Effects of Complement Regulators and Chemokine Receptors in Type 2 Diabetes

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation

BACKGROUND

The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine.

OBJECTIVE

This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation.

METHODS

The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis.

RESULTS

The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems.

CONCLUSION

GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

Systematic review and bioinformatics analysis of plasma and serum extracellular vesicles proteome in type 2 diabetes

Background

Type 2 diabetes (T2D) is a complex metabolic ailment marked by a global high prevalence and significant attention in primary healthcare settings due to its elevated morbidity and mortality rates. The pathophysiological mechanisms underlying the onset and progression of this disease remain subjects of ongoing investigation. Recent evidence underscores the pivotal role of the intricate intercellular communication network, wherein cell-derived vesicles, commonly referred to as extracellular vesicles (EVs), emerge as dynamic regulators of diabetes-related complications. Given that the protein cargo carried by EVs is contingent upon the metabolic conditions of the originating cells, particular proteins may serve as informative indicators for the risk of activating or inhibiting signaling pathways crucial to the progression of T2D complications.

Methods

In this study, we conducted a systematic review to analyze the published evidence on the proteome of EVs from the plasma or serum of patients with T2D, both with and without complications (PROSPERO: CRD42023431464).

Results

Nine eligible articles were systematically identified from the databases, and the proteins featured in these articles underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We identified changes in the level of 426 proteins, with CST6, CD55, HBA1, S100A8, and S100A9 reported to have high levels, while FGL1 exhibited low levels.

Conclusion

These proteins are implicated in pathophysiological mechanisms such as inflammation, complement, and platelet activation, suggesting their potential as risk markers for T2D development and progression. Further studies are required to explore this topic in greater detail.

New insight of complement system in the process of vascular calcification

The complement system defences against pathogenic microbes and modulates immune homeostasis by interacting with the innate and adaptive immune systems. Dysregulation, impairment or inadvertent activation of complement system contributes to the pathogenesis of some autoimmune diseases and cardiovascular diseases (CVD). Vascular calcification is the pivotal pathological basis of CVD, and contributes to the high morbidity and mortality of CVD. Increasing evidences indicate that the complement system plays a key role in chronic kidney diseases, atherosclerosis, diabetes mellitus and aging-related diseases, which are closely related with vascular calcification. However, the effect of complement system on vascular calcification is still unclear. In this review, we summarize current evidences about the activation of complement system in vascular calcification. We also describe the complex network of complement system and vascular smooth muscle cells osteogenic transdifferentiation, systemic inflammation, endoplasmic reticulum stress, extracellular matrix remodelling, oxidative stress, apoptosis in vascular calcification. Hence, providing a better understanding of the potential relationship between complement system and vascular calcification, so as to provide a direction for slowing the progression of this burgeoning health concern.

Serum stromal cell-derived factor-1 levels are associated with diabetic kidney disease in type 2 diabetic patients

The present study was designed to explore whether serum stromal cell-derived factor-1 (SDF-1) levels were associated with diabetic kidney disease (DKD). Serum SDF-1 levels were measured by sandwich ELISA. Patients with an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m² or a urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g for 3 months were identified as having DKD. Among the recruited type 2 diabetic patients, 18.71% (n = 32) were found to have DKD, and the serum SDF-1 levels of these patients were higher than those of patients without DKD (p < 0.05). Serum SDF-1 levels were positively correlated with cystatin C levels, the UACR and DKD incidence (r = 0.330, 0.183 and 0.186, respectively, p < 0.05) and inversely related to eGFR (r = -0.368, p < 0.001). After adjusting for other clinical covariates by multivariate logistic regression analyses, serum SDF-1 levels were found to be an independent contributor to DKD, and the odds ratio (95% confidence interval) was 1.438 (1.041-1.986). Furthermore, receiver operating characteristic analysis revealed that the optimal SDF-1 cutoff value for indicating DKD was 5.609 ng/mL (its corresponding sensitivity was 82.00%, and specificity was 46.90%). Our results demonstrated that serum SDF-1 levels were closely associated with DKD and could be considered a potent indicator for DKD in patients with T2D.