Risk Factors in Chronic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus Patients Attending a Tertiary Care Hospital in South Tamil Nadu, India: A Cross-sectional Study - S Priya; S Revanth; Hemamalini Basker; K Essakiraja (Journal of Clinical and Diagnostic Research)
Introduction: Chronic Kidney Disease (CKD) has become a major cause of global morbidity and mortality, especially in developing countries like India. When diagnosed at the end stage, the overall average cost incurred by the health system per haemodialysis session is INR 4,148, and the mean Out-of-Pocket Expenditure (OOPE) per patient is INR 2,838, which a poor can not afford. Indian CKD registry infers that Type 2 Diabetes Mellitus (T2DM) attributes for 31.2% of CKD cases. The high cost of haemodialysis and the expenditure spent out of pocket by impoverished patients warrants the need for the present study, which was aimed to assess CKD risk among newly diagnosed diabetics. Aim: To assess CKD risk among newly diagnosed diabetics using the Kidney Disease Improving Global Outcomes (KDIGO) 2021 guidelines, which utilise estimated Glomerular Filtration Rate (eGFR) and urine microalbuminuria as assessment tools. Materials and Methods: The present cross-sectional study was conducted in the Non Communicable Disease (NCD) Outpatient Department (OPD), Madurai Medical College (attached to the Government Rajaji Hospital), Madurai, Tamil Nadu, India, from July 1st 2022 to August 30th, 2022. Study was conducted among 200 newly diagnosed T2DM patients (diagnosed for less than 6 months), selected by consecutive sampling. After the initial administration of a questionnaire, blood and random spot urine samples were collected. CKD risk was assessed based on eGFR and urine microalbuminuria using the KDIGO 2021 guidelines, with eGFR calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Statistical analysis was done using Statistical Package for the Social Sciences (SPSS) software 21.0, employing the Chi-square test and Pearson’s correlation tests. Results: Of the total 200 study participants, 130 (65%) were females and 70 (35%) were males with a mean age of 47.74±10.35 years. Of the study participants, 106 (53%), belonged to the low-risk category, 74 (37%) to the moderate-risk category, and 20 (10%) to the high/very high-risk category for CKD. A statistically significant association was observed between alcohol intake and CKD (p-value=0.034). A significant negative correlation (p-value <0.001) was found between age and eGFR (Pearson’s coefficient=-0.400). In the present study, age and gender were not significant factors in CKD risk. While no link was found between carbonated drink consumption and CKD risk, alcohol intake did show a notable association. The study also noted a statistically significant negative correlation between age and eGFR. Conclusion: Identifying CKD risk in diabetic patients using KDIGO guidelines based on eGFR and microalbuminuria is crucial. Early detection is vital to slow progression, particularly from moderate to high-risk categories, thereby reducing personal and societal burdens. Regular monitoring with eGFR testing and follow-ups can improve outcomes and reduce the likelihood of advanced CKD. Collaborative efforts among primary care clinicians and specialists are essential for early CKD risk detection and management using these tools.