Table 1.

Biomarkers over time

	Basic model				Spline model							ΔAIC favors spline
Biomarker	β	P	P (FDR)	AICbasic	β1	P1	P1 (FDR)	β2	P2	P2 (FDR)	AICspline
PlGF	−0.0488	0.0091	0.0320	112.0	−0.13	0.0002	0.0015	0.149	0.0039	0.0355	109.7	True
Log(MCP1)	0.185	0.0001	0.0009	142.1	0.18	0.0029	0.0125	0.00889	0.9090	0.9270	147.4	False
Log(MIP1)	0.151	0.0095	0.0320	176.2	0.488	< 0.0001	< 0.0001	−0.652	< 0.0001	0.0002	160.1	True
IL15	−0.0534	0.0002	0.0015	95.2	−0.0875	0.0017	0.0092	0.0641	0.1330	0.2565	99.5	False
IL-7	−0.062	0.0007	0.0039	100.8	−0.0899	0.0032	0.0125	0.055	0.2300	0.3653	105.7	False
VEGF-A	0.146	0.0045	0.0203	157.9	−0.0588	0.3110	0.3817	0.415	< 0.0001	< 0.0001	143.2	True
Log(IL-6)	0.285	<  0.0001	0.0005	117.8	0.265	0.0003	0.0022	0.0418	0.4850	0.6236	123.1	False
Log(IL-8)	0.362	< 0.0001	< 0.0001	76.3	0.312	0.0000	0.0000	0.102	0.0254	0.0686	77.9	False

Data is from linear mixed effects models for the eight biomarkers that changed over time, after correction for multiple comparisons (see Additional file 1: Table S1 for data on all biomarkers). Biomarkers were used as dependent variables (scaled and standardized to z-scores) and time (hours) was used as predictor. For each biomarker, we tested two models, with or without restricted cubic splines (using three knots) to model time. Without splines, time is modeled with one parameter (β), and with splines, times is modeled with two parameters (β1 and β2). For each biomarker, we calculated the Akaike information criterion (AIC) for the two models. AIC may be used to compare model fits, where a lower AIC is preferable and penalizes models with additional predictors (and thereby protects against overfitting). For biomarkers with AIC_basic-AIC_spline < 2, we selected the basic model; otherwise we selected the spline model (selected model indicated with green shading). Data where p values are significant after correction for multiple comparisons [P (FDR)] are shown in italics. For example, for MIP1, the AIC selected the spline model, and both β1 (the linear component) and β2 (the cubic component) were significant, suggesting that MIP1 increased significantly during the first part of the study, and then decreased significantly during the second part of the study. In contrast, for IL-8, the AIC selected the non-spline model, and β was significant, suggesting that IL-8 increased continuously during the entire study duration. See Fig. 1 for visualizations of the significant effects