1. Volume 85, Issue 5, Pages 1214-1224 (May 2014)
Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study 
Monika A. Niewczas, Tammy L. Sirich, Anna V. Mathew, Jan Skupien, Robert P. Mohney, James H. Warram, Adam Smiles, Xiaoping Huang, Walker Walker, Jaeman Byun, Edward D. Karoly, Elizabeth M. Kensicki, Gerard T. Berry, Joseph V. Bonventre, Subramaniam Pennathur, Timothy W. Meyer, Andrzej S. Krolewski 
Kidney International 
Volume 85, Issue 5, Pages (May 2014)
DOI: /ki
Copyright © 2014 International Society of Nephrology Terms and Conditions

2. Figure 1
Stability of the common metabolites within individuals with type 2 diabetes in plasma samples taken 1–2 years apart. Spearman’s rank correlation coefficients (r) are presented per individual measurements. The line and number represent median value per specific class.
Kidney International  , DOI: ( /ki )
Copyright © 2014 International Society of Nephrology Terms and Conditions

3. Figure 2
Multivariate analysis (volcano plot) of all common metabolites measured on the Metabolon platform and their association with progression to end-stage renal disease (ESRD) are demonstrated as a fold difference (x-axis) and significance adjusted for multiple comparisons and presented as q-values (y-axis). Uremic solutes comprise metabolites of interest in a and amino acids are metabolites of interest in b. Uremic solutes are not displayed in b. Common and stable metabolites of interest are represented as red circles (), common metabolites that are not stable over time are represented as red empty circles (), and all other common metabolites are represented as gray circles (). Blue circles represent essential amino acids ().
Kidney International  , DOI: ( /ki )
Copyright © 2014 International Society of Nephrology Terms and Conditions

4. Figure 3
Logistic regression analysis of the effect of the plasma concentration of metabolites identified as uremic solutes on the risk of progression to end-stage renal disease (ESRD) in patients with type 2 diabetes (T2D). Data are odds ratios and 95% confidence intervals (OR, 95% CI) estimated for an effect of 1 s.d. change of the metabolite. AER, albumin excretion rate; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c.
Kidney International  , DOI: ( /ki )
Copyright © 2014 International Society of Nephrology Terms and Conditions

5. Figure 4
Logistic regression analysis of the effect of the plasma concentration of proteogenic amino acids and amino-acid derivatives on the risk of progression to end-stage renal disease (ESRD) in subjects with type 2 diabetes (T2D). Data are odds ratios and 95% confidence intervals (OR, 95% CI) estimated for an effect of 1 s.d. change of the metabolite. *Metabolite was not stable over time but is shown for its biological relevance. AER, albumin excretion rate; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c.
Kidney International  , DOI: ( /ki )
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6. Figure 5
Hierarchical cluster analysis (Ward’s method) of the metabolites significantly associated with progression to end-stage renal disease (ESRD). Separate clusters are marked in different colors. Distance scale is shown. C1–C6 represent respective clusters.
Kidney International  , DOI: ( /ki )
Copyright © 2014 International Society of Nephrology Terms and Conditions