Novel biomarkers for pre‐diabetes identified by metabolomics

Molecular Systems Biology, Journal Year: 2012, Volume and Issue: 8(1)

Published: Jan. 1, 2012

Article25 September 2012Open Access Novel biomarkers for pre-diabetes identified by metabolomics Rui Wang-Sattler Corresponding Author Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Search more papers this author Zhonghao Yu Christian Herder German Diabetes Center, Institute Clinical Diabetology, Leibniz Center at Heinrich Heine University, Düsseldorf, Ana C Messias Structural Biology, Anna Floegel Department Human Nutrition Potsdam-Rehbruecke, Nuthetal, Ying He Shanghai Bioinformation Technology, Shanghai, China Key Lab Systems Bioinformatics Institutes Biological Sciences, Chinese Academy Katharina Heim Genetics, Monica Campillos and Christina Holzapfel Else Kroener-Fresenius-Center Nutritional Medicine, University Hospital 'Klinikum rechts der Isar', Technische Universität Munich, Barbara Thorand Epidemiology II, Harald Grallert Tao Xu Erik Bader Cornelia Huth Kirstin Mittelstrass Angela Döring I, Christa Meisinger Gieger Genetic Prehn Genome Analysis Experimental Werner Roemisch-Margl Maren Carstensen Lu Xie Hisami Yamanaka-Okumura Nutrition, Health Biosciences, Tokushima Graduate School, Tokushima, Japan Guihong Xing Benxi Clinic, Central Hospital, Benxi, Uta Ceglarek Laboratory Chemistry Diagnostics, Leipzig, Joachim Thiery Guido Giani Biometrics Heiko Lickert Regeneration Research, Lin Yixue Li Heiner Boeing Hans-Georg Joost Martin Hrabé de Angelis Chair Wolfgang Rathmann Karsten Suhre Faculty Ludwig-Maximilians-Universität, Planegg-Martinsried, Physiology Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q), Doha, Holger Prokisch Annette Peters Thomas Meitinger Metabolic Diseases, Michael Roden Klinikum Isar, H-Erich Wichmann Informatics, Biometry Tobias Pischon Group, Max Delbrueck Medicine (MDC), Berlin-Buch, Jerzy Adamski Illig Hannover Unified Biobank, Hannover, Information 1,‡, Yu1,‡, Herder2,‡, Messias3,‡, Floegel4, He5,6, Heim7, Campillos8, Holzapfel1,9, Thorand10, Grallert1, Xu1, Bader1, Huth10, Mittelstrass1, Döring11, Meisinger10, Gieger12, Prehn13, Roemisch-Margl8, Carstensen2, Xie5, Yamanaka-Okumura14, Xing15, Ceglarek16, Thiery16, Giani17, Lickert18, Lin19, Li5,6, Boeing4, Joost4, Angelis13,20, Rathmann17, Suhre8,21,22, Prokisch7, Peters10, Meitinger7,23, Roden2,24, Wichmann11,25, Pischon4,26, Adamski13,20 Illig1,27 1Research 2German 3Institute 4Department 5Shanghai 6Key 7Institute 8Institute 9Else 10Institute 11Institute 12Institute 13Genome 14Department 15Benxi 16Institute 17German 18Institute 19Institute 20Chair 21Faculty 22Department 23Department 24Klinikum 25Institute 26Molecular 27Hannover ‡These authors contributed equally to work *Corresponding author. 85764 Munich-Neuherberg, Germany. Tel.:+49 89 3187 3978; Fax:+49 2428; E-mail: [email protected] Biology (2012)8:615https://doi.org/10.1038/msb.2012.43 PDFDownload PDF article text main figures. Peer ReviewDownload a summary the editorial decision process including letters, reviewer comments responses feedback. ToolsAdd favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Type 2 diabetes (T2D) can be prevented pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used approach identify candidate pre-diabetes. We quantified 140 metabolites 4297 fasting serum samples population-based Cooperative Region Augsburg (KORA) cohort. Our study revealed significant metabolic variation that are distinct from known risk indicators, such as glycosylated hemoglobin levels, insulin. three (glycine, lysophosphatidylcholine (LPC) (18:2) acetylcarnitine) had significantly altered levels IGT compared those normal tolerance, P-values ranging 2.4 × 10−4 2.1 10−13. Lower glycine LPC were found predictors not only but also T2D, independently confirmed European Prospective Investigation into Cancer (EPIC)-Potsdam Using metabolite–protein network analysis, seven T2D-related genes associated these IGT-specific multiple interactions four enzymes. The expression enzymes correlate changes metabolite concentrations linked diabetes. results may help developing novel strategies prevent T2D. Synopsis A targeted was relevance is further corroborated protein-metabolite interaction gene data. Three acetylcarnitine C2) controls. predict risks type (T2D). Seven (PPARG, TCF7L2, HNF1A, GCK, IGF1, IRS1 IDE) functionally metabolites. unique combination methodologies, prospective nested case–control, well cross-sectional studies, essential identification reported biomarkers. Introduction defined increased blood due pancreatic β-cell dysfunction insulin resistance without evidence specific causes, autoimmune destruction β-cells (Krebs et al, 2002; Stumvoll 2005; Muoio Newgard, 2008). state (i.e., (IFG) and/or (IGT)) slightly elevated precede T2D years (McGarry, Tabak 2012). development or delayed dietary physical activity (Tuomilehto 2001; Knowler 2002). However, no enable prevention been reported. Metabolomics studies allow involved disease mechanisms discovered monitoring level predisposed healthy ones (Shaham 2008; Newgard 2009; Zhao 2010; Pietilainen 2011; Rhee Wang Cheng 2012; Goek Altered serve diagnostic preventive action. Previous either based on small sample sizes Wopereis 2011) did consider influence common factors (Newgard 2009). Recently, case–control relative large (Rhee 2011), five branched-chain aromatic amino acids (Wang 2011). using various comprehensive large-scale approaches, measured concentration profiles (Yu 2012) (Holle 2005) baseline (survey 4 (S4)) follow-up (F4) (Rathmann Jourdan allowed us (i) reliably (ii) build networks understand diabetes-related pathways. Results Study participants Individuals physician-validated self-reporting 2010) excluded our avoid potential anti-diabetic medication non-fasting missing values (Figure 1A). Based both 2-h h post oral 75 g load), according WHO criteria (NGT), isolated IFG (i-IFG), newly diagnosed (dT2D) (WHO, 1999; Supplementary Table S1). sets include 91 dT2D patients 1206 non-T2D, 866 NGT, 102 i-IFG 238 IGT, KORA S4 1A; characteristics shown I). Of 1010 who participated surveys 1B, F4 survey S2), 876 them non-diabetic baseline. Out these, about 10% developed incident T2D) 1C). From 641 NGT baseline, 18% 118 IGT) 7 later 1D). S4→F4 II. Figure 1.Population description. screens cohort, (A), overlapped between (B) (C, D). Participant numbers shown. Normal (IGT), mellitus (dT2D). Non-T2D participants. Download figure PowerPoint 1. Characteristics laboratory parameters N Age (years) 63.5±5.5 64.1±5.2 65.2±5.2 65.9±5.4 Sex (female) (%) 52.2 30.4 44.9 41.8 BMI (kg/m2) 27.7±4.1 29.2±4 29.6±4.1 30.2±3.9 Physical (% >1 per week) 46.7 35.3 39.9 36.3 Alcohol intakea 20.2 20.5 25.2 24.2 Current smoker 14.8 10.8 10.9 23.1 Systolic BP (mm Hg) 131.7±18.9 138.9±17.9 140.7±19.8 146.8±21.5 HDL cholesterol (mg/dl) 60.5±16.4 55.7±15.9 55.7±15.1 50.0±15.8 LDL 154.5±39.8 152.1±37.7 155.2±38.6 146.1±44.6 Triglycerides 120.7±68.3 145.0±96.0 146.6±80.0 170.6±107.1 HbA1c 5.56±0.33 5.62±0.33 5.66±0.39 6.21±0.83 Fasting 95.6±7.1 114.2±3.7 104.5±9.7 133.2±31.7 Glucose 102.1±21.0 109.3±18.7 163.4±16.4 232.1±63.7 (μU/ml) 10.48±7.28 16.26±9.67 13.92±9.53 17.70±12.61 tolerance; i-IFG, glucose; dT2D, diabetes; BP, pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein. Percentages means±s.d. given each variable group (NGT, dT2D). ⩾20 g/day women; ⩾40 men. 2. (n=589) (n=876) Remained Developed 471 785 62.4±5.4 63.9±5.5 62.9±5.4 65.5±5.2 55.9 50.8 34.1 27.2±3.8 28.2±3.9 27.9±4 30.2±3.6 52.9 43.2 58.2 19.9 20.3 20.6 19.8 Smoker 14.6 9.3 12.0 14.3 129.6±18.2 134.2±18.7 132.4±18.6 137.8±19 61.3±16.8 58.9±16.2 60.0±16.5 51.9±12.4 153.9±38.4 156.9±42.7 154.5±39.5 157.7±41.6 118.1±63.9 129.5±79.0 125.0±70.0 151.2±74.2 5.54±0.33 5.59±0.34 5.6±0.3 5.8±0.4 94.7±6.9 96.6±7.1 97.7±8.8 106.1±10.1 98.2±20.5 109.9±16.8 109.3±28 145.9±32.3 9.91±6.48 11

Language: Английский

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The gut microbiota and the brain–gut–kidney axis in hypertension and chronic kidney disease

Nature Reviews Nephrology, Journal Year: 2018, Volume and Issue: 14(7), P. 442 - 456

Published: May 14, 2018

Language: Английский

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Metabolomics for clinical use and research in chronic kidney disease

Nature Reviews Nephrology, Journal Year: 2017, Volume and Issue: 13(5), P. 269 - 284

Published: March 6, 2017

Language: Английский

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A Combined Epidemiologic and Metabolomic Approach Improves CKD Prediction

Journal of the American Society of Nephrology, Journal Year: 2013, Volume and Issue: 24(8), P. 1330 - 1338

Published: May 17, 2013

Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry–based profiling on plasma from 1434 participants in Framingham Heart Study (FHS) who did not at baseline. During following 8 years, 123 individuals developed defined by an estimated GFR of <60 ml/min per 1.73 m2. Numerous metabolites were associated with incident including 16 achieved Bonferroni-adjusted significance threshold P≤0.00023. To explore how human kidney modulates these metabolites, we profiled arterial and renal venous nine individuals. Nine predicted FHS cohort decreased more than creatinine across circulation, suggesting they may reflect non–GFR-dependent functions, such as metabolism secretion. Urine isotope dilution studies identified citrulline choline markers kynurenic acid a marker In turn, analytes remained cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, proteinuria Addition multimarker panel clinical variables significantly increased c-statistic (0.77–0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 0.95; P<0.0001). Thus, addition data improve ability individual will develop identifying predictors risk are independent GFR.

Language: Английский

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Identification of serum metabolites associating with chronic kidney disease progression and anti-fibrotic effect of 5-methoxytryptophan

Nature Communications, Journal Year: 2019, Volume and Issue: 10(1)

Published: April 1, 2019

Abstract Early detection and accurate monitoring of chronic kidney disease (CKD) could improve care retard progression to end-stage renal disease. Here, using untargeted metabolomics in 2155 participants including patients with stage 1–5 CKD healthy controls, we identify five metabolites, 5-methoxytryptophan (5-MTP), whose levels strongly correlate clinical markers 5-MTP decrease CKD, mouse kidneys after unilateral ureteral obstruction (UUO). Treatment ameliorates interstitial fibrosis, inhibits IκB/NF-κB signaling, enhances Keap1/Nrf2 signaling mice UUO or ischemia/reperfusion injury, as well cultured human cells. Overexpression tryptophan hydroxylase-1 (TPH-1), an enzyme involved synthesis, reduces injury by attenuating inflammation whereas TPH-1 deficiency exacerbates fibrosis activating NF-κB inhibiting Nrf2 pathways. Together, our results suggest that may serve a target the treatment CKD.

Language: Английский

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Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study

Kidney International, Journal Year: 2014, Volume and Issue: 85(5), P. 1214 - 1224

Published: Jan. 15, 2014

Language: Английский

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Metabolomics in chronic kidney disease

Clinica Chimica Acta, Journal Year: 2013, Volume and Issue: 422, P. 59 - 69

Published: April 7, 2013

Language: Английский

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Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Nature Communications, Journal Year: 2015, Volume and Issue: 6(1)

Published: June 12, 2015

Metabolites are small molecules involved in cellular metabolism, which can be detected biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation blood serum levels 129 metabolites as measured by Biocrates platform. In a discovery sample 7,478 individuals European descent, find 4,068 genome- metabolome-wide significant (Z-test, P<1.09 × 10−9) associations between single-nucleotide polymorphisms (SNPs) metabolites, involving 59 independent SNPs 85 metabolites. Five fifty-nine new metabolite levels, were followed-up replication an (N=1,182). The novel located or near genes encoding transporter proteins enzymes (SLC22A16, ARG1, AGPS ACSL1) that have demonstrated biomedical pharmaceutical importance. further characterization genetic influences on metabolic phenotypes is important progress medical research. indicators physiological state body potential biomarkers disease. Here, Draisma et al. use study to identify associated with

Language: Английский

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A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population

Journal of the American Society of Nephrology, Journal Year: 2015, Volume and Issue: 27(4), P. 1175 - 1188

Published: Oct. 9, 2015

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function can be used to estimate filtration (e.g., established marker creatinine) or precede potentially contribute CKD development. Here, we applied a nontargeted metabolomics approach using gas liquid chromatography coupled mass spectrometry quantify 493 small human serum. The associations of these with GFR estimated on basis creatinine (eGFRcr) cystatin C levels were assessed ≤1735 participants KORA F4 study, followed replication 1164 individuals TwinsUK registry. After correction for multiple testing, 54 replicated metabolites significantly associated eGFRcr, six showed pairwise correlation (r≥0.50) measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, N-acetylcarnosine. Higher O-sulfo-L-tyrosine incident (eGFRcr <60 ml/min per 1.73 m(2)) study. In contrast creatinine, C-mannosyltryptophan pseudouridine little dependence sex. Furthermore, measured 200 AASK study was 0.78 both concentration, highly significant ESRD disappeared upon adjustment GFR. Thus, alternative complementary markers function. conclusion, our provides comprehensive list function-associated highlights potential novel that help improve estimation

Language: Английский

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Molecular pathways that drive diabetic kidney disease

Journal of Clinical Investigation, Journal Year: 2023, Volume and Issue: 133(4)

Published: Feb. 14, 2023

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

Language: Английский

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