Diagnostic and inclusion criteria in Alzheimer's disease clinical trials: A systematic review of the past decade

Abstract

Background

Dementia, mainly caused by Alzheimer's disease (AD), is a leading cause of mortality and disability in the elderly. However, inconsistencies in diagnostic and inclusion criteria challenge the design and comparability of AD clinical trials.

Objective

To review recent AD clinical trials, focusing on diagnostic methods and inclusion criteria, and identify trends and gaps to inform future research.

Methods

We systematically searched Web of Science, PubMed, and Google Scholar for AD clinical trials, extracting data on diagnostic criteria, disease stage, cognitive assessments, biomarker use, and participant age.

Results

Of the 27,471 articles screened, 71 studies were included in the final review. Most were conducted in North America (52%) and Europe (34%). NINCDS–ADRDA (56%) and NIA–AA 2011 (30%) were the most used diagnostic criterion, with the latter increasingly adopted in recent years. Over half focused on mild-to-moderate AD (56%), 16% on mild AD, and 13% included mild cognitive impairment/mild AD populations, with growing interest in early-stage interventions. However, only a minority reported notable cognitive improvements. The Mini-Mental State Examination was the most frequently used assessment tool (86%), though 36 different cutoff schemes were identified. Biomarkers were used in 38% of studies, mainly in the past three years, while others relied on symptom-based or imaging approaches. Participants ranged from 45 to 95 years old, with 50 as the most common lower age limit.

Conclusions

Symptom-based criteria still dominate AD trials. Given the limited efficacy of single interventions, future studies should consider multimodal, non-invasive approaches and prioritize objective biomarkers to enhance consistency and diagnostic precision.

Keywords

Alzheimer's disease diagnosis criteria electroencephalography inclusion criteria mini-Mental state examination neuropsychological assessments

Introduction

With global aging, the number of people with dementia is forecasted to reach 153 million by 2050, while annual worldwide costs already surpassed $1.3 trillion.¹ Alzheimer's disease (AD), the most common type of dementia, accounts for 60–80% of cases.² Despite billions in research funding, drug interventions have largely failed to yield significant cognitive improvement.³ While recently approved disease-modifying therapies (DMT), such as lecanemab and donanemab,^4,5 can slow disease progression, they do not provide a cure, underscoring the need for improved treatment strategies and early, accurate diagnosis.⁶

Over the past century, AD diagnostic criteria have evolved significantly. Early classifications required postmortem confirmation, but the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS–ADRDA, 1984) criteria introduced premortem clinical diagnosis,⁷ later refined by the International Working Group (IWG, 2007, 2014, 2021)^8–10 and the National Institute on Aging–Alzheimer's Association (NIA–AA, 2011, 2018).^11,12 These revisions reflect an evolving understanding of AD progression and the underlying amyloid and tau pathology. Most recently, in 2024, the Alzheimer's Association Workgroup (AA) incorporated plasma biomarker phosphorylated tau (p-tau) 217 into the diagnostic criteria, marking a shift toward blood-based biomarkers for non-invasive early detection.¹³ In contrast, clinical diagnostic systems such as ICD-10, DSM-IV, DSM-IV-TR, and DSM-5 continue to emphasize symptom-based dementia diagnosis after ruling out other causes.^14–16 However, significant heterogeneity remains in how diagnostic and inclusion criteria are applied in clinical research. Most existing guidelines are either biomarker-centered^8,9,12,13 or diease-centred,^7,15 often reflecting regional expertise¹⁷ or the perspectives of small expert groups.¹⁸ This variability in patient selection and diagnostic thresholds undermines study reliability, making consistent conclusions in AD trials challenging.¹⁷

Despite the critical role of standardized diagnostic frameworks in ensuring rigorous research,¹⁹ few studies have systematically analyzed the inclusion criteria used across AD-related randomized controlled trials (RCTs). To address this gap, our study reviews AD-related RCTs from the past decade, identifying key commonalities and discrepancies in diagnostic and inclusion criteria. By mapping these variations, our findings will provide valuable insights into current research practices and serve as a reference for clinicians and researchers, working toward greater diagnostic precision and treatment efficacy in AD.

Methods

Search strategy

This systematic review adhered to the guidelines of the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).²⁰ The PROSPERO-registered ID is CRD42023470858.

PubMed, Web of Science, and Google Scholar databases were used to identify studies for inclusion in this review, focusing on articles published between 01/01/2014 and 12/01/2024. The search used the terms: “Alzheimer's disease” or “AD” AND “Randomized Controlled Trial” or “RCT” or “Clinical Trials.” Previously published systematic reviews were also examined to identify additional relevant articles.

Inclusion criteria

Study inclusion criteria were: (1) AD cohort; (2) clinical RCT research article type; (3) well-defined and explicit diagnostic and inclusion criteria; (4) clinical registration number included; and (5) written in English.

Data extraction

The study selection process followed a three-step procedure (Figure 1). Duplicate articles were excluded. The titles and abstracts of the remaining articles were independently screened by two reviewers (Y.L. and S.L.) based on predefined inclusion criteria. Articles not meeting the inclusion criteria, such as those involving non-AD cohorts, other pathologies, other article types, or other clinical entities, were excluded. Finally, a third reviewer (S.C.) evaluated the quality and accuracy of the selected data. Any disagreements between reviewers were resolved through discussion until a consensus was reached.

Figure 1.

Flow diagram of the literature search and study selection process. Number of studies/articles (n) per step are indicated. * One eligible study was added post-review due to omission in search.

Results

Search results and study characteristics

A total of 27,471 articles were identified from 3 databases (PubMed, 2871; Web of Science, 4259; Google Scholar, 20,341). After excluding 3480 duplicates, 23,765 articles were removed for reasons such as non-AD focus, animal studies, and studies on healthy individuals. This left 226 articles, from which 59 were excluded for lacking a Clinical Trial Identifier, 51 for not applying clear diagnostic criteria, and 46 for not detailing inclusion criteria. During the peer review process, one additional eligible article missed in the initial search due to search strategy limitations, which was identified based on reviewer suggestion and met all inclusion criteria.²¹ It was therefore included in the final synthesis. In total, 71 studies were included in this systematic review. The selection process is detailed in Figure 1.

In addition, the authors of the included studies are mainly concentrated in the North America (52%), followed by Europe (34%), Asia (10%), and Oceania (4%). The United States is the leading country in AD research, followed by the United Kingdom, Sweden, Spain, and the Netherlands (Figure 2). We have also compiled the diagnostic and inclusion criteria from the studies analyzed, and the results are presented in Table 1.

Figure 2.

Distribution of the first authors’ affiliated countries in the included studies. The United States conducts the highest number of AD clinical studies.

Table 1.

Detailed inclusion criteria for all the included studies.

Disease state	Author	Publication year	Diagnose criteria	Age	Cognitive evaluation	Biomarkers or neuroimaging	Clinical Trial Identifier	Study phrase	Interventions	Results (Cognitive improvement or not)
Prodromal AD	Vladimir Coric²²	2015	Peterson diagnosis²³ for MCI and DSM-IV-TR for dementia exclusion	45–90	MMSE 24–30; CDR 0.5 with a memory box score ≤ 0.5	CSF for amyloid pathology positivity	NCT00890890	/	γ-secretase inhibitor: avagacestat	No (deterioration)
	Hilkka Soininen²⁴	2017	IWG-1 for prodromal AD and DSM-IV for dementia exclusion	55–85	MMSE ≥ 24(≥20 if education level ≤ 6 years)	CSF or MRI or (¹⁸F-FDG) PET for biomarker pathology positivity	NTR1705	/	LipiDiDiet	No (delay progression)
	Michael F. Egan²⁵	2019	NINCDS-ADRDA and DSM-IV-TR	55–85	MMSE 24–30;RBANS-Delayed Memory Index ≤ 85	PET for amyloid pathology positivity and MRI or CT consistent with AD diagnosis	NCT01953601	/	β-site amyloid precursor protein cleaving enzyme 1 inhibitor: verubecestat	No (deterioration)
	José Viña²⁶	2022	IWG-2	54–75	MMSE ≥ 24	PET for amyloid pathology positivity and MRI for vascular dementias exclusion	NCT01982578	II	a chemically defined polyphenol: genistein	No (delay progression)
	Charlotta Thunborg²⁷	2024	IWG-1 and IWG-2	60–85	MMSE ≥ 24; episodic memory impairment as -1 SD on at least 2 out of 8 tests; at least 1 being a memory test: FCSRT delayed free recall ≤ 8, FCSRT free recall-learning ≤ 22, WMS-R story delayed recall ≤ 75%; WMS-R delayed recall figures ≤ 75%;TMT- A ≥ 60 s, TMT-B ≥ 60 s, symbol digit substitution test ≥ 35 (120 s), category fluency ≤ 16 (60 s)	CSF for amyloid and tau pathology positivity or neuroimaging biomarker (Scheltens medial temporal lobe atrophy score of at least 1 and/or abnormal FDG-PET and/or PiB-PET compatible with AD type changes)	NCT03249688	/	multimodal lifestyle intervention with medical food	No (delay progression)
	Nicholas Levak²⁸	2024	IWG-1	60–85	MMSE ≥ 24	/	NCT03249688	/	multimodal lifestyle intervention: nutritional guidance, exercise, cognitive training, vascular/metabolic risk management, and social stimulation	No (delay progression)
prodromal AD and mild AD	Lutz Frölich²⁹	2019	IWG-2 for prodromal AD and NIA-AA 2012 for mild AD	≥55	MMSE ≥ 24 (prodromal),18–26 (mild);CDR 0 or 1 (prodromal), ≥1 (mild);RCSRT ≤ 20 and total ≤ 42 or WMVPAT ≥ 1 SD (prodromal);ADAS-cog 11 > 12 (mild)	PET or CSF for amyloid pathology positivity	NCT02240693;NCT02337907	II	novel phosphodiesterase type 9 inhibitor: BI 409306	No
prodromal or mild AD	Mark A. Mintun³⁰	2021	IWG-1	60–85	MMSE 20–28	PET or CSF for tau and amyloid pathology positivity	NCT03367403	II	anti-Aβ monoclonal antibody: donanemab	No (delay progression)
prodromal to mild AD	Edmond Teng ³¹	2022	NIA-AA 2011(MCI) for prodromal AD andNIA-AA 2011(AD) for mild AD	50–80	MMSE 20–30;CDR 0.5 or 1; RBANS- DMI ≤ 85	PET or CSF for amyloid pathology positivity	NCT03289143	II	anti-tau monoclonal antibody: semorinemab	No
prodromal to mild AD	Susanne Ostrowitzki³²	2022	NIA-AA 2011(MCI) for prodromal AD andNIA-AA 2011(AD) for mild AD	50–85	MMSE ≥ 22; CDR 0.5 or 1.0	PET or CSF for amyloid pathology positivity	NCT02670083	III	anti-Aβ monoclonal antibody: crenezumab	No
MCI due to AD or mild AD	Philip Scheltens³³	2018	NIA-AA 2011(MCI)and NIA-AA 2011(AD)	51–85	MMSE 21–30	CSF for amyloid and tau pathology positivity; MRI consistent with AD diagnosis	NCT 02389413	IIa	glutaminyl cyclase inhibitor: PQ912	No
	S. Budd Haeberlein³⁴	2022	NIA-AA 2011(MCI)and NIA-AA 2011(AD)	50–85	MMSE 24–30;CDR 0.5	PET for amyloid pathology positivity	NCT02484547NCT02477800	III	anti-Aβ monoclonal antibody: aducanumab	No (delay progression)
	Hana Florian³⁵	2023	NIA-AA 2011(MCI)	55–85	MMSE 22–30;CDR 0.5; RBANS-DMI ≤ 85; RMHIS ≤ 4	PET for amyloid pathology positivity	NCT02880956	II	anti-tau monoclonal antibody: tilavonemab	No
	C.J. C.H. van Dyck⁴	2023	NIA-AA 2011(MCI)and NIA-AA 2011(AD)	50–90	objective impairment in episodic memory as indicated by at least 1 standard deviation below the age-adjusted mean in the Wechsler Memory Scale IV–Logical Memory II.	PET or CSF for amyloid pathology positivity	NCT03887455	III	anti-Aβ monoclonal antibody: lecanemab	No (delay progression)
	Melanie Shulman³⁶	2023	NIA-AA 2011(MCI)and NIA-AA 2011(AD)	50–80	MMSE 22–30； ISLT or ISLR of 1 s.d; below the age-adjusted normative mean; CDR 0.5 (for MCI due to AD) or 0.5 or 1.0 (for mild AD dementia); CDR Memory Box score ≥ 0.5	PET or CSF for amyloid pathology positivity	NCT03352557	/	anti-tau monoclonal antibody: gosuranemaba	No
	R.J. Bateman³⁷	2023	NIA-AA 2011(MCI)and NIA-AA 2011(AD)	50–90	MMSE 22–30；CDR 0.5 or 1; FCSRT cueing index ≤ 0.67; FCSRT free recall ≤ 27	PET or CSF for amyloid pathology positivity	NCT03444870NCT03443973	III	anti-Aβ monoclonal antibody: gantenerumab	No
	Dean Ornish³⁸	2024	NIA-AA 2011(MCI)and NIA-AA 2011(AD)	45–90	MoCA ≥ 18	/	NCT04606420	II	intensive lifestyle changes	Yes
	Douglas Galasko³⁹	2024	NIA-AA 2011(MCI)	55–89	MMSE 17–30; CDR 0.5 or 1.0	CSF for amyloid pathology positivity and MRI consistent with AD diagnosis	NCT02925650	Ib	small molecule that reduces the translation of APP and Aβ: posiphen	No
	Young Hee Jung²¹	2024	NIA-AA 2011(AD)	55–90	MMSE ≥ 18; ISLT or ISLR of 1 sd; below the age-adjusted normative mean	PET or CSF for amyloid pathology positivity	NCT04260724	/	Personalized rTMS	Yes
Mild AD	Raj C. Shah⁴⁰	2014	NINCDS-ADRDA	55–85	MMSE ≥ 20	/	NCT01374438	/	mTOT modulating insulin sensitizer: MSDC-0160	No
	Martin Bystad⁴¹	2016	NINCDS-ADRDA	59–83	MMSE ≥ 18	/	NCT02518412	/	Transcranial direct current stimulation	No
	Anne Rijpma⁴²	2017	NIA-AA 2011(AD)	≥50	MMSE ≥ 20; GDS ≤ 6	MRI or CSF for pathology positivity	NTR3346	/	medical food: Souvenaid	No
	J. D. Huntley⁴³	2017	NINCDS-ADRDA	＞60	MMSE ＞ 22	/	ISRCTN43007027	/	working memory strategy training	No
	Lawrence S. Honig⁴⁴	2018	NINCDS-ADRDA	55–90	MMSE 20–26	PET or CSF for amyloid pathology positivity	NCT01900665	III	anti-Aβ monoclonal antibody: solanezumab	No
	Nanna A. Sobol⁴⁵	2018	NINCDS-ADRDA	50–90	MMSE ≥ 20	/	NCT01681602	/	aerobic exercise	Yes
	Matthew C. L. Phillips⁴⁶	2021	NINCDS-ADRDA	50–90	DSRS < 19	MRI or CT consistent with AD diagnosis	ACTRN1261800 1450202	/	ketogenic diet	No
	Dawn C. Matthews⁴⁷	2021	NINCDS-ADRDA and NIA-AA 2011(AD)	50–95	MMSE 19–27	FDG-PET for other dementia exclusion	NCT01703117	II	glutamate modulator: riluzole,	No (delay progression)
	Hui Jing Yu⁴⁸	2023	NINCDS-ADRDA	60–90	MMSE 20–26;CDR 0.5 or 1	PET for amyloid pathology positivity	NCT02551809	IIa	anti–Aβ active immunotherapeutic vaccine: UB-311	No
	Catherine J. Mummery⁴⁹	2023	NIA-AA 2011(MCI)	50–74	MMSE 20–27; CDR 1 or (0.5 with a Memory Score of 1)	CSF for amyloid and tau pathology positivity	NCT03186989	/	tau-targeting antisense oligonucleotide MAPTRx	No
	Yi Tang⁵⁰	2024	NIA-AA 2011(AD)	45–75	CDR 1.0	PET or CSF for amyloid pathology positivity	NCT 03920826	/	transcranial alternating current stimulation	No
mild or mild to moderate AD	Anne Rijpma⁵¹	2015	NINCDS-ADRDA	≥50	MMSE 20–26 (mild, RCT1); ≥20 (mild to moderate, RCT2); 14–24 (mild to moderate, RCT3)	/	NTR702; NTR1975; NTR1683;NTR2571	/	nutrient combination supplementation: Souvenaid	No
mild to moderate AD	Maurice W Dysken⁵²	2014	NINCDS-ADRDA	≥53	MMSE 12–26	/	NCT00235716	/	vitamin E and memantine	No (delay progression)
	Stephen Salloway⁵³	2014	NINCDS-ADRDA	50–88	MMSE 16–26;RMHIS ≤ 4	MRI consistent with AD diagnosis	NCT00575055 NCT00574132	III	anti-Aβ monoclonal antibody: bapineuzumab	No
	Douglas Galasko⁵⁴	2014	NINCDS-ADRDA	≥50	MMSE 14–26;RMHIS ≤ 4	/	NCT00566397	/	inhibitor of the receptor for advanced glycation end products:PF-04494700	No
	Lynne Shinto⁵⁵	2014	NINCDS-ADRDA	≥55	MMSE 15–26;CDR 0.5–1.0; CESDS ＜ 4.0	/	NCT00090402	/	omega-3 fatty acids and alpha lipoic acid	No
	Aaron H Burstein⁵⁶	2014	NINCDS-ADRDA	≥55	MMSE 14–26;RMHIS ≤ 4	MRI or CT consistent with AD diagnosis	NCT00566397	/	antagonist at the receptor for advanced glycation end products: TTP488	No
	Luigi Maria Edoardo Grimaldi⁵⁷	2014	DSM-IV	50–75	MMSE 20–26;RMHIS ≤ 4	/	NCT01075763	/	interferon beta-1a	No
	Laura M. Gault⁵⁸	2016	NINCDS-ADRDA	55–90	CSDD ≤ 10; RMHIS ≤ 4	/	NCT01527916	IIb	α7 nicotinic acetylcholine receptor agonist ABT-126 monotherapy	No
	Veronika Logovinsky⁵⁹	2016	NINCDS-ADRDA	≥50	MMSE 16–28	/	NCT01230853	/	anti-Aβ antibody: BAN2401	No
	Rik Vandenberghe⁶⁰	2016	NINCDS-ADRDA	50–88	MMSE 16–26 (mild 22–26, moderate 16–21); RMHIS ≤ 4	MRI consistent with AD diagnosis	NCT00667810 NCT00676143	III	anti-Aβ antibody: bapineuzumab	No
	Serge Gauthier⁶¹	2016	NIA-AA 2011(AD)	≤90	MMSE 14–26 (mild 20–26, moderate 14–19); CDR 1 or 2	PET or MRI or CSF for amyloid pathology positivity	NCT01689246	III	tau-aggregation inhibitor: leuco-methylthioninium bis (hydromethanesulfonate)	No
	Jeffrey L. Cummings⁶²	2016	NINCDS-ADRDA	50–90	MMSE 10–20	PET for amyloid pathology positivity	NCT01782742	/	retinoid×receptor agonis t:bexarotene	No
	Norman R. Relkin⁶³	2017	NINCDS-ADRDA	50–89	MMSE 16–26 (mild 21–26, moderate 16–20)	MRI consistent with AD diagnosis	NCT00818662	III	IV immunoglobulin	No
	Boris Decourt⁶⁴	2017	NINCDS-ADRDA	≥50	MMSE 12–26;HIS ≤ 4; GDS ＜ 10	MRI or CT consistent with AD diagnosis	NCT01094340	/	anti-cancer drug thalidomide	No
	Merce Boada⁶⁵	2017	NINCDS-ADRDA	55–85	MMSE 18–26	MRI or CT consistent with AD diagnosis	2007–000414–36	II	plasma exchange with albumin replacement	No (delay progression)
	Michael S. Rafii	2018	NINCDS-ADRDA	55–80	MMSE 17–26;MHIS ≤ 4	/	NCT00876863	II	nerve growth factor: adeno-associated viral vector	No
	Brian LawlorI⁶⁶	2018	NINCDS-ADRDA	>50	MMSE 12–27	/	NCT02017340	/	dihydropyridine calcium channel blocker: nilvadipine	No
	Jeffrey L. Cummings⁶⁷	2018	NINCDS-ADRDA	50–80	MMSE 18–26 (very mild 22–26, mild 20–26); GDS < 6; CDR-SB ≥ 0.5; ADAS-Cog ≥ 5	/	NCT01343966	II	anti-Aβ monoclonal antibody: crenezumab	No
	Terence Fullerton⁶⁸	2018	NINCDS-ADRDA	Mean 76.0	MMSE 10–24;NPI-total ≥ 10	/	NCT01712074	II	5-HT6 receptor antagonists:PF-05212377 (SAM-760)	No
	Ana-María Lacosta⁶⁹	2018	NINCDS-ADRDA	50–85	MMSE 15–26	/	NCT03113812	I	anti-Aβ40 vaccine：ABvac40	No
	Stephen Salloway⁷⁰	2018	NINCDS-ADRDA	50–80	MMSE 18–26;GDS < 6; CDR-SB ≥ 0.5; ADAS-Cog ≥ 5	PET for amyloid pathology positivity	NCT01397578	II	anti-Aβ monoclonal antibody: crenezumab	No
	Michael F. Egan⁷¹	2018	NINCDS-ADRDA and DSM-IV-TR	55–85	MMSE 15–26（mild 21–26, moderate 15–20)	MRI or CT consistent with AD diagnosis	NCT01739348	/	BACE-1 inhibitor: verubecestat	No (delay progression)
	Alireza Atri⁷²	2018	NINCDS-ADRDA	≥50	MMSE 12–22	/	NCT01955161; NCT0200664;NCT02006654	/	selective 5-hydroxytryptamine-6 receptor antagonist: idalopirdine	No
	Barbara R. Cardoso⁷³	2019	NINCDS-ADRDA	≥55	MMSE 14–26; RMHIS ≤ 4	MRI consistent with AD diagnosis	ACTRN12611001200976	/	supranutritional sodium selenate	No
	Clara Vila-Castelar⁷⁴	2019	NINCDS-ADRDA	61–88	MMSE 15–26	/	NCT03073876	/	donepezil	No
	Hélène Villars⁷⁵	2021	DSM-IV	Mean 82	MMSE 11–26	MRI or CT consistent with AD diagnosis	NCT01796314	/	therapeutic patient education	No
	Patrick Gavin Kehoe⁷⁶	2021	NINCDS-ADRDA	≥55	MMSE 15–28;RMHIS ≤ 5	MRI or CT consistent with AD diagnosis	ISRCTN93682878	/	angiotensin II receptor antagonist losartan	No
	Giacomo Koch⁷⁷	2022	IWG-2	51–85	MMSE 18–26;CDR 0.5 or 1	CSF for amyloid and tau pathology positivity	NCT03778151	/	repetitive transcranial magnetic stimulation	No (delay progression)
	Angela G. Juby⁷⁸	2022	NINCDS-ADRDA and DSM-IV	≥50	/	/	NCT04396015	/	nutritional source of ketones: medium chain triglyceride oil	Yes
	Glen Wunderlich⁷⁹	2023	NIA-AA 2011(AD)	≥55	MMSE 15–26	/	NCT02788513 (1346–0023)	II	glycine transporter-1 inhibitor: BI 425809	No
	Burak Yulug⁸⁰	2023	DSM-5	≥50	ADAS-cog ADAS ≥ 12; CDR-SB ≤ 2	/	NCT04044131	II	combined metabolic activators: 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%)	Yes
	Saeed Karima⁸¹	2023	NINCDS-ADRDA and DSM-IV	60–85	MMSE 10–26; HAM-D17 ≤ 12; CDR-SB 2–15.5	/	IRCT20170315033086N3	/	boswellic acids	Yes
	Cecilia Monteiro⁸²	2023	NIA-AA 2011(AD)	50–85	MMSE 16–21; CDR 1 or 2	PET or CSF for amyloid pathology positivity	NCT03828747	II	anti-tau monoclonal antibody: semorinemab	No
	Hsien-Yuan Lane⁸³	2023	NINCDS-ADRDA	50–100	MMSE 10–26;CDR 1	/	NCT03752463	/	endogenous antioxidants: sodium benzoate	Yes
	Yu-Chieh Hsu⁸⁴	2023	NINCDS-ADRDA and DSM-5 and NIA-AA 2011(AD)	50–90	MMSE 10–25; CDR 0.5 or 1 or 2	/	NCT05145881	/	multi-strain probiotic supplements	No
	Elaheh Foroumandi⁸⁵	2023	NINCDS-ADRDA	≥65	CDR ≤ 2	/	IRCT20160211026511N2	/	fenugreek seed extract supplementation: NeuroEPO plus	Yes
	Malika G. Fernando⁸⁶	2023	NINCDS-ADRDA	≥65	MMSE 15–25	/	SLCTR/2015/018	/	virgin coconut oil Supplementation	No
	Bruno Dubois⁸⁷	2023	NINCDS-ADRDA and DSM-IV-TR	≥50	MMSE 12–25	/	NCT01872598	III	tyrosine kinase inhibitor: masitinib.	No (delay progression)
	Saily Sosa⁸⁸	2023	NIA-AA 2011(AD)	≥50	GDS 3–5	MRI consistent with AD diagnosis	RPCEC00000232	II-III	recombinant human erythropoietin: : NeuroEPO plus	Yes
	E. Vijverberg⁸⁹	2024	NIA-AA 2018	50–85	MMSE 18–26;GDS ≤ 6	CSF for amyloid and tau positivity and MRI consistent with AD diagnosis	NCT04735536	II	a sigma-2receptor ligand:CT1812	No
Moderate to severe AD	Yun Jeong Hong⁹⁰	2024	NIA-AA 2011(AD)	45–90	MMSE 0–20;CDR ≥ 2	/	NCT0255 0665	/	Donepezil	No

ADAS-Cog: Alzheimer's Disease Assessment Scale–Cognitive Subscale; BPRS: Brief Psychiatric Rating Scale; CDRS: Clinical Dementia Rating Score; CDR-SB: Clinical Dementia Rating–Sum of Boxes; CDR-GS: CDR−Global Score; CES-D: Center for Epidemiologic Studies Depression Scale; CESDS: Center for Epidemiological Studies Depression; CSDD: Cornell Scale for Depression in Dementia; CSF, cerebrospinal fluid; CSS: Cornell scale score; CT, computed tomography; FAST: Functional Assessment Staging of Alzheimer's disease; GDS: Geriatric Depression Scale; GMS: Geriatric Mental Schedule; HAMD: Hamilton Depression Scale; HIS: Hachinski Ischemic Scale; MCI, mild cognitive impairment; MoCA: Montreal Cognitive Assessment; MMSE: Mini-Mental State Examination; MRI, magnetic resonance imaging; NPI: Neuropsychiatric Inventory; PET, positron emission tomography; FCSRT: Free and Cued Selective Reminding Test; WMS-R: Wechsler Memory Scale-revised; TMT: Trail Making Test; RBANS: Repeatable Battery for the Assessment of Neuropsychological Status; RBANS-DMI: Repeated battery of tests for the assessment of neuropsychological status-delayed memory DSRS: dementia severity rating scale; RCSRT: Free and cued selective reminder tests; RMHIS: Rosen-modified Hachinski ischemic score; WMVPAT: Wechsler Memory Visual Paired Association Test.; ISLT: International Shopping List Test Immediate Recall; ISLR: International Shopping List Test Delayed Recall; /: Indicates non-relevance.

Diagnostic criteria

Overview. We found the most commonly used diagnostic criteria were the earliest NINCDS-ADRDA criteria (n = 40, 56%), followed by the NIA-AA 2011 (n = 20, 30%). The DSM-IV were used in 7% of studies (n = 5), while the IWG-1 and IWG-2 were each used in 6% (n = 4). The DSM-IV-TR criteria accounted for 4% (n = 3), and DSM-5 was used in 3% (n = 2). The NIA-AA 2018 and NIA-AA 2012 each represented 1% (n = 1). Moreover, 16% of those studies (n = 11) employed a combination of two or more diagnostic criteria. The use of NIA-AA 2011 has become increasingly popular in recent years. The results are shown in Figure 3.

Figure 3.

Utilization of diagnostic criteria in AD research. (A) Overall distribution of diagnostic frameworks across studies. (B) Trends in the adoption of each criterion over time.

NINCDS–ADRDA. In the studies that used the NINCDS-ADRDA criteria, 83% (33 out of 40) applied them exclusively: 25 (76%) focused on diagnosing mild to moderate AD,^{52–56,58–60,62–70,72–74,76,83,85,86,91} 7 on mild AD,^{40,41,43–46,92} and 1 on mild or mild to moderate stages.⁵¹ The remaining 7 studies combined NINCDS-ADRDA with other criteria: 2 paired it with DSM-IV for mild to moderate AD,^78,81 2 with DSM-IV-TR for similar stages,^71,87 and 1 for prodromal AD.²⁵ Moreover, 1 study integrated NINCDS-ADRDA with NIA-AA 2011 for mild AD,⁴⁷ while another combined NINCDS-ADRDA, NIA-AA 2011, and DSM-5 for mild to moderate AD diagnosis.⁸⁴

NIA–AA. The NIA–AA (2011) criteria represented a significant advancement in AD diagnostics by covering preclinical AD, MCI due to AD, and AD dementia,^11,93,94 applicable in both clinical and research settings. In our analysis, 4 studies used the AD dementia criteria¹¹ for diagnosing mild to moderate AD,^61,79,82,88 while 3 for mild AD.^21,42,50 There studies applied the MCI due to AD criteria⁹³ to identify MCI or mild AD.^35,39,49 In addition, 8 studies combined MCI due to AD criteria with the AD dementia criteria: 6 targeted MCI or mild AD,^{33,34,36–38,95} and 2 focused on prodromal to mild AD.^31,32 Notably, only one study used NIA-AA 2018 research framework¹² for diagnosing mild to moderate AD in 2024.⁸⁹

IWG. The IWG criteria were the first to include pathological biomarkers in AD diagnosis, emphasizing clinical symptoms as primary and biomarkers as supplementary.⁹⁶ This contrasts with the America Association Group criteria, which prioritize biomarkers.¹³ This study shows that the IWG criteria are infrequently applied in clinical research. IWG-1 and IWG-2 are both mainly used for diagnosing prodromal or mild AD (n = 6),^24,26–30 with one study also using IWG-2 for diagnosing mild to moderate AD.⁷⁷

DSM-IV, DSM-IV-TR, and DSM-5. The common diagnostic characteristics of AD in DSM-IV, DSM-IV-TR, and DSM-5 include the presence of significant cognitive impairment, memory loss, functional decline, and the exclusion of other potential causes. However, early-stage AD cannot be diagnosed based solely on these criteria. In our studies, two investigations employed DSM-IV exclusively for diagnosing mild to moderate AD,^57,75 while one study used DSM-5 for the same purpose.⁸⁰ Two studies utilized the NINCDS-ADRDA criteria in conjunction with DSM-IV-TR for diagnosing mild to moderate AD,^71,87 and one study applied the same approach for diagnosing prodromal AD.²⁵ One study combined the Peterson criteria for MCI²³ with DSM-IV-TR for dementia exclusion in the diagnosis of prodromal AD.²² Furthermore, one study integrated the NINCDS-ADRDA criteria with DSM-5 and the 2011 NIA-AA criteria for diagnosing mild to moderate AD.⁸⁴

Disease states

Overview. From the NIA–AA (2011) criteria, AD is recognized as a continuous disease process, with distinct clinical features and varying degrees of cognitive impairment at each stage.⁹⁴ According to the 2020 Alzheimer's Association report, AD is further categorized into five stages: preclinical AD, MCI due to AD, and mild, moderate, and severe AD.⁹⁷ In this review, we found that the majority of studies (56%, n = 40) focused on the mild to moderate stages of AD, followed by studies on mild AD (16%, n = 11). MCI or mild AD accounted for 13% (n = 9), while prodromal AD and prodromal to mild AD accounted for 8% (n = 6) and 6% (n = 4), respectively. Only one study specifically investigated moderate to severe stages of AD. Moreover, as early diagnostic approaches continue to develop, research is increasingly focusing on the early stages of the disease. The distribution of research stages is shown in Figure 4.

Figure 4.

Selection of AD stages in research. (A) Proportional representation of studies focusing on different disease phases. (B) Temporal trends in research targeting various AD stages.

AD dementia stage. The mild-to-moderate AD stage is the most commonly studied disease state in our research, with the majority of studies focusing on drug development. Of these studies, 5 targeted Phase III trials,^{53,60,61,63,87} 1 targeted Phase IIb,⁵⁸ 1 focused on Phase II-III,⁸⁸ 9 on Phase II,^{65,67,68,70,79,80,82,89,91} and 1 on Phase I.⁶⁹ Most interventions at this stage did not result in significant improvements in cognitive function, like humanized monoclonal antibody^60,67,82,98; omega-3 fatty acids⁵⁵; inhibitor of the receptor for advanced glycation end products⁵⁶; α7 nicotinic acetylcholine receptor agonist⁵⁸ and virgin coconut oil.⁸⁶ Only 5 studies reported positive outcomes,^{38,80,81,83,85} while three observed a reduction in cognitive decline.^24,77,78 Additionally, one study investigating donepezil for the treatment of moderate to severe AD also yielded negative results.⁹⁰

Early AD stage. We found a total of 11 studies focused on mild AD,^40–50 6 on prodromal AD,^22,24–28 4 on both prodromal and/or mild AD,^29–32 and 9 on MCI due to AD or mild AD.^{21,33–39,95} We observed that many studies on the early stages of AD also reported negative results, such as transcranial direct current stimulation,⁴¹ humanized monoclonal antibody,^31,44 phosphodiesterase 9 inhibitor.²⁹ And some drugs even accelerate cognitive decline.^22,25 Meanwhile, 7 studies did find a reduction in disease progression, including aducanumab,³⁴ donanemab,³⁰ lecanemab,⁹⁵ LipiDiDiet,²⁴ multimodal lifestyle intervention with medical food,²⁷ glutamate modulator,⁴⁷ and polyphenol chemical.²⁶ However, only 8 studies showed cognitive function improvement, including intensive lifestyle changes,³⁸ personalized rTMS.²¹ aerobic exercise,⁴⁵ medium chain triglyceride oil,⁷⁸ combined metabolic activators,⁸⁰ boswellic acids,⁸¹ sodium benzoate.⁸³ and fenugreek seed extract supplementation.⁸⁵ These results suggest that monotherapy yields minimal cognitive benefits in early-stage AD.

Cognitive assessment scales

Overview. Neuropsychological assessment scales are vital in clinical neurology and research, helping to evaluate the nature and extent of cognitive impairments caused by brain lesions, identify the affected cognitive domains, and determine the severity of cognitive dysfunction.⁹⁹ We have sorted and summarized the types of cognitive assessment scales and the MMSE cutoff points for each stage of AD. The analysis revealed considerable variation in the cognitive assessment scales used, with MMSE being the most frequently cited in research. However, the MMSE cutoff points showed significant variability, as illustrated in Figure 5.

Figure 5.

Use of cognitive assessment tools. (A) Prevalence of different cognitive function scales. (B) Variation in MMSE cutoff scores across AD stages.

Type of cognitive assessment scales. The MMSE, first introduced in 1975, is a widely used neuropsychological tool primarily designed to assess cognitive function and aid in the diagnosis of dementia and other neurological disorders.¹⁰⁰ Our review found that the vast majority of studies (86%, n = 61) used the MMSE as part of their cognitive assessment protocols (Figure 5A). Of the remaining studies, Montreal Cognitive Assessment (MoCA),³⁸ Clinical Dementia Rating (CDR),^50,85 Geriatric Depression Scale (GDS),⁸⁸ and ADAS-cog⁸⁰ were often preferred, with one study not using any screening scale.⁷⁸ Among the MMSE-based studies, 43% (n = 26) used MMSE scores as the sole cognitive assessment screening scale,^{24,26,28,30,33,40,41,43–45,47,51,59,62,63,65,66,69,71,72,74,75,79,86,87,101} while 21 studies incorporated CDR or CDR-SB,^{22,29,31,32,34–37,39,48,49,55,61,67,70,77,81–84,90} 7 combined it with the Rosen Modified Hachinski Ischemic Score (RMHIS),^{35,53,54,56,60,73,76} and 2 used it alongside the Geriatric Depression Scale (GDS).^42,89

MMSE cutoff points. Our analysis reveals that MMSE score definitions varied across different stages of the disease (Figure 5B). For the mild to moderate stages of AD, MMSE cutoff points exhibited the greatest variability, with 19 distinct types identified: the most commonly used ranges were 15–26 (n = 5)^{55,69,71,74,79} and 18–26 (n = 5),^{65,67,70,77,89} then followed by 14–26 (n = 4)^54,56,61,73 and 16–26 (n = 3)^53,60,63; while 20–26,^51,57 12–26,^64,101 and 10–26^81,83 were reported in 2 studies each. The remaining 12 cutoff ranges appeared in only one study each, with the lowest cutoff set at 10^62,68,84 and the highest at 28.^59,76 For mild AD, 7 cutoff types were identified: 22–30 (n = 5),^{32,35–37,43} 20–30 (n = 4),^31,40,42,45 20–26 (n = 3),^44,48,51 18–30 (n = 2),^21,41 19–27 (n = 1),⁴⁷ 20–28 (n = 1),³⁰ and 20–27 (n = 1).⁴⁹ For prodromal AD, four cutoff ranges were used: 24–30 (n = 7),^22,24–29 22–30 (n = 1),³² 20–28 (n = 1),³⁰ and 20–30 (n = 1).³¹ In the case of MCI due to AD, four cutoff types were also observed: 22–30 (n = 3),^35–37 21–30 (n = 1),³³ 24–30 (n = 1),³⁴ and 17–30 (n = 1).³⁹ While moderate to severe AD was classified with a range of 0–20 (n = 1).⁹⁰

Biomarkers and neuroimaging examination

Biomarkers and neuroimaging are essential for the early and accurate diagnosis of AD, as they detect pathological changes before symptoms appear and provide objective evidence to support clinical assessments. In our included studies, 38% conducted AD-related biomarker testing, 20% utilized neuroimaging methods consistent with AD diagnosis, and 42% used neither approach (traditional symptom-based diagnosis) (Figure 6A). Of the studies focused on AD pathology, 6 used CSF testing to confirm amyloid positivity,^{22,33,39,49,77,89} 7 employed PET imaging,^{25,26,34,35,48,62,70} and 15 used either CSF or PET or MRI for biomarker pathology positivity.^{4,21,24,27,29–32,36,37,42,44,50,61,82} For neuroimaging techniques, 7 studies incorporated MRI or CT to support an AD diagnosis,^{46,56,64,65,71,75,76} and 5 studies relied on MRI findings consistent with AD.^{53,60,63,73,88} And one utilized FDG-PET to exclude other types of dementia.⁴⁷ Moreover, in recent years, the use of biomarkers has increased, possibly driven by the growing number of studies focusing on early-stage AD, as shown in Figure 6B.

Figure 6.

Integration of biomarkers in AD research. (A) Overview of the use of pathological biomarkers, neuroimaging techniques, and clinical symptom-based diagnostic approaches. (B) Annual trends in studies incorporating biomarkers and neuroimaging. (C) Distribution of the minimum age of participants across included studies.

Age range

The prevalence of AD increases significantly with age, affecting approximately 3%, 17%, and 32% of individuals aged 65–74, 75–84, and ≥85 years, respectively.¹⁰² Nevertheless, AD dementia is not an inevitable consequence of aging nor solely caused by advanced age.¹⁰³ The NINCDS-ADRDA criteria indicate AD onset typically between ages 40 and 90, with incidence highest in those ≥65 years.⁷ Similarly, ICD-10, DSM-IV, and DSM-IV-TR apply 65 years as the age threshold for differentiating early from late AD.

The studies included in this review displayed diverse age range definitions, even for the same disease stage, with 30 distinct ranges identified. For the mild to moderate AD stage, 9 studies included participants aged ≥50 years,^{26,51,59,64,72,78,80,87,88} 5 used ≥55 years,^{55,56,73,76,79} and 3 applied the 50–85 range.^69,82,89 For mild AD, 2 studies used the 50–90 range,^45,46 while other ranges spanned from a minimum age of 45⁵⁰ to a maximum of 95.⁴⁷ In prodromal AD, 2 studies reported using the 55–85 range,^24,25 another 2 used 60–85,^27,28 while individual studies adopted 45–90²² and 54–75²⁶ ranges. For MCI due to AD or mild AD, 2 studies used the 50–90 range,^4,37 while others included 45–90,³⁸ 50–80,³⁶ 50–85,³⁴ 51–85,³³ 55–89,³⁹ and 55–90,²¹ with each range reported in a single study. Among all studies, ages 45, 50, 55, and 60 were the most common starting points, as shown in Figure 6C.

Discussion

Diagnostic criteria in clinical research

Accurate diagnostic and inclusion criteria are fundamental to ensuring the validity and comparability of RCTs in AD.¹⁰⁴ Our analysis reveals that the NINCDS–ADRDA criteria, despite being developed four decades ago, remain widely used in clinical research, particularly for mild-to-moderate AD. The NIA–AA (2011) criteria, which cover all AD stages, are also extensively applied. Meanwhile, the DSM-IV, DSM-IV-TR, and DSM-5 criteria primarily focus on diagnosing Alzheimer's-type dementia and are often supplemented with additional criteria to rule out other neurodegenerative and psychiatric disorders. However, the adoption of biomarker-based frameworks, such as IWG and NIA–AA (2018), remains limited in clinical trials.

The symptom-based diagnostic criteria, including NINCDS-ADRDA, ICD-10, DSM-IV, DSM-IV-TR, and DSM-5, are categorized as first-generation criteria,¹⁰⁵ valued for their simplicity and ease of use but limited to diagnosing AD dementia, as they cannot identify early AD. Second-generation criteria, including the IWG and NIA-AA series, improve diagnostic accuracy by incorporating biomarker-based diagnostics, allowing for early AD diagnosis. However, due to gaps in understanding AD pathogenesis and limitations in biomarker detection technologies during earlier years, their initial implementation was constrained. The latest third-generation criteria incorporate blood-based biomarkers,¹³ marking a new era of non-invasive AD screening and simplifying the diagnostic process. A detailed comparison of the advantages and limitations of each diagnostic framework is provided in the Supplemental Material.

Cognitive assessment: applications and limitations

Cognitive assessment scales play a vital role in AD screening and diagnosis. The MMSE remains the most widely used tool, appearing in 86% of reviewed studies due to its ease of administration and ability to differentiate dementia from normal cognition.¹⁰⁶ However, 37% of studies relied solely on MMSE, despite evidence that it lacks sensitivity in detecting early-stage AD.¹⁰⁷ Moreover, the MMSE has been criticized for its limitations in detecting mild dementia and MCI, particularly due to its uneven assessment of cognitive domains and low sensitivity to executive function deficits.¹⁰⁸ Research has shown that MMSE fails to reliably detect cognitive decline in highly educated individuals, while also overestimating impairment in individuals with lower education levels.¹⁰⁹ Given these limitations, relying solely on MMSE may lead to misclassification of patients, including the incorrect inclusion of non-AD participants in clinical trials, thereby affecting study accuracy and reproducibility. Multidomain assessments, such as incorporating CDR, ADAS-cog, or ADL, have been shown to improve sensitivity in early AD detection and enhance disease stratification in clinical research.¹¹⁰

The MMSE cutoffs lack a standardized criterion and are influenced by factors such as age, education level, and cultural background of the assessed population.¹⁰⁹ Our analysis found that 26 is the most commonly used threshold for defining cognitive impairment.^{29,44,48,51,60,61,63,67,71} Other frequently used cutoffs include 24,^{22,24–26,29} as well as 22^35,43 and 20 points.⁶² Interestingly, higher cutpoint like 27⁴⁷ and 28³⁰ have been utilized in some studies. For mild to moderate AD, we found a remarkable diversity, with 19 distinct cutoffs reported across different studies. This indicates that the variability in using MMSE for disease stratification is substantial, potentially leading to difficulties in comparing and replicating research results. Therefore, more objective assessment methods are needed to address the limitations of the MMSE scale.

Advances and challenges in biomarker-based diagnosis

The integration of biomarkers has improved AD diagnostic accuracy, yet their use in RCTs remains inconsistent. Our review found that less than forty percent of studies incorporated biomarkers, with nearly half relying exclusively on traditional clinical assessments (Figure 6A). The limited adoption of biomarkers may be caused by the collection carries risks and discomfort for CSF, and expensive for PET imaging. Moreover, both methods may not effectively detect early cognitive decline, can lead to over-diagnosis, and offer limited prognostic value as not all patients with abnormal biomarkers progress to AD.^111,112 In addition, although MRI is frequently used in clinical practice for the diagnosis of AD, particularly features such as medial temporal lobe atrophy, hippocampal atrophy, hippocampal volume loss, enlarged lateral ventricles, cortical thinning, and posterior cortical atrophy, these abnormalities typically emerge when the disease has already progressed to the dementia stage.¹¹³ So CSF, PET and MRI all have limited utility for early screening of AD. We need to develop noninvasive, straightforward, and cost-effective screening methods, such as blood biomarkers or electroencephalography (EEG).

In recent years, plasma phosphorylated tau biomarkers, particularly p-tau 217, have emerged as a major breakthrough in AD diagnostics. Studies have shown that p-tau 217 has diagnostic accuracy comparable to CSF and PET biomarkers in detecting early AD pathology.¹¹⁴ Compared to p-tau 181, p-tau 217 demonstrates higher specificity in distinguishing AD from other neurodegenerative diseases and correlates strongly with Aβ burden and neuronal degeneration.¹¹⁵ Moreover, p-tau 217 detection is less invasive and more cost-effective than CSF and PET, making it a promising tool for early screening, disease monitoring, and patient stratification in clinical trials.

In parallel, EEG-based biomarkers are gaining recognition for early AD diagnosis.¹¹⁶ EEG provides real-time insights into brain activity, making it a promising tool for detecting synaptic dysfunction in early AD.¹¹⁷ Altered EEG patterns, such as increased delta/theta power and reduced global field synchronization, correlate with CSF Aβ₄₂, p-tau, and t-tau levels.¹¹⁸ Furthermore, increased theta/gamma and alpha3/alpha2 ratios in MCI patients are associated with higher AD conversion rates.^119,120 The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations suggest that resting-state EEG can provide objective electrophysiological evidence for early screening, disease progression monitoring, and treatment evaluation of AD.¹²¹ They also proposed a new ATPN diagnostic framework for AD, where P represents electrophysiological biomarkers provided by EEG.¹²¹ Given its low cost, accessibility, and high temporal resolution, EEG could also serve as a valuable complement to cognitive assessments and biomarker-based diagnostics.

Therapeutic strategies and future directions

For AD treatment, it is widely recognized that earlier intervention leads to better therapeutic outcomes. However, both earlier studies targeting mild-to-moderate AD and recent research focusing on early-stage AD, including prodromal AD, MCI due to AD, and mild AD, have predominantly investigated interventions such as anti-Aβ monoclonal antibodies,^{37,53,58–60,67,70} Aβ inhibitors,^25,32,54 tau-aggregation inhibitors,^49,61 and anti-tau monoclonal antibodies,^31,35,36,82 yet none have shown significant cognitive improvement.

In recent 10 years, only aducanumab, lecanemab, and donanemab have received FDA approval for slowing cognitive decline in MCI and mild AD.^5,34,95 However, the clinical efficacy of these drugs is limited and associated with serious adverse effects, even after 18 months of treatment, which are far less pronounced than 6 months of oral 10 mg donepezil treatment.¹²² Meanwhile, a recent meta-analysis reported that rTMS demonstrated greater efficacy, better tolerability, and fewer side effects than the current three DMT drugs.¹²³ In particular, a personalized rTMS protocol administered over four weeks (20 sessions) for early-stage AD patients resulted in significant improvements in both cognitive function and daily performance.²¹ Furthermore, some studies suggests that multimodal strategies, such as the RECODE program,^124,125 lifestyle or dietary changes may outperform single-drug therapies.^28,38,126 These non-invasive, low-risk interventions merit further exploration to evaluate their potential role in AD management.

Study limitations

This systematic review has several limitations that should be acknowledged. First, the reliance on three databases (Web of Science, PubMed, and Google Scholar) may have excluded relevant studies indexed in other databases, such as Embase or Scopus, potentially introducing selection bias. Second, although 27,471 articles were initially screened, only 71 studies met the inclusion criteria, which may limit the generalizability of the findings, particularly for underrepresented populations or research methodologies. Third, the broad time span of included studies (2014–2024) may have contributed to the limited adoption of more recent diagnostic frameworks, such as IWG-2021 and NIA-AA 2018, which integrate biomarkers more comprehensively. Since older studies relied heavily on symptom-based criteria (e.g., NINCDS-ADRDA, NIA-AA 2011), the review may underrepresent emerging biomarker-driven diagnostic approaches, leading to a potential bias toward traditional clinical diagnostic methods.

Conclusion

Our review summarized the diagnostic and detailed inclusion criteria adopted in clinical studies of AD in the past 10 years, mainly concluded as follows: (1) The NINCDS–ADRDA criteria are the most prevalent used to diagnose advanced AD, the less commonly used NIA–AA (2011) criteria encompass a wider range of AD stages; (2) Earlier studies primarily focused on mild-to-moderate AD, but recent years have seen an increase in research on early-stage AD. However, most single-drug therapies or standalone interventions have failed to produce significant cognitive improvements at either early or advanced stages of AD dementia; (3) The MMSE remains the most widely used cognitive assessment tool for AD screening, but its cutoff points vary considerably across studies; (4) The use of biomarkers in clinical research is limited, with many studies relying on traditional clinical methods; (5) The age ranges for study inclusion varied, most studies focused on participants aged 50 to 90 years. Our findings emphasize the need for harmonizing AD diagnostic criteria and explore combination therapies for AD treatment. Non-invasive and low-risk interventions deserve more attention, while more objective and practical cognitive assessment tools, such as blood biomarker and EEG, could serve as valuable supplements to traditional cognitive scales.

Supplemental Material

sj-docx-1-alr-10.1177_25424823251362444 - Supplemental material for Diagnostic and inclusion criteria in Alzheimer's disease clinical trials: A systematic review of the past decade

Supplemental material, sj-docx-1-alr-10.1177_25424823251362444 for Diagnostic and inclusion criteria in Alzheimer's disease clinical trials: A systematic review of the past decade by Yumei Liu, Siyan Chen, Sha Li, Zian Pei, Shuhan Fan and Yi Guo in Journal of Alzheimer's Disease Reports

Footnotes

Acknowledgements

We would like to thank Editage () for English language editing.

ORCID iDs

Yumei Liu

Siyan Chen

Sha Li

Zian Pei

Shuhan Fan

Yi Guo

Author contributions

Yumei Liu: Conceptualization; Data curation; Formal analysis; Resources; Writing – original draft.

Siyan Chen: Writing – review & editing.

Sha Li: Writing – review & editing.

Zian Pei: Visualization.

Shuha Fan: Visualization.

Yi Guo: Formal analysis; Project administration; Writing – review & editing.

Funding

This review was supported by National Natural Science Foundation of China (82371471), the Shenzhen Science and Technology Innovation Commission (KCXFZ20201221173400001, KCXFZ20201221173411032, and SGDX20210823103805042) and Natural Science Fund of Guangdong Province (2021A1515010983).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data supporting the findings of this study are available in this article. Further inquiries can be directed to the corresponding authors.

Supplemental material

Supplemental material for this article is available online.

References

Wimo

Handels

Antikainen

, et al. Dementia prevention: the potential long-term cost-effectiveness of the FINGER prevention program. Alzheimers Dement 2023; 19: 999–1008.

Alzheimer's Association. 2020 Alzheimer's disease facts and figures. Alzheimers Dement 2020; 16: 391–460.

Imbimbo

Balducci

Ippati

, et al. Initial failures of anti-tau antibodies in Alzheimer’s disease are reminiscent of the amyloid-β story. Neural Regen Res 2023; 18: 117–118.

Van Dyck

Swanson

Aisen

, et al. Lecanemab in early Alzheimer’s disease. N Engl J Med 2023; 388: 9–21.

Sims

Zimmer

Evans

, et al. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical trial. JAMA 2023; 330: 512–527.

Alzheimer's Association. Alzheimer’s disease facts and figures. Alzheimers Dement 2023; 19: 1598–1695.

McKhann

Drachman

Folstein

, et al. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer's disease. Neurology 1984; 34: 939–944.

Dubois

Feldman

Jacova

, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007; 6: 734–746.

Dubois

Feldman

Jacova

, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 2014; 13: 614–629.

10.

Dubois

Villain

Frisoni

, et al. Clinical diagnosis of Alzheimer's disease: recommendations of the international working group. Lancet Neurol 2021; 20: 484–496.

11.

McKhann

Knopman

Chertkow

, et al. 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 2011; 7: 263–269.

12.

Jack Jr

Bennett

Blennow

, et al. NIA-AA Research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement 2018; 14: 535–562.

13.

Jack

Andrews

Beach

, et al. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's association workgroup. Alzheimers Dement 2024; 20: 5143–5169.

14.

World Health Organization. The ICD-10 classification of metal and behavioural disoders:diagnostic criteria for research. Geveva: World Health Organization, 1993, p.248.

15.

American Psychatric Association. Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition. Washington, DC: American Psychiatric Association, 1994.

16.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disoders, Fourth Edition; Text Revision. Washington, DC: American Psychiatric Association, 2000.

17.

Riello

Albini

Galluzzi

, et al. Prescription practices of diagnostic imaging in dementia: a survey of 47 Alzheimer's centres in northern Italy. Int J Geriatr Psychiatry 2003; 18: 577–585.

18.

Chételat

Arbizu

Barthel

, et al. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. Lancet Neurol 2020; 19: 951–962.

19.

Cummings

Lee

Ritter

, et al. Alzheimer's disease drug development pipeline: 2019. Alzheimers Dement (N Y) 2019; 5: 272–293.

20.

Moher

Liberati

Tetzlaff

, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6: e1000097.

21.

Jung

Jang

Park

, et al. Effectiveness of personalized hippocampal network–targeted stimulation in Alzheimer disease. JAMA Netw Open 2024; 7: e249220.

22.

Coric

Salloway

van Dyck

, et al. Targeting prodromal Alzheimer disease with avagacestat: a randomized clinical trial. JAMA Neurol 2015; 72: 1324–1333.

23.

Petersen

. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256: 183–194.

24.

Soininen

Solomon

Visser

, et al. 24-month Intervention with a specific multinutrient in people with prodromal Alzheimer's disease (LipiDiDiet): a randomised, double-blind, controlled trial. Lancet Neurol 2017; 16: 965–975.

25.

Egan

Kost

Voss

, et al. Randomized trial of verubecestat for prodromal Alzheimer's disease. N Engl J Med 2019; 380: 1408–1420.

26.

Vina

Escudero

Baquero

, et al. Genistein effect on cognition in prodromal Alzheimer's disease patients. The GENIAL clinical trial. Alzheimers Res Ther 2022; 14: 164.

27.

Thunborg

Wang

Rosenberg

, et al. Integrating a multimodal lifestyle intervention with medical food in prodromal Alzheimer's disease: the MIND-AD(mini) randomized controlled trial. Alzheimers Res Ther 2024; 16: 118.

28.

Levak

Lehtisalo

Thunborg

, et al. Nutrition guidance within a multimodal intervention improves diet quality in prodromal Alzheimer's disease: multimodal preventive trial for Alzheimer's disease (MIND-AD(mini)). Alzheimers Res Ther 2024; 16: 147.

29.

Frölich

Wunderlich

Thamer

, et al. Evaluation of the efficacy, safety and tolerability of orally administered BI 409306, a novel phosphodiesterase type 9 inhibitor, in two randomised controlled phase II studies in patients with prodromal and mild Alzheimer's disease. Alzheimers Res Ther 2019; 11: 18.

30.

Mintun

Evans

, et al. Donanemab in early Alzheimer's disease. N Engl J Med 2021; 384: 1691–1704.

31.

Teng

Manser

Pickthorn

, et al. Safety and efficacy of semorinemab in individuals with prodromal to mild Alzheimer disease a randomized clinical trial. JAMA Neurol 2022; 79: 758–767.

32.

Ostrowitzki

Bittner

Sink

, et al. Evaluating the safety and efficacy of crenezumab vs placebo in adults with early Alzheimer disease two phase 3 randomized placebo-controlled trials. JAMA Neurol 2022; 79: 1113–1121.

33.

Scheltens

Hallikainen

Grimmer

, et al. Safety, tolerability and efficacy of the glutaminyl cyclase inhibitor PQ912 in Alzheimer's disease: results of a randomized, double-blind, placebo-controlled phase 2a study. Alzheimers Res Ther 2018; 10: 107.

34.

Haeberlein

Aisen

Barkhof

, et al. Two randomized phase 3 studies of aducanumab in early Alzheimer's disease. J Prev Alzheimers Dis 2022; 9: 197–210.

35.

Florian

Wang

Arnold

, et al. Tilavonemab in early Alzheimer's disease: results from a phase 2, randomized, double-blind study. Brain 2023; 146: 2275–2284.

36.

Shulman

Kong

O'Gorman

, et al. TANGO: a placebo-controlled randomized phase 2 study of efficacy and safety of the anti-tau monoclonal antibody gosuranemab in early Alzheimer's disease. Nat Aging 2023; 3: 1591–1601.

37.

Bateman

Smith

Donohue

, et al. Two phase 3 trials of gantenerumab in early Alzheimer's disease. N Engl J Med 2023; 389: 1862–1876.

38.

Ornish

Madison

Kivipelto

, et al. Effects of intensive lifestyle changes on the progression of mild cognitive impairment or early dementia due to Alzheimer's disease: a randomized, controlled clinical trial. Alzheimers Res Ther 2024; 16: 122.

39.

Galasko

Farlow

Lucey

, et al. A multicenter, randomized, double-blind, placebo-controlled ascending dose study to evaluate the safety, tolerability, pharmacokinetics (PK) and pharmacodynamic (PD) effects of posiphen in subjects with early Alzheimer's disease. Alzheimers Res Ther 2024; 16: 151.

40.

Shah

Matthews

Andrews

, et al. An evaluation of MSDC-0160, a prototype mTOT modulating insulin sensitizer, in patients with mild Alzheimer's disease. Curr Alzheimer Res 2014; 11: 564–573.

41.

Bystad

Grønli

Rasmussen

, et al. Transcranial direct current stimulation as a memory enhancer in patients with Alzheimer's disease: a randomized, placebo-controlled trial. Alzheimers Res Ther 2016; 8: 13.

42.

Rijpma

van der Graaf

Lansbergen

, et al. The medical food souvenaid affects brain phospholipid metabolism in mild Alzheimer's disease: results from a randomized controlled trial. Alzheimers Res Ther 2017; 9: 51.

43.

Huntley

Hampshire

Bor

, et al. Adaptive working memory strategy training in early Alzheimer's disease: randomised controlled trial. Br J Psychiatry 2017; 210: 61–66.

44.

Honig

Vellas

Woodward

, et al. Trial of solanezumab for mild dementia due to Alzheimer's disease. N Engl J Med 2018; 378: 321–330.

45.

Sobol

Dall

Høgh

, et al. Change in fitness and the relation to change in cognition and neuropsychiatric symptoms after aerobic exercise in patients with mild Alzheimer's disease. J Alzheimers Dis 2018; 65: 137–145.

46.

Phillips

MCL

Deprez

Mortimer

GMN

, et al. Randomized crossover trial of a modified ketogenic diet in Alzheimer's disease. Alzheimers Res Ther 2021; 13: 51.

47.

Matthews

Xiangling

Dowd

, et al. Riluzole, a glutamate modulator, slows cerebral glucose metabolism decline in patients with Alzheimer's disease. Brain 2021; 144: 3742–3755.

48.

Hui Jing

Dickson

Pei-Ning

, et al. Safety, tolerability, immunogenicity, and efficacy of UB-311 in participants with mild Alzheimer's disease: a randomised, double-blind, placebo-controlled, phase 2a study. EBioMedicine 2023; 94: 104665.

49.

Mummery

Börjesson-Hanson

Blackburn

, et al. Tau-targeting antisense oligonucleotide MAPT(rx) in mild Alzheimer's disease: a phase 1b, randomized, placebo-controlled trial. Nat Med 2023; 29: 1437–1447.

50.

Tang

Xing

Sun

, et al. TRanscranial AlterNating current stimulation FOR patients with mild Alzheimer's disease (TRANSFORM-AD): a randomized controlled clinical trial. Alzheimers Res Ther 2024; 16: 203.

51.

Rijpma

Meulenbroek

van Hees

, et al. Effects of Souvenaid on plasma micronutrient levels and fatty acid profiles in mild and mild-to-moderate Alzheimer's disease. Alzheimers Res Ther 2015; 7: 51.

52.

Dysken

Guarino

Vertrees

, et al. Vitamin E and memantine in Alzheimer's disease: clinical trial methods and baseline data. Alzheimers Dement 2014; 10: 36–44.

53.

Salloway

Sperling

Fox

, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease. N Engl J Med 2014; 370: 322–333.

54.

Galasko

Bell

Mancuso

, et al. Clinical trial of an inhibitor of RAGE-Aβ interactions in Alzheimer disease. Neurology 2014; 82: 1536–1542.

55.

Shinto

Quinn

Montine

, et al. A randomized placebo-controlled pilot trial of omega-3 fatty acids and alpha lipoic acid in Alzheimer's disease. J Alzheimers Dis 2014; 38: 111–120.

56.

Burstein

Grimes

Galasko

, et al. Effect of TTP488 in patients with mild to moderate Alzheimer's disease. BMC Neurol 2014; 14: 12.

57.

Grimaldi

Zappalà

Iemolo

, et al. A pilot study on the use of interferon beta-1a in early Alzheimer's disease subjects. J Neuroinflammation 2014; 11: 30.

58.

Gault

Lenz

Ritchie

, et al. ABT-126 monotherapy in mild-to-moderate Alzheimer's dementia: randomized double-blind, placebo and active controlled adaptive trial and open-label extension. Alzheimers Res Ther 2016; 8: 44.

59.

Logovinsky

Satlin

Lai

, et al. Safety and tolerability of BAN2401–a clinical study in Alzheimer's disease with a protofibril selective Aβ antibody. Alzheimers Res Ther 2016; 8: 14.

60.

Vandenberghe

Rinne

Boada

, et al. Bapineuzumab for mild to moderate Alzheimer's disease in two global, randomized, phase 3 trials. Alzheimers Res Ther 2016; 8: 18.

61.

Gauthier

Feldman

Schneider

, et al. Efficacy and safety of tau-aggregation inhibitor therapy in patients with mild or moderate Alzheimer's disease: a randomised, controlled, double-blind, parallel-arm, phase 3 trial. Lancet 2016; 388: 2873–2884.

62.

Cummings

Zhong

Kinney

, et al. Double-blind, placebo-controlled, proof-of-concept trial of bexarotene Xin moderate Alzheimer's disease. Alzheimers Res Ther 2016; 8: 4.

63.

Relkin

Thomas

Rissman

, et al. A phase 3 trial of IV immunoglobulin for Alzheimer disease. Neurology 2017; 88: 1768–1775.

64.

Decourt

Drumm-Gurnee

Wilson

, et al. Poor safety and tolerability hamper reaching a potentially therapeutic dose in the use of thalidomide for Alzheimer's disease: results from a double-blind, placebo-controlled trial. Curr Alzheimer Res 2017; 14: 403–411.

65.

Boada

Anaya

Ortiz

, et al. Efficacy and safety of plasma exchange with 5% albumin to modify cerebrospinal fluid and plasma amyloid-β concentrations and cognition outcomes in Alzheimer's disease patients: a multicenter, randomized, controlled clinical trial. J Alzheimers Dis 2017; 56: 129–143.

66.

Lawlor

Segurado

Kennelly

, et al. Nilvadipine in mild to moderate Alzheimer disease: a randomised controlled trial. PLoS Med 2018; 15: e1002660.

67.

Cummings

Cohen

van Dyck

, et al. ABBY: a phase 2 randomized trial of crenezumab in mild to moderate Alzheimer disease. Neurology 2018; 90: e1889–e1897.

68.

Fullerton

Binneman

David

, et al. A phase 2 clinical trial of PF-05212377 (SAM-760) in subjects with mild to moderate Alzheimer's disease with existing neuropsychiatric symptoms on a stable daily dose of donepezil. Alzheimers Res Ther 2018; 10: 38.

69.

Lacosta

Pascual-Lucas

Pesini

, et al. Safety, tolerability and immunogenicity of an active anti-aβ(40) vaccine (ABvac40) in patients with Alzheimer's disease: a randomised, double-blind, placebo-controlled, phase I trial. Alzheimers Res Ther 2018; 10: 12.

70.

Salloway

Honigberg

Cho

, et al. Amyloid positron emission tomography and cerebrospinal fluid results from a crenezumab anti-amyloid-beta antibody double-blind, placebo-controlled, randomized phase II study in mild-to-moderate Alzheimer's disease (BLAZE). Alzheimers Res Ther 2018; 10: 96.

71.

Egan

Kost

Tariot

, et al. Randomized trial of verubecestat for mild-to-moderate Alzheimer's disease. N Engl J Med 2018; 378: 1691–1703.

72.

Atri

Frölich

Ballard

, et al. Effect of idalopirdine as adjunct to cholinesterase inhibitors on change in cognition in patients with Alzheimer disease: three randomized clinical trials. JAMA 2018; 319: 130–142.

73.

Cardoso

Roberts

Malpas

, et al. Supranutritional sodium selenate supplementation delivers selenium to the central nervous system: results from a randomized controlled pilot trial in Alzheimer's disease. Neurotherapeutics 2019; 16: 192–202.

74.

Vila-Castelar

Kaplan

, et al. Attention measures of accuracy, variability, and fatigue detect early response to donepezil in Alzheimer's disease: a randomized, double-blind, placebo-controlled pilot trial. Arch Clin Neuropsychol 2019; 34: 277–289.

75.

Villars

Cantet

de Peretti

, et al. Impact of an educational programme on Alzheimer's disease patients’ quality of life: results of the randomized controlled trial THERAD. Alzheimers Res Ther 2021; 13: 152.

76.

Kehoe

Turner

Howden

, et al. Safety and efficacy of losartan for the reduction of brain atrophy in clinically diagnosed Alzheimer's disease (the RADAR trial): a double-blind, randomised, placebo-controlled, phase 2 trial. Lancet Neurol 2021; 20: 895–906.

77.

Koch

Casula

Bonni

, et al. Precuneus magnetic stimulation for Alzheimer's disease: a randomized, sham-controlled trial. Brain 2022; 145: 3776–3786.

78.

Juby

Blackburn

Mager

. Use of medium chain triglyceride (MCT) oil in subjects with Alzheimer's disease: a randomized, double-blind, placebo-controlled, crossover study, with an open-label extension. Alzheimers Dement (N Y) 2022; 8: e12259.

79.

Wunderlich

Blahova

Garcia

, et al. Efficacy and safety of the novel GlyT1 inhibitor BI 425809 in Alzheimer's dementia: a randomized controlled trial. Alzheimers Res Ther 2023; 15: 24.

80.

Yulug

Altay

, et al. Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial. Transl Neurodegener 2023; 12: 4.

81.

Karima

Aghamollaii

Mahmoodi Baram

, et al. Boswellic acids improve clinical cognitive scores and reduce systemic inflammation in patients with mild to moderate Alzheimer's disease. J Alzheimers Dis 2023; 94: 359–370.

82.

Monteiro

Toth

Brunstein

, et al. Randomized phase II study of the safety and efficacy of semorinemab in participants with mild-to-moderate Alzheimer disease: lauriet. Neurology 2023; 101: e1391–e1401.

83.

Lane

Wang

Lin

. Endogenous antioxidants predicted outcome and increased after treatment: a benzoate dose-finding, randomized, double-blind, placebo-controlled trial for Alzheimer's disease. Psychiatry Clin Neurosci 2023; 77: 102–109.

84.

Hsu

Huang

Tsai

, et al. Efficacy of probiotic supplements on brain-derived neurotrophic factor, inflammatory biomarkers, oxidative stress and cognitive function in patients with Alzheimer's dementia: a 12-week randomized, double-blind active-controlled study. Nutrients 2023; 16: 16.

85.

Foroumandi

Javan

Moayed

, et al. The effects of fenugreek seed extract supplementation in patients with Alzheimer's disease: a randomized, double-blind, placebo-controlled trial. Phytother Res 2023; 37: 285–294.

86.

Fernando

Silva

Fernando

, et al. Effect of virgin coconut oil supplementation on cognition of individuals with mild-to-moderate Alzheimer's disease in Sri Lanka (VCO-AD study): a randomized placebo-controlled trial. J Alzheimers Dis 2023; 96: 1195–1206.

87.

Dubois

López-Arrieta

Lipschitz

, et al. Masitinib for mild-to-moderate Alzheimer's disease: results from a randomized, placebo-controlled, phase 3, clinical trial. Alzheimers Res Ther 2023; 15: 39.

88.

Sosa

Bringas

Urrutia

, et al. NeuroEPO plus (NeuralCIM(®)) in mild-to-moderate Alzheimer's clinical syndrome: the ATHENEA randomized clinical trial. Alzheimers Res Ther 2023; 15: 215.

89.

Vijverberg

de Haan

Scheijbeler

, et al. A pilot electroencephalography study of the effect of CT1812 treatment on synaptic activity in patients with mild to moderate Alzheimer's disease. J Prev Alzheimers Dis 2024; 11: 1809–1817.

90.

Hong

Han

Youn

, et al. Safety and tolerability of donepezil 23 mg with or without intermediate dose titration in patients with Alzheimer's disease taking donepezil 10 mg: a multicenter, randomized, open-label, parallel-design, three-arm, prospective trial. Alzheimers Res Ther 2019; 11: 37.

91.

Rafii

Tuszynski

Thomas

, et al. Adeno-associated viral vector (serotype 2)-nerve growth factor for patients with Alzheimer disease: a randomized clinical trial. JAMA Neurol 2018; 75: 834–841.

92.

Dickson

Wang

93.

Albert

DeKosky

Dickson

, et al. 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 2011; 7: 270–279.

94.

Sperling

Aisen

Beckett

, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the national institute on aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011; 7: 280–292.

95.

van Dyck

Swanson

Aisen

, et al. Lecanemab in early Alzheimer's disease. N Engl J Med 2023; 388: 9–21.

96.

Dubois

Villain

Schneider

, et al. Alzheimer disease as a clinical-biological construct-an international working group recommendation. JAMA Neurol 2024; 81: 1304–1311.

97.

Harper

. 2020 Alzheimer’s association facts and figures. Alzheimers Dement 2020; 16: 391–460.

98.

Doody

Thomas

Farlow

, et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer's disease. N Engl J Med 2014; 370: 311–321.

99.

Crawford

Parker

McKinlay

. A Handbook of Neuropsychological Assessment. London: Routledge, 1992.

100.

Folstein

McHugh

. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189–198.

101.

Dysken

Sano

Asthana

, et al. Effect of vitamin E and memantine on functional decline in Alzheimer disease: the TEAM-AD VA cooperative randomized trial. JAMA 2014; 311: 33–44.

102.

Hebert

Weuve

Scherr

, et al. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology 2013; 80: 1778–1783.

103.

Nelson

Head

Schmitt

, et al. Alzheimer's disease is not “brain aging": neuropathological, genetic, and epidemiological human studies. Acta Neuropathol 2011; 121: 571–587.

104.

Schaumberg

Ishak

. Considerations for regulatory application of RWD-generated external comparators. https://www.raps.org/news-and-articles/news-articles/2021/7/considerations-for-regulatory-application-of-rwd-g. Regulatory Focus. Published online 29 July 2021.

105.

. Diagnostic criteria for Alzheimer's disease: from clinical to pathophysiologic diagnosis. J Shandong Univ Med Ed 2017; 55: 14–20.

106.

Freitas

Simões

Alves

, et al. Montreal Cognitive assessment: validation study for mild cognitive impairment and Alzheimer disease. Alzheimer Dis Assoc Disord 2013; 27: 37–43.

107.

Martin

Healy

McElwaine

, et al. Screening for cognitive impairment in older general hospital patients: comparison of the six-item cognitive impairment test with the mini-mental state examination. A reply. Int J Geriatr Psychiatry 2012; 27: 763–764.

108.

Nasreddine

Phillips

Bédirian

, et al. The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53: 695–699.

109.

Tombaugh

McIntyre

. The mini-mental state examination: a comprehensive review. J Am Geriatr Soc 1992; 40: 922–935.

110.

Wei

Chen

Lin

, et al. Normative data of mini-mental state examination, Montreal cognitive assessment, and Alzheimer’s disease assessment scale-cognitive subscale of community-dwelling older adults in Taiwan. Dement Geriatr Cogn Disord 2022; 51: 365–376.

111.

Younes

Albert

Moghekar

, et al. Identifying changepoints in biomarkers during the preclinical phase of Alzheimer’s disease. Front Aging Neurosci 2019; 11: 74.

112.

Suckling

, et al. Frequency and longitudinal clinical outcomes of Alzheimer's AT (N) biomarker profiles: a longitudinal study. Alzheimers Dement 2019; 15: 1208–1217.

113.

Lin

Shi

, et al. Uncovering stage-specific neural and molecular progression in Alzheimer's disease: implications for early screening. Alzheimers Dement 2025; 21: e70182.

114.

Palmqvist

Janelidze

Quiroz

, et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA 2020; 324: 772–781.

115.

Janelidze

Bali

Ashton

, et al. Head-to-head comparison of 10 plasma phospho-tau assays in prodromal Alzheimer’s disease. Brain 2023; 146: 1592–1601.

116.

Rossini

Di Iorio

Vecchio

, et al. Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophysiol 2020; 131: 1287–1310.

117.

de Wilde

Overk

Sijben

, et al. Meta-analysis of synaptic pathology in Alzheimer's disease reveals selective molecular vesicular machinery vulnerability. Alzheimers Dement 2016; 12: 633–644.

118.

Smailovic

Koenig

Kåreholt

, et al. Quantitative EEG power and synchronization correlate with Alzheimer's disease CSF biomarkers. Neurobiol Aging 2018; 63: 88–95.

119.

Moretti

Pievani

Pini

, et al. Cerebral PET glucose hypometabolism in subjects with mild cognitive impairment and higher EEG high-alpha/low-alpha frequency power ratio. Neurobiol Aging 2017; 58: 213–224.

120.

Roh

Park

, et al. Region and frequency specific changes of spectral power in Alzheimer's disease and mild cognitive impairment. Clin Neurophysiol 2011; 122: 2169–2176.

121.

Babiloni

Arakaki

Azami

, et al. Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: recommendations of an expert panel. Alzheimers Dement 2021; 17: 1528–1553.

122.

Espay

Kepp

Herrup

. Lecanemab and donanemab as therapies for Alzheimer's disease: An illustrated perspective on the data. eNeuro 2024; 11: ENEURO.0319-23.2024.

123.

Terao

Kodama

. Comparative efficacy, tolerability, and acceptability of aducanumab, lecanemab, and donanemab with repetitive transcranial magnetic stimulation on cognitive function in mild cognitive impairment and Alzheimer's disease: A systematic review and network meta-analysis. J Psychopharmacol 2025. Epub ahead of print 19 May 2025: DOI: https://doi.org/10.1177/02698811251340901.

124.

Bredesen

. Reversal of cognitive decline: a novel therapeutic program. Aging (Albany NY) 2014; 6: 707–717.

125.

Rao

Kumar

Gregory

, et al. ReCODE: a personalized, targeted, multi-factorial therapeutic program for reversal of cognitive decline. Biomedicines 2021; 9: 1348.

126.

Roach

Rapozo

Hara

, et al. A remotely coached multimodal lifestyle intervention for Alzheimer's disease ameliorates functional and cognitive outcomes. J Alzheimers Dis 2023; 96: 591–607.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.02 MB

0.00 MB