👤 Francesco Piarulli

Type 2 diabetes mellitus (T2DM) increases the risk of coronary heart disease (CHD) by 2-4 fold, and is associated with endothelial dysfunction, dyslipidaemia, insulin resistance, and chronic hyperglycaemia. The aim of this investigation was to assess, by a multimarker mass spectrometry approach, the predictive role of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus. The study considered 34 patients with both T2DM and CHD, 31 patients with T2DM and without CHD, and 30 patients without diabetes with a diagnosis of CHD. Plasma samples of subjects were analysed through a multiplexed targeted liquid chromatography mass spectrometry (LC-MS)-based assay, namely Multiple Reaction Monitoring (MRM), allowing the simultaneous detection of peptides derived from a protein of interest. Gene Ontology (GO) Analysis was employed to identify enriched GO terms in the biological process, molecular function, or cellular component categories. Non-parametric multivariate methods were used to classify samples from patients and evaluate the relevance of the analysed proteins' panel. A total of 81 proteins were successfully quantified in the human plasma samples. Gene Ontology analysis assessed terms related to blood microparticles, extracellular exosomes and collagen-containing extracellular matrix. Preliminary evaluation using analysis of variance (ANOVA) of the differences in the proteomic profile among patient groups identified 13 out of the 81 proteins as significantly different. Multivariate analysis, including cluster analysis and principal component analysis, identified relevant grouping of the 13 proteins. The first main cluster comprises apolipoprotein C-III, apolipoprotein C-II, apolipoprotein A-IV, retinol-binding protein 4, lysozyme C and cystatin-C; the second one includes, albeit with sub-grouping, alpha 2 macroglobulin, afamin, kininogen 1, vitronectin, vitamin K-dependent protein S, complement factor B and mannan-binding lectin serine protease 2. Receiver operating characteristic (ROC) curves obtained with the 13 selected proteins using a nominal logistic regression indicated a significant overall distinction (p < 0.001) among the three groups of subjects, with area under the ROC curve (AUC) ranging 0.91-0.97, and sensitivity and specificity ranging from 85 to 100%. Targeted mass spectrometry approach indicated 13 multiple circulating proteins as possible biomarkers of cardiovascular damage progression associated with T2DM, with excellent classification results in terms of sensitivity and specificity. Show less

The contribution of the Plantar Fascia (PF) and Long Plantar Ligament (LPL), two ligaments extending from the hindfoot to the forefoot, to arch stability has been studied in the past using in vivo, in vitro, and in silico methodologies. In silico studies were based on one single model obtained from one single subject and did not account for the known inter-subject morphological and biomechanical variations. In the present study, we developed computational dynamic models of nine different legs obtained from nine different individuals to evaluate the role of the LPL and PF in arch support, accounting for biological differences between subjects. These models were validated by comparing the simulation results against experimental results from the corresponding cadaver legs. After validation, we simulated body weight conditions for each model by applying a vertical load to the tibia, starting from zero and increasing linearly to 720 N. Kinematic and dynamic parameters, including the variation of the medial arch angle and of the navicular height, as well as the passive forces developed by the LPL and PF, were used to evaluate the contribution of these ligaments to arch support under body weight. The results indicate that a total collapse of the medial longitudinal arch occurred only when both the LPL and PF were absent, but a stable arch was maintained when either one of these two ligament structures were present. The results varied significantly among the specific models, highlighting the importance of using multiple models to account for inter-subject morphological differences. Show less