Serum and Urine Metabolites and Kidney Function.

BACKGROUND: Metabolites represent a read-out of cellular processes underlying states of health and disease. METHODS: We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with "X" in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities (ARIC) Study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and UACR in longitudinal analyses. RESULTS: At visit 5 of the ARIC study, the mean age of participants was 76 years (SD 6), 56% were women, mean eGFR was 62 ml/min/1.73m2 (SD 20), and median urine albumin-to-creatinine level (UACR) was 13 mg/g (IQR 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected p < 4.0E-5 for serum analyses and p < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including 7 shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate, N2,N2-dimethylguanosine, hydroxy-N6,N6,N6-trimethyllysine, X-13844, and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (HR 2.3 [1.6,3.2], p = 2.7E-5) and urine X-12117 (HR 1.7 [1.3,2.2], p = 1.9E-5) were risk factors for UACR doubling, whereas urine sebacate (HR 0.86 [0.80,0.92], p = 1.9E-5) was inversely associated. Compared to clinical characteristics alone, including the top 5 endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (AUCs for eGFR decline: clinical model = 0.79, clinical + metabolites model = 0.87, p = 8.1E-6; for UACR doubling: clinical model = 0.66, clinical + metabolites model = 0.73, p = 2.9E-5). CONCLUSIONS: Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Associations of Urine and Plasma Metabolites with Kidney Failure and Death in a CKD Cohort.

RATIONALE & OBJECTIVE: Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease. STUDY DESIGN: Prospective metabolome-wide association study. SETTING & PARTICIPANTS: Persons with CKD enrolled in the German CKD Study (GCKD) with metabolite measurements; with external validation within the Atherosclerosis Risk in Communities Study. EXPOSURES: 1,513 urine and 1,416 plasma metabolites (Metabolon, Inc.) measured at study entry using untargeted mass spectrometry. OUTCOMES: Main endpoints were kidney failure (KF), and a composite endpoint of KF, eGFR <15 mL/min/1.73m2, or 40% decline in eGFR (CKE). Death from any cause was a secondary endpoint. After a median of 6.5 years follow-up, 500 persons experienced KF, 1,083 experienced CKE and 680 died. ANALYTICAL APPROACH: Time-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design, with external validation. RESULTS: 5,088 GCKD participants were included in analyses of urine metabolites and 5,144 in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (hazard ratio: 1.95, 95% confidence interval: 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, CKE, and death. External validation of the identified associations of metabolites with KF or CKE revealed direction-consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported. LIMITATIONS: Use of observational data and semi-quantitative metabolite measurements at a single time point. CONCLUSIONS: The observed associations between metabolites and KF, CKE or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts.

SLC17 transporters mediate renal excretion of Lac-Phe in mice and humans.

N-lactoyl-phenylalanine (Lac-Phe) is a lactate-derived metabolite that suppresses food intake and body weight. Little is known about the mechanisms that mediate Lac-Phe transport across cell membranes. Here we identify SLC17A1 and SLC17A3, two kidney-restricted plasma membrane-localized solute carriers, as physiologic urine Lac-Phe transporters. In cell culture, SLC17A1/3 exhibit high Lac-Phe efflux activity. In humans, levels of Lac-Phe in urine exhibit a strong genetic association with the SLC17A1-4 locus. Urine Lac-Phe levels are also increased following a Wingate sprint test. In mice, genetic ablation of either SLC17A1 or SLC17A3 reduces urine Lac-Phe levels. Despite these differences, both knockout strains have normal blood Lac-Phe and body weights, demonstrating that urine and plasma Lac-Phe pools are functionally and biochemically de-coupled. Together, these data establish SLC17 family members as the physiologic urine transporters for Lac-Phe and uncover a biochemical pathway for the renal excretion of this signaling metabolite.

metaGWASmanager: a toolbox for an automated workflow from phenotypes to meta-analysis in GWAS consortia.

SUMMARY: This article introduces the metaGWASmanager, which streamlines genome-wide association studies within large-scale meta-analysis consortia. It is a toolbox for both the central consortium analysis group and participating studies to generate homogeneous phenotypes, minimize unwanted variability from inconsistent methodologies, ensure high-quality association results, and implement time-efficient quality control workflows. The toolbox features a plug-in-based approach for customization of association testing. RESULTS: The metaGWASmanager toolbox has been successfully deployed in both the CKDGen and MetalGWAS Initiative consortia across hundreds of participating studies, demonstrating its effectiveness in GWAS analysis optimization by automating routine tasks and ensuring the value and reliability of association results, thus, ultimately promoting scientific discovery. We provide a simulated data set with examples for script customization so that readers can reproduce the pipeline at their convenience. AVAILABILITY AND IMPLEMENTATION: GitHub: https://github.com/genepi-freiburg/metaGWASmanager.

Conditional survival to assess prognosis in patients with chronic lymphocytic leukemia.

Biomarkers in chronic lymphocytic leukemia (CLL) allow assessment of prognosis. However, the validity of current prognostic biomarkers based on a single assessment point remains unclear for patients who have survived one or more years. Conditional survival (CS) studies that address how prognosis may change over time, especially in prognostic subgroups, are still rare. We performed CS analyses to estimate 5-year survival in 1-year increments, stratified by baseline disease characteristics and known risk factors in two community-based cohorts of CLL patients (Freiburg University Hospital (n = 316) and Augsburg University Hospital (n = 564)) diagnosed between 1984 and 2021. We demonstrate that 5-year CS probability is stable (app. 75%) for the entire CLL patient cohort over 10 years. While age, sex, and stage have no significant impact on CS, patients with high-risk disease features such as non-mutated IGHV, deletion 17p, and high-risk CLL-IPI have a significantly worse prognosis at diagnosis, and 5-year CS steadily decreases with each additional year survived. Our results confirm that CLL patients have a stable survival probability with excess mortality and that the prognosis of high-risk CLL patients declines over time. We infer that CS-based prognostic information is relevant for disease management and counseling of CLL patients.

Implication of transcription factor FOXD2 dysfunction in syndromic congenital anomalies of the kidney and urinary tract (CAKUT).

Congenital anomalies of the kidney and urinary tract (CAKUT) are the predominant cause for chronic kidney disease below age 30 years. Many monogenic forms have been discovered due to comprehensive genetic testing like exome sequencing. However, disease-causing variants in known disease-associated genes only explain a proportion of cases. Here, we aim to unravel underlying molecular mechanisms of syndromic CAKUT in three unrelated multiplex families with presumed autosomal recessive inheritance. Exome sequencing in the index individuals revealed three different rare homozygous variants in FOXD2, encoding a transcription factor not previously implicated in CAKUT in humans: a frameshift in the Arabic and a missense variant each in the Turkish and the Israeli family with segregation patterns consistent with autosomal recessive inheritance. CRISPR/Cas9-derived Foxd2 knockout mice presented with a bilateral dilated kidney pelvis accompanied by atrophy of the kidney papilla and mandibular, ophthalmologic, and behavioral anomalies, recapitulating the human phenotype. In a complementary approach to study pathomechanisms of FOXD2-dysfunction-mediated developmental kidney defects, we generated CRISPR/Cas9-mediated knockout of Foxd2 in ureteric bud-induced mouse metanephric mesenchyme cells. Transcriptomic analyses revealed enrichment of numerous differentially expressed genes important for kidney/urogenital development, including Pax2 and Wnt4 as well as gene expression changes indicating a shift toward a stromal cell identity. Histology of Foxd2 knockout mouse kidneys confirmed increased fibrosis. Further, genome-wide association studies suggest that FOXD2 could play a role for maintenance of podocyte integrity during adulthood. Thus, our studies help in genetic diagnostics of monogenic CAKUT and in understanding of monogenic and multifactorial kidney diseases.

Advancing proteomics in nephrology: unraveling causal pathways and therapeutic targets.

Proteomics has illuminated disease pathophysiology, unearthed novel biomarkers, and bolstered risk assessment strategies. In nephrology, observational analyses unveil biomarkers associated with adverse outcomes, whereas genetics offer insights into causal pathways. Mendelian randomization offers the potential to link the two, uncovering causal relationships between biomarkers and kidney function. Lanktree et al. demonstrate Mendelian randomization's utility in identifying additional proteins affecting kidney function and kidney disease progression.

Circulating Proteins and Mortality in CKD: A Proteomics Study of the AASK and ARIC Cohorts.

Rationale & Objective: Proteomics could provide pathophysiologic insight into the increased risk of mortality in patients with chronic kidney disease (CKD). This study aimed to investigate associations between the circulating proteome and all-cause mortality among patients with CKD. Study Design: Observational cohort study. Setting & Participants: Primary analysis in 703 participants in the African American Study of Kidney Disease and Hypertension (AASK) and validation in 1,628 participants with CKD in the Atherosclerosis Risk in Communities (ARIC) study who attended visit 5. Exposure: Circulating proteins. Outcome: All-cause mortality. Analytical Approach: Among AASK participants, we evaluated the associations of 6,790 circulating proteins with all-cause mortality using multivariable Cox proportional hazards models. Proteins with significant associations were further studied in ARIC Visit 5 participants with CKD. Results: In the AASK cohort, the mean age was 54.5 years, 271 (38.5%) were women, and the mean measured glomerular filtration rate (GFR) was 46 mL/min/1.73 m2. The median follow-up was 9.6 years, and 7 distinct proteins were associated with all-cause mortality at the Bonferroni-level threshold (P < 0.05 of the 6,790) after adjustment for demographics and clinical factors, including baseline measured estimated GFR and proteinuria. In the ARIC visit 5 cohort, the mean age was 77.2 years, 903 (55.5%) were women, the mean estimated GFR was 54 mL/min/1.73 m2 and median follow-up was 6.9 years. Of the 7 proteins found in AASK, 3 (ß2-microglobulin, spondin-1, and N-terminal pro-brain natriuretic peptide) were available in the ARIC data, with all 3 significantly associated with death in ARIC. Limitations: Possibility of unmeasured confounding. Cause of death was not known. Conclusions: Using large-scale proteomic analysis, proteins were reproducibly associated with mortality in 2 cohorts of participants with CKD. Plain-Language Summary: Patients with chronic kidney disease (CKD) have a high risk of premature death, with various pathophysiological processes contributing to this increased risk of mortality. This observational cohort study aimed to investigate the associations between circulating proteins and all-cause mortality in patients with CKD using large-scale proteomic analysis. The study analyzed data from the African American Study of Kidney Disease and Hypertension (AASK) study and validated the findings in the Atherosclerosis Risk in Communities (ARIC) Study. A total of 6,790 circulating proteins were evaluated in AASK, and 7 proteins were significantly associated with all-cause mortality. Three of these proteins (ß2-microglobulin, spondin-1, and N-terminal pro-brain natriuretic peptide (BNP)) were also measured in ARIC and were significantly associated with death. Additional studies assessing biomarkers associated with mortality among patients with CKD are needed to evaluate their use in clinical practice.

Proteomics analysis of plasma from middle-aged adults identifies protein markers of dementia risk in later life.

A diverse set of biological processes have been implicated in the pathophysiology of Alzheimer's disease (AD) and related dementias. However, there is limited understanding of the peripheral biological mechanisms relevant in the earliest phases of the disease. Here, we used a large-scale proteomics platform to examine the association of 4877 plasma proteins with 25-year dementia risk in 10,981 middle-aged adults. We found 32 dementia-associated plasma proteins that were involved in proteostasis, immunity, synaptic function, and extracellular matrix organization. We then replicated the association between 15 of these proteins and clinically relevant neurocognitive outcomes in two independent cohorts. We demonstrated that 12 of these 32 dementia-associated proteins were associated with cerebrospinal fluid (CSF) biomarkers of AD, neurodegeneration, or neuroinflammation. We found that eight of these candidate protein markers were abnormally expressed in human postmortem brain tissue from patients with AD, although some of the proteins that were most strongly associated with dementia risk, such as GDF15, were not detected in these brain tissue samples. Using network analyses, we found a protein signature for dementia risk that was characterized by dysregulation of specific immune and proteostasis/autophagy pathways in adults in midlife ~20 years before dementia onset, as well as abnormal coagulation and complement signaling ~10 years before dementia onset. Bidirectional two-sample Mendelian randomization genetically validated nine of our candidate proteins as markers of AD in midlife and inferred causality of SERPINA3 in AD pathogenesis. Last, we prioritized a set of candidate markers for AD and dementia risk prediction in midlife.

A genome-wide association study identifies 41 loci associated with eicosanoid levels.

Eicosanoids are biologically active derivatives of polyunsaturated fatty acids with broad relevance to health and disease. We report a genome-wide association study in 8406 participants of the Atherosclerosis Risk in Communities Study, identifying 41 loci associated with 92 eicosanoids and related metabolites. These findings highlight loci required for eicosanoid biosynthesis, including FADS1-3, ELOVL2, and numerous CYP450 loci. In addition, significant associations implicate a range of non-oxidative lipid metabolic processes in eicosanoid regulation, including at PKD2L1/SCD and several loci involved in fatty acyl-CoA metabolism. Further, our findings highlight select clearance mechanisms, for example, through the hepatic transporter encoded by SLCO1B1. Finally, we identify eicosanoids associated with aspirin and non-steroidal anti-inflammatory drug use and demonstrate the substantial impact of genetic variants even for medication-associated eicosanoids. These findings shed light on both known and unknown aspects of eicosanoid metabolism and motivate interest in several gene-eicosanoid associations as potential functional participants in human disease.

Transcriptome- and proteome-wide association studies nominate determinants of kidney function and damage.

BACKGROUND: The pathophysiological causes of kidney disease are not fully understood. Here we show that the integration of genome-wide genetic, transcriptomic, and proteomic association studies can nominate causal determinants of kidney function and damage. RESULTS: Through transcriptome-wide association studies (TWAS) in kidney cortex, kidney tubule, liver, and whole blood and proteome-wide association studies (PWAS) in plasma, we assess for effects of 12,893 genes and 1342 proteins on kidney filtration (glomerular filtration rate (GFR) estimated by creatinine; GFR estimated by cystatin C; and blood urea nitrogen) and kidney damage (albuminuria). We find 1561 associations distributed among 260 genomic regions that are supported as putatively causal. We then prioritize 153 of these genomic regions using additional colocalization analyses. Our genome-wide findings are supported by existing knowledge (animal models for MANBA, DACH1, SH3YL1, INHBB), exceed the underlying GWAS signals (28 region-trait combinations without significant GWAS hit), identify independent gene/protein-trait associations within the same genomic region (INHBC, SPRYD4), nominate tissues underlying the associations (tubule expression of NRBP1), and distinguish markers of kidney filtration from those with a role in creatinine and cystatin C metabolism. Furthermore, we follow up on members of the TGF-beta superfamily of proteins and find a prognostic value of INHBC for kidney disease progression even after adjustment for measured glomerular filtration rate (GFR). CONCLUSION: In summary, this study combines multimodal, genome-wide association studies to generate a catalog of putatively causal target genes and proteins relevant to kidney function and damage which can guide follow-up studies in physiology, basic science, and clinical medicine.

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.

In vivo kinetics of early, non-random methylome and transcriptome changes induced by DNA-hypomethylating treatment in primary AML blasts.

Despite routine use of DNA-hypomethylating agents (HMAs) in AML/MDS therapy, their mechanisms of action are not yet unraveled. Pleiotropic effects of HMAs include global methylome and transcriptome changes. We asked whether in blasts and T-cells from AML patients HMA-induced in vivo demethylation and remethylation occur randomly or non-randomly, and whether gene demethylation is associated with gene induction. Peripheral blood AML blasts from patients receiving decitabine (20 mg/m2 day 1-5) were serially isolated for methylome analyses (days 0, 8 and 15, n = 28) and methylome-plus-transcriptome analyses (days 0 and 8, n = 23), respectively. T-cells were isolated for methylome analyses (days 0 and 8; n = 16). We noted massive, non-random demethylation at day 8, which was variable between patients. In contrast, T-cells disclosed a thousand-fold lesser, random demethylation, indicating selectivity of the demethylation for the malignant blasts. The integrative analysis of DNA demethylation and transcript induction revealed 87 genes displaying a significant inverse correlation, e.g. the tumor suppressor gene IFI27, whose derepression was validated in two AML cell lines. These results support HMA-induced, non-random early in vivo demethylation events in AML blasts associated with gene induction. Larger patient cohorts are needed to determine whether a demethylation signature may be predictive for response to this treatment.

Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease.

Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts-including Mendelian randomization, colocalization analyses, and proteome-wide association studies-can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.

Alterations in the Circulating Proteome Associated with Albuminuria.

SIGNIFICANCE STATEMENT: We describe circulating proteins associated with albuminuria in a population of African American Study of Kidney Disease and Hypertension with CKD (AASK) using the largest proteomic platform to date: nearly 7000 circulating proteins, representing approximately 2000 new targets. Findings were replicated in a subset of a general population cohort with kidney disease (ARIC) and a population with CKD Chronic Renal Insufficiency Cohort (CRIC). In cross-sectional analysis, 104 proteins were significantly associated with albuminuria in the Black group, of which 67 of 77 available proteins were replicated in ARIC and 68 of 71 available proteins in CRIC. LMAN2, TNFSFR1B, and members of the ephrin superfamily had the strongest associations. Pathway analysis also demonstrated enrichment of ephrin family proteins. BACKGROUND: Proteomic techniques have facilitated understanding of pathways that mediate decline in GFR. Albuminuria is a key component of CKD diagnosis, staging, and prognosis but has been less studied than GFR. We sought to investigate circulating proteins associated with higher albuminuria. METHODS: We evaluated the cross-sectional associations of the blood proteome with albuminuria and longitudinally with doubling of albuminuria in the African American Study of Kidney Disease and Hypertension (AASK; 38% female; mean GFR 46; median urine protein-to-creatinine ratio 81 mg/g; n =703) and replicated in two external cohorts: a subset of the Atherosclerosis Risk in Communities (ARIC) study with CKD and the Chronic Renal Insufficiency Cohort (CRIC). RESULTS: In cross-sectional analysis, 104 proteins were significantly associated with albuminuria in AASK, of which 67 of 77 available proteins were replicated in ARIC and 68 of 71 available proteins in CRIC. Proteins with the strongest associations included LMAN2, TNFSFR1B, and members of the ephrin superfamily. Pathway analysis also demonstrated enrichment of ephrin family proteins. Five proteins were significantly associated with worsening albuminuria in AASK, including LMAN2 and EFNA4, which were replicated in ARIC and CRIC. CONCLUSIONS: Among individuals with CKD, large-scale proteomic analysis identified known and novel proteins associated with albuminuria and suggested a role for ephrin signaling in albuminuria progression.

Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study.

OBJECTIVE: The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis. RESEARCH DESIGN AND METHODS: In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments. RESULTS: There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk. CONCLUSIONS: We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.

Uromodulin and its association with urinary metabolites: the German Chronic Kidney Disease Study.

BACKGROUND: The progression of chronic kidney disease (CKD), a global public health burden, is accompanied by a declining number of functional nephrons. Estimation of remaining nephron mass may improve assessment of CKD progression. Uromodulin has been suggested as a marker of tubular mass. We aimed to identify metabolites associated with uromodulin concentrations in urine and serum to characterize pathophysiologic alterations of metabolic pathways to generate new hypotheses regarding CKD pathophysiology. METHODS: We measured urinary and serum uromodulin levels (uUMOD, sUMOD) and 607 urinary metabolites and performed cross-sectional analyses within the German Chronic Kidney Disease study (N = 4628), a prospective observational study. Urinary metabolites significantly associated with uUMOD and sUMOD were used to build weighted metabolite scores for urine (uMS) and serum uromodulin (sMS) and evaluated for time to adverse kidney events over 6.5 years. RESULTS: Metabolites cross-sectionally associated with uromodulin included amino acids of the tryptophan metabolism, lipids and nucleotides. Higher levels of the sMS [hazard ratio (HR) = 0.73 (95% confidence interval 0.64; 0.82), P = 7.45e-07] and sUMOD [HR = 0.74 (95% confidence interval 0.63; 0.87), P = 2.32e-04] were associated with a lower risk of adverse kidney events over time, whereas uUMOD and uMS showed the same direction of association but were not significant. CONCLUSIONS: We identified urinary metabolites associated with urinary and serum uromodulin. The sUMOD and the sMS were associated with lower risk of adverse kidney events among CKD patients. Higher levels of sUMOD and sMS may reflect a higher number of functional nephrons and therefore a reduced risk of adverse kidney outcomes.

Integrated proteomic and metabolomic modules identified as biomarkers of mortality in the Atherosclerosis Risk in Communities study and the African American Study of Kidney Disease and Hypertension.

BACKGROUND: Proteins and metabolites are essential for many biological functions and often linked through enzymatic or transport reactions. Individual molecules have been associated with all-cause mortality. Many of these are correlated and might jointly represent pathways or endophenotypes involved in diseases. RESULTS: We present an integrated analysis of proteomics and metabolomics via a local dimensionality reduction clustering method. We identified 224 modules of correlated proteins and metabolites in the Atherosclerosis Risk in Communities (ARIC) study, a general population cohort of older adults (N = 4046, mean age 75.7, mean eGFR 65). Many of the modules displayed strong cross-sectional associations with demographic and clinical characteristics. In comprehensively adjusted analyses, including fasting plasma glucose, history of cardiovascular disease, systolic blood pressure and kidney function among others, 60 modules were associated with mortality. We transferred the network structure to the African American Study of Kidney Disease and Hypertension (AASK) (N = 694, mean age 54.5, mean mGFR 46) and identified mortality associated modules relevant in this disease specific cohort. The four mortality modules relevant in both the general population and CKD were all a combination of proteins and metabolites and were related to diabetes / insulin secretion, cardiovascular disease and kidney function. Key components of these modules included N-terminal (NT)-pro hormone BNP (NT-proBNP), Sushi, Von Willebrand Factor Type A, EGF And Pentraxin (SVEP1), and several kallikrein proteases. CONCLUSION: Through integrated biomarkers of the proteome and metabolome we identified functions of (patho-) physiologic importance related to diabetes, cardiovascular disease and kidney function.