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Visualization and Quantification of Mesenchymal Cell Adipogenic Differentiation Potential with a Lineage Specific Marker
Published on: March 31, 2018
Elevated circulating adipocyte-fatty acid binding protein levels predict incident cardiovascular events in a
Wing Sun Chow1, Annette Wai Kwan Tso, Aimin Xu
1Department of Medicine, The University of Hong Kong, Hong Kong.
Background:
Obesity is closely associated with various cardiovascular diseases (CVDs). Adipose tissue inflammation and perturbation of adipokine secretion may contribute to the pathogenesis of CVD. This study aimed to evaluate whether the 2 most abundant adipokines, adipocyte-fatty acid binding protein (A-FABP) and adiponectin, are independent risk factors predisposing to CVD.
Method And Results:
We investigated prospectively the 12-year development of CVD in relation to the baseline levels of A-FABP and adiponectin in a population-based community cohort comprising 1847 Chinese subjects recruited from the Hong Kong Cardiovascular Risk Factors Prevalence Study 2 (CRISPS 2) cohort without previous CVD. Baseline serum levels of A-FABP, adiponectin, and C-reactive protein (CRP), an established biomarker predictive of CVD, were measured. In all, 182 (9.9%) of the 1847 Chinese subjects developed CVD during a median follow-up of 9.4 years. The CVD group had more traditional risk factors, higher baseline levels of A-FABP and CRP (both P<0.001), but similar adiponectin levels (P=0.881) compared with the non-CVD group. In Cox regression analysis including both biomarkers, the adjusted HR for A-FABP and CRP for subjects above the optimal cutoff values were 1.57 (95% CI, 1.14 to 2.16; P=0.006) and 1.60 (95% CI, 1.12 to 2.27; P=0.01), respectively, after adjustment for traditional risk factors. The category-free net reclassification index, but not the c-statistic, showed improvement in predictive performance by the addition of A-FABP to the traditional risk factor model (P=0.017).
Conclusions:
Circulating A-FABP level predicts the development of CVD after adjustment for traditional risk factors in a community-based cohort. Its clinical use for CVD prediction warrants further validation.
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