高血压性心脏病生物标志物的筛选：基于中国重庆市7家医疗机构的数据分析研究

目的:筛选高血压性心脏病(HHD)的影响因素,建立HHD的预测模型,为HHD的发生提供预警。方法:选取中国重庆市某医科院校数据研究院平台2016年1月1日至2019年12月31日主要诊断为高血压性心脏病和高血压患者。通过单因素分析、多因素分析筛选HHD的影响因素,采用R语言分别构建Logistics模型、随机森林(RF)模型和极限梯度上升(XGBoost)模型。结果:单因素分析筛选出60项差异指标,多因素分析筛选出18项差异指标(P0.05)。Logistics模型、RF模型、XGBoost模型曲线下面积(AUC)分别为0.979、0.983和0.990。结论:本文建立的3种HHD预测模型结果稳定,其中XGBoost模型对于HHD的发生具有良好的诊断效应。     

Platelet biomarkers for a descending cognitive function: A proteomic approach

Memory loss is the most common clinical sign in Alzheimer''s disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in‐depth platelet proteomic analysis using TMT‐LC‐MS/MS in the populations with mild cognitive impairment (MCI, MMSE = 18–23), severe cognitive impairments (AD, MMSE = 2–17), and the age‐/sex‐matched normal cognition controls (MMSE = 29–30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP‐activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS‐DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD.     

Blood‐based biomarkers for Down syndrome and Alzheimer's disease: A systematic review

Down syndrome (DS) occurs due to triplication of chromosome 21. Individuals with DS face an elevated risk for development of Alzheimer's disease (AD) due to increased amyloid beta (Aβ) resulting from the over‐expression of the amyloid precursor protein found on chromosome 21. Diagnosis of AD among individuals with DS poses particular challenges resulting in an increased focus on alternative diagnostic methods such as blood‐based biomarkers. The aim of this review was to evaluate the current state of the literature of blood‐based biomarkers found in individuals with DS and particularly among those also diagnosed with AD or in prodromal stages (mild cognitive impairment [MCI]). A systematic review was conducted utilizing a comprehensive search strategy. Twenty‐four references were identified, of those, 22 fulfilled inclusion criteria were selected for further analysis with restriction to only plasma‐based biomarkers. Studies found Aβ to be consistently higher among individuals with DS; however, the link between Aβ peptides (Aβ1‐42 and Aβ1‐40) and AD among DS was inconsistent. Inflammatory‐based proteins were more reliably found to be elevated leading to preliminary work focused on an algorithmic approach with predominantly inflammatory‐based proteins to detect AD and MCI as well as predict risk of incidence among DS. Separate work has also shown remarkable diagnostic accuracy with the use of a single protein (NfL) as compared to combined proteomic profiles. This review serves to outline the current state of the literature and highlights the potential plasma‐based biomarkers for use in detecting AD and MCI among this at‐risk population.     

CLADES: A classification‐based machine learning method for species delimitation from population genetic data

Species are considered to be the basic unit of ecological and evolutionary studies. As multilocus genomic data are increasingly available, there have been considerable interests in the use of DNA sequence data to delimit species. In this study, we show that machine learning can be used for species delimitation. Our method treats the species delimitation problem as a classification problem for identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purposes. Each pair of known populations is combined to form training samples with a label of “same species” or “different species”. We use support vector machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: “same species” or “different species”. Given multilocus genomic data of multiple related organisms or populations, our method (called CLADES) performs species delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in delimitation, for example with short species divergence time and gene flow.     

冠状动脉粥样硬化性心脏病8号染色体基因扫描

冠状动脉粥样硬化性心脏病(Coronary heart disease,CHD)的全基因组扫描研究在世界各大研究中心展开,关于CHD易感基因位点的报道多集中于1号、3号、9号、11号、16号染色体,对8号染色体的研究报道甚少。在汉族人群中未见关于CHD的8号染色体的基因扫描研究。文章旨在查找汉族人群中CHD易感基因位点,选取8号染色体上间隔10 cM遗传距离的13个微卫星遗传位点,采用DNA混合池的方法对CHD患者组156例和正常对照组1 000例DNA样本进行基因扫描,经卡方检验分析患者组和对照组每个位点的等位基因频率差异。发现在患者组与对照组中D8S264位点(8p23.3-p23.2)及D8S285位点(8q12.1)的等位基因频率差异有统计学意义(P<0.05)。汉族人群中CHD患者8号染色体上8p23.3-p23.2、8q12.1区域可能存在CHD易感基因,需要进行候选基因突变筛查。     

Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)

Abstract

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes.

Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression.

Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12–3.32, p?=?0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism.

Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.

• Clinical significance

• More insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolism

• Biomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.

• Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

     

基于“心合小肠”理论探讨肠道菌群是冠心病干预的新靶点

心脑血管疾病的高发病率与高病死率对患者造成了极大的生理与心理压力,同时心脑血管疾病对人体器官具有巨大的危害性且并发症呈现多样性。因此,寻求新的干预靶点是治疗心脑血管疾病的关键。以肠道菌群为代表的微生物干预治疗技术为心脑血管疾病的治疗提供了新靶点,同时肠道菌群对心脑血管疾病的干预作用是以中医“心合小肠”理论为重要依据。本文以冠心病为例,通过分析中医心与小肠生理、病理及经络联系,西医结合肠道菌群及其不同代谢产物对冠心病的主要调节作用,在“心合小肠”为重要理论依据的基础上揭示肠道菌群对冠心病治疗的重要靶向作用。

     

Regulation of G protein function: Implications for heart disease

Heterotrimeric GTP-binding and -hydrolyzing proteins (G proteins) link members of a family of seven-helix transmembrane receptors (G protein-coupled receptors, GPCR) to intracellular effectors. The coupling mechanism involves the G protein completing a cycle of activation, dissociation into and subunits, deactivation, and reassociation. At the center of this cycle is the subunit, in which activation by GPCR, GTPase activity, and regulation of effector are combined. Whereas G's functional domains and residues had already been inferred from mutagenesis studies, the recent solution of the crystal structure has elucidated the structural basis of subunit function. It is now clear that an irregularity in any GPCR pathway component could cause a physiological defect. This is confirmed by the identification of mutations in GPCR and G's in various human diseases. Although several cardiomyopathies are associated with abnormal GPCR function, mutations are unlikely in these disorders. The last few years, other aspects of G protein function have moved into focus: e.g. posttranslational modifications; effector regulation by subunits; GTPase activating protein (GAP) activity of effectors; G protein expression levels etc. When comparing the regulation of G protein functional activity in cAMP and in inositol phosphate generating pathways, an extrapolation can be made to data on the status of these pathways in some cardiovascular diseases.Abbreviations AC adenylate cyclase - GPCR G protein-coupled receptor - PLC phospholipase C - GAP GTPase activating protein - PTX pertussis toxin - Ptdins(4,5)P ₂ phosphatidylinositol 4,5-bisphosphate - Ins(1,4,5)P ₃ inositol 1,4,5-trisphosphate - CCh carbachol     

Cell therapy for ischaemic heart disease: focus on the role of resident cardiac stem cells

Myocardial infarction results in loss of cardiomyocytes, scar formation, ventricular remodelling, and eventually heart failure. In recent years, cell therapy has emerged as a potential new strategy for patients with ischaemic heart disease. This includes embryonic and bone marrow derived stem cells. Recent clinical studies showed ostensibly conflicting results of intracoronary infusion of autologous bone marrow derived stem cells in patients with acute or chronic myocardial infarction. Anyway, these results have stimulated additional clinical and pre-clinical studies to further enhance the beneficial effects of stem cell therapy. Recently, the existence of cardiac stem cells that reside in the heart itself was demonstrated. Their discovery has sparked intense hope for myocardial regeneration with cells that are obtained from the heart itself and are thereby inherently programmed to reconstitute cardiac tissue. These cells can be detected by several surface markers (e.g. c-kit, Sca-1, MDR1, Isl-1). Both in vitro and in vivo differentiation into cardiomyocytes, endothelial cells and vascular smooth muscle cells has been demonstrated, and animal studies showed promising results on improvement of left ventricular function. This review will discuss current views regarding the feasibility of cardiac repair, and focus on the potential role of the resident cardiac stem and progenitor cells. (Neth Heart J 2009;17:199–207.)     

基于不同特征挖掘方法结合广义提升回归模型估测安徽省土壤pH

为探讨不同特征挖掘方法与广义提升回归模型相结合在数字土壤制图中的应用,本研究首先使用递归特征消除和过滤式两种特征筛选方法对环境协变量进行筛选,再分别使用原始环境协变量、筛选后的最优变量组合作为自变量,建立基于广义提升回归模型和随机森林模型的安徽省土壤pH预测模型并进行制图。结果表明: 引入两种特征挖掘方法均可有效提高广义提升回归模型和随机森林模型预测土壤pH的精度,并且可以起到降维的作用;相较于随机森林模型,广义提升回归模型的验证集预测精度略低,在训练集中,广义提升回归模型的精度却远高于随机森林模型,模型解释度高,整体效果较好;随机森林模型的主要参数ntree和mtry对于模型的影响程度较低,而不同参数对于广义提升回归模型的预测精度影响较大,不同参数组合模型精度不同,建模前需要进行调参。空间制图结果表明,安徽省土壤pH呈“南酸北碱”趋势。