Biomarkers to predict or measure steroid resistance in idiopathic nephrotic syndrome: A systematic review

Abstract

In this systematic review we have sought to summarise the current knowledge concerning biomarkers that can distinguish between steroid-resistant nephrotic syndrome and steroid-sensitive nephrotic syndrome. Additionally, we aim to select biomarkers that have the best evidence-base and should be prioritised for further research. Pub med and web of science databases were searched using “steroid resistant nephrotic syndrome AND biomarker”. Papers published between 01/01/2012 and 10/05/2022 were included. Papers that did not compare steroid resistant and steroid sensitive nephrotic syndrome, did not report sensitivity/specificity or area under curve and reviews/letters were excluded. The selected papers were then assessed for bias using the QUADAS-2 tool. The source of the biomarker, cut off, sensitivity/specificity, area under curve and sample size were all extracted. Quality assessment was performed using the BIOCROSS tool. 17 studies were included, comprising 15 case-control studies and 2 cross-sectional studies. Given the rarity of nephrotic syndrome and difficulty in recruiting large cohorts, case-control studies were accepted despite their limitations. We present a range of candidate biomarkers along with scores relating to the quality of the original publications and the risk of bias to inform future investigations. None of the selected papers stated whether the authors were blinded to the patient’s disease when assessing the index test in the cohort. Highlighting a key problem in the field that needs to be addressed. These candidate biomarkers must now be tested with much larger sample sizes. Using new biobanks such as the one built by the NURTuRE-INS team will be very helpful in this regard.

Full text

RESEARCH ARTICLE
Biomarkers to predict or measure steroid
resistance in idiopathic nephrotic syndrome:
A systematic review
Carl J. MayID
1
*, Nathan P. Ford
2
, Gavin I. Welsh
1
, Moin A. Saleem
1,3
1Bristol Renal, University of Bristol, Bristol, United Kingdom, 2University of Cape Town, Cape Town, South
Africa, 3Bristol Royal Hospital for Children, Bristol, United Kingdom
*carl.may@bristol.ac.uk
Abstract
In this systematic review we have sought to summarise the current knowledge concerning
biomarkers that can distinguish between steroid-resistant nephrotic syndrome and steroid-
sensitive nephrotic syndrome. Additionally, we aim to select biomarkers that have the best
evidence-base and should be prioritised for further research. Pub med and web of science
databases were searched using “steroid resistant nephrotic syndrome AND biomarker”.
Papers published between 01/01/2012 and 10/05/2022 were included. Papers that did not
compare steroid resistant and steroid sensitive nephrotic syndrome, did not report sensitiv-
ity/specificity or area under curve and reviews/letters were excluded. The selected papers
were then assessed for bias using the QUADAS-2 tool. The source of the biomarker, cut off,
sensitivity/specificity, area under curve and sample size were all extracted. Quality assess-
ment was performed using the BIOCROSS tool. 17 studies were included, comprising 15
case-control studies and 2 cross-sectional studies. Given the rarity of nephrotic syndrome
and difficulty in recruiting large cohorts, case-control studies were accepted despite their
limitations. We present a range of candidate biomarkers along with scores relating to the
quality of the original publications and the risk of bias to inform future investigations. None of
the selected papers stated whether the authors were blinded to the patient’s disease when
assessing the index test in the cohort. Highlighting a key problem in the field that needs to
be addressed. These candidate biomarkers must now be tested with much larger sample
sizes. Using new biobanks such as the one built by the NURTuRE-INS team will be very
helpful in this regard.
Introduction
The kidneys are responsible for many functions vital to sustaining life. They regulate blood
pressure, monitor blood pH balance and remove waste products from the blood [1]. The glo-
merulus is the site of ultrafiltration where small solutes are excreted while proteins and macro-
molecules are retained [2]. This permselectivity is achieved thanks to the highly specialised
structure of the glomerular filtration barrier [3]. The breakdown of the architecture of this
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OPEN ACCESS
Citation: May CJ, Ford NP, Welsh GI, Saleem MA
(2025) Biomarkers to predict or measure steroid
resistance in idiopathic nephrotic syndrome: A
systematic review. PLoS ONE 20(2): e0312232.
https://doi.org/10.1371/journal.pone.0312232
Editor: Rajendra Bhimma, University of KwaZulu-
Natal, SOUTH AFRICA
Received: April 25, 2023
Accepted: October 2, 2024
Published: February 13, 2025
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0312232
Copyright: ©2025 May et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data is
contained within the paper.
Funding: The author(s) received no specific
funding for this work.

barrier leads to runaway proteinuria resulting in the clinical triad of oedema, hypoalbumine-
mia and proteinuria [4]. This collection of symptoms is termed nephrotic syndrome. It is often
classified according to its apparent histopathological presentation. Nephrotic syndrome has
many classifications. It can be primary, when the problem arises within the kidney itself, or
secondary when the disease pathogenesis commences outside the kidney, as in lupus or HIV
associated nephropathy. Primary nephrotic syndrome can be genetic or non-genetic.
Nephrotic syndrome is the most common glomerular disease of childhood. It has an annual
incidence between 1 and 17 cases per 100,000 [5–10]. There are currently over 70 genes that
have been implicated in the pathogenesis of nephrotic syndrome. The pathogenesis of non-
genetic or idiopathic nephrotic syndrome (INS) is not well understood. The seminal work of
Shalhoub et al and many others since has demonstrated a role for a circulating permeability
factor. This factor may be derived from either T-Cells [11,12], B-Cells [13] or immature mye-
loid cells [14]. INS is treated with steroids. Steroid sensitive nephrotic syndrome (SSNS) has a
very good prognosis with less than 5% progressing to chronic kidney disease [15]. However,
between 10 and 20% of patients are steroid resistant (steroid resistant nephrotic syndrome,
SRNS) and have a 50% risk of developing end-stage renal failure within 5 years of diagnosis
[16]. Even amongst patients who do respond to steroid treatment a subset of these will prog-
ress to steroid-resistance end-stage renal failure patients requiring dialysis and or transplant.
NS can also be characterised by its histopathological features. Focal Segmental Glomeruloscel-
rosis (FSGS) progresses more rapidly to end-stage renal failure compared to minimal change
disease (MCD) [17,18]. Histopathological variants have limited correlation with the pathogen-
esis of the different NS entities, however, renal biopsies of SRNS generally show FSGS [19].
It is vital to preserve kidney function by using effective treatments as soon as possible. Cur-
rently steroid-resistant patients are identified by their lack of response to a course of steroid
treatment. This exposes patients to the unnecessary side-effects of a futile treatment. There is a
clear need to be able to differentiate between steroid-sensitive and steroid-resistant patients
quickly and accurately.
The use of high-quality biomarkers that can distinguish between steroid-sensitive and ste-
roid-resistant forms of idiopathic nephrotic syndrome would be a paradigm shift for nephrolo-
gists and their patients. Instead of being exposed to ultimately useless steroid treatment and
enduring the side-effects, steroid-resistant patients could have a simple blood or urine test and
proceed to treatment with secondary agents such as calcineurin inhibitors, alkylating agents,
mycophenolate mofetil or rituximab.
To summarise what is currently known about potential biomarkers we carried out a system-
atic literature review. Then applied a quality appraisal tool to identify the most promising bio-
marker(s) for future more intensive efforts.
Methods
Eligibility criteria
Original research articles that compared biomarkers between known steroid sensitive and ste-
roid resistant nephrotic syndrome patients were included. Review articles, conference pro-
ceedings, abstracts and letters to the editor were reviewed as a source for original studies but
excluded from final review. Studies looking at individual candidate biomarkers and panels
were included, but studies reliant on kidney biopsies were not included. A decision was made
a priori to limit the review to biomarkers from blood, plasma, or urine samples, but not to
focus on studies of kidney biopsies, which are invasive and pose a risk of harm for the patient.
Studies were not excluded based on patient group characteristics beyond having a steroid sen-
sitive and a steroid resistant group.
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Competing interests: The authors have declared
that no competing interests exist.

Study screening and selection
Databases were searched for “Steroid Resistant Nephrotic Syndrome AND Biomarker”. This
strategy is deliberately narrow and prioritises specificity over selectivity. In this way we hoped
to identify biomarkers that were directly relevant to steroid resistance. The search results from
PubMed and Web of Science were exported to EndNote and screened for duplicates. Studies
published between 1
st
January 2012 until 10
th
May 2022 were included to focus the review to
recent techniques and results. Duplicates were omitted and the titles and abstract of the articles
were screened, and the full text of potentially eligible studies was reviewed. Papers were
screened by CM without using any automated tools.
Data extraction
The sensitivity and specificity or Area Under the Curve (AUC) were extracted for each candi-
date biomarker or panel. The quality of the data was assessed using BIOCROSS [20] and bias
and applicability was scored using QUADAS-2 [21]. CM collected the data and performed the
bias and applicability assessment without using any automated tools.
The required characteristics were extracted and tabulated manually by CM and are shown
in Table 1.
Data synthesis and analysis
Data was synthesised and analysed using Excel (Microsoft) and Prism (Graphpad).
BIOCROSS assessment
BIOCROSS is a quality assessment tool that is used to quantify the quality of the data that sup-
ports candidate biomarkers. The methodology of this tool is covered including details of the
scoring is covered here [20]. Briefly, the tool includes 10-items covering 5 domains: ‘Study
rational’, ‘Design/Methods’, ‘Data analysis’, ‘Data interpretation’ and ‘Biomarker measure-
ment’, aiming to assess different quality features of biomarker cross-sectional studies. Each of
the 10 items has three issues to consider. If each issue is covered then the publication will score
2 for that item, if only one or two of the issues are covered then the paper will score 1 and if
none of the issues are covered then the score will be 0. A total score of 20 is available for papers
that cover all the issues across all items and domains.
QUADAS-2 assessment
The QUADAS-2 tool assesses the design and publication of biomarker data for applicability
and risk of bias across four domains: patient selection, index test, reference standard, and flow
and timing. It allows researchers to compare the risk of bias across different studies. The spe-
cific methodology is covered in detail here [21].
Registration and protocol
This systematic review was not prospectively registered, and no review protocol has been
made available.
Missing data
The exclusion criteria were designed such that all included papers have the requisite data for
inclusion in the synthesis.
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Results
Adapted from Page et al [22].Most publications were excluded from the review because they
either didn’t compare steroid sensitive patients with steroid resistant patients, or they didn’t
report either sensitivity and specificity or AUC. After applying the inclusion/exclusion criteria
and screening for eligibility, 17 studies were taken through to review, as hown in Fig 1. The
most common sample was urine (9 studies) then serum (5 studies) then plasma (2 studies).
Table 1. Summary table of candidate biomarkers and panels.
Candidate Biomarker Paper Source Cut Off Sensitivity Specificity AUC Sample
Size
BIOCROSS
Score
Ref
1Nephronectin Watany
et al,2018
Serum 1.215 ng/ml 74.2% 92.0% 0.896 40 SSNS
40 SRNS
18/20 [23]
2A panel of Vitamin D Binding Protein,
Adiponectin and MMP-2
Agrawal et al
2020
Plasma NR NR NR 0.78 24 SSNS
13 SRNS
15/20 [24]
3A panel of Vitamin D Binding Protein,
Neutrophil Gelatinase-Associated Lipocalin,
Fetuin-1, Prealbumin, Alpha-1-Acid
Glycoprotein 2, Acid Glycoprotein, Alpha-
2-Macroglobulin, Alpha-1-B Glycoprotein,
Thyroxine-Binding Globulin and Hemopexin
Bennet et al
2017
Urine N/A 80.0 80.6 0.92 25 SSNS
25 SRNS
14/20 [25]
4Vitamin D Binding Protein Bennet et al
2016
Urine NR 80.0 83.0 0.92 28 SSNS
24 SRNS
14/20 [26]
5A panel of 50S Ribosomal Protein L32,
S-Adenosylmethionine Decarboxylase α
Chain, FK506-Binding Protein 1A
and 30S ribosomal protein S11
Bai et al 2013 Urine 88.89% 91.00% NR 32 SSNS
9 SRNS
12/20 [27]
6Haptoglobin Wen et al
2012
Serum 1.279 μg/ml 85.0% 96.3% NR 54 SSNS
52 SRNS
16/20 [28]
7Vitamin D Binding Protein and Neutrophil
Gelatinase-Associated Lipocalin
Choudhary
et al 2020
Urine 303.81 ng/
ml and 13.1
ng/ml
82.0% and
86.0%
78.0% and
89.0%
NR 28 SSNS
28 SRNS
16/20 [29]
8Urinary Protein Carbonyl Content Gopal et al
2017
Urine 7.02 nmol/
mg
83.3% 85.2% 0.803 SSNS 47
SRNS 23
14/20 [30]
9P-Glycoprotein and MRP-1 Prasad et al
2021
PBMCs 7.13 and
9.62%
90.0 and
80.7%
80.0 and
90%
NR SSNS 171
SRNS 83
17/20 [31]
10 Soluble Urokinase Plasminogen Activator
Receptor
Peng et al
2015
Serum NR 73.5% 79.2% 0.80 SSNS 108
SRNS 68
17/20 [32]
11 Endothelin-1 Ahmed et al
2019
Serum 24.6 pg/mL 90.5% 84.0% 0.88 30 SSNS
25 SRNS
14/20 [33]
12 Soluble Urokinase Plasminogen Activator
Receptor
Mousa et al
2018
Serum 33.17 ng/
mL
100% 100% 0.99 25 SSNS
25 SRNS
14/20 [34]
13 Urinary Protein Bound Sialic Acid Gopal et al
2016
Urine 2.71 μg/mg 75% 75.5% 0.814 47 SSNS
23 SRNS
15/20 [35]
14 Interleukin-7, Interleukin-9 and Monocyte
Chemoattractant Protein-1
Agrawal et al
2021
Plasma NR 0.643 0.846 NR 26 SSNS
14 SRNS
13/20 [36]
15 Neutrophil Gelatinase-Associated Lipocalin
/Creatinine ratio
Nickavar
et al 2016
Urine 1.15ng/mg 100% 100% NR 25 SSNS
23 SRNS
14/20 [37]
16 N-Acetyl-beta-D Glucosaminodase
/creatinine ratio
Mishra et al
2012
Urine 108.9u/g 78.8% 100.0% NR 10 FENS
8 SRNS
15/20 [38]
17 Interleukin-8 Ahmed et al
2019
Urine 35.3 pg/mg 93% 85% 0.94 40 SSNS
25 SRNS
13/20 [39]
SRNS, steroid resistant nephrotic syndrome; SSNS, steroid sensitive nephrotic syndrome; BIOCROSS marks are out of 20 and scored across 5 domains: study rationale,
design/methods, data analysis, data interpretation and biomarker measurement.
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Table 1 shows the source paper and key descriptors for the candidate biomarker or panel.
The BIOCROSS score indicates the quality of the source article, the higher the number the bet-
ter the quality [20].
All of the identified studies fell afoul of the same reporting errors. None of them reported
whether consecutive or random sampling was employed (Table 2). Similarly, there was not
enough information provided by any of the identified manuscripts concerning blinding.
Nephronectin
Nephronectin is a basal lamina protein found in glomerular basement membrane [40]. It is
produced by the podocytes and is downregulated following podocyte injury [41]. In glomeru-
lar diseases such as focal segmental glomerulosclerosis and membranous nephropathy,
nephronectin is also known to be downregulated [41]. Such are these changes in nephronectin
expression during and following injury that nephronectin has been identified as a marker of
Fig 1. PRISMA flow diagram.
https://doi.org/10.1371/journal.pone.0312232.g001
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Table 2. Responses to the QUADAS-2 assessment.
Patient Selection
Could the selection of patients have introduced bias?
Paper SQ1 SQ2 SQ3 Applicability
1: Watany et al Not Reported No Yes None
2: Agrawal et al Not Reported No Not Reported Unknown
3: Bennet et al Not Reported No Yes None
4: Bennet et al Not Reported No Yes None
5: Bai et al Not Reported No Not Reported Unknown
6: Wen et al Not Reported No Yes None
7: Choudhary et al Not Reported Yes Yes None
8: Gopal et al Not Reported No Yes None
9: Prasad et al Not Reported No Yes None
10: Peng et al Not Reported No Yes None
11: Ahmed et al Not Reported No Yes None
12: Mousa et al Not Reported No Yes None
13: Gopal et al Not Reported No Yes None
14: Agrawal et al Not Reported No Not Reported Unknown
15: Nickavar et al Not Reported Yes Yes None
16: Mishra et al Not Reported No Yes None
17: Ahmed et al Not Reported No Yes None
Index Test
Could the conduct or the interpretation of the test introduced bias?
Paper SQ1 SQ2 Applicability
1: Watany et al Not Reported No No
2: Agrawal et al Not Reported Not Applicable No
3: Bennet et al Not Reported No No
4: Bennet et al Not Reported No No
5: Bai et al Not Reported No No
6: Wen et al Not Reported No No
7: Choudhary et al Not Reported No No
8: Gopal et al Not Reported No No
9: Prasad et al Not Reported No No
10: Peng et al Not Reported No No
11: Ahmed et al Not Reported No No
12: Mousa et al Not Reported No No
13: Gopal et al Not Reported No No
14: Agrawal et al Not Reported No No
15: Nickavar et al Not Reported No No
16: Mishra et al Not Reported No No
17: Ahmed et al Not Reported No No
Reference Standard
Could the reference standard, its conduct or its interpretation have introduced bias?
introduced bias?
Paper SQ1 SQ2 Applicability
1: Watany et al Yes Not Reported No
2: Agrawal et al Yes Not Reported No
3: Bennet et al Yes Not Reported No
(Continued)
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Table 2. (Continued)
4: Bennet et al Yes Not Reported No
5: Bai et al Yes Not Reported No
6: Wen et al Yes Not Reported No
7: Choudhary et al Yes Not Reported No
8: Gopal et al Yes Not Reported No
9: Prasad et al Yes Not Reported No
10: Peng et al Yes Not Reported No
11: Ahmed et al Yes Not Reported No
12: Mousa et al Yes Not Reported No
13: Gopal et al Yes Not Reported No
14: Agrawal et al Yes Not Reported No
15: Nickavar et al Yes Not Reported No
16: Mishara et al Yes Not Reported No
17: Ahmed et al Yes Not Reported No
Flow and Timing Overall risk of bias
Could the patient flow have introduced bias?
Paper SQ1 SQ2 SQ3
1: Watany et al Not enough info Yes Yes Low Risk
2: Agrawal et al Yes Yes Yes Unknown Risk
3: Bennet et al Not enough info Yes Yes Low Risk
4: Bennet et al Not enough info Yes Yes Low Risk
5: Bai et al Not enough info Yes Yes Unknown Risk
6: Wen et al Yes Yes Yes Low Risk
7: Choudhary et al Not enough info Yes Yes Low Risk
8: Gopal et al Not enough info Yes Yes Low Risk
9: Prasad et al Yes Yes Yes Low Risk
10: Peng et al Not enough info Yes Yes Low Risk
11: Ahmed et al Yes Yes Yes Low Risk
12: Mousa et al Not enough info Yes Yes Low Risk
13: Gopal et al Yes Yes Yes Low Risk
14: Agrawal et al Yes Yes Yes Unknown Risk
15: Nickavar et al Yes Yes Yes Low Risk
16: Mishara et al Yes Yes Yes Low Risk
17: Ahmed et al Yes Yes Yes Low Risk
DOMAIN 1: PATIENT SELECTION Risk of bias: Could the selection of patients have introduced bias?
Signalling question 1: Was a consecutive or random sample of patients enrolled? Signalling question 2: Was a case-control design avoided? Signalling question 3: Did the
study avoid inappropriate exclusions?
DOMAIN 2: INDEX TEST Risk of Bias: Could the conduct or interpretation of the index test have introduced bias?
Signalling question 1: Were the index test results interpreted without knowledge of the results of the reference standard? Signalling question 2: If a threshold was used,
was it pre-specified?
DOMAIN 3: REFERENCE STANDARD Risk of Bias: Could the reference standard, its conduct, or its interpretation have introduced bias?
Signalling question 1: Is the reference standard likely to correctly classify the target condition? Signalling question 2: Were the reference standard results interpreted
without knowledge of the results of the index test?
DOMAIN 4: FLOW AND TIMING Risk of Bias: Could the patient flow have introduced bias?
Signalling question 1: Was there an appropriate interval between index test and reference standard? Signalling question 2: Did all patients receive the same reference
standard?
For each signalling question we have looked at the appropriate papers and assessed if they have answered the question. Responses that reduce the risk of bias have been
marked in green while those that possibly increase bias have been marked in red. These have been taken together to assess the overall risk of bias.
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kidney repair following kidney damage [42,43]. One study, published in 2018, repurposed
nephronectin as a marker of kidney repair during the early stages of corticosteroid treatment
[23]. For these purposes nephronectin shows promise. However, patients will still be treated
with steroids. They may well be removed if there is no evidence of repair (indicated by
increased levels of nephronectin); however, ideally a biomarker for steroid resistance would be
able to distinguish patients prior to treatment.
Vitamin D Binding Protein (VDBP)
It has been found that vitamin D deficiency is associated to a greater degree with SRNS com-
pared to SSNS [44]. It has been postulated that this marked vitamin D deficiency is due to the
increased urinary loss of VDBP in SRNS versus SSNS [26]. VDBP is sufficiently small to pass
through the glomerular filtration barrier. Proximal tubular cells reabsorb the lost VDBP via
cubulin and megalin receptors. Hence chronic tubular injury could reduce this reabsorption
leading to a greater loss of VDBP in the urine [26]. Hinting at the importance of VDBP as a
biomarker of steroid resistance is the presence of VDBP, either on its own or as part of a panel,
in four of the seventeen studies. Vitamin D Binding Protein (VDBP) is a circulating protein
that binds to vitamin D to create a store of Vitamin D so that rapid vitamin D deficiency can
be avoided [45]. Vitamin D deficiency is more pronounced in SRNS than in SSNS, and VDBP
can be used to distinguish between these two conditions [29].
Adiponectin (ADIPOQ)
ADIPOQ is a hormone, released by adipocytes, that helps to improve insulin sensitivity and is
anti-inflammatory [46]. Low levels of adiponectin is correlated with albuminuria in mice and
humans [47]. ADIPOQ knockout mice demonstrate significant podocyte injury and albumin-
uria. Adiponectin therapy in this model restores podocyte foot processes [48]. Elevated levels
of serum adiponectin have been reported in patients with FSGS, chronic kidney disease, end-
stage renal disease, those on dialysis and transplant recipients [49–51]. Total serum levels of
adiponectin rise following the onset of nephrotic syndrome with notable changes in the ratios
of the three adiponectin isoforms [52]. A recent study noticed that levels start lower and show
a significant decrease following steroid treatment in children with SSNS whereas in children
with SRNS levels start higher and increase following treatment [24]. It is under this context
that Agrawal proposes using Adiponectin as an early indicator that steroids are working.
Matrix Metalloproteinase-2 (MMP-2)
MMP-2 is a metalloproteinase that acts on collagen IV [53]. It is normally expressed by the
mesangial cells in the glomerulus; however, during times of inflammation expression levels by
the mesangial cells increase and podocytes begin to express MMP-2 [54]. Indeed, increased
levels of MMP-2 in the sera have been seen in animal models of chronic kidney disease and in
humans with chronic kidney disease [55–57]. MMP-2 can also activate MMP-1 and MMP-9
leading to further extracellular matrix remodelling (ECM) [58]. Increased MMP-2 in the
serum and urine has been associated with progressive kidney fibrosis in chronic kidney disease
[54,59–62]. In children with SRNS there is a higher urinary MMP-2/creatinine ratio than in
SDNS. This suggests that there may be ECM remodelling in both instances but that in SRNS
there is a higher risk of renal fibrosis [63]. One study reported that MMP-2 was elevated in
SSNS patients following treatment [24]. Again, this suggests that MMP-2 is useful as an early
indicator that steroids may be working but does not help patients avoid steroid exposure
altogether.
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Neutrophil Gelatinase-Associated Lipocalin (NGAL)
NGAL is a small 25kDa protein within the lipocalin family [64]. Though initially found in neu-
trophils, NGAL is expressed by many epithelial cells [65]. It has been widely shown that NGAL
expression is upregulated following renal injury and as such is a powerful biomarker for AKI
[66–69]. NGAL is a marker for chronic kidney disease progression and is significantly
increased in patients with SRNS compared to those with SSNS (AUC0.91 p = <0.0001) [65].
However, it has also been found that calcineurin inhibitors, such as cyclosporine A, can
increase NGAL levels [70].
Fetuin-A
Fetuin-A is a carrier protein that has roles in insulin signalling and protease inhibition [71]. It
is central to the pathogenesis of a myriad of conditions including insulin resistance, type 2 dia-
betes, metabolic disorders, cardiovascular disease and brain disorders [72–75]. In the kidney
Fetuin-A protects the integrity of the tissues and levels drop dramatically as chronic kidney
disease progresses [76]. Fetuin-A is significantly elevated in the urine during SRNS, suggesting
a depletion in the serum leading to a lack of protease inhibition. This is an intriguing hypothe-
sis since there is a body of work supporting the role of a circulating protease in idiopathic
nephrotic syndrome [77,78].
Prealbumin
Prealbumin can be a sign of a hypercatabolic state often due to increased degradation of mus-
cle mass [79].
Acid Glycoprotein 1 (AGP-1)
AGP-1 is an acute phase protein released by hepatocytes in response to infection and inflam-
mation [80]. It is generated from active vitamin D [81]. Urinary secretion of AGP-1 in healthy
individuals is very low. However urinary secretion is detectable patients in a range of renal dis-
eases including nephrotic syndrome [81].
Alpha 1 Acid Glycoprotein 2 (AGP-2)
AGP-2 is very similar to AGP-1, with only 21 out of 181 amino acids being different [82].
AGP-1 isoforms outnumber AGP-2 by a ratio of 3:1 in the plasma under normal conditions,
however this ratio is known to change in diseased states [83,84].
Alpha 2 Macroglobulin (A2MCG)
A2MCG accounts for 3–5% of plasma protein and is mainly synthesised in the liver [85]. It is a
broad-spectrum protease which traps proteases in its “molecular cage” much like a Venus fly
trap would trap its prey [86,87]. A2MCG has a bait region which allows it to trap active prote-
ases [88]. However, somewhat counterintuitively, A2MCG has been shown to enhance the
activity of the protease thrombin by inhibiting the anticoagulant protein C/protein S system
[89]. There is evidence that the circulating factor in INS is a protease, which makes A2MCGs
inclusion here interesting [11,77,90].
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Thyroxine Binding Globulin (TBG)
TBG is a serine protease inhibitor, which again could have clear implications for the activity of
the circulating factor if it is indeed a serine protease [91]. TBG is lost in the urine of nephrotic
patients in sufficient quantities to cause subclinical or overt hypothyroidism [92].
Alpha 1 B Glycoprotein (A1BG)
A1BG is a 54.3 kDa protein of unknown function [93,94]. It has been found to be elevated in
certain cancers [95].
Haptoglobin
When erythrocytes are lysed the free heme group from haemoglobin can react with molecular
oxygen to form superoxide. Haptoglobin binds haemoglobin in the circulation and protects
tissues from oxidative damage [96]. Haptoglobin is mainly synthesised in the liver and the
lungs, then secreted into the plasma [97]. In addition to its function as an antioxidant, hapto-
globin also plays roles in angiogenesis, immunoregulation, the inhibition of nitric oxide and it
stimulates tissue repair [98]. There is a significant increase in serum haptoglobin in SRNS
patients versus SSNS patients [28].
Urinary Protein Carbonyl Content (UPCC)
Chronic oxidative stress can result in systemic inflammation. In turn this can lead to secretion
of pro-inflammatory cytokines and an exacerbation of proteinuria [99]. An imbalance between
oxidants and anti-oxidants has long been known to exist in idiopathic nephrotic syndrome
[100]. Since the oxidative stress is said to be higher in SRNS Vs SSNS then it stands to reason
that there would be more UPCC [101], and this has been reported [35]. The pro-inflammatory
cytokines induced in response to chronic oxidative stress can damage podocytes [102,103].
P-glycoprotein
P-glycoprotein is encoded by the MDR-1 gene, it is a transporter that causes the efflux of toxins
and drugs that are between 300 and 2000 kDa [104]. Cyclosporine is a P-glycoprotein antago-
nist that reduces the activity of the transporter allowing corticosteroids to accumulate within
the cell to achieve a therapeutic effect [105]. The activity of P-glycoprotein was found to be sig-
nificantly higher in the peripheral blood mononuclear cells (PBMCs) of steroid resistant
patients compared to steroid sensitive patients [106]. P-glycoprotein expression is higher on
the lymphocytes of steroid resistant patients versus steroid sensitive [107]. Additionally, glo-
merular expression of P-glycoprotein is significantly increased in those who frequently relapse
and or steroid resistant or steroid dependent [108]. Genetic differences affecting the activity of
P-glycoprotein can have an impact on the outcome of drug therapy [109] and a significant
increase in the prevalence of a SNP in the MDR1 gene (G2677T/A) amongst SRNS patients
compared to SSNS has been reported [110].
Multi Drug Resistance Protein 1 (MRP-1)
MRP-1 is pump responsible for the efflux of drugs acting on similar substrates to P-glycopro-
tein, but with a preference for heavy metal anions and toxins out of cells [111]. In contrast
with most ABC transporters that are located on the apical membrane of cells and pump out
into the urine or bile, MRP-1 is located on the basolateral membrane and pumps out into the
interstitium [112,113]. It is expressed throughout the body but particularly highly in PBMCs
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and the kidney [114]. Increased expression of MRP-1 is known to be associated with SRNS
and can be assayed for [31].
Soluble Urokinase Plasminogen Activator Receptor (suPAR)
suPAR fulfils criteria of a circulating protein signalling to the kidney. It can readily be found in
the circulation. It is generated by immature myeloid cells [14]. It can act directly on the podo-
cyte via interaction with αvβIII integrin [115] or activates proximal tubular cell mitochondria
[116]. The former has a deleterious effect on the podocytes, leading to foot process effacement
and downregulation of podocin and nephrin [115]. This can affect the structure and function
of the glomerulus [117–120]. Urinary suPAR is known to increase in FSGS and positively cor-
relates with disease severity [121,122]. Additionally it has been shown to be able to predict
recurrence of disease in transplanted patients [123]. Urinary and serum levels of suPAR can
also stratify cases of minimal change disease and FSGS [124]. suPAR has been hypothesised to
be a circulating factor driving the pathogenesis of nephrotic syndrome. However, there is con-
troversy over the role of suPAR as a circulating factor, with many studies not corroborating
the original reports, and showing highly variable levels in cohorts of NS patients [125–128].
Moreover, suPAR is a known inflammatory mediator and it has been associated with other
conditions [129]. Notably suPAR levels can be influenced by diabetes and obesity [130,131].
Interleukin-7 (IL-7)
IL-7 is secreted by T cells. T cells were postulated to be the source of the illusive circulating fac-
tor driving INS almost fifty years ago [132]. IL-7 is a cytokine that supports host defence by
regulating the homeostasis of the cells of the immune system such that congenital deficiency of
IL-7 leads to severe immunodeficiency [133]. In the mouse model of Adriamycin nephropathy,
IL-7 has been shown to lead to impaired barrier function, podocyte apoptosis, impaired activa-
tion of nephrin and actin cytoskeleton dysregulation [134].
Interleukin-9 (IL-9)
IL-9, also released by T cells, seems to act antagonistically to IL-7 on the podocyte in nephrotic
syndrome pathogenesis. IL-9 can dramatically improve glomerular function in the Adriamycin
nephropathy model. However, it is known to increase in the serum of patients with primary
FSGS hence it’s utility here as a biomarker [135].
Interleukin-8 (IL-8)
IL-8 is released by T-cells or resident kidney cells in response to pro-inflammatory stimuli
[136]. Within the kidney IL-8 is produced by mesangial cells [137], podocytes [138] and tubu-
lar epithelial cells [139]. IL-8 is known to affect the functioning of the glomerular basement
membrane [140]. It has been shown in rats that IL-8 treatment decreases the synthesis of hepa-
rin sulfate proteoglycans leading to proteinuria [141].
Monocyte Chemoattractant Protein-1 (MCP-1)
MCP-1 is a chemokine that recruits monocytes from the bone marrow to sites of inflammation
[142]. There is a significant increase in the infiltration and accumulation of macrophages in
the glomeruli of children with SRNS versus SSNS. Interestingly, there is an increase of MCP-1
in the urine but not in the serum of FSGS patients which suggests that the MCP-1 is being pro-
duced by the kidney [143]. It is known that the mesangial [144] cells of the glomerulus can
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produce MCP-1 [145–147]. Persistently elevated levels of MCP-1 in the urine could demon-
strate continuing inflammation within the kidney and a resistance to steroid treatment [148].
50S ribosomal protein L32 (50SL32) and 30S ribosomal protein S11
(30SS11)
50SL32 and 30SS11 are both subunit proteins that make up ribosomes.
S-adenosylmethionine decarboxylase αchain (SAMDC)
SAMDC is a critical enzyme involved in the synthesis of polyamines. It is an amino acid decar-
boxylase that is essential to life [149].
FK506-binding protein 1A (FKBP12)
FKBP12 is the target for the immunosuppressant drug tacrolimus, also known as FK506.
FK506 binds to FKBP12 leading to the inhibition of calcineurin which has a frontline treat-
ment for nephrotic syndrome [150,151]. Within the glomeruli of the kidney FKBP12 is
expressed exclusively by the podocytes [152].
Table 3 shows the identified candidate biomarkers separated by source and whether they
are predictive or evaluative.
Created by author. The biomarkers were ranked according to their sample size which ran-
ged from 18 participants for a study of the N-Acetyl-beta-D Glucosaminodase (NAG)/creati-
nine ratio to 254 participants for a study on P-glycoprotein and MRP-1. The biomarkers were
also ranked by their BIOCROSS score giving the lowest scoring paper (candidate 5 [27]) point
and the highest scoring paper (candidate 1 [23]) 17 points. The full data is shown in Table 3.
Additionally, biomarkers were scored by their sensitivity/specificity according to the ranges
they fell within (Fig 2). Papers that did not disclose sensitivity/specificity values received 0
points. The candidate biomarkers have been separated according to whether they are predic-
tive or evaluative and which biological specimen they can be found in.
Discussion
The field of clinical nephrology is working toward finding predictive biomarkers for SRNS to
save patients being exposed to futile steroid treatments. Unfortunately for these kinds of stud-
ies INS as a whole is rare and SRNS even more so. The importance of undertaking studies with
an adequate sample size is demonstrated by the two studies for candidate biomarkers 12 and
15 [34,37]. Both reported 100% diagnostic accuracy for the candidate markers under investi-
gation (serum suPAR as an evaluative marker and NGAL/creatinine ratio as a predictive uri-
nary marker). These molecules need now to be evaluated prospectively. It was recently done
for suPAR in the prediction of outcomes in septic acute kidney injury [153].
The search terms were set up to very specifically identify studies looking at SRNS. The ter-
minology used by nephrologists and renal scientists is a challenge. Focal segmental glomerulo-
sclerosis (FSGS) is often but not always steroid resistant. Equally SRNS usually presents
histopathologically as FSGS, but again, not always. This has led to some using the terms FSGS
and SRNS interchangeably. SRNS is a clinical phenotype, indeed it is the key characteristic of
interest in this systematic review. Hence SRNS was prioritised in the search strategy. This will
have biased specificity at the expense of selectivity in the identified studies and biomarkers.
We are satisfied that this sacrifice was necessary and will have led to the identification of credi-
ble candidate biomarkers for future analysis.
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We did not set a lower limit for sample size. Nephrotic syndrome and its subdivisions are
rare diseases hence recruitment can be difficult. We accept that smaller sample sizes of patient
groups are likely to be less representative and more prone to error however, we wanted to gen-
erate a list of candidate biomarkers in the field for further validation.
Table 3. Candidate biomarker ranking.
Rank Candidate Biomarker Paper Sample Size
Score
BIOCROSS Rank
Score
Sensitivity/
Specificity Rank
Sum
Predictive Urinary Biomarkers
17 VDBP and NGAL Choudhary et al
2020
10 13 4 27
213 Urinary Protein Bound Sialic Acid Gopal et al 2016 13 10 2 25
38 UPCC Gopal et al 2017 12 4 6 22
416 N-Acetyl-beta-D Glucosaminodase /creatinine ratio Mishra et al
2012
1 10 10 21
517 IL-8 Ahmed et al
2019
11 2 6 19
615 NGAL/Creatinine ratio Nickavar et al
2016
5 4 10 19
Rank Candidate Biomarker Paper Sample Size
Score
BIOCROSS
Rank Score
Sensitivity/
Specificity Rank
Sum
Predictive Serum/Plasma Biomarkers
16 Haptoglobin Wen et al 2012 15 13 10 38
210 suPAR Peng et al 2015 16 15 2 33
32 A panel of VDBP, ADIPOQ and MMP-2 Agrawal et al
2020
2 10 0 12
414 IL-7, IL-9 and MCP-1 Agrawal et al
2021
3 2 2 7
Rank Candidate Biomarker Paper Sample Size
Score
BIOCROSS
Rank Score
Sensitivity/
Specificity Rank
Sum
Evaluative Urinary Biomarkers
14 VDBP Bennet et al
2016
8 4 4 16
23 A panel of VDBP, NGAL, Fetuin-1, Prealbumin, Alpha-1-Acid
Glycoprotein 2, AGP1, A2MCG, A1BG, TBG and Hemopexin
Bennet et al
2017
6 4 4 14
33 A panel of VDBP, NGAL, Fetuin-1, Prealbumin, Alpha-1-Acid
Glycoprotein 2, AGP1, A2MCG, A1BG, TBG and Hemopexin
Bennet et al
2017
6 4 4 14
45 A panel of 50SL32, SAMDC, FKBP12 and 30SS11 Bai et al 2013 4 1 8 13
Rank Candidate Biomarker Paper Sample Size
Score
BIOCROSS
Rank Score
Sensitivity/
Specificity Rank
Sum
Evaluative Serum/Plasma Biomarkers
11 Nephronectin Watany
et al,2018
14 17 6 37
212 suPAR Mousa et al
2018
7 4 10 21
311 Endothelin-1 Ahmed et al
2019
9 4 8 21
Rank Candidate Biomarker Paper Sample Size
Score
BIOCROSS
Rank Score
Sensitivity/
Specificity Rank
Sum
Evaluative PBMC Biomarkers
19 P-Glycoprotein and MRP-1 Prasad et al
2021
17 15 6 38
https://doi.org/10.1371/journal.pone.0312232.t003
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1 Nephronectin 7 Vitamin D Binding Protein and
Neutrophil Gelatinase-Associated
Lipocalin
13 Urinary Protein Bound Sialic Acid
2 Vitamin D Binding Protein, Adiponectin and Metalloproteinase-2 8 Urinary Protein Carbonyl Content 14 Interleukin-7, Interleukin-9 and
Monocyte Chemoattractant Protein-
1
3 Vitamin D Binding Protein, Neutrophil Gelatinase-Associated Lipocalin,
Fetuin-1, Prealbumin, Alpha-1-Acid Glycoprotein 2, AGP1, Alpha-
2-Macroglobulin, Alpha-1-B Glycoprotein, Thyroxine-Binding Globulin
and Hemopexin
9 P-Glycoprotein 15 Neutrophil Gelatinase-Associated
Lipocalin /Creatinine
4 Vitamin D Binding Protein 10 Soluble Urokinase Plasminogen
Activator Receptor
16 N-Acetyl-beta-D Glucosaminodase
/Creatinine
5 50S Ribosomal Protein L32, S-Adenosylmethionine Decarboxylase αChain,
FK506-Binding Protein 1A and 30S ribosomal protein S11
11 Endothelin-1 17 Interleukin-8
6 Haptoglobin 12 Soluble Urokinase Plasminogen
Activator Receptor
https://doi.org/10.1371/journal.pone.0312232.t004
Fig 2. Specificity and sensitivity of candidate biomarkers and panels.
https://doi.org/10.1371/journal.pone.0312232.g002
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Owing to the scarcity of NS patients some of the studies incorporated here were cross-sec-
tional. This has meant that the cohorts in these studies incorporate a mixture of steroid naïve,
those who are currently on steroids and those who have met diagnostic criteria for being either
steroid sensitive or steroid resistant. Comparisons between these groups must be done with
great caution. Many of the studies in this review would have been stronger if they had more
clearly explained why they chose a cross-sectional design, and if they had taken steps to reduce
the influence of confounding variables on their results.
While some of the presented studies controlled for the exposure of subjects to immunosup-
pressive treatment, many did not. It is vital when looking for biomarkers of steroid resistance
to receive sufficient information concerning treatment to understand the context of the bio-
markers in those subjects. Given the paucity of information in many of the identified studies
concerning the treatment regimen, the authors recommend that future studies pay particular
attention to this aspect of their biomarker studies.
Overall, studies were considered to have low risk of bias. Three studies were rated as having
an unknown risk of bias [24,27,36], which was because of an inadequate description of the
exclusion criteria, and as such it is impossible to determine how generalisable their study
group is and how well the studies addressed potential confounding factors.
The main limitations of studies reported to date include sampling method, study design,
application of cut-off threshold, investigator blinding, and timing of testing.
None of the included studies reported a sampling method for patient recruitment. Given
the rarity of INS it is likely that studies used consecutive rather than random sampling to
recruit patients, but this is not evaluable. Choice of study design is also a concern. Most identi-
fied in this review used a case-control design. It is preferable to avoid case-controlled study
design, because it can be difficult to identify an appropriate control group to reduce risk of
bias [154]. However, such designs have practical advantage for recruiting cases when the dis-
ease is rare. The reporting of control selection is therefore critical to assess risk of bias.
None of the selected papers pre-specified the threshold cut-off. By selecting the cut-off after
the analysis, the data is shown in its best light and is therefore likely to lead to an overestima-
tion of the abilities of the index test. Since all the papers that used a cut-off failed to pre-specify,
they remain comparable. However, it is worth pointing out that the same cut-offs would be
unlikely to yield the same sensitivity/specificity values in a new cohort. Where possible,
authors should pre-specify the cut-offs to increase the accuracy of their sensitivities and
specificities.
Future studies are also encouraged to undertake assessor blinding, which enables the
impartial analysis of the index test. None of the studies included in this review provided any
details about blinding of the results of the index test of reference standard.
It is also important to report when the index test was performed relative to when the sam-
ples were taken. Progression of the pathophysiology will have an impact on the abundance of
the biomarkers being tested. Again, to make an accurate assessment of risk of bias studies need
to be given all the information. Almost half of studies included in this review (8/17) did not
report any information regarding when the index test took place relative to the reference
standard.
To improve the clinical management of patients with steroid resistant nephrotic syndrome,
the goal is to identify predictive markers that will obviate the need to expose these patients to
toxic, ineffective treatment.
Candidate biomarkers identified in this review were ranked according to a combination of
their sample size, the BIOCROSS score and their sensitivity/specificity. Though P-glycoprotein
and MRP-1 scored the most according to these criteria, it is less clinically useful since these are
evaluative rather than predictive biomarkers. Haptoglobin was the most rigorously tested
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predictive marker with the most promising sensitivity/specificity values, closely followed by
suPAR.
The biggest flaw with the current evidence-base is how reliant these biomarker studies are
on case-controlled studies. Many of the limitations of the studies included here could be over-
come by adopting the PRoBE (Prospective-Specimen Collection Retrospective Blinded Evalua-
tion) approach using a biobank such as NURTuRE-INS, which currently contains samples
from 742 INS patients [155,156]. NURTuRE-INS is a well-defined prospective cohort that col-
lects blood and urine from both steroid resistant and steroid sensitive idiopathic nephrotic
syndrome patients. The bio samples are collected during periods of active disease relapse) and
remission providing vital internal controls for each patient. Despite the multi-centre approach,
samples are all handled to the same exacting protocol. The 23 renal centres across the UK col-
lect, process, and freeze the samples at -80˚C within 2 hours. There is a chronic kidney disease
arm of NURTuRE that could provide an additional source of control samples. These could
control for markers of inflammatory processes common to multiple kidney diseases.
The PRoBE approach deals with several biases that can be inherent with retrospective case-
control study designs. Spectrum bias occurs when case-patients with clear cut examples of the
disease (usually severe and/or well-documented) are compared with carefully selected, particu-
larly healthy controls [157]. The manuscripts selected for review here did not describe how
patients were sampled (e.g., consecutive, or random), without such information it is difficult
to judge the risk of spectrum bias. However, this bias can be avoided when using the PRoBE
approach. Subjects in the cohort are identified as patients or controls, then the study group is
randomly selected from these sub-groups.
Additionally, by using nested subgroups the discovery and evaluation phases of biomarker
identification can be carried out in the same population [156].
suPAR and haptoglobin have emerged from this systematic review as the most promising
biomarkers for the prospective distinction between steroid resistant and steroid sensitive vari-
ants of idiopathic nephrotic syndrome.
Haptoglobin is known to be an acute phase protein which is elevated in many inflammatory
diseases and helps to coordinate the immune response [158]. Whilst the role, if indeed it has
one, in NS pathogenesis is yet to be elucidated [159], it is known to regulate the function of
lymphocytes and macrophages and control tissue damage in the context of inflammation
[160]. Therefore, it is logical that haptoglobin could indicate steroid responsiveness.
suPAR is known to exert direct effects on the podocyte and has been shown to downregu-
late nephrin and podocin via activation of the αβV III integrin [161,162]. When cultured
podocytes are treated with suPAR they respond by upregulating expression of TRPC6, which
can also be achieved by treating blood samples from FSGS patients [163]. This further under-
pins a role for TRPC6 in the pathogenesis of nephrotic syndrome [90]. It has been suggested
that suPAR could be the putative circulating factor in INS [164,165]. Though this has been
disputed [166,167]. However, in addition to its possible role as a biomarker for steroid respon-
siveness, urinary suPAR has shown utility in predicting recurrence of FSGS following a kidney
transplant [122].
It is our strong recommendation that work continues to investigate the utility of these
markers using the PRoBE approach on a cohort such as NURTuRE-INS.
Supporting information
S1 Checklist. PRISMA 2020 checklist.
(DOCX)
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S1 Table. Literature search results. Supplementary table one shows all of the papers identified
by our search strategy and the reason for their exclusion (if applicable).
(XLSX)
Author Contributions
Conceptualization: Carl J. May.
Data curation: Carl J. May.
Formal analysis: Carl J. May.
Investigation: Carl J. May.
Supervision: Nathan P. Ford.
Writing – original draft: Carl J. May.
Writing – review & editing: Nathan P. Ford, Gavin I. Welsh, Moin A. Saleem.
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