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.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Pancreatic cancer is a fatal malignancy with an increasing incidence in Shanghai, China. A genome-wide association study (GWAS) and other work have shown that ABO alleles are associated with pancreatic cancer risk. We conducted a population-based case-control study involving 256 patients with pathologically confirmed pancreatic ductal adenocarcinoma (PDAC) and 548 healthy controls in Shanghai, China, to assess the relationships between GWAS-identified ABO alleles and risk of PDAC. Carriers of the C allele of rs505922 had an increased cancer risk [adjusted odds ratio (OR) = 1.42, 95% confidence interval (CI): 1.02-1.98] compared to TT carriers. The T alleles of rs495828 and rs657152 were also significantly associated with an elevated cancer risk (adjusted OR = 1.58, 95% CI: 1.17-2.14; adjusted OR = 1.51, 95% CI: 1.09-2.10). The rs630014 variant was not associated with risk. We did not find any significant gene-environment interaction with cancer risk using a multifactor dimensionality reduction (MDR) method. Haplotype analysis also showed that the haplotype CTTC was associated with an increased risk of PDAC (adjusted OR = 1.46, 95% CI: 1.12-1.91) compared with haplotype TGGT. GWAS-identified ABO variants are thus also associated with risk of PDAC in the Chinese population.
ABO Blood-Group System ; genetics ; Adenocarcinoma ; genetics ; Aged ; Alleles ; Asian Continental Ancestry Group ; genetics ; Case-Control Studies ; China ; Confidence Intervals ; Female ; Gene-Environment Interaction ; Genome-Wide Association Study ; Genotype ; Haplotypes ; Humans ; Male ; Middle Aged ; Odds Ratio ; Pancreatic Neoplasms ; genetics ; Polymorphism, Single Nucleotide ; Risk Factors