A Novel Dried Blood Spot Detection Strategy for Characterizing Cardiovascular Diseases - Liu, Linsheng; Jin, Xurui; Wu, Yangfeng; Yang, Mei; Xu, Tao; Li, Xianglian; Ren, Jianhong; Yan, Lijing L. (Frontiers in Cardiovascular Medicine, 2020)
Cardiovascular diseases (CVDs) are the leading causes of death in China. Conventional diagnostic methods are dependent on advanced instruments, which are expensive, inaccessible and inconvenient in underdeveloped areas. To build a novel dried blood spot detection strategy for imaging cardiovascular diseases, in this study, a total of 12 compounds including seven amino acids (Ile, Leu, Val, Phe, Tyr, and Trp), three amino acid derivatives (choline, betaine, and TMAO), L-carnitine, and creatinine, were screened for their ability to identify CVD. A rapid and reliable method was established for the quantitative analysis of the 12 compounds in dried blood spot (DBS). A total of 526 CVD patients and 200 healthy volunteers in five provinces of China were recruited, and divided into the following groups: stroke, coronary heart disease, diabetes and high blood pressure. The orthogonal projection to latent structures-discriminant analysis model (OPLSDA) was used to characterize the difference between each CVD group. Marked differences between the groups based on the OPLSDA model were observed. Based on the model, the patients in the three training sets were mostly accurately categorized into the appropriate group. In addition, the ROC curves and logistic regression of each metabolite chosen by the OPLSDA model had excellent predictive value in both the test and validation groups. Dried blood spot detection of 12 biomarkers was sensitive and powerful for characterizing different types of CVD. Such differentiation may reduce unnecessary invasive coronary angiography, enhance predictive value, and complement current diagnostic methods.