Non-adherence to cardiometabolic medication as assessed by LC-MS/MS in urine and its association with kidney and cardiovascular outcomes in type 2 diabetes mellitus.

AIMS/HYPOTHESIS: Non-adherence to medication is a frequent barrier in the treatment of patients with type 2 diabetes mellitus, potentially limiting the effectiveness of evidence-based treatments. Previous studies have mostly relied on indirect adherence measures to analyse outcomes based on adherence. The aim of this study was to use LC-MS/MS in urine-a non-invasive, direct and objective measure-to assess non-adherence to cardiometabolic drugs and analyse its association with kidney and cardiovascular outcomes. METHODS: This cohort study includes 1125 participants from the PROVALID study, which follows patients with type 2 diabetes mellitus at the primary care level. Baseline urine samples were tested for 79 cardiometabolic drugs and metabolites thereof via LC-MS/MS. An individual was classified as totally adherent if markers for all drugs were detected, partially non-adherent when at least one marker for one drug was detected, and totally non-adherent if no markers for any drugs were detected. Non-adherence was then analysed in the context of cardiovascular (composite of myocardial infarction, stroke and cardiovascular death) and kidney (composite of sustained 40% decline in eGFR, sustained progression of albuminuria, kidney replacement therapy and death from kidney failure) outcomes. RESULTS: Of the participants, 56.3% were totally adherent, 42.0% were partially non-adherent, and 1.7% were totally non-adherent to screened cardiometabolic drugs. Adherence was highest to antiplatelet and glucose-lowering agents and lowest to lipid-lowering agents. Over a median (IQR) follow-up time of 5.10 (4.12-6.12) years, worse cardiovascular outcomes were observed with non-adherence to antiplatelet drugs (HR 10.13 [95% CI 3.06, 33.56]) and worse kidney outcomes were observed with non-adherence to antihypertensive drugs (HR 1.98 [95% CI 1.37, 2.86]). CONCLUSIONS/INTERPRETATION: This analysis shows that non-adherence to cardiometabolic drug regimens is common in type 2 diabetes mellitus and negatively affects kidney and cardiovascular outcomes.

Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios.

BACKGROUND: There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. METHODS: Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. RESULTS: In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17-1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47-1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39-1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). CONCLUSION: The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

The kidney-more than the sum of its cellular parts.

The kidney is a complex organ composed of a plethora of highly specialized cells that talk to each other. The tubuloglomerular feedback is a prototypical example of cell-cell interactions adapting glomerular filtration rate. With the advent of single-cell sequencing techniques, spatial transcriptomics, and bioinformatical cell-cell interaction analyses, we are now able to much better decipher the complex physiology and pathophysiology of these crosstalks, and identify new biomarkers as well as novel treatment options.

Using Infodemiology Metrics to Assess Public Interest in Liver Transplantation: Google Trends Analysis.

BACKGROUND: Liver transplantation (LT) is the only curative treatment for end-stage liver disease. Less than 10% of global transplantation needs are met worldwide, and the need for LT is still increasing. The death rates on the waiting list remain too high. OBJECTIVE: It is, therefore, critical to raise awareness among the public and health care providers and in turn increasingly acquire donors. METHODS: We performed a Google Trends search using the search terms liver transplantation and liver transplant on October 15, 2020. On the basis of the resulting monthly data, the annual average Google Trends indices were calculated for the years 2004 to 2018. We not only investigated the trend worldwide but also used data from the United Network for Organ Sharing (UNOS), Spain, and Eurotransplant. Using pairwise Spearman correlations, Google Trends indices were examined over time and compared with the total number of liver transplants retrieved from the respective official websites of UNOS, the Organización Nacional de Trasplantes, and Eurotransplant. RESULTS: From 2004 to 2018, there was a significant decrease in the worldwide Google Trends index from 78.2 in 2004 to 20.5 in 2018 (-71.2%). This trend was more evident in UNOS than in the Eurotransplant group. In the same period, the number of transplanted livers increased worldwide. The waiting list mortality rate was 31% for Eurotransplant and 29% for UNOS. However, in Spain, where there are excellent awareness programs, the Google Trends index remained stable over the years with comparable, increasing LT numbers but a significantly lower waiting list mortality (15%). CONCLUSIONS: Public awareness in LT has decreased significantly over the past two decades. Therefore, novel awareness programs should be initialized.

A Meta-Analysis of Human Transcriptomics Data in the Context of Peritoneal Dialysis Identifies Novel Receptor-Ligand Interactions as Potential Therapeutic Targets.

Peritoneal dialysis (PD) is one therapeutic option for patients with end-stage kidney disease (ESKD). Molecular profiling of samples from PD patients using different Omics technologies has led to the discovery of dysregulated molecular processes due to PD treatment in recent years. In particular, a number of transcriptomics (TX) datasets are currently available in the public domain in the context of PD. We set out to perform a meta-analysis of TX datasets to identify dysregulated receptor-ligand interactions in the context of PD-associated complications. We consolidated transcriptomics profiles from twelve untargeted genome-wide gene expression studies focusing on human cell cultures or samples from human PD patients. Gene set enrichment analysis was used to identify enriched biological processes. Receptor-ligand interactions were identified using data from CellPhoneDB. We identified 2591 unique differentially expressed genes in the twelve PD studies. Key enriched biological processes included angiogenesis, cell adhesion, extracellular matrix organization, and inflammatory response. We identified 70 receptor-ligand interaction pairs, with both interaction partners being dysregulated on the transcriptional level in one of the investigated tissues in the context of PD. Novel receptor-ligand interactions without prior annotation in the context of PD included BMPR2-GDF6, FZD4-WNT7B, ACKR2-CCL2, or the binding of EPGN and EREG to the EGFR, as well as the binding of SEMA6D to the receptors KDR and TYROBP. In summary, we have consolidated human transcriptomics datasets from twelve studies in the context of PD and identified sets of novel receptor-ligand pairs being dysregulated in the context of PD that warrant investigation in future functional studies.

Coregulation Analysis of Mechanistic Biomarkers in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder leading to deterioration of kidney function and end stage kidney disease (ESKD). A number of molecular processes are dysregulated in ADPKD but the exact mechanism of disease progression is not fully understood. We measured protein biomarkers being linked to ADPKD-associated molecular processes via ELISA in urine and serum in a cohort of ADPKD patients as well as age, gender and eGFR matched CKD patients and healthy controls. ANOVA and t-tests were used to determine differences between cohorts. Spearman correlation coefficient analysis was performed to assess coregulation patterns of individual biomarkers and renal function. Urinary epidermal growth factor (EGF) and serum apelin (APLN) levels were significantly downregulated in ADPKD patients. Serum vascular endothelial growth factor alpha (VEGFA) and urinary angiotensinogen (AGT) were significantly upregulated in ADPKD patients as compared with healthy controls. Arginine vasopressin (AVP) was significantly upregulated in ADPKD patients as compared with CKD patients. Serum VEGFA and VIM concentrations were positively correlated and urinary EGF levels were negatively correlated with urinary AGT levels. Urinary EGF and AGT levels were furthermore significantly associated with estimated glomerular filtration rate (eGFR) in ADPKD patients. In summary, altered protein concentrations in body fluids of ADPKD patients were found for the mechanistic markers EGF, APLN, VEGFA, AGT, AVP, and VIM. In particular, the connection between EGF and AGT during progression of ADPKD warrants further investigation.

Canagliflozin reduces inflammation and fibrosis biomarkers: a potential mechanism of action for beneficial effects of SGLT2 inhibitors in diabetic kidney disease.

AIMS/HYPOTHESIS: The sodium-glucose cotransporter 2 (SGLT2) inhibitor canagliflozin slows progression of kidney function decline in type 2 diabetes. The aim of this study was to assess the effect of the SGLT2 inhibitor canagliflozin on biomarkers for progression of diabetic kidney disease (DKD). METHODS: A canagliflozin mechanism of action (MoA) network model was constructed based on an in vitro transcriptomics experiment in human proximal tubular cells and molecular features linked to SGLT2 inhibitors from scientific literature. This model was mapped onto an established DKD network model that describes molecular processes associated with DKD. Overlapping areas in both networks were subsequently used to select candidate biomarkers that change with canagliflozin therapy. These biomarkers were measured in 296 stored plasma samples from a previously reported 2 year clinical trial comparing canagliflozin with glimepiride. RESULTS: Forty-four proteins present in the canagliflozin MoA molecular model overlapped with proteins in the DKD network model. These proteins were considered candidates for monitoring impact of canagliflozin on DKD pathophysiology. For ten of these proteins, scientific evidence was available suggesting that they are involved in DKD progression. Of these, compared with glimepiride, canagliflozin 300 mg/day decreased plasma levels of TNF receptor 1 (TNFR1; 9.2%; p < 0.001), IL-6 (26.6%; p = 0.010), matrix metalloproteinase 7 (MMP7; 24.9%; p = 0.011) and fibronectin 1 (FN1; 14.9%; p = 0.055) during 2 years of follow-up. CONCLUSIONS/INTERPRETATION: The observed reduction in TNFR1, IL-6, MMP7 and FN1 suggests that canagliflozin contributes to reversing molecular processes related to inflammation, extracellular matrix turnover and fibrosis. Trial registration ClinicalTrials.gov NCT00968812.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes.

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

Molecular, histological, and clinical phenotyping of diabetic nephropathy: valuable complementary information?

The incidence and prevalence of diabetic kidney disease is increasing. Observational and interventional studies suggest that the pathophysiology varies between individuals and within a patient over time. There is a huge clinical need to describe the molecular processes that modulate diabetic kidney disease. "Omics" experiments combined with bioinformatical analysis tools might allow for profiling of patients on an individual or at least group level to improve prediction of prognosis and guide targeted therapy.

Endogenous factors and mechanisms of renoprotection and renal repair.

BACKGROUND: An imbalance between renal damaging molecules and nephroprotective factors contributes to the development and progression of kidney diseases. Molecules with renoprotective properties might serve as biomarkers, drug targets as well as therapeutic options themselves. MATERIALS AND METHODS: For this review, we generated a set of renoprotective factors based on GeneRIF (Gene Reference Into Function) information available at NCBI's PubMed. The final set of manually curated renoprotective factors was investigated with respect to tissue-specific expression, subcellular location distribution and involvement in biological processes using information from gene ontology as well as information from protein-protein interaction databases. We furthermore investigated the factors in the context of clinical trials of renal disease and diabetes. RESULTS: One hundred and ninety-three factors could be retrieved from the set of GeneRIFs on nephroprotection and renal repair. A large number of factors were either secretory molecules or plasma membrane receptors. Next to the elevated expression in renal tissue, also higher expression in connective tissue and pancreas was observed. The proteins could be assigned to the broad functional categories of cell proliferation and signalling, inflammatory response, apoptosis, blood pressure regulation as well as cellular response to different kinds of insults such as hypoxia, heat or mechanical stimulus. Eight factors are studied in clinical trials with additional ones being targeted by compounds. CONCLUSIONS: We have generated a set of renoprotective factors based on the literature information, which was functionally annotated and evaluated with respect to tested compounds in kidney disease and diabetes clinical trials.

Validation of a protein panel for the noninvasive detection of recurrent non-muscle invasive bladder cancer.

CONTEXT: About 50-70% of patients with non-muscle invasive bladder cancer (NMIBC) experience relapse of disease. OBJECTIVE: To establish a panel of protein biomarkers incorporated in a multiplexed microarray (BCa chip) and a classifier for diagnosing recurrent NMIBC. MATERIALS AND METHODS: Urine samples from 45 patients were tested. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: A multi biomarker panel (ECadh, IL8, MMP9, EN2, VEGF, past recurrences, BCG therapies and stage at diagnosis) was identified yielding an area under the curve of 0.96. DISCUSSION AND CONCLUSION: This biomarker panel represents a potential diagnostic tool for noninvasive diagnosis of recurrent NMIBC.

Renal microRNA- and RNA-profiles in progressive chronic kidney disease.

BACKGROUND: MicroRNAs (miRNAs) contribute to chronic kidney disease (CKD) progression via regulating mRNAs involved in renal homeostasis. However, their association with clinical outcome remains poorly understood. MATERIALS AND METHODS: We performed miRNA and mRNA expression profiling on renal biopsy sections by qPCR (miRNA) and microarrays (mRNA) in a discovery (n = 43) and in a validation (n = 29) cohort. miRNAs differentiating stable and progressive cases were inversely correlated with putative target mRNAs, which were further characterized by pathway analysis using KEGG pathways. RESULTS: miR-30d, miR-140-3p, miR-532-3p, miR-194, miR-190, miR-204 and miR-206 were downregulated in progressive cases. These seven miRNAs correlated with upregulated 29 target mRNAs involved in inflammatory response, cell-cell interaction, apoptosis and intra-cellular signalling. In particular, miR-206 and miR-532-3p were associated with distinct biological processes via the expression of their target mRNAs: Reduced expression of miR-206 in progressive disease correlated with the upregulation of target mRNAs participating in inflammatory pathways (CCL19, CXCL1, IFNAR2, NCK2, PTK2B, PTPRC, RASGRP1 and TNFRSF25). Progressive cases also showed a lower expression of miR-532-3p and an increased expression of target transcripts involved in apoptosis pathways (MAP3K14, TNFRSF10B/TRAIL-R2, TRADD and TRAF2). In the validation cohort, we confirmed the decreased expression of miR-206 and miR-532-3p, and the inverse correlation of these miRNAs with the expression of nine of the 12 target genes. The levels of the identified miRNAs and the target mRNAs correlated with clinical parameters and histological damage indices. CONCLUSIONS: These results suggest the involvement of specific miRNAs and mRNAs in biological pathways associated with the progression of CKD.

Metallothioneins and renal ageing.

BACKGROUND: Human lifespan is increasing continuously and about one-third of the population >70 years of age suffers from chronic kidney disease. The pathophysiology of the loss of renal function with ageing is unclear. METHODS: We determined age-associated gene expression changes in zero-hour biopsies of deceased donor kidneys without laboratory signs of impaired renal function, defined as a last serum creatinine >0.96 mg/dL in females and >1.18 mg/dL in males, using microarray technology and the Significance Analysis of Microarrays routine. Expression changes of selected genes were confirmed by quantitative polymerase chain reaction and in situ hybridization and immunohistochemistry for localization of respective mRNA and protein. Functional aspects were examined in vitro. RESULTS: Donors were classified into three age groups (<40, 40-59 and >59 years; Groups 1, 2 and 3, respectively). In Group 3 especially, genes encoding for metallothionein (MT) isoforms were more significantly expressed when compared with Group 1; localization studies revealed predominant staining in renal proximal tubular cells. RPTEC/TERT1 cells overexpressing MT2A were less susceptible towards cadmium chloride-induced cytotoxicity and hypoxia-induced apoptosis, both models for increased generation of reactive oxygen species. CONCLUSIONS: Increased expression of MTs in the kidney with ageing might be a protective mechanism against increased oxidative stress, which is closely related to the ageing process. Our findings indicate that MTs are functionally involved in the pathophysiology of ageing-related processes.

Drugs meeting the molecular basis of diabetic kidney disease: bridging from molecular mechanism to personalized medicine.

Diabetic kidney disease (DKD) is a complex, multifactorial disease and is associated with a high risk of renal and cardiovascular morbidity and mortality. Clinical practice guidelines for diabetes recommend essentially identical treatments for all patients without taking into account how the individual responds to the instituted therapy. Yet, individuals vary widely in how they respond to medications and therefore optimal therapy differs between individuals. Understanding the underlying molecular mechanisms of variability in drug response will help tailor optimal therapy. Polymorphisms in genes related to drug pharmacokinetics have been used to explore mechanisms of response variability in DKD, but with limited success. The complex interaction between genetic make-up and environmental factors on the abundance of proteins and metabolites renders pharmacogenomics alone insufficient to fully capture response variability. A complementary approach is to attribute drug response variability to individual variability in underlying molecular mechanisms involved in the progression of disease. The interplay of different processes (e.g. inflammation, fibrosis, angiogenesis, oxidative stress) appears to drive disease progression, but the individual contribution of each process varies. Drugs at the other hand address specific targets and thereby interfere in certain disease-associated processes. At this level, biomarkers may help to gain insight into which specific pathophysiological processes are involved in an individual followed by a rational assessment whether a specific drug's mode of action indeed targets the relevant process at hand. This article describes the conceptual background and data-driven workflow developed by the SysKid consortium aimed at improving characterization of the molecular mechanisms underlying DKD at the interference of the molecular impact of individual drugs in order to tailor optimal therapy to individual patients.

Molecular disease presentation in diabetic nephropathy.

Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on protein-protein interaction data.

Disease map-based biomarker selection and pre-validation for bladder cancer diagnostic.

CONTEXT: Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of de novo and recurrent bladder cancer. OBJECTIVE: To identify a highly sensitive and specific biomarker candidate set with potential clinical utility in bladder cancer. MATERIALS AND METHODS: Urinary biomarker concentrations were determined by ELISA. The performance of individual markers and marker combinations was assessed using ROC analysis. RESULTS: A five-biomarker panel (IL8, MMP9, VEGFA, PTGS2 and EN2) was defined from the candidate set. DISCUSSION AND CONCLUSION: This panel showed a better overall performance than the best individual marker. Further validation studies are needed to evaluate its clinical utility in bladder cancer.

Mass spectrometric/bioinformatic identification of a protein subset that characterizes the cellular activity of anticancer peptides.

The preclinical study of the mechanism of action of anticancer small molecules is challenging due to the complexity of cancer biology and the fragmentary nature of available data. With the aim of identifying a protein subset characterizing the cellular activity of anticancer peptides, we used differential mass spectrometry to identify proteomic changes induced by two peptides, LR and [d-Gln(4)]LR, that inhibit cell growth and compared them with the changes induced by a known drug, pemetrexed, targeting the same enzyme, thymidylate synthase. The quantification of the proteome of an ovarian cancer cell model treated with LR yielded a differentially expressed protein data set with respect to untreated cells. This core set was expanded by bioinformatic data interpretation, the biologically relevant proteins were selected, and their differential expression was validated on three cis-platinum sensitive and resistant ovarian cancer cell lines. Via clustering of the protein network features, a broader view of the peptides' cellular activity was obtained. Differences from the mechanism of action of pemetrexed were inferred from different modulation of the selected proteins. The protein subset identification represents a method of general applicability to characterize the cellular activity of preclinical compounds and a tool for monitoring the cellular activity of novel drug candidates.

Identification of endophenotypes supporting outcome prediction in hemodialysis patients based on mechanistic markers of statin treatment.

Background: Statins are widely used to reduce the risk of cardiovascular disease (CVD). Patients with end-stage renal disease (ESRD) on hemodialysis have significantly increased risk of developing CVD. Statin treatment in these patients however did not show a statistically significant benefit in large trials on a patient cohort level. Methods: We generated gene expression profiles for statins to investigate the impact on cellular programs in human renal proximal tubular cells and mesangial cells in-vitro. We subsequently selected biomarkers from key statin-affected molecular pathways and assessed these biomarkers in plasma samples from the AURORA cohort, a double-blind, randomized, multi-center study of patients on hemodialysis or hemofiltration that have been treated with rosuvastatin. Patient clusters (phenotypes) were created based on the identified biomarkers using Latent Class Model clustering and the associations with outcome for the generated phenotypes were assessed using Cox proportional hazards regression models. The multivariable models were adjusted for clinical and biological covariates based on previously published data in AURORA. Results: The impact of statin treatment on mesangial cells was larger as compared with tubular cells with a large overlap of differentially expressed genes identified for atorvastatin and rosuvastatin indicating a predominant drug class effect. Affected molecular pathways included TGFB-, TNF-, and MAPK-signaling and focal adhesion among others. Four patient clusters were identified based on the baseline plasma concentrations of the eight biomarkers. Phenotype 1 was characterized by low to medium levels of the hepatocyte growth factor (HGF) and high levels of interleukin 6 (IL6) or matrix metalloproteinase 2 (MMP2) and it was significantly associated with outcome showing increased risk of developing major adverse cardiovascular events (MACE) or cardiovascular death. Phenotype 2 had high HGF but low Fas cell surface death receptor (FAS) levels and it was associated with significantly better outcome at 1 year. Conclusions: In this translational study, we identified patient subgroups based on mechanistic markers of statin therapy that are associated with disease outcome in patients on hemodialysis.

Molecular models of the cardiorenal syndrome.

Molecular profiling techniques have provided extensive sets of molecular features characterizing clinical phenotypes, but further extrapolation to mechanistic molecular models of disease pathophysiology faces major challenges. Here, we describe a computational procedure for delineating molecular disease models utilizing omics profiles, and exemplify the methodology on aspects of the cardiorenal syndrome describing the clinical association of declining kidney function and increased cardiovascular event rates. Individual molecular features as well as selected molecular processes were identified as linking cardiovascular and renal pathology as a combination of cross-organ mediators and common pathophysiology. The molecular characterization of the disease presents as a set of molecular processes together with their interactions, composing a molecular disease model of the cardiorenal syndrome. Integrating omics profiles describing aspects of cardiovascular disease and respective profiles for advanced chronic kidney disease on molecular interaction networks, computation of disease term-specific subgraphs, and complemented by subgraph segmentation allowed delineation of disease term-specific molecular models, at their intersection providing contributors to cardiorenal pathology. Building such molecular disease models allows in a generic way to integrate multi-omics sources for generating comprehensive sets of molecular processes, on such basis providing rationale for biomarker panel selection for further characterizing clinical phenotypes.

OntoloViz: a GUI for interactive visualization of ranked disease or drug lists using the MeSH and ATC ontologies.

Motivation: Structured vocabularies for drugs and diseases represent, besides their primary use for annotating scientific literature or scientific information in general, a valuable resource for visualizing aggregated information. The Medical Subject Headings (MeSH) and Anatomical Therapeutic Chemical (ATC) ontologies are widely used structured vocabularies for diseases and drugs, respectively. Their hierarchical tree-like structure can be used as a basis for creating intuitive visual displays for specific diseases and drugs within their higher-order classifications. Such displays are helpful means to contextualize diseases and drugs in various settings such as in drug repositioning. However, there are few tools that can harness the potential of these structured ontologies to create informative visual representations without extensive programming and data processing skills. Results: We have developed OntoloViz, a Graphical User Interface (GUI) for visualizing annotated lists of drugs or diseases in the context of their MeSH or ATC ontologies in an intuitively interpretable sunburst layout. Minimum input is a list of disease or drug names. Users in addition have the option to specify numerical parameters for the input lists to enhance the visualization, e.g. to visualize term frequencies. The GUI allows values to be propagated upwards in the respective ontology tree structure thus facilitating exploration of gene and drug lists. We present two use cases for OntoloViz, namely (i) a graphical representation of clinically tested drugs for coronavirus disease (COVID-19) based on ATC Classification and (ii) a graphical representation of literature annotation of human diseases on the MeSH ontology. Availability and implementation: The OntoloViz package can be retrieved from PyPi. The source code along with test data, template, and documentations are available at GitHub (https://github.com/Delta4AI/OntoloViz).