OBJECT: The most severe complication of type 1 diabetes (T1DM) is diabetic nephropathy. It is associated with a high risk of cardiovascular complications and premature death and requires early detection to be efficiently treated. The clinical practice to diagnose diabetic nephropathy is also a non-optimal and tedious set up based on albumin excretion rate in multiple overnight or 24h urine samples. Conversely, in this study, these independent diagnostic data are used to provide a realistic testing case for applying (1)H NMR metabonomics of serum in a diagnostic fashion. MATERIALS AND METHODS: 182 T1DM and 21 non-diabetic (non-T1DM) individuals were studied. The (1)H NMR of serum at 500 MHz was targeted at two molecular windows: lipoprotein lipids and low-molecular-weight metabolites. RESULTS: T1DM and non-T1DM individuals were exclusively separated by (1)H NMR. For diabetic nephropathy diagnosis in the T1DM patients, (1)H NMR data (and clinical biochemistry data) gave a sensitivity of 87.1% (83.9%) and a specificity of 87.7% (95.9%). The predictive values of positive and negative tests were 89.0% (95.5%) and 83.6% (79.2%), respectively. CONCLUSIONS: (1)H NMR metabonomics clearly distinguishes metabolic characteristics of T1DM and appears approximately as good a means to diagnose diabetic nephropathy from serum as an advanced set of biochemical variables.
OBJECT: The most severe complication of type 1 diabetes (T1DM) is diabetic nephropathy. It is associated with a high risk of cardiovascular complications and premature death and requires early detection to be efficiently treated. The clinical practice to diagnose diabetic nephropathy is also a non-optimal and tedious set up based on albumin excretion rate in multiple overnight or 24h urine samples. Conversely, in this study, these independent diagnostic data are used to provide a realistic testing case for applying (1)H NMR metabonomics of serum in a diagnostic fashion. MATERIALS AND METHODS: 182 T1DM and 21 non-diabetic (non-T1DM) individuals were studied. The (1)H NMR of serum at 500 MHz was targeted at two molecular windows: lipoprotein lipids and low-molecular-weight metabolites. RESULTS: T1DM and non-T1DM individuals were exclusively separated by (1)H NMR. For diabetic nephropathy diagnosis in the T1DM patients, (1)H NMR data (and clinical biochemistry data) gave a sensitivity of 87.1% (83.9%) and a specificity of 87.7% (95.9%). The predictive values of positive and negative tests were 89.0% (95.5%) and 83.6% (79.2%), respectively. CONCLUSIONS: (1)H NMR metabonomics clearly distinguishes metabolic characteristics of T1DM and appears approximately as good a means to diagnose diabetic nephropathy from serum as an advanced set of biochemical variables.
Authors: Joanne T Brindle; Henrik Antti; Elaine Holmes; George Tranter; Jeremy K Nicholson; Hugh W L Bethell; Sarah Clarke; Peter M Schofield; Elaine McKilligin; David E Mosedale; David J Grainger Journal: Nat Med Date: 2002-11-25 Impact factor: 53.440
Authors: John C Lindon; Elaine Holmes; Mary E Bollard; Elizabeth G Stanley; Jeremy K Nicholson Journal: Biomarkers Date: 2004 Jan-Feb Impact factor: 2.658
Authors: Olivier Cloarec; Marc-Emmanuel Dumas; Andrew Craig; Richard H Barton; Johan Trygg; Jane Hudson; Christine Blancher; Dominique Gauguier; John C Lindon; Elaine Holmes; Jeremy Nicholson Journal: Anal Chem Date: 2005-03-01 Impact factor: 6.986
Authors: G N Chmurny; B D Hilton; D Halverson; G N McGregor; J Klose; H J Issaq; G M Muschik; W J Urba; M L Mellini; R Costello Journal: NMR Biomed Date: 1988-06 Impact factor: 4.044
Authors: M T Hyvönen; Y Hiltunen; W El-Deredy; T Ojala; J Vaara; P T Kovanen; M Ala-Korpela Journal: J Am Chem Soc Date: 2001-02-07 Impact factor: 15.419
Authors: Richard L Klein; M Brent McHenry; Kerry H Lok; Steven J Hunter; Ngoc-Anh Le; Alicia J Jenkins; Deyi Zheng; Andrea J Semler; W Virgil Brown; Timothy J Lyons; W Timothy Garvey Journal: Metabolism Date: 2004-10 Impact factor: 8.694
Authors: S S Soedamah-Muthu; H M Colhoun; M J Thomason; D J Betteridge; P N Durrington; G A Hitman; J H Fuller; K Julier; M I Mackness; H A W Neil Journal: Atherosclerosis Date: 2003-04 Impact factor: 5.162
Authors: Ahmed M Mehdi; Emma E Hamilton-Williams; Alexandre Cristino; Anette Ziegler; Ezio Bonifacio; Kim-Anh Le Cao; Mark Harris; Ranjeny Thomas Journal: JCI Insight Date: 2018-03-08
Authors: Kelli M Sas; Pradeep Kayampilly; Jaeman Byun; Viji Nair; Lucy M Hinder; Junguk Hur; Hongyu Zhang; Chengmao Lin; Nathan R Qi; George Michailidis; Per-Henrik Groop; Robert G Nelson; Manjula Darshi; Kumar Sharma; Jeffrey R Schelling; John R Sedor; Rodica Pop-Busui; Joel M Weinberg; Scott A Soleimanpour; Steven F Abcouwer; Thomas W Gardner; Charles F Burant; Eva L Feldman; Matthias Kretzler; Frank C Brosius; Subramaniam Pennathur Journal: JCI Insight Date: 2016-09-22
Authors: Ville-Petteri Mäkinen; Tuulia Tynkkynen; Pasi Soininen; Carol Forsblom; Tomi Peltola; Antti J Kangas; Per-Henrik Groop; Mika Ala-Korpela Journal: Metabolomics Date: 2011-08-05 Impact factor: 4.290
Authors: James R Bain; Robert D Stevens; Brett R Wenner; Olga Ilkayeva; Deborah M Muoio; Christopher B Newgard Journal: Diabetes Date: 2009-11 Impact factor: 9.461
Authors: Franck Desmoulin; Michel Galinier; Charlotte Trouillet; Matthieu Berry; Clément Delmas; Annie Turkieh; Pierre Massabuau; Heinrich Taegtmeyer; Fatima Smih; Philippe Rouet Journal: PLoS One Date: 2013-04-03 Impact factor: 3.240
Authors: Laura Brugnara; Maria Vinaixa; Serafín Murillo; Sara Samino; Miguel Angel Rodriguez; Antoni Beltran; Carles Lerin; Gareth Davison; Xavier Correig; Anna Novials Journal: PLoS One Date: 2012-07-11 Impact factor: 3.240
Authors: Ville-Petteri Mäkinen; Pasi Soininen; Carol Forsblom; Maija Parkkonen; Petri Ingman; Kimmo Kaski; Per-Henrik Groop; Mika Ala-Korpela Journal: Mol Syst Biol Date: 2008-02-12 Impact factor: 11.429