Association of Lipid, Inflammatory, and Metabolic Biomarkers With Age at Onset for Incident Coronary Heart Disease in Women

Dugani, Sagar; Moorthy, M. Vinayaga; Li, Chunying; Demler, Olga; Alsheikh‐Ali, Alawi A.; Ridker, Paul M.; Glynn, Robert J.; Mora, Samia

doi:10.1001/jamacardio.2020.7073

Cited by 102 publications

(82 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The finding of the gender-smoking-habits association (H03) found significant (p < 0.05) support [21]. The age-group Survival-Status association (H 04 ) finding is significant (p < 0.05), rejected [24] but supported [15].…”

Section: Discussionmentioning

confidence: 98%

“…Smoking and diabetes were not found necessary toward HF death. Sagar B. Dugani et al [15] examined a coronary HD dataset w.r.t gender to see whether risk profiles differed by age at the start and diabetes, lipoprotein insulin resistance had the highest relative risk of more than 50 clinical and biomarker risk factors. In the women, diabetes had the highest aHR of any clinical factor.…”

Section: Effect Of Abnormal Sc and Ss On Hf Patientmentioning

confidence: 99%

“…Sagar B. Dugani et. al[15] studies on coronary HD dataset and researched on whether risk profiles vary by age at the beginning. Authors used 28024 women dataset and found diabetes and lipoprotein insulin resistance had a relative risk of over 50 clinical and biomarker risk factors.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Cardiac Diagnostic Feature and Demographic Identification Models: A Futuristic Approach for Smart Healthcare Using Machine Learning

Kumar¹,

Verma²,

Dahiya³

et al. 2021

Preprint

View full text Add to dashboard Cite

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p &lt; 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p &gt; 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Effect Of Abnormal Sc and Ss On Hf Patientmentioning

confidence: 99%

See 1 more Smart Citation

Cardiac Diagnostic Feature and Demographic Identification Models: A Futuristic Approach for Smart Healthcare Using Machine Learning

Kumar¹,

Verma²,

Dahiya³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, increased oxidative stress reduces nitric oxide, which increases the destruction of this molecule [31,52,53]. Increases in ROS and inflammation and decreases in bioavailable nitric oxide raise interstitial collagen deposition and crosslinking, associated with interstitial fibrosis and impaired myocardial relaxation [31,52,53]. Then, the profibrotic TGF-β1-SMAD signaling pathway increases myocardial collagen and fibronectin content and interstitial fibrosis in concert with impaired insulin signaling [54][55][56][57][58].…”

Section: Late Stagementioning

confidence: 99%

Evidence for human diabetic cardiomyopathy

et al. 2021

View full text Add to dashboard Cite

Growing interest has been accumulated in the definition of worsening effects of diabetes in the cardiovascular system. This is associated with epidemiological data regarding the high incidence of heart failure (HF) in diabetic patients. To investigate the detrimental effects both of hyperglycemia and insulin resistance, a lot of preclinical models were developed. However, the evidence of pathogenic and histological alterations of the so-called diabetic cardiomyopathy (DCM) is still poorly understood in humans. Here, we provide a stringent literature analysis to investigate unique data regarding human DCM. This approach established that lipotoxic-related events might play a central role in the initiation and progression of human DCM. The major limitation in the acquisition of human data is due to the fact of heart specimen availability. Postmortem analysis revealed the end stage of the disease; thus, we need to gain knowledge on the pathogenic events from the early stages until cardiac fibrosis underlying the end-stage HF.

show abstract

“…Недавно опубликованы результаты обсервационного исследования [5], включавшего данные о 28 024 женщинах в возрасте 45 лет и старше, которые участвовали в крупном рандомизированном клиническом исследовании (РКИ) по оценке эффективности применения низких доз ацетилсалициловой кислоты и витамина Е с целью первичной профилактики осложнений ССЗ. В ходе выполнения анализа оценивали более 50 факторов риска развития осложнений ССЗ, включая клинические показатели, концентрацию в крови липидов, биомаркеров и так называемых метаболических факторов риска.…”

unclassified

Clinical Efficacy and Mechanisms of Actions of the Glucagon-Like Peptide-1 Receptor Agonists in Patients with Type 2 Diabetes Mellitus and High Risk of Cardiovascular Diseases

Gilarevsky

2021

Kardiologiia

View full text Add to dashboard Cite

show abstract

Association of Lipid, Inflammatory, and Metabolic Biomarkers With Age at Onset for Incident Coronary Heart Disease in Women

Cited by 102 publications

References 63 publications

Cardiac Diagnostic Feature and Demographic Identification Models: A Futuristic Approach for Smart Healthcare Using Machine Learning

Cardiac Diagnostic Feature and Demographic Identification Models: A Futuristic Approach for Smart Healthcare Using Machine Learning

Evidence for human diabetic cardiomyopathy

Clinical Efficacy and Mechanisms of Actions of the Glucagon-Like Peptide-1 Receptor Agonists in Patients with Type 2 Diabetes Mellitus and High Risk of Cardiovascular Diseases

Contact Info

Product

Resources

About