- Access to this full-text is provided by Springer Nature.
- Learn more
Download available
Content available from BMC Geriatrics
This content is subject to copyright. Terms and conditions apply.
S T U D Y P R O T O C O L Open Access
GERO Cohort Protocol, Chile, 2017–2022:
Community-based Cohort of Functional
Decline in Subjective Cognitive Complaint
elderly
Andrea Slachevsky
1,2,3,4,5*
, Pedro Zitko
1,6,7
, David Martínez-Pernía
1,3,8
, Gonzalo Forno
1,2,3
, Felipe A. Court
1,9,10
,
Patricia Lillo
1,11,12
, Roque Villagra
1,13
, Claudia Duran-Aniotz
8
, Teresa Parrao
1,14
, Rodrigo Assar
1,15
, Paulina Orellana
1
,
Carolina Toledo
1
, Rodrigo Rivera
16
, Agustín Ibañez
8,17,18,19,20
, Mario A. Parra
19,21
, Christian González-Billault
1,5,10,22
,
Helena Amieva
23
and Daniela Thumala
1,24
Abstract
Background: With the global population aging and life expectancy increasing, dementia has turned a priority in
the health care system. In Chile, dementia is one of the most important causes of disability in the elderly and the
most rapidly growing cause of death in the last 20 years. Cognitive complaint is considered a predictor for
cognitive and functional decline, incident mild cognitive impairment, and incident dementia. The GERO cohort is
the Chilean core clinical project of the Geroscience Center for Brain Health and Metabolism (GERO). The objective
of the GERO cohort is to analyze the rate of functional decline and progression to clinical dementia and their
associated risk factors in a community-dwelling elderly with subjective cognitive complaint, through a population-
based study. We also aim to undertake clinical research on brain ageing and dementia disorders, to create data and
biobanks with the appropriate infrastructure to conduct other studies and facilitate to the national and international
scientific community access to the data and samples for research.
(Continued on next page)
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this article are included in the article's Creative Commons
licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons
licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: andrea.slachevsky@uchile.cl
1
Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
2
Neuropsychology and Clinical Neuroscience Laboratory (LANNEC),
Physiopathology Department - Institute of Biomedical Sciences (ICBM),
Neuroscience and East Neuroscience Departments, Faculty of Medicine,
University of Chile, Santiago, Chile
Full list of author information is available at the end of the article
Slachevsky et al. BMC Geriatrics (2020) 20:505
https://doi.org/10.1186/s12877-020-01866-4
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(Continued from previous page)
Methods: The GERO cohort aims the recruitment of 300 elderly subjects (> 70 years) from Santiago (Chile), following
them up for at least 3 years. Eligible people are adults not diagnosed with dementia with subjective cognitive complaint,
which are reported either by the participant, a proxy or both. Participants are identified through a household census. The
protocol for evaluation is based on a multidimensional approach including socio-demographic, biomedical, psychosocial,
neuropsychological, neuropsychiatric and motor assessments. Neuroimaging, blood and stool samples are also obtained.
This multidimensional evaluation is carried out in a baseline and 2 follow-ups assessments, at 18 and 36 months. In
addition, in months 6, 12, 24, and 30, a telephone interview is performed in order to keep contact with the participants
and to assess general well-being.
Discussion: Our work will allow us to determine multidimensional risks factors associated with functional decline and
conversion to dementia in elderly with subjective cognitive complain. The aim of our GERO group is to establish the
capacity to foster cutting edge and multidisciplinary research on aging in Chile including basic and clinical research.
Trial registration: NCT04265482 in ClinicalTrials.gov. Registration Date: February 11, 2020. Retrospectively Registered.
Keywords: Cognitive aging, Subjective cognitive complaint, Dementia, Alzheimer, Functional decline, Geroscience
Background
Population ageing, driven by rising life expectancies and
declining fertility rates, is one of the most important
transformations the world is undergoing today. World
population over 60 years old is now 12% and is expected
to reach 21.5% by the year 2050. Within the same
period, the increase in the population over 80 years old
will be even more pronounced, going from 1.7 to 4.5%
of the population [1]. This demographic change is ad-
vancing faster in Latin America (LA) than in European
and North American countries [2,3]: by 2025, the total
number of individuals over 60 years old will reach ap-
proximately 57 million [4]. Among the countries in this
region, Chile shows one of the fastest life expectancy
growth rates [2,3,5]. By 2050, Chileans older than 60
years will increase from the current 15.7% of the popula-
tion to 32.9%, while people older than 80 years will reach
10.3% [5].
This population ageing is associated with a strong in-
crease in the number of people living with dementia,
which is estimated to reach 140 million by 2050. De-
mentia is the most significant global challenge for health
and social care in the twenty-first century [6]. In Chile,
dementia is the leading cause of dependency (36%) in
older people [7–9]. The National Survey of Dependency
in the Elderly reported an estimated prevalence of de-
mentia of 7.0% (women 7.7%, men 5.9%) in people aged
60 years and older [7]. This prevalence is equivalent to
what is reported in a systematic review of epidemiologic
studies of dementia in Latin America [10]. In addition,
the number of deaths attributed to dementia in Chile
has increased by 526% from 1990 to 2010, which means
that dementia is the most rapidly growing cause of death
in the last 20 years [11].
Most of the dementia syndromes are preceded by a
prodromal phase characterized by the presence of a
broad range of very subtle manifestations of cognitive
decline. Common presentations are, amongst others,
concerns about cognitive decline, also known as subject-
ive cognitive complaint (SCC), of people who may or
may not have deficits in objective testing [12], reported
either by the person her/his-self, or by an informant,
mild cognitive impairment (MCI), and the recently pro-
posed mild behavioral impairment [13]. Although many
subjects with SCC and MCI are at high risk to progress
to a dementia syndrome (i.e. conversion rates to demen-
tia range from 2 to 15% per year in subjects with MCI),
some of them remain stable over time while others re-
vert to healthy cognition, particularly in epidemiological
settings [14–16]. This uncertain prognosis makes cogni-
tive complaints and MCI important construct in terms
of targeting interventions for secondary prevention in
dementia [14,17].
On the other hand, studies focused on the risk factors
associated with functional decline (FD), i.e. the ability to
perform daily routines, are less known. The determination
of FD has been commonly used as a critical line dividing
between predementia and dementia stages. However, the
notion that FD starts only at the stage of dementia has
been challenged with several studies showing that minor
impairment in complex activities of daily life (ADL) pre-
cedes dementia in many years [18,19], and is already
present at the stage of MCI [20,21]. Moreover, standard-
izing the degree of functional impairment that is associ-
ated with dementia rather than MCI has been problematic
and the categorical classification of MCI and Alzheimer’s
Disease (AD) has been criticized [22]. Studying the
amount and trajectories of FD could allow overcoming
limitations associated with categorical outcomes, such as
the conversion to dementia. Moreover, impairment in the
ability to perform everyday activities and the eventual loss
of independence are major concerns for older adults [23].
Finally, predicting the risk of FD and the risk of de-
mentia is associated with a complex interplay of non-
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 2 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
modifiable and modifiable risk factors such as overall
health and lifestyle factors [24]. The interplay of these
factors could be divided into five dimensions: i) biologic,
ii) neuroimaging, iii) clinical phenotype (cognition, be-
havioral, motor and functional domains), iii) metabolic,
systemic diseases and habits and v) psychosocial. A
multidimensional assessment including all dimensions
mentioned above would increase either the differenti-
ation of healthy and pathologic brain aging and the pre-
diction of the risk of FD and dementia [25].
To the best of our knowledge, no previous study has
reported multidimensional risk factors (biomedical, im-
aging, psychosocial, and clinical) associated with the
prognosis of elderly with SCC on the evolution of FD.
Also, there is a scarcity of cohort study on cognitive de-
cline in Latin-American and no studies have been car-
ried out in Chilean on risks associated with progression
to dementia. The present paper reports the aims and de-
sign of a cohort study, being conducted in Chile by the
Geroscience Center for Brain Health and Metabolism
(GERO).
Objectives of the study
The general objective of this study is to analyze the rate
of functional decline and progression to clinical demen-
tia and their associated risk factors (biomedical, imaging,
psychosocial, and clinical) in a community-dwelling eld-
erly with SCC, through a population-based study. The
specific objectives are to determine: i) longitudinal evo-
lution of biomarkers measured from blood, stool and
structural and functional magnetic resonance neuroim-
aging (MRI), ii) evolution of health-related outcomes, in-
cluding quality of life, comorbidity and risk factors, and
iii) mortality rates. We also aim to build the capacity to
undertake clinical research on brain ageing and demen-
tia disorders and to create data and biobanks with the
appropriate infrastructure to conduct other studies and
facilitate to the national and international scientific com-
munity access to the data and samples for research.
The GERO cohort is the core clinical project of the
GERO program grant, which is supported by the Fund
for Research Centers in Priority Areas Program (FON-
DAP) of the Chilean national research and development
agency (ANID, for its acronym in Spanish). GERO is ini-
tially funded for 5 years, and its main aim is to establish
a center for studying brain aging in Chile, including
basic and clinical research.
Methods/design
Setting
The cohort recruits the potential participants from the
general population, using a door-to-door strategy. The
sample framework corresponds to the territories
assigned to three primary healthcare centers selected by
convenience according to their socioeconomic hetero-
geneity, which belong to three different districts in
Santiago (Chile): Macul, Peñalolen and La Reina. The
sample considers a two-stage selection process. The first
stage includes a sample of quadrants within each terri-
tory, where the contact to all houses is attempted. The
second stage proceeded when in a home is found more
than one potential eligible participant, choosing one ran-
domly. Territories encompassed a population between
14,937 and 39,458 people [26], of which between 4.6 and
8.0% is expected to be older than 70 years old. Follow up
of the participants is performed in the Memory and
Neuropsychiatry Clinic (CMYN, for its acronym in
Spanish) at the Universidad de Chile, located next to the
Hospital Salvador, hospital of reference for territories in-
cluded in the sample.
Participant, eligibility, inclusion, and exclusion
Subjects are eligible for the study if they fulfil the follow-
ing criteria: i) 70 years old or older; ii) presence of a
knowledgeable informant and/or presence of a contact
that allows the follow up of the participant, and iii) being
affiliated to the public health insurance.
Eligible participants are invited to the study and re-
ceive a first evaluation to confirm the following criteria:
Inclusion criteria: Eligibility criteria plus:
–Subjective cognitive complaint either self-reported
or reported by a knowledgeable informant.
–Clinical Dementia Scale—frontotemporal lobar
degeneration (CDR-FTLD) equal or inferior to 0.5
[27].
–Signed informed consent.
Exclusion criteria:
–Report of medical diagnosis of dementia.
–Mini-mental State Examination (MMSE) < 21 and
Pfeffer questionnaire > 2 [28,29].
–Institutionalization (for example, living in an elderly
home or a skilled nursing facility).
–Illiteracy, meaning that is not able to write or read.
–Visual and auditory acuity not adequate for
neuropsychological testing.
–Important limitation of mobility incompatible with
the availability to be independent in daily life
activities and/or attending a clinical center for
further evaluation.
–Report of medical diagnosis of Parkinson’s disease.
–Report of medical diagnosis of one or more of the
following conditions causing severe impairment in
functionality: any psychiatric or neurological
disorders, brain tumor, subdural hematoma,
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 3 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
progressive supranuclear palsy, or history of head
trauma.
–Report of medical diagnosis of stroke occurred in
the last 3 months.
–Presence of a fatal disease (less than 1 year of
survival).
Field work during the first contact
The recruitment process considers two steps. First, a lay
team contacts each home to determine the presence of
eligible individuals. In positive cases, the person receives
a second visit by a trained psychologist who proceeds to
check for eligibility. In case of acceptance, the inclusion
and exclusion criteria protocol are applied. If the subject
fulfils the criteria, the psychologist schedules a medical
interview. Following this evaluation, a neurologist de-
cides if the subject fulfil the inclusion criteria of the co-
hort (see Fig. 1).
The fieldwork is preceded by an outreach campaign
(flyers, local radio advertisements, and presentations to
community-organized groups) raising awareness about
the visit of interviewers and the relevance of participat-
ing in the study. Rates of contact and response are mon-
itored permanently, and the procedures around the
contact and first interview are checked in the field and
also by telephone to a subsample of the participants.
Contact to homes is attempted up to three times on dif-
ferent days and hours before considering it frustrated.
The fieldwork started in November 2017 and is expected
to finish at the middle of 2020. Up to date, the recruit-
ment has not been completed.
The lay team and psychologists involved in the first
contact and recruitment received specific training on
their labor in the field. The lay team completed a whole
training week, which included theoretical and practical
elements. Psychologists received a 12 weeks length train-
ing, which covers several sessions of neuropsychological
assessment.
Sample size
The sample size needs to satisfy two criteria, one con-
cerned with the statistical power required to explore
multiple associations with outcomes, and other related
to the feasibility to perform a wide range of assessments
to each participant assuming costs and logistics. Both
criteria meant a trade-off between the tolerance to un-
certainty around the parameters to be estimated and the
number of assessments that would be investigated
throughout the study. The final sample chosen was 300
participants. This number allows maintaining the integ-
rity of the original protocol and permits to test associa-
tions equivalent to an Odds Ratio (OR) around 1.5
(Cohen’s equal to 0,22) in cases of exposition and prob-
ability of the outcome close to 50%, using a significance
of 5%. It is expected to follow each participant 3 years,
accumulating roughly 900 person-years of follow up.
Follow-up and retention strategy
Socio-demographic, health-related outcomes (quality of
life, arthrometric measures and risk factors), clinical
stages and symptoms, psychosocial, neuropsychological,
neuropsychiatric, motor, neuroimaging, blood bio-
markers, stool, and genetic samples will be performed as
baseline evaluation and every 18 months, with the excep-
tion of the genetic study that will be performed only at
baseline and neuroimaging at baseline and 36 months.
Patients’health status, functionality, and involvement in
the GERO cohort will be monitored every 6 months by a
telephonic questionnaire in order to assess general well-
being and keep contact with the participants.
To avoid a significant attrition of the sample the fol-
lowing strategies have been considered: to recruit only
people who have at least one person that can facilitate
the contact with him or her, it means a person who can
be contacted for asking about the location of the partici-
pant; telephone contact every 6 months; and domicile
visit in case of absence of contact or attending to assess-
ment appointments. Additionally, all transport costs of
participants are being covered by the GERO cohort ad-
ministration, as well as any food that is required during
the days of assessment. Initially, the end of the follow up
of the cohort is programmed for October 2022.
Assessments and measurements
The protocol considers an intensive and deep multidi-
mensional study of factors related to the prognosis of
FD and dementia development. The range of assess-
ments includes: socio-demographic, health-related out-
comes (quality of life, arthrometric measures and risk
factors), clinical stages and symptoms, psychosocial,
neuropsychological, neuropsychiatric, motor, neuroimag-
ing, blood biomarkers, genetic and stool samples to per-
form gut microbiome studies (see Table 1, Fig. 1and
Additional file 1). Neuroimaging protocol will allow
assessing brain atrophy, structural and functional con-
nectivity and white matter lesions [83,84] (see Add-
itional file 1). GERO biological samples of whole blood,
buffy coat, plasma, serum, and peripheral mononuclear
cells are taken and processed according to the guidelines
published in 2015 [85]. Samples are stored in our GERO
biobank for long-term storage at −80 °C or in liquid ni-
trogen (see details in annex). Stool samples are being
collected using standardized kits and DNA extracted
using the protocol Q suggested by the international hu-
man microbiome standards (IHMS SOP 06 V1). Data
are recorded in an ad-hoc platform developed by bio-
informatics and bioengineers personal of GERO (see
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 4 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Fig. 1 Flow Chart of Study Procedure
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 5 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 1 Schedule of enrolment, assessments and close-out
Enrolment Assessment Close-
out
TIMEPOINT -t
1
t
1
t
2
t
3
ENROLMENT:
Eligibility screen
Informed consent
X
X
ASSESSMENTS:
Functionality
Technological - Activities of Daily Living Questionnaire (T-ADLQ) [30]. X X X
Everyday Cognition Scale (ECog) [31]. X X X
Pfeffer Functional Activities Questionnaire (PQAF) [29]. X X X
Socio-demographic
a
[67] Marital status. X X X
Education. X
Occupational background. X
Ethnicity. X
Individual and household income. X X X
Assets inventory. X X X
Health insurance. X X X
Household conformation. X X X
Social network information. X X X
Health, risk factors,
anthropometric and laboratory
assessment
b
Health related quality of life (EQ-5D) [32]. X X X
Tabacum and alcohol consumption (Alcohol Use Disorders Identification
Test, AUDIT) [33].
XXX
Audition and vision section of the Chilean National Health Survey [34]. X X X
Physical activity, sedentarism and diet [34]. X X X
Oral health thought the Oral Health Impact Profile (OHIP). X X X
Frailty: Fried Frailty Phenotype and the Frail Questionnaires [35,36]. X X X
Anthropometric measurements: weight, body mass index (BMI), systolic and
diastolic blood pressure (seat and standing).
XXX
Framingham Cardiovascular Risk Scale. X X X
Laboratory evaluation: hemogram, glycaemia, lipid profile, level of vitamin
B12 and folic acid, thyroid hormone (TSH and free T4) and hepatic profile.
XXX
Health inventory on 18 health conditions (including cardiovascular events). X X X
Psychological assessment
c
Engagement in stimulating activities. X X X
Ageing related losses. X X X
Personality traits [37,38]. X X X
Psychological well-being [39]. X X X
Geriatric Depression Scale - Brink and Yesavage [40]. X X X
Depression, Anxiety and Stress Scale (DASS-21) [41]. X X X
Coping processes [42]. X X X
Social integration. X X X
Cognitive reserve scale [43]. X X X
Stage and clinical symptoms
d
Clinical Dementia Rating for Frontotemporal Lobar Degeneration (CDR-
FTLD)-eight domains [44].
XXX
Alzheimer Disease- 8 (AD8) [45,46]. X X X
Neuropsychological evaluation Global Cognitive Function:
- Minimental-State Examination (MMSE) [47]. X X X
- Montreal Cognitive Examination (MoCA) [48]. X X X
- Addenbrooke’s Cognitive Examination (ACE III) [49]. X X X
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 6 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Additional file 1). A schematic representation of instru-
ments and assessments is presented in Table 1.
Data analysis plan
The GERO cohort offers a unique opportunity for mul-
tiple analyses to identify, correlate and analyze multidi-
mensional factors related to FD and progression to
dementia in elderlies with SCC.
In broad terms, a descriptive of baseline measurements
(either outcomes or potential predictive factors) will be
performed. The procedure will be repeated at each
measurement time, every 18 months. Random effect
models will be used for describing trajectories of partici-
pant subgroups and the whole cohort according to main
variables, using Markov-Chain Montecarlo procedures
[86,87].
Table 1 Schedule of enrolment, assessments and close-out (Continued)
Enrolment Assessment Close-
out
Memory:
- Short Term Memory Binding Test [50,51].
- Free and Cued Selective Reminding Test (FCRST) [52–54]
- Supermarket task [55].
XXX
Executive functions:
- Ineco Frontal Screening [56].
- Verbal fluency test [57].
- Color Trail Test Part B [58,59].
XXX
Language: Sydney Language Battery (Sydbat) [60]. X X X
Visuo-constructive abilities: Rey Complex Fig [61,62].. X X X
Social Cognition: MiniSea [63]. X X X
Motor assessment
e
Soft neurological signs:
-Heidelberg Neurological Soft Signs [64].
-Edinburgh Motor Assessment (EMAS) [65].
XXX
Balance: simple-task, dual-task (including cognitive task), and sensorimotor
task.
XXX
Walking assessment: carrying a cup with water, and counting backwards
from 100.
XXX
Other scales:
- Tinetti test [66].
- Activities-Specific Balance Confidence Scale (ABC) [67].
- Timed up and go [68].
XXX
Neuroimaging
f
Three whole-brain sequences:
- High-resolution T1-weighted magnetic resonance image (MRI).
- Resting-state functional magnetic resonance images (RS-fMRI)
- Diffusion tensor-based images (DTI).
- Axial T2 and Flair sequences to detect infarcts and white-matter alterations.
XX
Gut microbiome 16S analysis from stool samples [69]. X X X
Biomarkers Six inflammatory biomarkers, IL-2, IL-6, IL-10, TNFα, SAP and CRP [70–74]. X X
Genetic study Family pedigree through a questionnaire in accordance to Goldman criteria
[75].
X
Candidate genes associated with neurodegenerative diseases (ApoE, TREM2
and MAPT) using real time PCR analysis.
X
Genome-Wide Association Study (GWAS) [76]. X
a
This module used standard items taken from previous studies [34]
b
Chile has its own prices to valuate health states using EQ5D [32,77]. Items for tabacum consumption, physical activity, sedentarism, diet evaluation were taken
from the National Health Survey 2009–2010, many of them in accordance to PAHO monitoring instruments [34]. AUDIT instrument has been validated in Chile
[33]. Health inventory includes items for diagnosis, past and current treatment [78]. Operational measure of frailty includes a brief 5 items scale: unintentional
weight loss, weakness, exhaustion, slow gait, and low physical activity [79,80]. Framingham scale (validated in Chile) includes diabetes, hypertension,
dyslipidemia, tabacum consumption, male gender and age as risk factor of cardiovascular disease [81]
c
Instruments previously validated for the Chilean population. Instruments developed by GERO (engagement in stimulating activities, aging related losses and
social integration) and validated in a pilot study with a sample of 250 elderlies
d
AD8 has been validated in Chile [82]
e
Balance is evaluated using a Bertec FP4060–05-PT force platform (Bertec Corporation, Columbus, Ohio, USA). Electro-cardi o-physiological and electrodermal
activity is collected through a BIOPAC MP150 device (BIOPAC Systems Inc., Goleta, CA, USA). A custom-made MATLAB script is used to present the stimuli and
send triggers to the AcqKnowledge software (BIOPAC) in sync with the onset of the stimuli
f
For a more detailed information of the neuroimaging protocol see Additional file 1
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 7 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The association between variables and outcomes will be
explored broadly using different machine learning
methods, such as elastic net procedure, random forest
procedure, based-tree methods, and support vector ma-
chines [88]. These procedures are suitable for leading with
multi-collinearity and also high dimensional data (e.g. the
number of predictive variables is larger than the number
of participants in the cohort). Interpretation of causality
will be conducted using standard random effect models
and eventually structural equation modelling [89].
Missing data and loss of follow up of participants are
common in observational studies, mainly in cohorts.
Firstly, cases with missing data in any outcome will be
explored and compared with cases without missing data
describing any pattern. Secondly, two strategies will be
followed to estimate results: i) to analyze only cases with
complete information (i.e. assuming that missing data is
completely at random); and ii) imputing data according
to multivariate imputation by chained equation tech-
niques [90,91]. The analysis will be performed using the
statistical software R.
Coordination with local health services
The GERO cohort has been carefully designed to avoid
undermining the usual care of participants in their com-
mon health services facilities. Even more, a linkage be-
tween the health assessments provided by the cohort
and the usual health care has been promoted.
In cases when the cohort’s assessment detects a new health
condition (diabetes, depression, hypertension, etc.) the partic-
ipants are derived to the primary healthcare center of their
territory. In the case of detection of a significant neurological
disorder (Dementia syndrome, Parkinson, etc.) the partici-
pants are directly derived to specialized care according to
their Health District, communicating the decision to the pri-
mary health care.
Primary care health centers, specialized care polyclinics
and the direction of the Health District involved have been
informed about the study and jointly the protocol of deriv-
ation and communication were established.
Regulation of access to data/biospecimens
The access to data and biospecimens is regulated by the
GERO directorate in accordance with the local Institu-
tional Review Board authorization. A bilateral agreement
must be signed before sharing of data. Access to the ser-
ver will not be granted.
Ethics
The project was approved September 2016 by the Ethic
Committee of the Servicio de Salud Metropolitano
Oriente, Santiago (Chile). A written informed consent to
participate in the study is obtained for all participants of
the GERO cohort.
Outreach/dissemination and clinical impact of the GERO
cohort
Our group, in collaboration with the Ministry of Health,
the Hospital del Salvador and other faculties of the Uni-
versity of Chile, created in 2018 the CMYN, a clinical fa-
cility that houses one of the three Memory Units of the
Chilean’s Dementia Plan, and it is conformed by a multi-
disciplinary team (neurologist, psychiatrist, nurse, neuro-
psychologist, clinical psychologist, occupational therapist,
speech therapist and social worker). Nowadays, GERO
and CMYN train professionals in primary care centers
and neurology, psychiatric and geriatric residents in brain
ageing and dementia. Additionally, we perform outreach
activities on geroscience, brain ageing, and dementia to
the broad community, mainly in the three districts of the
GERO cohort and in Hospital Salvador, and to the scien-
tific and health community. We designed a brochure to
inform about the GERO cohort and performed broader
dissemination through media (print, television, radio). Fi-
nally, our group lead a policy paper on dementia to inform
public policy [92].
Discussion
The current paper presents the study protocol of a Chil-
ean cohort in brain aging and dementia: the GERO co-
hort study. This project mainly focuses in identifying
risk factors associated with functional decline and pro-
gression to clinical dementia in the elderly with SCC by
determine factors related to biomedical, clinical and psy-
chosocial variables.
To date, important contributions have been realized in the
Latin-America region allowing to know the prevalence and
incidence of dementia [7,10,93], the subjective memory
complaints in people with and without dementia [94], the
neuropsychiatric symptoms as a risk of dementia [95], the
unawareness of memory impairment in dementia [96], and
biomarkers profiles in AD and MCI [97] in non-Caucasian
population. However, study on the risk to conversion to de-
mentia in elderly subjective cognitive complaint and MCI
hadbeenperformedmainlyinNorthAmericaandEurope
[98–107]. Epidemiologic studies of pathologic brain aging,
SCC and MCI from Latin America and in particular Chile
are still scarce in comparison with those from northern
countries [9] and none of them have addressed risk factors
related to FD, and its relationship with the progression to de-
mentia. The transference of data collected from longitudinal
and transversal European and North American studies to
Latin-America population is limited due to the important
differences in genetic, medical and social factors associated
with FD and the risk of Dementia. In this context, the study
of risk factors associated with dementia in non-Caucasian
population has emerged as priority area in research [108].
GERO cohort address an under represented population in
the literature [9].
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 8 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
From a public health perspective, identification of SCC
and MCI subjects with higher risk of pathologic trajector-
ies would be valuable as a diagnostic tool to focus preven-
tion in subjects at higher risk of FD and dementia.
Additionally, between SCC and MCI, SCC could be a bet-
ter valuable target for public policy interventions, since it
is built on the subjective self or informant-reported per-
ceptions, which are closer to the awareness that could tar-
get health care consultation. Moreover, the diagnosis of
MCI required the demonstration of an objective decline in
cognitive performance limiting the diagnosis of MCI in
primary care centers. Also, the concept of MCI has been
criticized due to overlap of MCI and AD dementia sug-
gesting ambiguity in the MCI concept and the criteria of
MCI are continuing evolving [22].
The collective approach will allow us to i) improve
diagnosis for neurodegenerative disease, ii) evaluate age-
related risk factors and genetic variations linked to neu-
rodegeneration, iii) understand how molecular mecha-
nisms involved in aging lead to neurodegeneration and
iv) explore novel biomarkers to evaluate the onset and
progression of neurodegeneration. GERO’s aim is to es-
tablish a center for studying brain aging in Chile, includ-
ing basic, translational and clinical research.
The availability of a GERO biobank will allow fostering
translational studies by collecting peripheral samples for
research use to improve our understanding of health
and disease in the Chilean population. Additionally, bio-
logical analysis and associated clinical data are necessary
to contribute in early diagnosis, prognostic and treat-
ment for the aging population [14]. The development of
and ad-hoc platform constitute an important step to the
development of a brain aging registry in Chile who could
contribute to advance in research in brain aging by col-
lecting either epidemiologic data and data from other
sources, such us from clinical practice with patients with
brain disorders [109]. GERO translational approach
combines basic and clinical scientists who are targeted
to fills the void in aging research that exists in our coun-
try, specifically toward the interface between aging and
neurodegenerative diseases.
Furthermore, GERO platform will provide strategies,
methods and tools to conduct longitudinal studies on a
community base in populations with diverse epidemio-
logical settings.
Main strengths and weakness of the GERO cohort
The main strengths of the GERO cohort is recruitment
of participants at their home, allowing recruiting either
people attending clinical center and people not attend-
ing. People not attending clinical center probably repre-
sent a higher risk group under-represented in previous
studies [110]. Moreover, feasibility of memory clinical-
based study in Latin America is limited, due to the
important barriers to the diagnosis of dementia in
Latino-America and generally consultation for memory
problems occur in late stage of dementia disorder [9];
second, GERO cohort implemented a multidimensional-
based evaluation, categorized in five main levels: i) bio-
markers, ii) neuroimaging, iii) clinical phenotype (cogni-
tion, neuropsychiatric, motor and functionality), iv)
metabolic, systemic diseases, and habits and v) psycho-
social. This multidimensional approach is in line with
evidence explaining FD of older adults with cognitive
impairment by multiple factors [111]. Dementia also is
umbrella term that include several diseases with import-
ant variability of genetic, neural, and behavioral manifes-
tations [112], therefore a multilevel approach including
molecular biomarkers, neuroimaging, genetic and clin-
ical phenotypic allow a better characterization [25].
Third, we will explore predictive algorithms that will
eventually predict rates of FD and conversion to demen-
tia [113–115]. The development of bioinformatics and
modelling algorithms during data analysis will allow the
integration of complex data from multiple sources to
build a comprehensive interaction model in our local
aging population, which expect to uncover complex de-
terminants of aging and brain diseases. Finally, establish-
ing comprehensive databases for studies on aging can
create the opportunity to formulate and validate tools
for early detection of people who are at increased risk of
late-life cognitive impairment, to identify important tar-
gets (risk factors) for preventive interventions, and to
test such interventions in randomized control trials.
Potential limitations
One of the main limitations of our study is a relatively
small sample size. Due to our research strategy to
prioritize a multidimensional and extensive evaluation,
time and budget constraint, we limited the cohort size.
Nevertheless, we selected a continuous outcome and will
study the rate of change rather than a categorical out-
come that allow to overcome possible limitations due to
the sample size [22,116]. Also due to budget constraint,
we do not include determination of specific biomarkers
for Alzheimer’s disease in spinal fluid and with PET (Pet
amyloid and tau) [117], nevertheless we store blood sam-
ple that will allow to study blood-based biomarkers
when available. Finally, as explained above, we have im-
plemented a strategy to avoid limitation associated with
attrition and missing data.
Final message
Our work will allow us to determine multidimensional
risks factors associated with the prognosis of elderly with
cognitive complaint on functional decline in Chilean
population. The GERO cohort will help to design public
health policies tailored to prevent aging disease, and
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 9 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
contribute to a better understanding of cognitive impair-
ment and dementia in Latin America and the world.
GERO’s aim is to establish a center for studying Brain
Ageing in Chile including basic and clinical research.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12877-020-01866-4.
Additional file 1.
Abbreviations
AD: Alzheimer’s disease; ADL: Activities of daily life; ANID: National Research
and Development Agency (ANID, for its acronym in Spanish); CDR-
FTLD: Clinical dementia scale - frontotemporal lobar degeneration;
CMYN: Memory and Neuropsychiatry Clinic (CMYN, for its acronym in
Spanish); DSM-V: Diagnostic and statistical manual for mental disorders fifth
edition; FONDAP: Fund for research centers in priority areas program;
GERO: Geroscience Center for Brain Health and MetabolismFDFunctional
decline; LA: Latin America; MCI: Mild cognitive impairment; MMSE: Mini-
mental State Examination; MRI: Magnetic resonance neuroimaging; OR: Odd’s
ratio; SCC: Subjective cognitive complaint
Acknowledgements
Servicio de Salud Metropolitano Oriente, Hospital del Salvador and CESFAM
Santa Julia (Macul), CESFAM Ossandón (La Reina), CESFAM Juan Pablo II (La
Reina), and CESFAM Cardenal Silvia Henríquez (Peñalolén) for their
contribution to the realization of the cohort.
Authors’contributions
Designed the study: AS, PZ, FC, PL, RV, RA, AI, CG, HA, DT. Field work and
coordination of study: AS, DMP, GFM, PL, RV, CDA, TP, PO, CT, DT. Draft
Manuscript: AS, PZ, DMP, GFM, PO, DT. Wrote and revised the manuscript
critically: AS, PZ, DMP, GFM, FC, CDA, TP, AI, CG, DT. All authors read and
approved the final manuscript.
Funding
ANID/FONDAP/15150012; AS and AI are partially supported by the
Interamerican Development Bank (IDB) and the Multi-partner consortium to
expand dementia research in Latin America (ReDLat)which is supported by
grants from the National Institutes of Health (R01AG057234), Alzheimer’s
Association (SG-20-725707), Rainwater Charitable Foundation, and The Global
Brain Health Institute. AI is partially supported by grants from CONICET,
ANID/FONDECYT Regular 602 /1171200, FONCyT-PICT 2017–1818, FONCyT-
PICT 2017–1820, ANID/FONDAP 15150012, Alzheimer’s Association GBHI ALZ
UK-20-639295, and NIH NIA R01 AG057234.
The contents of this publication are solely the responsibility of the
authors and does not represent the official views of these institutions. The
funding body has not involvement in the design of the study and collection,
analysis, and interpretation of data and in manuscript writing.
Availability of data and materials
Not applicable.
Ethics approval and consent to participate
The study protocol was approved by the Ethic Committee of the Servicio de
Salud Metropolitano Oriente, Santiago (Chile) on September 2016. A written
informed consent to participate in the study is obtained for all participants
of the GERO cohort.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests. The “Geroscience
Center for Brain Health and Metabolism”is funded by the National Agency
of Research and Technology (“Agencia Nacional de Investigación y
Tecnología, ANIT”), entity under the Chilean Ministry of Science and
Technology, through the Priority Areas Research Center Founding (“Fondo
de Financiamiento de Centros de Investigación en Áreas Prioritarias,
FONDAP”) N° 1510012. The project was founded in 2015 with US$5.284.791
(equivalent to national currency), for a period of 5 years (2015–2020).
(https://www.gerochile.cl/web/).
Author details
1
Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile.
2
Neuropsychology and Clinical Neuroscience Laboratory (LANNEC),
Physiopathology Department - Institute of Biomedical Sciences (ICBM),
Neuroscience and East Neuroscience Departments, Faculty of Medicine,
University of Chile, Santiago, Chile.
3
Memory and Neuropsychiatric Clinic
(CMYN) Neurology Department, Hospital del Salvador and Faculty of
Medicine, University of Chile, Santiago, Chile.
4
Department of Neurology and
Psychiatry, Clínica Alemana-Universidad del Desarrollo, Santiago, Chile.
5
Department of Neurosciences, Faculty of Medicine, Universidad de Chile,
Santiago, Chile.
6
Health Service & Population Research Department, IoPPN,
King’s College London, London, UK.
7
Escuela de Salud Pública, Universidad
de Chile, Santiago, Chile.
8
Center for Social and Cognitive Neuroscience
(CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile.
9
Center for Integrative Biology, Faculty of Sciences, Universidad Mayor,
Santiago, Chile.
10
The Buck Institute for Research on Aging, Novato, USA.
11
South Neurology Department, Faculty of Medicine, University of Chile,
Santiago, Chile.
12
Unidad de Neurología, Hospital San José, Santiago, Chile.
13
East Neurology Department, Faculty of Medicine, University of Chile,
Santiago, Chile.
14
Facultad de Psicología, Universidad Alberto Hurtado,
Santiago, Chile.
15
Institute of Biomedical Sciences (ICBM), Faculty of Medicine,
University of Chile, Santiago, Chile.
16
Neuroradiologic Department, Instituto
de Neurocirugia Asenjo, SSMO, Santiago, Chile.
17
Cognitive Neuroscience
Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina.
18
National
Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
19
Universidad Autónoma del Caribe, Barranquilla, Colombia.
20
Global Brain
Health Institute (GBHI), University of California San Francisco (UCSF),
California, USA.
21
Psychology Department, School of Psychological Sciences
& Health, University of Strathclyde, Glasgow, UK.
22
Department of Biology,
Faculty of Sciences, Universidad de Chile, Santiago, Chile.
23
INSERM,
Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux,
F-33000 Bordeaux, France.
24
Escuela de Psicologia, Facultad de Ciencias
Sociales, University of Chile, Santiago, Chile.
Received: 19 May 2020 Accepted: 3 November 2020
References
1. United Nations. World population prospects: the 2015 revision, key findings
and advance tables. Working paper no. ESA/P/WP.241.World population
prospects. New York: United Nations; 2015.
2. Bongaarts J. Trends in senescent life expectancy. Popul Stud (Camb). 2009;
63(3):203–13.
3. Bongaarts J. Human population growth and the demographic transition.
Philos Trans R Soc Lond Ser B Biol Sci. 2009;364(1532):2985–90.
4. Comisión Económica para América Latina y el Caribe [CEPAL] -Economic
Commission for Latin America and the Caribbean. Panorama Social de
América Latina (LC/G. 2635-P). Santiago; 2014.
5. Thumala D, Kennedy B, Calvo E, Gonzalez-Billault C, Zitko P, Lillo P, Villagra
R, Ibáñez A, Assar R, Andrade M, et al. Aging and health policies in Chile:
new agendas for research. Health Syst Reform. 2017;3(4):253–60.
6. Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D,
Ballard C, Banerjee S, Burns A, Cohen-Mansfield J, et al. Dementia
prevention, intervention, and care. Lancet. 2017;390(10113):2673–734.
7. Custodio N, Wheelock A, Thumala D, Slachevsky A. Dementia in Latin
America: epidemiological evidence and implications for public policy. Front
Aging Neurosci. 2017;9:221.
8. Fuentes P, Albala C. Aging and dementia in Chile. Dement Neuropsychol.
2014;8(4):317–22.
9. Parra MA, Baez S, Allegri R, Nitrini R, Lopera F, Slachevsky A, Custodio N, Lira
D, Piguet O, Kumfor F, et al. Dementia in Latin America: assessing the
present and envisioning the future. Neurology. 2018;90(5):222–31.
10. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global
prevalence of dementia: a systematic review and metaanalysis. Alzheimers
Dement. 2013;9(1):63–75 e62.
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 10 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
11. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J,
Adair T, Aggarwal R, Ahn SY, et al. Global and regional mortality from 235
causes of death for 20 age groups in 1990 and 2010: a systematic analysis for
the global burden of disease study 2010. Lancet. 2012;380(9859):2095–128.
12. Mendonca MD, Alves L, Bugalho P. From subjective cognitive complaints to
dementia: who is at risk?: a systematic review. Am J Alzheimers Dis Other
Dement. 2016;31(2):105–14.
13. Ismail Z, Aguera-Ortiz L, Brodaty H, Cieslak A, Cummings J, Fischer CE,
Gauthier S, Geda YE, Herrmann N, Kanji J, et al. The mild behavioral
impairment checklist (MBI-C): a rating scale for neuropsychiatric symptoms
in pre-dementia populations. J Alzheimers Dis. 2017;56(3):929–38.
14. Cooper C, Sommerlad A, Lyketsos CG, Livingston G. Modifiable predictors of
dementia in mild cognitive impairment: a systematic review and meta-
analysis. Am J Psychiatry. 2015;172(4):323–34.
15. Kielb S, Rogalski E, Weintraub S, Rademaker A. Objective features of
subjective cognitive decline in a United States national database.
Alzheimers Dement. 2017;13(12):1337–44.
16. Rabin LA, Smart CM, Crane PK, Amariglio RE, Berman LM, Boada M, Buckley
RF, Chetelat G, Dubois B, Ellis KA, et al. Subjective cognitive decline in older
adults: an overview of self-report measures used across 19 international
research studies. J Alzheimers Dis. 2015;48(Suppl 1):S63–86.
17. Stewart R. Subjective Cognitive Impairment. Geriatric Psychiatry. 2012;25:
445–50.
18. Peres K, Helmer C, Amieva H, Orgogozo JM, Rouch I, Dartigues JF,
Barberger-Gateau P. Natural history of decline in instrumental activities of
daily living performance over the 10 years preceding the clinical diagnosis
of dementia: a prospective population-based study. J Am Geriatr Soc. 2008;
56(1):37–44.
19. Marshall GA, Zoller AS, Lorius N, Amariglio RE, Locascio JJ, Johnson KA,
Sperling RA, Rentz DM. Functional activities questionnaire items that best
discriminate and predict progression from clinically Normal to mild
cognitive impairment. Curr Alzheimer Res. 2015;12(5):493–502.
20. Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO,
Nordberg A, Backman L, Albert M, Almkvist O, et al. Mild cognitive
impairment--beyond controversies, towards a consensus: report of the
international working group on mild cognitive impairment. J Intern Med.
2004;256(3):240–6.
21. Adams HH, Hibar DP, Chouraki V, Stein JL, Nyquist PA, Renteria ME, Trompet
S, Arias-Vasquez A, Seshadri S, Desrivieres S, et al. Novel genetic loci
underlying human intracranial volume identified through genome-wide
association. Nat Neurosci. 2016;19(12):1569–82.
22. Morris JC. Revised criteria for mild cognitive impairment may compromise
the diagnosis of Alzheimer disease dementia. Arch Neurol. 2012;69(6):700–8.
23. Rog LA, Park LQ, Harvey DJ, Huang CJ, Mackin S, Farias ST. The independent
contributions of cognitive impairment and neuropsychiatric symptoms to
everyday function in older adults. Clin Neuropsychol. 2014;28(2):215–36.
24. Livingston G, Baio G, Sommerlad A, de Lusignan S, Poulimenos S, Morris S,
Rait G, Hoe J. Effectiveness of an intervention to facilitate prompt referral to
memory clinics in the United Kingdom: cluster randomised controlled trial.
PLoS Med. 2017;14(3):e1002252.
25. Gross AL, Mungas DM, Leoutsakos JS, Albert MS, Jones RN. Alzheimer's
disease severity, objectively determined and measured. Alzheimers Dement
(Amst). 2016;4:159–68.
26. Departamento de Estadísticas y Gestión de la Información SdSMOS:
Población. Chile: Ministerio de Salud; 2014.
27. Knopman DS, Kramer JH, Boeve BF, Caselli RJ, Graff-Radford NR, Mendez MF,
Miller BL, Mercaldo N. Development of methodology for conducting clinical
trials in frontotemporal lobar degeneration. Brain. 2008;131(Pt 11):2957–68.
28. Folstein M, Anthony JC, Parhad I, Duffy B, Gruenberg EM. The meaning of
cognitive impairment in the elderly. J Am Geriatr Soc. 1985;33:228–35.
29. Pfeffer RI, Kurosaki TT, Harrah CH Jr, Chance JM, Filos S. Measurement of functional
activities in older adults in the community. J Gerontol. 1982;37(3):323–9.
30. Muñoz-Neira C, Lopez O, Riveros R, Nuñez-Huasaf J, Flores P, Slachevsky A.
The technology-activities of daily living questionnaire: a version with a
technology-related subscale. Dement Geriatr Cogn Disord. 2012;33:361–71.
31. Farias ST, Mungas D, Reed BR, Cahn-Weiner D, Jagust W, Baynes K, Decarli C.
The measurement of everyday cognition (ECog): scale development and
psychometric properties. Neuropsychology. 2008;22(4):531–44.
32. Zarate V, Kind P, Valenzuela P, Vignau A, Olivares-Tirado P, Munoz A. Social
valuation of EQ-5D health states: the Chilean case. Value Health. 2011;14:
1135–41.
33. Donoso MP: Análisis de Resultados del Alcohol Use Disorders Identification
Test (AUDIT) Resultados Escala: Noveno Estudio Nacional de Drogas en
Población General de Chile. Chile: SENDA Ministerio del Interior y Seguridad
Pública; 2015.
34. ENS: Encuesta Nacional de Salud 2009–2010. Santiago; 2010.
35. Fried PA, Watkinson B. Differential effects on facets of attention in
adolescents prenatally exposed to cigarettes and marihuana. Neurotoxicol
Teratol. 2001;23(5):421–30.
36. Avila-Funes JA, Helmer C, Amieva H, Barberger-Gateau P, Le Goff M, Ritchie
K, Portet F, Carriere I, Tavernier B, Gutierrez-Robledo LM, et al. Frailty among
community-dwelling elderly people in France: the three-city study. J
Gerontol A Biol Sci Med Sci. 2008;63(10):1089–96.
37. Berger-Sieczkowski E, Gruber B, Stogmann E, Lehrner J. Differences regarding
the five-factor personality model in patients with subjective cognitive decline
and mild cognitive impairment. Neuropsychiatr. 2019;33(1):35–45.
38. McCrae RR, Costa PT Jr. Validation of the five-factor model of personality
across instruments and observers. J Pers Soc Psychol. 1987;52(1):81–90.
39. Ryff CD, Keyes CL. The structure of psychological well-being revisited. J Pers
Soc Psychol. 1995;69(4):719–27.
40. Hoyl T, EV, Marín P. Depresión en el adulto mayor: evaluación preliminar de
la efectividad, como instrumento de tamizaje, de la versión de 5 ítems de la
Escala de Depresión Geriátrica. Rev Med Chil. 2000;128:1199–204.
41. Gurrola GM, Balcazar P, Bonilla MP, Virseda JA. Estructura Factorial y
Consistencia Interna de la Escala de Depresión, Ansiedad y Estrés (DASS-21)
en una muestra no clínica. Psicología y Ciencia Social. 2006;8(2):3–7.
42. Rothermund K, Brandtstadter J. Coping with deficits and losses in later life:
from compensatory action to accommodation. Psychol Aging. 2003;18(4):
896–905.
43. Leon I, Garcia-Garcia J, Roldan-Tapia L. Estimating cognitive reserve in
healthy adults using the cognitive reserve scale. PLoS One. 2014;9(7):
e102632.
44. Knopman DS, Weintraub S, Pankratz VS. Language and behavior domains
enhance the value of the clinical dementia rating scale. Alzheimers Dement
(Amst). 2011;7:293–9.
45. Galvin JE, Roe CM, Coats MA, Morris JC. Patient's rating of cognitive ability:
using the AD8, a brief informant interview, as a self-rating tool to detect
dementia. Arch Neurol. 2007;64(5):725–30.
46. Galvin JE, Roe CM, Xiong C, Morris JC. Validity and reliability of the AD8
informant interview in dementia. Neurology. 2006;67(11):1942–8.
47. Folstein MFF, S. E. McHugh, P. R.: Mini-mental state: a practical method for
grading the cognitive state of patients for clinician. J Psychiatry Res 1975,
12(1):189–198.
48. Delgado C, Araneda A, Behrens MI. Validation of the Spanish-language
version of the Montreal cognitive assessment test in adults older than 60
years. Neurologia. 2017;34(6):376–85.
49. Bruno D, Slachevsky A, Fiorentino N, Rueda DS, Bruno G, Tagle AR, Olavarria
L, Flores P, Lillo P, Roca M, et al. Argentinian/Chilean validation of the
Spanish-language version of Addenbrooke's cognitive examination III for
diagnosing dementia. Neurologia. 2020;35(2):82–88.
50. Parra MA, Abrahams S, Logie RH, Sala SD. Age and binding within-dimension
features in visual short-term memory. Neurosci Lett. 2009;449(1):1–5.
51. Parra MA, Abrahams S, Logie RH, Della Sala S. Visual short-term memory
binding in Alzheimer's disease and depression. J Neurol. 2010;257(7):1160–9.
52. Auriacombe S, Helmer C, Amieva H, Berr C, Dubois B, Dartigues JF. Validity
of the free and cued selective reminding test in predicting dementia: the
3C study. Neurology. 2010;74(22):1760–7.
53. Delgado C, Munoz-Neira C, Soto A, Martinez M, Henriquez F, Flores P,
Slachevsky A. Comparison of the psychometric properties of the “word”and
“picture”versions of the free and cued selective reminding test in a
Spanish-speaking cohort of patients with mild Alzheimer’s disease and
cognitively healthy controls. Arch Clin Neuropsychol. 2016;31(2):165–75.
54. Slachevsky A, Barraza P, Hornberger M, Munoz-Neira C, Flanagan E,
Henriquez F, Bravo E, Farias M, Delgado C. Neuroanatomical comparison of
the “word”and “picture”versions of the free and cued selective reminding
test in Alzheimer’s disease. J Alzheimers Dis. 2018;61(2):589–600.
55. Tu S, Wong S, Hodges JR, Irish M, Piguet O, Hornberger M. Lost in spatial
translation - a novel tool to objectively assess spatial disorientation in
Alzheimer's disease and frontotemporal dementia. Cortex. 2015;67:83–94.
56. Torralva T, Roca M, Gleichgerrcht E, Lopez P, Manes F. INECO frontal
screening (IFS): a brief, sensitive, and specific tool to assess executive
functions in dementia. J Int Neuropsychol Soc. 2009;15(5):777–86.
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 11 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
57. Olabarrieta-Landa L, Rivera D, Galarza-Del-Angel J, Garza MT, Saracho CP,
Rodriguez W, Chavez-Oliveros M, Rabago B, Leibach G, Schebela S, et al.
Verbal fluency tests: normative data for the Latin American Spanish
speaking adult population. NeuroRehabilitation. 2015;37(4):515–61.
58. Reitan RM. The relation of the trail making test to organic brain damage. J
Consult Psychol. 1955;19(5):393–4.
59. Arango-Lasprilla JC, Rivera D, Aguayo A, Rodriguez W, Garza MT, Saracho CP,
Rodriguez-Agudelo Y, Aliaga A, Weiler G, Luna M, et al. Trail making test:
normative data for the Latin American Spanish speaking adult population.
NeuroRehabilitation. 2015;37(4):639–61.
60. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF,
Ogar JM, Rohrer JD, Black S, Boeve BF, et al. Classification of primary
progressive aphasia and its variants. Neurology. 2011;76(11):1006–14.
61. Rey A. Test de copie d'une figure complexe. Paris: Centre de Psychologie
Appliquée; 1959.
62. Rivera D, Perrin PB, Morlett-Paredes A, Galarza-Del-Angel J, Martinez C, Garza
MT, Saracho CP, Rodriguez W, Rodriguez-Agudelo Y, Rabago B, et al. Rey-
Osterrieth complex figure - copy and immediate recall: normative data for
the Latin American Spanish speaking adult population. NeuroRehabilitation.
2015;37(4):677–98.
63. Bertoux M, Volle E, de Souza LC, Funkiewiez A, Dubois B, Habert MO. Neural
correlates of the mini-SEA (social cognition and emotional assessment) in
behavioral variant frontotemporal dementia. Brain Imaging Behav. 2014;8(1):1–6.
64. Schroder J, Niethammer R, Geider FJ, Reitz C, Binkert M, Jauss M, Sauer H.
Neurological soft signs in schizophrenia. Schizophr Res. 1991;6(1):25–30.
65. Elamin M, Bennett G, Symonds A, Pal S, Abrahams S, Parra M, Thomas B,
Connick P: Introducing a Brief Screening a Tool for Motor Signs in Patients
with Dementia (P6.203). Neurology 2015;84(14 Supplement).
66. Tinetti ME. Performance-oriented assessment of mobility problems in elderly
patients. J Am Geriatr Soc. 1986;34(2):119–26.
67. Powell LE, Myers AM. The activities-specific balance confidence (ABC) scale.
J Gerontol A Biol Sci Med Sci. 1995;50A(1):M28–34.
68. Podsiadlo D, Richardson S. The timed “up & go”: a test of basic functional
mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142–8.
69. Costea PI, Coelho LP, Sunagawa S, Munch R, Huerta-Cepas J, Forslund K,
Hildebrand F, Kushugulova A, Zeller G, Bork P. Subspecies in the global
human gut microbiome. Mol Syst Biol. 2017;13(12):960.
70. Araujo DM, Lapchak PA. Induction of immune system mediators in the
hippocampal formation in Alzheimer's and Parkinson's diseases: selective effects on
specific interleukins and interleukin receptors. Neuroscience. 1994;61(4):745–54.
71. D'Anna L, Abu-Rumeileh S, Fabris M, Pistis C, Baldi A, Sanvilli N, Curcio F,
Gigli GL, D'Anna S, Valente M. Serum Interleukin-10 levels correlate with
cerebrospinal fluid amyloid Beta deposition in Alzheimer disease patients.
Neurodegener Dis. 2017;17(4–5):227–34.
72. Kim YS, Lee KJ, Kim H. Serum tumour necrosis factor-alpha and interleukin-6
levels in Alzheimer's disease and mild cognitive impairment.
Psychogeriatrics. 2017;17(4):224–30.
73. Tennent GA, Lovat LB, Pepys MB. Serum amyloid P component prevents
proteolysis of the amyloid fibrils of Alzheimer disease and systemic
amyloidosis. Proc Natl Acad Sci U S A. 1995;92(10):4299–303.
74. Gong C, Wei D, Wang Y, Ma J, Yuan C, Zhang W, Yu G, Zhao Y. A meta-
analysis of C-reactive protein in patients with Alzheimer's disease. Am J
Alzheimers Dis Other Dement. 2016;31(3):194–200.
75. Goldman JS, Farmer JM, Wood EM, Johnson JK, Boxer A, Neuhaus J, Lomen-
Hoerth C, Wilhelmsen KC, Lee VM, Grossman M, et al. Comparison of family
histories in FTLD subtypes and related tauopathies. Neurology. 2005;65(11):
1817–9.
76. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C,
DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, et al. Meta-analysis of
74,046 individuals identifies 11 new susceptibility loci for Alzheimer's
disease. Nat Genet. 2013;45(12):1452–8.
77. EuroQol G. EuroQol--a new facility for the measurement of health-related
quality of life. Health Policy. 1990;16(3):199–208.
78. Global Burden Disease: Chile. 2010. http://www.healthdata.org/sites/default/
files/files/country_profiles/GBD/ihme_gbd_country_report_chile.pdf.
79. Morley JE, Malmstrom TK, Miller DK. A simple frailty questionnaire (frail)
predicts outcomes in middle aged African Americans. J Nutr Health Aging.
2012;16:601–8.
80. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J,
Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence
for a phenotype. J Gerontol. 2001;56A:146–56.
81. Icaza G, Nunez L, Marrugat J, Mujica V, Escobar MC, Jimenez AL, Perez P,
Palomo I. Estimation of coronary heart disease risk in Chilean subjects based
on adapted Framingham equations. Rev Med Chil. 2009;137(10):1273–82.
82. Munoz C, Nunez J, Flores P, Behrens PM, Slachevsky A. Usefulness of a brief
informant interview to detect dementia, translated into Spanish (AD8-Ch).
Rev Med Chil. 2010;138(8):1063–5.
83. Pievani M, Filippini N, van den Heuvel MP, Cappa SF, Frisoni GB. Brain
connectivity in neurodegenerative diseases--from phenotype to
proteinopathy. Nat Rev Neurol. 2014;10(11):620–33.
84. Ferrarini L, van Lew B, Reiber JH, Gandin C, Galluzzo L, Scafato E, Frisoni GB,
Milles J, Pievani M, Group IW. Hippocampal atrophy in people with memory
deficits: results from the population-based IPREA study. Int Psychogeriatr.
2014;26(7):1067–81.
85. O'Bryant SE, Gupta V, Henriksen K, Edwards M, Jeromin A, Lista S, Bazenet C,
Soares H, Lovestone S, Hampel H, et al. Guidelines for the standardization of
preanalytic variables for blood-based biomarker studies in Alzheimer’s
disease research. Alzheimers Dement. 2015;11:549–60.
86. Browne WJ. MCMC estimation in MLwiN (version 2.13) Centre for Multilevel
Modelling. Bristol: Centre for Multilevel Modelling, University of Bristol; 2015.
87. Christensen R, Johnson W, Branscum A, Hanson TE. Bayesian ideas and data
analysis: an introduction for scientists and statisticians. USA: CRC Press; 2010.
88. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical
learning with applications in R. New York: Springer; 2017.
89. Hoyle RH. Handbook of structural equation modeling. New York: The Guilford
Press; 2012.
90. Steyerberg E. Clinical Prediction Model: Ch. 8: Case Study on Dealing with
Missing Data. Stat Biol Health. 2009:139–58.
91. Steyerberg E. Clinical Prediction Models: Ch. 7: A Practical Approach to
Development, Validation, and Updating. Stat Biol Health. 2009:115–37.
92. Slachevsky A, Leon T, Gajardo J, Rivero P. Policy Paper “Prevención y
abordaje integral de las demencias. Avances y desafíos para la Política
Pública en materia de trastornos neurocognitivos”. Santiago: Universidad de
Chile; Comisión Futuro del Senado; COPRAD; GERO; 2019.
93. Rodriguez JJL, Ferri CP, Acosta D, Guerra M, Huang Y, Jacob KS,
Krishnamoorthy ES, Salas A, Sosa AL, Acosta I, et al. Prevalence of dementia
in Latin America, India, and China: a population-based cross-sectional
survey. Lancet. 2008;372:464–74.
94. Group DR. Subjective memory deficits in people with and without
dementia: findings from the 10/66 dementia research group pilot studies in
low- and middle-income countries. J Am Geriatr Soc. 2009;57:2118–24.
95. Acosta I, Borges G, Aguirre-Hernandez R, Sosa AL, Prince M.
Neuropsychiatric symptoms as risk factors of dementia in a Mexican
population: a 10/66 dementia research group study. Alzheimers Dement.
2018;14:271–9.
96. Mograbi DC, Brown RG, Salas C, Morris RG. Emotional reactivity and
awareness of task performance in Alzheimer’s disease. Neuropsychologia.
2012;50:2075–84.
97. Villarreal AE, O’Bryant SE, Edwards M, Grajales S, Britton GB. Serum-based
protein profiles of Alzheimer’s disease and mild cognitive impairment in
elderly Hispanics. Neurodegener Dis Manag. 2016;6:203–13.
98. Jagger C, Andersen K, Breteler MM, Copeland JR, Helmer C, Baldereschi
M, Fratiglioni L, Lobo A, Soininen H, Hofman A, et al. Prognosis with
dementia in Europe: a collaborative study of population-based cohorts.
Neurologic diseases in the elderly research group. Neurology. 2000;
54(11 Suppl 5):S16–20.
99. Purser JL, Fillenbaum GG, Pieper CF, Wallace RB. Mild cognitive impairment
and 10-year trajectories of disability in the Iowa established populations for
epidemiologic studies of the elderly cohort. J Am Geriatr Soc. 2005;53(11):
1966–72.
100. Muller M, Tang MX, Schupf N, Manly JJ, Mayeux R, Luchsinger JA. Metabolic
syndrome and dementia risk in a multiethnic elderly cohort. Dement Geriatr
Cogn Disord. 2007;24(3):185–92.
101. Melis RJ, Marengoni A, Rizzuto D, Teerenstra S, Kivipelto M, Angleman SB,
Fratiglioni L. The influence of multimorbidity on clinical progression of
dementia in a population-based cohort. Plos One. 2013;8(12):e84014.
102. Helmer C, Peres K, Letenneur L, Guttierez-Robledo LM, Ramaroson H,
Barberger-Gateau P, Fabrigoule C, Orgogozo JM, Dartigues JF. Dementia in
subjects aged 75 years or over within the PAQUID cohort: prevalence and
burden by severity. Dement Geriatr Cogn Disord. 2006;22(1):87–94.
103. Delva F, Touraine C, Joly P, Edjolo A, Amieva H, Berr C, Rouaud O, Helmer C,
Peres K, Dartigues JF. ADL disability and death in dementia in a French
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 12 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
population-based cohort: new insights with an illness-death model.
Alzheimers Dement. 2016;12(8):909–16.
104. Peres K, Brayne C, Matharan F, Grasset L, Helmer C, Letenneur L, Foubert-Samier
A, Baldi I, Tison F, Amieva H, et al. Trends in prevalence of dementia in French
farmers from two epidemiological cohorts. J Am Geriatr Soc. 2017;65(2):415–20.
105. Qian J, Wolters FJ, Beiser A, Haan M, Ikram MA, Karlawish J, Langbaum JB,
Neuhaus JM, Reiman EM, Roberts JS, et al. APOE-related risk of mild
cognitive impairment and dementia for prevention trials: an analysis of four
cohorts. PLoS Med. 2017;14(3):e1002254.
106. Ouvrard C, Meillon C, Dartigues JF, Avila-Funes JA, Amieva H.
Psychosocioeconomic precariousness, cognitive decline and risk of developing
dementia: a 25-year study. Dement Geriatr Cogn Disord. 2016;41(3–4):137–45.
107. Avila-Funes JA, Carcaillon L, Helmer C, Carriere I, Ritchie K, Rouaud O, Tzourio C,
Dartigues JF, Amieva H. Is frailty a prodromal stage of vascular dementia?
Results from the Three-City study. J Am Geriatr Soc. 2012;60(9):1708–12.
108. Babulal GM, Quiroz YT, Albensi BC, Arenaza-Urquijo E, Astell AJ, Babiloni C,
Bahar-Fuchs A, Bell J, Bowman GL, Brickman AM, et al. Perspectives on ethnic
and racial disparities in Alzheimer's disease and related dementias: update and
areas of immediate need. Alzheimers Dement. 2019;15(2):292–312.
109. Krysinska K, Sachdeva PS, Breitner J, Kivipelto M, Kukull W, Brodaty H.
Dementia registries around the globe and their applications: a systematic
review. Alzheimers Dement. 2017;13:1031–47.
110. Dufouil C, Dubois B, Vellas B, Pasquier F, Blanc F, Hugon J, Hanon O, Dartigues
JF, Harston S, Gabelle A, et al. Cognitive and imaging markers in non-
demented subjects attending a memory clinic: study design and baseline
findings of the MEMENTO cohort. Alzheimers Res Ther. 2017;9(1):67.
111. Delgado C, Vergara R, Martinez M, Musa G, Henriquez F, Slachevsky A.
Differential contribution of Neuropsychiatric symptoms and cognition on
complex functionality across Alzheimer disease. J Alzheimer’s Dis. 2019;67:
381–92.
112. Ahmed RM, Devenney EM, Irish M, Ittner A, Naismith S, Ittner LM, Rohrer JD,
Halliday GM, Eisen A, Hodges JR, et al. Neuronal network disintegration:
common pathways linking neurodegenerative diseases. J Neurol Neurosurg
Psychiatry. 2016;87(11):1234–41.
113. Nori VS, Hane CA, Martin DC, Kravetz AD, Sanghavi DM. Identifying incident
dementia by applying machine learning to a very large administrative
claims dataset. PLoS One. 2019;14(7):e0203246.
114. Grassi M, Rouleaux N, Caldirola D, Loewenstein D, Schruers K, Perna G, Dumontier
M, Alzheimer's Disease Neuroimaging I. A novel ensemble-based machine learning
algorithm to predict the conversion from mild cognitive impairment to Alzheimer's
disease using socio-demographic characteristics, clinical information, and
neuropsychological measures. Front Neurol. 2019;10:756.
115. Shao Y, Zeng QT, Chen KK, Shutes-David A, Thielke SM, Tsuang DW.
Detection of probable dementia cases in undiagnosed patients using
structured and unstructured electronic health records. BMC Med Inform
Decis Mak. 2019;19(1):128.
116. Aguirre-Acevedo DC, Jaimes-Barragan F, Henao E, Tirado V, Munoz C,
Reiman EM, Tariot PN, Langbaum JB, Lopera F. Diagnostic accuracy of CERA
D total score in a Colombian cohort with mild cognitive impairment and
Alzheimer's disease affected by E280A mutation on presenilin-1 gene. Int
Psychogeriatr. 2016;28(3):503–10.
117. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB,
Holtzman DM, Jagust W, Jessen F, Karlawish J, et al. NIA-AA research
framework: toward a biological definition of Alzheimer's disease. Alzheimers
Dement. 2018;14(4):535–62.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Slachevsky et al. BMC Geriatrics (2020) 20:505 Page 13 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Content uploaded by Gonzalo Forno
Author content
All content in this area was uploaded by Gonzalo Forno on Nov 26, 2020
Content may be subject to copyright.