scholarly journals Physician and Patient Attitudes Toward Screening for Kidney Complications in Type 2 Diabetes: Is There A Role for Patient Self-Advocacy?

2018 ◽  
Vol 2 (1) ◽  
pp. 72-74
Author(s):  
Bart Frischknecht ◽  
Lindsay Giudice ◽  
Robert M Perkins ◽  
Joel E Urbany
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Prognostic Value ◽  
Diabetic Kidney Disease ◽  
Patient Attitudes ◽  
Urine Protein ◽  
Self Advocacy ◽  
Urine Albumin ◽  
Urine Specimens

Urine protein screening rates among patients with type 2 diabetes are suboptimal despite evidence supporting its efficacy in preventing or slowing the progression of diabetic kidney disease (DKD) [1]. Testing of untimed urine specimens to quantify albumin (urine albumin-creatinine, or uACR)-the recommended screening test based on combination of simplicity, cost, and cardiovascular and renal prognostic value-is particularly underutilized, despite longitudinal efforts to improve awareness and screening among physicians [2].

scholarly journals Optimal Early Diagnosis and Monitoring of Diabetic Kidney Disease in Type 2 Diabetes Mellitus: Addressing the Barriers to Albuminuria Testing

2021 ◽  
Vol 12 ◽  
pp. 215013272110036
Author(s):  
Elena A. Christofides ◽  
Niraj Desai
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Urine Albumin ◽  
Increased Risk ◽  
Ckd Stage ◽  
Stage Renal Disease ◽  
End Stage ◽  
Kidney Health

Chronic kidney disease (CKD) in patients with type 2 diabetes (T2D) is associated with increased risk of end-stage renal disease (ESRD) and cardiovascular disease (CVD). Urine albumin-to-creatinine ratio (UACR) is a sensitive and early indicator of kidney damage, which should be used routinely to accurately assess CKD stage and monitor kidney health. However, this test currently is performed in only a minority of patients with T2D. Here, we review the importance of albuminuria testing and current barriers that hinder patient access to UACR testing and describe solutions to such testing in a community clinical setting.

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

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 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.

Sign in / Sign up

Export Citation Format

Share Document