Use of clinical chemistry health outcomes and PFAS chain length to predict 28-day rodent oral toxicity

Current literature suggests PFAS carbon chain length may be a predictive variable of toxicity. If so, statistical modeling may be used to help predict toxicity, thus improving the efficiency of PFAS regulation development. Data were analyzed using one-way ANOVAs, Tukey's HSD post hoc tests, and simple linear regressions. A dataset was predicted using modeling from this data. Analysis indicated that 11 of 15 health outcomes showed significant differences in mean values. Two of 15 health outcomes were analyzed using simple linear regressions, with statistically significant results. After predictive modeling generated a theoretical dataset, unpaired t-tests comparing the results of an actual dataset indicated no significant differences among the mean values of the two health outcomes. Therefore, predictive statistical modeling may be used to predict health outcomes for PFAS exposure.

Automated summary

Current literature suggests that PFAS carbon chain length may affect its toxicity, and statistical modeling can be used to predict toxicity and improve the efficiency of PFAS regulation development. In this study, data analysis and predictive modeling were conducted, showing significant differences in mean values for 11 out of 15 health outcomes. Simple linear regressions were used for two health outcomes, yielding statistically significant results. Comparison between the results of an actual dataset and a theoretically generated dataset indicated no significant differences in the mean values of the two health outcomes. Therefore, predictive statistical modeling can be used to predict health outcomes for PFAS exposure.