Generalized Functional Linear Models: Efficient Modeling for High-dimensional Correlated Mixture Exposures.

Bing Song ZHANG; Hai Bin YU; Xin PENG; Hai Yi YAN; Si Ran LI; Shutong LUO; Hui Zi WEIREN; Zhu Jiang ZHOU; Ya Lin KUANG; Yi Huan ZHENG; Chu Lan OU; Lin Hua LIU; Yuehua HU; Jin Dong NI

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Generalized Functional Linear Models: Efficient Modeling for High-dimensional Correlated Mixture Exposures.

Author: Bing Song ZHANG ¹ ; Hai Bin YU ¹ ; Xin PENG ¹ ; Hai Yi YAN ¹ ; Si Ran LI ¹ ; Shutong LUO ² ; Hui Zi WEIREN ³ ; Zhu Jiang ZHOU ¹ ; Ya Lin KUANG ¹ ; Yi Huan ZHENG ¹ ; Chu Lan OU ¹ ; Lin Hua LIU ⁴ ; Yuehua HU ⁵ ; Jin Dong NI ⁶
Author Information

1. Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan 523808, Guangdong, China.
2. Herbert Wertheim School of Public Health, Warrant College, University of California San Diago 92037, U.S.A.
3. Office of Quality Management, Hospital of Huangjiang Dongguan, Dongguan 523750, Guangdong, China.
4. Dongguan Key Laboratory of Environmental Medicine, School of Public Health, Guangdong Medical University, Dongguan 523808, Guangdong, China.
5. Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
6. Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan 523808, Guangdong, China;Maternal and Child Research Institute, Shunde Women and Children's Hospital, Guangdong Medical University, Foshan 528300, Guangdong, China.
Publication Type:Journal Article
Keywords: Correlated exposures; Environmental epidemiology; Functional data analysis; High-dimensional data; Mixture exposure modeling
MeSH: Humans; Environmental Exposure/analysis*; Linear Models; Nutrition Surveys; Environmental Pollutants; Body Mass Index
From: Biomedical and Environmental Sciences 2025;38(8):961-976
CountryChina
Language:English
Abstract: OBJECTIVE:Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects.
METHODS:We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.
RESULTS:We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity.
CONCLUSION:GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.