Purpose To explore the value of CT-based radiomics in the preoperative prediction of lymphatic invasion of node-negative gastric cancer,and to construct a nomogram combined with clinical variables.Materials and Methods The clinical and CT imaging data of 173 gastric cancer patients with lymph node negative and pathologically confirmed gastric cancer in the Sir Run Run Shaw Hospital from January 2019 to June 2021 were retrospectively analyzed.A total of 60 cases with lymphovascular invasion(LVI)positive patients and 113 cases with LVI negative patients were included,and randomly divided into train cohort(n=121)and test cohort(n=52)at 7∶3.Based on the train cohort,the clinical model,the radiomics model,the fusion model were constructed and verified in the test cohort.Clinical data and conventional CT features included age,gender,tumor marker,tumor location,tumor morphology,enhancement range,etc.The clinical significant variables were selected through univariate and multivariate analysis to establish the clinical model.The tumor regions of interest were segmented and radiomics features were extracted by using the 3D-Slicer software.Key features were screened through least absolute shrinkage and selection operator regression analysis,and then the radiomics model was constructed with random forest algorithm,and converted to random forest score(RF score).The fusion model was constructed via combining clinical significant variables and RF score,and visualized as a nomogram.The receiver operator characteristic curve and area under curve(AUC)were used to evaluate the prediction performance of the models.Decision curve analysis was used to calculate the clinical practicability.Results The radiomics model was superior to the clinical model.The radiomics model AUC of the train cohort and the test cohort were 0.872(0.810 to 0.935)and 0.827(0.707 to 0.947),the clinical model AUC were 0.767(0.682 to 0.852)and 0.761(0.610 to 0.913).The nomogram further improved the predictive efficiency,the AUC in train cohort and test cohort reached 0.898(0.842 to 0.953)and 0.844(0.717 to 0.971),respectively.Decision curve analysis demonstrated clinical benefits of nomogram.Conclusion The radiomics model can be used to preoperatively predict LVI of node-negative gastric cancer.The nomogram can further improve the prediction efficiency.

Objective:To explore the relationship between normal serum creatinine(Scr) level and diabetic kidney disease(DKD) in patients with type 2 diabetes mellitus(T2DM).Methods:This was a prospective cohort study. Patients with yet not DKD who were regularly followed up at six centers of Li′s United Clinic in Taiwan, China from January 1, 2002 to December 31, 2018 were selected. At baseline, clinic information and lab tests were collected. According to whether the patients developed DKD during the follow-up period, they were divided into DKD group and non-DKD(NDKD) group. The exposure factor was the Scr(μmol/L) value, and it was used as a categorical variable. According to the quartiles of Scr, they were divided into 4 groups: Q1 group(Scr<57.68 μmol/L), Q2 group(57.68 μmol/L≤Scr<68.51 μmol/L), Q3 group(68.51 μmol/L≤Scr<80.44 μmol/L) and Q4 group(Scr≥80.44 μmol/L). The Cox regression model was used to explore the relationship between Scr level and the incidence of DKD. Receiver operating characteristic(ROC) curve was used to analyze the predictive effect of normal level Scr on DKD. Results:A total of 2 202 T2DM patients without DKD at baseline were included. After a follow-up period of(5.2±2.17) years, there were 966 patients in the DKD group and 1 236 patients in the NDKD group. Compared with the NDKD group, the DKD group had older age, longer duration of diabetes, higher BMI, SBP, DBP, LDL-C, Scr, and UACR(all P<0.05). Cox regression analysis results showed that compared with the Q1 group as the reference, the risk of developing DKD in the Q2, Q3, and Q4 groups after adjusting for confounding factors was 1.394, 1.688, and 2.821 times higher, respectively(all P<0.05). ROC curve analysis results showed that the area under the curve(AUC) for predicting DKD occurrence using normal serum creatinine level was 0.70(95% CI 0.678-0.722), with an optimal cutoff value of 74.27 μmol/L, sensitivity of 0.54, and specificity of 0.76. The cumulative risk plot showed that after adjusting for confounding factors, patients in the Q4 group had a higher cumulative risk of developing DKD compared to the Q1, Q2, and Q3 groups(all P<0.05). Conclusion:Scr is an independent risk factor for developing DKD in patients with T2DM. The higher the Scr level, the greater the risk, especially when Scr is greater than 74.27 μmol/L.

Objective To explore the predictive effect of empowerment and theory of planned behavior(TPB)on the four core behaviors of type 2 diabetes mellitus(T2DM)self-management,and analyze the influence path of TPB model and behavioral intention on the four core behaviors.Methods A total of 500 T2DM patients who were hospitalized in the Endocrinology Department of three tertiary general hospitals in different provinces from December 2022 to May 2023 were selected for investigation.Hierarchical multiple regression analysis was used to explore the predictive effects of empowerment and TPB models on self-management behaviors.Bootstrap method was used to analyze the influence path of TPB model and behavior intention on self-management behaviors.Results Empowerment had a predictive effect on self-management behavior,which was improved after the addition of TPB,with medication R2=0.194,blood glucose monitoring R2=0.308,regular diet R2=0.337 and regular exercise R2=0.343,respectively.Mediation effect analyses revealed that attitude,subjective norm,and perceived behavioral control were observed to predict behavior intention across the four behaviors,and the three except through the behavior intention indirectly affect behavior,can also directly affect the behavior.Conclusions Empowerment can predict self-management behavior,and adding TPB to this can improve the prediction effect.Attitude,subjective norms and perceived control can indirectly or directly affect self-management behavior through behavioral intention.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Objective:To evaluate the relationship between hemoglobin(Hb) level and the risk of diabetic retinopathy(DR) in patients with type 2 diabetes mellitus(T2DM).Methods:This study was a prospective cohort study. A total of 1 730 T2DM patients without DR, who received regular management at the Li′s Clinic in Taiwan, China starting from 2002, were selected as the study population. All patients underwent annual dilated fundus examination by professional ophthalmologists. General patient information and laboratory results were collected and analyzed. Based on the occurrence of DR during patient follow-up, patients were divided into the DR group and the non-DR(NDR) group. The impact of Hb levels on DR was explored using a generalized linear mixed model, and the relationship between Hb levels and DR was studied using Cox proportional hazards regression model.Results:After an average follow-up of 9.79 years, 481 patients with DR were detected. Compared with NDR group, DR group displayed a longer course of diabetes, higher rates of cataract, insulin use, and anemia, and higher systolic blood pressure, HbA 1C, and UACR as well as lower Hb. The results of the generalized linear mixed model showed a negative correlation between Hb and the occurrence of DR( β=-0.015, P<0.001). The Cox proportional hazards regression model showed that, after adjusting for confounding variables and based on quartiles of average Hb levels during follow-up, the risk of developing DR increased by 56.9% in the Q1 group(Hb≤127 g/L) compared to the Q4 group(Hb≥142 g/L). The cumulative risk plot showed that, after adjusting for confounding variables, the Q1 group had the highest cumulative risk of developing DR, and the difference was statistically significant( P<0.05). Conclusion:Hb was negatively correlated with DR, and the lower Hb levels were associated with the occurrence of DR, independent of other influencing factors.

Objective To explore the relationship between different obesity indicators and carotid intima-media thickness(CIMT)in patients with type 2 diabetes mellitus(T2DM).Methods A total of 1762 T2DM patients who visited the Endocrinology Department of Changzhou Second People's Hospital Affiliated with Nanjing Medical University and the Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Traditional Chinese Medicine from January 2019 to February 2022 were enrolled in this study.They were divided into youth group(18～44 years old,n=402),middle aged group(45～59 years old,n=1032),and elderly group(≥60 years old,n=328)according to WHO age classification criteria.The influencing factors for CIMT thickening in T2DM patients were analyzed using binary logistic regression,and the evaluation of the predictive effect of different obesity indicators on CIMT thickening was evaluated by receiver operating characteristic(ROC)curves.Results The subcuta-neous fat area,visceral fat area(VFA),neck circumference(NC),BMI,WC,cardiac metabolic index(CMI),Chinese visceral fat index(CAVI),visceral fat index,triglyceride glucose index,body roundness index,lipid aggregation index,HbA1c,DBP,TC,TG,HDL-C,LDL-C were lower in the middle aged and elderly groups than in youth group(P＜0.05).Binary logistic regression showed that VFA,NC,CMI in young T2DM patients,CAVI in middle aged T2DM patients,and NC in elderly T2DM patients were influ-encing factors for CIMT thickening.ROC curve analysis showed that VFA in young T2DM patients,CAVI in middle aged T2DM patients,and NC in elderly T2DM patients had a better predictive effect on CIMT thickening,with areas under the ROC curve of 0.567,0.574,and 0.573 respectively.Conclusion VFA,CAVI,and NC have a certain predictive effect on CIMT thickening in young,middle aged,and elderly T2DM patients.

Objective:To evaluate the effect of urinary albumin creatinine ratio (UACR) on diabetic retinopathy (DR) in patients with type 2 diabetes. Receiver operating characteristic (ROC) curve was applied to find the cut-off value of UACR for diagnosing DR.Methods:A prospective cohort study of 2 490 patients with type 2 diabetes was conducted with a mean follow-up of 7 years ranging from 3 to 10 years. Dilated fundus examination was performed once a year, and patient history and clinical data were collected and analyzed. Patients were divided into three groups according to the UACR: Q1, normal urinary albumin group (UACR<30 mg/g), Q2, microalbuminuria group (30 mg/g≤UACR≤299 mg/g), and Q3, macroalbuminuria group (UACR>300 mg/g), respectively. Cox regression analysis was used to explore the influence of UACR and other factors on DR, and ROC curve was drawn to evaluate the value of UACR in diagnosis of DR.Results:Cox regression analysis showed that UACR was the risk factor of DR( HR=1.108, 95% CI 1.023-1.241, P<0.001). It showed that the patients in Q3 group had the highest risk of proliferative DR ( HR=3.128, 95% CI 2.025-4.831, P<0.001), the patients in Q2 group followed( HR=1.918, 95% CI 1.355-2.714, P<0.001), and the patients in Q1 group were the lowest. ROC curve analysis showed that area under UACR curve was 0.746(95% CI 0.681-0.812, P<0.001), and the cut-off value, sensitivity, and specificity for the diagnosis of PDR were 54.12mg/g, 0.769, and 0.653, respectively. Conclusion:The UACR can predict the progression of PDR in type 2 diabetes patients, therefore it may be used as a preliminary predictor for the progression of DR.

Objective:To analyze the association between HbA 1C variability and the incidence of diabetes retinopathy in patients with type 2 diabetes mellitus(T2DM). Methods:All the patients with type 2 diabetes receiving regular follow-up were enrolled from Lee′s Joint Clinic from 2002 to 2014. Demographic and laboratory data like HbA 1C were collected including fundal examination. According to HbA 1C variability, which was defined as the difference between baseline and last available HbA 1C, participants were divided into three groups, stable group with HbA 1C variability of ±10%, increase group(>10%), and decline group(<-10%). Results:A total of 3 657 T2DM participants were recruited including 662(13.4%) participants with diabetes retinopathy. Blood glucose gradually rose from ideal level [HbA 1C(7.04±1.35)%] and HbA 1C was up to (9.11±1.96)% at the end of follow-up in increase group. HbA 1C gradually fell to (7.27±1.12)% from (10.05±1.99)% of baseline in decline group. HbA 1C of the third group remained relatively stable. Adjusted for age, body mass index, systolic blood pressure, pulse pressure, duration of diabetes, mean HbA 1C of follow-up, glaucoma and so on, logistic regression revealed that participants in stable group( OR=0.800, 95% CI 0.645-0.992) and increase group( OR=0.706, 95% CI 0.548-0.909) had a lower risk of diabetes retinopathy than decline group( P<0.05). Conclusion:HbA 1C variability is an important risk factor of diabetes retinopathy in patients with T2DM. Patients with blood glucose declined had increased risk of diabetes retinopathy as compared to those with rising HbA 1C.

Objective:To investigate the effect of a 2-year resistance and aerobic training on reducing the risk of cardiovascular disease in patients with prediabetes.Methods:A total of 248 patients with prediabetes were enrolled from Chinese and Western Medicine Hospital Affiliated to Nanjing University of Chinese Medicine from January to April 2014, and Danyang People′s Hospital and The First Affiliated Hospital of Guangxi Medical University from May to December 2014.Based on random number table method, the patients were divided into 3 groups: the resistance training group (RT group, 82 cases), the aerobic training group (AT group, 83 cases) and control group (83 cases). Participants in the RT group and the AT group underwent a total of 24 months of exercise training. Changes in indicators (blood glucose,blood lipid, etc.) at baseline and the end of 12 and 24 months among the groups were compared.Results:After intervention, glycosylated hemoglobin (HbA1c), low density lipoprotein cholesterol (LDL-C), blood pressure and homeostasis model 2 insulin resistance index (HOMA2-IR) in the RT and AT groups tended to decrease, and the steady state model 2 β cell function index (HOMA2-β) tended to increase. At the end of 24 months, HbA1c [5.80 (5.43, 6.20) %, 5.70 (5.50, 6.00)% vs. 6.20 (5.70, 6.60) %, all P≤ 0.01], LDL-C [3.07 (2.69, 3.58) mmol/L, 2.97 (2.62, 3.95) mmol/L vs. 3.21(2.54, 3.78) mmol/L, all P<0.05] and HOMA2-IR [0.96 (0.82, 1.47), 1.20 (0.99, 1.43) vs. 1.34 (1.09, 1.51), all P<0.05] were significantly decreased in the RT and AT groups than in the control group. In addition, HOMA2-β [84.50 (60.55, 107.33), 93.00 (78.60, 119.75) vs. 53.40 (37.70, 80.40), all P = 0.001] was significantly increased in the AT and RT groups compared with that in the control group. There were no significant differences in triglyceride (TG) and high-density lipoproteincholesterol (HDL-C) levels between the training groups and the control group (all P>0.05). After adjusting for age, sex and blood pressure, the cardiovascular risk of prediabetes was significantly reduced in RT ( P =0.017) and AT groups ( P =0.018). The Cox regression analyses showed that both the resistance training (HR=0.419, 95 %CI =0.415-0.942, P=0.037) and the aerobic training ( HR=0.310, 95 %CI=0.447-0.866, P=0.026) were protective factors for cardiovascular disease in prediabetic patients after adjustment of age, sex, statins, body mass index and waist-to-hip ratio, which reduced the risks of cardiovascular disease in prediabetic patients by 58.1% and 69.0%, respectively. Conclusions:Two years of aerobic and resistance training interventions have obvious advantages on glycemic and insulin resistance control in prediabetes patients. The resistance training can reduce the risk of cardiovascular disease, and it is, thus, recommended for prediabetic patients without obvious exercise contraindications.