Multiplex Immunoassay Profiling of Hormones Involved in Metabolic Regulation

Pancreatic cancer-associated diabetes mellitus is characterized by reduced β-cell secretory capacity, rather than insulin resistance

AIMS

The early distinction of pancreatic cancer associated diabetes (PaCDM) in patients with elderly diabetes is critical. However, PaCDM and type 2 diabetes mellitus (T2DM) remain indistinguishable. We aim to address the differences between the pancreatic and gut endocrine hormones of patients with PaCDM and T2DM.

METHODS

A total of 44 participants underwent mixed meal tolerance test (MMTT). Fasting and postprandial concentrations of insulin, C-peptide, glucagon, pancreatic polypeptide (PP), glucagon-like peptide-1 (GLP-1), and gastric inhibitory peptide (GIP) were measured. Insulin sensitivity and secretion indices were calculated. One-way ANOVA with post-hoc analysis was used for statistical analysis.

RESULTS

Insulin and C-peptide responses to MMTT were blunted in PaCDM patients compared with T2DM. Baseline concentrations and AUCs differed. PaCDM patients showed lower insulin secretion capacity but better insulin sensitivity than T2DM patients. The peak concentration and AUC of PP in T2DM group were higher than healthy controls, but in accordance with PaCDM. PaCDM patients presented lower baseline GLP-1 concentration than T2DM patients. No between-group differences were found for glucagon and GIP.

CONCLUSIONS

PaCDM patients had a lower baseline and postprandial insulin and C-peptide secretion than T2DM patients. Reduced insulin secretion and improved peripheral sensitivity were found in PaCDM patients compared with T2DM.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.