Introduction
Sulfur is the third most abundant mineral element in the human body after calcium and phosphorus. Many important molecules contain sulfur, including the amino acids methionine and cysteine, hydrogen sulfide, and glutathione (1,2). Sulfur is also required for sulfation of drugs and toxins, including uremic toxins such as p-cresol. Dietary protein is the principal source of sulfur; inorganic sulfate (SO4) from water, food preservatives, and other sources contributes to a lesser extent. Animal protein has a higher proportion of the sulfur-containing amino acids cysteine and methionine than plant protein and is the main source of sulfur in Western society. Although sulfur is an important mineral element, animal protein contributes to the daily dietary acid load (34–5), which is associated with poor outcomes in individuals with CKD, including bone demineralization, muscle wasting, insulin resistance, and more rapid loss of kidney function (6789–10).
The potential benefits of restricting animal protein are many and include reducing the daily acid load, improving bicarbonate and phosphorus levels, and attenuating glomerular hyperfiltration and albuminuria (11121314151617181920–21). In most cases, dietary protein restriction involves reducing animal protein intake and, by default, sulfur intake. Hence, protein restriction may have adverse effects on sulfur metabolism and patient outcomes. We therefore evaluated the association between urinary SO4 levels, the end product of sulfur metabolism, and kidney and patient survival among participants in the African American Study of Kidney Disease and Hypertension (AASK). We hypothesized that higher urinary SO4, as a reflection of high protein intake and consequently daily acid load is associated with higher risk of death and loss of kidney function.
Materials and Methods
Study Participants
The details of the AASK have been published previously (2223–24). Briefly, Blacks aged 18–70 years with CKD attributed to hypertension were eligible for the study. Key exclusion criteria included elevated fasting or random blood glucose, treatment for diabetes, or a urinary protein to creatinine ratio >2.5. Participants (N=1094) were randomized to ramipril, metoprolol, or amlodipine, and to one of two BP goals (usual mean arterial pressure goal of 102–107 mm Hg or a low mean arterial pressure goal of ≤92 mm Hg). Baseline (prerandomization) urine samples were available for 1057 participants, and these individuals were included in this analysis. The AASK was overseen by the Institutional Review Boards of the participating sites and was performed under the principles embodied in the Declaration of Helsinki.
Measurements
Trained personnel obtained baseline demographic, clinical, and laboratory data using standardized forms. The 24-hour urine samples were confirmed to have been collected according to the AASK protocol and were necessary before randomization. Urine SO4 was measured by barium precipitation on a Beckman AU 680 analyzer (Beckman Coulter, Brea, CA). The analytical measurement range is 2.5–80 mEq/L. Daily urinary SO4 excretion was determined from 24-hour urine volumes. Serum total CO2 was measured using either the kinetic ultraviolet method (Roche Hitachi 747 autoanalyzer; Roche, Indianapolis, IN) or a CO2 electrode (Beckman CX3 Delta autoanalyzer; Beckman Coulter). Urinary protein excretion was expressed as protein/creatinine (PCR) obtained from the 24-hour urine collection. Daily dietary protein intake (g/d) was calculated from 24-hour urine urea nitrogen excretion using the equation: 6.25×[urine urea nitrogen+(weight×0.031)] (25). Net endogenous acid production (NEAP) was calculated using the formula: –10.2+(54.5×protein intake in g/d)/urine potassium in mEq/d (26).
Statistical Analyses
Participants were categorized by tertiles of daily SO4 excretion. Continuous variables are presented as means with SD unless otherwise specified. Categorical variables are presented as percentages. Significance tests were performed using analysis of variance for continuous variables and chi-squared tests for dichotomous variables.
The longitudinal outcome of interest was a composite of death, dialysis, or 50% reduction in measured GFR. These events were adjudicated by the AASK outcomes committee. Kaplan–Meier curves were constructed to display event-free survival by tertiles of urinary SO4. A series of Cox regression models were fit to relate the composite outcome to daily urinary SO4 excretion using the lowest tertile as the reference. Follow-up time was censored at the end of the trial phase of the AASK or permanent loss to follow-up. The initial model (model 1) was unadjusted, followed by adjustment for age, sex, randomized group, iothalamate measured GFR, urinary PCR, body mass index (BMI), and serum total CO2 (model 2). A subsequent model also adjusted for protein intake, urinary potassium as a reflection of alkali intake, urinary phosphate, and serum phosphate (model 3). Model 4 also adjusted for systolic BP, history of heart disease, and smoking history and was considered our main model of interest. Similar Cox models were constructed for the outcomes of death alone or a kidney-specific outcome of 50% reduction in measured GFR or ESKD. A cubic spline regression analysis adjusted for variables in model 4 was performed using daily urinary SO4 excretion as the predictor variable. Knots were placed at quartiles of daily urinary SO4, and the median value (26.3 mEq/d) was the reference point. The variance inflation factor of variables potentially correlated with urine sulfate (protein intake, serum and urine phosphate, serum and urine potassium, GFR, BMI, serum total CO2) was determined, and there was no evidence of multicollinearity (variance inflation factor ≤3.40 for each variable).
Results
Table 1 presents baseline characteristics of the 1057 participants for whom urinary SO4 measurements were obtained. Participants in the highest urinary SO4 tertile were more likely to be men and have higher BMI, protein intake, measured GFR, and urinary ammonium and phosphate excretion, and lower urinary PCR. Although protein intake was highest among those in the highest urine SO4 tertile group, mean daily protein intake was not excessive.
Table 1. -
Baseline characteristics by tertiles of urinary
sulfate
| Variable |
Urine SO4, mEq/d |
P Value |
| Total Population (N=1057) |
Tertile 1 (N=353) |
Tertile 2 (N=352) |
Tertile 3 (N=352) |
| Urine SO4, mEq/d |
26.3 (17.6–37.4) |
14.8 (11.1–17.6) |
26.4 (23.6–30.4) |
43.4 (37.4–51.6) |
N/A |
| Age, yr |
54 (11) |
55 (11) |
54 (11) |
54 (11) |
0.27 |
| Men |
61 |
46 |
59 |
80 |
<0.001 |
| Heart disease |
51 |
50 |
52 |
52 |
0.78 |
| Current smoker |
30 |
35 |
29 |
24 |
0.009 |
| Past smoker |
28 |
23 |
31 |
32 |
| Never smoker |
42 |
42 |
40 |
44 |
| SBP, mm Hg |
150 (24) |
152 (26) |
150 (22) |
149 (24) |
0.34 |
| BMI, kg/m2
|
31 (7) |
29 (7) |
30 (6) |
32 (7) |
<0.001 |
| Protein intake, g/d |
69 (26) |
48 (13) |
66 (12) |
93 (25) |
<0.001 |
| Protein intake, g/kg per day |
0.78 (0.24) |
0.59 (0.15) |
0.77 (0.16) |
0.97 (0.24) |
|
| NEAP, mEq/d |
82 (37) |
86 (44) |
79 (33) |
82 (34) |
0.05 |
| ACE-I/ARB use |
39 |
37 |
36 |
45 |
0.04 |
| Diuretic use |
64 |
61 |
62 |
69 |
0.03 |
| Measured GFR, ml/min per 1.73 m2
|
47 (14) |
43 (14) |
47 (13) |
50 (13) |
<0.001 |
| GFR <30 ml/min per 1.73 m2
|
16 |
23 |
14 |
9 |
<0.001 |
| Urine protein/creatinine, mg/g |
81 (30–350) |
108 (41–518) |
74 (30–323) |
61 (22–244) |
<0.001 |
| Total CO2, mEq/L |
25.1 (3) |
24.8 (3.2) |
25.1 (2.9) |
25.5 (2.7) |
0.005 |
| Total CO2<22 mEq/L, % |
12 |
16 |
11 |
9 |
0.014 |
| K+, mEq/L |
4.2 (0.6) |
4.3 (0.7) |
4.2 (0.6) |
4.2 (0.6) |
0.47 |
| Serum anion gap, mEq/L |
10 (2.4) |
10.3 (2.6) |
9.9 (2.2) |
9.9 (2.4) |
0.02 |
| Urine NH4, mEq/d |
19.5 (13.2–28.1) |
13.2 (8.7–18.3) |
19.5 (14.6–25.3) |
28.7 (21.1–37.2) |
<0.001 |
| Urine PO4, mg/d |
63.3 (30.2) |
39.9 (16.4) |
61.6 (19.4) |
88.6 (30.3) |
<0.001 |
Data presented as median (interquartile range), mean (SD), or %. N/A, not applicable; SBP, systolic BP; BMI, body mass index; NEAP, net endogenous acid production; ACE-I, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker.
Figure 1 shows baseline values of urinary SO4, protein intake, urine potassium, and NEAP by categories of measured GFR. Urinary SO4, protein intake, and urinary potassium levels were lower among those with lower measured GFR. There was no significant difference in NEAP across the measured GFR categories.
;)
Figure 1.: Urinary sulfate and potassium levels and protein intake are lower with lower measured GFR, but net endogenous acid production is similar across GFR categories. (A) urinary SO4, (B) protein intake, (C) urinary potassium, and (D) net endogenous acid production (NEAP) at baseline.
Figure 2 shows correlations between urinary SO4 and protein intake, urinary potassium, NEAP, and serum total CO2 at baseline. Urinary SO4 was strongly correlated with protein intake (r=0.84) and urinary potassium but to a lesser extent (r=0.53). There was no meaningful correlation between urinary SO4 and NEAP or total CO2.
Figure 2.: Urinary sulfate is strongly associated with protein intake but not net endogenous acid production. Correlation between urinary sulfate and (A) protein intake, (B) urinary potassium, (C) NEAP, and (D) serum total CO2 at baseline.
Table 2 shows the number of composite events (death, dialysis, or 50% reduction in measured GFR) experienced among AASK participants according to baseline urinary SO4 excretion. The number of events was lowest among those in the highest urinary SO4 tertile. Figure 3 shows that event-free (death, dialysis, or 50% reduction in measured GFR) survival in the lowest urinary SO4 tertile started to decline at a greater rate than other groups after about 1 year of follow-up.
Table 2. -
Number and incidence rate (per 1000 patient years) of the composite outcome of death, dialysis, or 50% reduction in measured GFR
| Daily SO4 Excretion |
Number of Events |
Follow-Up Time (Patient Years) |
Incidence Rate (95% Confidence Interval) (per 1000 Patient Years) |
| Tertile 1 |
137 |
1383 |
99 (84–117) |
| Tertile 2 |
109 |
1489 |
73 (61–88) |
| Tertile 3 |
94 |
1467 |
64 (52–78) |
| Total |
340 |
4340 |
78 (70–87) |
Figure 3.: Kaplan–Meier event-free survival plot by tertiles of urine sulfate.
Table 3 shows unadjusted and adjusted hazard ratios (HR) for the composite outcome of death, dialysis, or 50% reduction in measured GFR by tertile of urinary SO4. In the unadjusted model, the hazard of the composite outcome was 37% lower for those in the highest tertile and 28% lower for those in the middle tertile compared with the lowest tertile. Additional adjustment did not substantially change the HR for either group. In our main model (model 4), those in the middle tertile had a 27% lower hazard, and those in the highest tertile had a 44% lower hazard of death, dialysis, or 50% reduction in measured GFR. Table 3 also shows HR and 95% confidence intervals (CI) for the outcomes of (1) death alone and (2) a kidney-specific outcome of ESKD or 50% reduction in measured GFR after adjusting for variables in model 4. For death alone, there was a 25% lower hazard in the middle tertile and a 53% lower hazard in the highest tertile, although neither was statistically significant because of a low number of deaths during follow-up (n=83). For the kidney-specific outcome (ESKD or 50% reduction in measured GFR), the hazard of experiencing these events was 27% lower in the middle tertile and 41% lower in the highest tertile compared with the lowest tertile.
Table 3. -
Unadjusted and adjusted hazard ratios of the composite outcome of death, dialysis, or 50% reduction in measured GFR; death alone; and dialysis or 50% reduction in GFR by tertiles of urinary
sulfate excretion
| Outcome |
Urinary SO4
|
| Tertile 1 |
Tertile 2 |
Tertile 3 |
|
Death, dialysis, or 50% reduction in GFR
|
|  Model 1 |
Reference |
0.72 (0.56 to 0.92) |
0.63 (0.49 to 0.82) |
|  Model 2 |
Reference |
0.77 (0.6 to 1) |
0.69 (0.52 to 0.93) |
|  Model 3 |
Reference |
0.73 (0.55 to 0.97) |
0.56 (0.37 to 0.84) |
|  Model 4 |
Reference |
0.73 (0.55 to 0.96) |
0.56 (0.37 to 0.84) |
|
Death
|
|  Model 4 |
Reference |
0.75 (0.43 to 1.3) |
0.47 (0.22 to 1) |
|
Dialysis or 50% reduction in GFR
|
|  Model 4 |
Reference |
0.73 (0.53 to 1.01) |
0.59 (0.37 to 0.95) |
Data shown as hazard ratio (95% confidence interval). The lowest tertile served as the reference group. Model 1: unadjusted. Model 2: adjusted for age, sex, randomized treatment group, measured GFR, urine protein/creatinine, body mass index, and serum total CO2. Model 3: adjusted for model 2 variables and protein intake, urinary potassium, urinary phosphate, and serum phosphate. Model 4: adjusted for model 2 variables and systolic BP, heart disease, and smoking.
Figure 4 shows the cubic spline regression plot between urinary SO4 and the composite outcome using median daily urinary SO4 excretion (26.3 mEq/d) as the reference point. The lowest risk of the composite outcome was with a urinary SO4 value of around 35–45 mEq/d. There was a linear and inverse association with the composite outcome among those with urinary SO4 levels below this range.
Figure 4.: Cubic spline regression plot of the association between urinary SO 4 and the primary composite outcome of death, dialysis, or 50% reduction in measured GFR. The solid line represents the hazard ratio, and the dashed lines represent the 95% confidence intervals. The median value (26.3 mEq/d) served as the reference point. Adjusted for age, sex, randomized treatment group, measured GFR, urinary protein/creatinine, body mass index, serum total CO2, protein intake, urinary potassium, urinary phosphate, serum phosphate, systolic BP, heart disease, and smoking.
Because urinary sulfate is tightly linked with protein intake, we evaluated the association between protein intake tertiles and the composite outcome in these participants. The model used here adjusted for the same variables as in model 4, excluding protein intake. Compared with those in the lowest tertile, there was no appreciable difference in the hazard of the composite outcome among those in the middle tertile (HR 0.86; 95% CI, 0.64 to 1.15) or the highest tertile (HR 0.98; 95% CI, 0.67 to 1.45) of protein intake.
Discussion
Sulfur is an essential mineral; however, sulfur’s primary dietary source is animal protein. Due to its link with protein intake, we postulated that higher urinary SO4 would be associated with greater risk of adverse outcomes in individuals with CKD. Although urinary SO4 was directly associated with protein intake, higher urinary SO4 was not associated with loss of kidney function or mortality among AASK participants. On the contrary, those in the highest tertile of urinary SO4 had around a 45% lower hazard of CKD progression or death during follow-up after adjusting for important confounders such as measured GFR, protein intake, and potassium intake. The observation that urinary SO4 and protein intake were strongly correlated yet only SO4 was associated with risk of adverse clinical events suggests that SO4 exerts its effects independently of protein intake. These findings are similar to those observed in kidney transplant recipients in whom higher urinary SO4 was associated with lower mortality and graft failure (27,28). Similarly, individuals with diabetes and higher urinary SO4 levels had lower risk of CKD progression (29,30). Thus, higher urinary SO4 is associated with more favorable outcomes in individuals with CKD.
There are several potential reasons why higher urinary SO4 is associated with more favorable outcomes in CKD. First, urinary SO4 excretion was higher in individuals with higher GFR. Thus, individuals with less severe CKD may not have been advised to reduce dietary protein intake, or may have had preserved sulfate clearance due to higher GFR. On the other hand, those with lower urinary SO4 could have been consuming a low protein diet or have impaired SO4 excretion with SO4 accumulation. Another potential explanation is that those with lower GFR may accumulate uremic toxins and medications that are metabolized through sulfation pathways. Hence, low urinary SO4 could be a sign of increased proximal SO4 reabsorption via the sodium-sulfate co-transporter (NaS1) to meet these metabolic demands. Nevertheless, we observed a strong and statistically significant association between higher urinary SO4 and lower risk of CKD progression or death, even after adjusting for both protein intake and GFR. Importantly, GFR was measured, and not estimated, in AASK participants, increasing confidence in the findings.
Another possibility is that urinary SO4 reflects total body sulfur abundance, and lower urinary sulfate excretion could signal a state of sulfur deficiency (28). Sulfur is a component of a variety of important molecules, including the sulfur-containing amino acids methionine and cysteine. Methionine is an essential amino acid, whereas cysteine can be synthesized as long as sulfur is available. Sulfur is principally stored as glutatione (31), and sulfur deficiency may favor cysteine and protein synthesis at the expense of other molecules such as glutathione. Glutathione is a powerful antioxidant whose activity decreases with protein restriction. Lower glutathione levels are associated with greater reperfusion injury in AKI in animal models and humans (3233–34), and glutathione suppresses prostaglandin synthesis pathways (35,36). In addition, glutathione plays a role in regulating cholesterol. For example, diets supplemented with sulfur-containing amino acids increased levels of reduced glutathione and lowered serum cholesterol (3738–39). Sulfur is also an elemental component of hydrogen sulfide, an endogenous neuromodulator. In animal models of kidney disease, exogenous administration of hydrogen sulfide reduced oxidative stress and promoted anti-inflammatory pathways (40414243–44). Thus, lower urinary sulfate levels may signal a state of sulfur deficiency that adversely affects the production and function of important regulatory molecules and pathways. Although important for metabolism, sulfur in excess can cause gastrointestinal side effects and neurotoxicity.
Despite a strong correlation between urinary SO4 and protein intake, there was no correlation of SO4 with NEAP. There was, however, a modest association between urinary SO4 and urinary potassium excretion, suggesting that alkali precursors ameliorate the effect of dietary acid load from protein. We calculated protein intake using urinary urea excretion, which does not discriminate between plant and animal protein sources (45). Others have found that lower fruit and vegetable intake correlated with high NEAP and was associated with more rapid progression of CKD when protein intake was assessed by food diaries rather than urinary urea excretion (4647–48). Dietary diaries were not performed in the AASK. Nevertheless, quantification of protein intake from urea nitrogen is a standard approach. However, it does not distinguish between animal- and vegetable-derived protein.
Sulfate is the end product of sulfur metabolism, irrespective of whether it comes from organic or inorganic sources. Sulfites are often added to dried produce, processed meats, and other foods, but it is difficult to distinguish the quantity or proportion of SO4 derived from these food additives. However, sulfites are generally thought to be more harmful than beneficial in human health because they can worsen allergies and asthma, and cause intestinal inflammation (49,50), and therefore they are unlikely to explain the findings. Other limitations of this study include its retrospective nature. Despite our best efforts to control for potential confounders, residual confounding may still be present, and we cannot prove causation. The AASK included African Americans with CKD attributed to hypertension, which could affect the generalizability of this study. However, similar findings have been observed in other cohorts such as individuals with diabetes or those who have received a kidney transplant. We are unaware if participants were instructed to be on a low-protein diet. Nevertheless, our findings were observed after adjusting for protein intake, and protein intake itself was not associated with an increased risk of CKD progression or death here. We cannot completely rule out the possibility that low urinary SO4 reflects reduced filtration of SO4 due to lower GFR. Plasma SO4 levels could help determine if this were the case; however, measuring plasma SO4 levels requires advanced techniques that are beyond the scope of this study. Despite these limitations, this study has important strengths. The AASK is a well-characterized cohort with a large sample size, carefully collected prospective data, and long-term follow-up. Furthermore, GFR was directly measured rather than estimated, which is particularly important, given the strong association between kidney function and the outcomes of interest.
In conclusion, higher urinary SO4 levels are associated with more favorable kidney and patient survival in AASK participants. Similar findings have been observed in individuals with diabetes and those who have received a kidney transplant. Future studies investigating the role of sulfur on health in patients with kidney disease are warranted.
Disclosures
S. Beddhu reports consultancy for Bayer and Reata; research funding from Bayer, Boehringer Ingelheim, and Novartis; and an advisory or leadership role for CJASN and Kidney Reports. K.L. Raphael reports consultancy for AstraZeneca and research funding from the Department of Veterans Affairs (I01 CX001695). All remaining authors have nothing to disclose.
Funding
This study was funded by the Harold Amos Medical Faculty Development Program of the Robert Wood Johnson Foundation (grant 11160).
Acknowledgments
We wish to acknowledge and thank the study participants, investigators, and staff of the African American Study of Kidney Disease and Hypertension. Portions of these data were included in a poster presentation at Kidney Week 2016.
Author Contributions
A. Azim wrote the original draft of the manuscript; S. Beddhu and K.L. Raphael were responsible for conceptualization, data curation, and project administration; S. Beddhu, J. Murray, and K.L. Raphael were responsible for the investigation; J. Murray and K.L. Raphael were responsible for the methodology; K.L. Raphael was responsible for the formal analysis, funding acquisition, resources, software, supervision, validation, and visualization; and all authors reviewed and edited the manuscript.
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