Autism and ADHD in the Era of Big Data; An Overview of Digital Resources for Patient, Genetic and Clinical Trials Information

Abstract

Even in the era of information “prosperity” in the form of databases and registries that compile a wealth of data, information about ASD and ADHD remains scattered and disconnected. These data systems are powerful tools that can inform decision-making and policy creation, as well as advancing and disseminating knowledge. Here, we review three types of data systems (patient registries, clinical trial registries and genetic databases) that are concerned with ASD or ADHD and discuss their features, advantages and limitations. We noticed the lack of ethnic diversity in the data, as the majority of their content is curated from European and (to a lesser extent) Asian populations. Acutely aware of this knowledge gap, we introduce here the framework of the Neurodevelopmental Disorders Database (NDDB). This registry was designed to serve as a model for the national repository for collecting data from Saudi Arabia on neurodevelopmental disorders, particularly ASD and ADHD, across diverse domains.

Full text

Citation: Abomelha, F.M.;
AlDhalaan, H.; Ghaziuddin, M.;
Al-Tassan, N.A.; Al-Mubarak, B.R.
Autism and ADHD in the Era of Big
Data; An Overview of Digital
Resources for Patient, Genetic and
Clinical Trials Information. Genes
2022,13, 1551. https://doi.org/
10.3390/genes13091551
Academic Editor: Gudrun Rappold
Received: 25 June 2022
Accepted: 25 August 2022
Published: 28 August 2022
Publisher’s Note: MDPI stays neutral
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4.0/).
genes
G C A T
T A C G
G C A T
Review
Autism and ADHD in the Era of Big Data; An Overview of
Digital Resources for Patient, Genetic and Clinical
Trials Information
Faris M. Abomelha 1,2, Hesham AlDhalaan 1,3 , Mohammad Ghaziuddin 4, Nada A. Al-Tassan 1,2,3
and Bashayer R. Al-Mubarak 1,2,3,*
1King Salman Center for Disability Research, P.O. Box 94682, Riyadh 11614, Saudi Arabia
2Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, P.O. Box 3354,
Riyadh 11211, Saudi Arabia
3Center for Autism Research, King Faisal Specialist Hospital and Research Center, P.O. Box 3354,
Riyadh 11211, Saudi Arabia
4Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
*Correspondence: bal-mubarak@kfshrc.edu.sa
Abstract:
Even in the era of information “prosperity” in the form of databases and registries that
compile a wealth of data, information about ASD and ADHD remains scattered and disconnected.
These data systems are powerful tools that can inform decision-making and policy creation, as well
as advancing and disseminating knowledge. Here, we review three types of data systems (patient
registries, clinical trial registries and genetic databases) that are concerned with ASD or ADHD and
discuss their features, advantages and limitations. We noticed the lack of ethnic diversity in the data,
as the majority of their content is curated from European and (to a lesser extent) Asian populations.
Acutely aware of this knowledge gap, we introduce here the framework of the Neurodevelopmental
Disorders Database (NDDB). This registry was designed to serve as a model for the national repository
for collecting data from Saudi Arabia on neurodevelopmental disorders, particularly ASD and ADHD,
across diverse domains.
Keywords: Autism; ADHD; database; patient registry; genetic variation database; clinical trials
1. Introduction
In the fields of health and biomedical research, the terms “patient registry” and
“database” are mainly used to describe the collection and storage of datasets in an orga-
nized system that enables users to query, retrieve and sometimes extract information [
1
,
2
].
Currently, there is no consistent definition of the term “patient registry” or a set of defining
characteristics in the literature. This makes the distinction between patient registries and
simple databases or non-registry data repositories less clear. Regardless of terminology,
both types of data systems can be sources of large amounts of valuable information for vari-
ous stakeholders. Such data systems could be designed to serve each stakeholder ’s specific
interests and needs. Healthcare stakeholders include patients, healthcare providers (pro-
fessionals and facilities), governmental parties, biopharmaceutical industries, regulatory
agencies and researchers (academics and scientists).
For individuals with disabilities, be it intellectual, developmental or physical, reg-
istries have far-reaching benefits beyond optimizing healthcare. They could serve as a tool,
assisting with achieving the best possible quality of life for those afflicted by these condi-
tions. Information obtained from these registries can be leveraged to improve various areas
of life, such as education, employment, as well as the social and physical environments.
Here, we describe the different types of patient registries, clinical trial registries and
genetic databases concerned with ASD and ADHD. We also discuss their strengths and
limitations. In our view, these two types of developmental disabilities deserve special
Genes 2022,13, 1551. https://doi.org/10.3390/genes13091551 https://www.mdpi.com/journal/genes

Genes 2022,13, 1551 2 of 10
attention for many reasons that could be boiled down to (1) the challenges associated with
diagnosis and (2) the benefits of early intervention. Lack of definitive clinical, genetic
or biochemical tests (except for rare monogenic forms) and the fact that these conditions
exist along a spectrum, with subtle or overlapping features, render them harder to identify.
Moreover, appropriate intervention, if introduced early in life, can significantly improve
the quality of life of those affected and their families.
2. ASD Patient Data Systems
Because there is no clear distinction between the terms “database” and “registry”,
we used the all-encompassing term “data system” throughout this section when referring
to registries/databases. We initially aimed to review and summarize the key features
of patient data systems dedicated to ASD and ADHD; however, we were only able to
find information related to ASD, but none related to ADHD. Because trying to list all the
existing data systems would be infeasible, we focused on identifying those described in
the published literature and/or those with an official webpage. Our search was limited to
articles and websites primarily available in English. Only active data systems are reviewed
in this section (retired or obsolete data systems were deemed irrelevant, or those for which
adequate information was not available were excluded). A total of nine data systems were
identified; their key features, goals, strengths and limitations are summarized in Table S1.
Although all the covered data systems invariably served a similar purpose and collected
similar variables, four different terms have been used in the literature/websites to identify
them (database, registry, research registry and archive). While there is no formal definition
that discriminates one term from the other, we noticed that the methods of data collection
and patient recruitment were usually different (Table S2).
All of the reviewed data systems have the same primary goal, that is advancing
research by accumulating high quality data and linking researchers with potential par-
ticipants and with one another. Under-representation of children from disadvantaged
backgrounds or non-English speaking families is a major limitation that is common to most
of these data systems.
The SFARI Base (https://www.sfari.org/resource/sfari-base/ (accessed on 15 August
2022)) [
3
] and the NIMH Data Archive (NDAR) (https://nda.nih.gov/about.html (accessed
on 15 August 2022)) [
4
] are examples of premier big data resources, containing data derived
from a large number of subjects, i.e., >250 k and >360 k, respectively. These figures are
expected to rise due to the SFARI Base’s ongoing recruitment process and researchers
depositing their new findings in NDAR. The SFARI Base is an online portal supported by
Simons Foundation Autism Research Initiative that serves as a central database, housing
various types of autism-related data, as well as banking biospecimens collected from the
following four different cohorts: the Simons Simplex Collection (SSC), Simons Searchlight,
Simons Foundation Powering Autism Research for Knowledge (SPARK) and Autism
Inpatient Collection (AIC). The Interactive Autism Network (IAN), initially launched as a
web-based portal for connecting researchers with individuals with ASD and their families,
was established in 2006. This registry recruited over 60,000 participants before the project
was closed in 2019 and transferred to SPARK [5].
While the SFARI Base contains data directly contributed by participants, NDAR,
funded by the NIH, only holds data contributed by investigators and does not act as a
repository for biospecimens. Although the SFARI Base and NDAR house an extensive
set of data, they are generally limited in their geographical coverage (except for Simons
Searchlight), being restricted to subjects residing in the USA or research projects funded by
US-funding bodies (mainly the NIH). The International Collaboration for Autism Registry
(iCARE), on the other hand, stands out as being the only one with multinational population
coverage, spanning 6 countries, however, the limited access (data only available to member-
sites) could be considered as a major downside [6].
Among the data systems reviewed herein, the Autism Genetic Resource Exchange
(AGRE) is the longest standing. It was founded in 1997 as a collaborative venture by

Genes 2022,13, 1551 3 of 10
parents, physicians and researchers to accelerate the scientific progress of autism research.
Currently, AGRE is supported by the Autism Speaks advocacy organization and funded
by the National Institute of Mental Health [
7
]. Moreover, the MSSNG project houses data
generated by whole genome sequencing of thousands of individuals from the AGRE and
other cohorts [
8
]. This database was designed to create a resource of high quality “big data”
that are available to researchers by leveraging cloud-based computing services. The latest
data release summary (2019–2020) reports the accumulation of data from over 11 thousand
individuals (including 5134 with ASD) that belong to families with either one or multiple
affected members.
The Autism Treatment Network (ATN) Registry is another USA-based registry sup-
ported by Autism Speaks that was established to advance understanding and care delivery
for children and adolescents with ASD and their families. The registry was curated to col-
lect baseline measures at annual follow-up visits. The data can be queried by investigators
to reach evidence-based recommendations or best practices in the management of ASD.
Since its official launch in 2008, the registry cumulated over 7000 subjects from the USA
and Canada [9].
As is the case with Autism Speaks, Autistica is a registered charity that was founded
with the aim of improving the quality of life of individuals with ASD by facilitating and
funding research. It is composed of a network of 50 UK child health teams and self-referral
that has successfully recruited more than 13 thousand families [10].
Distinct from the other registers, the Autism Register is a continuation of a pre-existing
paper-based register that was created in 1999 for the purpose of recording the incidence
of ASD in the state of West Australia. In 2018, the register transitioned to an online portal
that was designed to serve research by linking interested families with ongoing projects,
identifying core mutual characteristics across ASD individuals and ultimately utilizing the
outcome for better planning of essential health and educational services [9,11].
Readers can refer to Table S1 for a detailed comparison of all nine data systems.
3. ASD and ADHD Genetic Variation Databases
Although ASD and ADHD are two distinct disorders, they often co-exist or manifest
as comorbidities in rare syndromes with recognized genetic causes, such as Fragile X and
tuberous sclerosis. The genetic etiology of sporadic or non-syndromic forms of ASD and
ADHD remains largely elusive [
12
,
13
]. The precise genetic and biological mechanisms that
underpin the non-syndromic form of these disorders also remain largely elusive. To date,
100s of genomic regions (comprising of single or multiple genes) have been implicated in
ASD or ADHD susceptibility. The exponential growth in genomic discoveries witnessed in
the past decade or so have been facilitated by the availability of genome-wide association
studies and next generation sequencing. This abundance of data can be put to optimal use
through the process of curation and collation into accessible databases. In this section, we
will review ASD-related genomic databases that are publicly available and currently active
(not retired, obsolete or under construction). We compiled a non-exhaustive list of seven
databases, including the Autism database, SFARI GENE, Autism Knowledge database and
four more (see supporting information). Of note, to date, there is only one genetic database
dedicated to ADHD, which is named ADHDgene [
14
]; however, its content has not been
updated since 2014 and more recently, it has been removed from the web.
3.1. Autism Database (AutDB)
The database was created in 2007 by MindSpec, a non-profit organization with a
mission to accelerate research on neurodevelopmental disorders by using cutting-edge
bioinformatics tools for ongoing cataloguing of genes and variants associated with ASD
susceptibility [
15
]. Its content is built on data extracted from published clinical and scientific
studies, manually curated by expert researchers. The database in its latest version (AutDB
2.0) comprises of five interactive modules, including the human gene, animal model,
protein interaction (PIN), copy number variants (CNV) and gene scoring. The human

Genes 2022,13, 1551 4 of 10
gene module, the core component in which all other modules are integrated, represents an
exhaustive up-to-date reference for all human genes with a documented link to ASD. The
genes compiled within this module are classified according to the type of supporting genetic
evidence into rAut (genes implicated in rare monogenic forms); sAut (genes implicated
in syndromic forms; iAut (small risk-conferring candidate genes); and fAut (functional
candidates biologically relevant to ASD).
3.2. SFARI GENE
This database is a project funded by the Simons Foundation Autism Research Initiative
(SFARI) and developed by MindSpec (SFARIGenes news [
16
]). Launched as a licensed
version of AutDB in 2008, it serves the same aims and follows a similar framework and
portal design. In a similar way to AutDB, this database applies a system biology approach
through the implementation of the following three interactive data modules: human gene,
animal models and CNV. The data contained in each module are derived entirely from
peer-reviewed literature. Although very similar in concept, SFARI GENE differs from
AutDB in the following three main aspects: (1) the animal models module includes only
those created in mice, (2) it does not include any analytical tools, such as PIN and (3) it uses
a more comprehensive scoring criteria that evaluates all available evidence and not only
what is collected from genetic studies.
3.3. Autism Knowledge Database (AutismKB)
This database was first launched in 2011 by the Wei group from the Center for Bioin-
formatics at Peking University, China [
17
]. The primary goal of this database was to build
a knowledge portal that provides comprehensive reviews and analyses of published infor-
mation on ASD genomics. As with previous databases, AutismKB is comprised of data
primarily originating from peer-reviewed literature. As for gene functional annotation, the
developers collect extensive information on molecular function, genomic variants, homol-
ogous genes, reported animal models and expression profile from secondary databases.
Entries are also linked to three additional neurological disorders databases (AlzGene [18],
SzGene [
19
] and PDGene (http://www.pdgene.org/, accessed on 15 August 2022)) to iden-
tify overlapping genes. The second version of the knowledgebase (AutismKB 2.0), released
in 2018, integrated both the KOBAS enrichment analysis tool and variant pathogenicity
prediction tools to achieve a more sophisticated ranking [20].
3.4. VariCarta
This database is the most recent addition, launched in 2019 as part of ASD sequenc-
ing studies by Pavlidis Lab (Michael Smith Laboratories at the University of British
Columbia [
21
]). What distinguishes it from the databases above is that it specializes
in cataloguing single nucleotide variants (SNVs) with extensive annotation. The database
was developed with the intention to address a number of issues pertaining to data aggrega-
tion across cohorts and studies, such as methodological inconsistencies, subject overlap
and variations in variant reporting formats.
3.5. Structural Variant Databases
The above-mentioned databases are concerned with listing submicroscopic genetic
events (CNVs and SNVs) that occur only in the protein-coding regions of the human
genome. Currently, only two databases serve as a catalogue of microscopic genetic events
(structural variations existing at the chromosomal level), the Autism Chromosome Rear-
rangement Database (ACRD) [
22
] and the Autism Genetic Database (AGD) [
23
]. ACRD
was one of the research initiatives supported by The Center for Applied Genomics at The
Hospital for Sick Children in Canada. The database was curated in 2004 to house autism-
related chromosomal abnormalities and cytogenetic break points. The last update of the
database content was in 2014, listing 1695 breakpoints and 372 gain/loss events across
almost all chromosomes. While the scope of ACRD and AGD overlap, AGD offers the

Genes 2022,13, 1551 5 of 10
advantage of incorporating four main groups of non-coding RNAs, including microRNAs,
small nucleolar RNAs, Piwi-interacting RNAs and small interfering RNAs. At the time
of writing this manuscript, the AGD website (http://wren.bcf.ku.edu/ (accessed on 12
January 2021)) was removed from the public domain (access attempted in February 2021)
and the database was retired (personal communication with the author Prof Talebizadeh).
4. Clinical Trials on ASD/ADHD
Despite continuous efforts, no effective medication has been approved for the treat-
ment of core ASD symptoms [
24
]. Current treatments are primarily aimed at alleviating
ASD-associated symptoms (e.g., irritability, inattention and hyperactivity) and are mainly
based on compounds repurposed from other conditions with similar symptoms. However,
potential targets for drug discovery or development continue to emerge as a result of the
explosion of genomics and system neuroscience findings [
25
,
26
]. On the other hand, there
are at least five approved medications (including stimulants and non-stimulants), with
widely demonstrated effectiveness, for core ADHD symptoms. While these medications
are safe and effective for the majority of patients, a considerable proportion of patients
have inadequate responses, poor tolerance, contraindication or experience side effects.
These issues, in addition to long-term effectiveness, are the focus of nearly all drugs in
development for ADHD [
27
,
28
]. For both disorders, non-pharmacological interventions,
such as behavioral-based therapies and social skills training, are usually recommended,
and in some cases are regarded as a standard addition to drug treatment. Only a few types
of behavioral therapies (e.g., behavioral parent training and applied behavioral analysis)
are supported by sufficient evidence; however, the efficacy of these therapies, either alone
or in combination with other behavioral-based interventions and/or medical treatments,
can vary between cases and may be influenced by the sequence at which the intervention
was delivered or the age at which it was introduced [27,29].
In this section, we will provide an overview of the clinical trials for ASD and ADHD.
Here, only clinical trials listed in public registries recognized by the International Com-
mittee of Medical Journal Editors (ICMJE) and accepted as primary registries in the WHO
ICTRP Network were considered. Around 18 ICMJE-recognized clinical trial registries
exist across different continents (Table S3). The search for ADHD and ASD clinical trials
was conducted by entering one of the following terms: “autism” or “autism spectrum
disorder” or “attention deficit and hyper activity disorder” or, “attention deficit disorder
with hyperactivity” or “ADHD” into the condition/disease search field of the registry
website. The term/synonym that returned the highest number of search results was then
selected for subsequent analysis. Registries that returned zero results, did not have the
option to filter trials by condition/disease or displayed a technical error at the time of
search were excluded from the analysis (see Table S4 for information).
Among the reviewed registries, ClinicalTrials.gov (accessed on 15 August 2022) houses
the largest number of clinical studies on ASD and ADHD (Table S4). This database is
maintained by the National Library of Medicine located at the National Institutes of Health
(NIH) and it contains information on clinical studies that span a wide range of medical
conditions conducted not only in the USA but also in 220 different countries. This, perhaps,
is not surprising for reasons including, but not limited to, the following: (1) the USA,
despite the emergence of potential competitors, still maintains its position as a world leader
in R&D, with a reported expenditure of more than half a trillion US dollars for 2018 alone,
accounting for ~28% of the global spending [
30
]; (2) an annual budget of 30 billion US
dollars is allocated from federal funds to the NIH, a large proportion of which (>80%) goes
to support biomedical and health research [
31
]; (3) USA is the hub for the world’s largest
pharmaceutical and biomedical companies.
The majority of the clinical trials listed within the registries analyzed herein adopted an
interventional design strategy. Primary research is broadly categorized into interventional
and observational studies. Interventional studies, also referred to as experimental studies,
are those where the investigator(s) assign the participants to groups that either receive or

Genes 2022,13, 1551 6 of 10
do not receive a certain intervention to determine its effect on health-related outcomes.
Contrary to the former, in observational studies, the investigator(s) does not interfere as
part of the study design; instead, they merely observe and document the relationship
between the factors and outcomes throughout the natural course of events. It is worth
noting that patient registries can be considered a type of observational study as they can
serve as grounds for generating and testing a hypothesis through their readily accessible
wealth of data. Undoubtedly, each type of clinical study has its own advantages and
limitations. For instance, conducting interventional studies may raise major ethical issues
if addressing the research question requires subjecting participants to harm or withholding
an effective and proven intervention deliberately. In such cases, observational studies can
be the obvious option. However, they are often prone to confounding and bias and their
results can be disputable.
Another common feature of the clinical trials reviewed herein is that they almost exclu-
sively target children (school-aged and adolescents). The relative lack of studies conducted
on adults means that there is little evidence to guide intervention in adults because as
individuals transition from childhood to adulthood, the brain undergoes dynamic changes
(due to compensatory mechanisms and gene environment interplay across the lifespan)
that, in turn, can alter their needs and their response to intervention.
5. Neurodevelopmental Disorders Database (NDDB)
The overwhelming majority of information on individuals with ASD/ADHD that
is made available through different types of data systems (genetic databases, patient
registries and clinical trials registries) is largely curated from European (and to a lesser
extent) Asian populations. Other populations/ethnic groups are severely understudied
and, consequently, are underrepresented.
Our group is interested in studying neurodevelopmental disorders, particularly ASD
and ADHD in the Saudi population. Among the six GCC countries, Saudi Arabia is
the largest in terms of area and population size. However, no precise estimates exist
and the available data are mainly anecdotal and lack nationwide coverage. In addition,
government or non-government-maintained ASD or ADHD registries/databases are non-
existent. Acutely aware of this significant gap in knowledge, we have created a demo
database, the Neurodevelopmental Disorders Database (NDDB), with the intention of
developing it into a public website that is accessible to the general public with restrictions.
In this section, we provide an overview and description of the NDDB framework.
5.1. Purpose of NDDB
(1) To provide a searchable listing of individuals with neurodevelopmental disorders
(primarily ASD and ADHD).
(2) To provide a central repository of searchable epidemiological, clinical, behavioural
and genetic data.
5.2. Potential Data Contributors/Users
Data contributors or users are those that are anticipated to deposit data and may as well
benefit from knowledge of the data and examples include hospitals, schools, rehabilitation
centers, government bodies and researchers.
5.3. Design, Access and Privacy Protection
The database proposed in this paper is implemented using NoSQL, accessible via a
Web API portal, that will be hosted and stored at King Salman Center for Disability Research
(KSCDR) data center. Web-based applications allow the database to be compatible with
most platforms; therefore, it accessible by a broader community. The database structure is
illustrated in Figure 1and is based on client–server architecture (Figure 2). Safeguards will
be implemented to protect the collected health information in compliance with The Health
Insurance Portability and Accountability Act of 1996 (HIPAA) privacy and security rules.

Genes 2022,13, 1551 7 of 10
The data, in both forms (in transit and at rest), will be non-identifiable and encrypted with
the AES-256 algorithm.
Genes 2022, 13, x FOR PEER REVIEW 7 of 10
5.2. Potential Data Contributors/Users
Data contributors or users are those that are anticipated to deposit data and may as
well benefit from knowledge of the data and examples include hospitals, schools, rehabil-
itation centers, government bodies and researchers.
5.3. Design, Access and Privacy Protection
The database proposed in this paper is implemented using NoSQL, accessible via a
Web API portal, that will be hosted and stored at King Salman Center for Disability Re-
search (KSCDR) data center. Web-based applications allow the database to be compatible
with most platforms; therefore, it accessible by a broader community. The database struc-
ture is illustrated in Figure 1 and is based on client–server architecture (Figure 2). Safe-
guards will be implemented to protect the collected health information in compliance with
The Health Insurance Portability and Accountability Act of 1996 (HIPAA) privacy and
security rules. The data, in both forms (in transit and at rest), will be non-identifiable and
encrypted with the AES-256 algorithm.
Figure 1. Schematic illustration of the database’s general structure and data flow. The database will
include the following three modules: client-side, server-side, and management console. Users will
input the data variables (listed in Table S5) through a form-based interface that does not require
special skills.
Figure 1. Schematic illustration of the database’s general structure and data flow. The database will
include the following three modules: client-side, server-side, and management console. Users will
input the data variables (listed in Table S5) through a form-based interface that does not require
special skills.

Genes 2022,13, 1551 8 of 10
Genes 2022, 13, x FOR PEER REVIEW 8 of 10
Figure 2. Screen shots of the user interface: (A) the homepage, (B) the About Us page, (C) signup
page, (D) log in page; (E,F) search function. Users will be able to query data by keyword, data cate-
gory or collaborator (contributing entity). User level privileges are shown in Table S6.
6. Conclusions
Patient registries, genetic databases and clinical trial registries may have different
primary aims; however, they all serve one ultimate purpose, that is, improving patient
care and overall quality of life. Patient registries can help us to understand the natural
history of a condition, identify potential research participants for clinical or preclinical
studies and provide epidemiologic information. Genetic databases, on the other hand, can
serve as a gateway for more in-depth understanding of disease mechanisms from mode
of inheritance to revealing underlying biological pathways. The gained knowledge, there-
fore, holds promise for uncovering drug targets, developing diagnostic tests and applica-
tion of precision medicine that can be evaluated through clinical trials.
In the world of big data, ADHD lags behind ASD; the reasons may not be readily
apparent. Why does ADHD receive less attention compared to ASD? Is it because it can
be more manageable or in some cases, symptoms can wane with age? These questions
remain to be answered.
Finally, the fast-paced development and adoption of data sharing indicates that we
are not far from harnessing the full potential of big data to achieve optimal health and
well-being for all. An ideal system would be one that enables interoperability within and
Figure 2.
Screen shots of the user interface: (
A
) the homepage, (
B
) the About Us page, (
C
) signup
page, (
D
) log in page; (
E
,
F
) search function. Users will be able to query data by keyword, data
category or collaborator (contributing entity). User level privileges are shown in Table S6.
6. Conclusions
Patient registries, genetic databases and clinical trial registries may have different
primary aims; however, they all serve one ultimate purpose, that is, improving patient
care and overall quality of life. Patient registries can help us to understand the natural
history of a condition, identify potential research participants for clinical or preclinical
studies and provide epidemiologic information. Genetic databases, on the other hand, can
serve as a gateway for more in-depth understanding of disease mechanisms from mode of
inheritance to revealing underlying biological pathways. The gained knowledge, therefore,
holds promise for uncovering drug targets, developing diagnostic tests and application of
precision medicine that can be evaluated through clinical trials.
In the world of big data, ADHD lags behind ASD; the reasons may not be readily
apparent. Why does ADHD receive less attention compared to ASD? Is it because it can be
more manageable or in some cases, symptoms can wane with age? These questions remain
to be answered.
Finally, the fast-paced development and adoption of data sharing indicates that we
are not far from harnessing the full potential of big data to achieve optimal health and
well-being for all. An ideal system would be one that enables interoperability within and
across organizational, regional and national boundaries, while safeguarding privacy and

Genes 2022,13, 1551 9 of 10
data security. Such a model can offer timely and seamless mobility of information across
the complete spectrum of care and services.
Supplementary Materials:
The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/genes13091551/s1, Table S1. List of ASD patient data systems.
Table S2. Data system terminology and data collection methods. Table S3. List of ICMJE-recognized
clinical trial registries. Table S4. ASD and ADHD clinical trials from selected registries. Table S5. Data
elements and categories. Table S6. User privileges.
Author Contributions:
N.A.A.-T. conceived the idea of the NDDB, F.M.A. and B.R.A.-M. designed
the proto NDDB, H.A. and N.A.A.-T. contributed to the manuscript, M.G. critically reviewed the
manuscript and B.R.A.-M. wrote the manuscript. All authors have read and agreed to the published
version of the manuscript.
Funding:
This work and related APC was funded by King Salman Center for Disability Research
Grant# (RG-20190011).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest:
The authors declare no conflict of interest and the sponsors had no role in the
design, execution, interpretation, or writing of the study.
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