scholarly journals A non-laboratory-based risk score for predicting diabetic kidney disease in Chinese patients with type 2 diabetes

Oncotarget ◽  
2017 ◽  
Vol 8 (60) ◽  
pp. 102550-102558 ◽  
Author(s):  
Mian Wu ◽  
Junxi Lu ◽  
Lei Zhang ◽  
Fengjing Liu ◽  
Si Chen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Risk Score ◽  
Diabetic Kidney Disease ◽  
Chinese Patients ◽  
Diabetic Kidney

Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes

2019 ◽  
Vol 95 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Guozhi Jiang ◽  
Andrea On Yan Luk ◽  
Claudia Ha Ting Tam ◽  
Fangying Xie ◽  
Bendix Carstensen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Kidney Function ◽  
Diabetic Kidney Disease ◽  
Chinese Patients ◽  
Diabetic Kidney ◽  
Kidney Function Decline

scholarly journals Susceptibility of ApoB and PCSK9 Genetic Polymorphisms to Diabetic Kidney Disease Among Chinese Diabetic Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Liang Ma ◽  
Shaoting Wang ◽  
Hailing Zhao ◽  
Meijie Yu ◽  
Xiangling Deng ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Chinese Patients ◽  
Diabetic Patients ◽  
Diabetic Kidney ◽  
Increased Risk

This study aimed to investigate the susceptibility of 8 polymorphisms in ApoB and PCSK9 genes to diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus. This is a case-control association study, including 575 DKD cases and 653 controls. Genotypes were determined using ligase detection reaction method, and data are analyzed using STATA software. The genotype distributions of rs1042034 and rs12720838 differed significantly between the two groups (P < 0.001 and P = 0.008, respectively). After adjusting for confounding factors, the mutations of rs1042034 and rs12720838 were associated with the significantly increased risk of DKD. For instance, carriers of rs1042034 T allele (CT and TT genotypes) were 1.07 times more likely to have DKD than carriers of rs1042034 CC genotype [odds ratio (OR) = 1.07, 95% confidence interval (CI): 1.03–1.10, P < 0.001]. Further, haplotype T-A-G-T in ApoB gene was overrepresented in cases (18.10%) compared with controls (12.76%) (PSimulated = 0.045), and haplotype T-A-G-T was associated with a 33% increased risk of DKD (OR = 1.33, 95% CI: 1.04, 1.70). In further haplotype-phenotype analysis, significant association was only noted for hypertension and omnibus haplotypes in ApoB gene (PSimulated = 0.001). Our findings indicate that ApoB gene is a candidate gene for DKD in Chinese patients with type 2 diabetes mellitus.

scholarly journals Association between serum 25-hydroxyvitamin D and diabetic kidney disease in Chinese patients with type 2 diabetes

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0214728
Author(s):  
Shaofeng Xie ◽  
Liji Huang ◽  
Wen Cao ◽  
Yongxin Hu ◽  
Hongping Sun ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Chinese Patients ◽  
Diabetic Kidney ◽  
Hydroxyvitamin D ◽  
25 Hydroxyvitamin D

Albuminuria Is More Closely Associated with Vascular Endothelial Function than eGFR in Type 2 Diabetes with Diabetic Kidney Disease

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 443-P
Author(s):  
YOSHINORI KAKUTANI ◽  
MASANORI EMOTO ◽  
YUKO YAMAZAKI ◽  
KOKA MOTOYAMA ◽  
TOMOAKI MORIOKA ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Endothelial Function ◽  
Diabetic Kidney Disease ◽  
Vascular Endothelial ◽  
Vascular Endothelial Function ◽  
Diabetic Kidney

scholarly journals Association Between Classical and Emerging Risk Factors for Diabetic Kidney Disease and Albuminuria in a Cohort of Type 2 Diabetes Mellitus Patients

2021 ◽  
Vol 18 (3) ◽  
pp. 17-25
Author(s):  
Stoiţă Marcel ◽  
Popa Amorin Remus
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Significant Positive Association ◽  
Diabetic Kidney

Abstract The presence of albuminuria in patients with type 2 diabetes mellitus is a marker of endothelial dysfunction and also one of the criteria for diagnosing diabetic kidney disease. The present study aimed to identify associations between cardiovascular risk factors and renal albumin excretion in a group of 218 patients with type 2 diabetes mellitus. HbA1c values, systolic blood pressure, diastolic blood pressure were statistically significantly higher in patients with microalbuinuria or macroalbuminuria compared to patients with normoalbuminuria (p <0.01). We identified a statistically significant positive association between uric acid values and albuminuria, respectively 25- (OH)2 vitamin D3 deficiency and microalbuminuria (p <0.01).

scholarly journals Diabetic kidney disease: Act now or pay later

2008 ◽  
Vol 11 (4) ◽  
pp. 988-991
Author(s):  
Robert C Atkins ◽  
Paul Zimmet
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
International Society ◽  
Diabetic Kidney Disease ◽  
International Diabetes Federation ◽  
Diabetic Kidney ◽  
Global Pandemic

In 2003, the International Society of Nephrology and the International Diabetes Federation launched a booklet called “Diabetes in the Kidney: Time to act” [1] to highlight the global pandemic of type 2 diabetes and diabetic kidney disease. ration (PZ)

AI-based prediction model for the risk of diabetic kidney disease in patients with type 2 diabetes mellitus

2021 ◽  
Author(s):  
Ning Zhang ◽  
Rui Fan ◽  
Jing Ke ◽  
Qinghua Cui ◽  
Dong ZHAO
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Prediction Model ◽  
Diabetic Kidney Disease ◽  
Feature Selection Method ◽  
Training Set ◽  
Test Set ◽  
Diabetic Kidney

Abstract BackgroundMicroalbuminuria is the main characteristic of Diabetic kidney disease (DKD), but it fluctuates greatly under the influence of blood glucose. Our aim was to establish some common clinical variables which could be easily collected to predict the risk of DKD in patients with type 2 diabetes. Methods and resultsWe build an artificial intelligence (AI) model to quantitively predict the risk of DKD based on the biomedical parameters from 1239 patients. An information entropy-based feature selection method was applied to screen out the risk factors of DKD. The dataset was divided with 4/5 into the training set and 1/5 into the test set. By using the selected risk factors, 5-fold cross-validation is applied to train the prediction model and it finally got AUC of 0.72 and 0.71 in the training set and test set respectively. In addition, we provide a method of calculating risk factors’ contribution for individuals to provide personalized guidance for treatment. We set up web-based application available on http://www.cuilab.cn/dkd for self-check and early warning. ConclusionsWe establish a feasible prediction model for DKD and suggest the degree of risk contribution of each indicator for each individual, which has certain clinical significance for early intervention and prevention.

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