Articles
The effect of social networks on the relation between
Alzheimer’s disease pathology and level of cognitive
function in old people: a longitudinal cohort study
David A Bennett, Julie A Schneider, Yuxiao Tang, Steven E Arnold, Robert S Wilson
Lancet Neurol 2006; 5: 406–12
Published Online
April 4, 2006
DOI:10.1016/S1474-4422(06)
70417-3
Rush Alzheimer’s Disease
Center (D A Bennett MD,
J A Schneider MD,
R S Wilson PhD), Department of
Neurological Sciences
(D A Bennett, J A Schneider,
R S Wilson), Department of
Pathology (J A Schneider),
Department of Behavioral
Science (R S Wilson), and Rush
Institute for Healthy Aging and
Department of Internal
Medicine (Y Tang PhD), Rush
University Medical Center,
Chicago, IL, USA; and Center for
Neurobiology and Behavior,
University of Pennsylvania,
Philadelphia, PA, USA
(S E Arnold MD)
Correspondence to:
Dr David A Bennett, Rush
Alzheimer’s Disease Center, Rush
University Medical Center, 600 S
Paulina, Suite 1028, Chicago,
IL 60612, USA
dbennett@rush.edu
406
Summary
Background Few data are available about how social networks reduce the risk of cognitive impairment in old age. We
aimed to measure this effect using data from a large, longitudinal, epidemiological clinicopathological study.
Methods 89 elderly people without known dementia participating in the Rush Memory and Aging Project underwent
annual clinical evaluation. Brain autopsy was done at the time of death. Social network data were obtained by
structured interview. Cognitive function tests were Z scored and averaged to yield a global and specific measure of
cognitive function. Alzheimer’s disease pathology was quantified as a global measure based on modified Bielschowsky
silver stain. Amyloid load and the density of paired helical filament tau tangles were also quantified with antibodyspecific immunostains. We used linear regression to examine the relation of disease pathology scores and social
networks to level of cognitive function.
Findings Cognitive function was inversely related to all measures of disease pathology, indicating lower function at
more severe levels of pathology. Social network size modified the association between pathology and cognitive
function (parameter estimate 0·097, SE 0·039, p=0·016, R²=0·295). Even at more severe levels of global disease
pathology, cognitive function remained higher for participants with larger network sizes. A similar modifying
association was observed with tangles (parameter estimate 0·011, SE 0·003, p=0·001, R²=0·454). These modifying
effects were most pronounced for semantic memory and working memory. Amyloid load did not modify the relation
between pathology and network size. The results were unchanged after controlling for cognitive, physical, and social
activities, depressive symptoms, or number of chronic diseases.
Interpretation These findings suggest that social networks modify the relation of some measures of Alzheimer’s
disease pathology to level of cognitive function.
Introduction
Several clinicopathological studies over the past two
decades have shown that many elderly people with
extensive pathology of Alzheimer’s disease do not clinically
manifest cognitive impairment.1–4 This ability to tolerate
the pathology of this disease without obvious clinical
consequences is increasingly referred to as cognitive or
neural reserve.1,5 Identification of factors associated with
neural reserve has important implications for disease
prevention. For example, one such factor is education.
Clinicopathological studies suggest that the relation
between quantitative measures of Alzheimer’s disease
pathology and level of cognition differ by duration of
formal education.6 Another potential factor that could
modify this relation is social networks. Social networks
have been related to a reduced risk of death and a reduction
in a wide variety of adverse health outcomes in old people.7
Several studies have also examined the relation between
the extent of social ties and cognitive function and
dementia. Most,8–10 but not all,11 showed that people with
more extensive social networks were at reduced risk of
cognitive impairment. Little is known about the cellular,
molecular, and neuropathology of social networks and
potential neurobiological mechanisms underlying this
association. Although social networks could be directly
related to the accumulation of Alzheimer’s disease
pathology, it seems more likely that social network size is
related to reserve capacity capable of reducing the likelihood
that the disease pathology will be clinically expressed as
cognitive impairment. We aimed to test this hypothesis
using data from the Rush Memory and Aging Project—a
large, longitudinal, epidemiological, clinicopathological
study of ageing and Alzheimer’s disease.
Methods
Participants and procedures
Participants were elderly people without known dementia
in the Rush Memory and Aging Project12 (see
acknowledgments). Each participant gave written
informed consent and an anatomical gift act for brain
donation. The study was approved by the Institutional
Review Board of Rush University Medical Center. More
than 1100 people have agreed to participate and have
completed their baseline clinical assessment. The overall
annual follow-up rate of survivors exceeds 90%, and the
autopsy rate exceeds 75%. Post-mortem data were
available for analysis from the first 89 people.
All participants underwent a uniform structured clinical
assessment that included a medical history, neurological
examination, and neuropsychological performance
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Articles
testing. Neuropsychological test results were reviewed by
a board-certified neuropsychologist who gave an opinion
about the presence and severity of cognitive impairment.
Each participant was assessed in person by a physician.
On the basis of this evaluation, and review of the cognitive
testing and the neuropsychologist’s opinion, participants
were classified with respect to Alzheimer’s disease and
other common conditions with the potential to affect
cognitive function, according to the recommendations of
the joint working group of the National Institute of
Neurologic and Communicative Disorders and Stroke
and the Alzheimer’s Disease and Related Disorders
Association (NINCDS/ADRDA)13 as previously described.12
Annual follow-up evaluations were identical in all
essential details and were done by examiners unaware of
previously obtained data. At the time of death, all available
clinical data were reviewed and a summary diagnostic
opinion was given as to the most likely clinical diagnosis
at death. Summary diagnoses were made by reviewers
unaware of all post-mortem data.
21 cognitive performance tests were administered each
year. Details of the cognitive function tests have been
previously reported.12,14 Briefly, one test, the mini-mental
state examination (MMSE), was used to describe the
cohort but was not used in analyses. A second test,
complex ideational material, was used for diagnostic
classification but was not used in the composite measure
of cognition. The remaining 19 tests were used to assess
five domains of cognitive function. There were seven
tests of episodic memory including immediate and
delayed recall of story A from logical memory and of the
east Boston story, and word list memory, recall, and
recognition. Three measures assessed semantic memory
including a 15-item version of the Boston naming test,
verbal fluency, and a 15-item reading test. There were
three tests of working memory including digit span
forward and backward and digit ordering. There were
four tests of perceptual speed, including symbol digit
modalities test, number comparison, and two indices
from a modified version of the Stroop neuropsychological
screening test. Finally, there were two tests of visuospatial
ability, including a 15-item version of judgment of line
orientation, and a 16-item version of the Raven’s standard
progressive matrices.
The primary outcome measure in the study was a global
measure of cognitive function. We focused on the
continuous measure of cognition, rather than a
dichotomous variable of dementia or Alzheimer’s disease,
because doing so allowed us to fully examine the spectrum
of both pathology and cognition, and their association
with social networks, in the most direct way and with the
greatest statistical power. Furthermore, because factors
affecting neural reserve are likely to affect some cognitive
systems more than others, we did a series of secondary
analyses to explore five different cognitive abilities. This
approach is identical to that we have taken in similar
analyses with data from another study.6,15 The raw scores
http://neurology.thelancet.com Vol 5 May 2006
from the 19 tests were converted to Z scores and averaged
to yield a global cognitive summary, and measures of five
different cognitive abilities as previously described.12,14
We quantified social network size with three sets of
standard questions about the number of children, family,
and friends of each participant and how often they
interacted with them.10 Because we wanted to measure
the influence of premorbid social networks on the
relation between pathology and cognition, we restricted
the analyses to social network data from the baseline
assessment. Participants were asked about the number
of children they have and see monthly. They were asked
about the number of relatives (besides spouse and
children) and other close friends to whom they feel close
and with whom they felt at ease and could talk to about
private matters and could call upon for help, and how
many of these people they see monthly. Social network
size was the number of these individuals seen at least
once per month.10
We also assessed five potential mediators and covariates
that could account for or confound the association of
social networks with cognition, as previously reported.
We only used data from the baseline evaluation to be
concurrent with the assessment of social networks. We
assessed current participation in nine cognitively
stimulating activities,14 five physical activities,12 and six
social activities.10 Depressive symptoms were assessed
with a ten-item version of the Center for Epidemiologic
Studies depression scale.12 We measured seven chronic
diseases—diabetes, hypertension, heart disease, cancer,
thyroid disease, head injury, and stroke.12
Brains of deceased participants were removed, weighed,
cut into 1-cm-thick coronal slabs, and immersion fixed in
4% paraformaldehyde for 72 h. Tissue blocks from the
mid-frontal gyrus, the superior temporal gyrus, the inferior
parietal gyrus, the entorhinal cortex proper, and the
hippocampus (CA1/subiculum) were embedded in
paraffin, sectioned at 6 µm and stained with a modified
Bielschowsky silver stain. Neuritic plaques, diffuse plaques,
and neurofibrillary tangles were counted in the region that
appeared to have the maximum density of each pathological
index as previously described, resulting in 15 measures.16 A
composite measure of global Alzheimer’s disease pathology
was created as previously described by dividing each raw
count by the standard deviation of the mean for the same
neuropathological index in that region and averaging the
scaled scores to yield the composite measures.16
Multiple tissue blocks from entorhinal cortex proper,
hippocampus (CA1/subiculum), superior frontal cortex,
dorsolateral prefrontal cortex, inferior temporal cortex,
angular gyrus cortex, anterior cingulate cortex, and
calcarine cortex were embedded in paraffin and cut into
20-µm sections. Up to 24 sections were available for each
case for each post-mortem index. Amyloid-β was labelled
with an N-terminus directed monoclonal antibody
(10D5, courtesy Elan Pharmaceuticals; 1:1000).
Immunohistochemistry was done as previously described17
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Articles
Baseline
Proximate to death
Age (years)
84·3 (5·6)
87·2 (5·9)
Female
55·1%
..
White non-Hispanic
94·4%
..
Education (years)
14·4 (3·3)
..
6·9 (4·9)
..
with diaminobenzidine as the reporter with 2·5% nickel
sulphate to enhance immunoreaction product contrast.
All sections were run with identical incubation times on
an automated immunohistochemical stainer (Biogenex,
San Ramon, CA, USA) in precisely timed runs. Paired
helical filament (PHF) tau was labelled with an antibody
specific for phosphorylated tau, AT8 (Innogenetics, San
Ramon, CA, 1:1000). Positive and negative (no primary
antibody) control sections were included in all runs. A
systematic random sampling scheme was used to capture
video images of amyloid-β stained sections for quantitative
analysis of amyloid deposition.17 The region of interest
was outlined at low power with StereoInvestigator
software version 6 (MicroBrightfield, Colchester, VT,
USA) and an Olympus BX-51 microscope with an attached
motorised stage. A grid of predetermined size was
randomly placed over the outlined area to sample about
50% of the region of interest. After camera and
illumination calibration, the magnification was raised to
×200 and 24-bit colour images obtained at each sampling
site with a motorised stage employed to automatically
position the tissue before each capture.
We quantified amyloid-β load by image processing in
an automated, multistage computational image analysis
protocol algorithm, as previously described,18 with the
addition of a defective pixel removal procedure to exclude
faulty pixels. Mean fraction (% area) per region and per
person were computed. Quality of segmentation was
controlled by visual inspection on a randomly selected
subset of images. Values for all regions were averaged to
yield a composite measure of amyloid-β deposition.
Quantification of tangle density per mm² was done
with the stereological mapping station described above.
Briefly, after the region was delimited at low power, a grid
Demographic
Social networks (number)
Mediators and covariates
Cognitive activity
3·4 (1·0)
..
Physical activity
2·1 (2·6)
..
Social activity
2·3 (0·7)
..
Depressive symptoms
1·5 (1·8)
..
Chronic diseases
1·7 (1·2)
..
Cognitive function
MMSE
25·8 (4·7)
24·0 (7·6)
Global cognition
–0·28 (0·82)
–0·48 (0·98)
Episodic memory
–0·42 (1·01)
–0·57 (1·10)
Semantic memory
–0·12 (0·77)
–0·44 (1·12)
Working memory
–0·04 (0·93)
–0·31 (1·24)
Perceptual speed
–0·32 (1·11)
–0·75 (1·21)
Visuospatial ability
–0·08 (0·94)
–0·24 (1·07)
Pathological
Global AD pathology
..
Amyloid load
..
0·70 (0·63)
3·60 (4·08)
Neurofibrillary tangles
..
5·58 (7·02)
Data are mean (SD) unless otherwise indicated. MMSE=mini-mental state examination.
AD=Alzheimer’s disease.
Table 1: Selected clinical characteristics at baseline, and clinical and
pathological characteristics at the last assessment before death for
participants in the Rush Memory and Aging Project
Main effects
Estimate (SE)
With interaction term
p
Estimate (SE)
p
Model 1 (disease pathology)
Intercept
Global disease pathology
Social networks
0·034 (0·203)
0·868
0·256 (0·229)
0·267
–0·581 (0·142)
0·0001
–1·242 (0·301)
0·0001
0·012 (0·018)
0·519
–0·026 (0·023)
0·273
0·097 (0·039)
0·016
Social networks × global disease pathology
Model 2 (amyloid)
Intercept
–0·290 (0·202)
0·154
–0·133 (0·221)
0·550
Amyloid load
–0·052 (0·025)
0·039
–0·119 (0·047)
0·015
0·015 (0·019)
0·439
–0·005 (0·023)
0·815
0·010 (0·006)
0·104
Social networks
Social networks × amyloid load
Model 3 (tangles)
Intercept
Neurofibrillary tangles
Social networks
Social networks × neurofibrillary tangles
0·014 (0·177)
0·936
0·289 (0·186)
0·124
–0·070 (0·011)
<0·0001
–0·140 (0·023)
<0·0001
0·008 (0·016)
0·642
–0·032 (0·020)
0·104
0·011 (0·003)
0·001
All models controlled for age, sex, and education.
Table 2: Global cognition as a function of social networks, three different pathological indices, and interaction between each pathological index and
social networks
408
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Statistical analysis
Multiple linear regression was used to examine the extent
to which social network size was related to each of the
three measures of Alzheimer’s disease pathology. We
then constructed a linear regression model that examined
global cognition as a function of the global measure of
disease pathology and social networks. We then made a
second model which included the main effects for disease
pathology and social networks and also included an
interaction term. The interaction term directly tests the
hypothesis that the number of people in the social network
modifies the effect of a unit of pathology on level of
cognitive function. To see if the effects were present for
one type of pathology but not the other, we repeated these
models for amyloid load and PHFtau tangles. To
determine whether the association was due to cognitive,
physical, or social activities, depression, or chronic
diseases, we repeated all three sets of models controlling
for these covariates. To determine whether social networks
modified the relation of pathology to some cognitive
abilities but not others, we subsequently examined the
relation of all three post-mortem indices to separate
summary measures of five different cognitive abilities. All
models were adjusted for age, sex, and education, and
were validated graphically and analytically. Analyses were
done with SAS/STAT software version 8 (SAS Institute,
Cary, NC, USA) on a SunUltraSparc workstation.19
Role of the funding source
The sponsors of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report. The corresponding author had full
access to all the data in the study and had final
responsibility for the decision to submit for publication.
Results
Participants were about 81 years of age, had about 14 years
of education, and were predominantly white, nonHispanic (table 1). Mean MMSE was nearly 26 at baseline.
Global cognitive function and other cognitive scores at
baseline ranged from close to the mean for the entire
cohort at baseline for working memory to nearly half a
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2
High social networks
Low social networks
1
0
–1
–2
–3
–4
Global cognition
of predetermined size was randomly placed over the
entire region by the software program. Total magnification
was raised to ×400 and the program was engaged to direct
the motorised stage on the microscope to stop at each
intersection point of the grid for sampling. The operator
focused on each field visualised on the video monitor
within the superimposed counting frame. All objects
within the 150×150 µm counting frame that did not touch
the exclusion lines of the box (bottom and left sides) were
counted. Neurofibrillary tangles labeled with AT8 have a
characteristic appearance and location in the neuronal
cell body or as ghost tangles. The density of tangles (per
mm²) was averaged within region and subsequently
across regions.
–5
0
0·5
1·0
1·5
Global pathology
2·0
2·5
2
High social networks
Low social networks
1
0
–1
–2
–3
–4
–5
0
10
20
30
40
Neurofibrillary tangles (PHFtau)
Figure 1: Predicted association between pathology and global cognitive
function score proximate to death
Upper=global Alzheimer’s disease pathology. Lower=PHFtau tangles. Red
line=90th percentile of social network size (13 participants). Blue line=10th
percentile of social network size (two participants). Dotted lines indicate 95%
CIs. Both models controlled for age, sex, education, and main effects for social
networks and each pathological index.
standard unit below the mean for episodic memory. At
the last assessment before death, age was just over
87 years, mean MMSE score was 24, and cognitive scores
ranged from about a third to three quarters of a standard
unit below the mean for the entire cohort at baseline.
Overall, these data suggest that participants had very good
cognitive scores at baseline and, on average, experienced
substantial cognitive decline over the study period.
The average social network size of seven is similar to
that reported in other community-based studies of old
people.10 Social networks were related to social activity
(r=0·36, p<0·0001) and cognitive activity (0·23, p=0·031),
but not to physical activity (–0·09, p=0·43), depression
(0·01, p=0·90), chronic disease (0·12, p=0·273), or
income (0·11, p=0·53). Social networks were not related
to the global Alzheimer’s disease pathology score
(parameter estimate=–0·073, p=0·50), or to measures of
amyloid-β load (–0·062, p=0·57) or the density of PHFtau
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Articles
Episodic memory
Estimate (SE)
p
Semantic memory
Working memory
Perceptual speed
Estimate (SE)
Estimate (SE)
Estimate (SE)
p
p
Visuospatial ability
p
Estimate (SE)
p
Model 1
Intercept
0·201 (0·266)
0·452
0·272 (0·284)
0·340
0·343 (0·323)
0·292
–0·260 (0·327)
0·429
0·241 (0·305)
0·432
Social network
–0·006 (0·027)
0·836
–0·035 (0·028)
0·206
–0·018 (0·033)
0·582
–0·015 (0·033)
0·646
–0·043 (0·029)
0·142
Global disease pathology
–1·240 (0·349)
0·0007
–1·236 (0·366)
0·001
–0·894 (0·425)
0·039
–1·156 (0·429)
0·009
–0·628 (0·386)
0·109
Social network × disease
pathology
0·065 (0·046)
0·156
0·116 (0·047)
0·016
0·054 (0·056)
0·333
0·087 (0·056)
0·121
0·061 (0·049)
0·215
–0·189 (0·257)
0·466
–0·090 (0·257)
0·728
–0·037 (0·294)
0·899
–0·695 (0·313)
0·029
–0·059 (0·264)
0·824
0·007 (0·027)
0·800
–0·012 (0·026)
0·637
0·009 (0·030)
0·780
0·006 (0·031)
0·846
–0·026 (0·026)
0·329
–0·136 (0·055)
0·016
–0·124 (0·054)
0·024
–0·051 (0·063)
0·422
–0·077 (0·065)
0·239
–0·024 (0·055)
0·663
0·009 (0·007)
0·242
0·013 (0·007)
0·081
–0·002 (0·008)
0·842
0·008 (0·009)
0·378
0·005 (0·007)
0·495
Model 2
Intercept
Social network
Amyloid
Social network × amyloid
Model 3
Intercept
0·271 (0·212)
0·204
0·372 (0·242)
0·128
0·526 (0·282)
0·065
–0·211 (0·282)
0·458
0·232 (0·282)
0·413
Social network
–0·017 (0·022)
0·438
–0·049 (0·025)
0·050
–0·053 (0·030)
0·079
–0·021 (0·029)
0·481
–0·047 (0·028)
0·096
Tangles
–0·148 (0·027)
<0·0001
–0·154 (0·030)
<0·0001
–0·146 (0·036)
0·0001
–0·132 (0·035)
0·0003
–0·070 (0·036)
0·057
0·009 (0·004)
0·023
0·015 (0·004)
0·0005
0·015 (0·005)
0·005
0·010 (0·005)
0·056
0·008 (0·005)
0·114
Social network × tangles
All models controlled for age, sex, and education.
Table 3: Global cognition before death in different domains as a function of interaction of social networks
tangles (–0·088, p=0·42) in linear regression models
controlling for age, sex, and education.
In an analysis of global cognition as a function of
Alzheimer’s disease pathology and social networks, the
average global cognitive score was about 0·6 (p=0·0001)
of a unit lower for each unit of global disease pathology,
but social networks were not related to cognition; the
adjusted R² for the model was 0·250 (table 2, main effect
in model 1). We then repeated the analysis with a term
added for the interaction between pathology and social
networks. There was an interaction, and the adjusted R²
for the model increased to 0·295. To illustrate this effect,
we plotted the predicted relation between the global
measure of disease pathology and the global cognitive
score for participants with two different social network
sizes: the 90th percentile (13 members) and the 10th
percentile (two members; figure 1). The change in
cognitive function across disease pathologies was more
pronounced for people with smaller social network sizes
than for those with larger ones.
We did a similar series of analyses with amyloid-β load
and PHFtau tangles. Corresponding to each one percent
increase in amyloid, the average cognitive function score
was 0·05 units lower (p=0·039; table 2). The interaction
between amyloid and social networks was not significant.
In separate analyses, the average cognitive function score
was about 0·07 (p<0·0001) standard unit lower for each
neurofibrillary tangle (per mm²) with an adjusted R² of
0·385 (table 2). The interaction between tangles and
social networks was significant and R² increased to
0·454. To illustrate this effect, we plotted the predicted
relation between tangle density and the global cognitive
score for participants with two different social network
sizes (figure 1). Again the divergence of the lines suggests
410
a protective effect of social network size on the association
of tangles with cognition. We next repeated all three sets
of models five times adding terms for cognitive, physical,
and social activities, depressive symptoms, and chronic
medical conditions to ensure that these variables did not
confound or mediate the association of social networks
with cognition. The results were essentially unchanged
when examined separately or all five together. We also
constructed a model that included a term for the
interaction of education with measures of disease
pathology and the modifying effects of social networks
were unchanged.
Memory and other forms of cognition are not unitary
processes but are composed of dissociable systems that
mediate different types of information processing. To see
if social networks modified the relation between measures
of disease pathology to some forms of cognition but not
others, we undertook three sets of analyses, one for each
pathological measure, separately for five different
cognitive domains assessed proximate to death, namely
episodic memory, semantic memory, working memory,
perceptual speed, and visuopsatial ability (table 3). Social
networks modified the association of neurofibrillary
tangles with episodic memory, semantic memory, and
working memory. The effect for semantic memory was
especially striking, with the adjusted R² for the model
increasing from 0·210 to 0·318 with the addition of the
interaction term. Social networks also modified the
relation of global Alzheimer’s disease pathology with
semantic memory.
Discussion
We found that the extent of social networks modified the
relation between some measures of Alzheimer’s disease
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pathology and level of cognitive function assessed
proximate to death. The effect was evident with several
measures of pathology acquired with different methodologies, but was strongest for neurofibrillary tangles. The
effect persisted after controlling for various potentially
confounding variables. It was evident across multiple
domains of cognition, but was most evident for semantic
memory, which is the repository of knowledge about the
world and is fundamentally involved in unique human
cognitive processes such as language. These data provide
evidence that the extent of social networks, or something
related to social networks, provides some type of reserve
which reduces the deleterious effect of Alzheimer’s
disease pathology on cognitive abilities in old age.
In most physiological systems, considerable tissue
destruction must take place for function to be
compromised and signs and symptoms of disease to
become evident. Although structurally and functionally
complex, the nervous system can also tolerate injury and
pathology without expressing it clinically as functional
impairment. Some type of reserve has likewise been
hypothesised to protect the human brain from expressing
the pathology of Alzheimer’s disease as impaired
cognitive function.1 Clinical pathological studies report
that education1,6 or variables related to education20 can
protect the brain from the pathology of Alzheimer’s
disease.
To modify the relation between pathology and cognition,
social networks must reflect either the strength of the
neural systems underlying neurocognition or tap into
systems that support other cognitive processes to reduce
the deleterious effect of disease pathology. We are
unaware of any study that has yet examined the relation
between social networks and Alzheimer’s disease
pathology. Data about the relation of social networks to
cognition come from epidemiological studies limited to
clinical measures.8–11 Although the basis for this
association is unknown, most research has emphasised
the potential benefits for an individual of having a large
social network.21 Even though people with larger social
networks are more likely to engage in cognitive, physical,
and social activities, all of which are associated with
decreased risk of cognitive impairment and dementia,10,22–24
our findings were unchanged after controlling for
participation in these activities. Although those with
larger social networks might be less depressed, which is
also associated with cognitive impairment and dementia,25
controlling for depression had only a marginal effect on
our findings. Finally, controlling for comorbid conditions
did not alter our results.
The importance of social connectedness has been
recognised since antiquity. However, not all individuals
are equally capable of developing and maintaining
friendships and social ties. For example, several
neurodevelopmental disorders are characterised, in part,
by an impaired ability to develop social ties, including the
autism spectrum disorders, fragile X syndrome, and
http://neurology.thelancet.com Vol 5 May 2006
schizophrenia.26 People with these conditions have
deficits in social cognition. Social cognition is supported
by an extensive system of limbic and associational cortical
and subcortical brain regions.26,27 Many of these regions
also support episodic memory, semantic memory, and
other cognitive functions. These systems allow us to
make symbolic representations of the characteristics of
self and non-self, thoughts and feelings, and other aspects
of the social environment, and endow us with the
capability of viewing ourselves from the perspective of
another person (so-called theory of mind). Focal brain
lesions, including strokes, can impair aspects of social
behaviour while leaving other cognitive abilities relatively
intact.28
Neurodegenerative
diseases,
including
Parkinson’s disease, frontotemporal dementia, and
Alzheimer’s disease are known to be associated with
impaired aspects of social behaviour.29,30 Thus, it is
possible that aspects of cognitive processing that allow
people to develop and maintain large social networks
might also provide a reserve against the development of
cognitive impairment despite the accumulation of
Alzheimer’s disease pathology, or otherwise compensate
for the effects of degeneration of non-social cognitive
systems.
Recruitment of alternative brain regions in response to
injury due to ageing and neurodegenerative diseases has
been well documented in neuroimaging studies. For
example, when undertaking a cognitive task, ageing is
associated with increased activation in regions not
activated by younger people.31 This pattern is thought to
reflect compensation for age-related damage by activating
alternative neural networks.32 Old people with mild
Alzheimer’s disease also activate additional brain regions
to do a cognitive task at a similar level compared with
those without the disease.33 The observations that
alternative networks are activated in response to brain
damage rather than being on-line and in use at all times
would explain the absence of a main effect of social
networks on cognitive function but with the robust
modifying effect becoming evident as Alzheimer’s disease
pathology accumulates.
Our study has several strengths. The ability to link
social networks to several measures of disease pathology
and to multiple cognitive domains assessed proximate to
death in a single study offers a unique integrative
approach to hypothesis testing. All analyses were on
people from a single cohort with high rates of follow-up
and brain autopsy. Uniform structured procedures were
followed with blinding to previously collected data and
blinding of personnel collecting post-mortem data to
clinical data. The study also has limitations. Although the
associations are correlative and causation cannot be
proven with complete confidence, some hypotheses are
not amenable to clinical trials. Although we controlled
for many potential confounders, we did not examine the
quality of the networks nor did we have information
about networks early in life.34,35
411
Articles
Contributors
DAB obtained funding, and was involved in study design, data
collection, statistical analysis and interpretation, and writing and editing
of manuscript. JAS, SEA, and RSW were involved in design of data
collection, interpretation of analyses, and critical editing of manuscript.
YT was involved in statistical analysis and interpretation and critical
editing of manuscript. All authors had full access to all study data and
take full responsibility for the integrity of the data.
Conflicts of interest
We have no conflicts of interest.
Acknowledgments
We are indebted to the residents from the following groups
participating in the Rush Memory and Aging Project: Fairview Village,
Wyndemere, Luther Village, The Holmstad, Windsor Park Manor,
Covenant Village, Bethlehem Woods, King-Bruwaert House, Friendship
Village, Mayslake Village, The Moorings, Washington Jane Smith,
Victory Lakes, Village Woods, Franciscan Village, Victorian Village, The
Breakers of Edgewater, The Oaks, St. Paul Home, The Imperial, Frances
Manor, Peace Village, Alden Waterford, Marian Village, The Birches,
Elgin Housing Authority, Renaissance, Holland Home, Trinity United
Church Of Christ, St. Andrews-Phoenix, Green Castle, Kingston Manor,
Lawrence Manor, Community Renewal-Senior Ministry, Garden House,
and the residents of the Chicago metropolitan area. We thank the staff
of the Rush Alzheimer’s Disease Center and Institute for Healthy
Aging. This research was supported by National Institute on Aging
Grant R01AG17917.
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