Home Impact of renal function impairment on kappa free light chain index
Article Open Access

Impact of renal function impairment on kappa free light chain index

  • Martin Schmidauer ORCID logo , Fabian Föttinger , Klaus Berek , Michael Auer , Robert Barket , Franziska Di Pauli , Nik Krajnc , Markus Ponleitner , Anne Zinganell , Tobias Zrzavy , Florian Deisenhammer , Janette Walde , Gabriel Bsteh and Harald Hegen EMAIL logo
Published/Copyright: April 21, 2025
Download Article (PDF)
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 9

Abstract

Objectives

To investigate whether renal function impacts CSF κ-FLC concentration and/or κ-FLC index.

Methods

Patients with non-inflammatory neurological diseases were eligible. κ-FLC index was calculated as (CSF κ-FLC/serum κ-FLC)/albumin quotient. Structural equation modeling (SEM) was used to evaluate the direct influence of GFR on serum κ-FLC concentration and albumin quotient (Qalb), and via these two variables the indirect influence on CSF κ-FLC concentration.

Results

A total of 129 patients with a median age of 65 years and 42 % females were included. κ-FLC index ranged from 0.57 to 3.56 and glomerular filtration rate (GFR) ranged from 17 to 128 mL/min/1.73 m2. While a correlation of GFR with CSF κ-FLC concentration was observed (r= −0.52, p<0.001), there was no statistically significant correlation with κ-FLC index (r=0.14, p=0.113). SEM revealed that higher age was associated with lower GFR (β= −0.53), which led to higher serum κ-FLC concentration (β= −0.45) and higher Qalb (β= −0.17), while CSF κ-FLC concentration increased with serum κ-FLC concentration (β=0.75) and Qalb (β=0.39), indicating that GFR did not directly influence CSF κ-FLC concentration (RMSEA=0.043).

Conclusions

CSF κ-FLC concentration is not directly affected by renal function. The κ-FLC index compensates for renal function effects by factoring in serum κ-FLC concentration and Qalb. κ-FLC index can be interpreted without considering renal function.

Keywords: kappa free light chain; cerebrospinal fluid; renal function; creatinine; GFR

Introduction

κ-Free light chains (κ-FLC) in the cerebrospinal fluid (CSF) are an emerging biomarker in multiple sclerosis (MS). κ-FLC are produced by plasma cells in excess to intact immunoglobulins and accumulate in the intrathecal compartment in case of inflammatory diseases of the central nervous system, such as MS [1]. The detection of an intrathecal κ-FLC synthesis is considered as alternative to CSF-restricted oligoclonal bands (OCB) [2], as the κ-FLC index shows similar diagnostic accuracy [3] and has considerable advantages ranging from analytic aspects such as fast and rater-independent method [2] to conceptional aspects such as a metric variable with superior performance in early prognostication of MS disease course [4], 5].

When using biomarkers, potential confounders have to be considered, particularly within a routine clinical setting where patients are much more heterogenous than in clinical studies. Previous research reported that impaired renal function might affect κ-FLC and albumin concentrations not only in serum, but also in the CSF [6], 7]. κ-FLC are primarily cleared by renal elimination, consequently it is plausible that renal dysfunction could alter, at least indirectly, κ-FLC in the CSF. Due to the lack of conclusive evidence on the effect of renal function impairment on CSF κ-FLC concentration and/or the κ-FLC index, we undertook the present study [8].

Materials and methods

Patients and samples

Patients who were treated at the Departments of Neurology of the Medical University of Innsbruck (MUI) and the Medical University of Vienna (MUV), and had CSF and serum sampling for routine diagnostic purposes were screened applying the following criteria [9]: CSF red blood cell count ≤500/μL, CSF white blood cell count ≤10/μL, CSF total protein ≤1 g/L, absence of intrathecal IgG, IgM and IgA synthesis [10], and negative OCB [11]. Medical records of the remaining patients with available CSF and serum sample volume (to measure κ-FLC) as well as with results on serum creatinine were studied, and patients with non-inflammatory neurological diseases (NIND) and symptomatic controls (SC) according to the guidelines by the BioMS consortium [12], were included (Figure 1).

Figure 1: Inclusion of patients by various inclusion criteria. *According to Auer and Hegen formulae (Eur J Neurol. 2016 Apr;23 (4):713–21). CSF, cerebrospinal fluid; FLC, free light chain; GFR, glomerular filtration rate; Ig, immunoglobulin; NIND, non-inflammatory neurological disease; OCB, oligoclonal bands; RBC, red blood cell count; SC, symptomatic control; TP, total protein; WBC, white blood cell count.
Figure 1:

Inclusion of patients by various inclusion criteria. *According to Auer and Hegen formulae (Eur J Neurol. 2016 Apr;23 (4):713–21). CSF, cerebrospinal fluid; FLC, free light chain; GFR, glomerular filtration rate; Ig, immunoglobulin; NIND, non-inflammatory neurological disease; OCB, oligoclonal bands; RBC, red blood cell count; SC, symptomatic control; TP, total protein; WBC, white blood cell count.

CSF samples were collected by lumbar puncture. Serum samples were collected concomitantly within 30 min by venous puncture. All samples were centrifuged at 2000 g for 10 min at room temperature [12] before storage.

Objectives

To investigate whether renal function impacts CSF κ-FLC concentration and κ-FLC index.

Determination of albumin and κ-free light chains

Laboratory analysis was done at the Neuroimmunology Laboratory, Department of Neurology, Medical University of Innsbruck. Albumin and κ-FLC in CSF and serum were measured by nephelometry (Atellica; Siemens, Erlangen, Germany) using the N albumin and N Latex FLC kappa assay [13], 14], respectively, according to the manufacturer’s instructions.

Calculation of the κ-FLC index

The κ-FLC index was calculated using the following formula:

κ − FLC   index = κ  − FLC C S F   /   κ − FLC S e r u m Albumin C S F   /   Albumin S e r u m

Determination of renal function

Creatinine concentrations in serum had been measured for routine diagnostic purpose at the time of lumbar puncture. Creatinine concentrations were determined using an automated enzymatic assay (in patients recruited at the MUI), or via Jaffé method (in patients at the MUV) [15]. Glomerular filtration rate (GFR) was calculated using the 2021 CKD-EPI equation [16].

Statistical analysis

Categorical variables were expressed as frequencies and percentages, and continuous variables as median and 25th, 75th percentile or minimum and maximum, as appropriate. For group comparisons, the Mann-Whitney U-test was applied. Spearman correlation coefficient (r) was used for correlation analysis.

Structural equation modeling (SEM) was used to evaluate the direct influence of renal function on serum κ-FLC concentration and CSF/serum albumin quotient (Qalb), as well as its indirect impact on CSF κ-FLC concentration mediated via these two variables (Figure 2). Influence of age on renal function, and of sex and age on serum κ-FLC and Qalb were taken into account [17], 18]. Model quality was given by Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA) [19], 20].

Figure 2: Variables defining the structural equation model. CSF, cerebrospinal fluid; Qalb, CSF/serum albumin quotient; GFR, glomerular filtration rate.
Figure 2:

Variables defining the structural equation model. CSF, cerebrospinal fluid; Qalb, CSF/serum albumin quotient; GFR, glomerular filtration rate.

A p-value <0.05 was considered statistically significant. All statistical analyses were performed in R [21].

Ethics

The study was approved by the Ethics Committees of the Medical Universities of Innsbruck (approval number: 1050/2023) and Vienna (approval number: 1668/2023). We adhered to the declaration of Helsinki and national regulations during all study procedures.

Results

A total of 129 patients (42 % females, median age of 65 years) with NIND and SC were included into the study. The κ-FLC index ranged from 0.57 to 3.56. The median GFR was 88 mL/min/1.73 m2 ranging from 17 to 128 mL/min/1.73 m2. Further details on demographics and CSF findings are shown in Table 1. Diagnoses of patients are given in Supplemental Table S1.

Table 1:

Demographics and CSF findings.

Age (years) 65 (54–75)
Sex (female) 54 (42)
WBC, /mL 1 (1–2)
CSF albumin, g/L 0.25 (0.18–0.32)
Serum albumin, g/L 38.0 (34.0–41.2)
CSF κ-FLC, mg/L 0.17 (0.11–0.26)
Serum κ-FLC, mg/L 16.7 (12.6–22.3)
Qκ-FLC (×10−3) 10.1 (7.3–13.5)
Qalb (×10−3) 6.6 (4.8–8.7)
κ-FLC index 1.57 (1.35–1.81)
Creatinine, mg/L 9.1 (7.6–11.8)
GFR (mL/min/1.73 m2) 88 (58–100)

  1. Data are given as median (25th–75th percentile) and n (%), as appropriate. CSF, cerebrospinal fluid; Qalb, CSF/serum albumin quotient; GFR, glomerular filtration rate; Qκ-FLC, CSF/serum κ-FLC, quotient.

Correlation of albumin and κ-FLC with renal function

Lower GFR was associated with lower serum albumin (r=0.32, p<0.001, Figure 3A) but higher serum κ-FLC concentration (r= −0.59, p<0.001, Figure 3B). The GFR showed a statistically non-significant negative correlation with CSF albumin (r= −0.17, p=0.056, Figure 3C) and a negative correlation with CSF κ-FLC concentration (r= −0.52, p<0.001, Figure 3D). Lower GFR was associated with higher Qalb (r= −0.25, p=0.004, Figure 3E) while the correlation between GFR and Qκ-FLC did not reach statistical significance (r= −0.16, p=0.067, Figure 3F). The κ-FLC index was not statistically significantly correlated with GFR (r=0.14, p=0.113, Figure 4).

Figure 3: Correlations between renal function and various CSF/serum parameters. CSF, cerebrospinal fluid; FLC, free light chain; GFR, glomerular filtration rate.
Figure 3:

Correlations between renal function and various CSF/serum parameters. CSF, cerebrospinal fluid; FLC, free light chain; GFR, glomerular filtration rate.

Figure 4: Lacking correlation between renal function and κ-FLC index. FLC, free light chain; GFR, glomerular filtration rate.
Figure 4:

Lacking correlation between renal function and κ-FLC index. FLC, free light chain; GFR, glomerular filtration rate.

Higher patients’ age was correlated with lower GFR (r= −0.56, p<0.001, Supplemental Figure S1A). The CSF and serum albumin and κ-FLC concentrations as well as the Qalb and Qκ-FLC showed correlations with age as shown in Supplemental Figure S1B–G. There was no correlation between κ-FLC index and age (r= −0.020, p=0.817). Differences due to sex were generally small and are shown in Supplemental Figure S2. Male patients showed higher albumin concentrations in CSF and serum than female patients, while κ-FLC concentrations in CSF and serum as well as Qalb, Qκ-FLC and the κ-FLC index were similar. As supplemental, correlation analyses of the various CSF/serum parameters and of the κ-FLC index with serum creatinine are provided in Supplemental Figure S3 and Figure S4. Sensitivity analyses including only patients with chronic renal dysfunction (Supplemental Figure S5 and Figure S6) revealed qualitatively the same results.

Structural equation modeling

To decipher the connections – and not just correlations – between the variables forming the κ-FLC index, i.e., CSF and serum κ-FLC concentration as well as Qalb, and GFR, as well as patient-specific variables such as age and sex, we utilized structural equation modeling (Figure 2).

We confirmed that age was connected to GFR (β= −0.53, p<0.001, step 1). GFR influenced serum κ-FLC concentration (β= −0.45, p<0.001). There was also a negative but statistically non-significant correlation between GFR and Qalb (β= −0.17, p=0.07). Age determined Qalb levels (β=0.33, p=<0.001), and females tended to have lower Qalb levels compared to males (β=−0.30, p=0.066) (step 2).

We observed that Qalb (β=0.39, p<0.001) as well as serum κ-FLC concentration (β=0.75, p<0.001) determined CSF κ-FLC concentration (step 3).

The statistical significance of the connections of the variables as shown in Figure 2 was assured by a CFI of 0.996 and RMSEA of 0.043 showing that GFR was only indirectly related to CSF κ-FLC concentration (Table 2).

Table 2:

Results of the structural equation model showing the causal connection between the variables.

Estimate Standard error p-Value
CSF κ-FLC concentration
 Serum κ-FLC concentration 0.746 0.040 <0.001
 Qalb 0.393 0.040 <0.001
Serum κ-FLC concentration
 GFR −0.447 0.092 <0.001
 Age 0.048 0.093 0.608
 Sex −0.191 0.159 0.230
Qalb
 GFR −0.169 0.093 0.070
 Age 0.326 0.094 <0.001
 Sex −0.298 0.162 0.066
GFR
 Age −0.531 0.075 <0.001

  1. CSF, cerebrospinal fluid; FLC, free light chain; GFR, glomerular filtration rate; Qalb, CSF/serum albumin quotient.

As supplemental, results of SEM using serum creatinine are provided in Supplemental Table S2. Sensitivity analysis including only patients with chronic renal dysfunction (Supplemental Table S3) revealed qualitatively the same results.

Discussion

In this study, we aimed to determine whether renal function impairment influences CSF κ-FLC concentration and/or the κ-FLC index. We provide evidence that renal impairment has no direct impact on κ-FLC in the CSF. Utilizing SEM, we could show that i) renal function impacts both serum κ-FLC concentration and Qalb, and ii) that this impact is mediated indirectly via serum κ-FLC concentration and Qalb on CSF κ-FLC concentration (Table 2 and Supplemental Table S2). This explains why κ-FLC index does not significantly change with renal impairment. Thus, κ-FLC index can interpreted without considering renal function (Figure 4, Supplemental Figure S4).

Under physiological conditions, κ-FLC in the CSF compartment stem exclusively from passive transportation from blood via diffusion. Thus, CSF κ-FLC levels are dependent on both the amount of serum κ-FLC as well as the blood-CSF-barrier (BCB) function [22]; the latter is described by the well-established age-dependent Qalb [23]. Thus, it is recommended to use the κ-FLC index to account for these influences [2].

Regarding the impact of renal function, we confirmed some previous findings. We observed that patients with impaired renal function showed lower serum albumin concentrations [6]. This is attributed to several interrelated factors. Under healthy conditions, only minimal amounts of large-molecular weight proteins such as albumin pass through the glomerular filtration barrier, where they are reabsorbed in the proximal tubules. However, in chronically damaged kidneys, the glomerular barrier becomes leaky, resulting in albumin loss that exceeds the reuptake capacity of the proximal tubules (proteinuria) [6], 24]. Additionally, disruptions in protein metabolism (reduced albumin synthesis, increased catabolism) and overall homeostasis (loss of albumin-binding capacity) may contribute to this phenomenon [24].

Impaired renal function was also associated with significantly increased serum κ-FLC concentration. This is primarily due to decreased renal clearance [7], 8], 17]. In contrast to large molecule albumin, κ-FLC as low-molecular-weight proteins are filtered almost freely through the glomeruli and reabsorbed by the proximal tubules [25]. In case of lower GFR, reduced filtration from the bloodstream leads to increase of κ-FLC in serum [26], whereas a damaged glomerular filtration barrier plays no relevant role [24], 25].

CSF concentrations of albumin and of κ-FLC were higher in patients with decreased renal function – at least at first sight, by univariate analysis [8]. However, this might be the result of higher age in patients with renal impairment. It is well-known that older people show higher CSF protein levels [27] due to decreased CSF flow rate [22].

This means that with regard to the Qalb, there are two influencing factors, i.e. an effect of patients’ age via CSF albumin [23] as well as of decreased renal function via serum albumin. Besides that, males showed slightly higher Qalb than females which is in line with previous studies that observed a sex-effect for CSF protein levels [28], 18].

Due to these complex direct and indirect effects on CSF κ-FLC, we utilized SEM which allows to determine whether different variables are causally linked – and not just simply correlated as for example by regression analyses. As discussed above, the assumptions underlying this model are based on established, consecutive, (patho)physiological mechanisms driving CSF κ-FLC levels (Figure 2). As renal function is supposed to be relatively stable (at least in chronic kidney disease) in an individual (e.g., at a reduced level), the effects on serum κ-FLC (e.g., an elevation) and serum albumin concentrations (e.g., a decrease) are indirectly passed on CSF κ-FLC. Furthermore, we considered effects of age and sex in the SEM, as these are independent of renal function (e.g., on Qalb). We provide evidence that age was connected to GFR (step 1) and that GFR influenced serum κ-FLC concentration and to some extent Qalb; furthermore, that age and to some extent sex determined Qalb (step 2), and that finally Qalb and serum κ-FLC concentration determined CSF κ-FLC concentration (step 3). This implicates that there is no direct effect of renal function on κ-FLC in the CSF, but rather indirect effects which are mediated via Qalb and serum κ-FLC concentration. This connection explains why overall κ-FLC index was not affected by GFR (Figure 2), as this term (as shown in the methods section) includes both Qalb and serum κ-FLC. As a consequence, κ-FLC index can be interpreted without considering renal function.

So far, there is only one study that investigated the impact of renal function on κ-FLC index reporting – contrary to our findings – a possible impact of GFR [8]. While the cohort of 139 patients with “physiological CSF” profile is principally comparable to our cohort, the reported overall correlation of κ-FLC index and GFR was weak. In patients below 60 years, the authors did not observe any difference in κ-FLC index between those with normal and impaired renal function. In the group of patients above 60 years, the reported difference was based on the comparison of groups with only 15 patients, instead of using the whole cohort in a model. Furthermore, the use of an age cut-off of 60 years was arbitrary [8].

Here, we provide further evidence on the robustness of the κ-FLC index. The κ-FLC index as biomarker for intrathecal immunoglobulin synthesis has been introduced in clinical routine and will also be implemented in the 2024 revised MS diagnostic criteria as an alternative to OCB. Whether the κ-FLC index will replace OCB or will be implemented in a two-step approach as a screening tool, e.g., screening with the κ-FLC index followed by OCB testing in borderline cases, has still to be determined [2]. The German Society for Cerebrospinal Fluid Diagnostics and Clinical Neurochemistry highlighted κ-FLC and OCB as complementary markers due to their unique strengths and limitations and suggested using both in routine diagnostic work-up [29].

There are some limitations of the present study. It was a retrospective study with all inherent attributes, e.g., the inclusion of patients dependent on the availability of CSF and serum volume. A general limitation obtaining normal CSF is the fact that a reasonable number of healthy volunteers is practically impossible to recruit. By selecting patients with NIND and SC without relevant blood-CSF barrier dysfunction (95th percentile of Qalb was 13.1) we are confident that the population was appropriate to answer the question whether renal function impacts CSF κ-FLC and κ-FLC index. Patients with inflammatory neurological diseases had to be excluded as this would have biased the results (due to stark inter-individual difference in intrathecal κ-FLC synthesis). As most of the patients with decreased renal function showed chronic kidney dysfunction, we have to state that our findings cannot be extrapolated to patients with acute kidney failure. Furthermore, renal function (i.e., serum creatinine) was determined by different methods (enzymatic assay at MUI, Jaffé method at MUV). However, the vast majority (91 %) were retrieved from the MUI and sensitivity analysis (Figure S7, Figure S8, Table S4) reveal similar results. With an RBC count of <500/mL, blood contamination did not impact our results. Finally, it needs to be acknowledged that our results cannot be extrapolated to patients with significant blood-CSF barrier dysfunction. Further studies are needed to address this research question.

Altogether, we provide evidence that renal impairment has no direct impact on CSF κ-FLC levels. The κ-FLC index can be reliably interpreted in patients with non-inflammatory neurological diseases irrespective of renal function.

Corresponding author: Harald Hegen, PD, MD, PhD, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria, E-mail:
Gabriel Bsteh and Harald Hegen contributed equally to this work.

  1. Research ethics: The study was approved by the Ethics Committees of the Medical Universities of Innsbruck (approval number: 1050/2023) and Vienna (approval number: 1668/2023). We adhered to the declaration of Helsinki and national regulations during all study procedures.

  2. Informed consent: Informed consent was obtained from all participants.

  3. Author contributions: MS: Drafting the manuscript, acquisition of data, interpretation of data. FF: Acquisition of data, revision of the manuscript for content. KB: Revision of the manuscript for content. MA: Revision of the manuscript for content. RB: Revision of the manuscript for content. FDP: Revision of the manuscript for content. NK: Acquisition of data, revision of the manuscript for content. MP: Revision of the manuscript for content. AZ: Revision of the manuscript for content. TZ: Revision of the manuscript for content. FD: Revision of the manuscript for content. JW: Statistical analysis and interpretation of data, revision of the manuscript for content. GB: Revision of the manuscript for content. HH: Drafting the manuscript, study concept, statistical analysis and interpretation of data. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: Martin Schmidauer has participated in meetings sponsored by or received travel grants from Novartis, Sanofi-Genzyme and Amgen. Fabian Föttinger has participated in meetings sponsored by, received speaker honoraria and travel funding from Novartis. Klaus Berek has participated in meetings sponsored by and received travel funding or speaker honoraria from Roche, Teva, Merck, Biogen, Sanofi and Novartis. He is associate editor of Frontiers in Immunology / Neurology, Section Multiple Sclerosis and Neuroimmunology. Michael Auer has received speaker honoraria and/or travel grants from Biogen, Merck, Novartis, and Sanofi Genzyme, Horizon Therapeutics/Amgen and Zentiva. Robert Barket has participated in meetings sponsored by or received travel grants from Novartis, Janssen-Cilag, and Sanofi-Genzyme. He received honoraria from Janssen-Cilag and Biogen. Franziska Di Pauli has participated in meetings sponsored by, received honoraria (lectures, advisory boards, consultations), or travel funding from Bayer, Biogen, Celgene BMS, Merck, Novartis, Sanofi-Genzyme, Teva, and Roche. Her institution has received research grants from Roche. Nik Krajnc has participated in meetings sponsored by, received speaker honoraria or travel funding from Alexion, BMS/Celgene, Janssen, Merck, Novartis, Roche, and Sanofi. He held a grant for a Multiple Sclerosis Clinical Training Fellowship Programme from the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). Markus Ponleitner has received speaker or consulting honoraria from Amicus and Novartis and participated in meetings sponsored by and received travel funding from Amicus, Merck, Novartis and Sanofi-Genzyme. Anne Zinganell has participated in meetings sponsored by, received speaking honoraria or travel funding from Biogen, Merck, Novartis, Sanofi-Genzyme, Janssen, Bristol Myers Squibb and Teva. Tobias Zrzavy has participated in meetings sponsored by or received travel funding from Biogen, Merck, Novartis, Roche, and Teva. Florian Deisenhammer has participated in meetings sponsored by or received honoraria for acting as an advisor/speaker for Alexion, Almirall, Biogen, Celgene, Merck, Novartis, Roche, and Sanofi-Genzyme. His institution received scientific grants from Biogen and Sanofi-Genzyme. Janette Walde has nothing to disclose. Gabriel Bsteh has participated in meetings sponsored by, received speaker honoraria or travel funding from Biogen, Celgene/BMS, Janssen, Lilly, Medwhizz, Merck, Novartis, Roche, Sanofi-Genzyme and Teva, and received honoraria for consulting Adivo Associates, Biogen, Celgene/BMS, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva. He has received unrestricted research grants from Celgene/BMS and Novartis. He serves on the Executive Committee Member of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). Harald Hegen has participated in meetings sponsored by, received speaker honoraria or travel funding from Bayer, Biogen, Bristol Myers Squibb, Horizon, Janssen, Merck, Novartis, Sanofi-Genzyme, Siemens, Teva, and received honoraria for acting as consultant for Biogen, Bristol Myers Squibb, Novartis, Roche, Sanofi-Genzyme, and Teva.

  6. Research funding: This study was funded by Novartis.

  7. Data availability: Data available on request from the authors.

References

1. Hegen, H, Berek, K, Deisenhammer, F. Cerebrospinal fluid kappa free light chains as biomarker in multiple sclerosis-from diagnosis to prediction of disease activity. Wien Med Wochenschr 1946. 2022;172:337–45. https://doi.org/10.1007/s10354-022-00912-7.Search in Google Scholar PubMed PubMed Central

2. Cerebrospinal fluid kappa free light chains for the diagnosis of multiple sclerosis: a consensus statement – Harald Hegen, Georgina Arrambide, Sharmilee Gnanapavan, Batia Kaplan, Michael Khalil, Ruba Saadeh, Charlotte Teunissen, Hayrettin Tumani, Luisa Maria Villar, Maria Alice V Willrich, Henrik Zetterberg, Florian Deisenhammer, 2023. Available from: https://journals.sagepub.com/doi/full/10.1177/13524585221134217?rfr_dat=cr_pub++0pubmed&url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org.Search in Google Scholar

3. Hegen, H, Walde, J, Berek, K, Arrambide, G, Gnanapavan, S, Kaplan, B, et al.. Cerebrospinal fluid kappa free light chains for the diagnosis of multiple sclerosis: a systematic review and meta-analysis. Mult Scler J 2023;29:169–81. https://doi.org/10.1177/13524585221134213.Search in Google Scholar PubMed PubMed Central

4. Berek, K, Bsteh, G, Auer, M, Di Pauli, F, Grams, A, Milosavljevic, D, et al.. Kappa-free light chains in CSF predict early multiple sclerosis disease activity. Neurol Neuroimmunol Neuroinflammation 2021;8:e1005. https://doi.org/10.1212/nxi.0000000000001005.Search in Google Scholar

5. Hegen, H, Berek, K, Bsteh, G, Auer, M, Altmann, P, Di Pauli, F, et al.. Kappa free light chain and neurofilament light independently predict early multiple sclerosis disease activity-a cohort study. EBioMedicine 2023;91:104573. https://doi.org/10.1016/j.ebiom.2023.104573.Search in Google Scholar PubMed PubMed Central

6. Lang, J, Katz, R, Ix, JH, Gutierrez, OM, Peralta, CA, Parikh, CR, et al.. Association of serum albumin levels with kidney function decline and incident chronic kidney disease in elders. Nephrol Dial Transplant Off Publ Eur Dial Transpl Assoc – Eur Ren Assoc 2018;33:986–92. https://doi.org/10.1093/ndt/gfx229.Search in Google Scholar PubMed PubMed Central

7. Jenner, E. Serum free light chains in clinical laboratory diagnostics. Clin Chim Acta 2014;427:15–20. https://doi.org/10.1016/j.cca.2013.08.018.Search in Google Scholar PubMed

8. Konen, FF, Schwenkenbecher, P, Wurster, U, Jendretzky, KF, Möhn, N, Gingele, S, et al.. The influence of renal function impairment on kappa free light chains in cerebrospinal fluid. J Cent Nerv Syst Dis 2021;13:11795735211042166. https://doi.org/10.1177/11795735211042166..Search in Google Scholar PubMed PubMed Central

9. Teunissen, C, Menge, T, Altintas, A, Álvarez-Cermeño, JC, Bertolotto, A, Berven, FS, et al.. Consensus definitions and application guidelines for control groups in cerebrospinal fluid biomarker studies in multiple sclerosis. Mult Scler Houndmills Basingstoke Engl 2013;19:1802–9. https://doi.org/10.1177/1352458513488232.Search in Google Scholar PubMed

10. Auer, M, Hegen, H, Zeileis, A, Deisenhammer, F. Quantitation of intrathecal immunoglobulin synthesis – a new empirical formula. Eur J Neurol 2016;23:713–21. https://doi.org/10.1111/ene.12924.Search in Google Scholar PubMed

11. Hegen, H, Zinganell, A, Auer, M, Deisenhammer, F. The clinical significance of single or double bands in cerebrospinal fluid isoelectric focusing. A retrospective study and systematic review. PLoS One 2019;14:e0215410. https://doi.org/10.1371/journal.pone.0215410.Search in Google Scholar PubMed PubMed Central

12. Teunissen, CE, Petzold, A, Bennett, JL, Berven, FS, Brundin, L, Comabella, M, et al.. A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 2009;73:1914–22. https://doi.org/10.1212/wnl.0b013e3181c47cc2.Search in Google Scholar PubMed PubMed Central

13. Hoedemakers, RMJ, Pruijt, JFM, Hol, S, Teunissen, E, Martens, H, Stam, P, 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;50:489–95. https://doi.org/10.1515/cclm.2011.793.Search in Google Scholar PubMed

14. Velthuis, HT, Knop, I, Stam, P, van den Broek, M, Bos, HK, Hol, S, et al.. N Latex FLC – new monoclonal high-performance assays for the determination of free light chain kappa and lambda. Clin Chem Lab Med 2011;49:1323–32. https://doi.org/10.1515/cclm.2011.624.Search in Google Scholar PubMed

15. Mazzachi, BC, Peake, MJ, Ehrhardt, V. Reference range and method comparison studies for enzymatic and Jaffé creatinine assays in plasma and serum and early morning urine. Clin Lab 2000;46:53–5.Search in Google Scholar

16. Inker, LA, Eneanya, ND, Coresh, J, Tighiouart, H, Wang, D, Sang, Y, et al.. New creatinine- and Cystatin C–based equations to estimate GFR without race. N Engl J Med 2021;385:1737–49. https://doi.org/10.1056/nejmoa2102953.Search in Google Scholar

17. Long, TE, Indridason, OS, Palsson, R, Rognvaldsson, S, Love, TJ, Thorsteinsdottir, S, et al.. Defining new reference intervals for serum free light chains in individuals with chronic kidney disease: results of the iStopMM study. Blood Cancer J 2022;12:133. https://doi.org/10.1038/s41408-022-00732-3.Search in Google Scholar PubMed PubMed Central

18. McCudden, CR, Brooks, J, Figurado, P, Bourque, PR. Cerebrospinal fluid total protein reference intervals derived from 20 years of patient data. Clin Chem 2017;63:1856–65. https://doi.org/10.1373/clinchem.2017.278267.Search in Google Scholar PubMed

19. Hu, L, Bentler, PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 1999;6:1–55. https://doi.org/10.1080/10705519909540118.Search in Google Scholar

20. MacCallum, RC, Browne, MW, Sugawara, HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1996;1:130–49. https://doi.org/10.1037/1082-989x.1.2.130.Search in Google Scholar

21. RStudio Team. RStudio: integrated development for R. RStudio. Boston, MA: PBC; 2020. Available from: http://www.rstudio.com/.Search in Google Scholar

22. Reiber, H. Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta 2001;310:173–86. https://doi.org/10.1016/s0009-8981(01)00573-3.Search in Google Scholar PubMed

23. Hegen, H, Auer, M, Zeileis, A, Deisenhammer, F. Upper reference limits for cerebrospinal fluid total protein and albumin quotient based on a large cohort of control patients: implications for increased clinical specificity. Clin Chem Lab Med 2016;54:285–92. https://doi.org/10.1515/cclm-2015-0253.Search in Google Scholar PubMed

24. Albumin uptake and processing by the proximal tubule: physiological, pathological, and therapeutic implications [Internet]. Available from: https://journals.physiology.org/doi/epdf/10.1152/physrev.00014.2021.Search in Google Scholar

25. Desjardins, L, Liabeuf, S, Lenglet, A, Lemke, HD, Vanholder, R, Choukroun, G, et al.. Association between free light chain levels, and disease progression and mortality in chronic kidney disease. Toxins 2013;5:2058–73. https://doi.org/10.3390/toxins5112058.Search in Google Scholar PubMed PubMed Central

26. Miettinen, TA, Kekki, M. Effect of impaired hepatic and renal function on [131] bence jones protein catabolism in human subjects. Clin Chim Acta 1967;18:395–407. https://doi.org/10.1016/0009-8981(67)90036-8.Search in Google Scholar

27. Garton, MJ, Keir, G, Lakshmi, MV, Thompson, EJ. Age-related changes in cerebrospinal fluid protein concentrations. J Neurol Sci 1991;104:74–80. https://doi.org/10.1016/0022-510x(91)90218-v.Search in Google Scholar PubMed

28. Castellazzi, M, Morotti, A, Tamborino, C, Alessi, F, Pilotto, S, Baldi, E, et al.. Increased age and male sex are independently associated with higher frequency of blood–cerebrospinal fluid barrier dysfunction using the albumin quotient. Fluids Barriers CNS 2020;17:14. https://doi.org/10.1186/s12987-020-0173-2.Search in Google Scholar PubMed PubMed Central

29. Konen, FF, Wurster, U, Schwenkenbecher, P, Gerritzen, A, Groß, CC, Eichhorn, P, et al.. Oligoclonal bands and kappa free light chains: competing parameters or complementary biomarkers? Autoimmun Rev 2025;24:103765. https://doi.org/10.1016/j.autrev.2025.103765.Search in Google Scholar PubMed

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0007).

Received: 2025-01-03
Accepted: 2025-04-08
Published Online: 2025-04-21
Published in Print: 2025-08-26

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Macroprolactinaemia – some progress but still an ongoing problem
  4. Review
  5. Understanding the circulating forms of cardiac troponin: insights for clinical practice
  6. Opinion Papers
  7. New insights in preanalytical quality
  8. IFCC recommendations for internal quality control practice: a missed opportunity
  9. Genetics and Molecular Diagnostics
  10. Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models
  11. General Clinical Chemistry and Laboratory Medicine
  12. Pre-analytical phase errors constitute the vast majority of errors in clinical laboratory testing
  13. Improving the efficiency of quality control in clinical laboratory with an integrated PBRTQC system based on patient risk
  14. IgA-type macroprolactin among 130 patients with macroprolactinemia
  15. Prevalence and re-evaluation of macroprolactinemia in hyperprolactinemic patients: a retrospective study in the Turkish population
  16. Defining dried blood spot diameter: implications for measurement and specimen rejection rates
  17. Screening primary aldosteronism by plasma aldosterone-to-angiotensin II ratio
  18. Assessment of serum free light chain measurements in a large Chinese chronic kidney disease cohort: a multicenter real-world study
  19. Beyond the Hydrashift assay: the utility of isoelectric focusing for therapeutic antibody and paraprotein detection
  20. Direct screening and quantification of monoclonal immunoglobulins in serum using MALDI-TOF mass spectrometry without antibody enrichment
  21. Effect of long-term frozen storage on stability of kappa free light chain index
  22. Impact of renal function impairment on kappa free light chain index
  23. Standardization challenges in antipsychotic drug monitoring: insights from a national survey in Chinese TDM practices
  24. Potential coeliac disease in children: a single-center experience
  25. Vitamin D metabolome in preterm infants: insights into postnatal metabolism
  26. Candidate Reference Measurement Procedures and Materials
  27. Development of commutable candidate certified reference materials from protein solutions: concept and application to human insulin
  28. Reference Values and Biological Variations
  29. Biological variation of serum cholinesterase activity in healthy subjects
  30. Hematology and Coagulation
  31. Diagnostic performance of morphological analysis and red blood cell parameter-based algorithms in the routine laboratory screening of heterozygous haemoglobinopathies
  32. Cancer Diagnostics
  33. Promising protein biomarkers for early gastric cancer: clinical performance of combined detection
  34. Infectious Diseases
  35. The accuracy of presepsin in diagnosing neonatal late-onset sepsis in critically ill neonates: a prospective study
  36. Corrigendum
  37. The Unholy Grail of cancer screening: or is it just about the Benjamins?
  38. Letters to the Editor
  39. Analytical validation of hemolysis detection on GEM Premier 7000
  40. Reconciling reference ranges and clinical decision limits: the case of thyroid stimulating hormone
  41. Contradictory definitions give rise to demands for a right to unambiguous definitions
  42. Biomarkers to measure the need and the effectiveness of therapeutic supplementation: a critical issue
https://doi.org/10.1515/cclm-2025-0007
Keywords for this article
kappa free light chain; cerebrospinal fluid; renal function; creatinine; GFR
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Macroprolactinaemia – some progress but still an ongoing problem
  4. Review
  5. Understanding the circulating forms of cardiac troponin: insights for clinical practice
  6. Opinion Papers
  7. New insights in preanalytical quality
  8. IFCC recommendations for internal quality control practice: a missed opportunity
  9. Genetics and Molecular Diagnostics
  10. Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models
  11. General Clinical Chemistry and Laboratory Medicine
  12. Pre-analytical phase errors constitute the vast majority of errors in clinical laboratory testing
  13. Improving the efficiency of quality control in clinical laboratory with an integrated PBRTQC system based on patient risk
  14. IgA-type macroprolactin among 130 patients with macroprolactinemia
  15. Prevalence and re-evaluation of macroprolactinemia in hyperprolactinemic patients: a retrospective study in the Turkish population
  16. Defining dried blood spot diameter: implications for measurement and specimen rejection rates
  17. Screening primary aldosteronism by plasma aldosterone-to-angiotensin II ratio
  18. Assessment of serum free light chain measurements in a large Chinese chronic kidney disease cohort: a multicenter real-world study
  19. Beyond the Hydrashift assay: the utility of isoelectric focusing for therapeutic antibody and paraprotein detection
  20. Direct screening and quantification of monoclonal immunoglobulins in serum using MALDI-TOF mass spectrometry without antibody enrichment
  21. Effect of long-term frozen storage on stability of kappa free light chain index
  22. Impact of renal function impairment on kappa free light chain index
  23. Standardization challenges in antipsychotic drug monitoring: insights from a national survey in Chinese TDM practices
  24. Potential coeliac disease in children: a single-center experience
  25. Vitamin D metabolome in preterm infants: insights into postnatal metabolism
  26. Candidate Reference Measurement Procedures and Materials
  27. Development of commutable candidate certified reference materials from protein solutions: concept and application to human insulin
  28. Reference Values and Biological Variations
  29. Biological variation of serum cholinesterase activity in healthy subjects
  30. Hematology and Coagulation
  31. Diagnostic performance of morphological analysis and red blood cell parameter-based algorithms in the routine laboratory screening of heterozygous haemoglobinopathies
  32. Cancer Diagnostics
  33. Promising protein biomarkers for early gastric cancer: clinical performance of combined detection
  34. Infectious Diseases
  35. The accuracy of presepsin in diagnosing neonatal late-onset sepsis in critically ill neonates: a prospective study
  36. Corrigendum
  37. The Unholy Grail of cancer screening: or is it just about the Benjamins?
  38. Letters to the Editor
  39. Analytical validation of hemolysis detection on GEM Premier 7000
  40. Reconciling reference ranges and clinical decision limits: the case of thyroid stimulating hormone
  41. Contradictory definitions give rise to demands for a right to unambiguous definitions
  42. Biomarkers to measure the need and the effectiveness of therapeutic supplementation: a critical issue
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 16.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2025-0007/html
Scroll to top button