Serum 6-Bromotryptophan Levels Identified as a Risk Factor for CKD Progression

Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico

Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (β = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.

Association of Integrated Proteomic and Metabolomic Modules with Risk of Kidney Disease Progression

Key Points

Integrated analysis of proteome and metabolome identifies modules associated with CKD progression and kidney failure. Ephrin transmembrane proteins and podocyte-expressed CRIM1 and NPNT emerged as central components and warrant experimental and clinical investigation.

Background

Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes.

Methods

We present an integrated analysis of 4091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort study (N =1708; average follow-up for kidney failure, 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method, and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors, including the eGFR and urine protein–creatinine ratio. Associations were identified in a discovery sample (random two thirds, n =1139) and then evaluated in a replication sample (one third, n =569).

Results

We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components. Modules and principal component loadings were projected onto the replication sample, which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane-related terms were identified as overrepresented in these modules. Transmembrane–ephrin receptor activity displayed the largest odds (odds ratio=13.2, P value = 5.5×10−5). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure (P value = 2.6×10−5).

Conclusions

This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.

The metabolomic profiling of total fat and fat distribution in a multi-cohort study of women and men

Currently studies aiming for the comprehensive metabolomics profiling of measured total fat (%) as well as fat distribution in both sexes are lacking. In this work, bioimpedance analysis was applied to measure total fat (%) and fat distribution (trunk to leg ratio). Liquid chromatography-mass spectrometry-based untargeted metabolomics was employed to profile the metabolic signatures of total fat (%) and fat distribution in 3447 participants from three Swedish cohorts (EpiHealth, POEM and PIVUS) using a discovery-replication cross-sectional study design. Total fat (%) and fat distribution were associated with 387 and 120 metabolites in the replication cohort, respectively. Enriched metabolic pathways for both total fat (%) and fat distribution included protein synthesis, branched-chain amino acids biosynthesis and metabolism, glycerophospholipid metabolism and sphingolipid metabolism. Four metabolites were mainly related to fat distribution: glutarylcarnitine (C5-DC), 6-bromotryptophan, 1-stearoyl-2-oleoyl-GPI (18:0/18:1) and pseudouridine. Five metabolites showed different associations with fat distribution in men and women: quinolinate, (12Z)-9,10-dihydroxyoctadec-12-enoate (9,10-DiHOME), two sphingomyelins and metabolonic lactone sulfate. To conclude, total fat (%) and fat distribution were associated with a large number of metabolites, but only a few were exclusively associated with fat distribution and of those metabolites some were associated with sex*fat distribution. Whether these metabolites mediate the undesirable effects of obesity on health outcomes remains to be further investigated.

Molecular pathways that drive diabetic kidney disease

Kidney disease is a major driver of mortality among patients with diabetes and diabetic kidney disease (DKD) is responsible for close to half of all chronic kidney disease cases. DKD usually develops in a genetically susceptible individual as a result of poor metabolic (glycemic) control. Molecular and genetic studies indicate the key role of podocytes and endothelial cells in driving albuminuria and early kidney disease in diabetes. Proximal tubule changes show a strong association with the glomerular filtration rate. Hyperglycemia represents a key cellular stress in the kidney by altering cellular metabolism in endothelial cells and podocytes and by imposing an excess workload requiring energy and oxygen for proximal tubule cells. Changes in metabolism induce early adaptive cellular hypertrophy and reorganization of the actin cytoskeleton. Later, mitochondrial defects contribute to increased oxidative stress and activation of inflammatory pathways, causing progressive kidney function decline and fibrosis. Blockade of the renin-angiotensin system or the sodium-glucose cotransporter is associated with cellular protection and slowing kidney function decline. Newly identified molecular pathways could provide the basis for the development of much-needed novel therapeutics.

Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities Study

Rationale & Objective

Novel metabolite biomarkers of kidney failure with replacement therapy (KFRT) may help identify people at high risk for adverse kidney outcomes and implicated pathways may aid in developing targeted therapeutics.

Study Design

Prospective cohort.

Setting & Participants

The cohort included 3,799 Atherosclerosis Risk in Communities study participants with serum samples available for measurement at visit 1 (1987-1989).

Exposure

Baseline serum levels of 318 metabolites.

Outcomes

Incident KFRT, kidney failure (KFRT, estimated glomerular filtration rate <15 mL/min/1.73 m², or death from kidney disease).

Analytical Approach

Because metabolites are often intercorrelated and represent shared pathways, we used a high dimension reduction technique called Netboost to cluster metabolites. Longitudinal associations between clusters of metabolites and KFRT and kidney failure were estimated using a Cox proportional hazards model.

Results

Mean age of study participants was 53 years, 61% were African American, and 13% had diabetes. There were 160 KFRT cases and 357 kidney failure cases over a mean of 23 years. The 314 metabolites were grouped in 43 clusters. Four clusters were significantly associated with risk of KFRT and 6 were associated with kidney failure (including 3 shared clusters). The 3 shared clusters suggested potential pathways perturbed early in kidney disease: cluster 5 (15 metabolites involved in alanine, aspartate, and glutamate metabolism as well as 5-oxoproline and several gamma-glutamyl amino acids), cluster 26 (6 metabolites involved in sugar and inositol phosphate metabolism), and cluster 34 (21 metabolites involved in glycerophospholipid metabolism). Several individual metabolites were also significantly associated with both KFRT and kidney failure, including glucose and mannose, which were associated with higher risk of both outcomes, and 5-oxoproline, gamma-glutamyl amino acids, linoleoylglycerophosphocholine, 1,5-anhydroglucitol, which were associated with lower risk of both outcomes.

Limitations

Inability to determine if the metabolites cause or are a consequence of changes in kidney function.

Conclusions

We identified several clusters of metabolites reproducibly associated with development of KFRT. Future experimental studies are needed to validate our findings as well as continue unraveling metabolic pathways involved in kidney function decline.

Metabolite Biomarkers of CKD Progression in Children

BACKGROUND AND OBJECTIVES

Metabolomics facilitates the discovery of biomarkers and potential therapeutic targets for CKD progression.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We evaluated an untargeted metabolomics quantification of stored plasma samples from 645 Chronic Kidney Disease in Children (CKiD) participants. Metabolites were standardized and logarithmically transformed. Cox proportional hazards regression examined the association between 825 nondrug metabolites and progression to the composite outcome of KRT or 50% reduction of eGFR, adjusting for age, sex, race, body mass index, hypertension, glomerular versus nonglomerular diagnosis, proteinuria, and baseline eGFR. Stratified analyses were performed within subgroups of glomerular/nonglomerular diagnosis and baseline eGFR.

RESULTS

Baseline characteristics were 391 (61%) male; median age 12 years; median eGFR 54 ml/min per 1.73 m²; 448 (69%) nonglomerular diagnosis. Over a median follow-up of 4.8 years, 209 (32%) participants developed the composite outcome. Unique association signals were identified in subgroups of baseline eGFR. Among participants with baseline eGFR ≥60 ml/min per 1.73 m², two-fold higher levels of seven metabolites were significantly associated with higher hazards of KRT/halving of eGFR events: three involved in purine and pyrimidine metabolism (N6-carbamoylthreonyladenosine, hazard ratio, 16; 95% confidence interval, 4 to 60; 5,6-dihydrouridine, hazard ratio, 17; 95% confidence interval, 5 to 55; pseudouridine, hazard ratio, 39; 95% confidence interval, 8 to 200); two amino acids, C-glycosyltryptophan, hazard ratio, 24; 95% confidence interval 6 to 95 and lanthionine, hazard ratio, 3; 95% confidence interval, 2 to 5; the tricarboxylic acid cycle intermediate 2-methylcitrate/homocitrate, hazard ratio, 4; 95% confidence interval, 2 to 7; and gulonate, hazard ratio, 10; 95% confidence interval, 3 to 29. Among those with baseline eGFR <60 ml/min per 1.73 m², a higher level of tetrahydrocortisol sulfate was associated with lower risk of progression (hazard ratio, 0.8; 95% confidence interval, 0.7 to 0.9).

CONCLUSIONS

Untargeted plasma metabolomic profiling facilitated discovery of novel metabolite associations with CKD progression in children that were independent of established clinical predictors and highlight the role of select biologic pathways.

NAT8 Variants, N-Acetylated Amino Acids, and Progression of CKD

BACKGROUND AND OBJECTIVES

Genetic variants in NAT8, a liver- and kidney-specific acetyltransferase encoding gene, have been associated with eGFR and CKD in European populations. Higher circulating levels of two NAT8-associated metabolites, N-δ-acetylornithine and N-acetyl-1-methylhistidine, have been linked to lower eGFR and higher risk of incident CKD in the Black population. We aimed to expand upon prior studies to investigate associations between rs13538, a missense variant in NAT8, N-acetylated amino acids, and kidney failure in multiple, well-characterized cohorts.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We conducted analyses among participants with genetic and/or serum metabolomic data in the African American Study of Kidney Disease and Hypertension (AASK; n=962), the Atherosclerosis Risk in Communities (ARIC) study (n=1050), and BioMe, an electronic health record-linked biorepository (n=680). Separately, we evaluated associations between rs13538, urinary N-acetylated amino acids, and kidney failure in participants in the German CKD (GCKD) study (n=1624).

RESULTS

Of 31 N-acetylated amino acids evaluated, the circulating and urinary levels of 14 were associated with rs13538 (P<0.05/31). Higher circulating levels of five of these N-acetylated amino acids, namely, N-δ-acetylornithine, N-acetyl-1-methylhistidine, N-acetyl-3-methylhistidine, N-acetylhistidine, and N2,N5-diacetylornithine, were associated with kidney failure, after adjustment for confounders and combining results in meta-analysis (combined hazard ratios per two-fold higher amino acid levels: 1.48, 1.44, 1.21, 1.65, and 1.41, respectively; 95% confidence intervals: 1.21 to 1.81, 1.22 to 1.70, 1.08 to 1.37, 1.29 to 2.10, and 1.17 to 1.71, respectively; all P values <0.05/14). None of the urinary levels of these N-acetylated amino acids were associated with kidney failure in the GCKD study.

CONCLUSIONS

We demonstrate significant associations between an NAT8 gene variant and 14 N-acetylated amino acids, five of which had circulation levels that were associated with kidney failure.

Urine 6-Bromotryptophan: Associations with Genetic Variants and Incident End-Stage Kidney Disease

Higher serum 6-bromotryptophan has been associated with lower risk of chronic kidney disease (CKD) progression, implicating mechanisms beyond renal clearance. We studied genetic determinants of urine 6-bromotryptophan and its association with CKD risk factors and incident end-stage kidney disease (ESKD) in 4,843 participants of the German Chronic Kidney Disease (GCKD) study. 6-bromotryptophan was measured from urine samples using mass spectrometry. Patients with higher levels of urine 6-bromotryptophan had higher baseline estimated glomerular filtration rate (eGFR, p < 0.001). A genome-wide association study of urine 6-bromotryptophan identified two significant loci possibly related to its tubular reabsorption, SLC6A19, and its production, ERO1A, which was also associated with serum 6-bromotryptophan in an independent study. The association between urine 6-bromotryptophan and time to ESKD was assessed using Cox regression. There were 216 ESKD events after four years of follow-up. Compared with patients with undetectable levels, higher 6-bromotryptophan levels were associated with lower risk of ESKD in models unadjusted and adjusted for ESKD risk factors other than eGFR ( Collapse

Key Words

• prognostic markers
• kidney diseases
• biomarkers
• epidemiology
• genetics research
• outcomes research
• risk factors

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MESH Headings

• Amino Acid Transport Systems, Neutral/genetics
• Female
• Genetic Predisposition to Disease/genetics
• Genetic Variation/genetics
• Glomerular Filtration Rate/genetics
• Humans
• Kidney Failure, Chronic/epidemiology
• Kidney Failure, Chronic/genetics
• Kidney Failure, Chronic/urine
• Male
• Membrane Glycoproteins/genetics
• Middle Aged
• Oxidoreductases/genetics
• Proportional Hazards Models
• Risk Factors
• Tryptophan/analogs & derivatives
• Tryptophan/urine

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Grants

• R21 DK112087 NIDDK NIH HHS
• UL1 RR025005 NCRR NIH HHS
• R01 HL086694 NHLBI NIH HHS
• U01 HG004402 NHGRI NIH HHS
• HHSN268201700002C NHLBI NIH HHS
• HHSN268201700001I NHLBI NIH HHS
• HHSN268201700004I NHLBI NIH HHS
• HHSN268201700004C NHLBI NIH HHS
• R01 HL087641 NHLBI NIH HHS
• HHSN268201700003I NHLBI NIH HHS
• HHSN268201700005C NHLBI NIH HHS
• HHSN268201700001C NHLBI NIH HHS
• HHSN268201700003C NHLBI NIH HHS
• HHSN268201700002I NHLBI NIH HHS
• HHSN268201700005I NHLBI NIH HHS

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Affiliation(s)

Peggy Sekula Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
Adrienne Tin Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA The Memory Impairment and Neurodegenerative Dementia Center, University of Mississippi Medical Center, Jackson, MS, USA
Ulla T Schultheiss Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany Division of Nephrology, Department of Medicine, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
Seema Baid-Agrawal Department of Nephrology and Transplant Center, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
Robert P Mohney Metabolon Inc., Durham, NC, USA
Inga Steinbrenner Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
Bing Yu School of Public Health, The University of Texas Health Science Center at Houston, Houston, USA
Shengyuan Luo Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
Eric Boerwinkle School of Public Health, The University of Texas Health Science Center at Houston, Houston, USA
Kai-Uwe Eckardt Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
Josef Coresh Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
Morgan E Grams Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, MD, USA
Anna Kӧttgen Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

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Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression

OBJECTIVE

Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers.

MATERIALS AND METHODS

All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes.

RESULTS

Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days.

DISCUSSION

NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis.

CONCLUSION

For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.

Blood and urine biomarkers in chronic kidney disease: An update

INTRODUCTION

Chronic kidney disease (CKD) is a silent disease. Most CKD patients are unaware of their condition during the early stages of the disease which poses a challenge for healthcare professionals to institute treatment or start prevention. The trouble with the diagnosis of CKD is that in most parts of the world, it is still diagnosed based on measurements of serum creatinine and corresponding calculations of eGFR. There are controversies with the current staging system, especially in the methodology to diagnose and prognosticate CKD.

OBJECTIVE

The aim of this review is to examine studies that focused on the different types of samples which may serve as a good and promising biomarker for early diagnosis of CKD or to detect rapidly declining renal function among CKD patient.

METHOD

The review of international literature was made on paper and electronic databases Nature, PubMed, Springer Link and Science Direct. The Scopus index was used to verify the scientific relevance of the papers. Publications were selected based on the inclusion and exclusion criteria.

RESULT

63 publications were found to be compatible with the study objectives. Several biomarkers of interest with different sample types were taken for comparison.

CONCLUSION

Biomarkers from urine samples yield more significant outcome as compare to biomarkers from blood samples. But, validation and confirmation with a different type of study designed on a larger population is needed. More comparison studies on different types of samples are needed to further illuminate which biomarker is the better tool for the diagnosis and prognosis of CKD.