Levels and clinical significance of serum homocysteine (Hcy), high-density lipoprotein cholesterol (HDL-C), vaspin, and visfatin in elderly patients with different types of coronary heart disease

Guan, Jing; Wu, Li; Xiao, Qian; Pan, Li

doi:10.21037/apm-21-1001

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…The visualizations completed for the top four covariates were concordant with current literature around the relationship between these covariates and coronary artery disease: there is strong epidemiological and physiological evidence for the link between increased age and cholesterol as major risk factors for coronary artery disease [26][27][28]. The non-linear relationship between cholesterol and coronary artery disease matches survival-modeling and restricted PLOS ONE cubic spline analysis from other studies [28][29][30][31][32][33][34][35][36]. Furthermore, multiple genetic and sociological studies have found that family history is a significant risk factor for coronary artery disease [37][38][39][40].…”

Section: Plos Onesupporting

confidence: 74%

Use of machine learning to identify risk factors for coronary artery disease

Huang

2023

PLoS ONE

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Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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Section: Plos Onesupporting

confidence: 74%

Use of machine learning to identify risk factors for coronary artery disease

Huang

2023

PLoS ONE

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“…Recent studies have shown that elevated Hcy can promote lipid deposition in foam cells and accelerate the progression of atherosclerosis [ 46 ]. Clinical studies have found that the plasma expression of Hcy is elevated in CHD [ 47 , 48 ] and is related to coronary artery stenosis degree [ 49 ]. Therefore, Hcy can predict major adverse cardiovascular events and has important clinical significance in determining the condition and prognosis of CHD patients [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Predictive Significance of High-Sensitivity C-Reactive Protein Combined with Homocysteine for Coronary Heart Disease in Patients with Anxiety Disorders

Liu

Hua

Wang

2022

BioMed Research International

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Background. Currently, there are few studies on biomarkers for predicting coronary heart disease (CHD) with anxiety disorders. Objective. To explore risk factors and investigate the predictive value of common clinical peripheral blood indicators, such as high-sensitivity C-reactive protein (hs-CRP) and homocysteine (Hcy) for CHD patients with anxiety disorders. Methods. One hundred fifty-three hospitalized patients with chest pain as the main symptom and a Hamilton Anxiety Scale score > 14 were recruited from October 2020 to September 2021 in the hospital. Then, they were divided into an anxiety disorder with CHD group (observation group, n = 64 ) and a simple anxiety disorder group (control group, n = 89 ), according to coronary angiography (CAG) findings. Patients’ demographic and clinical messages were collected and compared. Diabetes mellitus and hypertension, body mass index (BMI), and peripheral blood interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), homocysteine (Hcy), fibrinogen, D-dimer, cortisol, and norepinephrine expression levels were compared. Binary logistic regression analysis screened independent risk factors of CHD patients with anxiety disorders. The effectiveness of independent risk factors in predicting CHD with anxiety disorders was analyzed using receiver operating characteristic (ROC) curves. Results. IL-6, hs-CRP, and Hcy levels of anxiety disorder in the CHD group were significantly higher than those in the simple anxiety disorder group. Binary multiple logistic regression analysis indicated that IL-6, hs-CRP, and Hcy were independent risk factors for CHD in patients with anxiety disorders. hs-CRP and Hcy levels were positively correlated with the Gensini score. ROC curve analysis indicated that the detection of hs-CRP or Hcy alone or the combined detection of the 2 had clinical predictive value for CHD in patients with anxiety disorders, and the area under the curve (AUC) of the combined detection of the 2 was significantly larger than that of any single factor alone (vs. hs-CRP, P = 0.045 ; vs. Hcy, P = 0.045 ). Conclusion. IL-6, hs-CRP, and Hcy are related to CHD with anxiety disorders. Serum levels of the combined detection of hs-CRP and Hcy have a high clinical predictive value for CHD in patients with anxiety disorders.

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“…Risk of CAD is monitored by lipid profile estimation especially the LDL and non-HDL-C levels. In a multi ethnic study conducted in patients (aged 52.3±17.9 years) with atherosclerotic cardiovascular disease, LDL levels were found to be higher than normal (9). The Copenhagen general population study conducted in 13,015 statin-treated patients found higher levels of apo B and Non-HDL-C were associated with the increased risk of all-cause mortality and myocardial infarction (6).…”

Section: Resultsmentioning

confidence: 98%

Homocysteine in CAD patients-Does it matter?

Sowndarya¹,

Jain²,

Shenoy³

et al. 2022

Biomedicine

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Introduction and Aim: Homocysteine (Hcy) is considered as an independent risk factor for coronary artery disease (CAD). Hyperhomocysteinemia (HHcy) may be caused due to the deficiency of vitamin B12, folic acid (FA), and pyridoxine (B6) or due to genetic polymorphism in methylenetetrahydrofolate reductase (MTHFR). A pilot study was undertaken to investigate Hcy levels and its association with lipid profile in patients with CAD. Methods: Lipid profile values were obtained from laboratory reports and the Hcy levels were estimated by enzymatic cycling assay using the commercial kits in autoanalyzer in subjects with CAD (n=12). Results: Hcy (14.58 ± 8.32 µmol/L), LDL (134.75 ± 45.02 mg/dl) and non-HDL (150.25 ± 56.89 mg/dl) levels were found to be elevated in patients with CAD. Conclusion: Dyslipidemia was associated with variable Hcy levels. Few patients with HHcy showed concurrent upsurge in LDL and non-HDL levels.

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Levels and clinical significance of serum homocysteine (Hcy), high-density lipoprotein cholesterol (HDL-C), vaspin, and visfatin in elderly patients with different types of coronary heart disease

Cited by 12 publications

References 17 publications

Use of machine learning to identify risk factors for coronary artery disease

Use of machine learning to identify risk factors for coronary artery disease

Predictive Significance of High-Sensitivity C-Reactive Protein Combined with Homocysteine for Coronary Heart Disease in Patients with Anxiety Disorders

Homocysteine in CAD patients-Does it matter?

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