subgroups. In a broad COPD patient population, comorbidity profiles could
        hold prognostic relevance. The findings of this study enhance the
        understanding of the comorbidity landscape in COPD and highlights the
        importance of comorbidity assessment in clinical management.

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