Share

January 2023; 10 (1) Research ArticleOpen Access

Kappa Free Light Chain Biomarkers Are Efficient for the Diagnosis of Multiple Sclerosis

A Large Multicenter Cohort Study

Michael Levraut, Sabine Laurent-Chabalier, Xavier Ayrignac, Kévin Bigaut, Manon Rival, Sanae Squalli, Hélène Zéphir, Tifanie Alberto, Jean-David Pekar, Jonathan Ciron, Damien Biotti, Bénédicte Puissant-Lubrano, Jean-Philippe Camdessanché, Yannick Tholance, Olivier Casez, Bertrand Toussaint, Jeanne Marion, Thibault Moreau, Daniela Lakomy, Audrey Thomasset, Elisabeth Maillart, Delphine Sterlin, Aude Maurousset, Auriane Rocher, David Axel Laplaud, Edith Bigot-Corbel, Pierre-Olivier Bertho, Jean Pelletier, Joseph Boucraut, Pierre Labauge, Thierry Vincent, Jérôme De Sèze, Isabelle Jahn, Barbara Seitz-Polski, Eric Thouvenot, Christine Lebrun-Frenay; on behalf of Société Francophone de la Sclérose En Plaques (SFSEP)
First published November 14, 2022, DOI: https://doi.org/10.1212/NXI.0000000000200049
Full PDF
Citation
Permissions

Make Comment

See Comments

Downloads
728

Share

Abstract

Background and Objectives Kappa free light chains (KFLC) seem to efficiently diagnose MS. However, extensive cohort studies are lacking to establish consensus cut-offs, notably to rule out non-MS autoimmune CNS disorders. Our objectives were to (1) determine diagnostic performances of CSF KFLC, KFLC index, and KFLC intrathecal fraction (IF) threshold values that allow us to separate MS from different CNS disorder control populations and compare them with oligoclonal bands' (OCB) performances and (2) to identify independent factors associated with KFLC quantification in MS.

Methods We conducted a retrospective multicenter study involving 13 French MS centers. Patients were included if they had a noninfectious and nontumoral CNS disorder, eligible data concerning CSF and serum KFLC, albumin, and OCB. Patients were classified into 4 groups according to their diagnosis: MS, clinically isolated syndrome (CIS), other inflammatory CNS disorders (OIND), and noninflammatory CNS disorder controls (NINDC).

Results One thousand six hundred twenty-one patients were analyzed (675 MS, 90 CIS, 297 OIND, and 559 NINDC). KFLC index and KFLC IF had similar performances in diagnosing MS from nonselected controls and OIND (p = 0.123 and p = 0.991 for area under the curve [AUC] comparisons) and performed better than CSF KFLC (p < 0.001 for all AUC comparisons). A KFLC index of 8.92 best separated MS/CIS from the entire nonselected control population, with better performances than OCB (p < 0.001 for AUC comparison). A KFLC index of 11.56 best separated MS from OIND, with similar performances than OCB (p = 0.065). In the multivariate analysis model, female gender (p = 0.003), young age (p = 0.013), and evidence of disease activity (p < 0.001) were independent factors associated with high KFLC index values in patients with MS, whereas MS phenotype, immune-modifying treatment use at sampling, and the FLC analyzer type did not influence KFLC index.

Discussion KFLC biomarkers are efficient tools to separate patients with MS from controls, even when compared with other patients with CNS autoimmune disorder. Given these results, we suggest using KFLC index or KFLC IF as a criterion to diagnose MS.

Classification of Evidence This study provides Class III evidence that KFLC index or IF can be used to differentiate patients with MS from nonselected controls and from patients with other autoimmune CNS disorders.

Glossary

AUC=
area under the curve;
CIS=
clinically isolated syndrome;
FLC=
free light chain;
IF=
intrathecal fraction;
KFLC=
Kappa free light chain;
LDL=
lower detectable limit;
LFLC=
lambda FLC;
NINDC=
noninflammatory CNS disorder control;
OCB=
oligoclonal band;
OIND=
other inflammatory CNS disorder;
SC=
symptomatic control

MS is the most common chronic inflammatory and demyelinating CNS disease worldwide. It mainly presents as relapsing subacute clinical demyelinating events that progressively lead to neurologic disabilities. For the past 2 decades, clinical research has focused on how to diagnose MS as early as possible and prevent relapse and handicap by initiating disease-modifying treatments as soon as possible.

In the last MS diagnostic criteria update,1 biomarkers have an essential role in the diagnostic workup of patients presenting with a typical first demyelinating event. Clinical and MRI abnormalities can ensure MS diagnosis to fulfill dissemination in space and time criteria. CSF oligoclonal IgG bands (OCB) detection can avoid fulfilling the dissemination in time to treat patients with early MS. OCB detection reflects intrathecal B-cell activity, which are critical but nonspecific, effector cells in MS.2,3 Nevertheless, the level of intrathecal B-cell activity could be an exciting field of research to separate MS from other CNS inflammatory diseases4 and target treatment. As OCB are a qualitative biomarker, their detection does not permit quantification of the intrathecal B-cell activity.5 Moreover, isoelectric focusing is a time-consuming procedure that requires an experienced biologist to provide reliable results.6 Therefore, novel diagnostic biomarkers are being investigated, including blood and CSF free light chains (FLCs), small immunoglobulin compounds that may reflect and quantify B-cell activity. Kappa FLC (KFLC) has shown good overall performance in diagnosing MS, whereas lambda FLC (LFLC) performance seems to be lower than OCB.7,-,15 FLC measurement has the advantage of being an automatized, quantitative, easy to perform, and labor- and time-saving procedure.16

In practice, evaluation of the CSF and serum KFLC and albumin provides the level of intrathecal KFLC synthesis by calculating the KFLC index or KFLC intrathecal fraction (KFLC IF), including blood-CSF barrier permeability data.7,17 Both biomarkers could help diagnose OCB negative,18 and IgA or IgM OCB positive patients with MS19 and identify intrathecal immunoglobulin synthesis in patients presenting with one isolated band on isoelectric focusing.20 KFLC could also predict the risk of developing a second clinical attack in patients presenting with a first clinical demyelinating event.21,-,25 However, the use of either the KFLC index, which refers to linear modeling of a locally synthesized fraction of KFLC, or the KFLC IF, which refers to hyperbolic modeling of KFLC intrathecal synthesis, is not clear.26,27 Even if the KFLC IF seems closer to the pathophysiology, some studies show that both biomarkers perform equally in diagnosing MS.27,-,29 There is no consensus concerning the KFLC index or KFLC IF cut-off to use in daily practice to confirm MS-related immunoglobulin intrathecal synthesis. Moreover, only a few studies have investigated the performance of KFLC in ruling out non-MS CNS inflammatory disorders.30 Finally, studies comparing the performance of the KFLC index and KFLC IF in a large cohort of patients are lacking.

The primary research question of this study was: Does KFLC biomarkers (CSF KFLC, KFLC index, and KFLC IF) allow to separate patients with MS from different controls (a nonselected CNS disorder population, and a non-MS autoimmune CNS disorder population) accurately? And compare their performances to OCB.

Methods

Standard Protocol Approvals, Registrations, and Patient Consents

According to French laws, the patients received transparent, fair, and appropriate research information, and nonopposition participation was available. The study received approval from the institutional review board of the University Hospital of Nice, and the protocol was registered on ClinicalTrial.gov (NCT05088473).

Patients and Controls

Thirteen French MS centers participated in this retrospective cohort study. Patients were eligible if they had (1) a diagnostic workup for a suspected CNS disorder and (2) eligible data for CSF and serum KFLC, albumin, and a known OCB status, up to March 31, 2021. Patients were excluded if the final diagnosis was (1) peripheral neurologic disorder, (2) infectious CNS disease, (3) CNS tumor, or if they had a monoclonal gammopathy or severe chronic kidney injury (glomerular filtration rate < 30 mL/min/1.73 m2) to avoid FLC misinterpretation.31

Peripheral neurologic diseases, CNS infections, and tumors were excluded so that the other inflammatory CNS disorder (OIND) group was as close as possible to clinical and/or radiologic MS mimickers. According to the final diagnoses, patients were separated into groups according to previously published classification criteria32: MS, clinically isolated syndrome (CIS), other inflammatory neurologic disorder (OIND), noninflammatory neurologic disorder (NIND), and symptomatic control (SC) groups. Patients classified as SC and NIND were grouped into a noninflammatory neurologic disorder control (NINDC) group. All patients with MS fulfilled the 2017 McDonald criteria.

Collected Data

The following data were collected based on patients' medical records: age, sex, immune modifying drug ongoing at sampling, final diagnosis, CSF and serum albumin, white blood cells, IgG, OCB status, and KFLC. When available, LFLC data were collected. For patients with MS/CIS, the type of clinical demyelinating event and evidence of disease activity (combined variable of the presence of an ongoing clinical demyelinating symptom at CSF sampling and/or gadolinium-enhanced T1 lesions on a full 30-day time lag MRI) were collected.

Laboratory Features

FLC were measured in each center's laboratory using a turbidimetric or nephelometric analyzer; 9 centers used the turbidimeter Optilite (The Binding Site, Birmingham, UK), 2 used the turbidimeter SPAPLUS (The Binding Site, Birmingham, UK), one the nephelometer BN ProSpec (Siemens Healthcare Diagnostics Products GmbH), and one the nephelometer BNII (Siemens Healthcare Diagnostics Products GmbH) with the serum-free light chain immunoassay Freelite (The Binding Site, Birmingham, UK). FLC measurements were performed according to the manufacturer's instructions. Serum and CSF albumin levels were determined with the same turbidimetric or nephelometric analyzer. Four of the 13 centers used FLC as part of the diagnostic workup, and data were measured on fresh samples. For the other centers, paired CSF and serum were immediately centrifuged after sampling and stored in polypropylene tubes within 2 hours at −80°C until assay. eTable 1 (links.lww.com/NXI/A753) summarizes the laboratory data according to each center. The assessment of OCB was performed by isoelectric focusing and subsequent immunoglobulin using IgG-specific antibody staining, according to each center's laboratory routine care, fulfilling the recommended standards for CSF analysis.5 In each center, OCB patterns were evaluated by experienced biologists and classified as negative or positive. A cut-off ≥ 2 CSF-restricted bands were used to define OCB positivity.

Free Light Chain Biomarkers Determination

The FLC index and the FLC IF were determined for each patient.

  1. The FLC index was calculated with the formula: FLC index=QFLC /Qalb with QFLC = CSF FLC/serum FLC and Qalb= CSF albumin/serum albumin

  2. The FLC IF was calculated with the formulas17,27: FLC IF=(FLCloc /CSF FLC)×100 with FLCloc = (QFLC /QFLC(lim)) × serum FLC and QKFLC(lim)=3.27 (Qalb2 + 33)0.5 – 8.2 (×10-3) QLFLC(lim) = 3.1276 × Qalb0.8650

For patients whose CSF FLC concentration was below the analyzer's lower detectable limit (LDL), we assigned an empirical value equal to the LDL divided by 2.17 The attributed CSF KFLC concentrations were 0.17 mg/L for the Optilite and the BNII, 0.19 mg/L for the SPAPLUS, and 0.03 mg/L for the BN ProSpec.

Statistical Analysis

Categorical variables were expressed as counts with percentages, and continuous variables as means and SD or medians and interquartile ranges because of their distribution. Normality and heteroskedasticity of the baseline demographic, clinical, and biology characteristics were assessed using the Shapiro-Wilk and Levene tests, respectively.

Differences between groups were tested using a Wilcoxon-Mann-Whitney test to compare 2 groups of non-normally distributed data. The Kruskal-Wallis test with post hoc Conover was applied to reach more than 2 groups. For normally distributed continuous variables, a Student t-test was used. A Fischer exact test or a χ2 test was performed for categorical variables. All analyses were 2-tailed.

To evaluate FLC biomarkers' diagnostic performance, we first pooled all patients presenting with a demyelinating disease (MS and CIS groups) and compared them with control groups (OIND and NINDC). Then, the MS group was compared with OIND. Logistic regressions were implemented for each population to obtain the ROC curves used to measure the diagnostic capacity (area under the curve [AUC], 95% CI) of the different FLC biomarkers. The DeLong method assessed the comparison of each FLC biomarker by comparing their areas under the ROC curves with their 95% CI. The Youden index allowed the definition of each biomarker's optimal threshold values.

The patients were classified as positive or negative for each FLC biomarker to define categorical variables from the obtained thresholds. The diagnostic capacity of each FLC biomarker of interest was compared with OCB via the categorical variables. Comparisons of sensitivities and specificities were made using the McNemar test, and comparisons of positive and negative predicted values were made using the Moskowitz and Pepe33 method.

The identification of independent clinical factors associated with KFLC index values in patients with MS was first performed using a univariate linear regressions implementation for each element to be tested (age, sex, type of analyzer, type of sample, type of MS, disease activity, immune treatment ongoing at sampling, and type of clinical event). The clinical factors whose p value was less than 0.2 were retained for the multivariate analysis in the univariate analysis.

The selection of variables in the multiple linear regression was made step by step, removing the least nonsignificant variable (p value > 0.05). The model retained all the variables whose p value was below the significance level of 5%. For all comparisons, p values <0.05 were considered statistically significant. Comparisons of sensitivity, specificity, and positive and negative predictive values were made using the R statistical software version 3.2.1 mix tools package (sesp.mcnemar function and pv.rpv function of the R DTComPair packages). All other analyses were made with SAS version 9.4 (SAS Institute, Inc). Figures were done using the online application EasyMedStat (version 3.14, easymedstat.com).

Data Availability

Data not provided in the article because of space limitations may be shared (anonymized) at the request of the corresponding author to replicate procedures and results.

Results

Paired CSF and serum samples were available for 1,917 eligible patients, and 1,621 patients (MS group [n = 675], CIS group [n = 90], OIND group [n = 297] and NINDC group [n = 559]) were included in the analysis (eFigure 1, links.lww.com/NXI/A753). Serum and CSF LFLC were available for 825 patients.

Definite diagnoses are available in supplementary data (eTable 2, links.lww.com/NXI/A753). Both MS and CIS groups had comparable baseline characteristics except for the OCB status (84 vs 63% positivity, respectively, p < 0.001) and IgG index values (median of 0.82 vs 0.64, respectively, p = 0.001). Both control groups had different baseline characteristics compared with patients with MS. In particular, immune-modifying drugs at sampling were more frequent in the OIND group (17%) than in the MS group (9%), p = 0.003. All baseline characteristics are available in Table 1, and details concerning disease-modifying treatment are available in supplementary data (eTable 3, links.lww.com/NXI/A753).

View this table:
Table 1

Demographic and Clinical Data of Patients at Serum/CSF Sampling

Patients With MS and CIS Presented Higher FLC Biomarkers Than Both Control Groups

The CSF KFLC (Figure 1A), KFLC index (Figure 1B), and KFLC IF (Figure 1C) values were higher in the MS group (median = 4.36 mg/L [1.75–9.96], 64.2 [24.4–147.5], and 94.6% [86.7–97.6], respectively) than in the CIS group (median = 2.04 mg/L [0.46–5.66], 29.0 [6.3–80.5], and 88.3% [55.8–96.2], respectively), the OIND group (median = 0.17 mg/L [0.17–0.93], 3.8 [2.2–9.5], and 3.2% [−48.9 to 64.5], respectively), and the NINDC group (median = 0.17 mg/L [0.17–0.17], 2.4 [1.6–3.5], and −47.2% [−98.3 to 8.8], respectively). p Value was <0.001 for all MS or CIS vs OIND and NINDC comparisons. Although focusing on MS phenotype, progressive patients with MS (n = 83) presented with similar median CSF KFLC concentration than relapsing patients with MS (n = 592), p = 0.292, and lower KFLC index (p = 0.023) and KFLC IF (p = 0.043) values. All data are presented in eFigure 2 (links.lww.com/NXI/A753).

Figure 1
Figure 1 Comparison of the Different FLC Biomarkers Values Between Groups

CIS = clinically isolated syndrome; CSF KFLC = CSF kappa free light chains; CSF LFLC = CSF lambda free light chains; IF = intrathecal fraction; KFLC IF = kappa free light chains IF; KFLC index = kappa free light chains index; LFLC IF = lambda free light chains intrathecal fraction; LFLC index = lambda free light chains index; MS = multiple sclerosis; NINDC = noninflammatory neurologic disorder controls; ns = nonsignificant; OIND = other inflammatory neurologic disorder. *p < 0.05, **p < 0.01, ***p < 0.001.

The CSF LFLC (Figure 1D), LFLC index (Figure 1E), and LFLC IF (Figure 1F) values were higher in the MS group than in the NINDC group, p Value <0.001 for all comparisons. Median values of all 3 LFLC biomarkers were similar between MS and CIS groups.

Both KFLC Index and KFLC IF Performed Better Than Other FLC Biomarkers in Diagnosing MS

MS/CIS groups were compared with OIND/NINDC groups to evaluate the diagnostic performance of each biomarker in the entire cohort. KFLC index (AUC 0.939) and KFLC IF (AUC 0.942) had similar diagnostic performances, p = 0.123, and performed better than CSF KFLC (AUC 0.914, p < 0.001 for both comparisons) in diagnosing MS/CIS (Figure 2A). Optimal cut-off values were 8.92 and 67.3% for KFLC index and KFLC IF, respectively (eTable 4, links.lww.com/NXI/A753).

Figure 2
Figure 2 FLC Biomarkers ROC Curve Analysis and Ability to Separate MS (±CIS) From Other Control Populations

Panel A shows diagnostic performances to separate MS and CIS from patients with NINDC and OIND (n = 1,621 for KFLC biomarkers and n = 811 for LFLC biomarkers). Panel B shows diagnostic performances to separate MS from patients with SC (n = 842 patients for KFLC biomarkers and n = 415 for LFLC biomarkers). Panel C shows diagnostic performances to separate MS from patients with NIND (n = 1,067 for KFLC biomarkers and n = 541 for LFLC biomarkers). Panel D shows diagnostic performances to separate MS from patients with OIND (n = 972 patients for KFLC biomarkers and n = 479 for LFLC biomarkers). CIS = clinically isolated syndrome; FLC = free light chain; IF = intrathecal fraction; KFLC = Kappa free light chain; LFLC = lambda free light chain; NINDS = noninflammatory CNS disorder control; OIND = other inflammatory CNS disorder.

Patients with MS were then compared with those with SC (Figure 2B). In this situation, KFLC IF performed better than the KFLC index (p = 0.008), and optimal cut-off values were 9.11 for KFLC index and 46.3% for KFLC IF (eTable 5, links.lww.com/NXI/A753). When patients with MS were compared with patients with NIND (Figure 2C), the KFLC IF performed better than the KFLC index (p = 0.002), and optimal cut-off values were 8.39 and 38.1% for KFLC index and KFLC IF, respectively (eTable 6, links.lww.com/NXI/A753).

Patients with MS were then compared with patients with OIND (Figure 2D). In this situation, the KFLC index (AUC 0.896) and KFLC IF (AUC 0.894) had similar diagnostic performance (p = 0.991) and performed better than CSF KFLC (AUC 0.865, p < 0.001 for both comparisons). Optimal cut-off values were 11.56 and 67.9% for KFLC index and KFLC IF, respectively (eTable 7, links.lww.com/NXI/A753). In all situations, CSF LFLC, LFLC index, and LFLC IF had lower performances than KFLC biomarkers.

Because different threshold values were obtained depending on the chosen control population, we compared the performances of the different thresholds in other situations to ensure their relevance. When applying KFLC biomarker thresholds obtained in separating MS from NIND to separate MS from OIND, the AUC of all 3 biomarkers decreased (p = 0.025 for CSF KFLC, p = 0.045 for KFLC index, and p < 0.001 for KFLC IF comparisons). The same results were obtained when applying KFLC biomarker thresholds obtained in separating MS from SC to separate MS from OIND. All data are shown in Table 2.

View this table:
Table 2

Comparison of the Different CSF KFLC, KFLC Index, and KFLC IF Obtained Thresholds Across Groups

KFLC Index and KFLC IF Performed Better Than OCB in Diagnosing MS/CIS From All Controls

For this analysis, we evaluated the performances of binary KFLC biomarkers (according to the obtained threshold values) in separating MS/CIS from OIND/NINDC and compared them to OCB. A KFLC index >8.92 (AUC 0.887) and a KFLC IF > 67.3% (AUC 0.892) performed better than OCB (AUC 0.861) in diagnosing MS/CIS (p < 0.001 for KFLC index vs OCB comparison, and for KFLC IF vs OCB comparison). These results are translated by a higher sensitivity and the same specificity, favoring KFLC biomarkers instead of OCB for MS/CIS diagnosis. Data are shown in Table 3.

View this table:
Table 3

CSF KFLC, KFLC Index, KFLC IF, and OCB Diagnostic Performances in Separating MS/CIS From Controls (n = 1,621)

KFLC Index and KFLC IF Tend to Perform Better Than OCB in Separating MS From OIND

To evaluate the ability of KFLC biomarkers to separate MS from OIND, KFLC binary variables, according to obtained cut-off, were compared with OCB status. A KFLC index >11.56 (AUC 0.835) and KFLC IF > 67.9% (AUC 0.831) tend to perform better than OCB (AUC 0.814) in diagnosing MS (p = 0.065 for KFLC index vs OCB comparison, and p = 0.138 for KFLC IF vs OCB comparison). Again, these results are translated by a higher sensitivity and the same specificity, favoring KFLC biomarkers instead of OCB for MS diagnosis. All data are shown in Table 4.

View this table:
Table 4

CSF KFLC, KFLC Index, KFLC IF, and OCB Performances in Separating MS (n = 675) From Other Inflammatory CNS Disorders (n = 297)

Adding OCB to KFLC Biomarkers as a Combinate Variable Does Not Increase Diagnostic Performances but Increases Specificity for MS Diagnosis

Because KFLC biomarkers and OCB's diagnostic performances were similar in separating MS from OIND, we analyzed whether a combined variable adding OCB to KFLC biomarkers performed better than KFLC biomarkers alone. The combined KFLC index variable performed similarly to KFLC index alone (AUC = 0.835 vs 0.826, p = 0.231), as for the KFLC IF (AUC = 0.831 vs 0.828, p = 0.666). Nonetheless, the double positive OCB/KFLC combined variable had higher specificity (p < 0.001) and lower sensitivity (p < 0.001) for MS diagnosis. The results are shown in eTable 8 (links.lww.com/NXI/A753).

Young Age, Female Gender, and Evidence of Disease Activity Were Independent Factors Associated With a High KFLC Index in MS

We then evaluated whether some clinical factors of interest independently influenced KFLC index values in patients with MS. In a univariate analysis, age, female gender, evidence of disease activity, MS phenotype, and the type of analyzer influenced KFLC index values, whereas the type of clinical event and immune-modifying drug consumption at sampling did not. In the multivariate analysis model, only young age (p = 0.013), female gender (p = 0.003), and evidence of disease activity (p < 0.001) were associated with high KFLC index values in patients with MS (Table 5).

View this table:
Table 5

Clinical Factors Influencing KFLC Index Values in MS

To better appreciate if age and gender influenced our results, we compared the performances of the KFLC thresholds between men and women and between 3 ranges of age (<30, between 30 and 55, and >55 years old). We found that gender did not influence our threshold diagnostic performances, whereas the AUC of all KFLC biomarkers were not statistically different between men and women (eTable 9, links.lww.com/NXI/A753). Similar results were found when comparing KFLC biomarker performances according to age. As shown in eTable 10, the sensitivity of KFLC biomarkers decreased with advanced age, whereas specificity increased.

KFLC Biomarkers Seem to Be Reproducible Across Centers

To evaluate the reproducibility of KFLC biomarkers, we compared, in patients with MS, median CSF KFLC, KFLC index, and KFLC IF values between each center that included at least 40 patients. The other centers were pooled together as another center. There was no statistical difference in median KFLC biomarker values between Marseille, Montpellier, Nantes, Nice, and other centers (p = 0.067, p = 0.268, and p = 0.091, for CSF KFLC, KFLC index, and KFLC IF comparisons, respectively) (eTable 11, links.lww.com/NXI/A753).

Second, the KFLC index and KFLC IF threshold values were applied separately in each center that included at least 100 patients with available data for MS, NINDC, and OIND groups. All KFLC thresholds seemed to separate MS from the dedicated control populations similarly (eTable 12, links.lww.com/NXI/A753).

Primary Research Question

Does KFLC biomarkers correctly separate patients having MS from patients having other CNS disorders, particularly other autoimmune CNS disorders? This study provides Class III evidence that KFLC index or IF can be used to differentiate patients with MS from nonselected controls and from patients with other autoimmune CNS disorders.

Discussion

Our study shows that the KFLC index and KFLC IF are valuable tools to detect MS, even compared with other noninfectious and nontumoral inflammatory neurologic disorders. As reported previously, LFLC biomarkers could not separate MS from controls with a reasonable accuracy.7,13,15 KFLC index and KFLC IF performed better than CSF KFLC, reinforcing the need to weight FLC values with a blood-brain barrier permeability factor such as albumin quotient to increase diagnostic performances.

In this cohort, a KFLC index >8.92 or a KFLC IF > 67.3% permit to separate MS from controls, with better sensitivity and the same specificity as OCB. However, these KFLC thresholds seemed similar to OCB in separating MS from OIND, with a trend that favors KFLC biomarkers when increasing threshold values. Moreover, predictive studies23,34 found that KFLC index predict second clinical attack or MRI dissemination in space and time, in patients with CIS, with a good accuracy, slightly better than OCB.23,34 Because KFLC measurement is an easier way to prove intrathecal B-cell activity, with at least as good diagnostic performances than OCB, KFLC index or KFLC IF could be used in clinical practice instead of OCB as first-line MS biomarker. Nonetheless, using a combination of KFLC and OCB significantly increases specificity for MS diagnosis. Therefore, adding OCB to KFLC biomarkers may be helpful in atypical cases. Based on our results, the results of other diagnostic studies,7,-,14 the ones of predictive studies,21,23,34 and the knowledge into MS red flags,1,35,36 we propose an algorithm that integrate KFLC biomarkers to diagnose MS (eFigure 3, links.lww.com/NXI/A753).

Even if our threshold values seem close to each other, we found that using lower KFLC thresholds to separate MS from OIND statistically decreases diagnostic performance. As already reported, it highlights that some controls may present pathologic intrathecal B-cell activity.28,30,37,38 There is growing evidence that CNS B-cell activity is higher in MS than in other autoimmune disorders, as pointed out by pathologic analyses, showing a substantial perivascular B-cell infiltrate in MS compared with other inflammatory diseases.4 Süße et al.30 reported similar results using KFLC IF, showing that using a 78.6% KFLC IF cut-off could separate MS-related myelitis (n = 26) from NMOSD-related myelitis (n = 9) with a good accuracy. These constants reinforce the interest in using quantitative biomarkers to measure CSF B-cell activity in suspected patients with MS. Of note, threshold values will always depend on the chosen control population. Therefore, the preciseness of our results should be used with caution in clinical practice, and the use of a 11.6 or 12 KFLC index threshold could be valuable to separate MS from OIND.

Our KFLC index thresholds seem consistent with currently published ones ranging from 3.045 to 10.62.7,-,13 The different chosen control populations can explain such discrepancy. For example, Leurs et al.7 identified a 6.6 KFLC index threshold to separate MS/CIS from controls. Only 30% of their 219 control patients had an inflammatory neurologic disorder explaining the lower cut-off obtained in this study. Another explanation could be the poor performance of the analyzers in detecting a low amount of FLC. For example, Sanz Diaz et al.12 attributed a practical value of 0.0001 mg/L to all patients with undetectable CSF KFLC concentrations (157/197 controls), leading to the report of a low KFLC index cut-off of 3.045. In our cohort, for undetectable CSF KFLC data, we assessed an empirical value based on the LDL of the analyzer divided by 2. In doing so, we may not have underestimated threshold values. Recently, Saadeh et al.14 reported, on a series of 1,359 patients (1,204 patients without MS), a median CSF KFLC value of 0.16 mg/L for controls, which seems similar to our empirical attributed value (0.17 mg/L).

Only a few studies focused on establishing a KFLC IF cut-off value to separate MS from controls.30,37 Most of the studies focused on the "presence or absence" of intrathecal KFLC synthesis based on a QKFLC>QKFLC(lim), leading to a KFLC IF>0% as a binary result. However, many patients without MS may present with low intrathecal B-cell activity resulting in a lack of specificity in such an approach. Our results support that using a KFLC IF > 67.9% to assess an MS-specific intrathecal B-cell activity separates patients more accurately.

The use of either the KFLC index or KLFC IF to prove intrathecal immunoglobulin synthesis is debated. Nonetheless, both biomarkers use CSF and serum KFLC concentration and include the albumin quotient as the blood-brain barrier permeability correction variable. In our study, both performed equally to separate MS from controls and OIND. These results are in line with others.27 Based on these results, both the KFLC index or KFLC IF can be used in daily practice.

In MS, OCB positivity is slightly modified over disease duration or disease-modifying treatment use.39,40 This is a crucial point to consider while a diagnostic biomarker needs to be efficient at the diagnostic workup. In our study, nearly 10% of the patients took an immune-modifying treatment at sampling, which did not influence KFLC index values. Moreover, we found that the KLFC index was not modified by the type of clinical demyelinating event nor by the MS phenotype (progressive vs relapsing), which allows its use in any clinical situation raising suspicion of MS. However, patients with evidence of MS activity at sampling presented with higher KFLC index values. This result is in line with predictive studies that showed that KFLC could predict MS disability20 and second clinical attack in patients with CIS.24,34,41,42

Of note, added to evidence of disease activity, we found that age and gender were independent KFLC index influencing factors in MS. KFLC sensitivity for MS diagnosis decreased in men, older patients, and inactive disease at sampling. Therefore, clinicians should be careful when interpreting a KFLC biomarker in an older man with suspected inactive MS, as a result, it could be negative. These findings may explain the observed difference in KFLC values between progressive and relapsing MS in the univariate analysis (and not in the multivariate analysis), whereas patients with progressive MS were older and more often men. Finally, we pointed out that median KFLC biomarkers values in patients with MS were similar between centers. The obtained KFLC thresholds permitted to correctly separate patients in each available center. These findings reinforce the reproducibility of such a biologic tool and its use in MS diagnostic workup. Nonetheless, dedicated prospective studies must confirm these results.

Our study is one of the first that evaluate the diagnostic performance of FLC biomarkers in a large multicenter cohort of MS and non-MS inflammatory patients. However, it has a few drawbacks. First, MS diagnosis was made according to the 2017 criteria, and dissemination in time data was not collected. Therefore, some patients with CIS could have been diagnosed with MS because of an OCB positive status, leading to a possible bias in comparing OCB and KFLC biomarker performance. However, this limitation tended to overestimate the OCB positivity in the MS group, which could not lead to an overestimation of KFLC biomarkers' diagnostic performance. We pooled patients with MS and CIS in the first analysis to avoid this limitation. Second, KFLC was measured using different analyzers and samples, in different laboratories.

We included the analyzer and sample types in the multivariate analysis model to evaluate their impact on our results to avoid this limitation. We found that both did not influence KFLC index values. However, the distribution of samples between the different analyzers was heterogeneous. Therefore, dedicated analyzers' comparative studies must be performed in MS to evaluate the consistency of FLC measurement, as it has been shown, in other disorders, that there were differences between the FLC measurement platforms.43,-,45 Third, we assigned an empirical FLC value equal to the LDL of the analyzer divided by 2 in patients with nondetectable FLC concentrations. We may not have overestimated KLFC biomarkers performances, whereas most patients with undetectable KFLC were not in the MS group. Finally, we excluded patients with CNS infections and/or tumors. Some of these patients may present elevated CSF B-cell activity, and their exclusion may have influenced KFLC biomarkers thresholds in this cohort. However, infections and tumors are not typical MS-mimicking diseases, and many other examinations allow to distinguish MS from such disorders.

In conclusion, KFLC is an automated and reliable variable that permits quantifying the CSF B-cell activity by calculating the KFLC index or KFLC IF. As supported by our results and the findings of other studies, the higher the KFLC biomarker value is, the higher the specificity for MS diagnosis will be. Based on these findings, we propose to use either a KFLC index >11.56 or a KFLC IF > 67.9% in patients presenting with atypical clinical demyelinating event suggestive of an inflammatory CNS disorder to favor MS from OIND. Otherwise, a KFLC index >8.92 or a KFLC IF > 67.3% can be used to favor MS diagnosis.

Study Funding

The authors report no targeted funding.

Disclosure

M. Levraut and S. Laurent-Chabalier report no disclosure related to this study. X. Ayrignac has received consulting and lecturing fees, travel grants, and research support from Alexion Pharmaceuticals, Biogen Idec, Genzyme, Gilead, Novartis, Merck Serono, Roche, Sanofi Aventis, and Teva Pharma, none related to this study. K. Bigaut reports fees as consultant or for presentations in partnership with Biogen, Roche, Novartis, and BMS/Celgene, none related to this study. M. Rival and S. Squalli report no disclosure related to this study. H. Zéphir, T. Alberto, and J.D. Pekar received free kappa free light chain reagents from Binding Site. J. Ciron has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biogen, Novartis, Merck, Sanofi-Genzyme, Roche, Celgene-BMS, and Alexion Pharmaceuticals, none related to this study. D. Biotti, B. Puissant-Lubrano, J.P. Camdessanché, Y. Tholance, O. Casez, B. Toussaint, and J. Marion report no disclosure related to this study. T. Moreau, D. Lakomy, and A. Thomasset report financial support from Binding Site for one kappa free light chain reagent. E. Maillart, D. Sterlin, A. Maurousset, A. Rocher, D. Laplaud, E. Bigot-Corbel, P.O. Bertho, J. Pelletier, J. Boucraut, P. Labauge, T. Vincent, J. De Sèze, I. Jahn, B. Seitz-Polski, E. Thouvenot, and C. Lebrun-Frénay report no disclosure related to this study. Go to Neurology.org/NN for full disclosures.

Appendix 1 Authors

Table

Appendix 2 Coinvestigators

Table

Footnotes

  • Go to Neurology.org/NN for full disclosures. Funding information is provided at the end of the article.

  • The Article Processing Charge was funded by the authors.

  • Société Francophone de la Sclérose En Plaques (SFSEP) coinvestigators are listed in the appendix at the end of the article.

  • Submitted and externally peer reviewed. The handling editor was Friedemann Paul, MD.

  • Class of Evidence: NPub.org/coe

  • Received April 7, 2022.
  • Accepted in final form September 7, 2022.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

References

  1. 1.↵
    1. Thompson AJ,
    2. Banwell BL,
    3. Barkhof F, et al.
    Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162-173.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Comi G,
    2. Bar-Or A,
    3. Lassmann H, et al.
    Role of B cells in multiple sclerosis and related disorders. Ann Neurol. 2021;89(1):13-23.
    OpenUrl
  3. 3.↵
    1. Li R,
    2. Patterson KR,
    3. Bar-Or A
    . Reassessing B cell contributions in multiple sclerosis. Nat Immunol. 2018;19(7):696-707.
    OpenUrl
  4. 4.↵
    1. Machado-Santos J,
    2. Saji E,
    3. Tröscher AR, et al.
    The compartmentalized inflammatory response in the multiple sclerosis brain is composed of tissue-resident CD8+ T lymphocytes and B cells. Brain. 2018;141(7):2066-2082.
    OpenUrlCrossRefPubMed
  5. 5.↵
    1. Freedman MS,
    2. Thompson EJ,
    3. Deisenhammer F, et al.
    Recommended standard of cerebrospinal fluid analysis in the diagnosis of multiple sclerosis: a consensus statement. Arch Neurol. 2005;62(6):865-870.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Franciotta D,
    2. Lolli F
    . Interlaboratory reproducibility of isoelectric focusing in oligoclonal band detection. Clin Chem. 2007;53(8):1557-1558.
    OpenUrlFREE Full Text
  7. 7.↵
    1. Leurs CE,
    2. Twaalfhoven H,
    3. Lissenberg-Witte BI, et al.
    Kappa free light chains is a valid tool in the diagnostics of MS: a large multicenter study. Mult Scler. 2020;26(8):912-923.
    OpenUrl
  8. 8.↵
    1. Ferraro D,
    2. Bedin R,
    3. Natali P, et al.
    Kappa index versus CSF oligoclonal bands in predicting multiple sclerosis and infectious/inflammatory CNS disorders. Diagnostics (Basel). 2020;10(10):856.
    OpenUrl
  9. 9.↵
    1. Menéndez-Valladares P,
    2. García-Sánchez MI,
    3. Adorna Martínez M,
    4. García De Veas Silva JL,
    5. Bermudo Guitarte C,
    6. Izquierdo Ayuso G
    . Validation and meta-analysis of kappa index biomarker in multiple sclerosis diagnosis. Autoimmun Rev. 2019;18(1):43-49.
    OpenUrl
  10. 10.↵
    1. Senel M,
    2. Tumani H,
    3. Lauda F, et al.
    Cerebrospinal fluid immunoglobulin kappa light chain in clinically isolated syndrome and multiple sclerosis. PLoS One. 2014;9(4):e88680.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Presslauer S,
    2. Milosavljevic D,
    3. Huebl W, et al.
    Validation of kappa free light chains as a diagnostic biomarker in multiple sclerosis and clinically isolated syndrome: a multicenter study. Mult Scler. 2016;22(4):502-510.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Sanz Diaz CT,
    2. de Las Heras Flórez S,
    3. Carretero Perez M,
    4. Hernández Pérez MA,
    5. Martín García V
    . Evaluation of kappa index as a tool in the diagnosis of multiple sclerosis: implementation in routine screening procedure. Front Neurol. 2021;12:676527.
    OpenUrl
  13. 13.↵
    1. Senel M,
    2. Mojib-Yezdani F,
    3. Braisch U, et al.
    CSF free light chains as a marker of intrathecal immunoglobulin synthesis in multiple sclerosis: a blood-CSF barrier related evaluation in a large cohort. Front Immunol. 2019;10:641.
    OpenUrl
  14. 14.↵
    1. Saadeh RS,
    2. Bryant SC,
    3. McKeon A, et al.
    CSF kappa free light chains: cut-off validation for diagnosing multiple sclerosis. Mayo Clinic Proc. 2022;97(4):738-751.
    OpenUrl
  15. 15.↵
    1. Levraut M,
    2. Landes C,
    3. Mondot L, et al.
    Kappa free light chains, soluble interleukin-2 receptor, and interleukin-6 help explore patients presenting with brain white matter hyperintensities. Front Immunol. 2022;13:864133.
    OpenUrl
  16. 16.↵
    1. Duranti F,
    2. Pieri M,
    3. Centonze D,
    4. Buttari F,
    5. Bernardini S,
    6. Dessi M
    . Determination of kFLC and k index in cerebrospinal fluid: a valid alternative to assess intrathecal immunoglobulin synthesis. J Neuroimmunology. 2013;263(1-2):116-120.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Reiber H,
    2. Zeman D,
    3. Kušnierová P,
    4. Mundwiler E,
    5. Bernasconi L
    . Diagnostic relevance of free light chains in cerebrospinal fluid – the hyperbolic reference range for reliable data interpretation in quotient diagrams. Clinica Chim Acta. 2019;497:153-162.
    OpenUrl
  18. 18.↵
    1. Ferraro D,
    2. Trovati A,
    3. Bedin R, et al.
    Cerebrospinal fluid kappa and lambda free light chains in oligoclonal band-negative patients with suspected multiple sclerosis. Eur J Neurol. 2020;27(3):461-467.
    OpenUrl
  19. 19.↵
    1. Hannich MJ,
    2. Dressel A,
    3. Budde K,
    4. Petersmann A,
    5. Nauck M,
    6. Süße M
    . Kappa free light chains in the context of blood contamination, and other IgA- and IgM-related cerebrospinal fluid disease pattern. Cells. 2021;10(3):616.
    OpenUrl
  20. 20.↵
    1. Süße M,
    2. Feistner F,
    3. Holbe C, et al.
    Diagnostic value of kappa free light chains in patients with one isolated band in isoelectric focusing. Clinica Chim Acta. 2020;507:205-209.
    OpenUrl
  21. 21.↵
    1. Rathbone E,
    2. Durant L,
    3. Kinsella J, et al.
    Cerebrospinal fluid immunoglobulin light chain ratios predict disease progression in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2018;89(10):1044-1049.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    1. Menéndez-Valladares P,
    2. García-Sánchez MI,
    3. Cuadri Benítez P, et al.
    Free kappa light chains in cerebrospinal fluid as a biomarker to assess risk conversion to multiple sclerosis. Mult Scler J - Exp Translational Clin. 2015;1:205521731562093.
    OpenUrl
  23. 23.↵
    1. Berek K,
    2. Bsteh G,
    3. Auer M, et al.
    Kappa-free light chains in CSF predict early multiple sclerosis disease activity. Neurol Neuroimmunol Neuroinflamm. 2021;8(4):e1005.
    OpenUrlAbstract/FREE Full Text
  24. 24.↵
    1. Gaetani L,
    2. Di Carlo M,
    3. Brachelente G, et al.
    Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis. J Neuroimmunology. 2020;339:577108.
    OpenUrl
  25. 25.↵
    1. Schwenkenbecher P,
    2. Konen FF,
    3. Wurster U, et al.
    The persisting significance of oligoclonal bands in the dawning era of kappa free light chains for the diagnosis of multiple sclerosis. Int J Mol Sci. 2018;19(12):3796.
    OpenUrl
  26. 26.↵
    1. Süße M,
    2. Reiber H,
    3. Grothe M, et al.
    Free light chain kappa and the polyspecific immune response in MS and CIS – application of the hyperbolic reference range for most reliable data interpretation. J Neuroimmunology. 2020;346:577287.
    OpenUrl
  27. 27.↵
    1. Hegen H,
    2. Walde J,
    3. Milosavljevic D, et al.
    Free light chains in the cerebrospinal fluid. Comparison of different methods to determine intrathecal synthesis. Clin Chem Lab Med (Cclm). 2019;57(10):1574-1586.
    OpenUrl
  28. 28.↵
    1. Emersic A,
    2. Anadolli V,
    3. Krsnik M,
    4. Rot U
    . Intrathecal immunoglobulin synthesis: the potential value of an adjunct test. Clinica Chim Acta. 2019;489:109-116.
    OpenUrl
  29. 29.↵
    1. Rosenstein I,
    2. Rasch S,
    3. Axelsson M, et al.
    Kappa free light chain index as a diagnostic biomarker in multiple sclerosis: a real-world investigation. J Neurochem. 2021;159(3):618-628.
    OpenUrl
  30. 30.↵
    1. Süße M,
    2. Feistner F,
    3. Grothe M,
    4. Nauck M,
    5. Dressel A,
    6. Hannich MJ
    . Free light chains kappa can differentiate between myelitis and non-inflammatory myelopathy. Neurol Neuroimmunol Neuroinflamm. 2020;7(6):e892.
    OpenUrlAbstract/FREE Full Text
  31. 31.↵
    1. Konen FF,
    2. Schwenkenbecher P,
    3. Wurster U, et al.
    The influence of renal function impairment on kappa free light chains in cerebrospinal fluid. J Cent Nerv Syst Dis. 2021;13:117957352110421.
    OpenUrl
  32. 32.↵
    1. Teunissen C,
    2. Menge T,
    3. Altintas A, et al.
    Consensus definitions and application guidelines for control groups in cerebrospinal fluid biomarker studies in multiple sclerosis. Mult Scler. 2013;19(13):1802-1809.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. Moskowitz CS,
    2. Pepe MS
    . Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials. 2006;3(3):272-279.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Arrambide G,
    2. Espejo C,
    3. Carbonell-Mirabent P, et al.
    The kappa free light chain index and oligoclonal bands have a similar role in the McDonald criteria. Brain. 2022:awac220.
  35. 35.↵
    1. Filippi M,
    2. Preziosa P,
    3. Banwell BL, et al.
    Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain. 2019;142(7):1858-1875.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Solomon AJ,
    2. Naismith RT,
    3. Cross AH
    . Misdiagnosis of multiple sclerosis: impact of the 2017 McDonald criteria on clinical practice. Neurology. 2019;92(1):26-33.
    OpenUrlPubMed
  37. 37.↵
    1. Hegen H,
    2. Milosavljevic D,
    3. Schnabl C, et al.
    Cerebrospinal fluid free light chains as diagnostic biomarker in neuroborreliosis. Clin Chem Lab Med (Cclm). 2018;56(8):1383-1391.
    OpenUrl
  38. 38.↵
    1. Cavalla P,
    2. Caropreso P,
    3. Limoncelli S, et al.
    Kappa free light chains index in the differential diagnosis of multiple sclerosis from Neuromyelitis optica spectrum disorders and other immune-mediated central nervous system disorders. J Neuroimmunology. 2020;339:577122.
    OpenUrl
  39. 39.↵
    1. Greenfield AL,
    2. Dandekar R,
    3. Ramesh A, et al.
    Longitudinally persistent cerebrospinal fluid B cells can resist treatment in multiple sclerosis. JCI Insight. 2019;4(6):e126599.
    OpenUrl
  40. 40.↵
    1. Kowarik MC,
    2. Astling D,
    3. Lepennetier G, et al.
    Differential effects of fingolimod and natalizumab on B cell repertoires in multiple sclerosis patients. Neurotherapeutics. 2021;18(1):364-377.
    OpenUrl
  41. 41.↵
    1. Makshakov G,
    2. Nazarov V,
    3. Kochetova O,
    4. Surkova E,
    5. Lapin S,
    6. Evdoshenko E
    . Diagnostic and prognostic value of the cerebrospinal fluid concentration of immunoglobulin free light chains in clinically isolated syndrome with conversion to multiple sclerosis. PLoS One. 2015;10(11):e0143375.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Salavisa M,
    2. Paixão P,
    3. Ladeira AF, et al.
    Prognostic value of kappa free light chains determination in first-ever multiple sclerosis relapse. J Neuroimmunology. 2020;347:577355.
    OpenUrl
  43. 43.↵
    1. Hoedemakers RMJ,
    2. Pruijt JFM,
    3. Hol S, et al.
    Clinical comparison of new monoclonal antibody-based nephelometric assays for free light chain kappa and lambda to polyclonal antibody-based assays and immunofixation electrophoresis. Clin Chem Lab Med. 2011;50(3):489-495.
    OpenUrlPubMed
  44. 44.↵
    1. Messiaen AS,
    2. De Sloovere MMW,
    3. Claus PE, et al.
    Performance evaluation of serum free light chain analysis: nephelometry vs turbidimetry, monoclonal vs polyclonal reagents. Am J Clin Pathol. 2017;147(6):611-622.
    OpenUrl
  45. 45.↵
    1. Sasson SC,
    2. McGill K,
    3. Wienholt L, et al.
    Comparison of the Freelite serum free light chain (SFLC) assay with serum and urine electrophoresis/immunofixation and the N Latex FLC assay. Pathology. 2015;47(6):564-569.
    OpenUrl

Letters: Rapid online correspondence

No comments have been published for this article.
Comment

REQUIREMENTS

You must ensure that your Disclosures have been updated within the previous six months. Please go to our Submission Site to add or update your Disclosure information.

Your co-authors must send a completed Publishing Agreement Form to Neurology Staff (not necessary for the lead/corresponding author as the form below will suffice) before you upload your comment.

If you are responding to a comment that was written about an article you originally authored:
You (and co-authors) do not need to fill out forms or check disclosures as author forms are still valid
and apply to letter.

Submission specifications:

  • Submissions must be < 200 words with < 5 references. Reference 1 must be the article on which you are commenting.
  • Submissions should not have more than 5 authors. (Exception: original author replies can include all original authors of the article)
  • Submit only on articles published within 6 months of issue date.
  • Do not be redundant. Read any comments already posted on the article prior to submission.
  • Submitted comments are subject to editing and editor review prior to posting.

More guidelines and information on Disputes & Debates

Compose Comment

More information about text formats

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Author Information
NOTE: The first author must also be the corresponding author of the comment.
First or given name, e.g. 'Peter'.
Your last, or family, name, e.g. 'MacMoody'.
Your email address, e.g. [email protected]
Your role and/or occupation, e.g. 'Orthopedic Surgeon'.
Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.
Publishing Agreement
NOTE: All authors, besides the first/corresponding author, must complete a separate Publishing Agreement Form and provide via email to the editorial office before comments can be posted.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Vertical Tabs

You May Also be Interested in

Back to top
Advertisement

Related Articles

  • No related articles found.

Alert Me