Statistics
We used linear mixed models based on the univariate repeated measurement analysis of variance (ANOVA) to evaluate the change in health outcomes between clean air and candles and cooking, respectively. The models included the outcome of interest and as fixed effects exposure, time, exposure-order, day, and time-exposure interaction. As a random effect, we included participant ID. Time was divided into baseline (0 h), five hours, and 24 h; the exposure was clean air, candles, or cooking; order corresponding to the order in which the participant received the exposure, while day indicated whether the exposure took place on participants’ first, second or third day. The statistical measures of interest were the exposure, and time-exposure interaction as an effect of any of these terms would indicate a difference associated with the exposure. We initially fitted a model with interaction (Model 1). For models where the interaction term was not statistically significant, the interaction term was left out. Instead, we examined the mean change in the outcomes following the three exposures (5 to 24 h) adjusted for baseline values (0 h) (Model 2). In case of non-normal distributions, analyses were performed on log-transformed outcome variables. This was true for cytokines in nasal lavage, serum CRP, and gene expression. For DNA damages, outcomes were analysed on a cube root scale. Before conducting the statistical analyses on the 1007 metabolomics bins, we decided to use a false-discovery rate of 