Abstract
Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health - increased Nϵ-carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health - increased glucosepane; and impaired renal health - increased BCAAs and decreased Nϵ-(γ-glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary Nϵ-fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% (cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 - 7, 26 - 28, and 34 - 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health.
| Original language | English |
|---|---|
| Article number | 4851323 |
| Journal | Oxidative medicine and cellular longevity |
| Volume | 2019 |
| DOIs | |
| Publication status | Published - 2019 |
Bibliographical note
Funding Information:This work was carried out with financial support of the European Union Framework Programme 7 FP7 2007-2013 under grant agreement no. 244995 (BIOCLAIMS Project) and the Austrian Federal Ministry of Science, Research and Economy to Karl-Franzens University of Graz and Medical University of Graz. The authors thank Petra Kieslinger, Daniela Berger, Verena Schaberl, Agnes Schriebl, Theopisti Maimari, Natalie Walter, Cornelia Missbrenner, Nicole Hacker, and Verena Zachhuber for excellent technical assistance.
Funding Information:
Masania Jinit [email protected] 1 Faustmann Gernot [email protected] 2 3 Anwar Attia [email protected] 1 Hafner-Giessauf Hildegard [email protected] 2 Rajpoot Nasir [email protected] 4 Grabher Johanna [email protected] 2 Rajpoot Kashif [email protected] 5 Tiran Beate [email protected] 6 Obermayer-Pietsch Barbara [email protected] 7 Winklhofer-Roob Brigitte M. [email protected] 3 Roob Johannes M. [email protected] 2 Rabbani Naila [email protected] 1 https://orcid.org/0000-0001-7659-443X Thornalley Paul J. [email protected] 1 8 Malaguti Marco 1 Warwick Medical School Clinical Sciences Research Laboratories University of Warwick University Hospital Coventry CV2 2DX UK warwick.ac.uk 2 Clinical Division of Nephrology Department of Internal Medicine Medical University of Graz 8036 Graz Austria medunigraz.at 3 Human Nutrition & Metabolism Research and Training Center (HNMRC) Institute of Molecular Biosciences Karl Franzens University of Graz Universitätsplatz 2 8010 Graz Austria uni-graz.at 4 Department of Computer Sciences University of Warwick Coventry CV4 7AL UK warwick.ac.uk 5 School of Computer Science University of Birmingham Edgbaston Birmingham B15 2TT UK birmingham.ac.uk 6 Clinical Institute of Medical and Clinical Laboratory Diagnostics Medical University of Graz 8036 Graz Austria medunigraz.at 7 Clinical Division of Endocrinology Department of Internal Medicine Medical University of Graz 8036 Graz Austria medunigraz.at 8 Diabetes Research Center Qatar Biomedical Research Institute (QBRI) Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar hbku.edu.qa 2019 19 11 2019 2019 23 05 2019 07 09 2019 11 09 2019 19 11 2019 2019 Copyright © 2019 Jinit Masania et al. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The publication of this article was funded by Qatar National Library. Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health—increased N ε -carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health—increased glucosepane; and impaired renal health—increased BCAAs and decreased N ε -( γ -glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary N ε -fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% ( cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 – 7, 26 – 28, and 34 – 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health. Bundesministerium für Wissenschaft, Forschung und Wirtschaft Seventh Framework Programme 244995
Publisher Copyright:
© 2019 Jinit Masania et al.
ASJC Scopus subject areas
- Biochemistry
- Ageing
- Cell Biology
