27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
Among Aβ-positive individuals, greater CR related to attenuated clinical progression in predementia stages of AD, but accelerated cognitive decline after the onset of dementia. 2 key findings →
Design: longitudinal study Participants: 175 Evidence: moderate
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Selection criteria We included randomised controlled trials (RCTs) and quasi-RCTs in which cognitive training via interactive computerised technology was compared with an active or inactive control intervention. 5 key findings →
Participants: 660 Evidence: high
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No statistically significant pooled association emerged between MetS and incident dementia and AD. 2 key findings →
Design: systematic review Participants: 18,313 Evidence: high
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Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. 1 key finding →
Design: cross-sectional Evidence: moderate
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The prevalence of A+T+N+F+ profile was 66.24% in clinically diagnosed AD dementia patients; similarly, the majority of individuals with reduced FDG-PET were AD dementia subjects. 1 key finding →
Design: longitudinal study Participants: 551 Evidence: moderate
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Study objectives To determine the effect of self-reported clinical diagnosis of obstructive sleep apnea (OSA) on longitudinal changes in brain amyloid PET and CSF biomarkers (Aβ42, T-tau, and P-tau) in cognitively normal (NL), mild cognitive impairment (MCI), and Alzheimer's disease (AD) elderly. 2 key findings →
Design: longitudinal study Participants: 516 Evidence: moderate
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The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. 1 key finding →
Participants: 785 Evidence: low
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Qualitatively, our review of 57 MCI versus HC comparisons suggests substantial inconsistency; 9 showed no group difference, 8 showed MCI > HC and 22 showed HC > MCI across the brain, and 18 showed regionally-mixed directions of effect. 3 key findings →
Design: meta-analysis Evidence: high
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Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. 1 key finding →
Evidence: moderate
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Our findings support the association of increased neuroinflammation during the progression of MCI and AD, relative to HCs. 4 key findings →
Design: meta-analysis Evidence: high
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Available data consistently show that risk of progression increases across the preclinical AD stages, where Stage 3 shows a risk of progression comparable to MCI due to AD. 2 key findings →
Design: cross-sectional Evidence: moderate
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The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. 1 key finding →
Participants: 883 Evidence: moderate
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Recently, increasing evidence has suggested the association between gut dysbiosis and Alzheimer's disease (AD) progression, yet the role of gut microbiota in AD pathogenesis remains obscure. 3 key findings →
Design: clinical trial Evidence: low
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Multiple linear regression models identified sources of signal covariance between neuroinflammation and brain connectivity profiles, in relation to the diagnostic group (patients, controls) and cognitive status. 3 key findings →
Participants: 28 Evidence: low
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Alzheimer disease (AD) is a major cause of age-related dementia. 1 key finding →
Evidence: low
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Most approaches to machine learning from electronic health data can only predict a single endpoint. 1 key finding →
Participants: 1,909 Evidence: low
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Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. 2 key findings →
Evidence: low
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The trend changes in the hippocampus subfields and further illustrates that SCD is the preclinical stage of AD earlier than aMCI. 1 key finding →
Design: longitudinal study Evidence: moderate
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Loss of function of TREM2, a key receptor selectively expressed by microglia in the brain, contributes to the development of Alzheimer's disease (AD). 3 key findings →
Evidence: low
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CSF synaptic biomarkers, particularly NPTX2, which regulates synaptic homeostasis, relate to cognition and predict progression in AD beyond Aβ1-42 and Tau. 2 key findings →
Evidence: moderate
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Alzheimer's disease (AD) is a devastating and progressive neurodegenerative disease for which there is no cure. 5 key findings →
Evidence: low
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TP42/40 demonstrated value in the identification of individuals suffering a-MCI, in the prediction of progression to dementia, and in the detection of underlying AD pathology revealed by FDG-PET, Amyloid-PET and CSF biomarkers, being, thus, consistently associated with all the well-established indicators of AD. 3 key findings →
Participants: 145 Evidence: low
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Our novel neuropsychological approach improved the staging of MCI by (1) capturing individuals at an early symptomatic stage, (2) minimizing false-positive cases, and (3) identifying a late MCI group further along the disease trajectory. 2 key findings →
Evidence: low
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No disease-modifying therapy is currently available for Alzheimer's disease, but therapies are in development, and one may become available in the near future. 1 key finding →
Evidence: low
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Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled.
This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.
Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.
Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.
We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.
The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.
See the whole rubric drawn out → — four charts covering all 27,380 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.
This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 27,380 records, built mostly from automated pulls of Europe PMC (60.6% of the library) and OpenAlex (18.6%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.
1,032 records (3.8% of the library) are not peer-reviewed — 962 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.
See the full, machine-readable breakdown of this library's coverage →