Assessment of CYP1A2 enzyme activity in relation to type-2 diabetes and habitual caffeine intake

Abstract

Background: Coffee consumption is a known inducer of cytochrome P450 1A2 (CYP1A2) enzyme activity. We recently observed that a group of type-2 diabetes patients consumed more caffeine (coffee) on a daily basis than non-type-2 diabetes controls. Here, we investigated whether type-2 diabetes cases may metabolize caffeine faster than non-type-2 diabetes controls.

Methods: To estimate CYP1A2 enzyme activity, an established marker of caffeine metabolism, we quantified the paraxanthine/caffeine concentration ratio in saliva in 57 type-2 diabetes and 146 non-type-2 diabetes participants in a case-control field study. All participants completed validated questionnaires regarding demographic status, health and habitual caffeine intake, and were genotyped for the functional -163C > A polymorphism of the CYP1A2 gene.

Results: In the diabetes group, we found a larger proportion of participants with the highly inducible CYP1A2 genotype. Furthermore, the paraxanthine/caffeine ratio, time-corrected to mitigate the impact of different saliva sampling times with respect to the last caffeine intake, was higher than in the control group. Participants who reported habitually consuming more caffeine than the population average showed higher CYP1A2 activity than participants with lower than average caffeine consumption. Multiple regression analyses revealed that higher caffeine intake was potentially an important mediator of higher CYP1A2 activity.

Conclusions: Estimated CYP1A2 enzyme activity, and thus speed of caffeine metabolism, was higher in our type-2 diabetes group; this was possibly due to higher intake of caffeine, a known inducer of CYP1A2 enzyme activity. Given the fairly small sample sizes, the results need to be considered as preliminary and require validation in larger populations.

Keywords: Caffeine; HPLC; Paraxanthine; Phenotyping.

Fig. 1

Relationship between sampling time and salivary paraxanthine/caffeine ratio. Solid line: mean observed ratio estimates based on data of Spigset et al. [14]. Error bars show standard deviation across the mean of observed ratio data (n = 12). Dotted line: ratio based on fitted curve (dotted line) using a second-order polynomial model: Y = A + (B x X) + (C x X ² ). Best-fit values (95 % confidence intervals): A = 0.016 (-0.206 - 0.238); B = 0.141 (0.090 - 0.191); C = -0.004 (-0.006 - -0.002). Equation: Y = 0.016 + (0.141 x X) + (-0.004 x X ² ); Y = paraxanthine/caffeine ratio; X = time interval between final caffeine intake and saliva sampling

Fig. 2

Paraxanthine/caffeine ratios in type-2 diabetes patient and non-type-2 diabetes control groups. Boxplots represent paraxanthine/caffeine ratios corrected to an “ideal” time interval between last caffeine intake and saliva sampling of 6 h (box: 25^th percentile, median and 75^th percentile; whiskers = 10^th to 90^th percentiles; dots: individual data points outside of the whisker range). Statistics compared type-2 diabetes patient (n = 57) and non-type-2 diabetes control (n = 146) groups by independent samples t-test on square-rooted data (2-tailed; equal variances assumed). Statistical analysis with the non-parametric Mann-Whitney U-test on non-transformed corrected paraxanthine/caffeine ratios confirmed the robustness of the result: T2D vs. non-T2D: mean rank 120.93 vs. 94.61; exact sig. 2-tailed: p = 0.004)