1.
The Impact of the Triglyceride-Glucose Index on Poor Prognosis in NonDiabetic Patients Undergoing Percutaneous Coronary Intervention.
Yang, J, Tang, YD, Zheng, Y, Li, C, Zhou, Q, Gao, J, Meng, X, Zhang, K, Wang, W, Shao, C
Frontiers in endocrinology. 2021;:710240
Abstract
BACKGROUND The triglyceride-glucose index (TyG index) is a valuable marker for predicting adverse cardiovascular events in diabetic patients. However, for nondiabetic patients, whether the TyG index is independently related to poor prognosis remains unclear. This cohort study assessed the association of the TyG index with future cardiovascular risk in nondiabetic subjects who received percutaneous coronary intervention (PCI). METHODS We consecutively enrolled 5,489 nondiabetic patients who underwent PCI. All experimental subjects were divided into three groups based on their TyG index, which was determined by the equation ln (fasting triglyceride (mg/dl) × fasting blood glucose (mg/dl)/2). The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE), including all-cause death, nonfatal myocardial infarction (MI), nonfatal stroke, and target vessel revascularization (TVR). RESULTS A total of 386 MACCE were documented during a median 29-month follow-up. The Kaplan-Meier survival results indicated that among the three groups, there was no obvious difference in any endpoints. Further Cox regression analyses suggested that the TyG index was not independently related to adverse cardiovascular outcomes for nondiabetic patients who underwent PCI (HR: 0.77, 95% CI 0.56-1.16, P = 0.210 for MACCE). Subgroup analysis suggested that the TyG index was independently relevant to MACCE for patients with low-density lipoprotein cholesterol (LDL-C) lower than 1.8 mmol/L. CONCLUSION The TyG index is not an effective predictive factor for adverse cardiovascular prognosis in nondiabetic patients who underwent PCI. However, in subjects with LDL-C lower than 1.8mmol/L, it may predict future cardiovascular risk.
2.
Serum ARCHITECT PIVKA-II reference interval in healthy Chinese adults: Sub-analysis from a prospective multicenter study.
Yan, C, Hu, J, Yang, J, Chen, Z, Li, H, Wei, L, Zhang, W, Xing, H, Sang, G, Wang, X, et al
Clinical biochemistry. 2018;:32-36
Abstract
BACKGROUND Protein induced by vitamin K absence or antagonist-II (PIVKA-II) has been widely used as a biomarker for liver cancer diagnosis in Japan for decades. However, the reference intervals for serum ARCHITECT PIVKA-II have not been established in the Chinese population. Thus, this study aimed to measure serum PIVKA-II levels in healthy Chinese subjects. METHODS This is a sub-analysis from the prospective, cross-sectional and multicenter study (ClinicalTrials.gov Identifier: NCT03047603). A total of 892 healthy participants (777 Han and 115 Uygur) with complete health checkup results were recruited from 7 regional centers in China. Serum PIVKA-II level was measured by ARCHITECT immunoassay. All 95% reference ranges were estimated by nonparametric method. RESULTS The distribution of PIVKA-II values showed significant difference with ethnicity and sex, but not age. The 95% reference range of PIVKA-II was 13.62-40.38 mAU/ml in Han Chinese subjects and 15.16-53.74 mAU/ml in Uygur subjects. PIVKA-II level was significantly higher in males than in females (P < 0.001). The 95% reference range of PIVKA-II was 15.39-42.01 mAU/ml in Han males while 11.96-39.13 mAU/ml in Han females. CONCLUSIONS The reference interval of serum PIVKA-II on the Architect platform was established in healthy Chinese adults. This will be valuable for future clinical and laboratory studies performed using the Architect analyzer. Different ethnic backgrounds and analytical methods underline the need for redefining the reference interval of analytes such as PIVKA-II, in central laboratories in different countries.
3.
Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD.
Sun, W, Kechris, K, Jacobson, S, Drummond, MB, Hawkins, GA, Yang, J, Chen, TH, Quibrera, PM, Anderson, W, Barr, RG, et al
PLoS genetics. 2016;(8):e1006011
Abstract
Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10-10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10-392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.