Stefanos Roumeliotis

C-Terminal Fragment of Agrin (CAF): A Novel Marker for Progression of Kidney Disease in Type 2 Diabetics

Background Diabetes is the leading cause of CKD in the developed world. C-terminal fragment of agrin (CAF) is a novel kidney function and injury biomarker. We investigated whether serum CAF predicts progression of kidney disease in type 2 diabetics. Methods Serum CAF levels were measured in 71 elderly patients with diabetic nephropathy using a newly developed commercial ELISA kit (Neurotune®). Estimated glomerular filtration rate (eGFR) and proteinuria in spot urine were assessed at baseline and after 12 months follow up. The presence of end stage renal disease (ESRD) was evaluated after 24 months follow-up. Correlation and logistic regression analyses were carried out to explore the associations of serum CAF levels with GFR, proteinuria, GFR loss and incident ESRD. Renal handling of CAF was tested in neurotrypsin-deficient mice injected with recombinant CAF. Results We found a strong association of serum CAF levels with eGFR and a direct association with proteinuria both at baseline (r = 0.698, p<0.001 and r = 0. 287, p = 0.02) as well as after 12 months follow-up (r = 0.677, p<0.001 and r = 0.449, p<0.001), respectively. Furthermore, in multivariate analysis, serum CAF levels predicted eGFR decline at 12 months follow-up after adjusting for known risk factors (eGFR, baseline proteinuria) [OR (95%CI) = 4.2 (1.2–14.5), p = 0.024]. In mice, injected CAF was detected in endocytic vesicles of the proximal tubule. Conclusion Serum CAF levels reflect renal function and are highly associated with eGFR and proteinuria at several time points. Serum CAF was able to predict subsequent loss of renal function irrespective of baseline proteinuria in diabetic nephropathy. CAF is likely removed from circulation by glomerular filtration and subsequent endocytosis in the proximal tubule. These findings may open new possibilities for clinical trial design, since serum CAF levels may be used as a selection tool to monitor kidney function in high-risk patients with diabetic nephropathy.

Adiponectin Plasma Levels and Albuminuria in Patients with Type 2 Diabetes and Different Stages of Diabetic Kidney Disease

Adiponectin is an inflammatory cytokine produced by adipose tissue and its protective role has been recognized in the pathogenesis of obesity. A lower concentration in obesity patients is noted, in conditions of resistance to insulin, diabetes mellitus, and CKD. Patients with type 2 diabetes mellitus have a potential risk of atherosclerosis, while low concentrations of adiponectin are considered as predictor for the occurrence of complications in patients with type 2 diabetes. The aim of this study was to investigate in patients with type 2 diabetes mellitus with and without diabetic nephropathy the correlation of adiponectin levels and CKD stage or degree of albuminuria. We studied 119 patients with type 2 diabetes mellitus with different stage of renal function, the levels of plasma adiponectin, and the BMI. A statistically significant difference of plasma adiponectin levels was noted between the initial and end stages of CKD, the highest levels seen in ESKD patients. Also, the levels of adiponectin were elevated in patients with greater albuminuria (statistically significant difference between groups 1 and 3, p=0.05). The levels of adiponectin were found to decrease with increasing the stage of obesity (ANOVA, p<0.05). Finally, the group of patients receiving glitazones had higher plasma adiponectin levels compared to those not receiving. It concluded that the levels of adiponectin increase with the deterioration of renal function and with enhancement of albuminuria, while decreasing as the stage of obesity worsens. The administration of glitazones was associated with increased plasma levels of adiponectin.

Matrix Gla protein T-138C polymorphism is associated with carotid intima media thickness and predicts mortality in patients with diabetic nephropathy

AIMS We sought to determine the predictive value of Matrix Gla Protein MGP T-138C polymorphism in relation to all-cause mortality, cardiovascular mortality and cardiovascular events in patients with diabetic nephropathy (DN). METHODS MGP T-138C polymorphism was assessed in 40 diabetic patients without nephropathy and 118 patients at different stages of DN, including patients on hemodialysis. Measurement of carotid intima-media thickness (cIMT) was performed using real-time B-mode ultrasonography. Plasma levels of dephoshorylated uncarboxylated Matrix Gla Protein (dp-ucMGP) were determined in a subgroup of 67 patients by ELISA. Mortality and cardiovascular events were assessed during a 7year follow-up. RESULTS TT homozygotes for the MGP T-138C polymorphism had higher values of cIMT compared to combined TC and CC genotypes (P=0.006) whereas no association was observed between cIMT and dp-ucMGP levels. MGP T-138C polymorphism was a strong independent predictor of cIMT (P<0.0001), after adjustment for several well-known atherosclerosis risk factors. Patients with TT genotype presented a significantly higher all-cause and cardiovascular mortality risk compared to patients with TC and CC genotypes (P=0.01 and P=0.04 respectively), after adjustment for several traditional risk factors. CONCLUSIONS MGP T-138C polymorphism is a strong and independent predictor of increased cIMT as well as all-cause and cardiovascular mortality in DN patients.

Integrating medical scientific knowledge with the semantically quantified self

The assessment of risk in medicine is a crucial task, and depends on scientific knowledge derived by systematic clinical studies on factors affecting health, as well as on particular knowledge about the current status of a particular patient. Existing non-semantic risk prediction tools are typically based on hardcoded scientific knowledge, and only cover a very limited range of patient states. This makes them rapidly out of date, and limited in application, particularly for patients with multiple co-occurring conditions. In this work we propose an integration of Semantic Web and Quantified Self technologies to create a framework for calculating clinical risk predictions for patients based on self-gathered biometric data. This framework relies on generic, reusable ontologies for representing clinical risk, and sensor readings, and reasoning to support the integration of data represented according to these ontologies. The implemented framework shows a wide range of advantages over existing risk calculation.

Navigating Health Literacy Using Interactive Data Visualisation

It is commonly concluded that health literacy focuses on individual skills to obtain, process and understand health information and services necessary to make appropriate health decisions. To achieve this, an individual first needs to obtain an adequate level of health literacy. However, nowadays, the information that individuals encounter with regards to their health, the amount, credibility and quality of the data make it difficult for one to make judgments on their health and disease progression, let alone make informed decisions on behaviour change. In this paper, we will report our work in providing patients with efficient ways to explore and understand the relevant health literacy. We focus on two data types: 1) harvested medical evidence from PubMed on cardiorenal disease and its comorbidities, 2) data collected from patients including from PHR and wearable sensors. Our work provides ways for patients to visualise this data meaningfully. Our work aims to improve the health literacy for the general public and increase the populations understanding of the medical field, thus helping users to make informed decision with regards to their care.