Download Laboratory Statistics and Quality Control

Journal Club
Estimated Glomerular Filtration
Rate and Albuminuria Are
Associated with Biomarkers of
Cardiac Injury in a PopulationBased Cohort Study:
The Maastricht Study
R.J.H. Martens, et al.
April 2017
www.clinchem.org/content/63/4/887.full
© Copyright 2017 by the American Association for Clinical Chemistry
Introduction
Background
•
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)
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Question
Why is it important to distinguish between eGFR and albuminuria?
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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
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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
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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?
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Table 1. Clinical characteristics of the study population
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Table 1 continued Data are presented as n (%), mean±SD, or median
(interquartile range).
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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.
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Figure 1. Associations of eGFR with hs-cTnT, hs-cTnI and NT-proBNP
for eGFR expressed as a continuous and as a categorical variable
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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.
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Figure 2. Associations of albuminuria with hs-cTnT, hs-cTnI and NTproBNP for albuminuria expressed as a continuous and as a categorical
variable
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Question
What can we learn from the changes in the regression
coefficients / ratios when sequentially adding (groups
of) potential confounders to our regression models?
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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
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