Gene Variation of Endoplasmic Reticulum Aminopeptidases 1 and 2, and Risk of Blood Pressure Progression and Incident Hypertension among 17,255 Initially Healthy Women

Recent studies have demonstrated the importance of endoplasmic reticulum aminopeptidase (ERAP) in blood pressure (BP) homeostasis. To date, no large prospective, genetic–epidemiological data are available on genetic variation within ERAP and hypertension risk. The association of 45 genetic variants of ERAP1 and ERAP2 was investigated in 17,255 Caucasian female participants from the Women’s Genome Health Study. All subjects were free of hypertension at baseline. During an 18-year follow-up period, 10,216 incident hypertensive cases were identified. Multivariable linear, logistic, and Cox regression analyses were performed to assess the relationship of genotypes with baseline BP levels, BP progression at 48 months, and incident hypertension assuming an additive genetic model. Linear regression analyses showed associations of four tSNPs (ERAP1: rs27524; ERAP2: rs3733904, rs4869315, and rs2549782; all p<0.05) with baseline systolic BP levels. Three tSNPs (ERAP1: rs27851, rs27429, and rs34736, all p<0.05) were associated with baseline diastolic BP levels. Multivariable logistic regression analysis showed that ERAP1 rs27772 was associated with BP progression at 48 months (p=0.0366). Multivariable Cox regression analysis showed an association of three tSNPs (ERAP1: rs469783 and rs10050860; ERAP2: rs2927615; all p<0.05) with risk of incident hypertension. Analyses of dbGaP for genotype–phenotype association and GTEx Portal for gene expression quantitative trait loci revealed five tSNPs with differential association of BP and nine tSNPs with lower ERAP1 and ERAP2 mRNA expression levels, respectively. The present study suggests that ERAP1 and ERAP2 gene variation may be useful for risk assessment of BP progression and the development of hypertension.

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Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

10.21203/rs.3.rs-915700/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Xiaoying Wang ◽

Yue Han ◽

Xun Li

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Correlation Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Pearson Correlation ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Abstract P254: Daytime Napping is Associated With Incident Stroke in Community

Circulation ◽

10.1161/circ.141.suppl_1.p254 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Xuting Jin ◽

Bin Yan ◽

Ruohan Li ◽

Ya Gao ◽

Jingjing Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

Life Quality ◽

Health Study ◽

Sleep Habits ◽

Community Based ◽

Cox Regression Analysis ◽

Increased Risk ◽

Registration Number ◽

The Relationship

Introduction: There are conflicting reports regarding whether daytime napping is a risk factor for cardiovascular events. The purpose of this study was to investigate the relationship between daytime napping and incident stroke within a community-based cohort study. Hypothesis: We assessed the hypothesis that the duration and the frequency of daytime napping may be associated with incident stroke. Methods: Participants without previous stroke were enrolled in the present prospective study from the Sleep Heart Health Study (registration number, NCT00005275). Daytime napping were assessed with a self-reported Sleep Habits Questionnaire. Duration of daytime napping was divided into the following categories: no naps, 0-30 min, 31-60 min, or >60 min. Frequency of naps were categorised as: no naps, 1-2 times/week, 3-4 times/week, 5-6 times/week, or daily. After combining nap duration and frequency, participants were further divided into groups with regular long naps (≥5 times per week and >30 min), regular short naps (≥5 times per week and ≤30 min), irregular naps or no naps. Subsequently, participants were followed up until the first stroke occurred between the date of the completed questionnaire and the final censoring date. Cox regression analysis was used to estimate the relationship between daytime napping and incident stroke. Results: The present study enrolled 4757 participants, of which 220 participants (4.6%) experienced incident stroke during an average follow-up of 10.6 years. There was a higher rate of stroke among participants taking longer and more frequent naps than others. Multivariate Cox regression analysis indicated that, when compared with participants with no naps, those with a nap duration of ≥60 min or of 31-60 min had a higher risk of stroke (HR, 2.182; 95% CI, 1.443-3.301; HR, 1.594; 95% CI, 1.003-2.531, respectively). Moreover, there was an increased risk of stroke among participants taking daily daytime naps (HR, 1.563; 95% CI, 1.059-2.307) or napping 5-6 times per week (HR, 1.548; 95% CI, 1.026-2.335) than those with no naps. And after combining nap duration and frequency, regular long naps and regular short naps were also associated with higher risk of incident stroke (HR, 1.903; 95% CI, 1.182-3.065; HR, 1.451; 95% CI, 1.010-2.084, respectively). Conclusions: In conclusion, daytime napping of long duration and high frequency may increase the risk of incident stroke in community. Modification of sleep habits may improve the life quality among those elderly community-based population.

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Association of masseter area and radiodensity with three-month survival after proximal anterior circulation occlusion

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2020-015837 ◽

2020 ◽

Vol 13 (1) ◽

pp. 25-29 ◽

Cited By ~ 1

Author(s):

Iisa Lindström ◽

Sara Protto ◽

Niina Khan ◽

Jussi Hernesniemi ◽

Niko Sillanpää ◽

...

Keyword(s):

Regression Analysis ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Cox Regression ◽

Multivariable Logistic Regression Analysis ◽

Anterior Circulation ◽

Term Survival ◽

Cox Regression Analysis ◽

Kaplan Meier

BackgroundMasseter area (MA), a surrogate for sarcopenia, appears to be useful when estimating postoperative survival, but there is lack of consensus regarding the potential predictive value of sarcopenia in acute ischemic stroke (AIS) patients. We hypothesized that MA and density (MD) evaluated from pre-interventional CT angiography scans predict postinterventional survival in patients undergoing mechanical thrombectomy (MT).Materials and methods312 patients treated with MT for acute occlusions of the internal carotid artery (ICA) or the M1 segment of the middle cerebral artery (M1-MCA) between 2013 and 2018. Median follow-up was 27.4 months (range 0–70.4). Binary logistic (alive at 3 months, OR <1) and Cox regression analyses were used to study the effect of MA and MD averages (MAavg and MDavg) on survival.ResultsIn Kaplan–Meier analysis, there was a significant inverse relationship with both MDavg and MAavg and mortality (MDavg P<0.001, MAavg P=0.002). Long-term mortality was 19.6% (n=61) and 3-month mortality 12.2% (n=38). In multivariable logistic regression analysis at 3 months, per 1-SD increase MDavg (OR 0.61, 95% CI 0.41 to 0.92, P=0.018:) and MAavg (OR 0.57, 95% CI 0.35 to 0.91, P=0.019) were the independent predictors associated with lower mortality. In Cox regression analysis, MDavg and MAavg were not associated with long-term survival.ConclusionsIn acute ischemic stroke patients, MDavg and MAavg are independent predictors of 3-month survival after MT of the ICA or M1-MCA. A 1-SD increase in MDavg and MAavg was associated with a 39%–43% decrease in the probability of death during the first 3 months after MT.

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Impact in delay of start of chemotherapy and surgery on pCR and survival in breast cancer: A pooled analysis of individual patient data from six prospectively randomized neoadjuvant trials.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.571 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 571-571 ◽

Cited By ~ 2

Author(s):

Sibylle Loibl ◽

Gustavo Werutsky ◽

Valentina Nekljudova ◽

Sabine Seiler ◽

Jens Uwe Blohmer ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Disease Free Survival ◽

Pooled Analysis ◽

Multivariable Logistic Regression Analysis ◽

Time Interval ◽

Cox Regression Analysis ◽

Study Results ◽

Survival Endpoints

571 Background: Time interval from diagnostic biopsy to neoadjuvant chemotherapy (NACT) start (TBC) and from last chemotherapy application to surgery (TCS) are influenced by many factors. It is unclear whether a delay of systemic therapy or surgery impacts patients (pts) outcome. Methods: 9127 pts with early BC from 6 German neoadjuvant trials receiving an anthracycline-taxane based NACT were included. pCR (ypT0/is ypN0), disease free survival (DFS) and overall survival (OS) were compared according to TBC and TCS length (cut-off of ≤4 vs >4weeks (w)), overall and in subgroups (BC subtypes [luminal, HER2+, triple-negative breast cancer (TNBC)] and pCR [yes vs no] for survival endpoints) adjusted by study. Results: Data on TBC were available for 8072 pts, on TCS for 6420, on follow-up (FU) for 7889. Median age was 49 yrs, 25.6% had cT3-4, 48.6% N+, 44.1% G3, 46.0% luminal, 26.4% TNBC, 27.6% HER2+ tumors. Median (m) FU-time was 65 months [0-201]. mTBC was 23 days [0-228] (67.5% ≤4w vs 32.5% >4w), mTCS was 28d [0-204] (53.7% ≤4w vs 46.3% >4w), with inter-study variability for mTBC ranging from 14 to 32d and for mTCS ranging from 24 to 29d from the oldest to the most recently conducted study. TBC did not influence the pCR rate, neither in all patients nor in subgroups. At multivariable logistic regression analysis TBC length did not independently predict pCR. TBC did not influence DFS or OS, neither in all patients nor in subgroups. TCS<4w was associated with a trend towards a better DFS in all patients (HR=1.11 95%CI (0.99-1.24), p=0.08) and in pts not achieving pCR (HR=1.12, 95%CI (0.99-1.26), p=0.08). No difference was observed within BC subtypes. OS was not impacted by TCS length. At multivariable Cox regression analysis TBC or TCS≤4 vs >4w did not independently influence DFS or OS. Conclusions: A delay in starting NACT does not impact the pCR rate, DFS or OS and results are independent of the subgroup. However, early surgery after NACT in pts without pCR seems to influence outcome. Our analysis is explorative, but indicates for the first time, that time interval of starting NACT and undergoing surgery might be uncritical. Further research is ongoing.

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Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.641487 ◽

2021 ◽

Vol 11 ◽

Author(s):

Peng Liu ◽

Jinhong Wei ◽

Feiyu Mao ◽

Zechang Xin ◽

Heng Duan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Endoplasmic Reticulum ◽

Regression Analysis ◽

Endoplasmic Reticulum Stress ◽

Cox Regression ◽

Prognostic Biomarker ◽

Cancer Genome ◽

Survival Prediction ◽

Training Cohort ◽

Cox Regression Analysis

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v1 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

10.21203/rs.3.rs-50552/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Genome ◽

Regression Analyses ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Tcga Dataset

Abstract Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

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Pulmonary hypertension: state of problem and analysis of referential center work (results of first Ukrainian Register)

Ukrainian Journal of Cardiology ◽

10.31928/1608-635x-2019.4.5672 ◽

2019 ◽

Vol 26 (4) ◽

pp. 56-72

Author(s):

G. D. Radchenko ◽

I. O. Zhyvylo ◽

Yu. M. Sirenko

Keyword(s):

Blood Pressure ◽

Pulmonary Hypertension ◽

Regression Analysis ◽

Survival Rate ◽

Connective Tissue ◽

Systolic Blood Pressure ◽

Right Ventricular ◽

Cox Regression ◽

Cox Regression Analysis ◽

The Right

The aim – to analyze the structure of patients who were treated in specialized referential center; to evaluate Ukrainian reality of survival of patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) who were treated at the referential center and to determine the predictors of death. Materials and methods. Data of 359 patients with pulmonary hypertension were included in the study. To assess survival, 281 patients (52 (18.5 %) with CTEPH and 229 (81.5 %)) with PAH were examined who were treated at the center of pulmonary hypertension of M.D. Strazhesko Institute of Cardiology of NAMS of Ukraine. The diagnosis of pulmonary hypertension was based on the data of the catheterization of the right heart. Survival was determined by the method of constructing the Kaplan – Meier curves. The observation period was 51 months. Predictors were determined using binary logistic regression and Cox regression analysis. Results and discussion. The survival rate of the overall patient cohort was 93.3 %, 86.8 % and 81.5 % at stages one, two, and three, respectively. The best survival rate was in patients with PAH associated with congenital heart diseases – CHD (92.7 %) compared to patients with idiopathic PAH (67.5 %, long rank р=0.002), PAH associated with connective tissue diseases (49.7 %, long rank р=0.001) and CTEPH (83.2 %, long rank р=0.04). According to the univariate Cox analysis, the predictors of death were: functional class IV according to the WHO (OR=4.94, 95 % CI 2.12–11.48), presence of ascites (OR=4.52, 95 % CI 2.21–9.24), PAH associated with connective tissue disease (OR=3.07, 95 % CI 1.07–8.87), PAH associated with CHD (OR=0.28, 95 % CI 0.11–0.68), heart rate at the background of treatment > 105 beats per minute (OR=7.85, 95 % CI 1.83–33.69), office systolic blood pressure < 100 mm Hg (OR=2.78, 95 % CI 1.26–6.1), the distance of the 6-minute test at the background of treatment < 340 m (OR=3.47, 95 % CI 1.01–12.35), NT-proBNP level > 300 pg/ml (OR=4.98, 95 % CI 1.49–16.6), right atrial area > 22 cm2 (OR=14.2, 95 % CI 1.92–104.89), right ventricular area in diastole (OR=1.08, 95 % CI 1.03–1.14), right ventricular area in systole (OR=1.08, 95 % CI 1.02–1.11), 1 mm Hg increase of mean pressure in the right atrium (OR=1.02, 95 % CI 1.02–1.19). In multivariate Cox regression analysis, independent predictors of death were ascites, office systolic blood pressure < 100 mm Hg, and NT-proBNP level > 300 pg/ml. PAH associated with CHD reduced the likelihood of death. Conclusions. These are the first results of the study that was conducted at the only Ukrainian center for the diagnosis and treatment of pulmonary hypertension. They show that the structure of patients with PAH in Ukraine is significantly different from that in centers of other countries, though the survival rates are comparable. Quite simple indicators are found which can be evaluated in routine practice and which are independent predictors of death.

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Clinical and Genetic Determinants of Heart Failure: Optimized by Machine Learning and Mendelian Randomization

10.21203/rs.3.rs-670567/v1 ◽

2021 ◽

Author(s):

liao li zhen ◽

chen zhi chong ◽

li wei dong ◽

liao xin xue ◽

zhuang xiao dong

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Heart Failure ◽

Blood Pressure ◽

Regression Analysis ◽

Mendelian Randomization ◽

Cox Regression ◽

Atherosclerosis Risk In Communities ◽

Cox Regression Analysis ◽

Potential Risk Factors

Abstract Background: Identifying unrecognized, potentially modifiable risk factors is essential for heart failure (HF) management.Methods: The Atherosclerosis Risk in Communities (ARIC) study was used for machine learning (ML) to establish the top 20 important variables as potential risk factors for HF. Multivariable Cox regression analysis was performed in an explorative manner to find independent factors for HF and Mendelian randomization (MR) analysis to address causality.Results: Of the 14,842 participants included in the ARIC analysis, 20.4% of participants (3,028) were identified as HF. The 20 variables with the highest importance selected by ML were creatinine, glucose, age, previous coronary artery disease (CAD), systolic blood pressure, fibrinogen, albumin, income, diabetes, magnesium, insulin, white blood cell, hemoglobin, sodium, education, phosphorus, diastolic blood pressure, protein-c, heart rate and body mass index (BMI). Cox regression analysis demonstrated 19 independently associated variables except sodium. MR analysis provided evidence supporting that genetically determined BMI, CAD, diabetes and education was causally associated with HF.Conclusions: The ML plus MR framework was useful in identifying important causal factors of HF. BMI, CAD, diabetes, and education not only served as excellent prognostic factors for HF, but therapeutics targeted at these factors were likely to prevent HF effectively.

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