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. 2023 Jun 23;18(6):e0278441. doi: 10.1371/journal.pone.0278441

Evaluation of renal markers in systemic autoimmune diseases

Hari Krishnamurthy 1,*, Yuanyuan Yang 1, Qi Song 1, Karthik Krishna 1, Vasanth Jayaraman 1, Tianhao Wang 1, Kang Bei 1, John J Rajasekaran 1
Editor: Alessandro Granito2
PMCID: PMC10289317  PMID: 37352246

Abstract

Renal involvement is a common occurrence in subjects with systemic autoimmune diseases. The renal manifestation and its severity depend on the underlying condition and may reversely complicate the clinical course of autoimmune diseases. Renal function markers have been widely used in the assessment of normal functioning of kidneys including glomerular filtration rate and concentrating and diluting capacity of the kidney. An increase or decrease in the values of these markers may indicate kidney dysfunction. In this study, a number of critical renal markers were examined in seropositive autoimmune diseases including systemic lupus erythematosus (SLE), connective tissue disorder (CTD), and rheumatoid arthritis (RA). The data from three cohorts of subjects enrolled in renal markers and autoimmune antibody testing between January 2015 to August 2019 were retrospectively studied. The prevalence of renal markers that were out of the reference range and their average levels in female and male subgroups across SLE, CTD, and RA cohorts were compared and analyzed. The levels of renal markers are significantly affected by the presence of autoantibodies, in particular eGFR, cystatin C, and albumin. Autoantibodies were also more frequent in subjects with severe renal function damage. Close follow-up of both renal markers and autoantibodies may potentially assist in the early diagnosis of kidney diseases and improve the survival and life expectancy of autoimmune patients.

Introduction

Systemic autoimmune diseases are a large and heterogeneous group of immunologically mediated disorders that originated from complex genetic and environmental factors and are characterized by the production of autoantibodies [1, 2]. Renal involvement is a common occurrence in subjects with systemic autoimmune diseases [3]. The renal manifestation and its severity depend on the underlying disease and may reversely complicate the clinical course of autoimmune diseases [4]. As observed in previous studies, impairment of renal functions may occur in a variable prevalence in different systemic autoimmune diseases, such as approximately 50% in systemic lupus erythematosus (SLE) [5], roughly 5% in systemic scleroderma (SSc) [6], a variable occurrence in Sjögren syndrome [7, 8], and rare manifestation in rheumatoid arthritis (RA) [9]. For most systemic autoimmune diseases, renal involvement can be of significant prognostic value and often warrants specific immunosuppressive treatment [10]. Thus, it is important to diagnose and manage them at an early stage.

A variety of methods are available to assist clinicians to assess renal functions and injuries. The estimated glomerular filtration rate (eGFR) is generally regarded as the most important measure of overall renal function [11]. Decreased GFR is generally accompanied by other renal functional variables. Urea is the waste product of protein metabolism and should be almost eliminated through urinary excretion. Blood urea nitrogen (BUN) quantifies the accumulation of urea in the blood and has been widely used to assess renal functions as well as cardiovascular diseases [12]. Serum creatinine supplemented BUN for renal assessment since mid-1900s and remains a laboratory parameter to estimate GFR [13]. Serum creatinine is the product of the nonenzymatic dehydration of muscle creatine and is usually formed at a relatively constant rate. Creatinine can be freely filtered by the glomerulus and not reabsorbed by the renal tubules. To improve the use of serum creatinine to estimate GFR, several serum creatinine-based equations have been developed [14]. Serum cystatin C, a low molecular weight protein in the cysteine proteinase inhibitor family, is another marker that has been considered enthusiastically to estimate GFR [15]. Unlike creatinine, serum cystatin C concentration appears to be independent of age, sex, and muscle mass. Beyond GFR, Albumin is one of the most prognostically significant biomarkers of kidney disease outcomes and even cardiovascular disease and death [16]. Electrolytes and minerals are frequently used to screen for an electrolyte or acid-based imbalance which may affect bodily organ function [17].

The purpose of this study was to evaluate a comprehensive panel of renal markers among seropositive autoimmune patients and seronegative controls. The renal function panel measured two critical calculated parameters and 11 markers which have been widely employed in clinical practice to monitor the physiologic status of the kidney. Three cohorts of patients were enclosed in this study and they were tested serologically for SLE, RA, CTD, and renal markers respectively. We have further attempted to analyze the renal markers’ levels by females and males in relation to reference ranges. The frequency of autoantibodies across all three cohorts was investigated based on different stages of renal function damage.

Materials and methods

Serum samples

Three cohorts of retrospective study samples were included in this study, as shown in Table 1. The samples’ medical information was collected between January 2015 to August 2019 and tested in the Vibrant America Clinical Laboratory (San Carlos, CA, USA). The waiver of consent for In Vitro Diagnostic Device study using leftover human specimens that are not individually identifiable was approved by the Western Institutional Review Board (WIRB) (work order #1-1098539-1).

Table 1. Demographics of the three cohorts in this study.

Cohort 1 Cohort 2 Cohort 3
Panels tested SLE + Renal Function CTD + Renal Function RA + Renal Function
Number of subjects 13841 9995 20681
Genders 8954 F / 4887 M 6293 F / 3702 M 13482 F / 7199 M
Average age (±SD) 46 (±16) 46 (±16) 47 (±16)
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Renal function panel

The Renal function panel included electrolytes (sodium, potassium, chloride, total bicarbonate), minerals (calcium, phosphorus, magnesium), protein (albumin, cystatin C), waste products (BUN, creatinine), and two calculated values (BUN/creatinine ratio, estimated glomerular filtration rate (eGFR)). These markers were quantitatively determined at Vibrant America Clinical Laboratory (San Carlos, CA, USA), a CLIA-certified clinical laboratory. Detailed information regarding the markers’ measurement is in S1 File. eGFR was calculated from serum creatinine using the CKD-EPI equation for adults (> 18 years old) and Bedside IDMS-traceable Schwartz GFR Calculator for Children (≤ 18 years old). The reference range for normal levels of renal markers is detailed in S2 File.

Systemic Lupus Erythematosus (SLE) panel

The SLE panel included antinuclear antibody (ANA) and anti-dsDNA antibody. The ANA detection was performed with a solid phase bio-chip immunofluorescence assay, VibrantTM ANA HEp-2 (Vibrant America, LLC, San Carlos, CA, USA). A sample was considered ANA positive (ANA+) if any specific staining (homogeneous, centromere, speckled, nucleolar, peripheral) was observed to be greater than the negative controls. 1:40 dilution was used for screening and was reflexed depending on the assay results (1:80, 1:160, 1:320, 1:640, and so on). The elderly, especially women, are prone to develop low-tittered autoantibodies in the absence of clinical autoimmune disease. Anti-dsDNA antibody was detected using a solid phase bio-chip immunofluorescence assay that reports qualitative and semi-quantitative results. A seropositive SLE subject is whose ANA and anti-dsDNA testing results were both positive. A seronegative control is ANA and/or anti-dsDNA antibody were negative.

Connective Tissue Disorder (CTD) panel

The CTD panel included ANA and 10 anti-extractable nuclear antigens (ENA). The testing principles and assay process of detecting ANA and 10 anti-ENA were very similar to the procedures described in our previous work [18]. The 10 anti-ENA antibodies including SSA(Ro), SSB(La), RNP/Sm, Jo-1, Sm, Scl-70, Chromatin, Centromere, Histone, RNA polymerase III was tested. SSA(Ro), SSB(La), RNP/Sm, and Jo-1 were detected using a solid phase bio-chip immunofluorescence assay that reports qualitative and semi-quantitative results. The assessment and interpretation of the results were following the international guideline announced by the European autoimmunity standardization initiative and the International Union of Immunologic Societies/World Health Organization/Arthritis Foundation/Centers for Disease Control and Prevention autoantibody standardizing committee. A seropositive CTD subject is whose ANA and more than one of the anti-ENAs testing results were positive. A seronegative control is whose ANA and/or anti-ENAs testing results were negative.

Rheumatoid Arthritis (RA) panel

The RA panel included anti-RF IgM (Roche Diagnostics, Risch-Rotkreuz, Switzerland) and anti-CCP3 IgG and IgA (Inova Diagnostics, San Diego, CA, USA). The interpretation of the results strictly followed the protocol suggested by the assay provider companies. A seropositive RA subject is someone who has at least one antibody at borderline of or more than an index value of 0.95. A seronegative control is if the concentrations of the antibodies to all the markers in the panel were equal to or less than the cut-off values.

Data analysis

Clinical data from the de-identified subjects were included in a database retrieved through MySQL workbench 8.0.12 and analyzed using R for Windows version 3.5.1. Two-tail student T test was performed to determine whether there is significant difference between data sets and P<0.05 is considered as significant. In all histogram figures, P values less than 0.05 were given *, P values less than 0.01 were given **, P values less than 0.001 were given ***, P values less than 0.0001 were given ****, and P values more than 0.05 were not labeled.

Results

Renal makers in SLE subjects

Cohort 1 consists of 126 seropositive SLE subjects and 13715 seronegative controls. Fig 1 shows the prevalence of SLE and control subjects for carrying higher-than-reference-range (blue, above axis) and lower-than-reference-range renal markers (orange, below axis). Among them, 43.4% of SLE subjects had high BUN/creatinine levels compared with 32.5% of controls (P<0.0001). This result is, however, opposite to the observation with BUN, which were carried by more SLE subjects when lower than reference range (P<0.0001) and by more controls when higher than the reference range (P<0.0001). Low sodium level was also found to be significantly more frequent in SLE subjects (P<0.0001).

Fig 1. Prevalence of out-of-range renal markers in seropositive SLE subjects and seronegative controls.

Fig 1

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The results of subjects with higher-than-reference-range renal markers are shown in blue. The results of the subject with lower-than-reference-range renal markers are shown in orange.

We split this cohort into female and male subgroups and assessed their levels of renal markers, respectively, shown in Table 2. The mean eGFR level was lower in the female SLE subjects but not much different in the male group. Beyond eGFR, the mean values of BUN and cystatin C were higher in both female and male SLE subjects than respective controls. The mean calcium level was higher solely in SLE female subjects while phosphate level was higher solely in the SLE male subjects.

Table 2. Average levels of renal markers among seropositive SLE subjects and seronegative controls.

Renal Markers (Unit) Female Male
Seropositive SLE Control P value Seropositive SLE Control P value
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
eGFR 82.7±22.2 90.5±20.8 <0.001 80.3±27.8 85.2±18.7 0.415
BUN/creatinine 20.1±5.3 19.3±6.1 0.145 18.2±5.1 17.3±5.5 0.413
BUN (mg/dL) 15.6±4.9 13.9±4.7 <0.00001 21.0±10 16.4±5.5 0.024
Creatinine (Mg/Dl) 0.8±0.2 0.7±0.2 0.129 1.1±0.4 1.0±0.2 0.081
Cystatin C (mg/L) 1.0±0.3 0.9±0.2 <0.00001 1.2±0.4 1.0±0.3 0.006
Albumin (G/Dl) 4.4±0.4 4.6±0.3 <0.001 4.5±0.4 4.6±0.3 0.138
Sodium (Mmol/L) 141.5±3.8 141.5±2.8 0.864 141.3±4.1 142.0±2.8 0.423
Potassium (Mmol/L) 4.4±0.4 4.4±0.4 0.799 4.9±1.2 4.5±0.4 0.068
Chloride (Mmol/L) 101.0±3.5 101.3±2.8 0.430 100.5±3.9 101.0±2.9 0.535
Total bicarbonate (Mmol/L) 22.5±3 22.3±2.9 0.603 22.3±3.4 22.7±3.1 0.497
Calcium (Mg/Dl) 9.8±0.4 9.6±0.4 <0.001 9.6±0.4 9.7±0.4 0.407
Phosphate (Mg/Dl) 3.8±0.6 3.7±0.5 0.055 3.7±0.6 3.5±0.6 <0.000001
Magnesium (Mg/Dl) 2.1±0.2 2.1±0.2 0.415 2.1±0.2 2.1±0.2 0.270
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Renal markers in CTD subjects

Cohort 2 consists of 695 seropositive CTD subjects and 9300 seronegative controls. The percentages of CTD and control subjects for carrying higher-than-reference-range (blue, above axis) and lower-than-reference-range renal markers (orange, below axis) are shown in Fig 2. In this cohort, 33.2% of the CTD subjects had low eGFR compared with 26.8% in controls (P<0.001). BUN/creatinine was also observed to be low in 5.1% of CTD subjects which is more frequent than 2.9% in controls (P<0.01). Other than these two calculated parameters, the higher-than-reference-range renal markers that were more frequent in CTD subjects include creatinine, cystatin C, calcium, phosphate, and magnesium. The lower-than-reference-range renal markers that were more frequent in CTD subjects include albumin, sodium, chloride, calcium.

Fig 2. Prevalence of out-of-range renal markers in seropositive CTD subjects and seronegative controls.

Fig 2

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The results of subjects with higher-than-reference-range renal markers are shown in blue; and the results of subjects with lower-than-reference-range renal markers are shown in orange.

Cohort 2 was split into female and male subgroups and the mean levels of renal marker in each group were shown in Table 3. The mean eGFR was significantly lower in both the female and male CTD subjects while there was no difference for BUN/creatinine. The mean levels of BUN, creatinine, cystatin C were higher in both female and male CTD subjects while albumin and total bicarbonate were higher in seronegative controls. The other markers did not show any statistical difference between seropositive and seronegative controls in this cohort.

Table 3. Average levels of renal markers among seropositive CTD subjects and seronegative controls.

Renal Markers (Unit) Female Male
Seropositive CTD Control P value Seropositive CTD Control P value
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
eGFR 87.2±20.8 92.2±20.9 <0.000001 76.5±21.9 83.7±18 <0.001
BUN/creatinine 19.7±6.3 19.5±6.1 0.344 16.5±7.2 17.1±5.3 0.324
BUN (mg/dL) 14.4±4.9 13.8±4.7 <0.01 18.9±7.8 16.4±5.4 <0.001
Creatinine (Mg/Dl) 0.7±0.2 0.7±0.2 0.016 1.1±0.3 1.0±0.2 0.003
Cystatin C (mg/L) 0.9±0.2 0.9±0.2 <0.000001 1.1±0.4 1.0±0.2 <0.0001
Albumin (G/Dl) 4.5±0.3 4.6±0.3 <0.00001 4.5±0.4 4.6±0.3 <0.00001
Sodium (Mmol/L) 141.5±3.2 141.6±2.9 0.184 141.3±3.2 142.0±2.8 0.013
Potassium (Mmol/L) 4.4±0.4 4.4±0.4 0.229 4.5±0.7 4.5±0.4 0.506
Chloride (Mmol/L) 101.4±3 101.5±2.8 0.492 100.5±3.2 101.1±2.8 0.035
Total Bicarbonate (Mmol/L) 22.0±2.8 22.3±3 0.033 22.3±3.2 22.8±3.1 0.046
Calcium (Mg/Dl) 9.6±0.5 9.6±0.4 0.668 10.1±2 9.7±0.4 0.013
Phosphate (Mg/Dl) 3.8±0.5 3.7±0.5 0.090 4.4±3.1 3.5±0.6 <0.001
Magnesium (Mg/Dl) 2.1±0.2 2.1±0.2 0.097 2.1±0.2 2.1±0.2 0.276
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Renal markers in RA subjects

Cohort 3 consists of 3304 seropositive RA subjects and 17377 seronegative controls. Fig 3 demonstrates the prevalence of RA and control subjects with higher-than-reference-range (blue, above axis) or lower-than-reference-range renal markers (orange, below axis). Low eGFR and high BUN/Creatinine were more prominent in the seropositive group than the controls (35.6% vs. 23.3% for eGFR, 36.8% vs. 33.9% for BUN/Creatinine). Furthermore, high levels of BUN, creatinine, cystatin C, potassium, calcium, magnesium, and low levels of albumin, sodium, chloride were found to be more frequent among RA subjects (P<0.05).

Fig 3. Prevalence of out-of-range renal markers in seropositive RA subjects and seronegative controls.

Fig 3

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The results of subjects with higher-than-reference-range renal markers are shown in blue; and the results of subjects with lower-than-reference-range renal markers are shown in orange.

The mean levels of the renal markers in both female and males are presented in Table 4. eGFR was elevated in both the female and male RA subjects while BUN/Creatinine was lowered in the female RA subjects. BUN, creatinine, cystatin C were elevated in both gender groups. Several markers’ average levels were greater in the control groups including albumin, sodium, chloride, calcium, and phosphate.

Table 4. Average levels of renal markers among seropositive RA subjects and seronegative controls.

Renal Markers (Unit) Female Male
Seropositive RA Control p value Seropositive RA Control p value
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
eGFR 86.9±21 94.2±20.3 <0.000001 80.5±17.8 84.7±17.6 <0.000001
BUN/creatinine 20.3±6.5 19.5±6.1 <0.000001 17.5±5.9 17.3±5.2 0.163
BUN (mg/dL) 14.8±5.1 13.8±4.7 <0.000001 17.2±5.7 16.5±5.5 <0.00001
Creatinine (Mg/Dl) 0.8±0.6 0.7±0.2 <0.001 1.0±0.2 1.0±0.2 0.042
Cystatin C (mg/L) 0.9±0.3 0.9±0.2 <0.000001 1.0±0.3 0.9±0.2 <0.000001
Albumin (G/Dl) 4.5±0.3 4.6±0.3 <0.000001 4.6±0.3 4.7±0.3 <0.000001
Sodium (Mmol/L) 141.5±3 141.7±2.9 0.002 141.8±2.9 142.1±2.8 0.006
Potassium (Mmol/L) 4.4±0.4 4.4±0.4 0.461 4.5±0.4 4.5±0.4 0.053
Chloride (Mmol/L) 101.2±3 101.6±2.9 <0.000001 100.9±3.1 101.2±2.8 0.002
Total Bicarbonate (Mmol/L) 22.5±3 22.3±2.9 0.004 22.6±3.1 22.7±3.1 0.313
Calcium (Mg/Dl) 9.6±0.5 9.6±0.4 0.002 9.6±0.5 9.7±0.4 0.034
Phosphate (Mg/Dl) 3.7±0.5 3.7±0.5 0.002 3.4±0.6 3.5±0.6 <0.000001
Magnesium (Mg/Dl) 2.1±0.2 2.1±0.2 0.060 2.1±0.2 2.1±0.2 0.308
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Evaluation of autoantibodies at different stages of renal damage

In this study, we divided all three cohort subjects into three stages in terms of kidney damage based on their eGFR: 90 or above indicating normal GFR; 60 to 89 indicating mild decreased GFR; below 60 indicating moderate to severe decreased GFR. The frequency of 14 autoantibodies were examined by stages and displayed in Fig 4. There is an apparent trend that autoantibodies became more frequent as eGFR decreased, which indicates worse renal damage. ANA, anti-histone, and anti-CCP were the three most prevalent autoantibodies among all renal damaged subjects and their presence were significantly more frequent than that in subjects with normal GFRs (P<0.05).

Fig 4. Frequencies of individual autoantibodies in three stages of kidney diseases.

Fig 4

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Autoantibodies increase with a decrease in eGFR.

Discussion

Patients with autoimmune diseases usually have increased mortality due to multiple factors that include an increased susceptibility to organ damage. The survival of patients with autoimmune diseases has improved tremendously in the past few decades, which is attributed to early diagnosis, novel treatments, and better supportive care for organ failures. However, patients with autoimmune diseases still have a higher mortality rate than that of the general population. Renal disease is a major organ manifestation of autoimmune diseases, and it may lead to kidney failure in a proportion of patients over time. A number of previous studies have focused on the prevalence of specific renal dysfunctions among certain types of autoimmune diseases, but a comprehensive evaluation of clinically significant renal markers related with autoimmune serology conditions have not been investigated.

Kidney can be a frequent target in CTD because kidney has abundant connective tissues and active blood supplies. Patients with CTD-associated renal involvement are often asymptomatic, at least in an early disease course, or report nonspecific symptoms. Direct involvement of kidneys is usually less common in RA but it can be complication of therapy. Our study assessed a total of 13 renal function markers in three cohorts of subjects with positive/negative serology in SLE, CTD, or RA. We further divided each cohort into female and male subgroups and analyzed the mean levels of these renal markers. We observed significantly lower eGFRs in seropositive CTD and RA subjects compared with respective controls. In the cohort 2, more seropositive CTD subjects had eGFR lower than 60 (33.2% in CTD, 26.8% in control, P<0.001). The mean eGFR reduced to 5 when at least one of the anti-ENA markers appear in female subjects and the number becomes 7.2 with male subjects. A similar trend was also observed in cohort 3. More seropositive CTD subjects had eGFR lower than 60 (35.6% in RA, 23.3% in control, P<0.0001). The mean eGFR reduced 7.3 when either RF or anti-CCP appear in female subjects as compared with 4.2 in male subjects. It is also worthy to note that male subjects with positive autoimmune serology had the lowest average eGFR across all cohorts. These observations are in accordance with the co-existence of renal dysfunction and autoimmune diseases [19].

The other renal function markers also provided valuable correlation information. The BUN/creatinine ratio, as a differential marker of acute or chronic renal disease, is usually between 10:1 and 20:1 in healthy population but rises when the kidney blood flow decreases. In our study, the mean BUN/creatinine values were higher among females than males (P<0.05) but only the female RA subjects showed a statistical difference compared with controls (<0.000001). BUN and creatinine as individual markers were believed to have limited utilization in characterizing renal functions because they are easily interfered by increased dietary protein intake, hyper catabolism, corticosteroid use, or gastrointestinal bleeding. However, in this study, they displayed differences between seropositive groups and controls across all three cohorts except for creatinine in the SLE group. Cystatin C, as an emerging marker other than creatinine to calculate eGFR, did present a higher level of difference between the seropositive subjects and respective controls across all three cohorts. Similarly, higher mean levels of albumin were also prominently shown in majority of the seropositive subjects except for SLE males. eGFR, Cystatin C, BUN, Calcium, and Albumin showed a significant association with seropositive females in the SLE group. Lupus nephritis is a common manifestation of SLE. Its onset is between 3–5 years after SLE onset. Lupus nephritis is more common in women. This is consistent with our results. Regular monitoring of kidney function by measuring renal markers and preventing the decline of kidney function is the primary treatment of Lupus nephritis [20].

In terms of electrolytes and minerals, renal dysfunctions are often accompanied by elevations in potassium, phosphate, magnesium and decreases in sodium and calcium. The electrolytes and minerals are less specific to kidney, but they are a good indication for organ dysfunction induced chemical imbalance. Potassium is considered to be the most convincing electrolyte marker of renal failure and hyperkalemia, which is the most significant and life-threatening complication of renal failure [21]. From our observation, there was no certain trend and the electrolytes are similar in seropositive subjects and respective controls with a few exceptions labeled in Figs 1–3. We hypothesized that the levels of these chemicals might be highly sensitive to diet and physical activity. These environmental factors may play an important role in regulating electrolytes and minerals.

Moreover, we observed a significantly greater prevalence of autoantibodies in more severe kidney damaged subjects. ANA, anti-histone, and anti-CCP were the three most prevalent autoantibodies in all renal damaged subjects and their presence were significantly more frequent than that in subjects with normal GFRs (P<0.05). Several mechanisms have been envisioned for renal involvement in systemic autoimmune diseases. Autoimmunity induced renal damage may be resulted from a systemic disturbance of immunity and accompanied by reduced tolerance to normal cellular and extracellular proteins [22]. Glomerular, tubular and vascular structures often become targets due to the loss of immunity balance. One hypothesis is that renal tissue may harbor self-antigens [23]. Autoantigens have been widely accepted as direct indicators in autoimmune disease; however, very few of them have been speculated to cause tissue injuries in kidney. Another theory is that the kidneys may become affected by the autoantigen outside the kidney [4]. The high flow, high-pressure perm-selective filtration function of the glomerulus may drive non-renal autoantigens to become renal targets during physiological process. Circulating autoantigens can accumulate in glomeruli and deposit as a target antigen because of their physio-chemical properties that predispose them to the glomerular structure. Moreover, antigen and antibodies may be neither derived nor deposited within the kidneys but the interaction between them may cause the disease [24]. The investigation of the mechanism based on the above speculations is currently underway. There were some limitations in our study that should be considered while reviewing the data. This was a retrospective study performed on de-identified serological data. The clinical conditions of the individuals were unknown and hence not part of the analysis. The testing was performed based on physician orders which would have an inherent bias on who was getting tested.

In conclusion, to the best of our knowledge this is the first retrospective study of patients with seropositive autoimmune diseases whose renal markers were examined and compared with seronegative controls. The renal markers reported in this paper are based on a large cohort of controlled samples and an extended list of autoantibodies. The clinical utility of testing for renal markers along with autoantibodies is immediately apparent and may potentially assist in early diagnosis of kidney diseases and improve survival and life expectancy of autoimmune patients.

Supporting information

S1 File. Measurement of renal functional panel markers.

(DOCX)

Click here for additional data file. (15.4KB, docx)
S2 File. Reference range for normal levels of renal markers.

(DOCX)

Click here for additional data file. (24.4KB, docx)

Data Availability

All relevant data are within the paper. Any additional data will be available upon request from Vibrant America LLC by sending an email to our bioinformatics team at bioinformatics@vibrant-america.com or hari@vibrantsci.com.

Funding Statement

Vibrant America provided funding for this study in the form of salaries for authors [YY, QS, KK, VJ, TW, KB, HK, JJR]. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Latika Gupta

19 Dec 2022

PONE-D-22-31509Evaluation of Renal Markers in Systemic Autoimmune DiseasesPLOS ONE

Dear Dr. Krishnamurthy,

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Additional Editor Comments:

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[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Summary and general comments:

In the manuscript “Evaluation of Renal Markers in Systemic Autoimmune Diseases” the authors took a correlation approach to investigate the association between several renal markers and kidney disease in patients with systemic autoimmune diseases. To achieve such aim, the authors compared retrospectively 13 renal markers together with autoantibodies in 3 cohorts of patients: 1) 126 with systemic lupus erythematosus (SLE), 2) 695 with connective tissue diseases (CTDs) and 3) 3304 with rheumatoid arthritis (RA).

The results showed that:

1. Renal markers were elevated in the 3 cohorts of patients comparatively to controls.

2. Some autoantibodies such ANA, anti-Histone and anti-CCP were associated with kidney damage and lower eGFRs.

3. All the renal markers except for electrolytes were good in assessing renal involvement in patients with autoimmune diseases.

Overall, the paper is well organized and I enjoyed reading it but there are some rectifications and explanations that need to be addressed.

Specific comments:

#1 In the text, the authors mention 4 tables that do not exist in the manuscript version submitted for review. Please provide the manuscript with the cited tables.

#2 The last paragraph of the introduction is a bit misleading. For instance, the authors mention 13841 SLE patients when in reality it is 126 patients and 13715 controls. And then who were these control subjects, and why did each of the 3 cohorts have its own control group?

#3 In the statistical analysis section, the authors did not specify the type of statistical tests used.

#4 In the results section, the authors did not detail the quantitative measurements of the different markers assessed in the patients. It would be more appropriate to compare the means or to perform non-parametric tests followed by ROC analysis to evaluate the sensitivity and specificity of the markers in detecting renal damage. Eventually, the authors could perform a logistic regression to estimate the incremental risk for each one-unit increase in each marker and build a predictive model for kidney damage.

#5 In SLE patients, the association of anti-DNA antibodies with renal damage has been known for a long time and numerous human and mouse studies have confirmed the pathogenic role of these autoantibodies. The authors should discuss the discrepancy of their results with the literature.

Reviewer #2: 1. About leftover samples: when, on an average, were the samples processed after being leftover and is there any effect on the sample results because of the leftover time.

2. The titre and the strength of ANA control is not mentioned anywhere in manuscript.

3. Renal markers in SLE: Among the 43.4% of SLE subjects, is there any clinical data about how many were having or developed nephritis or other renal complications, that way the results would be translated more effectively bedside. in various other studies, serum creatinine level remains statistically significant risk factor for developing nephritis in SLE patients, what about the serum creatinine levels in this study, was that statistically significant? is there any clinical data available about those who have low sodium levels because some studies have demonstrated low sodium level as an inflammatory marker of lupus, the current study would further add strength to that association in case. Was the mean Calcium level in female SLE subjects and high phosphate level in male SLE patients statistically significant? there are some studies which show inverse relation between hypocalcemia and disease activity in SLE patients.

3. Renal markers in RA: 1. Any clinical data available in these RA patients who had high creatinine, BUN, Potassium, Calcium and Magnesium and low sodium?

2. Many studies demonstrated serum Uric acid as an independent predictor of renal disease in patients with RA, but serum uric acid has not been evaluated in this present study.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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Attachment

Submitted filename: Manuscript Review.docx

Click here for additional data file. (17.2KB, docx)
PLoS One. 2023 Jun 23;18(6):e0278441. doi: 10.1371/journal.pone.0278441.r002

Author response to Decision Letter 0

13 Apr 2023

Responses to Reviewers:

We sincerely thank the Reviewers for their insightful comments and helpful feedback, which have led to significant improvements in the manuscript. A point-by-point response is provided below. The line numbers referred to in the responses below correspond to the “Renal Markers and Autoimmunity (revised with track changes)”.

Reviewer #1

Summary and general comments:

In the manuscript “Evaluation of Renal Markers in Systemic Autoimmune Diseases” the authors took a correlation approach to investigate the association between several renal markers and kidney disease in patients with systemic autoimmune diseases. To achieve such aim, the authors compared retrospectively 13 renal markers together with autoantibodies in 3 cohorts of patients: 1) 126 with systemic lupus erythematosus (SLE), 2) 695 with connective tissue diseases (CTDs) and 3) 3304 with rheumatoid arthritis (RA).

The results showed that:

1. Renal markers were elevated in the 3 cohorts of patients comparatively to controls.

2. Some autoantibodies such ANA, anti-Histone and anti-CCP were associated with kidney damage and lower eGFRs.

3. All the renal markers except for electrolytes were good in assessing renal involvement in patients with autoimmune diseases.

Overall, the paper is well organized and I enjoyed reading it but there are some rectifications and explanations that need to be addressed.

We thank the Reviewer for their study summary. This accurately represents the ideas that we wanted to put forth.

Specific comments:

#1 In the text, the authors mention 4 tables that do not exist in the manuscript version submitted for review. Please provide the manuscript with the cited tables.

We uploaded the tables as a separate document. The tables have been added to the revised manuscript. (Table 1:86-87, Table 2: 154-155, Table 3: 173-174, Table 4: 188-190)

#2 The last paragraph of the introduction is a bit misleading. For instance, the authors mention 13841 SLE patients when in reality it is 126 patients and 13715 controls. And then who were these control subjects, and why did each of the 3 cohorts have its own control group?

The lines have been reworded for clarity on the study population (line numbers 73-74). This was a retrospective study of seropositive and seronegative data from individuals who were tested on the autoimmune and kidney function panels.

#3 In the statistical analysis section, the authors did not specify the type of statistical tests used.

Two-tail student T test was performed to determine the p value. It has been incorporated in the ‘data analysis’ sub-section under ‘Materials and Methods’ (line numbers 132-133).

#4 In the results section, the authors did not detail the quantitative measurements of the different markers assessed in the patients. It would be more appropriate to compare the means or to perform non-parametric tests followed by ROC analysis to evaluate the sensitivity and specificity of the markers in detecting renal damage. Eventually, the authors could perform a logistic regression to estimate the incremental risk for each one-unit increase in each marker and build a predictive model for kidney damage.

The markers used in the study are routinely used in clinical laboratories and their sensitivities and specificities are well-known. We do plan to apply logistic regression to estimate the incremental risk for each one-unit increase in each marker in a subsequent study using a larger data set.

#5 In SLE patients, the association of anti-DNA antibodies with renal damage has been known for a long time and numerous human and mouse studies have confirmed the pathogenic role of these autoantibodies. The authors should discuss the discrepancy of their results with the literature.

We appreciate and thank the author for this insightful comment. Our results, as indicated in Table 2, showed some significant correlations which were not elaborated in the discussion section. We have added the following to the manuscript to highlight this association (line numbers 239-244).

“eGFR, Cystatin C, BUN, Calcium, and Albumin showed a significant association with seropositive females in the SLE group. Lupus nephritis is a common manifestation of SLE. Its onset is between 3-5 years after SLE onset. Lupus nephritis is more common in women. This is consistent with our results. Regular monitoring of kidney function by measuring renal markers and preventing the decline of kidney function is the primary treatment of Lupus nephritis.”

Reviewer #2

1. About leftover samples: when, on an average, were the samples processed after being leftover and is there any effect on the sample results because of the leftover time.

We did not use leftover samples. We performed a retrospective analysis of the clinical laboratory data after de-identifying samples under WIRB (work order #1-1098539-1)

2. The titre and the strength of ANA control is not mentioned anywhere in manuscript.

1:40 dilution was used for screening and was reflexed depending on the assay results (1:80, 1:160, 1:320, 1:640, and so on). This is included in the revised manuscript (line numbers 103-104). The clinical interpretation is left to the provider.

3. Renal markers in SLE: Among the 43.4% of SLE subjects, is there any clinical data about how many were having or developed nephritis or other renal complications, that way the results would be translated more effectively bedside. in various other studies, serum creatinine level remains statistically significant risk factor for developing nephritis in SLE patients, what about the serum creatinine levels in this study, was that statistically significant? is there any clinical data available about those who have low sodium levels because some studies have demonstrated low sodium level as an inflammatory marker of lupus, the current study would further add strength to that association in case. Was the mean Calcium level in female SLE subjects and high phosphate level in male SLE patients statistically significant? there are some studies which show inverse relation between hypocalcemia and disease activity in SLE patients.

We conducted a retrospective analysis using de-identified samples of individuals that visited Vibrant Clinical Lab for routine tests. The study is limited to serological markers and does not consider the clinical aspect. We recognized this as a limitation as indicated in line numbers 254-257. Serum creatinine levels were observed and the results are summarized in tables 2,3, and 4. A significant association was observed in female rheumatoid arthritis patients. Calcium levels were found to be significant in female SLE patients and phosphate levels were statistically significant in male SLE patients as indicated in table 2 (line numbers 154-155).

4. Renal markers in RA: 1. Any clinical data available in these RA patients who had high creatinine, BUN, Potassium, Calcium and Magnesium, and low sodium?

As mentioned earlier, we conducted a retrospective analysis using de-identified samples of individuals that visited Vibrant Clinical Lab for routine tests. The study is limited to serological markers and does not consider the clinical aspect. We recognized this as a limitation and have added it to the manuscript (line numbers 254-257 )

5. Many studies demonstrated serum Uric acid as an independent predictor of renal disease in patients with RA, but serum uric acid has not been evaluated in this present study.

Serum uric acid is an independent marker for the prediction of renal disease. However, it is not part of the routine chemistries to access kidney damage in clinical laboratories. We will consider this in future studies.

Attachment

Submitted filename: Rebuttal Letter.docx

Click here for additional data file. (10.1KB, docx)

Decision Letter 1

Alessandro Granito

29 May 2023

Evaluation of Renal Markers in Systemic Autoimmune Diseases

PONE-D-22-31509R1

Dear Dr. Krishnamurthy,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Alessandro Granito

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the revision. All questions were adressed.

I feel that the manuscript is of sufficient quality and detail to Accept.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File. Measurement of renal functional panel markers.

    (DOCX)

    Click here for additional data file. (15.4KB, docx)
    S2 File. Reference range for normal levels of renal markers.

    (DOCX)

    Click here for additional data file. (24.4KB, docx)
    Attachment

    Submitted filename: Manuscript Review.docx

    Click here for additional data file. (17.2KB, docx)
    Attachment

    Submitted filename: Rebuttal Letter.docx

    Click here for additional data file. (10.1KB, docx)

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