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> Disorders > Mental or Behavioral Dysfunction > Developmental Disabilities

Developmental Disabilities

Developmental Disabilities is a diverse group of chronic conditions that emerge during the developmental period and involve impairments in physical, learning, language, or behavior areas.
These disabilities originate before adulthood, have a lasting impact, and can affect an individual's functioning and quality of life.
Common examples include intellectual disabilities, autism spectrum disorders, and cerebral palsy.
Indviduals with Developmental Disabilities may require specialized services and support to reach their full potential.
This Medical Subject Heading (MeSH) term provides a comprehensive overview of this complex field, offering researchers and clinicians a concise, authorative resource for understanding the nuances of these lifelong conditions.

Most cited protocols related to «Developmental Disabilities»

1
Ferroptosis Regulation Annotation Database
Cited 78 times
Annotation data sets in FerrDb belong to three categories (Table 1). Genes were annotated as drivers, suppressors and markers. Small molecules were annotated as inducers and inhibitors. Drivers, suppressors, inducers and inhibitors are regulators of ferroptosis: drivers and inducers positively regulate ferroptosis, while suppressors and inhibitors negatively regulate ferroptosis. Markers do not regulate ferroptosis, but they indicate the occurrence of ferroptosis. Ferroptosis affects the development of disease in two ways. Ferroptosis was then annotated to either aggravate or alleviate an illness.
To be annotated as a ferroptosis regulator, genes and small molecules must possess explicit evidence to prove their regulatory role in ferroptosis. This kind of evidence is generally represented by an author statement of the role of the regulator in an original article. Genes that only undergo abundance, modification or stability change or are merely a component of a functional signaling axis or interaction network were annotated as markers. To annotate ferroptosis’ effect on diseases, evidence based on a growth test in cell lines or animal models was required.
In comparison with revealing a small molecule’s role, confirming a gene’s function is more challenging. We therefore dedicated more effort to gene annotation. A confidence level was assigned to each annotation to indicate its reliability (Table 2). Experimental reproducibility is correlated with results consistency, so the number of experiments was used as a score of the accuracy of the regulatory role of annotated genes. Critical cases (e.g. article retraction, conflicting results) that may affect the annotation reliability were highlighted by a caution statement. Other noteworthy information (e.g. inconsistent gene symbols) that seems less likely to impair annotation quality was denoted with a remark.
Zhou N, & Bao J. (2020). FerrDb: a manually curated resource for regulators and markers of ferroptosis and ferroptosis-disease associations. Database: The Journal of Biological Databases and Curation, 2020, baaa021.
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Publication 2020
Animal Model Cell Lines Developmental Disabilities Epistropheus Ferroptosis Gene Annotation Genes Genes, Regulator inhibitors Operator, Genetic
2
Correlates of Psychopathology Protocol
Cited 48 times
Table S2 in the Supplemental Material provides measurement details about the correlates of psychopathology reported here, including personality functioning, life impairment, family histories and developmental histories of psychiatric disorders, and measures of brain integrity.
Caspi A., Houts R.M., Belsky D.W., Goldman-Mellor S.J., Harrington H., Israel S., Meier M.H., Ramrakha S., Shalev I., Poulton R, & Moffitt T.E. (2014). The p Factor: One General Psychopathology Factor in the Structure of Psychiatric Disorders?. Clinical psychological science : a journal of the Association for Psychological Science, 2(2), 119-137.
Publication 2013
Brain Developmental Disabilities
3
Pseudotime Trajectory of Mouse Embryos
Cited 45 times
After the single cell gene count matrix was generated, each cell was assigned to its original mouse embryo based on the RT barcode. Reads mapping to each embryo were aggregated to generate “bulk RNA-seq” for each embryo. For sex separation of embryos, we counted reads mapping to a female-specific non-coding RNA (Xist) or chrY genes (except Erdr1 which is in both chrX and chrY). Embryos were readily separated into females (more reads mapping to Xist than chrY genes) and males (more reads mapping to chrY genes than Xist).
Pseudotemporal ordering of whole mouse embryos was done by Monocle 257 . Briefly, an aggregated gene expression matrix was constructed as described above. Differentially expressed genes across different development conditions were identified with differentialGeneTest function of Monocle 257 . The top 2,000 genes with the lowest q value were used to construct the pseudotime trajectory using Monocle 257 . Each embryo was assigned a pseudotime value based on its position along the trajectory.
Cao J., Spielmann M., Qiu X., Huang X., Ibrahim D.M., Hill A.J., Zhang F., Mundlos S., Christiansen L., Steemers F.J., Trapnell C, & Shendure J. (2019). The single cell transcriptional landscape of mammalian organogenesis. Nature, 566(7745), 496-502.
Publication 2019
Cells Cytosol Developmental Disabilities Dietary Fiber Embryo Females Gene Expression Genes Genes, vif Males Mus RNA, Untranslated RNA-Seq
4
Automated Variant Filtering Pipeline for Developmental Disorders
Cited 41 times
An automated variant filtering pipeline was used to narrow down the number of putative diagnostic variants (figure 2). First, common (>1% minor allele frequency) and non-functional (not protein-altering) variants were filtered out. Second, potentially pathogenic variants in known disease genes were selected in by comparison against an in-house database of genes consistently implicated in specific developmental disorders, the Developmental Disorders Genotype-to-Phenotype database (DDG2P). This database includes more than 1000 genes that have been consistently implicated in specific developmental disorders and is updated regularly with newly implicated genes (table 1; appendix 1; appendix 2). Each gene in DDG2P is associated with a specific developmental phenotype or syndrome via a particular genetic mechanism (autosomal dominant, autosomal recessive, or X-linked) and mutation consequence on the gene product (loss of function, activating mutation, increased gene dosage, etc). The use of DDG2P enabled any rare variant in a known DD gene with a predictable effect on the gene product to be flagged on the basis of inheritance, genotype, and likely mutational consequence. Large, rare CNVs overlapping non-DDG2P genes were also flagged based on a series of size thresholds (>100 kb for losses and >250 kb for gains where the inheritance was either de novo or segregated with disease, and >500 kb for any genic CNV for which the inheritance was unclear).

Variant filtering logic for clinical reporting within the study

Genomic variants were filtered on the basis of six factors, of which the first five were automated and the final one was done manually: (1) frequency, prevalence of the variant in the general population (MAF ≤1%); (2) function, most severe predicted functional consequence, such as LOF, defined by specific sequence ontology terms (transcript ablation, splice donor variant, splice acceptor variant, stop-gained, frameshift variant, stop-lost, initiator codon variant, in-frame insertion, in-frame deletion, missense variant, transcript amplification, and coding sequence variant); (3) location, genomic location compared with DDG2P of published genes; (4) variant type, genotype (eg, heterozygous or homozygous) and loss or gain for small CNVs (which were only considered when they contained entire genes in which LOF or dominant negative mutations had been previously reported, and gains were only considered when they overlapped genes in which increased gene dosage mutations had been previously reported); (5) inheritance, aspects of the pipeline that are dependent on inheritance information derived from parental data are shaded; and (6) phenotype, patient phenotype was manually compared against published phenotypes for a particular gene. MAF=minor allele frequency. CNV=copy number variant. LOF=loss of function. DDG2P=Developmental Disorders Genotype-to-Phenotype database.

Changes to DDG2P over time

July, 2012November, 2012July, 2013November, 2013
Total reportable genes*81987510751128
Genes added..6020160
Genes removed†..417

In addition to genes being added or removed, annotations for existing genes can also change (eg, to include multiple modes or mechanisms). The November, 2013 version was used for the analysis presented here and includes 1128 reportable genes. DDG2P=Developmental Disorders Genotype-to-Phenotype database.

DDG2P also contains non-reportable categories when there is insufficient evidence associating a gene and developmental disorder (appendix 1).

The selection of variants for reporting is based on the strongest available evidence of gene function and no variants yet reported have been retracted because of changes in the DDG2P list.

Wright C.F., Fitzgerald T.W., Jones W.D., Clayton S., McRae J.F., van Kogelenberg M., King D.A., Ambridge K., Barrett D.M., Bayzetinova T., Bevan A.P., Bragin E., Chatzimichali E.A., Gribble S., Jones P., Krishnappa N., Mason L.E., Miller R., Morley K.I., Parthiban V., Prigmore E., Rajan D., Sifrim A., Swaminathan G.J., Tivey A.R., Middleton A., Parker M., Carter N.P., Barrett J.C., Hurles M.E., FitzPatrick D.R, & Firth H.V. (2015). Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet (London, England), 385(9975), 1305-1314.
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Publication 2015
Codon, Terminator Deletion Mutation Developmental Disabilities Diagnosis Frameshift Mutation Gene Amplification Gene Annotation Gene Deletion Genes Genetic Diversity Genome Genotype Hereditary Diseases Heterozygote Homozygote Missense Mutation Multiple Pterygium Syndrome, Autosomal Dominant Mutation Open Reading Frames Operator, Genetic Parent Pathogenicity Patients Pattern, Inheritance Phenotype Proteins Reading Frames Tissue Donors
5
Adolescent Cognition and Emotion Project
Cited 37 times
Participants were individuals who completed at least the first study session (Time 1 Session 1) of the Temple University (TU) Adolescent Cognition and Emotion (ACE) Project, a prospective longitudinal study of the development of depressive and anxiety disorders in adolescence, with TU IRB approval. Caucasian and African American male and female adolescents, ages 12 – 13, and their mothers or primary female caretakers (hereafter referred to as “mothers”) were recruited from Philadelphia area public and private middle schools. Participants were recruited in two main ways. First, with the Philadelphia School District’s (PSD) permission and provision of demographic and contact information, we mailed a letter of introduction and description of Project ACE to parents of Caucasian and African-American students aged 12–13 years old (approximate N = 8,662) attending some schools in the PSD. Although mothers could call or return a prepaid postcard indicating their interest in Project ACE, most families were recruited by this method through follow-up phone calls from project staff inviting mothers and their adolescent children to participate. Second, advertisements describing Project ACE were placed in Philadelphia area newspapers (e.g., Metro Kids, community newspapers) and mothers (approximate N = 134) called in to indicate their interest.
Mothers interested in participation with their adolescent children from either recruitment method initially completed a screening instrument over the phone to determine eligibility. Inclusion criteria for the study were: 1) the adolescent child was 12 or 13 years old; 2) the adolescent child self-identified as Caucasian/White, African American/Black or Biracial (adolescents could be Hispanic or non-Hispanic as long as they also identified as White or Black); and 3) the mother was also willing to participate. Exclusion criteria were: 1) there was no mother/primary female caretaker available to participate (e.g., mother had died); 2) either the adolescent or mother did not read or speak English well enough to be able to complete the study assessments; and 3) either the adolescent or mother was mentally retarded, had a severe learning disability or other cognitive impairment, had a severe developmental disorder (e.g., autism), was psychotic, or exhibited any other medical or psychiatric problem that would prevent either of them from being able to complete the study assessments. As long as they could adequately complete the study assessments, adolescents or mothers with mild learning disabilities or cognitive/sensory impairments were eligible for participation. Mothers and adolescents who met all study inclusion and exclusion criteria were scheduled for a Time 1 Session 1 (T1S1) assessment, at which time mothers provided written consent and the adolescents provided written assent to participate in Project ACE. The recruitment target was a sample size of 500 adolescents (125 in each gender by race group) and their mothers.
Alloy L.B., Black S.K., Young M.E., Goldstein K.E., Shapero B.G., Stange J.P., Boccia A.S., Matt L.M., Boland E.M., Moore L.C, & Abramson L.Y. (2012). Cognitive Vulnerabilities and Depression versus Other Psychopathology Symptoms and Diagnoses in Early Adolescence. Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53, 41(5), 539-560.
Publication 2012
Adolescent Adolescents, Female African American Anxiety Disorders Autistic Disorder Child Cognition Developmental Disabilities Disorders, Cognitive Eligibility Determination Emotions Gender Hispanics Learning Disabilities Males Mental Disorders Mothers Parent Racial Groups Student White Person Woman

Most recents protocols related to «Developmental Disabilities»

1
Comorbid Psychiatric Disorders in Patients
The proportion of patients with COD was not significantly different for male (45.4%) and female (52.0%) patients (p = 0.139). The prevalence rates for the types of co-occurring psychiatric diagnoses are shown in Table 2. Among patients with COD, anxiety (22.9%) and mood disorders (17.3%) were the two most common psychiatric disorders. About one in five had more than one COD (21.4%), with a higher prevalence rate of multiple CODs among females (30.3%) than males (18.0%). Having anxiety disorders were significantly more prevalent among females (30.3%) than males (19.8%).

Prevalence of co-occurring psychiatric disorders

Total(N = 611)Females(N = 175)Males(N = 434)Females versus malesref
N%N%N%ORp-value
Without COD32252.78448.023754.6
With COD28947.39152.019745.41.300.139
Psychiatric diagnoses1
- Anxiety disorders F40-4914022.95330.38619.81.760.005
- Mood disorders F30-3910617.33620.67016.11.350.191
- ADHD F90.0-F90.97912.92112.05813.40.880.650
- Personality disorders F60-697011.52715.4439.91.660.053
- Multiple CODs13121.453*30.37818.01.98< 0.001

1 Other psychiatric diagnoses (n = 17) included Schizophrenia, F20-F29 (n = 8); Behavioral syndromes associated with physiological disorders and physical factors (n = 16); Mental retardation, F70-F79 (n = 6); Pervasive and specific developmental disorders, F80-89 (n = 16); Behavioral disorders, F91-F98 (n = 8)

Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
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Publication 2023
Anxiety Disorders Behavior Disorders Cods Developmental Disabilities Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Females Intellectual Disability Males Mental Disorders Mood Mood Disorders Patients Personality Disorders Physical Examination physiology Schizophrenia Syndrome Woman
2
Midlife Factors and Alzheimer's Disease
Participants were drawn from the WRAP, a longitudinal study designed to identify midlife factors associated with the development of Alzheimer’s disease.39 (link),40 (link) Enrolment of participants began in 2001, with the first follow-up visit occurring 2 to 4 years after the baseline visit and all additional visits occurring at 2-year intervals thereafter. WRAP participants were free of dementia at enrolment (mean age 54 years). All study procedures were approved by the University of Wisconsin School of Medicine and Public Health Institutional Review Board and are in concordance with the Declaration of Helsinki.
At each study visit, participants completed comprehensive neuropsychological assessment and multiple questionnaires related to a broad array of factors, including lifestyle, modifiable risk factors, medical history and memory functioning. Sleep measures were added in two stages to the WRAP assessment protocol. To be eligible for the primary analyses, participants needed to have completed the full set of sleep measures at least once and be free of dementia at time of sleep assessment (n = 619). To be eligible for secondary analyses, participants needed to have completed at least one of the sleep questionnaires described below and had completed a Pittsburgh Compound B (PiB) PET scan.
Du L., Langhough R., Hermann B.P., Jonaitis E., Betthauser T.J., Cody K.A., Mueller K., Zuelsdorff M., Chin N., Ennis G.E., Bendlin B.B., Gleason C.E., Christian B.T., Plante D.T., Chappell R, & Johnson S.C. (2023). Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer’s Prevention. Brain Communications, 5(2), fcad039.
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Publication 2023
Developmental Disabilities Ethics Committees, Research factor A Neuropsychological Tests Pittsburgh compound B Positron-Emission Tomography Presenile Dementia Sleep
3
Cognitive Impairment in Type 2 Diabetes
Two hundred and ten individuals were willing to join this study (May 10, 2021, to July 1, 2022). The exclusion criteria for the two groups were as follows: type 1 diabetes mellitus, impaired fasting glucose or impaired glucose tolerance58 (link), hypertension, hypoglycemia (blood sugar levels < 3.9 mmol/L), hyperlipidemia, serious eye diseases (e.g., blindness), symptoms of neurological conditions (e.g., cerebral infarction or hemorrhage), history of neurological abnormality (e.g., Parkinson’s disease), severe head injuries or chronic head discomfort (e.g., migraine), BMI > 31 kg/m2, left- or mixed-handedness, substance (tobacco, alcohol, or psychoactive drug) abuse, taking medications that may affect cognition and memory within 6 months, specific abnormalities detected on conventional MRI scans or any other factors that may influence brain structure or function (e.g., extreme physical weakness, chronic infections, and other endocrine diseases). Patients with T2DM were diagnosed by two experienced endocrinologists following international clinical standards59 . MCI was evaluated via Mini-Mental State Examination (MMSE) and MoCA-B (21 ≤ MoCA-B score < 26, and MMSE score > 24 were diagnosed with MCI)60 ,61 (link).
Participants with brain tumors (n = 3), neuropsychiatric diseases (n = 4) (e.g., major depression or schizophrenia), or developmental disorders (n = 4) were excluded. Finally, 37 patients with T2DM-MCI, 93 patients with T2DM-NCI, and 69 NC were enrolled in this study. The source of patients with T2DM and NC corresponded with our previous study37 (link). This study was approved by the ethics committee of The First Affiliated Hospital of Guangzhou University of Chinese Medicine (ID: NO. JY [2020] 288). Written informed consent was obtained from all participants. In addition, the study was conducted following approved guidelines.
Wu J., Fang Y., Tan X., Kang S., Yue X., Rao Y., Huang H., Liu M., Qiu S, & Yap P.T. (2023). Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity. Scientific Reports, 13, 3940.
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Publication 2023
Asthenia Blindness Blood Glucose Brain Brain Neoplasms Cerebral Infarction Chinese Chronic Infection Cognition Congenital Abnormality Craniocerebral Trauma Developmental Disabilities Diabetes Mellitus, Insulin-Dependent Drug Abuse Endocrine System Diseases Endocrinologists Ethanol Ethics Committees, Clinical Eye Disorders Glucose Head Hemorrhage High Blood Pressures Hyperlipidemia Hypoglycemia Major Depressive Disorder Memory Migraine Disorders Mini Mental State Examination MRI Scans Nervous System Abnormality Nervous System Disorder Patients Pharmaceutical Preparations Physical Examination Psychotropic Drugs Schizophrenia Tobacco Products
4
Comprehensive Clinical Data Extraction for Refugee Health
We extracted data from clinical records (including clinician notes and letters, referrals, health assessments/reports, available detention health documentation, Australian Immunisation Register (AIR) records, and/or school feedback/reports) using a standardised audit form created within the CAReHR™ database. This form included demographic information, details on detention (duration and location), reported trauma experience, symptoms, physical or mental health diagnoses made during clinical review, and treatment and other supports provided. We included data from any time during or after held detention to capture the spectrum of impact.
We reported a range of clinical outcomes selected by clinical consensus, previous Australian refugee health research, and national guidelines for new arrival screening [20 (link), 21 (link)]. We focused reporting on outcomes that were present at the time of first clinical assessment or became apparent in subsequent evaluation, documenting longer-term evolution of symptomatology where possible. Mental health diagnoses were included if they had been made by a child psychiatrist, paediatrician or psychologist, or where symptoms met Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria [22 ]. Developmental disorders were included if diagnosed by a paediatrician or through a standardised multidisciplinary assessment (e.g. Autism Diagnostic Observation Schedule). Whilst standardised measurements for childhood trauma are often not validated for refugee/asylum seeker populations, our survey broadly covers domains of the Adverse Childhood Experience Rating Scores [23 (link)]. Absence of documentation was considered to be negative for symptoms and diagnosis. Six clinicians (ST, HG, GP, RH, TV, ILR) completed the initial data extraction; three investigators (ST, HG, GP) performed a secondary review to check for accuracy.
Tosif S., Graham H., Kiang K., Laemmle-Ruff I., Heenan R., Smith A., Volkman T., Connell T, & Paxton G. (2023). Health of children who experienced Australian immigration detention. PLOS ONE, 18(3), e0282798.
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Publication 2023
ARID1A protein, human Autistic Disorder Biological Evolution Child Developmental Disabilities Diagnosis Immunization Mental Health Pediatricians Physical Examination Population Group Psychiatrist Psychologist Refugees Wounds and Injuries
5
Disability Types and Substance Use Disorders
This study used diagnosis codes to identify 4 common types of disabling conditions: physical (eg, spinal cord injuries and mobility impairment), sensory (eg, blind or visual impairments, and deaf or hard of hearing), developmental (eg, Down syndrome, autism, and other intellectual or developmental disabilities), and cognitive (ie, TBI). See eTable 3 in Supplement 1 for diagnostic codes. These conditions were informed by the US Census Bureau’s American Community Survey standardized disability types26 and by their association with increased risk for SUD. Each disability type was indicated through a dichotomous variable. A summary variable indicated the presence of any of these disabling conditions. Because a considerable body of research focuses on OUD treatment for individuals with co-occurring substance use and mental disorders, we did not examine these groups separately.
Thomas C.P., Stewart M.T., Ledingham E., Adams R.S., Panas L, & Reif S. (2023). Quality of Opioid Use Disorder Treatment for Persons With and Without Disabling Conditions. JAMA Network Open, 6(3), e232052.
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Publication 2023
Autistic Disorder Cognition Developmental Disabilities Diagnosis Dietary Supplements Disabled Persons Down Syndrome Hearing Impaired Persons Human Body Mental Disorders Physical Examination Range of Motion, Articular Spinal Cord Injuries Substance Use Visually Impaired Persons

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More about "Developmental Disabilities"

Developmental Disabilities (DD) is a broad term encompassing a diverse array of chronic conditions that emerge during the developmental period, often involving impairments in physical, learning, language, or behavioral domains.
These lifelong disabilities, which can have a lasting impact on an individual's functioning and quality of life, typically originate before adulthood.
Common examples of DDs include intellectual disabilities, autism spectrum disorders (ASDs), and cerebral palsy.
Individuals with DDs may require specialized services and support to reach their full potential.
This complex field offers researchers and clinicians a wide range of topics to explore, from genetic factors and neurodevelopmental processes to evidence-based interventions and assistive technologies.
Synonyms and related terms for Developmental Disabilities include developmental disorders, neurodevelopmental disorders, intellectual and developmental disabilities (I/DD), and special needs.
Abbreviations such as DD and I/DD are also commonly used.
Key subtopics within the realm of Developmental Disabilities include (but are not limited to): - Etiology and risk factors (e.g., genetic, environmental, and prenatal influences) - Diagnostic criteria and assessment methodologies - Early intervention and educational strategies - Behavioral and communication challenges - Sensory processing and motor skill difficulties - Comorbid conditions (e.g., epilepsy, mental health disorders) - Assistive technologies and adaptive equipment - Caregiver support and family-centered approaches - Transitioning to adulthood and independent living When conducting research in the field of Developmental Disabilities, researchers may utilize tools and techniques such as SAS version 9.4 for data analysis, Pertussis toxin for neurological studies, TRIzol reagent for RNA extraction, Agilent 2100 Bioanalyzer for gene expression profiling, Isoflurane for animal anesthesia, Platinum PCR SuperMix for molecular assays, and MOG35–55 peptide for experimental autoimmune encephalomyelitis models.
By leveraging the insights and resources available within the Developmental Disabilities domain, researchers and clinicians can work towards improving the lives of individuals with these diverse and complex conditions.