Open Access

Kolade Oluwagbemigun, Dan Ziegler, Alexander Strom, Margit Heier, Gidon Bönhof, Michael Roden, Wolfgang Rathmann, Christa Meisinger, Annette Peters, Stefanie M. Hauck, Agnese Petrera, Moritz F. Sinner, Stefan Kääb, Barbara Thorand, Christian Herder

• Background We sought to investigate the association between circulating inflammatory and cardiovascular proteomics biomarkers and cardiac autonomic nervous dysfunction–sensitive heart rate variability indices. Methods Using the population‐based KORA (Cooperative Health Research in the Region of Augsburg) cohort, 233 proteomics biomarkers were quantified in baseline plasma samples of 1389 individuals using proximity extension assay technology. Five heart rate variability indices (Rényi entropy of the histogram with order [α] 4, total power of the density spectra, SD of word sequence, SD of the short‐term normal‐to‐normal interval variability, compression entropy) were assessed at baseline in 982 individuals and in 407 individuals at baseline and at 14‐year follow‐up. Three unbiased multivariable selection models followed by linear or linear mixed‐effects models with multiple testing correction were used to determine the association between proteomics biomarkers and heart rateBackground We sought to investigate the association between circulating inflammatory and cardiovascular proteomics biomarkers and cardiac autonomic nervous dysfunction–sensitive heart rate variability indices. Methods Using the population‐based KORA (Cooperative Health Research in the Region of Augsburg) cohort, 233 proteomics biomarkers were quantified in baseline plasma samples of 1389 individuals using proximity extension assay technology. Five heart rate variability indices (Rényi entropy of the histogram with order [α] 4, total power of the density spectra, SD of word sequence, SD of the short‐term normal‐to‐normal interval variability, compression entropy) were assessed at baseline in 982 individuals and in 407 individuals at baseline and at 14‐year follow‐up. Three unbiased multivariable selection models followed by linear or linear mixed‐effects models with multiple testing correction were used to determine the association between proteomics biomarkers and heart rate variability indices. Results C‐C motif chemokine 23 was positively associated, while peptidoglycan recognition protein nd fibroblast growth factor 21 were negatively associated with Rényi entropy of the histogram with order (α) 4 cross‐sectionally. Tumor necrosis factor–related activation‐induced cytokine and growth/differentiation factor 15 were negatively associated with compression entropy cross‐sectionally. Over time, interleukin‐6 receptor subunit α and macrophage colony‐stimulating factor were positively and negatively associated with total power of the density spectra, respectively. Additionally, myoglobin and agouti‐related protein were positively and negatively associated with SD of the short‐term normal‐to‐normal interval variability, respectively. Gastrotropin and agouti‐related protein were positively and negatively associated with compression entropy, respectively. Conclusions This study identified novel circulating proteins associated with heart rate variability indices. These proteins could improve our understanding of the pathophysiology underlying cardiac autonomic nervous dysfunction.…