scholarly journals Identification of Two Long Non-Coding RNAs AC010082.1 and AC011443.1 as Biomarkers of Coronary Heart Disease Based on Logistic Stepwise Regression Prediction Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Liu ◽  
Lanchun Liu ◽  
Jialiang Gao ◽  
Jie Wang ◽  
Yongmei Liu
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Microarray Data ◽  
Stepwise Regression ◽  
Validation Cohort ◽  
Healthy Controls ◽  
Training Set ◽  
Test Set ◽  
Cerna Network

Coronary heart disease (CHD) is a global health concern with high morbidity and mortality rates. This study aimed to identify the possible long non-coding RNA (lncRNA) biomarkers of CHD. The lncRNA- and mRNA-related data of patients with CHD were downloaded from the Gene Expression Omnibus database (GSE113079). The limma package was used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively). Then, miRcode, TargetScan, miRDB, and miRTarBase databases were used to form the competing endogenous RNA (ceRNA) network. Furthermore, SPSS Modeler 18.0 was used to construct a logistic stepwise regression prediction model for CHD diagnosis based on DElncRNAs. Of the microarray data, 70% was used as a training set and 30% as a test set. Moreover, a validation cohort including 30 patients with CHD and 30 healthy controls was used to verify the hub lncRNA expression through real-time reverse transcription-quantitative PCR (RT-qPCR). A total of 185 DElncRNAs (114 upregulated and 71 downregulated) and 382 DEmRNAs (162 upregulated and 220 downregulated) between CHD and healthy controls were identified from the microarray data. Furthermore, through bioinformatics prediction, a 38 lncRNA-21miRNA-40 mRNA ceRNA network was constructed. Next, by constructing a logistic stepwise regression prediction model for 38 DElncRNAs, we screened two hub lncRNAs AC010082.1 and AC011443.1 (p < 0.05). The sensitivity, specificity, and area under the curve were 98.41%, 100%, and 0.995, respectively, for the training set and 93.33%, 91.67%, and 0.983, respectively, for the test set. We further verified the significant upregulation of AC010082.1 (p < 0.01) and AC011443.1 (p < 0.05) in patients with CHD using RT-qPCR in the validation cohort. Our results suggest that lncRNA AC010082.1 and AC011443.1 are potential biomarkers of CHD. Their pathological mechanism in CHD requires further validation.

scholarly journals A 5-Year Survival Prediction Model for Chronic Heart Failure Patients Induced by Coronary Heart Disease with Traditional Chinese Medicine Intervention

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Guan ◽  
Guo-Hua Dai ◽  
Wu-Lin Gao ◽  
Xue Zhao ◽  
Zhen-Hao Cai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Chinese Medicine ◽  
Chronic Heart Failure ◽  
Traditional Chinese Medicine ◽  
Prediction Model ◽  
Protective Factor ◽  
Survival Prediction ◽  
Training Set

Objective. This study aimed to construct a 5-year survival prediction model of coronary heart disease (CHD) induced chronic heart failure (CHF), which is supported by the traditional Chinese medicine (TCM) factor, and to verify the model. Methods. Inpatients from January 1, 2012, to December 31, 2017, in seven hospitals in Shandong Province were studied. The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). In the training set, the least absolute shrinkage selection operator regression was first used to screen the independent variables. Logistic regression was then applied to construct a survival prediction model. The following nomogram visualizes the prediction model results. Finally, C-indices, calibration curves, and decision curves were used to discriminate and calibrate the established model and evaluate its practicability in the clinic. Bootstrap resampling and the verification set were used for internal and external verification, respectively. Results. A total of 424 eligible patients were included in the model construction and verification. In this 5-year survival prediction model of patients with CHF induced by CHD, eight independent predictors were included. The series of C-indices for the training set, bootstrap resamples, and verification set was 0.885, 0.867, and 0.835, respectively, demonstrating the credibility of our model. Additionally, the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis of the training and verification sets showed that this 5-year survival prediction model was good in discrimination, calibration, and clinical practicability. Conclusion. This work highlights eight independent factors affecting 5-year mortality in patients with CHF induced by CHD after discharge and further helps reallocate medical resources rationally by precisely identifying high-risk groups. The constructed prediction model not only plays a credible role in prediction but also demonstrates TCM intervention as a protective factor for the 5-year death of patients with CHF induced by CHD, thereby advancing the use of TCM in CHF.

Abstract P416: An Externally Validated and Highly Sensitive Artificial Intelligence-driven Integrated Genetic-epigenetic DNA Test for Incident Coronary Heart Disease Prediction

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Meesha Dogan ◽  
Robert Philibert
Keyword(s):  
Dna Methylation ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Score ◽  
Digital Pcr ◽  
Test Set ◽  
Dna Test ◽  
Logistic Regression Models ◽  
Chd Risk

Despite being largely preventable, every year, coronary heart disease (CHD) affects millions of Americans. Currently, primary prevention of CHD starts with an understanding of several risk factors (e.g. age, gender, diabetes) that are aggregated to derive an incident CHD risk score using risk calculators such as the Framingham Risk Score (FRS) and ASCVD Pooled Cohort Equation (PCE). Unfortunately, this approach involves multiple tests and its lack in sensitivity could result in ineffective treatment recommendations to patients for the prevention of CHD. As an alternative, we have developed and independently validated a single, simpler, AI-driven, digital PCR (dPCR)-based integrated genetic-epigenetic DNA test that can more sensitively estimate the 3-year incident CHD risk for both men and women. Our technology accounts for the inherited and acquired (lifestyle/environment) risks for CHD through three genetic (SNP) and three epigenetic (DNA methylation) biomarkers, respectively, and can be performed on DNA from blood or saliva. The incident CHD risk prediction model was developed using genome-wide DNA methylation and genotype data from the Framingham Heart Study (FHS) Offspring cohort (n=1172 in training set, n=512 in test set) and was validated in an Intermountain Healthcare (IM) cohort (n=80 in validation set, n=79 in test set). The final prediction model is an ensemble of SVM, Random Forest and Logistic Regression models, and its performance in the FHS and IM test sets is summarized in Table 1. The FRS and PCE risk calculators were implemented on all FHS and IM cohort data. A clinically implementable version of this tool as part of its translation into a Laboratory Developed Test incorporates standard Taqman assays for genotyping and custom dPCR assays for DNA methylation quantification. The strong correlation between our dPCR assay values and the Illumina array values are shown in Figure 1, indicating successful dPCR assay translation. We are also extending this tool to include more ethnically diverse cohorts.

scholarly journals Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings

2005 ◽  
Vol 12 (5) ◽  
pp. 459-464 ◽  
Author(s):  
Guizhou Hu ◽  
Martin M. Root
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Empirical Model ◽  
Complex Disease ◽  
Prediction Models ◽  
Longitudinal Research ◽  
Study Data ◽  
Individual Risk ◽  
Data Set

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

Adaptive mining prediction model for content recommendation to coronary heart disease patients

2013 ◽  
Vol 17 (3) ◽  
pp. 881-891 ◽  
Author(s):  
Jae-Kwon Kim ◽  
Jong-Sik Lee ◽  
Dong-Kyun Park ◽  
Yong-Soo Lim ◽  
Young-Ho Lee ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Content Recommendation

AI-based prediction model for the risk of diabetic kidney disease in patients with type 2 diabetes mellitus

2021 ◽  
Author(s):  
Ning Zhang ◽  
Rui Fan ◽  
Jing Ke ◽  
Qinghua Cui ◽  
Dong ZHAO
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Prediction Model ◽  
Diabetic Kidney Disease ◽  
Feature Selection Method ◽  
Training Set ◽  
Test Set ◽  
Diabetic Kidney

Abstract BackgroundMicroalbuminuria is the main characteristic of Diabetic kidney disease (DKD), but it fluctuates greatly under the influence of blood glucose. Our aim was to establish some common clinical variables which could be easily collected to predict the risk of DKD in patients with type 2 diabetes. Methods and resultsWe build an artificial intelligence (AI) model to quantitively predict the risk of DKD based on the biomedical parameters from 1239 patients. An information entropy-based feature selection method was applied to screen out the risk factors of DKD. The dataset was divided with 4/5 into the training set and 1/5 into the test set. By using the selected risk factors, 5-fold cross-validation is applied to train the prediction model and it finally got AUC of 0.72 and 0.71 in the training set and test set respectively. In addition, we provide a method of calculating risk factors’ contribution for individuals to provide personalized guidance for treatment. We set up web-based application available on http://www.cuilab.cn/dkd for self-check and early warning. ConclusionsWe establish a feasible prediction model for DKD and suggest the degree of risk contribution of each indicator for each individual, which has certain clinical significance for early intervention and prevention.

Preference for full-fat over low-fat foods among individuals suffering from coronary heart disease and healthy controls

2009 ◽  
Vol 98 (4) ◽  
pp. 489-497 ◽  
Author(s):  
Erika Rapp ◽  
Åsa Öström ◽  
Walter Osika ◽  
Anders Englund ◽  
Judith Annett ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Healthy Controls ◽  
Low Fat

Asymmetric and symmetric dimethylarginine in patients presenting with risk factors for coronary heart disease

Open Medicine ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. 659-664
Author(s):  
Marina Ilic ◽  
Radmila Pavlovic ◽  
Gordana Lazarevic ◽  
Tatjana Cvetkovic ◽  
Gordana Kocic ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Risk Factor ◽  
Heart Disease ◽  
Diabetic Patients ◽  
Healthy Controls ◽  
Middle Aged ◽  
Control Groups ◽  
Symmetric Dimethylarginine ◽  
Patient Groups

AbstractThe aim of the present study was to investigate asymmetric (ADMA) and symmetric dimethylarginine (SDMA) production in patients presenting with one or more risk factor (RF) for coronary heart disease (CHD). Patients and methods: Overall, 113 participants were enrolled in the study, including 45 patients presenting with risk for CHD (27 male and 18 female; aged 55.9 ± 6.4 years), 30 sex and age-matched middle-aged healthy controls (16 male and 14 female; aged 56.3 ± 8.4 years), and 38 young healthy controls (38 male; aged 24.6 ± 3.9 years). Results: No significant differences for ADMA and SDMA were recorded between patients groups presenting with risk for CHD. However, ADMA and SDMA were significantly higher in all examined patient groups (≥3 and 1–2 RF, hypertensive and non-hypertensive, obese and non-obese, diabetics and non-diabetics) compared with both control groups (middle-aged and young controls) (p<0.001). ADMA significantly correlated with SDMA in ≥3 RF (p<0.05), hypertensive (p<0.05), non-obese (p<0.05), non-diabetics (p<0.01), as well in middle-aged (p<0.05) and young controls (p<0.001). Conclusion: Significantly higher ADMA and SDMA were found between patients presenting with risk for CHD (≥3 and 1–2 RF, hypertensive and nonhypertensive, obese and non-obese, diabetics and non-diabetics) and healthy, middle-aged and young controls. ADMA significantly correlated with SDMA in ≥3 RF, hypertensive, non-obese and non-diabetic patients, as well as in middle-aged and young controls.

Serum microRNA signatures for the detection of pancreatobiliary cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15718-e15718
Author(s):  
Shuichi Mitsunaga ◽  
Shogo Nomura ◽  
Kazuo Hara ◽  
Yukiko Takayama ◽  
Makoto Ueno ◽  
...  
Keyword(s):  
Validation Cohort ◽  
External Validation ◽  
Diagnostic Value ◽  
Healthy Controls ◽  
Mirna Signature ◽  
Training Set ◽  
Linear Discriminant ◽  
Serum Mirna ◽  
Validation Set ◽  
Sensitivity Specificity

e15718 Background: The diagnostic value of serum microRNAs (miRNA) in a highly sensitive microarray for pancreatobiliary cancer (PBca) has been demonstrated. This study attempted to build and validate a signature comprised of multiple serum miRNA markers for discriminating PBca from healthy controls. Methods: A multicenter prospective study on the diagnostic performance of serum miRNAs was conducted. The patients (pts) with treatment-naïve PBca and healthy participants aged ≥60 years were enrolled. Clinical data and sera were collected. Target population was randomly divided to training or validation cohort with an allocation ratio of 2:1. Twenty-nine serum miRNA markers on the microarray data were analyzed. Using any combinations of the markers, a Fisher’s linear discriminant analysis was performed, and the resulting sensitivity, specificity and AUC of ROC curve to discriminate PBca from healthy controls were calculated for each combination. Marker combinations with a sensitivity/specificity (SN/SP) of ≥80%/90% and high AUC in comparison with AUC of CA19-9 were defined as the diagnostic miRNA signature, which were selected in the training cohort. Next, the signatures were screened out which showed a good reproducibility in the validation cohort. As an independent external cohort, PBca pts and healthy with pooled frozen sera were enrolled and the identified miRNA signatures were further validated. Results: Total of 546 participants (80 healthy and 223 PBca in training set, 40 healthy and 104 PBca in validation set, 49 healthy and 50 PBca in external validation set) were analyzed in this study. Four serum miRNA combinations were identified as the diagnostic miRNA signature. In the training set, four miRNA signatures, consisted of 10 miRNAs, were developed. For the best-performed miRNA signature, the SN/SP and AUC in the validation and external validation cohorts were 84/90% and 0.95 (CA19-9: 73/95% and 0.88) and 84/90% and 0.93 (CA19-9: 80/94% and 0.87), respectively. Conclusions: The diagnostic serum miRNA signatures for PBca were identified in this study.

Prognostic value of a brief loneliness questionnaire for patients with coronary heart disease: Proposal for a prediction model

2021 ◽  
Author(s):  
Mitti Blakoe ◽  
Anne Vinggaard Christensen ◽  
Pernille Palm ◽  
Ida Elisabeth Højskov ◽  
Lars Thrysoee ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Prognostic Value
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