Cluster analysis of blood biomarkers to identify molecular patterns in pulmonary fibrosis: assessment of a multicentre, prospective, observational cohort with independent validation

Although the age of the genome gave us much insight about how our organs fail with disease, it also suggested that diseases do not arise from mutations alone; rather, they develop as we age. In this Review, we examine how wound healing might act to ignite disease. Wound healing works well when we are younger, repairing damage from accidents, environmental assaults, and battles with pathogens. Yet, with age and accumulation of mutations and tissue damage, the repair process can devolve, leading to inflammation, fibrosis, and neoplastic signaling. We discuss healthy wound responses and how our bodies might misappropriate these pathways in disease. Although we focus predominantly on epithelial-based (lung and skin) diseases, similar pathways might operate in cardiac, muscle, and neuronal diseases.

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Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

Lin,

Li,

Wang

et al. 2024

BMC Geriatr

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Background Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definitions. The advancements in machine learning offer new opportunities for improving the measurement and assessment of loneliness through the development of risk assessment models. Methods Data from the 2018 Chinese Longitudinal Healthy Longevity Survey, involving about 16,000 participants aged ≥ 65 years, were used. The study examined the relationships between loneliness and factors such as functional limitations, living conditions, environmental influences, age-related health issues, and health behaviors. Using R 4.4.1, seven assessment models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC. Results Loneliness prevalence among elderly Chinese was 23.4%. Analysis identified 15 evaluative factors and evaluated seven models. Multi-layer perceptron stands out for its strong nonlinear mapping capability and adaptability to complex data, making it one of the most effective models for assessing loneliness risk. Conclusion The study found a 23.4% prevalence of loneliness among elderly individuals in China. SHAP values indicated that marital status has the strongest evaluative value across all forecasting periods. Specifically, elderly individuals who are never married, widowed, divorced, or separated are more likely to experience loneliness compared to their married counterparts.

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Cluster analysis of blood biomarkers to identify molecular patterns in pulmonary fibrosis: assessment of a multicentre, prospective, observational cohort with independent validation

Cited by 5 publications

References 30 publications

Biomarker-defined endotypes of pulmonary fibrosis

Biomarker-defined endotypes of pulmonary fibrosis

Imperfect wound healing sets the stage for chronic diseases

Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

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