1. Estimated Glomerular Filtration Rate and Albuminuria Are Associated with Biomarkers of Cardiac Injury in a Population-Based Cohort Study:
The Maastricht Study
R.J.H. Martens, et al.
April 2017
© Copyright 2017 by the American Association for Clinical Chemistry

2. Introduction Background Aim
Chronic kidney disease is associated with cardiovascular disease and mortality, but whether this association is continuous (from end-stage renal disease up to mild kidney dysfunction) is unclear. In addition, the underlying mechanisms are largely unclear.
Associations of eGFR and albuminuria and, biomarkers of cardiac disease (troponins and natriuretic peptides) may help to understand the above issues.
Aim
To examine the associations of eGFR and albuminuria with: high sensitivity cardiac troponins (hs-cTN) T and I and, N-terminal pro-brain natriuretic peptide (NT-proBNP) in The Maastricht Study (Maastricht, the Netherlands)

3. Question
Why is it important to distinguish between eGFR and albuminuria?

4. Materials & Methods Study Population
3103 participants (between 40 – 75 years of age)
The Maastricht Study: an observational, prospective, population-based cohort study with oversampling of type 2 diabetes participants
GFR estimated with the CKD-EPI equation based on serum creatinine and serum cystatin C
24h urinary albumin excretion based on the average of 2 collections

5. Materials & Methods Biomarkers of Cardiac Injury
measured from serum samples stored at -80°C for 1-4 years
hs-cTnT: Roche Cobas 6000 analyzer / Elecsys Troponin T hs assay
hs-cTnI: Abbott Architect i2000 SR Analyzer / Architect STAT hs Troponin I assay
NT-proBNP Roche Cobas 6000 analyzer / Elecsys proBNP II assay
Primary Statistical Analyses
multivariable linear regression analyses
associations of eGFR and albuminuria with hs-cTnT and hs-cTnI compared with a test for comparing correlations measured on the same individuals

6. Question
Why is it important to consider the assay characteristics and distributions of hs-cTnT and hs-cTnI when comparing their associations with eGFR and albuminuria?

7. Table 1. Clinical characteristics of the study population

8. Table 1 continued Data are presented as n (%), mean±SD, or median (interquartile range).

9. Table 2. Associations of eGFR with biomarkers of cardiac injury
betas represent the ratio of geometric mean concentrations of cardiac biomarkers in the respective eGFRcrcys category relative to participants with an eGFRcrcys ≥90mL min−1 (1.73m2)−1.

10. Figure 1. Associations of eGFR with hs-cTnT, hs-cTnI and NT-proBNP for eGFR expressed as a continuous and as a categorical variable

11. Table 3. Associations of albuminuria with biomarkers of cardiac injury.
betas represent the ratio of geometric mean concentrations of cardiac biomarkers in the respective albuminuria category relative to participants with a UAE <15 mg/24 h.

12. Figure 2. Associations of albuminuria with hs-cTnT, hs-cTnI and NT-proBNP for albuminuria expressed as a continuous and as a categorical variable

13. Question
What can we learn from the changes in the regression coefficients / ratios when sequentially adding (groups of) potential confounders to our regression models?

14. Conclusions
eGFR and albuminuria were mutually independently associated with hs-cTnT, hs-cTnI and NT-proBNP These associations were already present at levels of eGFR and albuminuria that do not fulfill the criteria for chronic kidney disease eGFR was more strongly associated with hs-cTnT than with hs-cTnI but for albuminuria the associations with hs-cTnT and hs-cTnI were similar