Factors Predicting Reversion from Mild Cognitive
Impairment to Normal Cognitive Functioning: A
Perminder S. Sachdev1,2,3*, Darren M. Lipnicki1, John Crawford1, Simone Reppermund1,
Nicole A. Kochan1,2, Julian N. Trollor1,5, Wei Wen1,2,3, Brian Draper1,3,4, Melissa J. Slavin1,3, Kristan Kang1,
Ora Lux1,6, Karen A. Mather1,2, Henry Brodaty1,3,4, the Sydney Memory, Ageing Study Team1
1Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales Medicine, Sydney, New South Wales, Australia, 2Neuropsychiatric Institute, Prince
of Wales Hospital, Sydney, New South Wales, Australia, 3Primary Dementia Collaborative Research Centre, School of Psychiatry, University of New South Wales Medicine,
Sydney, New South Wales, Australia, 4Academic Department for Old Age Psychiatry, Prince of Wales Hospital, Sydney, New South Wales, Australia, 5Department of
Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales Medicine, Sydney, New South Wales, Australia, 6South-Eastern Area
Laboratory Services, Prince of Wales Hospital, Sydney, New South Wales, Australia
Introduction: Mild cognitive impairment (MCI) is associated with an increased risk of developing dementia. However, many
individuals diagnosed with MCI are found to have reverted to normal cognition on follow-up. This study investigated factors
predicting or associated with reversion from MCI to normal cognition.
Methods: Our analyses considered 223 participants (48.9% male) aged 71–89 years, drawn from the prospective,
population-based Sydney Memory and Ageing Study. All were diagnosed with MCI at baseline and subsequently classified
with either normal cognition or repeat diagnosis of MCI after two years (a further 11 participants who progressed from MCI
to dementia were excluded). Associations with reversion were investigated for (1) baseline factors that included diagnostic
features, personality, neuroimaging, sociodemographics, lifestyle, and physical and mental health; (2) longitudinal change in
potentially modifiable factors.
Results: There were 66 reverters to normal cognition and 157 non-reverters (stable MCI). Regression analyses identified
diagnostic features as most predictive of prognosis, with reversion less likely in participants with multiple-domain MCI
(p=0.011), a moderately or severely impaired cognitive domain (p=0.002 and p=0.006), or an informant-based memory
complaint (p=0.031). Reversion was also less likely for participants with arthritis (p=0.037), but more likely for participants
with higher complex mental activity (p=0.003), greater openness to experience (p=0.041), better vision (p=0.014), better
smelling ability (p=0.040), or larger combined volume of the left hippocampus and left amygdala (p,0.040). Reversion was
also associated with a larger drop in diastolic blood pressure between baseline and follow-up (p=0.026).
Discussion: Numerous factors are associated with reversion from MCI to normal cognition. Assessing these factors could
facilitate more accurate prognosis of individuals with MCI. Participation in cognitively enriching activities and efforts to
lower blood pressure might promote reversion.
Citation: Sachdev PS, Lipnicki DM, Crawford J, Reppermund S, Kochan NA, et al. (2013) Factors Predicting Reversion from Mild Cognitive Impairment to Normal
Cognitive Functioning: A Population-Based Study. PLoS ONE 8(3): e59649. doi:10.1371/journal.pone.0059649
Editor: Stephen D. Ginsberg, Nathan Kline Institute and New York University School of Medicine, United States of America
Received December 4, 2012; Accepted February 15, 2013; Published March 27, 2013
Copyright: ? 2013 Sachdev et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The study was funded by National Health & Medical Research Council of Australia Program Grant (ID 350833) and Capacity Building Grant (ID568940).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Perminder Sachdev has received payment for lectures by Eli Lilly and Pfizer. Henry Brodaty has received payment for lectures by Pfizer,
Janssen, and Novartis, is on the Alzheimer’s Advisory Boards of these companies and Lunbeck, consults for Wyeth, and has received institutional grants for an
Alzheimer’s drug trial from Sanofi and Medivation. All other authors have declared that no competing interests exist. This does not alter the authors’ adherence to
all the PLOS ONE policies on sharing data and materials.
* E-mail: firstname.lastname@example.org
The diagnosis of mild cognitive impairment (MCI) is increas-
ingly being used in epidemiological studies of cognitive disorders as
well as in the clinic . As a nosological entity, MCI conveys
important health implications, in particular an increased risk of
developing dementia in the near future. This is evident from
studies of MCI patients presenting to memory disorders clinics, in
whom the annual rate of progression to dementia is reported to be
between 10% and 15% . While rates of progression are lower in
population-based studies, between 6% and 10%, these are still
higher than the 1% to 2% annualised incidence rates of dementia
and Alzheimer’s disease (AD) in the general older population .
The significance of an MCI categorisation sits uneasily with
longitudinal data suggesting that MCI may be unstable, with many
individuals found to be cognitively normal on follow-up [3–6].
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The rates of reversion to normal vary from 4.5%  to as high as
53% . A number of explanations can account for this
variability, including individuals with normal cognition on
follow-up having been initially misdiagnosed with MCI due to
the use of very liberal criteria [3,8] or inappropriate normative
neuropsychological data against which the individual’s perfor-
mance was compared . It is also possible that MCI was
diagnosed during a temporary decline in cognitive functioning
associated with depression , mild psychiatric conditions or stress
, or on the basis of poor cognitive test performance arising
from general ill-health or poor motivation . It has also been
suggested that ‘unstable’ MCI represents a pre-MCI condition that
will develop into ‘stable’ MCI with time, prior to which cognitive
impairment is subtle and only manifests under certain circum-
stances . There are some causes of MCI that are truly
reversible, such as metabolic disorders and deficiency syndromes,
and others with an acute phase of impairment that subsequently
improves, including traumatic brain injury, substance use and
cerebrovascular events. Finally, it is possible that some individuals
with MCI improve because of pharmacological intervention or
lifestyle change, including increased cognitive and physical activity
and reduced stress .
The health and social implications of an MCI diagnosis make it
important to identify factors indicative of a good prognosis. While
a number of factors used to diagnose MCI are reportedly
associated with reversion [3,10,13–18], non-diagnostic factors
have received relatively little attention [10,14,15]. There are
numerous factors associated with cognition in the elderly not used
in diagnosing MCI, and many of these could help in predicting
reversion to normal cognition and/or be suitable targets for
remedial strategies. The aim of the present study was to identify
factors associated with reversion from MCI to normal cognition
from among a broad range of factor types, including socio-
demographic, neuroimaging, lifestyle, physical and mental health,
diagnostic, and personality characteristics.
Participants were from the Sydney Memory and Ageing Study
(MAS), a longitudinal study of community-dwelling individuals
aged 70 to 90 years recruited randomly from areas of Sydney,
Australia, through the electoral roll. A full description of the
recruitment procedures has been previously published .
Participants were excluded if they had a previous diagnosis of
dementia, psychotic symptoms or a diagnosis of schizophrenia or
bipolar disorder, multiple sclerosis, motor neuron disease,
developmental disability, progressive malignancy, or if they had
medical or psychological conditions that may have prevented them
from completing assessments. Participants were also excluded if
they had a Mini-Mental State Examination (MMSE)  score of
,24 adjusted for age, education and non-English speaking
background  at study entry, or if they had received a diagnosis
of dementia after comprehensive baseline assessment. The
representativeness of the MAS sample was assessed through
comparisons with geographically-relevant census data .
Figure 1 depicts the recruitment and selection process for the
study. The total MAS sample comprised 1037 participants. We
excluded 164 individuals deemed to be not of English-speaking
background (English acquired after 10 years of age) because
neuropsychological test norms for this group are lacking. There
were 320 participants from the remaining 873 diagnosed with
MCI at baseline.
The study was approved by the ethics committees of the
University of New South Wales and the South Eastern Sydney and
Illawarra Area Health Service, and each participant gave written
Participants underwent face-to-face neuropsychological assess-
ments by trained psychology graduates, using a battery of tests to
assess functioning within five cognitive domains: memory,
language, attention/processing speed, visuospatial and executive
functioning (see Table S1). Consensus diagnoses of MCI were
made by a panel of psychogeriatricians, neuropsychiatrists and
clinical and research neuropsychologists using current interna-
tional consensus criteria . MCI was diagnosed in individuals
who met all of the following: self or informant complaint of decline
in memory or other cognitive function; cognitive impairment on
testing (performance on at least one test measure 1.5 SD or more
below published normative values, with adjustment for age and/or
education where possible); no dementia on the basis of DSM-IV
criteria ; and no or minimal impairment in instrumental
activities of daily living attributable to cognitive impairment (total
average score ,3.0 on the Bayer Activity of Daily Living (ADL)
Scale  adjusted for physical impairment). The test adminis-
trators and clinical consensus panel were blind to baseline
diagnoses when making assessments and diagnoses at follow-up.
Measures and Criteria for Categorical Classifications
Measures were obtained in part from interviews and question-
naires addressing sociodemographic factors, lifestyle, and aspects
of cardiac, physical, mental and general health (listed in Table 1).
This included the Goldberg Anxiety Scale , and most
participants (93.9%) had an informant who completed a phone
interview and additional questionnaires, including the Bayer ADL
Scale. The 15-item version of the Geriatric Depression Scale 
Figure 1. Flow diagram of sample selection.
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and the neuroticism, openness, and conscientiousness scales of the
NEO-Five Factor Inventory  were given to participants for
self-completion and return by mail. The personality items were
added to the Sydney MAS after the study began, and thus not
received by all participants.
A brief physical examination conducted by a trained research
assistant included measures of seated blood pressure (BP), height
and weight, a 6-metre timed walk , the Brief Smell
Identification Test (BSIT) , and a corrected vision test using
a 3-metre Standard Contrast LogMAR chart. Venous blood was
collected following an overnight fast, and lithium heparin, EDTA
plasma and serum aliquots frozen at –80uC. Total cholesterol was
measured in heparin plasma aliquots using a Beckman LX20
Analyser by a timed-endpoint method (Fullerton, California,
USA), and homocysteine levels determined from EDTA plasma
aliquots using reverse phase HPLC with fluorometric detection
after derivatization with 4-aminosulfonyl-7-fluorobenzo-2-oxa1,3-
diazole (CV 6.7% at 11.7 umol/L, 6% at 30.0 umol/L) (BioRad
Munich, Germany). APOE genotyping was performed using
standard procedures, as described previously .
Around half (52.3%) of all MAS participants consented to an
MRI scan, which was performed on either a Philips 3 T Achieva
Quasar Dual scanner or a Philips 3 T Integra Quasar Dual scanner
(Philips Medical System, Best, The Netherlands). With both
scanners, T1-weighted structural images were acquired using the
turbo field echo sequence: TR=6.39 ms, TE=2.9 ms, flip
angle=8u, matrix size=256 6 256, FOV=256 6 256 6 190,
and slice thickness=1 mm with no gap between; yielding 1616
1 mm3isotropic voxels. T2-weighted fluid attenuated inversion
recovery images were also acquired, to evaluate white matter
hyperintensities. The sequence parameters were: TR=10000 ms,
TE=110 ms, TI=2800; matrix size=512 6 512; slice thick-
Table 1. Sociodemographic, lifestyle and health
characteristics of reverters and non-reverters at baselinea.
Age, mean (SD), y 78.61 (4.47)78.48 (4.45).847
Males 34 (51.5) 75 (47.8).610
Education, mean (SD), y12.29 (3.94)11.23 (3.53).049
Married or de facto24 (36.9) 71 (45.2).255
Hypertension52 (78.8)130 (82.8).480
Antihypertensives37 (56.1)97 (61.8).426
Coronary artery disease 14 (21.2)27 (17.2).480
Atrial fibrillation5 (7.8) 11 (7.1).863
Other heart diseasec
7 (10.6) 21 (13.4).569
Systolic BP, mean (SD), mmHg145.72 (19.00) 144.05 (20.16).573
Diastolic BP, mean (SD), mmHg82.83 (10.81)81.46 (9.39) .353
BMI, mean (SD), kg/m2
26.57 (3.99)27.24 (4.56).309
Diabetes10 (15.2)20 (12.7) .630
Hypoglycemics5 (7.6) 14 (8.9).743
High cholesterol diagnosis 37 (56.1) 88 (56.1).999
Hypolipidemics33 (50.0)71 (45.2).514
Stroke4 (6.1) 5 (3.2) .324
Migraines6 (9.1) 22 (14.0).311
Kidney disease1 (1.5)7 (4.5).277
Arthritis29 (45.3)97 (61.8) .025
Apnea 4 (6.1)6 (3.8).467
Anemia7 (10.6) 19 (12.3) .727
GDS score, mean (SD) 2.30 (1.94)2.18 (1.81).659
History of depression9 (13.6) 24 (15.3).751
GAS score, mean (SD)1.0 (1.8)1.4 (2.2).170
Antidepressants5 (7.6)15 (9.6).637
Antianxiety agents 1 (1.5)10 (6.4) .127
Alcohol consumption .486
Abstainer 7 (10.6) 13 (8.3)
#1 drink/day36 (54.5)76 (48.4)
.1 drink/day 23 (34.8)68 (43.3)
Never32 (48.5) 74 (47.1)
Past31 (47.0)75 (47.8)
Current 3 (4.5)8 (5.1)
Mental activity, mean (SD)d
2.70 (0.82)2.26 (0.84)
Physical activity, mean (SD)e
1.52 (0.96)1.65 (1.11) .385
Social activity .249
,5 (contacts/month)6 (9.4)22 (14.2)
5–10 (contacts/month)12 (18.8) 40 (25.8)
.10 (contacts/month)46 (71.9)93 (60.0)
Table 1. Cont.
Poor to fair 7 (10.6)21 (13.4)
Good 23 (34.8)69 (43.9)
Very good to excellent 36 (54.5)67 (42.7)
6-m walk time, mean (SD), s9.10 (2.73) 9.58 (2.86).255
BSIT score, mean (SD) 9.52 (2.09)8.90 (2.19).054
Visual acuity, mean (SD)f
0.71 (0.19)0.63 (0.20) .014
Apolipoprotein E e4 allele 12 (19.4)49 (32.7) .051
Homocysteine, mean (SD), umol/L 11.46 (5.02) 12.46 (4.51).164
Cholesterol, mean (SD), mmol/L4.59 (0.96)4.82 (1.09).150
eGFR ,60 ml/min/1.73 m2
20 (32.8) 66 (43.1).163
BMI=body mass index; BP=blood pressure; BSIT=Brief Smell Identification
Test; eGFR=estimated glomerular filtration rate; GAS=Goldberg Anxiety Scale;
GDS=Geriatric Depression Scale.
aData presented as No. (%) unless otherwise indicated.
bMaximum n, with small amounts of missing data for some factors.
cAny of cardiac arrhythmia, cardiomyopathy, or heart valve disease.
dAverage days/week of participation in mental activities.
eNumber of different physical activities participated in.
fArbitrary units, averaged for the two eyes.
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ness=3.5 mm with no gap between slices; yielding a spatial
resolution of 0.488 6 0.488 6 3.5 mm3/voxel. Statistical
Parametric Mapping 5 (SPM5) software (Wellcome Trust Centre
for Neuroimaging, UK) was used to process and analyse the T1
images. Briefly, the major steps included: (1) segmentation of T1
images into grey matter, white matter, and cerebrospinal fluid; (2)
registration of grey and white matter maps to our group average
template, using the DARTEL toolbox  in SPM5 to obtain the
DARTEL flow fields; (3) each individual’s DARTEL flow field was
applied to the automated anatomical labeling template  in
Montreal Neurological Institute space to create each subject’s grey
matter region of interest mask; (4) grey matter volumes of each
region of interest (see Table S2) were calculated from the warped
automated anatomical labeling mask. Total brain volume was
calculated as the sum of grey and white matter, and intracranial
volume as the sum of total brain volume and cerebrospinal fluid.
The total volume of white matter hyperintensities for each
participant was determined using a computer algorithm described
in detail elsewhere .
Participants were classified as hypertensive if meeting one of:
previous diagnosis and current treatment, or either systolic
BP$140 mmHg or diastolic BP$90 mmHg (as per JNC-7 values)
. A self-reported previous diagnosis of heart attack or angina
was taken as coronary artery disease, and of cardiac arrhythmia,
cardiomyopathy or heart valve disease as ‘other heart disease’.
History of depression reflected self-reports of both a previous
diagnosis and treatment. Current mental activity was calculated as
the average days/week of participation in 13 activities (e.g.,
reading books); physical activity as the sum of participation across
eight listed activities (e.g., bicycling), a valid other reported activity
(e.g., yoga), and walking; and social activity as the average number
of face-to-face contacts with friends or relatives per month.
Alcohol consumption was measured in terms of the average
number of standard drinks (10 g alcohol) per day over the past
year. Visual acuity was calculated as 1.78 minus log10(line number)
and averaged across eyes.
MCI was subtyped as either amnestic or non-amnestic
(depending on whether or not objective testing revealed a memory
impairment), and as either single- or multiple-domain (given the
number of cognitive domains with impairment) . We also
calculated Z scores reflecting a participant’s overall performance
in each cognitive domain relative to the study sample, adjusted for
age, education, and sex . Across all domains, the participant’s
worst level of performance was categorised as low (above
21.0 SD),mildly impaired (between
21.5 SD), moderately impaired (between 21.5 inclusive and
22.0 SD), or severely impaired (equal to or less than 22.0 SD).
Figure 1 shows that 86 of the 320 individuals with MCI at
baseline were missing a classification at follow-up: 17 were
deceased, 38 declined, and 31 could not be reliably diagnosed
(primarily due to insufficient neuropsychological data). Compared
to the 234 individuals with a follow-up classification, those without
were significantly older, had lower systolic BP, consumed less
alcohol, and reported less participation in mental and physical
activities and lower levels of self-rated health. The missing
individuals also had lower MMSE scores, a higher proportion of
informant-based non-memory complaints, and higher neuroticism
scores (see Table S3).
Descriptive statistics were computed for the participants who
reverted to normal cognition upon follow-up and those who did
not (i.e., those with stable MCI or who had progressed to
dementia). For all measures, we first conducted simple compar-
isons between reverters and non-reverters using either t- or x2tests.
We then performed logistic regressions to identify measures that
individually discriminated between reverters and non-reverters at
a significance level of p,0.10 when controlling for age and sex
(and intracranial volume for neuroimaging data). Each discrim-
inating measure was assigned to one of six sets: cognitive reserve,
sensory, health and genetic, neuroimaging, personality, and
diagnostic. For each of these sets we then performed a separate
multivariable logistic regression featuring the discriminating
measures assigned to that particular set. For example, the
regression for the cognitive reserve set featured education and
mental activity, whereas that for the sensory set featured BSIT
score and visual acuity. All regressions were controlled for age and
sex (and intracranial volume for the neuroimaging set). We did not
attempt a comprehensive multivariable regression containing the
discriminating variables from all six sets. This was because only
subgroups of our sample had neuroimaging or personality scale
data, limiting the number of participants with data for all
discriminating measures and leaving us with an events per variable
value that prevented a valid overall regression from being
performed (as per ).
The analyses of baseline predictors were supplemented by an
investigation of associations between reversion and longitudinal
change over the follow-up interval in factors potentially modifiable
by lifestyle alteration, medication, or other intervention. Blood
pressure, body mass index, depression, cholesterol and homocys-
teine levels, alcohol consumption, and mental, physical, and social
activity were analysed, but smoking was not included as there were
very few changes in status (3.4% of participants). Descriptive
statistics were computed and reverters and non-reverters com-
pared, first with ANOVA or x2tests and then with logistic
regressions controlling for age and sex.
All analyses were performed using IBM SPSS Statistics 20, and
our final conclusions were based on regression outcomes where
Final Sample of Reverters and Non-reverters
For the 234 individuals with classifications at both baseline and
follow-up, the duration between these time-points ranged from
17.1 to 29.9 months (mean 6 SD=23.061.5). As shown in
Figure 1, 66 were classified at follow-up as having normal
cognition, 157 were re-diagnosed with MCI, and 11 had
developed dementia. We excluded the individuals with dementia
from any further analyses, meaning our non-reverter group was
comprised only of individuals with stable MCI. The final number
of participants included in our sample of reverters and non-
reverters was 223; their ages ranged from 71 to 89 years (mean 6
SD=78.5264.45), and 48.9% were male.
Simple Comparisons of Reverters and Non-reverters
As shown in Table 1, there were only a few sociodemographic,
health and lifestyle characteristics on which reverters and non-
reverters differed significantly (p,0.05). There was no difference in
age, but reverters were more mentally active and had more years
of education. The two groups did not differ in terms of
cardiovascular risk factors or indicators of physical health, except
for lower rates of arthritis and better visual acuity in reverters.
Statistical trends (p,0.10) also favoured reverters as having better
smelling ability (BSIT scores) and a decreased likelihood of an
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APOE e4 allele. Mental health status, in terms of depression,
anxiety and use of psychotropic drugs, were not discrepant.
Table 2 shows baseline diagnostic and neuroimaging charac-
teristics and personality scale scores of reverters and non-reverters.
Reverters had higher MMSE scores, whereas rates of moderate or
severe impairment of a cognitive domain were higher in non-
reverters. The pattern of informant versus self-reported memory
complaints and rates of multiple-domain MCI also differed
between reverters and non-reverters, but the pattern of non-
memory complaints and rates of amnestic MCI did not.
Personality scale score differences included lower levels of
neuroticism and greater levels of openness in reverters than non-
reverters. There was no difference in conscientiousness between
these groups. Reverters had greater white matter volumes, which
combined with a trend towards greater grey matter volumes led to
them also having significantly greater total brain volumes. In
contrast, intracranial volume, a measure of premorbid brain size,
did not differ between reverters and non-reverters. There were
many regions of interest for which volume was greater in reverters
than non-reverters, most notably the hippocampus and amygdala
of the left hemisphere (Table 2 only shows regions of interest that
were significantly different with adjusted comparisons; see Table
S2 for a full list).
Adjusted Comparisons of Reverters and Non-reverters
For the sociodemographic, health and lifestyle, diagnostic, and
personality variables, the results of univariate logistic regression
analyses controlling for age and sex (Table 3) were mostly very
similar to the results found using simple comparisons. The only
discrepancies in the adjusted analyses were trends for more
education (initially significantly) and lower homocysteine levels in
reverters. However, for the neuroimaging data, many of the
variables found to differ significantly between reverters and non-
reverters with simple comparisons no longer discriminated
between these groups when age, sex and intracranial volume
were controlled for. This outcome is most likely contributed to by
the greater proportion of males in the reverter group (a difference
evident as a statistical trend).
Table 3 also shows the results of six multivariable logistic
regressions, one for each of the six sets of discriminating measures:
cognitive reserve, sensory, health and genetic, neuroimaging,
personality, and diagnostic. These analyses revealed that, of the
variables in the cognitive reserve set, only mental activity was an
independent predictor of reversion. For the sensory variables, both
better smelling ability and better visual acuity independently
predicted reversion. Of the set of health and genetic variables
investigated, reversion was independently predicted by an absence
of arthritis. The only independent predictor from among the
personality variables was a greater level of openness. A number of
independent predictors were identified from among the set of
diagnostic variables. These included absences of either an
informant-based memory complaint or multiple-domain MCI,
and there not being moderate or severe impairment of a cognitive
domain. The multivariable regression model for the neuroimaging
set identified no significant effects, suggesting (on the basis of the
univariate results) that left hippocampus and left amygdala
volumes predicted reversion in a mutually dependent manner.
Figure 2 summarises these findings and shows a Nagelkerke R2
value for each of the six multivariable regressions. These values
approximate the relative importance of each set of variables for
predicting reversion, and suggest that the set of diagnostic
variables accounted for the largest amount of variability.
Table 2. Baseline diagnostic characteristics, brain region
volumes and personality scale scores of reverters and non-
FactorReverters Non-revertersp value
28.8 (1.3)28.0 (1.5).001
Bayer ADL Scale score1.5 (0.6) 1.6 (0.6).268
Informant, No. (%) 39 (61.9) 108 (76.1)
Self-report only, No. (%)24 (38.1)34 (23.9)
Non-memory complaint .886
Informant, No. (%)20 (40.8) 50 (42.0)
Self-report only, No. (%)29 (59.2) 69 (58.0)
No, No. (%)27 (41.5) 77 (50.0)
Yes, No. (%)38 (58.5) 77 (50.0)
No, No. (%) 58 (89.2)90 (59.2)
Yes, No. (%)7 (10.8)62 (40.8)
Performance in worst domain
Low, No. (%)30 (45.5) 24 (16.7)
Mildly impaired, No. (%)25 (37.9)42 (29.2)
Moderately impaired, No. (%) 9 (13.6) 48 (33.3)
Severely impaired, No. (%)2 (3.0) 30 (20.8)
Brain region volumes(n=37) (n=92)
Grey matter, l0.756 (0.080) 0.726 (0.109).086d
White matter, l0.387 (0.038) 0.371 (0.042) .045
Total brain volume, l 1.144 (0.101)1.097 (0.137).037d
Cerebrospinal fluid, l0.441 (0.129)0.444 (0.122) .871
Intracranial volume, l1.584 (0.184)1.542 (0.201).269
13977 (26864) 9330 (11776).317d
Region of interest,emm3
Hippocampus (left) 3519 (365) 3281 (451).005
Amygdala (left) 868 (112) 787 (125) .001
Caudate (left)3278 (455)3006 (572) .011
Caudate (right)3399 (462) 3153 (550) .018
Putamen (left) 2662 (579)2423 (505) .021
Cerebellum 7b (right)1814 (279) 1683 (282) .017
Cerebellum 8 (right) 6528 (1421)6099 (948) .021
Personality scale scores (n=39) (n=96)
Neuroticism12.1 (8.2) 15.2 (5.7).034d
Openness 28.3 (6.3)25.4 (5.5).009
Conscientiousness 34.4 (6.1)33.6 (5.7).453
ADL=Activity of Daily Living; MCI=mild cognitive impairment; MMSE=Mini-
Mental State Examination; WMH=white matter hyperintensities.
aData presented as mean (SD) unless stated otherwise.
bMaximum n, with small amounts of missing data for some factors.
cAdjusted for age and education.
dResult for t-test for unequal variances.
eFull list of regions of interest in Table S2.
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Associations between Reversion and Longitudinal
Table 4 shows baseline and follow-up values for continuously-
measured factors. Across all participants, there were significant
decreases from baseline to follow-up in systolic and diastolic BP,
cholesterol, and mental and physical activity, and a significant
increase in homocysteine. A greater fall in diastolic BP for
reverters than non-reverters was the only significant difference
between these groups. As seen for categorically-measured factors
in Table 5, social activity change patterns were similar but alcohol
consumption increased in proportionally more reverters and
decreased in proportionally more non-reverters. Regressions
controlling for age and sex confirmed greater falls in diastolic
BP and increased drinking (vs. unchanged drinking) as associated
with significantly greater chances of reversion (odds ratio 1.03,
95% confidence interval 1.00–1.06, p=0.026 and 3.25, 1.07–9.89,
The diastolic BP difference between reverters and non-reverters
was further explored by analysing patterns of antihypertensive use.
A significantly greater proportion of non-reverters than reverters
had either stopped or begun using antihypertensives at follow-up;
reverters were much more consistent in their use across baseline
and follow-up (see Table 5).
We identified factors predicting or associated with reversion
from MCI to normal cognition. Factors most indicative of
prognosis were diagnostic features, with any of a diagnosis of
multiple-domain MCI, moderate or severe impairment of a
cognitive domain, or an informant-based memory complaint
signalling a reduced chance of reversion. Arthritis was also
Table 3. Baseline factors associated with reversion from MCI to normal cognition.
OR (95% CI)p value OR (95% CI)p value
Education 1.08 (1.00–1.17).061 1.05 (0.96–1.14).292
Mental activity 1.90 (1.31–2.74).0011.79 (1.22–2.62).003
BSIT score 1.19 (1.01–1.39).034 1.19 (1.01–1.40).040
Visual acuity9.17 (1.56–53.94) .014 9.35 (1.56–55.86).014
Health and Genetic
Arthritis 0.51 (0.28–0.92) .0250.51 (0.27–0.96).037
Homocysteine 0.93 (0.86–1.01) .0970.93 (0.86–1.01).096
Apolipoprotein E e4 allele 0.48 (0.24–1.00).049 0.48 (0.22–1.03).058
Hippocampus (left)1.001 (1.000–1.002) .0400.999 (0.997–1.002) .622
Amygdala (left) 1.005 (1.001–1.009).011 1.005 (0.998–1.013).162
Caudate (left) 1.001 (1.000–1.002).052 1.000 (0.998–1.002).928
Caudate (right) 1.001 (1.000–1.002).074 1.000 (0.998–1.002).850
Putamen (left) 1.001 (1.000–1.001).088 1.000 (0.999–1.001).754
Cerebellum 7b (right)1.001 (1.000–1.003).081 1.001 (0.998–1.004).601
Cerebellum 8 (right)1.000 (1.000–1.001) .0971.000 (0.999–1.001) .858
Neuroticism scale score0.93 (0.87–0.99).0220.94 (0.89–1.01) .078
Openness scale score1.09 (1.02–1.17).012 1.08 (1.00–1.15).041
Informant memory complaint0.50 (0.26–0.95) .033 0.44 (0.21–0.93).031
Performance in worst domain
Low Reference– Reference–
Mildly impaired 0.49 (0.24–1.02).057 0.58 (0.26–1.29).182
Moderately impaired 0.15 (0.06–0.36)
,.001 0.20 (0.08–0.55).002
Severely impaired0.05 (0.01–0.24)
,.001 0.10 (0.02–0.52).006
MMSE score 1.48 (1.17–1.87).001 1.23 (0.96–1.59) .102
Multiple-domain MCI 0.17 (0.07–0.40)
,.0010.27 (0.10–0.75) .011
BSIT=Brief Smell Identification Test; CI=confidence interval; MCI=mild cognitive impairment; MMSE=Mini-Mental State Examination; OR=odds ratio.
aSix multivariable regressions were conducted, one for each of the sets of variables labelled cognitive reserve, sensory, health and genetic, neuroimaging, personality,
and diagnostic. For example, the regression for the cognitive reserve set featured education and mental activity, whereas that for the sensory set featured BSIT score
and visual acuity. All ORs are adjusted for age and sex (neuroimaging results are also adjusted for intracranial volume).
Reversion from MCI to Normal Cognition
PLOS ONE | www.plosone.org6 March 2013 | Volume 8 | Issue 3 | e59649
associated with less reversion. Participants were more likely to
revert if they had a greater level of mental activity, better control
of BP, greater openness to experience, a larger left hippocampus/
amygdala, better visual acuity, or better smelling ability.
Interpretation of the Factors Associated with Reversion
The diagnosis of MCI requires an expressed concern (com-
plaint) about memory and/or other cognitive difficulties, such as
forgetfulness, inability to remember names, word-finding difficul-
ties, getting lost, or difficulty in solving complex problems. The
Figure 2. Baseline factors associated with reversion from mild
cognitive impairment to normal cognitive functioning. Univar-
iate analyses identified measures that discriminated between reverters
and non-reverters. Each of these measures was assigned to one of six
sets of related variables: cognitive reserve, sensory, health and genetic,
neuroimaging, personality, and diagnostic. For each of these sets we
performed a separate multivariable regression containing the discrim-
inating measures assigned to that set, controlling for age and sex (and
intracranial volume for the neuroimaging set). A separate Nagelkerke R2
value is shown for each of the six sets. The factors on the right hand
side are those from among the variables in the relevant set that were
independently associated with reversion (p,0.05).
Table 4. Potentially modifiable continuously-measured characteristics of reverters and non-reverters at baseline and follow-upa.
Baseline Follow-upBaselineFollow-up Time effect Interaction
Systolic BP, mmHg146.13 (18.86) 140.41 (18.78)144.22 (20.15) 141.32 (19.74).006 .367
Diastolic BP, mmHg82.94 (10.85) 77.82 (9.58)81.40 (9.37) 80.05 (10.79)
26.51 (4.00) 26.69 (4.26)27.28 (4.60) 27.38 (4.64).307 .774
GDS score 2.37 (1.92)2.34 (2.36)2.20 (1.84)2.51 (2.36) .386.280
2.71 (0.82)2.57 (0.93) 2.26 (0.84)2.19 (0.77).043 .478
1.54 (0.96)1.36 (1.02) 1.65 (1.11) 1.38 (1.05)
Cholesterol, mmol/L4.66 (0.97)4.62 (1.05) 4.85 (1.11) 4.57 (1.11).014 .060
Homocysteine, umol/L 11.43 (4.81)13.40 (4.21)12.17 (4.37) 13.52 (4.56)
BP=blood pressure; BMI=body mass index; GDS=Geriatric Depression Scale.
aData presented as mean (SD).
bMaximum n, with small amounts of data missing for some factors.
cAverage days/week of participation in mental activities.
dNo. physical activities participated in.
Table 5. Change in potentially modifiable categorically-
measured characteristics and antihypertensive use from
baseline to follow-upa.
Unchanged 182 (82.7)54 (83.1) 128 (82.6)
Increase14 (6.4)8 (12.3)6 (3.9)
Decrease 24 (10.9)3 (4.6)21 (13.5)
Unchanged 126 (61.2)40 (64.5)86 (59.7)
Increase35 (17.0) 8 (12.9) 27 (18.8)
Decrease 45 (21.8) 14 (22.6)31 (21.5)
Not used73 (32.7) 27 (40.9)46 (29.3)
Baseline and follow-up 125 (56.1) 37 (56.1)88 (56.1)
Baseline only9 (4.0) 0 (0.0)9 (5.7)
Follow-up only 16 (7.2)2 (3.0)14 (8.9)
aData presented as No. (%).
bMaximum n, with small amounts of data missing for some factors for either
baseline or follow-up.
cResults comparing reverters and non-reverters.
dChange between abstainer, #1 drink/day, and .1 drink/day.
eChange between ,5, 5–10, and .10 contacts/month.
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concern can be either self-reported or made by a knowledgeable
informant. We found that reversion was less likely when concerns
about a participant’s memory were expressed by an informant.
This could be because concerns raised by friends or family
members reflect more severe problems than self-reported con-
cerns, which may arise from relatively minor age-related changes
in cognitive functioning that are salient to the individual but
unlikely to be perceived as pathological by others . Supporting
this idea is a previous report that self-reported complaints do not
predict cognitive decline .
Our finding that individuals with multiple-domain MCI are less
likely than those with single-domain MCI to revert to normal
cognition is consistent both with previous reports [3,13,15–18] and
with findings that multiple-domain MCI is more likely to transition
to AD, which we have previously reported . We also found
that individuals with moderate or severe impairment in any one
domain were less likely to revert. Other studies have similarly
shown poorer cognitive performance to be associated with MCI
that is either persistent or progressive [3,14–16]. It is arguable that
low cognitive performance not exceeding mild impairment may
reflect temporary effects of extraneous factors such as depression,
fatigue, poor motivation or intercurrent illness which are likely to
change with time, prompting reconsideration of a cross-sectional
diagnosis of MCI.
Our analysis of neuroimaging data revealed that reverters had
significantly larger volumes of the left hippocampus and left
amygdala than non-reverters. There have been previous reports of
a non-significantly greater hippocampus volume in reverters than
in non-reverters  and smaller volumes of the hippocampus,
amygdala and caudate in MCI patients who progressed to AD
than in patients with stable MCI or healthy controls . Further,
a recent meta-analysis found that MCI patients consistently
showed less grey matter in the hippocampus and amygdala than
healthy controls . Our results support these structural changes
as an element of stable or progressive MCI. It is unclear why we
found effects for the left hemisphere only. Differences in right
hippocampus and right amygdala volumes between reverters and
non-reverters were apparent with simple analyses (see Table S2),
but not when sex, age and intracranial volume were controlled for.
MCI reverters in our sample were less likely than non-reverters
to have arthritis. This finding warrants cautious interpretation,
given that the relationship between arthritis and AD is inconsistent
[42–45] and complicated by the effect of pain on cognitive
performance and the potential impact of long-term use of anti-
We also found that reverters reported more frequent engage-
ment in mental activities like reading books, suggesting that they
had a higher degree of brain or cognitive reserve than non-
reverters. According to this conceptualization, individuals with
high brain reserve have a greater buffer in the process of their
decline before they reach a threshold for diagnosis of dementia
. The superior performance may be related to an efficient set
of neural networks or a wider repertoire of conscious and
preconscious cognitive strategies, and education enriches these.
This repertoire arguably permits high reserve individuals to
compensate for loss more effectively than those with a limited
repertoire. There is also evidence that complex mental activity is
protective against cognitive decline and the development of
incident dementia . The finding of higher brain reserve in
the reverter group however is against expectation in one sense, as
individuals with high reserve reportedly develop cognitive
symptoms at a later stage of pathology than those with low
reserve , and they should therefore be more likely to decline.
We do not think that this was a factor in our study as normative
data were corrected for education (where possible). Further, some
individuals may have increased their level of cognitive activity after
a perception of mild cognitive problems, and thereby reversed
their deficits. The exact mechanism cannot be determined from
the current data.
A greater level of cognitive reserve in reverters could help to
account for some of our other findings, including the association
between openness to experience and reversion. Openness may
facilitate cognitive reserve by promoting active engagement in
cognitively enriching activities that protect against cognitive
decline . The association between visual acuity and reversion
can also be interpreted along these lines, with poor vision likely to
prevent engagement in many cognitively enriching activities and
thus limit the capacity to develop or maintain cognitive reserve.
An alternative interpretation is that sensory loss is a marker for
accelerated cognitive ageing with a greater likelihood of later
developing AD . Consistent with this idea is our finding that
reverters also had better performance on a smell identification test.
It has been previously found that individuals with MCI have
poorer olfactory discrimination than controls [51–53], and that
poorer performance on the BSIT is associated with an increased
chance of progressing to AD . Also previously demonstrated is
a relationship between olfactory discrimination and AD pathology
in the brain, even in individuals cognitively normal at the time of
death . Our olfactory identification and visual acuity findings
support sensory loss as a marker for cognitive ageing.
The data also suggest that good control of BP is associated with
increased likelihood of reversion. The reverters were more
consistent in their antihypertensive drug usage and achieved a
greater reduction in their diastolic BP over the two years. This is
consistent with a previous finding  and reports that
hypertension is associated with lower cognitive performance
. The relationship of diastolic BP to cognitive decline may
be U-shaped, with both ,60 mm Hg and .110 mm Hg
associated with greater decline . While antihypertensive
treatment has been shown to decrease the risk of stroke,
cardiovascular events and heart failure, the effect of the control
of systolic BP on cognitive decline and the onset of dementia has
been inconsistent . More work is needed to examine the effect
of the lowering of diastolic BP on cognitive decline in non-
The finding of an increase in alcohol consumption being
associated with reversion was surprising, although tempered by the
fact that in the majority (82.7%) of the sample, alcohol use was
stable between baseline and follow-up. More reverters (12.3% vs.
3.9%) increased their alcohol use in this period (all from #1 to .1
drink/day), while more non-reverters (13.5% vs. 4.6%) decreased
it (71.4% from .1 to #1 drink/day and 28.6% from #1 drink/
day to abstainer). While there is evidence of the protective effect of
moderate alcohol use against incident dementia, the published
literatures is less clear on the effect on cognitive decline and
predementia syndromes . It is perhaps reasonable to conclude
that light to moderate alcohol use is not deleterious in those with
MCI, but the evidence is not persuasive enough to recommend
initiation of alcohol use or increase in its quantity for the purpose
of preventing decline or reversing MCI.
Strengths and Limitations
Our study has numerous strengths, including the use of
comprehensive assessment protocols at both baseline and follow-
up, a large sample of participants with MCI at baseline, and a
diverse range of factors investigated as potentially associated with
reversion. No previous study appears to have simultaneously
achieved all three of these. Our study also has limitations. The
Reversion from MCI to Normal Cognition
PLOS ONE | www.plosone.org8 March 2013 | Volume 8 | Issue 3 | e59649
sample was population-based and participants diagnosed with
MCI were not seeking help for cognitive difficulties. Individuals
diagnosed with MCI in the clinic are likely to be a select group
with potentially lower rates and different predictors of reversion. A
number of individuals who had MCI at baseline were excluded
from our analyses for missing a follow-up diagnostic classification.
These individuals differed significantly from those remaining in the
study in ways that suggest a reduced likelihood of reversion from
MCI to normal cognition, including lower MMSE scores and
mental activity and higher levels of neuroticism. Accordingly, we
may be reporting an overestimated prevalence of reversion. An
association between age and reversion was not found by us but has
been reported for a sample with a mean age younger than ours
. Individuals younger than 70 years may show different
predictors of reversion . Further, our follow-up duration of two
years provides only a narrow window into what can be a slow
progression of neurodegenerative disease in older individuals. A
longer follow-up is needed to determine the extent to which
unstable MCI represents a very early MCI stage of serious
cognitive decline. A final limitation is our lack of consideration for
transitory cognitive impairment associated with factors like stress,
acute illness or poor motivation, with MCI diagnosed under such
conditions at greater than normal chances of reversion to normal
Conclusions and Implications
A sizeable proportion of individuals categorised as MCI revert
back to normal cognition. It is possible for reverters to have been
misclassified initially, to have unstable MCI, or to have made true
improvements in cognitive functioning upon follow-up. Knowing
which individuals classified as MCI are more likely to revert to
normal could help optimise the allocation of resources among
MCI patients, with those considered least likely to revert receiving
greater levels of intervention and follow-up contact. We have
identified a number of diagnostic and other factors, albeit in a
population-based sample, that may help determine if an MCI
patient is likely to revert. Future research should aim to expand
upon these findings by developing a predictive model that includes
factors considered by clinicians as appropriate for routine use. For
example, while MRI scans checking for hippocampus/amygdala
atrophy are likely to be impractical, tests of visual acuity, olfactory
identification, and personality might be suitably included in
screening protocols. Our findings also suggest that continuing use
of mild to moderate amounts of alcohol is not deleterious, and lead
us to endorse both good control of BP and asking patients about
their engagement in cognitively enriching activities. For patients
with low engagement, activities suited to their interests, abilities
and capacities should be identified and encouraged.
sources and demographic adjustments used in diagnos-
ing MCI in the Sydney MAS.
Cognitive domains, tests, normative data
reverters and non-reverters in the MRI subsample.
Baseline region of interest volumes (mm3) for
with or without a diagnostic classification at follow-up.
Baseline characteristics of participants either
DNA samples were extracted by Genetic Repositories Australia. APOE
genotyping was performed by Arezoo Assareh and Karen A. Mather in the
laboratory of Peter Schofield and John Kwok at Neuroscience Research
Australia (NeuRA). Neuroimaging was performed at the NeuRA Imaging
Centre. Blood samples were collected by South Eastern Area Laboratory
Service. We thank the Sydney MAS participants.
Conceived and designed the experiments: PSS NAK JNT BD MJS HB.
Analyzed the data: DML JC. Wrote the paper: PSS DML. Obtained
research funds: PSS HB. Supervised data gathering and/or analysis: PSS
SR NAK JNT BD MJS KK. Neuroimaging: WW. Genetic testing: KAM.
Blood sample analysis: OL.
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PLOS ONE | www.plosone.org 10March 2013 | Volume 8 | Issue 3 | e59649