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.

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.

Computational drug repositioning of clopidogrel as a novel therapeutic option for focal segmental glomerulosclerosis.

Focal segmental glomerulosclerosis (FSGS) is a glomerular lesion often associated with nephrotic syndrome. It is also associated with a high risk of progression to end-stage kidney disease. Current treatment of FSGS is limited to systemic corticosteroids or calcineurin inhibition, along with inhibitors of the renin-angiotensin-aldosterone system. FSGS is heterogeneous in etiology, and novel therapies targeting specific, dysregulated molecular pathways represent a major unmet medical need. We have generated a network-based molecular model of FSGS pathophysiology using previously established systems biology workflows to allow computational evaluation of compounds for their predicted interference with molecular processes contributing to FSGS. We identified the anti-platelet drug clopidogrel as a therapeutic option to counterbalance dysregulated FSGS pathways. This prediction of our computational screen was validated by testing clopidogrel in the adriamycin FSGS mouse model. Clopidogrel improved key FSGS outcome parameters and significantly reduced urinary albumin to creatinine ratio (P < 0.01) and weight loss (P < 0.01), and ameliorated histopathological damage (P < 0.05). Clopidogrel is used to treat several cardiovascular diseases linked to chronic kidney disease. Clopidogrel's favorable safety profile and its efficacy in the adriamycin mouse FSGS model thus recommend it as an attractive drug repositioning candidate for clinical trial in FSGS.

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).

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.

Exosomal mitochondrial tRNAs and miRNAs as potential predictors of inflammation in renal proximal tubular epithelial cells.

Exosomes have emerged as a valuable repository of novel biomarkers for human diseases such as chronic kidney disease (CKD). From a healthy control group, we performed microRNA (miRNA) profiling of urinary exosomes and compared it with a cell culture model of renal proximal tubular epithelial cells (RPTECs). Thereby, a large fraction of abundant urinary exosomal miRNAs could also be detected in exosomes derived from RPTECs, indicating them as a suitable model system for investigation of CKD. We subsequently analyzed exosomes from RPTECs in pro-inflammatory and pro-fibrotic states, mimicking some aspects of CKD. Following cytokine treatment, we observed a significant increase in exosome release and identified 30 dysregulated exosomal miRNAs, predominantly associated with the regulation of pro-inflammatory and pro-fibrotic-related pathways. In addition to miRNAs, we also identified 16 dysregulated exosomal mitochondrial RNAs, highlighting a pivotal role of mitochondria in sensing renal inflammation. Inhibitors of exosome biogenesis and release significantly altered the abundance of selected candidate miRNAs and mitochondrial RNAs, thus suggesting distinct sorting mechanisms of different non-coding RNA (ncRNA) species into exosomes. Hence, these two exosomal ncRNA species might be employed as potential indicators for predicting the pathogenesis of CKD and also might enable effective monitoring of the efficacy of CKD treatment.

Clonal Hematopoiesis of Indeterminate Potential and Diabetic Kidney Disease: A Nested Case-Control Study.

Introduction: The disease trajectory of diabetic kidney disease (DKD) shows a high interindividual variability not sufficiently explained by conventional risk factors. Clonal hematopoiesis of indeterminate potential (CHIP) is a proposed novel cardiovascular risk factor. Increased kidney fibrosis and glomerulosclerosis were described in mouse models of CHIP. Here, we aim to analyze whether CHIP affects the incidence or progression of DKD. Methods: A total of 1419 eligible participants of the PROVALID Study were the basis for a nested case-control (NCC) design. A total of 64 participants who reached a prespecified composite endpoint within the observation period (initiation of kidney replacement therapy, death from kidney failure, sustained 40% decline in estimated glomerular filtration rate or sustained progression to macroalbuminuria) were identified and matched to 4 controls resulting in an NCC sample of 294 individuals. CHIP was assessed via targeted amplicon sequencing of 46 genes in peripheral blood. Furthermore, inflammatory cytokines were analyzed in plasma via a multiplex assay. Results: The estimated prevalence of CHIP was 28.91% (95% CI 22.91%-34.91%). In contrast to other known risk factors (albuminuria, hemoglobin A1c, heart failure, and smoking) and elevated microinflammation, CHIP was not associated with incident or progressive DKD (hazard ratio [HR] 1.06 [95% CI 0.57-1.96]). Conclusions: In this NCC study, common risk factors as well as elevated microinflammation but not CHIP were associated with kidney function decline in type 2 diabetes mellitus.

Assessment of Fibrinogen-like 2 (FGL2) in Human Chronic Kidney Disease through Transcriptomics Data Analysis.

Fibrinogen-like 2 (FGL2) was recently found to be associated with fibrosis in a mouse model of kidney damage and was proposed as a potential therapeutic target in chronic kidney disease (CKD). We assessed the association of renal FGL2 mRNA expression with the disease outcome in two independent CKD cohorts (NEPTUNE and Innsbruck CKD cohort) using Kaplan Meier survival analysis. The regulation of FGL2 in kidney biopsies of CKD patients as compared to healthy controls was further assessed in 13 human CKD transcriptomics datasets. The FGL2 protein expression in human renal tissue sections was determined via immunohistochemistry. The regulators of FGL2 mRNA expression in renal tissue were identified in the co-expression and upstream regulator analysis of FGL2-positive renal cells via the use of single-cell RNA sequencing data from the kidney precision medicine project (KPMP). Higher renal FGL2 mRNA expression was positively associated with kidney fibrosis and negatively associated with eGFR. Renal FGL2 mRNA expression was upregulated in CKD as compared with healthy controls and associated with CKD progression in the Innsbruck CKD cohort (p-value = 0.0036) and NEPTUNE cohort (p-value = 0.0048). The highest abundance of FGL2 protein in renal tissue was detected in the thick ascending limb of the loop of Henle and macula densa, proximal tubular cells, as well as in glomerular endothelial cells. The upstream regulator analysis identified TNF, IL1B, IFNG, NFKB1, and SP1 as factors potentially inducing FGL2-co-expressed genes, whereas factors counterbalancing FGL2-co-expressed genes included GLI1, HNF1B, or PPARGC1A. In conclusion, renal FGL2 mRNA expression is elevated in human CKD, and higher FGL2 levels are associated with fibrosis and worse outcomes.

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.

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.

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.

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions.

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

Non-adherence to antidiabetic and cardiovascular drugs in type 2 diabetes mellitus and its association with renal and cardiovascular outcomes: A narrative review.

Cardiovascular and renal complications are a major burden for individuals with type 2 diabetes mellitus (T2DM). Besides lifestyle interventions, current guidelines recommend combination drug therapy to prevent or delay the incidence and progression of comorbidities. However, non-adherence to pharmacotherapy is common in chronic conditions such as T2DM and a barrier to successful disease management. Numerous studies have associated medication non-adherence with worse outcome as well as higher health care costs. This narrative review provides (i) an overview on adherence measures used within and outside research settings, (ii) an estimate on the prevalence of non-adherence to antidiabetic and cardiovascular drugs in T2DM, and (iii) specifically focuses on the association of non-adherence to these drugs with renal and cardiovascular outcomes.

Extracellular matrix protein signature of recurrent spontaneous cervical artery dissection.

OBJECTIVE: To assess whether connective tissue disorder is evident in patients with spontaneous cervical artery dissection and therefore identify patients at risk of recurrence using a cutting-edge quantitative proteomics approach. METHODS: In the ReSect study, all patients with spontaneous cervical artery dissection treated at the Innsbruck University Hospital since 1996 were invited to attend a standardized clinical follow-up examination. Protein abundance in skin punch biopsies (n = 50) was evaluated by a cutting-edge quantitative proteomics approach (liquid chromatography-mass spectrometry) that has hitherto not been applied to such patients. RESULTS: Patients with 1-time single-vessel (n = 19) or multiple-vessel (n = 13) dissections did not differ between each other or compared to healthy controls (n = 12) in protein composition. Patients with recurrent spontaneous cervical artery dissection (n = 6), however, showed significantly different expression of 25 proteins compared to the other groups combined. Literature review and Gene Ontology term annotation check revealed that 13 of the differently expressed proteins play a major role in the structural integrity of connective tissue or are linked to connective tissue disorders. These proteins showed clustering to a collagen/elastin cluster and one consisting of desmosome related proteins. CONCLUSION: This study unravels an extracellular matrix protein signature of recurrent spontaneous cervical artery dissection. In the long run and after large-scale validation, our findings may well assist in identifying patients at risk of recurrent spontaneous cervical artery dissection and thus guide therapy.

Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes.

The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in subjects with diabetic kidney disease, albeit with a large variability between individuals. Identifying novel biomarkers that predict response to therapy may help to tailor spironolactone therapy. We aimed to identify a set of metabolites for prediction of albuminuria response to spironolactone in subjects with type 2 diabetes. Systems biology molecular process analysis was performed a priori to identify metabolites linked to molecular disease processes and drug mechanism of action. Individual subject data and urine samples were used from 2 randomized placebo controlled double blind clinical trials (NCT01062763, NCT00381134). A urinary metabolite score was developed to predict albuminuria response to spironolactone therapy using penalized ridge regression with leave-one-out cross validation. Bioinformatic analysis identified a set of 18 metabolites linked to a diabetic kidney disease molecular model and potentially affected by spironolactone mechanism of action. Spironolactone reduced UACR relative to placebo by median -42% (25th to 75% percentile -65 to 6) and -29% (25th to 75% percentile -37 to -1) in the test and replication cohorts, respectively. In the test cohort, UACR reduction was higher in the lowest tertile of the baseline urinary metabolite score compared with middle and upper tertiles -58% (25th to 75% percentile -78 to 33), -28% (25th to 75% percentile -46 to 8), -40% (25th to 75% percentile -52% to 31), respectively, P = 0.001 for trend). In the replication cohort, UACR reduction was -54% (25th to 75% percentile -65 to -50), -41 (25th to 75% percentile -46% to 30), and -17% (25th to 75% percentile -36 to 5), respectively, P = 0.010 for trend). We identified a set of 18 urinary metabolites through systems biology to predict albuminuria response to spironolactone in type 2 diabetes. These data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.

Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis.

OBJECTIVES: To assess the association of public interest in coronavirus infections with the actual number of infected cases for selected countries across the globe. METHODS: We performed a Google TrendsTM search for "Coronavirus" and compared Relative Search Volumes (RSV) indices to the number of reported COVID-19 cases by the European Center for Disease Control (ECDC) using time-lag correlation analysis. RESULTS: Worldwide public interest in Coronavirus reached its first peak end of January when numbers of newly infected patients started to increase exponentially in China. The worldwide Google TrendsTM index reached its peak on the 12th of March 2020 at a time when numbers of infected patients started to increase in Europe and COVID-19 was declared a pandemic. At this time the general interest in China but also the Republic of Korea has already been significantly decreased as compared to end of January. Correlations between RSV indices and number of new COVID-19 cases were observed across all investigated countries with highest correlations observed with a time lag of -11.5 days, i.e. highest interest in coronavirus observed 11.5 days before the peak of newly infected cases. This pattern was very consistent across European countries but also holds true for the US. In Brazil and Australia, highest correlations were observed with a time lag of -7 days. In Egypt the highest correlation is given with a time lag of 0, potentially indicating that in this country, numbers of newly infected patients will increase exponentially within the course of April. CONCLUSIONS: Public interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic. Public interest is on average highest 11.5 days before the peak of newly infected cases.

Is There Decreasing Public Interest in Renal Transplantation? A Google TrendsTM Analysis.

BACKGROUND AND OBJECTIVES: Renal transplantation is the preferred form of renal replacement therapy for the majority of patients with end stage renal disease (ESRD). The Internet is a key tool for people seeking healthcare-related information. This current work explored the interest in kidney transplantation based on Internet search queries using Google TrendsTM. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: We performed a Google TrendsTM search with the search term "kidney transplantation" between 2004 (year of inception) and 2018. We retrieved and analyzed data on the worldwide trend as well as data from the United Network for Organ Sharing (UNOS), the Organización Nacional de Trasplantes (ONT), the Eurotransplant area, and the National Health Service (NHS) Transplant Register. Google TrendsTM indices were investigated and compared to the numbers of performed kidney transplants, which were extracted from the respective official websites of UNOS, ONT, Eurotransplant, and the NHS. RESULTS: During an investigational period of 15 years, there was a significant decrease of the worldwide Google TrendsTM index from 76.3 to 25.4, corresponding to an absolute reduction of -50.9% and a relative reduction by -66.7%. The trend was even more pronounced for the UNOS area (-75.2%), while in the same time period the number of transplanted kidneys in the UNOS area increased by 21.9%. Events of public interest had an impact on the search queries in the year of occurrence, as shown by an increase in the Google TrendsTM index by 39.2% in the year 2005 in Austria when a person of public interest received his second live donor kidney transplant. CONCLUSIONS: This study indicates a decreased public interest in kidney transplantation. There is a clear need to raise public awareness, since transplantation represents the best form of renal replacement therapy for patients with ESRD. Information should be provided on social media, with a special focus on readability and equitable access, as well as on web pages.

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.