Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes

Metabolic profile of urine of albuminuric and non-albuminuric nephropathic diabetic patients suggests TCA-cycle related biomarkers

AIM

Diabetic kidney disease (DKD) is a common and serious complication of diabetes. Moreover, ∼25 % of DKD patients are non-albuminuric, complicating diagnosis. This study aimed to identify potential urinary metabolic biomarkers in healthy and DKD patients, both with (A-DKD) and without albuminuria (NA-DKD).

METHODS

We analyzed urine samples from healthy controls (n = 23) and DKD (n = 17) patients. DKD patients were further split into NA-DKD (n = 5) and A-DKD (n = 12). Non-targeted proton nuclear magnetic resonance (1H NMR) metabolomics was used to explore metabolic differences.

RESULTS

DKD patients exhibited lower levels of citrate, hypoxanthine, formate, isobutyrate, glycine, phenylacetate, dimethylamine, and valine in urine samples, and higher levels of trans-aconitate, glycolate, and taurine. Citrate presented the strongest negative correlation (r = -0.65,p < 0.0001), followed by hypoxanthine (r = -0.49,p = 0.004), isobutyrate (r = -0.45,p = 0.004) and formate (r = -0.41,p = 0.009). On the other hand, glycolate (r = 0.46,p = 0.003), taurine (r = 0.47,p = 0.007), and trans-aconitate (r = 0.41,p = 0.05) were positively correlated with albuminuria. Glycine was decreased, and alanine was increased in NA-DKD compared to A-DKD. Receiver operating characteristic curve analysis identified citrate, hypoxanthine, and taurine as key predictors for distinguish patients with normal and higher levels of albuminuria.

CONCLUSION

Urinary metabolites related to tricarboxylic acid (TCA) and purine metabolism can potentially serve as a marker to individualize therapeutic choices in DKD, especially for non-albuminuric phenotype.

HPLC-MS/MS method for simultaneous analysis of plasma 2-hydroxybutyrate and 2-hydroxyisobutyrate: Development and clinical significance

Recent studies have identified relationships between diabetes mellitus and short-chain fatty acids, including 2-hydroxybutyrate (2-HB) and 2-hydroxyisobutyrate (2-HiB); 2-HB has been associated to the early stages of insulin resistance, while 2-HiB with the risk and progression of complications of Type 1 diabetes. Their metabolism and pathophysiological role in humans are not fully clarified. The possible association between 2-HB and 2-HiB and diabetes mellitus was investigated with a novel mass spectrometry-based assay, capable of discriminating plasma 2-HiB and 2-HB from their HB isomers. Accuracy and precision (RSD%) were always in the range 99-102% and 0.7-3.5%, respectively. The study involved samples from subjects with normal glucose tolerance (NGT) and Type 2 diabetes (T2D), originally included in a multicenter study investigating mechanisms involved in atherothrombosis. NGT subjects exhibited concentrations of 2-HB and 2-HiB of 61 (36) and 3.1 (1.9) µmol/L, median (interquartile range), respectively, that were significantly lower than those of the T2D patients, whose values were 74 (4.0) and 3.8 (2.9) µmol/L, respectively. The pattern of association of these molecules with clinical and metabolic variables is partially different: both compounds were directly related to male sex, BMI, HbA1c, and plasma glucose, 2-HiB also with age, systolic blood pressure, and HDL-cholesterol. Furthermore, they correlate with free fatty acids, glycerol, and triglyceride concentrations, but the latter correlation was negative for 2-HB and positive for 2-HiB. Results confirmed the clinical significance of 2-HB and 2-HiB, in differential association with metabolic features of T2D.

Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function

Dissecting the genetic mechanisms underlying urinary metabolite concentrations can provide molecular insights into kidney function and open possibilities for causal assessment of urinary metabolites with risk factors and disease outcomes. Proton nuclear magnetic resonance metabolomics provides a high-throughput means for urinary metabolite profiling, as widely applied for blood biomarker studies. Here we report a genome-wide association study meta-analysed for 3 European cohorts comprising 8,011 individuals, covering both people with type 1 diabetes and general population settings. We identify 54 associations (p < 9.3 × 10-10) for 19 of 54 studied metabolite concentrations. Out of these, 33 were not reported previously for relevant urinary or blood metabolite traits. Subsequent two-sample Mendelian randomization analysis suggests that estimated glomerular filtration rate causally affects 13 urinary metabolite concentrations whereas urinary ethanolamine, an initial precursor for phosphatidylcholine and phosphatidylethanolamine, was associated with higher eGFR lending support for a potential protective role. Our study provides a catalogue of genetic associations for 53 metabolites, enabling further investigation on how urinary metabolites are linked to human health.

Disruption of branched-chain amino acid homeostasis promotes the progression of DKD via enhancing inflammation and fibrosis-associated epithelial-mesenchymal transition

BACKGROUND AND AIMS

The disrupted homeostasis of branched-chain amino acids (BCAAs, including leucine, isoleucine, and valine) has been strongly correlated with diabetes with a potential causal role. However, the relationship between BCAAs and diabetic kidney disease (DKD) remains to be established. Here, we show that the elevated BCAAs from BCAAs homeostatic disruption promote DKD progression unexpectedly as an independent risk factor.

METHODS AND RESULTS

Similar to other tissues, the suppressed BCAAs catabolic gene expression and elevated BCAAs abundance were detected in the kidneys of type 2 diabetic mice and individuals with DKD. Genetic and nutritional studies demonstrated that the elevated BCAAs from systemic disruption of BCAAs homeostasis promoted the progression of DKD. Of note, the elevated BCAAs promoted DKD progression without exacerbating diabetes in the animal models of type 2 DKD. Mechanistic studies demonstrated that the elevated BCAAs promoted fibrosis-associated epithelial-mesenchymal transition (EMT) by enhancing the activation of proinflammatory macrophages through mTOR signaling. Furthermore, pharmacological enhancement of systemic BCAAs catabolism using small molecule inhibitor attenuated type 2 DKD. Finally, the elevated BCAAs also promoted DKD progression in type 1 diabetic mice without exacerbating diabetes.

CONCLUSION

BCAA homeostatic disruption serves as an independent risk factor for DKD and restoring BCAA homeostasis pharmacologically or dietarily represents a promising therapeutic strategy to ameliorate the progression of DKD.

Urinary metabolomics provide insights into coronary artery disease in individuals with type 1 diabetes

BACKGROUND

Type 1 diabetes increases the risk of coronary artery disease (CAD). High-throughput metabolomics may be utilized to identify metabolites associated with disease, thus, providing insight into disease pathophysiology, and serving as predictive markers in clinical practice. Urine is less tightly regulated than blood, and therefore, may enable earlier discovery of disease-associated markers. We studied urine metabolomics in relation to incident CAD in individuals with type 1 diabetes.

METHODS

We prospectively studied CAD in 2501 adults with type 1 diabetes from the Finnish Diabetic Nephropathy Study. 209 participants experienced incident CAD within the 10-year follow-up. We analyzed the baseline urine samples with a high-throughput targeted urine metabolomics platform, which yielded 54 metabolites. With the data, we performed metabolome-wide survival analyses, correlation network analyses, and metabolomic state profiling for prediction of incident CAD.

RESULTS

Urinary 3-hydroxyisobutyrate was associated with decreased 10-year incident CAD, which according to the network analysis, likely reflects younger age and improved kidney function. Urinary xanthosine was associated with 10-year incident CAD. In the network analysis, xanthosine correlated with baseline urinary allantoin, which is a marker of oxidative stress. In addition, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. Metabolomic state profiling supported the usage of CAD-associated urinary metabolites to improve prediction accuracy, especially during shorter follow-up. Furthermore, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. The network analysis further suggested glomerular filtration rate to influence the urinary metabolome differently between individuals with and without future CAD.

CONCLUSIONS

We have performed the first high-throughput urinary metabolomics analysis on CAD in individuals with type 1 diabetes and found xanthosine, 3-hydroxyisobutyrate, trans-aconitate, and 4-deoxythreonate to be associated with incident CAD. In addition, metabolomic state profiling improved prediction of incident CAD.

Liraglutide ameliorates diabetic kidney disease by modulating gut microbiota and L-5-Oxoproline

The gut microbiome-metabolites-kidney axis is a potential target for treating diabetic kidney disease (DKD). Our previous study found that Liraglutide attenuated DKD in rats by decreasing renal tubular ectopic lipid deposition (ELD) and serum metabolites levels, including L-5-Oxoproline (5-OP). However, the response of gut microbiome-metabolites-kidney axis to Liraglutide in DKD rats and the effect of 5-OP on ELD remain unknown. In this study, Sprague-Dawley rats were used as an animal model of DKD. They were subjected to a high fat diet, streptozotocin and uninephrectomy, followed by Liraglutide treatment (0.4 mg/kg d). Additionally, HK-2 cells were incubated with 30 mM glucose and 200 μM palmitate for 24h, and exposed to different concentrations of 5-OP. In DKD rats, Liraglutide dramatically improved the renal tubule structure. It increased the Simpson index (F = 4.487, p = 0.035) and reduced the Actinobacteria-to-Bacteroidetes ratio (F = 6.189, p = 0.014). At the genus level, Liraglutide increased the relative abundance of Clostridium, Oscillospira, Sarcina, SMB53, and 02d06 while decreasing that of Allobaculum. Meanwhile, 13 metabolites were significantly altered after Liraglutide treatment. Multi-omics analysis found that 5-OP levels were positively correlated with Clostridium abundance but negatively correlated with renal injury related indicators. In HK-2 cells, 5-OP significantly reduced the ELD in a dose-dependent manner through inhibiting the expression of SREBP1 and FAS. Overall, the renoprotective effect of Liraglutide in DKD rats is linked to the improvement of the gut microbiota composition and increased serum 5-OP levels, which may reduce ELD in renal tubular cells by lowering lipid synthesis.

The key metabolic signatures and biomarkers of polycyclic aromatic hydrocarbon-induced blood glucose elevation in chinese individuals exposed to diesel engine exhaust

Due to the complexity of environmental exposure factors and the low levels of exposure in the general population, identifying the key environmental factors associated with diabetes and understanding their potential mechanisms present significant challenges. This study aimed to identify key polycyclic aromatic hydrocarbons (PAHs) contributing to increased fasting blood glucose (FBG) concentrations and to explore their potential metabolic mechanisms. We recruited a highly PAH-exposed diesel engine exhaust testing population and healthy controls. Our findings found a positive association between FBG concentrations and PAH metabolites, identifying 1-OHNa, 2-OHPh, and 9-OHPh as major contributors to the rise in FBG concentrations induced by PAH mixtures. Specifically, each 10 % increase in 1-OHNa, 2-OHPh, and 9-OHPh concentrations led to increases in FBG concentrations of 0.201 %, 0.261 %, and 0.268 %, respectively. Targeted metabolomics analysis revealed significant alterations in metabolic pathways among those exposed to high levels of PAHs, including sirtuin signaling, asparagine metabolism, and proline metabolism pathway. Toxic function analysis highlighted differential metabolites involved in various dysglycemia-related conditions, such as cardiac arrhythmia and renal damage. Mediation analysis revealed that 2-aminooctanoic acid mediated the FBG elevation induced by 2-OHPh, while 2-hydroxyphenylacetic acid and hypoxanthine acted as partial suppressors. Notably, 2-aminooctanoic acid was identified as a crucial intermediary metabolic biomarker, mediating significant portions of the associations between the multiple different structures of OH-PAHs and elevated FBG concentrations, accounting for 16.73 %, 10.84 %, 10.00 %, and 11.90 % of these effects for 1-OHPyr, 2-OHFlu, the sum concentrations of 2- and 9-OHPh, and the sum concentrations of total OH-PAHs, respectively. Overall, our study explored the potential metabolic mechanisms underlying the elevated FBG induced by PAHs and identified 2-aminooctanoic acid as a pivotal metabolic biomarker, presenting a potential target for intervention.

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review)

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end‑stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in‑depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Characterizing poorly controlled type 2 diabetes using 1H-NMR metabolomics

INTRODUCTION

The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited.

OBJECTIVE

This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort.

METHOD

Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites.

RESULTS

The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage.

CONCLUSION

These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.

Low molecular weight fucoidan restores diabetic endothelial glycocalyx by targeting neuraminidase2: A new therapy target in glycocalyx shedding

BACKGROUND AND PURPOSE

Diabetic vascular complication is a leading cause of disability and mortality in diabetes patients. Low molecular weight fucoidan (LMWF) is a promising drug candidate for vascular complications. Glycocalyx injury predates the occurrence of diabetes vascular complications. Protecting glycocalyx from degradation relieves diabetic vascular complications. LMWF has the potential to protect the diabetes endothelial glycocalyx from shedding.

EXPERIMENTAL APPROACH

The protective effect of LMWF on diabetic glycocalyx damage was investigated in db/db mice and Human Umbilical Vein Endothelial Cells (HUVEC) through transmission electron microscopy and WGA labelling. The effect of LMWF on glycocalyx degrading enzymes expression was investigated. Neuraminidase2 (NEU2) overexpression/knockdown was performed in HUVECs to verify the important role of NEU2 in glycocalyx homeostasis. The interaction between NEU2 and LMWF was detected by ELISA and surface plasmon resonance analysis (SPR).

KEY RESULTS

LMWF normalizes blood indexes including insulin, triglyceride, uric acid and reduces diabetes complications adverse events. LMWF alleviates diabetic endothelial glycocalyx damage in db/db mice kidney/aorta and high concentration glucose treated HUVECs. NEU2 is up-regulated in db/db mice and HUVECs with high concentration glucose. Overexpression/knockdown NEU2 results in glycocalyx shedding in HUVEC. Down-regulation and interaction of LMWF with NEU2 is a new therapy target in glycocalyx homeostasis. NEU2 was positively correlated with phosphorylated IR-β.

CONCLUSION AND IMPLICATIONS

NEU2 is an effective target for glycocalyx homeostasis and LMWF is a promising drug to alleviate vascular complications in diabetes by protecting endothelial glycocalyx.

Exposome and Metabolome Analysis of Sugarcane Workers Reveals Predictors of Kidney Injury

Introduction

Sugarcane workers are exposed to potentially hazardous agrochemicals, including pesticides, heavy metals, and silica. Such occupational exposures present health risks and have been implicated in a high rate of kidney disease seen in these workers.

Methods

To investigate potential biomarkers and mechanisms that could explain chronic kidney disease (CKD) among this worker population, paired urine samples were collected from sugarcane cutters at the beginning and end of a harvest season in Guatemala. Workers were then separated into 2 groups, namely those with or without kidney function decline (KFD) across the harvest season. Urine samples from these 2 groups underwent elemental analysis and untargeted metabolomics.

Results

Urine profiles demonstrated increases in silicon, certain pesticides, and phosphorus levels in all workers, whereas heavy metals remained low. The KFD group had a reduction in estimated glomerular filtration rate (eGFR) across the harvest season; however, kidney injury marker 1 did not significantly change. Cross-harvest metabolomic analysis found trends of fatty acid accumulation, perturbed amino acid metabolism, presence of pesticides, and other known signs of impaired kidney function.

Conclusion

Silica and certain pesticides were significantly elevated in the urine of sugarcane workers with or without KFD. Future work should determine whether long-term occupational exposure to silica and pesticides across multiple seasons contributes to CKD in these workers. Overall, these results confirmed that multiple exposures are occurring in sugarcane workers and may provide insight into early warning signs of kidney injury and may help explain the increased incidence of CKD among agricultural workers.

Elucidating ascorbate and aldarate metabolism pathway characteristics via integration of untargeted metabolomics and transcriptomics of the kidney of high-fat diet-fed obese mice

Obesity is a major independent risk factor for chronic kidney disease and can activate renal oxidative stress injury. Ascorbate and aldarate metabolism is an important carbohydrate metabolic pathway that protects cells from oxidative damage. However the effect of oxidative stress on this pathway is still unclear. Therefore, the primary objective of this study was to investigate the ascorbate and aldarate metabolism pathway in the kidneys of high-fat diet-fed obese mice and determine the effects of oxidative stress. Male C57BL/6J mice were fed on a high-fat diet for 12 weeks to induce obesity. Subsequently, non-targeted metabolomics profiling was used to identify metabolites in the kidney tissues of the obese mice, followed by RNA sequencing using transcriptomic methods. The integrated analysis of metabolomics and transcriptomics revealed the alterations in the ascorbate and aldarate metabolic pathway in the kidneys of these high-fat diet-fed obese mice. The high-fat diet-induced obesity resulted in notable changes, including thinning of the glomerular basement membrane, alterations in podocyte morphology, and an increase in oxidative stress. Metabolomics analysis revealed 649 metabolites in the positive-ion mode, and 470 metabolites in the negative-ion mode. Additionally, 659 differentially expressed genes (DEGs) were identified in the obese mice, of which 34 were upregulated and 625 downregulated. Integrated metabolomics and transcriptomics analyses revealed two DEGs and 13 differential metabolites in the ascorbate and aldarate metabolic pathway. The expression levels of ugt1a9 and ugt2b1 were downregulated, and the ascorbate level in kidney tissue of obese mice was reduced. Thus, renal oxidative stress injury induced by high-fat diet affects metabolic regulation of ascorbate and aldarate metabolism in obese mice. Ascorbate emerged as a potential marker for predicting kidney damage due to high-fat diet-induced obesity.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

BACKGROUND

Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes.

OBJECTIVE

This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors.

METHODS

We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI.

RESULTS

Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR).

CONCLUSIONS

This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers

BACKGROUND

Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges.

METHODS

We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking.

RESULTS

Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate.

CONCLUSIONS

Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

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.

The citrate transporter SLC13A5 as a therapeutic target for kidney disease: evidence from Mendelian randomization to inform drug development

BACKGROUND

Solute carrier family 13 member 5 (SLC13A5) is a Na+-coupled citrate co-transporter that mediates entry of extracellular citrate into the cytosol. SLC13A5 inhibition has been proposed as a target for reducing progression of kidney disease. The aim of this study was to leverage the Mendelian randomization paradigm to gain insight into the effects of SLC13A5 inhibition in humans, towards prioritizing and informing clinical development efforts.

METHODS

The primary Mendelian randomization analyses investigated the effect of SLC13A5 inhibition on measures of kidney function, including creatinine and cystatin C-based measures of estimated glomerular filtration rate (creatinine-eGFR and cystatin C-eGFR), blood urea nitrogen (BUN), urine albumin-creatinine ratio (uACR), and risk of chronic kidney disease and microalbuminuria. Secondary analyses included a paired plasma and urine metabolome-wide association study, investigation of secondary traits related to SLC13A5 biology, a phenome-wide association study (PheWAS), and a proteome-wide association study. All analyses were compared to the effect of genetically predicted plasma citrate levels using variants selected from across the genome, and statistical sensitivity analyses robust to the inclusion of pleiotropic variants were also performed. Data were obtained from large-scale genetic consortia and biobanks, with sample sizes ranging from 5023 to 1,320,016 individuals.

RESULTS

We found evidence of associations between genetically proxied SLC13A5 inhibition and higher creatinine-eGFR (p = 0.002), cystatin C-eGFR (p = 0.005), and lower BUN (p = 3 × 10-4). Statistical sensitivity analyses robust to the inclusion of pleiotropic variants suggested that these effects may be a consequence of higher plasma citrate levels. There was no strong evidence of associations of genetically proxied SLC13A5 inhibition with uACR or risk of CKD or microalbuminuria. Secondary analyses identified evidence of associations with higher plasma calcium levels (p = 6 × 10-13) and lower fasting glucose (p = 0.02). PheWAS did not identify any safety concerns.

CONCLUSIONS

This Mendelian randomization analysis provides human-centric insight to guide clinical development of an SLC13A5 inhibitor. We identify plasma calcium and citrate as biologically plausible biomarkers of target engagement, and plasma citrate as a potential biomarker of mechanism of action. Our human genetic evidence corroborates evidence from various animal models to support effects of SLC13A5 inhibition on improving kidney function.

Abnormal lactate metabolism is linked to albuminuria and kidney injury in diabetic nephropathy

Diabetic nephropathy (DN) is characterized by abnormal kidney energy metabolism, but its causes and contributions to DN pathogenesis are not clear. To examine this issue, we carried out targeted metabolomics profiling in a mouse model of DN that develops kidney disease resembling the human disorder. We found a distinct profile of increased lactate levels and impaired energy metabolism in kidneys of mice with DN, and treatment with an angiotensin-receptor blocker (ARB) reduced albuminuria, attenuated kidney pathology and corrected many metabolic abnormalities, restoring levels of lactate toward normal while increasing kidney ATP content. We also found enhanced expression of lactate dehydrogenase isoforms in DN. Expression of both the LdhA and LdhB isoforms were significantly increased in kidneys of mice, and treatment with ARB significantly reduced their expression. Single-cell sequencing studies showed specific up-regulation of LdhA in the proximal tubule, along with enhanced expression of oxidative stress pathways. There was a significant correlation between albuminuria and lactate in mice, and also in a Southeast Asian patient cohort consisting of individuals with type 2 diabetes and impaired kidney function. In the individuals with diabetes, this association was independent of ARB and angiotensin-converting enzyme inhibitor use. Furthermore, urinary lactate levels predicted the clinical outcomes of doubling of serum creatinine or development of kidney failure, and there was a significant correlation between urinary lactate levels and biomarkers of tubular injury and epithelial stress. Thus, we suggest that kidney metabolic disruptions leading to enhanced generation of lactate contribute to the pathogenesis of DN and increased urinary lactate levels may be a potential biomarker for risk of kidney disease progression.

CKD Urine Metabolomics: Modern Concepts and Approaches

One of the primary challenges regarding chronic kidney disease (CKD) diagnosis is the absence of reliable methods to detect early-stage kidney damage. A metabolomic approach is expected to broaden the current diagnostic modalities by enabling timely detection and making the prognosis more accurate. Analysis performed on urine has several advantages, such as the ease of collection using noninvasive methods and its lower protein and lipid content compared with other bodily fluids. This review highlights current trends in applied analytical methods, major discoveries concerning pathways, and investigated populations in the context of urine metabolomic research for CKD over the past five years. Also, we are presenting approaches, instrument upgrades, and sample preparation modifications that have improved the analytical parameters of methods. The onset of CKD leads to alterations in metabolism that are apparent in the molecular composition of urine. Recent works highlight the prevalence of alterations in the metabolic pathways related to the tricarboxylic acid cycle and amino acids. Including diverse patient cohorts, using numerous analytical techniques with modifications and the appropriate annotation and explanation of the discovered biomarkers will help develop effective diagnostic models for different subtypes of renal injury with clinical applications.

Urinary metabolites associate with the presence of diabetic kidney disease in type 2 diabetes and mediate the effect of inflammation on kidney complication

AIMS

Diabetic kidney disease (DKD) is the one of the leading causes of end-stage kidney disease. Unraveling novel biomarker signatures capable to identify patients with DKD is favorable for tackle the burden. Here, we investigated the possible association between urinary metabolites and the presence of DKD in type 2 diabetes (T2D), and further, whether the associated metabolites improve discrimination of DKD and mediate the effect of inflammation on kidney involvement was evaluated.

METHODS

Two independent cohorts comprising 192 individuals (92 DKD) were analyzed. Urinary metabolites were analyzed by targeted metabolome profiling  and inflammatory cytokine IL-18 were measured by ELISA. Differentially expressed metabolites were selected and mediation analysis was carried out.

RESULTS

Seven potential metabolite biomarkers (i.e., S-Adenosyl-L-homocysteine, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid) were identified using the discovery and validation design. In the pooled analysis, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid were markedly and independently associated with DKD. The composite index of 7 potential metabolite biomarkers (CMI) mediated 32.99% of the significant association between the inflammatory IL-18 and DKD. Adding the metabolite biomarkers improved the discrimination of DKD.

CONCLUSIONS

In T2D, several associated urinary metabolites were identified to improve the prediction of DKD. Whether interventions aimed at reducing CMI also reduce the risk of DKD especially in patients with high IL-18 needs further investigations.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Urine myo-inositol as a novel prognostic biomarker for diabetic kidney disease: a targeted metabolomics study using nuclear magnetic resonance

BACKGROUND

As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients.

METHODS

We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity.

RESULTS

During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction.

CONCLUSION

Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.

Changes of Serum Bone Metabolism Indexes and Ultrasonic Bone Mineral Density in Patients with Diabetic Nephropathy at Different Stages and their effects on Diabetic Renal Microvascular Complications

Objective

To determine the changes in serum bone metabolism indexes and ultrasonic bone mineral density (BMD) in patients with diabetic nephropathy at different stages, and their effects on diabetic renal microvascular complications.

Methods

This is a clinical comparative study. One hundred twenty two diabetic patients admitted to the Baoding No.1 Central Hospital from January 2020 to March 2022 were selected as subjects and divided into three groups according to their actual condition: the simple diabetes (Group-A, 40 cases), diabetic nephropathy with micro urinary protein (Group-B, 40 cases) and diabetic nephropathy with massive proteinuria (Group-C, 42 cases). Another 36 healthy subjects were selected as the control group. Differences in serum bone metabolism indexes and ultrasound BMD levels were compared.

Results

Twenty five hydroxy-vitamin D, BGP, T-PINP and ultrasound BMD levels in the control group were > Group-A > Group-B > Group-C, PTH and β-CTX in the control group were < Group-A < Group-B < Group-C, statistically significant differences (p<0.05). The urinary albumin to urinary creatinine ratio (ACR) value in Group-B was significantly lower than Group-C (p<0.05). Logistic regression analysis showed that 25-hydroxy-vitamin D, PTH, BGP, β-CTX, T-PINP and ultrasound BMD were the influencing factors of diabetic renal microvascular complications (p<0.05).

Conclusion

Bone metabolism indexes and ultrasound bone mineral density are abnormally expressed in patients with diabetic nephropathy at different stages, which are closely related to the urine protein of patients. They have important clinical value in the diagnosis of early diabetic nephropathy.

Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

A significant portion of the health burden of diabetic kidney disease (DKD) is caused by both type 1 and type 2 diabetes which leads to morbidity and mortality globally. It is one of the most common diabetic complications characterized by loss of renal function with high prevalence, often leading to acute kidney disease (AKD). Inflammation triggered by gut microbiota is commonly associated with the development of DKD. Interactions between the gut microbiota and the host are correlated in maintaining metabolic and inflammatory homeostasis. However, the fundamental processes through which the gut microbiota affects the onset and progression of DKD are mainly unknown. In this narrative review, we summarised the potential role of the gut microbiome, their pathogenicity between diabetic and non-diabetic kidney disease (NDKD), and their impact on host immunity. A well-established association has already been seen between gut microbiota, diabetes and kidney disease. The gut-kidney interrelationship is confirmed by mounting evidence linking gut dysbiosis to DKD, however, it is still unclear what is the real cause of gut dysbiosis, the development of DKD, and its progression. In addition, we also try to distinguish novel biomarkers for early detection of DKD and the possible therapies that can be used to regulate the gut microbiota and improve the host immune response. This early detection and new therapies will help clinicians for better management of the disease and help improve patient outcomes.

Molecular pathways that drive diabetic kidney disease

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

Novel biomarkers for diabetic kidney disease

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics

Diabetic kidney disease is the main cause of end-stage renal disease worldwide. The prediction of the clinical course of patients with diabetic kidney disease remains difficult, despite the identification of potential biomarkers; therefore, novel biomarkers are needed to predict the progression of the disease. We conducted non-targeted metabolomics using plasma and urine of patients with diabetic kidney disease whose estimated glomerular filtration rate was between 30 and 60 mL/min/1.73 m². We analyzed how the estimated glomerular filtration rate changed over time (up to 30 months) to detect rapid decliners of kidney function. Conventional logistic analysis suggested that only one metabolite, urinary 1-methylpyridin-1-ium (NMP), was a promising biomarker. We then applied a deep learning method to identify potential biomarkers and physiological parameters to predict the progression of diabetic kidney disease in an explainable manner. We narrowed down 3388 variables to 50 using the deep learning method and conducted two regression models, piecewise linear and handcrafted linear regression, both of which examined the utility of biomarker combinations. Our analysis, based on the deep learning method, identified systolic blood pressure and urinary albumin-to-creatinine ratio, six identified metabolites, and three unidentified metabolites including urinary NMP, as potential biomarkers. This research suggests that the machine learning method can detect potential biomarkers that could otherwise escape identification using the conventional statistical method.

Characteristics of Normalization Methods in Quantitative Urinary Metabolomics—Implications for Epidemiological Applications and Interpretations

A systematic comparison is presented for the effects of seven different normalization schemes in quantitative urinary metabolomics. Morning spot urine samples were analyzed with nuclear magnetic resonance (NMR) spectroscopy from a population-based group of 994 individuals. Forty-four metabolites were quantified and the metabolite–metabolite associations and the associations of metabolite concentrations with two representative clinical measures, body mass index and mean arterial pressure, were analyzed. Distinct differences were observed when comparing the effects of normalization for the intra-urine metabolite associations with those for the clinical associations. The metabolite–metabolite associations show quite complex patterns of similarities and dissimilarities between the different normalization methods, while the epidemiological association patterns are consistent, leading to the same overall biological interpretations. The results indicate that, in general, the normalization method appears to have only minor influences on standard epidemiological regression analyses with clinical/physiological measures. Multimetabolite normalization schemes showed consistent results with the customary creatinine reference. Nevertheless, interpretations of intra-urine metabolite associations and nuanced understanding of the epidemiological associations call for comparisons with different normalizations and accounting for the physiology, metabolism and kidney function related to the normalization schemes.

Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease

Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Novel biomarkers and therapeutic approaches for diabetic retinopathy and nephropathy: Recent progress and future perspectives

The global burden due to microvascular complications in patients with diabetes mellitus persists and even increases alarmingly, the intervention and management are now encountering many difficulties and challenges. This paper reviews the recent advancement and progress in novel biomarkers, artificial intelligence technology, therapeutic agents and approaches of diabetic retinopathy and nephropathy, providing more insights into the management of microvascular complications.