Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT03687138 |
| Other study ID # |
EstMcBasNL_001 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 15, 2018 |
| Est. completion date |
February 28, 2022 |
Study information
| Verified date |
May 2022 |
| Source |
Hospital de Basurto |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Objective: To search for potential biomarkers obtained by non-invasive methods (24-hour urine
collection) that distinguish between patients diagnosed with systemic lupus erythematosus
with or without renal involvement, patients with non-autoimmune renal disease and healthy
donors.
Lupus nephritis is one of the most common and severe complications of systemic lupus
erythematosus, causing from asymptomatic mild proteinuria to rapidly progressive
glomerulonephritis with kidney failure. To date, kidney biopsy (an invasive medical procedure
with associated risks and complications) is essential for making a definitive diagnosis,
assessing the severity of the damage and deciding on the best treatment. In relation to this,
the identification of biomarkers using a non-invasive biological sample could help to
classify population groups, and this would be a great step forward in the clinical setting.
In this research project, we propose to conduct a case and control study. For this, we will
first carefully classify the study groups, using clinical data on patients and by testing a
pool of peptides described in the scientific literature in each of the sample groups, using
solid phase extraction combined with matrix-assisted laser desorption/ionization
time-of-flight mass spectrometry. Subsequently, we will carry out multivariate principal
component analysis on the data collected, and calculate corresponding receiver operating
characteristic curves, to enable us to identify the masses corresponding to peptides with
potential as biomarkers. We will then use classification algorithms to select sets of masses
that would allow us to distinguish the population groups, and generate statistical
classifiers for assessing the level of confidence in the model and its subsequent validation.
Description:
BACKGROUND AND STATUS QUO IN THE FIELD
Systemic lupus erythematosus (SLE) is a systemic, chronic autoimmune disease, affecting
connective tissue, and is considered a rare disease by the Spanish Foundation for Rare
Diseases (FEDER). Its clinical manifestations are varied, being able to affect most types of
tissue and organs especially the skin, joints, blood-forming organs, heart, lung, kidney and
nervous system (Galindo, 2008). A complex disease, it has a variable clinical course and
prognosis, characterised by alternating periods of remission and exacerbation (flares). For
this reason, it is important to establish the prognosis of each patient, as well as develop
and validate measures of disease activity and accumulated damage (Irastorza et al., 2012;
Galindo, 2008; Cervera, 1993).
The aetiology of SLE is still unknown. As well as a variable production of autoantibodies,
genetic and environmental factors may be involved in its pathogenesis. Indeed, it is likely
that the involvement of various different pathogenic and aetiological agents explains the
biological and clinical heterogeneity observed in patients with the disease.
The incidence of SLE varies with age, sex and ethnic group, and other characteristics of the
population under study. In Europe, recent data suggest an incidence of 1.5 cases/100,000 and
a prevalence of 122 cases/100,000), while specifically in Spain, the EPISER study found a
prevalence of 9 cases/100,000. The disease is known to be more common in Afro-Americans,
Hispanics and Asians, and moreover, is more severe in these groups, probably due to a mixture
of genetic, economic and cultural factors. The female-to-male sex ratio is around 10:1,
though the proportion of cases in females is lower among those with onset occurring during
childhood or after 65 years of age. In most patients, signs and symptoms of SLE develop
between 15 and 40 years of age, with a mean age of disease onset of 29 to 32 years old
(Galindo, 2008; Ruiz-Irastorza et al., 2012).
Between 30 and 50% of patients with SLE develop kidney disease, this being one of the first
manifestations in 10% of patients. The most common clinical manifestations include
proteinuria, micro-haematuria, urinary casts, kidney failure, and high blood pressure. This
condition is associated with higher risks of end-stage renal disease (ESRD) 5 to 10 years
after diagnosis and of death (Galindo, 2008; Aran et al., 2008; Ruiz-Irastorza et al., 2012;
Sanz, 2014).
Lupus nephritis (LN), a common complication of SLE, may vary in severity from asymptomatic
mild proteinuria to rapidly progressive glomerulonephritis with kidney failure. It is
classified on the basis of pathological findings in the glomerulus, the WHO classification
and updates thereof, being the most widely used in both clinical trials and daily practice.
The WHO system identifies six different classes of LN, classes III and IV corresponding to
the proliferative forms of the disease. This pathology-based classification, based on renal
biopsy, is of great important for assessing the intensity of treatment required to prevent
progression to ESRD (Silva-Fernández et al., 2008; Sanz, 2014). On the other hand, renal
biopsy is an invasive medical procedure, which requires highly-qualified personnel and is not
free of risks and complications. Currently, this test is essential for making a definitive
diagnosis, assessing the severity of the damage ("staging") and deciding on the best
treatment (Carpio, 2003; Ruiz-Irastorza et al., 2012).
Moreover, by the time patients are diagnosed, they may already be at an advanced stage of the
disease, making it more difficult to treat and manage. For this reason, methods for assessing
whether individuals are at risk of developing this condition at an earlier stage, enabling
them to be treated promptly, would increase the likelihood of treatments being successful and
thereby patient quality of life.
Proteomics provides key tools for obtaining data to improve diagnosis and/or prognosis, and
as such is one of the foundations of translational research. The proteome is a dynamic
entity, which is constantly changing in response to processes affecting cells, such as
stress, illness and medical treatments. Analysis of differences in protein and/or peptide
expression in samples from healthy individuals and patients with a given illness provides
very valuable information on the molecular and physiological basis of the illness (Pando &
Ferreira, 2007; Ali et al., 2005; Arrollo, 2005).
Biomarkers are molecules associated with in vivo biological processes that significantly
change with physiological conditions. They may be used in clinical practice for screening, to
establish a diagnosis, monitor disease activity, estimate prognosis with a given treatment,
or detect disease recurrence. In relation to this, proteomic analysis based on solid phase
extraction (SPE) in combination with matrix-assisted laser desorption/ionization
(MALDI)-time-of-flight (TOF) profiling allows inexpensive and rapid screening of peptides and
small proteins present in biological fluids. In our case, the fluid to analyse would be
urine, and this would enable sampling to be non-invasive, avoiding the risks associated with
biopsies.
Urine is likely to be the best body fluid for obtaining biomarkers for a kidney disease for
two reasons: 1. It is produced by the kidney, and hence any biomarkers identified may
directly reflect kidney function (Tianfu W. et al., 2013). 2. Samples can be obtained easily
and non-invasively
Regarding the assessment of LN using SPE combined with laser desorption/ionization-TOF mass
spectrometry, studies have been published by Suzuki et al. (2007) on childhood nephritis, and
by Mosley et al. (2006), investigating the use of urine profiles to distinguish patients with
active and inactive disease. Both of these groups used surface-enhanced laser
desorption/ionization, techniques that are mainly being replaced by SPE combined with
MALDI-TOF mass spectrometry (Callensen, Proteomics review), which has a higher resolving
power, provides more accurate mass measurements and is more sensitive. In relation to this,
several studies have been performed including those of Gonzáles et al. (2012) in tears, to
differentiate between individuals; Iloro et al. (2013) in urine, to monitor the toxicity of a
drug. The combination of MALDI-TOF mass spectrometry with the selective ability of SPE
techniques using, for example, reverse-phase resins (C18) and/or ion exchange (weak cation or
anion exchange) would make it possible to obtain better results than those of the
aforementioned studies. One of the strengths of this type of profiling analysis is that, as
well as being rapid and inexpensive, it is based on the intensity of a panel of biomarkers.
In this sense, several groups have described some proteomic biomarkers potentially associated
with susceptibility to LN and the underlying pathogenesis (Li et al, 2013; Wu et al., 2013;
Masanori et al., 2013; Zhu X et al., 2012; Nakatani et al., 2012). Already, there are some
studies that have contributed in the health field, example is using proteomics studies in
kidney biopsies, which have helped the reclassification of membranoproliferative
glomerulonephritis (MPGN) (Caster et al., 2015 ; Stoskar et al, 2012).. Alaiya and
collaborator (2014) conducted a study using proteomics kidney biopsy in which they determined
that there is no strong evidence to differentiate the two subcategories of class IV lupus
nephritis. Bruschi et al (2014) observed with typical proteomics antibodies in kidney NL also
found in serum. Yajuan Li, et al (2013).; Tianfu et al, (2013).; Masanori H. et al, (2013).;
Xuejing Zhu et al, (2012).; Nakatani et al., (2012), perform a description of possible
proteomic biomarkers associated with susceptibility to and pathogenesis of NL. These studies
demonstrate the potential for information to both clinicians and scientists in prognosis and
diagnosis of pathology in improving treatments, and in terms related to the etiology,
pathogenesis and disease activity (Caster et al., 2015).
In a first pilot study performed in OSI BILBAO BASURTO patients diagnosed with SLE with and
without NL, a differential protein pattern was observed between the two groups of patients
and also among patients with mild and significant renal involvement, by analyzing the ANOVA
(162 proteins p <0.01) (Rivera et al., 2018).
In this context and in order to delve into the molecular and physiological basis of disease,
it will seek to differentiate populations of patients diagnosed with SLE patients without NL
with no autoimmune nephropathy and healthy donors. The sample used is the 24-hour urine. Once
obtained and processed biological samples, analyzing them by MALDI TOF SPE in the Autoflex
III (bioGUNE) will be held.
METHODOLOGY
In this research project, we propose to conduct a case and control study. For this, we will
first carefully classify the study groups, using clinical data on patients. The Department of
Rheumatology at Basurto University Hospital provides consultations for autoimmune diseases,
mainly focusing on SLE, and these will be the source of the majority of patients with SLE and
LN and for comparison groups, as well as sample donors who are healthy or diagnosed with
non-immune diseases. Patients diagnosed with non-autoimmune lupus nephritis will be recruited
by the Nephrology Unit of the hospital.
Statistical analysis of the data
First, we will carry out a descriptive analysis of the variables and assess whether they are
associated with LN.
Second, we will determine whether the biomarkers tested are present in the study and
comparison populations, and then assess whether there are significant differences between the
study and comparison groups, for each of the candidate markers. Further, we will perform
multivariate analysis, and for this, as is common in profiling studies, we will use principal
component analysis. We will routinely calculate the receiver operating characteristic curves
as these will enable us to select masses corresponding to peptides with potential as
biomarkers. Finally, we will use classification algorithms (genetic algorithms and/or
supervised neural networks) to select sets of masses that would enable us to distinguish the
population groups, and generate statistical classifiers for assessing the level of confidence
in the model and its subsequent validation (Cellesen et al., 2009).
Limitations of the study:
Proteins are very labile and are prone to change under the influence of physical factors,
contact with plastic materials, among others. For this reason, to reduce potential noise in
the data associated with the plastic 24-hour urine collection containers to be used,
participants will each be provided with a container from the same batch. All samples will be
handed in to the research office of Basurto Hospital, standardising the way samples are
handled. Moreover, we will use protein LoBind tubes, plates, etc., to minimise binding of the
proteins to the plastic.
Ethical considerations.
The study will be conducted in accordance with the principles of the Declaration of Helsinki.
Copies of the Declaration and its amendments will be provided on request or can be found on
the website of the World Medical Association at http://wma.net/
en/30publications/10policies/b3/index.html.
The study is to be carried out according to the protocol and standard operating procedures
and this will ensure adherence to good practice guidelines, as described in the 1996
tripartite harmonised International Conference on Harmonisation Guideline on Good Clinical
Practice.
