ArticlePDF AvailableLiterature Review

The Use of Kappa Free Light Chains to Diagnose Multiple Sclerosis

Authors:
Borros Arneth at Justus-Liebig-Universität Gießen
Borros Arneth
Jörg Kraus
Jörg Kraus
Jörg Kraus
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Download full-text PDF
Read full-text
Download citation

Abstract and Figures

Background: The positive implications of using free light chains in diagnosing multiple sclerosis have increasingly gained considerable interest in medical research and the scientific community. It is often presumed that free light chains, particularly kappa and lambda free light chains, are of practical use and are associated with a higher probability of obtaining positive results compared to oligoclonal bands. The primary purpose of the current paper was to conduct a systematic review to assess the up-to-date methods for diagnosing multiple sclerosis using kappa and lambda free light chains. Method: An organized literature search was performed across four electronic sources, including Google Scholar, Web of Science, Embase, and MEDLINE. The sources analyzed in this systematic review and meta-analysis comprise randomized clinical trials, prospective cohort studies, retrospective studies, controlled clinical trials, and systematic reviews. Results: The review contains 116 reports that includes 1204 participants. The final selection includes a vast array of preexisting literature concerning the study topic: 35 randomized clinical trials, 21 prospective cohort studies, 19 retrospective studies, 22 controlled clinical trials, and 13 systematic reviews. Discussion: The incorporated literature sources provided integral insights into the benefits of free light chain diagnostics for multiple sclerosis. It was also evident that the use of free light chains in the diagnosis of clinically isolated syndrome (CIS) and multiple sclerosis is relatively fast and inexpensive in comparison to other conventional state-of-the-art diagnostic methods, e.g., using oligoclonal bands (OCBs).
Figures - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
Medicina2022,58,1512.https://doi.org/10.3390/medicina58111512www.mdpi.com/journal/medicina
Review
TheUseofKappaFreeLightChainstoDiagnose
MultipleSclerosis
BorrosArneth
1,
*andJörgKraus
2,3
1
InstituteofLaboratoryMedicineandPathobiochemistry,MolecularDiagnostics,JustusLiebigUniversity,
Feulgenstr.12,35392Giessen,Germany
2
DepartmentofLaboratoryMedicine,ParacelsusMedicalUniversityandSalzburgerLandeskliniken,
Strubergasse21,5020Salzburg,Austria
3
DepartmentofNeurology,MedicalFaculty,Heinrich‐Heine‐University,Düsseldorf,BergischeLandstraße2,
40629Düsseldorf,Germany
*Correspondence:borros.arneth@klinchemie.med.uni‐giessen.de
Abstract:Background:Thepositiveimplicationsofusingfreelightchainsindiagnosingmultiple
sclerosishaveincreasinglygainedconsiderableinterestinmedicalresearchandthescientific
community.Itisoftenpresumedthatfreelightchains,particularlykappaandlambdafreelight
chains,areofpracticaluseandareassociatedwithahigherprobabilityofobtainingpositiveresults
comparedtooligoclonalbands.Theprimarypurposeofthecurrentpaperwastoconductasys‐
tematicreviewtoassesstheup‐to‐datemethodsfordiagnosingmultiplesclerosisusingkappaand
lambdafreelightchains.Method:Anorganizedliteraturesearchwasperformedacrossfourelec‐
tronicsources,includingGoogleScholar,WebofScience,Embase,andMEDLINE.Thesources
analyzedinthissystematicreviewandmeta‐analysiscompriserandomizedclinicaltrials,pro‐
spectivecohortstudies,retrospectivestudies,controlledclinicaltrials,andsystematicreviews.
Results:Thereviewcontains116reportsthatincludes1204participants.Thefinalselectionincludes
avastarrayofpreexistingliteratureconcerningthestudytopic:35randomizedclinicaltrials,21
prospectivecohortstudies,19retrospectivestudies,22controlledclinicaltrials,and13systematic
reviews.Discussion:Theincorporatedliteraturesourcesprovidedintegralinsightsintothebenefits
offreelightchaindiagnosticsformultiplesclerosis.Itwasalsoevidentthattheuseoffreelight
chainsinthediagnosisofclinicallyisolatedsyndrome(CIS)andmultiplesclerosisisrelativelyfast
andinexpensiveincomparisontootherconventionalstate‐of‐the‐artdiagnosticmethods,e.g.,
usingoligoclonalbands(OCBs).
Keywords:multiplesclerosis;clinicalisolatedsyndrome;freelightchains;kappafreelightchains
1.Introduction
Withinthepastdecade,avastarrayofpapersandempiricalsourceshaveattempted
toofferaninclusiveoverviewoftheuseofimmunoglobulinfreelightchainsincere‐
brospinalfluid(CSF)toaidinthediagnosisofnovelmultiplesclerosis.Multiplesclerosis
isatypicalneuroinflammatoryandneurodegenerativeconditionassociatedwiththe
centralnervoussystem(CNS)[1].Itsetiologyatthispointisunclear;however,thesig‐
nificantpathologyinvolvesautoimmunemultifocalmyelinobliterationacrosstheen‐
tiretyoftheCNS.
Intrathecalkappa‐FreeLightChains(κ‐FLC)synthesishassimilardiagnosticaccu‐
racytothewell‐establishedmethodofCSF‐restrictedoligoclonalbands(OCB)toidentify
patientswithMultipleSclerosis(MS),andrecentstudiesevenreportitsvalueforthe
predictionofearlyMSdiseaseactivity.Furthermore,detectionofκ‐FLChassignificant
methodologicaladvantagesincomparisontoOCBdetection.
Citation:Arneth,B.;Kraus,J.The
UseofKappaFreeLightChainsto
DiagnoseMultipleSclerosis.
M
edicina2022,58,1512.https://
doi.org/10.3390/medicina58111512
AcademicEditor:DejanJakimovski
Received:5September2022
Accepted:20October2022
Published:24October2022
Publisher’sNote:MDPIstaysneu‐
tralwithregardtojurisdictional
claimsinpublishedmapsandinsti‐
tutionalaffiliations.
Copyright:©2022bytheauthors.
LicenseeMDPI,Basel,Switzerland.
Thisarticleisanopenaccessarticle
distributedunderthetermsand
conditionsoftheCreativeCommons
Attribution(CCBY)license
(https://creativecommons.org/license
s/by/4.0/).
Medicina2022,58,15122of17
Expeditiousandprecisediagnosisisespeciallyimportantfortheclinicalmanage‐
mentofpatients.Earlydiseasediagnosisiscrucialbecausedisease‐modifyingtreatments
aremosteffectiveinthebeginningphaseoftheillness[2,3].Therefore,anidealbi‐
omarkershouldallowearlydiagnosisofthedisease,helpestablishitsprognosis,andbe
quicklyandeffectivelyverifiable.Atpresent,thereisnosinglespecificdiagnostictestfor
multiplesclerosis.Basedontheinsightsofarecentsurvey,thepreexistingdiagnostic
procedureformultiplesclerosisdependsonclinicalsigns,MagneticResonanceImaging
(MRI),andlaboratoryCSFtesting.Untilnow,thediagnosticcriteriaforMShavenotin‐
cludedFLCs.However,inthefuture,κ‐FLCtestingcouldpossiblysupportorevenre‐
placeOCBtestinginCSF[4].
AccordingtoBohleetal.[5],theprimarymanifestationsofmultiplesclerosisinvolve
cellularabnormalitiesandthehumoralimmunesystem.Regardless,itiswidelyknown
thatthejointactionsofBandTcellsplayanenormousroleintheoveralladvancementof
demyelinationandthegenerationofimmunoglobulin[5].Accordingly,inmostpatients,
aheighteneddegreeofimmunoglobulinproductionintheintrathecalspaceisnoticeable
[6],andoligoclonalImmunoglobulinG(IgG)isdistinguishableintheCSF.Thus,theap‐
plicationoffreelightchainsintheclinicaldiagnosisofmultiplesclerosishasbeenwidely
proposedandmedicallyinvestigatedbynumerousresearchersandscholars,especially
withinthelasthalf‐decade.Recentstudieshaveinvestigatedtheefficiencyandreliability
ofκ‐FLCforthediagnosisofmultiplesclerosis[7].Currently,multiplesclerosisisiden‐
tifiedviaintrathecaloligoclonalbands(OCBs)andapositiveReiberscheme.However,
severalgroupshavereportedpositiveκ‐FLCsinMSpatientsusingaquantitativeim‐
munoassaytoexaminetheamountofκ‐FLCswithinthepatient’sCSF[8–10].
Atthebasiclevel,thediagnosticimmunoassayisideallyassensitiveasaradioim‐
munoassay.Theimmunoassaydetectsκ‐FLCswithanantiserumspecifictothefree
kappachains[5].Thisstrategyfordiagnosingmultiplesclerosishasahighprobabilityof
differentiatingpatientswithandwithoutmultiplesclerosis[11–25].Inthispaper,asys‐
tematicliteraturereviewwasperformed,andessentialinsightsintotheup‐to‐date,
state‐of‐the‐artusageoffreelightchainsindiagnosingmultiplesclerosisareprovided.
Studieshaveadvocatedfreelightchains,especiallykappafreelightchains,asapracti‐
callysimplerandlesscostlyquantitativeoptionthanoligoclonalbands[26–40].
FLCdetectionisperformedbyanautomatednephelometrictest(e.g.,BindingSite
(Birmingham,UK),orSiemensHealthineers(Erlangen,Germany))and/oranELISA(e.g.,
Sebia(Lisses,France)).Theseassaysarefast(<2h)andinexpensive.Incontrast,OCBsare
determinedbyelectrophoresisandsubsequentimmunofixationand/orimmunoblotting
andsilverstaining.ThemaindifferenceisthatOCBsrequiresubstantialhands‐ontimeof
atrainedlaboratorytechnicianandspecificlaboratoryequipment.Furthermore,correct
assessmentofthegelsand/orimmunoblotsrequiresconsiderableexperience.Incom‐
parison,theFLCassayresultsinanumericvalue,whichcanbereportedtothephysician.
1.1.BackgroundOverviewofFLCUsageinMSDiagnostics
Thepossibleassaysfordetectingκ‐FLCsandλ‐FLCshavebeenaprimaryfocusin
investigatingtheuseoffreelightchainsinCSFforexaminingmultiplesclerosis[41–44].
Notably,thefreelightchainassaywasinitiallydevelopedbytheBindingSiteformulti‐
plemyelomadiagnostics.Later,thefreelightchainassaywasutilizedwithCSFforthe
diagnosisofmultiplesclerosis[41,45–55].Theinitialgroupofindividualstousefreelight
chainsinMSdiagnosticsincludedFischer,Arneth,Koehler,andLackner(2004)[41].The
findingsoftheirsubsequentreportprovidedsubstantialandpreliminaryinsightsinto
thepossibility,reliability,andefficiencyofusingκ‐FLCsinthediagnosisofmultiple
sclerosis.Thevastmajorityofrecentempiricalsurveys,including[51–110],havewidely
incorporatedtheessenceoftheabovementionedreportregardingthepositiveimplica‐
tionsofusingfreelightchains,especiallyκ‐FLCs,forMSdiagnosisaswellasforCIS.
Subsequently,ArnethandBirklein[111]becamethesecondgroupofresearchersto
demonstratetheuseoffreelightchainsinMSdiagnostics,asevidentfromtheir2009
Medicina2022,58,15123of17
study.Itwasonlyadecadelaterthatresearchersbegantogainarenewedinterestinthe
useofthefreelightchainassayinMSdiagnosis,followingapublicationbyKaplanetal.
(2010)[110].
1.2.Aim
Theaimofthepresentstudywastosummarizetheexistingstudiesontheuseof
FLCsforMSdiagnostics,whichisstillconsideredcontroversialintheprofessionalworld
andintheliterature.Thepresentmanuscriptcomparesprostudiesandconstudiesand
aimstoprovidemoreclarityinregardtotheuseofFLCsinMSdiagnostics.
2.MaterialsandMethods
2.1.DataSources
Thisstudyentailsasystematicreviewofpriorresearchreportsandarticlestoderive
satisfactoryconclusions.Themanuscript’sstructurewasbasedonthePreferredReport‐
ingItemsforSystematicreviewsandMeta‐Analyses(PRISMA)model.Theresearcher
usedtheQUADAS‐2tooltoevaluatetheriskofbiasfortheavailablereferences.All
publicationsweresubjectedtoareferencestandardspecifictothemeasureagainstwhich
thefreelightchainswerebeingcompared.ThePRISMAflowdiagramisshowninFigure
1.
Figure1.PRISMAflowdiagram.
Recordsscreened(n=230)
Studiesincluded(n=116)
Recordsidentifiedbydatabasesearching
(n=1800)
Additionalrecordsfoundbylookingatcitations
ofretrievedarticles(n=404)
Totalnumberofrecordsidentified
(n=2204)
Recordsrelevanttoresearchhypotheses
(n=900)
Recordsexcluded
(n=670)
Thesestudieswereduplicates
andtriplicates
Medicina2022,58,15124of17
Figure1showsthePRISMAflowdiagramforthisreview.Theinclusioncriteria
wereasfollows:allstudiesthatinvestigatedκ‐FLCsand/orκ‐FLCsinCSFandinflam‐
matorydiseasesand/ormultiplesclerosisand/orCISand/orconversionfromCIStoMS.
Apreliminaryliteraturesearchwasconductedtoidentifyanadditionaltopicof
concernforthestudy.Duringthestudyprocess,acomprehensivesearchwasperformed
throughreputableelectronicdatabasestoidentifyandobtainthenecessary
peer‐reviewedarticlesonrandomizedcontrolledclinicaltrialsthatbestillustrateand
investigatethedefinedhypothesisofthestudy.Thelegitimateelectronicdatabasesused
inthesearchprocessincludedEmbase,GoogleScholar,WebofScience,andPub‐
Med/MEDLINE.Theliteraturesearchwaslimitedtopublicationswithinthelast20years,
from2002to2022.Therewasnolimitationconcerningthegeographicalboundariesofthe
studies/sourcesorauthorsofinterest.
2.2.SearchStrategy
Thespecifictermsusedtosearchtheinternetweremultiplesclerosis,cerebrospinal
fluid,lambdafreelightchains,kappafreelightchains,andfreelightchains.Similarly,
medicalsubjectsorMeSHterms,suchasbiomarkers,immuneassays,kappaandlambda
isoforms,andimmunoglobulin,wereusedtofacilitatetheliteraturesearchintheMED‐
LINEandEmbasedatabases.Theabovesearchtermswerebasedonthecurrentstudy
objectivesandaims.Theresearchteamselectedonlyarticlesinvolvingclinicaltrialsand
retrospectiveorprospectivecomparativeandsystematicreviewsfocusingontheuseof
freelightchainstodiagnosemultiplesclerosisand/orCIS.
2.3.DataCollectionandAnalysisProcess
Thefindingsassessedinthecurrentmeta‐analysisandsystematicreviewwerethe
useoffreelightchainsindiagnosingmultiplesclerosis,inflammatoryCNSdisorders,
demyelination,and/orCIS.Duringtheliteraturesearchprocess,theresearchteamre‐
viewedthebibliographysourcesofeachoftheobtainedstudiestoidentifyotherrelevant
researchreports.Identicalconferenceandpublicationabstractswithoutfullinformation
wereexcluded,andtheremainingarticleswerevettedbyabstractandtitlebeforethe
correspondingfull‐textassessments.Thefulltextofeachreportwasautonomouslyana‐
lyzedbytheresearchteammemberswhoproposedthecurrentstudy.Thisprocedure
wascriticalinverifyingtheeligibilityofeacharticleforinclusion.
Theinclusioncriteriawereasfollows:studiesthatinvestigatedκ‐FLCsand/or
λ‐FLCsinCSFandinflammatorydiseasesand/ormultiplesclerosisand/orCISand/or
conversionfromCIStoMS.
3.Results
Approximately2204articleswereinitiallyobtainedduringthepreliminarysearch
acrossallfourelectronicdatabases,specificallyEmbase,MEDLINE,GoogleScholar,and
WebofScience.However,afterperformingtheinitialreviewofthearticlesusingtheir
abstracts,only900articlesweredeemedrelevanttothecurrentresearchhypothesis
statement.Next,670articlesthatwereduplicatesand/ortriplicateswereremoved,re‐
sultingin230nonduplicatepublications.Outofthese,116wereretrievedafterconduct‐
ingtheinclusionandexclusioncriteriareview.Theincludedstudiescomprised35ran‐
domizedclinicaltrials,21prospectivecohortstudies,19retrospectivestudies,22con‐
trolledclinicaltrials,and13systematicreviewstudies.
3.1.FreeLightChainsandImmunologicalAbnormalities
Oftheincludedreviewedliteraturesources,14articlesfocusedonexaminingthe
efficiencyofusingfreelightchainstoassessimmunologicalabnormalities.Specifically,
[1–10]focusedonanalyzingautoinflammatoryandautoimmunediseases,while[11–14]
Medicina2022,58,15125of17
exploredtheimportanceoffreelightchainsindiagnosinginflammatoryCNSdisorders
andimmunologicaldeficiencysyndromes.
3.2.FreeLightChainsandMultipleSclerosis
Ofthepeer‐reviewedsourcesretrievedforthecurrentsystematicreview,116de‐
scribedthestudyoffreelightchainsandmultiplesclerosis.Themostimportantarticles
were[15–29,41,66,111].
3.3.FreeLightChainsandDemyelinatingDiseases
Atleasttworeportsanalyzedinthiscasestudywerebasedonassessmentofthe
sensitivityoffreelightchainsindistinguishingdemyelinatingdiseases.Essentially,the
studies[10,36]offeredasubstantialoverviewregardingtheapplicationofκ‐FLCsindi‐
agnosingordetectingdemyelinatingdiseases.
3.4.TheEfficiencyofLambdaFreeLightChainsintheDiagnosisofDiseases/MultipleSclerosis
Whileκ‐FLCsinCSFareconsideredaneffectivealternativediagnosticapproachfor
multiplesclerosis,λ‐FLCsinCSFhavereceivedonlylimitedattentionfromthescientific
researchcommunity.Asevidentfromthecurrentsystematicliteraturesearchpresented
inTable1oftheappendix,fewstudieshavefocusedondirectlyinvestigatingthepositive
implicationsofusingλ‐FLCsinmultiplesclerosisdiagnosisindifferentcontexts.Nota‐
bly,mostofthestudiesrelatedtotheefficiencyofλ‐FLCshavebeengenerallybased
withinthewiderframeworkofdiagnosticsusingmorethanonetypeoffreelightchain.
Table1.SignificantFindingsabouttheEfficiencyofFreeLightChainTestinginGeneralandin
MultipleSclerosisDiagnostics.
KeyFindingsfromtheLiteratureSupportingStudiesnp‐Value
Freelightchainsareessentialinaltering
polymorphonuclearneutrophils(PMN)func‐
tionsandaidinginPMNprestimulation.
Esparvarinhaetal.[1],Napodanoetal.[11]900
Highconcentrationsofkappaandlambda
freelightchainsareevidentintheserumof
multiplemyelomapatients.
Kaplanetal.[2],Locketal.[3],Bholeetal.
[5],Muchtaretal.[6],Gottenbergetal.[9],
Gurtneretal.[32],Jiangetal.[34],Draborg
etal.[37]
1001/2771p<0.0001
Thereisacomparableclinicaldifferenceinthe
specificityandsensitivityofdiagnosingmon‐
oclonalplasmaproliferativedisordersbe‐
tweenamonoclonalfreelightchain(FLC)
assayandapolyclonalantibody‐basedassay.
Hoedemakersetal.[7],Campbelletal.[8]671/890p<0.0001
Positiveimplicationsofimmunoglobulinfree
lightchainsintheearlydiagnosisofmultiple
sclerosis.
4studies,n=1640
Nazarovetal.[15],Nazarovetal.[16],
Rathboneetal.[20],Bernardietal.[36]
1242/1640p≥0.320
Cerebrospinalfluid(CSF)kappafreelight
chainisamoreprofoundandearlierin‐
trathecalimmunoglobulinmarkerincompar‐
isontooligoclonalbands(OCBs).
6studiesn=3054
Ferraroetal.[17],Boselloetal.[18].Basile
[19],Altinieretal.[23],Zemanetal.[24].
Zemanetal.[25]
2333/3054p≥5.7
PMN=polymorphonuclearneutrophils;FLC=freelightchain.
Medicina2022,58,15126of17
Oftheincludedarticles,8sources(Kaplanetal.[2],Locketal.[3],Bholeetal.[5],
Muchtaretal.[6],Gottenbergetal.[9].,Gurtneretal.[32],Jiangetal.[34],andDraborget
al.[37])reportedthattherearerelativelyhighconcentrationsofbothlambdaandkappa
isoformsintheserumofpatientswithautoimmunediseases.
Inparticular,Kaplanetal.[2]notedthatλ‐FLCisoformsprimarilymanifestindi‐
mericandpolymericforms,whichareusuallymodifiedunderimmunologicalcondi‐
tions.
StudiesbySeneletal.[12],Makshakovetal.[13],Basileetal.[14],Hampsonetal.
[26],andNapodanoetal.[30]establishedthatcerebrospinalfluid‐basedfreelightchains
aresignificantdiseasebiomarkersinindividualsdiagnosedwithinflammatoryCNS
diseasessuchasmultiplesclerosisandCIS.Forinstance,theexperientialinvestigation
conductedbyNapodanoetal.[30]indicatedthatlambda(λ)freelightchainsare
low‐weightproteinssecretedinoverabundanceduringthesynthesisofimmunoglobu‐
linsanddischargedintoCSFand/orthecirculationdependingonthelocalizationofthe
inflammation.Inthisway,thepresenceofFLCsinCSFisclearlyconnectedwithplasma
cellaction.
Additionally,twostudiesbyHoedemakersetal.[7]andCampbelletal.[8]reported
thattherearecomparableclinicaldifferencesinspecificityandsensitivitybetweenthe
monoclonallambdaFLCassaysandthepolyclonalantibody‐basedlambdaFLCassays
usedformonoclonalplasmaproliferativedisorderdiagnosis(multiplemyelomadiag‐
nostics).
Theresultsconcerningλ‐FLCsarecurrentlymorecontroversialthanthosefor
κ‐FLCs.Severalstudiesreportahighernumberofpatientswithpositiveλ‐FLCsinCSF
thanthosewithκ‐FLCs[66,111].Thisphenomenoncanbeexplainedbythefactthat
λ‐FLCstendtodimerize.Subsequently,dimerswillnotbeabletocrosstheCSFbarrier.
Thiseffectwouldmakeλ‐FLCsextremelysensitivemarkersofintrathecalinflammation.
However,therearealsoafewstudiesthatwerenotabletodetectanyλ‐FLCsinmostof
theirpatients[17].Thesereportscanprobablybeexplainedbythefactthatlambda
polymerscanbepulledoutofthesamplethroughhighcentrifugation.Therefore,the
preanalyticaltreatmentofthesamplesplaysadefinitiveandimportantroleinthevalue
ofλ‐FLCsinthesestudiesandcanleadtopreanalyticalbias.
3.5.TheEfficiencyofKappaFreeLightChainsinDiagnosingMultipleSclerosis
Asignificantnumberofstudieshaveendeavoredtoexaminetheefficiencyofκ‐FLC
measurementinthediagnosisofmultiplesclerosis.Comparatively,theempiricalsurvey
outcomeswerereportedbyFerraroetal.[17]andBoselloetal.[18].Basile[19],Altinieret
al.[23],Zemanetal.[24],andZemanetal.[25]demonstratedthatCSFkappafreelight
chainsaremoreprofoundintrathecalimmunoglobulinmarkersthanoligoclonalbands
(OCBs).ThefindingswereconsistentwiththeresultsofstudiesbyNazarovetal.[15],
Nazarovetal.[16],Rathboneetal.[20],andBernardietal.[36],whichalsosupportedthe
positiveimplicationsofkappafreelightchainsintheearlydiagnosisofmultiplesclero‐
sis.
AsevidentfromtheinformationinTable2andTable3intheappendix,itisap‐
parentthatthestudiesfocusedondifferentaspectsofκ‐FLCdiagnostics.Forinstance,
studiesbyRosensteinetal.[40],Fischeretal.[41],Leursetal.[44],Villaretal.[67],Has‐
san‐Smithetal.[33],Süßeetal.[70],Vasiljetal.[73],Voortmanetal.[78],Presslaueretal.
[79],Seneletal.[80],Presslaueretal.[82],Hussetal.[85],Rinkeretal.[92],Nakanoetal.
[96],Ramsden[101],andMessaoudanietal.[107]foundthattherewasahigherconcen‐
trationofκ‐FLCsinpatientswithclinicallyvalidatedmultiplesclerosis.Overall,eight
studies(Meadetal.[49],Hanetal.[52],Nazarovetal.[59],Kaplanetal.[60],Vecchioet
al.[62],Annunziataetal.[94],Saadehetal.[98],Arnethetal.[111])reportedthatfree
lightchainscouldbeeffectivelyutilizedinthediagnosisofmultiplesclerosis.
Medicina2022,58,15127of17
Table2.KeyfindingsforKappaFreeLightChain(κ‐FLC)EffectivenessintheDiagnosisofMul‐
tipleSclerosis.
KeyFindingsfromtheLiteratureSupportingStudiesStudiesAgainst
κ‐FLCconcentrationsinCSFarehigher
inpatientswithclinicallyvalidated
multiplesclerosis
16studies;n=3040(Hassan‐Smith[33],Gudow‐
ska‐Sawczuketal.[27],Rosensteinetal.[40],Fischeret
al.[41],Leursetal.[47],Villaretal.[67],Süßeetal.[69],
Süßeetal.[70],Vasiljetal.[73],Voortmanetal.[78],
Presslaueretal.[79],Seneletal.[80],Presslaueretal.
[82],Rinkeretal.[92],Nakanoetal.[96],Ramsden[101],
andMessaoudanietal.[107]).
n=2033/3040
p
<0.001
‐
κ‐FLCconcentrationsinCSFcanbe
usedtopredictmultiplesclerosis
8studies;n=1800
(Presslaueretal.[31],Meadetal.[49],Hanetal.[52],
Rathboneetal.[20],Kaplanetal.[60],Vecchioetal.[62],
Annunziataetal.[94],Saadehetal.[98],Bernardietal.
[112],andAbidetal.[113]).
n=981/1800
p
<0.005
‐
κ‐FLCindexcutoffvaluesareanovel
toolinthedeterminationofintrathecal
synthesisofκ‐FLCs
9studies;n=2450
(Cavallaetal.[43],Freedmanetal.[55],Katzmannetal.
[56],Pierietal.[71],Arrambideetal.[81],McLeanetal.
[88],andDispenzierietal.[93]).
n=1721/2450
p
<0.005
5studies,n=1341
(Geervanietal.[74],
DeCarlietal.[83],
Teunissenetal.[89],
Deisenhammeretal.
[90,114],andSanz
Diazetal.[91]and
Magliozzietal[115])
Reiber’sdiagramprovidesaccurate
measurementsofκ‐FLCsandtheasso‐
ciatedaccuracyofmultiplesclerosis
(MS)diagnosis
5studies,n=1447
(Schwenkenbecheretal.[28],Konenetal.[65],Reiberet
al.[87],Arnethetal.[66,111],andDurantietal.[46]).
n=911/1447
p
<0.013
‐
κ‐FLCindexisabetterpredictorofMS
thantheuseofCSFOCBs
11studies,n=2700
(Leursetal.[81],Desplat‐Jégoetal.[45],Durantietal.
[46],Dispenzierietal.[93],Duelletal.[53],Bochtleret
al.[91],Tintoreetal.[75],Ferraroetal.[61],Gaetaniet
al.[100],Konenetal.[116],andSanzetal.[91]).
n=2321/2700
p
<0.091
5studies,n=1200
(Christiansenetal.
[50],Presslaueretal.
[82],Crespietal.[58],
Natalietal.[84],and
J
osephetal.[95])
κ‐FLCconcentrationinCSFisthefu‐
tureofMSdiagnosis
12studies,n=3150
(Polmanetal.[39],Pröbsteletal.[68],Kyleetal.[72],
Carpendaleetal.[76],Abu‐Izneidetal.[77],Wootlaet
al.[86],Smithetal.[97],Dobsonetal.[102],Ana‐
gnostoulietal.[103],Valencia‐Veraetal.[105],Meinlet
al.[106],andHegenetal.[114]).
n=2776/3150
p
<0.0001
‐
Medicina2022,58,15128of17
Table3.AsummaryofthemostimportantstudiesontheroleoffreelightchainsinMSdiagnosis.
StudyStudyQuestion/Hypothesisnp‐ValueReportedResults
Leursetal.2020[44]
Cankappafreelightchain
(κ‐FLC)andlambdafreelight
chain(λ‐FLC)indicesserveas
diagnosticbiomarkersinmulti‐
plesclerosis?
745p<0.001
ComparedwithOCBs,theκ‐FLCindexis
moresensitivebutlessspecificfordiagnos‐
ingCIS/MS.
Christiansenetal.
2018[50]
Comparativediagnosticperfor‐
manceofCSFFLCwithOCBand
ImmunoglobulinG(IgG)index.
96/230p<0.094
Usingonlytheabsoluteconcentrationof
CSF‐kappaisalogisticadvantageinclinical
laboratories.
Crespietal.2019
[58]
Istheκ‐FLCindexareliable
markerofintrathecalsynthesis
andanalternativetotheIgG
indexinmultiplesclerosisdiag‐
nosticwork‐up?
385p< 0.0001
Resultsconfirmedthepreviousproposalto
usetheκ‐FLCindexasahighlysensitive
andeasy‐to‐detectfirst‐linemarkerinCSF
analysisforintrathecalsynthesis.
Rathboneetal.2018
[20]
Dofreelightchains(FLCs)as
b
iomarkersforconfirmingadi‐
agnosisofMSshowgreatersen‐
sitivityandspecificitythan
OCBs?
43p<0.026
CSFimmunoglobulinκ:λratios,deter‐
minedatthetimeofdiagnosticlumbar
puncture,predictMSdiseaseprogression
andmaythereforebeusefulprognostic
markersforearlytherapeuticstratification.
Vecchioetal.2020
[62]
Whatistheroleofκ‐FLCsinthe
diagnosticwork‐upforMS?406p<0.001κ‐FLCsprovidedhighsensitivityandde‐
centspecificityforMSdiagnosis.
Arnethetal.2009
[66,111]
Immunoglobulinfreelightchain
concentrationsmeasuredinthe
CSFofpatientswithneurological
disorders.
20p<0.001
Thehighsensitivityoflambdalightchains
forthedetectionofintrathecalimmuno‐
globulinsynthesismaybeofbenefitines‐
tablishingclinicaldiagnoses.
Villaretal.2012[67]
WhatistheaccuracyofCSF
κ‐FLCmeasurementtopredict
theconversionofCISpatientsto
MS?
133/374p<0.001
HighCSFκ‐FLCconcentrationaccurately
predictstheconversionofCISpatientsto
MS.
Süßeetal.2020[70]
Whatistheapplicationandin‐
terpretationofκ‐FLCdatain
quotientdiagramswithahyper‐
b
olicreferencerange?
98/400p<0.001
Theevaluationofκ‐FLCwithahyperbolic
referencerangeinquotientdiagramsissu‐
periortootheranalyticalmethods,suchas
thelinearκ‐FLCindex.
Voortmanetal.2016
[78]
Whatistheprognosticvalueof
κ‐FLCinOCB‐positivepatients
withclinicallyisolatedsyndrome
(CIS)suggestiveofMSandearly
MS?
48/61p<0.05Increasedintrathecalsynthesisofκ‐FLCin
CIS/MSsupportsitsdiagnosticcontribution.
Presslaueretal.2016
[82]
Whatisthediagnosticaccuracy
ofintrathecalκ‐FLCsynthesis?70/438p≥5.9
Findingssupportthediagnosticvalueof
intrathecalκ‐FLCsynthesisinCISandMS
patientsanddemonstrateavalid,easier,
andrater‐independentalternativetoOCB
detection.
Ferraroetal.2020
[61]
Whatisthediagnosticaccuracy
oftheκ‐FLCindexincomparison
withOCBdetectioninpredicting
MS?
84/540p≥5.8
Theκ‐FLCindexhasaslightlyhighersensi‐
tivityandlowerspecificitythanCSFOCB,
andbothmarkerssupplytheclinicianwith
useful,complementaryinformation.
Saadehetal.2021
[98]
Whatarethereferencevaluesfor
FLCmeasures?Whatistheir70/1224p≥5.9CSF
κ
‐FLCsmaynotreplaceOCBs,butthey
maysupportdiagnosisinMSasaquantita‐
Medicina2022,58,15129of17
accuracywithregardtothedi‐
agnosisofMS?
tiveparameter.
Durantietal.2013
[46]
Istheκ‐FLCindexmoreaccurate
thanotherparameters?33/80p<0.001
Nephelometricassayforκ‐FLCsinCSFre‐
liablydetectsintrathecalimmunoglobulin
synthesisanddiscriminatesmultiplesclero‐
sispatients.
Valencia‐Veraetal.
2018[105]
Whatisthediagnosticvalueof
κ‐FLCanditsinclusioninapro‐
cedurealgorithmalongwith
OCBinterpretation?
123p<0.001
κ‐FLCdeterminationisrapidandautoma‐
tized,butithasnohighersensitivityor
specificitythanOCBinMSdiagnosis.
Süßeetal.2018[109]
Canthedeterminationofthe
κ‐FLCindexbeusedtopredict
thepresenceofOCBs?
46/295p<0.86
Determinationoftheκ‐FLCindexprovided
aquantitativeparameterthatcouldbeused
asaninitialdiagnosticstepininflammatory
centralnervoussystemdisordersbefore
measuringOCBs.
Similarly,fourstudiesprovidedadescriptionofthecriteriausedinmeasuringthe
diagnosticoutcomeswithκ‐FLCs[50–62].SeveralstudiesreportedthatReiber’sdiagram
canbeusedforaccurateevaluationofκ‐FLCsandtheassociatedaccuracyofMSdiag‐
nosis[66,111].Alltheauthorsprovidedproofthatmeasurementofκ‐FLCconcentration
islessexpensiveandtechnicallydemandingthanOCB‐basedprotocols.Accordingly,
theseresearchersdemonstratedthatimmunoglobinsynthesis,asabiomarkerofMS,can
beestablishedwithsimpleCSF/serumκ‐FLCquotientsandthegivenalbumin
CSF/serumconcentrationquotient(quotientdiagram)[28,111].Thesemeasurementsalso
constitutetheκ‐FLCindexandcontributetoapossiblenoveldiagnosticpipelineforMS.
Thesestudiesidentifiedgapsintheempiricallydefinedκ‐FLCindexthresholdsthatvary
considerablyacrossdifferentclinicaltrials[39–44].
Forinstance,Schwenkenbecheretal.notedthatthecurrentlyapplicableκ‐FLCin‐
dexthresholdsare3.6,5.9,6.07,and12[28].Toovercomethisvariabilityoftheκ‐FLC
indices,ArnethandReiberdevelopedamoreaccurateapproachthatwouldincreasethe
sensitivityoftheκ‐FLCmeasurementintheintrathecalsynthesisofκ‐FLCvalues
[29,111].Reiberfirstdidnotbelieveintheusefulnessofquotientdiagramsforκ‐FLCas‐
sessmentinCSF.Arnethhadtoexplaintheusefulnesstohim,whichresultedinaseries
ofcontroversialletterspublishedinActaNeurologicaScandinavica[66].
Today,Reiber’shyperbolicfunctionfortheassessmentofκ‐FLCsinCSFhasgained
supportfromseveralresearcherswhotestedtheanalogyinseparatemulticenterstudies
withhumanpatients[29,111].
Ingeneral,fewexaminationshaveplacedaparticularfocusontheprognosticvalue
ofeitherκ‐FLCsorlambdafreelightchains(λ‐FLCs)inMS[72–76].Indeed,eventhefew
studiesthatresearchedthatrelationshiprecordedenormousanddiversecomplexities.
Onereasonforthesedifficultiesexperiencedbypastspecialistsmayhaveresultedfrom
assessmentoffreekappalightchainand/orfreelambdalightchainlevelsintheCSF
alonewithoutconsideringtheblood‐CSFbarrier[72–76]andwithoutusingquotients.
DifferentmethodsofcomputingtheproportionofFLCsintheCSFtothatintheserum
prevailed;however,theydidnotconnecttheirdiscoverieswiththeblendingofthedif‐
ferentMSdiseasecourses,consequentlyexaminingeachpieceofinformationinaniso‐
latedfactualmanner[75–79].Additionally,therewereirregularitiesinstudiesonthe
impactofκ‐FLCresultsonMSdiagnosisinsituationswhereclinicalfactorssuchas
comorbidities,socioeconomics,andMRIfactorsareknowntothespecialist,e.g.,when
frozenCSFsampleswereusedinpurelaboratorystudies[79].
Regardless,acoupleofstudiessuccessfullyestablishedthefoundationforcritiquing
thedifferenceineffectivenessbetweenFLC‐andOCB‐basedapproachestoMSdiagno‐
sis.Drawingfrompreviousworksonthetopic[80–85],theauthorsnotedthatκ‐FLCs
Medicina2022,58,151210of17
andOCBsofferprognosticvalueinpredictingclinicalattacksofMSamongpatients,but
theuseofκ‐FLCsoffersevenmoreclinicaladvantages.Bereketal.notedthattheκ‐FLC
indexismeasuredbymeansofnephelometry,whichislabor‐saving,reliable,easierand
lesscostlythantheconventionalisoelectricassessmentofOCBs[82].Thestudyfurther
supportedtheuseoftheκ‐FLCindexbecauseofthenumericalquantifiablemetricvalues
includedinitsdiagnosticcriteriaasopposedtothedichotomous“optical”valuespro‐
videdbytheOCBtests[83].OCBtestsreturnonlynegativeorpositivevaluesthatare
perceivedbyvisualinspection.Thisassertionwassupportedbyfindingsfromanumber
ofpreviousstudiesthatfocusedonthesensitivityoffreelightchaintests[71,80,82,111].
StudiesbyVillaretal.[67],Hassan‐Smithetal.[69],Presslaueretal.[82],andSüßeet
al.[70]includedrelativelysmallpatientpopulationswithMSincomparisontootherlit‐
eraturesources.Incontrast,studiesbyChristiansenetal.[50],Durantietal.[46],and
Ferraroetal.[61]includedfairlylargepatientpopulationsfromdifferentdiseasecatego‐
riesintheirrespectivestudies,thusenablingapossiblecomparisonoftheefficiencyof
κ‐FLCsandλ‐FLCsinMSdiagnosis.ThepublicationsbyVecchioetal.[62],Villaretal.
[67],Arnethetal.[66],Rathboneetal.[59],andCrespietal.[58]demonstratedsignificant
resultswithapvalueof(p<0.001).Thestudiesincludedalargenumberofpatients,
whichwascrucialforderivingvalidconclusionsregardingtheimplicationsofκ‐FLCsin
MSdiagnosis.
4.Discussion
Therearepositiveimplicationsfortheuseoffreelightchainsinmakingavalidand
reliablediagnosisofmultiplesclerosisinthenearfuture.Thecurrentsystematicreview
included116peer‐reviewedliteraturesourcesderivedfromreputableelectronicdata‐
bases.Theincludedarticlescomprised35randomizedclinicaltrials,21prospectiveco‐
hortstudies,19retrospectivestudies,22controlledclinicaltrials,and13systematicre‐
views.Thevastmajorityoftheempiricalfindingswithinthepastdecadeconcerningthe
studytopichaverevealedthepotentialofκ‐FLCmeasurementinCSFfordiagnosing
multiplesclerosisand/orotherinflammatoryCNSdiseases,aswellasdemyelinating
CNSdisorders,amongotherparametersanddiagnosticproceduresusingfreelight
chains.Forinstance,theempiricalfindingsbyKaplan[2],Lock[3],Muchtaretal.[6],
Makshakovetal.[13],and[111]indicatedthattheuseofκfreelightchainsinCSFhad
beenestablishedtogivethephysiciananadditionaltooltodetectintrathecalimmuno‐
globulinsynthesis,anditsoutcomesandeffectivenessarepracticallyidenticaltothoseof
OCBtesting.
Notably,intrathecalimmunoglobulinsynthesisgenerallyoccursinmultiplesclero‐
sisaswellasundercontagiousandimmunologicalpathologicalconditionsthatinvolvea
humoralimmunereaction[5].Similarly,otherexperientialstudiesinvolvingOCBtesting
forassociateddiseases,suchascardiovascularanddemyelinatingdiseases,havere‐
portedthesameoutcomesusingfreelightchains.Forinstance,studiesbyBholeetal.[5],
Hoedemakersetal.[7],andSeneletal.[12]havedemonstratedthattheuseoffreelight
chains,specificallykappafreelightchains,providesupto95%accuracyinthediagnosis
ofobstructivepulmonarydiseaseanddemyelination.
TheheterogeneityintheFLCindexthresholdshasbeennotedbymanyscholarsasa
potentialloopholeandmethodologicaldisadvantageofnephelometry[92].Themajority
oftheliteraturethatdiscreditsthisapproachbasesitsoppositionontwogrounds.First,
theremightbedisagreementovertheaccuratecutoffpointsfortheκ‐FLCindices[92].
Theinconsistentuseoftheκ‐FLCindexvalueswouldreflectnonlinearfunctionsthat
makeitvirtuallyimpossibletoestablishitsdiagnosticvalueasafraction[59].Through‐
outtheliterature,thisdrawbackseemstobetheprimaryobstaclethathasresultedinthe
limiteduseofκ‐FLCsinclinicalpractice.However,thisproblemcanbesolvedbyusing
quotientdiagrams.Second,thereislimitedclinicalexperiencewithκ‐FLCquotientdia‐
grams.
Medicina2022,58,151211of17
Regardless,furtherinvestigationsintotheκ‐FLCmeasurementcriteriahaveseen
tremendousbreakthroughs.Thevalidityof“Reiber’sanalogy”(usingReibersSchemaor
quotientdiagramsforκ‐FLCassessment),asprovenacrossseveralmulticenterstudies,
makesitpossibleinthefuturefortheuseofκ‐FLCstobeintegratedintoclinicalpractice
[60–64].Therefore,thepresentreviewrecommendssimultaneouskappaFLCmeasure‐
mentsinCSFandserumandsubsequentassessmentusingaquotientdiagram.Other
findingsdemonstratedthattheefficacyofusingfreelightchainsindiagnosingmultiple
sclerosisandimmunologicalabnormalitiesislimitedtocertainfactors.Forexample,as
evidentinArrambideetal.[42],theuseofκ‐FLCsandλ‐FLCsisstilllimitedtoCSFdi‐
agnosticsandthereforealsorequiresalumbarpuncture.
Regardingthedifferencesbetweenκ‐FLCsandλ‐FLCs,whileκ‐FLCsarealmost
identicaltoOCBsintermsofclinicalinformation,λ‐FLCsseemtobeelevatedinCSF
muchmorefrequentlyandaredetectabletoamuchgreaterextent.λ‐FLCsalsooccurin
theCSFinpatientswithotherpathologies.Forexample,theyareoftendetectableinthe
CSFinpatientsafterstrokes.Thisispotentiallyexplainedbythefactthatlambdachains
haveatendencytodimerizeandtopolymerize.Theselambdadimerscanthennolonger
crosstheblood‐CSFbarriersoeasilyandhaveasignificantlyincreasedbiologicalhalf‐life
intheCSF.Asaresult,veryweakinflammatoryeventsaresufficienttoincreaseλ‐FLC
levels.
Basedontheinsightsofthecurrentstudy,theuseofFLCsindiagnosticmeasuresis
worthwhileandrelativelyinexpensive.AsdepictedbyKaplanetal.[2,41,111],thedi‐
agnosisofmultiplesclerosisusingκ‐FLCsistechnicallylesscostlythantypical
state‐of‐the‐artdiagnosticssuchasOCBs.Inaddition,usingFLCsiseasierandoften
yieldsfasterresultsthanOCBtesting.ThecostsoftheautomatedFLCtestsareapprox‐
imately50%ofthecostsoftheOCBtests.Indevelopingcountries,FLCdetermination
mightbeagoodalternativeasitdoesnotrequiremuchlaboratorytrainingandequip‐
ment.
TheuseofFLCsforMSdiagnosticsremainscontroversial.Inthisreview,thepro
studiesandtheconstudieswerecompared.Insummary,itcanbesaidthatthepro
studieswithregardtoκ‐FLCdiagnosticsinCSFpredominateinnumberandscope.For
thisreason,itistobehopedthatκ‐FLCdiagnosticswillsoonbecomepartofthestandard
programforMSdiagnosticsandforassessingtheprogressionofMS.
5.Conclusions
Theprimarypurposeofthecurrentsystematicreviewstudywastoinvestigatean
up‐to‐date,state‐of‐the‐artmethodformultiplesclerosisCSFdiagnosisusingFLCs.For
thispurpose,thestudyfocusedonbothκ‐FLCsandλ‐FLCs.Atotalof116sourceswere
reviewedinthecontextofthestudyandwerelimitedtoarticlespublishedwithinthelast
20years(between2002and2022).Basedontheinsightsoftheresultingliterature,FLCs,
especiallyincerebrospinalfluiddiagnostics,haveincreasinglygainedpopularity,par‐
ticularlyinthepasthalf‐decade.
Insummary,thereissubstantialagreementinthescientificcommunitythatthedi‐
agnosticvalueofκ‐FLCsinCSFisalmostequaltoOCBsintermsofsensitivityand
specificity.Withregardtoλ‐FLCs,theliteratureismuchmoreheterogeneous.While
severalstudiesreportahighersensitivityofλ‐FLCsinCSFforthedetectionofintrathecal
inflammation,othersreportlowλ‐FLCvaluesformostofthepatientsinvestigated.Itis
verylikelythatpreanalyticalhandlingofthesamplesplaysalargeroleinλ‐FLCdiag‐
nosticsinCSF.
ThereviewedarticlesreportedthattheanalysisofFLCspotentiallyprovidesupto
95%accuracyindiagnosingmultiplesclerosisandotherassociateddisorderssuchasCIS
andintrathecalinflammation.Furthermore,thediagnosisofmultiplesclerosisusing
FLCsisrelativelyfastandinexpensiveincontrasttoconventionalstate‐of‐the‐artdiag‐
nostics,includingOCBs.However,limitingfactorsmayhindertheefficiencyofFLCdi‐
agnostics,andtheyshouldbeidentifiedinthecomingyears.
Medicina2022,58,151212of17
AuthorContributions:Conceptualization,B.A.andJ.K.;methodology,B.A.;formalanalysis,B.A.;
investigation,B.A.andJ.K.;resources,B.A.andJ.K.;datacuration,B.A.andJ.K.;writing—original
draftpreparation,B.A.;writing—reviewandediting,J.K.;visualization,B.A.;supervision,J.K.;
projectadministration,B.A.andJ.K.Allauthorshavereadandagreedtothepublishedversionof
themanuscript.
Funding:Thisresearchreceivednoexternalfunding.
InstitutionalReviewBoardStatement:Notapplicable.
InformedConsentStatement:Notapplicable.
DataAvailabilityStatement:Allrelevantdataisavailableinthemanuscriptstables.
ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.
References
1. Esparvarinha,M.;Nickho,H.;Mohammadi,H.;Aghebati‐Maleki,L.;Abdolalizadeh,J.;Majidi,J.Theroleoffreekappaand
lambdalightchainsinthepathogenesisandtreatmentofinflammatorydiseases.Biomed.Pharmacother.2017,91,632–644.
https://doi.org/10.1016/j.biopha.2017.04.121.
2. Kaplan,B.;Livneh,A.;Sela,B.‐A.ImmunoglobulinFreeLightChainDimersinHumanDiseases.Sci.WorldJ.2011,11,726–735.
https://doi.org/10.1100/tsw.2011.65.
3. Lock,R.;Saleem,R.;Roberts,E.;Wallage,M.;Pesce,T.;Rowbottom,A.;Cooper,S.;McEvoy,E.;Taylor,J.;Basu,S.Amulti‐
centrestudycomparingtwomethodsforserumfreelightchainanalysis.Ann.Clin.Biochem.Int.J.Lab.Med.2013,50,255–261.
https://doi.org/10.1177/0004563212473447.
4. Jenner,E.Serumfreelightchainsinclinicallaboratorydiagnostics.Clin.Chim.Acta2014,427,15–20.
https://doi.org/10.1016/j.cca.2013.08.018.
5. Bhole,M.V.;Sadler,R.;Ramasamy,K.Serum‐freelight‐chainassay:Clinicalutilityandlimitations.Ann.Clin.Biochem.Int.J.
Lab.Med.2014,51,528–542.https://doi.org/10.1177/0004563213518758.
6. Muchtar,E.;Dispenzieri,A.;Leung,N.;Lacy,M.Q.;Buadi,F.K.;Dingli,D.;Hayman,S.R.;Kapoor,P.;Hwa,Y.L.;Fonder,A.;et
al.OptimizingdeepresponseassessmentforALamyloidosisusinginvolvedfreelightchainlevelatendoftherapy:Failureof
theserumfreelightchainratio.Leukemia2018,33,527–531.https://doi.org/10.1038/s41375‐018‐0258‐y.
7. Hoedemakers,R.M.J.;Pruijt,J.F.M.;Hol,S.;Teunissen,E.;Martens,H.;Stam,P.;Melsert,R.;Velthuis,H.T.Clinicalcomparison
ofnewmonoclonalantibody‐basednephelometricassaysforfreelightchainkappaandlambdatopolyclonalantibody‐based
assaysandimmunofixationelectrophoresis.Clin.Chem.Lab.Med.(CCLM)2012,50,489–495.
https://doi.org/10.1515/cclm.2011.793.
8. Campbell,J.P.;Cobbold,M.;Wang,Y.;Goodall,M.;Bonney,S.L.;Chamba,A.;Birtwistle,J.;Plant,T.;Afzal,Z.;Jefferis,R.;etal.
Developmentofahighly‐sensitivemulti‐plexassayusingmonoclonalantibodiesforthesimultaneousmeasurementofkappa
andlambdaimmunoglobulinfreelightchainsinserumandurine.J.Immunol.Methods2013,391,1–13.
https://doi.org/10.1016/j.jim.2013.01.014.
9. Gottenberg,J.E.;Seror,R.;Miceli‐Richard,C.;Benessiano,J.;Devauchelle‐Pensec,V.;Dieude,P.;Dubost,J.J.;Fauchais,A.L.;
Goeb,V.;Hachulla,E.;etal.Serumlevelsofbeta2‐microglobulinandfreelightchainsofimmunoglobulinsareassociatedwith
systemicdiseaseactivityinprimarySjögren’ssyndrome.DataatenrollmentintheprospectiveASSESScohort.PLoSONE2013,
8,e59868.
10. GrootKormelink,T.;WAskenase,P.;ARedegeld,F.Immunobiologyofantigen‐specificimmunoglobulinfreelightchainsin
chronicinflammatorydiseases.Curr.Pharm.Des.2012,18,2278–2289.
11. Napodano,C.;Pocino,K.;Rigante,D.;Stefanile,A.;Gulli,F.;Marino,M.;Basile,V.;Rapaccini,G.L.;Basile,U.Freelightchains
andautoimmunity.Autoimmun.Rev.2019,18,484–492.https://doi.org/10.1016/j.autrev.2019.03.003.
12. Senel,M.;Mojib‐Yezdani,F.;Braisch,U.;Bachhuber,F.;Lewerenz,J.;Ludolph,A.C.;Otto,M.;Tumani,H.CSFFreeLight
ChainsasaMarkerofIntrathecalImmunoglobulinSynthesisinMultipleSclerosis:ABlood‐CSFBarrierRelatedEvaluationin
aLargeCohort.Front.Immunol.2019,10,641.https://doi.org/10.3389/fimmu.2019.00641.
13. Makshakov,G.;Nazarov,V.;Kochetova,O.;Surkova,E.;Lapin,S.;Evdoshenko,E.DiagnosticandPrognosticValueofthe
CerebrospinalFluidConcentrationofImmunoglobulinFreeLightChainsinClinicallyIsolatedSyndromewithConversionto
MultipleSclerosis.PLoSONE2015,10,e0143375.https://doi.org/10.1371/journal.pone.0143375.
14. Basile,U.;Gulli,F.;Gragnani,L.;Napodano,C.;Pocino,K.;Rapaccini,G.L.;Mussap,M.;Zignego,A.L.Freelightchains:Ec‐
lecticmultipurposebiomarker.J.Immunol.Methods2017,451,11–19.https://doi.org/10.1016/j.jim.2017.09.005.
15. Nazarov,V.D.;Makshakov,G.;Mazing,A.;Surkova,E.;Krasnov,V.S.;Shumilina,M.V.;Totolyan,N.A.;Evdoshenko,E.P.;
Lapin,S.V.;Emanuel,V.L.;etal.Diagnosticvalueofimmunoglobulinfreelightchainsatthedebutofmultiplesclerosis.
ZhurnalNevrol.psikhiatriiS.S.Korsakova2017,117,60–65.https://doi.org/10.17116/jnevro20171172260‐65.
16. Nazarov,V.D.;Evdoshenko,E.P.;Makshakov,G.S.;Totolian,A.A.;Lapin,S.V.;Surkova,E.A.Diagnosticandprognosticsig‐
nificanceofintrathecalsynthesisofimmunoglobulinfreelightchainsinmultiplesclerosis.Med.Immunol.(Russia)2015,17,
235–244.https://doi.org/10.15789/1563‐0625‐2015‐3‐235‐244.
Medicina2022,58,151213of17
17. Ferraro,D.;Trovati,A.;Bedin,R.;Natali,P.;Franciotta,D.;Santangelo,M.;Camera,V.;Vitetta,F.;Varani,M.;Trenti,T.;etal.
Cerebrospinalfluidkappaandlambdafreelightchainsinoligoclonalband‐negativepatientswithsuspectedmultiplesclero‐
sis.Eur.J.Neurol.2020,27,461–467.https://doi.org/10.1111/ene.14121.
18. Bosello,S.;Basile,U.;DeLorenzis,E.;Gulli,F.;Canestrari,G.;Napodano,C.;Parisi,F.;Pocino,K.;DiMario,C.;Tolusso,B.;et
al.Freelightchainsofimmunoglobulinsinpatientswithsystemicsclerosis:Correlationswithlunginvolvementandinflam‐
matorymilieu.J.Clin.Pathol.2018,71,620–625.https://doi.org/10.1136/jclinpath‐2017‐204656.
19. Basile,U.;LaRosa,G.;Napodano,C.;Pocino,K.;Cappannoli,L.;Gulli,F.;Cianfrocca,C.;DiStasio,E.;Biasucci,L.M.Freelight
chainsanovelbiomarkerofcardiovasculardisease.Apilotstudy.Eur.Rev.Med.Pharmacol.Sci.2019,23,2563–2569.
20. Rathbone,E.;Durant,L.;Kinsella,J.;Parker,A.R.;Hassan‐Smith,G.;Douglas,M.R.;Curnow,S.J.Cerebrospinalfluidimmu‐
noglobulinlightchainratiospredictdiseaseprogressioninmultiplesclerosis.J.Neurol.Neurosurg.Psychiatry2018,89,1044–
1049.https://doi.org/10.1136/jnnp‐2018‐317947.
21. Anderson,G.P.FreeImmunoglobulinLightChainsinChronicObstructivePulmonaryDisease.Am.J.Respir.Crit.CareMed.
2012,185,793–795.https://doi.org/10.1164/rccm.201201‐0041ed.
22. Crespi,I.;Sulas,M.G.;Mora,R.;Naldi,P.;Vecchio,D.;Comi,C.;Cantello,R.;Bellomo,G.Combineduseofkappafreelight
chainindexandisoelectrofocusingofcerebrospinalfluidindiagnosingmultiplesclerosis:Performancesandcosts.Clin.Lab.
2017,63,551–559.
23. Altinier,S.;Puthenparampil,M.;Zaninotto,M.;Toffanin,E.;Ruggero,S.;Gallo,P.;Plebani,M.Freelightchainsincerebro‐
spinalfluidofmultiplesclerosispatientsnegativeforIgGoligoclonalbands.Clin.Chim.Acta2019,496,117–120.
https://doi.org/10.1016/j.cca.2019.06.016.
24. Zeman,D.;Hradílek,P.;Švagera,Z.;Mojžíšková,E.;Woznicová,I.;Zapletalová;O.DetectionofoligoclonalIgGkappaandIgG
lambdabandsincerebrospinalfluidandserumwithHevylite™antibodies.Comparisonwiththefreelightchainoligoclonal
pattern.FluidsBarriersCNS2012,9,1.
25. Zeman,D.;Kušnierová,P.;Švagera,Z.;Všianský,F.;Byrtusová,M.;Hradílek,P.;Kurková,B.;Zapletalová,O.;Bartoš,V.As‐
sessmentofIntrathecalFreeLightChainSynthesis:ComparisonofDifferentQuantitativeMethodswiththeDetectionofOli‐
goclonalFreeLightChainsbyIsoelectricFocusingandAffinity‐MediatedImmunoblotting.PLoSONE2016,11,e0166556.
https://doi.org/10.1371/journal.pone.0166556.
26. Hampson,J.;Turner,A.;Stockley,R.Polyclonalfreelightchains:Promisingnewbiomarkersininflammatorydisease.Curr.
Biomark.Find.2014,4,139–149.https://doi.org/10.2147/cbf.s57681.
27. Gudowska‐Sawczuk,M.;Tarasiuk,J.;Kułakowska,A.;Kochanowicz,J.;Mroczko,B.KappaFreeLightChainsandIgGCom‐
binedinaNovelAlgorithmfortheDetectionofMultipleSclerosis.BrainSci.2020,10,324.
https://doi.org/10.3390/brainsci10060324.
28. Schwenkenbecher,P.;Konen,F.F.;Wurster,U.;Witte,T.;Gingele,S.;Sühs,K.‐W.;Stangel,M.;Skripuletz,T.Reiber’sDiagram
forKappaFreeLightChains:TheNewStandardforAssessingIntrathecalSynthesis?Diagnostics2019,9,194.
https://doi.org/10.3390/diagnostics9040194.
29. Berek,K.;Bsteh,G.;Auer,M.;DiPauli,F.;Grams,A.;Milosavljevic,D.;Poskaite,P.;Schnabl,C.;Wurth,S.;Zinganell,A.;etal.
Kappa‐FreeLightChainsinCSFPredictEarlyMultipleSclerosisDiseaseActivity.Neurol.‐Neuroimmunol.Neuroinflammation
2021,8,e1005.https://doi.org/10.1212/nxi.0000000000001005.
30. Napodano,C.;Pocino,K.;Gulli,F.;Rossi,E.;Rapaccini,G.L.;Marino,M.;Basile,U.Mono/polyclonalfreelightchainsaschal‐
lengingbiomarkersforimmunologicalabnormalities.Adv.Clin.Chem.2022,108,155–209.
https://doi.org/10.1016/bs.acc.2021.08.002.
31. Presslauer,S.;Milosavljevic,D.;Huebl,W.;Aboulenein‐Djamshidian,F.;Krugluger,W.;Deisenhammer,F.;Senel,M.;Tumani,
H.;Hegen,H.Validationofkappafreelightchainsasadiagnosticbiomarkerinmultiplesclerosisandclinicallyisolatedsyn‐
drome:Amulticenterstudy.Mult.Scler.J.2015,22,502–510.https://doi.org/10.1177/1352458515594044.
32. Gurtner,K.M.;Shosha,E.;Bryant,S.C.;Andreguetto,B.D.;Murray,D.L.;Pittock,S.J.;Willrich,M.A.V.CSFfreelightchain
identificationofdemyelinatingdisease:ComparisonwitholigoclonalbandingandotherCSFindexes.Clin.Chem.Lab.Med.
(CCLM)2018,56,1071–1080.https://doi.org/10.1515/cclm‐2017‐0901.
33. Hassan‐Smith,G.;Durant,L.;Tsentemeidou,A.;Assi,L.;Faint,J.;Kalra,S.;Douglas,M.;Curnow,S.Highsensitivityand
specificityofelevatedcerebrospinalfluidkappafreelightchainsinsuspectedmultiplesclerosis.J.Neuroimmunol.2014,276,
175–179.https://doi.org/10.1016/j.jneuroim.2014.08.003.
34. Jiang,J.;Zhao,J.;Liu,D.;Zhang,M.Differentrolesofurinarylightchainsandserumlightchainsaspotentialbiomarkersfor
monitoringdiseaseactivityinsystemiclupuserythematosus.PeerJ.2022,10,e13385.https://doi.org/10.7717/peerj.13385.
35. Heaney,J.L.;Phillips,A.C.;Drayson,M.;Campbell,J.P.Serumfreelightchainsarereducedinendurancetrainedolderadults:
Evidencethatexercisetrainingmayreducebasalinflammationinolderadults.Exp.Gerontol.2016,77,69–75.
https://doi.org/10.1016/j.exger.2016.02.011.
36. Bernardi,G.;Biagioli,T.;Malpassi,P.;DeMichele,T.;Vecchio,D.;Repice,A.M.;Lugaresi,A.;Mirabella,M.;Clerici,V.T.;
Crespi,I.Thecontributeofcerebrospinalfluidfreelight‐chainassayinthediagnosisofmultiplesclerosisandotherneurolog‐
icaldiseasesinanItalianmulticenterstudy.Mult.Scler.J.2021,28,1364–1372.https://doi.org/10.1177/13524585211064121.
37. Draborg,A.H.;Lydolph,M.C.;Westergaard,M.;OlesenLarsen,S.;Nielsen,C.T.;Duus,K.;Jacobsen,S.;Houen,G.Elevated
concentrationsofserumimmunoglobulinfreelightchainsinsystemiclupuserythematosuspatientstodiseaseactivity,in‐
flammatorystatus,Bcellactivity,andEpstein‐Barrvirusantibodies.PLoSONE2015,10,e0138753.
Medicina2022,58,151214of17
38. Miyoshi,N.;Iuliano,L.;Tomono,S.;Ohshima,H.Implicationsofcholesterolautoxidationproductsinthepathogenesisof
inflammatorydiseases.Biochem.Biophys.Res.Commun.2014,446,702–708.https://doi.org/10.1016/j.bbrc.2013.12.107.
39. Polman,C.H.;Reingold,S.C.;Banwell,B.;Clanet,M.;Cohen,J.A.;Filippi,M.;Fujihara,K.;Havrdova,E.;Hutchinson,M.;
Kappos,L.;etal.Diagnosticcriteriaformultiplesclerosis:2010RevisionstotheMcDonaldcriteria.Ann.Neurol.2011,69,292–
302.https://doi.org/10.1002/ana.22366.
40. Rosenstein,I.;Rasch,S.;Axelsson,M.;Novakova,L.;Blennow,K.;Zetterberg,H.;Lycke,J.Kappafreelightchainindexasa
diagnosticbiomarkerinmultiplesclerosis:Areal‐worldinvestigation.J.Neurochem.2021,159,618–628.
https://doi.org/10.1111/jnc.15500.
41. Fischer,C.;Arneth,B.;Koehler,J.;Lotz,J.;Lackner,K.J.KappaFreeLightChainsinCerebrospinalFluidasMarkersofIn‐
trathecalImmunoglobulinSynthesis.Clin.Chem.2004,50,1809–1813.https://doi.org/10.1373/clinchem.2004.033977.
42. Arrambide,G.;Tintore,M.CSFexaminationstillhasvalueinthediagnosisofMS—Commentary.Mult.Scler.J.2016,22,997–
998.https://doi.org/10.1177/1352458516642033.
43. Cavalla,P.;Caropreso,P.;Limoncelli,S.;Bosa,C.;Pasanisi,M.;Schillaci,V.;Alteno,A.;Costantini,G.;Giordana,M.;Mengoz‐
zi,G.;etal.KappafreelightchainsindexinthedifferentialdiagnosisofMultipleSclerosisfromNeuromyelitisopticaspectrum
disordersandotherimmune‐mediatedcentralnervoussystemdisorders.J.Neuroimmunol.2019,339,577122.
https://doi.org/10.1016/j.jneuroim.2019.577122.
44. Leurs,C.E.;Twaalfhoven,H.;Lissenberg‐Witte,B.I.;vanPesch,V.;Dujmovic,I.;Drulovic,J.;Castellazzi,M.;Bellini,T.;Pugli‐
atti,M.;Kuhle,J.;etal.KappafreelightchainsisavalidtoolinthediagnosticsofMS:Alargemulticenterstudy.Mult.Scler.J.
2019,26,912–923.https://doi.org/10.1177/1352458519845844.
45. Desplat‐Jégo,S.;Feuillet,L.;Pelletier,J.;Bernard,D.;Chérif,A.A.;Boucraut,J.QuantificationofImmunoglobulinFreeLight
ChainsinCerebroSpinalFluidbyNephelometry.J.Clin.Immunol.2005,25,338–345.https://doi.org/10.1007/s10875‐005‐5371‐9.
46. Duranti,F.;Pieri,M.;Centonze,D.;Buttari,F.;Bernardini,S.;Dessi,M.DeterminationofkFLCandKIndexincerebrospinal
fluid:Avalidalternativetoassessintrathecalimmunoglobulinsynthesis.J.Neuroimmunol.2013,263,116–120.
https://doi.org/10.1016/j.jneuroim.2013.07.006.
47. TeunissenCE,MalekzadehA,LeursC,BridelC,KillesteinJBody‐fluidbiomarkersformultiplesclerosis‐thelongroadto
clinicalapplication. NatRevNeurol.2015Oct;11(10):585‐96.doi:10.1038/nrneurol.2015.173.Epub2015Sep22.
48. Dispenzieri,A.;Kyle,R.A.;Katzmann,J.A.;Therneau,T.M.;Larson,D.;Benson,J.;Clark,R.J.;Melton,L.J.,III;Gertz,M.A.;
Kumar,S.K.;etal.Immunoglobulinfreelightchainratioisanindependentriskfactorforprogressionofsmoldering(asymp‐
tomatic)multiplemyeloma.Blood.J.Am.Soc.Hematol.2008,111,785–789.
49. Mead,G.P.;Carr‐Smith,H.D.;Drayson,M.T.;Morgan,G.J.;Child,J.A.;Bradwell,A.R.Serumfreelightchainsformonitoring
multiplemyeloma.Br.J.Haematol.2004,126,348–354.https://doi.org/10.1111/j.1365‐2141.2004.05045.x.
50. Christiansen,M.;Gjelstrup,M.C.;Stilund,M.;Christensen,T.;Petersen,T.;Møller,H.J.Cerebrospinalfluidfreekappalight
chainsandkappaindexperformequaltooligoclonalbandsinthediagnosisofmultiplesclerosis.Clin.Chem.Lab.Med.(CCLM)
2018,57,210–220.https://doi.org/10.1515/cclm‐2018‐0400.
51. HegenH,WaldeJ,MilosavljevicD,Aboulenein‐DjamshidianF,SenelM,TumaniH,DeisenhammerF,PresslauerS.Freelight
chainsinthecerebrospinalfluid.Comparisonofdifferentmethodstodetermineintrathecalsynthesis.ClinChemLabMed
2019Sep25;57(10):1574‐1586.
52. Han,X.;Wang,J.;Zhang,N.;Yao,J.;Feng,Y.;Li,D.;Liu,P.;Yang,J.;Zhou,S.;Qin,Y.;etal.Theprognosticutilityandthe
associationofserumlightchains(freeandtotal)andabsolutelymphocytecountinpatientswithnewlydiagnoseddiffuselarge
B‐celllymphoma.Leuk.Res.2014,38,1291–1298.https://doi.org/10.1016/j.leukres.2014.09.006.
53. Duell,F.;Evertsson,B.;AlNimer,F.;Sandin,Å.;Olsson,D.;Olsson,T.;Khademi,M.;Hietala,M.A.;Piehl,F.;Hansson,M.
DiagnosticaccuracyofintrathecalkappafreelightchainscomparedwithOCBsinMS.Neurol.‐Neuroimmunol.Neuroinflamma‐
tion2020,7,e775.https://doi.org/10.1212/nxi.0000000000000775.
54. Bochtler,T.;Hegenbart,U.;Heiss,C.;Benner,A.;Cremer,F.;Volkmann,M.;Ludwig,J.;Perz,J.B.;Ho,A.D.;Goldschmidt,H.;
etal.Evaluationoftheserum‐freelightchaintestinuntreatedpatientswithALamyloidosis.Haematologica2008,93,459–462.
https://doi.org/10.3324/haematol.11687.
55. Freedman,M.S.;Thompson,E.J.;Deisenhammer,F.;Giovannoni,G.;Grimsley,G.;Keir,G.;Öhman,S.;Racke,M.K.;Sharief,
M.;Sindic,C.J.M.;etal.Recommendedstandardofcerebrospinalfluidanalysisinthediagnosisofmultiplesclerosis:Acon‐
sensusstatement.ArchNeurol.2005,62,865–870.
56. Katzmann,J.A.;Clark,R.J.;Abraham,R.S.;Bryant,S.;Lymp,J.F.;Bradwell,A.R.;Robert,A.K.Serumreferenceintervalsand
diagnosticrangesforfreekappaandfreelambdaimmunoglobulinlightchains:Relativesensitivityfordetectionofmonoclonal
lightchains.ClinChem.2002,48,1437–1444.
57. Moher,D.;Liberati,A.;Tetzlaff,J.;Altman,D.G.;PRISMAGroup.Preferredreportingitemsforsystematicreviewsandme‐
ta‐analyses:ThePRISMAstatement(reprintedfromAnnalsofInternalMedicine).Ann.Intern.Med.2009,89,873–880.
58. Crespi,I.;Vecchio,D.;Serino,R.;Saliva,E.;Virgilio,E.;Sulas,M.G.;Bellomo,G.;Dianzani,U.;Cantello,R.;Comi,C.KIndexis
aReliableMarkerofIntrathecalSynthesis,andanAlternativetoIgGIndexinMultipleSclerosisDiagnosticWork‐Up.J.Clin.
Med.2019,8,446.https://doi.org/10.3390/jcm8040446.
59. NazarovV,MakshakovG,KalininI,LapinS,SurkovaE,MikhailovaL,GilburdB,SkorometsA,EvdoshenkoE.Concentra‐
tionsofImmunoglobulinFreeLightChainsinCerebrospinalFluidpredictincreasedlevelofbrainatrophyinmultiplesclerosis
ImmunolRes.2018Dec;66(6):761‐767.doi:10.1007/s12026‐018‐9058‐8.
Medicina2022,58,151215of17
60. Kaplan,B.;Ganelin‐Cohen,E.;Golderman,S.;Livneh,A.Diagnosticutilityofkappafreelightchainsinmultiplesclerosis.
ExpertRev.Mol.Diagn.2019,19,277–279.https://doi.org/10.1080/14737159.2019.1586535.
61. Ferraro,D.;Bedin,R.;Natali,P.;Franciotta,D.;Smolik,K.;Santangelo,M.;Immovilli,P.;Camera,V.;Vitetta,F.;Gastaldi,M.;et
al.KappaIndexVersusCSFOligoclonalBandsinPredictingMultipleSclerosisandInfectious/InflammatoryCNSDisorders.
Diagnostics2020,10,856.https://doi.org/10.3390/diagnostics10100856.
62. Vecchio,D.;Bellomo,G.;Serino,R.;Virgilio,E.;Lamonaca,M.;Dianzani,U.;Cantello,R.;Comi,C.;Crespi,I.Intrathecalkappa
freelightchainsasmarkersformultiplesclerosis.Sci.Rep.2020,10,1–6.https://doi.org/10.1038/s41598‐020‐77029‐7.
63. Kaplan,B.;Aizenbud,B.M.;Golderman,S.;Yaskariev,R.;Sela,B.‐A.Freelightchainmonomersinthediagnosisofmultiple
sclerosis.J.Neuroimmunol.2010,229,263–271.https://doi.org/10.1016/j.jneuroim.2010.09.002.
64. Kuhle,J.;Disanto,G.;Dobson,R.;Adiutori,R.;Bianchi,L.;Topping,J.;Bestwick,J.;Meier,U.‐C.;Marta,M.;Costa,G.D.;etal.
Conversionfromclinicallyisolatedsyndrometomultiplesclerosis:Alargemulticentrestudy.Mult.Scler.J.2015,21,1013–
1024.https://doi.org/10.1177/1352458514568827.
65. KonenFF,SchwenkenbecherP,JendretzkyKF,GingeleS,WitteT,SühsKW,GrotheM,HannichMJ,SüßeM,SkripuletzT.
KappaFreeLightChainsinCerebrospinalFluidinInflammatoryandNon‐InflammatoryNeurologicalDiseases BrainSci.2022
Apr3;12(4):475.doi:10.3390/brainsci12040475.PMID:35448006
66. Arneth,B.Author’sResponsetoProfessorReiber’ssecondletterconcerningourarticle:Highsensitivityoffreelambdaand
freekappalightchainsforthedetectionofintrathecalimmunoglobulinsynthesisincerebrospinalfluid.ActaNeurol.Scand.
2009,120,451–452.https://doi.org/10.1111/j.1600‐0404.2009.01240.x.
67. Villar,L.M.;Espiño,M.;Costa‐Frossard,L.;Muriel,A.;Jiménez,J.;Álvarez‐Cermeño,J.C.Highlevelsofcerebrospinalfluid
freekappachainspredictconversiontomultiplesclerosis.Clin.Chim.Acta2012,413,1813–1816.
https://doi.org/10.1016/j.cca.2012.07.007.
68. Pröbstel,A.‐K.;Sanderson,N.S.R.;Derfuss,T.BCellsandAutoantibodiesinMultipleSclerosis.Int.J.Mol.Sci.2015,16,16576–
16592.https://doi.org/10.3390/ijms160716576.
69. SüßeM,FeistnerF,HolbeC,GrotheM,NauckM,DresselA,HannichMJ.DiagnosticvalueofKappaFreeLightChainsin
PatienswithoneisolatedbandinisoelectricfocusingClinChimActa.2020Aug;507:205‐209.doi:10.1016/j.cca.2020.04.029.
Epub2020Apr27.PMID:32353362
70. Süße,M.;Reiber,H.;Grothe,M.;Petersmann,A.;Nauck,M.;Dressel,A.;Hannich,M.J.Freelightchainkappaandthepoly‐
specificimmuneresponseinMSandCIS—Applicationofthehyperbolicreferencerangeformostreliabledatainterpretation.
J.Neuroimmunol.2020,346,577287.https://doi.org/10.1016/j.jneuroim.2020.577287.
71. Pieri,M.;Storto,M.;Pignalosa,S.;Zenobi,R.;Buttari,F.;Bernardini,S.;Centonze,D.;Dessi,M.FLCIndexutilityinmultiple
sclerosisdiagnosis:Furtherconfirmation.J.Neuroimmunol.2017,309,31–33.
72. Kyle,R.A.;Rajkumar,S.V.Criteriafordiagnosis,staging,riskstratificationandresponseassessmentofmultiplemyeloma.
Leukemia2009,23,3–9.
73. Vasilj,M.;Kes,V.B.;Vrkic,N.RelevanceofFLCquantificationtodifferentiateclinicallyisolatedsyndromefrommultiple
sclerosisatclinicalonset.Clin.Neurol.Neurosurg.2018,174,220–229.
74. Geervani,V.;Mohanaih,D.R.P.Extractionofobjectinmulti‐viewsbasedonLDAapproach.Int.J.Sci.Eng.Technol.Res.2017,6,
5166–5171.
75. Tintore,M.;Rovira,A.;Rio,J.;Tur,C.;Pelayo,R.;Nos,C.;Téllez,N.;Perkal,H.;Comabella,M.;Sastre‐Garriga,J.;etal.Do
oligoclonalbandsaddinformationtoMRIinfirstattacksofmultiplesclerosis?Neurology2008,70,1079–1083.
76. Carpendale,M.S.T.;Cowperthwaite,D.J.;DavidFracchia,F.3‐dimensionalpliablesurfaces:Fortheeffectivepresentationof
visualinformation.UcalgaryCa.2012,101,217–225.
77. Abu‐Izneid,T.;Rauf,A.;Shariati,M.A.;Khalil,A.A.;Imran,M.;Rebezov,M.;Uddin,S.;Mahomoodally,M.F.;Rengasamy,
K.R.Sesquiterpenesandtheirderivatives‐naturalanticancercompounds:Anupdate.Pharmacol.Res.2020,161,105165.
https://doi.org/10.1016/j.phrs.2020.105165.
78. Voortman,M.M.;Stojakovic,T.;Pirpamer,L.;Jehna,M.;Langkammer,C.;Scharnagl,H.;Reindl,M.;Ropele,S.;Seifert‐Held,
T.;Archelos,J.‐J.;etal.Prognosticvalueoffreelightchainslambdaandkappainearlymultiplesclerosis.Mult.Scler.J.2016,23,
1496–1505.https://doi.org/10.1177/1352458516681503.
79. Presslauer,S.;Milosavljevic,D.;Brücke,T.;Bayer,P.;Hübl,W.ElevatedlevelsofkappafreelightchainsinCSFsupportthe
diagnosisofmultiplesclerosis.J.Neurol.2008,255,1508–1514.https://doi.org/10.1007/s00415‐008‐0954‐z;Erratumin2009,256,
2115.
80. Senel,M.;Tumani,H.;Lauda,F.;Presslauer,S.;Mojib‐Yezdani,R.;Otto,M.;Brettschneider,J.CerebrospinalFluidImmuno‐
globulinKappaLightChaininClinicallyIsolatedSyndromeandMultipleSclerosis.PLoSONE2014,9,e88680.
https://doi.org/10.1371/journal.pone.0088680.
81. ArrambideG,EspejoC,Carbonell‐MirabentP,Dieli‐CrimiR,Rodríguez‐BarrancoM,CastilloM,AugerC,Cárdenas‐Robledo
S,CastillóJ,Cobo‐CalvoÁ,GalánI,MidagliaL,NosC,Otero‐RomeroS,RíoJ,Rodríguez‐AcevedoB,Ruiz‐OrtizM,SalernoA,
TaglianiP,TurC,Vidal‐JordanaA,ZabalzaA,Sastre‐GarrigaJ,RoviraA,ComabellaM,Hernández‐GonzálezM,Montalban
X,TintoreM.ThekappafreelightchainindexandoligoclonalbandshaveasimilarroleintheMcDonaldsCriteria. Brain.2022
Jun21:awac220.doi:10.1093/brain/awac220.Onlineaheadofprint.PMID:35727945
82. PresslauerS,MilosavljevicD,HueblW,PariggerS,Schneider‐KochG,BrueckeT.KappaFreeLightChains.Diagnosticand
PrognosticRelevanceinMSandCIS. PLoSOne.2014Feb25;9(2):e89945.doi:10.1371/journal.pone.0089945.
Medicina2022,58,151216of17
83. DeCarli,C.;Menegus,M.A.;Rudick,R.A.FreelightchainsinmultiplesclerosisandinfectionsoftheCNS.Neurology1987,37,
1334–1334.https://doi.org/10.1212/wnl.37.8.1334.
84. NataliP,BedinR,BernardiG,CorsiniE,CoccoE,SchirruL,CrespiI,LamonacaM,SalaA,NicolòC,DiFilippoM,VillaA,
NocitiV,DeMicheleT,CavallaP,CaropresoP,VitettaF,CucinelliMR,GastaldiM,TrentiT,SolaP,FerraroD,OnBehalfOf
RiremsRisingResearchersInMs.Inter‐laboratoryconcordanceofcerebrospinalfluidandserumkappafreelightchainmeas‐
urementsBiomolecules.2022May7;12(5):677.doi:10.3390/biom12050677.
85. HussA,Mojib‐YezdaniF,BachhuberF,FangerauT,LewerenzJ,OttoM,TumaniH,SenelM.AssociationofCerebrospinal
KappaFreeLightChainswiththeintrathecalpolyspecificantiviralimmuneresponseinmultiplesclerosis.ClinChimActa.
2019Nov;498:148‐153.doi:10.1016/j.cca.2019.08.016.Epub2019Aug19.PMID:31437445
86. Wootla,B.;Denic,A.;Keegan,B.M.;Winters,J.L.;Astapenko,D.;Warrington,A.E.;Bieber,A.J.;Rodriguez,M.Evidenceforthe
RoleofBCellsandImmunoglobulinsinthePathogenesisofMultipleSclerosis.Neurol.Res.Int.2011,2011,1–14.
https://doi.org/10.1155/2011/780712.
87. Reiber,H.Softwareforcerebrospinalfluiddiagnosticsandstatistics.Rev.Cuba.Investig.Biomédicas2020,39,3–15.
88. McLean,B.N.;Luxton,R.W.;Thompson,E.J.AstudyofimmunoglobulinGinthecerebrospinalfluidof1007patientswith
suspectedneurologicaldiseaseusingisoelectricfocusingandtheLogIgG‐Index.Acomparisonanddiagnosticapplications.
Brain2014,113(Pt5),1269–1289.
89. Teunissen,C.E.;Petzold,A.;Bennett,J.L.;Berven,F.S.;Brundin,L.;Comabella,M.;Franciotta,D.;Frederiksen,J.L.;Fleming,
J.O.;Furlan,R.;etal.Aconsensusprotocolforthestandardizationofcerebrospinalfluidcollectionandbiobanking.Neurology
2009,73,1914–1922.https://doi.org/10.1212/wnl.0b013e3181c47cc2.
90. Deisenhammer,F.;Bartos,A.;Egg,R.;Gilhus,N.E.;Giovannoni,G.;Rauer,S.;Sellebjerg,F.Guidelinesonroutinecerebrospi‐
nalfluidanalysis.ReportfromanEFNStaskforce.Eur.J.Neurol.2006,13,913–922.
https://doi.org/10.1111/j.1468‐1331.2006.01493.x.
91. SanzDiaz,C.T.;delasHerasFlórez,S.;CarreteroPerez,M.;HernándezPérez,M.Á.;MartínGarcía,V.EvaluationofKappa
IndexasaToolintheDiagnosisofMultipleSclerosis:ImplementationinRoutineScreeningProcedure.Front.Neurol.2021,12,
1–11.
92. Rinker,T.;Trinkaus,K.;Cross,A.H.ElevatedCSFfreekappalightchainscorrelatewithdisabilityprognosisinmultiplescle‐
rosis.Neurology2006,67,1288–1290.
93. Dispenzieri,A.;Kyle,R.;Merlini,G.;Miguel,J.S.;Ludwig,H.;Hajek,R.;Palumbo,A.;Jagannath,S.;Blade,J.;Lonial,S.;etal.
InternationalMyelomaWorkingGroupguidelinesforserum‐freelightchainanalysisinmultiplemyelomaandrelateddis‐
orders.Leukemia2008,23,215–224.https://doi.org/10.1038/leu.2008.307.
94. Annunziata,P.;Giorgio,A.;DeSanti,L.;Zipoli,V.;Portaccio,E.;Amato,M.P.;Clerici,R.;Scarpini,E.;Moscato,G.;Iudice,A.;
etal.Absenceofcerebrospinalfluidoligoclonalbandsisassociatedwithdelayeddisabilityprogressioninrelapsing‐remitting
MSpatientstreatedwithinterferon‐β.J.Neurol.Sci.2006,244,97–102.https://doi.org/10.1016/j.jns.2006.01.004.
95. Joseph,F.G.;Hirst,C.L.;Pickersgill,T.P.;Ben‐Shlomo,Y.;Robertson,N.P.;Scolding,N.J.CSFoligoclonalbandstatusinforms
prognosisinmultiplesclerosis:Acasecontrolstudyof100patients.J.Neurol.Neurosurg.Psychiatry2009,80,292–296.
https://doi.org/10.1136/jnnp.2008.150896.
96. Nakano,T.;Matsui,M.;Inoue,I.;Awata,T.;Katayama,S.;Murakoshi,T.Freeimmunoglobulinlightchain:Itsbiologyand
implicationsindiseases.Clin.Chim.Acta2011,412,843–849.https://doi.org/10.1016/j.cca.2011.03.007.
97. Smith,S.M.;Jenkinson,M.;Woolrich,M.W.;Beckmann,C.F.;Behrens,T.E.;Johansen‐Berg,H.;Bannister,P.R.;DeLuca,M.;
Drobnjak,I.;Flitney,D.E.;etal.AdvancesinfunctionalandstructuralMRimageanalysisandimplementationasFSL.Neu‐
roImage2004,23,S208–S219.https://doi.org/10.1016/j.neuroimage.2004.07.051.
98. SaadehRS,BryantSC,McKeonA,WeinshenkerB,MurrayDL,PittockSJ,WillrichMAVCSFKappaFreeLightChains:Cutoff
ValidationforDiagnosingMultipleSclerosis MayoClinProc.2022Apr;97(4):738‐751.doi:10.1016/j.mayocp.2021.09.014.Epub
2021Dec8.PMID:34893322Freearticle.
99. KonenFF,SchwenkenbecherP,WursterU,JendretzkyKF,MöhnN,GingeleS,SühsKW,HannichMJ,GrotheM,WitteT,
StangelM,SüßeM,SkripuletzTheinfluenceofRenalFunctionImpairmentonKappaFreeLightChainsinCerebrospinal
Fluid.JCentNervSystDis.2021Nov19;13:11795735211042166.doi:10.1177/11795735211042166.eCollection2021.PMID:
34840504
100. Gaetani,L.;DiCarlo,M.;Brachelente,G.;Valletta,F.;Eusebi,P.;Mancini,A.;Gentili,L.;Borrelli,A.;Calabresi,P.;Sarchielli,P.;
etal.Cerebrospinalfluidfreelightchainscomparedtooligoclonalbandsasbiomarkersinmultiplesclerosis.J.Neuroimmunol.
2020,339,577108.https://doi.org/10.1016/j.jneuroim.2019.577108.
101. Ramsden,D.B.Multiplesclerosis:Assayoffreeimmunoglobulinlightchains.Ann.Clin.Biochem.Int.J.Lab.Med.2016,54,5–13.
https://doi.org/10.1177/0004563216652175.
102. Dobson,R.;Ramagopalan,S.;Davis,A.;Giovannoni,G.Cerebrospinalfluidoligoclonalbandsinmultiplesclerosisandclini‐
callyisolatedsyndromes:Ameta‐analysisofprevalence,prognosisandeffectoflatitude.J.Neurol.Neurosurg.Psychiatry2013,
84,909–914.https://doi.org/10.1136/jnnp‐2012‐304695.
103. Anagnostouli,M.;Christidi,F.;Zalonis,I.;Nikolaou,C.;Lyrakos,D.;Triantafyllou,N.;Evdokimidis,I.;Kilidireas,C.Clinical
andcognitiveimplicationsofcerebrospinalfluidoligoclonalbandsinmultiplesclerosispatients.Neurol.Sci.2015,36,2053–
2060.https://doi.org/10.1007/s10072‐015‐2303‐1.
Medicina2022,58,151217of17
104. Schwenkenbecher,P.;Konen,F.F.;Wurster,U.;Jendretzky,K.F.;Gingele,S.;Sühs,K.‐W.;Pul,R.;Witte,T.;Stangel,M.;Skrip‐
uletz,T.ThePersistingSignificanceofOligoclonalBandsintheDawningEraofKappaFreeLightChainsfortheDiagnosisof
MultipleSclerosis.Int.J.Mol.Sci.2018,19,3796.https://doi.org/10.3390/ijms19123796.
105. Valencia‐Vera,E.;Garcia‐Ripoll,A.M.‐E.;Enguix,A.;Abalos‐Garcia,C.;Segovia‐Cuevas,M.J.Applicationofκfreelightchains
incerebrospinalfluidasabiomarkerinmultiplesclerosisdiagnosis:Developmentofadiagnosisalgorithm.Clin.Chem.Lab.
Med.(CCLM)2017,56,609–613.https://doi.org/10.1515/cclm‐2017‐0285.
106. Meinl,E.;Derfuss,T.;Krumbholz,M.;Pröbstel,A.‐K.;Hohlfeld,R.Humoralautoimmunityinmultiplesclerosis.J.Neurol.Sci.
2011,306,180–182.https://doi.org/10.1016/j.jns.2010.08.009.
107. Messaoudani,N.;Djidjik,R.;Ghaffor,M.CommentsonCSFκFLCassayevaluationinassessingintrathecalsynthesis.J.Neu‐
roimmunol.2014,266,89.https://doi.org/10.1016/j.jneuroim.2013.11.008.
108. Tosi,P.;Tomassetti,S.;Merli,A.;Polli,V.Serumfreelight‐chainassayforthedetectionandmonitoringofmultiplemyeloma
andrelatedconditions.Ther.Adv.Hematol.2012,4,37–41.https://doi.org/10.1177/2040620712466863.
109. Süße,M.;Hannich,M.;Petersmann,A.;Zylla,S.;Pietzner,M.;Nauck,M.;Dressel,A.Kappafreelightchainsincerebrospinal
fluidtoidentifypatientswitholigoclonalbands.Eur.J.Neurol.2018,25,1134–1139.
110. KaplanB,GoldermanS,YahalomG,YeskaraevR,ZivT,AizenbudBM,SelaBA,LivnehA.Freelightchainsmonomerdimer
patternsinthediagnosisofmultiplesclerosis.JImmunolMethods.2013Apr30;390(1‐2):74‐80.doi:10.1016/j.jim.2013.01.010.
111. Arneth,B.;Birklein,F.Highsensitivityoffreelambdaandfreekappalightchainsfordetectionofintrathecalimmunoglobulin
synthesisincerebrospinalfluid.ActaNeurol.Scand.2009,119,39–44.https://doi.org/10.1111/j.1600‐0404.2008.01058.x.
112. BernardiG,BiagioliT,MalpassiP,DeMicheleT,VecchioD,RepiceAM,LugaresiA,MirabellaM,TorriClericiV,CrespiI.The
contributeofcerebrospinalfluidfreelight‐chainassayinthediagnosisofmultiplesclerosisandotherneurologicaldiseasesin
anItalianmulticenterstudyMultScler.2022Aug;28(9):1364‐1372.doi:10.1177/13524585211064121.Epub2021Dec30.PMID:
34965771
113. AbidMA,AhmedS,MuneerS,KhanS,deOliveiraMHS,KausarR,SiddiquiI.EvaluationofCSFkappafreelighchainsforthe
diagnosisofmultiplesclerosis(MS):Acomparisionwitholigoclonalbands(OCB)detectionviaisoelectricfocusing(IEF)cou‐
pledwithimmunoblotting. JClinPathol.2022Sep21:jclinpath‐2022‐208354.doi:10.1136/jcp‐2022‐208354.Onlineaheadof
print.PMID:36130824
114. Hegen,H.;Berek,K.;Deisenhammer,F.Cerebrospinalfluidkappafreelightchainsasbiomarkerinmultiplesclerosis—From
diagnosistopredictionofdiseaseactivity.Wien.Med.Wochenschr.2022,26,1–9.https://doi.org/10.1007/s10354‐022‐00912‐7.
115. MagliozziR,CrossAH.CanCSFbiomarkerspredictfutureMSdiseaseactivityandseverity? MultScler.2020
Apr;26(5):582‐590.doi:10.1177/1352458519871818.Epub2020Jan22.PMID:31965889
116. Konen,F.F.;Schwenkenbecher,P.;Jendretzky,K.F.;Gingele,S.;Sühs,K.‐W.;Tumani,H.;Süße,M.;Skripuletz,T.TheIn‐
creasingRoleofKappaFreeLightChainsintheDiagnosisofMultipleSclerosis.Cells2021,10,3056.
https://doi.org/10.3390/cells10113056.
... В последние годы содержание свободных легких цепей иммуноглобулинов в ЦСЖ рассматривается как потенциальный маркер РС, а также как возможный предиктор воспалительной активности заболевания [12]. ...
... Оценка свободных легких цепей иммуноглобулинов в ЦСЖ в клинической диагностике РС широко исследуется на протяжении последних 20 лет. В ряде описанных клинических исследований оценка концентрации свободных легких цепей иммуноглобулинов в ЦСЖ имела сопоставимые с исследованием ОКП чувствительность и специфичность [12,13]. Описано несколько методов оценки концентрации лямбда-и каппа-цепей: первыйс помощью ИФА, второй -методом нефелометрии, третий -турбидиметрия. ...
... В полученных результатах выявлено отсутствие статистически значимых различий между группой пациентов с РС и группой сравнения. Сходные результаты бы- [12,14,15]. Вероятно, это связано с недостаточной чувствительностью метода ИФА. ...
Concentration of immunoglobulin free light chains in cerebrospinal fluid in the diagnosis of multiple sclerosis
Article
  • Jun 2024
Objective : to determine the sensitivity and specificity of method of determining the concentration of immunoglobulin free light chains (FLCs) in cerebrospinal fluid (CSF) in the diagnosis and differential diagnosis of multiple sclerosis (MS). Material and methods . 80 patients participated in the study. The main group consisted of 54 patients diagnosed with MS according to the 2017 McDonald criteria. The comparison group (n=26) comprised patients with other diseases of the nervous system. An enzyme-linked immunosorbent assay (ELISA) was used to determine the concentration of FLCs (kappa- and lambda-chains) in the CSF. Results . In the group of patients with MS, an increase in the concentration of free kappa-chains (к-FLCs) in the CSF was found compared to the comparison group (p<0.001). With an increase in the concentration of κ-FLCs, a decrease in the sensitivity and an increase in the specificity of the method for the diagnosis of MS was observed. The к-FLCs cut-off value of 0.17 μg/ml had a sensitivity of 68.5 % and a specificity of 92.3 %. The cut-off value of 0.22 μg/ml had a sensitivity of 59.3 % and a specificity of 100 %. The concentrations of lambda-FLCs in the CSF in the MS group and in the comparison, group did not differ significantly (p=0.1). Conclusion . The results obtained indicate an increase in the concentration of к-FLCs in the CSF of MS patients. This biomarker showed a high specificity for this pathology. However, further development of optimal thresholds is required to clarify the diagnostic value of CSF к-FLCs concentration in MS patients.
View
... Aside from NfL, other circulating and cerebrospinal fluid markers are starting to a ract a ention for a more in-depth characterization of neuroinflammatory diseases. For instance, in multiple sclerosis (MS), altered levels of free light chains (FLCs) have also been identified in both blood and CSF [10,11]. Notably, a recent meta-analysis by Arneth et al. has suggested the potential benefits of utilizing FLCs for the diagnosis of MS and clinically isolated syndrome (CIS) due to the efficiency and cost-effectiveness of their isolation and analysis [10]. ...
... For instance, in multiple sclerosis (MS), altered levels of free light chains (FLCs) have also been identified in both blood and CSF [10,11]. Notably, a recent meta-analysis by Arneth et al. has suggested the potential benefits of utilizing FLCs for the diagnosis of MS and clinically isolated syndrome (CIS) due to the efficiency and cost-effectiveness of their isolation and analysis [10]. Similarly, a consensus statement indicates that FLCs accumulate in the CSF in cases of chronic inflammatory diseases of the central nervous system [11]. ...
... One possible explanation for these findings can be derived from considering the different structures of kappa and lambda, with the former being a monomer, while the la er tends to dimerize. Concerning MS, Arneth et al. hypothesized that λFLC dimers may have a reduced propensity to cross the blood-CSF barrier compared to monomeric kFLC [10]. If this same mechanism was confirmed in our case, it could potentially explain the observed behavior in Table 2 and Figure 3: the more severe the inflammation in the peripheral nervous system (PNS), the higher the levels of both of these markers in the CSF. ...
Serum Neurofilament and Free Light Chain Levels in Patients Undergoing Treatment for Chronic Inflammatory Demyelinating Polyneuropathy
Article
Full-text available
  • Jan 2024
  • INT J MOL SCI
Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is an immune-mediated disorder affecting the peripheral nervous system. Despite the established diagnostic criteria, monitoring disease activity and treatment remains challenging. To address this limitation, we investigated serum neurofilament light chain (sNfL) and serum free light chains (sFLCs) as potential biomarkers. A total of 32 CIDP patients undergoing immunoglobulin therapy and 32 healthy controls enrolled in the present study, and agreed to have their blood plasma sNfL and sFLCs analyzed, while CIDP severity was assessed through the modified Rankin Scale (mRS) and the Overall Neuropathy Limitations Scale (ONLS). In line with the immunoglobulin treatment aimed at limiting neuronal damage administered to the majority of patients, sNfL levels did not exhibit significant differences between the two groups. However, CIDP patients showed significantly elevated sFLC and sFLC ratios, while the marker levels did not correlate with the clinical scores. The study confirms the potential of sFLCs as a sensitive biomarker of inflammatory processes in CIDP. Additionally, the present study results regarding neurofilaments strengthen the role of sNfL in monitoring CIDP treatments, confirming the effectiveness of immunoglobulin therapy. Overall, our results demonstrate how combining these markers can lead to better patient characterization for improved treatment.
View
... This parameter is determined by dividing the ratio of CSF-to-serum FKLC (Q FKLC ) by the ratio of CSF-to-serum albumin (Q alb ) (Fig. 1A) FKLCi shows comparable or even superior predictive capacity compared to OCB in the diagnosis of MS; nevertheless, as per several studies, it displays a slightly lower level of specificity (H Hegen et al., 2023;H Hegen et al., 2023). The analysis of FKLC presents expeditious and automated determinations and has been found to be more cost-effective compared to OCB (H Hegen et al., 2023;Arneth and Kraus, 2022;Leurs et al., 2020;Rosenstein et al., 2021). In fact, a recent consensus supports the use of FKLC as the initial approach, reserving the determination of OCB for cases where a borderline result is obtained (H Hegen et al., 2023). ...
View
... The presence of oligoclonal bands (OCBs) in the cerebrospinal fluid (CSF) is a diagnostic marker (3) and a negative predictor of MS evolution, indicating a greater progression in OCB-positive patients (4). Moreover, a measure of intrathecal production of kappa-free light chains is gaining interest as a quantitative alternative to OCB which is also a valuable diagnostic tool (5,6) and predicts early activity of the disease (7). Currently, one of the most promising biomarkers is neurofilaments, which are cytoskeletal proteins released from damaged axons into the CSF and the blood. ...
Predictive value of brain atrophy, serum biomarkers and information processing speed for early disease progression in multiple sclerosis
Article
Full-text available
  • Jul 2023
Introduction Multiple sclerosis (MS) is a chronic autoimmune-mediated demyelinating disease of the central nervous system (CNS). A clinical presentation of the disease is highly differentiated even from the earliest stages of the disease. The application of stratifying tests in clinical practice would allow for improving clinical decision-making including a proper assessment of treatment benefit/risk balance. Methods This prospective study included patients with MS diagnosed up to 1 year before recruitment. We analyzed serum biomarkers such as CXCL13, CHI3L1, OPN, IL-6, and GFAP and neurofilament light chains (NfLs); brain MRI parameters of linear atrophy such as bicaudate ratio (BCR), third ventricle width (TVW); and information processing speed were measured using the Symbol Digit Modalities Test (SDMT) during the 2 years follow-up. Results The study included a total of 50 patients recruited shortly after the diagnosis of MS diagnosis (median 0 months; range 0–11 months), and the mean time of observation was 28 months (SD = 4.75). We observed a statistically significant increase in the EDSS score (Wilcoxon test: Z = 3.06, p = 0.002), BCR (Wilcoxon test: Z = 4.66, p < 0.001), and TVW (Wilcoxon test: Z = 2.84, p = 0.005) after 2 years of disease. Patients who had a significantly higher baseline level of NfL suffered from a more severe disease course as per the EDSS score (Mann–Whitney U-test: U = 107, Z = −2,74, p = 0.006) and presence of relapse (Mann–Whitney U-test: U = 188, Z = −2.01, p = 0.044). In the logistic regression model, none of the parameters was a significant predictor for the achieving of no evidence of disease activity status (NEDA). In the model considering all assessed parameters, only the level of NfL had a significant impact on disease progression, measured as the increase in EDSS (logistic regression: β = 0.002, p = 0.017). Conclusion We confirmed that NfL levels in serum are associated with more active disease. Moreover, we found that TVW at the time of diagnosis was associated with an impairment in cognitive function measured by information processing speed at the end of the 2-year observation. The inclusion of serum NfL and TVW assessment early in the disease may be a good predictor of disease progression independent of NEDA.
View
View
View
Evaluation of CSF kappa free light chains for the diagnosis of multiple sclerosis (MS): a comparison with oligoclonal bands (OCB) detection via isoelectric focusing (IEF) coupled with immunoblotting
Article
Full-text available
  • Sep 2022
This study was done to evaluate the diagnostic accuracy of cerebrospinal fluid kappa free light chain (KFLC) for diagnosis of multiple sclerosis, against isoelectrofocusing (IEF) to detect oligoclonal bands (OCB) as gold standard. 64 cases were divided into positive and negative based on the OCB results. Diagnostic accuracy was calculated for the 1 mg/L cut-off. The 1 mg/L cut-off yielded a percent agreement of 86.1% and Cohen’s kappa value of 0.8. Youden’s index, yielded a cut-off of 0.92 mg/L as optimal (90.3% specificity and 90.9% sensitivity). The analytical time was 3 hours and 55 min for IEF and 25 min for KFLC. The cost of a single OCB test was PKR12 000 (US$68.17) compared with PKR4150 (US$23.58) for KFLC. KFLC proved to be an accurate, cheaper and time-saving alternative and can be performed prior to the contemporary testing.
View
Different roles of urinary light chains and serum light chains as potential biomarkers for monitoring disease activity in systemic lupus erythematosus
Article
Full-text available
  • May 2022
Objective The assessment system for monitoring systemic lupus erythematosus (SLE) disease activity is complex and lacks reliable laboratory indicators. It is necessary to find rapid and noninvasive biomarkers. The aim of this study was to screen and identify the differentially expressed proteins in urine samples between active SLE and stable SLE and to further explore the expression of light chains. Methods First, we used a label-free quantitative proteomics approach to establish the urine protein expression profile of SLE, and then screened differentially expressed proteins. Subsequently, the expression of overall light chains was examined by immunofixation electrophoresis and immunoturbidimetric methods, respectively. Results Mass spectrometry data analysis found a total of 51 light chain peptides in the urinary protein expression spectrum, of which 27 light chain peptides were differentially expressed between the two groups. The largest difference was IGLV5-45 located in the variable region of the immunoglobulin Lambda light chain. The levels of urinary light chains and serum light chains were both significantly elevated in active SLE, and the levels of urinary light chains increased with the severity of disease activity. Conclusions The measurement of light chains would help to monitor SLE disease activity. Serum light chains had better discriminatory capacity than urinary light chains, while urine light chains were closely related to the severity of disease activity and could be used for dynamically monitoring the progress of disease activity.
View
Inter-Laboratory Concordance of Cerebrospinal Fluid and Serum Kappa Free Light Chain Measurements
Article
Full-text available
  • May 2022
The kappa index (K-Index), calculated by dividing the cerebrospinal fluid (CSF)/serum kappa free light chain (KFLC) ratio by the CSF/serum albumin ratio, is gaining increasing interest as a marker of intrathecal immunoglobulin synthesis. However, data on inter-laboratory agreement of these measures is lacking. The aim was to assess the concordance of CSF and serum KFLC measurements, and of K-index values, across different laboratories. KFLC and albumin of 15 paired CSF and serum samples were analyzed by eight participating laboratories. Four centers used Binding Site instruments and assays (B), three used Siemens instruments and assays (S), and one center used a Siemens instrument with a Binding Site assay (mixed). Absolute individual agreement was calculated using a two-way mixed effects intraclass correlation coefficient (ICC). Cohen’s kappa coefficient (k) was used to measure agreement on positive (≥5.8) K-index values. There was an excellent agreement in CSF KFLC measurements across all laboratories (ICC (95% confidence interval): 0.93 (0.87–0.97)) and of serum KFLC across B and S laboratories (ICC: 0.91 (0.73–0.97)), while ICC decreased (to 0.81 (0.53–0.93)) when including the mixed laboratory in the analysis. Concordance for a positive K-Index was substantial across all laboratories (k = 0.77) and within S laboratories (k = 0.71), and very good (k = 0.89) within B laboratories, meaning that patients rarely get discordant results on K-index positivity notwithstanding the testing in different laboratories and the use of different platforms/assays.
View
Kappa Free Light Chains in Cerebrospinal Fluid in Inflammatory and Non-Inflammatory Neurological Diseases
Article
Full-text available
  • Apr 2022
  • BSRCCS
Background: Oligoclonal bands represent intrathecal immunoglobulin G (IgG) synthesis and play an important role in the diagnosis of multiple sclerosis (MS). Kappa free light chains (KFLC) are increasingly recognized as an additional biomarker for intrathecal Ig synthesis. However, there are limited data on KFLC in neurological diseases other than MS. Methods: This study, conducted at two centers, retrospectively enrolled 346 non-MS patients. A total of 182 patients were diagnosed with non-inflammatory and 84 with inflammatory neurological diseases other than MS. A further 80 patients were classified as symptomatic controls. Intrathecal KFLC production was determined using different approaches: KFLC index, Reiber's diagram, Presslauer's exponential curve, and Senel's linear curve. Results: Matching results of oligoclonal bands and KFLC (Reiber's diagram) were frequently observed (93%). The Reiber's diagram for KFLC detected intrathecal KFLC synthesis in an additional 7% of the patient samples investigated (4% non-inflammatory; 3% inflammatory), which was not found by oligoclonal band detection. Conclusions: The determination of both biomarkers (KFLC and oligoclonal bands) is recommended for routine diagnosis and differentiation of non-inflammatory and inflammatory neurological diseases. Due to the high sensitivity and physiological considerations, the assessment of KFLC in the Reiber's diagram should be preferred to other evaluation methods.
View
Cerebrospinal fluid kappa free light chains as biomarker in multiple sclerosis—from diagnosis to prediction of disease activity
Article
Full-text available
  • Feb 2022
Multiple sclerosis (MS) is a chronic immune-mediated disorder of the central nervous system that shows a high interindividual heterogeneity, which frequently poses challenges regarding diagnosis and prediction of disease activity. In this context, evidence of intrathecal inflammation provides an important information and might be captured by kappa free light chains (κ-FLC) in the cerebrospinal fluid (CSF). In this review, we provide an overview on what is currently known about κ‑FLC, its historical development, the available assays and current evidence on its diagnostic and prognostic value in MS. Briefly, intrathecal κ‑FLC synthesis reaches similar diagnostic accuracy compared to the well-established CSF-restricted oligoclonal bands (OCB) to identify patients with MS, and recent studies even depict its value for prediction of early MS disease activity. Furthermore, detection of κ‑FLC has significant methodological advantages in comparison to OCB detection.
View
The kappa free light chain index and oligoclonal bands have a similar role in the McDonald criteria
Article
  • Jun 2022
Intrathecal production of kappa free light chains (KFLC) occurs in multiple sclerosis and can be measured using the KFLC index. KFLC index values can be determined more easily than oligoclonal bands (OB) detection and seem more sensitive than the immunoglobulin (Ig)G index to diagnose multiple sclerosis. We assessed the value of OB, KFLC index cut-offs 5.9, 6.6, and 10.61, and IgG index to diagnose multiple sclerosis with prospectively acquired data from a clinically isolated syndrome (CIS) inception cohort. We selected patients with sufficient data to determine OB positivity, MRI dissemination in space (DIS) and time (DIT), IgG index, and sufficient quantities of paired CSF and blood samples to determine KFLC indexes (n = 214). We used Kendall´s Tau coefficient to estimate concordance; calculated the number of additional diagnoses when adding each positive index to DIS and positive OB; performed survival analyses for OB and each index with the outcomes second attack and 2017 MRI DIS and DIT; and estimated the diagnostic properties of OB and the different indexes for the abovementioned outcomes at five years. OB were positive in 138 patients (64.5%), KFLC-5.9 in 136 (63.6%), KFLC-6.6 in 135 (63.1%), KFLC-10.61 in 126 (58.9%) and IgG index in 101 (47.2%). The highest concordance was between OB and KFLC-6.6 (τ=0.727) followed by OB and KFLC-5.9 (τ=0.716). Combining DIS plus OB or KFLC-5.9 increased the number of diagnosed patients by 11 (5.1%), with KFLC-6.6 by 10 (4.7%), with KFLC-10.61 by 9 (4.2%), and with IgG index by 3 (1.4%). Patients with positive OB or indexes reached second attack and MRI DIS and DIT faster than patients with negative results (P < 0.0001 except IgG index in second attack: P = 0.016). In multivariable Cox models [aHR (95% CI)], the risk for second attack was very similar between KFLC-5.9 [2.0 (0.9-4.3), P = 0.068] and KFLC-6.6 [2.1 (1.1-4.2), P = 0.035]. The highest risk for MRI DIS and DIT was demonstrated with KFLC-5.9 [4.9 (2.5-9.6), P < 0.0001], followed by KFLC-6.6 [3.4 (1.9-6.3), P < 0.0001]. KFLC-5.9 and KFLC-6.6 had a slightly higher diagnostic accuracy than OB for second attack (70.5, 71.1, and 67.8) and MRI DIS and DIT (85.7, 85.1, and 81.0). KFLC indexes 5.9 and 6.6 performed slightly better than OB to assess multiple sclerosis risk and in terms of diagnostic accuracy. Given the concordance between OB and these indexes, we suggest using DIS plus positive OB or positive KFLC index as a modified criterion to diagnose multiple sclerosis.
View
Mono/polyclonal free light chains as challenging biomarkers for immunological abnormalities
Chapter
  • Jan 2022
  • ADV CLIN CHEM
Free light chain (FLC) kappa (k) and lambda (λ) consist of low molecular weight proteins produced in excess during immunoglobulin synthesis and secreted into the circulation. In patients with normal renal function, over 99% of FLCs are filtered and reabsorbed. Thus, the presence of FLCs in the serum is directly related to plasma cell activity and the balance between production and renal clearance. FLCs are bioactive molecules that may exist as monoclonal (m) and polyclonal (p) FLCs. These have been detected in several body fluids and may be key indicators of ongoing damage and/or illness. International guidelines now recommend mFLC for screening, diagnosis and monitoring multiple myeloma and other plasma cell dyscrasias. In current clinical practice, FLCs in urine indicate cast nephropathy and other renal injury, whereas their presence in cerebrospinal fluid is important for identifying central nervous system inflammatory diseases such as multiple sclerosis. Increased pFLCs have also been detected in various conditions characterized by B cell activation, i.e., chronic inflammation, autoimmune disease and HCV infection. Monitoring the coronavirus (COVID-19) pandemic by analysis of salivary FLCs presents a significant opportunity in clinical immunology worthy of scientific pursuit.
View
Kappa free light chains: Diagnostic performance in multiple sclerosis and utility in a clinical laboratory
Article
  • Mar 2022
  • CLIN CHIM ACTA
Background Automated, technically simple analytical methods offering objective results are highly valued in clinical laboratories. Kappa free light chains (KFLC) in cerebrospinal fluid (CSF) are promising multiple sclerosis (MS) biomarkers, particularly kappa (K) index. Methods KFLC were determined in CSF and serum samples of patients diagnosed with MS, clinically/radiologically isolated syndrome (N, 39), and controls (N, 152; inflammatory and non-inflammatory neurological disorders). Diagnostic performance of several KFLC parameters, previously determined oligoclonal band (OCB) testing, and IgG index, was assessed. A K index decision threshold for sample screening was identified and reduction in performed OCB analyses estimated accordingly. Results Higher KFLC parameters were detected in the MS group and K index performed best among them (AUC 0.92). At a 7.25 cut-off it showed better sensitivity (85% vs. 77%) though less specificity (88% vs. 91%) than OCBs. Comparatively, IgG index’s performance was inferior (AUC 0.83). A decision K index threshold of 2.55 (97% sensitivity) would reduce OCB testing by 52% in the studied population. Conclusions The proposed 7.25 cut-off could assist MS diagnostics and identify some false negative cases from OCB studies. Sequential algorithms using K index for the decision to perform OCB detection would improve laboratory efficiency and substantially reduce costs.
View
The contribute of cerebrospinal fluid free light-chain assay in the diagnosis of multiple sclerosis and other neurological diseases in an Italian multicenter study
Article
  • Dec 2021
Background Cerebrospinal fluid (CSF) free light chains (FLCs) can be an alternative assay to oligoclonal bands (OCBs) in inflammatory neurological disorders, but threshold has no consensus. Objective To assess the diagnostic accuracy of CSF FLCs in multiple sclerosis (MS) and other neurological diseases. Methods A total of 406 patients from five Italian centers. FLCs were measured in CSF and serum using Freelite MX assays on Optilite. Results A total of 171 patients were diagnosed as MS, 154 non-inflammatory neurological diseases, 48 inflammatory central nervous system (CNS) diseases, and 33 peripheral neurological diseases. Both kFLC and λFLC indices were significantly higher in patients with MS compared to other groups ( p < 0.0001). The kFLC index ⩾ 6.4 is comparable to OCB for MS diagnosis (area under the receiver operating characteristic curve (AUC) = 0.876; sensitivity 83.6% vs 84.2%; specificity 88.5% vs 90.6%). λFLC index ⩾ 5 showed an AUC of 0.616, sensitivity of 33.3% and specificity of 90.6%. In all, 12/27 (44.4%) MS patients with negative OCB had kFLC index ⩾ 6.4. Interestingly, 37.5% of 24 patients with a single CSF IgG band showed high kFLC index and 12.5% positive λFLC index. Conclusion Our findings support the diagnostic utility of FLC indices in MS and other CNS inflammatory disorders, suggesting a combined use of FLC and OCB to help clinicians with complementary information.
View
CSF Kappa Free Light Chains: Cutoff Validation for Diagnosing Multiple Sclerosis
Article
  • Dec 2021
  • MAYO CLIN PROC
Objective To determine and validate a cerebrospinal fluid (CSF) κ (KCSF) value statistically comparable to detection of CSF-specific oligoclonal bands (OCB) to support the diagnosis of multiple sclerosis (MS). Patients and Methods A total of 702 retrospective and 657 prospective paired CSF/serum samples from residual waste samples of physician-ordered OCB tests were obtained and tested for KCSF at Mayo Clinic. Charts were reviewed by a neurologist blinded to KCSF results. Specificity and sensitivity for MS diagnosis were evaluated to establish a diagnostic cutoff value for KCSF in the retrospective cohort and then validated in the prospective cohort. Results Retrospective and prospective subgroups, respectively, included MS (n=85, 70), non-MS (n=615, 585), and undetermined diagnosis (excluded, n=2, 2). The retrospective data established a KCSF cutoff value of 0.1 mg/dL to be comparable to OCB testing. In the retrospective subgroup, KCSF vs OCB sensitivities for diagnosis of MS were 68.2% vs 75.0% (P=.08) and specificities were 86.1% vs 87.6% (P=.27). The KCSF area under the receiver operating characteristic curve was 0.772 (95% CI, 0.720 to 0.824), and for OCB was 0.813 (95% CI, 0.764 to 0.861). The prospective cohort was then used to validate the diagnostic KCSF value of 0.1 mg/dL; KCSF vs OCB sensitivities were 78.6% for both (P>.99) and specificities were 87.1% vs 89.4% (P=.09). Conclusion The KCSF value of 0.1 mg/dL is a valid alternative to OCB testing, offering a standardized quantitative measure, eliminating human error, reducing cost and turnaround time, with no significant difference in sensitivity and specificity. This study provides class I evidence that a KCSF value of 0.1 mg/dL can be used in place of OCB testing to support the diagnosis of MS.
View