scholarly journals Prescribing Trends of Antidiabetes Medications in Patients With Type 2 Diabetes and Diabetic Kidney Disease, a Cohort Study

Diabetes Care ◽  
2021 ◽  
pp. dc210529
Author(s):  
Samantha T. Harris ◽  
Elisabetta Patorno ◽  
Min Zhuo ◽  
Seoyoung C. Kim ◽  
Julie M. Paik
2020 ◽  
Vol 9 (7) ◽  
pp. 2028
Author(s):  
Hayato Tanabe ◽  
Haruka Saito ◽  
Noritaka Machii ◽  
Akihiro Kudo ◽  
Kenichi Tanaka ◽  
...  

The risk of developing diabetic kidney disease (DKD) in patients with undiagnosed diabetes mellitus (UD) has never been evaluated. We studied the burden of UD on the risk of developing DKD in the Japanese population in a single-center retrospective cohort study. The patients with type 2 diabetes mellitus, but without DKD (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 or proteinuria), were recruited from January 2018 to January 2019; medical records were scrutinized retrospectively from January 2003 until May 2019. The individuals, with diabetes that could not be denied based on past and current records, comprised the undiagnosed diabetes (UD) group whereas those with confirmed diagnosis comprised the diagnosed diabetes (DD) group. The group differences were tested using a Kaplan–Meier curve and Cox proportional hazards model. Among the 408 participants, 164 (40.2%) and 244 (59.8%) comprised the DD and UD groups, respectively. The baseline parameters, including age, male gender, and BMI were comparable between the groups, but the plasma glucose, HbA1c levels, and diabetic retinopathy prevalence were higher in the UD group. The risk of developing DKD (log rank test, p < 0.001), an eGFR of < 60 mL/min/1.73 m2 (p = 0.001) and proteinuria (p = 0.007) were also higher in the UD group. The unadjusted and adjusted hazard ratios for DKD were 1.760 ((95% CI: 1.323–2.341), p < 0.001) and 1.566 ((95% CI: 1.159–2.115), p = 0.003), respectively, for the UD group. In conclusion, this is the first report showing that UD is a strong risk factor for DKD. The notion that a longer duration of untreated diabetes mellitus is involved strongly in the risk of developing DKD warrants the need for the identification and monitoring of UD patients.


Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 443-P
Author(s):  
YOSHINORI KAKUTANI ◽  
MASANORI EMOTO ◽  
YUKO YAMAZAKI ◽  
KOKA MOTOYAMA ◽  
TOMOAKI MORIOKA ◽  
...  

2019 ◽  
Vol 95 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Guozhi Jiang ◽  
Andrea On Yan Luk ◽  
Claudia Ha Ting Tam ◽  
Fangying Xie ◽  
Bendix Carstensen ◽  
...  

2021 ◽  
Vol 18 (3) ◽  
pp. 17-25
Author(s):  
Stoiţă Marcel ◽  
Popa Amorin Remus

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).


2008 ◽  
Vol 11 (4) ◽  
pp. 988-991
Author(s):  
Robert C Atkins ◽  
Paul Zimmet

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)


2021 ◽  
Author(s):  
Ning Zhang ◽  
Rui Fan ◽  
Jing Ke ◽  
Qinghua Cui ◽  
Dong ZHAO

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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