<p><b>Objective: </b>Natriuretic peptide (NP) concentrations are increased in cardiovascular
diseases (CVD) but are associated with a lower diabetes risk. We investigated
associations of N-terminal pro-B-type NP (NT-proBNP) and mid-regional
pro-atrial NP (MR-proANP) with incident type 2 diabetes stratified by the
presence of CVD. </p>
<p><b> </b></p>
<p><b>Research Design and Methods:</b> Based on the Biomarkers for Cardiovascular Risk Assessment in
Europe-(BiomarCaRE) Consortium, we included 45,477 participants with NT-proBNP
measurements (1,707 developed type 2 diabetes over 6.5 years of median
follow-up; among these, 209 had CVD at baseline) and 11,537 participants with
MR-proANP measurements (857 developed type 2 diabetes over 13.8 years of median
follow-up; among these, 106 had CVD at baseline). The associations were
estimated using multivariable Cox regression models. </p>
<p> </p>
<p><b>Results: </b>Both NPs were inversely associated with incident type 2 diabetes (hazard
ratios [95%CI] per 1-standard deviation increase of log NP: 0.84 [0.79; 0.89]
for NT-proBNP and 0.77 [0.71; 0.83] for MR-proANP). The inverse association
between NT-proBNP and type 2 diabetes was significant in individuals without,
but not in individuals with CVD (0.81 [0.76; 0.86] vs 1.04 [0.90; 1.19]; <i>P</i>-multiplicative interaction= 0.001).
There was no significant difference in the association of MR-proANP with type 2
diabetes between individuals without and with CVD (0.75 [0.69; 0.82] vs 0.81
[0.66; 0.99]; <i>P</i>-multiplicative
interaction= 0.236). </p>
<p> </p>
<p><b>Conclusions:</b> NT-proBNP and MR-proANP are inversely associated with incident type 2
diabetes. However, the inverse association of NT-proBNP seems to be modified by
the presence of CVD. Further investigations are warranted to confirm our
findings and to investigate the underlying mechanisms.</p>
Cardiovascular risk assessment among type-2 diabetic subjects in selected areas of Bangladesh: concordances among without cholesterol-based WHO/ISH, Globorisk, and Framingham risk prediction tools
