Caffeine Consumption Contributes to Skin Intrinsic Fluorescence in Type 1 Diabetes

Document Type

Journal Article

Publication Date



Diabetes Technology and Therapeutics








Background: A variant (rs1495741) in the gene for the N-acetyltransferase 2 (NAT2) protein is associated with skin intrinsic fluorescence (SIF), a noninvasive measure of advanced glycation end products and other fluorophores in the skin. Because NAT2 is involved in caffeine metabolism, we aimed to determine whether caffeine consumption is associated with SIF and whether rs1495741 is associated with SIF independently of caffeine. Materials and Methods: SIF was measured in 1,181 participants with type 1 diabetes from the Epidemiology of Diabetes Interventions and Complications study. Two measures of SIF were used: SIF1, using a 375-nm excitation light-emitting diode (LED), and SIF14 (456-nm LED). Food frequency questionnaires were used to estimate mean caffeine intake. To establish replication, we examined a second type 1 diabetes cohort. Results: Higher caffeine intake was significantly associated with higher SIF1LED 375nm[0.6,0.2] (P=2×10-32) and SIF14LED 456nm[0.4,0.8] (P=7×10-31) and accounted for 4% of the variance in each after adjusting for covariates. When analyzed together, caffeine intake and rs1495741 both remained highly significantly associated with SIF1LED 375nm[0.6,0.2] and SIF14LED 456nm[0.4,0.8]. Mean caffeinated coffee intake was also positively associated with SIF1LED 375nm[0.6,0.2] (P=9×10-12) and SIF14LED 456nm[0.4,0.8] (P=4×10-12), but no association was observed for decaffeinated coffee intake. Finally, caffeine was also positively associated with SIF1LED 375nm[0.6,0.2] and SIF14LED 456nm[0.4,0.8] (P<0.0001) in the replication cohort. Conclusions: Caffeine contributes to SIF. The effect of rs1495741 on SIF appears to be partially independent of caffeine consumption. Because SIF and coffee intake are each associated with cardiovascular disease, our findings suggest that accounting for coffee and/or caffeine intake may improve risk prediction models for SIF and cardiovascular disease in individuals with diabetes.