Association Between Thromboelastography and Coronary Heart Disease.

BACKGROUND Thromboelastography (TEG) is a novel blood viscoelasticity detection method revealing blood coagulation status and has been reported to be helpful in predicting clinical outcomes in patients with cardiovascular diseases (CVD). In this study, we aimed to investigate the association between TEG and CVD. MATERIAL AND METHODS A single-center case-control study was performed. Individuals who took TEG tests at Tongji Hospital in Wuhan, China from 2015 to 2019 were included. The nearest-neighbor Mahalanobis matching with replacement, within propensity score calipers of 0.25 was used to control the covariate imbalance between CVD patients and controls. Logistic regression analyses were conducted to assess the relationship between TEG and CVD. Subgroup and sensitivity analyses were performed to evaluate the robustness of the association between TEG and CVD. RESULTS After matching, a total of 151 participants were included in this study, with 83 patients having CVD (49 patients having coronary heart disease [CHD] and 34 patients having an ischemic stroke). By comparison, CHD patients had a significantly higher maximum amplitude (MA) (P=0.02) than controls. After multivariable adjustment, MA (OR 1.11, 95% CI 1.01-1.24, P=0.04) was independently associated with CHD. The association between MA and CHD remained robust across subgroups and in sensitivity analyses. CONCLUSIONS The current study suggests that MA is significantly associated with CHD. Enhanced platelet reactivity as described by high MA might be associated with risk of CHD. The exact role of MA in the measurement of CHD risk needs to be further examined in large-scale prospective cohort studies.

Cohort profile for the Tongji Cardiovascular Health Study: a prospective multiomics cohort study.

PURPOSE: The Tongji Cardiovascular Health Study aimed to further explore the onset and progression mechanisms of cardiovascular disease (CVD) through a combination of traditional cohort studies and multiomics analysis, including genomics, metabolomics and metagenomics. STUDY DESIGN AND PARTICIPANTS: This study included participants aged 20-70 years old from the Geriatric Health Management Centre of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. After enrollment, each participant underwent a comprehensive series of traditional and novel cardiovascular risk factor assessments at baseline, including questionnaires, physical examinations, laboratory tests, cardiovascular health assessments and biological sample collection for subsequent multiomics analysis (whole genome sequencing, metabolomics study from blood samples and metagenomics study from stool samples). A biennial follow-up will be performed for 10 years to collect the information above and the outcome data. FINDINGS TO DATE: A total of 2601 participants were recruited in this study (73.4% men), with a mean age of 51.5±11.5 years. The most common risk factor is overweight or obesity (54.8%), followed by hypertension (39.7%), hyperlipidaemia (32.4%), current smoking (23.9%) and diabetes (12.3%). Overall, 13.1% and 48.7% of men and women, respectively, did not have any of the CVD risk factors (hypertension, hyperlipidaemia, diabetes, cigarette smoking and overweight or obesity). Additionally, multiomics analyses of a subsample of the participants (n=938) are currently ongoing. FUTURE PLANS: With the progress of the cohort follow-up work, it is expected to provide unique multidimensional and longitudinal data on cardiovascular health in China.

Sex-specific equations to estimate body composition: Derivation and validation of diagnostic prediction models using UK Biobank.

BACKGROUND & AIMS: Body mass index and waist circumference are simple measures of obesity. However, they do not distinguish between visceral and subcutaneous fat, or muscle, potentially leading to biased relationships between individual body composition parameters and adverse health outcomes. The purpose of this study was to develop and validate prediction models for volumetric adipose and muscle. METHODS: Based on cross-sectional data of 18,457, 18,260, and 17,052 White adults from the UK Biobank, we developed sex-specific equations to estimate visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and total thigh fat-free muscle (FFM) volumes, respectively. Volumetric magnetic resonance imaging served as the reference. We used the least absolute shrinkage and selection operator and the extreme gradient boosting methods separately to fit three sequential models, the inputs of which included demographics and anthropometrics and, in some, bioelectrical impedance analysis parameters. We applied comprehensive metrics to assess model performance in the temporal validation set. RESULTS: The equations that included more predictors generally performed better. Accuracy of the equations was moderate for VAT (percentage of estimates that differed <30% from the measured values, 70 to 78 in males, 64 to 69 in females) and good for ASAT (85 to 91 in males, 90 to 95 in females) and FFM (99 to 100 in both sexes). All the equations appeared precise (interquartile range of the difference, 0.89 to 1.76 L for VAT, 1.16 to 1.61 L for ASAT, 0.81 to 1.39 L for FFM). Bias of all the equations was negligible (-0.17 to 0.05 L for VAT, -0.10 to 0.12 L for ASAT, -0.07 to 0.09 L for FFM). The equations achieved superior cardiometabolic correlations compared with body mass index and waist circumference. CONCLUSIONS: The developed equations to estimate VAT, ASAT, and FFM volumes achieved moderate to good performance. They may be cost-effective tools to revisit the implications of diverse body components.

Machine learning improves risk stratification of coronary heart disease and stroke.

Background: Coronary heart disease (CHD) and cerebral ischemic stroke (CIS) are two major types of cardiovascular disease (CVD) that are increasingly exerting pressure on the healthcare system worldwide. Machine learning holds great promise for improving the accuracy of disease prediction and risk stratification in CVD. However, there is currently no clinically applicable risk stratification model for the Asian population. This study developed a machine learning-based CHD and CIS model to address this issue. Methods: A case-control study was conducted based on 8,624 electronic medical records from 2008 to 2019 at the Tongji Hospital in Wuhan, China. Two machine learning methods (the random down-sampling method and the random forest method) were integrated into 2 ensemble models (the CHD model and the CIS model). The trained models were then interpreted using Shapley Additive exPlanations (SHAP). Results: The CHD and CIS models achieved good performance with the areas under the receiver operating characteristic curve (AUC) of 0.895 and 0.884 in random testing, and 0.905 and 0.889 in sequential testing, respectively. We identified 4 common factors between CHD and CIS: age, brachial-ankle pulse wave velocity, hypertension, and low-density lipoprotein cholesterol (LDL-C). Moreover, carcinoembryonic antigen (CEA) was identified as an independent indicator for CHD. Conclusions: Our ensemble models can provide risk stratification for CHD and CIS with clinically applicable performance. By interpreting the trained models, we provided insights into the common and unique indicators in CHD and CIS. These findings may contribute to a better understanding and management of risk factors associated with CVD.

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM).

Background: Death due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention. Methods: We conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008-2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions. Results: CVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738-0.799), and D statistic was 4.738 (95% CI: 3.270-6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction. Conclusions: We developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator-calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD.

A population-based study of precision health assessments using multi-omics network-derived biological functional modules.

Recent technological advances in multi-omics and bioinformatics provide an opportunity to develop precision health assessments, which require big data and relevant bioinformatic methods. Here we collect multi-omics data from 4,277 individuals. We calculate the correlations between pairwise features from cross-sectional data and then generate 11 biological functional modules (BFMs) in males and 12 BFMs in females using a community detection algorithm. Using the features in the BFM associated with cardiometabolic health, carotid plaques can be predicted accurately in an independent dataset. We developed a model by comparing individual data with the health baseline in BFMs to assess health status (BFM-ash). Then we apply the model to chronic patients and modify the BFM-ash model to assess the effects of consuming grape seed extract as a dietary supplement. Finally, anomalous BFMs are identified for each subject. Our BFMs and BFM-ash model have huge prospects for application in precision health assessment.

Clinical perspectives and concerns of metformin as an anti-aging drug.

As percentages of elderly people rise in many societies, age-related diseases have become more prevalent than ever. Research interests have been shifting to delaying age-related diseases by delaying or reversing aging itself. We use metformin as an entry point to talk about the important molecular and genetic longevity-regulating mechanisms that have been extensively studied with it. Then we review a number of observational studies, animal studies, and clinical trials to reflect the clinical potentials of the mechanisms in lifespan extension, cardiovascular diseases, tumors, and neurodegeneration. Finally, we highlight remaining concerns that are related to metformin and future anti-aging research.

Evolution and Comprehensive Analysis of DNaseI Hypersensitive Sites in Regulatory Regions of Primate Brain-Related Genes.

How the human brain differs from those of non-human primates is largely unknown and the complex drivers underlying such differences at the genomic level remain unclear. In this study, we selected 243 brain-related genes, based on Gene Ontology, and identified 184,113 DNaseI hypersensitive sites (DHSs) within their regulatory regions. To performed comprehensive evolutionary analyses, we set strict filtering criteria for alignment quality and filtered 39,132 DHSs for inclusion in the investigation and found that 2,397 (~6%) exhibited evidence of accelerated evolution (aceDHSs), which was a much higher proportion that DHSs genome-wide. Target genes predicted to be regulated by brain-aceDHSs were functionally enriched for brain development and exhibited differential expression between human and chimpanzee. Alignments indicated 61 potential human-specific transcription factor binding sites in brain-aceDHSs, including for CTCF, FOXH1, and FOXQ1. Furthermore, based on GWAS, Hi-C, and eQTL data, 16 GWAS SNPs, and 82 eQTL SNPs were in brain-aceDHSs that regulate genes related to brain development or disease. Among these brain-aceDHSs, we confirmed that one enhanced the expression of GPR133, using CRISPR-Cas9 and western blotting. The GPR133 gene is associated with glioblastoma, indicating that SNPs within DHSs could be related to brain disorders. These findings suggest that brain-related gene regulatory regions are under adaptive evolution and contribute to the differential expression profiles among primates, providing new insights into the genetic basis of brain phenotypes or disorders between humans and other primates.

Single-cell transcriptomic landscape of nucleated cells in umbilical cord blood.

BACKGROUND: For both pediatric and adult patients, umbilical cord blood (UCB) transplant is a therapeutic option for a variety of hematologic diseases, such as blood cancers, myeloproliferative disorders, genetic diseases, and metabolic disorders. However, the level of cellular heterogeneity and diversity of nucleated cells in UCB has not yet been assessed in an unbiased and systemic fashion. In the present study, nucleated cells from UCB were subjected to single-cell RNA sequencing to simultaneously profile the gene expression signatures of thousands of cells, generating a rich resource for further functional studies. Here, we report the transcriptomes of 17,637 UCB cells, covering 12 major cell types, many of which can be further divided into distinct subpopulations. RESULTS: Pseudotemporal ordering of nucleated red blood cells identifies wave-like activation and suppression of transcription regulators, leading to a polarized cellular state, which may reflect nucleated red blood cell maturation. Progenitor cells in UCB also comprise 2 subpopulations with activation of divergent transcription programs, leading to specific cell fate commitment. Detailed profiling of cytotoxic cell populations unveiled granzymes B and K signatures in natural killer and natural killer T-cell types in UCB. CONCLUSIONS: Taken together, our data form a comprehensive single-cell transcriptomic landscape that reveals previously unrecognized cell types, pathways, and mechanisms of gene expression regulation. These data may contribute to the efficacy and outcome of UCB transplant, broadening the scope of research and clinical innovations.