Declining Physical Performance Associates with Serum FasL, miR-21, and miR-146a in Aging ...

15 Pages·2017·1.64 MB·English

Hindawi
BioMed Research International
Volume 2017, Article ID 8468469, 14 pages
https://doi.org/10.1155/2017/8468469
Research Article
Declining Physical Performance Associates with Serum FasL,
miR-21, and miR-146a in Aging Sprinters
ReetaKangas,1TimoTörmäkangas,1AriHeinonen,1MarkkuAlen,2,3HarriSuominen,1
VuokkoKovanen,1EijaK.Laakkonen,1andMarkoT.Korhonen1
1GerontologyResearchCenter,DepartmentofHealthSciences,UniversityofJyvaskyla,Jyvaskyla,Finland
2DepartmentofMedicalRehabilitation,OuluUniversityHospital,Oulu,Finland
3CenterforLifeCourseHealthResearch,UniversityofOulu,Oulu,Finland
CorrespondenceshouldbeaddressedtoReetaKangas;[email protected]
Received22April2016;Revised2November2016;Accepted21November2016;Published3January2017
AcademicEditor:DavidC.Hughes
Copyright©2017ReetaKangasetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Agingisassociatedwithsystemicinflammationandcellularapoptosisacceleratingphysiologicaldysfunctions.Whetherphysically
activewayoflifeaffectstheseassociationsisunclear.Thisstudymeasuredthelevelsofseruminflammatoryandapoptoticmolecules,
theirchangeover10years,andtheirassociationswithphysicalperformanceinsprint-trainedmaleathletes.HsCRP,cellcounts,
HGB,FasL,miR-21,andmiR-146aweremeasuredcross-sectionally(𝑛=67,18–90yrs)andserumFasL,miR-21,andmiR-146aand
theiraging-relatedassociationswithphysicalperformancewereassessedovera10-yearfollow-up(𝑛 = 49,50–90yrs).Thecross-
sectionalstudyshowedpositiveagecorrelationsforneutrophilsandnegativeforlymphocytes,redbloodcells,HGB,FasL,andmiR-
146a.Duringthe10-yearfollow-up,FasLdecreased(𝑃=0.017)andmiR-21(𝑃<0.001)andmiR-146a(𝑃=0.005)levelsincreased.
Whencombiningthemoleculelevels,aging,andphysicalperformance,FasLassociatedwithcountermovementjumpandbench
press(𝑃<0.001),miR-21andmiR-146awithkneeflexion(𝑃=0.023;𝑃<0.001),andbenchpress(𝑃=0.004;𝑃<0.001)andmiR-
146awithsprintperformance(𝑃<0.001).Thestudiedserummoleculeschangedinanage-dependentmannerandwereassociated
withdecliningphysicalperformance.Theyhavepotentialasbiomarkersofaging-relatedprocessesinfluencingthedevelopmentof
physiologicaldysfunctions.FurtherresearchisneededfocusingontheoriginsandtargetsofcirculatingmicroRNAstoclarifytheir
functioninvarioustissueswithaging.
1.Introduction a deficit of important cells, both recognized in aging. One
way to regulate immune cell homeostasis, and subsequent
Physical exercise affects inflammatory state. The common immuneresponse,isthroughFasligand-Fasreceptor(FasL-
understanding is that an acute bout of exercise results in Fas)interactiononthecellsurfaces,whichleadstotargetcell
a temporal inflammatory response, while regular training destruction [6]. There are indications that habitual training
has a protective anti-inflammatory effect [1–4]. In addition influences the apoptotic processes of the immune system.
to immune cells, skeletal muscle tissue contributes to the AccordingtoMoorenetal.[7],thebasallevelsofleukocyte
inflammatoryresponsebyreleasinginflammatorymolecules, apoptosisaswellasthelevelsofexercise-inducedapoptosis
such as TNF-𝛼 and IL-6, following of acute exercise [5]. aredistinctbetweenhighlytrainedandpoorlytrainedmen,
During inflammation, whether induced by age, disease, or suggesting training-induced adaptation to leukocyte home-
acuteexercise,controllingthecellularbalanceoftheinflam- ostasis.
matory cells is important. Apoptosis, or programmed cell Traces from inflammation and cellular apoptosis can
destruction,isacrucialmechanismformaintainingcellular be detected from the circulation by measuring specific
balanceinalltissues.Disruptionsinthecellularhomeostasis moleculesfromtheblood.Inadditiontotheclassicalinflam-
leadtoeitheranaccumulationofpoorlyfunctioningcellsor mation (e.g., CRP, IL6, and TNF-𝛼) and apoptotic markers
2 BioMedResearchInternational
Cross-sectional Group A Group B Group C Group D
2012
Age (yrs) 18to39 50to66 66to79 79to90
N 18 16 18 15
Follow-up Baseline End-point Baseline End-point Baseline End-point
baseline in2002,
end-point in2012
Age (yrs) 40to56 50to66 56to69 66to79 69to80 79to90
N 16 16 18 18 15 15
Figure1:Descriptionsofthestudydesignsneededinthecurrentstudy.
(cytochrome c, Fas, and FasL), the small RNA molecules In addition, the highest levels of miR-146a during exercise
called microRNAs (miRs) are novel and potentially even were found to correlate with peak VO2max [15], whereas
more sensitive tools for screening these physiological pro- miR-21 levels have been shown to be upregulated in males
cesses.miRsarenoncodingRNAsregulatinggeneexpression withlowVO2 max,indicatingthecontrastingrolesofthese
by blocking the translation of specific target mRNA into two miRs [18]. Nielsen et al. [17] demonstrated that miR-
proteins.miRsarereleased,eitheractivelyorpassively,from 146a decreased immediately after a bout of acute exercise,
differentcelltypesintothebloodstream,wheretheyreflect whereasthebasallevelsofmiR-21weredownregulatedafter
thegeneregulationchangesintheircellsoforigin.Theirrole 12weeksofendurancetraining.BothmiR-21andmiR-146a
inintercellularcommunicationhasalsobeenrecognized[8]. havealsobeenshowntoexistatdifferentlevelsintheplasma
Inhealthyconditions,circulatingmiRsmostlikelyoriginate ofyoungmaleenduranceandstrengthtrainedathletes[19].
from blood or epithelial cells or from organs with high Theabove-mentionedstudiesdemonstratethatthesespecific
vascularization,includingskeletalmusclecells[9].Owingto miRs detected in the circulation are affected differently by
trauma, cancer, cardiovascular disease, or other conditions exercise type and duration and have potential as indicators
influencingmetabolism,themiRsignatureinthecirculation ofphysiologicalchanges[15,16,19,20].
changes. InordertoevaluateifcirculatingFasL,miR-21,andmiR-
mir-21andmiR-146aareamongthemiRsthatseemtobe 146ahavethepotentialasbiomarkersoftrainingadaptations
responsive to various physiological stimuli. These miRs are inagingathletes,longitudinalstudiesareneeded.Theaimof
associated with aging-related processes such as senescence thisstudywastodeterminewhethercirculatingFasL,miR-
andinflammation,miR-21havingproinflammatoryandmiR- 21, and miR-146a levels change over 10-year period among
146a having anti-inflammatory effects [10, 11]. Both miRs competitivemalemasterssprinterswithalong-termtraining
induce apoptosis by targeting FasL-Fas signaling, which background and to assess their associations with physical
strengthenstheinterplayofthesemoleculeswitheachother performancemeasuresandaging.
and their role as regulators of immune cell homeostasis
[12,13].CirculatingmiR-21hasalsobeenshowntopromote 2.Methods
cachexia-relatedapoptosisinskeletalmusclecells[14].Phys-
ical exercise creates a whole body adaptive responses that 2.1.StudyDesignandEthics. Thisstudyispartofanongoing
also affect miR regulation and expression in different cell Athlete Aging Study (ATHLAS) [21–25] on young adult
types and subsequently in the circulation [15–17]. miR-21 athletesandmastersathletesfromdifferentsportdisciplines.
andmiR-146ahavealsobeenshowntobehighlyresponsive The participants were recruited from the memberships of
to physical exercise. The pioneering work by Baggish et Finnishathleticorganizations.Thepresentstudycomprised
al. [15] demonstrated that these miRs were associated with malesprinters.Thesprintershadalong-termbackgroundin
cardiovascular/musculoskeletal adaptations and low-grade sprinttrainingandhadbeensuccessfulinnationalorinterna-
inflammation and changed in response to acute exercise as tional100–400msprintingevents.Bothcross-sectionaland
well as sustained training. CirculatingmiR-146a levels were longitudinalstudydesignswereused(Figure1).Inthecross-
upregulatedbyacutecyclingexercisebeforeandafter90days sectionalanalysis(usingdatafromfollow-upmeasurements
ofsustainedrowtraining,whereasmiR-21wasonlyupregu- conductedin2012),thesprintersweredividedintofourage
latedbyacuteexercisepriortothesustainedtrainingperiod. groups:(A)18to39years(𝑛=18),(B)50to66years(𝑛=16),
BioMedResearchInternational 3
(C)66to79years(𝑛=18),and(D)79to90years(𝑛=15).In USA) according to the manufacturer’s protocol. Reactions
addition, baseline measurements (conducted in 2002) were were performed as duplicates and a common sample was
availablefor49oldermasterssprinters(aged50to90years addedtoeachplateinordertoobservewhethercontrolling
at follow-up) belonging to age groups B, C, and D, thereby forinterassayvariationwasnecessary.HsCRPmeasurements
allowing longitudinal analysis. Both the baseline and the atbaselineandfollow-upwereperformedwithanImmulite
follow-up measurements consisted of similar standardized 1000ImmunoassaySystem.Leukocytecountswerepartofthe
two-daymeasurements. standardmedicalcompletebloodcountanalysis.
Prior to the measurements, training and health history
wereelicitedandevaluatedusingquestionnaires.Theaverage
2.4.RNAExtractionandmiRAnalyses. TheRNAextraction
number of training years among the masters athletes at
methods and miR-21 and miR-146a analyses have been
baseline was 34.1 ± 15.1 years. More detailed self-reported
described previously [26]. Briefly, total RNA was isolated
traininghistories(trainingfrequency,sprint-specifictraining from100𝜇lofserumwithatotalRNApurificationkit(Nor-
hours, and other training hours) and their changes over 10
gen Biotek Corporation, Thorold, ON, Canada) according
years are presented in Tables 1 and 3. The sprint-specific
to the manufacturer’s protocol. Synthetic C. elegans Cel-
traininghoursperweekincludedsprints,jumps,andstrength 󸀠 󸀠
miR-39 (5 -UCA CCG GGU GUA AAU CAG CUU G-3 ,
training, while the other training hours included all other
Invitrogen) (25fM, concentration determined by dilution
notably strenuous physical exercises. Subjects over age 55
series) was added at the lysis step to all of the samples as
underwentamedicalexamination.Currentabilitytopartic-
a spike-in control in order to monitor the efficiency and
ipateinphysicallydemandingmeasurementwasassessedad
uniformity of the RNA extraction and qPCR procedure.
hoc,individuallybythestudyphysician.Thehealthofallthe
participants was in general good with no acute conditions RNA was reverse-transcribed to cDNA (𝑉tot = 10𝜇l) by
(infections,traumas)orfunctionallylimitingchronicneuro- a Taqman reverse transcription kit and the qPCR (𝑉tot =
logical,cardiovascular,endocrinological,ormusculoskeletal 10𝜇l) was performed with a Taqman Universal Mastermix
󸀠
conditions. In a few cases, some physical tests were not II NO Ung using Taqman miR assays (hsa-miR-21: 5 -
󸀠 󸀠
performedduetolocalmusculoskeletalpain. UAGCUUAUCAGACUGAUGUUGA-3 , hsa-miR-146a: 5 -
󸀠
Thestudywasconductedaccordingtotheguidelinesof UGAGAACUGAAUUCCAUGGGUU-3 ) (Device: Applied
theDeclarationofHelsinki.Allparticipantswereinformeda Biosystems,ABI7300).
priori about the possible risks and discomfort of the phys- Ct values less than 35 were accepted for the analysis.
ical and clinical measurements. Written informed consent, All the samples were normalized to a reference sample of
including permission for the use of the gathered data for an average sprint athlete across the plates. ΔCt values were
researchpurposesonly,wasprovidedbythestudysubjects. calculated as ΔCt = mean CtmiR-X − mean CtmiR(reference).
The study protocol was approved by the ethics committees Each reaction was performed in duplicate and the relative
oftheUniversityofJyva¨skyla¨(in2002forbaseline)andthe expressions were calculated by using the 2−ΔCt method. To
CentralFinlandHealthcareDistrict(in2012forfollow-up). observethepossiblecontaminationsandprimer-dimers,no
templatecontrols(NTCs)wereusedineithertheRTorqPCR
2.2. Physical Performance and Body Composition Measure- reactions.
ments. Participantswereinstructedtorest(noheavytraining
orcompetition)twodaysbeforethemeasurements.Onthe
2.5. Statistical Analyses. In the age-correlation analyses of
firstmeasurementday,theparticipantsperformedamaximal
the cross-sectional design, Pearson’s correlation coefficient
60m sprint twice on an indoor running track with spiked
wasusedforcontinuousvariablesandSpearman’scorrelation
running shoes. Explosive force production of the lower coefficient for ordinal variables. The paired sample 𝑡-test
limbs was measured by a vertical countermovement jump
for parametric variables and Wilcoxon signed rank test
(CMJ).Isometrickneeflexionforceandisometricupperlimb
for nonparametric variables were used in the longitudinal
force were measured by bench press performance using a
comparison in the 10-year follow-up design. In addition,
David 200 dynamometer (David Fitness and Medical Ltd.,
GeneralizedEstimatingEquations(GEE)modelswerecon-
Outokumpu,Finland).Thebestof2or3trialswasrecorded
structedto study theassociationofFasL,miR-21, andmiR-
in the subsequent analyses. In addition, the assessment of
146a with the performance measures over age. The model
total body fat and lean mass (LBM) was performed with
was based on the two measurements points (2002, 2012)
bioelectrical impedance (Spectrum II, RJL System, Detroit,
used in the longitudinal study with unstructured working
MI). More detailed descriptions of the measurements are
givenelsewhere[22–25].Participants’mealsduringthemea- correlation matrix specification and athletes’ age as the
surementdayswerearrangedbytheresearchorganizers. descriptive metric of time. As the effect of age is generally
nonlinear we used polynomial terms (quadratic and cubic)
2.3. Serum Analyses. To obtain serum, venous blood was ofagetoincludecurvatureinmodelingtheage-relatedeffect
collected on the second measurement day under standard of the FasL and the miRs on the outcomes. The results
fasting conditions at least 12h after exercise. The serum were adjusted for LBM. More detailed prediction equations
sampleswerestoredat−80∘Cuntilanalyzed.SerumFasLcon- based on GEE-models are presented in the supplemen-
centrationwasmeasuredusingaHumanFasLigand/TNFSF6 tary data (S1 in Supplementary Material available online at
Quantikine(cid:2) ELISA Kit (R&D Systems, Minneapolis, MN, https://doi.org/10.1155/2017/8468469).
4 BioMedResearchInternational
CI/coefficientofetermination −0.4to0.10.031−0.3to0.20.0070.0to0.60.096 0.7to0.90.707−−0.9to0.60.612−−0.7to0.20.220−−0.9to0.60.602 etesand(2)masters
%d hl
5 at
9 all
nsofthevariableswithage. Correlationwithage𝑃(onlyB,C,D)/value −(𝑛=46)0.176𝑃=0.241S(𝑛=46)−0.085𝑃=0.573S(𝑛=46)0.310P=0.036 S(𝑛=36)0.841P<0.001−(𝑛=35)0.782P<0.001−(𝑛=41)0.469P=0.002−(𝑛=38)0.776P<0.001 correlationsarepresented:(1)
atio of Age
el ntn D.
oupsandcorr %CI/coefficiedeterminatio −−0.8to0.50.434−−0.6to0.10.144−0.1to0.40.032 0.8to0.90.806−−0.9to0.70.656−−0.8to0.50.419−−0.9to0.60.604±dasmeansS
gr 95 nte
measuresindifferentage Correlationwithage𝑃(allgroups)/value −(𝑛=61)0.659P<0.001S(𝑛=64)−0.380P=0.002S(𝑛=64)0.179𝑃=0.157 S(𝑛=48)0.898P<0.001−(𝑛=50)0.810P<0.001−(𝑛=58)0.647P<0.001−(𝑛=55)0.777P<0.001 18to90yrs.Resultsareprese
ce es
n g
calperforma D79–90yrs𝑛=15() 3.1±1.1(𝑛=14)2.7±2.3(𝑛=14)2.0±2.5(𝑛=14) 10.81±1.30(𝑛=10)18.9±4.7(𝑛=11)190±49(𝑛=12)565±126(𝑛=11) athletesfroma
Table1:Self-reportedtraininghistoryandphysi ABC18–39yrs50–66yrs66–79yrs𝑛=18𝑛=16𝑛=18()()()Self-reportedtraining3.6±1.66.8±2.33.5±1.3Frequency(times/wk)(𝑛=15)(𝑛=14)2.6±1.73.2±3.27.0±5.5Sprinttraining(h/wk)(𝑛=14)0.4±0.61.3±2.02.5±4.2Othertraining(h/wk)(𝑛=14) Physicalperformance7.52±0.368.43±0.639.29±0.61Sprint60m(s)(𝑛=12)(𝑛=13)(𝑛=13)45.7±11.832.0±4.225.8±5.0CMJ(cm)(𝑛=15)(𝑛=12)(𝑛=12)326±86±227±3425357Isometrickneeflexion(N)(𝑛=17)(𝑛=15)(𝑛=14)1307±347945±159717±175Isometricbenchpress(N)(𝑛=17)(𝑛=14)(𝑛=13) Tableisformedbasedonthecross-sectionalstudydesign(2012)includingalltheSSpearman’scorrelationcoefficient.athletesonly.CMJ:countermovementjump.
BioMedResearchInternational 5
3.Results ofthevariation,respectively.Alownegativecorrelationwas
observed between age and HGB both when all the athletes
3.1.ParticipantCharacteristicsandSerumFasLandmiRLevels were included (𝑟 = −0.383, 95% CI = −0.6 to −0.2, 𝑃 =
in Different Age Groups. Participants’ characteristics in the 0.002) and when only the masters athletes were included
differentagegroupsandagecorrelationsofthevariablesare (𝑟 = −0.370, 95% CI = −0.6 to −0.1, 𝑃 = 0.010), with age
presentedinTables1and2.Theagecorrelationsarepresented accounting for 14.7% and 13.7% of the variation in HGB,
in two different ways: (1) inclusive of all the participants respectively. Of the studied serum molecules, a modest
(ages18to90)and(2)inclusiveonlyofthemastersathletes negativecorrelationbetweenageandFasLwasdetectedwhen
(ages50to90).Amodestnegativecorrelationwasobserved all the athletes were included in the analyses (𝑟 = −0.596,
betweenageandtrainingfrequencywhenalltheathleteswere 95%CI=−0.7to−0.4,𝑃 < 0.001),withageaccountingfor
includedintheanalyses(𝑟 = −0.659,95%CI=−0.8to−0.5, 35.5%ofthevariationinFasLconcentration.Amodestposi-
𝑃<0.001),withageaccountingfor43.3%ofthevariationin tivecorrelationwasdetectedbetweenageandmiR-146awhen
trainingfrequency.Thesamecorrelationwasverylowwhen alltheathleteswereincluded(𝑟 = −0.611,95%CI=−0.7to
onlythemastersathleteswereincluded(𝑟 = −0.176,95%CI −0.4,𝑃 < 0.001),withageaccountingfor37.3%ofthemiR-
=−0.4to0.1,𝑃 = 0.241),withageaccountingonlyfor3.1% 146a variation. No significant correlations between age and
ofthevariation.Amongthephysicalperformancemeasures, the serum molecules were detected when only masters
ahighpositivecorrelationwasfoundbetweenageand60m athleteswereincludedintheanalyses.
sprinttimeamongalltheathletes(𝑟=0.898,95%CI=0.8to
0.9,𝑃<0.001),withageaccountingfor80.6%ofthevariation
insprinttime.Thepatternwassimilarwhenonlythemasters 3.2.ChangesinthePhysical PerformanceofMastersAthletes
athletes were included in the analyses (𝑟 = 0.841, 95% CI over 10 Years. Changes in the training frequencies and
= 0.7 to 0.9, 𝑃 < 0.001), with age accounting for 70.7% of the change percentage of the specific physical performance
thevariationinsprinttime.Theotherphysicalperformance measures in 10 years are presented in Table 3. The results
measuresshowedamodesttohighnegativecorrelationwith areshownformasterssprintersasawholeanddividedinto
agewhenalltheathleteswereincluded(CMJ:𝑟=−0.81,95% 3 age groups (B: 50–66yrs, C: 66–79yrs, and D: 79–90yrs
CI=−0.9to−0.7,𝑃<0.001,65.6%;kneeflexion:𝑟=−0.647, presenting the end-point ages). The self-reported weekly
95% CI =−0.8 to −0.5,𝑃 < 0.001, 41.9%; andbench press: trainingfrequency,sprint,andothertraininghoursdecreased
𝑟 = −0.777, 95% CI = −0.9 to −0.6, 𝑃 < 0.001, 60.4%). when all the masters athletes were involved in the analyses
Whenonlythemastersathleteswereincluded,highnegative (𝑃 < 0.001,𝑃 < 0.001,and𝑃 = 0.003,resp.).Whengroup-
correlations were observed between age and CMJ and age ingtheathletes,thedeclinesinalltheself-reportedtraining
andbenchpress(CMJ:𝑟 = −0.782,95%CI=−0.9to−0.6, activities remained significant for the group B (𝑃 = 0.011,
𝑃 < 0.001;benchpress:𝑟 = −0.776,95%CI=−0.9to−0.6, 𝑃 = 0.009, and 𝑃 = 0.008, resp.). For group C, no signi-
𝑃 < 0.001),withageaccountingfor61.2%and60.2%ofthe ficant changes in the training activities over 10 years were
variation,respectively.Betweenageandkneeflexion,onlya reported. For the group D, weekly training frequency and
modestnegativecorrelationwasdetected(𝑟 = −0.469,95% sprinttraininghoursdecreasedsignificantly(𝑃 = 0.004,𝑃 =
CI=−0.7to−0.2,𝑃=0.002),withageaccountingfor22%of 0.023,resp.).Fromthephysicalperformancemeasures60m
thevariationinkneeflexionstrength. sprint,CMJandisometricbenchpresschangedduringthe10
Amongthebodyanthropometricvariables,modestneg- years’ follow-up period when all the masters sprinters were
ativecorrelationswereobservedbetweenageandheight,age included(𝑃<0.001forallmeasures).Whenthemastersspr-
and weight, and age and LBM when all the athletes were intersweredividedinto3agegroupsthechangesremained
included (height: 𝑟 = −0.512, 95% CI = 0.7 to −0.3, 𝑃 < for all groups B (𝑃 = 0.001, 𝑃 = 0.001, and 𝑃 = 0.036,
0.001;weight:𝑟 = −0.419,95%CI=−0.6to−0.2,𝑃 < 0.001; resp.),C(𝑃 = 0.002,𝑃 = 0.001,and𝑃 = 0.008,resp.),andD
LBM:𝑟 = −0.525,95%CI=−0.7to−0.3,𝑃 < 0.001),with (𝑃=0.005,𝑃<0.001,and𝑃<0.001,resp.),withthehighest
ageaccountingfor26.2%,17.6%,and27.6%ofthevariation, deteriorationseenamongtheoldestathletes(groupD).There
respectively.Ofthebloodcellcounts,lowtomodestnegative werenosignificantchangesinthekneeflexionstrengthin10
correlations were detected between age and lymphocyte years.
percentage and age and RBC when all the athletes were
included (LYM%: 𝑟 = −0.384, 95% CI = −0.6 to −0.2, 𝑃 = 3.3. Changes in the Serum hsCRP, FasL, miR-21, and miR-
0.002;RBC:𝑟 = −0.433,95%CI=−0.6to−0.2,𝑃 < 0.001), 146a Levels of Masters Athletes over 10 Years. The change
with age accounting for 14.7% and 18.7% of the variation, percentages of the blood parameters, hsCRP, FasL, miR-21,
respectively. In addition, a modest positive correlation was and miR-146a over 10 years are presented in Table 3. The
detectedbetweenageandneutrophilpercentage(𝑟 = 0.411, resultsareshownforthemasterssprintersbothasawholeand
95% CI = 0.2 to 0.6, 𝑃 = 0.001), with age accounting for dividedinto3agegroups(B:50–66yrs,C:66–79yrs,andD:
16.9%ofthevariationinneutrophils.Whenonlythemasters 79–90yrspresentingtheend-pointages).SerumhsCRPdid
athletes were included, the pattern was similar in slightly notchangeoverthe10-yearperiodamongtheathletes.The
weakeragecorrelations(LYM%:𝑟 = −0.315,95%CI=−0.6 serumFasLconcentrationsdecreased(𝑃=0.017)andserum
to0.0,𝑃 = 0.031;RBC:𝑟 = −0.370,95%CI=−0.6to−0.1, miR-21andmiR-146alevelsincreased(𝑃<0.001,𝑃<0.005,
𝑃 = 0.010; and NEUT%: 𝑟 = 0.326, 95% CI = 0.0 to 0.6, resp.)amongthemasterssprintersasasinglegroupduring
𝑃 = 0.025),withageaccountingfor9.9%,13.7%,and10.6% the 10 years. At the age-group level, the changes were
6 BioMedResearchInternational
95%CI/coefficientofdetermination −−0.7to0.20.215−−0.7to0.30.323−−0.7to0.30.318−0.5to0.00.091 −0.2to0.40.010−0.6to0.00.099−0.4to0.20.0130.0to0.60.106−−0.6to0.10.137 −−0.6to0.10.141 −−0.6to0.20.187 −0.4to0.20.010−0.5to0.10.033−0.4to0.20.014−0.4to0.20.011 ways:(1)oneincludingallcells,PLT:platelet,HGB:
woood
eue ntbl
variableswithage. Correlationwithag𝑃(onlyB,C,D)/val −𝑛=490.464()P=0.001−𝑛=490.568()P<0.001−𝑛=490.564()P<0.001−𝑛=490.301()P=0.035 𝑛=470.099()𝑃=0.509−𝑛=470.315()P=0.031−𝑛=470.112()𝑃=0.455𝑛=470.326()P=0.025−𝑛=470.370()P=0.010 −𝑛=470.375()P=0.009 −𝑛=470.443().002P=0 −𝑛=470.102()𝑃=0.999−𝑛=410.181()𝑃=0.258S𝑛=49−0.118()𝑃=0.419S𝑛=49−0.105()𝑃=0.473 orrelationsarepresentediUT:neutrophils,RBC:red
rticipantcharacteristicsandbloodparametersindifferentagegroupsandcorrelationsofthe BCDCorrelationwithage95%CI/coefficientof50–66yrs66–79yrs79–90yrs𝑃(allgroups)/valuedetermination(𝑛=16)(𝑛=18)(𝑛=15) −𝑛=67−−0.512()0.7to0.3±±±178.28.1172.84.7172.44.9P<0.0010.262−𝑛=67−−0.419()0.6to0.2±±±80.29.771.06.269.46.4P<0.0010.176−𝑛=67−−0.525()0.7to0.3±±±66.37.060.24.158.34.8P<0.0010.276−𝑛=67−0.028()0.3to0.2±±±13.96.111.13.711.23.6𝑃=0.8570.001 ±±−𝑛=65−5.72.25.40.80.057()0.3to0.2±5.61.0𝑛=17𝑛=14𝑃=0.652()()0.003±±−𝑛=65−−0.234.25.631.09.40.384()0.6to±35.96.2𝑛=17)𝑛=14P=0.0020.147(()±±−𝑛=65−11.51.910.92.40.174()0.4to0.1±11.53.0𝑛=17)𝑛=14𝑃=0.165(()0.030±±𝑛=6554.36.658.19.50.411()0.2to0.6±52.66.9𝑛=17)𝑛=14P=0.001(()0.169±±−𝑛=65−−4.90.44.40.30.443()0.6to0.2±4.90.4𝑛=17)𝑛=14P<0.000(()0.187±±210.0199.7±−𝑛=65−242.30.100()0.3to0.183.642.9𝑃=0.42944.30.010(𝑛=17)𝑛=14()±±±−𝑛=65−−152.9149.49.3140.38.00.383()0.6to0.2(𝑛=17)𝑛=14P=0.00212.4()0.147 ±±−𝑛=65−2.36.01.31.20.035()0.3to0.2±1.73.3𝑛=17)𝑛=14(()0.7840.001±±±−𝑛=59−−56.821.956.018.252.323.40.596()0.7to0.4𝑛=14(𝑛=14)𝑛=13P<0.001()()0.355S−𝑛=67−0.0950.3to0.1()±±±2.221.411.741.051.650.62𝑃=0.4440.009S−−𝑛=67−0.6110.7to0.4()±±±1.831.161.500.711.510.96P<0.0010.373±design(2012)includingalltheathletesfromages18to90yrs.ResultsarepresentedasmeansSD.Agechletes.LBM:leanbodymass,WBC:whitebloodcells,LYM:lymphocytes,MXD:mixedleukocytes,NESotein,FasL:Fas-ligand,andRE:relativeexpression.Spearman’scorrelationcoefficient.
Pa dyatpr
Table2: A18–39yrs(𝑛=18) odycomposition ±180.15.0 ±78.36.8 ±67.25.7 ±11.24.1 ±5.61.4 ±39.27.2 ±12.34.8 ±48.68.5 ±5.10.4 ±211.944.0 ±154.39.3 ±1.65.1 ±92.324.7 ±1.810.75 ±5.822.66 hecross-sectionalstucludingonlymasterssensitivityc-reactive
b tnh
Anthropometricsand Height(cm) Weight(kg) LBM(kg) Bodyfatmass(kg) Bloodcellcount WBC LYM% MXD% NEUT% RBC PLT HGB Serummolecules hsCRP(mg/L) FasL(pg/ml) miR-21(RE) miR-146a(RE) Tableisformedbasedontheathletesand(2)oneihemoglobin,hsCRP:hig
BioMedResearchInternational 7
Table3:Thechangeinphysicalperformancemeasures,self-reportedtrainingamounts,andserummoleculelevelsamongallandage-grouped
masterssprintersinthe10-yearfollow-up.
All B C D
50–90yrs 50–66yrs 66–79yrs 79–90yrs
Trainingfrequency
Change(times/wk) −0.89±1.22(𝑛=42) −0.91±1.15(𝑛=14) −0.73±1.43(𝑛=15) −1.06±1.09(𝑛=13)
𝑃value P<0.001 P=0.011 𝑃=0.067 P=0.004
Sprinttraining
Change(h/wk) −1.84±3.00(𝑛=42) −1.80±2.25(𝑛=14) −1.94±4.08(𝑛=15) −1.77±2.43(𝑛=13)
𝑃values P<0.001 P=0.009 𝑃=0.078 P=0.023
Othertraining
Change(h/wk) −1.02±2.62(𝑛=32) −1.53±1.61(𝑛=11) −0.93±3.44(𝑛=11) −0.56±2.49(𝑛=10)
𝑃value P=0.006 P=0.008 𝑃=0.221 P=0.424
Sprint60m(s)
11.9(6.7−16.5) 8.2(5.7−10.0) 12.2(5.7−16.4) 15.8(12.1−22.8)
Change% (𝑛=35) (n=13) (𝑛=12) (𝑛=10)
𝑃value P<0.001W P=0.001W P=0.002W P=0.005W
CMJ(cm)
Change% −15.4±10.2(𝑛=32) −9.2±6.3(𝑛=11) −15.7±10.0(𝑛=11) −22.0±10.4(𝑛=10)
𝑃value P<0.001 P=0.001 P=0.001 P<0.001
Kneeflexion(N)
Change% −1.8±23.6(𝑛=40) 5.5±29.0(𝑛=15) −3.6±20.1(𝑛=13) −9.07±18.0(𝑛=12)
𝑃value 𝑃=0.062 𝑃=0.896 𝑃=0.287 𝑃=0.111
Isometricbenchpress(N)
Change% −10.9±11.9(𝑛=36) −5.3±9.8(𝑛=14) −14.0±14.7(𝑛=12) −14.9±8.51(𝑛=10)
𝑃value P<0.001 P=0.036 P=0.008 P<0.001
hsCRP
Change% 112±349(𝑛=47) 203±512(𝑛=16) 71.5±230(𝑛=17) 56.8±220(𝑛=14)
𝑃value 𝑃=0.841 𝑃=0.404 𝑃=0.868 𝑃=0.220
FasL
Change% −5.3±26.3(𝑛=41) −18.3±16.7(𝑛=14) 1.22±29.1(𝑛=14) 1.78±27.9(𝑛=13)
𝑃value P=0.017 P=0.001 𝑃=0.746 𝑃=0.587
miR-21
77.0(1.1–689) 107(21.0–662) 37.9(−34.7–834) 136(21.3–995)
Change% (𝑛=49) (𝑛=16) (𝑛=18) (𝑛=15)
𝑃value P<0.001W P=0.007W 𝑃=0.267W P=0.017W
miR-146a
95.4(−7.4–631) 93.0(−3.0–1101) 18.6(−26.0–444) 143(53.6–861)
Change% (𝑛=49) (𝑛=16) (𝑛=18) (𝑛=15)
𝑃value P=0.005W 𝑃=0.079W 𝑃=0.500W P=0.011W
Theagerangesofthegroups(B,C,andD)representtheagesatfollow-up.Dataarepresentedasmeans±SDforparametricvariablesandasmedian(IQR)for
nonparametricvariables.W𝑃valuewascalculatedwithpaired𝑡-testfornormallydistributedvariablesandwithWilcoxonsignedranktestfornonparametric
W
variables. Wilcoxonsignedranktestfornonparametricvariables.CMJ=countermovementjump,hsCRP=highsensitivityC-reactiveprotein,andFasL:
Fas-ligand.
significantforFasLandformiR-21amongtheyoungestmas- model was constructed to combine the effects of a specific
terssprinters(groupB;𝑃 = 0.001;𝑃 = 0.007,resp.)andfor serummolecule(FasL,miR-21,ormiR-146a)andthephysical
miR-146aamongtheoldestgroup(D;𝑃 = 0.011).Thecha- performance measures (60m sprint, CMJ, knee flexion,
ngewasalsosignificantformiR-21intheoldestgroup(group or bench press) over time. The association curves for the
D,𝑃 = 0.017).Theoriginalvaluesforthebloodparameters physical performance measures according to the different
arepresentedinsupplementarydata(S2). measuredcirculatingmoleculelevelsarepresentedinFigures
2,3,and4.Onlysignificantortrendingcurvesarepresented.
3.4. FasL, miR-21, and miR-146a Associations with Physical The results for the models were adjusted with the LBM.
Performance Measures in the 10-Year Follow-Up. A GEE Figure 2 shows that the combination of the effects of FasL
8 BioMedResearchInternational
0.8 Countermovement jump
p (cm) Effect Estimate Std. Err. P value OPm vnaliubeuas
m
ment ju 0.6 FFaassLL×age −00.0.01045 00..000025 00..000077 <0.001
move 0.4 FasL×age2 −0.009 0.003 0.006
er Note. Age was divided by 75. Model included also main effects of age,
unt age2, and lean body mass.
o
C 0.2 aOmnibusWald-test for the joint significance of FasL,FasL×age, and
FasL×age2.
20 30 40 50 60 70 80 90
Age
FasL (pg/ml)
Lower quartile
Mean
Upper quartile
(a)
2000 Bench press
Omnibus
Effect Estimate Std. Err. P value P valuea
N) 1500 FasL 26.08 7.349 <0.001 <0.001
ess ( FasL×age −11.63 3.313 <0.001
h pr FasL×age2 1.21 0.370 0.001
c
en 1000 Note. Age was divided by 15. Model included also main effects of age,
B
age2, and lean body mass.
aOmnibusWald-test for the joint significance of FasL,FasL× age, and
500 FasL×age2.
20 30 40 50 60 70 80 90
Age
FasL (pg/ml)
Lower quartile
Mean
Upper quartile
(b)
Figure2:TheassociationofserumFasLconcentrationwithphysicalperformanceovertime.Valuesfortheyoungerparticipants(<40years)
withoutfollow-upmeasuresarepresentedontheleftsideoftheimages.Theassociationsarebasedonthefollow-updesign(𝑛=49,>40yrs).
Cross(×)indicatesthatcaseislocatedwithin0–37.5%,circle(I)within37.5–67.5%,andsquare(◻)within67.5–100%ofthecumulativeshare
oftheFasLdistribution.The3differentlinespresenttheassociationsofthedifferentserummarkerlevelswiththephysicalperformance
measures over time. The tables next to the curves present the model used in forming the prediction curves, in greater detail, including
statisticsonthemaineffectsofthestudiedserummarkerandthepossiblequadraticandcubiceffects.
and its interactions with age was statistically significant for FasLlevels.Forbenchpress,higherFasLlevelsattheyounger
the CMJ (𝑃 < 0.001) and bench press (𝑃 < 0.001). Also, agespredictedasteadierdeclineinperformancewhilelower
allcoefficientestimatesfortheseoutcomeswerestatistically FasL values predicted better sustained performance at the
significantindicatingbothasignificantlinearandquadratic olderages(Figure2(b)).
curvature term. The model for the CMJ, when the serum SignificanteffectsofmiR-21andagecombined(Figure3)
FasL concentration was taken into account (Figure 2(a)), weredetectedforkneeflexion(𝑃 = 0.023)andbenchpress
predictedasteeperdeclineinperformanceafterageof70with strength(𝑃 = 0.004).Statisticallysignificantcoefficientesti-
slightly higher FasL serum concentrations than with lower mates relate mainly to miR-21 interactionwith quadratic of
BioMedResearchInternational 9
0
0
5 Knee flexion strength
h (N) 400 Effect Estimate Std. Err. P value OPm vnaliubeuas
ngt miR-21 801 582 0.169 0.023
on stre 300 mmiiRR--2211××aaggee2 −166453 9420.55 00..017125
exi miR-21×age3 −13.5 6.96 0.053
fl
ee 0 Note. Age was divided by 15. Model included also main effects of age, age2, and
n 0
K 2 lean body mass.
aOmnibusWald-test for the joint significance of miR-21, miR-21×age,
00 miR-21×age2, and miR-21×age3.
1
20 30 40 50 60 70 80 90
Age
miR-21
Lower quartile
Mean
Upper quartile
(a)
0 Bench press
0
0
2
Omnibus
Effect Estimate Std. Err. P value P valuea
ess (N) 1500 mmiiRR--2211×age −667199 430111 00..009407 0.004
pr miR-21×age2 163 78.6 0.038
ch miR-21×age3 −13.2 6.47 0.042
n 0
Be 100 Note. Age was divided by 15. Model included also main effects of age, age2, and
lean body mass.
aOmnibusWald-test for the joint significance of miR-21, miR-21×age,
00 miR-21×age2, and miR-21×age3.
5
20 30 40 50 60 70 80 90
Age
miR-21
Lower quartile
Mean
Upper quartile
(b)
Figure3:TheassociationofserummiR-21levelwithphysicalperformanceovertime.Valuesfortheyoungerparticipants(<40yrs)without
follow-upmeasuresarepresentedontheleftsideoftheimages.Thepredictionsarebasedonthefollow-updesign(𝑛=49,>40yrs).Cross(×)
indicatesthatcaseislocatedwithin0–37.5%,circle(I)within37.5–67.5%,andsquare(◻)within67.5–100%ofthecumulativeshareofthemiR-
21-distribution.The3differentlinespresenttheassociationsofthedifferentserummarkerlevelswiththephysicalperformancemeasures
overtime.Thetablesnexttothecurvespresentthemodelusedinformingthepredictioncurves,ingreaterdetail,includingstatisticsonthe
maineffectsofthestudiedserummarkerandthepossiblequadraticandcubiceffects.
cubic terms of age indicating that curvature has a stronger Significant combination effects of miR-146a and age
roleintheprediction.Forkneeflexionstrength,whenserum (Figure4)weredetectedforsprint(𝑃 < 0.001),kneeflexion
miR-21 levels were taken into account, low miR-21 levels (𝑃<0.001),andbenchpressstrength(𝑃<0.001).ThemiR-
predicted best performance prior to age 60 and high levels 146a levels predicted the largest differences in 60m sprint
bestperformancebetweenages60and80(Figure3(a)).Low performanceaftertheageof70,afterwhichthelowestvalues
miR-21 levels predicted highest performance in the bench predicted the best sprint performance (Figure 4(a)). For
pressuntilage65,afterwhichthepredictioncurveswerevery knee flexion strength, the lowest miR-146a levels predicted
similartoeachother(Figure3(b)). the best performance until age 60, after which, until age
10 BioMedResearchInternational
8
1 Sprint
Omnibus
16 Effect Estimate Std. Err. P value P valuea
m (s) 14 mmiiRR--114466aa×age −87.3.453 22..4249 00..000011 <0.001
0 2 miR-146a×age2 −3.06 0.908 0.001
6 1
nt miR-146a×age3 0.367 0.108 0.001
Spri 10 Note. Age was divided by 20. Model included also main effects of age,age2, and
lean body mass.
8
aOmnibusWald-test for the joint significance of miR-146a, miR-146a×age,
5 miR-146a×age2, and miR-146a×age3.
6.
20 30 40 50 60 70 80 90
Age
miR-146a
Lower quartile
Mean
Upper quartile
(a)
0
0
5 Knee flexion strength
h (N) 400 Effect Estimate Std. Err. P value OPm vanliubeuas
gt miR-146a 759 202 <0.001 <0.001
n
xion stre 300 mmmiiiRRR---111444666aaa×××aaagggeee23 −−2276884.87 661.397.732 <<<000...000000111
e
e fl 0 Note. Age was divided by 20. Model included also main effects of age,age2, and
ne 20 lean body mass.
K
aOmnibusWald-test for the joint significance of miR-146a, miR-146a×age,
0 miR-146a×age2, and miR-146a×age3.
0
1
20 30 40 50 60 70 80 90
Age
miR-146a
Lower quartile
Mean
Upper quartile
(b)
0 Bench press
0
0
2 Omnibus
Effect Estimate Std. Err. P value
P valuea
ess (N) 1500 mmiiRR--114466aa×age −113175 4341..55 <00.0.00021 <0.001
h pr miR-146a×age2 21.7 5.39 <0.001
c
en 00 Note. Age was divided by 20. Model included also main effects of age,age2, and
B 0
1 lean body mass.
aOmnibusWald-test for the joint significance of miR-146a, miR-146a×age,
00 miR-146a×age2, and miR-146a×age3.
5
20 30 40 50 60 70 80 90
Age
miR-146a
Lower quartile
Mean
Upper quartile
(c)
Figure4:TheassociationofserummiR-146alevelwithphysicalperformanceovertime.Valuesfortheyoungerparticipants(<40years)
withoutfollow-upmeasuresarepresentedontheleftsideoftheimages.Thepredictionsarebasedonthefollow-updesign(𝑛=49,>40yrs).
Cross(×)indicatesthatcaseislocatedwithin0–37.5%,circle(I)within37.5–67.5%,andsquare(◻)within67.5–100%ofthecumulativeshare
ofthemiR-146adistribution.The3differentlinespresenttheassociationsofthedifferentserummarkerlevelswiththephysicalperformance
measuresovertime.Thetablesnexttothecurvespresentthemodelusedinformingthepredictioncurves,ingreaterdetail,includingstatistics
onthemaineffectsofthestudiedserummarkerandthepossiblequadraticandcubiceffects.