De Novo exposomic geospatial assembly of chronic disease regions with machine learning & network analysis.

IF 10.8 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL EBioMedicine Pub Date : 2025-02-01 Epub Date: 2025-01-31 DOI:10.1016/j.ebiom.2025.105575

Andrew Deonarine, Ayushi Batwara, Roy Wada, Puneet Sharma, Joseph Loscalzo, Bisola Ojikutu, Kathryn Hall

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Abstract

Background: Determining spatial relationships between diseases and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States.

Methods: Using aPEER we examined the relationship between 12 chronic diseases and 186 pollutants. PCA, K-means clustering, and map projection produced clusters of counties derived from pollutants, and the Jaccard correlation between these clusters with chronic disease geography (defined as groups of counties with high chronic disease prevalence rates) was calculated. Disease-pollution correlation matrices were used together with network analysis to identify the strongest disease-pollution relationships. Results were compared to LISA, Moran's I, univariate, elastic net, and random forest regression.

Findings: aPEER produced 68,820 human interpretable maps with distinct pollution-derived regions, and acetaldehyde/benzo(a)pyrene was found to be strongly associated with hypertension (J = 0.5316, p = 3.89 × 10^-208), stroke (J = 0.4517, p = 1.15 × 10^-127), and diabetes mellitus (J = 0.4425, p = 2.34 × 10^-127); formaldehyde/glycol ethers with COPD (J = 0.4545, p = 8.27 × 10^-131); and acetaldehyde/formaldehyde with stroke mortality (J = 0.4445, p = 4.28 × 10^-125). Methanol, acetaldehyde, and formaldehyde formed distinct regions in the southeast United States (which correlated with both the Stroke and Diabetes Belts) which were strongly associated with multiple chronic diseases. Pollutants predicted chronic disease geography with similar or superior areas under the curve compared to SDOH and preventive healthcare models (determined with random forest and elastic net methods). Conventional geospatial analysis methods did not identify these geospatial relationships, highlighting aPEER's utility.

Interpretation: aPEER identified a pollution-defined geographical region associated with chronic disease, highlighting the role of aPEER in epidemiological and geospatial analysis, and exposomics in understanding chronic disease geography.

Funding: This work was primarily funded by the BPHC, NHLBI (R03 HL157890) and the CDC, and this work was funded in part by grants from the NIH (U01 HG007691, R01 HL155107, and HL166137), the American Heart Association (AHA24MERIT1185447), and the EU (HorizonHealth 2021 101057619) to JL.

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基于机器学习和网络分析的慢性疾病区域的暴露性地理空间组装。

背景：确定疾病与暴露体之间的空间关系受到现有方法的限制。在美国，aPEER（暴露体和流行病学关系投影算法）使用机器学习（ML）和网络分析来寻找疾病与暴露体之间的空间关系。方法：应用aPEER分析了12种慢性疾病与186种污染物的关系。PCA、k -均值聚类和地图投影生成了来自污染物的县的聚类，并计算了这些聚类与慢性病地理（定义为慢性病患病率高的县组）之间的Jaccard相关性。采用疾病-污染相关矩阵和网络分析相结合的方法来识别最强的疾病-污染关系。结果与LISA、Moran’s I、单变量、弹性网和随机森林回归进行了比较。结果：aPEER绘制了68,820张具有不同污染来源区域的人类可解释地图，发现乙醛/苯并芘与高血压（J = 0.5316, p = 3.89 × 10-208）、中风（J = 0.4517, p = 1.15 × 10-127）和糖尿病（J = 0.4425, p = 2.34 × 10-127）密切相关；甲醛/乙二醇醚与COPD (J = 0.4545, p = 8.27 × 10-131)；乙醛/甲醛与脑卒中死亡率的关系（J = 0.4445, p = 4.28 × 10-125）。甲醇、乙醛和甲醛在美国东南部形成了不同的区域（与中风和糖尿病带相关），这些区域与多种慢性疾病密切相关。与SDOH和预防保健模型（随机森林和弹性网方法确定）相比，污染物预测慢性病地理曲线下的面积相似或更优。传统的地理空间分析方法无法识别这些地理空间关系，这凸显了aPEER的实用性。解释：aPEER确定了一个与慢性病相关的污染界定的地理区域，强调了aPEER在流行病学和地理空间分析中的作用，以及暴露学在了解慢性病地理学中的作用。经费：本工作主要由BPHC， NHLBI （R03 HL157890）和CDC资助，部分由NIH (U01 HG007691， R01 HL155107和HL166137)，美国心脏协会（AHA24MERIT1185447）和EU （HorizonHealth 2021 101057619）资助JL。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology

CiteScore

17.70

自引率

0.90%

发文量

579

审稿时长

5 weeks

期刊介绍： eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.