EPIDEMIOLOGY OF CHRONIC KIDNEY DISEASE IN DELTA DISTRICTS OF TAMILNADU : INSIGHTS FROM REGIONAL REGISTRY

Introduction:

1.Introduction

The delta region of Tamil Nadu state in India has a unique geography. The area is constituted by the region through which the river Cauvery flows spreading into a network of tributaries [1]. It meanders through the lush landscapes carving out the fertile delta region. The river Cauvery is the lifeline for agriculture in the delta region before completing its journey by flowing into the Bay of Bengal . Delta region includes Thanjavur, Tiruchirappalli, Ariyalur, Perambalur, Thiruvarur, Nagapattinam, Cuddalore and Pudukottai districts (Fig 1).

Chronic Kidney Disease (CKD) is a heterogenous disease with increased morbidity and mortality. The onset and progression of CKD is influenced by various determinants like genetic factors, ethnic factors, local geographic factors like climate, water and soil contaminants, prevailing infection profile, socioeconomic factors, quality and accessibility of health care. We surmise that the epidemiology of CKD in the delta region is unique. Understanding regional peculiarities in CKD epidemiology is essential for prioritizing resources and for implementing preventive strategies.

Our study aims to describe the epidemiology of CKD in the delta districts of Tamil Nadu and explore the gender differences in the epidemiology of CKD. We also sought to identify peculiarities of CKD epidemiology in delta region by comparing with national CKD cohort (Indian CKD cohort) [2].

Figure 1: Map showing districts of Tamilnadu- the yellow coloured region represents the delta districts.

Methods:

2.Materials and Methods:

2a.Study design and setting

A hospital-based registry of CKD was started in December 2021 by Thanjavur Trichy Nephrology Association (TANTRA). Cross-sectional data were collected from CKD patients attending the nephrology clinic of 15 centres located within delta districts from December 2021 to March 2024. CKD of any stage, including dialysis was included. Kidney transplant recipients with graft dysfunction were also enrolled. CKD patients hailing from non-delta districts were excluded. Both Public and private sector hospitals, teaching and non-teaching institutions participated in the study . This study was conducted as per the ethical principles laid down in Declaration of Helsinki. Ethical approval was obtained at the participating centres.

2b. Study Conduct:

CKD was diagnosed according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Demographic details like age, sex, socioeconomic status, rural/urban residence, occupation, family history of CKD was collected.  Socio-economic status was categorized according to 2021 revision of Prasad’s socio-economic status classification [4 ] Blood pressure, body mass index (BMI) and other clinical examination findings were recorded. Lab parameters like haemoglobin, urine routine, urine microscopy, urine protein creatinine ratio, blood urea and serum creatinine were recorded. The estimated glomerular filtration rate (eGFR) was calculated using creatinine based CKI-EPI formula. Native kidney disease in each patient was adjudicated by the treating Physician. Data at each centre was initially entered in a printed proforma and later fed into google form maintained by TANTRA.

2c.Statistical approach:

Continuous variables with normal distribution are expressed as mean ± SD and range. Skewed continuous data are expressed as median and interquartile range (IQR). Binary and categorical variables are summarised as frequency and percentage. For comparing mean between groups, student’s t-test is used. Proportion between groups is compared by Chi2 or proportion test. Locally weighted scatter-plot smoothing (LOWESS) is used to study the relationship between two continuous variables like eGFR and haemoglobin. For identifying predictors of haemoglobin, linear regression technique is used.  statistical analysis was done with statistical software package STATA version 17.

Results:

3.Results:

 

A total of 2202 patients residing at delta districts of Tamil Nadu were enrolled in the study. The representation of delta districts in the registry is charted in supplementary appendix  . About 25% of patients were from Tiruchirappalli district, 24% from Thanjavur district and 17% from Pudukottai district ( Table 1 ) The sample included CKD patients of age ranging from 14 – 93 years, with mean age of 57.2 ± 12.7 years. About 80% were from rural area  and 69% belonged to Middle class and 25% belong to Below Poverty Line ( Table 2 ).

 

Table 1

 

Patient representation from Delta districts of Tamilnadu

 

 

 

 

3a. Gender differences

 

The comparison between the epidemiological and clinical profile of Men and Women with CKD in the delta study is shown in Table 2.  About 70% of the patients in the delta cohort were men. Proportion of rural women seeking medical care for CKD is lower than men ( 76% vs 80% ,  P < 0.001) .Women with CKD present 2 years earlier than men with CKD ( 56 yrs Vs 58 yrs , P < 0.001) . Also they presented at later stages than men with CKD  (eGFR 22 ml /min /1.73 m2 Vs  26 ml /min /1.73 m2  , P < 0.001). The prevalence of DM , HTN, DKD, CTIN, CKD u, CGN  were comparable between men and women with CKD in the delta registry.  The mean systolic BP in women with CKD was higher when compared to men with CKD ( 134.7 mm of Hg Vs 132.3 mm of Hg, P  0.03). We have observed that obesity was more prevalent in women with CKD than in men with CKD  (17.2 % vs 7% , P <0.001 ). Females having CKD tend to be more anemic than males with CKD (  Hb 9.5 Vs 10.4 gm/dl, P<0.001). About 38% of women had stage 4 CKD  when compared with 31.06% men and 34.8% women presented with stage 5 CKD when compared with 31  % of men ( Table 3 ).

 

 

 

Table 2 : Demographic and clinical characteristics of participants in the Delta cohort

 

 

Table 3 : Distribution of CKD stages between sexes

3b.Difference between Delta cohort and ICKD cohort:

 

ICKD cohort had 4056 patients whereas delta CKD cohort had 2202 patients. Table 4 depicts the differences between the characteristics of the participants in the Delta study and the ICKD study.  Delta CKD study is a cross-sectional study whereas ICKD was a longitudinal one. Our patients were relatively older than ICKD cohort (57.2 yrs Vs 50.3 yrs , P <0.001). Proportion of men in delta registry was more than in ICKD registry (70.6% vs 67.1% , P < 0.001). Delta cohort was composed of 80% rural population compared to 66% in ICKD cohort (P<0.001).

 

The Median eGFR in delta cohort was 21 ml/min /1.73 m2 compared to 40.5 ml/min/1.73 m2 in ICKD study ( P <0.001). Though the mean age of the delta cohort is older than ICKD cohort (57.2 yrs Vs 50.3 yrs), the difference between the eGFR between them is significant even after considering the age related decline in eGFR in the delta cohort. Mean Hemoglobin in Delta cohort is 10.1 gm/dl whereas in ICKD cohort it was 11.8 gm/dl( P<0.001). The lower mean Hemoglobin in the delta study may be due to the advanced stages of CKD at presentation.

 

The prevalence of DM is higher in delta cohort (45.9% vs 37.5% , P<0.001) whereas prevalence of hypertension is lesser than in ICKD cohort (70% vs 87%, P <0.001). But the prevalence of DKD is similar (24%) in both groups. The most common cause of NKD in delta cohort is CKD u (28%) followed by DKD ( 23.5%) then CIN (11.5 %) . In ICKD the most common cause of CKD is DKD (24.9%) followed by CIN (23.2%), then CKD u (19.5%). CKD u is significantly more prevalent in delta cohort when compared to ICKD cohort (27.8% Vs 19.5%, P <0.001).The proportion of CGN is higher in ICKD study than in delta study (14.7% vs 9.2%, P<0.001). This difference might be due to some CGN being adjudged as HTN nephropathy in delta st

Table 4 : Comparison of the salient characteristics between Delta and ICKD cohort

 

 

 

 

Table 5 : Distribution of Native kidney disease in Delta Registry

 

 

3c. Additional findings:

Mean Hemoglobin in the delta cohort is 10.1 gm/dl ( IQR  8.8 to 11.5 ) . As the eGFR falls below 60 ml /min /1.73 m2, Hemoglobin begins to drop. The predictors of Hemoglobin in the delta registry are Sex, eGFR and diabetic kidney disease. Compared to women, men had 0.71gm/dl higher hemoglobin. For every 10 ml/min fall in eGFR, Hb declined by 0.6 gm/dl. Patients with DKD have a Hb that is 0.35 gm/dl higher than those with other native kidney diseases (Table 6 ).

eGFR is plotted against serum creatinine using LOWESS curve. If the standard cut off of 1.2 mg/dl for serum creatinine was used for diagnosing CKD, eGFR corresponding to this value is 60 ml/min/1.73m2.So by the time we diagnose CKD, the eGFR would have fallen from 120 ml/min to 60 ml/min. We need better biomarkers for diagnosing CKD.

In our study, we have observed the relationship between BMI and SBP is U shaped. Patients with BMI less than 19 and more than 30 have poor BP control.In patients with normal BMI range, SBP control was ideal . In patients with low BMI, Malnutrition-Inflammation syndrome, sympathetic nervous system overactivity, increased stress hormones, reduced vasodilatory adipokines may lead to increased BP.

 

 

 

Table 6 : Predictors of Hemoglobin in Delta registry

 

Conclusions:

India is a large country with extremely diverse social, economic, cultural, geographical differences among its huge population. A single registry may not be able to capture all the significant differences in epidemiology of CKD. Hence state level and much more precise regional level registries are required to better define epidemiology of CKD.

 

Patient in delta cohort present with advanced stages of CKD. Also patients present at older age in delta cohort when compared to National cohort. The delta cohort has 80% rural population. This calls for implementation of CKD screening programs in the rural area more vigorously.

 

 This study throws light on the epidemiological differences in the prevalence of CKD between men and women. Two thirds of patients are men in the delta cohort. Rural women are less represented in the registry. This discrepancy in our study might be due to the socio-economic and cultural barriers that prevent females from seeking healthcare.

 

Also women with CKD are more anemic , have increased incidence of hypertension  and also more obese when compared with men having CKD . The higher prevalence of anemia in females with CKD may be due to the lower baseline hemoglobin levels, blood loss through menstruation, low iron stores, and hormonal differences.

 

An important finding in the delta registry is the emergence of CKD u as the most common cause of CKD in the  delta districts of Tamilnadu. The prevalence of CKD u is higher than in National cohort. Contributing factors may include exposure to agrochemicals, water contaminants, heavy manual labor, temperature and climatic conditions , chronic dehydration and a probable genetic predisposition. More research is needed in this area to further elucidate the cause for CKD u.

 

 

Strengths of the study

 

There is a paucity of data regarding the epidemiology of CKD. This attempt at creating regional CKD registry is first of its kind. The strength of maintaining CKD registry will help us in quantifying disease burden, health infrastructure planning and also audit of existing health policies. Furthermore for clinicians, the registry can help in generating hypothesis regarding etiology of CKD, understanding its progression and planning clinical research. Both Public and Private sector hospitals are adequately represented in the study to ensure that all classes of people with CKD are included.

 

Limitations

Delta CKD registry was a hospital-based cross-sectional study with no longitudinal follow-up. Lack of funding limited elaborate investigations. Native kidney adjudication was done at the discretion of the treating physician. There was lack of data on the cardiovascular status and cardiovascular risk factors of the CKD patients.  Missing data limited comparison of urine PCR between groups.

I have no potential conflict of interest to disclose.

I did not use generative AI and AI-assisted technologies in the writing process.