scholarly journals Glycemic Control and Prevalence of Chronic Kidney Disease in Type‑2 Diabetes Mellitus Patients at Primary Healthcare Centers in Yogyakarta Province 2015

2017 ◽  
Vol 6 (2) ◽  
pp. 78-90
Author(s):  
Vitarani D. A. Ningrum ◽  
◽  
Zullies Ikawati ◽  
Ahmad H. Sadewa ◽  
Mohammad R. Ikhsan ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Glycemic Control ◽  
Primary Healthcare ◽  
Primary Healthcare Centers

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 Study of correlation between adequacy of dialysis and glycemic control in patients of type 2 diabetes mellitus with chronic kidney disease stage 5

2020 ◽  
Vol 8 (4) ◽  
pp. 1366
Author(s):  
Samrat Mitra ◽  
Sanghita Barui
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Glycemic Control ◽  
Chronic Kidney Disease Stage ◽  
Disease Stage ◽  
Random Blood Sugar

Background: The adequacy of haemodialysis in patients of type 2 diabetes mellitus with chronic kidney disease stage 5 depends on several clinical as well as laboratory parameters. Previous studies from Western literature have identified several clinical and laboratory markers for predicting adequacy of dialysis. There is a dearth of literature regarding the same in Indian patient populace. Authors aimed to find correlation, if any, between glycemic control and adequacy of dialysis in this cohort of patients.Methods: A set of 200 patients of type 2 diabetes mellitus who have undergone hemodialysis at a tertiary care hospital were included in the study. Random blood sugar (RBS), Glycated hemoglobin (HbA1c) were measured at admission. After 4 hours of dialysis, the urea reduction ratio (URR) and Kt/V was measured for each patient. The correlation coefficient as well as linear equation of the association between these variables were calculated. Standard statistical method and software were used in the process.Results: The study revealed a linear negative correlation between the variables RBS, HbA1c and URR as well as Kt/V. This suggests the importance of pre dialysis glycemic control in patients undergoing hemodialysis.Conclusions: Authors formulate the hypothesis that glycated hemoglobin and random blood sugar at admission correlate well with the outcome and adequacy of dialysis in patients of stage 5 chronic kidney disease undergoing haemodialysis.  Good glycemic control (HbA1c <6.5 % and RBS <120 mg/dL) have shown to be important predictive markers of adequate dialysis. The hypothesis needs to be tested with a larger study.

Insulin Glargine U100 Improved Glycemic Control and Reduced Nocturnal Hypoglycemia in Patients with Type 2 Diabetes Mellitus and Chronic Kidney Disease Stages 3 and 4

2019 ◽  
Vol 41 (10) ◽  
pp. 2008-2020.e3 ◽  
Author(s):  
Carolina C. Betônico ◽  
Silvia Maria O. Titan ◽  
Aécio Lira ◽  
Tatiana S. Pelaes ◽  
Maria Lúcia C. Correa-Giannella ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Glycemic Control ◽  
Insulin Glargine ◽  
Nocturnal Hypoglycemia ◽  
Disease Stages

scholarly journals Inequalities in the Global Burden of Chronic Kidney Disease Due to Type 2 Diabetes Mellitus: An Analysis of Trends from 1990 to 2019

2021 ◽  
Vol 18 (9) ◽  
pp. 4723
Author(s):  
Nóra Kovács ◽  
Attila Nagy ◽  
Viktor Dombrádi ◽  
Klára Bíró
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Data Exchange ◽  
Disease Burden ◽  
Socioeconomic Development ◽  
Developed Countries

The prevalence of type 2 diabetes mellitus (T2DM) and the burden of complications are increasing worldwide. Chronic kidney disease (CKD) is one serious complication. Our aim was to investigate the trends and inequalities of the burden of CKD due to T2DM between 1990 and 2019. Data were obtained from the Global Health Data Exchange database. Age-standardized incidence, mortality, and DALYs rates of CKD were used to estimate the disease burden across the Human Development Index (HDI). Joinpoint regression was performed to assess changes in trend, and the Gini coefficient was used to assess health inequality. A higher incidence was observed in more developed countries (p < 0.001), while higher mortality and DALYs rates were experienced in low and middle HDI countries in 2019 (p < 0.001). The trend of incidence has increased since 1990 (AAPC: 0.9–1.5%), while slight decrease was observed in low HDI countries in mortality (APC: −0.1%) and DALYs (APC: −0.2%). The Gini coefficients of CKD incidence decreased from 0.25 in 2006 to 0.23 in 2019. The socioeconomic development was associated with disease burden. Our findings indicate that awareness of complications should be improved in countries with high incidence, and cost-effective preventive, diagnostic, and therapeutic tools are necessary to implement in less developed regions.

Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

2021 ◽  
Vol 24 ◽  
pp. 157-166
Author(s):  
Wilailuck Tuntayothin ◽  
Stephen John Kerr ◽  
Chanchana Boonyakrai ◽  
Suwasin Udomkarnjananun ◽  
Sumitra Chukaew ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Prediction Model ◽  
Disease Prediction ◽  
Development And Validation
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