Diagnostic Sensitivity and Specificity of Cognitive Tests for Mild Cognitive Impairment and Alzheimer’s Disease in Patients with Down Syndrome: A Systematic Review and Meta-Analysis 1

Abstract

Background:

Improved health care for people with Down syndrome (DS) has resulted in an increase in their life expectancy therefore increasing comorbidities associated with age-related problems in this population, the most frequent being Alzheimer’s disease (AD). To date, several cognitive tests have been developed to evaluate cognitive changes related to the development of mild cognitive impairment (MCI) and AD in people with DS.

Objective:

Identify and evaluate available cognitive tests for the diagnosis of MCI and AD in people with DS.

Methods:

A systematic search of the Pubmed and PsycInfo databases was performed to identify articles published from January 1, 2000 and July 1, 2022. Keysearch terms were DS, AD or MCI, cognition, and assessment. Relevant studies assessing the diagnostic accuracy of cognitive tests for AD or MCI with standard clinical evaluation were extracted. Risk of bias was assessed using the QUADAS 2.

Results:

We identified 15 batteries, 2 intelligence scales, 14 memory tests, 11 executive, functioning tests, 11 motor and visuospatial functioning tests, 5 language tests, 3 attention tests, and 2 orientation tests. Analysis showed that the CAMCOG-DS present a fair to excellent diagnostic accuracy for detecting AD in patients with DS. However, for the diagnosis of MCI, this battery showed poor to good diagnostic accuracy.

Conclusion:

The findings highlight important limitations of the current assessment available for the screening of mild cognitive impairment and AD in patients with DS and support the need for more clinical trials to ensure better screening for this highly at-risk population.

Keywords

Alzheimer’s disease cognition Down syndrome mild cognitive impairment

INTRODUCTION

Down syndrome (DS) is one of the most common genetic disorders in North America. In Canada, the prevalence rate is 13.5 cases in 10 000 births approximately. In total, more than 45,000 people are affected by this syndrome [1]. DS is characterized by an alteration of the 21st chromosome pair and is mainly caused by the trisomy 21. DS is detected as early as the first trimester during pregnancy and can be screened through ultrasounds and blood samples for the measurement of biochemical markers or cytogenetic analysis [2]. The clinical signs are an intellectual disability affecting global cognitive abilities with characteristic morphological traits. Improved health care for this population has resulted in an increase in their life expectancy from an average of 45 years to almost 60 years. This improvement was accompanied by an increase in comorbidities associated with aging, the most frequent being Alzheimer’s disease (AD) with a lifetime risk over 90% [3].

It is supposed that, in people with DS, the additional chromosome at the 21st pair leads to an overexpression of the genes from this area, including the amyloid precursor protein gene, and would be responsible for some features of the phenotype such as AD [4]. The overexpression of the amyloid precursor protein gene generates an increase of the amyloid-β protein [5]. As proposed by a universally accepted hypothesis concerning the development of AD, the over production of amyloid-β generates an increase in amyloid-β peptides in the external neuronal environment [6]. Due to their adhesive property, these deposits gradually clump together to produce amyloid plaques. The large accumulation of these plaques eventually abnormally activate the tau proteins through a series of reactions [7]. In turn, the tau proteins accumulate inside the neuron leading to neurofibrillary degeneration along the axon thus preventing the circulation of the elements essential for the neuron’s survival [8]. This would ultimately result in cell death and therefore significant neuronal loss in patients who develop AD. These neuronal losses are thought to be the cause of the known symptoms of AD, including significant cognitive losses [9]. In people with DS, the appearance of amyloid plaques and neurofibrillary degeneration can appear as early as their twenties and reach a prevalence rate of almost 100% by their forties [5, 10]. DS is now recognized as a genetic form of AD along its rare autosomal dominant familial forms [11].

The brain damage observed during the development of AD occurs insidiously. The consequences of brain damage caused by the development of AD appear early on by cognitive deficits that can be objectified through standard cognitive tests. More specifically, the diagnostic criteria for this preclinical stage called mild cognitive impairment (MCI) are 1) the presence of a concern related to a decrease in the person’s cognitive functioning compared to their previous functioning, 2) a progressive and modest deterioration in at least one cognitive domain that is greater than what would be expected through normal aging, 3) retention of the ability to perform activities of daily living independently, and 4) without the presence of an altered level of consciousness or other disease that may better explain cognitive decline [12]. To date, few studies have looked at the cognitive damage observed during the MCI stage of the development of AD specifically in people with DS. From these studies, some have pointed out that people with DS present early on difficulties with executive functions and executive memory as well as deficits in explicit verbal memory [13, 14].

Gradually, the deterioration of cognitive functions can come to affect the daily living activities such as disorientation in previously known places. To overcome these difficulties, the patient will slowly need others to perform certain tasks. This advancement in the major neurocognitive disorder stage of AD, previously known as dementia, is gradual and can lead to a complete loss of one’s ability to take care of oneself and to use judgment. Since the major neurocognitive disorder stage is generally referred to AD, we will further refer to this stage as AD. AD is characterized by the same symptoms as MCI but these are now severe enough to affect the patient’s daily functioning [15]. It is, however, necessary for clinicians in this field to ensure that the difficulties observed do not arise during a delirium or that they could not be better explained by another disorder. To date, no treatment is available to cure patients with this AD.

The fact remains that the pre-existence of cognitive deficits characteristic of DS brings an additional difficulty in terms of screening for the development of AD. Indeed, traditional cognition assessment tools cannot be used in people with significant ID as in DS [11]. To date, several tools have been developed to screen for cognitive changes related to the development of AD specifically for people with DS [16, 17]. Three types of assessments are currently used to help screen for AD in this population: questionnaires administered to a tutor concerning the cognitive, behavioural, and personality functioning of the patients, neuropsychological tests administered directly to the patient or a combination of these two types of assessments. Despite their frequent use, questionnaires assessing the cognition of patients administered to tutors are subject to the subjectivity of the latter and can therefore lead to an assessment of cognitive functioning that is not exact, i.e., an evaluation of the cognitive performance of patients better or worse than it actually is. The present review therefore aims to identify all directly administered cognitive tests to patients for the assessment of MCI and AD in people with DS. Through a meta-analysis, we aimed to compare the diagnostic power of each available tests. This data collection will allow professionals in the field to have easy access to the various important information regarding the cognitive tools available to use and could ultimately allow them to adjust their screening methods when working with the DS population.

MATERIALS AND METHODS

Research strategy

We searched studies where neuropsychological tests were used for the diagnosis of MCI or AD through the databases Pubmed as well as Psychinfo. Both databases were selected since they are overwhelmingly composed of articles in the field of psychology, neuropsychology and medicine. The search was limited to studies published between 2000 and May 30, 2023. Studies were identified through the following research terms: 1) DS or trisomy 21, 2) Alzheimer or neurodegenerative disorder or neurodegenerative disease or dementia or mild cognitive impairment or neurocognitive disorder or neurocognitive deficit or neurodegenerative, 3) cognition or cognitive or attention or intellectual function or visuo-spatial function or executive function or memory or gnosia or language or praxis or information processing speed or higher order process, and 4) evaluation or assess or instrument, test or questionnaires or scale or inventory or checklist.

Selection criteria

Studies were eligible for this review if they 1) included men and women of over 18 years old with DS, 2) assessing cognitive functions, 3) used a clinician judgment in diagnosing MCI, AD or even dementia (these articles, however, needed to clearly refer to Alzheimer’s disease in their article), 4) incorporated a control group of patients with DS without MCI, AD or dementia. Specifically for the meta-analysis, an additional screening criterion relating to the presence of sensitivity and specificity scores of cognitive tests for MCI, AD or dementia in people with DS was applied. When these scores were not published, authors were contacted.

Study selection and data extraction

A sorting of titles and abstract and then full articles were assessed for inclusion. The index test, the reference standard, the severity of intellectual disability, the criteria for the diagnosis of MCI or AD, the recommended cut off scores and information regarding the diagnosis of MCI or AD were extracted. Moreover, the sensitivity and specificity scores were extracted and allowed to reconstruct two by two tables. When multiple sensitivity and specificity scores when given in a study, the recommended scores for specific cut-offs given by the authors were selected.

Assessment of methodological quality

The methodological quality of the studies included in the meta-analysis was measured using the QUADAS-2 tool as recommended for the evaluation of articles on diagnostic accuracy [18]. This tool assesses the risk of bias of each article according to four areas, that is, patient selection, test used, baseline diagnosis, and pace and time. To reduce individual bias, evaluations were performed by two reviewers independently. When a disagreement occurred in the evaluation, the most conservative estimate was selected, and the inclusion of articles was performed through mutual agreement between the evaluators.

Meta-analysis

For each study included in the meta-analysis, we built a 2×2 contingency table consisting of true positive, false positive, true negative and false negative based on concordance between cognitive tests results for the diagnosis and the reference standard carried out by a clinician. Studies evaluating more than one index tests to a reference test were analyzed separately. Study specific index test accuracy was evaluated in terms of sensitivity, specificity and diagnostic odds ratios with 95% confidence intervals. Pooled estimates were calculated using a bivariate random-effect model. For univariate analysis, forest plots for both sensitivity and specificity were produced. For bivariate analysis, forest plots of diagnostic odds ratios and SROC curves were generated. Specifically for tests where more than one study reported enough information to build the contingency tables, SROC curves with calculated area under the curve as well as sensitivity and specificity scores summaries with 95% confidence region ellipses were generated as recommended by [19]. Through this analysis, it will be possible to visualize how the different sensitivities and specificities of the included studies are related to each other as well as to illustrate the heterogeneity in between studies. The area under the ROC curve results were considered excellent for values ranging from 1 to 0.9, good for values between 0.89 to 0.8, fair for values between 0.79 to 0.7 0.7–0.8, poor for values between 0.69 to 0.6 and failed for values between 0.59 to 0.5 [20]. Statistical analysis were all performed in R with the Mada package.

RESULTS

A total of 81 studies reporting multiple cognitive assessments for the diagnosis of MCI and AD were selected for the review (Fig. 1). A summary of all cognitive tests used with the recommended cut off scores, when available, are presented (Table 1). Details regarding the population evaluated and information associated with the procedure are also included. We identified 63 assessments with a majority of batteries (15), some intelligence scales (2) as well as multiple tests measuring specific cognitive functions: memory (14), executive functioning (11), motor and visuospatial functioning (11), language (5), attention (3) and orientation (2). Across all studies, the most frequently used batteries for the diagnosis of MCI as well as for the diagnosis of AD are the Cambridge Cognitive Examination adapted for individuals with Down Syndrome (CAMCOG-DS), the Down Syndrome Mental Status Examination, the Down’s syndrome attention, memory, and executive function scales and the Severe impairment battery. Several tools measuring specific cognitive functions are also frequently used, including the Cued recall test, the Selective Reminding test, the Verbal Fluency test, the Tower of London, the Cats and Dogs Stroop test, and the Brief Praxis test.

Fig. 1

Flowchart of the literature search and study section.

Table 1

Cognitive assessement for the diagnosis of AD people with DS

Cognitive function evaluated	Index test	Recommended cutt off	Study	Total DS with MCI/early stage AD	Total DS with AD	Total DS control	Level of ID	MCI/AD diagnostic criteria	MCI/AD diagnosis
Global cognitive functions	Arizona Cognitive Test Battery	N/A	[21]	–	19	30	mild: 13mod to severe: 14	N/A	N/A
	Severe impairement battery	N/A	[22]	–	38	245	N/A	DSM-IV	N/A
		N/A	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[24]	–	71	43	mild: 40mod: 44severe to prof: 30	ICD-10 and DSM-IV	N/A
		N/A	[25]	–	10	21	N/A	ICD-10 and DSDS	N/A
		N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive evaluation
		N/A	[27]	–	N/A	N/A	N/A	DSM-IV	N/A
		N/A	[28]	–	10	10	N/A	NINCDS-ADRDA	not blind to cognitive evaluation
		N/A	[29]	–	5	17	mild: 13mod: 9	NINCDS-ADRDA	not blind to cognitive evaluation
		one year change of 12 points or more	[30]	–	Time 1:20Time 2:31	Time 1:52Time 2:41	borderline to mild: 36mod to severe: 34not documented: 1	NINCDS-ADRDA	N/A
		N/A	[31]	79	58	424	mild: 167mod: 161severe: 160prof: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[32]	–	73	68	mild: 17mod: 65prof: 43severe: 14unknown: 2	NINCDS-ADRDA and DSM-IV	not blind to cognitive evaluation
		performance ≤69	[33]	–	32	68	borderline to mild: 52mod to severe: 47not documented: 1	NINCDS-ADRDA	not blind to cognitive evaluation
		N/A	[34]	–	4	11	N/A	ICD-10 and DSM-IV TR	blind to cognitive evaluation
	DSMSE	N/A	[22]	–	38	245	N/A	DSM-IV	N/A
		N/A	[35]	–	16	14	N/A	ICD-10	not blind to cognitive evaluation
		N/A	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[36]	–	74	130	mild to mod: 98 severe to prof: 106	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[38]	–	74	107	mild to mod: 86severe to prof: 95	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[39]	–	71	164	mild to mod: 131severe to prof: 104	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[40]	7	40	–	N/A	N/A	not blind to cognitive evaluation
		N/A	[41]	–	69	7	mild: 0mod: 61severe: 15	ICD-10	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[43]	7	7	28	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[31]	79	58	424	mild: 167mod: 161severe: 160profound: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[44]	11	10	35	mild: 25mod: 19severe: 3	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	not blind to cognitive evaluation
		N/A	[46]	6	–	55	mild: 19mod: 42	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[47]	7	7	28	N/A	AAMR-IASSID	blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[50]	–	10 (combined with MCI)	77	N/A	N/A	not blind to cognitive evaluation
		N/A	[51]	4	3	N/A	N/A	N/A	not blind to cognitive evaluation
		N/A	[52]	10	8	184	N/A	N/A	N/A
		N/A	[53]	40	33	212	mild: 156mod: 113severe: 30	AAMR-IASSID	not blind to cognitive evaluation
	IBR mental exam	N/A	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
	CAMCOG-DS	N/A	[54]	–	3	38	N/A	ICD-10 and DSM-IV	blind to cognitive evaluation
		N/A	[55]	–	10	45	mild: 14mod: 29severe: 12	Roth et al., 1998	blind to cognitive evaluation
		Prodromal: mild ID, performance ≤82Prodromal: moderate ID, performance ≤64AD: mild ID, performance ≤80 AD: moderate ID, performance ≤56	[56]	31	35	277	mild: 91mod: 205severe to prof: 47	study specific criteria	blind to cognitive evaluation
		AD: mild ID, performance ≤68AD: moderate ID, performance ≤52	[57]	–	N/A	N/A	mild: 36 mod: 67	DSM-IV + ICD-10	N/A
		N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive scores
		N/A	[58]	–	19	39	N/A	DCR-10	N/A
		N/A	[59]	–	19	45	N/A	ICD-10	N/A
		N/A	[60]	–	10	39	N/A	ICD-10	not blind to cognitive evaluation
		N/A	[61]	39	49	194	mild: 44mod152severe: 69prof: 17	study specific criteria	N/A
		AD/MCI: Performance decline after three years <5.8 points	[62]	18	11	63	mild: 34mod: 34severe: 22	DSM-V, ICD-10	blind to cognitive scores
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
		N/A	[63]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive evaluation
		N/A	[64]	19	21	40	mild to mod: 62	study specific criteria	N/A
		N/A	[65]	14	13	14	N/A	study specific criteria	N/A
		N/A	[66]	28	26	85	mild: 46mod: 93	study specific criteria	N/A
		N/A	[67]	38	–	25	mild: 30mod: 33	study specific criteria	not blind to cognitive evaluation
		N/A	[68]	69	127	436	mild: 121mod: 313	study specific criteria	blind to cognitive evaluation
except for certain occasions
		N/A	[69]	–	196	395	mild:133moderate: 328severe to prof: 150	study specific criteria	not blind to cognitive evaluation
		5th percentile of asymptomatic patients with mild ID: 725th percentile of asymptomatic patients with moderate ID: 50	[70]	62	84	416	mild: 173mod: 389	study specific criteria	blind to cognitive evaluation
		N/A	[71]	38	46	150	mild: 59moderate: 133severe to prof: 42	study specific criteria	not blind to cognitive evaluation
		Prodromal: mild ID, performance ≤82Prodromal: moderate ID, performance ≤64AD: mild ID, performance ≤80AD: moderate ID, performance ≤56	[72]	24	11	48	mild: 49mod: 34	study specific criteria	not blind to cognitive evaluation
	Down’s syndrome attention, memory, and executive function scales	N/A	[73]	–	61	112	N/A	ICD-10	N/S
		N/A	[74]	14	13	14	mild to mod: 41	study specific criteria	N/A
		N/A	[3]	48	83	257	mild: 72mod: 175severe to prof: 98	N/A	N/A
		N/A	[75]	56	88	309	mild: 105mod: 240severe to prof: 116	study specific criteria	N/A
		N/A	[64]	19	21	40	mild to mod: 62	study specific criteria	N/A
		N/A	[76]	17	13	62	mild: 34mod: 34severe: 22unspecified: 2	ICD-10 and DSM-V	N/A
	LonDownS	N/A	[77]	–	51	130	mild: 71mod: 75severe: 30	N/A	N/A
		N/A	[11]	–	43	162	mild: 114mod: 130severe: 39	N/A	blind to cognitive evaluation
		N/A	[78]	–	28	14	mild: 58mod: 77severe to prof: 27unstated: 3	DSM-IV, ICD-10 and DSM-5	not blind to cognitive evaluation
	Palmer cognitive battery: 1-Color trail test2-Boston naming test3-Cotrolled oral word association test4-Early signs of Dementia Checklist	AD: 4 or more subtests failed out of 8	[79]	–	6	4	N/A	DSM-IV-TR	blind to cognitive evaluation
	Neuropsychological assessment of dementiain individuals with Intellectual Disabilities (NAID)	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive evaluation
		N/A	[21]	–	19	30	mild: 13mod to severe: 22	N/A	N/A
	Raven’s Coloured Progressive Matrices	N/A	[80]	–	10	49	N/A	DSM-IV-TR, ICD-10 DCR, DC-LD, DM-ID	not blind to cognitive evaluation
	Prudhoe Cognitive Function	AD: performance ≤1 standard deviation of the change scores	[81]	–	18	69	N/A	ICD-10	N/A
	Mini-Mental State Examination	N/A	[59]	–	19	45	N/A	ICD-10	N/A
		N/A	[25]	–	10	21	N/A	ICD-10 and DSDS	N/A
		N/A	[82]	–	52	285	mild to mod: 224severe to prof: 80	DSM-IV	N/A
		N/A	[83]	–	50	47	N/A	N/A	N/A
		N/A	[31]	79	58	424	mild: 167mod: 161severe: 160prof: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77Mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
	Rapid Assessment of Developmental Disabilities	AD: mild ID, performance ≤60AD: moderate ID, performance ≤30AD: severe ID, performance ≤20	[24]	–	71	43	mild: 40mod: 44severe to prof: 30	ICD-10 and DSM-IV	blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[34]	–	4	11	N/A	ICD-10 and DSM-IV TR	blind to cognitive evaluation
	Test of severe impairment	N/A	[35]	–	16	14	N/A	ICD-10	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[41]	–	69	7	mild: 0mod: 61severe: 15	ICD-10	not blind to cognitive evaluation
		N/A	[31]	79	58	424	mild: 167mod: 161severe: 160prof: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[44]	11	10	35	mild: 25mod: 19severe: 3	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[84]	14	28	50	borderline to mild: 46mod to prof: 45	NINCDS-ADRDA	not blind to cognitive evaluation
	Barcelona Test-Intellectual Disability	N/A	[67]	38	–	25	mild: 30mod: 33	study specific criteria	not blind to cognitive evaluation
		N/A	[72]	24	11	48	mild: 49mod: 34	study specific criteria	not blind to cognitive evaluation
	Wolfenbütteler Dementia Test for Individualswith Intellectual Disabilities	confirmed suspicion: score fall of at least five points	[85]	11	7	4	N/A	ICD-10	blind to cognitive evaluation
Memory	Cued recall test	AD: total score ≤23	[86]	5	7	44	N/A	ICD-10	N/A
		AD: total score ≤23	[87]	–	19	75	N/A	ICD-10	not blind to cognitive evaluation
		Prodromal: mild ID, total score ≤35Prodromal: moderate ID, total score ≤33AD: mild ID, total score ≤29AD: moderate ID, total score ≤28	[56]	31	20	220	mild: 85mod: 161severe to prof: 25	study specific criteria	blind to cognitive evaluation
		AD: total score ≤23	[88]	5	5	25	N/A	study specific criteria	not blind to cognitive evaluation
		AD: total score ≤23	[89]	–	15	75	Upper end of spectrum: 43middle range of spectrum: 39	N/A	blind to cognitive evaluation
		N/A	[40]	7	40	–	N/A	N/A	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	N/A
		N/A	[44]	11	10	35	mild: 25mod: 19severe: 3	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	N/A
		N/A	[75]	56	88	309	mild: 105mod: 240severe to prof: 116	study specific criteria	N/A
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[64]	19	21	40	mild to mod: 62severe to prof: 18	N/A	N/A
		N/A	[50]	–	10 (combined with MCI)	77	N/A	N/A	not blind to cognitive evaluation
		N/A	[46]	6	–	55	mild: 19mod: 42	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[66]	28	26	85	mild: 46mod: 93	study specific criteria	N/A
		N/A	[90]	40	29	192	mild: 144mod: 90severe: 27	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[52]	10	8	184	N/A	N/A	N/A
		N/A	[91]	6	–	117	mild: 38mod: 83severe: 2	study specific criteria	not blind to cognitive evaluation
		N/A	[53]	40	33	212	mild: 156mod: 113severe: 30	AAMR-IASSID	not blind to cognitive evaluation
		Cognitively stable: mild or moderate ID, total score ≤33AD or MCI: mild, moderate or severe, total score <20	[92]	43	42	247	mild: 143mod: 172severe: 17	study specific criteria	blind to cognitive scores
		N/A	[93]	4	1	64	mild: 40mod: 20severe: 11	study specific criteria	not blind to cognitive evaluation
		AD: total score ≤22AD: mild and moderate ID, free recal ≤15	[94]	–	12	88	mild: 50mod: 29severe: 22	study specific criteria	not blind to cognitive evaluation
		N/A	[68]	69	127	436	mild: 121mod: 313	study specific criteria	blind to cognitive evaluation
except for certain occasions
		N/A	[69]	–	196	395	mild:133mod: 328severe to prof: 150	study specific criteria	not blind to cognitive evaluation
		5th percentile of asymptomatic patients with mild ID:32 5th percentile of asymptomatic patients with moderate ID: 30	[70]	62	84	416	mild: 173mod: 389	study specific criteria	blind to cognitive evaluation
		N/A	[71]	38	46	150	mild: 59mod: 133severe to prof: 42	study specific criteria	not blind to cognitive evaluation
	Selective reminding test	AD: Performance ≤20% for 2 consecutive years	[86]	5	7	44	N/A	ICD-10	N/A
		AD: Performance ≤20% for 2 consecutive years	[14]	–	14	71	mild to mod: 29low mod: 19severe to prof: 42	ICD-10	N/A
		AD: Performance ≤20%	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		AD: Performance ≤20%	[36]	–	74	130	mild to mod: 98severe to prof: 106	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		AD: Performance ≤20%	[38]	–	74	107	mild to mod: 86severe to prof: 95	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[88]	5	5	25	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[39]	–	71	164	mild to mod: 131severe to prof: 104	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
	Auditory and visual sequential memory	N/A	[80]	–	10	49	N/A	DSM-IV-TR, ICD-10 DCR, DC-LD, DM-ID	not blind to cognitive evaluation
	Delayed auditory and visual memory (Barcelona Test-Intellectual Disability)	N/A	[74]	14	13	14	mild to mod: 41	study specific criteria	N/A
		N/A	[65]	14	13	14	N/A	study specific criteria	N/A
	Modified Fuld Object Memory Test	N/A	[95]	–	32	76	mild: 25mod: 53severe to prof: 30	DSM-IV	blind to cognitive evaluation
		N/A	[82]	–	52	285	mild to mod: 224severe to prog: 80	DSM-IV	N/A
		N/A	[34]	–	4	11	N/A	ICD-10 and DSM-IV TR	blind to cognitive evaluation
	New dot test	N/A	[95]	–	32	76	mild: 25mod: 53severe to prof: 30	DSM-IV	blind to cognitive evaluation
		N/A	[82]	–	52	285	mild to mod: 224severe to prof: 80	DSM-IV	N/A
	Object Recognition Memory	N/A	[96]	–	10	34	N/A	DSM-III-R	N/A
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
		N/A	[97]	56	46	68	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
	Remembering a belonging (Rivermead Behavioural Memory Test for Children)	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive evaluation
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
	Picture Recognition (Rivermead Behavioral Memory Test for Children)	N/A	[40]	7	40	–	N/A	N/A	not blind to cognitive evaluation
		N/A	[44]	11	10	35	mild: 25mod: 19severe: 3	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	N/A
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[93]	4	1	64	mild: 40mod: 20severe: 11	study specific criteria	not blind to cognitive evaluation
	Verbal learning-verbal short-term memory
(Barcelona Test-Intellectual Disability)	N/A	[65]	14	13	14	N/A	study specific criteria	N/A
	CANTAB Paired associates learning	N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
Sementic memory	Peabody picture vocabulary test	N/A	[23]	–	30	81	mild to mod: 57severe tp prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[36]	–	74	130	mild to mod: 98 severe to prof: 106	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[38]	–	74	107	mild to mod: 86severe to prof: 95	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	N/A
		N/A	[45]	3	3	46	N/A	N/A	N/A
	Sementic verbal fluency	N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103 Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	not blind to cognitive evaluation
		AD: Raw score performance ≤5.5	[98]	38	33	97	mild: 85mod: 73severe: 10	study specific criteria	not blind to cognitive evaluation
		N/A	[99]	–	37	133	mild: 80mod: 90	N/A	not blind to cognitive evaluation
	Boston naming test	N/A	[98]	38	33	97	mild: 85mod: 73severe: 10	study specific criteria	not blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
Executive functions	Verbal fluency test	N/A	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[36]	–	74	130	mild to mod: 98severe to prof: 106	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive evaluation
		N/A	[38]	–	74	107	mild to mod: 86severe to prof: 95	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[39]	–	71	164	mild to mod: 131severe to prof: 104	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[21]	–	19	30	mild: 13mod to severe: 22	N/A	N/A
		N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
		N/A	[43]	7	7	28	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[31]	79	58	424	mild: 167mod: 161severe: 160prof: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	not blind to cognitive evaluation
		N/A	[47]	7	7	28	N/A	AAMR-IASSID	blind to cognitive scores
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[50]	–	10 (combined with MCI)	77	N/A	N/A	not blind to cognitive evaluation
	Reversal learning	N/A	[96]	–	10	34	N/A	DSM-III-R	N/A
	Weigl sorting test	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive scores
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
		N/A	[67]	38	–	25	mild: 30mod: 33	study specific criteria	not blind to cognitive evaluation
	Tower of london	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive scores
		N/A	[21]	–	19	30	mild: 13mod to severe: 22	N/A	N/A
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
		N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
		N/A	[67]	38	–	25	mild: 30mod: 33	study specific criteria	not blind to cognitive evaluation
		N/A	[72]	24	11	48	mild: 49mod: 34	study specific criteria	not blind to cognitive evaluation
	Stroop Cats and dogs	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive scores
		N/A	[40]	7	40	–	N/A	N/A	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	N/A
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
		N/A	[90]						not blind to cognitive evaluation
		N/A	[50]	–	10 (combined with MCI)	77	N/A	N/A	not blind to cognitive evaluation
		N/A	[52]	10	8	184	N/A	N/A	M/I
	Digit span	N/A	[40]	7	40	–	N/A	N/A	not blind to cognitive evaluation
	Scrambled boxes	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive scores
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
	Spatial reversal	N/A	[26]	–	25	78	N/A	DSM-IV	blind to cognitive scores
		N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
	Day-night stroop	N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
	CANTAB intra-/extra-dimensionalset shift	N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
	Working memory (Barcelona Test-Intellectual Disability)	N/A	[65]	14	13	14	N/A	study specific criteria	N/A
Attention	Developmental NEuroPSYchological Assessment Bunny Cancelation	N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	not blind to cognitive evaluation
		N/A	[52]	10	8	184	N/A	N/A	N/A
	Picture cancellation task	MCI: performance ≤15AD: performance ≤6	[88]	5	5	25	N/A	study specific criteria	N/A
	simple reaction time (CANTAB)	N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
Langage	Peabody Picture Vocabulary Test	N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[80]	–	10	49	N/A	DSM-IV-TR, ICD-10 DCR, DC-LD, DM-ID	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	N/A	not blind to cognitive evaluation
	Test of Auditory Comprehension of Language—3	N/A	[80]	–	10	49	N/A	DSM-IV-TR, ICD-10 DCR, DC-LD, DM-ID	not blind to cognitive evaluation
	Mean Lenght of Utterance	N/A	[80]	–	10	49	N/A	DSM-IV-TR, ICD-10 DCR, DC-LD, DM-ID	not blind to cognitive evaluation
	Vocabulary (WASI)	N/A	[13]	17	13	62	mild: 34mod: 34severe: 62not specified: 2	DSM-V, ICD-10	blind to cognitive scores
	Vocabulary	N/A	[95]	–	32	76	mild: 25mod: 53severe to prof: 30	DSM-IV	blind to cognitive evaluation
Motor and visuospatial
performance	Beery visual motor integration test	N/A	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[36]	–	74	130	mild to mod: 98severe to prof: 106	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[39]	–	71	164	mild to mod: 131severe to prof: 104	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[43]	7	7	28	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[51]	4	3	N/A	N/A	N/A	not blind to cognitive evaluation
	Developmental Test of Visual-Motor Integration	N/A	[31]	79	58	424	mild: 167mod: 161severe: 160prof: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	not blind to cognitive evaluation
		N/A	[47]	7	7	28	N/A	AAMR-IASSID	blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[50]	–	10 (combined with MCI)	77	N/A	N/A	not blind to cognitive evaluation
	Block design (WISC)	N/A	[23]	–	30	81	mild to mod: 57severe to prof: 54	ICD-10	not blind to cognitive evaluation
		N/A	[38]	–	74	107	mild to mod: 86severe to prof: 95	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[36]	–	74	130	mild to mod: 98severe to prof: 106	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[37]	–	38	59	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[38]	–	74	107	mild to mod: 86severe to prof: 95	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[39]	–	71	164	mild to mod: 131severe to prof: 104	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[43]	7	7	28	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[31]	79	58	424	mild: 167mod: 161severe: 160prof: 66	study specific criteria	not blind to cognitive evaluation
		N/A	[45]	3	3	46	N/A	N/A	not blind to cognitive evaluation
	Block design (continued)	N/A	[47]	7	7	28	N/A	AAMR-IASSID	blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[90]	6	–	55	mild: 19mod: 42	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[49]	36	38	111	mild: 90mod: 83severe: 12	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[51]	4	3	N/A	N/A	N/A	not blind to cognitive evaluation
		N/A	[52]	10	8	184	N/A	N/A	N/A
	Landmark	AD: 4 or more errors	[96]	–	10	34	N/A	DSM-III-R	N/A
	Simple Visual Discrimination	N/A	[96]	–	10	34	N/A	DSM-III-R	N/A
	Developmental NEuroPSYchologicalAssessment-II (NEPSY-II) visuomotor precision	N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
	Tinetti Balance and Gait Assessment Tool	N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
	Brief praxis test	N/A	[95]	–	32	76	mild: 25mod: 53severe to prof: 30	DSM-IV	blind to cognitive evaluation
		N/A	[100]	–	28	62	mild to mod: 29low mod: 19severe to prof: 42	DSM-IV	blind to cognitive evaluation
		N/A	[27]	–	N/A	N/A	N/A	DSM-IV	N/A
		N/A	[28]	–	10	10	N/A	NINCDS-ADRDA	not blind to cognitive evaluation
		N/A	[24]	–	71	43	mild: 40mod: 44severe to prof: 30	ICD-10 and DSM-IV	N/A
		N/A	[82]	–	52	285	mild to mod: 224severe to prof: 80	DSM-IV	N/A
		N/A	[29]	–	5	17	mild: 13mod: 9	NINCDS-ADRDA	not blind to cognitive evaluation
		one year change of 11 points or more	[30]	–	Time 1:20Time 2:31	Time 1:52Time 2:41	borderline to mild: 36mod to severe: 34not documented: 1	NINCDS-ADRDA	N/A
		performance ≤62	[33]	–	32	68	borderline to mild: 52mod to severe: 47not documented: 1	NINCDS-ADRDA	not blind to cognitive evaluation
		N/A	[32]	–	73	68	mild: 17mod: 65prof: 43severe: 14unknown: 2	NINCDS-ADRDA and DSM-IV	not blind to cognitive evaluation
		N/A	[34]	–	N/A	N/A	N/A	ICD-10 and DSM-IV TR	blind to cognitive evaluation
		N/A	[32]	14	28	50	borderline to mild: 46mod to prof: 45	National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria for dementia	not blind to cognitive evaluation
	Purdue Pegboard	N/A	[40]	7	40	–	N/A	N/A	not blind to cognitive evaluation
		N/A	[42]	Time 1:9Time 2:8Time 3:7Time 4:7Time 5:4	Time 1:0Time 2:6Time 3:3Time 4:2Time 5:1	Time 1:103Time 2:101Time 3:53Time 4:37Time 5:12	N/A	study specific criteria	not blind to cognitive evaluation
		N/A	[48]	41	–	103	mild: 77mod: 67	study specific criteria	not blind to cognitive evaluation
		N/A	[90]	6	–	55	mild: 19mod: 42	AAMR-IASSID	not blind to cognitive evaluation
		N/A	[50]	–	10 (combined with MCI)	77	N/A	N/A	not blind to cognitive evaluation
		N/A	[52]	10	8	184	N/A	N/A	N/A
	Finger-nose pointing	N/A	[21]	–	19	30	mild: 13mod to severe: 22	N/A	N/A
		N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
	Gait assessement	N/A	[21]	–	19	30	mild: 13mod to severe: 22	N/A	N/A
Orientation		N/A	[95]	–	32	76	mild: 25mod: 53severe to prof: 30	DSM-IV	blind to cognitive evaluation
	Temporal orientation	N/A	[74]	14	13	14	mild to mod: 41	study specific criteria	N/A
		N/A	[65]	14	13	14	N/A	study specific criteria	N/A
	Orientation (CAMCOG)	N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
Intelligence scale	WISC	N/A	[86]	–	12	44	N/A	ICD-10	N/A
		N/A	[45]	3	3	46	N/A	N/A	not blind to cognitive evaluation
	Kaufmann Brief Intelligence Test 2	N/A	[97]	44	45	66	mild: 114mod: 135severe: 48	DSM-IV-TR, ICD-10	N/A
		N/A	[99]	–	37	133	mild: 80mod: 90	N/A	not blind to cognitive evaluation

Notes. AD = Alzheimer’s disease, CAMCOG-DS = Cambridge Cognitive Examination for people with Down syndrome, DCR-10 = Diagnostic Criteria for Research-10, DSM = Diagnostic and Statistical Manual, MCI = mild cognitive impairment, NINCDS-ADRDA = National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria for dementia, ID = intellectual disability, ICD = International Classification of Diseases, N/A = not available.

From the identified studies, several lacked data regarding the participants’ ID severity, sex ratios as well as the applied or recommended cut off scores for the cognitive tests used during the evaluation process or sensitivity and specificity scores. A total of twelve articles were deemed suitable for inclusion in the meta-analysis, as they provided sufficient relevant information. These articles, which were carefully selected for analysis, demonstrated a low or unclear risk of bias and applicability concern in various aspects such as patient selection, reference standard, and flow and timing scales (as indicated in Table 2). However, it should be noted that certain studies exhibited a high risk of bias in relation to the index test. This bias arose from the fact that clinicians were aware of the results generated by this particular test when diagnosing individuals with MCI or AD, leading to potential circular effect in the findings. For the diagnosis of AD, forest plots of sensitivity (Fig. 2), specificity (Fig. 3), and diagnostic odds ratio (Fig. 4) were performed. Forests plots of sensitivity (Fig. 5), specificity (Fig. 6), and diagnostic odds ratio (Fig. 7) for the diagnosis of MCI were also generated.

Table 2

Quality assessement of articles included in the meta-analysis

Study	Risk of bias				Applicability concerns			Comments
	Patient	Index test	Reference	Flow and	Patient	Index test	Reference
	selection		standard	timing	selection		standard
[87]								Missing information regarding the criterias used for the diagnosis of DS and ID.
[14]								Missing information regarding the criterias used for the diagnosis of DS and ID.
[100]								Missing information regarding the criterias used for the diagnosis of DS and ID.
								Clinicians were blind to neuropsychological evaluation.
[79]								Missing information regarding the criterias used for the diagnosis of DS and ID. Clinicians were blind to neuropsychological evaluation.
[81]								Missing information regarding the criterias used for the diagnosis of DS and ID.
[88]								Missing information regarding the diagnosis of AD.
								Clinicians were not blind to cognitive scores when diagnosing AD. No threshold prespecified.
[13]								Clinicians were not blind to cognitive scores when diagnosing AD.
[74]
[56]								Missing information regarding the diagnosis of AD. Clinicians were blind to neuropsychological evaluation.
[98]								Clinicians were not blind to cognitive scores when diagnosing AD.
[33]								Clinicians were not blind to cognitive scores when diagnosing AD.
[92]								Clinicians were blind to neuropsychological evaluation.
[94]								Missing information regardind the diagnosis of DS. Clinicians were not blind to cognitive scores when diagnosing AD.

Green, low risk/concern; red, high risk/concern; yellow, unclear risk/concern; DS, Down syndrome; ID, intellectual disability; AD, Alzheimer’s disease.

Fig. 2

Sensitivity forest plot of AD cognitive assessment.

Fig. 3

Specificity forest plot of AD cognitive assessment.

Fig. 4

DOR forest plot of AD cognitive assessment.

Fig. 5

Sensitivity forest plot of MCI cognitive assessment.

Fig. 6

Specificity forest plot of MCI cognitive assessment.

Fig. 7

DOR forest plot of MCI cognitive assessment.

Since the CAMCOG-DS is the only tool with enough data published, summary estimates of the sensitivity, specificity, and diagnostic odds ratios were generated specifically (Table 3). Considering that for this assessment it is recommended to use cut off scores according to the patient’s ID severity and to therefore ensure meaningful combination of studies, analysis were performed according to ID severities. To illustrate the performance of this diagnostic battery and the relation between the different sensitivities and specificities of included studies, SROC curves for the diagnosis of MCI (Figs. 8 9) and AD (Figs. 10 11) are presented. Analysis showed that the CAMCOG-DS present a fair diagnostic accuracy for detecting AD in patients with mild ID (AUC = 0.78) and excellent for moderate ID (AUC = 0.90). For the diagnosis of MCI, the CAMCOG-DS showed a good diagnostic accuracy for detecting MCI in patients with mild ID (AUC = 0.82) but a poor accuracy for patients with moderate ID (AUC = 0.65). A visual inspection of the SROC curves’ ellipse showed the presence of heterogeneity in between studies with large confidence intervals scores.

Table 3

Summary estimates of the CAMCOG-DS for sensitivity, specificity, and diagnostic odds ratio from the bivariate model

ID severy for diagnostic type	Mean sensitivity	Mean specificity	Mean DOR
	(95% CI)	(95% CI)	(95% CI)
Mild ID for MCI	77.6% (41.4–94.5%)	78.8% (59.1–90.5%)	12.88 (2.47–67.07)
Moderate ID for MCI	65.5% (47.2–79.7%)	56.2% (23.3–84.4%)	2.54 (0.53–12.13)
Mild ID for AD	71.4% (32.6–92.9%)	88.9% (81.5–93.6%)	22.34 (3.84–129.94)
Moderate ID for AD	89.6 % (64.6–97.6%)	75.2% (49.4–90.4%)	25.57 (9.13–71.61)

Fig. 8

SROC curve for the diagnosis of MCI in patients with DS and mild ID.

Fig. 9

SROC curve for the diagnosis of MCI in patients with DS and moderate ID.

Fig. 10

SROC curve for the diagnosis of AD in patients with DS and mild ID.

Fig. 11

SROC curve for the diagnosis of AD in patients with DS and moderate ID.

DISCUSSION

This study aimed to identify all cognitive tests used for the diagnosis of MCI or AD specifically for people with DS and to verify the diagnostic performance of the available tests for both diagnoses. This is the first systematic review including a meta-analysis for the MCI and AD diagnostic accuracy of cognitive assessments specifically for people with DS. The present review highlights the variety of available cognitive assessment of MCI and AD with multiple batteries, intelligence scales and various tests measuring specific cognitive functions while also presenting the different published recommended cut off scores.

Neuropsychological assessment

Neuropsychological assessment plays a crucial role in evaluating the development of AD in individuals with DS. Among the commonly used tools, the Down Syndrome Mental State Exam stands out for its ease of administration and its ability to measure orientation (i.e., day of the week, current season), recall of personal information, short-term memory, language, visuospatial construction, and praxis [16]. However, despite its frequent use in research, the Down Syndrome Mental State Exam presents a floor effect, even in individuals without dementia, in addition to having low sensitivity when compared to other cognitive tests [23]. Since this tool relies heavily on the patient’s verbal abilities, it could be that the presence of language difficulties associated with ID significantly affects the ability of patients to perform the tests integrated in this battery. The Severe impairment battery is a second cognitive battery frequently used in both research settings and clinics [101]. This battery includes a set of low-level tests that evaluate various cognitive domains. Specifically, the Severe impairment battery measures attention, language, memory, praxis, visuospatial functioning, construction, and orientation. Wallace et al. highlighted the ability of this tool to differentiate between individuals with DS and those with AD reporting a sensitivity of 0.66 and specificity of 0.66 [34]. However, the same study found that the Severe impairment battery did not demonstrate sufficient sensitivity to detect changes associated with MCI. It is worth noting that the tasks included in this battery are designed to be relatively easy, which may explain their suitability for individuals with severe cognitive impairment observed in AD. However, individuals with mild or moderate ID and less pronounced cognitive impairments may find these tasks too straightforward, potentially limiting the sensitivity of the battery for detecting MCI.

Other tools developed for people with ID are also used to screen for the presence of a neurocognitive disorder in people with DS, such as the CAMCOG-DS. This battery measures various cognitive domains such as orientation, abstract thought, perception, language comprehension and expression, memory, attention, praxis through the ability to draw simple and complex figures as well as the ability to perform complex tasks, and ultimately the ability to calculate [102]. The CAMCOG-DS has been validated in several languages, including English, German, Spanish, and Portuguese [57 , 103]. Calculated area under the curve generated specifically for the CAMCOG-DS showed that this battery presents better performance in detecting MCI and AD in patients with moderate ID. However, this battery presents some difficulty in detecting MCI and AD in patients with mild ID. This discrepancy between the diagnostic accuracy for MCI and AD could be explained by the presence of few primary studies with heterogenous sensitivity as well as specificity results. In their studies, Fonseca et al. pointed out that the attentional and executive scales of the CAMCOG-DS were not associated with the diagnosis of MCI as well as AD [13]. Thus, the tests included in these two scales may not be sufficiently sensitive to the changes observed during the development of AD for subjects with DS. Nevertheless, the CAMCOG-DS allows an evaluation of multiple cognitive functions without exhibiting a floor effect for patients with mild and moderate ID and therefore remains useful for monitoring the progression of AD in patients with cognitive symptoms [68]. More studies on the diagnostic accuracy of the CAMCOG-DS are needed to verify if this battery is suited for the screening of MCI for patients with DS presenting various cognitive deficits associated to their level of ID severity.

Several studies have highlighted the usefulness of specific tasks in screening for AD among individuals with DS such as, for exemple, the Selective Reminding test, the Cats and Dogs Stroop test, the Tower of London, the Verbal Fluency test, the Brief praxis test, and the Cued recall tests [13 , 92]. Recent studies have specifically investigated the effectiveness of the Cued recall test as a screening tool for detecting MCI in patients with DS, demonstrating promising results (56, 92]. The reported sensitivity values for the Cued recall test range from 0.60 to 0.70 for MCI and from 0.75 to 0.99 for AD. In terms of specificity, the reported values range from 0.73 to 0.79 for MCI and from 0.75 to 0.99 for AD [56 , 92]. These findings underscore the potential of the Cued recall test as a valuable tool for detecting cognitive decline and distinguishing between MCI and AD in individuals with DS.

Gold standard for MCI diagnosis

The study of deficits and assessment tools related to MCI in individuals with DS has gained increasing attention in recent years. However, the diagnosis of MCI in this population poses a significant challenge as there is currently no established gold standard procedure for clinical judgment. This lack of clarity in diagnostic criteria hampers the accurate identification of MCI in individuals with DS.

Some researchers have attempted to address this issue by categorizing individuals into different stages, such as preclinical AD, prodromal AD, and AD. This categorization is based on the understanding that individuals with DS show amyloid neuropathology by their mid-30 s, as observed in postmortem studies [5, 10]. Accordingly, individuals over the age of 35 without cognitive decline are classified as having preclinical AD, those with some cognitive decline but not yet in the dementia phase are classified as prodromal AD, and those with significant cognitive decline associated with dementia are classified as having AD. While involving clinicians in the decision-making process can ensure that observed cognitive decline is sufficiently significant and help differentiate between normal aging, prodromal AD, and dementia, this approach lacks transparency and the resulting groups may not be reproducible.

To address this diagnostic challenge, Esteba-Castillo et al. recently proposed a guide of neuropsychological criteria to assist clinicians in diagnosing MCI in middle-aged patients with DS [67]. According to their proposal, a diagnosis of MCI can be made if significant behavioral changes are reported by caregivers, there is no clinically relevant deterioration in adaptive skills reported by caregivers, the patient demonstrates a small dysexecutive pattern with notably lower scores in abstract thinking compared to previous assessments, and the patient exhibits difficulties in delayed verbal memory. However, these criteria have not yet been widely adopted in research or clinical practice.

Establishing a standardized and reproducible gold standard for MCI diagnosis in individuals with DS is crucial to improve diagnostic accuracy. Future studies should consider incorporating the proposed diagnostic criteria and further refine them based on empirical evidence. It is essential to develop a transparent and replicable approach that can effectively differentiate MCI from other stages of cognitive decline in individuals with DS. By addressing these challenges, researchers and clinicians can enhance the early detection and management of MCI in this high-risk population, ultimately improving outcomes and quality of life.

Strengths and limitations

In addition to the findings discussed above, it is important to acknowledge the strengths and limitations of the studies included in the review. First, it is important to note that the participant’s ID categorization was not homogeneous across all studies. Indeed, for the studies where this stratification was performed, some patients were identified according to their verbal capacities while others through their IQ scores, as recommended by the American Psychological Association before 2015. At present, the diagnostic criteria provided by the American Psychological Association recommend performing this evaluation according to the patient’s adaptive functioning with criteria for each of skills (i.e., conceptual, social and practical). While being more in line with the recommendation of the American Association of Intellectual Disabilities, this new classification provides information regarding the individual’s abilities and needs. To follow this new recommendation, recent studies instead classify the severity of the intellectual disability according to the person’s best lifelong adaptive functioning. This method ensures not to erroneously mix the difficulties associated with intellectual disability with those rather associated with the cognitive difficulties observed during the development of AD. It is important to mention that an important quantity of studies not provide sufficient information regarding the classification of intellectual disability severity or failed to categorize the ID of participants altogether. This lack of consistency made it challenging in comparing and pooling data, highlighting the need for future studies to adhere to standardized categorization methods based on the individual’s adaptive functioning.

Moreover, a notable limitation of the existing literature is the exclusion of individuals with severe and profound intellectual disability from analysis due to their inability to understand instructions or complete assessments. Some authors such as Krinsky-McHale et al., tried to include this specific group by analyzing descriptively their performance, while some combined moderate and severe patients together [13 , 94]. This exclusion leaves a significant gap in understanding the cognitive impairments observed in these patients when they develop AD. Given that individuals with severe to profound intellectual disability often require substantial assistance and exhibit daily dependence, it is crucial to gather information on their cognitive trajectory during both normal and pathological aging. Future research should aim to develop assessments that can be comprehended and completed by individuals with severe and profound intellectual disability, while also including a greater number of participants from these ID severity groups in study protocols.

Another limitation pertains to the heterogeneity in age inclusion criteria across studies. Some studies excluded young adults (18 to 35 years old), while others included them. This variability in age ranges complicates the comparison of findings and makes it challenging to establish a clear understanding of cognitive changes and impairments at different stages of life in individuals with DS.

Furthermore, important omissions in published articles, such as not providing separate scores for DS patients and patients with other disabilities or not disclosing the number of participants in each diagnostic group, hindered the ability to perform comprehensive analyses. Although efforts were made to contact authors and obtain the necessary information, only a few were able to provide the requested data. This limited availability of data sets resulted in the exclusion of a significant number of tools from the meta-analysis, reducing the overall robustness of the findings.

Conclusion

Despite these limitations, this review and meta-analysis provide valuable information on the cognitive assessment of MCI and AD in individuals with DS. The findings contribute significantly to the detection and understanding of AD development in this high-risk population. The review highlights the diverse range of available cognitive assessments for MCI and AD, encompassing various batteries, intelligence scales, and tests targeting specific cognitive functions. Moreover, it presents different recommended cut-off scores reported in the literature, enhancing the understanding of diagnostic thresholds.

Neuropsychological assessment plays a crucial role in evaluating AD development in individuals with DS. Notably, the Down Syndrome Mental State Exam and the Severe Impairment Battery are commonly used tools in this context. However, these instruments exhibit limitations such as floor effects, low sensitivity, and challenges in detecting changes associated with MCI. On the other hand, the CAMCOG-DS battery, designed specifically for individuals with intellectual disabilities, demonstrates better performance in detecting MCI and AD in patients with moderate intellectual disability. However, it encounters difficulties in identifying MCI in individuals with mild intellectual disability. Thus, further investigation is required to determine the diagnostic accuracy of the CAMCOG-DS and its suitability for screening MCI in patients with DS presenting various cognitive deficits associated with their level of ID severity. In addition to the aforementioned batteries, specific tasks like the Selective reminding test, the Brief praxis test, and the verbal fluency test show promise in screening for AD among individuals with DS. Indeed, they offer good sensitivity and specificity for AD, making them valuable in detecting cognitive decline. Moreover, the cued recall tests and the picture cancellation task presents proming results in helping to distinguish between cognitive decline associated to normal aging, the MCI stage, or AD.

To advance the field, future studies should address the methodological limitations outlined in this review. These include the lack of a standardized gold standard procedure for diagnosing MCI, the need for standardized categorization of intellectual disability severity, inclusion of individuals with severe and profound intellectual disability, and clearer reporting of study details. By addressing these limitations, researchers can enhance the understanding of cognitive impairments in DS and develop more accurate diagnostic tools for MCI and AD in this vulnerable population.

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

This work was supported by a scholarship to Patricia Alves Nadeau from the Canadian Institute of Health Research and from the Centre de recherche de l’Institut universitaire de gériatrie de Montréal.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data supporting the findings of this study are available on request from the corresponding author. Some data cannot be publicly available due to ethical restrictions.

References

Société canadienne du syndrome de Down. Canadian Down Syndrome Society (CDSS),https://cdss.ca/home-3/, Accessed 1 November 2020.

Naìtre et grandir (2019) Grossesse : Dépistage de la trisomie 21. Trimestre 1, https://naitreetgrandir.com/fr/grossesse/trimestre1/grossesse-trisomie-21-depistage/, Accessed 15 September 2022.

Fortea

, Vilaplana

, Carmona-Iragui

, Benejam

, Videla

, Barroeta

, Fernández

, Altuna

, Pegueroles

, Montal

, Valldeneu

, Giménez

, González-Ortiz

, Muñoz

, Estellés

, Illán-Gala

, Belbin

, Camacho

, Wilson

, Annus

, Osorio

, Videla

, Lehmann

, Holland

, Alcolea

, Clarimón

, Zaman

, Blesa

, Lleó

(2020) Clinical andbiomarker changes of Alzheimer’s disease in adults with Downsyndrome: A cross-sectional study. Lancet 395, 1988–1997.

Korenberg

, Kawashima

, Pulst

, Ikeuchi

, Ogasawara

, Yamamoto

, Schonberg

, West

, Allen

, Magenis

, Ikawa

, Taniguchi

, Epstein

(1990) Molecular definition of a region of chromosome 21 that causes features of the Down syndrome phenotype. Am J Hum Genet 47, 236–246.

Wiseman

, Al-Janabi

, Hardy

, Karmiloff-Smith

, Nizetic

, Tybulewicz

, Fisher

, Strydom

(2015) A genetic cause of Alzheimer disease: Mechanistic insights from Down syndrome. Nat Rev Neurosci 16, 564–574.

Hardy

, Higgins

(1992) Alzheimer’s disease: The amyloid cascade hypothesis. Science 256, 184–185.

Bloom

(2014) Amyloid-β and tau: The trigger and bullet in Alzheimer disease pathogenesis. JAMA Neurol 71, 505–508.

Braak

, Braak

(1998) Evolution of neuronal changes in the course of Alzheimer’s disease. J Neural Transm Suppl 53, 127–140.

Hanseeuw

, Betensky

, Jacobs

HIL

, Schultz

, Sepulcre

, Becker

, Cosio

DMO

, Farrell

, Quiroz

, Mormino

, Buckley

, Papp

, Amariglio

, Dewachter

, Ivanoiu

, Huijbers

, Hedden

, Marshall

, Chhatwal

, Rentz

, Sperling

, Johnson

(2019) Association of amyloid and tau with cognition in preclinical Alzheimer Disease: A longitudinal study. JAMA Neurol 76, 915–924.

10.

Mann

(1988) Alzheimer’s disease and Down’s syndrome. J Alzheimers Dis 6, 125–137.

11.

Firth

, Startin

, Hithersay

, Hamburg

, Wijeratne

, Mok

, Hardy

, Alexander

; LonDownS Consortium, Strydom

(2018) Aging related cognitive changes associated with Alzheimer’s disease in Down syndrome. J Alzheimers Dis 5, 741–751.

12.

Albert

, DeKosky

, Dickson

, Dubois

, Feldman

, Fox

, Gamst

, Holtzman

, Jagust

, Petersen

, Snyder

, Carrillo

, Thies

, Phelps

(2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 270–279.

13.

Fonseca

, Mattar

, Haddad

, Goncalves

, Miguel

, Guilhoto

, Zaman

, Holland

, Bottino

, Hoexter

(2019) Frontal-subcortical behaviors during Alzheimer’s disease in individuals with Down syndrome. Neurobiol Aging 78, 186–194.

14.

Krinsky-McHale

, Devenny

, Silverman

(2002) Changes in explicit memory associated with early dementia in adults with Down’s syndrome. J Intellect Disabil Res 46, 198–208.

15.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

CR Jr

, Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 263–269.

16.

Haxby

(1989) Neuropsychological evaluation of adults with Down’s syndrome: Patterns of selective impairment in non-demented old adults. J Ment Defic Res 33, 193–210.

17.

Hon

, Huppert

, Holland

, Watson

(1999) Neuropsychological assessment of older adults with Down’s syndrome: An epidemiological study using the Cambridge Cognitive Examination (CAMCOG). Br J Clin Psychol 38, 155–165.

18.

Zeng

, Zhang

, Kwong

, Zhang

, Li

, Sun

, Niu

, Du

(2015) The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: A systematic review. J Evid Based Med 8, 2–10.

19.

Reitsma

J B

, Glas

, Rutjes

, Scholten

, Bossuyt

, Zwinderman

(2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58, 982–990.

20.

Metz

(1978) Basic principles of ROC analysis. Semin Nucl Med 8, 283–298.

21.

Sinai

, Hassiotis

, Rantell

, Strydom

(2016) Assessing specific cognitive deficits associated with dementia in older adults with Down syndrome: Use and validity of the Arizona Cognitive Test Battery (ACTB). PloS One 11, e0153917.

22.

Tyrrell

, Cosgrave

, McCarron

, McPherson

, Calvert

, Kelly

, McLaughlin

, Gill

, Lawlor

(2001) Dementia in people with Down’s syndrome. Int J Geriatr Psychiatry 16, 1168–1174.

23.

Schupf

, Pang

, Patel

, Silverman

, Schubert

, Lai

, Kline

, Stern

, Ferin

, Tycko

, Mayeux

(2003) Onset of dementia isassociated with age at menopause in women with Down’s syndrome. Ann Neurol 54, 433–438.

24.

Walsh

, Doran

, Silverman

, Tournay

, Movsesyan

, Lott

(2015) Rapid assessment of cognitive function in down syndrome across intellectual level and dementia status. J Intellect Disabil Res 59, 1071–1079.

25.

Iulita

, Ower

, Barone

, Pentz

, Gubert

, Romano

, Cantarella

, Elia

, Buono

, Recupero

, Romano

, Castellano

, Bosco

, Di Nuovo

, Drago

, Caraci

, Cuello

(2016) An inflammatory and trophic disconnect biomarker profile revealed in Down syndrome plasma: Relation to cognitive decline and longitudinal evaluation. Alzheimers Dement 12, 1132–1148.

26.

Ball

, Holland

, Treppner

, Watson

, Huppert

(2008) Executive dysfunction and its association with personality and behaviour changes in the development of Alzheimer’s disease in adults with Down syndrome and mild to moderate learning disabilities. Br J Clin Psychol 47, 1–29.

27.

Head

, Doran

, Nistor

, Hill

, Schmitt

, Haier

, Lott

(2011) Plasma amyloid-β as a function of age, level of intellectual disability, and presence of dementia in Down syndrome. J Alzheimers Dis 23, 399–409.

28.

Powell

, Caban-Holt

, Jicha

, Robertson

, Davis

, Gold

, Schmitt

, Head

(2014) Frontal white matter integrity in adults with Down syndrome with and without dementia. Neurobiol Aging 35, 1562–1569.

29.

Lin

, Powell

, Caban-Holt

, Jicha

, Robertson

, Gold

, Davis

, Abner

, Wilcock

, Schmitt

, Head

(2016) H-MRS metabolites in adults with Down syndrome: Effects of dementia. Neuroimage Clin 11, 728–735.

30.

Koehl

, Harp

, Van Pelt

, Head

, Schmitt

(2020) Longitudinal assessment of dementia measures in Down syndrome. Alzheimers Dement (Amst) 12, e12075.

31.

Krinsky-McHale

, Zigman

, Lee

, Schupf

, Pang

, Listwan

, Kovacs

, Silverman

(2020) Promising outcome measures of early Alzheimer’s dementia in adults with Down syndrome. Alzheimers Dement (Amst) 12, e12044.

32.

Harp

, Koehl

, Pelt

, Hom

, Doran

, Head

, Lott

, Schmitt

(2021) Cognitive and behavioral domains that reliably differentiate normal aging and dementia in Down syndrome. Brain Sci 11, 1128.

33.

Wallace

, Harp

, Van Pelt

, Koehl

, Caban-Holt

, Anderson-Mooney

, Jicha

, Lightner

, Robertson

, Head

, Schmitt

(2021) Identifying dementia in Down syndrome with the Severe Impairment Battery, Brief Praxis Test and Dementia Scale for people with learning disabilities. J Intellect Disabil Res 65, 1085–1096.

34.

DiProspero

, Keator

, Phelan

, van Erp

TGM

, Doran

, Powell

, Van Pelt

, Schmitt

, Head

, Lott

, Yassa

(2022) Selective impairment of long-range default mode network functional connectivity as a biomarker for preclinical Alzheimer’s disease in people with Down syndrome. J Alzheimers Dis 85, 153–165.

35.

McCarron

, Gill

, Lawlor

, Beagly

(2002) A pilot study of the reliability and validity of the Caregiver Activity Survey - Intellectual Disability (CAS-ID). J Intellect Disabil Res 46, 605–612.

36.

Schupf

, Patel

, Pang

, Zigman

, Silverman

, Mehta

, Mayeux

(2007) Elevated plasma beta-amyloid peptide Abeta(42) levels, incident dementia, and mortality in Down syndrome. Arch Neurol 64, 1007–1013.

37.

Zigman

, Schupf

, Jenkins

, Urv

, Tycko

, Silverman

(2007) Cholesterol level, statin use and Alzheimer’s disease in adults with Down syndrome. Neurosci Lett 416, 279–284.

38.

Schupf

, Lee

, Wei

, Pang

, Chace

, Cheng

, Zigman

, Tycko

, Silverman

(2008) Estrogen receptor-alpha variants increase risk of Alzheimer’s disease in women with Down syndrome. Dement Geriatr Cogn Disord 25, 476–482.

39.

Chace

, Pang

, Weng

, Temkin

, Lax

, Silverman

, Zigman

, Ferin

, Lee

, Tycko

, Schupf

(2012) Variants in CYP17 and CYP19 cytochrome P450 genes are associated with onset of Alzheimer’s disease in women with down syndrome. J Alzheimers Dis 28, 601–612.

40.

Cody

, Piro-Gambetti

, Zammit

, Christian

, Handen

, Klunk

, Zaman

, Johnson

, Plante

, Hartley

(2020) Association of sleep with cognition and beta amyloid accumulation in adults with Down syndrome. Neurobiol Aging 93, 44–51.

41.

McCarron

, McCallion

, Reilly

, Mulryan

(2014) A prospective 14-year longitudinal follow-up of dementia in persons with Down syndrome. J Intellect Disabil Res 58, 61–70.

42.

Hartley

, Handen

, Devenny

, Tudorascu

, Piro-Gambetti

, Zammit

, Laymon

, Klunk

, Zaman

, Cohen

, Christian

(2020) Cognitive indicators of transition to preclinical and prodromal stages of Alzheimer’s disease in Down syndrome. Alzheimers Dement (Amst) 12, e12096.

43.

Henson

, Doran

, Christian

, Handen

, Klunk

, Lai

, Lee

, Rosas

, Schupf

, Zaman

, Lott

, Fagan

(2020) Cerebrospinal fluid biomarkers of Alzheimer’s disease in a cohort of adults with Down syndrome. Alzheimers Dement (Amst) 12, e12057.

44.

Rosas

, Hsu

, Mercaldo

, Lai

, Pulsifer

, Keator

, Brickman

, Price

, Yassa

, Hom

, Krinsky-McHale

, Silverman

, Lott

, Schupf

(2020) Alzheimer-related altered white matter microstructural integrity in Down syndrome: A model for sporadic AD? Alzheimers Dement (Amst) 12, e12040.

45.

Bazydlo

, Zammit

, Wu

, Dean

, Johnson

, Tudorascu

, Cohen

, Cody

, Ances

, Laymon

, Klunk

, Zaman

, Handen

, Alexander

, Christian

, Hartley

(2021) White matter microstructure associations with episodic memory in adults with Down syndrome: A tract-based spatial statistics study. J Neurodev Disord 13, 17.

46.

Fleming

, Piro-Gambetti

, Patrick

, Zammit

, Alexander

, Christian

, Handen

, Cohen

, Klunk

, Laymon

, Ances

, Plante

, Okonkwo

, Hartley

(2021) Physical activity and cognitive and imaging biomarkers of Alzheimer’s disease in down syndrome. Neurobiol Aging 107, 118–127.

47.

Hithersay

, Baksh

, Startin

, Wijeratne

, Hamburg

, Carter

; LonDownS Consortium; Strydom A (2021) Optimal age and outcome measures for Alzheimer’s disease prevention trials in people with Down syndrome. Alzheimers Dement 17, 595–604.

48.

Hom

, Kirby

K A

, Ricks-Oddie

, Keator

, Krinsky-McHale

, Pulsifer

, Rosas

, Lai

, Schupf

, Lott

, Silverman

(2021) Cognitive function during the prodromal stage of Alzheimer’s disease in Down syndrome: Comparing models. Brain Sci 11, 1220.

49.

Silverman

, Krinsky-McHale

, Lai

, Diana Rosas

, Hom

, Doran

, Pulsifer

, Lott

, Schupf

; Alzheimer’s Disease in Down Syndrome (ADDS) Consortium (2021) Evaluation of the National Task Group-Early Detection Screen for Dementia: Sensitivity to ‘mild cognitive impairment’ in adults with Down syndrome. J Appl Res Intellect Disabil 34, 905–915.

50.

Zammit

, Laymon

, Tudorascu

, Hartley

, Piro-Gambetti

, Johnson

, Stone

, Mathis

, Zaman

, Klunk

, Handen

, Cohen

, Christian

(2021) Patterns of glucose hypometabolism inDown syndrome resemble sporadic Alzheimer’s disease except for theputamen. Alzheimers Dement (Amst) 12, e12138.

51.

Brown

, Mak

, Clare

, Grigorova

, Beresford-Webb

, Walpert

, Jones

, Hong

, Fryer

, Coles

, Aigbirhio

, Tudorascu

, Cohen

, Christian

, Handen

, Klunk

, Menon

, Nestor

, Holland

, Zaman

(2022) Support vector machine learning and diffusion-derived structural networks predict amyloid quantity and cognition in adults with Down’s syndrome. Neurobiol Aging 115, 112–121.

52.

Grigorova

, Mak

, Brown

SSG

, Beresford-Webb

, Hong

, Fryer

, Coles

, Aigbirhio

, Tudorascu

, Cohen

, Christian

, Ances

, Handen

, Laymon

, Klunk

, Clare

ICH

, Holland

, Zaman

(2022) Amyloid-β and tau deposition influences cognitive and functional decline in Down syndrome. Neurobiol Aging 119, 36–45.

53.

Janelidze

, Christian

, Price

, Laymon

, Schupf

, Klunk

, Lott

, Silverman

, Rosas

, Zaman

, Mapstone

, Lai

, Ances

, Handen

, Hansson

(2022) Detection of brain tau pathology in Down syndrome using plasma biomarkers. JAMA Neurol 79, 797–807.

54.

Ball

, Holland

, Huppert

, Treppner

, Watson

, Hon

(2004) The modified CAMDEX informant interview is a valid and reliable tool for use in the diagnosis of dementia in adults with Down’s syndrome. J Intellect Disabil Res 48, 611–620.

55.

Ball

, Holland

, Hon

, Huppert

, Treppner

, Watson

(2006) Personality and behaviour changes mark the early stages of Alzheimer’s disease in adults with Down’s syndrome: Findings from a prospective population-based study. Int J Geriatr Psychiatry 21, 661–673.

56.

Benejam

, Videla

, Vilaplana

, Barroeta

, Carmona-Iragui

, Altuna

, Valldeneu

, Fernandez

, Giménez

, Iulita

, Garzón

, Bejanin

, Bartrés-Faz

, Videla

, Alcolea

, Blesa

, Lleó

, Fortea

(2020) Diagnosis of prodromal andAlzheimer’s disease dementia in adults with Down syndrome usingneuropsychological tests. Alzheimers Dement (Amst) 12, e12047.

57.

Esteba-Castillo

, Dalmau-Bueno

, Ribas-Vidal

, Vilà-Alsina

, Novell-Alsina

, García-Alba

(2013) [Adaptation and validationof CAMDEX-DS (Cambridge Examination for Mental Disorders of OlderPeople with Down’s Syndrome and others with intellectualdisabilities) in Spanish population with intellectual disabilities.]. Rev Neurol 57, 337–346.

58.

Beacher

, Daly

, Simmons

, Prasher

, Morris

, Robinson

, Lovestone

, Murphy

(2009) Alzheimer’s disease and Down’s syndrome: An in vivo MRI study. Psychol Med 39, 675–684.

59.

Mullins

, Daly

, Simmons

, Beacher

, Foy

, Lovestone

, Hallahan

, Murphy

(2013) Dementia in Down’s syndrome: An MRI comparison with Alzheimer’s disease in the general population. J Neurodev Disord 5, 19.

60.

Annus

, Wilson

, Hong

, Acosta-Cabronero

, Fryer

, Cardenas-Blanco

, Smith

, Boros

, Coles

, Aigbirhio

, Menon

, Zaman

, Nestor

, Holland

(2016) The pattern of amyloid accumulation in the brains of adults with Down syndrome. Alzheimers Dement 12, 538–545.

61.

Fortea

, Carmona-Iragui

, Benejam

, Fernández

, Videla

, Barroeta

, Alcolea

, Pegueroles

, Muñoz

, Belbin

, de Leon

, Maceski

, Hirtz

, Clarimón

, Videla

, Delaby

, Lehmann

, Blesa

, Lleó

(2018) Plasma and CSF biomarkers for thediagnosis of Alzheimer’s disease in adults with Down syndrome: Across-sectional study. Lancet Neurol 17, 860–869.

62.

Fonseca

, Haddad

, Mattar

, Oliveira

, Simon

, Guilhoto

, Busatto

, Zaman

, Holland

, Hoexter

, Bottino

(2019) The validity and reliability of the CAMDEX-DS for assessing dementia in adults with Down syndrome in Brazil. Braz J Psychiatry 41, 225–233.

63.

Fonseca

, Mattar

, Haddad

, Burduli

, McPherson

, Guilhoto

LMFF

, Yassuda

, Busatto

, Bottino

CMC

, Hoexter

, Chaytor

(2021) Neuropsychiatric symptoms of Alzheimer’s disease in down syndrome and its impact on caregiver distress. J Alzheimers Dis 81, 137–154.

64.

Lleó

, Carmona-Iragui

, Videla

, Fernández

, Benejam

, Pegueroles

, Barroeta

, Altuna

, Valldeneu

, Xiao

, Xu

, Núñez-Llaves

, Querol-Vilaseca

, Sirisi

, Bejanin

, Iulita

, Clarimón

, Blesa

, Worley

, Alcolea

, Fortea

, Belbin

(2021) VAMP-2 is a surrogate cerebrospinal fluid marker ofAlzheimer-related cognitive impairment in adults with Down syndrome. Alzheimers Res Ther 13, 119.

65.

Ramírez-Toraño

, García-Alba

, Bruña

, Esteba-Castillo

, Vaquero

, Pereda

, Maestú

, Fernández

(2021) Hypersynchronized magnetoencephalography brain networks inpatients with mild cognitive impairment and Alzheimer’s disease inDown syndrome. Brain Connect 11, 725–733.

66.

Benejam

, Aranha

, Videla

, Padilla

, Valldeneu

, Fernández

, Altuna

, Carmona-Iragui

, Barroeta

, Iulita

, Montal

, Pegueroles

, Bejanin

, Giménez

, González-OrtizS , Videla

, Bartrés-Faz

, Alcolea

, Blesa

, Lleó

, Fortea

(2022) Neural correlates of episodic memory in adults with Down syndrome and Alzheimer’s disease. Alzheimers Res Ther 14, 123.

67.

Esteba-Castillo

, Garcia-Alba

, Rodríguez-Hildago

, Vaquero

, Novell

, Moldenhauer

, Castellanos

MÁ

(2022) Proposeddiagnostic criteria for mild cognitive impairment in Down syndromepopulation. J Appl Res Intellect Disabil 35, 495–505.

68.

Videla

, Benejam

, Pegueroles

, Carmona-Iragui

, Padilla

, Fernández

, Barroeta

, Altuna

, Valldeneu

, Garzón

, Ribas

, Montal

, Arranz Martínez

, Rozalem Aranha

, Alcolea

, Bejanin

, Iulita

, Videla Cés

, Blesa

, Lleó

, Fortea

(2022) Longitudinal clinical and cognitive changes alongthe Alzheimer disease continuum in Down syndrome. JAMA NetwOpen 5, e2225573.

69.

Iulita

, Bejanin

, Vilaplana

, Carmona-Iragui

, Benejam

, Videla

, Barroeta

, Fernández

, Altuna

, Pegueroles

, Montal

, Valldeneu

, Giménez

, González-Ortiz

, Torres

, El Bounasri El Bennadi

, Padilla

, Rozalem Aranha

, Estellés

, Illán-Gala

, Belbin

, Valle-Tamayo

, Camacho

, Blessing

, Osorio

, Videla

, Lehmann

, Holland

, Zetterberg

, Blennow

, Alcolea

, Clarimón

, Zaman

, Blesa

, Lleó

, Fortea

(2023) Association of biological sex withclinical outcomes and biomarkers of Alzheimer’s disease in adultswith Down syndrome. Brain Commun 5, fcad074.

70.

Videla

, Benejam

, Carmona-Iragui

, Barroeta

, Fernández

, Arranz

, Azzahchi

S E

, Altuna

, Padilla

, Valldeneu

, Pegueroles

, Montal

, Aranha

, Vaqué-Alcázar

, Iulita

, Alcolea

, Bejanin

, Videla

, Blesa

, Lleó

, Fortea

(2023) Cross-sectional versus longitudinal cognitive assessments for the diagnosis of symptomatic Alzheimer’s disease in adults with Down syndrome. Alzheimers Dement, doi: 10.1002/alz.13073.

71.

Rozalem Aranha

, Iulita

, Montal

, Pegueroles

, Bejanin

, Vaqué-Alcázar

, Grothe

, Carmona-Iragui

, Videla

, Benejam

, Arranz

, Padilla

, Valldeneu

, Barroeta

, Altuna

, Fernández

, Ribas

, Valle-Tamayo

, Alcolea

, González-Ortiz

, Bargalló

, Zetterberg

, Blennow

, Blesa

, Wisniewski

, Busciglio

, Cuello

, Lleó

, Fortea

(2023) Basal forebrain atrophy along the Alzheimer’s disease continuum in adults with Down syndrome. Alzheimers Dement, doi: 10.1002/alz.12999.

72.

Rodríguez-Hidalgo

, García-Alba

, Novell

, Esteba-Castillo

(2023) The Global Deterioration Scale for Down Syndrome Population (GDS-DS): A rating scale to assess the progression of Alzheimer’s disease. Int J Environ Res Public Health 20, 5096.

73.

Hanney

, Prasher

, Williams

, Jones

, Aarsland

, Corbett

, Lawrence

, Yu

, Tyrer

, Francis

, Johnson

, Bullock

, Ballard

; MEADOWS trial researchers (2012) Memantine for dementia in adults older than 40 years with Down’s syndrome (MEADOWS): A randomised, double-blind, placebo-controlled trial. Lancet 379, 528–536.

74.

García-Alba

, Ramírez-Toraño

, Esteba-Castillo

, Bruña

, Moldenhauer

, Novell

, Romero-Medina

, Maestú

, Fernández

(2019) Neuropsychological and neurophysiologicalcharacterization of mild cognitive impairment and Alzheimer’sdisease in Down syndrome. Neurobiol Aging 84, 70–79.

75.

Bejanin

, Iulita

, Vilaplana

, Carmona-Iragui

, Benejam

, Videla

, Barroeta

, Fernandez

, Altuna

, Pegueroles

, Montal

, Valldeneu

, Giménez

, González-Ortiz

, Muñoz

, Padilla

, Aranha

, Estellés

, Illán-Gala

, Belbin

, Camacho

, Wilson

, Annus

, Osorio

, Videla

, Lehmann

, Holland

, Zetterberg

, Blennow

, Alcolea

, Clarimon

, Zaman

, Blesa

, Lleó

, Fortea

(2021) Association of a polipoprotein E ɛ4 allele with clinical and multimodal biomarker changes of Alzheimer disease in adults with down syndrome. JAMA Neurol 78, 937–947.

76.

Prado Mattar

, Uchida

, Haddad

, Shiozawa

, Reboucas da Silva

, Hoexter

, Busatto

, de Campos Bottin

, Fonseca

, Forlenza

(2022) Screening for dementia and cognitive decline in adults with Down syndrome: A novel approach using the Informant Questionnaire on cognitive decline in the elderly. Alzheimer Dis Assoc Disord 36, 162–167.

77.

Startin

, Hamburg

, Hithersay

, Davies

, Rodger

, Aggarwal

, Al-Janabi

, Strydom

(2016) The LonDownS adult cognitive assessment to study cognitive abilities and decline in Down syndrome. Wellcome Open Res 1, 11.

78.

Pape

, Al Janabi

, Ashto

, Hye

, Sheehan

, Gallagher

, Knight

, Prins

, Courtenay

, Jordanova

, Thomas

, Perumal

, Forbes

, Hassiotis

, Strydom

(2021) The reliability and validity of DSM 5 diagnostic criteria for neurocognitive disorder and relationship with plasma neurofilament light in a down syndrome population. Sci Rep 11, 13438.

79.

Palmer

(2006) Neuropsychological profiles of persons with mental retardation and dementia. Res Dev Disabil 27, 299–308.

80.

Iacono

, Torr

, Wong

(2010) Relationships amongst age, language and related skills in adults with Down syndrome. Res Dev Disabil 31, 568–576.

81.

Margallo-Lana

, Moore

, Kay

, Perry

, Reid

, Berney

, Tyrer

(2007) Fifteen-year follow-up of 92 hospitalized adults with Down’s syndrome: Incidence of cognitive decline, its relationship to age and neuropathology. J Intellect Disabil Res 51, 463–477.

82.

Sano

, Aisen

, Andrew

, Tsai

, Lai

, Dalton

; International Down Syndrome and Alzheimer’s Disease Consortium (2016) Vitamin E in aging persons with Down syndrome: A randomized, placebo-controlled clinical trial. Neurology 86, 2071–2076.

83.

Delaby

, Alcolea

, Carmona-Iragui

, Illán-Gala

, Morenas-Rodríguez

, Barroeta

, Altuna

, Estellés

, Santos-Santos

, Turon-Sans

, Muñoz

, Ribosa-Nogué

, Sala-Matavera

, Sánchez-Saudinos

, Subirana

, Videla

, Benejam

, Sirisi

, Lehmann

, Belbin

, Clarimon

, Blesa

, Pagonabarraga

, Rojas-Garcia

, Fortea

, Lleó

(2020) Differential levels of neurofilament light protein in cerebrospinalfluid in patients with a wide range of neurodegenerative disorders. Sci Rep 10, 9161.

84.

Harp

, Koehl

, Van Pelt

, Head

, Jicha

, Robertson

, Lightner

, Lott

, Schmitt

(2022) Primitive reflexes and dementia in adults with down syndrome. Neurol Clin Pract 12, 6–13.

85.

Kuske

, Wolff

, Gövert

, Müller

(2017) Early detection of dementia in people with an intellectual disability - a German pilot study. J Appl Res Intellect Disabil 30, 49–57.

86.

Devenny

, Krinsky-McHal

, Sersen

, Silverman

(2000) Sequence of cognitive decline in dementia in adults with Down’s syndrome. J Intellect Disabil Res 44, 654–665.

87.

Devenny

, Zimmerli

, Kittler

, Krinsky-McHale

(2002) Cued recall in early-stage dementia in adults with Down’s syndrome. J Intellect Disabil Res 46, 472–483.

88.

Krinsky-McHale

, Devenny

, Kittler

, Silverman

(2008) Selective attention deficits associated with mild cognitive impairment and early stage Alzheimer’s disease in adults with Down syndrome. Am J Ment Retard 113, 369–386.

89.

Benejam

, Fortea

, Molina-López

, Videla

(2015) Patterns ofperformance on the modified cued recall test in Spanish adults withDown syndrome with and without dementia. Am J Intellect DevDisabil 120, 481–489.

90.

Fleming

, Helsel

, Ptomey

, Rosas

, Handen

, Laymon

, Christian

, Head

, Mapstone

, Lai

, Krinsky-McHale

, Zaman

, Ances

, Lee

, Hartley

; Alzheimer’s Biomarker Consortium –Down Syndrome (ABC-DS) Consortium (2023) Weight loss and Alzheimer’s disease in Down syndrome. J Alzheimers Dis 91, 1215–1227.

91.

Hartley

, Handen

, Tudorascu

, Lee

, Cohen

, Piro-Gambetti

, Zammit

, Klunk

, Laymon

, Zaman

, Ances

, Sabbagh

, Christian

(2022) Role of tau deposition in early cognitive decline in Down syndrome. Alzheimers Dementia (Amst) 14, e12256.

92.

Krinsky-McHale

, Hartley

, Hom

, Pulsifer

, Clare

ICH

, Handen

, Lott

, Schupf

, Silverman

; Alzheimer’s Biomarker Consortium –Down Syndrome (ABC-DS) Consortium (2022) A modified Cued Recall Test for detecting prodromal AD in adults with Down syndrome. Alzheimers Dement (Amst) 14, e1236.

93.

Peven

, Handen

, Laymon

, Fleming

, Piro-Gambetti

, Christian

, Klunk

, Cohen

, Okonkwo

, Hartley

(2022) Physical activity, memory function, and hippocampal volume in adults with Down syndrome. Front Integr Neurosci 16, 919711.

94.

Sacco

, Falquero

, Bouis

, Akkaya

, Gallard

, Pichot

, Radice

, Bazin

, Montestruc

, Hiance-Delahaye

, Rebillat

(2022) Modified Cued Recall Test in the French population with Down syndrome: A retrospective medical records analysis. J Intellect Disabil Res 66, 690–703.

95.

Sano

, Aisen

, Dalton

, Andrews

, Tsai

, the International Down Syndrome and Alzheimer’s Disease Consortium (2005) Assessment of aging individuals with Down syndrome in clinical trials: Results of baseline measures. J Policy Pract Intellect Disabil 2, 126–138.

96.

Nelson

, Scheibel

, Ringman

, Sayre

(2007) An experimental approach to detecting dementia in Down syndrome: A paradigm for Alzheimer’s disease. Brain Cogn 64, 92–103.

97.

Startin

, Hamburg

, Hithersay

, Al-Janabi

, Mok

, Hardy

; LonDownS Consortium, Strydom

(2019) Cognitive markers of preclinical and prodromal Alzheimer’s disease in Down syndrome. Alzheimers Dement 15, 245–257.

98.

Pulsifer

, Evans

, Hom

, Krinsky-McHale

, Silverman

, Lai

, Lott

, Schupf

, Wen

, Rosas

(2020) Language skills as a predictor of cognitive decline in adults with Down syndrome. Alzheimers Dement 12, e12080.

99.

Mgaieth

, Baksh

, Startin

, Hamburg

, Hithersay

, Pape

, Zetterberg

, Ashton

, Tamayo-Elizalde

, Saini

, Idris

, Consortium

, Strydom

(2023) Exploring semantic verbal fluency patterns and their relationship to age and Alzheimer’s disease in adults with Down syndrome. Alzheimers Dement, doi: 10.1002/alz.13097.

100.

Lott

, Doran

, Walsh

, Hill

(2006) Telemedicine, dementia and Down syndrome: Implications for Alzheimer disease. Alzheimers Dement 2, 179–184.

101.

Saxton

, McGonigle

, Swihart

, Boller

(1993) The severe impairment battery. Pearson, San Antonio, TX..

102.

Holland

, Ball

(2009) The Cambridge Examination for Mental Disorders of Older People with Down’s Syndrome and Others with Intellectual Disabilities (CAMDEX-DS). In Neuropsychological Assessments of Dementia in Down Syndrome and Intellectual Disabilities, Prasher V, ed. Springer, London.

103.

Nübling

, Loosli

, Wlasich

, Prix

, Schönecker

, Freudelsperger

, Smrzka

, Strydom

, Zaman

, Benejam

, Missios

, Meister

, Danek

, Levin

(2020) [A German version of the Cambridge examination for mental disorders of older people with Down’s syndrome and others with intellectual disabilities: A diagnostic procedure for detecting dementia in people with Down’s syndrome.]. Z Gerontol Geriatr 53, 546–551.