Proteomics and Metabolomics in Kidney Disease, including Insights into Etiology, Treatment, and Prevention

ExpoNano: A Strategy Based on Hyper-Cross-Linked Polymers Achieves Urinary Exposome Assessment for Biomonitoring

Urinary analysis of exogenous and endogenous molecules constitutes an efficient, noninvasive approach to evaluate human health status. However, the exposome characterization of urinary molecules remains extremely challenging with current techniques. Herein, we develop an ExpoNano strategy based on hyper-cross-linked polymers (HCPs) to achieve ultrahigh-throughput measurement of exo/endogenous molecules in urine. The strategy includes a simple trapping-detrapping procedure (15 min) with HCPs in enzymatically treated urine, followed by mass spectrometer determination. Molecules that can be determined by ExpoNano have a wide range of molecular weight (75-837 Da) and Log Kow (octanol-water partition coefficient; -9.86 to 10.56). The HCPs can be repeatedly used five times without decreasing the trapping efficiency. Application of ExpoNano in a biomonitoring study revealed a total of 63 environmental chemicals detected in >50% of the urine pools collected from Chinese adults living in 13 cities, with a median concentration of 0.026-47 ng/mL, while nontargeted analysis detected an additional 243 exogenous molecules. Targeted and nontargeted analysis also detected 926 endogenous molecules in pooled urine. Collectively, the ExpoNano strategy demonstrates unique advantages over traditional urine analysis approaches, including a wide range of analytes, satisfactory trapping efficiency, high simplicity and reusability, and extremely reduced time demand and financial cost.

Comparing the metabolic signatures of obesity defined by waist circumference, waist-hip ratio, or BMI

OBJECTIVE

Measuring obesity is crucial for assessing health risks and developing effective prevention and treatment strategies. The most common methods used to measure obesity include BMI, waist circumference, and waist-hip ratio. This study aimed to determine the metabolic signatures associated with each measure of obesity in the Qatari population.

METHODS

Metabolomics profiling was conducted to identify, quantify, and characterize metabolites in serum samples from the study participants. Inverse rank normalization, principal component analysis, and orthogonal partial least square-discriminant analysis were used to analyze the metabolomics data.

RESULTS

This study revealed significant differences in metabolites associated with obesity based on different measurements. In men, phosphatidylcholine and phosphatidylethanolamine metabolites were significantly enriched in individuals classified as having obesity based on the waist-hip ratio. In women, significant changes were observed in leucine, isoleucine, and valine metabolism metabolites. Unique metabolites were found in the different categorization groups that could serve as biomarkers for assessing many obesity-related disorders.

CONCLUSIONS

This study identified unique metabolic signatures associated with obesity based on different measurements in the Qatari population. These findings contribute to a better understanding of the molecular pathways involved in obesity and may have implications for developing personalized prevention and treatment strategies.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Detection of pharmacolipidodynamic effects following the intravenous and oral administration of gefitinib to C57Bl/6JRj mice by rapid UHPLC-MS analysis of plasma

Omics-based biomarker technologies, including metabolic profiling (metabolomics/metabonomics) and lipidomics, are making a significant impact on disease understanding, drug development, and translational research. A wide range of patho-physiological processes involve lipids and monitoring changes in lipid abundance can give valuable insights into mechanisms of drug action, off target pharmacology and toxicity. Here we report changes, detected by untargeted LC-MS, in the plasma lipid profiles of male C57Bl/6JRj mice following the PO and IV administration of the epidermal growth factor receptor (EGFR) inhibitor gefitinib. Statistical analysis of the data obtained for both the IV and PO samples showed time-related changes in the amounts of lipids from several different classes. The largest effects were associated with a rapid onset of these changes following gefitinib administration followed by a gradual return by 24 h post dose to the type of lipid profile seen in predose samples. Investigation of the lipids responsible for the variance observed in the data showed that the PI, PC, LPC, PE and TG were subject to the largest disruption with both transient increases and decreases in relative amounts seen in response to administration of the drug. The pattern of the changes in the relative abundances of those lipids subject to variation appeared to be correlated to the pharmacokinetics of gefitinib (and its major metabolites). These observations support the concept of a distinct pharmacolipidodynamic relationship between drug exposure and plasma lipid abundance.

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Integrative analysis of metabolomics and proteomics reveals mechanism of berberrubine-induced nephrotoxicity

Berberrubine (BRB), a main metabolite of berberine, has stronger hypoglycemic and lipid-lowering activity than its parent form. We previously found that BRB could cause obvious nephrotoxicity, but the molecular mechanism involved remains unknown. In this study, we systematically integrated metabolomics and quantitative proteomics to reveal the potential mechanism of nephrotoxicity caused by BRB. Metabolomic analysis revealed that 103 significant- differentially metabolites were changed. Among the mentioned compounds, significantly upregulated metabolites were observed for phosphorylcholine, sn-glycerol-3-phosphoethanolamine, and phosphatidylcholine. The top three enriched KEGG pathways were the mTOR signaling pathway, central carbon metabolism in cancer, and choline metabolism in cancer. ERK1/2 plays key roles in all three metabolic pathways. To further confirm the main signaling pathways involved, a proteomic analysis was conducted to screen for key proteins (such as Mapk1, Mapk14, and Caspase), indicating the potential involvement of cellular growth and apoptosis. Moreover, combined metabolomics and proteomics analyses revealed the participation of ERK1/2 in multiple metabolic pathways. These findings indicated that ERK1/2 regulated the significant- differentially abundant metabolites determined via metabolomics analysis. Notably, through a cellular thermal shift assay (CETSA) and molecular docking, ERK1/2 were revealed to be the direct binding target involved in BRB-induced nephrotoxicity. To summarize, this study sheds light on the understanding of severe nephrotoxicity caused by BRB and provides scientific basis for its safe use and rational development.

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.

A systematic review of epigenetic interplay in kidney diseases: Crosstalk between long noncoding RNAs and methylation, acetylation of chromatin and histone

The intricate crosstalk between long noncoding RNAs (lncRNAs) and epigenetic modifications such as chromatin/histone methylation and acetylation offer new perspectives on the pathogenesis and treatment of kidney diseases. lncRNAs, a class of transcripts longer than 200 nucleotides with no protein-coding potential, are now recognized as key regulatory molecules influencing gene expression through diverse mechanisms. They modulate the epigenetic modifications by recruiting or blocking enzymes responsible for adding or removing methyl or acetyl groups, such as DNA, N6-methyladenosine (m6A) and histone methylation and acetylation, subsequently altering chromatin structure and accessibility. In kidney diseases such as acute kidney injury (AKI), chronic kidney disease (CKD), diabetic nephropathy (DN), glomerulonephritis (GN), and renal cell carcinoma (RCC), aberrant patterns of DNA/RNA/histone methylation and acetylation have been associated with disease onset and progression, revealing a complex interplay with lncRNA dynamics. Recent studies have highlighted how lncRNAs can impact renal pathology by affecting the expression and function of key genes involved in cell cycle control, fibrosis, and inflammatory responses. This review will separately address the roles of lncRNAs and epigenetic modifications in renal diseases, with a particular emphasis on elucidating the bidirectional regulatory effects and underlying mechanisms of lncRNAs in conjunction with DNA/RNA/histone methylation and acetylation, in addition to the potential exacerbating or renoprotective effects in renal pathologies. Understanding the reciprocal relationships between lncRNAs and epigenetic modifications will not only shed light on the molecular underpinnings of renal pathologies but also present new avenues for therapeutic interventions and biomarker development, advancing precision medicine in nephrology.

The Effect of L-Carnitine on Critical Illnesses Such as Traumatic Brain Injury (TBI), Acute Kidney Injury (AKI), and Hyperammonemia (HA)

L-carnitine (LC) through diet is highly beneficial for critical patients. Studies have found that acetyl-L-carnitine (ALC) can reduce cerebral edema and neurological complications in TBI patients. It significantly improves their neurobehavioral and neurocognitive functions. ALC has also been shown to have a neuroprotective effect in cases of global and focal cerebral ischemia. Moreover, it is an effective agent in reducing nephrotoxicity by suppressing downstream mitochondrial fragmentation. LC can reduce the severity of renal ischemia-reperfusion injury, renal cast formation, tubular necrosis, iron accumulation in the tubular epithelium, CK activity, urea levels, Cr levels, and MDA levels and restore the function of enzymes such as SOD, catalase, and GPx. LC can also be administered to patients with hyperammonemia (HA), as it can suppress ammonia levels. It is important to note, however, that LC levels are dysregulated in various conditions such as aging, cirrhosis, cardiomyopathy, malnutrition, sepsis, endocrine disorders, diabetes, trauma, starvation, obesity, and medication interactions. There is limited research on the effects of LC supplementation in critical illnesses such as TBI, AKI, and HA. This scarcity of studies highlights the need for further research in this area.

Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome

BACKGROUND

Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome.

METHODS

We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy).

RESULTS

Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells.

CONCLUSIONS

Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.

Plasma proteome analysis implicates novel proteins as potential therapeutic targets for chronic kidney disease: A proteome-wide association study

Chronic kidney disease (CKD) is prevalent globally with limited therapeutic drugs available. To systemically identify novel proteins involved in the pathogenesis of CKD and possible therapeutic targets, we integrated human plasma proteomes with the genome-wide association studies (GWASs) of CKD, estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) to perform proteome-wide association study (PWAS), Mendelian Randomization and Bayesian colocalization analyses. The single-cell RNA sequencing data of healthy human and mouse kidneys were analyzed to explore the cell-type specificity of identified genes. Functional enrichment analysis was conducted to investigate the involved signaling pathways. The PWAS identified 22 plasma proteins significantly associated with CKD. Of them, the significant associations of three proteins (INHBC, LMAN2, and SNUPN) were replicated in the GWASs of eGFR, and BUN. Mendelian Randomization analyses showed that INHBC and SNUPN were causally associated with CKD, eGFR, and BUN. The Bayesian colocalization analysis identified shared causal variants for INHBC in CKD, eGFR, and BUN (all PP4 > 0.75). The single-cell RNA sequencing revealed that the INHBC gene was sparsely scattered within the kidney cells. This proteomic study revealed that INHBC, LMAN2, and SNUPN may be involved in the pathogenesis of CKD, which represent novel therapeutic targets and warrant further exploration in future research.

Metagenomics and metaproteomics alterations are associated with kidney disease in opisthorchiasis hamsters fed a high-fat and high-fructose diet

BACKGROUND

Opisthorchis viverrini (O. viverrini, Ov) infection and consumption of high-fat and high-fructose (HFF) diet exacerbate liver and kidney disease. Here, we investigated the effects of a combination of O. viverrini infection and HFF diet on kidney pathology via changes in the gut microbiome and host proteome in hamsters.

METHODOLOGY/PRINCIPAL FINDINGS

Twenty animals were divided into four groups; 1) fed a normal diet not infected with O. viverrini (normal group), 2) fed an HFF diet and not infected with O. viverrini (HFF), 3) fed a normal diet and infected with O. viverrini (Ov), and 4) fed an HFF diet and infected with O. viverrini (HFFOv). DNA was extracted from fecal samples and the V3-V4 region of the bacterial 16S rRNA gene sequenced on an Illumina MiSeq sequencing platform. In addition, LC/MS-MS analysis was done. Histopathological studies and biochemical assays were also conducted. The results indicated that the HFFOv group exhibited the most severe kidney injury, manifested as elevated KIM-1 expression and accumulation of fibrosis in kidney tissue. The microbiome of the HFFOv group was more diverse than in the HFF group: there were increased numbers of Ruminococcaceae, Lachnospiraceae, Desulfovibrionaceae and Akkermansiaceae, but fewer Eggerthellaceae. In total, 243 host proteins were identified across all groups. Analysis using STITCH predicted that host proteome changes may lead to leaking of the gut, allowing molecules such as soluble CD14 and p-cresol to pass through to promote kidney disease. In addition, differential expression of TGF-beta-activated kinase 1 and MAP3K7-binding protein 2 (Tab2, involving renal inflammation and injury) are predicted to be associated with kidney disease.

CONCLUSIONS/SIGNIFICANCE

The combination of HFF diet and O. viverrini infection may promote kidney injury through alterations in the gut microbiome and host proteome. This knowledge may suggest an effective strategy to prevent kidney disease beyond the early stages.

Exploring the mechanism of Huanglian ointment in alleviating wound healing after anal fistula surgery through metabolomics and proteomics

Anal fistula is a common anal and intestinal disease. The wound of anal fistula surgery is open and polluting, which is the most difficult to heal among all surgical incisions. To investigate the mechanism of Huanglian ointment (HLO) on wound healing after anal fistula incision. The S. aureus infected wound in SD rats were used to imitate poor healing wound after anal fistula surgery. SD rats with wound sites (n = 24) were randomly divided into four groups (Control group, Model group, Potassium permanganate (PP) treatment group, and HLO treatment group). The wound healing rate was evaluated, HE staining was used to evaluate the pathological changes of each group, ELISA was used to detect the secretion of inflammatory factors in each group, and the mechanism was explored through metabolomics and proteomics in plasma rat. Compared to other groups, the rate of wound healing in the HLO group was higher on days 7 and 14. Histological analysis showed that collagen and fibroblast in HLO rats were significantly increased, inflammatory cells were reduced, and vascular endothelial permeability was increased. ELISA results showed that the secretion of inflammatory factors in HLO rats was significantly lower. Significant proteins and metabolites were identified in the wound tissues of the infected rats and HLO-treated rats, which were mainly attributed to Cdc42, Ctnnb1, Actr2, Actr3, Arpc1b, Itgam, Itgb2, Cttn, Linoleic acid metabolism, d-Glutamine and d-glutamate metabolism, Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylalanine metabolism, alpha-Linolenic acid metabolism, and Ascorbate and aldarate metabolism. In conclusion, this study showed that HLO can promote S. aureus infected wound healing, and the data provide a theoretical basis for the treatment of wounds after anal fistula surgery with HLO.

Causal association of plasma circulating metabolites with nephritis: a Mendelian randomization study

Background

Nephritis is a pivotal catalyst in chronic kidney disease (CKD) progression. Although epidemiological studies have explored the impact of plasma circulating metabolites and drugs on nephritis, few have harnessed genetic methodologies to establish causal relationships.

Methods

Through Mendelian randomization (MR) in two substantial cohorts, spanning large sample sizes, we evaluated over 100 plasma circulating metabolites and 263 drugs to discern their causal effects on nephritis risk. The primary analytical tool was the inverse variance weighted (IVW) analysis. Our bioinformatic scrutiny of GSE115857 (IgA nephropathy, 86 samples) and GSE72326 (lupus nephritis, 238 samples) unveiled anomalies in lipid metabolism and immunological characteristics in nephritis. Thorough sensitivity analyses (MR-Egger, MR-PRESSO, leave-one-out analysis) were undertaken to verify the instrumental variables' (IVs) assumptions.

Results

Unique lipoprotein-related molecules established causal links with diverse nephritis subtypes. Notably, docosahexaenoic acid (DHA) emerged as a protective factor for acute tubulointerstitial nephritis (ATIN) (OR1 = 0.84, [95% CI 0.78-0.90], p1 = 0.013; OR2 = 0.89, [95% CI 0.82-0.97], p2 = 0.007). Conversely, multivitamin supplementation minus minerals notably increased the risk of ATIN (OR = 31.25, [95% CI 9.23-105.85], p = 0.004). Reduced α-linolenic acid (ALA) levels due to lipid-lowering drugs were linked to both ATIN (OR = 4.88, [95% CI 3.52-6.77], p < 0.001) and tubulointerstitial nephritis (TIN) (OR = 7.52, [95% CI 2.78-20.30], p = 0.042). While the non-renal drug indivina showed promise for TIN treatment, the use of digoxin, hydroxocobalamin, and liothyronine elevated the risk of chronic tubulointerstitial nephritis (CTIN). Transcriptome analysis affirmed that anomalous lipid metabolism and immune infiltration are characteristic of IgA nephropathy and lupus nephritis. The robustness of these causal links was reinforced by sensitivity analyses and leave-one-out tests, indicating no signs of pleiotropy.

Conclusion

Dyslipidemia significantly contributes to nephritis development. Strategies aimed at reducing plasma low-density lipoprotein levels or ALA supplementation may enhance the efficacy of existing lipid-lowering drug regimens for nephritis treatment. Renal functional status should also be judiciously considered with regard to the use of nonrenal medications.

Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics

Current diagnostic methods for diabetic nephropathy (DN) lack precision, especially in early stages and monitoring progression. This study aims to find potential biomarkers for DN progression and evaluate their accuracy. Using serum samples from healthy controls (NC), diabetic patients (DM), early-medium stage DN (DN-EM), and late-stage DN (DN-L), researchers employed quantitative proteomics and Mfuzz clustering analysis revealed 15 proteins showing increased expression during DN progression, hinting at their biomarker potential. Combining Mfuzz clustering with weighted gene co-expression network analysis (WGCNA) highlighted five candidates (HMGB1, CD44, FBLN1, PTPRG, and ADAMTSL4). HMGB1 emerged as a promising biomarker, closely correlated with renal function changes. Experimental validation supported HMGB1's upregulation under high glucose conditions, reinforcing its potential as an early detection biomarker for DN. This research advances DN understanding and identifies five potential biomarkers, notably HMGB1, as a promising early monitoring target. These findings set the stage for future clinical diagnostic applications in DN.

The menopause-related gut microbiome: associations with metabolomics, inflammatory protein markers, and cardiometabolic health in women with HIV

OBJECTIVE

This study aimed to identify menopause-related gut microbial features, as well as their related metabolites and inflammatory protein markers, and link with cardiometabolic risk factors in women with and without HIV.

METHODS

In the Women's Interagency HIV Study, we performed shotgun metagenomic sequencing on 696 stool samples from 446 participants (67% women with HIV), and quantified plasma metabolomics and serum proteomics in a subset (~86%). We examined the associations of menopause (postmenopausal vs premenopausal) with gut microbial features in a cross-sectional repeated-measures design and further evaluated those features in relation to metabolites, proteins, and cardiometabolic risk factors.

RESULTS

Different overall gut microbial composition was observed by menopausal status in women with HIV only. We identified a range of gut microbial features that differed between postmenopausal and premenopausal women with HIV (but none in women without HIV), including abundance of 32 species and functional potentials involving 24 enzymatic reactions and lower β-glucuronidase bacterial gene ortholog. Specifically, highly abundant species Faecalibacterium prausnitzii , Bacteroides species CAG:98 , and Bifidobacterium adolescentis were depleted in postmenopausal versus premenopausal women with HIV. Menopause-depleted species (mainly Clostridia ) in women with HIV were positively associated with several glycerophospholipids, while negatively associated with imidazolepropionic acid and fibroblast growth factor 21. Mediation analysis suggested that menopause may decrease plasma phosphatidylcholine plasmalogen C36:1 and C36:2 levels via reducing abundance of species F. prausnitzii and Acetanaerobacterium elongatum in women with HIV. Furthermore, waist-to-hip ratio was associated with menopause-related microbes, metabolites, and fibroblast growth factor 21 in women with HIV.

CONCLUSIONS

Menopause was associated with a differential gut microbiome in women with HIV, related to metabolite and protein profiles that potentially contribute to elevated cardiometabolic risk.

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

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.

Kidney fibrosis: Emerging diagnostic and therapeutic strategies

An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.

Metabolomic profiles and pathogenesis of nephrolithiasis

PURPOSE OF REVIEW

Kidney stone disease is caused by supersaturation of urine with certain metabolites and minerals. The urine composition of stone formers has been measured to prevent stone recurrence, specifically calcium, uric acid, oxalate, ammonia, citrate. However, these minerals and metabolites have proven to be unreliable in predicting stone recurrence. Metabolomics using high throughput technologies in well defined patient cohorts can identify metabolites that may provide insight into the pathogenesis of stones as well as offer possibilities in therapeutics.

RECENT FINDINGS

Techniques including 1H-NMR, and liquid chromatography paired with tandem mass spectroscopy have identified multiple possible metabolites involved in stone formation. Compared to formers of calcium oxalate stones, healthy controls had higher levels of hippuric acid as well as metabolites involved in caffeine metabolism. Both the gut and urine microbiome may contribute to the altered metabolome of stone formers.

SUMMARY

Although metabolomics has offered several potential metabolites that may be protective against or promote stone formation, the mechanisms behind these metabolomic profiles and their clinical significance requires further investigation.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Glomerular proteomic profiling reveals early differences between preexisting and de novo type 2 diabetes in human renal allografts

BACKGROUND

Diabetes mellitus (DM), either preexisting or developing after transplantation, remains a crucial clinical problem in kidney transplantation. To obtain insights into the molecular mechanisms underlying PTDM development and early glomerular damage before the development of histologically visible diabetic kidney disease, we comparatively analysed the proteome of histologically normal glomeruli from patients with PTDM and normoglycaemic (NG) transplant recipients. Moreover, to assess specificities inherent in PTDM, we also comparatively evaluated glomerular proteomes from transplant recipients with preexisting type 2 DM (T2DM).

METHODS

Protocol biopsies were obtained from adult NG, PTDM and T2DM patients one year after kidney transplantation. Biopsies were formalin-fixed and embedded in paraffin, and glomerular cross-sections were microdissected. A total of 4 NG, 7 PTDM and 6 T2DM kidney biopsies were used for the analysis. The proteome was determined by liquid chromatography-tandem mass spectrometry. Relative differences in protein abundance and significantly dysregulated pathways were analysed.

RESULTS

Proteins involved in cell adhesion, immune response, leukocyte transendothelial filtration, and cell localization and organization were less abundant in glomeruli from PTDM patients than in those from NG patients, and proteins associated with supramolecular fibre organization and protein-containing complex binding were more abundant in PTDM patients. Overall, proteins related to adherens and tight junctions and those related to the immune system, including leukocyte transendothelial migration, were more abundant in NG patients than in transplanted patients with DM, irrespective of the timing of its development. However, proteins included in cell‒cell junctions and adhesion, insulin resistance, and vesicle-mediated transport were all less abundant in PTDM patients than in T2DM patients.

CONCLUSIONS

The glomerular proteome profile differentiates PTDM from NG and T2DM, suggesting specific pathogenetic mechanisms. Further studies are warranted to validate these results, potentially leading to an improved understanding of PTDM kidney transplant pathophysiology and to the identification of novel biomarkers.

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD

Purpose

Chronic kidney disease (CKD) progression is complex. There are not standardized methods for predicting the prognosis of CKD. Nicotinamide N-methyltransferase (NNMT) has been shown to be associated with renal fibrosis. This study aimed to validate NNMT as a prognostic biomarker of progressive CKD.

Patients and Methods

We explored the relationship between NNMT expression and CKD-related outcome variables using the NephroseqV5 and GEO databases. Additionally, a validation set of 37 CKD patients was enrolled to measure the correlation between NNMT expression levels and CKD outcomes. Furthermore, single-cell RNA sequencing data and the Human Protein Atlas were reanalyzed to investigate the expression specificity of NNMT in the kidney. Finally, to detect the status of NNMT expression with tubular fibrosis in vivo, we constructed a unilateral ureteral obstruction (UUO) mouse treated with an NNMT inhibitor.

Results

Analyzing the datasets showed that NNMT was expressed mainly in proximal tubule compartments. And patients with high NNMT expression levels had a significantly lower overall survival rate compared to those with low NNMT expression levels (P = 0.013). NNMT was independent of prognosis factors in the multivariate Cox regression model, and the AUCs for CKD progression at 1, 3, and 5 years were 0.849, 0.775, and 0.877, respectively. Pathway enrichment analysis indicated that NNMT regulates the biological processes of tubulointerstitial fibrosis (TIF). In the validation group, NNMT levels were significantly higher in the CKD group combined with interstitial fibrosis. In vivo, NNMT was a high expression in the UUO group, peaking at postoperative day 21. Treatment with an NNMT inhibitor improved renal tubular interstitial fibrosis, and expression levels of FN, α-SMA, VIM, and TGF-β1 were decreased compared with UUO (P < 0.05).

Conclusion

NNMT was expressed mainly in tubular renal compartments, and associated with CKD prognosis. It holds potential as a diagnostic biomarker for tubular fibrosis in CKD.

Integrated proteomic and metabolomic profiling of urine of renal anemia patients uncovers the molecular mechanisms of roxadustat

Roxadustat (FG-4592) is a hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) prescribed to patients with low hemoglobin associated with chronic kidney disease. Due to the various HIF-mediated adaptive responses, FG-4592 has attracted significant interest for therapeutic use against various diseases. However, the clinical application of Roxadustat remains limited due to a lack of understanding of its underlying mechanisms. Herein, we performed label-free quantitative liquid chromatography with tandem mass spectrometry (LC-MS-MS) proteomics and un-targeted metabolomics to study the protein and metabolite alterations in the urine of renal anemia patients before and after Roxadustat therapy. The results were validated by parallel reaction monitoring (PRM). A total of 46 proteins (including 15 upregulated and 31 downregulated proteins) and 207 metabolites were significantly altered after Roxadustat treatment in urine samples obtained from renal anemia patients. Then, the altered proteins were further validated by PRM. Finally, proteomics combined with metabolomics analysis revealed that the Ras signalling pathway, cysteine and methionine metabolism, arginine and proline metabolism, and cholesterol metabolism were the main pathways altered by Roxadustat treatment. The multi-omics analysis revealed that Roxadustat could alter the protein expression and reverse the potential metabolic changes to exert hypotensive, lipid metabolic regulation, and renoprotective effects in clinical practice.

Urinary Metabolomic Profile of Youth at Risk of Chronic Kidney Disease in Nicaragua

Key Points

Urinary concentrations of glycine, a molecule associated with thermoregulation, were elevated among youth from a high-risk region for chronic kidney disease of non-traditional etiology (CKDnt). Urinary concentrations of pyruvate, citric acid, and inosine were lower among youth at higher risk of CKDnt, suggesting renal stress. Metabolomic analyses may shed light on early disease processes or profiles or risk in the context of CKDnt.

Background

CKD of a nontraditional etiology (CKDnt) is responsible for high mortality in Central America, although its causes remain unclear. Evidence of kidney dysfunction has been observed among youth, suggesting that early kidney damage contributing to CKDnt may initiate in childhood.

Methods

Urine specimens of young Nicaraguan participants 12–23 years without CKDnt (n =136) were analyzed by proton nuclear magnetic resonance spectroscopy for 50 metabolites associated with kidney dysfunction. Urinary metabolite levels were compared by, regional CKDnt prevalence, sex, age, and family history of CKDnt using supervised statistical methods and pathway analysis in MetaboAnalyst. Magnitude of associations and changes over time were assessed through multivariable linear regression.

Results

In adjusted analyses, glycine concentrations were higher among youth from high-risk regions (β =0.82, [95% confidence interval, 0.16 to 1.85]; P = 0.01). Pyruvate concentrations were lower among youth with low eGFR (β = −0.36 [95% confidence interval, −0.57 to −0.04]; P = 0.03), and concentrations of other citric acid cycle metabolites differed by key risk factors. Over four years, participants with low eGFR experienced greater declines in 1-methylnicotinamide and 2-oxoglutarate and greater increases in citrate and guanidinoacetate concentrations.

Conclusion

Urinary concentration of glycine, a molecule associated with thermoregulation and kidney function preservation, was higher among youth in high-risk CKDnt regions, suggestive of greater heat exposure or renal stress. Lower pyruvate concentrations were associated with low eGFR, and citric acid cycle metabolites, such as pyruvate, likely relate to mitochondrial respiration rates in the kidneys. Participants with low eGFR experienced longitudinal declines in concentrations of 1-methylnicotinamide, an anti-inflammatory metabolite associated with anti-fibrosis in tubule cells. These findings merit further consideration in research on the origins of CKDnt.

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.

Light and heavy chain deposition disease with focal amyloid deposition diagnosed with mass spectrometry: a case report

BACKGROUND

Light and heavy chain deposition disease (LHCDD) is a rare condition characterised by the deposition of immunoglobulin components in the kidneys. Similarly, Amyloidosis is also caused by the deposition of light chain and/or heavy chain components of immunoglobulins which are folded into amyloid fibrils characterised by Congophilic deposits that exhibit apple-green birefringence under polarised light. Only a handful of reports describing LHCDD with amyloid fibril deposition have been previously published, however, none have characterized the composition of the deposited immunoglobulin components via mass spectrometry.

CASE PRESENTATION

We report a case of a 79-year-old Japanese woman with nephrotic syndrome. Bone marrow aspiration revealed a slight proliferation of plasma cells (under 10%). Immunofluorescence assessment of renal biopsy showed amyloid-like deposits in the glomerulus that were positive for IgA and kappa. Further, the Congo red staining of the deposits was faintly positive, and only a slight birefringence was detected. Electron microscopy confirmed fine fibrillar structures and non-amyloid deposits. Finally, mass spectrometry revealed that the deposits were composed of abundant amounts of light chain with small amounts of heavy chain. Therefore, the patient was diagnosed with LHCDD and focal amyloid deposition. Chemotherapy was subsequently initiated, which resulted in haematological and renal response. Under polarised light, faint birefringence with Congo red staining and periodic acid-methenamine silver positivity indicated that the deposits were mostly non-amyloid fibrils with a small component of amyloid fibrils. Generally, the diagnosis of heavy- and light-chain amyloidosis is defined by greater heavy chain deposition compared to the light chain. However, in our case, contrary to the definition, the light-chain deposition was far greater than that of the heavy-chain.

CONCLUSIONS

This is the first case of LHCDD with focal amyloid deposition diagnosed by analysing the glomerular deposits by mass spectrometry.

Proteomics analyses of acute kidney injury biomarkers in a rat exertional heat stroke model

The frequency of exertional heat stroke (EHS) increases with the gradual elevation of global temperatures during summer. Acute kidney injury (AKI) is a common complication of EHS, and its occurrence often indicates the worsening of a patient's condition or a poor prognosis. In this study, a rat model of AKI caused by EHS was established, and the reliability of the model was evaluated by HE staining and biochemical assays. The expression of kidney tissue proteins in the EHS rats was analyzed using label-free liquid chromatography-tandem mass spectrometry. A total of 3,129 differentially expressed proteins (DEPs) were obtained, and 10 key proteins were finally identified, which included three upregulated proteins (Ahsg, Bpgm, and Litaf) and seven downregulated proteins (medium-chain acyl-CoA synthetase 2 (Acsm2), Hadha, Keg1, Sh3glb1, Eif3d, Ambp, and Ddah2). The qPCR technique was used to validate these 10 potential biomarkers in rat kidney and urine. In addition, Acsm2 and Ahsg were double-validated by Western blotting. Overall, this study identified 10 reliable biomarkers that may provide potential targets for the treatment of AKI caused by EHS.

Rationale and design of a prospective, clinical study of kidney biopsies in people with type 2 diabetes and severely increased albuminuria (the PRIMETIME 2 study)

INTRODUCTION

Diabetic kidney disease is a severe complication of diabetes. The diagnosis is based on clinical characteristics such as persistently elevated albuminuria, hypertension and decline in kidney function, although this definition is not specific to kidney disease caused by diabetes. The only way to establish an accurate diagnosis-diabetic nephropathy-is by performing a kidney biopsy. The histological presentation of diabetic nephropathy can be associated with a heterogeneous range of histological features with many pathophysiological factors involved demonstrating the complexity of the condition. Current treatment strategies aim to slow disease progression and are not specific to the underlying pathological processes.This study will investigate the prevalence of diabetic nephropathy in individuals with type 2 diabetes (T2D) and severely elevated albuminuria. The deep molecular characterisation of the kidney biopsy and biological specimens may pave the way for improved diagnostic accuracy and a better understanding of the pathological processes involved and may also reveal new targets for individualised treatment.

METHODS AND ANALYSIS

In the PRecIsion MEdicine based on kidney TIssue Molecular interrogation in diabetic nEphropathy 2 study, research kidney biopsies will be performed in 300 participants with T2D, urine albumin/creatinine ratio ≥700 mg/g and estimated glomerular filtration ratio >30 mL/min/1.73 m2. Cutting-edge molecular technologies will be applied to the kidney, blood, urine, faeces and saliva samples for comprehensive multi-omics profiling. The associated disease course and clinical outcomes will be assessed by annual follow-up for 20 years.

ETHICS AND DISSEMINATION

The Danish Regional Committee on Health Research Ethics and the Knowledge Center on Data Protection (in the Capital Region of Denmark) have granted approval for the study. The results will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04916132.

Nutritional Epidemiology and Dietary Assessment for Patients With Kidney Disease: A Primer

Nutritional epidemiology seeks to understand nutritional determinants of disease in human populations using experimental and observational study designs. Though randomized controlled trials provide the strongest evidence of causality, the expense and difficulty of sustaining adherence to dietary interventions are substantial barriers to investigating dietary determinants of kidney disease. Therefore, nutritional epidemiology commonly employs observational study designs, particularly prospective cohort studies, to investigate long-term associations between dietary exposures and kidney disease. Due to the covarying nature and synergistic effects of dietary components, holistic characterizations of dietary exposures that simultaneously consider patterns of foods and nutrients regularly consumed are generally more relevant to disease etiology than single nutrients or foods. Dietary intakes have traditionally been self-reported and are subject to bias. Statistical methods including energy adjustment and regression calibration can reduce random and systematic measurement errors associated with self-reported diet. Novel approaches that assess diet more objectively are gaining popularity but have not yet fully replaced self-report and require refinement and validation in populations with chronic kidney disease. More accurate and frequent diet assessment in existing and future studies will yield evidence to better personalize dietary recommendations for the prevention and treatment of kidney disease.

Alterations in the Circulating Proteome Associated with Albuminuria

SIGNIFICANCE STATEMENT

We describe circulating proteins associated with albuminuria in a population of African American Study of Kidney Disease and Hypertension with CKD (AASK) using the largest proteomic platform to date: nearly 7000 circulating proteins, representing approximately 2000 new targets. Findings were replicated in a subset of a general population cohort with kidney disease (ARIC) and a population with CKD Chronic Renal Insufficiency Cohort (CRIC). In cross-sectional analysis, 104 proteins were significantly associated with albuminuria in the Black group, of which 67 of 77 available proteins were replicated in ARIC and 68 of 71 available proteins in CRIC. LMAN2, TNFSFR1B, and members of the ephrin superfamily had the strongest associations. Pathway analysis also demonstrated enrichment of ephrin family proteins.

BACKGROUND

Proteomic techniques have facilitated understanding of pathways that mediate decline in GFR. Albuminuria is a key component of CKD diagnosis, staging, and prognosis but has been less studied than GFR. We sought to investigate circulating proteins associated with higher albuminuria.

METHODS

We evaluated the cross-sectional associations of the blood proteome with albuminuria and longitudinally with doubling of albuminuria in the African American Study of Kidney Disease and Hypertension (AASK; 38% female; mean GFR 46; median urine protein-to-creatinine ratio 81 mg/g; n =703) and replicated in two external cohorts: a subset of the Atherosclerosis Risk in Communities (ARIC) study with CKD and the Chronic Renal Insufficiency Cohort (CRIC).

RESULTS

In cross-sectional analysis, 104 proteins were significantly associated with albuminuria in AASK, of which 67 of 77 available proteins were replicated in ARIC and 68 of 71 available proteins in CRIC. Proteins with the strongest associations included LMAN2, TNFSFR1B, and members of the ephrin superfamily. Pathway analysis also demonstrated enrichment of ephrin family proteins. Five proteins were significantly associated with worsening albuminuria in AASK, including LMAN2 and EFNA4, which were replicated in ARIC and CRIC.

CONCLUSIONS

Among individuals with CKD, large-scale proteomic analysis identified known and novel proteins associated with albuminuria and suggested a role for ephrin signaling in albuminuria progression.

Associations between prefrontal PI (16:0/20:4) lipid, TNC mRNA, and APOA1 protein in schizophrenia: A trans-omics analysis in post-mortem brain

Background

Though various mechanisms have been proposed for the pathophysiology of schizophrenia, the full extent of these mechanisms remains unclear, and little is known about the relationships among them. We carried out trans-omics analyses by comparing the results of the previously reported lipidomics, transcriptomics, and proteomics analyses; all of these studies used common post-mortem brain samples.

Methods

We collected the data from three aforementioned omics studies on 6 common post-mortem samples (3 schizophrenia patients and 3 controls), and analyzed them as a whole group sample. Three correlation analyses were performed for each of the two of three omics studies in these samples. In order to discuss the strength of the correlations in a limited sample size, the p-values of each correlation coefficient were confirmed using the Student's t-test. In addition, partial correlation analysis was also performed for some correlations, to verify the strength of the impact of each factor on the correlations.

Results

The following three factors were strongly correlated with each other: the lipid level of phosphatidylinositol (PI) (16:0/20:4), the amount of TNC mRNA, and the quantitative signal intensity of APOA1 protein. PI (16:0/20:4) and TNC showed a positive correlation, while PI (16:0/20:4) and APOA1, and TNC and APOA1 showed negative correlations. All of these correlations reached at p < 0.01. PI (16:0/20:4) and TNC were decreased in the prefrontal cortex of schizophrenia samples, while APOA1 was increased. Partial correlation analyses among them suggested that PI (16:0/20:4) and TNC have no direct correlation, but their relationships are mediated by APOA1.

Conclusion

The current results suggest that these three factors may provide new clues to elucidate the relationships among the candidate mechanisms of schizophrenia, and support the potential of trans-omics analyses as a new analytical method.

Urinary Proteins of Female Domestic Dog (Canis familiaris) during Ovarian Cycle

The presence and identity of non-volatile chemical signals remain elusive in canines. In this study, we aim to evaluate the urinary proteins of female domestic dogs in the estrus and anestrus phases to evidence the presence of non-volatile chemical signals and to elucidate their identities. We collected urine samples from eight female dogs in the estrus and anestrus phases. A total of 240 proteins were identified in the urine samples using liquid chromatography-mass spectrometry (LC-MS analysis). The comparison of the proteins revealed a significant difference between the estrus and anestrus urine. We identified proteins belonging to the lipocalin family of canines (beta-lactoglobulin-1 and beta-lactoglobulin-2, P33685 and P33686, respectively), one of whose function was the transport of pheromones and which was present only in the estrus urine samples. Moreover, proteins such as Clusterin (CLU), Liver-expressed antimicrobial peptide 2 (LEAP2), and Proenkephalin (PENK) were more abundant in the estrus urine when compared to the anestrus urine. LEAP2 was recently described as a ghrelin receptor antagonist and implicated in regulating food intake and body weight in humans and mice. Proenkephalin, a polypeptide hormone cleaved into opioid peptides, was also recognized as a candidate to determine kidney function. As of yet, none of these have played a role in chemical communication. Clusterin, an extracellular chaperone protecting from protein aggregation implicated in stress-induced cell apoptosis, is a plausible candidate in chemical communication, which is a claim that needs to be ascertained further. Data are available via ProteomeXchange with the identifier PXD040418.

Diabetic vascular diseases: molecular mechanisms and therapeutic strategies

Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.

Biomarker identification and pathway analysis of Astragalus membranaceus and Curcuma zedoaria couplet medicines on adenine-induced chronic kidney disease in rats based on metabolomics

Background: Chronic kidney disease (CKD) is usually insidious, and most affected individuals are asymptomatic until the disease becomes advanced. The effective treatment of CKD would rely on the incorporation of multidisciplinary approaches. Astragalus membranaceus (AM) and Curcuma zedoaria (CZ) have been widely used in the treatment of CKD. However, the mechanism of AM and CZ in the treatment of CKD is still unclear.

Methods: This study was designed to evaluate the effects of AM and CZ on adenine-induced rats and to investigate the underlying mechanism by using metabolomic analysis. Addition of 0.75% adenine to the diet of rats for 3 weeks induced the animal model of CKD. The rats in the treatment group were treated with AM and CZ (2.1 g/kg/day) for 4 weeks. Blood and kidney samples were collected for biochemical and histological examination. Ultra-high-performance liquid chromatography/Q Exactive HFX mass spectrometer (UHPLC-QE-MS) was applied to analyze metabolic profiling variations in the kidney.

Results: The results showed that AM and CZ could significantly reduce serum creatinine (Scr) and blood urea nitrogen (BUN) levels in CKD rats and alleviate renal pathological injury. By comparing the endogenous components of the normal group and the model group in positive ion mode and negative ion mode, a total of 365 and 155 different metabolites were screened, respectively. A total of 117 and 73 metabolites with significantly different expressions were identified between model group and AM and CZ group in positive ion mode and negative ion mode, respectively. The pivotal pathways affected by AM and CZ included nicotinate and nicotinamide metabolism, and glycine, serine and threonine metabolism. Furthermore, significant changes in metabolites in CKD rats after AM and CZ therapies were observed, including L-Threonine, D-pantothenic acid, and nicotinamide. Moreover, we found that AM and CZ significantly reduced renal fibrosis and inflammation in CKD rats, which may be related to the regulation of SIRT1/JNK signaling pathway.

Conclusion: In conclusion, AM and CZ significantly reduced renal fibrosis and inflammation in CKD rats, which may be related to the regulation of SIRT1/JNK signaling pathway. Furthermore, L-Threonine, D-pantothenic acid, and nicotinamide may be potential biomarkers for the progression and treatment of CKD.

Comparison of SPEED, S-Trap, and In-Solution-Based Sample Preparation Methods for Mass Spectrometry in Kidney Tissue and Plasma

Mass spectrometry is a powerful technique for investigating renal pathologies and identifying biomarkers, and efficient protein extraction from kidney tissue is essential for bottom-up proteomic analyses. Detergent-based strategies aid cell lysis and protein solubilization but are poorly compatible with downstream protein digestion and liquid chromatography-coupled mass spectrometry, requiring additional purification and buffer-exchange steps. This study compares two well-established detergent-based methods for protein extraction (in-solution sodium deoxycholate (SDC); suspension trapping (S-Trap)) with the recently developed sample preparation by easy extraction and digestion (SPEED) method, which uses strong acid for denaturation. We compared the quantitative performance of each method using label-free mass spectrometry in both sheep kidney cortical tissue and plasma. In kidney tissue, SPEED quantified the most unique proteins (SPEED 1250; S-Trap 1202; SDC 1197). In plasma, S-Trap produced the most unique protein quantifications (S-Trap 150; SDC 148; SPEED 137). Protein quantifications were reproducible across biological replicates in both tissue (R2 = 0.85–0.90) and plasma (SPEED R2 = 0.84; SDC R2 = 0.76, S-Trap R2 = 0.65). Our data suggest SPEED as the optimal method for proteomic preparation in kidney tissue and S-Trap or SPEED as the optimal method for plasma, depending on whether a higher number of protein quantifications or greater reproducibility is desired.

Proteomic Approaches and Potential Applications in Autosomal Dominant Polycystic Kidney Disease and Fabry Disease

Although rare, hereditary diseases, such as autosomal dominant polycystic kidney disease (ADPKD) and Fabry disease (FD) may significantly progress towards severe nephropathy. It is crucial to characterize it accurately, predict the course of the illness and estimate treatment effectiveness. A huge effort has been undertaken to find reliable biomarkers that might be useful for an early prevention of the disease progression and/or any invasive diagnostic procedures. The study of proteomics, or the small peptide composition of a sample, is a field of study under continuous development. Over the past years, several strategies have been created to study and define the proteome of samples from widely varying origins. However, urinary proteomics has become essential for discovering novel biomarkers in kidney disease. Here, the extracellular vesicles in human urine that contain cell-specific marker proteins from every segment of the nephron, offer a source of potentially valuable urinary biomarkers, and may play an essential role in kidney development and kidney disease. This review summarizes the relevant literature investigating the proteomic approaches and potential applications in the regular studies of ADPKD and FD.

Metabolomic profiling of overnight peritoneal dialysis effluents predicts the peritoneal equilibration test type

This study primarily aimed to evaluate whether peritoneal equilibration test (PET) results can be predicted through the metabolomic analysis of overnight peritoneal dialysis (PD) effluents. From a total of 125 patients, overnight PD effluents on the day of the first PET after PD initiation were analyzed. A modified 4.25% dextrose PET was performed, and the PET type was categorized according to the dialysate-to-plasma creatinine ratio at the 4-h dwell time during the PET as follows: high, high average, low average, or low transporter. Nuclear magnetic resonance (NMR)-based metabolomics was used to analyze the effluents and identify the metabolites. The predictive performances derived from the orthogonal projection to latent structure discriminant analysis (OPLS-DA) modeling of the NMR spectrum were estimated by calculating the area under the curve (AUC) using receiver operating characteristic curve analysis. The OPLS-DA score plot indicated significant metabolite differences between high and low PET types. The relative concentrations of alanine and creatinine were greater in the high transporter type than in the low transporter type. The relative concentrations of glucose and lactate were greater in the low transporter type than in the high transporter type. The AUC of a composite of four metabolites was 0.975 in distinguish between high and low PET types. Measured PET results correlated well with the total NMR metabolic profile of overnight PD effluents.

New insight of metabolomics in ocular diseases in the context of 3P medicine

Metabolomics refers to the high-through untargeted or targeted screening of metabolites in biofluids, cells, and tissues. Metabolome reflects the functional states of cells and organs of an individual, influenced by genes, RNA, proteins, and environment. Metabolomic analyses help to understand the interaction between metabolism and phenotype and reveal biomarkers for diseases. Advanced ocular diseases can lead to vision loss and blindness, reducing patients' quality of life and aggravating socio-economic burden. Contextually, the transition from reactive medicine to the predictive, preventive, and personalized (PPPM / 3P) medicine is needed. Clinicians and researchers dedicate a lot of efforts to explore effective ways for disease prevention, biomarkers for disease prediction, and personalized treatments, by taking advantages of metabolomics. In this way, metabolomics has great clinical utility in the primary and secondary care. In this review, we summarized much progress achieved by applying metabolomics to ocular diseases and pointed out potential biomarkers and metabolic pathways involved to promote 3P medicine approach in healthcare.

Testican-2 Is Associated with Reduced Risk of Incident ESKD

BACKGROUND

Testican-2 was recently identified as a podocyte-derived protein that is released into circulation by the kidneys and is positively correlated with eGFR and eGFR slope. However, whether higher testican-2 levels are associated with lower risk of ESKD is unknown.

METHODS

Aptamer-based proteomics assessed blood testican-2 levels among participants in the African American Study of Kidney Disease and Hypertension (AASK, n =703), the Chronic Renal Insufficiency Cohort (CRIC) study ( n =3196), and the Atherosclerosis Risk in Communities (ARIC) study ( n =4378). We compared baseline characteristics by testican-2 tertile and used Cox proportional hazards models to study the association of testican-2 with incident ESKD.

RESULTS

Higher testican-2 levels were associated with higher measured GFR (mGFR) in AASK, higher eGFR in the CRIC and ARIC studies, and lower albuminuria in all cohorts. Baseline testican-2 levels were significantly associated with incident ESKD in Cox proportional hazards models adjusted for age, sex, and race (model 1) and model 1+mGFR or eGFR+comorbidities (model 2). In model 3 (model 2+proteinuria), the associations between testican-2 (per SD increase) and incident ESKD were AASK (hazard ratio [HR]=0.84 [0.72 to 0.98], P =0.023), CRIC (HR=0.95 [0.89 to 1.02], P =0.14), ARIC (HR=0.54 [0.36 to 0.83], P =0.0044), and meta-analysis (HR=0.92 [0.86 to 0.98], P =0.0073).

CONCLUSIONS

Across three cohorts spanning >8000 individuals, testican-2 is associated with kidney health and prognosis, with higher levels associated with reduced risk of ESKD.

Integration of artificial intelligence and multi-omics in kidney diseases

Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

Altered serum metabolome associated with vascular calcification developed from CKD and the critical pathways

Introduction

Vascular calcification (VC) is more likely to be detected in the chronic kidney disease (CKD) population. The mechanism of VC development from CKD is different from that for simple VC and has always been a major research area. The aim of this study was to detect alterations in the metabolome during development of VC in CKD and to identify the critical metabolic pathways and metabolites involved in its pathogenesis.

Methods

Rats in the model group were given an adenine gavage combined with a high-phosphorus diet to imitate VC in CKD. The aorta calcium content was measured and used to divide the model group into a VC group and non-vascular calcification group (non-VC group). The control group was fed a normal rat diet and given a saline gavage. Ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) was used to determine the altered serum metabolome in the control, VC, and non-VC groups. The identified metabolites were mapped into the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/) for pathway and network analyses.

Result

There were 14 metabolites that changed significantly in the VC group, with three metabolic pathways playing critical roles in the pathogenesis of VC in CKD: steroid hormone biosynthesis; valine, leucine and isoleucine biosynthesis; and pantothenate and CoA biosynthesis.

Conclusion

Our results indicated changes in the expression of steroid sulfatase and estrogen sulfotransferase, and down-regulation of the in situ synthesis of estrogens in the VC group. In conclusion, the serum metabolome alters significantly during the pathogenesis of VC in CKD. The key pathways, metabolites, and enzymes we identified are worth further study and may become a promising therapeutic target for the treatment of VC in CKD.

Environmental Exposures and Kidney Disease

Accumulating evidence underscores the large role played by the environment in the health of communities and individuals. We review the currently known contribution of environmental exposures and pollutants on kidney disease and its associated morbidity. We review air pollutants, such as particulate matter; water pollutants, such as trace elements, per- and polyfluoroalkyl substances, and pesticides; and extreme weather events and natural disasters. We also discuss gaps in the evidence that presently relies heavily on observational studies and animal models, and propose using recently developed analytic methods to help bridge the gaps. With the expected increase in the intensity and frequency of many environmental exposures in the decades to come, an improved understanding of their potential effect on kidney disease is crucial to mitigate potential morbidity and mortality.

Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative "omics" approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis.

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing

Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

Silica nanoparticles aggravated the metabolic associated fatty liver disease through disturbed amino acid and lipid metabolisms-mediated oxidative stress

The metabolic associated fatty liver disease (MAFLD) is a public health challenge, leading to a global increase in chronic liver disease. The respiratory exposure of silica nanoparticles (SiNPs) has revealed to induce hepatotoxicity. However, its role in the pathogenesis and progression of MAFLD was severely under-studied. In this context, the hepatic impacts of SiNPs were investigated in vivo and in vitro through using ApoE^-/- mice and free fatty acid (FFA)-treated L02 hepatocytes. Histopathological examinations and biochemical analysis showed SiNPs exposure via intratracheal instillation aggravated hepatic steatosis, lipid vacuolation, inflammatory infiltration and even collagen deposition in ApoE^-/- mice, companied with increased hepatic ALT, AST and LDH levels. The enhanced fatty acid synthesis and inhibited fatty acid β-oxidation and lipid efflux may account for the increased hepatic TC/TG by SiNPs. Consistently, SiNPs induced lipid deposition and elevated TC in FFA-treated L02 cells. Further, the activation of hepatic oxidative stress was detected in vivo and in vitro, as evidenced by ROS accumulation, elevated MDA, declined GSH/GSSG and down-regulated Nrf2 signaling. Endoplasmic reticulum (ER) stress was also triggered in response to SiNPs-induced lipid accumulation, as reflecting by the remarkable ER expansion and increased BIP expression. More importantly, an UPLC-MS-based metabolomics analysis revealed that SiNPs disturbed the hepatic metabolic profile in ApoE^-/- mice, prominently on amino acids and lipid metabolisms. In particular, the identified differential metabolites were strongly correlated to the activation of oxidative stress and ensuing hepatic TC/TG accumulation and liver injuries, contributing to the progression of liver diseases. Taken together, our study showed SiNPs promoted hepatic steatosis and liver damage, resulting in the aggravation of MAFLD progression. More importantly, the disturbed amino acids and lipid metabolisms-mediated oxidative stress was a key contributor to this phenomenon from a metabolic perspective.

Bacteroides plebeius improves muscle wasting in chronic kidney disease by modulating the gut-renal muscle axis

Chronic kidney disease (CKD) affects approximately 10% of the global population. Muscle atrophy occurs in patients with almost all types of CKD, and the gut microbiome is closely related to protein consumption during chronic renal failure (CRF). This study investigated the effects of Bacteroides plebeius on protein energy consumption in rats with CKD, and our results suggest that Bacteroides plebeius may combat muscle atrophy through the Mystn/ActRIIB/SMAD2 pathway. A total of 5/6 Nx rats were used as a model of muscle wasting in CKD. The rats with muscle wasting were administered Bacteroides plebeius (2 × 10⁸  cfu/0.2 ml) for 8 weeks. The results showed that Bacteroides plebeius administration significantly inhibited muscle wasting in CKD. High-throughput 16 S rRNA pyrosequencing revealed that supplementation with Bacteroides plebeius rescued disturbances in the gut microbiota. Bacteroides plebeius could also enhance the barrier function of the intestinal mucosa. Bacteroides plebeius may modulate the gut microbiome and reduce protein consumption by increasing the abundance of probiotics and reducing damage to the intestinal mucosal barrier. Our findings suggest that Bacteroides plebeius may combat muscle atrophy through the Mystn/ActRIIB/SMAD2 pathway.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

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

BACKGROUND

Proteins and metabolites are essential for many biological functions and often linked through enzymatic or transport reactions. Individual molecules have been associated with all-cause mortality. Many of these are correlated and might jointly represent pathways or endophenotypes involved in diseases.

RESULTS

We present an integrated analysis of proteomics and metabolomics via a local dimensionality reduction clustering method. We identified 224 modules of correlated proteins and metabolites in the Atherosclerosis Risk in Communities (ARIC) study, a general population cohort of older adults (N = 4046, mean age 75.7, mean eGFR 65). Many of the modules displayed strong cross-sectional associations with demographic and clinical characteristics. In comprehensively adjusted analyses, including fasting plasma glucose, history of cardiovascular disease, systolic blood pressure and kidney function among others, 60 modules were associated with mortality. We transferred the network structure to the African American Study of Kidney Disease and Hypertension (AASK) (N = 694, mean age 54.5, mean mGFR 46) and identified mortality associated modules relevant in this disease specific cohort. The four mortality modules relevant in both the general population and CKD were all a combination of proteins and metabolites and were related to diabetes / insulin secretion, cardiovascular disease and kidney function. Key components of these modules included N-terminal (NT)-pro hormone BNP (NT-proBNP), Sushi, Von Willebrand Factor Type A, EGF And Pentraxin (SVEP1), and several kallikrein proteases.

CONCLUSION

Through integrated biomarkers of the proteome and metabolome we identified functions of (patho-) physiologic importance related to diabetes, cardiovascular disease and kidney function.