Latent class analysis (LCA), although minimally applied to the statistical analysis of mixtures, may serve as a useful tool for identifying individuals with shared real-life profiles of chemical exposures. Knowledge of these groupings and their risk of adverse outcomes has the potential to inform targeted public health prevention strategies. This example applies LCA to identify clusters of pregnant women from a case–control study within the LIFECODES birth cohort with shared exposure patterns across a panel of urinary phthalate metabolites and parabens, and to evaluate the association between cluster membership and urinary oxidative stress biomarkers. LCA identified individuals with: “low exposure,” “low phthalates, high parabens,” “high phthalates, low parabens,” and “high exposure.” Class membership was associated with several demographic characteristics. Compared with “low exposure,” women classified as having “high exposure” had elevated urinary concentrations of the oxidative stress biomarkers 8-hydroxydeoxyguanosine (19% higher, 95% confidence interval [CI] = 7, 32%) and 8-isoprostane (31% higher, 95% CI = −5, 64%). However, contrast examinations indicated that associations between oxidative stress biomarkers and “high exposure” were not statistically different from those with “high phthalates, low parabens” suggesting a minimal effect of higher paraben exposure in the presence of high phthalates. The presented example offers verification of latent class assignments through application to an additional data set as well as a comparison to another unsupervised clustering approach, k-means clustering. LCA may be more easily implemented, more consistent, and more able to provide interpretable output.
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Accompanying code for the LCA methods is available at GitHub repository “LCAmix” from user “carrollrm.” This is available as an R markdown file to lead viewers through a simple example of performing these methods.
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This research was supported by the Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS), National Institute of Health (Z1AES103321). Additional funding was provided by NIEHS (R01ES018872 and R01ES029531).
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The authors declare that they have no conflict of interest.
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Carroll, R., White, A.J., Keil, A.P. et al. Latent classes for chemical mixtures analyses in epidemiology: an example using phthalate and phenol exposure biomarkers in pregnant women. J Expo Sci Environ Epidemiol 30, 149–159 (2020). https://doi.org/10.1038/s41370-019-0181-y
- Latent class models
- Mixtures methods
- Oxidative stress