Salt, but not protein intake, is associated with accelerated disease progression in autosomal dominant polycystic kidney disease
Authors: Bart J. Kramers, Iris W. Koorevaar, Joost P.H. Drenth, Johan W. de Fijter, Antonio Gomes Neto, Dorien J.M. Peters, Priya Vart, Jack F. Wetzels, Robert Zietse, Ron T. Gansevoort, Esther Meijer
Journal: Kidney International (2020)

In autosomal dominant polycystic kidney disease (ADPKD), there are only scarce data on the effect of salt and protein intake on disease progression. Here we studied association of these dietary factors with the rate of disease progression in ADPKD and what the mediating factors are by analyzing an observational cohort of 589 patients with ADPKD. Salt and protein intake were estimated from 24-hour urine samples and the plasma copeptin concentration measured as a surrogate for vasopressin. The association of dietary intake with annual change in the estimated glomerular filtration rate (eGFR) and height adjusted total kidney volume (htTKV) growth was analyzed with mixed models. In case of significant associations, mediation analyses were performed to elucidate potential mechanisms. These patients (59% female) had a mean baseline age of 47, eGFR 64 mL/min/1.73m2 and the median htTKV was 880 mL. The mean estimated salt intake was 9.1 g/day and protein intake 84 g/day. During a median follow-up of 4.0 years, eGFR was assessed a median of six times and 24-hour urine was collected a median of five times. Salt intake was significantly associated with annual change in eGFR of −0.11 (95% confidence interval 0.20 – −0.02] mL/min/1.73m2) per gram of salt, whereas protein intake was not (−0.00001 [−0.01 – 0.01] mL/min/1.73m2) per gram of protein). The effect of salt intake on eGFR slope was significantly mediated by plasma copeptin (crude analysis: 77% mediation, and, adjusted analysis: 45% mediation), but not by systolic blood pressure. Thus, higher salt, but not higher protein intake may be detrimental in ADPKD. The substantial mediation by plasma copeptin suggests that this effect is primarily a consequence of a salt-induced rise in vasopressin. In autosomal dominant polycystic kidney disease (ADPKD), there are only scarce data on the effect of salt and protein intake on disease progression. Here we studied association of these dietary factors with the rate of disease progression in ADPKD and what the mediating factors are by analyzing an observational cohort of 589 patients with ADPKD. Salt and protein intake were estimated from 24-hour urine samples and the plasma copeptin concentration measured as a surrogate for vasopressin. The association of dietary intake with annual change in the estimated glomerular filtration rate (eGFR) and height adjusted total kidney volume (htTKV) growth was analyzed with mixed models. In case of significant associations, mediation analyses were performed to elucidate potential mechanisms. These patients (59% female) had a mean baseline age of 47, eGFR 64 mL/min/1.73m2 and the median htTKV was 880 mL. The mean estimated salt intake was 9.1 g/day and protein intake 84 g/day. During a median follow-up of 4.0 years, eGFR was assessed a median of six times and 24-hour urine was collected a median of five times. Salt intake was significantly associated with annual change in eGFR of −0.11 (95% confidence interval 0.20 – −0.02] mL/min/1.73m2) per gram of salt, whereas protein intake was not (−0.00001 [−0.01 – 0.01] mL/min/1.73m2) per gram of protein). The effect of salt intake on eGFR slope was significantly mediated by plasma copeptin (crude analysis: 77% mediation, and, adjusted analysis: 45% mediation), but not by systolic blood pressure. Thus, higher salt, but not higher protein intake may be detrimental in ADPKD. The substantial mediation by plasma copeptin suggests that this effect is primarily a consequence of a salt-induced rise in vasopressin. see commentary on page 831 see commentary on page 831 In chronic kidney disease (CKD), salt restriction is advocated to slow disease progression.1Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work GroupKDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.Kidney Int Suppl. 2013; 3: 1-150Abstract Full Text Full Text PDF Scopus (1579) Google Scholar Salt restriction lowers blood pressure and potentiates renoprotective effects of renin-angiotensin-aldosterone system (RAAS) blockade.2Humalda J.K. Navis G. Dietary sodium restriction: a neglected therapeutic opportunity in chronic kidney disease.Curr Opin Nephrol Hypertens. 2014; 23: 533-540Crossref PubMed Scopus (57) Google Scholar The role of dietary protein restriction in slowing progression of CKD is more controversial, although several meta-analyses indicate a beneficial, albeit small effect.3Kasiske B.L. Lakatua J.D. Ma J.Z. Louis T.A. A meta-analysis of the effects of dietary protein restriction on the rate of decline in renal function.Am J Kidney Dis. 1998; 31: 954-961Abstract Full Text Full Text PDF PubMed Scopus (348) Google Scholar,4Yan B. Su X. Xu B. et al.Effect of diet protein restriction on progression of chronic kidney disease: a systematic review and meta-analysis.PLoS One. 2018; 13e0206134Crossref PubMed Scopus (40) Google Scholar In autosomal dominant polycystic kidney disease (ADPKD) specifically, there are only scarce data on the renal effects of salt and protein intake. In the Consortium for Radiologic Imaging Studies in Polycystic Kidney Disease (CRISP) cohort, an observational study in 241 patients with ADPKD with early stage disease, higher urinary sodium excretion (indicating higher salt intake) was associated with more rapid kidney volume growth. In a post hoc analysis of the HALT Progression of Polycystic Kidney Disease (HALT-PKD) study, a randomized controlled trial in 1044 patients with later-stage ADPKD, sodium excretion was associated with steeper estimated glomerular filtration rate (eGFR) decline in patients with later-stage ADPKD but not in patients with early-stage ADPKD.5Torres V.E. Grantham J.J. Chapman A.B. et al.Potentially modifiable factors affecting the progression of autosomal dominant polycystic kidney disease.Clin J Am Soc Nephrol. 2011; 6: 640-647Crossref PubMed Scopus (104) Google Scholar,6Torres V.E. Abebe K.Z. Schrier R.W. et al.Dietary salt restriction is beneficial to the management of autosomal dominant polycystic kidney disease.Kidney Int. 2017; 91: 493-500Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar It has been suggested that an association with eGFR decline may be caused by salt restriction potentiating the renal protective effects of RAAS blockade, similar to non-ADPKD CKD.6Torres V.E. Abebe K.Z. Schrier R.W. et al.Dietary salt restriction is beneficial to the management of autosomal dominant polycystic kidney disease.Kidney Int. 2017; 91: 493-500Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar An alternative explanation could be that salt intake leads to accelerated disease progression in ADPKD by stimulation of vasopressin secretion. Vasopressin is known to be causally related to disease progression in ADPKD.7Boertien W.E. Meijer E. Li J. et al.Relationship of copeptin, a surrogate marker for arginine vasopressin, with change in total kidney volume and GFR decline in autosomal dominant polycystic kidney disease: results from the CRISP cohort.Am J Kidney Dis. 2013; 61: 420-429Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar, 8Torres V.E. Chapman A.B. Devuyst O. et al.Tolvaptan in patients with autosomal dominant polycystic kidney disease.N Engl J Med. 2012; 367: 2407-2418Crossref PubMed Scopus (1014) Google Scholar, 9Torres V.E. Chapman A.B. Devuyst O. et al.Tolvaptan in later-stage autosomal dominant polycystic kidney disease.N Engl J Med. 2017; 377: 1930-1942Crossref PubMed Scopus (303) Google Scholar One of the main factors for vasopressin secretion is plasma sodium concentration,10Zerbe R.L. Robertson G.L. Osmoregulation of thirst and vasopressin secretion in human subjects: Effect of various solutes.Am J Physiol. 1983; 244: E607-E614PubMed Google Scholar which increases after salt ingestion. As urinary urea excretion was not measured in the HALT-PKD trial, it is unclear whether protein intake was also associated with eGFR decline.6Torres V.E. Abebe K.Z. Schrier R.W. et al.Dietary salt restriction is beneficial to the management of autosomal dominant polycystic kidney disease.Kidney Int. 2017; 91: 493-500Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar The effect of a low protein intake on rate of eGFR decline has been studied in a post hoc analysis of the Modification of Diet in Renal Disease (MDRD) study, in which low protein diet was compared to a usual diet (study A) and a very low protein diet was compared to a low protein diet (study B). In the subgroup of 200 patients with ADPKD, there were no significant differences in either substudy; however, the results were deemed inconclusive by the investigators, among others due to lack of power.11Levey A.S. Greene T. Beck G.J. et al.Dietary protein restriction and the progression of chronic renal disease: what have all of the results of the MDRD study shown? Modification of Diet in Renal Disease Study Group.J Am Soc Nephrol. 1999; 10: 2426-2439Crossref PubMed Google Scholar,12Klahr S. Breyer J.A. Beck G.J. et al.Dietary protein restriction, blood pressure control, and the progression of polycystic kidney disease. Modification of Diet in Renal Disease Study Group.J Am Soc Nephrol. 1995; 5: 2037-2047Crossref PubMed Google Scholar Given these scarce and inconclusive data, we aimed to investigate the relation between salt and protein intake and renal function decline in ADPKD. To address this aim, we analyzed data of patients with ADPKD in a large observational cohort. We also aimed to study whether a potential association was mediated by vasopressin or by other potential mechanisms. The cohort flow is detailed in Figure 1. The baseline characteristics are shown in Table 1. Mean age was 47 ± 11 years, 59% of participants were female, eGFR was 64 ± 24 ml/min per 1.73 m2 and median height-adjusted total kidney volume (htTKV) was 880 ml (interquartile range [IQR]: 549, 1352). There were no significant differences in age, sex, eGFR, and htTKV in the 205 patients that were excluded due to insufficient follow-up data. Sodium excretion was 156 ± 65 mmol/24 hours at baseline, corresponding with an estimated salt intake of 9.1 ± 3.8 g. Urea excretion was 390 ± 132 mmol/24 hours, corresponding with an estimated protein intake of 84 ± 25 g. Sodium excretions and urea excretion during all visits in the Developing Interventions to Halt Progression of Autosomal Dominant Polycystic Kidney Disease (DIPAK) 1 trial and the DIPAK observational cohort are shown Figure 2.Table 1Baseline characteristicsCharacteristicsn = 589Age, yr47 ± 11Female sex245 (59)Weight, kg Females76 ± 15 Males90 ± 14Height, m Females1.70 ± 0.07 Males1.84 ± 0.07SBP, mm Hg133 ± 14DBP, mm Hg82 ± 10RAAS blockade, yes415 (71)eGFR, ml/min per 1.73 m2aEstimated by Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.64 ± 24htTKV, ml/m880 (549, 1352)Copeptin, pmol/l7.6 (4.5, 13.2)Mayo risk class 1A/1B, low-risk disease145 (26) 1C/1D/1E, high-risk disease385 (69) 2, atypical27 (5)PKD genotype PKD1 truncating241 (42) PKD1 nontruncating151 (26) PKD2128 (22) Unknown/not detected50 (9)Sodium excretion, mmol/24 h156 ± 65Estimated salt intake, g/24 h9.1 ± 3.8Urea excretion, mmol/24 h390 ± 132Estimated protein intake, g/24 h84 ± 25Urine volume, l/24 h2.3 ± 0.8DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; htTKV, height-adjusted total kidney volume; PKD, polycystic kidney disease; RAAS, renin-angiotensin-aldosterone system; SBP, systolic blood pressure.Variables are presented as mean ± SD, as median (interquartile range) in case of nonnormal distribution, or as n (%) for categorical variables.a Estimated by Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Open table in a new tab Figure 2Mean sodium excretion and urea excretion at the yearly visits, with estimated salt and protein intake. Whiskers indicate 5% to 95% range. BL, baseline.View Large Image Figure ViewerDownload Hi-res image Download (PPT) DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; htTKV, height-adjusted total kidney volume; PKD, polycystic kidney disease; RAAS, renin-angiotensin-aldosterone system; SBP, systolic blood pressure. Variables are presented as mean ± SD, as median (interquartile range) in case of nonnormal distribution, or as n (%) for categorical variables. During a median follow-up time of 4.0 years (IQR: 2.6, 5.0), eGFR was assessed 6 times (IQR: 5, 14) and 24-hour urine was collected 5 times (IQR: 4, 7). Average annual change in eGFR was −3.50 ml/min per 1.73 m2 per year (95% confidence interval [CI]: −3.70 to −3.29). Sodium excretion was strongly correlated with urea excretion (standardized β = 0.61, unstandardized β = 1.8 mmol urea per mmol sodium; 95% CI: 1.6 to 2.0; P < 0.001). In mixed model analysis, sodium excretion was univariably associated with change in eGFR (−0.16 ml/min per 1.73 m2 per year per 18 mmol of sodium; 95% CI: −0.24 to −0.08; P < 0.001), as was urea excretion (−0.03 ml/min per 1.73 m2 per year per 40 mmol of urea; 95% CI: −0.05 to −0.001; P = 0.04). In multivariable analysis, adjusted for age, sex, body surface area (BSA), baseline htTKV, and DNA mutation, the association of sodium excretion with change in eGFR remained statistically significant (Table 2). In contrast, the association between urea excretion and eGFR slope lost significance after adjustment for potential confounders (Table 2). Figure 3 graphs the relationship among sodium excretion and urea excretion and eGFR slope. Based on the excretions of sodium and urea, we estimated salt and protein intake. In the multivariable model, the association of salt intake with change in eGFR was −0.11 ml/min per 1.73 m2 per year per gram salt (95% CI: −0.20 to −0.02; P = 0.02), the association of protein intake with change in eGFR was not significant (−0.00001 ml/min per 1.73 m2 per year per gram protein; 95% CI: −0.01 to 0.01; P = 0.9) (Supplementary Table S1A). When we excluded the patients that used lanreotide during the DIPAK-1 trial, the results were essentially the same (Supplementary Table S1B).Table 2Associations of sodium and urea excretion with eGFR slope (n = 553)Sodium and urea excretion vs. eGFR slope (ml/min per 1.73 m2 per yr)Model 1Model 2Model 3Est. (95% CI)P ValueEst. (95% CI)P ValueEst. (95% CI)P ValueSodium excretion, per 18 mmolaEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).−0.12 (−0.20 to −0.03)0.006−0.11 (−0.21 to −0.02)0.02Urea excretion, per 40 mmolaEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).−0.02 (−0.05 to 0.01)0.2−0.002 (−0.03 to 0.03)0.9Age, per yraEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).0.01 (−0.01 to 0.03)0.40.01 (−0.01 to 0.03)0.30.01 (−0.01 to 0.03)0.4Female sexaEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).−0.08 (−0.56 to 0.40)0.7−0.01 (−0.50 to 0.47)0.9−0.08 (−0.57 to 0.40)0.7BSA, per m2aEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).0.07 (−1.05 to 1.20)0.9−0.16 (−1.28 to 0.97)0.80.08 (−1.06 to 1.22)0.9Log10 htTKV, ml/m−2.98 (−3.70 to −2.27)<0.001−3.05 (−3.76 to −2.33)<0.001−2.99 (−3.70 to −2.27)<0.001DNA mutation (ref: PKD2) PKD 1 truncating−1.25 (−1.78 to −0.72)<0.001−1.24 (−1.77 to −0.71)<0.001−1.25 (−1.78 to −0.73)<0.001 PKD 1 nontruncating−1.18 (−1.73 to −0.62)<0.001−1.14 (−1.70 to −0.58)<0.001−1.18 (−1.73 to −0.62)<0.001 Unknown−0.64 (−1.33 to 0.05)0.07−0.64 (−1.33 to 0.06)0.07−0.64 (−1.33 to 0.06)0.07BSA, body surface area; CI, confidence interval; eGFR, estimated glomerular filtration rate; Est, estimation; htTKV, height-adjusted total kidney volume; PKD, polycystic kidney disease; ref, reference.a Estimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept). Open table in a new tab BSA, body surface area; CI, confidence interval; eGFR, estimated glomerular filtration rate; Est, estimation; htTKV, height-adjusted total kidney volume; PKD, polycystic kidney disease; ref, reference. In univariate analysis, both sodium excretion and urea excretion were associated with htTKV growth (0.63% per year per 18 mmol sodium; 95% CI: 0.40 to 0.87; P < 0.001, and 0.18% per year per 40 mmol urea; 95% CI: 0.09 to 0.28; P < 0.001, respectively). The association of sodium excretion with htTKV growth remained significant after adjustment for age, sex, BSA, baseline htTKV, and DNA mutation, whereas the association of urea excretion lost significance (Table 3).Table 3Association of estimated salt intake and protein intake with annual htTKV growth (n = 283)Sodium and urea excretion vs. htTKV growth (% per yr)Model 1Model 2Model 3Est. (95% CI)P ValueEst. (95% CI)P ValueEst. (95% CI)P ValueSodium excretion, per 18 mmolaEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).0.31 (0.09 to 0.53)0.0070.44 (0.18 to 0.71)0.001Urea excretion, per 40 mmolaEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).0.01 (−0.07 to 0.09)0.8−0.09 (−0.19 to 0.01)0.09Age, per yraEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).−0.05 (−0.12 to 0.02)0.2−0.07 (−0.14 to 0.00)0.05−0.05 (−0.12 to 0.02)0.2Female sexaEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).−2.87 (−3.94 to −1.79)<0.001−3.09 (−4.13 to −2.04)<0.001−3.01 (−4.05 to −1.95)<0.001BSA, per m2aEstimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept).0.37 (−2.36 to 3.18)0.91.55 (−1.11 to 4.29)0.30.75 (−1.93 to 3.51)0.6Log10 baseline htTKV, ml/m0.46 (−1.32 to 2.27)0.60.58 (−1.14 to 2.32)0.50.40 (−1.32 to 2.14)0.7DNA mutation (ref: PKD2) PKD 1 truncating−1.38 (−2.64 to −0.10)0.03−1.48 (−2.70 to −0.25)0.02−1.41 (−2.63 to −0.17)0.03 PKD 1 nontruncating−0.92 (−2.29 to 0.48)0.2−1.08 (−2.40 to 0.26)0.1−0.85 (−2.19 to 0.50)0.2 Unknown−0.77 (−2.56 to 1.05)0.4−0.90 (−2.63 to 0.86)0.3−0.77 (−2.50 to 1.00)0.4Randomization group (lanreotide)−2.07 (−2.93 to −1.20)0.004−2.15 (−3.00 to −1.30)<0.001−2.21 (−3.05 to 1.35)<0.001BSA, body surface area; CI, confidence interval; eGFR, estimated glomerular filtration rate; Est, estimation; htTKV, height-adjusted total kidney volume; PKD, polycystic kidney disease; ref, reference.a Estimations and P values shown for the interactions of variables with time. The interaction with time signifies the effect of said variable on eGFR over time: that is, the effect on eGFR slope. Model 1 shows the association of sodium excretion with eGFR slope. Model 2 shows the association of urea excretion with eGFR slope. Model 3 shows the associations of sodium and urea excretion with eGFR slope in the same model. All models were adjusted for time, age, sex, BSA, and their interactions with time. The estimations for the variables not interacted with time (not shown) signify the effect of said variable on baseline eGFR (the intercept). Open table in a new tab BSA, body surface area; CI, confidence interval; eGFR, estimated glomerular filtration rate; Est, estimation; htTKV, height-adjusted total kidney volume; PKD, polycystic kidney disease; ref, reference. As sensitivity analyses, we repeated the linear mixed model analyses with salt and protein intake per kilogram ideal body weight, and salt and protein per kilogram actual body weight (Supplementary Table S1C and D). As additional sensitivity analyses, we repeated the analyses using baseline 24-hour excretions instead of mean excretions, we subsequently used median excretions instead of mean excretions. We excluded 24-hour urine collections if the 24-hour creatinine excretion was >30% different from that participant's average creatinine excretion. Finally, we performed a sensitivity analysis in which we adjusted for albuminuria. All of these analyses yielded essentially the same results. We tested for differences in the association between salt intake and annual change in eGFR across several subgroups (Figure 4). Higher salt intake was associated with more rapid eGFR decline or neutral eGFR in all subgroups. The interaction term between use of RAAS blockade and salt intake was significant (P = 0.02), with a stronger negative association in patients who did not use RAAS blockade. There was a trend toward a significant interaction with age (P = 0.06) and baseline eGFR (P = 0.07). Compared with patients that did not use RAAS blockade, RAAS blockade users had similar salt intake, but they were older, more often female, had lower eGFR, and had other baseline differences (Supplementary Table S2). There was significantly higher average salt intake in the younger patients than in the older patients (9.0 ± 2.7 g vs. 8.3 ± 2.5 g; P = 0.002). Salt intake was similar in patients with higher eGFR (8.8 ± 2.8 g) and patients with lower eGFR (8.4 ± 2.5 g; P = 0.07). We performed structural equation models to test for possible mediators of the association of excretion and eGFR slope. First, we tested whether the effect was mediated by an effect on blood pressure. In this model, the total effect of estimated salt intake on eGFR slope was estimated as −0.13 ml/min per 1.73 m2 per year per gram table salt (95% CI: −0.23 to −0.02; P = 0.03). The direct effect of blood pressure on eGFR slope was significant (−0.02 ml/min per 1.73 m2 per year per mm Hg; 95% CI: −0.03 to −0.01; P = 0.02). However, the direct effect of salt intake on systolic blood pressure was insignificant (P = 0.3). Thus, the indirect effect through systolic blood pressure was not significant (estimate: −0.005; 95% CI: −0.01 to 0.003; P = 0.3). Ergo, there was no significant mediation by systolic blood pressure. We tested whether the effect of salt intake on eGFR was mediated by plasma renin and plasma aldosterone in patients that did not use RAAS blockade (n = 58). In these patients, median plasma renin was 10.6 pg/ml (IQR: 6.5, 16.7) and median plasma aldosterone was 265 pg/ml (IQR: 181, 363). Both indirect effects were not significant (P = 0.3 and 0.4, respectively), indicating there was no statistically significant mediation by plasma renin or plasma aldosterone. Next, we investigated whether the association of sodium excretion and eGFR slope was mediated by copeptin (average of 2 values). There was high correlation between the 2 plasma copeptin measurements (Spearman coefficient: 0.85; P < 0.001) (Supplementary Figure S1). In a crude model, the total effect of salt intake on eGFR slope was estimated as −0.16 (95% CI: −0.23 to −0.09) ml/min per 1.73 m2 per year per gram table salt (P < 0.001). The indirect effect, mediated by copeptin, was estimated to be −0.12 (95% CI: −0.18 to −0.08) ml/min per 1.73 m2 per year per gram table salt (P < 0.001). Thus, the effect of salt intake on eGFR slope is mediated by copeptin by 77% (95% CI: 32% to 100%). After this crude analysis, the mediation model with copeptin was adjusted for potential confounders. In multivariable analysis, baseline age, sex, and eGFR were significantly associated with plasma copeptin on top of estimated salt intake. Sex and DNA mutation were significantly associated with eGFR slope on top of either salt intake or plasma copeptin. After adjustment for these variables, the total effect of salt intake on eGFR slope was −0.14 (95% CI: −0.23 to −0.04) ml/min per 1.73 m2 per year per gram table salt (P = 0.004), and the indirect effect was −0.06 (95% CI: −0.10 to −0.02; P = 0.004) (Figure 5). Thus, in the adjusted analysis, the effect of