ARTICLE OPEN
Are infections associated with cognitive decline and
neuroimaging outcomes? A historical cohort study using data
from the UK Biobank study linked to electronic health records
Rutendo Muzambi 1✉, Krishnan Bhaskaran1, Christopher T. Rentsch1,2, Liam Smeeth1, Carol Brayne3, Victoria Garfield4,
Dylan M. Williams 4,5, Nish Chaturvedi4 and Charlotte Warren-Gash 1
© The Author(s) 2022
While there is growing evidence of associations between infections and dementia risk, associations with cognitive impairment and
potential structural correlates of cognitive decline remain underexplored. Here we aimed to investigate the presence and nature of
any associations between common infections, cognitive decline and neuroimaging parameters. The UK Biobank is a large volunteer
cohort (over 500,000 participants recruited aged 40–69) with linkage to primary and secondary care records. Using linear mixed
effects models, we compared participants with and without a history of infections for changes in cognitive function during follow-
up. Linear regression models were used to investigate the association of infections with hippocampal and white matter
hyperintensity (WMH) volume. 16,728 participants (median age 56.0 years [IQR 50.0–61.0]; 51.3% women) had baseline and follow-
up cognitive measures. We found no evidence of an association between the presence of infection diagnoses and cognitive decline
for mean correct response time (slope difference [infections versus no infections]= 0.40 ms, 95% CI: −0.17–0.96 per year), visual
memory (slope difference 0.0004 log errors per year, 95% CI: −0.003–0.004, fluid intelligence (slope difference 0.007, 95% CI:
−0.010–0.023) and prospective memory (OR 0.88, 95% CI: 0.68–1.14). No evidence of an association was found between infection
site, setting or frequency and cognitive decline except for small associations on the visual memory test. We found no association
between infections and hippocampal or WMH volume. Limitations of our study include selection bias, potential practice effects and
the relatively young age of our cohort. Our findings do not support a major role for common midlife infections in contributing to
cognitive decline for this cohort. Further research is warranted in individuals with more severe infections, for infections occurring
later in life.
Translational Psychiatry          (2022) 12:385 ; https://doi.org/10.1038/s41398-022-02145-z
INTRODUCTION
Growing evidence from longitudinal studies supports the role of
common infections such as sepsis [1–4], pneumonia [4, 5], other
lower respiratory tract infections (LRTIs) [4], urinary tract infections
(UTIs) [4, 5], and skin and soft tissue infections (SSTIs) [4, 5], in
increasing the risk of dementia, though some findings have been
conflicting [6].
Dementia has a long preclinical phase which can take decades
to develop [7]. Before the clinical expression of dementia,
cognitive and neuropathological changes associated with demen-
tia progression can be observed but it is unclear whether
infections are relevant during this process [8, 9]. Identifying the
point at which infections may act before clinical onset of dementia
might allow interventions to be targeted and timed appropriately
to prevent or delay the onset of dementia.
Infection-related hospitalisations, particularly for sepsis and
pneumonia, have been associated with cognitive decline [10–14].
However, there is limited understanding of the relationship of
other sites of common infections diagnosed in different clinical
settings such as primary care with changes in cognitive function
over time. Other limitations of existing studies include either
relatively small study sizes, the use of a single global measure of
cognition rather than individual cognitive domains, or inadequate
confounder adjustment, given the wide range of potential
confounders [15–24].
A possible link between infections and other subclinical markers
of dementia risk such as hippocampal atrophy and white matter
hyperintensities (WMH) may exist. However, evidence on the link
between common infections and these neuroimaging markers is
scarce. Compared to cognitive function measures, neuroimaging
measures may be less prone to the effects of sociodemographic
influences such as education; investigating both cognitive decline
and neuroimaging measures may allow us to triangulate our
findings across different outcomes associated with dementia risk.
Therefore, we aimed to explore the association between
common infections and subclinical markers of dementia (cognitive
Received: 2 March 2022 Revised: 23 August 2022 Accepted: 1 September 2022
1Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK. 2Section of General Internal Medicine, Yale School of
Medicine, New Haven, CT 06510, USA. 3Cambridge Institute of Public Health, Cambridge University, Cambridge, UK. 4MRC Unit for Lifelong Health and Ageing at UCL, Institute of
Cardiovascular Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK. 5Department of Medical Epidemiology and Biostatistics, Karolinska Institutet,
Stockholm, Sweden. ✉email: Rutendo.Muzambi@lshtm.ac.uk
www.nature.com/tpTranslational Psychiatry
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decline, hippocampal volume and WMH volume) in a population
without pre-existing dementia or evidence of cognitive impair-
ment. We then assessed whether these associations differed by
infection site, clinical setting, frequency and timing of infections.
METHODS
Study design and population
We used data from the UK Biobank study, an ongoing prospective study
which recruited over 500,000 participants aged 40–69 between 2006 and
2010 from 22 assessment centres based in England, Wales and Scotland
[25]. Over 9 million individuals registered with the National Health Service
(NHS) who lived within 40 km of one of the UK Biobank assessment centres
were invited to take part in the study via postal invitations, however, the
response rate was low with 5.5% consenting to take part in the study and
attending the baseline assessment [25, 26]. The methodology of the UK
Biobank has been described previously and is summarised in the
Supplementary Information [27].
Linkage to primary care data for ~45% of the cohort has been made
available by UK Biobank for non-COVID research [28]. To minimise
exposure misclassification, our study population was limited to only
participants with linked primary and secondary care records (Fig. 1). We
excluded participants who had less than 12 months registration with a GP
practice to avoid incorporating historical diagnoses when defining our
exposure as previous studies suggest historical diagnoses may be recorded
within the first 12 months when a patient registers with a GP practice [29].
We defined baseline as the date participants attended the UK Biobank
baseline assessment. A subset of participants was invited to attend the first
repeat assessment (2012–13) and the first wave of the neuroimaging
assessments (2014 onwards). Cognitive function was assessed at all three
assessments.
Our study explored two subsets of the UK Biobank population, a
cognition subset, and a separate neuroimaging subset. For the cognition
subset, we included participants with valid measures of cognitive function
completed at baseline and at least one follow-up measure on the same
test. For our neuroimaging cohort, we included only participants with data
on hippocampal or WMH volume who attended the first neuroimaging
visit. We excluded participants with dementia or cognitive impairment at
baseline in both cohorts using self-report data and linked primary and
secondary care records. We excluded participants with a history of
dementia or cognitive impairment because the aim of our study was to
investigate whether infections were associated with earlier or subclinical
markers of dementia risk thus we were specifically interested in the stage
before clinical expression of dementia.
Exposures
Our exposures were one or more of the following common infections:
sepsis, pneumonia, other LRTIs, UTIs and SSTIs. We examined these as
With follow-up cognive funcon 
data on the same test (n=16,728)
•Reacon me (n=16,663)
•Visual memory (n=14,435)
•Verbal-numerical reasoning 
(n=5,755)
•Prospecve memory (n=5,861)
2,004 (12.0% had two follow-up 
cognive measures on the same 
test)
UK Biobank (n=502,473)
Parcipants withdrawing 
from study (n=29)
Parcipants without 
primary care records and
Less than 12 months 
registraon in GP records 
(n=324,426)
History of cognive 
impairment and 
demena (n=1,811)
Without baseline 
cognive funcon 
measures (n=1,082)
With data on the first neuroimaging 
visit (n=14,712)
Hippocampal volume 
(n=14,710)
White maer hyperintensies 
(n=14,397)
No measures on hippocampal 
volume or white maer 
hyperintensity volume 
measures at first imaging visit
(n=161,495, of whom 6,104 
Study populaon with no 
history of demena or 
cognive impairment 
Study populaon with linked primary
and secondary care data and at least 
12 months follow-up in GP data 
With baseline data on
cognive funcon tests 
(n=175,125)
No follow-up data on 
cognive measures 
(n=158,397, of whom   
5,956 [3.8%] died)
Fig. 1 Flow chart of study population. Flowchart depicting participants included and excluded from this study.
R. Muzambi et al.
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Translational Psychiatry          (2022) 12:385 
exposures in combination and separately. Sepsis is defined as a serious, life-
threatening condition caused by a dysregulated host response following a
range of infections [30]. Infections were identified in the 5 years up to UK
Biobank participants’ baseline assessment visit. This time period of 5 years
was chosen due to issues with the completeness of historical linked primary
care data. Infections were defined using Read codes in linked primary care
records and International Classification of Diseases (ICD)-10 codes in
hospital records. To increase the specificity of our infection definition,
participants were defined as having UTIs or SSTIs if they were also
prescribed antibiotics on the same date as infection diagnosis.
Outcomes
Cognitive function. Participants completed a 15min battery of compu-
terised cognitive function tests including measurement of reaction time
which is referred to in this study as the mean correct response time test,
visual memory (pairs matching test), fluid intelligence (verbal-numerical
reasoning) and prospective memory. Details of these tests and their
association with age have been reported elsewhere [31, 32]. Our outcome
measures for these tests were: (1) mean correct response time
(milliseconds) taken for a participant to correctly identify matching pairs
of cards (Data-field 404), (2) total number of incorrect matches in
participants who completed the visual memory test (Data-field 399), (3)
total number of incorrect answers on the fluid intelligence test (Data-field
20016) and (4) participants were scored 0 for the correct answer and 1 for
an incorrect answer at the first attempt for the prospective memory test
(one minus Data-field 20018). Lower scores in all cognitive tests indicated
better performance. We dealt with outliers for mean correct response time
using an approach consistent with the method outlined by the UK
Biobank. We therefore excluded response times under 50 milliseconds due
to anticipation and response times over 2000 milliseconds were excluded
as the UK Biobank indicates that the cards used for this web-based test
had disappeared by then.
Neuroimaging measures. We used measures of hippocampal volume
(Data-fields 25019 and 25020) with lower volumes being indicative of
Alzheimer’s disease pathology and WMH volume (Data-field 25781) with
higher volumes indicative of cerebral small vessel disease pathology
measures from the first imaging visit (Supplementary Information). We
assessed the total volume of WMH volume using postprocessed measures
from T1 and T2 weighted fluid attenuation inversion recovery (FLAIR)
imaging technique derived by the UK Biobank study [33, 34].
Covariates. Data from baseline assessment questionnaires, verbal inter-
view and linked primary and secondary care data were used to define
covariates. Demographic variables included age (years), sex, ethnicity
(White European, South Asian, African or Caribbean, Mixed or Other) and
years in full-time education based on the International Standard
Classification of Education (ISCED) 1997 (Supplementary Table 1) [35, 36].
Socioeconomic status was measured using the Townsend Deprivation
scores [37]. Potential lifestyle factors included body mass index (BMI, kg/
m2), smoking, alcohol intake frequency and physical activity. Diabetes was
ascertained using HbA1c and all data sources mentioned above. Other
comorbidities included anxiety and depression, asthma, chronic kidney
disease, chronic liver disease, chronic obstructive pulmonary disease, heart
failure, hypertension, inflammatory bowel disease, multiple sclerosis,
obstructive sleep apnoea, rheumatoid arthritis, psoriasis, severe mental
illness, stroke and traumatic brain injury (Supplementary Information).
Code lists for covariates ascertained in electronic health records can be
accessed at: https://doi.org/10.17037/DATA.00002573.
Missing data on covariates was minimal (≤3.6% in total) in both cohorts
and not all of these covariates were adjusted for in each analysis thus we
used a complete case analysis for all analyses (Supplementary Information).
Approval for the UK Biobank study was obtained from the North West
Multi-Centre Research Ethics Committee and the present research was
conducted under application number 7661. All participants of the study
provided written informed consent. Ethical approval was also obtained
from the London School of Hygiene and Tropical Medicine research ethics
committee (reference number 22721).
Statistical analysis
We developed a statistical analysis plan before conducting our analyses as
shown in the Supplementary Material pages 33–47. We descriptively
explored the potential for selection bias in our study by comparing the
baseline characteristics of participants included and excluded from our study.
Association between common infections and cognitive decline
For each continuous cognitive measure (mean correct response time,
visual memory and fluid intelligence), we fitted linear mixed models with
random intercept and slope effects using an unstructured covariance
matrix to estimate the rates of cognitive decline over follow-up in
participants with and without a history of infections. Interaction terms
were fitted between infections and time since baseline to assess the
difference in cognitive decline over follow-up. Due to skewed distribution
and zero value inflation of the visual memory error scores, a value of one
was added to the scores which were then logn-transformed. For the
dichotomous test (prospective memory), we used logistic regression
models for participants with correct recall at baseline to examine the
association between infections and cognitive decline with a binary variable
(coded as 0 for correct recall and 1 for incorrect recall at follow-up).
For linear mixed models, minimally adjusted models included age, sex
and an interaction term between time and infection. For all other analyses,
minimally adjusted models included age and sex. For each analysis, we
considered adjustment for all potential confounders described above and
adjusted for covariates that changed the main association estimates by an
important amount (9% or greater change in association magnitude) in the
fully adjusted model.
In our secondary analyses, we investigated the association between site,
clinical setting (GP-recorded or hospital-recorded), frequency (with the
number of infections modelled as a continuous variable) and timing of
infections expressed as year(s) since infection diagnosis in the 5 years up to
baseline (0 to <1 year, 1 to <2 years, 2 to <3 years, 3 to <4 years and 4 to 5
years). Given the potential interaction between inflammatory comorbidities,
such as diabetes, and dementia pathogenesis, we explored whether the
associations of infections on cognition differ by diabetes category (using a
binary variable for diabetes) and tested for the presence of effect
modification by fitting an interaction term [38]. We performed additional
analyses to compare the associations between infections and cognitive
decline by age group (40–49, 50–59 and 60+) and sex. This is because
increasing age is associated with greater trajectories of cognitive decline and
some studies have reported sex differences in cognitive decline [39, 40].
Association of common infections with hippocampal and
WMH volume
We log-transformed WMH volume due to a positively skewed distribution.
We used linear regression models to estimate the association between
infections and each structural neuroimaging measure. We used the same
strategy for confounder adjustment as the approach described for our
cognitive decline analyses. To aid interpretation, log-transformed WMH
volume was reported using exponentiated betas and was interpreted as
percentages. For example, exponentiated beta 1.01 refers to a 1% increase
in WMH volume.
Sensitivity analyses
We conducted a range of sensitivity analyses to test the robustness of our
findings. First, we found evidence of non-normal distribution for mean
correct response time. However, when we inverse or log transformed this
variable, we were unable to fit the models due to unstable standard errors
and models failing to converge. Thus, we used raw scores in our main
analyses and in a sensitivity analysis we specified an independent
covariance structure which allowed us to re-run our models using the
inverse transformed variable which showed evidence of a normal
distribution (Supplementary Information). Second, we repeated our main
analyses excluding participants diagnosed with infections during follow-up
to reduce misclassification of our exposure. Third, we repeated our main
analyses excluding participants whose current registration date with a GP
practice was <5 years. Given that infections were defined within 5 years
prior to baseline, this analysis ensured that all participants had at least 5
years of follow-up in which to capture infection diagnoses. Fourth,
previous studies suggest infections may have differing associations with
the left and right hippocampus thus we repeated our main analyses on
hippocampal volume separately for the left and right hippocampus [41].
Statistical analyses were performed in Stata MP (version 16.0) and
RStudio (version 4.1.0).
RESULTS
Of the 176,207 participants with linked primary and secondary
care records and no history of cognitive impairment or dementia
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Table 1. Baseline characteristics of participants included and excluded from the study for the cognitive function and neuroimaging cohorts.
Cohort with cognitive function measures Cohort with neuroimaging measures
Characteristics Included
(n= 16,728)
Excluded because of no
follow-up cognition measures
(158,383)
Included
(14,712)
Excluded because of no
neuroimaging measures
(161,495)
Any Infection 2971 (17.8%) 31,381 (19.8%) 2435 (16.6%) 32,214 (19.9%)
Mean age at baseline assessment
(years)
55.59 (7.5) 56.73 (8.1) 54.82 (7.5) 56.79 (8.1)
Median age at baseline assessment
(years)
56.0 (50.0–61.0) 58.0 (50.0–63.0) 55.0 (49.0–61.0) 58.0 (50.0–63.0)
Age category (years)
40–44 1650 (9.9%) 15,744 (9.9%) 1680 (11.4%) 15,824 (9.8%)
45–49 2458 (14.7%) 20,519 (13.0%) 2336 (15.9%) 20,768 (12.9%)
50–54 2887 (17.3%) 23,606 (14.9%) 2781 (18.9%) 23,833 (14.8%)
55–59 3721 (22.2%) 27,994 (17.7%) 3267 (22.2%) 28,645 (17.7%)
60–64 4048 (24.2%) 38,855 (24.5%) 3231 (22.0%) 39,925 (24.7%)
65+ 1964 (11.7%) 31,665 (20.0%) 1,417 (9.6%) 32,500 (20.1%)
Women 8576 (51.3%) 87,051 (55.0%) 7781 (52.9%) 88,387 (54.7%)
Ethnicity
White European 16,323 (97.6%) 150,683 (95.1%) 14,275 (97.0%) 153,339 (94.9%)
South Asian 110 (0.7%) 2776 (1.8%) 135 (0.9%) 2893 (1.8%)
African or Caribbean 76 (0.5%) 1630 (1.0%) 76 (0.5%) 1670 (1.0%)
Mixed or other 177 (1.1%) 2787 (1.8%) 185 (1.3%) 2851 (1.8%)
Missing 42 (0.3%) 507 (0.3%) 41 (0.3%) 742 (0.5%)
Diabetes status
No diabetes 14,762 (88.2%) 134,585 (85.0%) 13,203 (89.7%) 136,823 (84.7%)
Pre-diabetes 370 (2.2%) 5109 (3.2%) 279 (1.9%) 5256 (3.3%)
Undiagnosed diabetes 1104 (6.6%) 10,157 (6.4%) 884 (6.0%) 10,579 (6.6%)
Controlled diabetes 362 (2.2%) 5593 (3.5%) 256 (1.7%) 5781 (3.6%)
Uncontrolled diabetes 130 (0.8%) 2939 (1.9%) 90 (0.6%) 3056 (1.9%)
Educational attainment (years in
full-time education)
16.6 (4.4) 14.7 (5.2) 16.8 (4.3) 14.7 (5.2)
Baseline BMI, kg/m2 (mean) 26.7 (4.4) 27.6 (4.8) 26.5 (4.2) 27.6 (4.8)
Townsend deprivation
score (mean)
−2.2 (2.5) −1.4 (3.0) −2.1 (2.6) −1.4 (3.0)
Baseline number of days/week
moderate physical activity >10min
3.0 (2.0–5.0) 3.0 (2.0–5.0) 3.0 (2.0–5.0) 3.0 (2.0–5.0)
Smoking status
Never smoker 12,060 (72.1%) 104,733 (66.1%) 10,670 (72.5%) 106,714 (66.1%)
Ex-smoker 3650 (21.8%) 36,455 (23.0%) 3138 (21.3%) 37,117 (23.0%)
Current smoker 1001 (6.0%) 16,901 (10.7%) 889 (6.0%) 17,141 (10.6%)
Missing 17 (0.1%) 294 (0.2%) 15 (0.1%) 523 (0.3%)
Baseline alcohol intake frequency
Rarely or never 2,192 (13.1%) 31,244 (19.7%) 1860 (12.6%) 31,965 (19.8%)
1–8 times per month 6159 (36.8%) 59,815 (37.8%) 5,399 (36.7%) 60,854 (37.7%)
16 times per month-every day 8374 (50.1%) 67,161 (42.4%) 7447 (50.6%) 68,290 (42.3%)
Missing <5 163 (0.1%) 6 (0.0%) 386 (0.2%)
Comorbidities
Anxiety and depression 1,968 (11.8%) 20,832 (13.2%) 1660 (11.3%) 21,332 (13.2%)
Severe mental illness 187 (1.1%) 2150 (1.4%) 155 (1.1%) 2216 (1.4%)
Inflammatory bowel disease 708 (4.2%) 7788 (4.9%) 611 (4.2%) 7947 (4.9%)
Multiple Sclerosis 51 (0.3%) 612 (0.4%) 41 (0.3%) 630 (0.4%)
Rheumatoid arthritis 155 (0.9%) 2269 (1.4%) 126 (0.9%) 2314 (1.4%)
Psoriasis 460 (2.7%) 4227 (2.7%) 402 (2.7%) 4309 (2.7%)
Asthma 2099 (12.5%) 20,981 (13.2%) 1835 (12.5%) 21,410 (13.3%)
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at baseline, 16,728 (9.5%) had baseline and at least one follow-up
cognitive measure on the same test, and of whom 2,004 (12.0%)
had two follow-up cognitive measures. 14,712 participants
completed neuroimaging measurements at the first imaging visit
(Fig. 1). The mean time interval between baseline and the first or
second repeat cognitive function assessment was 4.0 years (sd
0.78) and 8.27 years (sd 1.6), respectively. Compared to participants
without follow-up cognitive measures, participants included in our
study were slightly younger, less likely to be female, had more
years in education, fewer infections and performed better on
baseline cognitive tests (Table 1). Further descriptive information
on infections in participants included and excluded from the study
is presented in Supplementary Table 2. 323 (1.0%) participants
excluded from the study had infection-related mortality compared
to 5 (0.2%) participants included in the study.
Characteristics of participants included in the two subsets of our
study, one with follow-up cognitive measures and the other who
attended the first imaging visit, are presented in Table 2 and
Supplementary Fig. 1. 11, 455 participants were included in both
cohorts. In our cognition cohort, the median age was 56.0 (IQR,
50.0–61.0) and 51.3% were female. 2,971 (17.8%) participants were
diagnosed with a previous infection at least 5 years prior to
baseline. These infections included 23 (0.1%) sepsis, 45 (0.3%)
pneumonia, 1,681 (10.1%) other LRTIs, 674 (4.0%) UTIs and 532
(3.2%) SSTIs. 6 participants (0.0%) had multiple infection diagnoses
on the same date from different infection sites. 2,770 participants
(93.2%) had GP-recorded infections and 201 (6.8%) had hospital-
recorded infections.
Figure 2 and Table 3 show no evidence for differences in
cognitive performance change over follow-up in participants with
a history of any infections compared to those without infections
for the mean correct response time (estimated difference in slope
[infections versus no infections]= 0.40 ms, 95% CI: −0.17–0.96 per
year), visual memory (estimated difference in slope 0.0004 log
errors per year, 95% CI: (−0.003–0.004), fluid intelligence
(estimated difference in slope 0.007, 95% CI: −0.010–0.023) and
prospective memory tests (OR 0.88, 95% CI: 0.68–1.14). The results
for all covariates for these models are shown in Supplementary
Table 3. No evidence of an association was found between the site
and clinical setting of infections with cognitive decline for any of
the tests, apart from visual memory. The log of the visual memory
errors increased by 0.011 (95% CI: 0.004–0.018) per year in
participants with a history of UTIs compared to those with no prior
infection.
Association between infections and neuroimaging outcomes
Figure 3 shows that a history of any infections and LRTIs excluding
pneumonia was associated with a 5% higher WMH volume
compared to no prior infection in minimally adjusted models. In
fully adjusted analyses, the difference reduced to a 2% higher
WMH volume for those with infections and included the null. No
evidence of an association was found between the presence or
site of infections with hippocampal volume in minimally or fully
adjusted models.
Additional secondary analyses and sensitivity analyses
Secondary analyses on the association between numbers of
infections and cognitive decline show that the increase over time
in the log of visual memory errors was slightly steeper by 0.006
(95% CI: 0.002 to 0.011) per year, for every additional infection
(Supplementary Table 4). No evidence of an association was found
between the number of infections beyond the first and cognitive
decline in all other tests. Supplementary Fig. 2 depicts the
association between the timing of infections in the 5 years prior to
baseline and cognitive decline in fully adjusted models. We found
no evidence of an association between common infections in
each year prior to baseline and cognitive decline according to any
of the tests. In further additional secondary analyses, we found
evidence of an interaction by diabetes for mean correct response
time (p for interaction= 0.015). Participants with diabetes and
infections performed better on mean correct response time
compared to participants with diabetes and no infection. In
stratified results, there was no evidence of an association between
common infections and cognitive decline in any of the cognitive
Table 1. continued
Cohort with cognitive function measures Cohort with neuroimaging measures
Characteristics Included
(n= 16,728)
Excluded because of no
follow-up cognition measures
(158,383)
Included
(14,712)
Excluded because of no
neuroimaging measures
(161,495)
Chronic kidney disease 141 (0.8%) 1950 (1.2%) 119 (0.8%) 1995 (1.2%)
Chronic liver disease 479 (2.9%) 6576 (4.2%) 328 (2.2%) 6789 (4.2%)
Chronic obstructive pulmonary
disease
114 (0.7%) 2798 (1.8%) 75 (0.5%) 2881 (1.8%)
Heart failure 208 (1.2%) 4233 (2.7%) 149 (1.0%) 4379 (2.7%)
Hypertension 2613 (15.6%) 33,785 (21.3%) 1982 (13.5%) 34,747 (21.5%)
Obstructive sleep apnoea 126 (0.8%) 1547 (1.0%) 97 (0.7%) 1591 (1.0%)
Stroke 116 (0.7%) 1756 (1.1%) 80 (0.5%) 1815 (1.1%)
Traumatic brain injury 118 (0.7%) 1260 (0.8%) 92 (0.6%) 1299 (0.8%)
Baseline cognitive function test performance
Mean correct response time
score baseline (milliseconds)
540.3 (102.2) 561.3 (118.8) 536.2 (99.8) 549.3 (106.9)
Pairs matching test score
(incorrect matches)
5.1 (2.9) 5.50 (3.3) 5.1 (2.8) 5.3 (3.1)
Fluid intelligence test score
(incorrect answers)
6.3 (2.0) 7.08 (2.1) 6.25 (2.0) 6.25 (2.0)
Prospective Memory test
(incorrect answer)
761 (13.0%) 14,323 (24.6%) 532 (12.7%) 229 (13.7%)
For data protection, table cells containing fewer than 5 participants were recorded as ‘<5’.
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Table 2. Baseline characteristics of participants included in the study with data on cognitive function measures and neuroimaging outcomes,
stratified by history of common infections.
Characteristics Cohort with cognitive function
measures (N= 16,728)
Cohort with neuroimaging
measures (N= 14,712)
No infection
(N= 13,757)
Any infection
(N= 2971)
No infection
(N= 12,277)
Any infection
(N= 2,435)
Mean age at baseline assessment (years) 55.4 (7.5) 56.4 (7.4) 54.7 (7.5) 55.5 (7.5)
Median age at baseline assessment (years) 56.0 (49.0–61.0) 57.0 (51.0–62.0) 55.0 (49.0–61.0) 56.0 (49.0–61.0)
Age category (years)
40–44 1415 (10.3%) 235 (7.9%) 1442 (11.7%) 238 (9.8%)
45–49 2054 (14.9%) 404 (13.6%) 1963 (16.0%) 373 (15.3%)
50–54 2410 (17.5%) 477 (16.1%) 2365 (19.3%) 416 (17.1%)
55–59 3056 (22.2%) 665 (22.4%) 2716 (22.1%) 551 (22.6%)
60–64 3279 (23.8%) 769 (25.9%) 2648 (21.6%) 583 (23.9%)
65+ 1543 (11.2%) 421 (14.2%) 1143 (9.3%) 274 (11.3%)
Women 6856 (49.8%) 1720 (57.9%) 6318 (51.5%) 1463 (60.1%)
Ethnicity
White European 13,417 (97.5%) 2906 (97.8%) 11,910 (97.0%) 2365 (97.1%)
South Asian 90 (0.7%) 20 (0.7%) 110 (0.9%) 25 (1.0%)
African or Caribbean 59 (0.4%) 17 (0.6%) 61 (0.5%) 15 (0.6%)
Mixed or Other 155 (1.1%) 22 (0.7%) 158 (1.3%) 27 (1.1%)
Missing 36 (0.3%) 6 (0.2%) 38 (0.3%) <5
Diabetes category
No diabetes 12,229 (88.9%) 2533 (85.3%) 11,062 (90.1%) 2141 (87.9%)
Pre-diabetes 276 (2.0%) 94 (3.2%) 220 (1.8%) 59 (2.4%)
Undiagnosed diabetes 891 (6.5%) 213 (7.2%) 721 (5.9%) 163 (6.7%)
Controlled diabetes 267 (1.9%) 95 (3.2%) 205 (1.7%) 51 (2.1%)
Uncontrolled diabetes 94 (0.7%) 36 (1.2%) 69 (0.6%) 21 (0.9%)
Educational attainment (years in full-time education) 16.7 (4.3) 16.2 (4.5) 16.9 (4.3) 16.2 (4.5)
Baseline BMI (mean) 26.6 (4.2) 27.5 (4.9) 26.4 (4.1) 27.1 (4.6)
Townsend deprivation score (mean) −2.2 (2.5) −2.1 (2.5) −2.1 (2.6) −2.0 (2.6)
Baseline number of days/week moderate physical activity >10min 3.0 (2.0–5.0) 3.0 (2.0–5.0) 3.0 (2.0–5.0) 3.0 (2.0–5.0)
Smoking status
Never Smoker 9994 (72.6%) 2066 (69.5%) 8989 (73.2%) 1681 (69.0%)
Ex-Smoker 2931 (21.3%) 719 (24.2%) 2548 (20.8%) 590 (24.2%)
Current smoker 820 (6.0%) 181 (6.1%) 729 (5.9%) 160 (6.6%)
Missing 12 (0.1%) 5 (0.2%) 11 (0.1%) <5
Baseline alcohol intake frequency
Rarely or never 1752 (12.7%) 440 (14.8%) 1510 (12.3%) 350 (14.4%)
1–8 times per month 5036 (36.6%) 1123 (37.8%) 4497 (36.6%) 902 (37.0%)
16 times per month-every day 6968 (50.7%) 1406 (47.3%) 6266 (51.0%) 1181 (48.5%)
Missing <5 <5 <5 <5
Comorbidities
Anxiety and depression 1494 (10.9%) 474 (16.0%) 1262 (10.3%) 398 (16.3%)
Severe mental illness 149 (1.1%) 38 (1.3%) 126 (1.0%) 29 (1.2%)
Inflammatory bowel disease 519 (3.8%) 189 (6.4%) 462 (3.8%) 149 (6.1%)
Multiple Sclerosis 34 (0.2%) 17 (0.6%) 30 (0.2%) 11 (0.5%)
Rheumatoid Arthritis 112 (0.8%) 43 (1.4%) 94 (0.8%) 32 (1.3%)
Psoriasis 362 (2.6%) 98 (3.3%) 319 (2.6%) 83 (3.4%)
Asthma 1511 (11.0%) 588 (19.8%) 1380 (11.2%) 455 (18.7%)
Chronic kidney disease 112 (0.8%) 29 (1.0%) 100 (0.8%) 19 (0.8%)
Chronic liver disease 359 (2.6%) 120 (4.0%) 249 (2.0%) 79 (3.2%)
Chronic obstructive pulmonary disease 44 (0.3%) 70 (2.4%) 28 (0.2%) 47 (1.9%)
Heart failure 143 (1.0%) 65 (2.2%) 113 (0.9%) 36 (1.5%)
R. Muzambi et al.
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tests in people with and without diabetes (Supplementary Table
5). We found no difference by sex and age in the association
between infections and cognitive decline for any of the cognitive
measures (Supplementary Tables 6, 7). Our sensitivity analyses did
not materially change our conclusions (Supplementary Tables
8–12).
DISCUSSION
To our knowledge, this is the largest longitudinal study to date
examining the association of common infections occurring in
midlife with subclinical markers of dementia risk (cognitive
decline, hippocampal volume and WMH volume). We did not
find evidence for an association between common infections and
cognitive decline for any of the cognitive measures, except for
visual memory. We found evidence of small associations between
UTIs and an increasing number of infections with worsening of
performance on the visual memory test over follow-up. Infections
were not associated with lower hippocampal or higher WMH
volume after adjustments for potential confounding.
Previous studies
Previous studies have yielded mixed findings for the association
between infections and cognitive impairment. In a US case-control
study of intensive care unit survivors (ICU) with a mean age of 65.9
years, no association was found between sepsis and cognitive
decline [42]. However, given that ICU stay and sepsis are
associated with mortality risk, the competing risk of mortality in
ICU survivors with sepsis may have weakened the associations
found in this study [43, 44]. Two prospective US studies with a
mean and median age, respectively, of 77 years found an
association between pneumonia or severe sepsis hospitalisation
and cognitive impairment [10, 12].
Differences in our findings compared to previous studies may
be due to heterogeneity in populations studied including their
ages, clinical setting, site of infection, cognitive measures and
domains assessed, and study design. Participants in our study
were younger (mean/median both 56 years) than those in
previous studies. Age is an important risk factor for cognitive
decline and dementia, with the risk of dementia doubling every 5
years after the age of 65 [45]. We found no evidence of an
association between hospitalised infections with cognitive decline,
however, we had a reduced power to detect an association as only
a small subset of 201 (6.8%) participants with infections had
hospitalised infections while those with hospital infections in
previous studies ranged from 827 to 1,529 participants [10, 12, 42].
These studies only assessed sepsis or pneumonia as individual
exposures; however, our study had insufficient statistical power to
Table 2. continued
Characteristics Cohort with cognitive function
measures (N= 16,728)
Cohort with neuroimaging
measures (N= 14,712)
No infection
(N= 13,757)
Any infection
(N= 2971)
No infection
(N= 12,277)
Any infection
(N= 2,435)
Hypertension 2020 (14.7%) 593 (20.0%) 1574 (12.8%) 408 (16.8%)
Obstructive sleep apnoea 86 (0.6%) 40 (1.3%) 77 (0.6%) 20 (0.8%)
Stroke 88 (0.6%) 28 (0.9%) 58 (0.5%) 22 (0.9%)
Traumatic brain injury 88 (0.6%) 30 (1.0%) 67 (0.5%) 25 (1.0%)
For data protection, table cells containing fewer than 5 participants were recorded as ‘<5’.
Fig. 2 Association of presence, site and setting (GP and hospital) with changes in cognitive performance over follow-up. Linear mixed
models with random intercept and slope used to illustrate fitted changes in cognitive function over time for the mean correct response time,
visual memory (log transformed) and fluid intelligence test. A Mean correct response time models adjusted for age (years), sex, time, baseline
test score, interaction term with time × infection status, ethnicity, BMI, years in full-time education, physical activity, alcohol consumption,
diabetes, anxiety and depression, COPD, multiple sclerosis, hypertension and heart failure. B Visual memory models adjusted for age (years),
sex, time, baseline test score, interaction term with time × infection status, ethnicity, BMI, smoking status, socioeconomic deprivation, physical
activity, alcohol frequency, years in full-time education, diabetes, anxiety and depression, COPD, hypertension, inflammatory bowel disease,
rheumatoid arthritis, obstructive sleep apnoea, and multiple sclerosis. C Fluid intelligence models adjusted for age (years), sex, time, baseline
test score, interaction term with time × infection status and years in full-time education.
R. Muzambi et al.
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Table 3. Association of site and clinical setting of common infections with cognitive decline.
Minimally adjusted Fully adjusted model
No. of
participants
β (95% CI) P value No. of
participants
β (95% CI) P value
Mean correct response time (Difference in slope compared with no infection)
Site of infection
No infection 13,707 Reference 13,275 Reference
Any infection 2956 0.47 (−0.09 to 1.03) 0.10 2809 0.40 (−0.17 to 0.96) 0.17
LRTIs 1682 0.50 (−0.21 to 1.22) 0.17 1598 0.34 (−0.39 to 1.07) 0.37
UTIs 672 0.39 (−0.70 to 1.47) 0.49 636 0.57 (−0.54 to 1.68) 0.31
SSTI 529 0.63 (−0.58 to 1.84) 0.31 507 0.53 (−0.70 to 1.75) 0.40
Clinical setting of infection
No infection 13,906 Reference 13,463 Reference
GP infection 2757 0.56 (−0.02 to 1.13) 0.06 2621 0.50 (−0.09 to 1.08) 0.10
No infection 16,464 Reference 15,896 Reference
Hospital-recorded
infections
199 −0.62 (−2.50 to 1.27) 0.52 188 −0.83 (−2.76 to 1.09) 0.40
Visual memory (Difference in slope compared with no infection)
Site of infection
No infection 11,873 Reference 11,481 Reference
Any infection 2562 0.00 (−0.00 to 0.00) 0.73 2436 0.00036 (−0.0034 to
0.0041)
0.85
LRTIs 1461 −0.0016 (−0.0063
to 0.00)
0.52 1387 −0.0015 (−0.0064 to
0.0033)
0.53
UTIs 579 0.011 (0.0040
to 0.018)
0.002 548 0.011 (0.0037
to 0.018)
0.003
SSTI 459 −0.0040 (−0.012 to
0.0040)
0.33 441 −0.0051 (−0.013 to
0.0030)
0.22
Clinical setting of infection
No infection 12,041 Reference 11,639 Reference
GP infection 2394 −0.00029 (−0.0041
to 0.0035)
0.88 2278 −0.00049 (−0.0044
to 0.0034)
0.80
No infection 14,267 Reference 13,759 Reference
Hospital-recorded
infections
168 0.010 (−0.0024
to 0.022)
0.11 158 0.0089 (−0.0039
to 0.022)
0.17
Fluid intelligence (Difference in slope compared with no infection)
Site of infection
No infection 4685 Reference 4673 Reference
Any infection 1070 0.0063 (−0.010
to 0.023)
0.46 1066 0.0066 (−0.010
to 0.023)
0.44
LRTIs 619 0.012 (−0.0091
to 0.033)
0.27 616 0.011 (−0.0092
to 0.033)
0.27
UTIs 245 0.0083 (−0.025
to 0.041)
0.62 244 0.0086 (−0.024
to 0.042)
0.61
SSTI 184 −0.018 (−0.055
to 0.019)
0.34 184 −0.0162 (−0.053
to 0.021)
0.39
Clinical setting
No infection 4743 Reference 4731 Reference
GP infection 1012 0.0051 (−0.012
to 0.022)
0.56 1008 0.0055 (−0.012
to 0.023)
0.53
No infection 5697 Reference 5681 Reference
Hospital-recorded
infections
58 0.021 (−0.044
to 0.085)
0.53 58 0.020 (−0.044
to 0.084)
0.55
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study the association of these infections with cognitive decline
individually. Given that exposed participants in our study were
predominantly diagnosed with more mild infections (GP-recorded
infections and other LRTIs), this may explain the lack of association
observed as it is hypothesised that more severe systemic
infections may be more likely to trigger the release of pro-
inflammatory cytokines and induce systemic inflammation (or
other pathways) which may lead to cognitive decline or dementia
[46]. Alternatively, it is also possible that hospitalised infections do
not play a role in cognitive decline and the association shown by
previous studies may be driven by factors related to hospitalisa-
tion and cognition.
Two case-control studies found lower hippocampal volume in
hospitalised individuals with sepsis compared to healthy controls
[41, 47]. These studies were limited by their small sample size (<45
participants each) and one study had inadequately matched
controls. Differences in study design and statistical method limited
comparability of these studies with our findings. White matter
lesions have been identified in septic shock patients who
developed acute brain dysfunction, however, there is a lack of
studies examining the association of common, predominantly
bacterial infections, other than sepsis with WMH or hippocampal
volume [48].
An explanation for our lack of evidence of an association
between infections, cognitive decline, hippocampal or WMH
volume could be that these neuroimaging or cognitive measures
may have lacked sufficient sensitivity to detect subtle brain
changes following infection in the prodromal phases. Alterna-
tively, it could be that our finding of small associations between
infections and cognitive decline on the visual memory test should
Table 3. continued
Prospective memory No. of
participants
OR (95% CI) P value No. of
participants
OR (95% CI) P value
Site of infection
No infection 4174 Reference 4,083 Reference
Any infection 926 0.83 (0.65 to 1.06) 0.14 894 0.88 (0.68 to 1.14) 0.33
LRTIs 549 072 (0.54 to 0.97) 0.03 532 0.76 (0.56 to 1.03) 0.07
UTIs 204 0.82 (0.51 to 1.33) 0.42 198 0.88 (0.53 to 1.46) 0.63
SSTI 154 1.58 (0.77 to 3.26) 0.21 147 1.74 (0.80 to 3.75) 0.16
Clinical setting
No infection 4221 Reference 4,127 Reference
GP infection 879 0.82 (0.64 to 1.06) 0.13 850 0.88 (0.68 to 1.14) 0.33
No infection 5053 Reference 4933 Reference
Hospital infection 47 1.07 (0.38 to 2.99) 0.90 44 0.95 (0.34 to 2.69) 0.93
Linear Mixed models results with random intercept and random slope. The associations of site of infection, GP infection and hospital infection were not
assessed in the same model but rather in three separate models. For analyses on site of infections, sepsis and pneumonia were not included due to a small
number of infections (23 and 45 participants, respectively). For mean correct response time, visual memory (log transformed) and fluid intelligence tests,
minimally adjusted: age (years), sex, time, baseline test score and time × infection status interaction term which represents the rate of decline by presence of
infection with the difference in slope compared to that of no infection (reference group). For mean correct response time, fully adjusted models additionally
adjusted for ethnicity, BMI, years in full-time education, physical activity, alcohol consumption, diabetes, anxiety and depression, COPD, multiple sclerosis,
hypertension and heart failure. For the visual memory test, fully adjusted models additionally adjusted for ethnicity, BMI, smoking status, socioeconomic
deprivation, physical activity, alcohol frequency, years in full-time education, diabetes, anxiety and depression, COPD, hypertension, inflammatory bowel
disease, rheumatoid arthritis, obstructive sleep apnoea, and multiple sclerosis. For the fluid intelligence test, fully adjusted models additionally included years
in full-time education. For the prospective memory test logistic regression was performed and the estimates reported are odds ratios. Minimally adjusted
models for this test include age (years) and sex and fully adjusted models additionally adjusted for physical activity in the fully adjusted models.
Fig. 3 Association of presence and site of common infections with hippocampal volume and WMH volume. WMH; White matter
hyperintensities. A represents the association between common infections and hippocampal volume. Minimally adjusted models for adjusted
for age (years) and sex. Fully adjusted models additionally adjusted for ethnicity, BMI, smoking, physical activity, alcohol consumption, years in
full-time education, diabetes, chronic obstructive pulmonary disease, asthma and hypertension (n= 14,239). B represents the association of
common infections with WMH volume. Minimally adjusted models for (B) adjusted for age (years) and sex and fully adjusted models
additionally adjusted for BMI, smoking and hypertension (14,357).
R. Muzambi et al.
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Translational Psychiatry          (2022) 12:385 
be interpreted with caution. Given the number of analyses on
multiple cognitive tests conducted in this study, it is possible that
these findings may have occurred by chance.
Strengths and limitations
Strengths of this study include the large size of the study
population and the use of repeated assessments over follow-up
assessing four individual domains of cognition. The extensive
information on sociodemographic and lifestyle factors and
comorbidities linked to primary and secondary care electronic
health records allowed for the adjustment of multiple confound-
ing variables. Furthermore, we explored medically recorded
common infections by infection site, severity, timing and dose
response as well as conducting extensive secondary and
sensitivity analyses.
Our study had several limitations. First, there was evidence of a
‘healthy volunteer’ bias in the cohort studied and in the overall UK
Biobank population [25]. In this study, this selection bias was likely
given that participants with poorer cognitive ability and fewer
years in education were more likely to have no follow-up cognitive
measures. This selective attrition would likely underestimate any
associations of common infections with cognitive decline thus
biasing our estimates towards the null. Selection bias may have
also occurred if individuals with more severe infections did not
attend the baseline assessment, biasing associations towards the
null. Second, our findings may also have been influenced by a
potential practice effect in which participants’ performance on the
same cognitive tests improved during follow-up due to familiarity
with the test [49]. Although both infected and uninfected groups
would have been expected to have a similar benefit of practice,
these practice effects may dilute any differences between the two
groups therefore potentially masking any association of infections
on cognitive decline [31, 49]. Third, cognitive tests were non-
standardised and visual memory test results have poor correlation
between the baseline and the first repeat assessment (r= 0.16)
which will likely have led to non-differential measurement error,
biasing effect estimates towards the null [31]. Fourth, although we
assessed a wide range of potential confounders, we cannot rule
out the possibility of residual confounding through unmeasured
confounders e.g. frailty and other forms of physical activity such as
light intensity physical activity or social activities. Fifth, we
restricted our analyses to only infections diagnosed within 5
years prior to baseline, therefore, infections occurring prior to this
period were not included in our primary analyses which may
underestimate our associations. Sixth, neuroimaging measures
were assessed at one time point in our study thus we were unable
to investigate longitudinal changes in hippocampal and WMH
volume over time. Seventh, our study focused on a population
without dementia or cognitive impairment at baseline as such our
findings cannot be generalised to individuals with existing
cognitive impairment/dementia. Eighth, participants in our study
had a mean age of ~55 years old which may limit the
generalisability of our findings to older adults, who are at a
greater risk of cognitive decline and dementia. Lastly, our study
population was healthier than the general population and had
more years in education compared to those excluded during
follow-up. This may have delayed cognitive decline thus reducing
the ability to detect a significant association between common
infections and cognitive decline.
CONCLUSION
In summary, our findings extend the scarce literature on
infections, cognitive decline and neuroimaging measures into a
younger age group than the majority of previous studies. We
found no evidence of accelerated cognitive decline in people
across the age groups of 40–69 (at UK Biobank recruitment) with a
history of common infections occurring in midlife compared to
those without infections in all cognitive tests except for visual
memory. Further studies are needed to assess the potential effects
of common infections on cognition at different life stages and in
more representative populations, including ethnic and other
diversity with sufficient sample sizes to test different types of
infection. These studies need careful attention to loss to follow-up
as well as incorporating the impacts of mortality. Future studies
should also explore trajectories of cognitive decline in people with
pre-existing cognitive impairment or dementia following infection
and investigate longitudinal changes in hippocampal volume,
WMH volume and other subclinical neuropathologies over time in
individuals with and without infections.
CODE AVAILABILITY
Analysis codes used in this study can be accessed at https://github.com/
RutendoMuzambi/InfectionsCognitionNeuroimaging.
DATA AVAILABILITY
Data are available to researchers through an application via https://
www.ukbiobank.ac.uk/enable-your-research/register.
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AUTHOR CONTRIBUTIONS
CWG, KB, LS, CB, RM, NC, CTR, VG, and DMW contributed to the conception and/or
design of the study. RM analysed the data and wrote the first draft of the paper. All
authors were involved in the data interpretation and revision of the paper.
FUNDING
RM is supported by an Alzheimer’s Society Ph.D. studentship 379 (AS-PhD-17-013).
CWG is supported by a Wellcome Intermediate Clinical Fellowship, Wellcome Trust
(201440/Z/16/Z). KB holds a Wellcome Senior Research Fellowship (220283/Z/20/Z)
and also held a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and
the Royal Society during this work. VG is jointly funded by a grant from Diabetes UK
and the British Heart Foundation (grant 15/0005250).
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41398-022-02145-z.
Correspondence and requests for materials should be addressed to Rutendo
Muzambi.
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R. Muzambi et al.
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