scholarly journals Glycemic Control and the Risk of Acute Kidney Injury in Patients With Type 2 Diabetes and Chronic Kidney Disease: Parallel Population-Based Cohort Studies in U.S. and Swedish Routine Care

Diabetes Care ◽  
2020 ◽  
Vol 43 (12) ◽  
pp. 2975-2982
Author(s):  
Yang Xu ◽  
Aditya Surapaneni ◽  
Jim Alkas ◽  
Marie Evans ◽  
Jung-Im Shin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Kidney Disease ◽  
Glycemic Control ◽  
Cohort Studies ◽  
Kidney Injury ◽  
Population Based ◽  
Routine Care

scholarly journals Glycemic control and the risk of acute kidney injury in patients with type 2 diabetes and chronic kidney disease: parallel population-based cohort studies in U.S. and Swedish routine care

2020 ◽  
Author(s):  
Yang Xu ◽  
Aditya Surapaneni ◽  
Jim Alkas ◽  
Marie Evans ◽  
Jung-Im Shin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Glycemic Control ◽  
Kidney Injury ◽  
Routine Care ◽  
Time Varying ◽  
Baseline Hba1c ◽  
Swedish Cohort ◽  
The U.S

<b>Objective: </b>Patients with diabetes and chronic kidney disease (CKD) have increased susceptibility to acute kidney injury (AKI), but mechanisms are unclear. We investigated the association of glycemic control with risk of AKI. <p><b>Research Design and Methods: </b>In two observational cohorts of U.S. (Geisinger Heath system, Pennsylvania) and Swedish (SCREAM project, Stockholm) adults with type-2 diabetes and confirmed CKD stages G3-G5 undergoing routine care, we evaluated associations between baseline and time-varying HbA1c with the incident AKI (defined as increase in creatinine ≥0.3 mg/dL over 48 hours, 1.5x creatinine over 7 days). </p> <p><b>Results: </b>In the U.S. cohort, there were 22877 patients (55% women) with median age 72 years and eGFR 52 ml/min/1.73 m<sup>2</sup>. In the Swedish cohort, there were 12157 patients (51% women) with median age 76 years and eGFR 51 ml/min/1.73 m<sup>2</sup>. During 3.1 and 2.3 years of follow-up, 7060 and 2619 AKI events were recorded in the U.S. and Swedish cohorts, respectively. The adjusted association between baseline HbA1c and AKI was similar in both cohorts. Compared to baseline HbA1c 6-6.9% (42-52 mmol/mol), the HR for AKI in patients with HbA1c>9% (75 mmol/mol) was 1.29 (95% CI 1.18-1.41) in Geisinger, and 1.33 (95% CI 1.13-1.57) in the Swedish cohort. Results were consistent in stratified analysis, when using death as competing risk, and when using time-varying HbA1c.</p> <p><b>Conclusions: </b>Higher HbA1c was associated with AKI in adults with type 2 diabetes and CKD, suggesting that improving glycemic control may reduce the risk of AKI.</p>

scholarly journals Glycemic control and the risk of acute kidney injury in patients with type 2 diabetes and chronic kidney disease: parallel population-based cohort studies in U.S. and Swedish routine care

2020 ◽  
Author(s):  
Yang Xu ◽  
Aditya Surapaneni ◽  
Jim Alkas ◽  
Marie Evans ◽  
Jung-Im Shin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Glycemic Control ◽  
Kidney Injury ◽  
Routine Care ◽  
Time Varying ◽  
Baseline Hba1c ◽  
Swedish Cohort ◽  
The U.S

<b>Objective: </b>Patients with diabetes and chronic kidney disease (CKD) have increased susceptibility to acute kidney injury (AKI), but mechanisms are unclear. We investigated the association of glycemic control with risk of AKI. <p><b>Research Design and Methods: </b>In two observational cohorts of U.S. (Geisinger Heath system, Pennsylvania) and Swedish (SCREAM project, Stockholm) adults with type-2 diabetes and confirmed CKD stages G3-G5 undergoing routine care, we evaluated associations between baseline and time-varying HbA1c with the incident AKI (defined as increase in creatinine ≥0.3 mg/dL over 48 hours, 1.5x creatinine over 7 days). </p> <p><b>Results: </b>In the U.S. cohort, there were 22877 patients (55% women) with median age 72 years and eGFR 52 ml/min/1.73 m<sup>2</sup>. In the Swedish cohort, there were 12157 patients (51% women) with median age 76 years and eGFR 51 ml/min/1.73 m<sup>2</sup>. During 3.1 and 2.3 years of follow-up, 7060 and 2619 AKI events were recorded in the U.S. and Swedish cohorts, respectively. The adjusted association between baseline HbA1c and AKI was similar in both cohorts. Compared to baseline HbA1c 6-6.9% (42-52 mmol/mol), the HR for AKI in patients with HbA1c>9% (75 mmol/mol) was 1.29 (95% CI 1.18-1.41) in Geisinger, and 1.33 (95% CI 1.13-1.57) in the Swedish cohort. Results were consistent in stratified analysis, when using death as competing risk, and when using time-varying HbA1c.</p> <p><b>Conclusions: </b>Higher HbA1c was associated with AKI in adults with type 2 diabetes and CKD, suggesting that improving glycemic control may reduce the risk of AKI.</p>

scholarly journals Machine learning approaches revealed metabolic signatures of incident chronic kidney disease in persons with pre-and type 2 diabetes

2020 ◽  
Author(s):  
Ada Admin ◽  
Jialing Huang ◽  
Cornelia Huth ◽  
Marcela Covic ◽  
Martina Troll ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Population Based ◽  
Learning Approaches ◽  
Clinical Variables

Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk of progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in persons with pre- and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

scholarly journals Prevalence of chronic kidney disease stages 3-5 among patients with type 2 diabetes mellitus in Bangladesh

2017 ◽  
Vol 11 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Muhammad Abdur Rahim ◽  
Palash Mitra ◽  
Hasna Fahmima Haque ◽  
Tasrina Shamnaz Samdani ◽  
Shahana Zaman ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Glycemic Control ◽  
Significant Risk ◽  
Duration Of Diabetes

Background and objectives: Diabetes mellitus is one of the most common causes of chronic kidney disease (CKD). The prevalence of CKD in type 2 diabetes mellitus (T2DM) in Bangladesh is not well described. The present study aimed to find out the prevalence of CKD stages 3-5 and its risk factors among selected Bangladeshi T2DM patients.Methods: This cross-sectional study was conducted in BIRDEM (Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders) General Hospital, Dhaka, Bangladesh from July to December 2015. Diagnosed adult T2DM patients were consecutively and purposively included in this study. Pregnant women, patients with diagnosed kidney disease due to non-diabetic etiology, acute kidney injury (AKI), AKI on CKD and patients on renal replacement therapy were excluded. Age, gender, body mass index (BMI) and laboratory parameters were recorded systematically in a predesigned data sheet. Diagnosis of CKD and its stages were determined according to Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guidelines 2012 and estimated glomerular filtration rate (eGFR). Estimated GFR was calculated by using Modification of Diet in Renal Disease (MDRD), Cockcroft-Gault (CG) and Chronic Kidney Disease Epidemiology (CKDEPI) creatinine based formula.Results: A total of 400 patients with T2DM of various durations were enrolled in the study. Out of 400 patients, 254 (63.5%), 259 (64.75%) and 218 (54.5%) cases had CKD stages 3-5 according to MDRD, C-G and CKD-EPI equations respectively. CKD was significantly more common in females (p<0.001) and in cases with long duration of diabetes (?5 years; p=0.007). CKD stages 3-5 were significantly associated with hypertension (?2=5.2125, p =0.02) and good control of diabetes (HbA1c <7%) as evidenced by higher proportion of CKD in them (73.3%) compared to those with poor glycemic control (52.1%).Conclusions: More than half of T2DM patients had CKD stages 3-5. Female gender, duration of diabetes and hypertension were significant risk factors and should be emphasized for the prevention of CKD in T2DM. Glycemic control may not reduce CKD in diabetes.IMC J Med Sci 2017; 11(1): 19-24

scholarly journals Machine learning approaches revealed metabolic signatures of incident chronic kidney disease in persons with pre-and type 2 diabetes

2020 ◽  
Author(s):  
Ada Admin ◽  
Jialing Huang ◽  
Cornelia Huth ◽  
Marcela Covic ◽  
Martina Troll ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Population Based ◽  
Learning Approaches ◽  
Clinical Variables

Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk of progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in persons with pre- and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

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