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Early detection of metabolic dysregulation using water T2 analysis of biobanked samples

Authors Mishra I, Jones C, Patel V, Deodhar S, Cistola DP

Received 23 July 2018

Accepted for publication 12 October 2018

Published 23 November 2018 Volume 2018:11 Pages 807—818


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Konstantinos Tziomalos

Ina Mishra,1,2 Clinton Jones,1,3 Vipulkumar Patel,1,2 Sneha Deodhar,1 David P Cistola1,2

1Nanoparticle Diagnostics Laboratory, Institute for Cardiovascular and Metabolic Diseases, Department of Physiology & Anatomy, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA; 2Center of Emphasis in Diabetes & Metabolism, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, 79905, USA; 3Texas College of Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA

Background: The ability to use frozen biobanked samples from cohort studies and clinical trials is critically important for biomarker discovery and validation. Here we investigated whether plasma and serum water transverse relaxation times (T2) from frozen biobanked samples could be used as biomarkers for metabolic syndrome (MetS) and its underlying conditions, specifically insulin resistance, dyslipidemia, and subclinical inflammation.
Methods: Plasma and serum aliquots from 44 asymptomatic, non-diabetic human subjects were biobanked at –80°C for 7–9 months. Water T2 measurements were recorded at 37°C on 50 µL of unmodified plasma or serum using benchtop nuclear magnetic resonance relaxometry. The T2 values for freshly drawn and once-frozen-thawed (“frozen”) samples were compared using Huber M-values (M), Lin concordance correlation coefficients (ρc), and Bland–Altman plots. Water T2 values from frozen plasma and serum samples were compared with >130 metabolic biomarkers and analyzed using multi-variable linear/logistic regression and ROC curves.
Results: Frozen plasma water T2 values were highly correlated with fresh (M=0.94, 95% CI 0.89, 0.97) but showed a lower level of agreement (ρc=0.74, 95% CI 0.62, 0.82) because of an average offset of –5.6% (−7.1% for serum). Despite the offset, frozen plasma water T2 was strongly correlated with markers of hyperinsulinemia, dyslipidemia, and inflammation and detected these conditions with 89% sensitivity and 91% specificity (100%/63% for serum). Using optimized cut points, frozen plasma and serum water T2 detected hyperinsulinemia, dyslipidemia, and inflammation in 23 of 44 subjects, including nine with an early stage of metabolic dysregulation that did not meet the clinical thresholds for prediabetes or MetS.
Conclusion: Plasma and serum water T2 values from once-frozen-thawed biobanked samples detect metabolic dysregulation with high sensitivity and specificity. However, the cut points for frozen biobanked samples must be calibrated independent of those for freshly drawn plasma and serum.

Keywords: metabolic syndrome, insulin resistance, hyperinsulinemia, dyslipidemia, inflammation, nuclear magnetic resonance relaxometry, metabolic health screening, diabetes prevention

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