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Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease.

ABSTRACT: Diabetic kidney disease is the leading cause of ESRD, but few biomarkers of diabetic kidney disease are available. This study used gas chromatography-mass spectrometry to quantify 94 urine metabolites in screening and validation cohorts of patients with diabetes mellitus (DM) and CKD(DM+CKD), in patients with DM without CKD (DM-CKD), and in healthy controls. Compared with levels in healthy controls, 13 metabolites were significantly reduced in the DM+CKD cohorts (P?0.001), and 12 of the 13 remained significant when compared with the DM-CKD cohort. Many of the differentially expressed metabolites were water-soluble organic anions. Notably, organic anion transporter-1 (OAT1) knockout mice expressed a similar pattern of reduced levels of urinary organic acids, and human kidney tissue from patients with diabetic nephropathy demonstrated lower gene expression of OAT1 and OAT3. Analysis of bioinformatics data indicated that 12 of the 13 differentially expressed metabolites are linked to mitochondrial metabolism and suggested global suppression of mitochondrial activity in diabetic kidney disease. Supporting this analysis, human diabetic kidney sections expressed less mitochondrial protein, urine exosomes from patients with diabetes and CKD had less mitochondrial DNA, and kidney tissues from patients with diabetic kidney disease had lower gene expression of PGC1? (a master regulator of mitochondrial biogenesis). We conclude that urine metabolomics is a reliable source for biomarkers of diabetic complications, and our data suggest that renal organic ion transport and mitochondrial function are dysregulated in diabetic kidney disease.

SUBMITTER: Sharma K

PROVIDER: S-EPMC3810086 | biostudies-other | 2013 Nov

REPOSITORIES: biostudies-other

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Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease.

Sharma Kumar K Karl Bethany B Mathew Anna V AV Gangoiti Jon A JA Wassel Christina L CL Saito Rintaro R Pu Minya M Sharma Shoba S You Young-Hyun YH Wang Lin L Diamond-Stanic Maggie M Lindenmeyer Maja T MT Forsblom Carol C Wu Wei W Ix Joachim H JH Ideker Trey T Kopp Jeffrey B JB Nigam Sanjay K SK Cohen Clemens D CD Groop Per-Henrik PH Barshop Bruce A BA Natarajan Loki L Nyhan William L WL Naviaux Robert K RK

Journal of the American Society of Nephrology : JASN 20131010 11

Diabetic kidney disease is the leading cause of ESRD, but few biomarkers of diabetic kidney disease are available. This study used gas chromatography-mass spectrometry to quantify 94 urine metabolites in screening and validation cohorts of patients with diabetes mellitus (DM) and CKD(DM+CKD), in patients with DM without CKD (DM-CKD), and in healthy controls. Compared with levels in healthy controls, 13 metabolites were significantly reduced in the DM+CKD cohorts (P≤0.001), and 12 of the 13 remai ...[more]

PMID: 23949796

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Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Project description:ObjectivesMitochondrial DNA (mtDNA) plays an important role in the pathogenesis of diabetes. Variants in mtDNA have been reported in diabetes, but studies on the whole mtDNA variants were limited. Our study aims to explore the association of whole mtDNA variants with diabetes and diabetic kidney disease (DKD).MethodsThe whole mitochondrial genome was screened by next-generation sequencing in cohort 1 consisting of 50 early-onset diabetes (EOD) patients with a maternally inherited diabetes (MID) family history. A total of 42 variants possibly associated with mitochondrial diseases were identified according to the filtering strategy. These variants were sequenced in cohort 2 consisting of 90 EOD patients with MID. The association between the clinical phenotype and these variants was analyzed. Then, these variants were genotyped in cohort 3 consisting of 1,571 type 2 diabetes mellitus patients and 496 subjects with normal glucose tolerance (NGT) to analyze the association between variants with diabetes and DKD.ResultsPatients with variants in the non-coding region had a higher percentage of obesity and levels of fasting insulin (62.1% vs. 24.6%, P = 0.001; 80.0% vs. 26.5% P < 0.001). The patients with the variants in rRNA had a higher prevalence of obesity (71.4% vs. 30.3%, P = 0.007), and the patients with the variants in mitochondrial complex I had a higher percentage of the upper tertile of FINS (64.3% vs. 34.3%, P = 0.049). Among 20 homogeneous variants successfully captured, two known variants (m.A3943G, m.A10005G) associated with other mitochondrial diseases were only in the diabetic group, but not in the NGT group, which perhaps indicated its possible association with diabetes. The prevalence of DKD was significantly higher in the group with the 20 variants than those without these variants (18.7% vs. 14.6%, P = 0.049) in the participants with diabetes of cohort 3.ConclusionMtDNA variants are associated with MID and DKD, and our findings advance our understanding of mtDNA in diabetes and DKD. It will have important implications for the individual therapy of mitochondrial diabetes.

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Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease.

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Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease.

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