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The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step‐by‐step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. Highlights We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early‐changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later‐changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

Modifiable factors can influence the risk for Alzheimer’s disease (AD) and serve as targets for intervention; however, the biological mechanisms linking these factors to AD are unknown. This study aims to identify plasma metabolites associated with modifiable factors for AD, including MIND diet, physical activity, smoking, and caffeine intake, and test their association with AD endophenotypes to identify their potential roles in pathophysiological mechanisms. The association between each of the 757 plasma metabolites and four modifiable factors was tested in the wisconsin registry for Alzheimer’s prevention cohort of initially cognitively unimpaired, asymptomatic middle-aged adults. After Bonferroni correction, the significant plasma metabolites were tested for association with each of the AD endophenotypes, including twelve cerebrospinal fluid (CSF) biomarkers, reflecting key pathophysiologies for AD, and four cognitive composite scores. Finally, causal mediation analyses were conducted to evaluate possible mediation effects. Analyses were performed using linear mixed-effects regression. A total of 27, 3, 23, and 24 metabolites were associated with MIND diet, physical activity, smoking, and caffeine intake, respectively. Potential mediation effects include beta-cryptoxanthin in the association between MIND diet and preclinical Alzheimer cognitive composite score, hippurate between MIND diet and immediate learning, glutamate between physical activity and CSF neurofilament light, and beta-cryptoxanthin between smoking and immediate learning. Our study identified several plasma metabolites that are associated with modifiable factors. These metabolites can be employed as biomarkers for tracking these factors, and they provide a potential biological pathway of how modifiable factors influence the human body and AD risk.

We examined the influence of common preanalytical factors on the measurement of Alzheimer's disease–specific biomarkers in human plasma. Amyloid β peptides (Aβ[1-40], Aβ[1-42]) and total Tau plasma concentrations were quantified using fully automated Roche Elecsys assays. Aβ(1-40), Aβ(1-42), and total Tau plasma concentrations were not affected by up to three freeze/thaw cycles, up to five tube transfers, the collection tube material, or the size; circadian rhythm had a minor effect. All three biomarkers were influenced by the anticoagulant used, particularly total Tau. Aβ concentrations began decreasing 1 hour after blood draw/before centrifugation and decreased by up to 5% and 10% at 2 and 6 hours, respectively. For separated plasma, time to measurement influenced Aβ levels by up to 7% after 6 hours and 10% after 24 hours. Our findings provide guidance for standardizing blood sample collection, handling, and storage to ensure reliable analysis of Alzheimer's disease plasma biomarkers in routine practice and clinical trials. •Blood-based Alzheimer's disease biomarkers were measured with fully automated and highly precise Roche Elecsys assays.•Preanalytical sample handling affected measured concentrations of biomarkers.•Time between sample collection and centrifugation, and between centrifugation and measurement, impacted the measured concentrations of Aβ peptides.•Type of anticoagulant substantially affected measured levels of total Tau.•Recommendations for standardized sample collection and processing are provided.

Objectives In this study, the relationship between preoperative neutrophil‐to‐lymphocyte ratio (NLR) and Alzheimer‐related biomarkers in cerebrospinal fluid (CSF) was investigated to determine whether high NLR is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy. Methods We selected 1000 patients from the perioperative neurocognitive disorder risk factor and prognosis (PNDRFAP) database and 999 patients from the perioperative neurocognitive disorder and biomarker lifestyle (PNDABLE) database. Patients in the PNDABLE database have been measured for Alzheimer‐related biomarkers in CSF (Aβ40, Aβ42, P‐tau, and tau protein). Mini‐mental state examination was used to assess the preoperative mental status of patients. POD was diagnosed using the confusion assessment method and assessed for severity using the memorial delirium assessment scale. Logistic regression analysis was utilized to explore the association of preoperative NLR with POD. What's more, we also performed sensitivity analysis by adding corrected confounders, and the results were almost unchanged. Spearman's rank correlation was used to determine the associations between NLR and Alzheimer‐related biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we use decision curves and the nomogram model to evaluate the efficacy of preoperative NLR in predicting POD; we also performed external validation using data from Qilu Hospital. Result Logistic regression results showed that an elevated preoperative NLR was a risk factor for the development of POD in patients (PNDRFAP: OR = 1.067, 95% CI 1.020–1.116; PNDABLE: OR = 1.182, 95% CI 1.048–1.335, p < .05). Spearman's rank correlation analysis showed a positive but weak correlation between NLR and P‐tau/T‐tau (R = .065). The mediating effect results indicate that NLR likely mediates the occurrence of POD through elevated tau protein levels (proportion: 47.47%). The results of the box plots showed statistically significant NLR and CSF biomarkers between the POD and non‐POD (NPOD) groups (p < .05), with higher NLR, P‐tau, and T‐tau in the POD group than in the NPOD group. In contrast, the NPOD group had higher Aβ42 levels compared to the POD group. In addition, we used R package to plot the decision curve and nomogram both suggesting a good predictive effect of preoperative NLR on the occurrence of POD. Conclusion Elevated preoperative NLR levels may be a risk factor for POD and likely mediate the development of POD through elevated P‐tau/T‐tau levels. This is the first prospective population‐based study to assess the relationship between the prevalence of POD and preoperative NLR levels. This study proposes that elevated preoperative NLR levels are a risk factor for POD, and elevated P‐tau and T‐tau are also associated with the development of POD. However, AÎ242 is a protective factor for POD. Preoperative NLR has the potential to act as a predictor for the development of POD. We can reduce the occurrence of POD through possible interventions to bring down preoperative NLR levels.

INTRODUCTION Whether Alzheimer's disease (AD) should be defined by symptoms, biological processes, or both, is a matter of debate. We aim to reconstruct the motivations, aims, and content of consecutive versions of AD diagnostic criteria. METHODS We systematically analyzed publications on AD diagnostic criteria between 1984 and 2024. RESULTS Early diagnosis and incorporating recent scientific findings are recurring aims for criteria revisions but aims and motivations for revising are often unclear or ambiguous and reflection on previous criteria is lacking. The subsequent criteria, except International Working Group (IWG) 2021/2024, consistently lower the threshold for diagnosing AD and increasingly focus on amyloid β and tau biomarkers. DISCUSSION Subsequent AD criteria show an increasing “biomarkerization,” but it is often unclear what problems revised criteria should solve and how effective they are. To overcome these limitations, future revisions should evaluate the effectiveness and impacts of previous criteria, and define clear problems and aims. Highlights Early diagnosis and incorporating scientific insights are recurring aims. The aims of new criteria are often not clearly articulated or ambiguous. The number of requirements for an AD diagnosis decreases over time. Consecutive criteria for research and clinical use did not result in clear terminology. The AD definition is increasingly narrowed to amyloid β and tau.

INTRODUCTION Evidence has emerged that cardiometabolic multimorbidity (CMM) is associated with dementia, but the underlying mechanisms are poorly understood. METHODS This population‐based study included 5704 older adults. Of these, data were available in 1439 persons for plasma amyloid‐β (Aβ), total tau, and neurofilament light chain (NfL) and in 1809 persons for serum cytokines. We defined CMM following two common definitions used in previous studies. Data were analyzed using general linear, logistic, and mediation models. RESULTS The presence of CMM was significantly associated with an increased likelihood of dementia, Alzheimer's disease (AD), and vascular dementia (VaD) (p < 0.05). CMM was significantly associated with increased plasma Aβ40, Aβ42, and NfL, whereas CMM that included visceral obesity was associated with increased serum cytokines. The mediation analysis suggested that plasma NfL significantly mediated the association of CMM with AD. DISCUSSION CMM is associated with dementia, AD, and VaD in older adults. The neurodegenerative pathway is involved in the association of CMM with AD. Highlights The presence of CMM was associated with increased likelihoods of dementia, AD, and VaD in older adults. CMM was associated with increased AD‐related plasma biomarkers and serum inflammatory cytokines. Neurodegenerative pathway was partly involved in the association of CMM with AD.

In the 115 years since the discovery of Alzheimer’s disease (AD), our knowledge, diagnosis, and therapeutics have significantly improved. Biomarkers are the primary tools for clinical research, diagnostics, and therapeutic monitoring in clinical trials. They provide much insightful information, and while they are not clinically used routinely, they help us to understand the mechanisms of this disease. This review charts the journey of AD biomarker discovery and development from cerebrospinal fluid (CSF) amyloid-beta 1-42 (Aβ42), total tau (T-tau), and phosphorylated tau (p-tau) biomarkers and imaging technologies to the next generation of biomarkers. We also discuss advanced high-sensitivity assay platforms for CSF Aβ42, T-tau, p-tau, and blood analysis. The recently proposed Aβ deposition/tau biomarker/neurodegeneration or neuronal injury (ATN) scheme might facilitate the definition of the biological status underpinning AD and offer a common language among researchers across biochemical biomarkers and imaging. Moreover, we highlight blood-based biomarkers for AD that offer a scalable alternative to CSF biomarkers through cost-saving and reduced invasiveness, and may provide an understanding of disease initiation and development. We discuss different groups of blood-based biomarker candidates, their advantages and limitations, and paths forward, from identification and analysis to clinical validation. The development of valid blood-based biomarkers may facilitate the implementation of future AD therapeutics and diagnostics.

INTRODUCTION How do reactions to a brain scan result differ between Black and White adults? The answer may inform efforts to reduce disparities in Alzheimer's disease (AD) diagnosis and treatment. METHODS Self‐identified Black (n = 1055) and White (n = 1451) adults were randomized to a vignette of a fictional patient at a memory center who was told a brain scan result. Measures of stigma and diagnosis confidence were compared between‐groups. RESULTS Black participants reported more stigma than White participants on four of seven domains in reaction to the patient at a memory center visit. Black participants’ confidence in an AD diagnosis informed by a brain scan and other assessments was 72.2 points (95% confidence interval [CI] 70.4 to 73.5), which was lower than the respective rating for White participants [78.1 points (95%CI 77.0 to 79.3)]. DISCUSSION Equitable access to early AD diagnosis will require public outreach and education that address AD stigma associated with a memory center visit.

Alzheimer’s disease (AD) is a growing global challenge, representing the most common neurodegenerative disorder and affecting millions of lives. As life expectancy continues to rise and populations expand, the number of individuals coping with the cognitive declines caused by AD is projected to double in the coming years. By 2050, we may see over 115 million people diagnosed with this devastating condition. Unfortunately, while we currently lack effective cures, there are preventative measures that can slow disease progression in symptomatic patients. Thus, research has shifted toward early detection and intervention for AD in recent years. With technological advances, we are now harnessing large datasets and more efficient, minimally invasive methods for diagnosis and treatment. This review highlights critical demographic insights, health conditions that increase the risk of developing AD, and lifestyle factors in midlife that can potentially trigger its onset. Additionally, we delve into the promising role of plant-based metabolites and their sources, which may help delay the disease’s progression. The innovative multi-omics research is transforming our understanding of AD. This approach enables comprehensive data analysis from diverse cell types and biological processes, offering possible biomarkers of this disease’s mechanisms. We present the latest advancements in genomics, transcriptomics, Epigenomics, proteomics, and metabolomics, including significant progress in gene editing technologies. When combined with machine learning and artificial intelligence, multi-omics analysis becomes a powerful tool for uncovering the complexities of AD pathogenesis. We also explore current trends in the application of radiomics and machine learning, emphasizing how integrating multi-omics data can transform our approach to AD research and treatment. Together, these pioneering advancements promise to develop more effective preventive and therapeutic strategies soon.

INTRODUCTION We examined the associations of polygenic risk score (PRS) with Alzheimer's disease (AD) and plasma biomarkers in the Chinese population. METHODS This population‐based study used baseline data from MIND‐China (2018; n = 4873) and follow‐up data from dementia‐free individuals (2014–2018; n = 2117). We measured AD‐related plasma biomarkers in a subsample (n = 1256). Data were analyzed using logistic and Cox regression models. RESULTS We developed PRS with (PRSAPOE) and without (PRSnon‐APOE) apolipoprotein E (APOE) gene. In the longitudinal analysis, PRSAPOE was associated with a multivariable‐adjusted hazards ratio of 1.91 (95% CI = 1.13–3.23) for AD. PRSAPOE in combination with demographics yielded discriminative (area under the curve [AUC]) and predictive(C‐statistic) accuracy of 0.80 (95% confidence interval [CI] = 0.77–0.84) and 0.80 (0.77–0.82), respectively. PRSnon‐APOE showed an association with AD risk similar to PRSAPOE. PRSAPOE, but not PRSnon‐APOE, was associated with reduced plasma Aβ42/Aβ40 ratio and increased Neurofilament light chain (NfL) (p < 0.05). DISCUSSION The PRS with and without APOE gene, in combination with demographics, shows good discriminative and predictive ability for AD. The AD‐related pathologies underlie AD risk associated with PRSAPOE. Highlights The PRSAPOE and PRSnon‐APOE were associated with AD risk in the Chinese population. The PRSAPOE and PRSnon‐APOE, in combination with demographics, showed good discriminative and predictive ability for AD. The AD‐related pathologies underlie the AD risk associated with PRSAPOE but not PRSnon‐APOE.