The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data.

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

Identification of Circulating Plasma Proteins as a Mediator of Hypertension-Driven Cardiac Remodeling: A Mediation Mendelian Randomization Study.

BACKGROUND: This study focused on circulating plasma protein profiles to identify mediators of hypertension-driven myocardial remodeling and heart failure. METHODS: A Mendelian randomization design was used to investigate the causal impact of systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure on 82 cardiac magnetic resonance traits and heart failure risk. Mediation analyses were also conducted to identify potential plasma proteins mediating these effects. RESULTS: Genetically proxied higher SBP, DBP, and pulse pressure were causally associated with increased left ventricular myocardial mass and alterations in global myocardial wall thickness at end diastole. Elevated SBP and DBP were linked to increased regional myocardial radial strain of the left ventricle (basal anterior, mid, and apical walls), while higher SBP was associated with reduced circumferential strain in specific left ventricular segments (apical, mid-anteroseptal, mid-inferoseptal, and mid-inferolateral walls). Specific plasma proteins mediated the impact of blood pressure on cardiac remodeling, with FGF5 (fibroblast growth factor 5) contributing 2.96% (P=0.024) and 4.15% (P=0.046) to the total effect of SBP and DBP on myocardial wall thickness at end diastole in the apical anterior segment and leptin explaining 15.21% (P=0.042) and 23.24% (P=0.022) of the total effect of SBP and DBP on radial strain in the mid-anteroseptal segment. Additionally, FGF5 was the only mediator, explaining 4.19% (P=0.013) and 4.54% (P=0.032) of the total effect of SBP and DBP on heart failure susceptibility. CONCLUSIONS: This mediation Mendelian randomization study provides evidence supporting specific circulating plasma proteins as mediators of hypertension-driven cardiac remodeling and heart failure.

Association of estimated carotid-femoral pulse wave velocity with frailty in middle-aged and older adults with cardiometabolic disease.

PURPOSE: The prevalence of frailty in individuals with cardiometabolic disease (CMD) has become a growing concern in public health. The purpose of this study was to investigate the association between estimated pulse wave velocity (ePWV) and frailty in middle-aged and older adults with CMD. METHODS: We analyzed data from 23,313 non-institutionalized adults with CMD from the National Health and Nutrition Examination Survey 2003-2018. Frailty status was determined using the frailty index, and logistic regression models were used to assess the association of ePWV with frailty risk. Multivariable logistic regression and propensity-score matching (PSM) were used to adjust for potential confounders. The restricted cubic spline regression model was used to evaluate the non-linear association between ePWV and frailty risk. RESULTS: After adjusting for potential confounding factors, we found that each one m/s increase in ePWV was associated with a 15% higher risk of frailty (odds ratio [OR] = 1.15, 95% confidence interval [CI] 1.12 to 1.18, P < 0.001). After PSM, the association remained significant (OR = 1.05, 95% CI 1.03 to 1.08, P < 0.001). The logistic models with restricted cubic splines showed a non-linear dose-response association, with the risk of frailty increasing more rapidly when ePWV exceeded 9.5 m/s. CONCLUSIONS: The findings of this study suggest that a higher level of ePWV is associated with an increased risk of frailty in middle-aged and older adults with CMD, and may serve as a viable alternative to directly measured cfPWV.

Association of remnant cholesterol with frailty: findings from observational and Mendelian randomization analyses.

BACKGROUND: Recent insights suggest that remnant cholesterol (RC) plays a role in cellular senescence, yet its specific contribution to frailty remains indeterminate. Through the integration of observational and mendelian randomization (MR) studies, this research explores the impact of elevated serum RC levels on frailty susceptibility. METHODS: A dual-method approach, combining an observational study with an MR study, was employed to investigate the connection between RC and frailty. The observational study included 11,838 participants from the National Health and Nutrition Examination Survey. Multivariable logistic regression and propensity score matching were employed to control for potential confounders. The non-linear relationship was assessed using restricted cubic splines. To circumvent observational study limitations, a two-sample MR analysis was conducted using the inverse-variance weighted method, leveraging genome-wide association studies (GWAS) data. RESULTS: After adjusting for potential confounding variables, the observational study identified a significant association between high serum RC levels and frailty in middle-aged and older adults (odds ratio [OR] = 1.67, 95% confidence interval [CI] = 1.20 to 2.33, P = 0.003), exhibiting a non-linear dose-response correlation (non-linear P = 0.011). This association persisted after propensity score matching (OR = 1.53, 95% CI = 1.14 to 2.06, P = 0.005). The MR study echoed these results, demonstrating a causal association of RC with the frailty index (ß = 0.059, 95% CI = 0.033 to 0.085, P = 1.05E-05), consistent with the observational findings (ß = 0.017, 95% CI = 0.008 to 0.026, P = 4.51E-04). CONCLUSION: This study provides evidence that higher RC levels amplify frailty risk in middle-aged and older adults, implying that the reduction of RC levels may present a promising strategy for frailty prevention and management.

Effect of botanical drugs in improving symptoms of hypertensive nephropathy: Analysis of real-world data, retrospective cohort, network, and experimental assessment.

Background/aim: Hypertensive nephropathy (HN) is a common complication of hypertension. Traditional Chinese medicine has long been used in the clinical treatment of Hypertensive nephropathy. However, botanical drug prescriptions have not been summarized. The purpose of this study is to develop a prescription for improving hypertensive nephropathy, explore the evidence related to clinical application of the prescription, and verify its molecular mechanism of action. Methods: In this study, based on the electronic medical record data on Hypertensive nephropathy, the core botanical drugs and patients' symptoms were mined using the hierarchical network extraction and fast unfolding algorithm, and the protein interaction network between botanical drugs and Hypertensive nephropathy was established. The K-nearest neighbors (KNN) model was used to analyze the clinical and biological characteristics of botanical drug compounds to determine the effective compounds. Hierarchical clustering was used to screen for effective botanical drugs. The clinical efficacy of botanical drugs was verified by a retrospective cohort. Animal experiments were performed at the target and pathway levels to analyze the mechanism. Results: A total of 14 botanical drugs and five symptom communities were obtained from real-world clinical data. In total, 76 effective compounds were obtained using the K-nearest neighbors model, and seven botanical drugs were identified as Gao Shen Formula by hierarchical clustering. Compared with the classical model, the Area under the curve (AUC) value of the K-nearest neighbors model was the best; retrospective cohort verification showed that Gao Shen Formula reduced serum creatinine levels and Chronic kidney disease (CKD) stage [OR = 2.561, 95% CI (1.025-6.406), p < 0.05]. With respect to target and pathway enrichment, Gao Shen Formula acts on inflammatory factors such as TNF-α, IL-1ß, and IL-6 and regulates the NF-κB signaling pathway and downstream glucose and lipid metabolic pathways. Conclusion: In the retrospective cohort, we observed that the clinical application of Gao Shen Formula alleviates the decrease in renal function in patients with hypertensive nephropathy. It is speculated that Gao Shen Formula acts by reducing inflammatory reactions, inhibiting renal damage caused by excessive activation of the renin-angiotensin-aldosterone system, and regulating energy metabolism.

Association of systemic inflammatory biomarkers with depression risk: Results from National Health and Nutrition Examination Survey 2005-2018 analyses.

Background/Aim: Depression has become a multiple disease worldwide, and is closely related to the systemic inflammatory response. Methods: Based on the data of the National Health and Nutrition Examination Survey (NHANES), this study included 2,514 depressive and 26,487 non-depressive adults. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) were used to quantify systemic inflammation. The multivariate logistic regression and inverse probability weighting methods were used to analyze the effect size of SII and SIRI on the risk of depression. Results: After adjusting for all confounders, the above associations of SII and SIRI with depression risk remained significant (SII, OR = 1.02, 95% CI = 1.01 to 1.02, p = 0.001; SIRI, OR = 1.06, 95% CI = 1.01 to 1.10, p = 0.016). Each 100-unit increase in SII was associated with a 2% increase in the risk of depression, while each one-unit increase in SIRI was associated with a 6% increase in the risk of depression. Conclusion: Systemic inflammatory biomarkers (SII and SIRI) significantly affected the risk of depression. SII or SIRI can serve as a biomarker of anti-inflammation treatment for depression.

Effect of dietary inflammatory potential on the aging acceleration for cardiometabolic disease: A population-based study.

Background/Aim: Optimized dietary patterns have been considered an important determinant of delaying aging in cardiometabolic disease (CMD). Dietary pattern with high-level dietary inflammatory potential is a key risk factor for cardiometabolic disease, and has drawn increasing attention. The aim of this study was to investigate whether dietary pattern with high dietary inflammatory potential was associated with aging acceleration in cardiometabolic disease. Materials and methods: We analyzed the cross-sectional data from six survey cycles (1999-2000, 2001-2002, 2003-2004, 2005-2006, 2007-2008, and 2009-2010) of the National Health and Nutritional Examination Surveys (NHANES). A total of 16,681 non-institutionalized adults and non-pregnant females with CMD were included in this study. Dietary inflammatory index (DII) was used to assess the dietary inflammatory potential. The two age acceleration biomarkers were calculated by the residuals from regressing chronologic age on Klemera-Doubal method biological age (KDM BioAge) or Phenotypic Age (PhenoAge), termed "KDMAccel" and "PhenoAgeAccel." A multivariable linear regression accounting for multistage survey design and sampling weights was used in different models to investigate the association between DII and aging acceleration. Four sensitivity analyses were used to ensure the robustness of our results. Besides, we also analyzed the anti-aging effects of DASH-type dietary pattern and "Life's Simple 7". Results: For 16,681 participants with CMD, compared with the first tertile of DII after adjusting for all potential confounders, the patients with second tertile of DII showed a 1.02-years increase in KDMAccel and 0.63-years increase in PhenoAgeAccel (KDMAccel, ß = 1.02, 95% CI = 0.64 to 1.41, P < 0.001; PhenoAgeAccel, ß = 0.63, 95% CI = 0.44 to 0.82, P < 0.001), while the patients with the third tertile of DII showed a 1.48-years increase in KDMAccel and 1.22-years increase in PhenoAgeAccel (KDMAccel, ß = 1.48, 95% CI = 1.02 to 1.94, P < 0.001; PhenoAgeAccel, ß = 1.22, 95% CI = 1.01 to 1.43, P < 0.001). In addition, DASH-type dietary pattern was associated with a 0.57-years reduction in KDMAccel (ß = -0.57, 95% CI = -1.08 to -0.06, P = 0.031) and a 0.54-years reduction in PhenoAgeAccel (ß = -0.54, 95% CI = -0.80 to -0.28, P < 0.001). The each one-unit increase in CVH score was associated with a 1.58-years decrease in KDMAccel (ß = -1.58, 95% CI = -1.68 to -1.49, P < 0.001) and a 0.36-years in PhenoAgeAccel (ß = -0.36, 95% CI = -0.41 to -0.31, P < 0.001). Conclusion: Among CMD, the dietary pattern with high dietary inflammatory potential was association with aging acceleration, and the anti-aging potential of DASH-type dietary pattern and "Life's Simple 7" should also be given attention, but these observations require future prospective validation.

Predicting Coupled Herbs for the Treatment of Hypertension Complicated with Coronary Heart Disease in Real-World Data Based on a Complex Network and Machine Learning.

Hypertension and coronary heart disease are the most common cardiovascular diseases, and traditional Chinese medicine is applied as an auxiliary treatment for common cardiovascular diseases. This study is based on 3 years of electronic medical record data from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine. A complex network and machine learning algorithm were used to establish a screening model of coupled herbs for the treatment of hypertension complicated with coronary heart disease. A total of 5688 electronic medical records were collected to establish the prescription network and symptom database. The hierarchical network extraction algorithm was used to obtain core herbs. Biological features of herbs were collected from public databases. At the same time, five supervised machine learning models were established based on the biological features of the coupled herbs. Finally, the K-nearest neighbor model was established as a screening model with an AUROC of 91.0%. Seventy coupled herbs for adjuvant treatment of hypertension complicated with coronary heart disease were obtained. It was found that the coupled herbs achieved the purpose of adjuvant therapy mainly by interfering with cytokines and regulating inflammatory and metabolic pathways. These results show that this model can integrate the molecular biological characteristics of herbs, preliminarily screen combinations of herbs, and provide ideas for explaining the value in clinical applications.

Summarizing the Effective Herbs for the Treatment of Hypertensive Nephropathy by Complex Network and Machine Learning.

Hypertensive nephropathy is a common complication of hypertension. Traditional Chinese medicine has been used in the clinical treatment of hypertensive nephropathy for a long time, but the commonly used prescriptions have not been summarized, and the basic therapeutic approaches have not been discussed. Based on data from 3 years of electronic medical records of traditional Chinese medicine used at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, a complex network and machine learning algorithm was used to explore the prescribed herbs of traditional Chinese medicine in the treatment of hypertensive nephropathy (HN). In this study, complex network algorithms were used to describe traditional Chinese medicine prescriptions for HN treatment. The Apriori algorithm was used to analyze the compatibility of these treatments with modern medicine. Data on the targets and regulatory genes related to hypertensive nephropathy and the herbs that affect their expression were obtained from public databases, and then, the signaling pathways enriched with these genes were identified on the basis of their participation in biological processes. A clustering algorithm was used to analyze the therapeutic pathways at multiple levels. A total of 1499 prescriptions of traditional Chinese medicines used for the treatment of hypertensive renal damage were identified. Fourteen herbs used to treat hypertensive nephropathy act through different biological pathways: huangqi, danshen, dangshen, fuling, baizhu, danggui, chenpi, banxia, gancao, qumai, cheqianzi, ezhu, qianshi, and niuxi. We found the formulae of these herbs and observed that they could downregulate the expression of inflammatory cytokines such as TNF, IL1B, and IL6 and the NF-κB and MAPK signaling pathways to reduce the renal inflammatory damage caused by excessive activation of RAAS. In addition, these herbs could facilitate the deceleration in the decline of renal function and relieve the symptoms of hypertensive nephropathy. In this study, the traditional Chinese medicine approach for treating hypertensive renal damage is summarized and effective treatment prescriptions were identified and analyzed. Data mining technology provided a feasible method for the collation and extraction of traditional Chinese medicine prescription data and provided an objective and reliable tool for use in determining the TCM treatments of hypertensive nephropathy.

Identification of Hypertension Subgroups through Topological Analysis of Symptom-Based Patient Similarity.

OBJECTIVE: To obtain the subtypes of the clinical hypertension population based on symptoms and to explore the relationship between hypertension and comorbidities. METHODS: The data set was collected from the Chinese medicine (CM) electronic medical records of 33,458 hypertension inpatients in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between July 2014 and May 2017. Then, a hypertension disease comorbidity network (HDCN) was built to investigate the complicated associations between hypertension and their comorbidities. Moreover, a hypertension patient similarity network (HPSN) was constructed with patients' shared symptoms, and 7 main hypertension patient subgroups were identified from HPSN with a community detection method to exhibit the characteristics of clinical phenotypes and molecular mechanisms. In addition, the significant symptoms, diseases, CM syndromes and pathways of each main patient subgroup were obtained by enrichment analysis. RESULTS: The significant symptoms and diseases of these patient subgroups were associated with different damaged target organs of hypertension. Additionally, the specific phenotypic features (symptoms, diseases, and CM syndromes) were consistent with specific molecular features (pathways) in the same patient subgroup. CONCLUSION: The utility and comprehensiveness of disease classification based on community detection of patient networks using shared CM symptom phenotypes showed the importance of hypertension patient subgroups.