Automated Author Profile

Á., Gil

Current S-Index

0.7

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.3

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

13.5%

Average FAIR Score per dataset

Total Citations

0

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Supplementary Material for: A Continuous Metabolic Syndrome Score Is Associated with Specific Biomarkers of Inflammation and CVD Risk in Prepubertal Children

Background/Aims: We aimed to evaluate the use of a continuous metabolic syndrome (MetS) score and to assess the associations of this score with risk biomarkers of inflammation, endothelial damage and cardiovascular disease (CVD) in prepubertal children. Methods: A total of 677 prepubertal children (295 obese, 146 overweight, and 236 normal-weight) were recruited. MetS traits, markers of inflammation, endothelial damage and CVD risk were measured, and a continuous MetS score was calculated, consisting of the sum/5 of the standardised scores of the MetS components. Results: The continuous MetS score was significantly associated with active plasminogen activator inhibitor-1 (r = 0.406, p < 0.001), adiponectin (r = -0.212, p < 0.001), resistin (r = 0.263, p < 0.001), C-reactive protein (r = 0.254, p < 0.001), tumour necrosis factor alpha (r = 0.120, p = 0.003), myeloperoxidase (r = 0.188, p < 0.001) and sE-selectin (r = 0.278, p < 0.001). Children in the normal-weight, overweight and obese groups with MetS totalled 0 (0%), 1 (0.7%) and 24 (8.7%), respectively, whereas the at-risk children identified using the continuous MetS score in each group totalled 2 (0.85%), 17 (11.6%) and 167 (56.6%), respectively. Conclusions: The association of the continuous MetS score with specific risk biomarkers of inflammation, endothelial damage and CVD supports its use in the early identification of children at increased risk of metabolic dysfunction.

Authors

  • Olza, J. ;
  • Aguilera, C.M. ;
  • Gil-Campos, M. ;
  • Leis, R. ;
  • Bueno, G. ;
  • Valle, M. ;
  • Cañete, R. ;
  • Tojo, R. ;
  • Moreno, L.A. ;
  • Á., Gil
0 Citations0 Mentions13% FAIR0.3 Dataset Index
10.6084/m9.figshare.5127400January 2015

Supplementary Material for: A Continuous Metabolic Syndrome Score Is Associated with Specific Biomarkers of Inflammation and CVD Risk in Prepubertal Children

Background/Aims: We aimed to evaluate the use of a continuous metabolic syndrome (MetS) score and to assess the associations of this score with risk biomarkers of inflammation, endothelial damage and cardiovascular disease (CVD) in prepubertal children. Methods: A total of 677 prepubertal children (295 obese, 146 overweight, and 236 normal-weight) were recruited. MetS traits, markers of inflammation, endothelial damage and CVD risk were measured, and a continuous MetS score was calculated, consisting of the sum/5 of the standardised scores of the MetS components. Results: The continuous MetS score was significantly associated with active plasminogen activator inhibitor-1 (r = 0.406, p < 0.001), adiponectin (r = -0.212, p < 0.001), resistin (r = 0.263, p < 0.001), C-reactive protein (r = 0.254, p < 0.001), tumour necrosis factor alpha (r = 0.120, p = 0.003), myeloperoxidase (r = 0.188, p < 0.001) and sE-selectin (r = 0.278, p < 0.001). Children in the normal-weight, overweight and obese groups with MetS totalled 0 (0%), 1 (0.7%) and 24 (8.7%), respectively, whereas the at-risk children identified using the continuous MetS score in each group totalled 2 (0.85%), 17 (11.6%) and 167 (56.6%), respectively. Conclusions: The association of the continuous MetS score with specific risk biomarkers of inflammation, endothelial damage and CVD supports its use in the early identification of children at increased risk of metabolic dysfunction.

Authors

  • Olza, J. ;
  • Aguilera, C.M. ;
  • Gil-Campos, M. ;
  • Leis, R. ;
  • Bueno, G. ;
  • Valle, M. ;
  • Cañete, R. ;
  • Tojo, R. ;
  • Moreno, L.A. ;
  • Á., Gil
0 Citations0 Mentions13% FAIR0.3 Dataset Index
10.6084/m9.figshare.5127400.v1January 2015