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Seifertetal. BMC Geriatrics (2022) 22:507
https://doi.org/10.1186/s12877-022-02955-2
RESEARCH
Mapping thecomplexity ofdementia:
factors inuencing cognitive function
attheonset ofdementia
Imke Seifert1*, Henrik Wiegelmann2, Marta Lenart‑Bugla3, Mateusz Łuc3, Marcin Pawłowski3, Etienne Rouwette4,
Joanna Rymaszewska3, Dorota Szcześniak3, Myrra Vernooij‑Dassen5, Marieke Perry6, René Melis6,
Karin Wolf‑Ostermann2, Ansgar Gerhardus1, on behalf of the SHARED consortium
Abstract
Background: Dementia is a multi‑factorial condition rather than a natural and inevitable consequence of ageing.
Some factors related to dementia have been studied much more extensively than others. To gain an overview of
known or suspected influential factors is a prerequisite to design studies that aim to identify causal relationships and
interactions between factors. This article aims to develop a visual model that a) identifies factors related to cognitive
decline that signal the onset of dementia, b) structures them by different domains and c) reflects on and visualizes the
possible causal links and interactions between these factors based on expert input using a causal loop diagram.
Method: We used a mixed‑method, step‑wise approach: 1. A systematic literature review on factors related to cogni‑
tive decline; 2. A group model building (GMB) workshop with experts from different disciplines; 3. Structured discus‑
sions within the group of researchers. The results were continuously synthesized and graphically transformed into a
causal loop diagram.
Results: The causal loop diagram comprises 73 factors that were structured into six domains: physical (medical) fac‑
tors (23), social health factors (21), psychological factors (14), environmental factors (5), demographic factors (5) and
lifestyle factors (3). 57 factors were identified in the systematic literature review, additionally 16 factors, mostly of the
social health cluster, were identified during the GMB session and the feedback rounds.
Conclusion: The causal loop diagram offers a comprehensive visualisation of factors related to cognitive decline and
their interactions. It supports the generation of hypotheses on causal relationships and interactions of factors within
and between domains.
Keywords: Dementia, Model, Theory, Risk factors, Cognition
© The Author(s) 2022. 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
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Introduction
Dementia is a multi-factorial condition that is a major
cause of disability and dependency among older people.
Worldwide, 50 million people live with a diagnosis of
dementia and the number is projected to reach 152 mil-
lion in 2050 [1]. It not only causes substantial challenges
for the individuals living with dementia themselves, but
also for their families and caregivers [2–4]. In addition
to physical and emotional distress, dementia causes sub-
stantial economic burdens for both individuals and socie-
ties [1].
Open Access
*Correspondence: imke.seifert@uni‑bremen.de
1 Department for Health Services Research, Institute of Public Health
and Nursing Research (IPP), Health Sciences Bremen, University
of Bremen, Grazer Straße 4 , 28359 Bremen, Germany
Full list of author information is available at the end of the article
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Page 2 of 8
Seifertetal. BMC Geriatrics (2022) 22:507
Previous research has identified a wide array of
factors potentially related to cognitive decline and
dementia. Well studied risk factors include genetic pre-
conditions, life-style related factors, such as physical
inactivity, tobacco use, unhealthy diets, harmful use of
alcohol, and medical conditions, such as hypertension,
diabetes, hypercholesterolemia, obesity and depression
[1, 5]. Other contributing factors, such as environmen-
tal and social health factors have been less intensively
researched, however there are strong indications that
these might influence the onset of dementia and its fur-
ther trajectory [6–9].
Although dementia is considered a multi-factorial
condition, risk factors have mainly been researched in
isolation. Only few maps and models on protective or
risk factors of dementia are available [10–12]. e life-
course model of Livingston, etal. [10] shows potentially
modifiable and non-modifiable risk factors of demen-
tia. It states that 40% of dementia is attributable to 12
potentially modifiable factors over the life course: from
early life, midlife to later life. ey note, however, that
their model does not include other potentially rele-
vant risk factors, such as diet and sleep. e bio-social
model of Spector, Orrell [11] disaggregates psychoso-
cial and biological processes with the aim of under-
standing the inter-relationship between these two and
distinguishing modifiable and non-modifiable factors.
Additionally, their model includes interventions with
potential benefits, although environmental factors are
neglected. All models show the multicausality of Alz-
heimer’s disease, but are not considered comprehen-
sive. Similarly, the model by Uleman etal. (2020) on
Alzheimer’s disease is also an incomplete representa-
tion of reality, missing the environmental and psycho-
logical factors. Together, these models offer important
and detailed insights on the relationships between the
factors, but they do not allow to capture the whole
picture of the multifactorial nature of pathogenesis
of dementia. Hence, in this study we aimed at apply-
ing the most comprehensive approach at the onset of
the disease by developing a causal loop diagram (CLD)
that a) identifies factors related to cognitive decline and
the onset of dementia, b) structures them by different
domains and c) reflects on and visualizes the links and
interactions between these factors.
is work is part of the SHARED study (Social Health
And Reserve in the Dementia patient journey), an inter-
national project funded by the EU Joint Programme –
Neurodegenerative Disease Research (JPND) that focuses
on the interaction between social health, cognitive and
biological factors on dementia using quantitative analy-
ses as well as performing qualitative studies to reveal
additional relevant social factors and relations with cog-
nitive reserve and function [13].
Methods
We deliberately decided on a methodological approach
that combines a comprehensive search of the literature
and a qualitative, participatory research method inte-
grating the knowledge of interdisciplinary experts. It
consists of three sequential and integrated steps: (1) a
systematic literature review; (2) a Group Model Build-
ing (GMB) session; (3) a structured iterative discussion
process (Fig.1).
Step 1: Systematic Literature Review
A systematic literature search of systematic reviews and
meta-analyses was conducted in five databases (Medline,
PsychINFO, CINAHL Complete, Cochrane Database of
Systematic Reviews and Epistemonikos); see Additional
file1. 1. Search strategy. is search provided a compre-
hensive synthesis of factors associated with cognitive
Fig. 1 Illustration of the applied methodical process (Step 1 – Step 3)
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Seifertetal. BMC Geriatrics (2022) 22:507
function in the context of dementia. Articles were eligible
if they reported either on empirical research on the influ-
ence (positive and/or negative) of one single factor or a
combination of factors on cognitive decline or dementia.
Only systematic reviews and meta-analyses were included.
e search was limited to human studies, English lan-
guage, journals and periodicals and time period 1.01.2009
– 1.08.2019. Most of the systematic reviews and meta-
analyses we included, covered studies from a wide time
range but mostly focusing on work published in the last
decade. e exclusion criteria were: non-English articles,
studies about pharmacological interventions (for example
drug tests), non-human studies (for example animal test-
ing), studies on other diseases (for example with no or a
weak link to dementia or about reverse causality).
e search was performed by six reviewers and was
based on two consecutive steps: (1) a title and abstract
screening and (2) a full-text screening. In the first step
articles where independently screened by two review-
ers. Any discrepancies were resolved by consensus with a
third reviewer. Secondly, all six reviewers independently
reviewed potentially relevant articles in full text. Refer-
ence lists of included studies were manually searched for
additional relevant studies. All selected publications were
uploaded into PRIMARY Excel Workbook for System-
atic Reviews for the screening process [14]. A PRISMA
flow diagram [15] was used to summarize study selection
(Additional file1. 2. Flowchart). e inter-rater reliability
of the eligibility criteria (exclusion/inclusion) was checked
using Fleiss Kappa test – a chance-corrected measure of
agreement between more than two reviewers. e agree-
ment reached 0.91, indicating an almost perfect agree-
ment [16].
Clusters for data extraction were developed in two
steps by the six reviewers who did the screening of the
literature and who were therefore familiar with the con-
tent of the data. First, the extraction of the data was car-
ried out deductively with an a priori designed system of
clusters based on already existing clusters in the litera-
ture for cognitive related disease [17], e.g [18, 19]. and
secondly, inductively modified based on the emerging
results as the process of the full-text review progressed.
e extracted data was then summarized in a prepared
data charting form containing title, author(s), country,
year, type of review, number of included studies, type of
diagnosis/health status, age range of study participants,
key findings, comments and seven clusters of factors: (1)
demographic, (2) socioeconomic, (3) lifestyle, (4) social
health, (5) psychological, (6) environmental and (7) phys-
ical. Factors reported in the articles were also categorized
whether their influence was “protective”, “increased risk”,
“no risk”, “unclear” or “no influence”. e results of the lit-
erature search were used as input to the GMB session.
Step 2: Group Model Building (GMB)
GMB is a participatory method for involving experts
in developing models, such as a causal loop diagram
(CLD), a theoretical model or a knowledge map. e
result contains the consensus of the experts on the
basis of a collective decision [20–22]. GMB is based
on system dynamics, a methodology to support deci-
sion making in a variety of complex domains. System
dynamics has been successfully applied on a variety of
topics related to health, such as chronic diseases, sub-
stance abuse epidemiology or health care capacity and
delivery [23, 24]. Within the SHARED project, GMB
was used to elicit and to structure knowledge from an
interdisciplinary group of experts and to combine this
knowledge with results of the systematic literature
review into a CLD.
A GMB session with 18 experts from different pro-
fessional disciplines was conducted over two days in
Bremen (Germany). Two experts participated via skype.
e session was led by an experienced GMB-facilitator
and member of the SHARED consortium (ER). All par-
ticipants were members of the SHARED consortium
and/or the INTERDEM (early and timely INTERven-
tions in DEMentia) platform (a pan-European network
of dementia researchers). e group consisted of experts
in psychology (n = 5), public health/ health services and
nursing research (n = 4), medicine (n = 4), epidemiol-
ogy (n = 3), and social science (n = 2). e experts were
from e Netherlands (n = 5), Poland (n = 5), Germany
(n = 4), Australia (n = 2), UK (n = 1) and Italy (n = 1). e
process of GMB started with defining the core variable
of the CLD, in this case “cognitive functioning”. Based on
this, the participants collected, discussed and prioritised
factors influencing the core variable and built a prelimi-
nary CLD model. On the second day factors identified
in the systematic literature review (see Step 1) were pre-
sented to the group. e factors were discussed, priori-
tised and integrated into the CLD. Factors were grouped
into thematic clusters. Clusters were initially deducted
from pre-existing clusters used by e.g. Uleman, et al.
[12], Kinderman [17], Korczyn, Halperin [25], which we
then inductively modified based on our data and in an
iterative process (Step 3). Of the seven categories iden-
tified in step 1, 6 clusters were formed. e clusters (1)
demographic, and (2) socioeconomic were combined
in one cluster “demographic factors”. e connection
between the factors (arrows) based either on expert’s
knowledge of the GMB participants or on results of the
literature review. For building the model in our GMB we
used Vensim DSS, version 8.0.0 [26], a special System
Dynamics software.
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Seifertetal. BMC Geriatrics (2022) 22:507
Step 3: Iterative structured discussion
After the GMB session an iterative process, consist-
ing of three feedback rounds, was initiated: first, a
slightly edited version of the CLD produced dur-
ing the session was sent to the SHARED consor-
tium members for feedback. The feedback suggested
adjustments at the level of the factors as well as at
the level of clusters. It also induced changes to the
relationships between factors. Based on the feed-
back a revised CLD was sent again to the SHARED
consortium members and discussed during an online
meeting. The revised CLD was sent to the SHARED
consortium members for final approval. For better
visualisation we transferred the CLD from Vensim to
the software Kumu Inc [26].
Results
e resulting CLD of step 1–3 comprises 73 factors
(Fig.2) that presumably directly or indirectly affect cog-
nitive functioning. ey have been grouped into six clus-
ters (Fig.2).
Fig. 2 Causal loop diagram of responsible factors in the development and trajectory of dementia. To see higher resolution of this causal loop
diagram, go to https:// kumu. io/ ImkeB remen/ causal‑ loop‑ diagr am‑ of‑ facto rs‑ assoc iated‑ with‑ demen tia
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Seifertetal. BMC Geriatrics (2022) 22:507
e physical cluster (blue, top right) is made up of 23
factors. Two factors (vascular pathology and cerebrovas-
cular disease) show a high number of relationships to
other factors, suggesting an important role for cognitive
functioning and dementia. e physical cluster has con-
nections with all other clusters.
e social health cluster (green, bottom right) com-
prises a total of 21 factors. Most of the relationships
between factors happen within the cluster. Outwards,
most relationships are to the psychological cluster, fol-
lowed by the physical cluster. e factor social engage-
ment seems to influence numerous other factors and is
at the same time influenced by many factors itself. e
cluster is closely linked to the physical and psychological
cluster, there are no connections to the other clusters.
irteen factors form the psychological cluster (yellow,
bottom left). Within this cluster the factors emotional
wellbeing, level of stress, psychological resilience, cop-
ing behaviour, and self-efficacy seem to be influenced by
several factors from other clusters. e cluster is closely
connected to the physical and social health cluster; there
are hardly any connections to the other clusters.
e environmental cluster (red, top left) comprises
eight factors. ese factors seem to be closely related to
factors from the physical cluster (i.e. cancer, microbiome,
vascular pathology, cerebral vascular disease, COPD). In
addition, there are links to the psychological cluster, e.g.,
there is a positive influence of sun exposure or green space
exposure on emotional wellbeing which in turn is associ-
ated with cognitive functioning. e cluster is only con-
nected to the physical, psychological and demographic
cluster.
Five factors (purple) are grouped together as the demo-
graphic cluster in the top left part of the CLD. Most rela-
tionships are within the cluster. Only a few factors have
links with other clusters. e factors education, profes-
sion and income are connected with all the clusters. e
cluster is connected to all other clusters, with the excep-
tion of the social health cluster.
e cluster of lifestyle factors (orange, top left) is the
smallest of the six thematic fields. It consists of only three
factors (level of physical activity, healthy diet patterns,
substance abuse (overuse of alcohol/ (passive) smoking/
use of drugs). e factors level of physical activity and
healthy diet patterns are closely related to some demo-
graphic and physical factors, e.g. high-level income, higher
level of education, obesity, and cerebrovascular disease.
e cluster is connected to the physical, psychological
and demographic cluster.
In total 57 factors of the CLD were identified in the sys-
tematic literature review. Further, 16 additional factors,
mostly of the social health cluster, were identified during
the GMB session and the feedback rounds (Additional
file1. 3. Clusters and related factors of the model). With
the exception of the factor brain reserve (part of the phys-
ical cluster), all factors added by the experts are part of
the psychological cluster (coping behaviour, emotional
wellbeing, personality traits, psychological resilience,
self-efficacy and social awareness) and the social health
cluster (autonomy, cognitive reserve, dignity, experience
of negative life events, norms and values towards help-
seeking/adherence, positive life events, quality of care/
welfare facilities, reciprocity (reciprocity is defined as a
dynamic characteristic of individual social ties. It refers
to the extent to which exchanges or transactions are even
or reciprocal [27]) and stigma).
Discussion
e objective of this research was to create an informed
and structured overview over the multitude of factors
influencing cognitive function at the onset and trajec-
tory of dementia and their interrelationships. Our CLD
reflects the results of an extensive literature review, a
two-days-workshop using group model building with an
interdisciplinary group of experts, followed by an itera-
tive development by a larger group of researchers.
We structured the factors into six clusters. Even though
it is visible that the clusters and their factors are inter-
related, most of the relationships exist within their own
clusters. e physical cluster comprises 23 factors, the
highest number of all clusters. is is not surprising,
since research on physical (bio-medical) factors have
long dominated the research and discourse on cognitive
decline [11]. All factors from this cluster were identi-
fied through the literature, while none was added by the
experts. Which also indicates that a lot of research has
already been done in this area. In contrast, in the social
health cluster nine of the 21 factors were identified by the
experts during the GMB session. is may in part result
from the composition of the group in which experts for
social health were well represented.
Interrelationships betweenfactors
Our CLD displays (possible) interrelationships between
factors. Our literature search indicated a great variability
in the intensity of the various relationships that have been
investigated. Especially in the non-medical fields and of
the (inter-)relationship between biological and psycho-
logical and social domains, more research is needed to
provide evidence for these interrelationships. With the
help of the CLD new hypotheses can be formulated. To
illustrate this, we use the example of stigma. e role of
stigma in cognitive decline could be investigated by ana-
lysing its relationship with social isolation, loneliness,
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Seifertetal. BMC Geriatrics (2022) 22:507
social interaction and ultimately cognitive functioning
(see Fig.3). As depicted in Fig.3, stigma is not directly
connected with cognitive functioning but rather via
mediating factors. Social engagement is leading directly
and social isolation via social interaction indirectly to
cognitive functioning.
Strengths andlimitations
To our knowledge this is the first study creating a com-
prehensive visualization of the factors influencing cog-
nitive functioning in dementia. A strength of the CLD
is the combination of methods that led to its develop-
ment. It consists of an extensive review of the literature,
a two-day-GMB session with a multidisciplinary group
of experts followed by structured discussions within the
group of experts. It should be noted that the review is
based only on articles from 2009–2019. However, we do
not expect to have missed relevant factors as a major part
of articles on this topic has been written during this time
span. Also, relevant factors detected before 2009 most
likely would have been picked up by the literature in the
years after.
e restriction of English articles can have an impact
on the diversity of the population. Most of the articles
were conducted and based on data from western socie-
ties which limits the generalisability. We also limited our
search to databases that are commonly used in the field
of dementia research.
A limitation might be the unequal representation of
disciplines by experts, which could have translated into
results. ere were more experts with a background in
social health, which might explain why social factors may
have competed in numbers with other clusters. However,
this may highlight a specific gap that has so far not been
visible in empirical research. e results of the system-
atic literature review (which were presented in the GMB
session as a basis for discussion) provided a synthesis of
factors associated with cognitive function in the context
of dementia but were not stratified by the strength of
evidence of each causative link. e arrows in the model
neither show the size of the association nor if the associa-
tion is positive or negative.
Another challenge was the choice of an optimal level of
aggregation of the factors given the heterogeneity of clus-
ters. While we focused at acquiring maximal consistency,
certain factors remain a challenge, as they can be further
identified in more detail (e.g., healthy diet patterns or
level of physical activity).
Another limitation of the results might be not con-
sidering cumulative effects of factors or weighting the
contribution of factors to cognitive decline. Further, the
arrows in the model do not reflect the strength of evi-
dence for the connections between the different factors.
Fig. 3 Illustrative example: Stigma and its relationship with other factors
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Seifertetal. BMC Geriatrics (2022) 22:507
Our CLD inrelation toother models
Our CLD is an attempt to visualize a wide array of fac-
tors influencing cognitive decline and their interrela-
tions. Hou, etal. [28] developed a model to predict the
risk of dementia, however missed to depict the (inter-)
relationship between the factors. e prominent model
by Livingston, etal. [10] focusses on a group of selected,
modifiable factors impacting cognitive decline over the
life course. Uleman, etal. [12] also used a GMB approach
for the development of a CLD model, but with the main
focus on Alzheimer’s disease. Uleman et al.’s model is
based exclusively on expert knowledge whereas our
model combines expert knowledge with the result of
a comprehensive literature review. Uleman et al. con-
ducted a network analysis as well as analyses of feedback
loops. Both models reflect the complexity of Alzheimer’s
and dementia respectively.
In the light of the existing models, our CLD is an
attempt to capture the bigger picture, such as the trajec-
tory from healthy cognition, cognitive decline to demen-
tia in general. Moreover, the developed CLD aggregates
the two-level knowledge – literature-based evidence and
expert knowledge discussed and gathered as a whole.
Conclusions andoutlook
is study aimed at creating a structured visualization
of the multitude of factors influencing cognitive func-
tioning at the onset and trajectory of dementia and
their interrelationships. e result was a highly complex
CLD. It can be used to formulate hypotheses of possible
pathways and to support a theory based on empirical
research. In the wider SHARED project, the CLD will
support structuring the analyses of data on interrela-
tionships of factors collected from more than 40 inter-
national cohort studies. e results of these analyses will
be fed into a new version of the CLD that presents path-
ways and indicates targets for preventive interventions
at the individual and the population level. ese inter-
ventions should then be evaluated in further research.
In this future work it would be valuable to integrate the
life experience of persons living with dementia in the
research process.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12877‑ 022‑ 02955‑2.
Additional le1. (1. Search strategy. 2. Flowchart. 3. Clusters and related
factors of the model).
Acknowledgements
Not applicable
Author contributions
IS, HW, MLB, ML, MP designed figure 1 and the model in figure 2. IS took
the lead in writing the manuscript. All authors discussed the results and
commented on the manuscript. The authors read and approved the final
manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. The Federal
Ministry of Education and Research (BMBF) (grantnumber: 01ED1905),
National Centre for Research and Development (NCBR) (grantnumber:
JPND/06/2020) and the EU Joint Programme – Neurodegenerative Disease
Research (JPND) (grantnumber: HESOCARE‑329–109) provide the fundings
for this study. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The data that support the findings of this study are available from the cor‑
responding author (Imke Seifert) but restrictions apply to the availability of
these data, which were used under license for the current study, and so are
not publicly available. Data are however available from the authors upon
reasonable request and with permission of the corresponding author (Imke
Seifert).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department for Health Services Research, Institute of Public Health and Nurs‑
ing Research (IPP), Health Sciences Bremen, University of Bremen, Grazer
Straße 4 , 28359 Bremen, Germany. 2 Department for Health Care Research,
Institute of Public Health and Nursing Research (IPP), Health Sciences Bremen,
University of Bremen, Bremen, Germany. 3 Department of Psychiatry, Wroclaw
Medical University, Wroclaw, Poland. 4 Methodology Department, Univer‑
sity of Nijmegen, Nijmegen, The Netherlands. 5 Faculty of Medical Sciences,
Radboud University Medical Center, Nijmegen, The Netherlands. 6 Department
of Geriatric Medicine, Radboud University Medical Center, Nijmegen, The
Netherlands.
Received: 29 November 2021 Accepted: 15 March 2022
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