侯丽波
中国医学科学院阜外医院 中国牛津国际医学研究中心
Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF. Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771-0.895) and validation set (AUC 0.798, 95% CI: 0.719-0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05). Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.
Frontiers in cardiovascular medicine 2021
BACKGROUND:Systemic studies of association of genome-wide DNA methylated sites with cardiovascular disease (CVD) in prospective cohorts are lacking. Our aim was to identify DNA methylation sites associated with the risk of CVD and further investigate their potential predictive value in CVD development for high-risk subjects.METHODS:We performed an epigenome-wide association study (EWAS) to identify CpGs related to CVD development in a Chinese population.We adopted a nested case-control design based on data from China PEACE Million Persons Project. A total of 83 cases who developed CVD events during follow-up and 83 controls who were matched with cases by age, sex, BMI, ethnicity, medications treatment and behavior risk factors were included in the discovery stage. Genome-wide DNA methylation from whole blood was detected using Infinium Human Methylation EPIC Beadchip (850 K). For significant CpGs [FDR(false discovery rate) < 0.005], we further validated in an independent cohort including 38 cases and 38 controls.RESULTS:In discovery set, we identified 8 significant CpGs (FDR < 0.005) associated with the risk of CVD after adjustment for cell components, demographic and cardiac risk factors and the first 5 principal components. Two of these identified CpGs (cg06901278 and cg09306458 in UACA) were replicated in another independent set (p < 0.05). Enrichment analysis in 787 individual genes from 1036 CpGs in discovery set revealed a significant enrichment for anatomical structure homeostasis as well as regulation of vesicle-mediated transport. Receiver operating characteristic (ROC) analysis showed that the model combined 8 CVD-related CpGs with baseline characteristics showed much better predictive effect for CVD occurrence compared with the model with baseline characteristics only [AUC (area under the curve) = 0.967, 95% CI (0.942 - 0.991); AUC = 0.621, 95% CI (0.536 - 0.706); p = 9.716E-15].CONCLUSIONS:Our study identified the novel CpGs associated with CVD development and revealed their additional predictive power in the risk of CVD for high-risk subjects.
BMC cardiovascular disorders 2021
BACKGROUND AND AIMS:Familial hypercholesterolemia (FH) is a an autosomal dominant disorder characterized by very high levels of low-density lipoprotein cholesterol (LDL-C). It is estimated that >85% of all FH-causing mutations involve genetic variants in the LDL receptor (LDLR). To date, 795 single amino acid LDLR missense mutations have been reported in the Leiden Open Variation Database (LOVD). However, the functional impact of these variants on the LDLR pathway has received little attention and remains poorly understood. We aim to establish a systematic functional prediction model for LDLR single missense mutations.METHODS:Using a combined structural modeling and bioinformatics algorithm, we developed an in silico prediction model called "Structure-based Functional Impact Prediction for Mutation Identification" (SFIP-MutID) for FH with LDLR single missense mutations. We compared the pathogenicity and functional impact predictions of our model to those of other conventional tools with experimentally validated variants, as well as in vitro functional test results for patients with LDLR variants.RESULTS:Our SFIP-MutID model systematically predicted 13,167 potential LDLR single amino acid missense substitutions with biological effects. The functional impact of 52 out of 54 specific mutations with reported in vitro experimental data was predicted correctly. Further functional tests on LDLR variants from patients were also consistent with the prediction of our model.CONCLUSIONS:Our LDLR structure-based computational model predicted the pathogenicity of LDLR missense mutations by linking genotypes with LDLR functional phenotypes. Our model complements other prediction tools for variant interpretation and facilitates the precision diagnosis and treatment of FH and atherosclerotic cardiovascular diseases.
Atherosclerosis 2019
OBJECTIVE:The calmodulin-binding transcription activator 2 (CAMTA2) promotes transcription of genes involved in cardiac hypertrophy through its interaction with Nkx2.5 and is an indispensable transcription coactivator for cardiac hypertrophy. We hypothesized that variants in the coding region of CAMTA2 would affect its function and confer a risk of cardiac hypertrophy.METHODS:The effects of the variant rs238234 on the activity of the atrial natriuretic factor promoter and on the cardiomyocytes hypertrophy were assessed in the H9C2 cell line and primary neonatal rat cardiomyocytes, respectively. Furthermore, the association of this variant with left ventricular hypertrophy (LVH) was tested in hypertensive patients with and without hypertrophy (N = 325 and 697), and this analysis was replicated in an independent population of 987 hypertensive patients without hypertrophy and 463 hypertensive patients with hypertrophy.RESULTS:We found that the G allele of rs238234 activated the atrial natriuretic factor promoter more strongly than the C allele. The cell size of cardiomyocytes was larger in the presence of the Ad-CAMTA2 G allele, and the G allele was associated with significantly increased susceptibility to LVH in hypertensive [odds ratio (OR), 1.29; P = 0.009]. In the discovery cohort, after adjusting for age and sex, the GG genotype was significantly associated with increased LVH risk (OR, 1.75; P = 0.015). There was little attenuation of the ORs (1.62; P < 0.05) when adjusting for BMI, heart rate, blood pressure, smoking, and drinking and further adjusting all covariates including lipid levels and other major risk factors. However, the GC genotype did not show any association with LVH using three regressive models. Replication in the second study yielded similar results.CONCLUSION:Our results provide evidence that the rs238234 GG genotype in the coding region of CAMTA2 may increase the risk of LVH by affecting the activation of Nkx2.5-dependent transcription.
Journal of hypertension 2016
