the 114 participants enrolled were selected from a cohort of 1,051 individuals , as representative of good and poor cognitive performers based on neurocognitive / psychological performance patterns .
the participants from the initial cohort are senior community - dwelling individuals living in the minho region of portugal and are representative of the country 's population in terms of age distribution , sex , and school years . participants who were unable to attend the clinical and neuropsychological sessions , were diagnosed with dementia and/or unable to understand informed consent , or who had disorders of the central nervous system or overt thyroid pathology , were excluded from the larger cohort .
the cohort was established in accordance with the principles expressed in the declaration of helsinki , and the work was approved by the portuguese national ethical committee ( comisso nacional de proteco de dados ) and by local ethics review boards .
a team of trained psychologists assessed the cognitive and mood profile of the participants as described in santos et al .
briefly , the cognitive profile was established using a battery of neurocognitive and psychological tests selected to evaluate short - term verbal memory , verbal working memory , response inhibition / cognitive flexibility , verbal fluency , multiple trial verbal learning and memory , high - level information processing speed , global cognitive status , and mood . using a principal component analysis ,
the neurocognitive / psychological test variables were grouped into 3 dimensions : memory ( mem ) , general and executive function ( genexec ) , and mood ( gds ) ( for details see santos et al . ) .
all the statistical analysis of the cognitive evaluation and individual clustering followed the method in our previous publication .
descriptive information regarding age , sex , school years , and scores for gds , mem , and genexec for all participants and for the good and poor cognitive performance groups is described in table e-1 at neurology.org/nn .
while the full characterization of the acquired immune system was performed for all 114 individuals , the innate immune system was analyzed for only 79 individuals ( table e-2 ) .
the presence of anti - cytomegalovirus ( cmv ) immunoglobulin g was determined , and only 7 of the 114 participants were considered nonimmune to cmv ; no correlations were observed between cmv antibody titers and the cognitive performance of the participants . all participants presented c - reactive protein levels below the limit associated with active inflammation / infection ( 10 mg / l ) .
information about anti - inflammatory / immunomodulatory medication was collected at the time of clinical interview ; 18 of the participants were receiving this type of therapy ( information provided in table e-1 ) .
blood was collected to edta blood collection tubes and processed for standard hospital leukogram ( braga 's hospital ) and multiparametric flow cytometry analyses on the same day of collection ( see e - methods for details ) .
leukogram analysis was conducted at the certified pathology laboratory of braga 's hospital , following standard procedures .
data for mem , genexec , and gds were used in the analysis as z scores , as previously determined by santos et al . to evaluate normal distribution of the variables , skewness and kurtosis values were calculated and the approximate normal distribution was defined for variables with absolute values of skewness below 3 and of kurtosis below 8 .
levene test was used to evaluate equality of variances . to compare immune systems ' cell populations ( cell counts per ml of blood ) between good and poor cognitive performance groups ( descriptive statistics of variables
are presented in table e-2 ) , an independent - sample t test was performed for variables with normal distribution and a mann - whitney u test for variables with non - normal distribution .
p values below 0.05 were considered significant , and to quantify the strength of the differences , cohen d was calculated as a measure of effect size ( 0.2 is considered a small effect size , 0.5 a medium effect size , and 0.8 a large effect size ) . to correlate immune systems ' cell populations ( cell counts ) with cognitive ( mem and genexec ) dimensions ,
a pearson correlation coefficient test was performed for variables with normal distribution and a spearman rank correlation coefficient test for variables with non - normal distribution .
the coefficient of determination ( r ) was calculated as a measure of effect size ( 0.0196 is considered a small effect size , 0.1300 a medium effect size , and 0.2600 a large effect size ) .
hierarchical regression analysis was performed to compare different models to predict mem or genexec dimensions ( dependent variables ) and to test whether successive models fit better than the previous ones .
variables with non - normal distribution and with tolerance values below 0.4 ( to avoid multicollinearity between variables ) were excluded from the model .
the statistical procedures were performed in ibm spss version 20 ( ibm corp , armonk , ny ) .
the 114 participants enrolled were selected from a cohort of 1,051 individuals , as representative of good and poor cognitive performers based on neurocognitive / psychological performance patterns .
the participants from the initial cohort are senior community - dwelling individuals living in the minho region of portugal and are representative of the country 's population in terms of age distribution , sex , and school years . participants who were unable to attend the clinical and neuropsychological sessions , were diagnosed with dementia and/or unable to understand informed consent , or who had disorders of the central nervous system or overt thyroid pathology , were excluded from the larger cohort .
the cohort was established in accordance with the principles expressed in the declaration of helsinki , and the work was approved by the portuguese national ethical committee ( comisso nacional de proteco de dados ) and by local ethics review boards .
a team of trained psychologists assessed the cognitive and mood profile of the participants as described in santos et al .
briefly , the cognitive profile was established using a battery of neurocognitive and psychological tests selected to evaluate short - term verbal memory , verbal working memory , response inhibition / cognitive flexibility , verbal fluency , multiple trial verbal learning and memory , high - level information processing speed , global cognitive status , and mood . using a principal component analysis ,
the neurocognitive / psychological test variables were grouped into 3 dimensions : memory ( mem ) , general and executive function ( genexec ) , and mood ( gds ) ( for details see santos et al . ) .
all the statistical analysis of the cognitive evaluation and individual clustering followed the method in our previous publication .
descriptive information regarding age , sex , school years , and scores for gds , mem , and genexec for all participants and for the good and poor cognitive performance groups is described in table e-1 at neurology.org/nn .
while the full characterization of the acquired immune system was performed for all 114 individuals , the innate immune system was analyzed for only 79 individuals ( table e-2 ) .
the presence of anti - cytomegalovirus ( cmv ) immunoglobulin g was determined , and only 7 of the 114 participants were considered nonimmune to cmv ; no correlations were observed between cmv antibody titers and the cognitive performance of the participants . all participants presented c - reactive protein levels below the limit associated with active inflammation / infection ( 10 mg / l ) .
information about anti - inflammatory / immunomodulatory medication was collected at the time of clinical interview ; 18 of the participants were receiving this type of therapy ( information provided in table e-1 ) .
blood was collected to edta blood collection tubes and processed for standard hospital leukogram ( braga 's hospital ) and multiparametric flow cytometry analyses on the same day of collection ( see e - methods for details ) .
leukogram analysis was conducted at the certified pathology laboratory of braga 's hospital , following standard procedures .
data for mem , genexec , and gds were used in the analysis as z scores , as previously determined by santos et al . to evaluate normal distribution of the variables , skewness and kurtosis values were calculated and the approximate normal distribution was defined for variables with absolute values of skewness below 3 and of kurtosis below 8 .
levene test was used to evaluate equality of variances . to compare immune systems ' cell populations ( cell counts per ml of blood ) between good and poor cognitive performance groups (
descriptive statistics of variables are presented in table e-2 ) , an independent - sample t test was performed for variables with normal distribution and a mann - whitney u test for variables with non - normal distribution .
p values below 0.05 were considered significant , and to quantify the strength of the differences , cohen d was calculated as a measure of effect size ( 0.2 is considered a small effect size , 0.5 a medium effect size , and 0.8 a large effect size ) . to correlate immune systems ' cell populations ( cell counts ) with cognitive ( mem and genexec ) dimensions ,
a pearson correlation coefficient test was performed for variables with normal distribution and a spearman rank correlation coefficient test for variables with non - normal distribution .
the coefficient of determination ( r ) was calculated as a measure of effect size ( 0.0196 is considered a small effect size , 0.1300 a medium effect size , and 0.2600 a large effect size ) .
hierarchical regression analysis was performed to compare different models to predict mem or genexec dimensions ( dependent variables ) and to test whether successive models fit better than the previous ones .
variables with non - normal distribution and with tolerance values below 0.4 ( to avoid multicollinearity between variables ) were excluded from the model .
the statistical procedures were performed in ibm spss version 20 ( ibm corp , armonk , ny ) .
when characterizing the overall immune system , no differences between good and poor cognitive performers were observed in the leukogram cell subpopulations ( lymphocytes , monocytes , neutrophils , basophils , and eosinophils ; figure e-1 ) .
a more detailed flow cytometry analysis of the acquired immune system cells revealed that , when compared to poor performers , good cognitive performers presented significantly higher b - cell counts ( figure 1a ; t(110 ) = 2.354 , p = 0.020 , d = 0.45 ) .
no differences were seen between good and poor cognitive performers regarding total t - cell counts ( figure 1b ; t(110 ) = 0.355 , p = 0.723 ) or the 2 main t - cell populations ( figure 1c ; cd4 [ t(110 ) = 0.617 , p = 0.539 ] and cd8 [ t(110 ) = 0.196 , p = 0.845 ] ) .
the cd4 and cd8 t - cell compartments ( naive , central memory [ cm ] , effector memory [ em ] , and late differentiated [ ld ] ) , whose proportion has been shown to change with aging , displayed a distribution pattern ( figure 1 , d and e ) in accordance with the described dynamics of these t - cell compartments in aging .
it is interesting that the poor cognitive performers presented higher numbers of em cd4 t cells ( figure 1d ; t(110 ) =  2.510 , p = 0.014 , d = 0.48 ) , with no differences in other cd4 t - cell compartments ( figure 1d ; naive t(109.9 ) = 1.770 , p = 0.080 ; cm t(110 ) = 0.780 , p = 0.480 ; ld u(110 ) = 1,545 , z = 0.108 , p = 0.919 ) , and lower numbers of naive cd8 t cells ( figure 1e ; t(93.75 ) = 2.600 , p = 0.011 , d = 0.54 ) , with no differences among the cd8 t - cell compartments ( figure 1e ; cm u(110 ) = 1,375 , z = 1.096 , p = 0.273 ; em t(110 ) = 0.774 , p = 0.440 ; ld t(110 ) = 0.332 , p = 0.746 ) .
the profile ( cell counts per ml of blood ) of good ( black ) and poor ( red ) cognitive performers with regard to b cells ( a ) ; t cells ( b ) ; cd4 and cd8 t - cell subpopulations ( c ) ; cd4 and cd8t - cell compartments ( d and e ) , subdivided as naive , central memory ( cm ) , effector memory ( em ) , and late differentiated ( ld ) t cells ; cd4 regulatory t cells ( tregs ) ( f ) , subdivided as naive tregs , activated tregs , and nonsuppressive tregs ( g ) .
each dot represents one individual and the line represents the mean of the group . * p < 0.05 . no differences were noted in total regulatory t cell ( treg ) counts ( figure 1f ; t(106 ) = 0.046 , p = 0.963 ) or in the treg compartments ( figure 1 g ; naive tregs t(106 ) = 1.667 , p = 0.098 ; activated tregs t(106 ) = 0.480 , p = 0.632 ; nonsuppressive tregs
u(106 ) = 1,387 , z = 0.361 , p = 0.718 ) , possibly indicating that this cell population is not associated with cognitive performance .
there were no differences in the dendritic and natural killer cell counts ( figure e-2 ) .
cognitive performance is known to relate directly or indirectly to sex , age , mood and , particularly , education .
therefore , we next investigated whether these factors influenced the differences observed in the immune system between distinct cognitive performers .
to do so , hierarchical linear regression models were used to predict the genexec and mem dimension performances .
the adaptive immune system variables appeared to be the most promising , so those that presented statistically significant correlations with genexec and mem ( table e-3 ) were included in the regression model to determine to what extent they were able to predict the cognitive performance in both domains .
the first block of variables included age , sex , and school years , which are known to strongly relate to cognitive performance .
the third ( last ) block included the immune system variables ( activated tregs , naive cd4 t cells , cm cd4 t cells , em cd4 t cells , naive cd8 t cells and b cells in the genexec model ; naive cd4 t cells , em cd4 t cells , naive cd8 t cells and b cells in the mem model ) .
the hierarchical linear regression analysis revealed that the first block predicted 38.6% ( adjusted r ) of the genexec dimension variance and that all variables were statistically significant predictors ( table 1 ; model 1 ) . adding the second block to the model increased the prediction of genexec significantly 5.1% ( r change ) , with the sex variable ceasing to be a significant predictor ( table 1 ; model 2 ) .
finally , the inclusion of the third block further increased the predictive power by 7.5% ( table 1 ; model 3 ) .
overall , model 3 , which comprises all the variables , predicted 47.9% ( adjusted r ) of the genexec variance .
moreover , in addition to age , mood , and school years , em cd4 t cells and b cells were statistically significant predictors of genexec .
this means that even when considering the variables previously known to affect cognitive function as predictors , the variables of the immune system proved to be good predictors ( although as expected with lower impact [ lower  ] ) .
altogether these observations indicate that better cognitive performers in the genexec dimension are not just younger individuals with higher education levels and better mood , as shown previously , but also those with lower em cd4 t - cell counts and higher b - cell counts in the blood .
hierarchical linear regression models to predict genexec with respect to the mem dimension , the hierarchical regression showed that the first block predicted almost 20% ( adjusted r ) of the mem dimension variance , with age and school years being statistically significant predictors ( table 2 ; model 1 ) .
the addition of the second block significantly increased the predictive power 10.7% ( r change ) , with mood being the variable with the highest impact on mem prediction ( table 2 ; model 2 ) .
the addition of the last block raised statistical significance by 7.6% ( table 2 ; model 3 ) for mem . following the genexec findings , in model 3
all variables comprehensively explained 33% of the mem dimension , in which , in addition to mood , em cd4 t cells were again statistically significant predictors .
the approach indicated that better cognitive performers in memory function have better mood scores and lower em cd4 t - cell counts .
hierarchical linear regression models to predict mem it is also important to highlight that in this cohort only 18 participants were receiving anti - inflammatory / immunoodulatory treatment , and no differences were detected in the cellular populations analyzed when comparing individuals receiving treatment to those not receiving treatment ( data not shown ) .
we also included this factor in the hierarchical regression models as a factor to control , and anti - inflammatory / immunomodulatory treatment showed no impact on cognitive function or on the correlation between em cd4 t cells and cognitive performance ( data not shown ) .
when characterizing the overall immune system , no differences between good and poor cognitive performers were observed in the leukogram cell subpopulations ( lymphocytes , monocytes , neutrophils , basophils , and eosinophils ; figure e-1 ) .
a more detailed flow cytometry analysis of the acquired immune system cells revealed that , when compared to poor performers , good cognitive performers presented significantly higher b - cell counts ( figure 1a ; t(110 ) = 2.354 , p = 0.020 , d = 0.45 ) .
no differences were seen between good and poor cognitive performers regarding total t - cell counts ( figure 1b ; t(110 ) = 0.355 , p = 0.723 ) or the 2 main t - cell populations ( figure 1c ; cd4 [ t(110 ) = 0.617 , p = 0.539 ] and cd8 [ t(110 ) = 0.196 , p = 0.845 ] ) .
the cd4 and cd8 t - cell compartments ( naive , central memory [ cm ] , effector memory [ em ] , and late differentiated [ ld ] ) , whose proportion has been shown to change with aging , displayed a distribution pattern ( figure 1 , d and e ) in accordance with the described dynamics of these t - cell compartments in aging .
it is interesting that the poor cognitive performers presented higher numbers of em cd4 t cells ( figure 1d ; t(110 ) =  2.510 , p = 0.014 , d = 0.48 ) , with no differences in other cd4 t - cell compartments ( figure 1d ; naive t(109.9 ) = 1.770 , p = 0.080 ; cm t(110 ) = 0.780 , p = 0.480 ; ld u(110 ) = 1,545 , z = 0.108 , p = 0.919 ) , and lower numbers of naive cd8 t cells ( figure 1e ; t(93.75 ) = 2.600 , p = 0.011 , d = 0.54 ) , with no differences among the cd8 t - cell compartments ( figure 1e ; cm u(110 ) = 1,375 , z = 1.096 , p = 0.273 ; em t(110 ) = 0.774 , p = 0.440 ; ld t(110 ) = 0.332 , p = 0.746 ) .
the profile ( cell counts per ml of blood ) of good ( black ) and poor ( red ) cognitive performers with regard to b cells ( a ) ; t cells ( b ) ; cd4 and cd8 t - cell subpopulations ( c ) ; cd4 and cd8t - cell compartments ( d and e ) , subdivided as naive , central memory ( cm ) , effector memory ( em ) , and late differentiated ( ld ) t cells ; cd4 regulatory t cells ( tregs ) ( f ) , subdivided as naive tregs , activated tregs , and nonsuppressive tregs ( g ) .
each dot represents one individual and the line represents the mean of the group . * p < 0.05 . no differences were noted in total regulatory t cell ( treg ) counts ( figure 1f ; t(106 ) = 0.046 , p = 0.963 ) or in the treg compartments ( figure 1 g ; naive tregs t(106 ) = 1.667 , p = 0.098 ; activated tregs t(106 ) = 0.480 , p = 0.632 ; nonsuppressive tregs
u(106 ) = 1,387 , z = 0.361 , p = 0.718 ) , possibly indicating that this cell population is not associated with cognitive performance .
there were no differences in the dendritic and natural killer cell counts ( figure e-2 ) .
cognitive performance is known to relate directly or indirectly to sex , age , mood and , particularly , education .
therefore , we next investigated whether these factors influenced the differences observed in the immune system between distinct cognitive performers .
to do so , hierarchical linear regression models were used to predict the genexec and mem dimension performances .
the adaptive immune system variables appeared to be the most promising , so those that presented statistically significant correlations with genexec and mem ( table e-3 ) were included in the regression model to determine to what extent they were able to predict the cognitive performance in both domains .
the first block of variables included age , sex , and school years , which are known to strongly relate to cognitive performance .
the third ( last ) block included the immune system variables ( activated tregs , naive cd4 t cells , cm cd4 t cells , em cd4 t cells , naive cd8 t cells and b cells in the genexec model ; naive cd4 t cells , em cd4 t cells , naive cd8 t cells and b cells in the mem model ) .
the hierarchical linear regression analysis revealed that the first block predicted 38.6% ( adjusted r ) of the genexec dimension variance and that all variables were statistically significant predictors ( table 1 ; model 1 ) . adding the second block to the model increased the prediction of genexec significantly 5.1% ( r change ) , with the sex variable ceasing to be a significant predictor ( table 1 ; model 2 ) .
finally , the inclusion of the third block further increased the predictive power by 7.5% ( table 1 ; model 3 ) .
overall , model 3 , which comprises all the variables , predicted 47.9% ( adjusted r ) of the genexec variance .
moreover , in addition to age , mood , and school years , em cd4 t cells and b cells were statistically significant predictors of genexec .
this means that even when considering the variables previously known to affect cognitive function as predictors , the variables of the immune system proved to be good predictors ( although as expected with lower impact [ lower  ] ) .
altogether these observations indicate that better cognitive performers in the genexec dimension are not just younger individuals with higher education levels and better mood , as shown previously , but also those with lower em cd4 t - cell counts and higher b - cell counts in the blood .
hierarchical linear regression models to predict genexec with respect to the mem dimension , the hierarchical regression showed that the first block predicted almost 20% ( adjusted r ) of the mem dimension variance , with age and school years being statistically significant predictors ( table 2 ; model 1 ) .
the addition of the second block significantly increased the predictive power 10.7% ( r change ) , with mood being the variable with the highest impact on mem prediction ( table 2 ; model 2 ) .
the addition of the last block raised statistical significance by 7.6% ( table 2 ; model 3 ) for mem . following the genexec findings , in model 3 all variables comprehensively explained 33% of the mem dimension , in which , in addition to mood , em cd4 t cells were again statistically significant predictors .
the approach indicated that better cognitive performers in memory function have better mood scores and lower em cd4 t - cell counts .
hierarchical linear regression models to predict mem it is also important to highlight that in this cohort only 18 participants were receiving anti - inflammatory / immunoodulatory treatment , and no differences were detected in the cellular populations analyzed when comparing individuals receiving treatment to those not receiving treatment ( data not shown ) .
we also included this factor in the hierarchical regression models as a factor to control , and anti - inflammatory / immunomodulatory treatment showed no impact on cognitive function or on the correlation between em cd4 t cells and cognitive performance ( data not shown ) .
this study shows that better cognitive performance in a healthy senior population is associated with lower numbers of em cd4 t cells , higher numbers of naive cd8 t cells and higher numbers of b cells .
furthermore , em cd4 t cells are predictors of genexec and mem , even when factors known to influence cognitive performance ( e.g. , age , sex , education and mood ) are taken into account .
the immune cell population strongly associated with cognitive performance in our healthy senior cohort is the em cd4 t cells population .
higher em cd4 t - cell numbers negatively predict mem and genexec performance . of interest
, other authors reported that patients diagnosed with probable alzheimer disease and cognitive deficits according to the mini - mental state examination presented with significantly lower levels of cd4 naive t cells ( cd45ra ) and an increase in the activated / naive cd4 t - cell ratio ( cd45ro / cd45ra ) compared with age - matched cognitively healthy individuals .
this suggests that pathologically related cognitive deficits are associated with alterations in the cd4 t - cell compartments . of note , we also observed more em cd4 t cells in the poor cognitive performers , even though the individuals enrolled in our study presented no detectable pathologic cognitive alteration .
the link between cd4 t cells and cognitive performance has been proposed based on preclinical studies .
in fact , systemic depletion of cd4 t cells in mice significantly reduced hippocampal neurogenesis and impaired reversal learning ; such effects were not observed for cd8 t cells neither for b cells .
recent studies showed that a substantial increase in cd4 t cells , but not in cd8 t cells , was observed in the meninges of mice that performed a cognitive task .
moreover , the recruited cells were activated cd4 t cells ( cd69 ; cd25foxp3 ) producing il-4 ( th2 phenotype ) and not ifn- ( th1 phenotype ) , suggesting that the cytokines produced by cd4 t cells may play a role in cognition . in fact , a more proinflammatory profile has been associated with cognitive impairments in older individuals . because em cd4 t cells are the major cytokine producers among t cells , the association found in our population between higher em cd4 t cells and poor cognition may be related to a more proinflammatory profile .
further studies are needed to better understand the interplay between the cd4 t - cell populations , their specific cytokine production profile , and cognitive performance .
this study also identified an association between b - cell counts and cognition ( individuals with better cognitive performance presented with higher b - cell counts ) .
we next used hierarchical regression models to investigate the impact of the immune system in both the mem and genexec cognitive domains .
the analysis revealed that all the immune system variables included in the models increased the predictive power of both cognitive domains almost 8% . because this effect is in addition to the effect of other factors known to explain cognitive performance in senior populations , the effect of the immune system variables
should be included when addressing cognitive differences among older individuals . regarding the models used in this study , all variables explained genexec better than mem .
we previously demonstrated that the predicted cognitive score ( a  latent  cognitive score calculated using structural equation modeling based on 3 identified cognitive dimensions [ general and executive function , memory , and a higher - level executive processing capacity ] and 4 predictors [ sex , age , school years , and gds ] ) is more strongly explained by genexec than by mem , which is in agreement with the present observations .
furthermore , in the structural equation model , the included variables explained 62% of the variability of the latent cognitive score , indicating that other unknown factors are also of relevance .
based on the current findings , the inclusion of immune system parameters as predictors ( mainly em cd4 t cells and/or b cells ) can probably increase the explanation of the variability of cognition observed among senior individuals . in summary , to the best of our knowledge , this is the first study identifying a phenotype of the immune system associated with poorer / better cognitive performance in individuals with healthy cognitive aging .
this study is potentially limited by the relatively small number of participants ; thus , the present findings must be confirmed in other human cohorts fully characterized for their neurocognitive / psychological performance patterns in order to establish these  immunomarkers  as predictors of cognitive performance in aging .
in addition , prospective studies must follow on how the immune system profile further relates to cognitive decline .
, n.s . , and m.c .- n . designed the study and established the cohort .
all authors contributed to the planning of the experiments , data interpretation , writing of the manuscript , and approval of the final version of the manuscript .
( contract health - f2 - 2010 - 259772 ) and co - financed by the portuguese north regional operational program ( on.2o novo norte ) under the national strategic reference framework ( qren ) , through the european regional development fund ( feder ) .
n. sousa is editor - in - chief for frontiers in behavioral neuroscience and associate editor for molecular neurodegeneration .
 objective : immunosenescence and cognitive decline are common markers of the aging process . taking into consideration the heterogeneity observed in aging processes and the recently described link between lymphocytes and cognition , we herein explored the possibility of an association between alterations in lymphocytic populations and cognitive performance.methods:in a cohort of cognitively healthy adults ( n = 114 ) , previously characterized by diverse neurocognitive / psychological performance patterns , detailed peripheral blood immunophenotyping of both the innate and adaptive immune systems was performed by flow cytometry.results:better cognitive performance was associated with lower numbers of effector memory cd4 + t cells and higher numbers of naive cd8 + t cells and b cells . 
 furthermore , effector memory cd4 + t cells were found to be predictors of general and executive function and memory , even when factors known to influence cognitive performance in older individuals ( e.g. , age , sex , education , and mood ) were taken into account.conclusions:this is the first study in humans associating specific phenotypes of the immune system with distinct cognitive performance in healthy aging . 
METHODS
Participant characterization.
Flow cytometry analysis.
Statistical analysis.
RESULTS
B cells, naive CD8
EM CD4
DISCUSSION
Supplementary Material
AUTHOR CONTRIBUTIONS
STUDY FUNDING
DISCLOSURE