[Construction of a risk prediction model for diabetes after kidney transplantation based on genome-wide association study].

Objective: To explore the clinical risk factors and susceptibility genes of diabetes after kidney transplantation (PTDM) and construct a risk prediction model for PTDM. Methods: The data of kidney transplant recipients who underwent follow-up in the Affiliated Lihuili Hospital, Ningbo University and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine from January 2001 to December 2022 were retrospectively analyzed. The recipients were divided into PTDM group and Non-PTDM group according to whether they were complicated with PTDM. The differences in clinical indicators between the two groups were compared, the risk factors affecting the incidence of PTDM were determined, and susceptibility genes of PTDM were screened by genome-wide association study (GWAS). PTDM risk prediction models based only on clinical indicators (Model 1) and clinical indicators combined with susceptibility genes (Model 2) were established respectively, and the predictive performance of the two prediction models was compared. Finally, the Nomogram of the optimal model was drawn, and the discrimination, calibration and clinical applicability of the model were evaluated. Results: A total of 113 kidney transplant recipients (70 males and 43 females) were included, with an average age of (46.2±10.8) years. There were 51 cases in PTDM group and 62 cases in Non-PTDM group. The related factors screened by GWAS and logistic regression analysis included family history of diabetes (OR=88.912, 95%CI: 5.827-1 356.601, P=0.001), preoperative triglyceride (TG) (OR=1.888, 95 %CI: 1.150-3.098, P=0.012), uric acid (UA) (OR=1.011, 95%CI: 1.000-1.022, P=0.045) and rs802707 (OR=10.046, 95%CI: 1.462-69.042, P=0.019). The area under the curve (AUC) of the receiver operating characteristics analysis (ROC) predicted by Model 1 for PTDM was 0.891 (95%CI: 0.811-0.972), with the sensitivity of 0.889 and the specificity of 0.742. The AUC of ROC curve predicted by Model 2 for PTDM was 0.930 (95%CI: 0.864-0.995), with the sensitivity of 0.885 and the specificity of 0.900. Conclusions: Family history of diabetes, preoperative TG and UA, and rs802707 are significantly associated with the occurrence of PTDM. In addition, the combination of susceptibility genes could improve the predictive ability of clinical indicators for the risk of PTDM.

[Effect of corneal e-value on myopia control in children and adolescents with orthokeratology].

Objective: To investigate the influence of corneal e-value on the effectiveness of orthokeratology in controlling myopia in children and adolescents. Methods: A retrospective cohort study was conducted, involving the data from 1 563 myopic patients (1 563 eyes) who underwent orthokeratology at the Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine from June 2015 to August 2021 and adhered to lens wear for at least 2 years. The cohort consisted of 737 males and 826 females with an average age of (10.84±2.13) years. Based on corneal e-value parameters obtained from corneal topography, patients were categorized into a low e-value group (n=425) and a high e-value group (n=1 138). Data on gender, age, parental myopia history, and baseline measures such as spherical equivalent (SE), axial length, and corneal e-value were collected. Differences in axial length change and corneal fluorescein staining rates were compared between the two groups at 1 and 2 years after the start of lens wear. A generalized linear mixed model was established with axial length change as the dependent variable to analyze the correlation between axial length change and baseline corneal e-value. Results: The initial age of the 1 563 myopic patients was (10.84±2.13) years, with a baseline SE of (-3.05±1.30) D. After 1 year of lens wear, the axial length change was (0.20±0.19) mm in the low e-value group and (0.24±0.20) mm in the high e-value group. After 2 years, the changes were (0.38±0.25) mm and (0.43±0.27) mm, respectively, with statistically significant differences (all P<0.05). The incidence of corneal staining after 1 year of lens wear was 9.2% (39/425) in the low e-value group and 14.1% (160/1 138) in the high e-value group. After 2 years, the rates were 15.8% (67/425) and 21.8% (248/1 138), respectively, with statistically significant differences (all P<0.05). After adjusting for parental myopia history, age, SE, and baseline axial length, the baseline corneal e-value was positively correlated with axial length change at 1 and 2 years after lens wear (all P<0.05). Conclusions: Corneal e-value is an independent factor influencing the effectiveness of orthokeratology in controlling myopia. A smaller corneal e-value is associated with slower axial length growth after orthokeratology, indicating better control of myopia in treated eyes.