Effectiveness of Early Advanced Glycation End Product Accumulation Testing in the Diagnosis of Diabetes: A Health Risk Factor Analysis Using the Body Mass Index as a Moderator - Zhang, Yi; Jiang, Tian; Liu, Chao; Hu, Honglin; Dai, Fang; Xia, Li; Zhang, Qiu (Frontiers in Endocrinology, 2022)
Objective To evaluate the value of non-invasive detection of advanced glycation end products (AGEs) in the early screening of type 2 diabetes mellitus (T2DM) in the community of China. Methods From January 2018 to January 2019, a total of 912 patients with community health physical examination and no history of T2DM were selected, excluding the results of missing value > 5%. Finally, 906 samples were included in the study, with a response rate of 99.3%. Non-invasive diabetic detection technology was used to detect AGEs in the upper arm skin of all participants, AGE accumulations were classified as ≤P25, P25∼P50, P50∼P75, and >P75; HbA1c, insulin, C-peptide, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), creatinine, urea, and other indicators were measured at the same time. Univariate analysis of variance was used to compare the differences in general data, biochemical indexes, skin AGE levels, and blood glucose among groups, and logistic regression analysis and latent category analysis were performed. Results In univariate analysis, SBP, FBG, HbA1c, and age were correlated with higher AGE (p < 0.01); TG, TC, HDL, UA, and gender were not positively correlated with AGE (p < 0.01). After controlling for covariates (waist circumference, hip circumference), AGE accumulation was interacted with other variables. The results of latent category analysis (LCA) showed that the health risk factors (HRFs), including age, systolic blood pressure, HbA1c, FBG, triglyceride, total cholesterol, HDL-C, and uric acid, were divided as three groups, and AGE is divided into four categories according to the quartile method, which were low risk (≤P25), low to medium risk (P25∼P50), medium to high (P50∼P75), and high risk (>P75), respectively. The association between the quartile AGE and risk factors of the OR values was 1.09 (95% CI: 1.42, 2.86), 2.61 (95% CI: 1.11, 6.14), and 5.41 (95% CI: 2.42, 12.07), respectively. The moderation analysis using the PROCESS program was used to analyze whether BMI moderated the link between risk factors and AGE accumulation. There was also a significant three-way interaction among HRFs, BMI, and gender for AGE accumulation in the total sample (β = -0.30). Conclusion Non-invasive skin detection of AGEs has a certain application value for the assessment of T2DM risk and is related to a variety of risk factors.