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Strong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available.
View Article and Find Full Text PDFEndocrine Hormones and Their Impact on Pubertal Gynecomastia.
J Clin Med
December 2024
Department of Aesthetic and Reconstructive Breast Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100144, China.
Pubertal gynecomastia (PG) is a common condition characterized by the abnormal development and hyperplasia of unilateral or bilateral breast tissue in adolescent males, affecting up to 50% of appropriately aged adolescents and exhibiting rising prevalence over recent years. The etiology of PG is multifaceted, encompassing physiological, pharmacological, and pathological factors. This narrative review synthesizes evidence from a comprehensive selection of peer-reviewed literature, including observational studies, clinical trials, systematic reviews, and case reports, to explore the pivotal role of endocrine hormones in the pathogenesis of PG.
View Article and Find Full Text PDFPredicting Heart Rate at the Anaerobic Threshold Using a Machine Learning Model Based on a Large-Scale Population Dataset.
J Clin Med
December 2024
Department of Cardiovascular Medicine, Sakakibara Heart Institute, Tokyo 183-0003, Japan.
For effective exercise prescription for patients with cardiovascular disease, it is important to determine the target heart rate at the level of the anaerobic threshold (AT-HR). The AT-HR is mainly determined by cardiopulmonary exercise testing (CPET). The aim of this study is to develop a machine learning (ML) model to predict the AT-HR solely from non-exercise clinical features.
View Article and Find Full Text PDFPoint-Based Prediction Model for Bladder Cancer Risk in Diabetes: A Random Survival Forest-Guided Approach.
J Clin Med
December 2024
JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
Previous epidemiological studies have shown that diabetes is associated with an increased risk of several cancers, including bladder cancer. However, prediction models for bladder cancer among diabetes patients remain scarce. This study aims to develop a scoring system for bladder cancer risk prediction among diabetes patients who receive routine care in general outpatient clinics using a machine learning-guided approach.
View Article and Find Full Text PDFDevelopment and validation of a risk prediction model for acute kidney injury in coronary artery disease.
BMC Cardiovasc Disord
January 2025
Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
Background: Acute Kidney Injury (AKI) is a sudden and often reversible condition characterized by rapid kidney function reduction, posing significant risks to coronary artery disease (CAD) patients. This study focuses on developing accurate predictive models to improve the early detection and prognosis of AKI in CAD patients.
Methods: We used Electronic Health Records (EHRs) from a nationwide CAD registry including 54 429 patients.
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