氣喘為全球不可忽視的重要公共衛生與兒童健康議題，影響氣喘之因素中環境因子如空氣中PM2.5、真菌孢子與基因易感性為重要之危險因子，有相同基因型的人暴露在不同程度污染的情況下，其氣喘表現的結果可能有所差異。因此，釐清基因與環境的交互作用，對於氣喘病程的控制、以及氣喘精準醫學的發展均具有實質的重要意義。基於前述研究背景，本研究之研究目的為發展高時空解析度之大範圍周界PM2.5與真菌孢子地理人工智慧模型 (Geospatial-artificial intelligence model, Geo-AI model)，並利用推估之PM2.5與真菌孢子濃度，釐清環境暴露與基因交互作用對兒童氣喘相關呼吸道症狀的影響。
本研究透過建立Geo-AI模型，模擬早上 (7:00–9:00) 與下午 (16:00–18:00) 通勤時段的PM2.5與早上通勤時段之真菌孢子濃度空間分布，在資料庫建置方面，小時PM2.5濃度觀測值蒐集自台灣本島之環境部空品監測站，並將其計算為通勤時段之平均濃度；此外，本研究於台南市設置50個真菌孢子採樣點，以採集並計算總真菌孢子濃度，同時蒐集空間解釋變數資料庫，包含：空氣污染物、氣象參數、土地利用與土地覆蓋資訊、地標資料、道路與衛星影像資料等，在地標資料的部分，本研究特別收集了寺廟與中式餐飲等資訊以探討特殊排放源對於PM2.5的影響，針對真菌孢子變化的部分，我們納入木材工廠、紡織、造紙業等與真菌有關之特殊排放源當作預測變數之一，之後結合機器學習演算法，建立Geo-AI推估模型，並透過機器學習可解釋性指標 ”沙普利加法解釋 (SHapley Additive exPlanations, SHAP)” 指標的方式探討各個重要變數對模型之貢獻程度；最後藉由測試資料、十折交叉驗證與分地區、季節驗證檢驗模式的穩健性與可外推性。
為探討氣喘易感基因與環境暴露對於兒童氣喘的影響，本研究以病例對照研究設計搭配全基因組關聯分析 (Genome-wide association study, GWAS) 探討環境基因交互作用對於氣喘嚴重程度的影響。本研究自台南市四間國小共招募168名6-12歲之氣喘與非氣喘受試者，蒐集其唾液以萃取DNA，並將檢體送至中研院基因體研究中心分析每位受試者的單核甘酸多態性 (Single-Nucleotide Polymorphism, SNP)，進而計算每位受試者氣喘易感基因之基因風險分數，同時透過中文之ISAAC兒童過敏及氣喘問卷 (The International Study of Asthma and Allergies in Childhood Questionnaires) 進行氣喘症狀調查，透過家長填寫之氣喘相關症狀問卷調查成果做為後續分析之氣喘自變數；另一方面，本研究藉由智慧型配戴裝置 (Fitbit Charge 4) 同步記錄學童上學路徑，做為後續通勤暴露評估之參考通勤路徑。健康資料庫建立完成後，本研究透過廣義加性模型 (Generalized additive model, GAM) 分析PM2.5、真菌孢子與基因風險分數 (Genetic risk score, GRS) 對於兒童氣喘呼吸道症狀嚴重程度的影響。
Geo-AI模型結果顯示，PM2.5預測能力於早上與下午通勤時間之R2分別為0.90與0.94，兩個時段之模型皆具有高度預測能力，真菌孢子預測能力則為0.96，模型推估成果可以應用於後續流行病學探討環境暴露與疾病之關係。在國小學童暴露PM2.5、真菌孢子、基因體與氣喘之關聯性分析部分，經GWAS研究發現4個SNPs與氣喘顯著相關，分別是：rs4862096、rs9925480、rs7630840以及rs151181731，其中每個 SNP 的基因變異皆達到顯著水準，表示其可能在氣喘的發病機制中扮演重要角色；進一步分析PM2.5、真菌孢子與GRS之交互作用，單污染物模型顯示國小學童暴露於真菌孢子與帶有氣喘易感基因會顯著提高呼吸道症狀風險，相對風險 (Relative risk, RR) 分別為1.287 (95% CI=1.079–1.536) 與1.043 (95% CI=1.027–1.060)，PM2.5也具有正相關但不顯著。雙污染物模型則顯示基因變異會調整原有之環境暴露風險，在交互作用模型中進一步證明，PM2.5與真菌孢子交互作用具有顯著正相關 (OR=1.048, 95% CI=1.016–1.081)，基因變異則使整體環境基因交互作用降低，但是不顯著，而在單一SNP與環境暴露的交互作用中則發現rs4862096、rs151181731及rs9925480與PM2.5及真菌孢子暴露有顯著交互作用，顯示基因變異對於調控環境暴露與氣喘之間關係的重要影響，分別是TENM3、C6orf118與SHISA9基因。未來需要更多研究探討顯著氣喘易感基因對人體影響的生理機制，並透過重複性研究探討顯著基因與環境暴露於不同族群的效應程度。

Asthma is one of the most common chronic diseases among children. Among the factors affecting asthma, PM2.5, fungal spores, and genetic susceptibility are factors which have great effects on asthma development. People who have the same genotype might have different asthma severity owing to genetic susceptibility. Thus, to clarify the interaction between gene and environmental exposures is important specifically for controlling asthma progression and developing precise medicine for asthma. The goal of this study is to develop prediction models to estimate the variation of PM2.5 and fungal spore concentrations with a high spatiotemporal resolution and use the estimations to investigate the relationship between environmental exposure, genetics, and childhood asthma exacerbation.
This study aims to establish Geo-AI models to simulate the spatial distribution of PM2.5 during morning (7:00–9:00) and evening (16:00–18:00) commuting hours, as well as fungal spore concentrations during morning commuting hours. For database construction, hourly PM2.5 concentrations were collected from the air quality monitoring stations and aggregated into mean concentrations for the commuting periods. Additionally, 50 fungal spore sampling sites were established in Tainan City to measure and calculate total fungal spore concentrations. A spatial explanatory variable database was compiled, encompassing air pollutants, meteorological parameters, land use and land cover information, landmarks, road networks, and satellite-derived data. Specifically, landmark data included information on temples and Chinese restaurants to investigate the influence of unique emission sources on PM2.5 concentrations. For fungal spore variations, emission sources related to fungi, such as timber factories, textiles, and paper industries, were incorporated as predictive variables. These datasets were integrated with machine learning algorithms to develop the Geo-AI estimation model. Model robustness and generalizability were further validated using test data, 10-fold cross-validation, and independent validations across different regions and seasons.
To investigate the impact of asthma susceptibility genes and environmental exposures on childhood asthma, this study utilized a case-control design combined with genome-wide association study (GWAS) to examine the gene-environment interactions affecting asthma severity. A total of 168 children aged 6–12 years, including asthmatic and non-asthmatic participants, were recruited from four elementary schools in Tainan City. Saliva samples were collected for DNA extraction and subsequently analyzed to identify single-nucleotide polymorphisms (SNPs) for each participant. Genetic risk scores (GRS) for asthma susceptibility were calculated for all subjects. Asthma symptoms were assessed using the questionnaire, completed by parents to provide self-reported data on asthma-related symptoms. To evaluate commuting exposures, children's commuting routes were recorded using wearable devices (Fitbit Charge 4), providing reference pathways for subsequent exposure assessments. Upon establishing the health database, this study employed a generalized additive model (GAM) to analyze the effects of PM2.5, fungal spores, and genetic risk scores on the severity of respiratory symptoms in children with asthma.
The Geo-AI model results indicated high predictive performance for PM2.5 concentrations, with R² values of 0.90 and 0.94 for the morning and dusk commuting periods, respectively. Similarly, the Geo-AI model demonstrated a high predictive accuracy for fungal spore concentrations, with an R2 of 0.96. The predictions could be applied to further investigations on the relationship between environmental exposures and health outcomes.
Regarding the associations among PM2.5 exposure, fungal spores, genomic data, and asthma in elementary school children, the GWAS identified four SNPs significantly associated with asthma: rs4862096, rs9925480, rs7630840, and rs151181731. Each SNP reached significance, suggesting a potential role in the pathogenesis of asthma. Further analysis of the interactions among PM2.5, fungal spores, and GRS revealed that exposure to fungal spores and children with a higher GRS significantly increased the risk of respiratory symptoms, with relative risks (RRs) of 1.287 (95% CI: 1.079–1.536) for fungal spores and 1.043 (95% CI: 1.027–1.060) for genetic susceptibility in single-pollutant models. While PM2.5 exposure showed a positive association with respiratory symptoms, it was not statistically significant. The two-pollutant model indicated that genetic variation modifies the existing environmental exposure risks. Interaction models further demonstrated a significant positive interaction between PM2.5 and fungal spores (OR=1.048, 95% CI: 1.016–1.081). However, GRS appeared to reduce the overall environmental gene interaction effect, though this reduction was not statistically significant. Significant interactions were observed between single SNPs and environmental exposures, specifically rs4862096, rs151181731, and rs9925480 with PM2.5 and fungal spore exposure. These findings highlight the critical role of genetic variation in modulating the relationship between environmental exposure and asthma, especially TENM3, C6orf118, and SHISA9 genes. Future studies are needed to explore the physiological mechanisms underlying the effects of significant asthma susceptibility genes on human health and to validate these findings through replication studies in diverse populations to assess the extent of gene-environmental effects.

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