Comparative Effect of Normal Protein and Low Protein Diet on Renal Function Reserve in Patients of Chronic Kidney Disease Stage 3 And 4: A Randomised Controlled Study

Comparative Effect of Normal Protein and Low Protein Diet on Renal Function Reserve in Patients of Chronic Kidney Disease Stage 3 And 4: A Randomised Controlled 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 Research Article Comparative Effect of Normal Protein and Low Protein Diet on Renal Function Reserve in Patients of Chronic Kidney Disease Stage 3 And 4: A Randomised Controlled Study Varuna Yadav, Himansu Mahapatra, Madhavi Tripathi, Lalit Pursnani, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7778811/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Renal functional reserve (RFR), defined as the difference between peak and baseline glomerular filtration rate (GFR), reflects the adaptive capacity of the kidney. This study evaluated how normal protein diet (NPD) and low protein diet (LPD) affect changes in both absolute (RFR abs ) and relative (RFR % ) RFR, as well as rate of GFR decline over six months in patients with CKD stages 3 and 4. Methodology: This six‑month randomized controlled trial enrolled adults (>18 years) with stage 3–4 CKD. Baseline assessments included clinical and laboratory parameters, eGFR, Creatinine clearance (CrCl) and two‑plasma technetium-99m Diethylenetriamine pentaacetic acid ( 99m Tc‑DTPA) method before and after a 1 g/kg protein load to calculate RFR abs and RFR % . Patients were randomized to either a normal protein diet (0.8 g/kg/day) or a low protein diet (0.6 g/kg/day). Dietary adherence monitored by recall, record and 24‑hour urinary urea nitrogen. All parameters were repeated at six months to compare RFR abs and RFR % changes by both measured GFR methods between diet groups. Patients having RFR below median value of the study population were considered to have low renal reserve; risk factors of low RFR were also assessed. Results: Of 135 patients (64 NPD, 71 LPD), there was significant increase in measured GFR after protein loading in both groups at baseline and at six months (p < 0.001). The magnitude of increase in GFR after protein loading didn’t differ between groups at baseline (p = 0.88), but significantly high in LPD group at six months (p = 0.03). The decline in RFR was significantly smaller in LPD than NPD after six months by both the methods of RFR estimation (RFR abs (DTPA): p = 0.002; RFR % (DTPA): p = 0.001 and RFR abs (CrCl): p=0.046; RFR % (CrCl): p=0.021; respectively), with no significant difference in eGFR change between groups. Lower baseline GFR, higher proteinuria and increased age were independent risk factors for low renal reserve. Conclusion: LPD preserved both absolute and relative RFR compared to NPD. Decline in RFR preceded fall in GFR, making it a potential early biomarker for CKD progression. Trial registration: - Clinical Trial Registry (India) CTRI – CTRI/2024/08/071864 dated 2 nd August 2024 Key words: Chronic kidney disease, creatinine clearance, technetium-99m Diethylenetriamine pentaacetic acid, low protein diet, normal protein diet, renal function reserve Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Renal Function Reserve (RFR), defined as the difference between peak and baseline GFR, provides a dynamic assessment of the kidney’s adaptability to physiological stressors such as protein intake, dopamine infusion, and states of hyperfiltration. 1 , 2 This adaptability is regulated by complex interactions among renal hemodynamics, hormonal responses, and tubuloglomerular feedback mechanisms. 2 In healthy individuals, RFR allows GFR to increase by up to 20% following a protein load, highlighting the kidney’s ability to respond efficiently to metabolic challenges. 3 Studies have shown progressive reduction in RFR during the course of CKD progression. Multiple factors influence GFR and RFR, including age, gender, hypertension, diabetes, obesity, and diet. 4 , 5 Notably, high sodium and animal protein intake increase glomerular hyperfiltration and accelerate CKD progression, 5,6 while low-protein diets (LPD) can reduce hyperfiltration and improve RFR, in diabetic nephropathy. 7 These studies on RFR primarily focused on cross-sectional comparisons and did not systematically assess the impact of dietary protein intake on RFR or its trajectory over time in moderate CKD. Whether a normal protein diet (NPD) or LPD better preserves RFR is still unclear. The measurement of RFR relies on accurate GFR assessment, yet the gold-standard method inulin clearance is impractical for routine clinical use. Estimated GFR (eGFR) formulas, though widely used, provide only a static approximation of renal function and lack the capacity to detect dynamic changes. 8 The two-plasma sample technetium-99m diethylenetriamine pentaacetic acid (DTPA) method offers more precise GFR measurement, 9 while emerging imaging modalities such as functional magnetic resonance imaging and contrast-enhanced ultrasound present promising non-invasive alternatives. 10 , 11 Creatinine clearance is widely used due to ease and cost-effectiveness but may be less sensitive and affected by tubular secretion and muscle mass. 12 , 13 However, optimization and validation of appropriate GFR estimation method are needed to enhance precision and clinical integration of RFR measurements. Common hemodynamic stimulants include intravenous dopamine, while metabolic stimulants frequently used are intravenous amino acids and oral protein loads. 12 The response magnitude depends on the type of stimulant, with intravenous amino acids and oral protein loads typically inducing substantial increases in GFR over 1 to 3 hours. 4 The type of protein used for oral loading also influences the response, with meat-based proteins generally causing greater GFR increases than vegetable-based proteins. 14 , 15 Although few studies have shown the relationship of protein diet and RFR, no randomised control study has been conducted to know the exact relationship of RFR in different stages of CKD through DTPA GFR estimation. Further, RFR is also not routinely evaluated in practice and its role as an early marker of CKD progression remains under explored. Given these gaps, the present study aims to compare the effects of normal and low-protein diets on RFR and disease progression in CKD stages 3 and 4, with the goal of refining dietary recommendations to help preserve renal adaptability and improve outcomes. Methods Study Design This single-centre, randomized, parallel-group study was conducted in the Department of Nephrology at a tertiary teaching hospital from August 2024 to January 2025 after obtaining Institutional Ethics Committee approval. Inclusion and Exclusion Criteria After obtaining written informed consent, eligible patients aged 18 to 60 years with stage 3 or 4 CKD, attending the nephrology outpatient clinic were included. Acute illness or unstable renal function within the past 4 weeks, neurological or psychiatric disorders, malignancy, pregnancy or lactation, previous renal transplantation, chronic steroid use, hypercalcemia, alcohol abuse, and chronic liver disease were excluded. Sample Size: In the absence of definitive studies specifically examining the relationship between protein diet and RFR, we based our sample size calculation on data from the MDRD trial 16 , the largest study to evaluate the impact of protein intake on GFR. This study reported a mean difference of 1.6 with a standard deviation of 2.7. To achieve 85% power at a 95% confidence level, the required sample size was 53 patients per group. Allowing for an anticipated 20% dropout rate, the total sample size was adjusted to 126 participants, equally allocated between the groups. Data Collection Demographic details and clinical history were recorded including age, sex, primary renal diagnosis, comorbid conditions, medication use, and lifestyle factors such as smoking and alcohol intake. Anthropometric measurements (weight, height, BMI) were noted. Baseline laboratory testing comprised complete blood count, kidney function tests (serum creatinine, blood urea nitrogen), liver function test, serum electrolytes (sodium, potassium, calcium, phosphate), lipid profile, and urinary albumin-to-creatinine ratio (UACR). Estimated GFR (eGFR) was calculated using the MDRD and CKD-EPI 2021 equations to determine CKD staging for inclusion. Measured GFR by Tc-DTPA Two-Plasma Sample Method Measured GFR was calculated using Russell’s two‑compartment formula for the two‑plasma sample 99m Tc‑DTPA method. In this approach, the injected dose (D) of tracer, expressed in becquerels (Bq) or megabecquerels (MBq), is divided by the difference between two exponential terms derived from the bi‑exponential plasma clearance curve. These terms incorporate coefficients (A and B, in Bq/mL) corresponding to the fast and slow components of tracer elimination, rate constants (k₁ and k₂, in min⁻¹), and sample collection times (t₁ and t₂, in minutes) at 60 and 180 minutes after injection. The final GFR is expressed in millilitres per minute normalized to 1.73 m² of body surface area (mL/min/1.73 m²). 9 Assessment of Renal Functional Reserve (RFR) at Baseline RFR was assessed by measuring GFR response to a standardized vegetarian protein load after an 8-hour overnight fast. The protein challenge consisted of a vegetarian protein bar combined with raw paneer (cottage cheese) to achieve 1 gram/kilogram body weight, based on their respective protein contents (15-20g per protein bar, 18g per 100g paneer). Patients consumed the entire load within 30 minutes under supervision, with hydration maintained to balance urine output throughout the process. GFR was measured using 99m Tc-DTPA clearance with the two-plasma-sample method, both at baseline (fasting) and post-protein load on two different days at least 48 hours apart. Blood samples were collected at 1 and 3 hours post-tracer injection to determine plasma clearance rates. Creatinine clearance (CrCl) method for RFR estimation was done in 100 patients. Preliminary hydration was achieved by giving 10 mL/kg of oral water within 10–15 minutes at the start of the test. Urine volume was recorded one hour after fluid loading (time 0, T0), and thereafter, additional oral water equal to urinary output was administered. Blood samples for serum creatinine were obtained at 30 and 90 minutes after T0, while urine collections for volume and urinary creatinine were taken at 60 and 120 minutes from T0. Subjects then received an oral protein load of 1 g/kg body weight (as described above) within 30 minutes. After 60 minutes, total urine volume was measured; following this, the sampling for blood and urine creatinine was repeated at the same intervals post-protein load. CrCl was calculated at each time point. The lowest CrCl before protein load was considered basal GFR, while the highest CrCl after the load was designated stimulated GFR. 13 Absolute RFR (RFR abs ) was calculated as the difference between post-protein and baseline GFR (mL/min/1.73 m²), while percentage RFR (RFR % ) was calculated as [(RFR abs /GFR baseline) × 100]. Present study also planned to estimate the cut off of RFR from its median value. Lesser to the median value was defined as low RFR. All the risk factors were coordinated with this value to assess the effect of these risk parameters with RFR. Randomization After baseline assessments, including the RFR estimation, patients were subjected to computer-generated randomization sequence with concealed allocation in a 1:1 ratio; one as Normal Protein Diet (NPD): 0.8 g protein/kg/day and other as Low Protein Diet (LPD): 0.6 g protein/kg/day. Individualized diet plans were provided by a renal dietitian to ensure adequate energy intake (30–35 kcal/kg/day) alongside assigned protein targets. Dietary adherence was monitored monthly via 3-day dietary recall and 24-hour dietary history which were recorded in a notebook. To confirm the dietary adherence, 24 hr urinary urea nitrogen measurement was also done every three months 24‑hour urinary urea nitrogen estimation, from which daily protein intake was calculated. Follow-Up and Reassessment of RFR at Six Months Patients followed their assigned diets for six months with monthly clinic visits for dietary counselling and clinical monitoring. At six months, all baseline clinical and laboratory tests were repeated, including measured GFR (both baseline and post-protein load) using the 99m Tc-DTPA method. RFR mentioned again as RFR abs and RFR % and the change in RFR after six months on the respective diet was calculated and compared between groups to assess the effect of protein intake on RFR. Statistical analysis Statistical analysis was conducted using SPSS version 25.0 (IBM, Chicago, USA). The Shapiro-Wilk test assessed data normality. Continuous variables were presented as mean ± standard deviation for normally distributed data and median with interquartile range for non-normally distributed data. Categorical variables were expressed as frequencies and percentages. For between-group comparisons of NPD and LPD groups, independent t-test was used for normally distributed variables (age, eGFR, DTPA GFR) while Mann-Whitney U test was applied for non-normally distributed variables (RFR abs , RFR % ) by both methods, at baseline and six months. Within-group changes were analyzed using paired t-test for normally distributed variables and Wilcoxon signed-rank test for non-normally distributed variables. For categorical variables, chi-square test was used for between-group comparisons, with Fisher's exact test applied when expected cell frequencies were less than 5, particularly for low-prevalence variables like alcohol consumption and ACE inhibitor use. Spearman rank correlation was used to assess the relationship between eGFR (MDRD, CKD-EPI), and DTPA GFR to determine their degree of association. Univariate and multivariate logistic regression identified risk factors for low renal reserve, with results expressed as beta coefficients, odds ratios, and 95% confidence intervals. Results Of the total 135 patients, 64 in the normal protein diet (NPD) group and 71 in the low protein diet (LPD) group shown in Consort diagram ( Fig. 1 ). Baseline demographic, clinical, and laboratory parameters for the NPD and LPD groups were comparable as shown in Table 1 . Median baseline RFR (absolute and %) was similar between NPD and LPD groups. Our study revealed a progressive decline in RFR with age: RFR% (DTPA) medians were 9.7 for 18–30, 7.04 for 30–45, and 5.78 for 45–60 years. The highest RFR was 45.5% in a 22-year-old male, while the lowest was − 11.8% in a 59-year-old female. Table 1 -Comparison of baseline characteristics between NPD and LPD. Baseline characteristics Total NPD LPD P value Age (years, mean ± SD) 45.97 ± 11.66 46.52 ± 11.17 45.48 ± 12.14 0.608‡ Male Gender 81 (60%) 43 (67.19%) 38 (53.52%) 0.106† Smoker 23 (17.04%) 15 (23.44%) 8 (11.27%) 0.090* Alcohol consumer 10 (7.41%) 6 (9.38%) 4 (5.63%) 0.418* Birth weight 2 kg 87 (64.44%) 41 (64.06%) 46 (64.79%) Lifestyle Sedentary 130 (96.30%) 61 (95.31%) 69 (97.18%) 0.414* Active 5 (3.70%) 3 (4.69%) 2 (2.82%) CKD stage CKD Stage 3 68 (50.37%) 33 (51.56%) 35 (49.30%) 0.793† CKD Stage 4 67 (49.63%) 31 (48.44%) 36 (50.70%) Basic disease DKD 20 (14.81%) 12 (18.75%) 8 (11.27%) 0.408* Hypertension 8 (5.93%) 6 (9.38%) 2 (2.82%) Others 107 (79.26%) 46 (71.88%) 61 (85.91%) ACE inhibitors 12 (8.89%) 4 (6.25%) 8 (11.27%) 0.374* ARB 93 (68.89%) 41 (64.06%) 52 (73.24%) 0.250† SGLT2 inhibitors 32 (23.70%) 17 (26.56%) 15 (21.13%) 0.45 General appearance Well nourished 103 (76.30%) 51 (79.69%) 52 (73.24%) 0.518* Malnourished 30 (22.22%) 13 (20.31%) 17 (23.94%) eGFR (MDRD, mL/min/1.73 m²) 30.4 (21.45–42.25) 29.4 (23.3–42.73) 30.4 (19.6–41.35) 0.252§ MAP (mmHg, mean ± SD) 97.22 ± 8.37 97.92 ± 9.01 96.59 ± 7.88 0.367‡ BMI (kg/m², mean ± SD) 22.59 ± 2.57 22.46 ± 2.19 22.71 ± 2.88 0.564‡ Hemoglobin (g/dL) 10.3 (9.45–11.95) 10.5 (9.4–11.95) 10.3 (9.55–11.85) 0.651§ Serum creatinine (mg/dL) 2.2 (1.6–2.8) 2.1 (1.662–2.8) 2.3 (1.6–2.85) 0.751§ Serum albumin (g/dL, mean ± SD) 4.0 ± 0.52 3.95 ± 0.6 4.06 ± 0.44 0.215‡ Total cholesterol (mg/dL) 140 (130–160) 140 (130–157) 139 (130–180) 0.677§ Triglycerides (mg/dL) 112 (92–150) 123.5 (98–152.75) 108 (84–138.5) 0.088§ UACR (mg/g) 0.6 (0.356–1.846) 0.52 (0.36–1.497) 0.74 (0.356–1.846) 0.716§ DTPA GFR – pre protein load 27 (19.5–40) 26 (20.075–40.028) 30 (19–40) 0.890§ DTPA GFR – post protein load 30 (22–43) 29 (22–43) 33 (20.5–43) 0.827§ RFR abs DTPA (mL/min/1.73 m²) 2 (1–3) 2 (1–3) 2 (1–3.2) 0.979§ RFR % DTPA 7.14 (3.798–12.549) 7.14 (2.308–13.202) 7.32 (4.191–11.438) 0.855§ RFR abs CrCl (mL/min/1.73 m²) (n = 100) 2.45(0.875–4.525) 2.8(0.9–4.5) 1.4(0.25–4.5) 0.17 ** RFR % CrCl (n = 100) 8.35(2.875–15.5) 6.3(3.9–18.6) 8.4(1.25–12.2) 0.22 ** Baseline characteristics compared between NPD and LPD group. Values are presented as mean ± standard deviation (SD) for normally distributed continuous variables, median (interquartile range, IQR) for non-normally distributed variables, and number (percentage) for categorical variables. Statistical analysis used: ‡ Independent t test, § Mann Whitney test, * Fisher's exact test, † Chi square test. NPD: Normal Protein Diet, LPD: Low Protein Diet, CKD: Chronic Kidney Disease, DKD: Diabetic Kidney Disease, ACE: Angiotensin-Converting Enzyme inhibitor, ARB: Angiotensin Receptor Blocker, SGLT2: Sodium-Glucose Cotransporter-2 inhibitor, BMI: Body Mass Index, eGFR: estimated Glomerular Filtration Rate, MAP: Mean Arterial Pressure, UACR: Urine Albumin-to-Creatinine Ratio, DTPA: Diethylenetriamine pentaacetic acid clearance, CrCl: Creatinine Clearance, RFR abs : Absolute Renal Functional Reserve, RFR % : Percentage Renal Functional Reserve. Effect of Protein diet on RFR and GFR At baseline, mean GFR increased significantly after protein loading in both NPD (34.33 ± 12.53 to 37.43 ± 13.45 mL/min/1.73 m², p < 0.001) and LPD groups (31.44 ± 11.69 to 34.31 ± 12.66 mL/min/1.73 m², p < 0.001), with no significant difference between groups (p = 0.88). At 6 months, a similar significant rise was seen in both NPD (32.38 ± 11.29 to 34.53 ± 12.31 mL/min/1.73 m², p = 0.02) and LPD groups (30.73 ± 11.38 to 32.99 ± 12.25 mL/ min/1.73 m², p < 0.001), but the between-group difference became significant (p = 0.03) ( Table 2 ). Table 2 Comparison of mean GFR before and after protein load (by DTPA method) at baseline and at 6 months in LPD and NPD groups. Time point Diet group GFR before protein load (Mean ± SD) GFR after protein load (Mean ± SD) P value Baseline NPD (n = 64) 34.33 ± 12.53 37.43 ± 13.45 < 0.001* 0.88 † LPD (n = 71) 31.44 ± 11.69 34.31 ± 12.66 < 0.001* 6 months NPD (n = 64) 32.38 ± 11.29 34.53 ± 12.31 0.02* 0.03 † LPD (n = 71) 30.73 ± 11.38 32.99 ± 12.25 < 0.001* Values are expressed as mean ± standard deviation (SD). Within-group comparisons (pre- vs. post-protein load) were performed using paired t-tests (*), while comparisons between groups (NPD vs. LPD) were made using independent t-tests (†). NPD: Normal Protein Diet; LPD: Low Protein Diet; GFR: Glomerular Filtration Rate; DTPA: Diethylenetriaminepentaacetic acid. Figure 2 (a) and (b) shows a significantly smaller percentage of decline in RFR abs and RFR % by DTPA method in the LPD group compared to the NPD group (p = 0.002 and p = 0.001, respectively). Bar graphs illustrating (a) RFR abs values and (b) RFR % at baseline and 6 months, along with the change after 6 months, in patients on a NPD and LPD. Values above each bar represent group medians. RFR abs : Absolute Renal Functional Reserve; RFR % : Percentage Renal Functional Reserve; NPD: Normal Protein Diet; LPD: Low Protein Diet. The median change in RFR abs and RFR % by CrCl method showed statistically significant difference between the diet groups with more decline in NPD group after 6 months (p = 0.043 and p = 0.043 respectively) ( Fig. 3 (a) and (b)) . Bar (a) depicts RFR abs (mL/min) and bar (b) depicts RFR % at baseline, at 6 months, and the change at 6 months in NPD and LPD groups. Values above each bar represent group medians. RFR abs : Absolute Renal Functional Reserve; RFR % : Percentage Renal Functional Reserve; NPD: Normal Protein Diet; LPD: Low Protein Diet. There was no statistically significant difference in change in eGFR between two diet groups at 6 months by both MDRD and CKD-EPI methods of eGFR (p = 0.932 and p = 0.793, respectively) ( Fig. 4 a and b ). Bar (a) shows median change in eGFR by the MDRD equation, and bar (b) shows median change in eGFR estimated by the CKD-EPI Creatinine 2021 equation, after 6 months in NPD and LPD groups. Values above each bar represent the group median change in eGFR. eGFR: estimated Glomerular Filtration Rate; MDRD: Modification of Diet in Renal Disease; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration; NPD: Normal Protein Diet; LPD: Low Protein Diet RFR measurements obtained from urine creatinine clearance and the two plasma 99m Tc-DTPA method were positively correlated, with a correlation coefficient of 0.619 (p = 0.03). Both eGFR MDRD and CKD-EPI Creatinine (2021) demonstrate strong, statistically significant correlations with gold standard DTPA GFR measurements, with correlation coefficients of 0.925 and 0.706 respectively. In patients with low renal reserve, the change in RFR % at six months was significantly greater in the LPD group (median + 4.31% [–1.261 to 8.2]) versus the NPD group (median + 0.25% [–3.631 to 2.963]), p = 0.011 ( Fig. 5 ). Low Protein Diet. Bar (a) shows the median RFR abs ( mL/min) and bar (b) the median RFR % at 6 months in the NPD and LPD groups. Values above each bar indicate group medians. RFR abs : Absolute Renal Functional Reserve; RFR % : Percentage Renal Functional Reserve; NPD: Normal Protein Diet; LPD: Low Protein Diet. Univariate analysis showed that increased age, lower eGFR, higher serum creatinine and higher UACR were significant risk factors for low RFR ( Table 3 ). BMI, serum albumin, gender, birth weight, hypertension, diabetes, and diabetic kidney disease were not significantly associated with low RFR (all p > 0.05). Multivariate analysis revealed no independent predictors for low RFR, likely due to interrelated variables. Table 3 Univariate logistic regression to assess significant factors affecting RFR % below median {<7.14}. Variable Beta coefficient SE P value OR 95% CI Age (years) 0.153 0.074 0.040 1.165 1.007–1.348 BMI (kg/m²) -0.025 0.129 0.849 0.976 0.758–1.256 eGFR (MDRD) (mL/min/1.73 m²) -0.172 0.055 0.002 0.842 0.757–0.937 CKD-EPI Creat (2021) (mL/min/1.73 m²) -0.315 0.130 0.032 0.730 0.548–0.974 DTPA GFR (mL/min) -1.419 0.500 0.005 0.242 0.091–0.645 S. creat(mg/dL) 0.048 0.024 0.046 1.049 1.001-1.100 S. Alb(g/dL) -0.777 0.758 0.305 0.460 0.104–2.031 UACR (mg/g) 0.095 0.030 0.002 0.910 0.858–0.966 Male 0.451 0.673 0.503 1.570 0.420–5.873 Birth weight 2 kg 0.701 0.895 0.434 2.015 0.348–11.654 Hypertension 0.000 1.250 1.000 1.000 0.086–11.597 Diabetes 1.326 0.815 0.104 3.764 0.762–18.586 Basic disease DKD -2.181 1.705 0.201 0.113 0.004–3.189 Univariate logistic regression analysis assessing factors associated with low renal reserve. Beta coefficient (β), standard error (SE), p-value, odds ratio (OR), and 95% confidence intervals (CI) are reported for each variable. BMI: Body Mass Index; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration (Creatinine 2021 equation); CI: Confidence Interval; DTPA GFR: Glomerular Filtration Rate measured by Diethylenetriamine Pentaacetic Acid clearance; DKD: Diabetic Kidney Disease; eGFR: Estimated Glomerular Filtration Rate (MDRD or CKD-EPI equations); NPD: Normal Protein Diet; LPD: Low Protein Diet; OR: Odds Ratio; RFR: Renal Functional Reserve; RFR % : Percentage Renal Functional Reserve; RFR abs : Absolute Renal Functional Reserve; SE: Standard Error; S. Alb: Serum Albumin; S. creat: Serum Creatinine; UACR: Urine Albumin-to-Creatinine Ratio. Dietary Adherence and Actual Protein Intake Comparison between actual and prescribed protein intake at 0,3 and 6 months is shown in Fig. 6 . Median measured protein intake, estimated from 24‑hour urinary urea nitrogen, closely matched the prescribed intake in both groups at 3 and 6 months, confirming good dietary adherence throughout the study. Bar (a) displays mean actual versus prescribed daily protein intake over 0, 3, and 6 months in the NPD group. Bar (b) displays the same for the LPD group. NPD: Normal Protein Diet; LPD: Low Protein Diet. Discussion This randomized controlled trial showed the relationship of change in RFR abs and RFR % between NPD and LPD in patients with stage 3 and 4 CKD. Our study is a novel contribution in being the first to simultaneously employ both gold standard 99m Tc-DTPA clearance and urine creatinine clearance method for comprehensive RFR estimation. Both dietary approaches influenced RFR abs and RFR % , their impact varied, with LPD showing a trend toward better preservation of RFR over six months without affecting the eGFR. Further, adherence to dietary protein diet by measuring 24 hour urinary urea nitrogen between the two groups and it confirms the adherence of dietary advise. The baseline demographic analysis revealed that our study population primarily comprised middle-aged individuals with a male predominance consistent with previous studies, evaluating RFR in CKD populations. 14 , 17 , 18 Multiple studies have shown that RFR decreases as CKD stages advance and with increasing age in alignment with our study. 19 , 20 Data also confirms that men tend to have higher GFR and RFR than women, linked to both larger kidney mass and differences in tubular transporter expression, consistent with our finding of the highest RFR in a young male and lowest in an older female. 21 , 22 Discrepancies in sex distributions may affect findings; some reports suggest females can have similar or higher tubular reserve capacity depending on the test method and underlying disease. 23 Our study found no significant association between birth weight and RFR unlike previous studies 24 , likely due to recall bias and approximate participant reporting. No gender differences in RFR were seen, contrasting some reports suggesting male hyperfiltration 25 or female hormonal protection 23 ; this may reflect our relatively homogeneous, middle-aged sample with similar renal function and diets. There is evidence that increased BMI is associated with higher single-nephron GFR and transiently higher total GFR. 4 Notably, the highest BMI noted in our study (31.1) was associated with a relatively preserved RFR (8.3%). However, single nephron GFR was not calculated in our study. In contrast, some prospective studies in living kidney donors have reported that higher BMI is associated with a greater decline in RFR following nephrectomy. However, this evidence remains mixed and may be influenced by confounding factors such as the donors’ age and the presence of comorbidities like hypertension within the study population, use of animal vs vegetarian protein ( Table 4 ) . 3 , 4 , 26 However, we observed no correlation between RFR and BMI, possibly due to a narrow BMI range and BMI’s limitations in reflecting body composition. Additionally recent study by Leone et al. suggested BMI alone is an imprecise measure for predicting RFR because fat and lean mass also affect renal function differently. 29 Various studies reported that the mean RFR abs in healthy subjects range at a higher end from 25 to 31 mL/min and at a lower end from 9–15 mL/min respectively. 18 , 27 Similarly, RFR % values in healthy individuals range at a higher side from 15% to 20% and at a lower side from 8% and 10% respectively. 3,4,14,17 Of note, studies employing creatinine clearance for GFR measurement have consistently observed higher RFR% values (range, 8–25%) compared to those using exogenous marker clearance (range, 4–8%). This pattern is further influenced by the type of protein used for renal stress: oral meat or animal protein loads generally elicit a greater GFR increment and thus higher RFR values than vegetarian or amino acid-based protocols, reinforcing the need to interpret RFR findings in light of methodological context. However our study did not include a healthy cohort for comparison (Table 4 ). Table 4 List of studies on RFR in normal healthy individuals. Serial No. Sample Stimulus RFR Measurement Results Remarks Authors and year 1 5 normal protein diet / 8 vegetarian diet Protein meal Creatinine, urinary inulin Max GFR of 171 ± 7.7 mL/min after 150 min; blunted RFR in reduced nephron mass Nephron number Bosch et al., 1983 3 2 7 AA, meat, milk protein Creatinine clearance Similar CrC increase after AA, meat, milk protein Type of protein Mansy, 1987 28 3 10 Three levels protein load Creatinine clearance Larger GFR increase seen with higher protein loads (up to 1.35 g/kg) Protein dose Rodriguez-Iturbe, 1988 29 4 17 vegetarian vs animal protein Creatinine clearance Animal protein produces larger/sustained GFR increase than vegetarian protein Diet type Kontessis, 1990 30 5 10 elderly AA infusion Creatinine clearance Percent GFR increase comparable to young, but lower baseline/fractional reserve Age Fliser, 1993 31 6 47 hypertensive donors Dopamine, 1.5 µg/kg/min 125I-iothalamate RFR % Pre donation 8% Dopamine, HTN Tent, 2012 32 7. 937 donors Dopamine, 1.5 µg/kg/min 125I-iothalamate RFR % Pre donation 9% Dopamine infusion results in increase plasma flow rather than GFR van Londen, 2018 33 8 105 female donors Dopamine, 1.5 µg/kg/min 125I-iothalamate RFR % Pre donation 8% Dopamine, female donors van Londen, 2018 33 Table depicts key studies investigating renal functional reserve (RFR) in normal healthy individuals, detailing sample size, stimulus method, RFR measurement techniques, results, and study remarks. AA: Amino acids, CrC: Creatinine clearance, DTPA: Diethylenetriamine pentaacetic acid, GFR: Glomerular filtration rate, NSAIDs: Nonsteroidal anti-inflammatory drugs, RFR: Renal functional reserve, RFR%: Percentage renal functional reserve, RFRabs: Absolute renal functional reserve All GFR values are expressed as mean ± standard deviation (SD) or percentages as reported in original studies. Our study included patients with CKD stage 3 and 4 with mean GFR of the study population as 31.9 ± 11.7 mL/min with median RFR abs and RFR % as 2 mL/min and 7.14% and a significant increase in GFR post protein loading. Previous studies have shown significant increase post protein loading similar to our study. 3 , 17 Contrary to our study, Uemasu et al. reported that patients with baseline GFR between 40 and 90 mL/min exhibited no significant increase in GFR post protein loading test. 34 Importantly, their study used glucagon as the stimulus and thiosulphate clearance for GFR measurement, whereas we used an oral protein load and different GFR estimation methods in a cohort with lower baseline GFR. Notably, glucagon typically produces a lower GFR response than protein or amino acids, as it primarily increases renal plasma flow rather than glomerular filtration, resulting in a blunted RFR response. 34 – 36 Study by Barai et al., has reported median RFR % values comparable to those observed in our study with CKD stages 3 and 4. 17 Similarly, Bosch et al. showed that RFR abs is severely blunted in CKD or reduced nephron mass states. 3 Data from living kidney donors show that both RFR abs and RFR % are partially maintained post-donation but reduced compared to healthy controls. 4 , 22 , 33 This highlights nephron mass as a crucial determinant of RFR, thus explaining the lower renal reserve observed in CKD as a state of diminished nephron number. Contrary to our study, Krishna et al., showed RFR % of 20 ± 13% and 21 ± 14% in patients with moderate renal failure and advanced renal failure respectively which was similar to that of normal healthy adults. 37 Also, in contrast to our study, Loo et al. demonstrated that healthy subjects and patients with renal disease had a mean RFR abs of 13.5 mL/min in patients with renal disease. 18 This difference in both the studies may be attributed to usage of a large amount of meat protein for GFR stimulation, whereas we employed vegetarian oral protein; additionally, their study population had a higher mean GFR compared to our cohort ( Table 5 ) . Table 5 List of studies on RFR in patients with Kidney Disease. Serial no. Condition / Population Type of Stimulus GFR Measurement Methods Key Results Summary Reference(s) 1. CKD stages 1–4 Protein meal, AA infusion Creatinine clearance, DTPA, Inulin Progressive loss of RFR with CKD stages; lower RFR in diabetes and hypertension Bosch et al. (1986), 3 2. 8 healthy, 9 CGN (GFR > 90), 8 CGN (GFR 40–90) Glucagon Thiosulphate clearance (THIO) Normal controls: increased GFR & ERPF; CGN with preserved GFR no ERPF increase Uemasu, 1991 34 3. 15 CKD patients Protein meal Creatinine clearance Preserved renal reserve in CKD; enalapril did not affect RFR Krishna, 1991 37 4. 32 CKD, 19 transplant, 12 donors, 62 healthy Protein meal Creatinine clearance Healthy RFR = 31 mL/min; CKD lower (13.5 mL/min); transplant intermediate Loo, 1994 18 5. Type 1 and Type 2 DM with/without nephropathy Amino acids, Protein load Creatinine clearance, EDTA Reduced RFR in diabetic nephropathy; no RFR in advanced disease; response varies with albuminuria Brouhard et al. (1990), 7 Sackmann et al. (2000) 38 6. 25 controls, 100 CKD Protein meal 25 controls, 100 CKD 99m Tc-DTPA Mean RFR (%): controls 23.4; CKD 1:19.08; CKD 2:15.4; CKD 3:8.9; CKD 4:6.7 Barai et al. (2010), 17 7. Essential hypertension Amino acids, Protein meal Creatinine clearance, EDTA Blunted RFR in hypertensive patients correlating with albuminuria and vascular resistance Gaipov et al. (2016) 39 8. 135 CKD (Stage 3 and 4) Vegetarian protein 99m Tc DTPA, Creatinine clearance Median RFR abs and RFR % by 99m Tc DTPA and Creatinine Clearance- 2, 7.14 ;and 2.45 and 8.35 respectively Present study This table summarizes studies evaluating renal functional reserve (RFR) in various renal disease populations, indicating clinical context, type of stimulus, GFR measurement methods, and key findings. AA: Amino acids, CKD: Chronic kidney disease, CGN: Chronic glomerulonephritis, CrC: Creatinine clearance, DTPA: Diethylenetriamine pentaacetic acid clearance, EDTA: Ethylenediaminetetraacetic acid clearance, ERPF: Effective renal plasma flow, GFR: Glomerular filtration rate RFR: Renal functional reserve, RFR%: Percentage renal functional reserve, RFRabs: Absolute renal functional reserve, THIO: Thiosulphate clearance. GFR values and RFR are presented as reported in individual studies, reflecting variations related to disease severity and patient condition. The values of RFR abs and RFR % estimated by both methods in our cohort were comparable and showed no statistically significant difference. Also various GFR measurement methods were also assessed in our study, demonstrating similar results across eGFR, creatinine clearance and DTPA clearance methods. This is supported by various previous studies. 40 Various previous studies have used creatinine clearance as a method of RFR estimation but none has compared it with gold standard DTPA clearance method similar to our study. 13 , 18 , 22 Although the difference between two methods was non-significant, but the variability in the range of values in creatinine clearance group was wider. This concordance suggests that, despite its limitations, creatinine clearance remains a feasible and acceptable method for RFR estimation 99m Tc-DTPA clearance. 99m Tc-DTPA, offers highly accurate and direct measurement of GFR, while creatinine clearance is a widely used, practical, and cost-effective alternative that can be performed quickly and noninvasively. Although some studies done in normal healthy individuals and CKD patients, 41 have shown overestimation of GFR by creatinine clearance method as compared to two plasma DTPA method, but these studies have not estimated the RFR, hence use of creatinine clearance for RFR estimation and if it is comparable to gold standard DTPA, needs further large scale studies. Additionally, Viberti et al. showed that dietary protein acutely modulates RFR, supporting the concept that glomerular hemodynamics are sensitive to protein intake. The short-term hyperfiltration response seen after protein loading may become maladaptive over time, further emphasizing the importance of dietary protein moderation in CKD management. Our study findings showing that post-protein stimulation GFR was higher in patients on LPD compared to those on NPD after 6 months similar to previous studies. 7 , 14 Although some reports found comparable GFR rises after acute protein loading regardless of diet, these involved shorter durations which may have limited dietary effects. 14 We also demonstrated that patients on a LPD exhibited significantly less decline in both RFR abs and RFR % over six months compared to those on a NPD. This suggests that a lower dietary protein intake helps preserve RFR and may protect against the glomerular injury and hyperfiltration commonly seen with higher protein intake. Our findings align closely with foundational research by Brouhard et al., who reported preserved RFR in diabetic nephropathy patients on LPD versus decline in those on NPD after 12 months on the particular diet. 7 The key difference in that they exclusively included diabetic nephropathy patients with higher eGFR and prescribed a normal protein diet of 1 g/kg/day, compared to our cohort with lower eGFR and a 0.8 g/kg/day protein intake. This makes our results more generalizable to real-world CKD patients and protein intake recommendations. Additionally, Viberti et al. showed that dietary protein acutely modulates RFR, further supporting our observations. 42 Our study observed that eGFR declined similarly in both the NPD and LPD groups. These results align with a meta-analysis by Fouque et al. and other similar studies have indicated LPD does not prevent GFR decline. 43 , 44 However some studies have shown that protein restriction may slow CKD progression. 45 , 46 The lack of significant dietary impact in our study likely reflects its short six-month duration; longer-term studies are needed to clarify dietary effects on CKD progression. Regarding risk factors associated with reduced RFR, multiple studies have identified lower baseline GFR, higher serum creatinine, older age, diabetes, hypertension, and proteinuria as significant contributors to diminished renal reserve. , 35 For instance, Barai et al. found that advancing CKD stage and reduced baseline GFR strongly predict lower RFR. 17 Similarly, Brouhard and LaGrone noted that diabetic nephropathy, with its characteristic hyperfiltration and glomerular injury, is linked to decreased RFR, highlighting the role of diabetes as a risk factor. 7 Other authors have implicated hypertension and increased albuminuria as modifiers of renal reserve by promoting glomerulosclerosis and nephron loss. Our study’s findings are congruent with these known risk factors. Univariate logistic regression analysis revealed that higher age, lower baseline eGFR (using both MDRD and CKD-EPI 2021 equations), higher serum creatinine and proteinuria were significantly associated with low RFR. These were similar to previous studies. 4,17,25 Although multivariate analysis did not confirm independent predictors, the trends align with established literature indicating that diminished baseline kidney function correlates with reduced adaptive renal capacity. The lack of statistical significance in multivariate models likely reflects sample size and interrelatedness of variables but does not negate clinical relevance. The major strengths of our study include its randomized controlled design, use of gold standard 99m Tc-DTPA clearance and urine creatinine clearance methods for GFR measurement, standardized protein loading protocol, rigorous dietary adherence monitoring through 24-hour urinary urea nitrogen, and comprehensive six-month follow-up with repeated RFR abs and RFR % assessments. The study's focus on RFR as a dynamic marker of renal health represents a novel approach that may enhance early detection of renal functional decline. Some limitations of our study include the short six-month follow-up period, which may be inadequate to assess long-term clinical outcomes such as CKD progression or cardiovascular events, and restriction to a cohort with stage 3 and 4 CKD. Additionally, the study did not control for non-protein dietary factors like sodium or phosphorus intake, which may independently influence kidney function and disease progression in this population. Clinical Implications and Future Directions Long-term studies are needed to determine if a low-protein diet (LPD) truly offers superior renal functional reserve (RFR) preservation compared to a normal protein diet (NPD) beyond six months. Future research should evaluate strategies combining dietary interventions (such as adding ketoanalogues to LPD) and pharmacological agents including ACE inhibitors, SGLT2 inhibitors, and mineralocorticoid antagonists for optimizing kidney health. Systematic investigation of other macronutrients, stratification by proteinuria status, and standardized RFR measurement protocol, potentially including single-nephron GFR, could yield important insights into renal adaptability and resilience, ultimately improving care and outcomes for high-risk CKD patients. Conclusion A low-protein diet preserves RFR, even when conventional measures such as eGFR remain unchanged in CKD patients, supporting the value of early dietary intervention to maintain kidney health. RFR assessment offers unique early insights into renal adaptability and resilience, making it a valuable surrogate endpoint for future studies on dietary and therapeutic strategies. Higher baseline function and lower proteinuria favoured better RFR. While short-term differences in kidney function (eGFR) between diets were limited, proteinuria strongly correlated with greater RFR loss, supporting a tailored protein-restricted approach, especially in early-stage CKD, proteinuria and those with low reserve. Notably, our study is the first to simultaneously use both gold standard 99m Tc-DTPA clearance and urine creatinine clearance for comprehensive RFR estimation. While urine creatinine clearance can be cumbersome, we found it to be cost-effective and comparable to DTPA clearance for RFR assessment. Abbreviations AA: Amino Acids ACE: Angiotensin-Converting Enzyme inhibitor ARB: Angiotensin Receptor Blocker BMI: Body Mass Index CKD: Chronic Kidney Disease CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration CrCl: Creatinine Clearance CTRI: Clinical Trial Registry India DKD: Diabetic Kidney Disease DTPA: Diethylenetriamine pentaacetic acid EDTA: Ethylenediaminetetraacetic acid clearance eGFR: Estimated Glomerular Filtration Rate ERPF: Effective Renal Plasma Flow GFR: Glomerular Filtration Rate HTN: Hypertension LPD: Low Protein Diet MAP: Mean Arterial Pressure MDRD: Modification of Diet in Renal Disease NPD: Normal Protein Diet NSAIDs: Nonsteroidal Anti-Inflammatory Drugs OR: Odds Ratio PET: Positron Emission Tomography RFR: Renal Functional Reserve RFR abs : Absolute Renal Functional Reserve RFR % : Relative Renal Functional Reserve SGLT2: Sodium-Glucose Cotransporter-2 inhibitor SD: Standard Deviation SE: Standard Error THIO: Thiosulphate clearance UACR: Urine Albumin-to-Creatinine Ratio UUN: Urine Urea Nitrogen Declarations Ethics approval and consent to participate: Ethical Committee clearance from institutional committee of Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) & Dr. Ram Manohar Lohia (R.M.L.) Hospital, New Delhi, India, vide number F.No TP (DM/MCH) 30/2023) /IEC/ABVIMS/RMLH 1405 dated 3rd August 2023. This study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Trial Registry - Clinical Trial Registry (India) CTRI – CTRI/2024/08/071864 dated 2 nd August 2024 Written informed consent was acquired from the participants prior to their inclusion in the study. All methods were carried out in accordance with relevant guidelines and protocols. • Consent for publication: Not applicable • Availability of data and materials: yes data will be shared when asked Point of contact for data and materials: Dr. Himansu Sekhar Mahapatra, Director Professor and Head, Department of Nephrology, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) & Dr. Ram Manohar Lohia (R.M.L.) Hospital, New Delhi, India Institutional Address: Room No 307, PGI BUILDING, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) & Dr. Ram Manohar Lohia (R.M.L.) Hospital, New Delhi, India , Phone: 9968474805, Email: [email protected] • Competing interests: we have no competing interest. • Funding: not applicable • Authors' contributions: Author I: Dr. Varuna Yadav: Conceptualization, Formal analysis, Writing - Original Draft. Author II: Dr. Himansu Sekhar Mahapatra: Methodology, Validation, Formal analysis, Data Curation. (CORRESPONDING AUTHOR) Author III: Dr. Madhavi Tripathi: DTPA procedure and techincality Author IV: Dr. Lalit Pursnani: Investigation, Resources, Writing - Review& Editing. Author V: Dr. MuthuKumar Balakrishnan: Supervision, Project administration, Critical revision of the manuscript. Author VI: Dr. Renju Binoy: Patient recruitment, Clinical data acquisition, Investigation. 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Kidney Int Rep. 2025;10(2):S322. 10.1016/j.ekir.2024.11 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7778811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536149642,"identity":"dced33e5-7077-4c01-8b56-a51537e687ef","order_by":0,"name":"Varuna Yadav","email":"","orcid":"","institution":"Dr. Ram Manohar Lohia Hospital","correspondingAuthor":false,"prefix":"","firstName":"Varuna","middleName":"","lastName":"Yadav","suffix":""},{"id":536149644,"identity":"aa123fd5-6288-4f73-80a8-d660142bb1b9","order_by":1,"name":"Himansu 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1","display":"","copyAsset":false,"role":"figure","size":14186,"visible":true,"origin":"","legend":"\u003cp\u003eConsort diagram depicts the flow of participants through the study, starting with 145 eligible patients who underwent baseline investigation including measurement of renal functional reserve (RFR). Following randomization, 70 were allocated to the normal protein diet (NPD) group and 75 to the low protein diet (LPD) group. After six months, 10 patients were lost to follow-up (NPD: 6; LPD: 4), resulting in a final cohort of 135 patients (NPD: 64; LPD: 71), in whom repeat biochemical assessments and renal functional reserve outcomes were analysed.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/97a02779f6d83fe74fe0753e.png"},{"id":94846176,"identity":"14ff0f14-307b-4e19-858e-d2517e3d366b","added_by":"auto","created_at":"2025-10-31 10:12:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange in RFR \u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eabs\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (a) and RFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e\u0026nbsp; (b) by DTPA method at 6 months between NPD and LPD.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/dce875650fd37bbf31a02d45.png"},{"id":94985523,"identity":"40306e45-25d3-4db0-990e-5eb397bd8867","added_by":"auto","created_at":"2025-11-03 06:58:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of change in RFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eabs \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e\u0026nbsp;(a) and RFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (b) by CrCl method between LPD and NPD\u0026nbsp; groups in patients over 6 months.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/264cd64f37ee5cd41f26b70a.png"},{"id":94846179,"identity":"5c9f8f00-6dad-4d04-a392-c45eda832f38","added_by":"auto","created_at":"2025-10-31 10:12:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange in eGFR (MDRD) (a) and eGFR (CKD EPI Creatinine 2021) (b) at 6 months between NPD and LPD.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/4905b6ec97edbce77a91298c.png"},{"id":94846178,"identity":"59da6d60-567c-42b6-89a8-fa9ae76e3c52","added_by":"auto","created_at":"2025-10-31 10:12:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of change in RFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eabs \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e(a) and RFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (b) at 6 months for patients with low RFR between NPD and LPD groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/e1449f0f738df59d86040674.png"},{"id":94846187,"identity":"266c50b9-32c9-4bca-826c-43d3ea27c3bb","added_by":"auto","created_at":"2025-10-31 10:12:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":179762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of protein intake in grams per 24 hours between actual and required protein intake in (a) NPD and (b) LPD groups at different timepoint.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/f523800e31825f04f2cf0def.png"},{"id":98433978,"identity":"bd7d253c-d90b-47fc-804c-e6674e54a176","added_by":"auto","created_at":"2025-12-17 16:51:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2464002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7778811/v1/00de7bd2-bbc8-444c-ba3c-e663650266e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Effect of Normal Protein and Low Protein Diet on Renal Function Reserve in Patients of Chronic Kidney Disease Stage 3 And 4: A Randomised Controlled Study ","fulltext":[{"header":"Background","content":"\u003cp\u003eRenal Function Reserve (RFR), defined as the difference between peak and baseline GFR, provides a dynamic assessment of the kidney\u0026rsquo;s adaptability to physiological stressors such as protein intake, dopamine infusion, and states of hyperfiltration.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This adaptability is regulated by complex interactions among renal hemodynamics, hormonal responses, and tubuloglomerular feedback mechanisms.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn healthy individuals, RFR allows GFR to increase by up to 20% following a protein load, highlighting the kidney\u0026rsquo;s ability to respond efficiently to metabolic challenges.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Studies have shown progressive reduction in RFR during the course of CKD progression. Multiple factors influence GFR and RFR, including age, gender, hypertension, diabetes, obesity, and diet.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Notably, high sodium and animal protein intake increase glomerular hyperfiltration and accelerate CKD progression,\u003csup\u003e5,6\u003c/sup\u003e while low-protein diets (LPD) can reduce hyperfiltration and improve RFR, in diabetic nephropathy.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These studies on RFR primarily focused on cross-sectional comparisons and did not systematically assess the impact of dietary protein intake on RFR or its trajectory over time in moderate CKD. Whether a normal protein diet (NPD) or LPD better preserves RFR is still unclear.\u003c/p\u003e\u003cp\u003eThe measurement of RFR relies on accurate GFR assessment, yet the gold-standard method inulin clearance is impractical for routine clinical use. Estimated GFR (eGFR) formulas, though widely used, provide only a static approximation of renal function and lack the capacity to detect dynamic changes.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The two-plasma sample technetium-99m diethylenetriamine pentaacetic acid (DTPA) method offers more precise GFR measurement,\u003csup\u003e9\u003c/sup\u003e while emerging imaging modalities such as functional magnetic resonance imaging and contrast-enhanced ultrasound present promising non-invasive alternatives.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eCreatinine clearance is widely used due to ease and cost-effectiveness but may be less sensitive and affected by tubular secretion and muscle mass.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e However, optimization and validation of appropriate GFR estimation method are needed to enhance precision and clinical integration of RFR measurements.\u003c/p\u003e\u003cp\u003eCommon hemodynamic stimulants include intravenous dopamine, while metabolic stimulants frequently used are intravenous amino acids and oral protein loads.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The response magnitude depends on the type of stimulant, with intravenous amino acids and oral protein loads typically inducing substantial increases in GFR over 1 to 3 hours.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The type of protein used for oral loading also influences the response, with meat-based proteins generally causing greater GFR increases than vegetable-based proteins.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAlthough few studies have shown the relationship of protein diet and RFR, no randomised control study has been conducted to know the exact relationship of RFR in different stages of CKD through DTPA GFR estimation. Further, RFR is also not routinely evaluated in practice and its role as an early marker of CKD progression remains under explored.\u003c/p\u003e\u003cp\u003eGiven these gaps, the present study aims to compare the effects of normal and low-protein diets on RFR and disease progression in CKD stages 3 and 4, with the goal of refining dietary recommendations to help preserve renal adaptability and improve outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003cp\u003e This single-centre, randomized, parallel-group study was conducted in the Department of Nephrology at a tertiary teaching hospital from August 2024 to January 2025 after obtaining Institutional Ethics Committee approval.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion and Exclusion Criteria\u003c/strong\u003e\u003cp\u003eAfter obtaining written informed consent, eligible patients aged 18 to 60 years with stage 3 or 4 CKD, attending the nephrology outpatient clinic were included. Acute illness or unstable renal function within the past 4 weeks, neurological or psychiatric disorders, malignancy, pregnancy or lactation, previous renal transplantation, chronic steroid use, hypercalcemia, alcohol abuse, and chronic liver disease were excluded.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Size:\u003c/h2\u003e\u003cp\u003eIn the absence of definitive studies specifically examining the relationship between protein diet and RFR, we based our sample size calculation on data from the MDRD trial \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, the largest study to evaluate the impact of protein intake on GFR. This study reported a mean difference of 1.6 with a standard deviation of 2.7. To achieve 85% power at a 95% confidence level, the required sample size was 53 patients per group. Allowing for an anticipated 20% dropout rate, the total sample size was adjusted to 126 participants, equally allocated between the groups.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic details and clinical history were recorded including age, sex, primary renal diagnosis, comorbid conditions, medication use, and lifestyle factors such as smoking and alcohol intake. Anthropometric measurements (weight, height, BMI) were noted. Baseline laboratory testing comprised complete blood count, kidney function tests (serum creatinine, blood urea nitrogen), liver function test, serum electrolytes (sodium, potassium, calcium, phosphate), lipid profile, and urinary albumin-to-creatinine ratio (UACR). Estimated GFR (eGFR) was calculated using the MDRD and CKD-EPI 2021 equations to determine CKD staging for inclusion.\u003c/p\u003e\n\u003ch3\u003eMeasured GFR by Tc-DTPA Two-Plasma Sample Method\u003c/h3\u003e\n\u003cp\u003eMeasured GFR was calculated using Russell\u0026rsquo;s two‑compartment formula for the two‑plasma sample \u003csup\u003e99m\u003c/sup\u003eTc‑DTPA method. In this approach, the injected dose (D) of tracer, expressed in becquerels (Bq) or megabecquerels (MBq), is divided by the difference between two exponential terms derived from the bi‑exponential plasma clearance curve. These terms incorporate coefficients (A and B, in Bq/mL) corresponding to the fast and slow components of tracer elimination, rate constants (k₁ and k₂, in min⁻\u0026sup1;), and sample collection times (t₁ and t₂, in minutes) at 60 and 180 minutes after injection. The final GFR is expressed in millilitres per minute normalized to 1.73 m\u0026sup2; of body surface area (mL/min/1.73 m\u0026sup2;).\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eAssessment of Renal Functional Reserve (RFR) at Baseline\u003c/h3\u003e\n\u003cp\u003eRFR was assessed by measuring GFR response to a standardized vegetarian protein load after an 8-hour overnight fast. The protein challenge consisted of a vegetarian protein bar combined with raw paneer (cottage cheese) to achieve 1 gram/kilogram body weight, based on their respective protein contents (15-20g per protein bar, 18g per 100g paneer). Patients consumed the entire load within 30 minutes under supervision, with hydration maintained to balance urine output throughout the process.\u003c/p\u003e\u003cp\u003eGFR was measured using \u003csup\u003e99m\u003c/sup\u003eTc-DTPA clearance with the two-plasma-sample method, both at baseline (fasting) and post-protein load on two different days at least 48 hours apart. Blood samples were collected at 1 and 3 hours post-tracer injection to determine plasma clearance rates.\u003c/p\u003e\u003cp\u003eCreatinine clearance (CrCl) method for RFR estimation was done in 100 patients. Preliminary hydration was achieved by giving 10 mL/kg of oral water within 10\u0026ndash;15 minutes at the start of the test. Urine volume was recorded one hour after fluid loading (time 0, T0), and thereafter, additional oral water equal to urinary output was administered. Blood samples for serum creatinine were obtained at 30 and 90 minutes after T0, while urine collections for volume and urinary creatinine were taken at 60 and 120 minutes from T0. Subjects then received an oral protein load of 1 g/kg body weight (as described above) within 30 minutes. After 60 minutes, total urine volume was measured; following this, the sampling for blood and urine creatinine was repeated at the same intervals post-protein load. CrCl was calculated at each time point. The lowest CrCl before protein load was considered basal GFR, while the highest CrCl after the load was designated stimulated GFR.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAbsolute RFR (RFR\u003csub\u003eabs\u003c/sub\u003e) was calculated as the difference between post-protein and baseline GFR (mL/min/1.73 m\u0026sup2;), while percentage RFR (RFR\u003csub\u003e%\u003c/sub\u003e) was calculated as [(RFR\u003csub\u003eabs\u003c/sub\u003e/GFR baseline) \u0026times; 100]. Present study also planned to estimate the cut off of RFR from its median value. Lesser to the median value was defined as low RFR. All the risk factors were coordinated with this value to assess the effect of these risk parameters with RFR.\u003c/p\u003e\n\u003ch3\u003eRandomization\u003c/h3\u003e\n\u003cp\u003eAfter baseline assessments, including the RFR estimation, patients were subjected to computer-generated randomization sequence with concealed allocation in a 1:1 ratio; one as Normal Protein Diet (NPD): 0.8 g protein/kg/day and other as Low Protein Diet (LPD): 0.6 g protein/kg/day. Individualized diet plans were provided by a renal dietitian to ensure adequate energy intake (30\u0026ndash;35 kcal/kg/day) alongside assigned protein targets. Dietary adherence was monitored monthly via 3-day dietary recall and 24-hour dietary history which were recorded in a notebook.\u003c/p\u003e\u003cp\u003eTo confirm the dietary adherence, 24 hr urinary urea nitrogen measurement was also done every three months 24‑hour urinary urea nitrogen estimation, from which daily protein intake was calculated.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFollow-Up and Reassessment of RFR at Six Months\u003c/h2\u003e\u003cp\u003ePatients followed their assigned diets for six months with monthly clinic visits for dietary counselling and clinical monitoring. At six months, all baseline clinical and laboratory tests were repeated, including measured GFR (both baseline and post-protein load) using the \u003csup\u003e99m\u003c/sup\u003eTc-DTPA method. RFR mentioned again as RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e and the change in RFR after six months on the respective diet was calculated and compared between groups to assess the effect of protein intake on RFR.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using SPSS version 25.0 (IBM, Chicago, USA). The Shapiro-Wilk test assessed data normality. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data and median with interquartile range for non-normally distributed data. Categorical variables were expressed as frequencies and percentages. For between-group comparisons of NPD and LPD groups, independent t-test was used for normally distributed variables (age, eGFR, DTPA GFR) while Mann-Whitney U test was applied for non-normally distributed variables (RFR\u003csub\u003eabs\u003c/sub\u003e, RFR\u003csub\u003e%\u003c/sub\u003e) by both methods, at baseline and six months. Within-group changes were analyzed using paired t-test for normally distributed variables and Wilcoxon signed-rank test for non-normally distributed variables. For categorical variables, chi-square test was used for between-group comparisons, with Fisher's exact test applied when expected cell frequencies were less than 5, particularly for low-prevalence variables like alcohol consumption and ACE inhibitor use. Spearman rank correlation was used to assess the relationship between eGFR (MDRD, CKD-EPI), and DTPA GFR to determine their degree of association. Univariate and multivariate logistic regression identified risk factors for low renal reserve, with results expressed as beta coefficients, odds ratios, and 95% confidence intervals.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the total 135 patients, 64 in the normal protein diet (NPD) group and 71 in the low protein diet (LPD) group shown in Consort diagram \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e Baseline demographic, clinical, and laboratory parameters for the NPD and LPD groups were comparable as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Median baseline RFR (absolute and %) was similar between NPD and LPD groups. Our study revealed a progressive decline in RFR with age: RFR% (DTPA) medians were 9.7 for 18\u0026ndash;30, 7.04 for 30\u0026ndash;45, and 5.78 for 45\u0026ndash;60 years. The highest RFR was 45.5% in a 22-year-old male, while the lowest was \u0026minus;\u0026thinsp;11.8% in a 59-year-old female.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e-Comparison of baseline characteristics between NPD and LPD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLPD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.97\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.52\u0026thinsp;\u0026plusmn;\u0026thinsp;11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.608\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (67.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (53.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (17.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (23.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (11.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (9.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (5.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.418*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (35.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (35.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (35.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.805*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (64.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (64.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (64.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifestyle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (96.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (95.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (97.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.414*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (3.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (4.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (2.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD Stage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (50.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (51.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (49.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.793\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD Stage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (49.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (48.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (50.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (14.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (18.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (11.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.408*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (9.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (2.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (79.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (71.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (85.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eACE inhibitors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (11.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.374*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eARB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (68.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (64.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (73.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGLT2 inhibitors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (23.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (26.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (21.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral appearance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWell nourished\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (76.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (79.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (73.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.518*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalnourished\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (20.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (23.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR (MDRD, mL/min/1.73 m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4 (21.45\u0026ndash;42.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4 (23.3\u0026ndash;42.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4 (19.6\u0026ndash;41.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAP (mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.367\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.564\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3 (9.45\u0026ndash;11.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5 (9.4\u0026ndash;11.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3 (9.55\u0026ndash;11.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.651\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2 (1.6\u0026ndash;2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1 (1.662\u0026ndash;2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (1.6\u0026ndash;2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum albumin (g/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.215\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (130\u0026ndash;160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (130\u0026ndash;157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (130\u0026ndash;180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (92\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.5 (98\u0026ndash;152.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (84\u0026ndash;138.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUACR (mg/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6 (0.356\u0026ndash;1.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (0.36\u0026ndash;1.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.356\u0026ndash;1.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTPA GFR \u0026ndash; pre protein load\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (19.5\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (20.075\u0026ndash;40.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (19\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.890\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTPA GFR \u0026ndash; post protein load\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (22\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (22\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (20.5\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.827\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eabs\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eDTPA (mL/min/1.73 m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1\u0026ndash;3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.979\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eDTPA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14 (3.798\u0026ndash;12.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14 (2.308\u0026ndash;13.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32 (4.191\u0026ndash;11.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eabs\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eCrCl (mL/min/1.73 m\u0026sup2;) (n\u0026thinsp;=\u0026thinsp;100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45(0.875\u0026ndash;4.525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8(0.9\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(0.25\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eCrCl (n\u0026thinsp;=\u0026thinsp;100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.35(2.875\u0026ndash;15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3(3.9\u0026ndash;18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4(1.25\u0026ndash;12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBaseline characteristics compared between NPD and LPD group. Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed continuous variables, median (interquartile range, IQR) for non-normally distributed variables, and number (percentage) for categorical variables. Statistical analysis used: \u0026Dagger; Independent t test, \u0026sect; Mann Whitney test, * Fisher\u0026apos;s exact test, \u0026dagger; Chi square test. NPD: Normal Protein Diet, LPD: Low Protein Diet, CKD: Chronic Kidney Disease, DKD: Diabetic Kidney Disease, ACE: Angiotensin-Converting Enzyme inhibitor, ARB: Angiotensin Receptor Blocker, SGLT2: Sodium-Glucose Cotransporter-2 inhibitor, BMI: Body Mass Index, eGFR: estimated Glomerular Filtration Rate, MAP: Mean Arterial Pressure, UACR: Urine Albumin-to-Creatinine Ratio, DTPA: Diethylenetriamine pentaacetic acid clearance, CrCl: Creatinine Clearance, RFR\u003csub\u003eabs\u003c/sub\u003e: Absolute Renal Functional Reserve, RFR\u003csub\u003e%\u003c/sub\u003e: Percentage Renal Functional Reserve.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEffect of Protein diet on RFR and GFR\u003c/h2\u003e\n \u003cp\u003eAt baseline, mean GFR increased significantly after protein loading in both NPD (34.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.53 to 37.43\u0026thinsp;\u0026plusmn;\u0026thinsp;13.45 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LPD groups (31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69 to 34.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.66 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with no significant difference between groups (p\u0026thinsp;=\u0026thinsp;0.88). At 6 months, a similar significant rise was seen in both NPD (32.38\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29 to 34.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.31 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.02) and LPD groups (30.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38 to 32.99\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25 mL/ min/1.73 m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but the between-group difference became significant (p\u0026thinsp;=\u0026thinsp;0.03) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of mean GFR before and after protein load (by DTPA method) at baseline and at 6 months in LPD and NPD groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiet group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGFR before protein load (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGFR after protein load (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPD (n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.43\u0026thinsp;\u0026plusmn;\u0026thinsp;13.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.88\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPD (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPD (n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.38\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.03\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPD (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.99\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eValues are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Within-group comparisons (pre- vs. post-protein load) were performed using paired t-tests (*), while comparisons between groups (NPD vs. LPD) were made using independent t-tests (\u0026dagger;). NPD: Normal Protein Diet; LPD: Low Protein Diet; GFR: Glomerular Filtration Rate; DTPA: Diethylenetriaminepentaacetic acid.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003e(a) and (b)\u003c/strong\u003e shows a significantly smaller percentage of decline in RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e by DTPA method in the LPD group compared to the NPD group (p\u0026thinsp;=\u0026thinsp;0.002 and p\u0026thinsp;=\u0026thinsp;0.001, respectively).\u003c/p\u003e\n \u003cp\u003eBar graphs illustrating (a) RFR\u003csub\u003eabs\u003c/sub\u003e values and (b) RFR\u003csub\u003e%\u003c/sub\u003e at baseline and 6 months, along with the change after 6 months, in patients on a NPD and LPD. Values above each bar represent group medians. RFR\u003csub\u003eabs\u003c/sub\u003e: Absolute Renal Functional Reserve; RFR\u003csub\u003e%\u003c/sub\u003e: Percentage Renal Functional Reserve; NPD: Normal Protein Diet; LPD: Low Protein Diet.\u003c/p\u003e\n \u003cp\u003eThe median change in RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e by CrCl method showed statistically significant difference between the diet groups with more decline in NPD group after 6 months (p\u0026thinsp;=\u0026thinsp;0.043 and p\u0026thinsp;=\u0026thinsp;0.043 respectively) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cstrong\u003e(a) and (b))\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBar (a) depicts RFR\u003csub\u003eabs\u003c/sub\u003e (mL/min) and bar (b) depicts RFR\u003csub\u003e%\u003c/sub\u003e at baseline, at 6 months, and the change at 6 months in NPD and LPD groups. Values above each bar represent group medians.\u003c/p\u003e\n \u003cp\u003eRFR\u003csub\u003eabs\u003c/sub\u003e: Absolute Renal Functional Reserve; RFR\u003csub\u003e%\u003c/sub\u003e: Percentage Renal Functional Reserve; NPD: Normal Protein Diet; LPD: Low Protein Diet.\u003c/p\u003e\n \u003cp\u003eThere was no statistically significant difference in change in eGFR between two diet groups at 6 months by both MDRD and CKD-EPI methods of eGFR (p\u0026thinsp;=\u0026thinsp;0.932 and p\u0026thinsp;=\u0026thinsp;0.793, respectively) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea \u003cstrong\u003eand b\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBar (a) shows median change in eGFR by the MDRD equation, and bar (b) shows median change in eGFR estimated by the CKD-EPI Creatinine 2021 equation, after 6 months in NPD and LPD groups. Values above each bar represent the group median change in eGFR. eGFR: estimated Glomerular Filtration Rate; MDRD: Modification of Diet in Renal Disease; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration; NPD: Normal Protein Diet; LPD: Low Protein Diet\u003c/p\u003e\n \u003cp\u003eRFR measurements obtained from urine creatinine clearance and the two plasma \u003csup\u003e99m\u003c/sup\u003eTc-DTPA method were positively correlated, with a correlation coefficient of \u003cstrong\u003e0.619 (p\u0026thinsp;=\u0026thinsp;0.03).\u003c/strong\u003e Both eGFR MDRD and CKD-EPI Creatinine (2021) demonstrate strong, statistically significant correlations with gold standard DTPA GFR measurements, with correlation coefficients of \u003cstrong\u003e0.925\u003c/strong\u003e and \u003cstrong\u003e0.706\u003c/strong\u003e respectively.\u003c/p\u003e\n \u003cp\u003eIn patients with low renal reserve, the change in RFR\u003csub\u003e%\u003c/sub\u003e at six months was significantly greater in the LPD group (median\u0026thinsp;+\u0026thinsp;4.31% [\u0026ndash;1.261 to 8.2]) versus the NPD group (median\u0026thinsp;+\u0026thinsp;0.25% [\u0026ndash;3.631 to 2.963]), p\u0026thinsp;=\u0026thinsp;0.011 \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow Protein Diet.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBar (a) shows the median RFR\u003csub\u003eabs\u003c/sub\u003e ( mL/min) and bar (b) the median RFR\u003csub\u003e%\u003c/sub\u003e at 6 months in the NPD and LPD groups. Values above each bar indicate group medians. RFR\u003csub\u003eabs\u003c/sub\u003e: Absolute Renal Functional Reserve; RFR\u003csub\u003e%\u003c/sub\u003e: Percentage Renal Functional Reserve; NPD: Normal Protein Diet; LPD: Low Protein Diet.\u003c/p\u003e\n \u003cp\u003eUnivariate analysis showed that increased age, lower eGFR, higher serum creatinine and higher UACR were significant risk factors for low RFR \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e BMI, serum albumin, gender, birth weight, hypertension, diabetes, and diabetic kidney disease were not significantly associated with low RFR (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Multivariate analysis revealed no independent predictors for low RFR, likely due to interrelated variables.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate logistic regression to assess significant factors affecting RFR\u003csub\u003e%\u003c/sub\u003e below median {\u0026lt;7.14}.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.007\u0026ndash;1.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.758\u0026ndash;1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR (MDRD) (mL/min/1.73\u0026nbsp;m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u0026ndash;0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD-EPI Creat (2021) (mL/min/1.73\u0026nbsp;m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u0026ndash;0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTPA GFR (mL/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u0026ndash;0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. creat(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001-1.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. Alb(g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u0026ndash;2.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUACR (mg/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.858\u0026ndash;0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.420\u0026ndash;5.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000-1453.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.348\u0026ndash;11.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u0026ndash;11.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.762\u0026ndash;18.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u0026ndash;3.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eUnivariate logistic regression analysis assessing factors associated with low renal reserve. Beta coefficient (\u0026beta;), standard error (SE), p-value, odds ratio (OR), and 95% confidence intervals (CI) are reported for each variable. BMI: Body Mass Index; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration (Creatinine 2021 equation); CI: Confidence Interval; DTPA GFR: Glomerular Filtration Rate measured by Diethylenetriamine Pentaacetic Acid clearance; DKD: Diabetic Kidney Disease; eGFR: Estimated Glomerular Filtration Rate (MDRD or CKD-EPI equations); NPD: Normal Protein Diet; LPD: Low Protein Diet; OR: Odds Ratio; RFR: Renal Functional Reserve; RFR\u003csub\u003e%\u003c/sub\u003e: Percentage Renal Functional Reserve; RFR\u003csub\u003eabs\u003c/sub\u003e: Absolute Renal Functional Reserve; SE: Standard Error; S. Alb: Serum Albumin; S. creat: Serum Creatinine; UACR: Urine Albumin-to-Creatinine Ratio.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDietary Adherence and Actual Protein Intake\u003c/h2\u003e\n \u003cp\u003eComparison between actual and prescribed protein intake at 0,3 and 6 months is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Median measured protein intake, estimated from 24‑hour urinary urea nitrogen, closely matched the prescribed intake in both groups at 3 and 6 months, confirming good dietary adherence throughout the study.\u003c/p\u003e\n \u003cp\u003eBar (a) displays mean actual versus prescribed daily protein intake over 0, 3, and 6 months in the NPD group. Bar (b) displays the same for the LPD group. NPD: Normal Protein Diet; LPD: Low Protein Diet.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis randomized controlled trial showed the relationship of change in RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e between NPD and LPD in patients with stage 3 and 4 CKD. Our study is a novel contribution in being the first to simultaneously employ both gold standard \u003csup\u003e99m\u003c/sup\u003eTc-DTPA clearance and urine creatinine clearance method for comprehensive RFR estimation. Both dietary approaches influenced RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e, their impact varied, with LPD showing a trend toward better preservation of RFR over six months without affecting the eGFR. Further, adherence to dietary protein diet by measuring 24 hour urinary urea nitrogen between the two groups and it confirms the adherence of dietary advise.\u003c/p\u003e\u003cp\u003eThe baseline demographic analysis revealed that our study population primarily comprised middle-aged individuals with a male predominance consistent with previous studies, evaluating RFR in CKD populations.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Multiple studies have shown that RFR decreases as CKD stages advance and with increasing age in alignment with our study.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Data also confirms that men tend to have higher GFR and RFR than women, linked to both larger kidney mass and differences in tubular transporter expression, consistent with our finding of the highest RFR in a young male and lowest in an older female.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Discrepancies in sex distributions may affect findings; some reports suggest females can have similar or higher tubular reserve capacity depending on the test method and underlying disease.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Our study found no significant association between birth weight and RFR unlike previous studies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, likely due to recall bias and approximate participant reporting. No gender differences in RFR were seen, contrasting some reports suggesting male hyperfiltration\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e or female hormonal protection\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e; this may reflect our relatively homogeneous, middle-aged sample with similar renal function and diets.\u003c/p\u003e\u003cp\u003eThere is evidence that increased BMI is associated with higher single-nephron GFR and transiently higher total GFR.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Notably, the highest BMI noted in our study (31.1) was associated with a relatively preserved RFR (8.3%). However, single nephron GFR was not calculated in our study. In contrast, some prospective studies in living kidney donors have reported that higher BMI is associated with a greater decline in RFR following nephrectomy. However, this evidence remains mixed and may be influenced by confounding factors such as the donors\u0026rsquo; age and the presence of comorbidities like hypertension within the study population, use of animal vs vegetarian protein \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e However, we observed no correlation between RFR and BMI, possibly due to a narrow BMI range and BMI\u0026rsquo;s limitations in reflecting body composition. Additionally recent study by Leone et al. suggested BMI alone is an imprecise measure for predicting RFR because fat and lean mass also affect renal function differently.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eVarious studies reported that the mean RFR\u003csub\u003eabs\u003c/sub\u003e in healthy subjects range at a higher end from 25 to 31 mL/min and at a lower end from 9\u0026ndash;15 mL/min respectively.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Similarly, RFR\u003csub\u003e%\u003c/sub\u003e values in healthy individuals range at a higher side from 15% to 20% and at a lower side from 8% and 10% respectively.\u003csup\u003e3,4,14,17\u003c/sup\u003e Of note, studies employing creatinine clearance for GFR measurement have consistently observed higher RFR% values (range, 8\u0026ndash;25%) compared to those using exogenous marker clearance (range, 4\u0026ndash;8%). This pattern is further influenced by the type of protein used for renal stress: oral meat or animal protein loads generally elicit a greater GFR increment and thus higher RFR values than vegetarian or amino acid-based protocols, reinforcing the need to interpret RFR findings in light of methodological context. However our study did not include a healthy cohort for comparison (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\u003eList of studies on RFR in normal healthy individuals.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStimulus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRFR Measurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRemarks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAuthors and year\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 normal protein diet / 8 vegetarian diet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtein meal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine, urinary inulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax GFR of 171\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 mL/min after 150 min; blunted RFR in reduced nephron mass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNephron number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBosch et al., 1983\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAA, meat, milk protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimilar CrC increase after AA, meat, milk protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eType of protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMansy, 1987\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThree levels protein load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLarger GFR increase seen with higher protein loads (up to 1.35 g/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProtein dose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRodriguez-Iturbe, 1988\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003evegetarian vs animal protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnimal protein produces larger/sustained GFR increase than vegetarian protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDiet type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKontessis, 1990\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 elderly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAA infusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercent GFR increase comparable to young, but lower baseline/fractional reserve\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFliser, 1993\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 hypertensive donors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDopamine, 1.5 \u0026micro;g/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125I-iothalamate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRFR\u003csub\u003e%\u003c/sub\u003e Pre donation 8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDopamine, HTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTent, 2012\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e937 donors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDopamine, 1.5 \u0026micro;g/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125I-iothalamate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRFR\u003csub\u003e%\u003c/sub\u003e Pre donation 9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDopamine infusion results in increase plasma flow rather than GFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003evan Londen, 2018\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 female donors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDopamine, 1.5 \u0026micro;g/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125I-iothalamate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRFR\u003csub\u003e%\u003c/sub\u003e Pre donation 8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDopamine, female donors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003evan Londen, 2018\u003csup\u003e33\u003c/sup\u003e\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\u003eTable depicts key studies investigating renal functional reserve (RFR) in normal healthy individuals, detailing sample size, stimulus method, RFR measurement techniques, results, and study remarks.\u003c/p\u003e\u003cp\u003eAA: Amino acids, CrC: Creatinine clearance, DTPA: Diethylenetriamine pentaacetic acid, GFR: Glomerular filtration rate, NSAIDs: Nonsteroidal anti-inflammatory drugs, RFR: Renal functional reserve, RFR%: Percentage renal functional reserve, RFRabs: Absolute renal functional reserve\u003c/p\u003e\u003cp\u003eAll GFR values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or percentages as reported in original studies.\u003c/p\u003e\u003cp\u003eOur study included patients with CKD stage 3 and 4 with mean GFR of the study population as 31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 mL/min with median RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e as 2 mL/min and 7.14% and a significant increase in GFR post protein loading. Previous studies have shown significant increase post protein loading similar to our study.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Contrary to our study, Uemasu et al. reported that patients with baseline GFR between 40 and 90 mL/min exhibited no significant increase in GFR post protein loading test.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Importantly, their study used glucagon as the stimulus and thiosulphate clearance for GFR measurement, whereas we used an oral protein load and different GFR estimation methods in a cohort with lower baseline GFR. Notably, glucagon typically produces a lower GFR response than protein or amino acids, as it primarily increases renal plasma flow rather than glomerular filtration, resulting in a blunted RFR response.\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Study by Barai et al., has reported median RFR\u003csub\u003e%\u003c/sub\u003e values comparable to those observed in our study with CKD stages 3 and 4.\u003csup\u003e17\u003c/sup\u003e Similarly, Bosch et al. showed that RFR\u003csub\u003eabs\u003c/sub\u003e is severely blunted in CKD or reduced nephron mass states.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Data from living kidney donors show that both RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e are partially maintained post-donation but reduced compared to healthy controls.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e This highlights nephron mass as a crucial determinant of RFR, thus explaining the lower renal reserve observed in CKD as a state of diminished nephron number. Contrary to our study, Krishna et al., showed RFR\u003csub\u003e%\u003c/sub\u003e of 20\u0026thinsp;\u0026plusmn;\u0026thinsp;13% and 21\u0026thinsp;\u0026plusmn;\u0026thinsp;14% in patients with moderate renal failure and advanced renal failure respectively which was similar to that of normal healthy adults.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Also, in contrast to our study, Loo et al. demonstrated that healthy subjects and patients with renal disease had a mean RFR\u003csub\u003eabs\u003c/sub\u003e of 13.5 mL/min in patients with renal disease.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e This difference in both the studies may be attributed to usage of a large amount of meat protein for GFR stimulation, whereas we employed vegetarian oral protein; additionally, their study population had a higher mean GFR compared to our cohort \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\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\u003eList of studies on RFR in patients with Kidney Disease.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial no.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCondition / Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eType of Stimulus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGFR Measurement Methods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Results Summary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference(s)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCKD stages 1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtein meal, AA infusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance, DTPA, Inulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProgressive loss of RFR with CKD stages; lower RFR in diabetes and hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBosch et al. (1986),\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 healthy, 9 CGN (GFR\u0026thinsp;\u0026gt;\u0026thinsp;90), 8 CGN (GFR 40\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlucagon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThiosulphate clearance (THIO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormal controls: increased GFR \u0026amp; ERPF; CGN with preserved GFR no ERPF increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUemasu, 1991\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 CKD patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtein meal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePreserved renal reserve in CKD; enalapril did not affect RFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKrishna, 1991\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 CKD, 19 transplant, 12 donors, 62 healthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtein meal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealthy RFR\u0026thinsp;=\u0026thinsp;31 mL/min; CKD lower (13.5 mL/min); transplant intermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLoo, 1994\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType 1 and Type 2 DM with/without nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmino acids, Protein load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance, EDTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduced RFR in diabetic nephropathy; no RFR in advanced disease; response varies with albuminuria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBrouhard et al. (1990),\u003csup\u003e7\u003c/sup\u003e Sackmann et al. (2000)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 controls, 100 CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtein meal 25 controls, 100 CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003csup\u003e99m\u003c/sup\u003eTc-DTPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean RFR (%): controls 23.4; CKD 1:19.08; CKD 2:15.4; CKD 3:8.9; CKD 4:6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBarai et al. (2010),\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEssential hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmino acids, Protein meal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreatinine clearance, EDTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBlunted RFR in hypertensive patients correlating with albuminuria and vascular resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGaipov et al. (2016)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 CKD (Stage 3 and 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVegetarian protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003csup\u003e99m\u003c/sup\u003eTc DTPA, Creatinine clearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedian RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e by \u003csup\u003e99m\u003c/sup\u003eTc DTPA and Creatinine Clearance- 2, 7.14 ;and 2.45 and 8.35 respectively\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePresent study\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\u003eThis table summarizes studies evaluating renal functional reserve (RFR) in various renal disease populations, indicating clinical context, type of stimulus, GFR measurement methods, and key findings.\u003c/p\u003e\u003cp\u003eAA: Amino acids, CKD: Chronic kidney disease, CGN: Chronic glomerulonephritis, CrC: Creatinine clearance, DTPA: Diethylenetriamine pentaacetic acid clearance, EDTA: Ethylenediaminetetraacetic acid clearance, ERPF: Effective renal plasma flow, GFR: Glomerular filtration rate RFR: Renal functional reserve, RFR%: Percentage renal functional reserve, RFRabs: Absolute renal functional reserve, THIO: Thiosulphate clearance. GFR values and RFR are presented as reported in individual studies, reflecting variations related to disease severity and patient condition.\u003c/p\u003e\u003cp\u003eThe values of RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e estimated by both methods in our cohort were comparable and showed no statistically significant difference. Also various GFR measurement methods were also assessed in our study, demonstrating similar results across eGFR, creatinine clearance and DTPA clearance methods. This is supported by various previous studies. \u003csup\u003e40\u003c/sup\u003eVarious previous studies have used creatinine clearance as a method of RFR estimation but none has compared it with gold standard DTPA clearance method similar to our study.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Although the difference between two methods was non-significant, but the variability in the range of values in creatinine clearance group was wider. This concordance suggests that, despite its limitations, creatinine clearance remains a feasible and acceptable method for RFR estimation \u003csup\u003e99m\u003c/sup\u003eTc-DTPA clearance. \u003csup\u003e99m\u003c/sup\u003eTc-DTPA, offers highly accurate and direct measurement of GFR, while creatinine clearance is a widely used, practical, and cost-effective alternative that can be performed quickly and noninvasively. Although some studies done in normal healthy individuals and CKD patients,\u003csup\u003e41\u003c/sup\u003e have shown overestimation of GFR by creatinine clearance method as compared to two plasma DTPA method, but these studies have not estimated the RFR, hence use of creatinine clearance for RFR estimation and if it is comparable to gold standard DTPA, needs further large scale studies.\u003c/p\u003e\u003cp\u003eAdditionally, Viberti et al. showed that dietary protein acutely modulates RFR, supporting the concept that glomerular hemodynamics are sensitive to protein intake. The short-term hyperfiltration response seen after protein loading may become maladaptive over time, further emphasizing the importance of dietary protein moderation in CKD management.\u003c/p\u003e\u003cp\u003eOur study findings showing that post-protein stimulation GFR was higher in patients on LPD compared to those on NPD after 6 months similar to previous studies.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Although some reports found comparable GFR rises after acute protein loading regardless of diet, these involved shorter durations which may have limited dietary effects.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWe also demonstrated that patients on a LPD exhibited significantly less decline in both RFR\u003csub\u003eabs\u003c/sub\u003e and RFR \u003csub\u003e%\u003c/sub\u003e over six months compared to those on a NPD. This suggests that a lower dietary protein intake helps preserve RFR and may protect against the glomerular injury and hyperfiltration commonly seen with higher protein intake. Our findings align closely with foundational research by Brouhard et al., who reported preserved RFR in diabetic nephropathy patients on LPD versus decline in those on NPD after 12 months on the particular diet.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The key difference in that they exclusively included diabetic nephropathy patients with higher eGFR and prescribed a normal protein diet of 1 g/kg/day, compared to our cohort with lower eGFR and a 0.8 g/kg/day protein intake. This makes our results more generalizable to real-world CKD patients and protein intake recommendations. Additionally, Viberti et al. showed that dietary protein acutely modulates RFR, further supporting our observations.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOur study observed that eGFR declined similarly in both the NPD and LPD groups. These results align with a meta-analysis by Fouque et al. and other similar studies have indicated LPD does not prevent GFR decline.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e However some studies have shown that protein restriction may slow CKD progression.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e The lack of significant dietary impact in our study likely reflects its short six-month duration; longer-term studies are needed to clarify dietary effects on CKD progression.\u003c/p\u003e\u003cp\u003eRegarding risk factors associated with reduced RFR, multiple studies have identified lower baseline GFR, higher serum creatinine, older age, diabetes, hypertension, and proteinuria as significant contributors to diminished renal reserve.\u003csup\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003eFor instance, Barai et al. found that advancing CKD stage and reduced baseline GFR strongly predict lower RFR.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Similarly, Brouhard and LaGrone noted that diabetic nephropathy, with its characteristic hyperfiltration and glomerular injury, is linked to decreased RFR, highlighting the role of diabetes as a risk factor.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Other authors have implicated hypertension and increased albuminuria as modifiers of renal reserve by promoting glomerulosclerosis and nephron loss.\u003c/p\u003e\u003cp\u003eOur study\u0026rsquo;s findings are congruent with these known risk factors. Univariate logistic regression analysis revealed that higher age, lower baseline eGFR (using both MDRD and CKD-EPI 2021 equations), higher serum creatinine and proteinuria were significantly associated with low RFR. These were similar to previous studies. \u003csup\u003e4,17,25\u003c/sup\u003eAlthough multivariate analysis did not confirm independent predictors, the trends align with established literature indicating that diminished baseline kidney function correlates with reduced adaptive renal capacity. The lack of statistical significance in multivariate models likely reflects sample size and interrelatedness of variables but does not negate clinical relevance.\u003c/p\u003e\u003cp\u003eThe major strengths of our study include its randomized controlled design, use of gold standard \u003csup\u003e99m\u003c/sup\u003eTc-DTPA clearance and urine creatinine clearance methods for GFR measurement, standardized protein loading protocol, rigorous dietary adherence monitoring through 24-hour urinary urea nitrogen, and comprehensive six-month follow-up with repeated RFR\u003csub\u003eabs\u003c/sub\u003e and RFR\u003csub\u003e%\u003c/sub\u003e assessments. The study's focus on RFR as a dynamic marker of renal health represents a novel approach that may enhance early detection of renal functional decline. Some limitations of our study include the short six-month follow-up period, which may be inadequate to assess long-term clinical outcomes such as CKD progression or cardiovascular events, and restriction to a cohort with stage 3 and 4 CKD. Additionally, the study did not control for non-protein dietary factors like sodium or phosphorus intake, which may independently influence kidney function and disease progression in this population.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications and Future Directions\u003c/h2\u003e\u003cp\u003eLong-term studies are needed to determine if a low-protein diet (LPD) truly offers superior renal functional reserve (RFR) preservation compared to a normal protein diet (NPD) beyond six months. Future research should evaluate strategies combining dietary interventions (such as adding ketoanalogues to LPD) and pharmacological agents including ACE inhibitors, SGLT2 inhibitors, and mineralocorticoid antagonists for optimizing kidney health. Systematic investigation of other macronutrients, stratification by proteinuria status, and standardized RFR measurement protocol, potentially including single-nephron GFR, could yield important insights into renal adaptability and resilience, ultimately improving care and outcomes for high-risk CKD patients.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA low-protein diet preserves RFR, even when conventional measures such as eGFR remain unchanged in CKD patients, supporting the value of early dietary intervention to maintain kidney health. RFR assessment offers unique early insights into renal adaptability and resilience, making it a valuable surrogate endpoint for future studies on dietary and therapeutic strategies. Higher baseline function and lower proteinuria favoured better RFR. While short-term differences in kidney function (eGFR) between diets were limited, proteinuria strongly correlated with greater RFR loss, supporting a tailored protein-restricted approach, especially in early-stage CKD, proteinuria and those with low reserve. Notably, our study is the first to simultaneously use both gold standard \u003csup\u003e99m\u003c/sup\u003eTc-DTPA clearance and urine creatinine clearance for comprehensive RFR estimation. While urine creatinine clearance can be cumbersome, we found it to be cost-effective and comparable to DTPA clearance for RFR assessment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eAA: Amino Acids\u003c/li\u003e\n \u003cli\u003eACE: Angiotensin-Converting Enzyme inhibitor\u003c/li\u003e\n \u003cli\u003eARB: Angiotensin Receptor Blocker\u003c/li\u003e\n \u003cli\u003eBMI: Body Mass Index\u003c/li\u003e\n \u003cli\u003eCKD: Chronic Kidney Disease\u003c/li\u003e\n \u003cli\u003eCKD-EPI: Chronic Kidney Disease Epidemiology Collaboration\u003c/li\u003e\n \u003cli\u003eCrCl: Creatinine Clearance\u003c/li\u003e\n \u003cli\u003eCTRI: Clinical Trial Registry India\u003c/li\u003e\n \u003cli\u003eDKD: Diabetic Kidney Disease\u003c/li\u003e\n \u003cli\u003eDTPA: Diethylenetriamine pentaacetic acid\u003c/li\u003e\n \u003cli\u003eEDTA: Ethylenediaminetetraacetic acid clearance\u003c/li\u003e\n \u003cli\u003eeGFR: Estimated Glomerular Filtration Rate\u003c/li\u003e\n \u003cli\u003eERPF: Effective Renal Plasma Flow\u003c/li\u003e\n \u003cli\u003eGFR: Glomerular Filtration Rate\u003c/li\u003e\n \u003cli\u003eHTN: Hypertension\u003c/li\u003e\n \u003cli\u003eLPD: Low Protein Diet\u003c/li\u003e\n \u003cli\u003eMAP: Mean Arterial Pressure\u003c/li\u003e\n \u003cli\u003eMDRD: Modification of Diet in Renal Disease\u003c/li\u003e\n \u003cli\u003eNPD: Normal Protein Diet\u003c/li\u003e\n \u003cli\u003eNSAIDs: Nonsteroidal Anti-Inflammatory Drugs\u003c/li\u003e\n \u003cli\u003eOR: Odds Ratio\u003c/li\u003e\n \u003cli\u003ePET: Positron Emission Tomography\u003c/li\u003e\n \u003cli\u003eRFR: Renal Functional Reserve\u003c/li\u003e\n \u003cli\u003eRFR\u003csub\u003eabs\u003c/sub\u003e: Absolute Renal Functional Reserve\u003c/li\u003e\n \u003cli\u003eRFR\u003csub\u003e%\u003c/sub\u003e: Relative Renal Functional Reserve\u003c/li\u003e\n \u003cli\u003eSGLT2: Sodium-Glucose Cotransporter-2 inhibitor\u003c/li\u003e\n \u003cli\u003eSD: Standard Deviation\u003c/li\u003e\n \u003cli\u003eSE: Standard Error\u003c/li\u003e\n \u003cli\u003eTHIO: Thiosulphate clearance\u003c/li\u003e\n \u003cli\u003eUACR: Urine Albumin-to-Creatinine Ratio\u003c/li\u003e\n \u003cli\u003eUUN: Urine Urea Nitrogen\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: Ethical Committee clearance from institutional committee of\u0026nbsp;\u003cstrong\u003eAtal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) \u0026amp; Dr. Ram Manohar Lohia (R.M.L.) Hospital, New Delhi, India,\u0026nbsp;\u003c/strong\u003evide number F.No TP (DM/MCH) 30/2023) /IEC/ABVIMS/RMLH 1405 dated 3rd August 2023.\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registry\u003c/strong\u003e- Clinical Trial Registry (India) CTRI – CTRI/2024/08/071864 dated 2\u003csup\u003end\u003c/sup\u003e August 2024\u003c/p\u003e\n\u003cp\u003eWritten informed consent was acquired from the participants prior to their inclusion in the study. All methods were carried out in accordance with relevant guidelines and protocols.\u003c/p\u003e\n\u003cp\u003e•\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Consent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003e•\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Availability of data and materials: yes data will be shared when asked\u003c/p\u003e\n\u003cp\u003ePoint of contact for data and materials:\u0026nbsp;\u003cstrong\u003eDr. Himansu Sekhar Mahapatra, Director Professor and Head, Department of Nephrology, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) \u0026amp; Dr. Ram Manohar Lohia (R.M.L.) Hospital, New Delhi, India\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Address: Room No 307, PGI BUILDING, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) \u0026amp; Dr. Ram Manohar Lohia (R.M.L.) Hospital, New Delhi, India , Phone: 9968474805, Email:[email protected]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e•\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Competing interests: we have no competing interest.\u003c/p\u003e\n\u003cp\u003e•\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Funding: not applicable\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor I: Dr. Varuna Yadav: Conceptualization, Formal analysis, Writing - Original Draft. Author II: \u0026nbsp;Dr. Himansu Sekhar Mahapatra: Methodology, Validation, Formal analysis, Data Curation. (CORRESPONDING AUTHOR)\u003c/p\u003e\n\u003cp\u003eAuthor III:\u0026nbsp;Dr. Madhavi Tripathi: DTPA procedure and techincality\u003c/p\u003e\n\u003cp\u003eAuthor IV: Dr. Lalit Pursnani: Investigation, Resources, Writing - Review\u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor V: Dr. MuthuKumar Balakrishnan: Supervision, Project administration, Critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author VI: Dr. Renju Binoy: Patient recruitment, Clinical data acquisition, Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther authors: Dr. Hari, Dr. Disha, and Dr. Vipin: Patient management, Data collection, Resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Professor Venkateshan Sekhar, management consultant for helping in statistical analysis. Miss Deepshikha for helping in diet charts and follow up.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJufar AH, Lankadeva YR, May CN, Cochrane AD, Bellomo R, Evans RG. Renal functional reserve: From physiological phenomenon to clinical biomarker and beyond. Am J Physiol Regul Integr Comp Physiol. 2020;319(6):R690\u0026ndash;702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/AJPREGU.00237.2020\u003c/span\u003e\u003cspan address=\"10.1152/AJPREGU.00237.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalsson R, Waikar SS. Renal Functional Reserve Revisited. 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The effects of dietary protein restriction and blood-pressure control on the progression of chronic renal disease. Modification of Diet in Renal Disease Study Group. N Engl J Med. 1994;330(13):877\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJM199403313301301\u003c/span\u003e\u003cspan address=\"10.1056/NEJM199403313301301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahapatra H, Khattar D, Kumar BM, Lakshman L, Suri N, Paul S. WCN25-3821 Use of 24 hours Urine Urea Nitrogen (UUN) and Urinary Sodium with Protein and salt consumption in three different dietary protein intake groups of Chronic Kidney Disease patients. Kidney Int Rep. 2025;10(2):S322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ekir.2024.11\u003c/span\u003e\u003cspan address=\"10.1016/j.ekir.2024.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7778811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7778811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRenal functional reserve (RFR), defined as the difference between peak and baseline glomerular filtration rate (GFR), reflects the adaptive capacity of the kidney. This study evaluated how normal protein diet (NPD) and low protein diet (LPD) affect changes in both absolute (RFR\u003csub\u003eabs\u003c/sub\u003e) and relative (RFR\u003csub\u003e%\u003c/sub\u003e) RFR, as well as rate of GFR decline over six months in patients with CKD stages 3 and 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e This six‑month randomized controlled trial enrolled adults (\u0026gt;18 years) with stage 3–4 CKD. Baseline assessments included clinical and laboratory parameters, eGFR,\u0026nbsp;Creatinine clearance (CrCl) and two‑plasma technetium-99m Diethylenetriamine pentaacetic acid (\u003csup\u003e99m\u003c/sup\u003eTc‑DTPA) method before and after a 1 g/kg protein load to calculate RFR\u003csub\u003eabs\u003c/sub\u003e\u0026nbsp;and RFR\u003csub\u003e%\u003c/sub\u003e. Patients were randomized to either a normal protein diet (0.8 g/kg/day) or a low protein diet (0.6 g/kg/day). Dietary adherence monitored by recall, record and 24‑hour urinary urea nitrogen. All parameters were repeated at six months to compare RFR\u003csub\u003eabs\u003c/sub\u003e\u0026nbsp;and RFR\u003csub\u003e%\u003c/sub\u003e changes by both measured GFR methods between diet groups. Patients having RFR below median value of the study population were considered to have low renal reserve; risk factors of low RFR were also assessed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOf 135 patients (64 NPD, 71 LPD), there was significant increase in measured GFR after protein loading in both groups at baseline and at six months (p \u0026lt; 0.001). The magnitude of increase in GFR after protein loading didn’t differ between groups at baseline (p = 0.88), but significantly high in LPD group at six months (p = 0.03). The decline in RFR was significantly smaller in LPD than NPD after six months by both the methods of RFR estimation (RFR\u003csub\u003eabs\u003c/sub\u003e\u0026nbsp;(DTPA): p = 0.002; RFR\u003csub\u003e%\u003c/sub\u003e\u0026nbsp;(DTPA): p = 0.001 and RFR\u003csub\u003eabs\u003c/sub\u003e\u0026nbsp;(CrCl): p=0.046; RFR\u003csub\u003e%\u0026nbsp;\u003c/sub\u003e(CrCl): p=0.021; respectively), with no significant difference in eGFR change between groups. Lower baseline GFR, higher proteinuria and increased age were independent risk factors for low renal reserve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e LPD preserved both absolute and relative RFR compared to NPD. Decline in RFR preceded fall in GFR, making it a potential early biomarker for CKD progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003e- Clinical Trial Registry (India) CTRI – CTRI/2024/08/071864 dated 2\u003csup\u003end\u003c/sup\u003e August 2024\u003c/p\u003e\n\u003cp\u003eKey words: Chronic kidney disease, creatinine clearance, technetium-99m Diethylenetriamine pentaacetic acid, low protein diet, normal protein diet, renal function reserve\u003c/p\u003e","manuscriptTitle":"Comparative Effect of Normal Protein and Low Protein Diet on Renal Function Reserve in Patients of Chronic Kidney Disease Stage 3 And 4: A Randomised Controlled Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 10:12:24","doi":"10.21203/rs.3.rs-7778811/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25f82a91-6d18-4918-bd20-95c06e832c1e","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T12:10:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 10:12:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7778811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7778811","identity":"rs-7778811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}