Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

Advances in the role of GPX3 in ovarian cancer (Review)

Ovarian cancer (OC) is the 5th most common malignancy in women, and the leading cause of death from gynecologic malignancies. Owing to tumor heterogeneity, lack of reliable early diagnostic methods and high incidence of chemotherapy resistance, the 5‑year survival rate of patients with advanced OC remains low despite considerable advances in detection and therapeutic approaches. Therefore, identifying novel therapeutic targets to improve the prognosis of patients with OC is crucial. The expression of glutathione peroxidase 3 (GPX3) plays a crucial role in the growth, proliferation and differentiation of various malignant tumors. In OC, GPX3 is the only antioxidant enzyme the high expression of which is negatively correlated with the overall survival of patients. GPX3 may affect lipid metabolism in tumor stem cells by influencing redox homeostasis in the tumor microenvironment. The maintenance of stemness in OC stem cells (OCSCs) is strongly associated with poor prognosis and recurrence in patients. The aim of the present study was to review the role of GPX3 in OC and investigate the potential factors and effects of GPX3 on OCSCs. The findings of the current study offer novel potential targets for drug therapy in OC, enhance the theoretical foundation of OC drug therapy and provide valuable references for clinical treatment.

Predictive biomarkers of rapidly developing insulin deficiency in children with type 1 diabetes

INTRODUCTION

The rate of progression to complete insulin deficiency varies greatly in type 1 diabetes. This constitutes a challenge, especially when randomizing patients in intervention trials aiming to preserve beta cell function. This study aimed to identify biomarkers predictive of either a rapid or slow disease progression in children with new-onset type 1 diabetes.

RESEARCH DESIGN AND METHODS

A retrospective, longitudinal cohort study of children (<18 years) with type 1 diabetes (N=46) was included at diagnosis and followed until complete insulinopenia (C-peptide <0.03 nmol/L). Children were grouped into rapid progressors (n=20, loss within 30 months) and slow progressors (n=26). A sex-matched control group of healthy children (N=45) of similar age was included for comparison. Multiple biomarkers were assessed by proximity extension assay (PEA) at baseline and follow-up.

RESULTS

At baseline, rapid progressors had lower C-peptide and higher autoantibody levels than slow. Three biomarkers were higher in the rapid group: carbonic anhydrase 9, corticosteroid 11-beta-dehydrogenase isozyme 1, and tumor necrosis factor receptor superfamily member 21. In a linear mixed model, 25 proteins changed over time, irrespective of group. One protein, a coxsackievirus B-adenovirus receptor (CAR) increased over time in rapid progressors. Eighty-one proteins differed between type 1 diabetes and healthy controls. Principal component analysis could not distinguish between rapid, slow, and healthy controls.

CONCLUSIONS

Despite differences in individual proteins, the combination of multiple biomarkers analyzed by PEA could not distinguish the rate of progression in children with new-onset type 1 diabetes. Only one marker was altered significantly when considering both time and group effects, namely CAR, which increased significantly over time in the rapid group. Nevertheless, we did find some markers that may be useful in predicting the decline of the C-peptide. Moreover, these could potentially be important for understanding type 1 diabetes pathogenesis.

Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes

Background

Observational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).

Methods

We conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.

Results

Our study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1-1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87-0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721-0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872-0.950], p = 1.52 × 10-5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963-0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778-0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008-1.206], p = 0.033).

Conclusion

In summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.