Optimizing diabetic kidney disease animal models:Insights from a meta-analytic approach

Diabetic kidney disease(DKD)is a prevalent complication of diabetes,often leading to end-stage renal disease.Animal models have been widely used to study the pathogenesis of DKD and evaluate potential therapies.However,current animal models often fail to fully capture the pathological characteristics of renal injury observed in clinical patients with DKD.Additionally,modeling DKD is often a time-consuming,costly,and labor-intensive process.The current review aims to summarize modeling strategies in the establishment of DKD animal models by utilizing meta-analysis related methods and to aid in the optimization of these models for future research.A total of 1215 articles were retrieved with the keywords of“diabetic kidney disease”and“animal experiment”in the past 10 years.Following screening,84 articles were selected for inclusion in the meta-analysis.Review manager 5.4.1 was employed to analyze the changes in blood glucose,glycosylated hemoglobin,total cholesterol,triglyceride,serum creatinine,blood urea nitrogen,and urinary albumin excretion rate in each model.Renal lesions shown in different models that were not suitable to be included in the metaanalysis were also extensively discussed.The above analysis suggested that combining various stimuli or introducing additional renal injuries to current models would be a promising avenue to overcome existing challenges and limitations.In conclusion,our review article provides an in-depth analysis of the limitations in current DKD animal models and proposes strategies for improving the accuracy and reliability of these models that will inspire future research efforts in the DKD research field.

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

BACKGROUND Type 2 diabetes mellitus(T2DM)is associated with periodontitis.Currently,there are few studies proposing predictive models for periodontitis in patients with T2DM.AIM To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.METHODS In this a retrospective study,300 patients with T2DM who were hospitalized at the First People’s Hospital of Wenling from January 2022 to June 2022 were selected for inclusion,and their data were collected from hospital records.We used logistic regression to analyze factors associated with periodontitis in patients with T2DM,and random forest and logistic regression prediction models were established.The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve(AUC).RESULTS Of 300 patients with T2DM,224 had periodontitis,with an incidence of 74.67%.Logistic regression analysis showed that age[odds ratio(OR)=1.047,95%confidence interval(CI):1.017-1.078],teeth brushing frequency(OR=4.303,95%CI:2.154-8.599),education level(OR=0.528,95%CI:0.348-0.800),glycosylated hemoglobin(HbA1c)(OR=2.545,95%CI:1.770-3.661),total cholesterol(TC)(OR=2.872,95%CI:1.725-4.781),and triglyceride(TG)(OR=3.306,95%CI:1.019-10.723)influenced the occurrence of periodontitis(P<0.05).The random forest model showed that the most influential variable was HbA1c followed by age,TC,TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showedthat in the training dataset, the AUC of the random forest model was higher than that of the logistic regressionmodel (AUC = 1.000 vs AUC = 0.851;P < 0.05). In the validation dataset, there was no significant difference in AUCbetween the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915;P > 0.05).CONCLUSION Both random forest and logistic regression models have good predictive value and can accurately predict the riskof periodontitis in patients with T2DM.

Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

Major chronic diseases such as Cardiovascular Disease(CVD),diabetes,and cancer impose a significant burden on people and healthcare systems around the globe.Recently,Deep Learning(DL)has shown great potential for the development of intelligentmobile Health(mHealth)interventions for chronic diseases that could revolutionize the delivery of health care anytime,anywhere.The aimof this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis,prognosis,management,and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.Type 2 Diabetes Mellitus(T2DMs)is a regular chronic disorder that is caused by the secretion of insulin,which leads to serious death-related issues and the most complicated ones.Coronary Heart Disease(CHD)is the most frequent issue related to T2DM patients.The major concern is recognizing the high possibility of CHD complications,yet the model is not available to identify it.This work introduces a deep learning technique that can predict heart disease effectively using a hybrid model,which integrates DNNs(Deep Neural Networks)with a Multi-Head Attention Model called MADNN.The scheme canbedesignedtoautomatically learnthe best-quality features fromElectronic Health Records(EHRs),and effectively combine heterogeneous and time-sequencedmedical data for predicting the risk of CVD.The analysis is done using the Kaggle dataset.The outcomes prove that the MADNN has improved accuracy by about 95%and indicates the precise accuracy is higher for the disease compared with SVM,CNN and ANN.

Effects of exercise on brain functions in diabetic animal models

Human life span has dramatically increased over several decades,and the quality of life has been considered to be equally important.However,diabetes mellitus(DM) characterized by problems related to insulin secretion and recognition has become a serious health problem in recent years that threatens human health by causing decline in brain functions and finally leading to neurodegenerative diseases.Exercise is recognized as an effective therapy for DM without medication administration.Exercise studiesusing experimental animals are a suitable option to overcome this drawback,and animal studies have improved continuously according to the needs of the experimenters.Since brain health is the most significant factor in human life,it is very important to assess brain functions according to the different exercise conditions using experimental animal models.Generally,there are two types of DM; insulin-dependent type 1 DM and an insulin-independent type 2 DM(T2DM); however,the author will mostly discuss brain functions in T2 DM animal models in this review.Additionally,many physiopathologic alterations are caused in the brain by DM such as increased adiposity,inflammation,hormonal dysregulation,uncontrolled hyperphagia,insulin and leptin resistance,and dysregulation of neurotransmitters and declined neurogenesis in the hippocampus and we describe how exercise corrects these alterations in animal models.The results of changes in the brain environment differ according to voluntary,involuntary running exercises and resistance exercise,and gender in the animal studies.These factors have been mentioned in this review,and this review will be a good reference for studying how exercise can be used with therapy for treating DM.

Progress in experimental models to investigate the in vivo and in vitro antidiabetic activity of drugs

Diabetes mellitus is one of the world's most prevalent and complex metabolic disorders,and it is a rapidly growing global public health issue.It is characterized by hyperglycemia,a condition involving a high blood glucose level brought on by deficiencies in insulin secretion,decreased activity of insulin,or both.Prolonged effects of diabetes include cardiovascular problems,retinopathy,neuropathy,nephropathy,and vascular alterations in both macro-and micro-blood vessels.In vivo and in vitro models have always been important for investigating and characterizing disease pathogenesis,identifying targets,and reviewing novel treatment options and medications.Fully understanding these models is crucial for the researchers so this review summarizes the different experimental in vivo and in vitro model options used to study diabetes and its consequences.The most popular in vivo studies involves the small animal models,such as rodent models,chemically induced diabetogens like streptozotocin and alloxan,and the possibility of deleting or overexpressing a specific gene by knockout and transgenic technologies on these animals.Other models include virally induced models,diet/nutrition induced diabetic animals,surgically induced models or pancreatectomy models,and non-obese models.Large animals or non-rodent models like porcine(pig),canine(dog),nonhuman primate,and Zebrafish models are also outlined.The in vitro models discussed are murine and human beta-cell lines and pancreatic islets,human stem cells,and organoid cultures.The other enzymatic in vitro tests to assess diabetes include assay of amylase inhibition and inhibition ofα-glucosidase activity.

Therapeutic Effect of Berberine on 60 Patients with Non－Insulin Dependent Diabetes Mellitus and Experimental Research

The effects of berberine on 60 cases with noninsulin dependent diabetes mellitus and ex-perimental research results were observed in this study. The results suggest berberine has significant ef-fects on noninsulin dependent diabetes mellitus patients and experimental diabetes in animals in the re-duction of blood glucose levels. The clinical symptoms basically disappeared and the level of serum insulinrose. The total effective rate was up to 90 percent and there were no signiticant side-effects. It was foundthat berberine has an effect on the recovery of pancreas islet cells, through pathological examination onthe animal subjects.

SARS-CoV-2 infection aggravates chronic comorbidities of cardiovascular diseases and diabetes in mice

Background:Cardiovascular diseases(CVDs)and diabetes mellitus(DM)are top two chronic comorbidities that increase the severity and mortality of COVID-19.However,how SARS-CoV-2 alters the progression of chronic diseases remain unclear.Methods:We used adenovirus to deliver h-ACE2 to lung to enable SARS-CoV-2 infection in mice.SARS-CoV-2’s impacts on pathogenesis of chronic diseases were studied through histopathological,virologic and molecular biology analysis.Results:Pre-existing CVDs resulted in viral invasion,ROS elevation and activation of apoptosis pathways contribute myocardial injury during SARS-CoV-2 infection.Viral infection increased fasting blood glucose and reduced insulin response in DM model.Bone mineral density decreased shortly after infection,which associated with impaired PI3K/AKT/mTOR signaling.Conclusion:We established mouse models mimicked the complex pathological symptoms of COVID-19 patients with chronic diseases.Pre-existing diseases could impair the inflammatory responses to SARS-CoV-2 infection,which further aggravated the pre-existing diseases.This work provided valuable information to better understand the interplay between the primary diseases and SARS-CoV-2 infection.

Diabetes and cognitive decline:Challenges and future direction

There is growing evidence that diabetes can induce cognitive decline and dementia.It is a slow,progressive cognitive decline that can occur in any age group,but is seen more frequently in older individuals.Symptoms related to cognitive decline are worsened by chronic metabolic syndrome.Animal models are frequently utilized to elucidate the mechanisms of cognitive decline in diabetes and to assess potential drugs for therapy and prevention.This review addresses the common factors and pathophysiology involved in diabetes-related cognitive decline and outlines the various animal models used to study this condition.

Glucagon-like peptide-1 receptor agonists in non-alcoholic fatty liver disease: An update

Non-alcoholic fatty liver disease(NAFLD)is the predominant cause of chronic liver disease worldwide.NAFLD progresses in some cases to non-alcoholic steatohepatitis(NASH),which is characterized,in addition to liver fat deposition,by hepatocyte ballooning,inflammation and liver fibrosis,and in some cases may lead to hepatocellular carcinoma.NAFLD prevalence increases along with the rising incidence of type 2 diabetes mellitus(T2DM).Currently,lifestyle interventions and weight loss are used as the major therapeutic strategy in the vast majority of patients with NAFLD.Glucagon-like peptide-1 receptor agonists(GLP-1RAs)are used in the management of T2DM and do not have major side effects like hypoglycemia.In patients with NAFLD,the GLP-1 receptor production is down-regulated.Recently,several animal and human studies have emphasized the role of GLP-1RAs in ameliorating liver fat accumulation,alleviating the inflammatory environment and preventing NAFLD progression to NASH.In this review,we summarize the updated literature data on the beneficial effects of GLP-1RAs in NAFLD/NASH.Finally,as GLP-1RAs seem to be an attractive therapeutic option for T2DM patients with concomitant NAFLD,we discuss whether GLP-1RAs should represent the first line pharmacotherapy for these patients.

灵芝螺旋藻复方胶囊辅助降血糖功能

该文探究灵芝螺旋藻复方胶囊对Ⅱ型糖尿病小鼠的降血糖作用。采用高热能饲料结合腹腔注射链脲佐菌素的方法,建立Ⅱ型糖尿病小鼠模型,随机将小鼠分为对照组、模型组和低、中、高剂量组。灵芝螺旋藻复方胶囊连续干预4周,测定小鼠体质量、脏器指数、血液相关指标并观察肝、胰腺、盲肠病理组织切片。结果显示,低、中、高剂量(相当于人体推荐用量5、10、30倍)灵芝螺旋藻复方胶囊能不同程度改善糖尿病小鼠脏器指数、血糖水平以及葡萄糖耐量;从病理组织切片观察到经过3个剂量组干预的肝细胞体积明显恢复且排列整齐、脂滴空泡减少,肝细胞的损伤与炎症明显改善;胰岛细胞边界随剂量的增大而变得逐渐清晰且有序;低、中剂量两个组除绒毛有少部分呈圆状环绕外,3个剂量组盲肠组织外膜、肌层、黏膜、绒毛形态均有明显改善。综上,低、中、高3个剂量灵芝螺旋藻复方胶囊均可有效改善Ⅱ型糖尿病小鼠的糖脂代谢,有助于维持血糖健康水平。

NADPH氧化酶4在1型糖尿病模型小鼠角膜病变中的致病作用及其机制

目的探讨还原型烟酰胺腺嘌呤二核苷酸氧化酶4(Nox4)在1型DM模型小鼠角膜病变中的致病作用及其可能机制。方法选择Nox4基因敲除(Nox4^(-/-))纯合子雄性小鼠40只,以鼠龄、性别匹配的野生型C57BL/6(Nox4^(+/+))小鼠120只作为对照。采用随机数字表法分别将2种小鼠随机分为DM组和非DM组,DM组小鼠采用链脲佐菌素腹腔内注射法构建1型DM模型。采用随机数字表法分别将Nox4^(+/+)小鼠DM组和非DM组分为普通饲料喂养小鼠和添加Nox4抑制剂GKT137831(GKT)饲料喂养小鼠。于DM造模后第16周采用酚红棉线法检测各组小鼠泪液分泌量;采用荧光素钠染色评分法评估角膜上皮完整性;采用激光扫描共聚焦显微镜观察角膜基质层神经纤维密度变化;采用CellROX荧光探针检测角膜上皮中活性氧簇(ROS)含量;采用免疫荧光染色法检测小鼠角膜上皮中E-Cadherin蛋白和核因子-κB(NF-κB)蛋白表达变化;采用角膜铺片TUBB3染色法检测角膜中央区神经纤维密度。结果Nox4^(+/+)小鼠DM组和非DM组泪液分泌量分别为(2.40±1.18)和(5.30±1.02)mm/min,差异有统计学意义(P<0.01);Nox4^(-/-)小鼠DM组泪液分泌量为(4.19±0.63)mm/min,明显多于Nox4^(+/+)小鼠DM组,差异有统计学意义(P<0.05);普通饲料喂养小鼠与GKT添加饲料喂养小鼠DM组泪液分泌量分别为(2.23±0.83)和(4.02±0.71)mm/min,差异有统计学意义(P<0.01)。与Nox4^(+/+)小鼠非DM组比较,Nox4^(+/+)小鼠DM组角膜荧光素染色评分显著升高,角膜神经纤维密度显著降低,角膜上皮中ROS荧光强度明显增强,E-Cadherin蛋白表达荧光强度减弱,NF-κB蛋白表达荧光强度增强。Nox4^(-/-)或GKT添加饲料喂养小鼠DM组与非DM组比较角膜上皮中ROS荧光增强,E-Cadherin蛋白表达荧光减弱。Nox4^(-/-)和GKT添加饲料喂养小鼠DM组角膜上皮细胞中NF-κB蛋白荧光强度均较弱,与非DM组强度一致。角膜铺片免疫荧光染色显示,Nox4^(+/+)小鼠DM组中TUBB3染色的神经纤维密度明显低于非DM组,Nox4^(-/-)或GKT添加饲料喂养小鼠DM组角膜基质层神经纤维与非DM组比较无明显减少。结论Nox4参与了糖尿病角膜病变的致病过程,其机制可能与氧化应激诱导ROS产物聚集,激活NF-κB介导的炎症反应有关。

2型糖尿病合并非酒精性脂肪性肝病患者肝纤维化检出率及预测因素分析

目的探讨2型糖尿病(T2DM)患者非酒精性脂肪性肝病(NAFLD)及其肝纤维化发生率和影响因素。方法2020年4月~2022年4月我院收治的482例T2DM患者,使用超声检查诊断NAFLD,检测杨氏模量值诊断肝纤维化。应用Lasso回归模型和有序多分类Logistic回归分析法筛查和分析T2DM患者罹患NAFLD严重程度的风险因素,绘制受试者工作特征(ROC)曲线评估风险因素预测NAFLD患者发生肝纤维化的效能。结果在482例T2DM患者中,诊断NAFLD者276例(57.3%),后者包括肝纤维化者58例(12.0%);T2DM/NAFLD/肝纤维化组年龄、周围神经病发生率、体质指数和杨氏模量值分别为(46.0±4.2)岁、82.8%、(28.3±3.2)kg/m^(2)和(10.8±2.2)kPa,均显著大于T2DM/NAFLD组【分别为(45.6±4.9)岁、45.4%、(27.9±3.1)kg/m^(2)和(5.3±1.0)kPa,P<0.05】或T2DM组【分别为(42.5±5.8)岁、44.2%、(25.0±2.7)kg/m^(2)和(4.8±0.6)kPa,P<0.05】;T2DM/NAFLD/肝纤维化组血清甘油三酯、尿酸、HOMA-IR、甘油三酯/血糖/体质指数、血清TNF-α和IL-6水平分别为(2.6±0.8)mmol/L、(355.2±24.6)μmol/L、(2.6±0.4)、(270.8±18.5)、(8.2±2.3)pg/ml和(13.6±2.4)pg/ml,显著大于T2DM/NAFLD组【分别为(2.2±0.4)mmol/L、(334.6±31.0)μmol/L、(2.6±0.4)、(219.1±40.4((1)))、(5.5±1.4)pg/ml和(9.7±2.1)pg/ml,P<0.05】或T2DM【分别为(2.1±0.5)mmol/L、(328.7±36.8)μmol/L、(2.3±0.7)、(207.9±39.1)、(5.3±1.1)pg/ml和(6.7±1.1)pg/ml,P<0.05】;LASSO回归和多因素Logistic回归分析结果显示,TyG-BMI(OR=1.012,P=0.000)、IL-6(OR=2.782,P=0.000)、TNF-α(OR=1.008,P=0.026)、UA(OR=1.530,P=0.000)和糖尿病周围神经病(OR=1.855,P=0.010)为T2DM患者罹患NAFLD严重程度的独立危险因素;ROC曲线分析结果显示,TyG-BMI、IL-6、TNF-α和UA预测患者发生肝纤维化的曲线下面积(AUC)分别为0.897、0.928、0.840和0.762。结论T2DM合并NAFLD患者可能会进展至肝纤维化,了解一些预测因子并进行有效的干预可能能延缓病情发展。

糖尿病慢性病管理模式的研究进展

通过对糖尿病慢性病管理模式的不同形式进行综述,提出目前该模式在我国实施存在的挑战及建议,以期为制定符合我国国情的糖尿病慢性病管理模式提供理论参考。

棕矢车菊素调节SDF-1α/CXCR4信号通路对妊娠糖尿病大鼠炎症反应的影响

目的探讨棕矢车菊素对妊娠糖尿病(GDM)大鼠炎症反应的影响及可能机制。方法将妊娠大鼠腹腔注射链脲佐菌素建立GDM模型,将建模成功大鼠分为GDM组,棕矢车菊素-L、M、H组,棕矢车菊素+CTCE-0214[基质细胞衍生因子1α(SDF-1α)/趋化因子受体4(CXCR4)激活剂]组,每组10只;另取10只健康妊娠大鼠作为对照组。棕矢车菊素-L、M、H组分别灌胃2、3、4 mg/kg棕矢车菊素,棕矢车菊素+CTCE-0214组腹腔注射10 mg/kg CTCE-0214并灌胃4 mg/kg棕矢车菊素,对照组和GDM组给予等量生理盐水。比较各组大鼠空腹血糖、空腹胰岛素以及胰岛素抵抗指数差异;酶联免疫吸附测定法检测大鼠胎盘组织中白细胞介素(IL)-1β、肿瘤坏死因子(TNF)-α水平;HE染色观察胎盘组织病理变化;TUNEL检测胎盘组织细胞凋亡;Western blot检测大鼠胎盘组织中SDF-1α/CXCR4通路相关蛋白水平。结果不同剂量棕矢车菊素干预GDM大鼠引起大鼠空腹血糖、空腹胰岛素、胰岛素抵抗指数水平降低,促炎细胞因子水平降低,改善GDM大鼠胎盘损伤和细胞凋亡,并抑制SDF-1α/CXCR4信号通路激活,呈剂量依赖性(P<0.05),SDF-1α/CXCR4激活剂和棕矢车菊素联合干预后抑制棕矢车菊素上述作用。结论棕矢车菊素可通过抑制SDF-1α/CXCR4信号通路激活来减轻GDM大鼠炎症反应和胰岛素抵抗。

Effects of liraglutide on metabolic syndrome in WBN/Kob diabetic fatty rats supplemented with a high-fat diet

Background:Liraglutide,a GLP-1 receptor agonist,has recently been used to treat metabolic syndrome(MS)because of its anti-diabetic and anti-obesity effects.We have previously shown that Wistar Bonn Kobori diabetic and fatty(WBN/Kob-Leprfa,WBKDF)rats fed a high-fat diet(HFD)developed MS including marked obesity,hyperglycemia,and dyslipidemia.To obtain further information on WBKDF-HFD rats as a severe MS model,we performed a pharmacological investigation into the anti-MS effects of liraglutide in this model.Methods:Seven-week-old male WBKDF-HFD rats were allocated to three groups(n=8 in each group):a vehicle group,a low-dose liraglutide group,and a high-dose liraglutide group.They received subcutaneous injections of either saline or liraglutide at doses of 75 or 300μg/kg body weight once daily for 4 weeks.Results:Results showed that liraglutide treatment reduced body weight gain and food intake in a dose-dependent manner.The marked hyperglycemia and the glucose tolerance were also significantly ameliorated in the liraglutide-treated groups.Moreover,liraglutide also reduced the plasma triglyceride concentration and liver fat accumulation.Conclusions:The present study demonstrated that liraglutide could significantly alleviate MS in WBKDF-HFD rats,and the reaction to liraglutide is similar to human patients with MS.WBKDF-HFD rats are therefore considered to be a useful model for research on severe human MS.

冠心病合并2型糖尿病患者冠状动脉正性重构的影响因素及临床预测模型构建

目的探讨冠心病合并2型糖尿病患者发生冠状动脉正性重构的影响因素并构建相关的临床预测模型用以早期识别高危患者,指导临床治疗。方法选择2016年1月—2023年6月于首都医科大学附属北京同仁医院心血管中心诊断的冠心病合并2型糖尿病患者104例,通过冠状动脉内超声(IVUS)对靶病变进行测量,并计算重构指数(RI),根据RI将患者分为正性重构组和非正性重构组,搜集患者的临床资料,对2组数据进行统计并构建临床预测模型。结果Logistic回归分析结果发现,低血钙[OR(95%CI)=1.544(1.263~1.927),P<0.001]、并发急性冠状动脉综合征(ACS)[OR(95%CI)=1.198(1.024~1.401),P=0.024]、高糖化血红蛋白(HbA1c)[OR(95%CI)=1.498(1.104~2.032),P=0.010]和高低密度脂蛋白胆固醇(LDL-C)[OR(95%CI)=1.275(1.139~1.428,P<0.001]是冠心病合并2型糖尿病患者冠状动脉正性重构的独立危险因素。基于以上危险因素构建冠心病合并2型糖尿病患者冠状动脉正性重构的列线图模型,预测发生率与实际发生率基本一致,模型内部验证曲线下面积(AUC)为0.937,且具有良好的临床适用度。结论根据冠心病合并2型糖尿病患者的危险因素构建的列线图模型对其发生冠状动脉正性重构具有较好的预测效能。

2型糖尿病患者代谢相关脂肪性肝病列线图预测模型的建立与验证

目的构建和验证2型糖尿病(type 2 diabetes mellitus,T2DM)患者代谢相关脂肪性肝病(metabolic associated fatty liver disease,MAFLD)的个体化预测模型。方法回顾性收集2020年1月至2022年12月上海市徐汇区大华医院收治的1592例T2DM患者的临床资料。根据超声/影像学检查结果将患者分为MAFLD组和非MAFLD组,比较两组患者的一般临床特征。采用二元Logistic回归构建和验证MAFLD的影响因素并建立列线图模型。采用受试者工作特征(receiver operating characteristic,ROC)曲线和一致性指数(concordance index,C-index)评价列线图模型区分度。采用bootstrap法验证列线图模型的拟合度和校准度,采用一致性曲线评估模型的预测效能,采用决策曲线分析(decision curve analysis,DCA)评价模型是否临床获益。结果纳入研究的1592例T2DM患者中,1085例(68.2%)患者为MAFLD,507例(31.8%)患者为非MAFLD。MAFLD组患者年龄(中位数:74岁比73岁)、有饮酒史患者比例[17.6%(191/1085)比13.0%(66/507)]、体重指数(中位数:22.5 kg/m^(2)比22.0 kg/m^(2))、24 h动态舒张压(中位数:76 mmHg比74 mmHg)、糖尿病持续时间[(14.9±10.2)年比(10.7±8.3)年]、糖化血红蛋白(中位数:8.5%比7.9%)、总胆固醇(中位数:5.2 mmol/L比4.8 mmol/L)、甘油三酯[(2.1±1.2)mmol/L比(1.8±1.2)mmol/L]和尿素氮[(7.1±3.1)mmol/L比(6.8±2.7)mmol/L]水平高于非MAFLD组,高密度脂蛋白胆固醇[(1.1±0.5)mmol/L比(1.2±0.4)mmol/L]、25-羟基维生素D(中位数:37.5 nmol/L比40.5 nmol/L)和血糖在目标范围内时间(中位数:75%比79%)低于非MAFLD组(P均<0.05)。多因素Logistic分析表明,饮酒史(OR=2.44,95%CI:1.56~3.83,P<0.001)、体重指数(OR=1.06,95%CI:1.01~1.12,P=0.045)、糖尿病病程(OR=1.05,95%CI:1.02~1.07,P=0.010)、糖化血红蛋白(OR=1.08,95%CI:1.01~1.17,P=0.032)、总胆固醇(OR=1.43,95%CI:1.23~1.65,P<0.001)、25-羟基维生素D(OR=0.91,95%CI:0.86~0.96,P=0.001)以及血糖在目标范围内时间(OR=0.98,95%CI:0.97~0.99,P<0.001)与T2DM人群发生MAFLD独立相关。将Logistic模型筛选的因素构建列线图模型,ROC曲线下面积为0.77(95%CI:0.71~0.83),C-index为0.76(95%CI:0.72~0.81)。一致性曲线显示预测概率与实际观察值一致性良好,DCA提示模型具有良好的临床获益。结论T2DM人群中,MAFLD患者更可能存在不良生活方式和代谢障碍性因素,更易存在25-羟基维生素D缺乏和血糖在目标范围内时间低。该模型可用于T2DM人群中MAFLD的预测。

糖尿病并发冠心病风险预测模型的构建、评价与验证

目的基于大数据筛选糖尿病(Diabetes Mellitus,DM)并发冠心病(Coronary Heart Disease,CHD)的关键因素,构建风险预测模型,为DM并发CHD患者的早期诊断和干预提供依据。方法首先,采用“mice”软件包对原始数据进行数据清洗,构建新数据集并对其进行差异分析;其次,对新数据集按7∶3随机分为建模组和验证组两组;再次,对建模组依次进行Lasso、单因素与多因素Logistic分析,筛选DM并发CHD的独立影响因素,并构建风险预测模型;最后,利用受试者工作特征曲线(Receiver Operating Characteristic,ROC)、决策曲线分析法(Decision Curve Analysis,DCA)、校准曲线(Calibration Curve,CC)对建模组和验证组进行内部评价和内部验证。结果共纳入2980例DM患者的临床数据(建模组2086例;验证组894例);建模组数据采用机器学习算法筛选出的影响因素有患病种类、高血压、高脂血、动脉粥样硬化、颈动脉狭窄、脂肪肝、其他慢性肝病、心肌梗死、心功能不全及心力衰竭、心律失常、其他内分泌疾病、年龄、高密度脂蛋白胆固醇(1~1.6 mmol/L)、低密度脂蛋白胆固醇(0~3.4 mmol/L)、总蛋白、磷脂以及部分活化凝血酶原时间等,并利用其成功构建风险预测模型;该模型的内部评价与内部验证的ROC曲线下的面积(Area Under Curve,AUC)分别是0.948和0.946,DCA净获益率分别为(1%~99%)和(1%~100%),且两组的校准曲线高度一致。结论DM并发CHD风险预测模型具有准确性、一致性、区分性与实用性等价值,可为患者的预防、诊断与治疗提供重要依据。

高血压合并糖尿病患者应用以KAP模式为导向的全程健康教育的作用价值

目的探究高血压合并糖尿病患者应用以知信行(Knowledge-attitude-practice,KAP)模式为导向的全程健康教育的作用价值。方法选取2021年1月—2023年1月庆城县人民医院收治的75例高血压合并糖尿病患者为研究对象,信封法分为常规组(n=37,常规干预)、观察组(n=38,以KAP模式为导向的全程健康教育),探究不同护理干预的应用价值。结果干预后,观察组血糖水平、血压水平、疾病不确定感评分均低于常规组,差异有统计学意义(P均<0.05);观察组自我管理能力分评分为(120.12±2.74)分,高于常规组的(117.88±2.13)分,差异有统计学意义(t=3.945,P<0.05)。结论高血压合并糖尿病患者应用以KAP模式为导向的全程健康教育,可改善血糖及血压水平,缓解患者疾病不确定感,提高患者自我管理能力。

2型糖尿病胰腺癌裸鼠模型的建立及活体成像观察

目的建立可动态观察成瘤过程并进行体内研究的2型糖尿病(T2DM)胰腺癌裸鼠模型。方法首先,通过慢病毒载体GV260转染人胰腺癌细胞(PANC-1细胞)构建能稳定表达萤火虫荧光素酶的胰腺癌细胞株(PANC-1-Luc细胞)。然后,将36只SPF级裸鼠随机分为对照组(n=12,血糖正常的胰腺癌裸鼠)和模型组(n=24,T2DM胰腺癌裸鼠)。对照组:先给予繁殖饲料喂养,之后将PANC-1-Luc细胞异位种植于裸鼠皮下;模型组:先给予高脂饲料喂养联合腹腔注射1%STZ,之后将PANC-1-Luc细胞异位种植于裸鼠皮下。用荧光活体成像系统和人工测量法同步动态监测2组裸鼠胰腺癌生长情况,绘制肿瘤生长曲线、分析荧光值与肿瘤体积的关系。显微镜下观察裸鼠皮下肿瘤及胰岛,验证造模是否成功;同时,通过免疫组化检测肿瘤组织Ki-67的表达来分析高血糖对裸鼠胰腺癌生长的影响。正态分布计量资料组间比较采用成组t检验,非正态分布计量资料组间比较采用Mann-Whitney U检验。结果确定PANC-1细胞慢病毒载体稳定转染的最佳病毒滴度为5×107 TU/mL,用嘌呤霉素筛选的最佳浓度为20μg/mL、最佳筛选时间为9天;PANC-1-Luc细胞的荧光值与细胞数量呈线性正相关,线性方程为y=42.56x-42504(r=0.977,P=0.004)。T2DM裸鼠模型血糖值为23.05(19.25~26.40)mmol/L,且每只裸鼠的血糖均高于11.1 mmol/L,与对照组裸鼠血糖值[6.15(5.20~7.30)mmol/L]相比,差异有统计学意义(Z=−8.45,P<0.001)。与对照组相比,模型组胰腺组织内胰岛数量减少、体积减小、形状不规则、边界模糊,同时移植瘤病理学检查确认镜下为胰腺癌组织,可判定T2DM裸鼠胰腺癌模型造模成功。模型组皮下肿瘤大小与荧光值呈线性正相关,线性方程为y=232348691x-8258608(r=0.911,P=0.031);模型组移植瘤Ki-67免疫组化阳性率显著高于对照组[(50.333±7.808)%vs(15.917±4.055)%,t=13.55,P<0.001],说明模型组肿瘤增殖较快。结论本研究所构建的T2DM裸鼠胰腺癌模型可模拟T2DM背景下胰腺癌发生、发展的病理过程,动态观察高血糖对体内胰腺癌细胞生长的影响,从而为T2DM背景下胰腺癌发生、发展的体内研究提供新的实验载体。