Metabolomic profiling to improve glomerular filtration rate estimation: a proof-of-concept study

Panel estimated Glomerular Filtration Rate (GFR): Statistical considerations for maximizing accuracy in diverse clinical populations

Assessing glomerular filtration rate (GFR) is critical for diagnosis, staging, and management of kidney disease. However, accuracy of estimated GFR (eGFR) is limited by large errors (>30% error present in >10-50% of patients), adversely impacting patient care. Errors often result from variation across populations of non-GFR determinants affecting the filtration markers used to estimate GFR. We hypothesized that combining multiple filtration markers with non-overlapping non-GFR determinants into a panel GFR could improve eGFR accuracy, extending current recognition that adding cystatin C to serum creatinine improves accuracy. Non-GFR determinants of markers can affect the accuracy of eGFR in two ways: first, increased variability in the non-GFR determinants of some filtration markers among application populations compared to the development population may result in outlying values for those markers. Second, systematic differences in the non-GFR determinants of some markers between application and development populations can lead to biased estimates in the application populations. Here, we propose and evaluate methods for estimating GFR based on multiple markers in applications with potentially higher rates of outlying predictors than in development data. We apply transfer learning to address systematic differences between application and development populations. We evaluated a panel of 8 markers (5 metabolites and 3 low molecular weight proteins) in 3,554 participants from 9 studies. Results show that contamination in two strongly predictive markers can increase imprecision by more than two-fold, but outlier identification with robust estimation can restore precision nearly fully to uncontaminated data. Furthermore, transfer learning can yield similar results with even modest training set sample size. Combining both approaches addresses both sources of error in GFR estimates. Once the laboratory challenge of developing a validated targeted assay for additional metabolites is overcome, these methods can inform the use of a panel eGFR across diverse clinical settings, ensuring accuracy despite differing non-GFR determinants.

Development and Validation of an Accurate Creatinine-Based Equation to Estimate Glomerular Filtration Rate for the Adult Indian Population: Design and Methods

Background

Existing creatinine-based equations to estimate glomerular filtration rate (GFR), developed primarily in populations of European and African American ancestry, do not accurately reflect the GFR in the Indian population due to differences in body composition, diet, and other factors. This manuscript describes the rationale and methodology for developing a creatinine-based equation for more accurate GFR estimation in Indian subjects.

Materials and Methods

This cross-sectional study will be conducted in India's two geographically and demographically diverse locations: Chandigarh (north) and Puducherry (south). Participants will include a representative sample from the general population and subjects with chronic kidney disease (CKD), with the latter being recruited from outpatient clinics. A total of 1558 subjects will be enrolled in the discovery and cross-validation cohort and 620 subjects in the external validation cohort. The reference standard for measured GFR (mGFR) will be the plasma clearance of iohexol. Stepwise multiple regression on log-transformed data will determine a set of variables that jointly predict mGFR and identify factors influencing mGFR and estimated (eGFR) in the study population. This study will also explore the performance of mGFR by iohexol measurement from dried blood spots against mGFR from plasma clearance of iohexol.

Conclusion

Developing a more reliable and accurate creatinine-based GFR estimating equation will improve CKD diagnosis, classification, and management. The findings will have substantial implications for CKD research in India and other regions with similar populations.

Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Key Points

Longitudinal untargeted metabolomics. Children with CKD have a circulating metabolome that changes over time.

Background

Understanding plasma metabolome patterns in relation to changing kidney function in pediatric CKD is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD.

Methods

The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05.

Results

There were 1156 person-visits (N : baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n =123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05.

Conclusions

We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.

Diagnostic standard: assessing glomerular filtration rate

Creatinine-based estimated glomerular filtration rate (eGFR) is imprecise at individual level, due to non-GFR-related serum creatinine determinants, including atypical muscle mass. Cystatin C has the advantage of being independent of muscle mass, a feature that led to the development of race- and sex-free equations. Yet, cystatin C-based equations do not perform better than creatinine-based equations for estimating GFR unless both variables are included together. The new race-free Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation had slight opposite biases between Black and non-Black subjects in the USA, but has poorer performance than that the previous version in European populations. The European Kidney Function Consortium (EKFC) equation developed in 2021 can be used in both children and adults, is more accurate in young and old adults, and is applicable to non-white European populations, by rescaling the Q factor, i.e. population median creatinine, in a potentially universal way. A sex- and race-free cystatin C-based EKFC, with the same mathematical design, has also be defined. New developments in the field of GFR estimation would be standardization of cystatin C assays, development of creatinine-based eGFR equations that incorporate muscle mass data, implementation of new endogenous biomarkers and the use of artificial intelligence. Standardization of different GFR measurement methods would also be a future challenge, as well as new technologies for measuring GFR. Future research is also needed into discrepancies between cystatin C and creatinine, which is associated with high risk of adverse events: we need to standardize the definition of discrepancy and understand its determinants.

Serum and Urine Metabolites and Kidney Function

Key Points

We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort. Metabolic profiling in serum and urine provides distinct and complementary insights into disease.

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 study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.

Results

At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m2 (SD 20), and median UACR level was 13 mg/g (interquartile range, 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 seven metabolites 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 (hazard ratio [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 of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves 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.

Consistency of metabolite associations with measured glomerular filtration rate in children and adults

Background

There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation.

Methods

We evaluated the consistency of associations between measured GFR (mGFR) and 887 common, known metabolites quantified by an untargeted chromatography- and spectroscopy-based metabolomics platform (Metabolon) performed on frozen blood samples from 580 participants in Chronic Kidney Disease in Children (CKiD), 674 participants in Modification of Diet in Renal Disease (MDRD) Study and 962 participants in African American Study of Kidney Disease and Hypertension (AASK). We evaluated metabolite-mGFR correlation association with metabolite class, molecular weight, assay platform and measurement coefficient of variation (CV). Among metabolites with strong negative correlations with mGFR (r < -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI).

Results

A total of 561 metabolites (63%) were negatively correlated with mGFR. Correlations with mGFR were highly consistent across study, sex, race and BMI categories (correlation of metabolite-mGFR correlations between 0.88 and 0.95). Amino acids, carbohydrates and nucleotides were more often negatively correlated with mGFR compared with lipids, but there was no association with metabolite molecular weight, liquid chromatography/mass spectrometry platform and measurement CV. Among 114 metabolites with strong negative associations with mGFR (r < -0.5), 27 were consistently not associated with age (height in children), sex or race.

Conclusions

The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review

BACKGROUND

There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.

PURPOSE

To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.

DATA SOURCES

Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.

STUDY SELECTION

Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.

DATA EXTRACTION

Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.

DATA SYNTHESIS

Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques.

LIMITATION

Results are mostly based on modeling studies and may be highly context-specific.

CONCLUSION

Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.

PRIMARY FUNDING SOURCE

Agency for Healthcare Quality and Research.

Evaluation of novel candidate filtration markers from a global metabolomic discovery for glomerular filtration rate estimation

Creatinine and cystatin-C are recommended for estimating glomerular filtration rate (eGFR) but accuracy is suboptimal. Here, using untargeted metabolomics data, we sought to identify candidate filtration markers for a new targeted assay using a novel approach based on their maximal joint association with measured GFR (mGFR) and with flexibility to consider their biological properties. We analyzed metabolites measured in seven diverse studies encompasing 2,851 participants on the Metabolon H4 platform that had Pearson correlations with log mGFR and used a stepwise approach to develop models to < -0.5 estimate mGFR with and without inclusion of creatinine that enabled selection of candidate markers. In total, 456 identified metabolites were present in all studies, and 36 had correlations with mGFR < -0.5. A total of 2,225 models were developed that included these metabolites; all with lower root mean square errors and smaller coefficients for demographic variables compared to estimates using untargeted creatinine. Seventeen metabolites were chosen, including 12 new candidate filtration markers. The selected metabolites had strong associations with mGFR and little dependence on demographic factors. Candidate metabolites were identified with maximal joint association with mGFR and minimal dependence on demographic variables across many varied clinical settings. These metabolites are excreted in urine and represent diverse metabolic pathways and tubular handling. Thus, our data can be used to select metabolites for a multi-analyte eGFR determination assay using mass spectrometry that potentially offers better accuracy and is less prone to non-GFR determinants than the current eGFR biomarkers.

Refining GFR estimation: a quest for the unobservable truth?

Assessing glomerular filtration rate (GFR), which is central to evaluating kidney health, remains challenging. Measured GFR is not widely available and lacks standardization. Estimated GFR can be highly inaccurate for some patients and has limited applicability to many patient populations, such as those who are acutely ill. Recent metabolomic advances show promise for identifying new filtration markers that might enhance GFR estimation. Improving GFR assessment will require refinement in both GFR measurement and estimation methods.

Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD

BACKGROUND

Children with CKD are at risk for impaired neurocognitive functioning. We investigated metabolomic associations with neurocognition in children with CKD.

METHODS

We leveraged data from the Chronic Kidney Disease in Children (CKiD) study and the Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease (NiCK) study. CKiD is a multi-institutional cohort that enrolled children aged 6 months to 16 years with eGFR 30-90 ml/min per 1.73 m 2 ( n =569). NiCK is a single-center cross-sectional study of participants aged 8-25 years with eGFR<90 ml/min per 1.73 m 2 ( n =60) and matched healthy controls ( n =67). Untargeted metabolomic quantification was performed on plasma (CKiD, 622 metabolites) and serum (NiCK, 825 metabolites) samples. Four neurocognitive domains were assessed: intelligence, attention regulation, working memory, and parent ratings of executive function. Repeat assessments were performed in CKiD at 2-year intervals. Linear regression and linear mixed-effects regression analyses adjusting for age, sex, delivery history, hypertension, proteinuria, CKD duration, and glomerular versus nonglomerular diagnosis were used to identify metabolites associated with neurocognitive z-scores. Analyses were performed with and without adjustment for eGFR.

RESULTS

There were multiple metabolite associations with neurocognition observed in at least two of the analytic samples (CKiD baseline, CKiD follow-up, and NiCK CKD). Most of these metabolites were significantly elevated in children with CKD compared with healthy controls in NiCK. Notable signals included associations with parental ratings of executive function: phenylacetylglutamine, indoleacetylglutamine, and trimethylamine N-oxide-and with intelligence: γ -glutamyl amino acids and aconitate.

CONCLUSIONS

Several metabolites were associated with neurocognitive dysfunction in pediatric CKD, implicating gut microbiome-derived substances, mitochondrial dysfunction, and altered energy metabolism, circulating toxins, and redox homeostasis.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_11_17_CJN0000000000000318.mp3.

External Validation of a Novel Multimarker GFR Estimating Equation

Key Points

Using multiple markers may improve GFR estimation especially in settings where creatinine and cystatin C are known to be limited. Panel eGFR is a novel multimarker eGFR equation consisting of age, sex, cystatin C, and nuclear magnetic resonance–measured creatinine, valine, and myo-inositol. eGFR-Cr and eGFR-Cr-CysC may underestimate measured GFR, while panel eGFR was unbiased among younger Black male individuals.

Background

Using multiple markers may improve accuracy in GFR estimation. We sought to externally validate and compare the performance of a novel multimarker eGFR (panel eGFR) equation among Black and White persons using the Genetic Epidemiology Network of Arteriopathy cohort.

Methods

We included 224 sex, race/ethnicity, and measured GFR (mGFR) category–matched persons, with GFR measured using urinary clearance of iothalamate. We calculated panel eGFR using serum creatinine, valine, myo-inositol, cystatin C, age, and sex. We compared its reliability with current eGFR equations (2021 CKD Epidemiology Collaboration creatinine [eGFR-Cr] and creatinine with cystatin C [eGFR-Cr-CysC]) using median bias, precision, and accuracy metrics. We evaluated each equation's performance in age, sex, and race subgroups.

Results

In the overall cohort, 49% were Black individuals, and mean mGFR was 79 ml/min per 1.73 m2. Panel eGFR overestimated mGFR (bias: −2.4 ml/min per 1.73 m2; 95% confidence interval [CI], −4.4 to −0.7), eGFR-Cr-CysC underestimated mGFR (bias: 4.8 ml/min per 1.73 m2; 95% CI, 2.1 to 6.7), while eGFR-Cr was unbiased (bias: 2.0 ml/min per 1.73 m2; 95% CI, −1.1 to 4.6). All equations had comparable accuracy. Among Black male individuals younger than 65 years, both eGFR-Cr (bias: 17.0 ml/min per 1.73 m2; 95% CI, 8.6 to 23.5) and eGFR-Cr-CysC (bias: 14.5 ml/min per 1.73 m2; 95% CI, 6.0 to 19.7) underestimated mGFR, whereas panel eGFR was unbiased (bias: 1.7 ml/min per 1.73 m2; 95% CI, −3.4 to 10.0). Metrics of accuracy for all eGFRs were acceptable in all subgroups except for panel eGFR in Black female individuals younger than 65 years (P30: 73.3%).

Conclusions

Panel eGFR can be used to estimate mGFR and may have utility among Black male individuals younger than 65 years where current CKD Epidemiology Collaboration equations are biased.

Multitargeted Internal Calibration for the Quantification of Chronic Kidney Disease-Related Endogenous Metabolites Using Liquid Chromatography-Mass Spectrometry

Accurate quantitative analysis in liquid chromatography-mass spectrometry (LC-MS) benefits from calibration curves generated in the same matrix as the study sample. In the case of endogenous compound quantification, as no blank matrix exists, the multitargeted internal calibration (MTIC) is an attractive and straightforward approach to avoid the need for extensive matrix similarity evaluation. Its principle is to take advantage of stable isotope labeled (SIL) standards as internal calibrants to simultaneously quantify authentic analytes using a within sample calibration. An MTIC workflow was developed for the simultaneous quantification of metabolites related to chronic kidney disease (CKD) using a volumetric microsampling device to collect 20 μL of serum or plasma, followed by a single-step extraction with acetonitrile/water and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Since a single concentration of internal calibrant is necessary to calculate the study sample concentration, the instrument response function was investigated to determine the best SIL concentration. After validation, the trueness of 16 endogenous analytes in authentic human serum ranged from 72.2 to 116.0%, the repeatability from 1.9 to 11.3%, and the intermediate precision ranged overall from 2.1 to 15.4%. The proposed approach was applied to plasma samples collected from healthy control participants and two patient groups diagnosed with CKD. Results confirmed substantial concentration differences between groups for several analytes, including indoxyl sulfate and cortisone, as well as metabolite enrichment in the kynurenine and indole pathways. Multitargeted methodologies represent a major step toward rapid and straightforward LC-MS/MS absolute quantification of endogenous biomarkers, which could change the paradigm of MS use in clinical laboratories.

Straightforward quantification of endogenous steroids with liquid chromatography-tandem mass spectrometry: Comparing calibration approaches

Different calibration strategies are used in liquid chromatography hyphenated to mass spectrometry (LC-MS) bioanalysis. Currently, the surrogate matrix and surrogate analyte represent the most widely used approaches to compensate for the lack of analyte-free matrices in endogenous compounds quantification. In this context, there is a growing interest in rationalizing and simplifying quantitative analysis using a one-point concentration level of stable isotope-labeled (SIL) standards as surrogate calibrants. Accordingly, an internal calibration (IC) can be applied when the instrument response is translated into analyte concentration via the analyte-to-SIL ratio performed directly in the study sample. Since SILs are generally used as internal standards to normalize variability between authentic study sample matrix and surrogate matrix used for the calibration, IC can be calculated even if the calibration protocol was achieved for an external calibration (EC). In this study, a complete dataset of a published and fully validated method to quantify an extended steroid profile in serum was recomputed by adapting the role of SIL internal standards as surrogate calibrants. Using the validation samples, the quantitative performances for IC were comparable with the original method, showing acceptable trueness (79%-115%) and precision (0.8%-11.8%) for the 21 detected steroids. The IC methodology was then applied to human serum samples (n = 51) from healthy women and women diagnosed with mild hyperandrogenism, showing high agreement (R² > 0.98) with the concentrations obtained using the conventional quantification based on EC. For IC, Passing-Bablok regression showed proportional biases between -15.0% and 11.3% for all quantified steroids, with an average difference of -5.8% compared to EC. These results highlight the reliability and the advantages of implementing IC in clinical laboratories routine to simplify quantification in LC-MS bioanalysis, especially when a large panel of analytes is monitored.

Estimating kidney function in patients with cancer: A narrative review

AIM

Accurate evaluation of glomerular filtration rate (GFR) is crucial in Oncology as drug eligibility and dosing depend on estimates of GFR. However, there are no clear guidelines on the optimal method of determining kidney function in patients with cancer. We aimed to summarize the evidence on estimation of kidney function in patients with cancer.

METHODS

We searched PubMed for literature discussing the performance of GFR estimating equations in patients with malignancy to create a table of the evidence for creatinine- and cystatin c-based equations. We further reviewed novel estimation techniques such as panel eGFR, real-time measured GFR, and functional magnetic resonance imaging.

RESULTS

The commonly used GFR estimating equations were derived from populations of patients without cancer. These equations may be less applicable in Oncology due to severe sarcopenia, inflammation, and other physiologic changes in patients with cancer. The Cockcroft-Gault equation currently dominates in clinical Oncology despite significant limitations and accumulating evidence for use of the CKD-EPICr formula. Additional considerations in the practice of Oncology include a recently developed equation (CamGFRv2, also called the Janowitz formula) and the use of cystatin c-based equations to overcome some of the barriers to accurate GFR estimation based on creatinine alone.

CONCLUSION

Overall, we suggest using the CKD-EPI equations (either cystatin c or creatinine-based) among patients with cancer in routine clinical practice and measured GFR for patients at a critical threshold for treatment decisions.

A Metabolomics study of metabolites associated with the glomerular filtration rate

BACKGROUND

Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates.

METHODS

An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The 145 samples were divided into four groups based on the patient's measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software.

RESULTS

A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function.

CONCLUSIONS

This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.

Novel Metabolites to Improve Glomerular Filtration Rate Estimation

INTRODUCTION

The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation.

METHODS

Fifty-three CKD participants were recruited from the Third Affiliated Hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by ultrahigh-performance liquid chromatography-tandem mass spectroscopy. All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst 5.0 for one-way analysis of variance, principal component analysis, and partial least squares-discriminant analysis and confirmed the metabolites whose levels increased or decreased with mGFR and variable importance in projection (VIP) values >1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient >0.8 and VIP >2 were identified.

RESULTS

We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites.

CONCLUSION

We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation.

Untargeted Metabolomics by Ultra-High-Performance Liquid Chromatography Coupled with Electrospray Ionization-Quadrupole-Time of Flight-Mass Spectrometry Analysis Identifies a Specific Metabolomic Profile in Patients with Early Chronic Kidney Disease

Chronic kidney disease (CKD) has emerged as one of the most progressive diseases with increased mortality and morbidity. Metabolomics offers new insights into CKD pathogenesis and the discovery of new biomarkers for the early diagnosis of CKD. The aim of this cross-sectional study was to assess metabolomic profiling of serum and urine samples obtained from CKD patients. Untargeted metabolomics followed by multivariate and univariate analysis of blood and urine samples from 88 patients with CKD, staged by estimated glomerular filtration rate (eGFR), and 20 healthy control subjects was performed using ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry. Serum levels of Oleoyl glycine, alpha-lipoic acid, Propylthiouracil, and L-cysteine correlated directly with eGFR. Negative correlations were observed between serum 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinyl glycine, Propenoylcarnitine, Uridine, and All-trans retinoic acid levels and eGFR. In urine samples, the majority of molecules were increased in patients with advanced CKD as compared with early CKD patients and controls. Amino acids, antioxidants, uremic toxins, acylcarnitines, and tryptophane metabolites were found in all CKD stages. Their dual variations in serum and urine may explain their impact on both glomerular and tubular structures, even in the early stages of CKD. Patients with CKD display a specific metabolomic profile. Since this paper represents a pilot study, future research is needed to confirm our findings that metabolites can serve as indicators of early CKD.

Identification of Metabolite Markers Associated with Kidney Function

Background

Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function.

Methods

Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory.

Results

From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study.

Conclusions

Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

BACKGROUND

Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).

METHODS

Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.

RESULTS

ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites.

CONCLUSION

ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C

Assessment of renal function relies on the estimation of the glomerular filtration rate (eGFR). Existing eGFR equations, usually based on serum levels of creatinine and/or cystatin C, are not uniformly accurate across patient populations. In the present study, we expanded a recent proof-of-concept approach to optimize an eGFR equation targeting the adult population with and without chronic kidney disease (CKD), based on a nuclear magnetic resonance spectroscopy (NMR) derived 'metabolite constellation' (GFRNMR). A total of 1855 serum samples were partitioned into development, internal validation and external validation datasets. The new GFRNMR equation used serum myo-inositol, valine, creatinine and cystatin C plus age and sex. GFRNMR had a lower bias to tracer measured GFR (mGFR) than existing eGFR equations, with a median bias (95% confidence interval [CI]) of 0.0 (-1.0; 1.0) mL/min/1.73 m2 for GFRNMR vs. -6.0 (-7.0; -5.0) mL/min/1.73 m2 for the Chronic Kidney Disease Epidemiology Collaboration equation that combines creatinine and cystatin C (CKD-EPI2012) (p < 0.0001). Accuracy (95% CI) within 15% of mGFR (1-P15) was 38.8% (34.3; 42.5) for GFRNMR vs. 47.3% (43.2; 51.5) for CKD-EPI2012 (p < 0.010). Thus, GFRNMR holds promise as an alternative way to assess eGFR with superior accuracy in adult patients with and without CKD.

Measured Glomerular Filtration Rate: The Query for a Workable Golden Standard Technique

Inulin clearance has, for a long time, been considered as the reference method to determine measured glomerular filtration rates (mGFRs). However, given the known limitations of the standard marker, serum creatinine, and of inulin itself, and the frequent need for accurate GFR estimations, several other non-radioactive (iohexol and iothalamate) and radioactive (51Cr-EDTA, 99mTc-DTPA, 125I iothalamate) exogenous mGFR filtration markers are nowadays considered the most accurate options to evaluate GFR. The availability of 51Cr-EDTA is limited, and all methods using radioactive tracers necessitate specific safety precautions. Serum- or plasma-based certified reference materials for iohexol and iothalamate and evidence-based protocols to accurately and robustly measure GFR (plasma vs. urinary clearance, single-sample vs. multiple-sample strategy, effect of sampling time delay) are lacking. This leads to substantial variation in reported mGFR results across studies and questions the scientific reliability of the alternative mGFR methods as the gold standard to evaluate kidney function. On top of the scientific discussion, regulatory issues are further narrowing the clinical use of mGFR methods. Therefore, this review is a call for standardization of mGFR in terms of three aspects: the marker, the analytical method to assess concentrations of that marker, and the procedure to determine GFR in practice. Moreover, there is also a need for an endogenous filtration marker or a panel of filtration markers from a single blood draw that would allow estimation of GFR as accurately as mGFR, and without the need for application of anthropometric, clinical, and demographic characteristics.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

A two-stage neural network prediction of chronic kidney disease

Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra-high dimensional datasets. This study aims to provide a two-stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.

Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives

Introduction

Analysis of blood for the evaluation of clinically relevant biomarkers requires precise collection and sample handling by phlebotomists and laboratory staff. An important consideration for the clinical application of metabolomics are the different anticoagulants utilized for sample collection. Most studies that have characterized differences in metabolite levels in various blood collection tubes have focused on single analytes. We define analyte levels on a global metabolomics platform following blood sampling using five different, but commonly used, clinical laboratory blood collection tubes (i.e., plasma anticoagulated with either EDTA, lithium heparin or sodium citrate, along with no additive (serum), and EDTA anticoagulated whole blood).

Methods

Using an untargeted metabolomics platform we analyzed five sample types after all had been collected and stored at -80°C. The biochemical composition was determined and differences between the samples established using matched-pair t-tests.

Results

We identified 1,117 biochemicals across all samples and detected a mean of 1,036 in the sample groups. Compared to the levels of metabolites in EDTA plasma, the number of biochemicals present at statistically significant different levels (p<0.05) ranged from 452 (serum) to 917 (whole blood). Several metabolites linked to screening assays for rare diseases including acylcarnitines, bilirubin and heme metabolites, nucleosides, and redox balance metabolites varied significantly across the sample collection types.

Conclusions

Our study highlights the widespread effects and importance of using consistent additives for assessing small molecule levels in clinical metabolomics. The biochemistry that occurs during the blood collection process creates a reproducible signal that can identify specimens collected with different anticoagulants in metabolomic studies.

Impact statement

In this manuscript, normal/healthy donors had peripheral blood collected using multiple anticoagulants as well as serum during a fasted blood draw. Global metabolomics is a new technology being utilized to draw clinical conclusions and we interrogated the effects of different anticoagulants on the levels of biochemicals from each of the donors. Characterizing the effects of the anticoagulants on biochemical levels will help researchers leverage the information using global metabolomics in order to make conclusions regarding important disease biomarkers.

Serum Myo-Inositol, Dimethyl Sulfone, and Valine in Combination with Creatinine Allow Accurate Assessment of Renal Insufficiency-A Proof of Concept

Evaluation of renal dysfunction includes estimation of glomerular filtration rate (eGFR) as the initial step and subsequent laboratory testing. We hypothesized that combined analysis of serum creatinine, myo-inositol, dimethyl sulfone, and valine would allow both assessment of renal dysfunction and precise GFR estimation. Bio-banked sera were analyzed using nuclear magnetic resonance spectroscopy (NMR). The metabolites were combined into a metabolite constellation (GFRNMR) using n = 95 training samples and tested in n = 189 independent samples. Tracer-measured GFR (mGFR) served as a reference. GFRNMR was compared to eGFR based on serum creatinine (eGFRCrea and eGFREKFC), cystatin C (eGFRCys-C), and their combination (eGFRCrea-Cys-C) when available. The renal biomarkers provided insights into individual renal and metabolic dysfunction profiles in selected mGFR-matched patients with otherwise homogenous clinical etiology. GFRNMR correlated better with mGFR (Pearson correlation coefficient r = 0.84 vs. 0.79 and 0.80). Overall percentages of eGFR values within 30% of mGFR for GFRNMR matched or exceeded those for eGFRCrea and eGFREKFC (81% vs. 64% and 74%), eGFRCys-C (81% vs. 72%), and eGFRCrea-Cys-C (81% vs. 81%). GFRNMR was independent of patients' age and sex. The metabolite-based NMR approach combined metabolic characterization of renal dysfunction with precise GFR estimation in pediatric and adult patients in a single analytical step.

KDIGO Controversies Conference on onco-nephrology: understanding kidney impairment and solid-organ malignancies, and managing kidney cancer

The association between kidney disease and cancer is multifaceted and complex. Persons with chronic kidney disease (CKD) have an increased incidence of cancer, and both cancer and cancer treatments can cause impaired kidney function. Renal issues in the setting of malignancy can worsen patient outcomes and diminish the adequacy of anticancer treatments. In addition, the oncology treatment landscape is changing rapidly, and data on tolerability of novel therapies in patients with CKD are often lacking. Caring for oncology patients has become more specialized and interdisciplinary, currently requiring collaboration among specialists in nephrology, medical oncology, critical care, clinical pharmacology/pharmacy, and palliative care, in addition to surgeons and urologists. To identify key management issues in nephrology relevant to patients with malignancy, KDIGO (Kidney Disease: Improving Global Outcomes) assembled a global panel of multidisciplinary clinical and scientific expertise for a controversies conference on onco-nephrology in December 2018. This report covers issues related to kidney impairment and solid organ malignancies as well as management and treatment of kidney cancer. Knowledge gaps, areas of controversy, and research priorities are described.

Potential for Novel Biomarkers in Diabetes-Associated Chronic Kidney Disease: Epigenome, Metabolome, and Gut Microbiome

Diabetes-associated chronic kidney disease is a pandemic issue. Despite the global increase in the number of individuals with this chronic condition together with increasing morbidity and mortality, there are currently only limited therapeutic options to slow disease progression. One of the reasons for this is that the current-day "gold standard" biomarkers lack adequate sensitivity and specificity to detect early diabetic chronic kidney disease (CKD). This review focuses on the rapidly evolving areas of epigenetics, metabolomics, and the gut microbiome as potential sources of novel biomarkers in diabetes-associated CKD and discusses their relevance to clinical practice. However, it also highlights the problems associated with many studies within these three areas-namely, the lack of adequately powered longitudinal studies, and the lack of reproducibility of results which impede biomarker development and clinical validation in this complex and susceptible population.

Kidney Disease, Race, and GFR Estimation

Assessment of GFR is central to clinical practice, research, and public health. Current Kidney Disease Improving Global Outcomes guidelines recommend measurement of serum creatinine to estimate GFR as the initial step in GFR evaluation. Serum creatinine is influenced by creatinine metabolism as well as GFR; hence, all equations to estimate GFR from serum creatinine include surrogates for muscle mass, such as age, sex, race, height, or weight. The guideline-recommended equation in adults (the 2009 Chronic Kidney Disease Epidemiology Collaboration creatinine equation) includes a term for race (specified as black versus nonblack), which improves the accuracy of GFR estimation by accounting for differences in non-GFR determinants of serum creatinine by race in the study populations used to develop the equation. In that study, blacks had a 16% higher average measured GFR compared with nonblacks with the same age, sex, and serum creatinine. The reasons for this difference are only partly understood, and the use of race in GFR estimation has limitations. Some have proposed eliminating the race coefficient, but this would induce a systematic underestimation of measured GFR in blacks, with potential unintended consequences at the individual and population levels. We propose a more cautious approach that maintains and improves accuracy of GFR estimates and avoids disadvantaging any racial group. We suggest full disclosure of use of race in GFR estimation, accommodation of those who decline to identify their race, and shared decision making between health care providers and patients. We also suggest mindful use of cystatin C as a confirmatory test as well as clearance measurements. It would be preferable to avoid specification of race in GFR estimation if there was a superior, evidence-based substitute. The goal of future research should be to develop more accurate methods for GFR estimation that do not require use of race or other demographic characteristics.

GFR Estimation Using a Panel of Filtration Markers in Shanghai and Beijing

RATIONALE & OBJECTIVES

Estimated glomerular filtration rate (eGFR) using creatinine and cystatin C (eGFRcr-cys) may be less accurate compared to measured GFR (mGFR) in China than in North America, Europe, and Australia due to variation across regions in their non-GFR determinants. The non-GFR determinants of β2-microglobulin (B2M) and β-trace protein (BTP) differ from those of creatinine and cystatin C. Thus, the average eGFR using all 4 markers (eGFRavg) could be more accurate than eGFRcr-cys in China.

STUDY DESIGN

Diagnostic test study.

SETTING & PARTICIPANTS

1,066 participants in Shanghai and Beijing with creatinine and cystatin C and 666 participants with all 4 filtration markers.

TESTS COMPARED

Index tests were previously developed equations for eGFR using creatinine, cystatin C, B2M, and BTP and combinations. The reference test was mGFR using plasma clearance of iohexol. We compared the performance of eGFRavg to eGFRcr-cys using the proportion of participants with errors in eGFR >30% of mGFR (1 - P30) and root mean square error (RMSE) of the regression of eGFR on mGFR on the logarithmic scale. We also compared classification and reclassification of mGFR categories using eGFRavg compared to eGFRcr-cys.

OUTCOMES

Accuracy was significantly better for eGFRavg (1 - P30 of 10.4% and RMSE of 0.214) compared to eGFRcr-cys (1 - P30 of 13.8% and RMSE of 0.232; P = 0.004 and P = 0.006, respectively). However, improvements in accuracy did not generally translate into significant improvement in classification or reclassification of mGFR categories.

LIMITATIONS

Study population may not be generalizable to clinical settings other than large urban medical centers in China.

CONCLUSIONS

A panel of endogenous filtration markers including B2M and BTP in addition to creatinine and cystatin C may improve GFR estimation in China. Further study is necessary to determine whether GFR estimation using B2M and BTP can be improved and whether these improvements lead to useful clinical applications.

Application of Metabolomics to Renal and Cardiometabolic Diseases

Metabolomics has been increasingly applied to study renal and related cardiometabolic diseases, including diabetes and cardiovascular diseases. These studies span cross-sectional studies correlating metabolites with specific phenotypes, longitudinal studies to identify metabolite predictors of future disease, and physiologic/interventional studies to probe underlying causal relationships. This chapter provides a description of how metabolomic profiling is being used in these contexts, with an emphasis on study design considerations as a practical guide for investigators who are new to this area. Research in kidney diseases is underlined to illustrate key principles. The chapter concludes by discussing the future potential of metabolomics in the study of renal and cardiometabolic diseases.

Novel associations between blood metabolites and kidney function among Bogalusa Heart Study and Multi-Ethnic Study of Atherosclerosis participants

INTRODUCTION

Chronic kidney disease (CKD) is a major public health challenge given its high global prevalence and associated risks of cardiovascular disease and progression to end stage renal disease. Although it is known that numerous metabolic changes occur in CKD patients, identifying novel metabolite associations with kidney function may enhance our understanding of the physiologic pathways relating to CKD.

OBJECTIVES

The objective of this study was to elucidate novel metabolite associations with kidney function among participants of two community-based cohorts with carefully ascertained metabolomics, kidney function, and covariate data.

METHODS

Untargeted ultrahigh-performance liquid chromatography-tandem mass spectrometry was used to detect and quantify blood metabolites. We used multivariate adjusted linear regression to examine associations between single metabolites and creatinine-based estimated glomerular filtration rate (eGFRcr) among 1243 Bogalusa Heart Study (BHS) participants (median eGFRcr: 94.4, 5th-95th percentile: 66.0-119.6 mL/min/1.73 m²). Replication, determined by statistical significance and consistent effect direction, was tested using gold standard measured glomerular filtration rate (mGFR) among 260 Multi-Ethnic Study of Atherosclerosis (MESA) participants (median mGFR: 72.0, 5th-95th percentile: 43.5-105.0 mL/min/1.73 m²). All analyses used Bonferroni-corrected alpha thresholds.

RESULTS

Fifty-one novel metabolite associations with kidney function were identified, including 12 from previously unrelated sub-pathways: N6-carboxymethyllysine, gulonate, quinolinate, gamma-CEHC-glucuronide, retinol, methylmalonate, 3-hydroxy-3-methylglutarate, 3-aminoisobutyrate, N-methylpipecolate, hydroquinone sulfate, and glycine conjugates of C₁₀H₁₂O₂ and C₁₀H₁₄O₂(1). Significant metabolites were generally inversely associated with kidney function and smaller in mass-to-charge ratio than non-significant metabolites.

CONCLUSION

The 51 novel metabolites identified may serve as early, clinically relevant, kidney function biomarkers.

Improving glomerular filtration rate estimation

Glomerular filtration rate (GFR) estimating equations are widely used, but their accuracy is limited by the need for race-ethnicity factors and imprecision of the estimated GFR. In this issue, Bukabau et al. evaluated the accuracy of GFR estimating equations and race-ethnicity factors in 2 centers in sub-Saharan Africa. The authors determined that the African American factor is not applicable and that newer equations are not more accurate than the Chronic Kidney Disease Epidemiology Collaboration equations recommended by current guidelines.

Untargeted metabolomics reveals N, N, N-trimethyl-L-alanyl-L-proline betaine (TMAP) as a novel biomarker of kidney function

The diagnosis and prognosis of chronic kidney disease (CKD) currently relies on very few circulating small molecules, which can vary by factors unrelated to kidney function. In end-stage renal disease (ESRD), these same small molecules are used to determine dialysis dose and dialytic clearance. Therefore, we aimed to identify novel plasma biomarkers to estimate kidney function in CKD and dialytic clearance in ESRD. Untargeted metabolomics was performed on plasma samples from patients with a single kidney, non-dialysis CKD, ESRD and healthy controls. For ESRD patients, pre- and post-dialysis plasma samples were obtained from several dialysis modalities. Metabolomics analysis revealed over 400 significantly different features in non-dialysis CKD and ESRD plasma compared to controls while less than 35 features were significantly altered in patients with a single kidney. N,N,N-trimethyl-L-alanyl-L-proline betaine (TMAP, AUROC = 0.815) and pyrocatechol sulfate (AUROC = 0.888) outperformed creatinine (AUROC = 0.745) in accurately identifying patients with a single kidney. Several metabolites accurately predicted ESRD; however, when comparing pre-and post-hemodialysis, TMAP was the most robust biomarker of dialytic clearance for all modalities (AUROC = 0.993). This study describes TMAP as a novel potential biomarker of kidney function and dialytic clearance across several hemodialysis modalities.

The Serum Metabolome Identifies Biomarkers of Dietary Acid Load in 2 Studies of Adults with Chronic Kidney Disease

BACKGROUND

Dietary acid load is a clinically important aspect of the diet that reflects the balance between acid-producing foods, for example, meat and cheese, and base-producing foods, for example, fruits and vegetables.

METHODS

We used metabolomics to identify blood biomarkers of dietary acid load in 2 independent studies of chronic kidney disease patients: the African American Study of Kidney Disease and Hypertension (AASK, n = 689) and the Modification of Diet in Renal Disease (MDRD, n = 356) study. Multivariable linear regression was used to assess the cross-sectional association between serum metabolites whose identity was known (outcome) and dietary acid load (exposure), estimated with net endogenous acid production (NEAP) based on 24-h urine urea nitrogen and potassium, and adjusted for age, sex, race, randomization group, measured glomerular filtration rate, log-transformed urine protein-to-creatinine ratio, history of cardiovascular disease, BMI, and smoking status.

RESULTS

Out of the 757 known, nondrug metabolites identified in AASK, 26 were significantly associated with NEAP at the Bonferroni threshold for significance (P < 6.6 × 10-5). Twenty-three of the 26 metabolites were also identified in the MDRD study, and 13 of the 23 (57%) were significantly associated with NEAP (P < 2.2 × 10-3), including 5 amino acids (S-methylmethionine, indolepropionylglycine, indolepropionate, N-methylproline, N-δ-acetylornithine), 2 cofactors and vitamins (threonate, oxalate), 1 lipid (chiro-inositol), and 5 xenobiotics (methyl glucopyranoside, stachydrine, catechol sulfate, hippurate, and tartronate). Higher levels of all 13 replicated metabolites were associated with lower NEAP in both AASK and the MDRD study.

CONCLUSION

Metabolomic profiling of serum specimens from kidney disease patients in 2 study populations identified 13 replicated metabolites associated with dietary acid load. Additional studies are needed to validate these compounds in healthy populations. These 13 compounds may potentially be used as objective markers of dietary acid load in future nutrition research studies.

Validation of a Metabolite Panel for a More Accurate Estimation of Glomerular Filtration Rate Using Quantitative LC-MS/MS

BACKGROUND

Clinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables.

METHODS

An ultra-performance liquid chromatography-tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 - P30); goal <10%].

RESULTS

The assay had a mean imprecision (≤10% intraassay, ≤6.9% interassay), linearity over the quantitative range (r 2 > 0.98), and analyte recovery (98.5%-113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 - P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups.

CONCLUSIONS

The new eGFRmet equation could serve as a confirmatory test for GFR estimation.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.