Objective To investigate the protective effect and underlying mechanism of human umbilical cord mesenchymal stem cell-derived exosomes (hucMSC-Exo) on renal ischemia-reperfusion injury (IRI). Methods hucMSC-Exos were isolated and characterized. A mouse renal IRI model was established and the animals were divided into Sham, IRI, IRI+hucMSC-Exo, IRI+hucMSC-Exo+JY-2 and Sham+JY-2 groups. Serum creatinine (Scr) and blood urea nitrogen (BUN) were measured. Hematoxylin-eosin (HE) staining was used to evaluate renal histopathology. Enzyme-linked immune absorbent assay was performed to determine serum interleukin (IL)-1β and IL-18 levels. Western blotting was used to detect the expression of activating transcription factor 3 (ATF3), Toll-like receptor 4 (TLR4), nuclear factor (NF)-κB, NOD-like receptor protein 3 (NLRP3), cysteineyl aspartate specific proteinase (Caspase)-1 p20 and Gasdermin D(GSDMD). Real-time fluorescent quantitative polymerase chain reaction was employed to measure ATF3, TLR4 and NF-κB messenger RNA (mRNA). Immunohistochemistry was conducted to examine NLRP3, Caspase-1 p20 and GSDMD. An in vitro hypoxia/reoxygenation (H/R) model was established in HK-2 cells and divided into Control, H/R, H/R+hucMSC-Exo, H/R+hucMSC-Exo+JY-2 and Control+JY-2 groups. Western blotting was used to detect the expression of ATF3, TLR4 and NF-κB. Real-time fluorescent quantitative polymerase chain reaction was used to measure NLRP3, GSDMD and Caspase-1 mRNA. Results HucMSC-Exos were successfully isolated and identified. Compared with the Sham group, the IRI group exhibited elevated Scr and BUN, higher tubular injury scores, increased protein expression levels of ATF3, TLR4, NF-κB p65, NLRP3, Caspase-1 p20 and GSDMD, and raised mRNA expression levels of ATF3, TLR4, NF-κB. Compared with the IRI group, the IRI+hucMSC-Exo group showed decreased Scr and BUN, lower tubular injury scores, up-regulated ATF3 protein and mRNA, down-regulated TLR4, NF-κB p65, NLRP3, Caspase-1 p20 and GSDMD protein, and declined TLR4 and NF-κB mRNA. Compared with the IRI+hucMSC-Exo group, the IRI+hucMSC-Exo+JY-2 group exhibited increased Scr and BUN levels, elevated renal tubular injury scores, decreased ATF3 protein expression levels, elevated protein expression levels of TLR4, NF-κB p65, NLRP3, Caspase-1 p20, and GSDMD, decreased ATF3 mRNA expression levels, and elevated mRNA expression levels of TLR4 and NF-κB. (all P < 0.05). Compared with the Control group, the expression levels of ATF3, TLR4 and NF-κB p65 proteins were increased in the H/R group, and the expression levels of NLRP3, Caspase-1 and GSDMD mRNA were increased. Compared with the H/R group, the expression level of ATF3 protein was increased, the expression levels of TLR4 and NF-κB p65 proteins were decreased, and the expression levels of NLRP3, Caspase-1 and GSDMD mRNA were decreased in the H/R+hucMSC-Exo group. Compared with the H/R+hucMSC-Exo group, the expression level of ATF3 protein was decreased, the expression levels of TLR4 and NF-κB p65 proteins were increased, and the expression levels of NLRP3, Caspase-1 and GSDMD mRNA were increased in the H/R+hucMSC-Exo+JY-2 group (all P < 0.05). Conclusions HucMSC-Exos alleviate renal IRI by up-regulating ATF3, thereby negatively regulating the TLR4/NF-κB signaling pathway and subsequently inhibiting pyroptosis.

BACKGROUND: Renal osteodystrophy (ROD) is a skeletal pathology associated with chronic kidney disease-mineral and bone disorder (CKD-MBD) that is characterized by aberrant bone mineralization and remodeling. ROD increases the risk of fracture and mortality in CKD patients. The underlying mechanisms of ROD remain elusive, partially due to the absence of an appropriate animal model. To address this gap, we established a stable mouse model of ROD using an optimized adenine-enriched diet and conducted exploratory analyses through ribonucleic acid sequencing (RNA-seq). METHODS: Eight-week-old male C57BL/6J mice were randomly allocated into three groups: control group ( n = 5), adenine and high-phosphate (HP) diet group ( n = 20), and the optimized adenine-containing diet group ( n = 20) for 12 weeks. We assessed the skeletal characteristics of model mice through blood biochemistry, microcomputed tomography (micro-CT), and bone histomorphometry. RNA-seq was utilized to profile gene expression changes of ROD. We elucidated the functions of differentially expressed genes (DEGs) using gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA). DEGs were validated via quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: By the fifth week, adenine followed by an HP diet induced rapid weight loss and high mortality rates in the mouse group, precluding further model development. Mice with optimized adenine diet-induced ROD displayed significant abnormalities in serum creatinine and blood urea nitrogen levels, accompanied by pronounced hyperparathyroidism and hyperphosphatemia. The femur bone mineral density (BMD) of the model mice was lower than that of control mice, with substantial bone loss and cortical porosity. ROD mice exhibited substantial bone turnover with an increase in osteoblast and osteoclast markers. Transcriptomic profiling revealed 1907 genes with upregulated expression and 723 genes with downregulated expression in the femurs of ROD mice relative to those of control mice. Pathway analyses indicated significant enrichment of upregulated genes in the sphingolipid metabolism pathway. The significant upregulation of alkaline ceramidase 1 ( Acer1 ), alkaline ceramidase 2 ( Acer2 ), prosaposin-like 1 ( Psapl1 ), adenosine A1 receptor ( Adora1 ), and sphingosine-1-phosphate receptor 5 ( S1pr5 ) were successfully validated in mouse femurs by qRT-PCR. CONCLUSIONS Optimized adenine diet mouse model may be a valuable proxy for studying ROD. RNA-seq analysis revealed that the sphingolipid metabolism pathway is likely a key player in ROD pathogenesis, thereby providing new avenues for therapeutic intervention.
Animals ; Mice ; Chronic Kidney Disease-Mineral and Bone Disorder/genetics* ; Male ; Disease Models, Animal ; Mice, Inbred C57BL ; Sphingolipids/metabolism* ; Transcriptome/genetics* ; Signal Transduction/genetics* ; X-Ray Microtomography ; Adenine

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Objective:To develop a deep learning-based segmentation model MT-UNet to automatically segment bone metastases and benign bone lesions in bone scintigraphy with SPECT/CT.Methods:A total of 93 patients (48 males and 45 females, age 28-84 years) who underwent bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from June 2023 to December 2023 were enrolled retrospectively in this study, with a total of 184 bone lesions (94 benign lesions and 90 metastatic tumors). The MT-UNet was employed to segment bone lesions in SPECT, CT and SPECT/CT images respectively. Comparative analysis with 8 segmentation models was performed. The training set and validation set were divided by using 5-fold cross-validation and transfer learning was introduced to further enhance the robustness of the model. An additional cohort of 22 patients (15 males and 7 females, age 37-87 years) who received bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from April 2023 to May 2023 were included, comprising 40 bone lesions (22 benign lesions and 18 metastatic tumors) as the test set of MT-UNet. Segmentation performance of different models was assessed using accuracy, sensitivity, specificity, AUC, intersection over union and Dice similarity coefficient (DSC). Delong test was used to compare the segmentation efficacy among different models in the test set.Results:In the validation set, MT-UNet demonstrated DSC of 0.940, 0.962, and 0.963 for SPECT, CT, and SPECT/CT bone lesion segmentation, respectively, which were outperformed other models. Following transfer learning implementation, the SPECT/CT model′s DSC was improved to 0.984. In the test set, MT-UNet maintained comparable segmentation performance to the validation set, with significant AUC differences among the three models ( Z values: from -15.42 to -9.27, all P<0.01). Compared with conventional image interpretation, MT-UNet-based segmentation reduced physician interpretation time from 164min to 102min. Conclusion:MT-UNet has shown good performance in automatic segmentation of bone metastases and benign bone lesions, and is expected to become an important part of SPECT/CT image intelligent diagnosis system for bone metastases.

Objective:To explore the correlation between serum nidogen-2 (NID-2) and thoracic aortic calcification in patients with chronic kidney disease (CKD), and construct a risk prediction model based on NID-2 to evaluate its value in predicting the risk of the severe thoracic aortic calcification and cardiovascular and cerebrovascular events in CKD patients.Methods:It was a prospective cohort study. Patients with CKD at stage 3 to 5D in the Zhongda Hospital Affiliated to Southeast University from January 2022 to January 2023 were enrolled. Syngo.via software was used to evaluate the volume of thoracic aortic calcification, and enzyme-linked immunosorbent assay was employed to determine the level of serum NID-2. According to the volume of thoracic aortic calcification, the patients were divided into three groups: no calcification group, mild calcification group and severe calcification group. The top 25% of the patients were defined as no or mild calcification group, and the latter 75% were defined as severe calcification group. The follow-up period was one year. During the follow-up period, cardiovascular and cerebrovascular events, as well as all-cause death among the enrolled patients were recorded. Logistic regression analysis was used to screen the influencing factors of thoracic aortic calcification. Based on the results of logistic regression analysis, a nomogram prediction model was constructed. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve were employed to evaluate the discrimination, calibration and clinical practicality of the nomogram model.Results:A total of 132 patients were included, with 91 males (68.94%) and age of (56.51±16.37) years. There were 60 CKD 3-5 stage patients (non-dialysis, 45.45%) and 72 CKD 5D patients (dialysis, 54.55%). Serum ND-2 levels differed significantly among healthy individuals, dialysis patients and non-dialysis patients ( H=70.651, P<0.001). There was no statistically significant difference in serum NID-2 level between the no or mild calcification group and the severe calcification group in dialysis patients ( Z=350.00, P=0.426). The serum NID-2 level in the severe calcification group was significantly higher than that in the no or mild calcification group in non-dialysis patients ( Z=242.00, P=0.019). In non-dialysis patients, there was a statistically significant correlation between serum NID-2 level and volume of thoracic aortic calcification ( r=0.40, P<0.001). In dialysis patients, there was no statistically significant correlation between serum NID-2 level and volume of each segment of thoracic aortic calcification (all P>0.05). The univariate logistic regression analysis showed that, age, hemoglobin, serum albumin, estimated glomerular filtration rate, NID-2, hypertension, type 2 diabetes mellitus and cerebral infarction were correlated factors of thoracic aortic calcification in non-dialysis patients (all P<0.05). Multivariate logistic regression analysis showed that age ( OR=1.22, 95% CI 1.08-1.50, P=0.010) was an independent correlated factor of thoracic aortic calcification in non-dialysis patients. The above related variables of univariate logistic regression analysis were incorporated into a nomogram to construct a predictive model for severe vascular calcification in non-dialysis patients, yielding an AUC of 0.94 (95% CI 0.89-0.99) in ROC curve, with a sensitivity of 83% and a specificity of 95%. A nomogram model based on above variables for predicting cardiovascular and cerebrovascular events in non-dialysis patients demonstrated an AUC of 0.95 (95% CI 0.90-1.00) in ROC curve, with a sensitivity of 95% and a specificity of 87%. Conclusions:In non-dialysis patients, serum NID-2 level in the severe calcification group is significantly higher than that in the no or mild calcification group. The serum NID-2 is a related factor of thoracic aortic calcification and cardiovascular and cerebrovascular events in non-dialysis patients. The nomogram prediction model constructed by combining NID-2 with age, hemoglobin, serum albumin, estimated glomerular filtration rate, hypertension, type 2 diabetes mellitus and cerebral infarction has a high predictive value for the risk of thoracic aortic calcification as well as cardiovascular and cerebrovascular events in non-dialysis patients.

Objective:To investigate the effects of baicalin on ferroptosis of mouse fibroblasts (Fbs) under high glucose treatment and its mechanism, and to provide a basis for the treatment of diabetic wounds.Methods:The study was an experimental study. Mouse Fbs were collected and divided into control group with conventional culture, high glucose group treated with glucose at final molarity of 30.0 mmol/L, and low baicalin group and high baicalin group pretreated with baicalin at final molarties of 5 and 10 μmol/L respectively and then treated as that in high glucose group. After 48 h of culture, the cell survival rate was detected by the cell counting kit-8, the reactive oxygen species level in cells was detected by the fluorescent probe method, the levels of malondialdehyde, glutathione, and ferrous ion in cells were detected by colorimetry, and the protein expression levels of solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4) in cells and nuclear factor-erythroid 2-related factor 2 (Nrf2) in cytoplasm and nucleus were detected by Western blotting. Another batch of mouse Fbs were collected and divided into control group, high glucose group, high baicalin group, and high baicalin+ML385 group. The cells in the first three groups were treated as before, the cells in the last group were pretreated with baicalin and ML385 of Nrf2 inhibitor at final molarties of 10 μmol/L and then treated as that in high glucose group. After 48 h of culture, the protein expression levels of SLC7A11 and GPX4 in cells and the protein expression level of Nrf2 in cytoplasm and nucleus were detected as before. Except that the sample number in detecting SLC7A11 and GPX4 was 4, the sample number in detecting other indexes was 3.Results:After 48 h of culture, the cell survival rates in control group, high glucose group, low baicalin group, and high baicalin group were (100.0±10.7)%, (70.0±5.0)%, (80.9±3.2)%, and (91.4±1.9)%, respectively. Compared with those in control group, the cell survival rate, the glutathione level, and SLC7A11 and GPX4 protein expression levels in cells, and nuclear Nrf2 protein expression level were significantly decreased in high glucose group ( P<0.05), and the levels of reactive oxygen species, malondialdehyde, and ferrous ion in cells, and cytoplasmic Nrf2 protein expression level were significantly increased in high glucose group ( P<0.05). Compared with those in high glucose group, the cell survival rate, glutathione level, SLC7A11 and GPX4 protein expression levels in cells, and nuclear Nrf2 protein expression level in low baicalin group and high baicalin group were significantly increased ( P<0.05), the reactive oxygen species and ferrous ion levels in cells, and cytoplasmic Nrf2 protein expression level in low baicalin group and high baicalin group were significantly decreased ( P<0.05), and the malondialdehyde level in cells in high baicalin group was significantly decreased ( P<0.05). Compared with those in low baicalin group, the cell survival rate, glutathione level, SLC7A11 and GPX4 protein expression levels in cells, and nuclear Nrf2 protein expression level in high baicalin group were significantly increased ( P<0.05), and the reactive oxygen species, malondialdehyde, and ferrous ion levels in cells, and cytoplasmic Nrf2 protein expression level in high baicalin group were significantly decreased ( P<0.05). After 48 h of culture, compared with those in control group, the nuclear Nrf2 protein expression level and SLC7A11 and GPX4 protein expression levels in cells were significantly decreased ( P<0.05), and the cytoplasmic Nrf2 protein expression level was significantly increased in high glucose group ( P<0.05); compared with those in high glucose group, the cytoplasmic Nrf2 protein expression level was significantly decreased ( P<0.05), and the nuclear Nrf2 protein expression level and SLC7A11 and GPX4 protein expression levels in cells were significantly increased in high baicalin group ( P<0.05); compared with those in high baicalin group, the cytoplasmic Nrf2 protein expression level was significantly increased ( P<0.05), and the nuclear Nrf2 protein expression level and SLC7A11 and GPX4 protein expression levels in cells were significantly decreased in high baicalin+ML385 group ( P<0.05). Conclusions:Baicalin can inhibit the occurrence of ferroptosis in cells by activating the Nrf2 signaling pathway and up-regulating the expressions of proteins related to SLC7A11/GPX4 axis in Fbs in high glucose treatment, thus increasing the cell survival rate.

Objective To investigate the association between UGT1A1 inhibitors-induced liver injury(DILI)and UGT1A1 gene polymorphisms through a pharmacogenomics approach.Methods Information on relevant drugs that may induce liver injury,blood routine tests,and liver function tests was collected from hospitalized patients diagnosed with DILI between June 2022 and June 2024.Relevant databases were searched to categorize DILI-associated drugs into UGT1A1 enzyme inhibitors and those without interaction with UGT1A1.Sanger sequenc-ing or MassARRAY SNP typing technology was utilized to detect and genotype the UGT1A1 gene.Results A total of 219 patients with drug-induced liver injury(DILI)were enrolled,including 98 males,with a mean age of 46.32±14.95 years.A literature search of relevant databases revealed that 20 drugs(16.26%,20/123)associated with DILI had inhibitory effects on the UGT1A1 enzyme.The proportion of DILI cases related to UGT1A1 inhibitors was 60.73%(133/219).Compared to non-UGT1A1 inhibitor-related DILI group,the UGT1A1 inhibitor-related DILI group exhibited significantly higher levels of ALT,AST,ALP,and GGT(P<0.05),while no significant differences were observed in age,gender,TBIL,IBIL,WBC,Hb,PLT,injury type,or injury grade(P>0.05).The prevalence of UGT1A1 polymorphisms was significantly higher in the UGT1A1 inhibitor-related DILI group(68.42%)com-pared to the non-UGT1A1 inhibitor-related DILI group(51.16%),with an odds ratio(OR)of 2.068(95%CI:1.183 to 3.617;χ2=6.58,P=0.010).There was also a significant difference in the distribution of genotypes between the UGT1A1 inhibitor-related and non-UGT1A1 inhibitor-related DILI groups(χ2=9.60,P=0.022).Univariate logistic regression analysis indicated that ALT and UGT1A1*6 were associated with UGT1A1 inhibitor-related DILI,while multivariate analysis confirmed that UGT1A1*6 was independently associated with UGT1A1 inhibitor-related DILI[OR(95%CI)=3.143(1.398 to 7.067),P=0.006].Conclusion The UGT1A1*6 allele increases the susceptibility to drug-induced liver injury(DILI)associated with UGT1A1 inhibitory drugs.

Objective To investigate the association between UGT1A1 inhibitors-induced liver injury(DILI)and UGT1A1 gene polymorphisms through a pharmacogenomics approach.Methods Information on relevant drugs that may induce liver injury,blood routine tests,and liver function tests was collected from hospitalized patients diagnosed with DILI between June 2022 and June 2024.Relevant databases were searched to categorize DILI-associated drugs into UGT1A1 enzyme inhibitors and those without interaction with UGT1A1.Sanger sequenc-ing or MassARRAY SNP typing technology was utilized to detect and genotype the UGT1A1 gene.Results A total of 219 patients with drug-induced liver injury(DILI)were enrolled,including 98 males,with a mean age of 46.32±14.95 years.A literature search of relevant databases revealed that 20 drugs(16.26%,20/123)associated with DILI had inhibitory effects on the UGT1A1 enzyme.The proportion of DILI cases related to UGT1A1 inhibitors was 60.73%(133/219).Compared to non-UGT1A1 inhibitor-related DILI group,the UGT1A1 inhibitor-related DILI group exhibited significantly higher levels of ALT,AST,ALP,and GGT(P<0.05),while no significant differences were observed in age,gender,TBIL,IBIL,WBC,Hb,PLT,injury type,or injury grade(P>0.05).The prevalence of UGT1A1 polymorphisms was significantly higher in the UGT1A1 inhibitor-related DILI group(68.42%)com-pared to the non-UGT1A1 inhibitor-related DILI group(51.16%),with an odds ratio(OR)of 2.068(95%CI:1.183 to 3.617;χ2=6.58,P=0.010).There was also a significant difference in the distribution of genotypes between the UGT1A1 inhibitor-related and non-UGT1A1 inhibitor-related DILI groups(χ2=9.60,P=0.022).Univariate logistic regression analysis indicated that ALT and UGT1A1*6 were associated with UGT1A1 inhibitor-related DILI,while multivariate analysis confirmed that UGT1A1*6 was independently associated with UGT1A1 inhibitor-related DILI[OR(95%CI)=3.143(1.398 to 7.067),P=0.006].Conclusion The UGT1A1*6 allele increases the susceptibility to drug-induced liver injury(DILI)associated with UGT1A1 inhibitory drugs.

Objective:To develop a deep learning-based segmentation model MT-UNet to automatically segment bone metastases and benign bone lesions in bone scintigraphy with SPECT/CT.Methods:A total of 93 patients (48 males and 45 females, age 28-84 years) who underwent bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from June 2023 to December 2023 were enrolled retrospectively in this study, with a total of 184 bone lesions (94 benign lesions and 90 metastatic tumors). The MT-UNet was employed to segment bone lesions in SPECT, CT and SPECT/CT images respectively. Comparative analysis with 8 segmentation models was performed. The training set and validation set were divided by using 5-fold cross-validation and transfer learning was introduced to further enhance the robustness of the model. An additional cohort of 22 patients (15 males and 7 females, age 37-87 years) who received bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from April 2023 to May 2023 were included, comprising 40 bone lesions (22 benign lesions and 18 metastatic tumors) as the test set of MT-UNet. Segmentation performance of different models was assessed using accuracy, sensitivity, specificity, AUC, intersection over union and Dice similarity coefficient (DSC). Delong test was used to compare the segmentation efficacy among different models in the test set.Results:In the validation set, MT-UNet demonstrated DSC of 0.940, 0.962, and 0.963 for SPECT, CT, and SPECT/CT bone lesion segmentation, respectively, which were outperformed other models. Following transfer learning implementation, the SPECT/CT model′s DSC was improved to 0.984. In the test set, MT-UNet maintained comparable segmentation performance to the validation set, with significant AUC differences among the three models ( Z values: from -15.42 to -9.27, all P<0.01). Compared with conventional image interpretation, MT-UNet-based segmentation reduced physician interpretation time from 164min to 102min. Conclusion:MT-UNet has shown good performance in automatic segmentation of bone metastases and benign bone lesions, and is expected to become an important part of SPECT/CT image intelligent diagnosis system for bone metastases.

Objective:To explore the correlation between serum nidogen-2 (NID-2) and thoracic aortic calcification in patients with chronic kidney disease (CKD), and construct a risk prediction model based on NID-2 to evaluate its value in predicting the risk of the severe thoracic aortic calcification and cardiovascular and cerebrovascular events in CKD patients.Methods:It was a prospective cohort study. Patients with CKD at stage 3 to 5D in the Zhongda Hospital Affiliated to Southeast University from January 2022 to January 2023 were enrolled. Syngo.via software was used to evaluate the volume of thoracic aortic calcification, and enzyme-linked immunosorbent assay was employed to determine the level of serum NID-2. According to the volume of thoracic aortic calcification, the patients were divided into three groups: no calcification group, mild calcification group and severe calcification group. The top 25% of the patients were defined as no or mild calcification group, and the latter 75% were defined as severe calcification group. The follow-up period was one year. During the follow-up period, cardiovascular and cerebrovascular events, as well as all-cause death among the enrolled patients were recorded. Logistic regression analysis was used to screen the influencing factors of thoracic aortic calcification. Based on the results of logistic regression analysis, a nomogram prediction model was constructed. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve were employed to evaluate the discrimination, calibration and clinical practicality of the nomogram model.Results:A total of 132 patients were included, with 91 males (68.94%) and age of (56.51±16.37) years. There were 60 CKD 3-5 stage patients (non-dialysis, 45.45%) and 72 CKD 5D patients (dialysis, 54.55%). Serum ND-2 levels differed significantly among healthy individuals, dialysis patients and non-dialysis patients ( H=70.651, P<0.001). There was no statistically significant difference in serum NID-2 level between the no or mild calcification group and the severe calcification group in dialysis patients ( Z=350.00, P=0.426). The serum NID-2 level in the severe calcification group was significantly higher than that in the no or mild calcification group in non-dialysis patients ( Z=242.00, P=0.019). In non-dialysis patients, there was a statistically significant correlation between serum NID-2 level and volume of thoracic aortic calcification ( r=0.40, P<0.001). In dialysis patients, there was no statistically significant correlation between serum NID-2 level and volume of each segment of thoracic aortic calcification (all P>0.05). The univariate logistic regression analysis showed that, age, hemoglobin, serum albumin, estimated glomerular filtration rate, NID-2, hypertension, type 2 diabetes mellitus and cerebral infarction were correlated factors of thoracic aortic calcification in non-dialysis patients (all P<0.05). Multivariate logistic regression analysis showed that age ( OR=1.22, 95% CI 1.08-1.50, P=0.010) was an independent correlated factor of thoracic aortic calcification in non-dialysis patients. The above related variables of univariate logistic regression analysis were incorporated into a nomogram to construct a predictive model for severe vascular calcification in non-dialysis patients, yielding an AUC of 0.94 (95% CI 0.89-0.99) in ROC curve, with a sensitivity of 83% and a specificity of 95%. A nomogram model based on above variables for predicting cardiovascular and cerebrovascular events in non-dialysis patients demonstrated an AUC of 0.95 (95% CI 0.90-1.00) in ROC curve, with a sensitivity of 95% and a specificity of 87%. Conclusions:In non-dialysis patients, serum NID-2 level in the severe calcification group is significantly higher than that in the no or mild calcification group. The serum NID-2 is a related factor of thoracic aortic calcification and cardiovascular and cerebrovascular events in non-dialysis patients. The nomogram prediction model constructed by combining NID-2 with age, hemoglobin, serum albumin, estimated glomerular filtration rate, hypertension, type 2 diabetes mellitus and cerebral infarction has a high predictive value for the risk of thoracic aortic calcification as well as cardiovascular and cerebrovascular events in non-dialysis patients.