Medical Genetics, Genomics and Bioinformatics-2022.

The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration [...].

Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

People with diabetes are more likely to have severe COVID-19 compared to the general population. Moreover, diabetes and COVID-19 demonstrate a certain parallelism in the mechanisms and organ damage. In this work, we applied bioinformatics analysis of associative molecular networks to identify key molecules and pathophysiological processes that determine SARS-CoV-2-induced disorders in patients with diabetes. Using text-mining-based approaches and ANDSystem as a bioinformatics tool, we reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance, and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The latter included SARS-CoV-2 entry receptors (ACE2 and DPP4), SARS-CoV-2 entry associated proteases (TMPRSS2, CTSB, and CTSL), and 332 human intracellular proteins interacting with SARS-CoV-2. A number of genes/proteins targeted by SARS-CoV-2 (ACE2, BRD2, COMT, CTSB, CTSL, DNMT1, DPP4, ERP44, F2RL1, GDF15, GPX1, HDAC2, HMOX1, HYOU1, IDE, LOX, NUTF2, PCNT, PLAT, RAB10, RHOA, SCARB1, and SELENOS) were found in the networks of vascular diabetic complications and insulin resistance. According to the Gene Ontology enrichment analysis, the defined molecules are involved in the response to hypoxia, reactive oxygen species metabolism, immune and inflammatory response, regulation of angiogenesis, platelet degranulation, and other processes. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity.

Hypoglycemia, Vascular Disease and Cognitive Dysfunction in Diabetes: Insights from Text Mining-Based Reconstruction and Bioinformatics Analysis of the Gene Networks.

Hypoglycemia has been recognized as a risk factor for diabetic vascular complications and cognitive decline, but the molecular mechanisms of the effect of hypoglycemia on target organs are not fully understood. In this work, gene networks of hypoglycemia and cardiovascular disease, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline, and Alzheimer's disease were reconstructed using ANDSystem, a text-mining-based tool. The gene network of hypoglycemia included 141 genes and 2467 interactions. Enrichment analysis of Gene Ontology (GO) biological processes showed that the regulation of insulin secretion, glucose homeostasis, apoptosis, nitric oxide biosynthesis, and cell signaling are significantly enriched for hypoglycemia. Among the network hubs, INS, IL6, LEP, TNF, IL1B, EGFR, and FOS had the highest betweenness centrality, while GPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR, and AQP7 demonstrated the highest cross-talk specificity. Hypoglycemia-related genes were overrepresented in the gene networks of diabetic complications and comorbidity; moreover, 14 genes were mutual for all studied disorders. Eleven GO biological processes (glucose homeostasis, nitric oxide biosynthesis, smooth muscle cell proliferation, ERK1 and ERK2 cascade, etc.) were overrepresented in all reconstructed networks. The obtained results expand our understanding of the molecular mechanisms underlying the deteriorating effects of hypoglycemia in diabetes-associated vascular disease and cognitive dysfunction.

Glucose Variability: How Does It Work?

A growing body of evidence points to the role of glucose variability (GV) in the development of the microvascular and macrovascular complications of diabetes. In this review, we summarize data on GV-induced biochemical, cellular and molecular events involved in the pathogenesis of diabetic complications. Current data indicate that the deteriorating effect of GV on target organs can be realized through oxidative stress, glycation, chronic low-grade inflammation, endothelial dysfunction, platelet activation, impaired angiogenesis and renal fibrosis. The effects of GV on oxidative stress, inflammation, endothelial dysfunction and hypercoagulability could be aggravated by hypoglycemia, associated with high GV. Oscillating hyperglycemia contributes to beta cell dysfunction, which leads to a further increase in GV and completes the vicious circle. In cells, the GV-induced cytotoxic effect includes mitochondrial dysfunction, endoplasmic reticulum stress and disturbances in autophagic flux, which are accompanied by reduced viability, activation of apoptosis and abnormalities in cell proliferation. These effects are realized through the up- and down-regulation of a large number of genes and the activity of signaling pathways such as PI3K/Akt, NF-κB, MAPK (ERK), JNK and TGF-ß/Smad. Epigenetic modifications mediate the postponed effects of glucose fluctuations. The multiple deteriorative effects of GV provide further support for considering it as a therapeutic target in diabetes.

Medical Genetics, Genomics and Bioinformatics Aid in Understanding Molecular Mechanisms of Human Diseases.

Molecular mechanisms of human disease progression often have complex genetic underpinnings, and sophisticated sequencing approaches coupled with advanced analytics [...].

Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications.

Glucose variability (GV) has been recognized recently as a promoter of complications and therapeutic targets in diabetes. The aim of this study was to reconstruct and analyze gene networks related to GV in diabetes and its complications. For network analysis, we used the ANDSystem that provides automatic network reconstruction and analysis based on text mining. The network of GV consisted of 37 genes/proteins associated with both hyperglycemia and hypoglycemia. Cardiovascular system, pancreas, adipose and muscle tissues, gastrointestinal tract, and kidney were recognized as the loci with the highest expression of GV-related genes. According to Gene Ontology enrichment analysis, these genes are associated with insulin secretion, glucose metabolism, glycogen biosynthesis, gluconeogenesis, MAPK and JAK-STAT cascades, protein kinase B signaling, cell proliferation, nitric oxide biosynthesis, etc. GV-related genes were found to occupy central positions in the networks of diabetes complications (cardiovascular disease, diabetic nephropathy, retinopathy, and neuropathy) and were associated with response to hypoxia. Gene prioritization analysis identified new gene candidates (THBS1, FN1, HSP90AA1, EGFR, MAPK1, STAT3, TP53, EGF, GSK3B, and PTEN) potentially involved in GV. The results expand the understanding of the molecular mechanisms of the GV phenomenon in diabetes and provide molecular markers and therapeutic targets for future research.

SGLT2 Inhibitor Empagliflozin and DPP4 Inhibitor Linagliptin Reactivate Glomerular Autophagy in db/db Mice, a Model of Type 2 Diabetes.

Recent data have indicated the emerging role of glomerular autophagy in diabetic kidney disease. We aimed to assess the effect of the SGLT2 inhibitor empagliflozin, the DPP4 inhibitor linagliptin, and their combination, on glomerular autophagy in a model of type 2 diabetes. Eight-week-old male db/db mice were randomly assigned to treatment with empagliflozin, linagliptin, empagliflozin-linagliptin or vehicle for 8 weeks. Age-matched non-diabetic db/+ mice acted as controls. To estimate glomerular autophagy, immunohistochemistry for beclin-1 and LAMP-1 was performed. Podocyte autophagy was assessed by counting the volume density (Vv) of autophagosomes, lysosomes and autolysosomes by transmission electron microscopy. LC3B and LAMP-1, autophagy markers, and caspase-3 and Bcl-2, apoptotic markers, were evaluated in renal cortex by western blot. Vehicle-treated db/db mice had weak glomerular staining for beclin-1 and LAMP-1 and reduced Vv of autophagosomes, autolysosomes and lysosomes in podocytes. Empagliflozin and linagliptin, both as monotherapy and in combination, enhanced the areas of glomerular staining for beclin-1 and LAMP-1 and increased Vv of autophagosomes and autolysosomes in podocytes. Renal LC3B and Bcl-2 were restored in actively treated animals. LAMP-1 expression was enhanced in the empagliflozin group; caspase-3 expression decreased in the empagliflozin-linagliptin group only. Mesangial expansion, podocyte foot process effacement and urinary albumin excretion were mitigated by both agents. The data provide further explanation for the mechanism of the renoprotective effect of SGLT2 inhibitors and DPP4 inhibitors in diabetes.

Urinary Excretion of Biomolecules Related to Cell Cycle, Proliferation, and Autophagy in Subjects with Type 2 Diabetes and Chronic Kidney Disease.

Dysregulation of cell cycle, proliferation, and autophagy plays a pivotal role in diabetic kidney disease. In this study, we assessed urinary excretion of molecular regulators of these processes that mediate their effects via the PI3K/AKT/mTOR pathway in subjects with long-term type 2 diabetes (T2D) and different patterns of chronic kidney disease (CKD). We included 140 patients with T2D and 20 non-diabetic individuals in a cross-sectional study. Urinary PTEN, Beclin-1, sirtuin 1 (SIRT1), Klotho, fibroblast growth factor 21 (FGF21), and connective tissue growth factor (CTGF) were assessed using ELISA. Patients with T2D, when compared to control, demonstrated increased excretion of PTEN, Beclin-1, SIRT1, FGF21, CTGF, and decreased urinary Klotho (all p < 0.05). In the diabetic group, PTEN, FGF21, and CTGF were significantly higher in patients with declined renal function, while Klotho was lower in those with elevated albuminuria. FGF21 and PTEN correlated inversely with the estimated glomerular filtration rate. There was a negative correlation between Klotho and urinary albumin-to-creatinine ratio. In multivariate models, Klotho and PTEN were associated with albuminuric CKD independently. The results provide further support for the role of PTEN, BECN1, FGF21, Klotho, and CTGF in development albuminuric and non-albuminuric CKD in diabetes.

Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.

Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the target range (3.9-10 mmol/L), above the target range, and below the target range in subjects with T1D managed with MDIs. The models were trained and tested on continuous glucose monitoring data obtained from 380 subjects with T1D. Two DL algorithms-multi-layer perceptron (MLP) and a convolutional neural network (CNN)-as well as two classic ML algorithms, random forest (RF) and gradient boosting trees (GBTs), were applied. The resulting models based on the DL and ML algorithms demonstrated high and similar accuracy in predicting target glucose (F1 metric: 96-98%) and above-target glucose (F1: 93-97%) within a 30 min prediction horizon. Model performance was poorer when predicting low glucose (F1: 80-86%). MLP provided the highest accuracy in low-glucose prediction. The results indicate that both DL (MLP, CNN) and ML (RF, GBTs) algorithms operating CGM data can be used for the simultaneous prediction of nocturnal glucose values within the target, above-target, and below-target ranges in people with T1D managed with MDIs.

Urinary Markers of Tubular Injury and Renal Fibrosis in Patients with Type 2 Diabetes and Different Phenotypes of Chronic Kidney Disease.

This study assessed the urinary excretion of markers and mediators of tubular injury and renal fibrosis in patients with type 2 diabetes (T2D) and non-albuminuric and albuminuric patterns of chronic kidney disease (CKD). One hundred and forty patients with long-term T2D and different patterns of CKD and twenty non-diabetic individuals were included. Urinary retinol-binding protein 4 (RBP-4), glutathione-S-transferase α1 and π (GST-α1 and GST-π), transforming growth factor ß (TGF-ß), type I and type IV collagen (Col1 and Col4), bone morphogenic protein 7 (BMP-7), and hepatocyte growth factor (HGF) were assessed by ELISA. Patients with T2D demonstrated increased urinary excretion of RBP-4, GST-π, Col4, BMP-7, and HGF (all p < 0.05 vs. control). The excretion of RBP-4, GST-π, Col1, and Col4 was increased in patients with elevated albumin-to-creatinine ratio (UACR; all p < 0.05 vs. control), while BMP-7 and HGF were increased innormoalbuminuric patients also (p < 0.05). Urinary RBP-4, GST-α1, Col1, Col4, and HGF correlated positively with UACR; meanwhile, no correlations with glomerular filtration rate were found. The results demonstrate that elevated urinary excretions of the markers of tubular injury (RBP-4, GST-π) and renal fibrosis (Col1, Col4), as well as HGF, an antifibrotic regulator, are associated with the albuminuric pattern of CKD in subjects with T2D.

Nocturnal Glucose Patterns with and without Hypoglycemia in People with Type 1 Diabetes Managed with Multiple Daily Insulin Injections.

Nocturnal hypoglycemia (NH) is a potentially dangerous and underestimated complication of insulin therapy. In this study, we aimed to determine which patterns of nocturnal glucose profiles are associated with NH in patients with type 1 diabetes (T1D) managed with multiple daily insulin injections. A dataset of continuous glucose monitoring (CGM) recordings obtained from 395 adult subjects with T1D was used for modeling. The clustering of CGM data was performed using a hierarchical clustering algorithm. Ten clusters without hypoglycemia and six clusters with NH episode(s) were identified. The differences among the clusters included initial and final glucose levels, glucose change during the night, and the presence of uptrends or downtrends. Post-midnight hyperglycemia was revealed in 5 out of 10 clusters without NH; in patterns with downtrends, initially elevated glucose prevented NH episodes. In clusters with initially near-normal glucose levels and downtrends, most episodes of NH were observed from midnight to 4 a.m.; if glucose was initially elevated, the episodes occurred at 2-4 a.m. or 4-6 a.m., depending on the time of the start of the downtrend. The results demonstrate the diversity of nocturnal glucose profiles in patients with T1D, which highlights the need for a differentiated approach to therapy adjustment.

Serum Cytokines and Growth Factors in Subjects with Type 1 Diabetes: Associations with Time in Ranges and Glucose Variability.

The detrimental effect of hyperglycemia and glucose variability (GV) on target organs in diabetes can be implemented through a wide network of regulatory peptides. In this study, we assessed a broad panel of serum cytokines and growth factors in subjects with type 1 diabetes (T1D) and estimated associations between concentrations of these molecules with time in ranges (TIRs) and GV. One hundred and thirty subjects with T1D and twenty-seven individuals with normal glucose tolerance (control) were included. Serum levels of 44 cytokines and growth factors were measured using a multiplex bead array assay. TIRs and GV parameters were derived from continuous glucose monitoring. Subjects with T1D compared to control demonstrated an increase in concentrations of IL-1ß, IL-1Ra, IL-2Rα, IL-3, IL-6, IL-7, IL-12 p40, IL-16, IL-17A, LIF, M-CSF, IFN-α2, IFN-γ, MCP-1, MCP-3, and TNF-α. Patients with TIR ≤ 70% had higher levels of IL-1α, IL-1ß, IL-6, IL-12 p70, IL-16, LIF, M-CSF, MCP-1, MCP-3, RANTES, TNF-α, TNF-ß, and b-NGF, and lower levels of IL-1α, IL-4, IL-10, GM-CSF, and MIF than those with TIR > 70%. Serum IL-1ß, IL-10, IL-12 p70, MCP-1, MCP-3, RANTES, SCF, and TNF-α correlated with TIR and time above range. IL-1ß, IL-8, IL-10, IL-12 p70, MCP-1, RANTES, MIF, and SDF-1α were related to at least one amplitude-dependent GV metric. In logistic regression models, IL-1ß, IL-4, IL-10, IL-12 p70, GM-CSF, HGF, MCP-3, and TNF-α were associated with TIR ≤ 70%, and MIF and PDGF-BB demonstrated associations with coefficient of variation values ≥ 36%. These results provide further insight into the pathophysiological effects of hyperglycemia and GV in people with diabetes.

Continuous Glucose Monitoring in Patients Following Simultaneous Pancreas-Kidney Transplantation: Time in Range and Glucose Variability.

Simultaneous pancreas-kidney transplantation (SPKT) can improve long-term patient survival and restore endogenous insulin secretion in recipients with type 1 diabetes (T1D). There are currently few data on glucose fluctuations assessed by continuous glucose monitoring (CGM) after SPKT. Aim: to evaluate CGM-derived time in range (TIR) and glucose variability (GV) in patients with T1D and functioning pancreatic grafts after SPKT. Fifty-four CGM recordings from 43 patients, 15 men and 28 women, aged 34 (31; 39) years were analyzed. Time since SKPT was up to 1 year (group 1, n = 13), from 1 to 5 years (group 2, n = 15), and from 5 to 12 years (group 3, n = 26). TIR (3.9-10 mmol/L), Time Above Range (TAR), Time Below Range (TBR), and GV parameters were estimated. There were no differences in mean glucose (5.5 [5.1; 6.2], 5.9 [5.4; 6.2], and 5.9 [5.6; 6.7] mmol/L), TIR (97.6 [92.8-99.1], 97.2 [93.2; 99.1], and 97.5 [93.4; 99]%); TAR (0, 1.8 [1.3; 3.7], and 2.5 [2; 5]%), TBR (5 [3.3; 12.7], 4.1 [2.2; 10.1], and 3.5 [1.3; 6.5]%) and GV parameters between three groups (all p > 0.05). Thus, recipients with functioning pancreatic grafts demonstrate remarkably high TIR and low GV after SPKT.

Factors associated with trabecular bone score in postmenopausal women with type 2 diabetes and normal bone mineral density.

BACKGROUND: Osteoporosis and type 2 diabetes (T2D) have been recognized as a widespread comorbidity leading to excess mortality and an enormous healthcare burden. In T2D, bone mineral density (BMD) may underestimate the risk of low-energy fractures as bone quality is reduced. It was hypothesized that a decrease in the trabecular bone score (TBS), a parameter assessing bone microarchitecture, may be an early marker of impaired bone health in women with T2D. AIM: To identify clinical and body composition parameters that affect TBS in postmenopausal women with T2D and normal BMD. METHODS: A non-interventional cross-sectional comparative study was conducted. Potentially eligible subjects were screened at tertiary referral center. Postmenopausal women with T2D, aged 50-75 years, with no established risk factors for secondary osteoporosis, were included. BMD, TBS and body composition parameters were assessed by dual-energy X-ray absorptiometry. In women with normal BMD, a wide range of anthropometric, general and diabetes-related clinical and laboratory parameters were evaluated as risk factors for TBS decrease using univariate and multivariate regression analysis and analysis of receiver operating characteristic (ROC) curves. RESULTS: Three hundred twelve women were initially screened, 176 of them met the inclusion criteria and underwent dual X-ray absorptiometry. Those with reduced BMD were subsequently excluded; 96 women with normal BMD were included in final analysis. Among them, 43 women (44.8%) showed decreased TBS values (≤ 1.31). Women with TBS ≤ 1.31 were taller and had a lower body mass index (BMI) when compared to those with normal TBS (Р = 0.008 and P = 0.007 respectively). No significant differences in HbA1c, renal function, calcium, phosphorus, alkaline phosphatase, PTH and 25(ОН)D levels were found. In a model of multivariate linear regression analysis, TBS was positively associated with gynoid fat mass, whereas the height and androgen fat mass were associated negatively (all P < 0.001). In a multiple logistic regression, TBS ≤ 1.31 was associated with lower gynoid fat mass (adjusted odd ratio [OR], 0.9, 95% confidence interval [CI], 0.85-0.94, P < 0.001), higher android fat mass (adjusted OR, 1.13, 95%CI, 1.03-1.24, P = 0.008) and height (adjusted OR, 1.13, 95%CI, 1.05-1.20, P < 0.001). In ROC-curve analysis, height ≥ 162.5 cm (P = 0.04), body mass index ≤ 33.85 kg/m2 (P = 0.002), gynoid fat mass ≤ 5.41 kg (P = 0.03) and android/gynoid fat mass ratio ≥ 1.145 (P < 0.001) were identified as the risk factors for TBS reduction. CONCLUSION: In postmenopausal women with T2D and normal BMD, greater height and central adiposity are associated with impaired bone microarchitecture.

Glucose variability in subjects with normal glucose tolerance: Relations with body composition, insulin secretion and sensitivity.

BACKGROUND AND AIMS: To estimate the determinants of glucose variability (GV) in young and middle-aged non-obese subjects with normal glucose tolerance (NGT) we assessed relations between GV parameters, body composition, insulin secretion and sensitivity indices. METHODS: Thirty individuals with normal body mass index (BMI) and twenty overweight subjects were included. 24-hour mean glucose, time in range, time above range (TAR), time below range (TBR), standard deviation (SD), coefficient of variation (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), J-index, lability index (LI), mean absolute glucose (MAG), M-value, high blood glucose index (HBGI), low blood glucose index (LBGI) were derived from continuous glucose monitoring. Body composition was assessed by DEXA. Insulin secretion and sensitivity was estimated by HOMA-IR and HOMA-B scores. RESULTS: Overweight subjects demonstrated higher mean glucose, CONGA, J-index and lower TBR, M-value and LBGI values. Mean glucose correlated positively with total, trunk, gynoid and android fat mass, while M-value and LBGI demonstrated negative correlations with these parameters. In multiple stepwise regression analysis, android fat mass was a predictor of mean glucose, CONGA, J-index, SD and MAGE, gynoid fat mass predicted J-index only, and total fat mass was associated inversely with MAG. Fasting insulin was a predictor of TAR, SD, CV, MAGE, MAG, LI and HBGI. HOMA-B was associated with CONGA, M-value and LBGI. CONCLUSION: In non-obese subjects with NGT mean glucose and GV parameters are related to fat mass and fat distribution. These relations can be mediated through insulin secretion and sensitivity.

Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes.

Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D.

Carotid Artery Disease in Subjects with Type 2 Diabetes: Risk Factors and Biomarkers.

Carotid atherosclerosis (CA) and, especially, carotid artery stenosis (CAS), are associated with a high risk of cardiovascular events in subjects with type 2 diabetes (T2D). In this study, we aimed to identify risk factors and biomarkers of subclinical CA and CAS in T2D individuals. High-resolution ultrasonography of carotid arteries was performed in 389 patients. Ninety-five clinical parameters were evaluated, including diabetic complications and comorbidities; antihyperglycemic, hypolipidemic, and antihypertensive therapy; indices of glycemic control and glucose variability (GV); lipid panels; estimated glomerular filtration rate (eGFR); albuminuria; blood cell count; and coagulation. Additionally, serum levels of calponin-1, relaxin, L-citrulline, and matrix metalloproteinase-2 and -3 (MMP-2, -3) were measured by ELISA. In univariate analysis, older age, male sex, diabetes duration, GV, diabetic retinopathy, chronic kidney disease, coronary artery disease, peripheral artery disease, and MMP-3 were associated with subclinical CA. In addition to these factors, long-term arterial hypertension, high daily insulin doses, eGFR, and L-citrulline were associated with CAS. In multivariate logistic regression, age, male sex, BMI, GV, and eGFR predicted CA independently; male sex, BMI, diabetes duration, eGFR, and L-citrulline were predictors of CAS. These results can be used to develop screening and prevention programs for CA and CAS in T2D subjects.

Optical uncaging of ADP reveals the early calcium dynamics in single, freely moving platelets.

Platelet activation is considered to be a cornerstone in pathogenesis of cardiovascular disease. The assessment of platelet activation at the single-cell level is a promising approach for the research of platelet function in physiological and pathological conditions. Previous studies used the immobilization of platelets on the surface, which significantly alters the activation signaling. Here we show that the use of photolabile "caged" analog of ADP allows one to track the very early stage of platelet activation in single, freely moving cells. In this approach, the diffusion step and ADP receptor ligation are separated in time, and a millisecond-timescale optical pulse may trigger the activation. The technique allows us to measure the delay (lag time) between the stimulus and calcium response in platelets. We also propose a simple model function for calcium peaks, which is in good agreement with the measured data. The proposed technique and model function can be used for in-depth studies of platelet physiology.

Multiplex Bead Array Assay of a Panel of Circulating Cytokines and Growth Factors in Patients with Albuminuric and Non-AlbuminuricDiabetic Kidney Disease.

A panel of cytokines and growth factors, mediating low-grade inflammation and fibrosis, was assessed in patients with type 2 diabetes (T2D) and different patterns of chronic kidney disease (CKD). Patients with long-term T2D (N = 130) were classified into four groups: no signs of CKD; estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 without albuminuria; albuminuria and eGFR ≥60 mL/min/1.73 m2; albuminuria and eGFR <60 mL/min/1.73 m2. Thirty healthy subjects were acted as control. Twenty-seven cytokines and growth factors were assessed in serum by multiplex bead array assay. Serum hs-CRP, urinary nephrin, podocine, and WFDC2 were measured by ELISA. Patients with T2D showed elevated IL-1Ra, IL-6, IL-17A, G-CSF, IP-10, MIP-1α, and bFGF levels; concentrations of IL-4, IL-12, IL-15, INF-γ, and VEGF were decreased. IL-6, IL-17A, G-CSF, MIP-1α, and bFGF correlated negatively with eGFR; IL-10 and VEGF demonstrated negative associations with WFDC2; no relationships with podocyte markers were found. Adjusted IL-17A and MIP-1α were predictors of non-albuminuric CKD, IL-13 predicted albuminuria with preserved renal function, meanwhile, IL-6 and hsCRP were predictors of albuminuria with eGFR decline. Therefore, albuminuric and non-albuminuric CKD in T2D patients are associated with different pro-inflammatory shifts in the panel of circulating cytokines.

Circulating Wnt1-inducible signaling pathway protein-1 (WISP-1/CCN4) is a novel biomarker of adiposity in subjects with type 2 diabetes.

BACKGROUND: Wnt1-inducible signaling pathway protein 1, or cellular communication network factor 4 (CCN4), a member of CCN family of secreted, extracellular matrix associated signaling proteins, recently was validated as a novel adipose tissue derived cytokine. OBJECTIVE: To assess the relationships between circulating CCN4, adipose tissue distribution and function, and chronic low-grade inflammation in subjects with type 2 diabetes. METHODS: We observed 156 patients with type 2 diabetes and 24 healthy controls. Serum levels of CCN4, hsCRP and alpha1-acid glycoprotein (alpha1-AGP) were measured by ELISA. Serum concentrations of leptin, resistin, visfatin, adipsin, adiponectin, IL-6, IL-8, IL-18 and TNF-alpha were determined by multiplex analysis. Fat mass and distribution was assessed by DEXA. Mean diameter of adipocytes was estimated in samples of subcutaneous adipose tissue. RESULTS: Patients with diabetes had higher levels of circulating CCN4, leptin, resistin, adipsin, visfatin, hsCRP, alpha1-AGP, and IL-6 (all p < 0.02). The CCN4 concentration correlated positively with percentage of fat mass in central abdominal area, as well as with leptin, resistin and visfatin levels; negative correlation was found between CCN4 and mean adipocyte diameter. In multiple regression analysis fat mass in central abdominal area was independent predictor for CCN4 concentration. CONCLUSION: In subjects with type 2 diabetes serum levels of CCN4 are associated with central abdominal fat mass and adipose tissue dysfunction.