Detection of Chronic Kidney Disease by Using Different Equations of Glomerular Filtration Rate in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Analysis

Abstract

IntroductionChronic kidney disease (CKD) is a global threat due to its high mortality. It is essential to know the actual magnitude of diabetic CKD to design a specific management program. However, there is limited knowledge regarding the most suitable equation to measure CKD in patients with Type 2 diabetes mellitus (T2DM). This paper aimed to analyze estimated glomerular filtration rate (eGFR) based on different equations to detect the CKD among T2DM. MethodsA hospital-based cross-sectional study was carried out, and a clinical registry was used to collect 4,042 T2DM patients from a large district hospital in Northeast Thailand. CKD patients were diagnosed when eGFR was less than 60 ml/min/1.73m2. Using Stata statistical software (StataCorp LP, College Station, TX), three standard equations, such as ‘modification of diet in renal disease (MDRD-4)’, ‘chronic kidney disease epidemiology collaboration (CKD-EPI)’, and ‘Cockcroft-Gault (C-G)' equations, were used to produce eGFR values to report and compare stages of CKD.ResultsThe mean age of the patients was 61.4 (± 10.7) years and male to female ratio was 1:1.9. According to the MDRD-4, CKD-EPI, and C-G equation, the prevalence of diabetic CKD was 21.4%, 21.9%, and 31.4%, respectively, and the frequency of CKD Stage 3 to 5 was found to be different among T2DM. About 3,789 (93.9%) measurements appeared to be classified as different stages of CKD (Stages 1 to 5) between MDRD-4 and CKD-EPI equations (kappa: 0.905; 95% confidence interval (CI): 0.83 - 0.97, p < 0.001). However, this study found that the above-mentioned agreement was 70.9% between CKD-EPI and C-G equation (kappa: 0.56, 95% CI: 0.44 - 0.67, p < 0.001).ConclusionsCKD-EPI equations can overcome the constraint of MDRD-4 and C-G equations to report CKD and can be used in patients with T2DM.

Detecting chronic kidney disease in diabetic adults by estimating glomerular filtration rate and Serum Creatinine

“…This is in agreement with Zaman et al; CKD prevalence by using CG (31.4%), by CKD-EPI (21.9%) and by sCr (18.6%) though prevalence was higher as the last is a hospital-based and ours is outpatient-based study. 31 And in agreement with Bouzid et al; prevalence using CG: 19.8% 32 and Fiseha et al; prevalence using CG: 23.8%. 33 All this resembles a worldwide over/underestimation of CKD with a wide variation depending on the used approach, obesity, high age, economic problems, and DM prevalence.…”

“…40 Those living in periphery are more likely to have CKD; in low income countries where unaffordable costly health service, unawareness, 41 unemployment and DM epidemic in Iraq, making CKD a leading cause of death and lately referred CKD. 36,[42][43][44] Study sample was categorized to five stages according to eGFR level 19 using two equations; standard CKD-EPI (the most accepted index one 23 ) and CG, comparing between above equations showed strong agreement: 85.5%, kappa: 0.73 (95% CI: 0.68-0.77, p < 0.001), 96.1% of CKD patients were with stage 3 that was higher by depending on CG which is in agreement with Zaman et al that found strong agreement: 70.9%, kappa: 0.56 (95% CI: 0.44-0.67, p < 0.001) and considered high CG prevalence as overestimation compared to CKD-EPI, 31 and in agreement with Kitiyakara et al 2012; 45 that is related to continuously increasing CKD (stage 3) prevalence among adult in correlation with increasing DM, advanced age, and obesity. 19 CG doesn't take diverse people in consideration and requires weight and height which isn't available in laboratories while CKD-EPI does not; CKD-EPI is better to assess CKD prevalence in diabetics despite strong agreement between them.…”

Associations between Body Mass Index and Chronic Kidney Disease in Type 2 Diabetes Mellitus Patients: Findings from the Northeast of Thailand

“…As a country of Southeast Asia, Thailand has experienced the effects of the epidemiological transition in recent years, and the prevalence of non-communicable diseases is rising [ 7 ]. Among the various reasons, behavioural factors like physical inactivity and increasing trend of junk food consumption are responsible for causing over-nutrition, which can gradually trigger microvascular complications in patients with diabetes mellitus (DM) [ 8 ].…”

Can glycated hemoglobin act as a reliable glycemic indicator in patients with diabetic chronic kidney disease? evidence from the Northeast of Thailand

“…4 Therefore, the rate of kidney disease is high in the North-eastern Thailand. 5 Although all diabetic patients are not at risk to develop CKD, minimal damage to the kidney is frequent. 6 Age, sex, race, positive family history, and high blood pressure are the known risk factors of diabetic CKD.…”