A combined epidemiologic and metabolomic approach improves CKD prediction

Plasma Trimethylamine N-Oxide Levels in Nondialysis Chronic Kidney Disease Patients Following Meal Challenge

Trimethylamine N-oxide (TMAO) is a uremic toxin accumulated in patients with chronic kidney disease (CKD) and involved with atherosclerosis. TMAO is derived from the gut microbiota (nutrient fermentation) and is found in some fish and seafood. This pilot study evaluated the acute effects of cod fish, red meat, and vegetable meal consumption on plasma TMAO levels in nondialysis CKD patients. Patients (five women, three men, 72.5 [7.7] years, body mass index [BMI], 25.6 [1.4] kg/m2, glomerular filtration rate, 32.5 [18.5] mL/min) consumed lunches containing codfish, red meat, and chickpeas, within a 7-day interval between meals. TMAO plasma levels were assessed using the HPLC-EM/EM method before and after the 4-h meal challenge. Patients presented TMAO plasma levels of 9.06 µM. Consumption of codfish significantly increases the TMAO levels (to 23.5 µM, p < 0.001) compared to red meat and chickpeas. We conclude that codfish, compared to red meat and vegetable meal, provoked a transient elevation in TMAO levels in patients with CKD. Therefore, this result suggests that it is prudent to prescribe fish with low TMAO content for these patients. The response to ingesting TMAO-rich foods in CKD patients needs more study.

Methylamine metabolites and progression to kidney failure in type 2 diabetes: An Asian and European prospective study

AIM

Unlike trimethylamine N-oxide (TMAO), the role of methylamine pathway metabolites in diabetic kidney disease (DKD) remains unclear. We investigated the association of circulating methylamines with progression of DKD in a prospective cohort study of patients with type 2 diabetes of two different ethnic backgrounds.

METHODS

We analyzed two independent cohorts: a European-origin cohort (SURDIAGENE France; n = 1,357) and an Asian-origin cohort (Khoo Teck Puat Hospital-DKD [KTPH-DKD] Singapore, n = 1,868). The primary composite renal outcome in SURDIAGENE was sustained doubling of serum creatinine or kidney failure with replacement therapy (KFRT), while the secondary outcome was 40% renal function loss (RFL40). In KTPH-DKD, KFRT was the primary outcome. Baseline betaine, carnitine, choline, trimethylamine and TMAO concentrations were measured in plasma by mass-spectrometry. Cox regression models were used to estimate the risk of DKD progression, adjusting for demographics, clinical parameters, and comorbidities.

RESULTS

Over a median follow-up of 7.1 years (IQR 4.5-10.7), we registered 75 composite renal outcomes in SURDIAGENE and over 10.7 years (IQR 7.0-11.8), 149 KFRT in KTPH-DKD. Choline was the only consistently associated with progression of DKD in both cohorts: HR [95%CI] per 1 SD = 1.29 [1.02;1.62], P = 0.033 for composite renal outcome, 1.11 [1.01;1.23], P = 0.028 for RFL40 in SURDIAGENE, and 1.84 [1.30;2.61], P < 0.001 for KFRT in KTPH-DKD.

CONCLUSION

Plasma choline is an independent risk factor for DKD progression in two independent type 2 diabetes populations. Interventional trials are needed to assess whether reducing dietary choline intake could mitigate severe renal outcomes in type 2 diabetes.

The microbiome-derived metabolite trimethylamine N-oxide is associated with chronic kidney disease risk

Previous studies have established a correlation between the microbiome-derived metabolite trimethylamine N-oxide (TMAO) and decreased renal function, but with great heterogeneity. Moreover, population-based evidence remains scarce, particularly in Chinese populations. We designed a meta-analysis and a population-based cross-sectional study in China to examine the associations between TMAO and chronic kidney disease (CKD). In meta-analysis, among 2125 pooled subjects with 1240 controls and 885 CKD patients, a significant association was observed between TMAO and CKD, with a standardized mean difference of - 0.93 (95% confidence interval: - 1.11, - 0.75). Meta-regression analysis identified gender, age, and body mass index (BMI) as significant heterogeneity factors. In our population-based study of 5584 subjects with an estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2 from Sijing community, 100 developed CKD in 2 years. We matched 195 controls by age and gender from the 5484 non-CKD subjects. Male subjects and alcohol consumers exhibited a lower risk of CKD with adjusted odds ratio (OR) of 0.471 (P < 0.05) and 0.320 (P < 0.05), respectively. When comparing subjects in the lowest tertile of TMAO, adjusted OR reached to 1.243 (P > 0.05) for those in the middle and 2.123 (P < 0.05) in the highest tertile (P for trend < 0.05). TMAO demonstrated a moderate capacity to distinguish CKD from non-CKD subjects (AUC = 0.614, P < 0.01). Our findings indicate TMAO is significantly associated with the risk of CKD, and suggest age, gender, and BMI may confound the relationship between TMAO and CKD. KEY POINTS: • Subjects with elevated TMAO levels have an increased risk of CKD. • TMAO demonstrates a moderate capacity to distinguish CKD from non-CKD cases. • Age, gender and BMI may confound the relationship between TMAO and CKD.

The effect of elevated levels of the gut metabolite TMAO on glucose metabolism after sleeve gastrectomy

Purpose：Bariatric surgery can effectively alleviate obesity and diabetes by regulation of the gut microbiota. This study aimed to investigate the change in the gut microbiota metabolite TMAO and to explore its effect on glucose metabolism after sleeve gastrectomy (SG).

Materials and methods：Diet-induced obesity mouse models were established, and the mice were randomly divided into four groups: an SG group, a sham-operated group pair-fed with the SG group (PF), a sham-operated group fed ad libitum (AL), and a lean control group (C). At 10 weeks post-surgery, the changes in glycogen content of liver, gut microbiota and the level of FMO3 in the liver were evaluated, and their correlation with TMAO production was analysed. The expression levels of the TMAO/PERK/FOXO1 pathway and the gluconeogenic genes G6PC and PCK1 were measured.

Results：At 10 weeks post-surgery, hepatocyte glycogen levels were restored, and serum TMA and TMAO levels were significantly increased. Faecal metagenomic sequencing results showed that the abundances of Ruminococcaceae and Lachnospiraceae, which were positively correlated with TMAO production, were significantly increased after surgery. While the changes in FMO3, the key enzyme producing TMAO in the liver was found decreased significantly after SG. The expression levels of the TMAO/PERK/FOXO1 pathway and the gluconeogenic genes G6PC and PCK1 were measured. Inconsistent with the changing trend of TMAO, the expression of PERK, FOXO1, PCK, and G6PC significantly decreased after SG.

Conclusions：SG can significantly reduce obesity and restore glucose metabolism. After surgery, TMAO metabolites increased in a microbiota-dependent manner.

Bidirectional Association of Gut Microbiota-Derived Trimethylamine N-Oxide and its Precursors with Estimated Glomerular Filtration Rate: A Cross-Lagged Cohort Study

BACKGROUND

It is unclear whether trimethylamine N-oxide (TMAO) and its precursors are bidirectionally associated with kidney dysfunction.

OBJECTIVES

This study aims to investigate whether increased TMAO and its precursors are linked to decreased estimated glomerular filtration rate (eGFR) and whether reduced eGFR is associated with elevated TMAO and its precursors.

METHODS

Our study consists of participants with creatinine, TMAO, and its precursors (choline, carnitine, and betaine) repeatedly measured from the Fuxin rural cohort. We utilized cross-lagged panel models to assess the potential bidirectional associations of TMAO and its precursors with eGFR. Age (≥60 and <60 y) and sex-specified associations and interaction effects were examined using multi-group cross-lagged panel models. The Bonferroni method was applied for multiple comparisons.

RESULTS

Of 1746 participants [mean age 59.4 ± 9.3 y, 584 (33%) male], TMAO was inversely related to eGFR after 2 years [cross-lagged coefficient, 95% confidence interval: -0.030, -0.058, -0.002, P = 0.035], and eGFR was negatively associated with carnitine after 2 years (-0.138, -0.198, -0.078, P < 0.001). Subgroup analysis showed significant associations between baseline TMAO and eGFR after 2 years in individuals aged 60 and older (-0.061, -0.107, -0.014, P = 0.011) and between baseline eGFR and carnitine after 2 years in individuals aged 60 and older (-0.093, -0.164, -0.022, P = 0.010), in those under 60 (-0.153, -0.226, -0.079, P < 0.001), and in females (-0.154, -0.229, -0.079, P < 0.001). Additionally, baseline eGFR is nominally associated with choline after 2 years in those aged under 60 (0.092, 0.017, 0.167, P = 0.017) and in males (0.114, 0.015, 0.213, P = 0.025).

CONCLUSIONS

Deceased eGFR is related to elevated serum carnitine concentrations and may be linked to choline. Conversely, elevated TMAO may be linked to reduced kidney function. This provides novel evidence that managing healthy kidney function helps keep TMAO and its precursors at optimal levels, whereas maintaining low TMAO concentrations reduces risk of kidney disease.

Metabolomics to Identify Unclassified Uremic Toxins: A Comprehensive Literature Review

A comprehensive review of known uremic retention molecules goes back to more than 10 years ago and did not consider metabolomic analyses. The present analysis searches for as of yet unclassified solutes retained in chronic kidney disease (CKD) by analyzing metabolites associated with relevant outcomes of CKD. This untargeted metabolomics-based approach is compared with a conventional targeted literature search. For the selected molecules, the literature was screened for arguments regarding toxic (harmful), beneficial, or neutral effects in experimental or clinical studies. Findings were independently crosschecked. In total, 103 molecules were selected. No literature on any effect was found for 55 substances, 3 molecules had no significant effect, and 13 others showed beneficial effects. For the remaining 32 compounds, we found at least one report of a toxic effect. Whereas 62.5% of the compounds with at least one study on a toxic effect was retrieved via the bottom-up approach, 69.2% of the substances originating from metabolomics-based approaches showed a beneficial effect. Our results suggest that untargeted metabolomics offer a more balanced view of uremic retention than the targeted approaches, with higher chances of revealing the beneficial potential of some of the metabolites.

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning

Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests fail to diagnose early CKD. Therefore, this study aimed to identify early metabolic biomarkers. First, cats were retrospectively divided into two populations to conduct a case-control study, comparing the urinary and serum metabolome of healthy (n = 61) and CKD IRIS stage 2 cats (CKD2, n = 63). Subsequently, longitudinal validation was conducted in an independent population comprising healthy cats that remained healthy (n = 26) and cats that developed CKD2 (n = 22) within one year. Univariate, multivariate, and machine learning-based (ML) approaches were compared. The serum-to-urine ratio of 3-hydroxykynurenine was identified as a single biomarker candidate, yielding a high AUC (0.844) and accuracy (0.804), while linear support vector machine-based modelling employing metabolites and clinical parameters enhanced AUC (0.929) and accuracy (0.862) six months before traditional diagnosis. Furthermore, analysis of variable importance indicated consistent key serum metabolites, namely creatinine, SDMA, 2-hydroxyethanesulfonate, and aconitic acid. By enabling accurate diagnosis at least six months earlier, the highlighted metabolites may pave the way for improved diagnostics, ultimately contributing to timely disease management.

Targeted metabolomics identified novel metabolites, predominantly phosphatidylcholines and docosahexaenoic acid-containing lipids, predictive of incident chronic kidney disease in middle-to-elderly-aged Chinese adults

BACKGROUND

Evidence is limited regarding the association of circulating metabolites with decline of kidney function, letting alone their value in prediction of development of chronic kidney disease (CKD).

METHODS

This study included 3802 participants aged 64.1 ± 7.4 years from the Dongfeng-Tongji cohort, among whom 3327 were CKD-free at baseline (estimated glomerular filtration rate [eGFR] > 60 ml/min per 1.73 m2). We measured baseline levels of 211 metabolites with liquid chromatography coupled with mass spectrometry, including 25 amino acids, 12 acyl-carnitines, 161 lipids, and 13 other metabolites.

RESULTS

The mean (SD) absolute annual change in eGFR was -0.14 ± 4.11 ml/min per 1.73 m2 per year, and a total of 472 participants who were free of CKD at baseline developed incident CKD during follow-up of 4.6 ± 0.2 years (14.2 %). We identified a total of 22 metabolites associated with annual eGFR change and survived Bonferroni correction for multiple testing, including seven metabolites associated with eGFR increase (six being docosahexaenoic acid [DHA]-containing lipids) and 15 associated with eGFR decline (nine being phosphatidylcholines [PCs]). Among them, eight metabolites obtained non-zero coefficients in least absolute shrinkage and selection operator (LASSO) regression on incident CKD, indicating predictive potential, including one amino acid (arginine), one acyl-carnitine (C2), one lysophosphatidylcholine (LPC 22:6), two PCs (32:1 and 34:3), one triacylglycerol (TAG 56:8 [22:6]) and two other metabolites (inosine, niacinamide), and the composite score of these eight metabolites showed an odds ratio (OR) of 8.79 (95 % confidence interval [CI]: 7.49, 10.32; P < 0.001) per SD increase in association with incident CKD. The addition of the metabolite score increased the c-statistic of the reference model of traditional risk factors (including baseline eGFR) by 0.065 (95 % CI: 0.046 to 0.084; P = 3.39 × 10-11) to 0.765 (0.742 to 0.788) in 1000 repetitions of 10-fold cross-validation, while the application of two advanced machine learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost) models produced similar c-statistics, to 0.753 (0.729 to 0.777) and 0.778 (0.733 to 0.824) with increases of 0.074 (0.055 to 0.093; P = 4.11 × 10-14) and 0.073 (0.032 to 0.114; P = 4.00 × 10-4), respectively.

CONCLUSIONS

In this study, we identified 22 metabolites associated with longitudinal eGFR change, nine of which were PCs and six were DHA-containing lipids. We screened out a panel of eight metabolites which improved prediction for the development of CKD by 9 % beyond traditional risk factors including baseline eGFR. Our findings highlighted involvement of lipid metabolism in kidney function impairment, and provided novel predictors for CKD risk.

Amino acid metabolism in kidney health and disease

Amino acids form peptides and proteins and are therefore considered the main building blocks of life. The kidney has an important but under-appreciated role in the synthesis, degradation, filtration, reabsorption and excretion of amino acids, acting to retain useful metabolites while excreting potentially harmful and waste products from amino acid metabolism. A complex network of kidney transporters and enzymes guides these processes and moderates the competing concentrations of various metabolites and amino acid products. Kidney amino acid metabolism contributes to gluconeogenesis, nitrogen clearance, acid-base metabolism and provision of fuel for tricarboxylic acid cycle and urea cycle intermediates, and is thus a central hub for homeostasis. Conversely, kidney disease affects the levels and metabolism of a variety of amino acids. Here, we review the metabolic role of the kidney in amino acid metabolism and describe how different diseases of the kidney lead to aberrations in amino acid metabolism. Improved understanding of the metabolic and communication routes that are affected by disease could provide new mechanistic insights into the pathogenesis of kidney diseases and potentially enable targeted dietary or pharmacological interventions.

The human gut microbiome in health and disease: time for a new chapter?

The gut microbiome, composed of the colonic microbiota and their host environment, is important for many aspects of human health. A gut microbiome imbalance (gut dysbiosis) is associated with major causes of human morbidity and mortality. Despite the central part our gut microbiome plays in health and disease, mechanisms that maintain homeostasis and properties that demarcate dysbiosis remain largely undefined. Here we discuss that sorting taxa into meaningful ecological units reveals that the availability of respiratory electron acceptors, such as oxygen, in the host environment has a dominant influence on gut microbiome health. During homeostasis, host functions that limit the diffusion of oxygen into the colonic lumen shelter a microbial community dominated by primary fermenters from atmospheric oxygen. In turn, primary fermenters break down unabsorbed nutrients into fermentation products that support host nutrition. This symbiotic relationship is disrupted when host functions that limit the luminal availability of host-derived electron acceptors become weakened. The resulting changes in the host environment drive alterations in the microbiota composition, which feature an elevated abundance of facultatively anaerobic microbes. Thus, the part of the gut microbiome that becomes imbalanced during dysbiosis is the host environment, whereas changes in the microbiota composition are secondary to this underlying cause. This shift in our understanding of dysbiosis provides a novel starting point for therapeutic strategies to restore microbiome health. Such strategies can either target the microbes through metabolism-based editing or strengthen the host functions that control their environment.

NAD+ enhancers as therapeutic agents in the cardiorenal axis

Cardiorenal diseases represent a complex interplay between heart failure and renal dysfunction, being clinically classified as cardiorenal syndromes (CRS). Recently, the contributions of altered nicotinamide adenine dinucleotide (NAD+) metabolism, through deficient NAD+ synthesis and/or elevated consumption, have proved to be decisive in the onset and progress of cardiorenal disease. NAD+ is a pivotal coenzyme in cellular metabolism, being significant in various signaling pathways, such as energy metabolism, DNA damage repair, gene expression, and stress response. Convincing evidence suggests that strategies designed to boost cellular NAD+ levels are a promising therapeutic option to address cardiovascular and renal disorders. Here, we review and discuss the implications of NAD+ metabolism in cardiorenal diseases, focusing on the propitious NAD+ boosting therapeutic strategies, based on the use of NAD+ precursors, poly(ADP-ribose) polymerase inhibitors, sirtuin activators, and other alternative approaches, such as CD38 blockade, nicotinamide phosphoribosyltransferase activation and combined interventions.

Early Metabolomic Profiling as a Predictor of Renal Function Six Months After Kidney Transplantation

BACKGROUND

Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week after kidney transplantation that predict renal function at six months.

METHODS

We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one week after transplant were analyzed using liquid chromatography-mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine > 1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score > 1.5, which was used to optimize model performance.

RESULTS

The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine > 1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, the metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine.

CONCLUSIONS

Early metabolomic profiling after transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.

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.

Metabolic Crosstalk in Multimorbidity: Identifying Compensatory Effects Among Diabetes, Hypertension, and Dyslipidemia

Context

Metabolomics is becoming increasingly popular for detecting markers that indicate the presence of a specific disease. However, it is usually applied to studying individual ailments, yielding results that may not be directly relevant to people with multiple health conditions.

Objective

Our study proposes a different approach to explore metabolic crosstalk between various disease states.

Design Setting and Patients

We conducted a study on subjects at medium to high risk of developing coronary artery disease. We measured the plasma levels of 83 metabolites using nuclear magnetic resonance and analyzed the connections between these metabolites and various risk factors such as diabetes, hypertension, and dyslipidemia. Linear regression and multivariate analysis were combined for this purpose.

Results

Inspection of the metabolic maps created by our analysis helped us efficiently compare profiles. In this way, it was possible to discover opposing metabolic features among single conditions and their combination. Furthermore, we found compensating metabolic effects between diabetes, hypertension, and dyslipidemia involving mainly ketone body metabolism and fatty acid β-oxidation.

Conclusion

Our study introduces a novel approach to investigating how metabolism reacts to the simultaneous presence of multiple health conditions. This has allowed the detection of potential compensatory effects between diabetes, hypertension, and dyslipidemia, highlighting the complexity of metabolic crosstalk in patients with comorbidities. A better understanding of metabolic crosstalk like this could aid in developing focused treatments, resulting in improved therapeutic results.

Deficiency of flavin-containing monooxygenase 3 protects kidney function after ischemia-reperfusion in mice

The kidney is vulnerable to ischemia and reperfusion (I/R) injury that can be fatal after major surgery. Currently, there are no effective treatments for I/R-induced kidney injury. Trimethylamine N-oxide (TMAO) is a gut-derived metabolite linked to many diseases, but its role in I/R-induced kidney injury remains unclear. Here, our clinical data reveals an association between preoperative systemic TMAO levels and postoperative kidney injury in patients after post-cardiopulmonary bypass surgery. By genetic deletion of TMAO-producing enzyme flavin-containing monooxygenase 3 (FMO3) and dietary supplementation of choline to modulate TMAO levels, we found that TMAO aggravated acute kidney injury through the triggering of endoplasmic reticulum (ER) stress and worsened subsequent renal fibrosis through TGFβ/Smad signaling activation. Together, our study underscores the negative role of TMAO in I/R-induced kidney injury and highlights the therapeutic potential through the modulation of TMAO levels by targeting FMO3, thereby mitigating acute kidney injury and preventing subsequent renal fibrosis.

Trimethylamine N-oxide: a meta-organismal axis linking the gut and fibrosis

BACKGROUND

Tissue fibrosis is a common pathway to failure in many organ systems and is the cellular and molecular driver of myriad chronic diseases that are incompletely understood and lack effective treatment. Recent studies suggest that gut microbe-dependent metabolites might be involved in the initiation and progression of fibrosis in multiple organ systems.

MAIN BODY OF THE MANUSCRIPT

In a meta-organismal pathway that begins in the gut, gut microbiota convert dietary precursors such as choline, phosphatidylcholine, and L-carnitine into trimethylamine (TMA), which is absorbed and subsequently converted to trimethylamine N-oxide (TMAO) via the host enzyme flavin-containing monooxygenase 3 (FMO3) in the liver. Chronic exposure to elevated TMAO appears to be associated with vascular injury and enhanced fibrosis propensity in diverse conditions, including chronic kidney disease, heart failure, metabolic dysfunction-associated steatotic liver disease, and systemic sclerosis.

CONCLUSION

Despite the high prevalence of fibrosis, little is known to date about the role of gut dysbiosis and of microbe-dependent metabolites in its pathogenesis. This review summarizes recent important advances in the understanding of the complex metabolism and functional role of TMAO in pathologic fibrosis and highlights unanswered questions.

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.

Plasma Proteins Associated with Chronic Histopathologic Lesions on Kidney Biopsy

Key Points

Proteomic profiling identified 35 blood proteins associated with chronic histopathologic lesions in the kidney. Testican-2 was expressed in the glomerulus, released by the kidney into circulation, and inversely associated with glomerulosclerosis severity. NELL1 was expressed in tubular epithelial cells, released by the kidney into circulation, and inversely associated with interstitial fibrosis and tubular atrophy severity.

Background

The severity of chronic histopathologic lesions on kidney biopsy is independently associated with higher risk of progressive CKD. Because kidney biopsies are invasive, identification of blood markers that report on underlying kidney histopathology has the potential to enhance CKD care.

Methods

We examined the association between 6592 plasma protein levels measured by aptamers and the severity of interstitial fibrosis and tubular atrophy (IFTA), glomerulosclerosis, arteriolar sclerosis, and arterial sclerosis among 434 participants of the Boston Kidney Biopsy Cohort. For proteins significantly associated with at least one histologic lesion, we assessed renal arteriovenous protein gradients among 21 individuals who had undergone invasive catheterization and assessed the expression of the cognate gene among 47 individuals with single-cell RNA sequencing data in the Kidney Precision Medicine Project.

Results

In models adjusted for eGFR, proteinuria, and demographic factors, we identified 35 proteins associated with one or more chronic histologic lesions, including 20 specific for IFTA, eight specific for glomerulosclerosis, and one specific for arteriolar sclerosis. In general, higher levels of these proteins were associated with more severe histologic score and lower eGFR. Exceptions included testican-2 and NELL1, which were associated with less glomerulosclerosis and IFTA, respectively, and higher eGFR; notably, both of these proteins demonstrated significantly higher levels from artery to renal vein, demonstrating net kidney release. In the Kidney Precision Medicine Project, 13 of the 35 protein hits had cognate gene expression enriched in one or more cell types in the kidney, including podocyte expression of select glomerulosclerosis markers (including testican-2) and tubular expression of several IFTA markers (including NELL1).

Conclusions

Proteomic analysis identified circulating proteins associated with chronic histopathologic lesions, some of which had concordant site-specific expression within the kidney.

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients

Objective

The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients.

Methods

We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model.

Results

For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR.

Conclusion

Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.

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.

The Gut Microbial Metabolite Trimethylamine N -oxide, Incident CKD, and Kidney Function Decline

Key Points

In community-based US adults, higher plasma trimethylamine N -oxide levels associated with higher risk of incident CKD and greater rate of kidney function decline. Findings from our study support future clinical trials to examine whether lowering plasma trimethylamine N -oxide levels may prevent CKD development and progression.

Background

Trimethylamine N -oxide (TMAO) is a gut microbiota–derived metabolite of dietary phosphatidylcholine and carnitine. Experimentally, TMAO causes kidney injury and tubulointerstitial fibrosis. Little is known about prospective associations between TMAO and kidney outcomes, especially incident CKD. We hypothesized that higher plasma TMAO levels would be associated with higher risk of incident CKD and greater rate of kidney function decline.

Methods

We included 10,564 participants from two community-based, prospective cohorts with eGFR ≥60 ml/min per 1.73 m2 to assess incident CKD. TMAO was measured using targeted mass spectrometry at baseline and one follow-up visit. Creatinine and cystatin C were measured up to four times during follow-up and used to compute eGFR. Incident CKD was defined as an eGFR decline ≥30% from baseline and a resulting eGFR <60 ml/min per 1.73 m2. Time-varying Cox models assessed the association of serial TMAO measures with incident CKD, adjusting for sociodemographic, lifestyle, diet, and cardiovascular disease risk factors. Linear mixed models assessed the association with annualized eGFR change in 10,009 participants with at least one follow-up eGFR measure without exclusions for baseline eGFR levels.

Results

During a median follow-up of 9.4 years (interquartile range, 9.1–11.6 years), 979 incident CKD events occurred. Higher TMAO levels were associated with higher risk of incident CKD (second to fifth versus first quintile hazard ratio [95% confidence interval]=1.65 [1.22 to 2.23], 1.68 [1.26 to 2.25], 2.28 [1.72 to 3.02], and 2.24 [1.68 to 2.98], respectively) and greater annualized eGFR decline (second to fifth versus first quintile annualized eGFR change=−0.21 [−0.32 to −0.09], −0.17 [−0.29 to −0.05], −0.35 [−0.47 to −0.22], and −0.43 [−0.56 to −0.30] ml/min per 1.73 m2, respectively) with monotonic dose–response relationships. These associations were consistent across different racial/ethnic groups examined. The association with eGFR decline was similar to or larger than that seen for established CKD risk factors, including diabetes, per 10 mm Hg of higher systolic BP, per 10 years of older age, and Black race.

Conclusions

In community-based US adults, higher serial measures of plasma TMAO were associated with higher risk of incident CKD and greater annualized kidney function decline.

Antibiotic-induced intestinal microbiota depletion can attenuate the acute kidney injury to chronic kidney disease transition via NADPH oxidase 2 and trimethylamine-N-oxide inhibition

Intestinal microbiota and their metabolites affect systemic inflammation and kidney disease outcomes. Here, we investigated the key metabolites associated with the acute kidney injury (AKI)-to chronic kidney disease (CKD) transition and the effect of antibiotic-induced microbiota depletion (AIMD) on this transition. In 61 patients with AKI, 59 plasma metabolites were assessed to determine the risk of AKI-to-CKD transition. An AKI-to-CKD transition murine model was established four weeks after unilateral ischemia-reperfusion injury (IRI) to determine the effects of AIMD on the gut microbiome, metabolites, and pathological responses related to CKD transition. Human proximal tubular epithelial cells were challenged with CKD transition-related metabolites, and inhibitory effects of NADPH oxidase 2 (NOX2) signals were tested. Based on clinical metabolomics, plasma trimethylamine N-oxide (TMAO) was associated with a significantly increased risk for AKI-to-CKD transition [adjusted odds ratio 4.389 (95% confidence interval 1.106-17.416)]. In vivo, AIMD inhibited a unilateral IRI-induced increase in TMAO, along with a decrease in apoptosis, inflammation, and fibrosis. The expression of NOX2 and oxidative stress decreased after AIMD. In vitro, TMAO induced fibrosis with NOX2 activation and oxidative stress. NOX2 inhibition successfully attenuated apoptosis, inflammation, and fibrosis with suppression of G2/M arrest. NOX2 inhibition (in vivo) showed improvement in pathological changes with a decrease in oxidative stress without changes in TMAO levels. Thus, TMAO is a key metabolite associated with the AKI-to-CKD transition, and NOX2 activation was identified as a key regulator of TMAO-related AKI-to-CKD transition both in vivo and in vitro.

Revealing novel biomarkers for diagnosing chronic kidney disease in pediatric patients

Pediatric chronic kidney disease (CKD) is a clinical condition characterized by progressive renal function deterioration. CKD diagnosis is based on glomerular filtration rate, but its reliability is limited, especially at the early stages. New potential biomarkers (citrulline (CIT), symmetric dimethylarginine (SDMA), S-adenosylmethionine (SAM), n-butyrylcarnitine (nC4), cis-4-decenoylcarnitine, sphingosine-1-phosphate and bilirubin) in addition to creatinine (CNN) have been proposed for early diagnosis. To verify the clinical value of these biomarkers we performed a comprehensive targeted metabolomics study on a representative cohort of CKD and healthy pediatric patients. Sixty-seven children with CKD and forty-five healthy children have been enrolled in the study. Targeted metabolomics based on liquid chromatography-triple quadrupole mass spectrometry has been used for serum and plasma samples analysis. Univariate data analysis showed statistically significant differences (p < 0.05) in the concentration of CNN, CIT, SDMA, and nC4 among healthy and CKD pediatric patients. The predictive ability of the proposed biomarkers was also confirmed through specificity and sensitivity expressed in Receiver Operating Characteristic curves (AUC = 0.909). In the group of early CKD pediatric patients, AUC of 0.831 was obtained, improving the diagnostic reliability of CNN alone. Moreover, the models built on combined CIT, nC4, SDMA, and CNN allowed to distinguish CKD patients from healthy control regardless of blood matrix type (serum or plasma). Our data demonstrate potential biomarkers in the diagnosis of early CKD stages.

Metabolomic and lipidomic landscape of porcine kidney associated with kidney perfusion in heart beating donors and donors after cardiac death

Transplant centers are currently facing a lack of tools to ensure adequate evaluation of the quality of the available organs, as well as a significant shortage of kidney donors. Therefore, efforts are being made to facilitate the effective use of available organs and expand the donor pool, particularly with expanded criteria donors. Fulfilling a need, we aim to present an innovative analytical method based on solid-phase microextraction (SPME) - chemical biopsy. In order to track changes affecting the organ throughout the entire transplant procedure, porcine kidneys were subjected to multiple samplings at various time points. The application of small-diameter SPME probes assured the minimal invasiveness of the procedure. Porcine model kidney autotransplantation was executed for the purpose of simulating two types of donor scenarios: donors with a beating heart (HBD) and donors after cardiac death (DCD). All renal grafts were exposed to continuous normothermic ex vivo perfusion. Following metabolomic and lipidomic profiling using high-performance liquid chromatography coupled to a mass spectrometer, we observed differences in the profiles of HBD and DCD kidneys. The alterations were predominantly related to energy and glucose metabolism, and differences in the levels of essential amino acids, purine nucleosides, lysophosphocholines, phosphoethanolamines, and triacylglycerols were noticed. Our results indicate the potential of implementing chemical biopsy in the evaluation of graft quality and monitoring of renal function during perfusion.

Gastric Fluid Metabolomics Predicting the Need for Surfactant Replacement Therapy in Very Preterm Infants Results of a Case-Control Study

Respiratory distress syndrome (RDS) is a major morbidity of prematurity. In this case-control study, we prospectively evaluated whether untargeted metabolomic analysis (gas chromatography-mass spectrometry) of the gastric fluid could predict the need for surfactant in very preterm neonates. 43 infants with RDS necessitating surfactant (cases) were compared with 30 infants who were not treated with surfactant (controls). Perinatal-neonatal characteristics were recorded. Significant differences in gastric fluid metabolites (L-proline, L-glycine, L-threonine, acetyl-L-serine) were observed between groups, but none could solely predict surfactant administration with high accuracy. Univariate analysis revealed significant predictors of surfactant administration involving gastric fluid metabolites (L-glycine, acetyl-L-serine) and clinical parameters (gestational age, Apgar scores, intubation in the delivery room). Multivariable models were constructed for significant clinical variables as well as for the combination of clinical variables and gastric fluid metabolites. The AUC value of the first model was 0.69 (95% CI 0.57-0.81) and of the second, 0.76 (95% CI 0.64-0.86), in which acetyl-L-serine and intubation in the delivery room were found to be significant predictors of surfactant therapy. This investigation adds to the current knowledge of biomarkers in preterm neonates with RDS, but further research is required to assess the predictive value of gastric fluid metabolomics in this field.

Downregulation of the kidney glucagon receptor, essential for renal function and systemic homeostasis, contributes to chronic kidney disease

The glucagon receptor (GCGR) in the kidney is expressed in nephron tubules. In humans and animal models with chronic kidney disease, renal GCGR expression is reduced. However, the role of kidney GCGR in normal renal function and in disease development has not been addressed. Here, we examined its role by analyzing mice with constitutive or conditional kidney-specific loss of the Gcgr. Adult renal Gcgr knockout mice exhibit metabolic dysregulation and a functional impairment of the kidneys. These mice exhibit hyperaminoacidemia associated with reduced kidney glucose output, oxidative stress, enhanced inflammasome activity, and excess lipid accumulation in the kidney. Upon a lipid challenge, they display maladaptive responses with acute hypertriglyceridemia and chronic proinflammatory and profibrotic activation. In aged mice, kidney Gcgr ablation elicits widespread renal deposition of collagen and fibronectin, indicative of fibrosis. Taken together, our findings demonstrate an essential role of the renal GCGR in normal kidney metabolic and homeostatic functions. Importantly, mice deficient for kidney Gcgr recapitulate some of the key pathophysiological features of chronic kidney disease.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Metabolic Rewiring and Communication: An Integrative View of Kidney Proximal Tubule Function

The kidney proximal tubule is a key organ for human metabolism. The kidney responds to stress with altered metabolite transformation and perturbed metabolic pathways, an ultimate cause for kidney disease. Here, we review the proximal tubule's metabolic function through an integrative view of transport, metabolism, and function, and embed it in the context of metabolome-wide data-driven research. Function (filtration, transport, secretion, and reabsorption), metabolite transformation, and metabolite signaling determine kidney metabolic rewiring in disease. Energy metabolism and substrates for key metabolic pathways are orchestrated by metabolite sensors. Given the importance of renal function for the inner milieu, we also review metabolic communication routes with other organs. Exciting research opportunities exist to understand metabolic perturbation of kidney and proximal tubule function, for example, in hypertension-associated kidney disease. We argue that, based on the integrative view outlined here, kidney diseases without genetic cause should be approached scientifically as metabolic diseases.

Blueberry, cranberry, raspberry, and strawberry as modulators of the gut microbiota: target for treatment of gut dysbiosis in chronic kidney disease? From current evidence to future possibilities

Gut dysbiosis is common in patients with chronic kidney disease (CKD) and is associated with uremic toxin production, inflammation, oxidative stress, and cardiovascular disease development. Therefore, healthy dietary patterns are essential modulators of gut microbiota. In this context, studies suggest that consuming berry fruits, rich in polyphenols and nutrients, may positively affect the gut microbiota, promoting the selective growth of beneficial bacteria and improving clinical status. However, studies on the effects of berry fruits on gut microbiota in CKD are scarce, and a better understanding of the possible mechanisms of action of berry fruits on gut microbiota is needed to guide future clinical studies and clinical practice in CKD. The objective was to discuss how berry fruits (blueberry, cranberry, raspberry, and strawberry) could be a therapeutic strategy to modulate the gut microbiota and possibly reverse the dysbiosis in CKD. Overall, available evidence shows that berry fruits can promote an increase in diversity by affecting the abundance of mucus-producing bacteria and short-chain fatty acids. Moreover, these fruits can increase the expression of mRNA involved in tight junctions in the gut such as occludin, tight junction protein 1 (TJP1), and mucin. Studies on the exact amount of berries leading to these effects show heterogeneous findings. However, it is known that, with 5 mg/day, it is already possible to observe some effects in animal models. Wild berries could possibly improve the uremic condition by reducing the levels of uremic toxins via modulation of the gut microbiota. In the long term, this could be an excellent strategy for patients with CKD. Therefore, clinical studies are encouraged to evaluate better these effects on CKD as well as the safe amount of these fruits in order to promote a better quality of life or even the survival of these patients.

Circulating metabolites associated with kidney function decline and incident CKD: a multi-platform population-based study

Background

Investigation of circulating metabolites associated with kidney function and chronic kidney disease (CKD) risk could enhance our understanding of underlying pathways and identify new biomarkers for kidney function.

Methods

We selected participants from the population-based Rotterdam Study with data on circulating metabolites and estimated glomerular filtration rate based on serum creatinine (eGFRcreat) available at the same time point. Data on eGFR based on serum cystatin C (eGFRcys) and urine albumin-to-creatinine ratio (ACR) were also included. CKD was defined as eGFRcreat <60 ml/min per 1.73 m2. Data on circulating metabolites (ntotal = 1381) was obtained from the Nightingale and Metabolon platform. Linear regression, linear mixed, and Cox proportional-hazards regression analyses were conducted to study the associations between metabolites and kidney function. We performed bidirectional two-sample Mendelian randomization analyses to investigate causality of the identified associations.

Results

We included 3337 and 1540 participants with data from Nightingale and Metabolon, respectively. A total of 1381 metabolites (243 from Nightingale and 1138 from Metabolon) were included in the analyses. A large number of metabolites were significantly associated with eGFRcreat, eGFRcys, ACR, and CKD, including 16 metabolites that were associated with all four outcomes. Among these, C-glycosyltryptophan (HR 1.50, 95%CI 1.31;1.71) and X-12026 (HR 1.46, 95%CI 1.26;1.68) were most strongly associated with CKD risk. We revealed sex differences in the associations of 11-ketoetiocholanolone glucuronide and 11-beta-glucuronide with the kidney function assessments. No causal associations between the identified metabolites and kidney function were observed.

Conclusion

Our study indicates that several circulating metabolites are associated with kidney function which are likely to have potential as biomarkers, rather than as molecules involved in the pathophysiology of kidney function decline.

Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD

RATIONALE & OBJECTIVE

Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study.

STUDY DESIGN

Observational cohort study.

SETTING & PARTICIPANTS

A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy.

EXPOSURE

Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy.

OUTCOME

Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records.

ANALYTICAL APPROACH

Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals.

RESULTS

We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD.

LIMITATIONS

Potential underreporting of CKD cases because of case identification via electronic health records.

CONCLUSIONS

Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD.

PLAIN-LANGUAGE SUMMARY

The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.

Tubular dysfunction impairs renal excretion of pseudouridine in diabetic kidney disease

Plasma nucleosides-pseudouridine (PU) and N2N2-dimethyl guanosine (DMG) predict the progression of type 2 diabetic kidney disease (DKD) to end-stage renal disease, but the mechanisms underlying this relationship are not well understood. We used a well-characterized model of type 2 diabetes (db/db mice) and control nondiabetic mice (db/m mice) to characterize the production and excretion of PU and DMG levels using liquid chromatography-mass spectrometry. The fractional excretion of PU and DMG was decreased in db/db mice compared with control mice at 24 wk before any changes to renal function. We then examined the dynamic changes in nucleoside metabolism using in vivo metabolic flux analysis with the injection of labeled nucleoside precursors. Metabolic flux analysis revealed significant decreases in the ratio of urine-to-plasma labeling of PU and DMG in db/db mice compared with db/m mice, indicating significant tubular dysfunction in diabetic kidney disease. We observed that the gene and protein expression of the renal tubular transporters involved with nucleoside transport in diabetic kidneys in mice and humans was reduced. In conclusion, this study strongly suggests that tubular handling of nucleosides is altered in early DKD, in part explaining the association of PU and DMG with human DKD progression observed in previous studies.NEW & NOTEWORTHY Tubular dysfunction explains the association between the nucleosides pseudouridine and N2N2-dimethyl guanosine and diabetic kidney disease.

A systematic review of metabolomic findings in adult and pediatric renal disease

Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.

Gut dysbiosis: Ecological causes and causative effects on human disease

The gut microbiota plays a role in many human diseases, but high-throughput sequence analysis does not provide a straightforward path for defining healthy microbial communities. Therefore, understanding mechanisms that drive compositional changes during disease (gut dysbiosis) continues to be a central goal in microbiome research. Insights from the microbial pathogenesis field show that an ecological cause for gut dysbiosis is an increased availability of host-derived respiratory electron acceptors, which are dominant drivers of microbial community composition. Similar changes in the host environment also drive gut dysbiosis in several chronic human illnesses, and a better understanding of the underlying mechanisms informs approaches to causatively link compositional changes in the gut microbiota to an exacerbation of symptoms. The emerging picture suggests that homeostasis is maintained by host functions that control the availability of resources governing microbial growth. Defining dysbiosis as a weakening of these host functions directs attention to the underlying cause and identifies potential targets for therapeutic intervention.

Plasma Tryptophan-Kynurenine Pathway Metabolites and Risk for Progression to End-Stage Kidney Disease in Patients With Type 2 Diabetes

OBJECTIVE

We sought to study the associations between plasma metabolites in the tryptophan-kynurenine pathway and the risk of progression to end-stage kidney disease (ESKD) in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Plasma tryptophan, kynurenine, 3-hydroxykynurenine, kynurenic acid, and xanthurenic acid concentrations were measured in discovery (n = 1,915) and replication (n = 346) cohorts. External validation was performed in Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n = 1,312). The primary outcome was a composite of incident ESKD (progression to estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2, sustained dialysis, or renal death). The secondary outcome was annual eGFR decline.

RESULTS

In the discovery cohort, tryptophan was inversely associated with risk for ESKD, and kynurenine-to-tryptophan ratio (KTR) was positively associated with risk for ESKD after adjustment for clinical risk factors, including baseline eGFR and albuminuria (adjusted hazard ratios [HRs] 0.62 [95% CI 0.51, 0.75] and 1.48 [1.20, 1.84] per 1 SD). High levels of kynurenic acid and xanthurenic acid were associated with low risks of ESKD (0.74 [0.60, 0.91] and 0.74 [0.60, 0.91]). Consistently, high levels of tryptophan, kynurenic acid, and xanthurenic acid were independently associated with a slower eGFR decline, while a high KTR was predictive of a faster eGFR decline. Similar outcomes were obtained in the replication cohort. Furthermore, the inverse association between kynurenic acid and risk of ESKD was externally validated in CRIC participants with diabetes (adjusted HR 0.78 [0.65, 0.93]).

CONCLUSIONS

Accelerated catabolism of tryptophan in the kynurenine pathway may be involved in progressive loss of kidney function. However, shunting the kynurenine pathway toward the kynurenic acid branch may potentially slow renal progression.

Exploration of Metabolomic Markers Associated With Declining Kidney Function in People With Type 2 Diabetes Mellitus

Background

Metabolomics, the study of small molecules in biological systems, can provide valuable insights into kidney dysfunction in people with type 2 diabetes mellitus (T2DM), but prospective studies are scarce. We investigated the association between metabolites and kidney function decline in people with T2DM.

Methods

The Edinburgh Type 2 Diabetes Study, a population-based cohort of 1066 men and women aged 60 to 75 years with T2DM. We measured 149 serum metabolites at baseline and investigated individual associations with baseline estimated glomerular filtration rate (eGFR), incident chronic kidney disease [CKD; eGFR <60 mL/min/(1.73 m)2], and decliner status (5% eGFR decline per year).

Results

At baseline, mean eGFR was 77.5 mL/min/(1.73 m)2 (n = 1058), and 216 individuals had evidence of CKD. Of those without CKD, 155 developed CKD over a median 7-year follow-up. Eighty-eight metabolites were significantly associated with baseline eGFR (β range -4.08 to 3.92; PFDR < 0.001). Very low density lipoproteins, triglycerides, amino acids (AAs), glycoprotein acetyls, and fatty acids showed inverse associations, while cholesterol and phospholipids in high-density lipoproteins exhibited positive associations. AA isoleucine, apolipoprotein A1, and total cholines were not only associated with baseline kidney measures (PFDR < 0.05) but also showed stable, nominally significant association with incident CKD and decline.

Conclusion

Our study revealed widespread changes within the metabolomic profile of CKD, particularly in lipoproteins and their lipid compounds. We identified a smaller number of individual metabolites that are specifically associated with kidney function decline. Replication studies are needed to confirm the longitudinal findings and explore if metabolic signals at baseline can predict kidney decline.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review.

Sources of information

Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics."

Methods

A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience.

Key findings

Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy.

Limitations

There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory.

Implications

Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

Prognostic Significance of Amino Acid and Biogenic Amines Profiling in Chronic Kidney Disease

There is a pressing need for more precise biomarkers of chronic kidney disease (CKD). Plasma samples from 820 subjects [231 with CKD, 325 with end-stage kidney disease (ESKD) and 264 controls] were analyzed by liquid chromatography with tandem mass spectrometry (LC-MS/MS) to determine a metabolic profile of 28 amino acids (AAs) and biogenic amines to test their value as markers of CKD risk and progression. The kynurenine/tryptophan ratio showed the strongest correlation with estimated glomerular filtration rate values (coefficient = -0.731, p < 0.0001). Models created with orthogonal partial least squares-discriminant analysis (OPLS-DA) containing the metabolic signature showed a high goodness of fit and predictability for controls/CKD (R2X:0.73:R2Y:0.92:Q2:0.92, p < 0.0001) and lower values for CKD/ESKD (R2X:0.56:R2Y:0.59:Q2:0.55, p < 0.0001). Based on generated VIP scores, the most relevant markers for segregating samples into control/CKD and CKD/ESKD groups were citrulline (1.63) and tryptophan (1.47), respectively. ROC analysis showed that the addition of the metabolic profile to a model including CKD classic risk factors improved the AUC from 86.7% (83.6-89.9) to 100% (100-100) for CKD risk (p < 0.0001) and from 63.0% (58.2-67.8) to 96.5% (95.3-97.8) for the risk of progression from CKD to ESKD (p < 0.0001). Plasma concentrations of AAs and related amines may be useful as diagnostic biomarkers of kidney disease, both for CKD risk and for progression of CKD patients to ESKD.

Tryptophan Metabolites Target Transmembrane and Immunoglobulin Domain-Containing 1 Signaling to Augment Renal Tubular Injury

Chronic kidney disease (CKD) is characterized by the accumulation of uremic toxins and renal tubular damage. Tryptophan-derived uremic toxins [indoxyl sulfate (IS) and kynurenine (Kyn)] are well-characterized tubulotoxins. Emerging evidence suggests that transmembrane and immunoglobulin domain-containing 1 (TMIGD1) protects tubular cells and promotes survival. However, the direct molecular mechanism(s) underlying how these two opposing pathways crosstalk remains unknown. We posited that IS and Kyn mediate tubular toxicity through TMIGD1 and the loss of TMIGD1 augments tubular injury. Results from the current study showed that IS and Kyn suppressed TMIGD1 transcription in tubular cells in a dose-dependent manner. The wild-type CCAAT enhancer-binding protein β (C/EBPβ) enhanced, whereas a dominant-negative C/EBPβ suppressed, TMIGD1 promoter activity. IS down-regulated C/EBPβ in primary human renal tubular cells. The adenine-induced CKD, unilateral ureteric obstruction, and deoxycorticosterone acetate salt unilateral nephrectomy models showed reduced TMIGD1 expression in the renal tubules, which correlated with C/EBPβ expression. C/EBPβ levels negatively correlated with the IS and Kyn levels. Inactivation of TMIGD1 in mice significantly lowered acetylated tubulin, decreased tubular cell proliferation, caused severe tubular damage, and worsened renal function. Thus, the current results demonstrate that TMIGD1 protects renal tubular cells from renal injury in different models of CKD and uncovers a novel mechanism of tubulotoxicity of tryptophan-based uremic toxins.

Circulating metabolites improve the prediction of renal impairment in patients with type 2 diabetes

INTRODUCTION

Low glomerular filtration rate (GFR) is a leading cause of reduced lifespan in type 2 diabetes. Unravelling biomarkers capable to identify high-risk patients can help tackle this burden. We investigated the association between 188 serum metabolites and kidney function in type 2 diabetes and then whether the associated metabolites improve two established clinical models for predicting GFR decline in these patients.

RESEARCH DESIGN AND METHODS

Two cohorts comprising 849 individuals with type 2 diabetes (discovery and validation samples) and a follow-up study of 575 patients with estimated GFR (eGFR) decline were analyzed.

RESULTS

Ten metabolites were independently associated with low eGFR in the discovery sample, with nine of them being confirmed also in the validation sample (ORs range 1.3-2.4 per 1SD, p values range 1.9×10-2-2.5×10-9). Of these, five metabolites were also associated with eGFR decline (ie, tiglylcarnitine, decadienylcarnitine, total dimethylarginine, decenoylcarnitine and kynurenine) (β range -0.11 to -0.19, p values range 4.8×10-2 to 3.0×10-3). Indeed, tiglylcarnitine and kynurenine, which captured all the information of the other three markers, improved discrimination and reclassification (all p<0.01) of two clinical prediction models of GFR decline in people with diabetes.

CONCLUSIONS

Further studies are needed to validate our findings in larger cohorts of different clinical, environmental and genetic background.

Urinary Metabolite Profile Predicting the Progression of CKD

Key Points

As a biomarker, urinary metabolites could bridge the gap between genetic abnormalities and phenotypes of diseases. We found that levels of betaine, choline, fumarate, citrate, and glucose were significantly correlated with kidney function and could predict kidney outcomes, providing prognostic biomarkers in CKD.

Background

Because CKD is caused by genetic and environmental factors, biomarker development through metabolomic analysis, which reflects gene-derived downstream effects and host adaptation to the environment, is warranted.

Methods

We measured the metabolites in urine samples collected from 789 patients at the time of kidney biopsy and from urine samples from 147 healthy participants using nuclear magnetic resonance. The composite outcome was defined as a 30% decline in eGFR, doubling of serum creatinine levels, or end-stage kidney disease.

Results

Among the 28 candidate metabolites, we identified seven metabolites showing (1 ) good discrimination between healthy controls and patients with stage 1 CKD and (2 ) a consistent change in pattern from controls to patients with advanced-stage CKD. Among the seven metabolites, betaine, choline, glucose, fumarate, and citrate showed significant associations with the composite outcome after adjustment for age, sex, eGFR, the urine protein–creatinine ratio, and diabetes. Furthermore, adding choline, glucose, or fumarate to traditional biomarkers, including eGFR and proteinuria, significantly improved the ability of the net reclassification improvement (P < 0.05) and integrated discrimination improvement (P < 0.05) to predict the composite outcome.

Conclusion

Urinary metabolites, including betaine, choline, fumarate, citrate, and glucose, were found to be significant predictors of the progression of CKD. As a signature of kidney injury–related metabolites, it would be warranted to monitor to predict the renal outcome.

Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome

BACKGROUND

Nephrotic syndrome is common in children and adults worldwide, and steroid-sensitive nephrotic syndrome (SSNS) accounts for 80%. Aberrant metabolism involvement in early SSNS is sparsely studied, and its pathogenesis remains unclear. Therefore, the goal of this study was to investigate the changes in initiated SSNS patients-related metabolites through serum and urine metabolomics and discover the novel potential metabolites and metabolic pathways.

METHODS

Serum samples (27 SSNS and 56 controls) and urine samples (17 SSNS and 24 controls) were collected. Meanwhile, the non-targeted analyses were performed by ultra-high-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) to determine the changes in SSNS. We applied the causal inference model, the DoWhy model, to assess the causal effects of several selected metabolites. An ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to validate hits (D-mannitol, dulcitol, D-sorbitol, XMP, NADPH, NAD, bilirubin, and α-KG-like) in 41 SSNS and 43 controls. In addition, the metabolic pathways were explored.

RESULTS

Compared to urine, the metabolism analysis of serum samples was more clearly discriminated at SSNS. 194 differential serum metabolites and five metabolic pathways were obtained in the SSNS group. Eight differential metabolites were identified by establishing the diagnostic model for SSNS, and four variables had a positive causal effect. After validation by targeted MS, except XMP, others have similar trends like the untargeted metabolic analysis.

CONCLUSION

With untargeted metabolomics analysis and further targeted quantitative analysis, we found seven metabolites may be new biomarkers for risk prediction and early diagnosis for SSNS.

Metabolomic Profiles, Ideal Cardiovascular Health, and Risk of Heart Failure and Atrial Fibrillation: Insights From the Framingham Heart Study

Background The American Heart Association's framework "ideal cardiovascular health" (CVH) focuses on modifiable risk factors to reduce cardiovascular disease (CVD). Metabolomics provides important pathobiological insights into risk factors and CVD development. We hypothesized that metabolomic signatures associate with CVH status, and that metabolites, at least partially, mediate the association of CVH score with atrial fibrillation (AF) and heart failure (HF). Methods and Results We studied 3056 adults in the FHS (Framingham Heart Study) cohort to evaluate CVH score and incident outcomes of AF and HF. Metabolomics data were available in 2059 participants; mediation analysis was performed to evaluate the mediation of metabolites in the association of CVH score and incident AF and HF. In the smaller cohort (mean age, 54 years; 53% women), CVH score was associated with 144 metabolites, with 64 metabolites shared across key cardiometabolic components (body mass index, blood pressure, and fasting blood glucose) of the CVH score. In mediation analyses, 3 metabolites (glycerol, cholesterol ester 16:1, and phosphatidylcholine 32:1) mediated the association of CVH score with incident AF. Seven metabolites (glycerol, isocitrate, asparagine, glutamine, indole-3-proprionate, phosphatidylcholine C36:4, and lysophosphatidylcholine 18:2), partly mediated the association between CVH score and incident HF in multivariable-adjusted models. Conclusions Most metabolites that associated with CVH score were shared the most among 3 cardiometabolic components. Three main pathways: (1) alanine, glutamine, and glutamate metabolism; (2) citric acid cycle metabolism; and (3) glycerolipid metabolism mediated CVH score with HF. Metabolomics provides insights into how ideal CVH status contributes to the development of AF and HF.

Qishen granules regulate intestinal microecology to improve cardiac function in rats with heart failure

Introduction

Qishen Granule (QSG), a clinically approved traditional Chinese medicine, has been researched for treating heart failure (HF) for many years. However, the effect of QSG on intestinal microecology remains unconfirmed. Therefore, this study aimed to elucidate the possible mechanism of QSG regulating HF in rats based on intestinal microecological changes.

Methods

A rat model with HF induced by myocardial infarction was prepared by left coronary artery ligation. Cardiac functions were assessed by echocardiography, pathological changes in the heart and ileum by hematoxylin-eosin (HE) and Masson staining, mitochondrial ultrastructure by transmission electron microscope, and gut microbiota by 16S rRNA sequencing.

Results

QSG administration improved cardiac function, tightened cardiomyocytes alignment, decreased fibrous tissue and collagen deposition, and reduced inflammatory cell infiltration. Electron microscopic observation of mitochondria revealed that QSG could arrange mitochondria neatly, reduce swelling, and improve the structural integrity of the crest. Firmicutes were the dominant component in the model group, and QSG could significantly increase the abundance of Bacteroidetes and Prevotellaceae_NK3B31_group. Furthermore, QSG significantly reduced plasma lipopolysaccharide (LPS), improved intestinal structure, and recovered barrier protection function in rats with HF.

Conclusion

These results demonstrated that QSG was able to improve cardiac function by regulating intestinal microecology in rats with HF, suggesting promising therapeutic targets for HF.

Gut microbiota-derived metabolite trimethylamine-N-oxide and stroke outcome: a systematic review

Introduction

The relationship between baseline trimethylamine N-oxide (TMAO) levels and stroke outcomes remains unclear. Therefore, this systematic review aimed to summarize the existing relevant research.

Methods

We searched for studies on the association between baseline plasma levels of TMAO and stroke outcomes in the PubMed, EMBASE, Web of Science, and Scopus databases from their inception to 12 October 2022. Two researchers independently reviewed the studies for inclusion and extracted the relevant data.

Results

Seven studies were included in the qualitative analysis. Among them, six studies reported the outcome of acute ischemic stroke (AIS) and one study of intracerebral hemorrhage (ICH), respectively. Furthermore, no study reported the outcome of subarachnoid hemorrhage. Among patients with AIS, high baseline TMAO levels were associated with unfavorable functional outcomes or mortality at 3 months, as well as a high hazard ratio of mortality, recurrence, or major adverse cardiac event. Moreover, TMAO levels showed predictive utility for unfavorable functional outcomes or mortality at 3 months. Among patients with ICH, high TMAO levels were associated with unfavorable functional outcomes at 3 months, regardless of whether the TMAO value was considered a continuous or a categorical variable.

Conclusion

Limited evidence indicates that high baseline plasma levels of TMAO may be associated with poor stroke outcomes. Further studies are warranted to confirm the relationship between TMAO and stroke outcomes.

Metabolomic Investigation of Blood and Urinary Amino Acids and Derivatives in Patients with Type 2 Diabetes Mellitus and Early Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease; however, few biomarkers of its early identification are available. The aim of the study was to assess new biomarkers in the early stages of DKD in type 2 diabetes mellitus (DM) patients. This cross-sectional pilot study performed an integrated metabolomic profiling of blood and urine in 90 patients with type 2 DM, classified into three subgroups according to albuminuria stage from P1 to P3 (30 normo-, 30 micro-, and 30 macroalbuminuric) and 20 healthy controls using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI* MS). From a large cohort of separated and identified molecules, 33 and 39 amino acids and derivatives from serum and urine, respectively, were selected for statistical analysis using Metaboanalyst 5.0. online software. The multivariate and univariate algorithms confirmed the relevance of some amino acids and derivatives as biomarkers that are responsible for the discrimination between healthy controls and DKD patients. Serum molecules such as tiglylglycine, methoxytryptophan, serotonin sulfate, 5-hydroxy lysine, taurine, kynurenic acid, and tyrosine were found to be more significant in the discrimination between group C and subgroups P1-P2-P3. In urine, o-phosphothreonine, aspartic acid, 5-hydroxy lysine, uric acid, methoxytryptophan, were among the most relevant metabolites in the discrimination between group C and DKD group, as well between subgroups P1-P2-P3. The identification of these potential biomarkers may indicate their involvement in the early DKD and 2DM progression, reflecting kidney injury at specific sites along the nephron, even in the early stages of DKD.

Kidney-specific metabolomic profiling in machine perfusate

Given their accessibility and relevance to established clinical workflows, blood and urine have been the major focus of investigation in metabolomics studies of human kidney disease. In this issue, Liu et al. describe the application of metabolomics to perfusate from donor kidneys subjected to hypothermic machine perfusion. In addition to providing an elegant model for investigating kidney metabolism, this study highlights the limitations of allograft quality assessment and identifies metabolites of interest in kidney ischemia.

Identifying disease progression in chronic kidney disease using proton magnetic resonance spectroscopy

Chronic kidney disease (CKD) affects approximately 10% of the world population, higher still in some developing countries, and can cause irreversible kidney damage eventually leading to kidney failure requiring dialysis or kidney transplantation. However, not all patients with CKD will progress to this stage, and it is difficult to distinguish between progressors and non-progressors at the time of diagnosis. Current clinical practice involves monitoring estimated glomerular filtration rate and proteinuria to assess CKD trajectory over time; however, there remains a need for novel, validated methods that differentiate CKD progressors and non-progressors. Nuclear magnetic resonance techniques, including magnetic resonance spectroscopy and magnetic resonance imaging, have the potential to improve our understanding of CKD progression. Herein, we review the application of magnetic resonance spectroscopy both in preclinical and clinical settings to improve the diagnosis and surveillance of patients with CKD.