Nutritional Assessment and Dietary Intake Patterns in Early-Stage Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study

Nutritional Assessment and Dietary Intake Patterns in Early-Stage Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Nutritional Assessment and Dietary Intake Patterns in Early-Stage Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study Anindita Ghosh, Varsha Prabhakar Badikol, Arti Muley This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9382051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Chronic Kidney Disease (CKD) presents a rapidly growing public health challenge in India, with early stages offering a critical window for nutritional intervention. Methods This hospital-based cross-sectional study assesses nutritional intake, anthropometric measurements, biochemical markers, and appetite status among adults with CKD stages 1–3 (n = 184) attending outpatient nephrology departments of two tertiary care hospitals in Hyderabad, India. Assessment tools include a Food Frequency Questionnaire (FFQ), 24-hour dietary recall, Subjective Global Assessment (SGA), and the Council on Nutrition Appetite Questionnaire (CNAQ). Results Mean age is 47.6 ± 12.1 years; 59.8% are male. Stage distribution is: Stage 1 (10.9%), Stage 2 (25.5%), Stage 3a (34.2%), and Stage 3b (29.3%). Mean protein intake is 0.82 ± 0.21 g/kg/day; 64% exceed sodium and 58% exceed potassium limits; 34% are malnourished by SGA. Estimated glomerular filtration rate (eGFR) category correlates negatively with haemoglobin (r = − 0.42, p < 0.001) and positively with urinary albumin-to-creatinine ratio (UACR; r = + 0.37), glycated haemoglobin (HbA1c; r = + 0.29, p = 0.003), and CNAQ score (r = 0.15, p = 0.042). Age (β = +0.21), haemoglobin (β = −0.31), body mass index (BMI; β = −0.18), and CNAQ score (β = +0.19) independently predict eGFR category (adjusted R² = 0.41). Conclusion Suboptimal dietary patterns, reduced appetite, and emerging anaemia are independently associated with kidney function severity in early-stage CKD. Systematic nutritional assessment and individualised dietary counselling should be integrated into routine CKD care. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Chronic kidney disease nutritional status dietary intake appetite eGFR protein–energy wasting KDIGO India Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Chronic Kidney Disease (CKD) is defined as abnormalities of kidney structure or function, present for a minimum of three months, with implications for health. According to the KDIGO 2024 Clinical Practice Guideline, CKD diagnosis requires either a decreased estimated glomerular filtration rate (eGFR < 60 mL/min/1.73 m²) or markers of kidney damage irrespective of eGFR, including albuminuria, structural abnormalities on imaging, urine sediment abnormalities, electrolyte disturbances from tubular disorders, or histological abnormalities. CKD is classified by cause, GFR category (G1–G5), and albuminuria category (A1–A3)—the CGA framework—enabling risk stratification, prognostication, and individualized management ( 1 ). Undiagnosed early-stage disease remains a principal driver of the global CKD burden ( 2 ). Current estimates indicate that CKD affects approximately 674 million people globally (8–10% of the population), rising to approximately 850 million when acute kidney injury and undiagnosed dysfunction are included ( 3 , 4 ). About 78% of this burden falls on low- and middle-income countries, driven by rising rates of diabetes, hypertension, and ageing populations ( 4 , 5 ). In India, a recent systematic review and meta-analysis of 18 community-based studies reported a pooled CKD prevalence of 13.24% (95% CI: 10.52–16.22%) among adults, with a rising trend from 11.12% (2011–2017) to 16.38% (2018–2023) ( 6 ). CKD is projected to become a leading cause of premature mortality by 2040 ( 4 ). Crucially, patients in early stages ( 1 – 3 ) are frequently asymptomatic yet already vulnerable to nutritional deterioration, underscoring the importance of timely dietary assessment and intervention. Nutrition plays a central role in CKD progression and patient well-being ( 7 ). Protein–energy wasting (PEW), driven by poor appetite, uraemic inflammation, and dietary restrictions, is highly prevalent across all stages and is a strong independent predictor of morbidity and mortality ( 8 , 9 ). Micronutrient imbalances are equally common: impaired renal activation of vitamin D contributes to mineral and bone disorders, while reduced erythropoietin synthesis and limited iron absorption precipitate anaemia ( 10 , 11 ). Importantly, patients in early CKD may simultaneously face undernutrition from anorexia and overnutrition from calorie-dense but nutrient-poor diets ( 12 ). Indian dietary patterns are predominantly cereal–pulse based, with rice as the staple grain in Telangana and Hyderabad ( 13 , 14 ). While such patterns align with plant-based dietary principles, high cooking salt use, saturated fat intake, and sugary food consumption remain areas of concern specific to this population. Dietary Recommendations and Guidelines The KDIGO 2024 CKD Clinical Practice Guideline recommends a predominantly plant-based dietary pattern with individualised protein targets: 0.55–0.60 g/kg/day for metabolically stable non-diabetic adults at stages 3–5, 0.6–0.8 g/kg/day for those with diabetes, and 0.8–1.0 g/kg/day for stages 1–2 to maintain adequacy ( 1 ). The KDOQI 2020 Nutrition in CKD guidelines further specify that adequate energy intake of 25–35 kcal/kg/day is essential to prevent catabolism, with preference for complex carbohydrates including whole grains and millets ( 15 ). Sodium restriction (< 2 g sodium/day, equivalent to < 5 g salt/day) is universally recommended. Potassium and phosphorus restrictions are guided by individual biochemical profiles and should not be universally imposed. Micronutrient monitoring—particularly of vitamin D, iron, and vitamin B12—is essential across all stages ( 17 ). These principles align with the Dietary Guidelines for Indians (ICMR-NIN, 2024), supporting the feasibility of culturally adapted CKD nutrition strategies ( 16 ). Research Question and Objectives Despite increasing recognition of nutritional disturbances in early-stage CKD, limited evidence exists on the integrated relationship between dietary intake, nutritional status, appetite, and kidney function among Indian patients in non-dialysis stages 1–3. The present study was designed to address this gap. Primary Research Question What is the nutritional status and dietary intake profile of adults with early-stage CKD, and how are these factors associated with kidney function (eGFR)? Primary Hypothesis Patients with early-stage CKD exhibit suboptimal dietary intake and measurable nutritional impairment, and these nutritional indicators are significantly associated with kidney function (eGFR). Primary Outcome Association between nutritional indicators (dietary intake adequacy, appetite score, anthropometric and biochemical markers) and kidney function (eGFR category). Secondary Outcomes To assess the prevalence of malnutrition and appetite impairment; exploring dietary intake patterns relative to recommended targets and the relationship between biochemical markers and CKD severity. METHODOLOGY Study Design and Setting A hospital-based cross-sectional observational study was conducted between November 2024 and March 2025 in the outpatient nephrology departments of two nephrology-specialised tertiary care hospitals in Hyderabad, India. Only clinically stable ambulatory patients were enrolled. Hospitalised patients were excluded to avoid confounding from acute illness on nutritional status. This study was designed and reported in conformance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional studies. Study Population and Sample Size The study population comprised adults (≥ 18 years) with CKD stages 1–3, defined according to KDIGO 2024 guidelines ( 1 ) and classified using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Stage-specific eGFR thresholds applied were: Stage 1 (≥ 90 mL/min/1.73 m² with kidney damage markers), Stage 2 (60–89 mL/min/1.73 m²), Stage 3a (45–59 mL/min/1.73 m²), and Stage 3b (30–44 mL/min/1.73 m²). Both vegetarian and non-vegetarian participants were included. The sample size of 184 was justified for the study’s primary analytical objective: detecting a significant correlation between nutritional indicators and eGFR. Based on a prior study of nutritional status in non-dialysis CKD patients reporting a correlation coefficient of approximately r = 0.30 between SGA scores and kidney function indices, a sample size calculation using the Fisher z-transformation yielded a minimum of 112 participants to achieve 80% power at α = 0.05 (two-tailed)( 1 ). Adjusting for a 4% non-response rate and the exploratory multi-variable nature of the study, the final target was set at 184. This sample adequately powers the primary correlation and regression analyses conducted. Inclusion and Exclusion Criteria Inclusion criteria Adults aged 18–65 years; confirmed CKD stages 1–3 (non-dialysis-dependent); clinically stable and willing to provide written informed consent. Exclusion criteria CKD stages 4–5 (eGFR < 30 mL/min/1.73 m²) or end-stage renal disease; current dialysis or prior kidney transplantation; age 65 years; pregnancy or lactation; acute or chronic infection, malignancy, liver cirrhosis, advanced cardiac disease, severe azotaemia, dyselectrolytaemia, or malabsorptive disorders (e.g., inflammatory bowel disease, short bowel syndrome); adherence to a strict vegan diet. Ethics and Consent The study protocol was approved by the Institutional Ethics Committee of Symbiosis International (Deemed University) (Ref. no. SIU/IEC/719). The study was conducted in accordance with the Declaration of Helsinki. The trial was retrospectively registered with the Clinical Trials Registry of India (CTRI/2024/06/068985) on 18 June 2024. Written informed consent was obtained from all participants prior to enrolment. Data Collection Data were collected through structured questionnaire-based interviews administered by trained registered dietitians via face-to-face interaction after taking patients consent. Clinical records were cross-verified from hospital documentation. Anthropometry: Height and weight were measured using WHO-recommended standardised protocols; BMI was calculated. Body composition (fat mass, fat-free mass, visceral fat) was assessed using the Karada Scan bioelectrical impedance analyser. Biochemistry: Laboratory values including hemoglobin, serum creatinine (for eGFR calculation), serum urea, serum electrolytes, HbA1c, serum albumin, and spot urine albumin-to-creatinine ratio (UACR) were retrieved from hospital records within a maximum of four weeks of the interview date. Nutritional Status: Evaluated using the Subjective Global Assessment (SGA) tool ( 19 ). A standardised nutrition-focused physical examination (NFPE) assessing subcutaneous fat loss, muscle wasting, and oedema was performed by a trained dietitian ( 20 ). Appetite Assessment: Assessed using the Council on Nutrition Appetite Questionnaire (CNAQ), a validated eight-item instrument; scores ≥ 28 indicate good appetite and < 28 indicate poor appetite ( 21 ). Dietary Intake: Captured using a validated semi-quantitative Indian Food Frequency Questionnaire (FFQ) ( 22 ) and a single 24-hour dietary recall administered using the National Institute of Nutrition (NIN) multiple-pass methodology. Nutrient composition was analysed using the Indian Food Composition Tables (IFCT 2017) ( 23 ). Dietary recall is acknowledged to carry inherent recall bias and day-to-day variability, particularly for composite Indian dishes where nutrient composition varies by household preparation. Standard portion models and visual aids were used to improve estimation accuracy. Sociodemographic Data: Age, sex, education, occupation, income (Kuppuswamy scale, updated 2024) ( 24 ), and lifestyle practices (physical activity, tobacco, alcohol) were documented. Food Preparation Practices: Cooking methods, salt addition practices, and use of preserved or processed foods were assessed through structured probing questions. Pilot Testing Before initiating the main survey, the questionnaire was pilot tested on 15 CKD patients to evaluate clarity, cultural appropriateness, and feasibility of the tools (FFQ, 24-hour recall, SGA, CNAQ). Minor modifications were made to food portion examples and terminology based on participant and interviewer feedback. Pilot data were not included in the final analysis. The flow diagram is provided in Fig. 1 . Statistical Analysis Data were entered and validated in Microsoft Excel and exported to SPSS version 26.0 (IBM Corp., Armonk, NY, USA) for analysis. For analytical purposes, CKD stages were coded ordinally as 1 (Stage 1) through 4 (Stage 3b), such that higher values represent more advanced disease and lower estimated glomerular filtration rate (eGFR). Distribution normality was assessed using the Shapiro-Wilk test. Continuous variables are reported as mean ± standard deviation (SD) or median (interquartile range [IQR]) depending on distribution. Categorical variables are presented as frequencies and percentages. Group differences across CKD stages for normally distributed continuous variables were assessed by one-way ANOVA; non-normally distributed continuous variables (e.g., UACR) were assessed by the Kruskal–Wallis H test with post-hoc Dunn’s test. Categorical variables were compared using the chi-square test or Fisher’s exact test as appropriate. Bivariate associations between continuous variables were examined using Pearson’s correlation coefficient. Multivariable linear regression was performed with eGFR category as the dependent variable, adjusting for clinically relevant confounders (age, sex, BMI, hemoglobin, CNAQ score). Given the large number of statistical comparisons in the food-group frequency analyses, a Benjamini–Hochberg FDR correction was applied, and results with a q-value < 0.05 are considered significant. Statistical significance for primary and secondary analyses was set at p < 0.05. RESULTS Sociodemographic and Clinical Profile Among 184 participants (mean age 47.6 ± 12.1 years; 59.8% male), the distribution across CKD stages was: Stage 1 (n = 20, 10.9%), Stage 2 (n = 47, 25.5%), Stage 3a (n = 63, 34.2%), and Stage 3b (n = 54, 29.3%). The majority of participants were in the 41–60-year age group (46.2%). Most participants were married (95.7%), educated to graduation level or above (50.5%), and 73.9% consumed a non-vegetarian diet. A high proportion (76.5%) had at least one chronic comorbidity: 38% had both Type 2 Diabetes Mellitus (T2DM) and hypertension, 22.3% had hypertension alone, and 14.1% had T2DM alone. Sociodemographic and lifestyle characteristics are summarised in Table 1 . Table 1 Baseline sociodemographic and clinical characteristics of CKD participants (n = 184) Variable Category n (%) Age 18–30 years 13 (7.1) 31–40 years 54 (29.3) 41–60 years 85 (46.2) > 60 years 32 (17.4) Sex Male 110 (59.8) Female 74 (40.2) Diet Type Non-vegetarian 136 (73.9) Vegetarian 48 (26.1) Comorbidity T2DM + Hypertension 70 (38.0) Hypertension only 41 (22.3) T2DM only 26 (14.1) None 47 (25.5) Physical Activity 5 h/week 40 (21.7) CKD Stage Stage 1 (eGFR ≥ 90 mL/min/1.73 m²) 20 (10.9) Stage 2 (eGFR 60–89) 47 (25.5) Stage 3a (eGFR 45–59) 63 (34.2) Stage 3b (eGFR 30–44) 54 (29.3) Association Between CKD Stage and Sociodemographic, Lifestyle, and Medical History Variables Table 2 presents the association between CKD stages and key sociodemographic and lifestyle variables. Age showed a statistically significant relationship with CKD progression (p < 0.001), with individuals aged 41–60 years increasingly represented in more advanced stages, particularly stages 3a and 3b. Gender did not show a statistically significant association (p = 0.085), though males were more prevalent in earlier stages. Occupational status was significantly linked to CKD stage (p < 0.001): the proportion of unemployed individuals rose sharply in advanced stages. Income level (p < 0.001) and educational attainment (p < 0.001) showed inverse associations with CKD severity, with lower income and education levels concentrated in stages 3a and 3b. Regarding lifestyle variables, a significant association was found between diet type and CKD stage (p = 0.003), with non-vegetarians disproportionately represented in advanced stages. Tobacco use (p = 0.023) and alcohol consumption (p < 0.001) were significantly associated with CKD severity. The presence of multiple comorbidities; T2DM combined with hypertension was strongly associated with advanced CKD (p = 0.002). Family history of chronic diseases did not show a statistically significant association (p = 0.356). Table 2 Sociodemographic, lifestyle, and medical history variables by CKD stage (n = 184) Variable Category Stage 1 (n = 20) n (%) Stage 2 (n = 47) n (%) Stage 3a (n = 63) n (%) Stage 3b (n = 54) n (%) p-value Age 18–30 3 (15.0) 4 (8.5) 4 (6.3) 2 (3.7) < 0.001* 31–40 10 (50.0) 22 (46.8) 16 (25.4) 6 (11.1) 41–60 6 (30.0) 15 (31.9) 33 (52.4) 31 (57.4) Above 60 1 (5.0) 6 (12.8) 10 (15.9) 15 (27.8) Gender Male 16 (80.0) 29 (61.7) 39 (61.9) 26 (48.1) 0.085 Female 4 (20.0) 18 (38.3) 24 (38.1) 28 (51.9) Diet Type Vegetarian 12 (60.0) 9 (19.1) 16 (25.4) 11 (20.4) 0.003* Non-vegetarian 8 (40.0) 38 (80.9) 47 (74.6) 43 (79.6) Comorbidity T2DM only 4 (20.0) 7 (14.9) 13 (20.6) 2 (3.7) 0.002* Hypertension only 6 (30.0) 14 (29.8) 12 (19.0) 9 (16.7) Both 2 (10.0) 12 (25.5) 23 (36.5) 33 (61.1) None 8 (40.0) 14 (29.8) 15 (23.8) 10 (18.5) Alcohol intake Daily/Weekly 4 (20.0) 13 (27.7) 33 (52.4) 6 (11.1) < 0.001* Occasionally 8 (40.0) 13 (27.7) 13 (20.6) 21 (38.9) No/Don't recall 8 (40.0) 21 (44.7) 17 (27.0) 27 (50.0) Tobacco use Daily/Weekly 2 (10.0) 10 (21.3) 10 (15.9) 13 (24.1) 0.023* Occasionally 3 (15.0) 4 (8.5) 19 (30.2) 2 (3.7) No/Don't recall 15 (75.0) 33 (70.2) 34 (54.0) 39 (72.2) * p < 0.05. T2DM = Type 2 Diabetes Mellitus. Nutrition-Focused Physical Examination Physical examination revealed that 58% of participants showed some degree of fat depletion, most commonly in the upper arm (25%), thoracic/lumbar region (18%), and orbital region (15%). Muscle wasting was identified in 51% of participants, most prominently at the acromion (14%) and dorsal hand/interosseous region (14%), reflecting widespread protein–energy wasting even in this non-dialysis cohort. Oedema was present in 82% of participants, predominantly as pedal oedema (38%) and leg oedema (25%). Subjective Global Assessment (SGA) classified 66% of participants as well-nourished or at very mild risk (SGA-A), 32% as moderately malnourished (SGA-B), and 2% as severely malnourished (SGA-C).Approximately one-third of the early-stage CKD cohort showed significant nutritional impairment (Fig. 2 ). Dietary Intake and Nutritional Adequacy Mean energy intake was 28.4 ± 5.1 kcal/kg/day, which falls within the KDIGO 2024-recommended range of 25–35 kcal/kg/day. However, 22% of participants fell below the lower threshold (< 25 kcal/kg/day), placing them at risk for muscle catabolism. Mean protein intake was 0.82 ± 0.21 g/kg/day, which exceeds the KDIGO 2024 recommendation for stages 3–5 (0.55–0.60 g/kg/day) but remains within the acceptable range for stages 1–2 (0.8–1.0 g/kg/day). Stratified analysis revealed that Stage 3b patients had a mean protein intake of 0.73 ± 0.18 g/kg/day, approaching the lower limit of adequacy. Sixty-four percent of participants exceeded the recommended sodium intake (> 2 g/day or > 5 g salt/day). Sodium excess in this cohort predominantly reflected high cooking salt use and sodium-rich condiments (e.g., pickles, papads, commercial masala blends). Fifty-eight percent exceeded potassium thresholds. Association Between CKD Stage and Food Group Consumption Key findings include: (i) chickpea consumption declined significantly in Stage 3b (48.1% non-consumption, p = 0.005),(ii) intake of soft drinks, fresh fruit juices, and sweet snacks declined sharply in Stage 3b (p < 0.001 each) (iii) the ‘Others’ grain category showed significant variation (p = 0.021), and (iv) yoghurt consumption fluctuated across stages, with reduced intake in Stage 3a (44.4%) compared to Stage 3b (87%). After Benjamini–Hochberg FDR correction, associations with chickpeas, beverages (soft drinks, fruit juice), and sweet/bakery snacks remained statistically significant (q < 0.05). Detailed associations of CKD stage with food group consumption (cereals, pulses, vegetables, fruits, dairy, animal proteins, beverages, and snacks) are provided in Supplementary Tables S1–S8 (Supplementary file). Overall, rice, lentils, and dairy were common across all stages, while advanced CKD participants showed reduced intake of potassium-rich fruits/vegetables and processed foods. Association Between CKD Stage and Biochemical Parameters Table 3 presents key biochemical markers stratified by CKD stage. The urine albumin-to-creatinine ratio (UACR) showed a highly significant progressive increase from Stage 1 (31.71 ± 13.12 mg/g) to Stage 3b (111.00 ± 94.20 mg/g) (F = 7.852, p < 0.001). HbA1c was significantly associated with CKD stage (F = 2.697, p = 0.047), with higher values in Stages 3a (6.51%) and 3b (6.26%). Hemoglobin showed a declining trend (Stage 1: 12.87 ± 1.49 g/dL; Stage 3b: 11.35 ± 1.93 g/dL) that did not reach statistical significance (p = 0.222). Uric acid demonstrated a non-significant increasing trend across stages (p = 0.515). Table 3 Association between CKD stages and biochemical parameters Variable eGFR Category n Mean ± SD F-value p-value Hemoglobin (g/dL) Stage 1 20 12.87 ± 1.49 1.477 0.222 Stage 2 47 12.54 ± 1.81 Stage 3a 63 11.24 ± 2.01 Stage 3b 54 11.35 ± 1.93 UACR (mg/g) Stage 1 20 31.71 ± 13.12 7.852 < 0.001* Stage 2 47 39.13 ± 14.43 Stage 3a 63 81.68 ± 114.90 Stage 3b 54 111.00 ± 94.20 Uric Acid (mg/dL) Stage 1 20 6.31 ± 0.73 0.765 0.515 Stage 2 47 6.60 ± 1.50 Stage 3a 63 6.65 ± 1.28 Stage 3b 54 6.80 ± 1.13 HbA1c (%) Stage 1 20 5.58 ± 0.60 2.697 0.047* Stage 2 47 6.03 ± 1.49 Stage 3a 63 6.51 ± 1.70 Stage 3b 54 6.26 ± 0.97 * p < 0.05; UACR = urine albumin-to-creatinine ratio. Group differences assessed by one-way ANOVA (normally distributed variables) or Kruskal–Wallis H test (UACR). Appetite Status and Dietary Restriction Patterns Across CKD Stages Appetite assessment using the CNAQ revealed a progressive decline with advancing CKD stage. In Stage 2, 28 participants had good appetite versus 19 with poor appetite. This ratio reversed in Stage 3a (18 good vs. 45 poor) and remained unfavourable in Stage 3b (15 good vs. 39 poor). These findings establish appetite loss as an early and clinically important manifestation of nutritional risk, even before Stage 3b. Dietary restriction burden escalated with disease stage: from minimal restriction in Stage 1 (no restriction, n = 7; low-protein only, n = 4) to complex multi-component regimens in Stage 3b (low-protein + sodium + potassium, n = 18). The increasing dietary burden in later stages contributes to appetite fatigue and compromised dietary adherence (Fig. 3 ). Correlations Between Nutritional and Clinical Variables with eGFR Category Pearson correlation analysis demonstrated that advancing age (r = + 0.34, p < 0.001) and the presence of chronic comorbidities (r = + 0.28, p < 0.001) were positively associated with a more advanced CKD stage. Higher occupational status (r = − 0.35), income (r = − 0.374), and educational attainment (r = − 0.25) were negatively associated with eGFR category (all p < 0.001). Haemoglobin was negatively correlated with eGFR category (r = − 0.42, p < 0.001). Urinary albumin-to-creatinine ratio (UACR) was positively associated with eGFR category (r = + 0.37, p < 0.001). HbA1c showed a positive association with eGFR category (r = + 0.29, p = 0.003). CNAQ appetite score (r = 0.15, p = 0.042) and dietary preference/restriction score (r = 0.224, p = 0.002) were positively correlated with eGFR category. Among dietary variables, chicken (r = + 0.171, p = 0.021), eggs (r = + 0.206, p = 0.005), and mutton (r = + 0.162, p = 0.028) showed modest positive associations with more advanced CKD stages. Full correlation results are presented in Table 4 . Table 4 Pearson correlation of demographic, lifestyle, dietary, and nutritional variables with eGFR category Variable Category r p-value Demographic Age + 0.340 < 0.001* Gender + 0.174 0.018* Occupational Status −0.346 < 0.001* Income range −0.374 < 0.001* Education −0.251 < 0.001* Family Members −0.043 0.563 Lifestyle & Medical History Diet Type + 0.170 0.021* Physical Activity −0.136 0.066 Tobacco use + 0.070 0.347 Alcohol intake −0.034 0.651 Chronic Medical Condition + 0.280 < 0.001* Family History −0.091 0.221 Biochemical Haemoglobin −0.420 < 0.001* UACR + 0.370 < 0.001* HbA1c + 0.290 0.003* Animal Protein Intake Chicken + 0.171 0.021* Fish + 0.063 0.394 Eggs + 0.206 0.005* Mutton + 0.162 0.028* Appetite & Dietary CNAQ Score + 0.150 0.042* Dietary Preferences/Restrictions + 0.224 0.002* * p < 0.05; r = Pearson correlation coefficient. eGFR category coded 1 (Stage 1, best kidney function) to 4 (Stage 3b, worst kidney function). A positive r indicates association with more advanced CKD (declining eGFR); a negative r indicates association with better-preserved kidney function. Multivariable Linear Regression: Independent Predictors of eGFR Category Multivariable linear regression with eGFR category as the dependent variable and age, sex, body mass index (BMI), haemoglobin, and CNAQ score as independent predictors yielded an adjusted R² = 0.41, F(5, 178) = 34.7, p < 0.001, indicating that the model explained 41% of the variance in CKD stage severity. Haemoglobin and CNAQ score emerged as the strongest modifiable predictors of kidney function. Older age (β = +0.21, p < 0.001) and lower appetite as measured by CNAQ score (β = +0.19, p = 0.003) were independently associated with more advanced CKD stages. Conversely, higher haemoglobin (β = −0.31, p < 0.001) and higher BMI (β = −0.18, p = 0.019) were independently associated with less advanced disease. Sex was not a significant independent predictor (β = +0.08, p = 0.186). Individual predictor results are presented in Table 5 . Table 5 Multivariable linear regression—independent predictors of eGFR category (n = 184) Predictor B (Unstd.) SE Beta (Std.) 95% CI p-value Age (years) 0.042 0.011 + 0.210 0.020, 0.064 < 0.001* Sex (male = 1) 0.118 0.089 + 0.082 −0.058, 0.294 0.186 BMI (kg/m²) −0.038 0.016 −0.180 −0.070, − 0.006 0.019* Hemoglobin (g/dL) −0.185 0.034 −0.310 −0.252, − 0.118 < 0.001* CNAQ Score 0.041 0.013 + 0.190 0.015, 0.067 0.003* * p < 0.05; B = unstandardised coefficient; SE = standard error; Beta = standardised coefficient; CI = confidence interval. eGFR category coded 1 (Stage 1, best kidney function) to 4 (Stage 3b, worst kidney function). A positive Beta indicates association with more advanced CKD (declining eGFR); a negative Beta indicates association with better-preserved kidney function. Adjusted R² = 0.41, F (5,178) = 34.7, p < 0.001. DISCUSSION This hospital-based cross-sectional study provides one of the first comprehensive integrative assessments of nutritional status, dietary intake, appetite, and biochemical markers among non-dialysis-dependent CKD patients (stages 1–3) in Hyderabad, India. The primary hypothesis — that early-stage CKD patients exhibit suboptimal nutritional status significantly associated with kidney function — was confirmed. Approximately one-third of participants were malnourished by SGA criteria, over half demonstrated measurable muscle or fat loss, and dietary patterns were frequently inconsistent with KDIGO 2024 recommendations. These disturbances were present across all stages, reinforcing the case for early nutritional intervention. The finding that 64% of participants exceeded sodium limits and 58% exceeded potassium limits despite adequate overall energy intake further underscores that dietary quality, not quantity alone, is compromised in this population. Multivariable regression identified age, haemoglobin, BMI, and CNAQ appetite score as independent predictors of eGFR category (adjusted R² = 0.41), with haemoglobin and appetite emerging as the strongest modifiable predictors. The association between lower haemoglobin and more advanced CKD is biologically plausible: erythropoietin deficiency, iron sequestration due to chronic inflammation, and reduced oral intake of iron-rich foods collectively drive anaemia as a consequence and amplifier of kidney function decline ( 11 ). Consistent with this, Pearson correlation analysis confirmed a significant negative association between haemoglobin and eGFR category (r = − 0.42, p < 0.001), indicating that lower haemoglobin corresponded with more advanced CKD stages across the cohort. The independent role of the CNAQ score extends prior evidence from haemodialysis populations to the critical and underexplored early CKD window, suggesting that appetite monitoring should be incorporated into routine outpatient nephrology care from the earliest stages. The positive correlation between CNAQ score and eGFR category (r = + 0.15, p = 0.042) observed in this study supports this, confirming that reduced appetite is associated with more advanced disease even at stages 1–3. The finding that 64% of participants exceeded the recommended sodium limit is consistent with South Asian dietary patterns characterised by high cooking salt and sodium-rich condiments (pickles, chutneys, papads) ( 14 ). In contrast to Western cohorts where processed food drives sodium excess, the primary source in this population was home cooking. Interventions targeting cooking salt reduction and use of low-sodium condiments are likely to yield greater impact than processed food avoidance alone. Evidence from South Asia supports that a 1 g/day reduction in sodium intake is achievable through behavioural counselling without compromising cultural acceptability ( 18 ). The high prevalence of potassium exceedance (58%) similarly warrants attention, particularly given the positive association between dietary restriction score and eGFR category (r = + 0.22, p = 0.002), suggesting that patients with more advanced disease are already self-restricting yet remain non-adherent to recommended limits. The high oedema prevalence (82%) in this early-stage cohort requires careful contextualisation. Oedema at stages 1–3 most likely reflects comorbidity-driven mechanisms — sodium and water retention from hypertension and diabetic nephropathy-associated hypoalbuminaemia — rather than uraemic fluid overload, which predominates in stages 4–5. Clinicians should interpret oedema in the context of comorbidity burden and serum albumin, rather than as an intrinsic early-CKD phenomenon. The significant positive association between chronic comorbidity burden and eGFR category (r = + 0.28, p < 0.001) observed in this cohort further supports this contextualisation, confirming that comorbidity accumulation tracks closely with advancing CKD stage. Socioeconomic determinants strongly shaped CKD severity in this cohort. Lower income, unemployment, and limited educational attainment were significantly associated with more advanced CKD stages, consistent with the international literature characterising CKD as a disease of inequity ( 25 , 26 ). Specifically, higher occupational status (r = − 0.35), greater income (r = − 0.37), and higher educational attainment (r = − 0.25) were each negatively associated with eGFR category (all p < 0.001), indicating that socioeconomic advantage is linked to less advanced disease in this population. Findings from Ghana underscore the need for region-specific contextualisation. In the Indian setting, structural factors including out-of-pocket healthcare expenditure, delayed health-seeking behaviour, and limited nutritional knowledge in lower socioeconomic groups represent modifiable targets for public health policy ( 27 ). Vegetarian dietary patterns were positively associated with better kidney function outcomes (p = 0.003). The modest but significant positive associations observed between animal protein sources — chicken (r = + 0.17, p = 0.021), eggs (r = + 0.21, p = 0.005), and mutton (r = + 0.16, p = 0.028) — and more advanced CKD stages are consistent with this direction, though causality cannot be inferred from this cross-sectional design. Evidence from the CRIC prospective cohort study shows that adherence to plant-based dietary patterns is associated with significantly lower risk of CKD progression and all-cause mortality ( 28 ). This supports a shift from generalised protein restriction to quality-driven dietary planning emphasising plant-forward eating, which confers acid-buffering benefits through higher dietary alkali load and reduced phosphorus bioavailability compared to animal-source foods ( 31 , 32 ). The progressive deterioration of glycaemic control (HbA1c) and proteinuria (UACR) across CKD stages reaffirms the synergistic role of diabetes and hypertension in nephropathy progression ( 29 ). The positive associations of both UACR (r = + 0.37, p < 0.001) and HbA1c (r = + 0.29, p = 0.003) with eGFR category observed in this cohort confirm that greater albuminuria and poorer glycaemic control co-occur with more advanced CKD stages, consistent with their established roles as markers of nephropathy severity. While uric acid and haemoglobin did not achieve statistical significance across stages, the observed trends align with established mechanisms of hyperuricaemia-mediated tubular injury and CKD-associated anaemia and are clinically relevant ( 30 ). Strengths and Limitations Key strengths include the comprehensive multidimensional approach incorporating dietary, anthropometric, biochemical, and clinical nutritional indicators; use of multiple validated tools (SGA, CNAQ, FFQ, 24-hour recall, NFPE); enrolment from two tertiary care hospitals providing clinical diversity; and an exclusive focus on the underexplored early non-dialysis CKD population. Reporting adhered to the STROBE checklist. Limitations include the cross-sectional design, which precludes causal inference. Dietary assessments relied on a single 24-hour dietary recall combined with an FFQ which may be subject to recall bias and portion estimation error, particularly for composite Indian dishes. Future studies should consider using multiple repeated 24 hours dietary recalls to capture day to day dietary variability. The hospital-based sampling frame may over-represent more advanced or comorbid patients, limiting generalisability. Exclusion of those > 65 years and strict vegans restrict applicability to those groups. The moderate sample size limits reliable subgroup analyses by stage and sex. Multiple comparisons in food-group analyses were mitigated by FDR correction, though residual type I error risk cannot be excluded. Future Directions Longitudinal cohort studies are needed to determine whether early nutritional intervention—particularly appetite support and sodium reduction—can meaningfully slow eGFR decline in Indian CKD patients. Randomised controlled trials testing culturally adapted plant-forward dietary interventions (e.g., incorporating millets, pulses, and regionally available vegetables in place of refined grains and high-salt condiments) are a priority. Integration of digital health tools for real-time dietary monitoring may enhance adherence in resource-constrained nephrology settings. CONCLUSION Adults with early-stage non-dialysis-dependent CKD in Hyderabad, India exhibit clinically significant nutritional impairment, including suboptimal energy and protein intake, high sodium and potassium intake, reduced appetite, emerging anaemia, and measurable protein–energy wasting—even in stages 1–3 before advanced uraemia develops. Age, hemoglobin, BMI, and appetite score independently predicted eGFR category, explaining 41% of its variance. These findings confirm that nutritional decline begins early in the CKD trajectory and that nutritional indicators are meaningful clinical markers of kidney function status. Integrating systematic nutritional assessment—using validated tools such as SGA, CNAQ, and dietitian-led dietary recall—into routine early CKD outpatient management is not merely an adjunct but a clinical necessity. Individualized dietary counselling must address cooking salt reduction, protein quality optimisation, and the promotion of plant-forward dietary patterns adapted to regional culinary practices. Addressing socioeconomic determinants and providing structured nutrition education are equally essential for equitable CKD outcomes. This study provides an evidence base for integrated nutritional care in the Indian nephrology setting and supports development of locally contextualised CKD dietary guidelines. Abbreviations CKD Chronic Kidney Disease eGFR Estimated Glomerular Filtration Rate KDIGO Kidney Disease:Improving Global Outcomes KDOQI Kidney Disease Outcomes Quality Initiative FFQ Food Frequency Questionnaire SGA Subjective Global Assessment CNAQ Council on Nutrition Appetite Questionnaire NFPE Nutrition-Focused Physical Examination BMI Body Mass Index UACR Urine Albumin-to-Creatinine Ratio PEW Protein–Energy Wasting T2DM Type 2 Diabetes Mellitus HbA1c Glycated Haemoglobin NIN National Institute of Nutrition IFCT Indian Food Composition Tables FDR False Discovery Rate SD Standard Deviation IQR Interquartile Range SPSS Statistical Package for the Social Sciences CTRI Clinical Trials Registry of India STROBE Strengthening the Reporting of Observational Studies in Epidemiology EWS Economically Weaker Section MIG Middle Income Group LIG Lower Income Group Declarations Ethics approval and consent to participate: Approved by the Institutional Ethics Committee of Symbiosis International (Deemed University) (Ref. no. SIU/IEC/719) and conducted in accordance with the Declaration of Helsinki. Trial registered with CTRI (CTRI/2024/06/068985) on 18 June 2024. Written informed consent was obtained from all participants. Consent for publication: Not applicable. Availability of data and materials: The dataset supporting the conclusions of this article is included within the article and its additional file. Competing interests: The authors declare no competing interests. Funding: This research received no specific funding from any public, commercial, or not-for-profit agency. Authors’ contributions: AG and VPB conducted data collection, data analysis, and drafted the manuscript. AM conceptualised and supervised the study. AG and AM critically reviewed the manuscript. AM approved the final version. All authors read and approved the submitted manuscript. Acknowledgements: The authors gratefully acknowledge the support of nephrology staff and registered dietitians at the participating hospitals in Hyderabad, and all patients who volunteered for this study. References Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117–S314. Shlipak MG, Tummalapalli SL, Boulware LE, Grams ME, Ix JH, Jha V, et al. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2021 Jan;99(1):34–47. Guo J, Jiao W, Xia S, Xiang X, Zhang Y, Ge X, et al. The global, regional, and national patterns of change in the burden of chronic kidney disease from 1990 to 2021. BMC Nephrol. 2025 Mar 13;26(1):136. Francis A, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, et al. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol. 2024 July;20(7):473–85. Saran R, Robinson B, Abbott KC, Bragg-Gresham J, Chen X, Gipson D, et al. US Renal Data System 2019 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2020 Jan;75(1):A6–7. Talukdar R, Ajayan R, Gupta S, Biswas S, Parveen M, Sadhukhan D, et al. Chronic Kidney Disease Prevalence in India: A Systematic Review and Meta‐Analysis From Community‐Based Representative Evidence Between 2011 to 2023. Nephrology. 2025 Jan;30(1):e14420. Kistler BM, Moore LW, Benner D, Biruete A, Boaz M, Brunori G, et al. The International Society of Renal Nutrition and Metabolism Commentary on the National Kidney Foundation and Academy of Nutrition and Dietetics KDOQI Clinical Practice Guideline for Nutrition in Chronic Kidney Disease. J Ren Nutr. 2021 Mar;31(2):116-120.e1. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. 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Factors influencing dietary patterns among the youth from higher educational institutions in India. Front Nutr. 2023 Mar 28;10:1134455. Durga AV, Manorenj S. Dietary pattern in Adult Patients with Acute Stroke in South India: A Case-Control Study from a Tertiary Care Center in Hyderabad. J Neurosci Rural Pract. 2019 Apr;10(02):283–93. Liu D, Li Q, Jia R, He W, Zhao X, Pan M, et al. Type 2 diabetes mellitus with chronic kidney disease benefits from long-term restriction of dietary protein intake: a 10-year retrospective cohort study. BMC Nutr. 2025 July 5;11(1):131. Indian Council of Medical Research – National Institute of Nutrition (ICMR-NIN). Dietary Guidelines for Indians 2024. Hyderabad: ICMR-NIN; 2024. Table of Contents for National Kidney Foundation 2017 Spring Clinical Meetings Abstracts, April 18 – 22, 2017. J Ren Nutr. 2017 Mar;27(2):140–1. Rhee CM, Wang AYM, Biruete A, Kistler B, Kovesdy CP, Zarantonello D, et al. Nutritional and Dietary Management of Chronic Kidney Disease Under Conservative and Preservative Kidney Care Without Dialysis. J Ren Nutr. 2023 Nov;33(6):S56–66. Chan M, Kelly J, Batterham M, Tapsell L. Malnutrition (Subjective Global Assessment) Scores and Serum Albumin Levels, but not Body Mass Index Values, at Initiation of Dialysis are Independent Predictors of Mortality: A 10-Year Clinical Cohort Study. J Ren Nutr. 2012 Nov;22(6):547–57. Hummell AC, Cummings M. Role of the nutrition‐focused physical examination in identifying malnutrition and its effectiveness. Nutr Clin Pract. 2022 Feb;37(1):41–9. Wilson MMG, Thomas DR, Rubenstein LZ, Chibnall JT, Anderson S, Baxi A, et al. Appetite assessment: simple appetite questionnaire predicts weight loss in community-dwelling adults and nursing home residents. Am J Clin Nutr. 2005 Nov;82(5):1074–81. Sudha V, Radhika G, Sathya RM, Ganesan A, Mohan >V. Reproducibility and validity of an interviewer-administered semi-quantitative food frequency questionnaire to assess dietary intake of urban adults in southern India. Int J Food Sci Nutr. 2006 Jan;57(7–8):481–93. Longvah T, An̲antan̲ I, Bhaskarachary K, Venkaiah K. Indian food composition tables. Hyderabad, Telangana State, India: National Institute of Nutrition, Indian Council of Medical Research; 2017. 536 p. Mandal I, Hossain SR. Update of modified Kuppuswamy scale for the year 2024. Int J Community Med Public Health. 2024 June 28;11(7):2945–6. Grant CH, Salim E, Lees JS, Stevens KI. Deprivation and chronic kidney disease—a review of the evidence. Clin Kidney J. 2023 June 30;16(7):1081–91. Ishimura N, Inoue K, Maruyama S, Nakamura S, Kondo N. Income Level and Impaired Kidney Function Among Working Adults in Japan. JAMA Health Forum. 2024 Mar 1;5(3):e235445. Adjei DN, Stronks K, Adu D, Beune E, Meeks K, Smeeth L, et al. Cross-sectional study of association between socioeconomic indicators and chronic kidney disease in rural–urban Ghana: the RODAM study. BMJ Open. 2019 May;9(5):e022610. Zhang L, Wang F, Wang L, Wang W, Liu B, Liu J, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. The Lancet. 2012 Mar;379(9818):815–22. Kistler BM, Moore LW, Benner D, Biruete A, Boaz M, Brunori G, et al. The International Society of Renal Nutrition and Metabolism Commentary on the National Kidney Foundation and Academy of Nutrition and Dietetics KDOQI Clinical Practice Guideline for Nutrition in Chronic Kidney Disease. J Ren Nutr. 2021 Mar;31(2):116-120.e1. Kang DH, Nakagawa T. Uric acid and chronic renal disease: Possible implication of hyperuricemia on progression of renal disease. Semin Nephrol. 2005 Jan;25(1):43–9. Carrero JJ, González-Ortiz A, Avesani CM, Bakker SJL, Bellizzi V, Chauveau P, et al. Plant-based diets to manage the risks and complications of chronic kidney disease. Nat Rev Nephrol. 2020 Sept;16(9):525–42. Joshi S, Hashmi S, Shah S, Kalantar-Zadeh K. Plant-based diets for prevention and management of chronic kidney disease: Curr Opin Nephrol Hypertens. 2020 Jan;29(1):16–21. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx TableofContent.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 07 May, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 11 Apr, 2026 Submission checks completed at journal 11 Apr, 2026 First submitted to journal 10 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9382051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9382051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109470586,"identity":"415a28c5-191e-4f24-9165-69671b6e4bde","added_by":"auto","created_at":"2026-05-18 13:01:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282239,"visible":true,"origin":"","legend":"\u003cp\u003edisplays the flow diagram of the research plan.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9382051/v1/594a418222b7ff884d1e823f.png"},{"id":109470588,"identity":"d701de00-d1cd-4975-97ba-6b9e1f320da3","added_by":"auto","created_at":"2026-05-18 13:01:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":549930,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of muscle/fat loss, edema, and Subjective Global Assessment (SGA) categories in CKD patients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9382051/v1/f7ff9707589bfe635016e694.png"},{"id":109470590,"identity":"1a364b46-3319-447d-9ba3-9850e204003c","added_by":"auto","created_at":"2026-05-18 13:01:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186946,"visible":true,"origin":"","legend":"\u003cp\u003eAppetite status (CNAQ Scores) and dietary restriction patterns across CKD stages\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9382051/v1/aba61a2f5cc7409f5fc1f8b0.png"},{"id":109906779,"identity":"f8fc13e7-c93e-4dc3-87ab-4343d8b446db","added_by":"auto","created_at":"2026-05-25 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13:01:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":56301,"visible":true,"origin":"","legend":"","description":"","filename":"TableofContent.docx","url":"https://assets-eu.researchsquare.com/files/rs-9382051/v1/5293f735e0018b354b508350.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional Assessment and Dietary Intake Patterns in Early-Stage Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChronic Kidney Disease (CKD) is defined as abnormalities of kidney structure or function, present for a minimum of three months, with implications for health. According to the KDIGO 2024 Clinical Practice Guideline, CKD diagnosis requires either a decreased estimated glomerular filtration rate (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;) or markers of kidney damage irrespective of eGFR, including albuminuria, structural abnormalities on imaging, urine sediment abnormalities, electrolyte disturbances from tubular disorders, or histological abnormalities. CKD is classified by cause, GFR category (G1\u0026ndash;G5), and albuminuria category (A1\u0026ndash;A3)\u0026mdash;the CGA framework\u0026mdash;enabling risk stratification, prognostication, and individualized management (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Undiagnosed early-stage disease remains a principal driver of the global CKD burden (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrent estimates indicate that CKD affects approximately 674\u0026nbsp;million people globally (8\u0026ndash;10% of the population), rising to approximately 850\u0026nbsp;million when acute kidney injury and undiagnosed dysfunction are included (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). About 78% of this burden falls on low- and middle-income countries, driven by rising rates of diabetes, hypertension, and ageing populations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In India, a recent systematic review and meta-analysis of 18 community-based studies reported a pooled CKD prevalence of 13.24% (95% CI: 10.52\u0026ndash;16.22%) among adults, with a rising trend from 11.12% (2011\u0026ndash;2017) to 16.38% (2018\u0026ndash;2023) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). CKD is projected to become a leading cause of premature mortality by 2040 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Crucially, patients in early stages (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) are frequently asymptomatic yet already vulnerable to nutritional deterioration, underscoring the importance of timely dietary assessment and intervention.\u003c/p\u003e \u003cp\u003eNutrition plays a central role in CKD progression and patient well-being (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Protein\u0026ndash;energy wasting (PEW), driven by poor appetite, uraemic inflammation, and dietary restrictions, is highly prevalent across all stages and is a strong independent predictor of morbidity and mortality (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Micronutrient imbalances are equally common: impaired renal activation of vitamin D contributes to mineral and bone disorders, while reduced erythropoietin synthesis and limited iron absorption precipitate anaemia (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Importantly, patients in early CKD may simultaneously face undernutrition from anorexia and overnutrition from calorie-dense but nutrient-poor diets (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Indian dietary patterns are predominantly cereal\u0026ndash;pulse based, with rice as the staple grain in Telangana and Hyderabad (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). While such patterns align with plant-based dietary principles, high cooking salt use, saturated fat intake, and sugary food consumption remain areas of concern specific to this population.\u003c/p\u003e\n\u003ch3\u003eDietary Recommendations and Guidelines\u003c/h3\u003e\n\u003cp\u003eThe KDIGO 2024 CKD Clinical Practice Guideline recommends a predominantly plant-based dietary pattern with individualised protein targets: 0.55\u0026ndash;0.60 g/kg/day for metabolically stable non-diabetic adults at stages 3\u0026ndash;5, 0.6\u0026ndash;0.8 g/kg/day for those with diabetes, and 0.8\u0026ndash;1.0 g/kg/day for stages 1\u0026ndash;2 to maintain adequacy (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The KDOQI 2020 Nutrition in CKD guidelines further specify that adequate energy intake of 25\u0026ndash;35 kcal/kg/day is essential to prevent catabolism, with preference for complex carbohydrates including whole grains and millets (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Sodium restriction (\u0026lt;\u0026thinsp;2 g sodium/day, equivalent to \u0026lt;\u0026thinsp;5 g salt/day) is universally recommended. Potassium and phosphorus restrictions are guided by individual biochemical profiles and should not be universally imposed. Micronutrient monitoring\u0026mdash;particularly of vitamin D, iron, and vitamin B12\u0026mdash;is essential across all stages (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These principles align with the Dietary Guidelines for Indians (ICMR-NIN, 2024), supporting the feasibility of culturally adapted CKD nutrition strategies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Question and Objectives\u003c/h2\u003e \u003cp\u003eDespite increasing recognition of nutritional disturbances in early-stage CKD, limited evidence exists on the integrated relationship between dietary intake, nutritional status, appetite, and kidney function among Indian patients in non-dialysis stages 1\u0026ndash;3. The present study was designed to address this gap.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrimary Research Question\u003c/strong\u003e \u003cp\u003eWhat is the nutritional status and dietary intake profile of adults with early-stage CKD, and how are these factors associated with kidney function (eGFR)?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrimary Hypothesis\u003c/strong\u003e \u003cp\u003ePatients with early-stage CKD exhibit suboptimal dietary intake and measurable nutritional impairment, and these nutritional indicators are significantly associated with kidney function (eGFR).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrimary Outcome\u003c/strong\u003e \u003cp\u003eAssociation between nutritional indicators (dietary intake adequacy, appetite score, anthropometric and biochemical markers) and kidney function (eGFR category).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSecondary Outcomes\u003c/strong\u003e \u003cp\u003eTo assess the prevalence of malnutrition and appetite impairment; exploring dietary intake patterns relative to recommended targets and the relationship between biochemical markers and CKD severity.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eA hospital-based cross-sectional observational study was conducted between November 2024 and March 2025 in the outpatient nephrology departments of two nephrology-specialised tertiary care hospitals in Hyderabad, India. Only clinically stable ambulatory patients were enrolled. Hospitalised patients were excluded to avoid confounding from acute illness on nutritional status. This study was designed and reported in conformance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional studies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Sample Size\u003c/h3\u003e\n\u003cp\u003eThe study population comprised adults (\u0026ge;\u0026thinsp;18 years) with CKD stages 1\u0026ndash;3, defined according to KDIGO 2024 guidelines (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and classified using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Stage-specific eGFR thresholds applied were: Stage 1 (\u0026ge;\u0026thinsp;90 mL/min/1.73 m\u0026sup2; with kidney damage markers), Stage 2 (60\u0026ndash;89 mL/min/1.73 m\u0026sup2;), Stage 3a (45\u0026ndash;59 mL/min/1.73 m\u0026sup2;), and Stage 3b (30\u0026ndash;44 mL/min/1.73 m\u0026sup2;). Both vegetarian and non-vegetarian participants were included.\u003c/p\u003e \u003cp\u003eThe sample size of 184 was justified for the study\u0026rsquo;s primary analytical objective: detecting a significant correlation between nutritional indicators and eGFR. Based on a prior study of nutritional status in non-dialysis CKD patients reporting a correlation coefficient of approximately r\u0026thinsp;=\u0026thinsp;0.30 between SGA scores and kidney function indices, a sample size calculation using the Fisher z-transformation yielded a minimum of 112 participants to achieve 80% power at α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed)(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Adjusting for a 4% non-response rate and the exploratory multi-variable nature of the study, the final target was set at 184. This sample adequately powers the primary correlation and regression analyses conducted.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003e Adults aged 18\u0026ndash;65 years; confirmed CKD stages 1\u0026ndash;3 (non-dialysis-dependent); clinically stable and willing to provide written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003eCKD stages 4\u0026ndash;5 (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u0026sup2;) or end-stage renal disease; current dialysis or prior kidney transplantation; age\u0026thinsp;\u0026lt;\u0026thinsp;18 or \u0026gt;\u0026thinsp;65 years; pregnancy or lactation; acute or chronic infection, malignancy, liver cirrhosis, advanced cardiac disease, severe azotaemia, dyselectrolytaemia, or malabsorptive disorders (e.g., inflammatory bowel disease, short bowel syndrome); adherence to a strict vegan diet.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthics and Consent\u003c/h2\u003e \u003cp\u003eThe study protocol was approved by the Institutional Ethics Committee of Symbiosis International (Deemed University) (Ref. no. SIU/IEC/719). The study was conducted in accordance with the Declaration of Helsinki. The trial was retrospectively registered with the Clinical Trials Registry of India (CTRI/2024/06/068985) on 18 June 2024. Written informed consent was obtained from all participants prior to enrolment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected through structured questionnaire-based interviews administered by trained registered dietitians via face-to-face interaction after taking patients consent. Clinical records were cross-verified from hospital documentation.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAnthropometry: Height and weight were measured using WHO-recommended standardised protocols; BMI was calculated. Body composition (fat mass, fat-free mass, visceral fat) was assessed using the Karada Scan bioelectrical impedance analyser.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBiochemistry: Laboratory values including hemoglobin, serum creatinine (for eGFR calculation), serum urea, serum electrolytes, HbA1c, serum albumin, and spot urine albumin-to-creatinine ratio (UACR) were retrieved from hospital records within a maximum of four weeks of the interview date.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNutritional Status: Evaluated using the Subjective Global Assessment (SGA) tool (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A standardised nutrition-focused physical examination (NFPE) assessing subcutaneous fat loss, muscle wasting, and oedema was performed by a trained dietitian (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAppetite Assessment: Assessed using the Council on Nutrition Appetite Questionnaire (CNAQ), a validated eight-item instrument; scores\u0026thinsp;\u0026ge;\u0026thinsp;28 indicate good appetite and \u0026lt;\u0026thinsp;28 indicate poor appetite (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDietary Intake: Captured using a validated semi-quantitative Indian Food Frequency Questionnaire (FFQ) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and a single 24-hour dietary recall administered using the National Institute of Nutrition (NIN) multiple-pass methodology. Nutrient composition was analysed using the Indian Food Composition Tables (IFCT 2017) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Dietary recall is acknowledged to carry inherent recall bias and day-to-day variability, particularly for composite Indian dishes where nutrient composition varies by household preparation. Standard portion models and visual aids were used to improve estimation accuracy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSociodemographic Data: Age, sex, education, occupation, income (Kuppuswamy scale, updated 2024) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and lifestyle practices (physical activity, tobacco, alcohol) were documented.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFood Preparation Practices: Cooking methods, salt addition practices, and use of preserved or processed foods were assessed through structured probing questions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003ePilot Testing\u003c/h3\u003e\n\u003cp\u003eBefore initiating the main survey, the questionnaire was pilot tested on 15 CKD patients to evaluate clarity, cultural appropriateness, and feasibility of the tools (FFQ, 24-hour recall, SGA, CNAQ). Minor modifications were made to food portion examples and terminology based on participant and interviewer feedback. Pilot data were not included in the final analysis. The flow diagram is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were entered and validated in Microsoft Excel and exported to SPSS version 26.0 (IBM Corp., Armonk, NY, USA) for analysis. For analytical purposes, CKD stages were coded ordinally as 1 (Stage 1) through 4 (Stage 3b), such that higher values represent more advanced disease and lower estimated glomerular filtration rate (eGFR). Distribution normality was assessed using the Shapiro-Wilk test. Continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range [IQR]) depending on distribution. Categorical variables are presented as frequencies and percentages.\u003c/p\u003e \u003cp\u003eGroup differences across CKD stages for normally distributed continuous variables were assessed by one-way ANOVA; non-normally distributed continuous variables (e.g., UACR) were assessed by the Kruskal\u0026ndash;Wallis H test with post-hoc Dunn\u0026rsquo;s test. Categorical variables were compared using the chi-square test or Fisher\u0026rsquo;s exact test as appropriate. Bivariate associations between continuous variables were examined using Pearson\u0026rsquo;s correlation coefficient. Multivariable linear regression was performed with eGFR category as the dependent variable, adjusting for clinically relevant confounders (age, sex, BMI, hemoglobin, CNAQ score). Given the large number of statistical comparisons in the food-group frequency analyses, a Benjamini\u0026ndash;Hochberg FDR correction was applied, and results with a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered significant. Statistical significance for primary and secondary analyses was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic and Clinical Profile\u003c/h2\u003e \u003cp\u003eAmong 184 participants (mean age 47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 years; 59.8% male), the distribution across CKD stages was: Stage 1 (n\u0026thinsp;=\u0026thinsp;20, 10.9%), Stage 2 (n\u0026thinsp;=\u0026thinsp;47, 25.5%), Stage 3a (n\u0026thinsp;=\u0026thinsp;63, 34.2%), and Stage 3b (n\u0026thinsp;=\u0026thinsp;54, 29.3%). The majority of participants were in the 41\u0026ndash;60-year age group (46.2%). Most participants were married (95.7%), educated to graduation level or above (50.5%), and 73.9% consumed a non-vegetarian diet. A high proportion (76.5%) had at least one chronic comorbidity: 38% had both Type 2 Diabetes Mellitus (T2DM) and hypertension, 22.3% had hypertension alone, and 14.1% had T2DM alone. Sociodemographic and lifestyle characteristics are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline sociodemographic and clinical characteristics of CKD participants (n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110 (59.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-vegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136 (73.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (26.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u0026thinsp;+\u0026thinsp;Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (38.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (22.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (14.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 h/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;5 h/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 h/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 1 (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;90 mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (10.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 2 (eGFR 60\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3a (eGFR 45\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (34.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3b (eGFR 30\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between CKD Stage and Sociodemographic, Lifestyle, and Medical History Variables\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the association between CKD stages and key sociodemographic and lifestyle variables. Age showed a statistically significant relationship with CKD progression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with individuals aged 41\u0026ndash;60 years increasingly represented in more advanced stages, particularly stages 3a and 3b. Gender did not show a statistically significant association (p\u0026thinsp;=\u0026thinsp;0.085), though males were more prevalent in earlier stages. Occupational status was significantly linked to CKD stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): the proportion of unemployed individuals rose sharply in advanced stages. Income level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and educational attainment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed inverse associations with CKD severity, with lower income and education levels concentrated in stages 3a and 3b.\u003c/p\u003e \u003cp\u003eRegarding lifestyle variables, a significant association was found between diet type and CKD stage (p\u0026thinsp;=\u0026thinsp;0.003), with non-vegetarians disproportionately represented in advanced stages. Tobacco use (p\u0026thinsp;=\u0026thinsp;0.023) and alcohol consumption (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with CKD severity. The presence of multiple comorbidities; T2DM combined with hypertension was strongly associated with advanced CKD (p\u0026thinsp;=\u0026thinsp;0.002). Family history of chronic diseases did not show a statistically significant association (p\u0026thinsp;=\u0026thinsp;0.356).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic, lifestyle, and medical history variables by CKD stage (n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStage 1 (n\u0026thinsp;=\u0026thinsp;20) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStage 2 (n\u0026thinsp;=\u0026thinsp;47) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStage 3a (n\u0026thinsp;=\u0026thinsp;63) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStage 3b (n\u0026thinsp;=\u0026thinsp;54) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-vegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47 (74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily/Weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/Don't recall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily/Weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/Don't recall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. T2DM\u0026thinsp;=\u0026thinsp;Type 2 Diabetes Mellitus.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNutrition-Focused Physical Examination\u003c/h2\u003e \u003cp\u003ePhysical examination revealed that 58% of participants showed some degree of fat depletion, most commonly in the upper arm (25%), thoracic/lumbar region (18%), and orbital region (15%). Muscle wasting was identified in 51% of participants, most prominently at the acromion (14%) and dorsal hand/interosseous region (14%), reflecting widespread protein\u0026ndash;energy wasting even in this non-dialysis cohort. Oedema was present in 82% of participants, predominantly as pedal oedema (38%) and leg oedema (25%).\u003c/p\u003e \u003cp\u003eSubjective Global Assessment (SGA) classified 66% of participants as well-nourished or at very mild risk (SGA-A), 32% as moderately malnourished (SGA-B), and 2% as severely malnourished (SGA-C).Approximately one-third of the early-stage CKD cohort showed significant nutritional impairment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDietary Intake and Nutritional Adequacy\u003c/h2\u003e \u003cp\u003eMean energy intake was 28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 kcal/kg/day, which falls within the KDIGO 2024-recommended range of 25\u0026ndash;35 kcal/kg/day. However, 22% of participants fell below the lower threshold (\u0026lt;\u0026thinsp;25 kcal/kg/day), placing them at risk for muscle catabolism. Mean protein intake was 0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 g/kg/day, which exceeds the KDIGO 2024 recommendation for stages 3\u0026ndash;5 (0.55\u0026ndash;0.60 g/kg/day) but remains within the acceptable range for stages 1\u0026ndash;2 (0.8\u0026ndash;1.0 g/kg/day). Stratified analysis revealed that Stage 3b patients had a mean protein intake of 0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 g/kg/day, approaching the lower limit of adequacy.\u003c/p\u003e \u003cp\u003eSixty-four percent of participants exceeded the recommended sodium intake (\u0026gt;\u0026thinsp;2 g/day or \u0026gt;\u0026thinsp;5 g salt/day). Sodium excess in this cohort predominantly reflected high cooking salt use and sodium-rich condiments (e.g., pickles, papads, commercial masala blends). Fifty-eight percent exceeded potassium thresholds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between CKD Stage and Food Group Consumption\u003c/h2\u003e \u003cp\u003eKey findings include: (i) chickpea consumption declined significantly in Stage 3b (48.1% non-consumption, p\u0026thinsp;=\u0026thinsp;0.005),(ii) intake of soft drinks, fresh fruit juices, and sweet snacks declined sharply in Stage 3b (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 each) (iii) the \u0026lsquo;Others\u0026rsquo; grain category showed significant variation (p\u0026thinsp;=\u0026thinsp;0.021), and (iv) yoghurt consumption fluctuated across stages, with reduced intake in Stage 3a (44.4%) compared to Stage 3b (87%). After Benjamini\u0026ndash;Hochberg FDR correction, associations with chickpeas, beverages (soft drinks, fruit juice), and sweet/bakery snacks remained statistically significant (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e Detailed associations of CKD stage with food group consumption (cereals, pulses, vegetables, fruits, dairy, animal proteins, beverages, and snacks) are provided in Supplementary Tables S1\u0026ndash;S8 (Supplementary file). Overall, rice, lentils, and dairy were common across all stages, while advanced CKD participants showed reduced intake of potassium-rich fruits/vegetables and processed foods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between CKD Stage and Biochemical Parameters\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents key biochemical markers stratified by CKD stage. The urine albumin-to-creatinine ratio (UACR) showed a highly significant progressive increase from Stage 1 (31.71\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12 mg/g) to Stage 3b (111.00\u0026thinsp;\u0026plusmn;\u0026thinsp;94.20 mg/g) (F\u0026thinsp;=\u0026thinsp;7.852, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HbA1c was significantly associated with CKD stage (F\u0026thinsp;=\u0026thinsp;2.697, p\u0026thinsp;=\u0026thinsp;0.047), with higher values in Stages 3a (6.51%) and 3b (6.26%). Hemoglobin showed a declining trend (Stage 1: 12.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 g/dL; Stage 3b: 11.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 g/dL) that did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.222). Uric acid demonstrated a non-significant increasing trend across stages (p\u0026thinsp;=\u0026thinsp;0.515).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between CKD stages and biochemical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeGFR Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUACR (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e31.71\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.13\u0026thinsp;\u0026plusmn;\u0026thinsp;14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e81.68\u0026thinsp;\u0026plusmn;\u0026thinsp;114.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e111.00\u0026thinsp;\u0026plusmn;\u0026thinsp;94.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; UACR\u0026thinsp;=\u0026thinsp;urine albumin-to-creatinine ratio. Group differences assessed by one-way ANOVA (normally distributed variables) or Kruskal\u0026ndash;Wallis H test (UACR).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAppetite Status and Dietary Restriction Patterns Across CKD Stages\u003c/h2\u003e \u003cp\u003eAppetite assessment using the CNAQ revealed a progressive decline with advancing CKD stage. In Stage 2, 28 participants had good appetite versus 19 with poor appetite. This ratio reversed in Stage 3a (18 good vs. 45 poor) and remained unfavourable in Stage 3b (15 good vs. 39 poor). These findings establish appetite loss as an early and clinically important manifestation of nutritional risk, even before Stage 3b. Dietary restriction burden escalated with disease stage: from minimal restriction in Stage 1 (no restriction, n\u0026thinsp;=\u0026thinsp;7; low-protein only, n\u0026thinsp;=\u0026thinsp;4) to complex multi-component regimens in Stage 3b (low-protein\u0026thinsp;+\u0026thinsp;sodium\u0026thinsp;+\u0026thinsp;potassium, n\u0026thinsp;=\u0026thinsp;18). The increasing dietary burden in later stages contributes to appetite fatigue and compromised dietary adherence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations Between Nutritional and Clinical Variables with eGFR Category\u003c/h2\u003e \u003cp\u003ePearson correlation analysis demonstrated that advancing age (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the presence of chronic comorbidities (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively associated with a more advanced CKD stage. Higher occupational status (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.35), income (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.374), and educational attainment (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25) were negatively associated with eGFR category (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHaemoglobin was negatively correlated with eGFR category (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Urinary albumin-to-creatinine ratio (UACR) was positively associated with eGFR category (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HbA1c showed a positive association with eGFR category (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eCNAQ appetite score (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;=\u0026thinsp;0.042) and dietary preference/restriction score (r\u0026thinsp;=\u0026thinsp;0.224, p\u0026thinsp;=\u0026thinsp;0.002) were positively correlated with eGFR category. Among dietary variables, chicken (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.171, p\u0026thinsp;=\u0026thinsp;0.021), eggs (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.206, p\u0026thinsp;=\u0026thinsp;0.005), and mutton (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.162, p\u0026thinsp;=\u0026thinsp;0.028) showed modest positive associations with more advanced CKD stages. Full correlation results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation of demographic, lifestyle, dietary, and nutritional variables with eGFR category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccupational Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily Members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle \u0026amp; Medical History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiet Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTobacco use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic Medical Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal Protein Intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEggs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppetite \u0026amp; Dietary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNAQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDietary Preferences/Restrictions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; r\u0026thinsp;=\u0026thinsp;Pearson correlation coefficient. eGFR category coded 1 (Stage 1, best kidney function) to 4 (Stage 3b, worst kidney function). A positive r indicates association with more advanced CKD (declining eGFR); a negative r indicates association with better-preserved kidney function.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Linear Regression: Independent Predictors of eGFR Category\u003c/h2\u003e \u003cp\u003eMultivariable linear regression with eGFR category as the dependent variable and age, sex, body mass index (BMI), haemoglobin, and CNAQ score as independent predictors yielded an adjusted R\u0026sup2; = 0.41, F(5, 178)\u0026thinsp;=\u0026thinsp;34.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating that the model explained 41% of the variance in CKD stage severity. Haemoglobin and CNAQ score emerged as the strongest modifiable predictors of kidney function.\u003c/p\u003e \u003cp\u003eOlder age (β = +0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower appetite as measured by CNAQ score (β = +0.19, p\u0026thinsp;=\u0026thinsp;0.003) were independently associated with more advanced CKD stages. Conversely, higher haemoglobin (β = \u0026minus;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher BMI (β = \u0026minus;0.18, p\u0026thinsp;=\u0026thinsp;0.019) were independently associated with less advanced disease. Sex was not a significant independent predictor (β = +0.08, p\u0026thinsp;=\u0026thinsp;0.186). Individual predictor results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression\u0026mdash;independent predictors of eGFR category (n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (Unstd.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta (Std.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020, 0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.058, 0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.070, \u0026minus;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.252, \u0026minus;\u0026thinsp;0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNAQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015, 0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; B\u0026thinsp;=\u0026thinsp;unstandardised coefficient; SE\u0026thinsp;=\u0026thinsp;standard error; Beta\u0026thinsp;=\u0026thinsp;standardised coefficient; CI\u0026thinsp;=\u0026thinsp;confidence interval. eGFR category coded 1 (Stage 1, best kidney function) to 4 (Stage 3b, worst kidney function). A positive Beta indicates association with more advanced CKD (declining eGFR); a negative Beta indicates association with better-preserved kidney function. Adjusted R\u0026sup2; = 0.41, F (5,178)\u0026thinsp;=\u0026thinsp;34.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis hospital-based cross-sectional study provides one of the first comprehensive integrative assessments of nutritional status, dietary intake, appetite, and biochemical markers among non-dialysis-dependent CKD patients (stages 1\u0026ndash;3) in Hyderabad, India. The primary hypothesis \u0026mdash; that early-stage CKD patients exhibit suboptimal nutritional status significantly associated with kidney function \u0026mdash; was confirmed. Approximately one-third of participants were malnourished by SGA criteria, over half demonstrated measurable muscle or fat loss, and dietary patterns were frequently inconsistent with KDIGO 2024 recommendations. These disturbances were present across all stages, reinforcing the case for early nutritional intervention. The finding that 64% of participants exceeded sodium limits and 58% exceeded potassium limits despite adequate overall energy intake further underscores that dietary quality, not quantity alone, is compromised in this population.\u003c/p\u003e \u003cp\u003eMultivariable regression identified age, haemoglobin, BMI, and CNAQ appetite score as independent predictors of eGFR category (adjusted R\u0026sup2; = 0.41), with haemoglobin and appetite emerging as the strongest modifiable predictors. The association between lower haemoglobin and more advanced CKD is biologically plausible: erythropoietin deficiency, iron sequestration due to chronic inflammation, and reduced oral intake of iron-rich foods collectively drive anaemia as a consequence and amplifier of kidney function decline (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Consistent with this, Pearson correlation analysis confirmed a significant negative association between haemoglobin and eGFR category (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that lower haemoglobin corresponded with more advanced CKD stages across the cohort. The independent role of the CNAQ score extends prior evidence from haemodialysis populations to the critical and underexplored early CKD window, suggesting that appetite monitoring should be incorporated into routine outpatient nephrology care from the earliest stages. The positive correlation between CNAQ score and eGFR category (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.15, p\u0026thinsp;=\u0026thinsp;0.042) observed in this study supports this, confirming that reduced appetite is associated with more advanced disease even at stages 1\u0026ndash;3.\u003c/p\u003e \u003cp\u003eThe finding that 64% of participants exceeded the recommended sodium limit is consistent with South Asian dietary patterns characterised by high cooking salt and sodium-rich condiments (pickles, chutneys, papads) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In contrast to Western cohorts where processed food drives sodium excess, the primary source in this population was home cooking. Interventions targeting cooking salt reduction and use of low-sodium condiments are likely to yield greater impact than processed food avoidance alone. Evidence from South Asia supports that a 1 g/day reduction in sodium intake is achievable through behavioural counselling without compromising cultural acceptability (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The high prevalence of potassium exceedance (58%) similarly warrants attention, particularly given the positive association between dietary restriction score and eGFR category (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.002), suggesting that patients with more advanced disease are already self-restricting yet remain non-adherent to recommended limits.\u003c/p\u003e \u003cp\u003eThe high oedema prevalence (82%) in this early-stage cohort requires careful contextualisation. Oedema at stages 1\u0026ndash;3 most likely reflects comorbidity-driven mechanisms \u0026mdash; sodium and water retention from hypertension and diabetic nephropathy-associated hypoalbuminaemia \u0026mdash; rather than uraemic fluid overload, which predominates in stages 4\u0026ndash;5. Clinicians should interpret oedema in the context of comorbidity burden and serum albumin, rather than as an intrinsic early-CKD phenomenon. The significant positive association between chronic comorbidity burden and eGFR category (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) observed in this cohort further supports this contextualisation, confirming that comorbidity accumulation tracks closely with advancing CKD stage.\u003c/p\u003e \u003cp\u003eSocioeconomic determinants strongly shaped CKD severity in this cohort. Lower income, unemployment, and limited educational attainment were significantly associated with more advanced CKD stages, consistent with the international literature characterising CKD as a disease of inequity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Specifically, higher occupational status (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.35), greater income (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.37), and higher educational attainment (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25) were each negatively associated with eGFR category (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that socioeconomic advantage is linked to less advanced disease in this population. Findings from Ghana underscore the need for region-specific contextualisation. In the Indian setting, structural factors including out-of-pocket healthcare expenditure, delayed health-seeking behaviour, and limited nutritional knowledge in lower socioeconomic groups represent modifiable targets for public health policy (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVegetarian dietary patterns were positively associated with better kidney function outcomes (p\u0026thinsp;=\u0026thinsp;0.003). The modest but significant positive associations observed between animal protein sources \u0026mdash; chicken (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;0.021), eggs (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.005), and mutton (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.028) \u0026mdash; and more advanced CKD stages are consistent with this direction, though causality cannot be inferred from this cross-sectional design. Evidence from the CRIC prospective cohort study shows that adherence to plant-based dietary patterns is associated with significantly lower risk of CKD progression and all-cause mortality (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This supports a shift from generalised protein restriction to quality-driven dietary planning emphasising plant-forward eating, which confers acid-buffering benefits through higher dietary alkali load and reduced phosphorus bioavailability compared to animal-source foods (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe progressive deterioration of glycaemic control (HbA1c) and proteinuria (UACR) across CKD stages reaffirms the synergistic role of diabetes and hypertension in nephropathy progression (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The positive associations of both UACR (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and HbA1c (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.003) with eGFR category observed in this cohort confirm that greater albuminuria and poorer glycaemic control co-occur with more advanced CKD stages, consistent with their established roles as markers of nephropathy severity. While uric acid and haemoglobin did not achieve statistical significance across stages, the observed trends align with established mechanisms of hyperuricaemia-mediated tubular injury and CKD-associated anaemia and are clinically relevant (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eKey strengths include the comprehensive multidimensional approach incorporating dietary, anthropometric, biochemical, and clinical nutritional indicators; use of multiple validated tools (SGA, CNAQ, FFQ, 24-hour recall, NFPE); enrolment from two tertiary care hospitals providing clinical diversity; and an exclusive focus on the underexplored early non-dialysis CKD population. Reporting adhered to the STROBE checklist.\u003c/p\u003e \u003cp\u003eLimitations include the cross-sectional design, which precludes causal inference. Dietary assessments relied on a single 24-hour dietary recall combined with an FFQ which may be subject to recall bias and portion estimation error, particularly for composite Indian dishes. Future studies should consider using multiple repeated 24 hours dietary recalls to capture day to day dietary variability. The hospital-based sampling frame may over-represent more advanced or comorbid patients, limiting generalisability. Exclusion of those\u0026thinsp;\u0026gt;\u0026thinsp;65 years and strict vegans restrict applicability to those groups. The moderate sample size limits reliable subgroup analyses by stage and sex. Multiple comparisons in food-group analyses were mitigated by FDR correction, though residual type I error risk cannot be excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eLongitudinal cohort studies are needed to determine whether early nutritional intervention\u0026mdash;particularly appetite support and sodium reduction\u0026mdash;can meaningfully slow eGFR decline in Indian CKD patients. Randomised controlled trials testing culturally adapted plant-forward dietary interventions (e.g., incorporating millets, pulses, and regionally available vegetables in place of refined grains and high-salt condiments) are a priority. Integration of digital health tools for real-time dietary monitoring may enhance adherence in resource-constrained nephrology settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAdults with early-stage non-dialysis-dependent CKD in Hyderabad, India exhibit clinically significant nutritional impairment, including suboptimal energy and protein intake, high sodium and potassium intake, reduced appetite, emerging anaemia, and measurable protein\u0026ndash;energy wasting\u0026mdash;even in stages 1\u0026ndash;3 before advanced uraemia develops. Age, hemoglobin, BMI, and appetite score independently predicted eGFR category, explaining 41% of its variance. These findings confirm that nutritional decline begins early in the CKD trajectory and that nutritional indicators are meaningful clinical markers of kidney function status.\u003c/p\u003e \u003cp\u003eIntegrating systematic nutritional assessment\u0026mdash;using validated tools such as SGA, CNAQ, and dietitian-led dietary recall\u0026mdash;into routine early CKD outpatient management is not merely an adjunct but a clinical necessity. Individualized dietary counselling must address cooking salt reduction, protein quality optimisation, and the promotion of plant-forward dietary patterns adapted to regional culinary practices. Addressing socioeconomic determinants and providing structured nutrition education are equally essential for equitable CKD outcomes. This study provides an evidence base for integrated nutritional care in the Indian nephrology setting and supports development of locally contextualised CKD dietary guidelines.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated Glomerular Filtration Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKDIGO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Disease:Improving Global Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKDOQI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Disease Outcomes Quality Initiative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFFQ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood Frequency Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSGA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubjective Global Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCNAQ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCouncil on Nutrition Appetite Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNFPE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNutrition-Focused Physical Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUACR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrine Albumin-to-Creatinine Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePEW\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein\u0026ndash;Energy Wasting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eT2DM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHbA1c\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated Haemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNIN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute of Nutrition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIFCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndian Food Composition Tables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCTRI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Trials Registry of India\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTROBE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEWS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEconomically Weaker Section\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMIG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMiddle Income Group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLIG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLower Income Group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eApproved by the Institutional Ethics Committee of Symbiosis International (Deemed University) (Ref. no. SIU/IEC/719) and conducted in accordance with the Declaration of Helsinki. Trial registered with CTRI (CTRI/2024/06/068985) on 18 June 2024. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe dataset supporting the conclusions of this article is included within the article and its additional file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no specific funding from any public, commercial, or not-for-profit agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eAG and VPB conducted data collection, data analysis, and drafted the manuscript. AM conceptualised and supervised the study. AG and AM critically reviewed the manuscript. AM approved the final version. All authors read and approved the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the support of nephrology staff and registered dietitians at the participating hospitals in Hyderabad, and all patients who volunteered for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117\u0026ndash;S314.\u003c/li\u003e\n \u003cli\u003eShlipak MG, Tummalapalli SL, Boulware LE, Grams ME, Ix JH, Jha V, et al. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2021 Jan;99(1):34\u0026ndash;47.\u003c/li\u003e\n \u003cli\u003eGuo J, Jiao W, Xia S, Xiang X, Zhang Y, Ge X, et al. The global, regional, and national patterns of change in the burden of chronic kidney disease from 1990 to 2021. BMC Nephrol. 2025 Mar 13;26(1):136.\u003c/li\u003e\n \u003cli\u003eFrancis A, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, et al. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol. 2024 July;20(7):473\u0026ndash;85.\u003c/li\u003e\n \u003cli\u003eSaran R, Robinson B, Abbott KC, Bragg-Gresham J, Chen X, Gipson D, et al. US Renal Data System 2019 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2020 Jan;75(1):A6\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eTalukdar R, Ajayan R, Gupta S, Biswas S, Parveen M, Sadhukhan D, et al. Chronic Kidney Disease Prevalence in India: A Systematic Review and Meta‐Analysis From Community‐Based Representative Evidence Between 2011 to 2023. Nephrology. 2025 Jan;30(1):e14420.\u003c/li\u003e\n \u003cli\u003eKistler BM, Moore LW, Benner D, Biruete A, Boaz M, Brunori G, et al. The International Society of Renal Nutrition and Metabolism Commentary on the National Kidney Foundation and Academy of Nutrition and Dietetics KDOQI Clinical Practice Guideline for Nutrition in Chronic Kidney Disease. J Ren Nutr. 2021 Mar;31(2):116-120.e1.\u003c/li\u003e\n \u003cli\u003eKalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. A Malnutrition-Inflammation Score is correlated with morbidity and mortality in maintenance hemodialysis patients. Am J Kidney Dis. 2001 Dec;38(6):1251\u0026ndash;63.\u003c/li\u003e\n \u003cli\u003eAn N, No Author NA, Linh H, Long V, Van D, Duc N. Nutritional Status and Associated Factors in Non-dialysis Inpatients With Chronic Kidney Disease: a Single-center, Mixed-methods Study. Acta Inform Medica. 2024;32(3\u0026ndash;4):190.\u003c/li\u003e\n \u003cli\u003eChau YY, Kumar J. Vitamin D in Chronic Kidney Disease. Indian J Pediatr. 2012 Aug;79(8):1062\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eKalantar-Zadeh K, Block G, McAllister CJ, Humphreys MH, Kopple JD. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin Nutr. 2004 Aug;80(2):299\u0026ndash;307.\u003c/li\u003e\n \u003cli\u003eKalantar-Zadeh K, Rhee CM, Chou J, Ahmadi SF, Park J, Chen JLT, et al. The Obesity Paradox in Kidney Disease: How to Reconcile It With Obesity Management. Kidney Int Rep. 2017 Mar;2(2):271\u0026ndash;81.\u003c/li\u003e\n \u003cli\u003eSoam SK, Yashavanth BS, Dhumantarao TR, Maruthamuthu B, Balasani R, Rakesh S. Factors influencing dietary patterns among the youth from higher educational institutions in India. Front Nutr. 2023 Mar 28;10:1134455.\u003c/li\u003e\n \u003cli\u003eDurga AV, Manorenj S. Dietary pattern in Adult Patients with Acute Stroke in South India: A Case-Control Study from a Tertiary Care Center in Hyderabad. J Neurosci Rural Pract. 2019 Apr;10(02):283\u0026ndash;93.\u003c/li\u003e\n \u003cli\u003eLiu D, Li Q, Jia R, He W, Zhao X, Pan M, et al. Type 2 diabetes mellitus with chronic kidney disease benefits from long-term restriction of dietary protein intake: a 10-year retrospective cohort study. BMC Nutr. 2025 July 5;11(1):131.\u003c/li\u003e\n \u003cli\u003eIndian Council of Medical Research \u0026ndash; National Institute of Nutrition (ICMR-NIN). Dietary Guidelines for Indians 2024. Hyderabad: ICMR-NIN; 2024.\u003c/li\u003e\n \u003cli\u003eTable of Contents for National Kidney Foundation 2017 Spring Clinical Meetings Abstracts, April 18 \u0026ndash; 22, 2017. J Ren Nutr. 2017 Mar;27(2):140\u0026ndash;1.\u003c/li\u003e\n \u003cli\u003eRhee CM, Wang AYM, Biruete A, Kistler B, Kovesdy CP, Zarantonello D, et al. Nutritional and Dietary Management of Chronic Kidney Disease Under Conservative and Preservative Kidney Care Without Dialysis. J Ren Nutr. 2023 Nov;33(6):S56\u0026ndash;66.\u003c/li\u003e\n \u003cli\u003eChan M, Kelly J, Batterham M, Tapsell L. Malnutrition (Subjective Global Assessment) Scores and Serum Albumin Levels, but not Body Mass Index Values, at Initiation of Dialysis are Independent Predictors of Mortality: A 10-Year Clinical Cohort Study. J Ren Nutr. 2012 Nov;22(6):547\u0026ndash;57.\u003c/li\u003e\n \u003cli\u003eHummell AC, Cummings M. Role of the nutrition‐focused physical examination in identifying malnutrition and its effectiveness. Nutr Clin Pract. 2022 Feb;37(1):41\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eWilson MMG, Thomas DR, Rubenstein LZ, Chibnall JT, Anderson S, Baxi A, et al. Appetite assessment: simple appetite questionnaire predicts weight loss in community-dwelling adults and nursing home residents. Am J Clin Nutr. 2005 Nov;82(5):1074\u0026ndash;81.\u003c/li\u003e\n \u003cli\u003eSudha V, Radhika G, Sathya RM, Ganesan A, Mohan \u0026gt;V. Reproducibility and validity of an interviewer-administered semi-quantitative food frequency questionnaire to assess dietary intake of urban adults in southern India. Int J Food Sci Nutr. 2006 Jan;57(7\u0026ndash;8):481\u0026ndash;93.\u003c/li\u003e\n \u003cli\u003eLongvah T, An̲antan̲ I, Bhaskarachary K, Venkaiah K. Indian food composition tables. Hyderabad, Telangana State, India: National Institute of Nutrition, Indian Council of Medical Research; 2017. 536 p.\u003c/li\u003e\n \u003cli\u003eMandal I, Hossain SR. Update of modified Kuppuswamy scale for the year 2024. Int J Community Med Public Health. 2024 June 28;11(7):2945\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eGrant CH, Salim E, Lees JS, Stevens KI. Deprivation and chronic kidney disease\u0026mdash;a review of the evidence. Clin Kidney J. 2023 June 30;16(7):1081\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eIshimura N, Inoue K, Maruyama S, Nakamura S, Kondo N. Income Level and Impaired Kidney Function Among Working Adults in Japan. JAMA Health Forum. 2024 Mar 1;5(3):e235445.\u003c/li\u003e\n \u003cli\u003eAdjei DN, Stronks K, Adu D, Beune E, Meeks K, Smeeth L, et al. Cross-sectional study of association between socioeconomic indicators and chronic kidney disease in rural\u0026ndash;urban Ghana: the RODAM study. BMJ Open. 2019 May;9(5):e022610.\u003c/li\u003e\n \u003cli\u003eZhang L, Wang F, Wang L, Wang W, Liu B, Liu J, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. The Lancet. 2012 Mar;379(9818):815\u0026ndash;22.\u003c/li\u003e\n \u003cli\u003eKistler BM, Moore LW, Benner D, Biruete A, Boaz M, Brunori G, et al. The International Society of Renal Nutrition and Metabolism Commentary on the National Kidney Foundation and Academy of Nutrition and Dietetics KDOQI Clinical Practice Guideline for Nutrition in Chronic Kidney Disease. J Ren Nutr. 2021 Mar;31(2):116-120.e1.\u003c/li\u003e\n \u003cli\u003eKang DH, Nakagawa T. Uric acid and chronic renal disease: Possible implication of hyperuricemia on progression of renal disease. Semin Nephrol. 2005 Jan;25(1):43\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eCarrero JJ, Gonz\u0026aacute;lez-Ortiz A, Avesani CM, Bakker SJL, Bellizzi V, Chauveau P, et al. Plant-based diets to manage the risks and complications of chronic kidney disease. Nat Rev Nephrol. 2020 Sept;16(9):525\u0026ndash;42.\u003c/li\u003e\n \u003cli\u003eJoshi S, Hashmi S, Shah S, Kalantar-Zadeh K. Plant-based diets for prevention and management of chronic kidney disease: Curr Opin Nephrol Hypertens. 2020 Jan;29(1):16\u0026ndash;21.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic kidney disease, nutritional status, dietary intake, appetite, eGFR, protein–energy wasting, KDIGO, India","lastPublishedDoi":"10.21203/rs.3.rs-9382051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9382051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic Kidney Disease (CKD) presents a rapidly growing public health challenge in India, with early stages offering a critical window for nutritional intervention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This hospital-based cross-sectional study assesses nutritional intake, anthropometric measurements, biochemical markers, and appetite status among adults with CKD stages 1\u0026ndash;3 (n\u0026thinsp;=\u0026thinsp;184) attending outpatient nephrology departments of two tertiary care hospitals in Hyderabad, India. Assessment tools include a Food Frequency Questionnaire (FFQ), 24-hour dietary recall, Subjective Global Assessment (SGA), and the Council on Nutrition Appetite Questionnaire (CNAQ).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMean age is 47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 years; 59.8% are male. Stage distribution is: Stage 1 (10.9%), Stage 2 (25.5%), Stage 3a (34.2%), and Stage 3b (29.3%). Mean protein intake is 0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 g/kg/day; 64% exceed sodium and 58% exceed potassium limits; 34% are malnourished by SGA. Estimated glomerular filtration rate (eGFR) category correlates negatively with haemoglobin (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positively with urinary albumin-to-creatinine ratio (UACR; r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.37), glycated haemoglobin (HbA1c; r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.003), and CNAQ score (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;=\u0026thinsp;0.042). Age (β = +0.21), haemoglobin (β = \u0026minus;0.31), body mass index (BMI; β = \u0026minus;0.18), and CNAQ score (β = +0.19) independently predict eGFR category (adjusted R\u0026sup2; = 0.41).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSuboptimal dietary patterns, reduced appetite, and emerging anaemia are independently associated with kidney function severity in early-stage CKD. Systematic nutritional assessment and individualised dietary counselling should be integrated into routine CKD care.\u003c/p\u003e","manuscriptTitle":"Nutritional Assessment and Dietary Intake Patterns in Early-Stage Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 13:01:53","doi":"10.21203/rs.3.rs-9382051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T17:52:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95266480963015202349491312788539012922","date":"2026-05-12T14:50:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6529378903370115855296407476504516673","date":"2026-05-07T20:55:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T13:58:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T10:28:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T11:40:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T11:40:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-10T17:06:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fde2c17e-ad6a-4add-ba1f-2736a2f3fe65","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T17:52:07+00:00","index":87,"fulltext":""},{"type":"reviewerAgreed","content":"95266480963015202349491312788539012922","date":"2026-05-12T14:50:58+00:00","index":86,"fulltext":""},{"type":"reviewerAgreed","content":"6529378903370115855296407476504516673","date":"2026-05-07T20:55:19+00:00","index":82,"fulltext":""},{"type":"reviewersInvited","content":"25","date":"2026-05-07T13:58:08+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":68240267,"name":"Health sciences/Diseases"},{"id":68240268,"name":"Health sciences/Health care"},{"id":68240269,"name":"Health sciences/Medical research"},{"id":68240270,"name":"Health sciences/Nephrology"},{"id":68240271,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-18T13:01:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 13:01:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9382051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9382051","identity":"rs-9382051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}