Abstract
Introduction
Chronic kidney disease (CKD) is a major public health concern in India, with a disproportionate impact on underserved populations. Shifting epidemiological patterns indicate increasing metabolic risk in tribal communities. This study sought to assess the prevalence of CKD and explore its associated determinants among tribal communities in Kerala, South India.
Methods
A community-based cross-sectional study was conducted among 2029 tribal adults aged ≥ 30 years across 3 districts in Kerala, selected through multistage random sampling. Data collection involved structured interviews, anthropometry, point-of-care blood tests, and spot urine analysis. Based on Kidney Disease: Improving Global Outcomes (KDIGO) 2024 criteria, CKD was defined using estimated glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR). Multivariable logistic regression was employed to identify factors independently associated with CKD.
Results
A total of 2029 individuals were included in this study (1209 females and 820 males) from 18 distinct tribal communities. The mean age was 50.1 (± 13.1) years. The prevalence of CKD was 22.6% (95% confidence interval [CI]: 20.7%–24.7%), higher in females (24.2%, 95% CI: 21.8%–26.8%) than in males (20.4%, 95% CI: 17.8%–23.2%). Most cases were in early risk stages, reflecting a significant subclinical burden. CKD was positively associated with older age, hypertension, diabetes, lower education, and low body mass index (BMI).
Conclusion
This is the first large-scale population-based study to report a high prevalence of CKD among indigenous tribal communities in India. Effective CKD control in the indigenous communities in India necessitates an equity-focused strategy addressing both clinical and social determinants. The findings underscore the need for culturally adapted screening and prevention interventions integrated within primary health care systems.
Keywords: chronic kidney disease, cross-sectional study, health inequities, India, noncommunicable diseases, tribal populations
Graphical abstract
CKD is a growing global public health concern, particularly in low- and middle-income countries such as India, where the burden of noncommunicable diseases is increasing sharply. Characterized by a progressive and often irreversible decline in renal function, CKD significantly contributes to premature mortality, reduced quality of life, and escalating health care costs.1 Recent national estimates indicate that 13% of Indian adults are affected by CKD, marking a significant increase from previous figures and highlighting the growing impact of major risk factors such as diabetes mellitus and hypertension.2
CKD places a significant burden on individuals and their families, and exerts substantial pressure on health care systems, particularly in low- and middle-income countries.3 However, CKD often remains underdiagnosed and undertreated, as its early stages are usually asymptomatic, allowing disease progression until irreversible kidney damage occurs.4 Efforts to address CKD require a thorough understanding of its prevalence and risk factors across various subpopulations, particularly those with limited access to health care resources.
Tribal communities in India have historically shown a low prevalence of noncommunicable diseases, largely attributed to their physically active lifestyles, traditional dietary practices, and minimal exposure to urbanization. However, recent evidence points to a rapid epidemiological transition and a sharp increase in metabolic disorders such as type 2 diabetes and hypertension among tribal groups, likely attributable to changes in dietary patterns, environmental degradation, and increased integration with mainstream society.5,6
These trends are particularly evident in Kerala, a state undergoing advanced demographic and health transitions, where tribal communities reside predominantly in geographically remote and socioeconomically marginalized regions.7
The convergence of rising metabolic risk factors and systemic health care disparities in tribal populations poses a significant but underrecognized risk for CKD.8 Although extensive data exist on the prevalence and risk factors of CKD in the general population, there is a notable paucity of epidemiological studies focusing on high-risk and underserved subgroups, including tribal populations.9
In Kerala, no population-based data currently exist to quantify the burden of CKD or to elucidate its associated risk factors in tribal communities. Because of the growing public health implications, there is a critical need for robust epidemiological data on CKD to inform early detection and prevention strategies in these populations. This study aimed to estimate the prevalence of CKD by objective assessment of its biomarkers and investigate its determinants, including demographic, metabolic, and environmental factors, among tribal populations in Kerala. Through a community-based, cross-sectional design, this study sought to address a significant gap in the literature and provide actionable insights to guide public health policy and targeted interventions in a uniquely vulnerable and understudied population.
Methods
Study Design and Setting
A cross-sectional, community-based study was conducted among tribal populations in 3 districts of Kerala, namely Malappuram, Wayanad, and Thiruvananthapuram. These districts were selected because of their significant tribal populations, blend of tribal communities, and geographic diversity.
The study targeted adult participants aged ≥ 30 years, based on the framework of the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke in India.10 The data collection and analysis for this study were conducted over 2 years (from September 2022 to July 2024), as part of a broader study focused on examining multimorbidity among tribal populations in Kerala. This manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines,11 and the STROBE reporting checklist was used during editing (Supplementary Table S1).
Sample Size Calculation and Sampling Technique
The sample size was estimated based on an anticipated prevalence of 23%, with a relative precision of 10% (absolute precision of 2.3%) and a 95% confidence level. To account for clustering within hamlets, a design effect of 1.5 was applied. This resulted in a required sample size of 1924, which was further increased by 15% to accommodate potential nonresponse, leading to a final target sample size of 2264.
Tribal hamlets in the selected districts were first identified and listed according to their population size. From each district, 25 hamlets were randomly selected. Within each selected hamlet, 15 households were chosen using systematic random sampling. All eligible individuals aged ≥ 30 years in these households were invited to participate in the study.
Data Collection Procedure
A trained research team, comprising a study coordinator and research nurses, conducted the data collection. Before initiating the survey, the team engaged with local tribal leaders, health personnel, and panchayat (local self-government institutions) authorities to explain the study objectives and establish rapport. Written informed consent was obtained from all participants in their local language, with the assistance of tribal promoters for illiterate participants.
Measurements and Assessments
Sociodemographic characteristics, educational status, health literacy, and general health status were assessed through structured questionnaires. Behavioral and lifestyle factors, including alcohol and tobacco use, were evaluated using tools adapted from the World Health Organization STEPS instrument.12 Biochemical assessments included capillary blood tests with point-of-care devices to measure fasting blood glucose, hemoglobin levels, and serum creatinine (Supplementary Table S2). Practical considerations and regulatory constraints necessitated the use of point-of-care devices for biochemical testing, because venous blood collection and storage were not permitted by the Tribal Department or the institute ethics committee. To assess reliability, a validation exercise was performed on 10 healthy volunteers comparing biochemical measurements with central laboratory values. The coefficient of variation was < 6% for all measurements, demonstrating good agreement and suitability for field use. Fasting samples were collected with prior notice to participants, with ≤ 2 follow-up visits for those unable to provide a fasting sample initially.
Anthropometric measurements, including height, weight, and waist circumference, were taken using standardized techniques. Spot urine samples were collected separately from participants to assess ACR. The urine samples were also used to measure sodium, potassium, and creatinine concentrations. Urine analyses were conducted in a central laboratory accredited by the National Accreditation Board for Testing and Calibration Laboratories (Supplementary Table S2).
Operational Definitions
Tribal communities in this study refer to indigenous populations recognized by the Government of India as Scheduled Tribes.13 These groups are characterized by distinct social, cultural, and economic practices, often living in geographically isolated areas with limited access to health care and other public services.
Sex was defined as the biological classification of participants as male or female, consistent with WHO definition.
High waist circumference was defined as ≥ 90 cm for males and ≥ 80 cm for females.14
High BMI was defined as ≥ 23 kg/m2 (overweight or obese), and low BMI was defined as < 18.5 kg/m2 (underweight).15
Anemia was defined according to World Health Organization criteria as hemoglobin concentration < 13.0 g/dl in males and < 12.0 g/dl in females.16
Hypertension was defined as systolic blood pressure ≥ 140 mm Hg and/or diastolic blood pressure ≥ 90 mm Hg, or self-report of previous diagnosis by a health professional.
Diabetes mellitus was defined as a fasting blood sugar level of ≥ 126 mg/dl or a self-reported history of treatment for diabetes.
eGFR was calculated using the CKD-Epidemiology Collaboration (CKD-EPI) 2021 creatinine equation, which incorporates age, sex, and serum creatinine concentration.17 In addition, we estimated eGFR using Cockcroft–Gault and the Modification of Diet in Renal Disease–4 equations as part of sensitivity analyses.18,19
Serum creatinine was measured using point-of-care testing devices, and eGFR was expressed in ml/min per 1.73 m2.
Salt intake was estimated in g/d using a sex-specific formula based on spot urine sodium and creatinine concentrations, height, and estimated 24-hour creatinine excretion, as described by Kawasaki et al.20 A daily intake of > 5 g was classified as high, consistent with World Health Organization recommendations.21
CKD Assessment
CKD was defined using the KDIGO guidelines,22 which classifies kidney function and damage based on the eGFR and ACR. The classification is color-coded as follows (Figure 1).
Figure 1.
Distribution of CKD stages based on Kidney Disease: Improving Global Outcomes classification among study participants (n = 2029). CKD, chronic kidney disease; GFR, glomerular filtration rate.
Low risk (green): Individuals with an eGFR in category G1 (≥ 90 ml/min per 1.73 m2) and albuminuria in category A1 (ACR < 30 mg/g) are considered to have normal kidney function without evidence of kidney damage and are not classified as having CKD.
Moderate risk (yellow): Individuals with an eGFR in category G2 (60–89 ml/min per 1.73 m2) and albuminuria in category A2 (ACR: 30–300 mg/g) or A3 (ACR > 300 mg/g) are considered to be at moderate risk, indicating mildly reduced kidney function with evidence of kidney damage.
High risk (orange): This group includes individuals with an eGFR in category G3a (45–59 ml/min per 1.73 m2) and ACR ≥ 30 mg/g (A2 or A3), or eGFR in category G3b (30–44 ml/min per 1.73 m2) with moderate albuminuria (A2). These combinations reflect moderately to severely reduced kidney function with albuminuria and indicate a high risk of CKD progression and cardiovascular complications.
Very high risk (red): Individuals with an eGFR in category G3b (30–44 ml/min per 1.73 m2) and severe albuminuria (A3), G4 (15–29 ml/min per 1.73 m2) with ACR ≥ 30 mg/g, or G5 (< 15 ml/min per 1.73 m2), with or without albuminuria (including those on dialysis), fall under the very high-risk category. This reflects advanced kidney dysfunction or kidney failure, with a markedly increased risk of progression to end-stage kidney disease and death.
As per the KDIGO 2024 guidelines, participants with G1A1 and G2A1 were excluded from the numerator of prevalence estimates because they represent normal or mildly decreased kidney function with minimal clinical significance.
Other participants (G1A2, G2A2, G3aA1, G3bA1, G3aA2, G1A3, G2A3, G3aA3, G3bA2, G3bA3, G4A1, G4A2, G4A3, G5A1, G5A2, and G5A3) were classified as having CKD.
Statistical Analysis
Descriptive statistics were calculated for the socio-demographic, clinical, and behavioral characteristics of the study participants. The prevalence of CKD was estimated along with 95% CIs. For continuous variables, means and SDs were calculated, whereas categorical variables were summarized using frequencies and percentages.
A total of approximately 2450 individuals were approached, of whom 2333 consented and participated in the survey, reflecting a 5% nonresponse rate. Of the 2333 individuals who participated in the survey, 2029 individuals with complete serum creatinine and urine ACR data were included in the final analysis to estimate CKD prevalence, based on KDIGO 2024 guidelines. We estimated a 95% CI for all prevalence estimates. Chi-square tests for categorical variables and t tests for continuous variables were performed to compare the characteristics of individuals with and without CKD. A multivariable logistic regression analysis was conducted to assess variables associated with CKD. The regression models were adjusted for potential confounders to account for any bias affecting the relationship between key risk factors (age, sex, educational status, hypertension, and diabetes status) and CKD. A P-value < 0.05 was considered statistically significant for all analyses. All statistical analyses were performed using the statistical software R version 4.4.3. The packages used included “stats” for regression and hypothesis testing, and “forestplot” and “ggplot2” for data visualization.
Ethical Considerations
The study was approved by the Institutional Ethics Committee of Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum (Approval No. SCT/IEC/1511/DEC-2019). The study adhered to ethical guidelines, ensuring confidentiality and the voluntary participation of respondents. Participants with abnormal findings, such as elevated blood glucose or abnormal ACR and eGFR, were referred to the nearest health care facility for follow-up care, facilitated by tribal promoters. Biomedical waste generated during the study was safely disposed of according to local health regulations.
Results
Participant Characteristics
A total of 2029 individuals were included in this study, comprising 1209 females and 820 males. The study population covered 18 distinct tribal groups. The mean age was 50.1 years (SD = 13.1), with females slightly older (mean = 50.4 years) than males (mean = 49.7 years) (Table 1). Participants were grouped into 4 age categories, with more than half belonging to the 30 to 49 years age group: 633 females (52.4%) and 433 males (52.8%). Educational attainment differed by sex; a higher proportion of males (47.8%) had secondary school education than females (37.4%). In comparison, education up to secondary school and above was reported by 29.9% of females and 26.5% of males (Table 1). Tobacco use was prevalent among both sexes, reported by 62.9% of males and 43.5% of females.
Table 1.
Sociodemographic, behavioral, clinical, and comorbidity characteristics of the study population (n = 2029)
| Variables | Female (n = 1209) | Male (n = 820) | Total (N = 2029) |
|---|---|---|---|
| Age, mean (SD) 50.1 (13.1) | 50.4 (13.1) | 49.7 (13.2) | 50.1 (13.1) |
| Age group, yrs, n (%) | |||
| 30–49 | 633 (52.4) | 433 (52.8) | 1066 (52.6) |
| 50–59 | 227 (18.8) | 164 (20.0) | 391 (19.3) |
| 60–69 | 239 (19.8) | 154 (18.8) | 393 (19.4) |
| 70+ | 110 (9.1) | 69 (8.4) | 179 (8.8) |
| Educational status, n (%)a | |||
| No formal schooling | 395 (32.7) | 211 (25.7) | 606 (30.0) |
| Till secondary school | 452 (37.4) | 392 (47.8) | 844 (41.6) |
| Secondary school & above | 361 (29.9) | 217 (26.5) | 578 (28.5) |
| Behavioral risk factors | |||
| Tobacco usage, n (%) | 526 (43.5) | 516 (62.9) | 1042 (51.4) |
| Alcohol consumption, n (%) | 77 (6.4) | 408 (49.8) | 485 (23.9) |
| BMI, n (%)a | |||
| Normal (18.5–22.9) | 485 (40.1) | 358 (43.7) | 843 (41.6) |
| Underweight (< 18.5) | 206 (17.0) | 115 (14.0) | 321 (15.8) |
| Overweight & obese (≥ 23) | 518 (42.8) | 346 (42.2) | 864 (42.6) |
| Waist circumference, n (%)a | |||
| High (male, > 90 cm; female > 80 cm) | 425 (35.4) | 78 (9.6) | 503 (25.0) |
| Normal | 774 (64.6) | 737 (90.4) | 1511 (75.0) |
| Salt intake, n (%)a | |||
| High (> 5 mg/d) | 173 (14.3) | 261 (31.9) | 434 (21.4) |
| Normal | 1036 (85.7) | 558 (68.1) | 1594 (78.6) |
| Comorbidities | |||
| Hypertension, n (%) | 621 (51.4) | 473 (57.7) | 1094 (53.9) |
| Diabetes, n (%)a | 373 (31.5) | 271 (33.9) | 644 (32.4) |
| Anemia, n (%) | 560 (46.3) | 305 (37.2) | 865 (42.6) |
BMI, body mass index.
Missing data reported.
Alcohol consumption showed a marked sex difference, reported by 49.8% of males compared with only 6.4% of females. Nearly half of females (42.8%) and 42.2% of males were classified as overweight or obese. Underweight status was more common among females (17%) than among males (14%). Abdominal obesity was observed in 35.4% of females compared with 9.6% of males. High salt intake was more frequently reported by males (31.9%) than by females (14.3%).
In our study population, 608 participants (30.0%) were identified as hypertensive based on self-reported history, whereas 486 (24.0%) were newly identified through blood pressure measurements during the screening. Hypertension and diabetes were relatively more prevalent among males (57.7% and 33.9%, respectively) than in females (51.4% and 31.5%, respectively). Anemia was more common in females (46.3%) than in males (37.2%).
Prevalence of CKD
The overall prevalence of CKD was 22.6% (95% CI: 20.7%–24.7%), with 292 cases among females (24.2%, 95% CI: 21.8%–26.8%) and 167 cases among males (20.4%, 95% CI: 17.8%–23.2%) (Table 2). Based on KDIGO guidelines, most participants were in the G1-A1 (43.8%) or G2-A1 (33.6%) stages. More advanced stages were less common, with 0.8% and 0.05% of participants classified as G4-A1 and G5-A1, respectively. A relatively large proportion had increased albuminuria despite normal or mildly reduced eGFR, as observed in G1-A2 (4.8%) and G2-A2 (4.0%) (Figure 1). The age-standardized prevalence of CKD was 20.6%, with a relatively higher prevalence among females (21.8%) than males (18.9%).
Table 2.
Comparison of sociodemographic, behavioral, clinical, and comorbidity characteristics among patients with and without CKD
| Variables | CKD (N = 459) | No CKD (N = 1570) |
|---|---|---|
| Sex, n (%) | ||
| Male | 167 (20.4) | 653 (79.6) |
| Female | 292 (24.2) | 917 (75.8) |
| Age group, yrs, n (%) | ||
| 30–49 | 142 (13.3) | 924 (86.7) |
| 50–59 | 106 (27.1) | 285 (72.9) |
| 60–69 | 120 (30.5) | 273 (69.5) |
| 70+ | 91 (50.8) | 88 (49.2) |
| Educational status, n (%)a | ||
| No formal schooling | 201 (33.2) | 405 (66.8) |
| Till secondary school | 166 (19.7) | 678 (80.3) |
| Secondary school & above | 92 (15.9) | 486 (84.1) |
| Behavioral risk factors | ||
| Tobacco usage, n (%) | 267 (25.6) | 775 (74.4) |
| Alcohol consumption, n (%) | 105 (21.6) | 380 (78.4) |
| BMI, n (%)a | ||
| Normal (18.5–22.9) | 180 (21.4) | 663 (78.6) |
| Underweight (< 18.5) | 98 (30.5) | 223 (69.5) |
| Overweight & obese (≥ 23) | 180 (20.8) | 684 (79.2) |
| Waist circumference, n (%)a | ||
| High (male, > 90 cm; female, > 80 cm) | 120 (23.9) | 383 (76.1) |
| Normal | 335 (22.2) | 1176 (77.8) |
| Salt Intake, n (%)a | ||
| High (> 5 mg/d) | 122 (28.1) | 312 (71.9) |
| Normal | 336 (21.1) | 1258 (78.9) |
| Comorbidities | ||
| Hypertension, n (%) | 322 (29.4) | 772 (70.6) |
| Diabetes, n (%)a | 205 (31.8) | 439 (68.2) |
| Anemia, n (%) | 211 (24.4) | 654 (75.6) |
BMI, body mass index; CKD, chronic kidney disease.
Missing data reported.
Multivariable Analysis of CKD Determinants
In the unadjusted model (Figure 2a), CKD was more prevalent in females than in males (P = 0.045); however, this association was not evident after adjustment (P = 0.074). Age demonstrated a strong, positive association with CKD, with adjusted odds ratios (ORs) (in comparison with the reference group of 30–49 years) elevated for individuals aged 50 to 59 years (OR: 1.9, 95% CI: 1.4–2.5), those aged 60–69 years (OR: 1.9, 95% CI: 1.4–2.6), and those aged ≥ 70 years (OR: 3.9, 95% CI: 2.7–5.8), all with P-values < 0.001 (Figure 2b). Participants with a low level of education had higher adjusted odds of CKD (OR: 1.5, 95% CI: 1.0–2.0; P = 0.026) than those with higher education (Figure 2b).
Figure 2.
(a) Factors associated with chronic kidney disease (unadjusted). (b) Factors associated with chronic kidney disease (adjusted). BMI, body mass index.
Although tobacco use was associated with CKD in the unadjusted analysis (P = 0.001), this association was not evident after adjustment (P = 0.219), suggesting possible confounding effects. Low BMI remained associated with CKD in both unadjusted and adjusted models (adjusted P = 0.003). Comorbid conditions, including hypertension (OR: 1.6, 95% CI: 1.2–2.1) and diabetes (OR: 1.6, 95% CI: 1.3–2.1), showed strong and consistent associations with CKD in both models (adjusted P < 0.001 for both). Alcohol use or anemia did not show any association with CKD in either model.
Sensitivity Analyses
We performed sensitivity analyses (Supplementary Table S3) to compare eGFR estimates derived from the CKD-EPI, Cockcroft–Gault, and Modification of Diet in Renal Disease-4 equations across different BMI categories. The mean eGFR values were lowest among underweight participants. However, the CKD-EPI equation yielded comparatively higher eGFR estimates in this group than in the other equations. Similarly, the prevalence of CKD was lowest in underweight participants when eGFR was calculated using the CKD-EPI equation compared with the other 2 methods.
Discussion
Our study reveals a high prevalence of CKD, affecting approximately 1 in 5 adults aged > 30 years within the tribal population of Kerala. The prevalence based on KDGIO guidelines was slightly higher among females than among males. Notably, the age-standardized prevalence closely mirrored the crude prevalence, suggesting that the elevated burden of CKD cannot be attributed solely to age distribution within the study population. Most CKD cases were detected at early stages (G1-A2, G2-A2, and G3aA1), underscoring a significant hidden burden of subclinical disease.
Multivariable analysis identified several independent variables associated with CKD, including increasing age, educational status, female sex, lower BMI, hypertension, and diabetes mellitus. Accurate estimation of CKD prevalence using standardized criteria, such as those outlined by the KDIGO guidelines, is critical for comparability across studies and for informing clinical and public health responses. The KDIGO framework, which incorporates both glomerular filtration rate and albuminuria categories, enables the identification of early-stage CKD and stratification of individuals by risk of progression and complications. In the present study, application of KDIGO staging criteria revealed that a significant proportion of cases were in the early stages, which may be overlooked in clinical practice without systematic screening. These findings highlight the importance of using guideline-based definitions to uncover the hidden burden of disease and to facilitate early intervention strategies, particularly in vulnerable and underserved populations such as tribal communities. The finding that approximately 1 in 5 adults aged > 30 years in Kerala's tribal population is affected by CKD has significant public health implications. It is considerably higher than national estimates reported in general population-based studies from India, which typically range between 10.5% and 16.2%.2 In rural South India, prevalence ranged from 6.3% using the Modification of Diet in Renal Disease equation to 16.5% using the Cockcroft–Gault equation, illustrating how estimation methods can influence results.23 In urban areas, prevalence was 8.7% for CKD and 7.1% for albuminuria, with nearly 80% of patients with CKD showing elevated glycated hemoglobin levels.24 In addition, it exceeds the prevalence reported in many urban25 and semiurban26 cohorts, suggesting that tribal communities, often presumed to be protected from lifestyle-related disorders, may now be experiencing an epidemiological shift. Compared with studies such as the Screening and Early Evaluation of Kidney Disease study,25 which reported a CKD prevalence of 17.2% among urban and rural populations, the burden in this tribal cohort is disproportionately high.27 Although some regional studies have identified pockets of high CKD prevalence, such as the Uddanam region of Andhra Pradesh, where CKD of unknown etiology is prevalent,28 the underlying risk profile and pathophysiological mechanisms may differ substantially. Tribal populations, such as those in Konduru, Andhra Pradesh, tend to have a higher prevalence, likely because of the limited health care access and other factors, including cases of CKD of unknown origin, that remain poorly understood.29 In our study, the strong association of CKD with known metabolic risk factors such as hypertension and diabetes suggest that classical pathways of kidney disease progression are increasingly relevant even in traditionally low-risk tribal populations. A notable proportion of younger individuals in our study presented with reduced eGFR in the absence of proteinuria, suggestive of CKD of unknown etiology. Our findings underscore the urgent need to expand screening, preventive, and management strategies to include tribal communities, who may otherwise be excluded from mainstream noncommunicable disease programs. Our findings call for region-specific research into potential environmental, genetic, and health care access-related contributors to the elevated CKD burden. The markedly higher prevalence of CKD among individuals with lower educational attainment (33.2%) compared with those with higher education levels (15.9%) highlights a significant social gradient in disease burden, underscoring the critical role of social determinants in shaping health outcomes. Education is a key proxy for socioeconomic status and is closely linked to health literacy, access to health care, occupational exposures, dietary patterns, and the ability to engage in preventive behaviors.30 The inverse relationship between education and CKD prevalence observed in this study aligns with broader global and national evidence that lower socioeconomic groups bear a disproportionate burden of noncommunicable diseases.
This disparity suggests that CKD is not merely a biomedical condition but also a manifestation of social disadvantage. From a public health perspective, these findings reinforce the need for equity-focused interventions that prioritize early detection and management of CKD among disadvantaged populations.31 Addressing the social gradient in CKD requires a multisectoral approach, integrating health promotion, education, poverty reduction, and improved health care access. Public health policy must adopt a targeted, equity-oriented approach. First, CKD screening programs should be integrated into primary health care services in tribal and socioeconomically deprived areas, with simplified protocols and culturally sensitive communication tailored to low-literacy populations. Second, community health workers (e.g., ASHAs) and tribal promoters should be trained to identify and refer individuals at risk, leveraging their trusted role within communities. Third, health education campaigns promoting awareness about CKD risk factors such as hypertension and diabetes should be expanded and made linguistically and culturally appropriate. Fourth, social protection measures such as subsidized access to essential medicines, renal function tests, and transportation to health care facilities should be extended to low-income and educationally disadvantaged groups. Lastly, policies must address upstream determinants by improving access to quality education, nutrition, and environmental protections, which collectively contribute to kidney health. These multifaceted interventions are essential to reduce the social gradient in CKD and promote health equity among tribal populations and other marginalized communities. The observation of a disproportionately high prevalence of CKD among underweight individuals in the tribal population warrants careful consideration. We acknowledge that the CKD-EPI 2021 equation may slightly overestimate the glomerular filtration rate in Indian populations because of differences in muscle mass and body composition. However, previous studies have demonstrated its accuracy and precision.
Mulay and Gokhale32 reported that the CKD-EPI 2009 equation was the most accurate for predicting eGFR in an Indian population, showing minimal bias and high precision, whereas Yan et al33 noted that the 2021 equation slightly overestimates eGFR but avoids race-based adjustments present in the 2009 equation. Based on these findings, we used the CKD-EPI 2021 equation and additionally performed a sensitivity analysis using Cockcroft–Gault and Modification of Diet in Renal Disease-4 equations. The consistency of higher advanced CKD prevalence among underweight participants across all equations supports the conclusion that these findings reflect a true health concern rather than a methodological artifact. Although obesity is widely recognized as a traditional risk factor for CKD, emerging evidence, including our findings, suggests that low BMI may be independently associated with impaired kidney function, particularly in socioeconomically marginalized groups.34 In this study, underweight individuals exhibited a significantly higher burden of CKD compared with those with normal or higher BMI. This may reflect underlying malnutrition, chronic inflammation, or undiagnosed comorbidities such as tuberculosis or parasitic infections, which are more prevalent in resource-limited settings.35,36 Moreover, low BMI could indicate sarcopenia and reduced muscle mass, which can affect creatinine-based estimations of glomerular filtration rate,37,38 potentially leading to overestimation of CKD prevalence. In addition, occupational and environmental stressors such as prolonged heat exposure and dehydration, common in labor-intensive and underserved communities, may further contribute to kidney dysfunction and the higher CKD burden observed in this group.39
Nevertheless, the association between undernutrition and kidney disease suggests a complex interplay between poverty, food insecurity, infectious disease burden, and limited access to preventive health care. These findings underscore the need to broaden the clinical and public health understanding of CKD beyond obesity- and diabetes-centric models.
Policy interventions should focus on improving nutritional status through food supplementation programs, routine nutritional screening, and addressing upstream determinants such as poverty and sanitation. In addition, CKD risk assessment tools may need to be recalibrated or validated for undernourished populations to ensure accurate diagnosis and management in such vulnerable groups.
Strengths and Limitations
This study has several important strengths. To our knowledge, it is among the first community-based investigations to systematically assess the prevalence and determinants of CKD in tribal communities in Kerala using the standardized KDIGO 2024 classification. A major strength lies in the inclusion of participants from 18 distinct tribal groups, enhancing the diversity and representativeness of the sample and allowing for broader generalizability of findings across tribal communities in the region. The use of population-based sampling further supports the external validity of the results. Comparison of age distributions showed that the study population and the general tribal population are comparable to each other (Supplementary Figure S1). In addition, the study incorporated both eGFR and urine ACR, enabling accurate CKD risk staging and the identification of early-stage disease often missed in routine care. The inclusion of detailed demographic, anthropometric, and clinical data allowed for comprehensive multivariable analysis of both metabolic and social determinants of CKD.
The study has limitations. First, its cross-sectional design limits the ability to infer causality between identified risk factors and CKD. Second, CKD diagnosis was based on a single serum creatinine and urine ACR measurement, without the confirmatory 3-month follow-up required by KDIGO to establish chronicity. This may have resulted in overestimating CKD prevalence, particularly in early-stage cases.
Biochemical testing was performed using point-of-care devices instead of central laboratory assays, which, given the different field conditions, could result in some small measurement inaccuracies. Third, creatinine-based eGFR may be less reliable in undernourished individuals, a prevalent subgroup in the study cohort. Fourth, though most urine samples were collected in the morning, logistical difficulties and occasional lapses in following instructions, such as participants forgetting to collect the first-morning sample, occurred. As a result, a small proportion (< 2%) of specimens were likely collected later in the morning, which may have caused a slight overestimation of albuminuria. Fifth, data on potential environmental and occupational exposures (e.g., water quality, use of traditional medicines, and agrochemicals) and genetic susceptibility, which may be relevant in tribal populations, were not collected.
Conclusion
This community-based study reveals a substantially high prevalence of CKD among tribal populations in Kerala, with 1 in 5 adults aged > 30 years affected. Most cases were in early stages, indicating a significant hidden burden. In our analysis, CKD was independently associated with advancing age, female sex, low BMI, hypertension, and diabetes, with disproportionately higher prevalence among underweight and less-educated individuals, reflecting a strong social gradient. These findings underscore the urgent need for targeted, culturally appropriate screening, early intervention, and health system strengthening in tribal communities to address the increasing burden of CKD and its social determinants.
Disclosure
All the authors declared no competing interests.
Acknowledgments
We thank all the participants in our study. We thank the Scheduled Tribes Development Department, Kerala, and the Kerala Forests and Wildlife Department.
Funding
This project is funded by the Department of Science and Technology (DST), Government of India (Project 6117), and is conducted by the Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST). The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding institutions.
Data Availability Statement
The data supporting this study's findings are available at a reasonable request and addressed to the corresponding author.
Author Contributions
PJ, MV, JVT, and SG conceived the study. PJ and SI were involved in coordinating and supervising data collection for the study. PJ, SI, HK, and RV were involved in data analysis and initial interpretation. All the authors critically reviewed the manuscript and approved the definitive version.
Footnotes
Figure S1. Comparison of age distribution between the general Tribal population and study participants.
Table S1. STROBE checklist.
Table S2. Summary of biochemical and anthropometric measurements employed in the study.
Table S3. Chronic kidney disease stages based on eGFR (Cockcroft–Gault, MDRD4, and CKD-EPI 2021) across BMI categories.
Supplementary Material
Figure S1. Comparison of age distribution between the general Tribal population and study participants. Table S1. STROBE checklist. Table S2. Summary of biochemical and anthropometric measurements employed in the study. Table S3. Chronic kidney disease stages based on eGFR (Cockcroft–Gault, MDRD4, and CKD-EPI 2021) across BMI categories.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1. Comparison of age distribution between the general Tribal population and study participants. Table S1. STROBE checklist. Table S2. Summary of biochemical and anthropometric measurements employed in the study. Table S3. Chronic kidney disease stages based on eGFR (Cockcroft–Gault, MDRD4, and CKD-EPI 2021) across BMI categories.
Data Availability Statement
The data supporting this study's findings are available at a reasonable request and addressed to the corresponding author.