P oll C ardio KG : A Dynamic Knowledge Graph of Interaction Between Pollution and Cardiovascular Diseases (2024)

Abstract: In recent decades, environmental pollution has become a pressing global health concern. According to the World Health Organization (WHO), a significant portion of the population is exposed to air pollutant levels exceeding safety guidelines. Cardiovascular diseases (CVDs) — including coronary artery disease, heart attacks, and strokes — are particularly significant health effects of this exposure. In this paper, we investigate the effects of air pollution on cardiovascular health by constructing a dynamic knowledge graph based on extensive biomedical literature. This paper provides a comprehensive exploration of entity identification and relation extraction, leveraging advanced language models. Additionally, we demonstrate how in-context learning with large language models can enhance the accuracy and efficiency of the extraction process. The constructed knowledge graph enables us to analyze the relationships between pollutants and cardiovascular diseases over the years, providing deeper insights into the long-term impact of cumulative exposure, underlying causal mechanisms, vulnerable populations, and the role of emerging contaminants in worsening various cardiac outcomes.

## introduction over the past few decades, the increasing levels of environmental pollution have emerged as a formidable global health crisis. the world health organization (who) has reported that nearly 99% of the world's population breathes air that contains pollutant levels exceeding established safety guidelines @xcite . this exposure is particularly severe in low-and middle-income countries, where industrial activities, urbanization, and insufficient regulatory measures exacerbate air quality issues. among the various health effects attributed to pollution, its impact on cardiovascular diseases (cvds) is particularly concerning @xcite . cvds encompass a wide range of conditions, including coronary artery disease, heart attacks, strokes, and heart failure. these diseases are not only prevalent but also represent a leading cause of morbidity and mortality worldwide. the mechanisms by which air pollutants influence cardiovascular health are multifaceted, involving both direct effects on the cardiovascular system and indirect effects through exacerbating existing risk factors such as hypertension and diabetes. recent studies have demonstrated that pollutants such as fine particulate matter (p m ), heavy metals, and toxic gases have a significant impact on cardiac health @xcite @xcite . research conducted by the united states environmental protection agency (epa) indicates that exposure to elevated concentrations of p m 2.5 , even over a short duration of a few hours to weeks, can trigger heart attacks and fatalities associated with cardiovascular diseases. prolonged exposure to these pollutants is linked to an increased risk of cardiovascular mortality and a reduction in life expectancy @xcite . furthermore, a substantial body of epidemiological evidence reveals a strong correlation between air pollutants and rising rates of cardiovascular diseases, including heart failure @xcite . animal studies also corroborate these findings, illustrating that exposure to pollutants can elevate the likelihood of conditions such as thrombosis and atherosclerosis @xcite . these insights underscore the urgent need for comprehensive public health interventions aimed at reducing air pollution and mitigating its adverse effects on cardiovascular health. however, current findings do not demonstrate how cumulative exposure impacts cardiovascular health over decades, the precise causal pathways by which these pollutants lead to cardiovascular damage. additionally, it is crucial to identify which populations are most vulnerable to specific pollutants and the reasons behind their susceptibility, which may include genetic predispositions, environmental factors, pre-existing health conditions, and lifestyle choices. moreover, there is a growing concern regarding new pollutants and emerging contaminants-such as microplastics and various industrial chemicals-that remain underresearched in the context of cardiovascular health. motivated by these gaps, in this study, we seek to address several of these issues by constructing a comprehensive knowledge graph 'pollcardiokg' that visualizes the causal relationships between pollutants and cardiovascular diseases, based on an extensive review of scientific pubmed abstracts. we also investigate how varying concentrations of pollutants affect cardiovascular health differently across diverse populations. this knowledge will enhance our understanding of the mechanisms by which pollutants contribute to cardiovascular diseases and inform the development of more effective public health strategies to mitigate their impact. the remainder of this article is organized as follows: in the next section, we provide a brief overview of related works conducted by previous researchers in this field. in section 3, we detail our methodology for dataset collection, data representation, and knowledge graph generation. section 4 presents our results along with a thorough analysis of the experimental findings. finally, in section 5, we draw conclusions based on our analysis, and in section 6, we discuss the limitations of our study. ## results and discussions in this section, we evaluate the performance of various large language models (llms) in extracting entities and their relationships. furthermore, we conducted a temporal analysis to examine the variation of each pollutant, disease, and their interrelationships over time. after the pre-processing step outlined in section 3, which involved 4,716 pubmed abstracts, we compiled a comprehensive list of 300 unique pollutants and approximately 936 unique disease names. we then evaluated the performance of various large language models (llms) for entity extraction, comparing their outputs with expert annotations. our analysis revealed that both llama-2-7b and llama-3-8b models often struggled with accurately identifying disease names, frequently mislabeling conditions. for example, the llama models generated 'stroke' in place of 'decreased hdl level' and identified 'cardiovascular risk' instead of 'ischemic heart disease'. to quantify the performance of these models, we calculated the mean entity extraction accuracy (meea), defined by 1 subsequently, we constructed the knowledge graph, named 'pollcardiokg', which encapsulates a total of 1,907 nodes, consisting of 1,029 pollutant nodes and 878 disease nodes, connected by 5,618 edges. this graph provides a comprehensive view of the complex relationships between pollutants and cardiovascular diseases. to enhance its granularity, multiple nodes were created for the same pollutant to represent varying concentration levels, where such data was available. this structure allows for the differentiation of effects based on pollutant exposure levels, offering deeper insights into potential dose-response relationships. figure 4 presents a snippet of the knowledge graph, where pollutant nodes are highlighted in red, and disease nodes are marked in blue. the directed edges between these nodes represent relationships such as causality or association. furthermore, we utilized the study year of each abstract as the time variable for this analysis to examine how pollutant levels and disease rates have changed over time. this approach allows us to identify long-term trends and patterns, as well as to detect any unusual or unexpected fluctuations in both pollutant concentrations and disease incidence. ## conclusions this study explores the causal relationships between environmental pollutants and cardiovascular diseases by constructing a comprehensive knowledge graph. analyzing around 4,700 pubmed abstracts, we identified key pollutants and their associated health risks, including hypertensive disease and ischemic stroke. the knowledge graph reveals both direct and complex interrelations, offering a valuable tool for understanding the cardiovascular impacts of air pollution. future work will integrate multimodal data to further enhance the analysis of indirect effects and expand the graph's scope for public health insights.