Nutrition & Metabolism (May 2023)

Serum cholinesterase is associated with incident diabetic retinopathy: the Shanghai Nicheng cohort study

  • Rong Yu,
  • Xiaoqi Ye,
  • Xiangning Wang,
  • Qiang Wu,
  • Lili Jia,
  • Keqing Dong,
  • Zhijun Zhu,
  • Yuqian Bao,
  • Xuhong Hou,
  • Weiping Jia

DOI
https://doi.org/10.1186/s12986-023-00743-2
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 9

Abstract

Read online

Abstract Background Serum cholinesterase (ChE) is positively associated with incident diabetes and dyslipidemia. We aimed to investigate the relationship between ChE and the incidence of diabetic retinopathy (DR). Methods Based on a community-based cohort study followed for 4.6 years, 1133 participants aged 55–70 years with diabetes were analyzed. Fundus photographs were taken for each eye at both baseline and follow-up investigations. The presence and severity of DR were categorized into no DR, mild non-proliferative DR (NPDR), and referable DR (moderate NPDR or worse). Binary and multinomial logistic regression models were used to estimate the risk ratio (RR) and 95% confidence interval (CI) between ChE and DR. Results Among the 1133 participants, 72 (6.4%) cases of DR occurred. The multivariable binary logistic regression showed that the highest tertile of ChE (≥ 422 U/L) was associated with a 2.01-fold higher risk of incident DR (RR 2.01, 95%CI 1.01-4.00; P for trend < 0.05) than the lowest tertile (< 354 U/L). The multivariable binary and multinomial logistic regression showed that the risk of DR increased by 41% (RR 1.41, 95%CI 1.05–1.90), and the risk of incident referable DR was almost 2-fold higher than no DR (RR 1.99, 95%CI 1.24–3.18) with per 1-SD increase of loge-transformed ChE. Furthermore, multiplicative interactions were found between ChE and elderly participants (aged 60 and older; P for interaction = 0.003) and men (P for interaction = 0.044) on the risk of DR. Conclusions In this study, ChE was associated with the incidence of DR, especially referable DR. ChE was a potential biomarker for predicting the incident DR.

Keywords