Chitinase 3-like 1 (CHI3L1) is an extracellular monomeric single-chain glycoprotein expressed by many types of cells. Its elevated levels were found in cerebrospinal fluid in central nervous system (CNS) inflammatory diseases patients. The aim of the study was 1) to validate the reference interval of cerebrospinal fluid (CSF) CHI3L1 in a control group; 2) to measure the CHI3L1 concentration in different diagnosis groups .including multiple sclerosis (MS); and 3) to correlate those values with other biomarkers of axonal damage or neuroinflammation in different grous.
The study included 132 CSF samples sent to the Department of Clinical Biochemistry, Institute of Laboratory Diagnostics, University Hospital Ostrava. Concentrations of CHI3L1, CXCL13 chemokine, neurofilament light chains, and phosphorylated neurofilament heavy chains were determined by enzyme-linked immunosorbent assays. IgG oligoclonal bands were detected by isoelectric focusing in agarose gels followed by immunofixation. IgM and FLC oligoclonal bands were analyzed by IEF followed by affinity immunoblotting. The group consisted of 42 patients with multiple sclerosis, 14 with clinically isolated syndrome, 11 with other central nervous system inflammatory diseases, 46 with non-inflammatory diseases of the central nervous system, 4 with inflammatory diseases of the peripheral nervous system, and 15 controls.
The estimated reference values of CHI3L1 were 28.6–182.5 μg.L-1. Statistically significant differences of CSF CHI3L1 concentrations were found among diagnosis groups (p < 0.0001), after age adjustment (p = 0.002). There was a statistically significant relationship between CHI3L1 and NFL in the MS group (rs = 0.460; P = 0.002), and between CHI3L1 and pNFH in the MS group (rs = 0.691; P < 0.001). No statistically significant difference was found in the categorical comparison of CHI3L1 in the MS group and other diagnostic groups as well as when using the Mann-Whitney U test for CHI3L1 with additional parameters with and without oligoclonal bands present.
CSF CHI3L1 values differ depending on diagnosis and correlate significantly with concentrations of the axonal damage markers CSF neurofilament light chains, and CSF phosphorylated neurofilament heavy chains, but not with CSF concentrations of the inflammatory marker CXCL13. Thus, CSF CHI3L1 could be another promising prognostic, albeit probably etiologically nonspecific, biomarker of MS.
All relevant data is available within the paper and its Supporting Information files.
Multiple sclerosis (MS) is a chronic disease affecting the central nervous system. In recent years several biochemical markers in cerebrospinal fluid have been suggested as prognostic tools [
CHI3L1, also known as YKL-40, belongs to the chitin glycoside hydrolase 18 family. Unlike true chitinases, it lacks enzymatic activity. It is a glycoprotein produced by a wide variety of cells, such as macrophages, chondrocytes, synovial cells, osteoblasts, neutrophils, and astrocytes [
CHI3L1 is expressed in astrocytes in the brain tissue of patients with multiple sclerosis, and is associated with reactive gliosis in different neuropathological states, particularly those associated with neuroinflammation. A correlation between the time course of the CHI3L1 concentration and the CSF viral load was shown in lentiviral encephalitis [
MS is a demyelinating disease associated with increasing and decreasing inflammation, gliosis, and variable axonal loss. Therefore, we expect to find increased concentrations of CHI3L1 in MS patients.
The aim of the study was 1) to validate the reference interval (RI) of cerebrospinal fluid (CSF) chitinase 3-like 1 (CHI3L1) in a control group; 2) to measure the CHI3L1 concentration in different diagnosis groups, including MS; and 3) to correlate those values with other biomarkers of axonal damage or neuroinflammation in different groups.
RIs were estimated on the basis of the guidelines of the Clinical and Laboratory Standards Institute (CLSI C28-A3), which recommends the use of nonparametric tests for statistical data processing and the evaluation of data according to gender and age [
Our study includes 132 patients of the Moravian-Silesian region of the Czech Republic whose CSF samples were sent for analysis to the Institute of Laboratory Diagnostics, Department of Clinical Biochemistry, University Hospital Ostrava. Informed consent was obtained from all patients at the University Hospital Ostrava who were included in the study. The study was approved by the Ethics Committee of the University Hospital Ostrava as a part of the project ‘CSF biomarkers of multiple sclerosis’ (reference number 400/2017). Patients were subdivided into diagnosis groups: MS (n = 42; 33 women, average age 39.5 ± 12.1 years; 9 men, average age 36.6 ± 10.5 years), clinically isolated syndrome (CIS; n = 14; 8 women, average age 28.8 ± 8.3 years; 6 men, average age 28.3 ± 8.9 years), other central nervous system inflammatory diseases (OIND; n = 11; 5 women, average age 78.4 ± 21.2 years; 6 men, average age 62.3 ± 12.1 years), inflammatory diseases of the peripheral nervous system (IDPNS; n = 4;; 4 men, average age 53.5 ± 13.3 years), non-inflammatory diseases of the central nervous system (NIND; n = 46; 32 women, average age 51.8 ± 17.1 years; 14 men, average age 59.1 ± 11.1 years), and controls (n = 15; 11 women, average age 41.7 ± 15.2 years; 4 men, average age 40.2 ± 15.5 years). For diagnosis of multiple sclerosis, we used the 2017 Revisions of the McDonald Criteria [
CSF samples were collected into polypropylene tubes (Sarstedt, Nümbrecht, Germany). Samples were centrifuged at 390 × g for 10 minutes at room temperature, and the supernatants for determination of CHI3L1, CXCL13, neurofilament light chains (NFL), and phosphorylated neurofilament heavy chains (pNFH) were then aliquoted into at least 3 vials (0.3 ml per vial) and stored at -70 °C until analysed.
The concentrations of CHI3L1 (Quantikine ELISA Human Chitinase-3-like 1 Immunoassay, REF DC3L10, R&D Systems, USA&Canada), CXCL13 (CXCL13 ELISA, REF EQ6811-9601-L, Euroimmun AG), NFL (NF-light ELISA, REF 10–7001, IVD CE, UmanDiagnostics AB, Umeå, Sweden), and pNFH (Neurofilament “pNf-H” ELISA, REF EQ 6561–9601, IVD CE, Euroimmun AG, Lübeck, Germany) were determined by ELISA. Undiluted CSF was used for CXCL13 and pNf-H whereas 1/2 and 1/50 dilution was used for NFL and CHI3L1, respectively.
The quality control samples supplied by the manufacturers of the diagnostic kits were used for accurate and reproducible measurement of CHI3L1, CXCL13, and pNHF; for NFL measurements, the patient sample was used, because the diagnostic kit did not include a quality control sample. Kit manufacturers reported that analytical sensitivity was 3.55 ng.L-1 for CHI3L1, 10.7 ng.L-1 for CXCL13, 32 ng.L-1 for NFL, and 27 ng.L-1 for pNFH. All samples were measured in duplicate. The mean coefficients of variation for CHI3L1, CXCL13, NFL, and pNFH were 5.8%, 5.2%, 1.9%, and 3.3%, respectively.
IgG oligoclonal bands (oIgG) were detected by isoelectric focusing in agarose gels followed by immunofixation, using a commercial kit on a Hydrasys instrument (Hydragel 9 CSF isofocusing, Cat. No. 4355, Sebia).
IgM and FLC oligoclonal bands (oIgM, oFLC) were analyzed by IEF focusing followed by affinity immunoblotting as originally described by Sindic and Laterre [
The statistical analysis was rendered using Excel, Stata version 13, MedCal version 17.9.7., R and NCSS 2007 software [
Basic descriptive statistics, including frequency tables, medians, arithmetic means, standard deviations and percentiles, were used to describe the results. With the Shapiro-Wilk test of normality, the normality of the parameters CHI3L1, CXCL13, NFL, and pNFH was verified.
The normality hypothesis was rejected; therefore, non-parametric tests were used, including the Kruskal-Wallis rank test and the two-sample Wilcoxon rank-sum (Mann-Whitney) test.
The relationship between the parameters was assessed by Spearman’s correlation coefficient. Data values were classified as positive and negative. Fisher’s exact test was used to test categorized data. Conformity between assay results was measured using the kappa index with confidence intervals of 95%. Statistical tests were evaluated using a significance level of 5%.
First, we validated the diagnostic kit for CHI3L1 determination. The coefficients of variation were comparable to the values supplied by the manufacturer (
Control | Intra-Assay Precision | Inter-Assay Precision | ||||
---|---|---|---|---|---|---|
Level 1 | Level 2 | Level 3 | Level 1 | Level 2 | Level 3 | |
n | 10 | 10 | 10 | 5 | 5 | 5 |
Mean (ng.L-1) | 335 | 1030 | 2084 | 417 | 1109 | 2094 |
SD (ng.L-1) | 15.9 | 52.2 | 105.1 | 22.2 | 63.0 | 135.5 |
CV (%) | 4.8 | 5.1 | 5.0 | 5.3 | 5.7 | 6.5 |
CVd | 4.7 | 4.3 | 4.7 | 5.3 | 5.8 | 6.9 |
n, number of measurements; SD, Standard deviation; CV (%), coefficient of variation;
†the declared value of coefficient of variation from the manufacturer
Theoretical concentration (μg.L-1) | Measured concentration (μg.L-1) | Average (μg.L-1) | Recovery (%) | |
---|---|---|---|---|
1. measurement | 2. measurement | |||
32.3 | 34.0 | 28.3 | 31.2 | 96.4 |
149.8 | 162.0 | 158.0 | 160.0 | 106.8 |
267.5 | 289.0 | 295.0 | 292.0 | 109.2 |
385.3 | 340.5 | 356.0 | 348.3 | 90.4 |
503.0 | 512.0 | 535.0 | 523.5 | 104.1 |
The estimated reference interval (28.6–182.5 μg.L-1) showed age-related increase (CHI3L1 = 41.546 + 1,072 * Age, P = 0.0017), sex-related difference was not found (P = 0.837) (
A total of 132 patient samples were included in the analysis, which evaluated the correlation between CHI3L1 levels and several other biochemical markers. Characteristics of the studied groups are presented in
MS | ||||||
---|---|---|---|---|---|---|
Variable | n | Min | Max | Mean | Median | SD |
Age (year) | 42 | 18.00 | 69.00 | 38.833 | 38.00 | 11.95 |
CSF CHI3L1 (μg.L-1) | 42 | 35.70 | 392.00 | 139.35 | 130.50 | 68.80 |
CSF NFL (ng.L-1) | 42 | 97.00 | 4044.00 | 1036.45 | 721.00 | 875.43 |
CSF pNFH (ng.L-1) | 35 | 87.31 | 1985.82 | 339.56 | 284.05 | 320.91 |
CSF CXCL13 (ng.L-1) | 42 | 10.70 | 265.50 | 37.43 | 10.70 | 61.16 |
CIS | ||||||
Age (year) | 14 | 16.00 | 44.00 | 28.57 | 27.00 | 8.86 |
CSF CHI3L1 (μg.L-1) | 14 | 38.50 | 269.00 | 83.92 | 65.70 | 58.29 |
CSF NFL (ng.L-1) | 14 | 139.00 | 1204.00 | 531.14 | 479.50 | 311.42 |
CSF pNFH (ng.L-1) | 10 | 104.18 | 475.28 | 188.35 | 159.37 | 105.92 |
CSF CXCL13 (ng.L-1) | 14 | 10.70 | 116.10 | 32.56 | 23.55 | 30.26 |
OIND | ||||||
Age (year) | 11 | 21.00 | 85.00 | 56.00 | 56.00 | 19.11 |
CSF CHI3L1 (μg.L-1) | 11 | 75.30 | 503.00 | 216.03 | 187.00 | 127.97 |
CSF NFL (ng.L-1) | 7 | 161.00 | 10073.00 | 2055.43 | 690.00 | 3553.81 |
CSF pNFH (ng.L-1) | 9 | 109.96 | 22400.00 | 3686.63 | 682.55 | 7161.84 |
CSF CXCL13 (ng.L-1) | 7 | < 10.70 | 80040.00 | 11464.36 | 24.10 | 30239.04 |
IDPNS | ||||||
Age (year) | 4 | 37.00 | 73.00 | 53.50 | 52.00 | 15.35 |
CSF CHI3L1 (μg.L-1) | 4 | 89.70 | 230.00 | 139.38 | 118.90 | 64.77 |
CSF NFL (ng.L-1) | 4 | 354.00 | 27149.00 | 7430.75 | 1110.00 | 13151.77 |
CSF pNFH (ng.L-1) | 4 | 226.88 | 23100.00 | 6121.19 | 578.93 | 11321.79 |
CSF CXCL13 (ng.L-1) | 1 | 10.70 | 10.70 | 10.70 | 10.70 | 0.00 |
NIND | ||||||
Age (year) | 46 | 12.00 | 79.00 | 54.07 | 55.00 | 16.08 |
CSF CHI3L1 (μg.L-1) | 46 | 61.10 | 386.00 | 163.37 | 140.00 | 83.56 |
CSF NFL (ng.L-1) | 31 | 210.00 | 60600.00 | 3997.87 | 744.00 | 10994.63 |
CSF pNFH (ng.L-1) | 43 | 141.73 | 15500.00 | 1573.79 | 501.34 | 2892.56 |
CSF CXCL13 (ng.L-1) | 24 | 10.70 | 96.20 | 17.01 | 10.70 | 21.57 |
Control | ||||||
Age (year) | 15 | 18.00 | 67.000 | 41.200 | 39.000 | 15.8439 |
CSF CHI3L1 (μg.L-1) | 15 | 32.30 | 188.00 | 85.84 | 76.90 | 42.24 |
CSF NFL (ng.L-1) | 7 | 108.00 | 644.00 | 324.14 | 303.00 | 182.20 |
CSF pNFH (ng.L-1) | 14 | 85.85 | 526.05 | 235.18 | 241.16 | 123.74 |
CSF CXCL13 (ng.L-1) | 5 | < 10.70 | < 10.70 | < 10.70 | < 10.70 | 0.00 |
MS, multiple sclerosis; CIS, clinically isolated syndrome; OIND, other central nervous system inflammatory diseases; IDPNS, inflammatory diseases of the peripheral nervous system; NIND, non-inflammatory diseases of the central nervous system; n, number of patients; Min, minimal concentration; Max, maximal concentration; SD, standard deviation
A statistically significant difference was found by the nonparametric Kruskal-Wallis test between CHI3L1 and diagnosis groups (p < 0.0001). After age-adjusted analysis according to regression dependence CHI3LI = 51.874 + 1.997 * Age, OIND diagnosis group had higher CHI3L1 levels than other diagnosis groups, (p = 0.002),
Upper graph: non-adjusted levels of CHI3L1 Statistically significant differences were found in the IDPNS, MS, NIND and OIND groups. Lower graph: Age adjusted levels of CHI3L1. Statistically significant differences were found only in the MS and OIND group.
The regression relationship between studied parameters was evaluated using Passing–Bablok regression (
rs = Spearman correlation coefficient. The correlation between CHI3L1 and NFL and pNFH in cerebrospinal fluid, P < 0.0001.
At the same time, we evaluated the correlation between the CHI3L1 concentrations and other studied parameters in individual diagnostic groups. There was a statistically significant relationship between CHI3L1 and NFL in the MS (rs = 0.460; P = 0.002), NIND (rs = 0.503; P = 0.003), and OIND (rs = 0.964; P < 0.001) diagnosis groups, and between CHI3L1 and pNFH in the MS (rs = 0.691; P < 0.001) and NIND diagnosis groups (rs = 0.691; P < 0.001) (
Parameters | Diagnosis | ||||||
---|---|---|---|---|---|---|---|
MS | CIS | NIND | OIND | Control | All | ||
CHI3L1 vs. NFL | rs | 0.460 | 0.422 | 0.503 | 0.964 | 0.857 | 0.595 |
P | 0.002 | 0.131 | 0.003 | <0.001 | 0.0137 | <0.0001 | |
n | 42 | 14 | 31 | 7 | 7 | 105 | |
CHI3L1 vs. pNFH | rs | 0.691 | -0.091 | 0.691 | 0.550 | 0.662 | 0.744 |
P | <0.001 | 0.802 | <0.001 | 0.125 | 0.010 | <0.0001 | |
n | 35 | 10 | 43 | 9 | 14 | 115 | |
CHI3L1 vs. CXCL13 | rs | 0.302 | 0.421 | 0.391 | 0.371 | 0.205 | |
P | 0.052 | 0.133 | 0.059 | 0.4131 | n.a. | 0.0482 | |
n | 42 | 14 | 24 | 7 | 93 |
MS, multiple sclerosis; CIS, clinically isolated syndrome; OIND, other central nervous system inflammatory diseases, NIND, non-inflammatory diseases of the central nervous system, IDPNS, inflammatory diseases of the peripheral nervous system; rs, Spearman’s correlation coefficient; n.a., not applicable
Methods (n) | Fisher exact test P | Sample distribution | Diagnosis | ||||
---|---|---|---|---|---|---|---|
CIS | MS | IDPNS | NIND | OIND | |||
CHI3L1 (117) | 0.0770 | Neg | 13 | 37 | 3 | 35 | 6 |
Pos | 1 | 5 | 1 | 11 | 5 | ||
CXCL13 (104) | 0.0004 | Neg | 8 | 32 | 4 | 28 | 4 |
Pos | 9 | 11 | 0 | 2 | 6 | ||
NFL (106) | 0.3775 | Neg | 13 | 25 | 2 | 19 | 4 |
Pos | 3 | 18 | 2 | 16 | 4 | ||
pNFH (104) | <0.0001 | Neg | 11 | 33 | 2 | 26 | 4 |
Pos | 0 | 2 | 2 | 18 | 6 | ||
oIgG (106) | <0.0001 | Neg | 5 | 5 | 8 | 18 | 9 |
Pos | 11 | 38 | 0 | 6 | 6 | ||
oIgM (55) | 0.0001 | Neg | 3 | 7 | 1 | 17 | 3 |
Pos | 4 | 16 | 0 | 1 | 3 | ||
oFLC Kappa (132) | <0.0001 | Neg | 2 | 6 | 3 | 46 | 6 |
Pos | 13 | 37 | 1 | 10 | 8 | ||
oFLC Lambda (132) | <0.0001 | Neg | 7 | 15 | 3 | 53 | 9 |
Pos | 8 | 28 | 1 | 3 | 5 |
The Cohen´s kappa statistic was used to compare the assays based on clinical interpretation (positive and negative results) because the methods had different reference intervals (
Positive values were as follows: CHI3L1 > 194.7 μg.L-1; NF-L > 900 ng.L-1 [
CHI3L1 vs. NFL | CHI3L1 vs. pNFH | CHI3L1 vs CXCL13 | |
---|---|---|---|
Weighted Kappa (n) | 0.243 (105) | 0.436 (115) | 0.287 (93) |
95% CI | 0.058–0.428 | 0.242–0.630 | 0.067–0.507 |
Standard error | 0.0942 | 0.0988 | 0.112 |
95% CI, 95% confidence interval
The Mann-Whitney U test was chosen to compare parameters (CHI3L1, pNFH, NFL and CXCL13) with and without the presence of oligoclonal bands (oIgG, oIgM, oFLC; positive results indicate more than 2 bands in cerebrospinal fluid absent from serum). Statistically significant differences were not found with the presence of oligoclonal bands for CHI3L1, but were found with oIgG for pNFH and CXCL13 (P = 0.0061; resp. P <0.0001), with oIgM for CXCL13 (P = 0.0279), with oFLC lambda for NFL and CXCL13 (P = 0.0348; resp. P <0.0001), oFLC kappa and CXCL13 (P <0.0001).
In this study we tested CHI3L1 as a marker of multiple sclerosis. The CHI3L1 ELISA diagnostics kit is sensitive enough to measure CHI3L1 in cerebrospinal fluid. We also estimated the normal CSF CHI3L1 values that were show to be independent of gender but associated with age. The CHI3L1 concentration increased with increasing age. This is consistent with published data showing increased levels of CSF CHI3L1 in patients with CNS inflammation compared with healthy individuals, and an increase with increasing age, consistent with the hypothesis that lower-grade inflammatory processes are induced in the aging brain [
The dependence of CH3L1 concentration in different diagnostic groups was studied. An elevated level of CSF CHI3L1 was found in patients with MS, but it was much higher in patients with other inflammatory neurological diseases. Modvig et al. [
At the same time, a number of studies dealt with the study of CHI3L1 in relation to CNS tissue damage. Burman et. [
Concurrently, CSF CHI3L1 concentration correlates significantly with CSF NFL and even more with CSF pNFH concentrations, making pNFH kit more convenient for routine analysis. The correlation between CHI3L1 and NFs varies depending on the diagnosis.
We also assessed the correlation between CHI3L1 and CXCL13. CXCL13 is a potent B cell chemoattractant. In cerebrospinal fluid, it is expressed by monocytes and especially macrophages in perivascular inflammatory lesions and scattered parenchymal cells [
The present study also provides the first results that compare CHI3L1, NFL, pNFH, and CXCL13 with IgG, IgM, and FLC kappa and FLC lambda oligoclonal bands. Our reason for conducting the comparison is the fact that intrathecal synthesis of IgG as well as of free light chains (FLC) is detectable in the majority of patients with multiple sclerosis and less frequently in other, mostly inflammatory, nervous system diseases. In addition, a positive finding of intrathecal FLC synthesis can be a marker of disease progression [
Since CSF CHI3L1 concentrations correlated much more closely with CSF pNFH, an established biomarker of disease progression than with inflammatory biomarker CSF CXCL13 and oFLC, we hypothesize that CSF CHI3L1 levels might reflect the extent of tissue damage rather than the degree of inflammatory activity. Lowest CSF CHI3L1 level found in the CIS group might further support this hypothesis, although patients with CIS (i.e., not fulfilling the diagnostic criteria of MS after the first episode consistent with demyelination) are probably at lower risk of progression to CDMS than before the implementation of 2017 revision of MS diagnostic criteria.
Our study has several weaknesses. Although three CSF and serum aliquots were frozen for each patient in the study, there was not always enough material, especially cerebrospinal fluid, for all tests. At the same time, the patient population was obtained during a 3-year study (n = 356), but in some patients no definitive diagnosis has been established. Therefore, these patients were not included in the final evaluation, which also affected the unequal representation of patients in the individual diagnostic groups. Finally, detailed clinical characteristics of patients within individual diagnostic groups were not taken into account in the analysis.
In this study, we tested a diagnostic kit for the determination of CHI3L1 concentrations in biological fluids. The CHI3L1 ELISA assay has adequate sensitivity and is suitable for CSF analysis. The data showed good correlation and moderate conformity between CHI3L1 and pNFH concentrations. When assessing the relationship of CHI3L1 concentrations and diagnosis, correlations were found between the concentrations of CHI3L1 and NFL in the MS, NIND, and OIND groups, and between the concentrations of CHI3L1 and pNFH in the MS and NIND groups. The results support the hypothesis that CSF CHI3L1 could be another promising prognostic, albeit probably etiologically nonspecific, biomarker of MS.
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We wish to thank the laboratory technicians L. Fürstová, I. Faruzelová, R. Malečková, R. Výtisková and B. Strakošová for their expert technical assistance. We thank Ing. F. Všianský for help with the statistical analysis.