Document Type : Original Article
Authors
Department of Clinical Pathology, Faculty of Medicine, Minia University
Abstract
Highlights
Conclusions
Overall, the findings of this study informed that anti-alpha enolase antibody and RDW levels could be considered an appropriate diagnostic marker in patients with active SLE and could differentiate them from patients with stable conditions.
Acknowledgement
I want to thank the contributions of Prof. Dr. Samar Ahmed Soliman, Department of Rheumatology and Rehabilitation, Faculty of Medicine, Minia University for her helpful insights and assistance with literature review and manuscript preparation. Her expertise added credibility to the findings of my study.
Financial support and sponsorship: Nil
Conflict of Interest: Nil
Keywords
Main Subjects
Introduction
Lupus, conventionally called systemic lupus erythematosus (SLE) is an autoimmune disorder in which the body immune system erroneously conquers various organs. It is a disease of flares and remissions. Lupus attacks mainly women in childbearing period, although other population get lupus, too.(1) Lupus is considered a deadly disease in the 20th century due to deficient diagnostic tools and therapeutic modalities(2). Therefore, identifying markers for early detection of the disease activity and advancement of treatment options become urgent necessity.
The cytoplasmic and glycolytic enzyme families include alpha-enolase. Infections and autoimmune illnesses can manifest with antibodies that target alpha-enolase. The anti-alpha enolase antibodies (AAE-Abs) cause endothelial damage in autoimmune disorders by triggering cell death, disrupting the fibrinolytic system, activating the complement classical cascade, and generating immune-logical complexes.(3)
As part of a full blood count, an automated hematology analyzer measures the red cell distribution width (RDW) to determine the variability of red cells. When combined with other red cell indices, RDW helps clinicians distinguish between various forms of anemia.(4) Irrespective of infection, anemia, or nutritional deficits, a large body of research suggests that elevated RDW values are associated with several autoimmune disorders.(5) As a result, RDW could be useful for estimating the incidence of certain disorders.
Aim of the Work:
This research set out to determine if serum AAE-Abs and RDW may be useful indicators for SLE disease activity prediction.
Subjects and Methods
The present investigation was place between March 2021 and April 2022 at the Clinical Pathology Department and the Rheumatology and Rehabilitation Department at the Faculty of Medicine, Minia University, Minia, Egypt.
Previous research indicated that in order to achieve 80% power at the 0.05 significance level, this study included 60 individuals with SLE. Based on the criteria outlined by Petri et al. (6), patients were diagnosed with SLE. Two groups were formed from the subclassification.
Twenty-five patients with active lupus erythematosus disease (SLEDAI) and a score more than 4 were included, whereas thirty patients with inactive lupus erythematosus (SLEDAI) and a score less than 4 were also included. For this research, we enlisted the help of 30 healthy individuals to act as a control group.
Patients with other autoimmune disorders, cardiovascular, kidney and liver diseases, acute infection, chronic inflammatory diseases, benign or malignant hematological diseases, as well as those with a history of blood transfusion 3 months prior participation in this study, were excluded.
Each participant gave his/her written, informed consent, and the Local Ethical Committee (No.817/08/2021) gave the approval to the study.
A comprehensive medical history, physical examination, and battery of laboratory testing were administered to both healthy controls and patients with SLE in this research. All of the following tests were performed: complete blood count (CBC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), urine analysis, 24-hour protein in urine test, kidney and liver function tests, rheumatoid factor (RF), antinuclear antibody (ANA), anti-dsDNA, complement (C)3 and C4, evaluation of serum levels of anti-alpha enolase antibody (AAE-Abs), and RDW.
Blood sampling protocol:
About 8 ml of venous blood was withdrawn from each subject by using a disposable plastic syringe after disinfection of skin with isopropyl alcohol (70%) swaps. This sample was divided as follow:
Methods
1- Complete blood count (CBC): It was performed using Sysmex XN-1000TM AUTOMATED HEMATOLOGY ANALYSER, France.
2- Renal function tests (blood urea & serum creatinine) and Liver function tests (ALT, AST and serum Albumin): following the instructions provided by the manufacturer, we used the auto-analyzer SELECTRA PRO XL from ELIT ech Group, clinical chemistry automation systems, Netherlands, in conjunction with commercially available kits.
3- CRP: using GENRUI, Biotech Inc, kinetic assay, China immunofluorescence method.
4-RF: using latex agglutination method.
5- ESR: Determined by conventional Westergren method.
6-Urine analysis:
Scientifically, by means of the dipstick method for urine we examined pH, sugar, proteins, ketones, bilirubin, White blood cells, nitrites, leukocyte esterase.
7- 24 hr. protein in urine:
Collection of urine started at 8:00 am by discarding the first urine specimen at 8 am, all then after quantity of urine throughout the following 24 hours, up to the sample at the following 8:00 am which was collected. Used a large container with label including patient identification, date and time of collection. Using auto-analyzer SELECTRA PRO XL, ELITech Group, clinical chemistry automation systems, Netherlands, for doing the test by using this equation:
Total protein * total urine volume / 100.000
incubate the wells for 30 minutes as in step 2.
For use requiring bichromatic measurements, 620nm can be used as a reference wavelength Quality Control
It is essential that the prediluted ANA ELISA High Positive absorbance be higher than that of the low positive absorbance, which in turn must be higher than that of the negative control prediluted ELISA control.
incubate the wells for 30 minutes as in step 2.
Quality Control
Pre-Assay Preparation
ELISA Procedure
Calculation and Interpretation
Parameters of RDW taken from CBC from Sysmex XN-1000TM AUTOMATED HEMATOLOGY ANALYSER, France.
By using the cell counter, Currently, there are two RDW measurements that are used: RDW-CV (red cell distribution width - coefficient of variation) and RDW-SD (red cell distribution width - standard deviation).
Using the distribution curve width and the mean cell size, the RDW-CV was computed. The mean cell size standard deviation was divided by the red blood cell MCV and then multiplied by 100 to get the percentage.
In femtoliters (fL), the RDW-SD measured the true width of the red cell distribution curve. Twenty percent above the baseline is where the distribution curve was found to be widest. The RDW-SD more faithfully represents the variation in red cell size since it is an actual measurement and is thus unaffected by the MCV. Adults typically have an RDW-SD between 40.0 and 55.0 fl.
Test principle
The quantity of anti-alpha enolase antibody (anti-αENOL Ab) in samples was assessed using a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) using the kit. A micro elisa well was prepared by adding an anti-alpha enolase antibody (anti-αENOL Ab), followed by incubation and washing. The anti-alpha enolase antibody (anti-αENOL Ab) was tagged with HRP before being added. The unbound enzyme was removed after further incubation and washing. Then, Chromogen Solution A and B were added, causing the liquid's color to shift to blue. The acid eventually caused the color to become yellow. The sample's anti-alpha enolase antibody (anti-αENOL Ab) presence was confirmed by measuring the absorbency (OD value) using a micro plate reader with a 450nm wavelength and comparing the result to the critical value.
Assay Procedure:
The standard well was supplemented with 50 μl of diluted standard, while the testing sample well on the assay plate received 40 μl of sample dilution. Following this, 10 μl of testing sample (with a final dilution degree of 5 times) was added, and the mixture was gently shaken before being incubated at 37 oC for 30 minutes.
Except for the blank well, 50μl of HRP-conjugate reagent was applied to every well. Shaken gently, the mixture was then incubated at 37 ℃ for 30 minutes.
Statistical analysis:
Research was conducted using SPSS, version 20, for statistical analysis. The Kolmogorov Smirnov test was used to check whether the quantitative data was normally distributed. We used Student's t-test to compare two groups, analysis of variance (ANOVA) to compare all three groups, and a Bonferroni post hoc test to find the least significant statistical difference between each pair of groups for normally distributed variables that were reported as mean + standard deviation (SD). For variables that did not follow a normal distribution, we used the median (interquartile range, or IQR) and compared the two sets of data using the Kruskal-Wallas and Mann-Whitney U tests. When applicable, we used a Chi-square test or Fisher's exact test to compare qualitative values, which were presented as numbers (%). We used the Spearman correlation test for non-parametric data and Pearson's correlation test for non-parametric variables to investigate relationships between them. On a scale from 0 to 1, values of the correlation coefficient (r) were as follows: 0.024 = weak, 0.25-0.49 = fair, 0.5-0.74 = moderate, and > 0.75 = high. We used receiver operating characteristic (ROC) curves to determine the best serum AAE-Abs and RDW cutoff values for SLE disease activity prediction. It was considered significant when the p-value was less than 0.05.
Results
The participants of this study are allocated into 3 groups: group I: it included 30 patients with active SLE, they were 23 females and 7 males with age ranging from 18 to 43 with mean +SD of 39.2+10.1 years; group II: it comprised 30 patients with inactive SLE, they were 24 females and 6 males ranging in age from 20 to 45 with mean + SD of 42+11 years, and group III: it consisted of 30 healthy controls, they were 22 females and 8 males, their ages ranged from 19 to 44 with mean +SD of 41+12.1 years.
Table 1 shows no statistically significant differences between the studying groups as
regards age, gender, occupation, residence and duration of illness, however the SLEDAI score was significantly higher in group I versus group II (12.3±6.46 vs.1.47±0.99,p> 0.00).
Discussion
Breaking of tolerance to nuclear self-antigens and the generation of pathogenic autoantibodies define SLE, an autoimmune illness. The skin, eyes, kidneys, heart, muscles, and joints are just some of the organs that may be affected by this difficult and complicated condition.(7)
SLE's aetiology is still a mystery, although environmental, hormonal, and genetic variables, as well as an unbalanced immune system, all play a part.(8)
A higher rate of SLE in females has long been documented (49.6 per 100,000), and it is more common in women of childbearing age (one in 500 cases). Biphasic patterns were discovered in males between 30 and 39 years old and 60 and 69 years old, with a prevalence rate of 6 per 100,000.(9)
These results showed that there were highly statistically significant increased levels of anti- alpha enolase ab in group I when compared to group II and group III also between group II with group III (P=<0.001).
Consistently, (10)(11) reported that In a study that examined the serum levels of anti-alpha-enolase Ab in SLE patients, the active group had significantly higher levels than the stable and control groups. α-enolase is a part of the complex extracellular trappings (NETs), which are released by neutrophils through an active process called NETosis. Lupus flares cause the production of circulating α-enolase antibodies because NETosis is a key factor in lupus autoimmunity.(12)
Reported(13)(14) that there was a positive correlation between anti-alpha enolase antibody, Anti ds DNA antibody and Complements (C3 and C4) in SLE patient groups it was due to increasing production of antibodies like anti-alpha enolase antibody and Anti-dsDNA antibody during flares of SLE that helped in consumption of complements in glomeruli of kidney leading to renal affection.
Reported(10)(11) that there was strong degree of specificity and sensitivity (100%, 100%) respectively of anti- alpha enolase antibody in active group than stable and control groups.
In the current work there were highly statistically significant increasedlevels of RDW in group I when compared with group II (p=0.005), or withgroup III (P= <0.001) and group II with group III (P= 0.001).
In agreement with these results (15) (16) (17) (18)reported that the levels of RDW inpatients with SLE in active group were significantly higher than those in stableand control groups through a study that investigating RDW in SLE patients,sustained autoantibodies that were formed in SLE caused activation of naturalkiller (NK) cells. NK cells caused lysis of the target cells. The hematopoieticsystem was very much vulnerable to these effects. Thus, autoantibodies caused.
Our analysis of the area under the ROC curve revealed that the cut off value of AAE- Abs was > 5.4 ng/ml that was the optimal value for accurate prediction of disease activity in SLE patients with an area under curve 1.0 that corresponded to PPV and NPV of 100% for each.it has been reported that NPV of 100% is desirable to minimize the oversight of patients who are at risk. Similarly, the area under ROC curve for RDW was 0.982 at a cut off value > 12.5% that yielded a sensitivity,specificity ,PPV,NPV of 100%,95%,100%,and 90.9% respectively. These results indicated that each AAE-Abs and RDW ca be used as a sole predictors for disease activity I SLE patients.
Undoubtedly, the current study has some limitations. first, the relatively small number of sample size. Second, the study was case-control and hospital based, therefore, the possibility of over estimating the studied markers could not be excluded. Third, complete autoimmune work-up was not done. Finally, the study data were obtained from a single center.
References