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Tuesday, April 10, 2018

New biological research framework for Alzheimer’s to spur discovery

NIA, Alzheimer’s Association convene effort to update disease definition, speed testing.

The research community now has a new framework toward developing a biologically-based definition of Alzheimer’s disease. This proposed “biological construct” is based on measurable changes in the brain and is expected to facilitate better understanding of the disease process and the sequence of events that lead to cognitive impairment and dementia. With this construct, researchers can study Alzheimer’s, from its earliest biological underpinnings to outward signs of memory loss and other clinical symptoms, which could result in a more precise and faster approach to testing drug and other interventions.

The National Institute on Aging (NIA), part of the National Institutes of Health, and the Alzheimer’s Association (AA) convened the effort, which as the “NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease,” appears in the April 10, 2018 edition of Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association . Drafts were presented at several scientific meetings and offered online, where the committee developing the framework gathered comments and ideas which informed the final published document. The framework, as it undergoes testing and as new knowledge becomes available, will be updated in the future.

The framework will apply to clinical trials and can be used for observational and natural history studies as well, its authors noted. They envision that this common language approach will unify how different stages of the disease are measured so that studies can be easily compared and presented more clearly to the medical field and public.

“In the context of continuing evolution of Alzheimer’s research and technologies, the proposed research framework is a logical next step to help the scientific community advance in the fight against Alzheimer’s,” said NIA Director Richard J. Hodes, M.D. “The more accurately we can characterize the specific disease process pathologically defined as Alzheimer’s disease, the better our chances of intervening at any point in this continuum, from preventing Alzheimer’s to delaying progression,”

Evolution in thinking

This framework reflects the latest thinking in how Alzheimer’s disease begins perhaps decades before outward signs of memory loss and decline may appear in an individual. In 2011, NIA-AA began to recognize this with the creation of separate sets of diagnostic guidelines that incorporated recognition of a preclinical stage of Alzheimer’s and the need to develop interventions as early in the process as possible. The research framework offered today builds from the 2011 idea of three stages — pre-clinical, mild cognitive impairment and dementia  — to a biomarker-based disease continuum.

The NIA-AA research framework authors, which included 20 academic, advocacy, government and industry experts, noted that the distinction between clinical symptoms and measurable changes in the brain has blurred. The new research framework focuses on biomarkers grouped into different pathologic processes of Alzheimer’s which can be measured in living people with imaging technology and analysis of cerebral spinal fluid samples. It also incorporates measures of severity using biomarkers and a grading system for cognitive impairment. 

“We have to focus on biological or physical targets to zero in on potential treatments for Alzheimer’s,” explained Eliezer Masliah, M.D., director of the Division of Neuroscience at the NIA. “By shifting the discussion to neuropathologic changes detected in biomarkers to define Alzheimer’s, as we look at symptoms and the range of influences on development of Alzheimer’s, I think we have a better shot at finding therapies, and sooner.”

In an accompanying editorial , Masliah and NIA colleagues, including Dr. Hodes, highlighted both the promise and limitations of the biological approach. They noted that better operational definitions of Alzheimer’s are needed to help better understand its natural history and heterogeneity, including prevalence of mimicking conditions. They also emphasized that the research framework needs to be extensively tested in diverse populations and with more sensitive biomarkers.

Batching and matching biomarkers

The NIA-AA research framework proposes three general groups of biomarkers—beta-amyloid, tau and neurodegeneration or neuronal injury—and leaves room for other and future biomarkers. Beta-amyloid is a naturally occurring protein that clumps to form plaques in the brain. Tau, another protein, accumulates abnormally forming neurofibrillary tangles which block communication between neurons. Neurodegeneration or neuronal injury may result from many causes, such as aging or trauma, and not necessarily Alzheimer’s disease.

Researchers can use measures from a study participant and identify beta-amyloid (A), tau (T) or neurodegeneration or neuronal injury (N) to characterize that person’s combination of biomarkers in one of eight profiles. For example, if a person has a positive beta-amyloid (A+) biomarker but no tau (T-), he or she would be categorized as having “Alzheimer’s pathologic change.” Only those with both A and T biomarkers would be considered to have Alzheimer’s disease, along a continuum. The N biomarker group provides important pathologic staging information about factors often associated with Alzheimer’s development or worsening of symptoms.

Framework for certain research only

The authors emphasized that the NIA-AA research framework is neither a diagnostic criteria nor guideline for clinicians. It is intended for research purposes, requiring further testing before it could be considered for general clinical practice, they noted.

They also stressed that the biological approach to Alzheimer’s is not meant to supplant other measures, such as neuropsychological tests, to study important aspects of the disease such as its cognitive outcomes. In some cases, the article pointed out, biomarkers may not be available or requiring them would be counterproductive for particular types of research.

The authors acknowledge that the research framework may seem complex, but stress that it is flexible and may be employed to answer many research questions, such as how cognitive outcomes differ among various biomarker profiles, and what the influence of age is on those relationships.

In its commentary the NIA leadership developed a table to help explain how the proposed framework might be used and where it might not apply:

About the National Institute on Aging : The NIA leads the federal government effort conducting and supporting research on aging and the health and well-being of older people. The NIA is designated as the lead NIH institute for information on Alzheimer’s disease. It provides information on age-related cognitive change and neurodegenerative disease, including participation in clinical studies, specifically on its website at www.nia.nih.gov/health/alzheimers .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

Jack CR et al. NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease . Alzheimers Dement . 2018 Apr 10. doi: 10.1016/j.jalz.2018.02.018

Silverberg N et al. NIA commentary on the NIA-AA research framework: Towards a biological definition of Alzheimer’s disease . Alzheimers Dement . 2018 Apr 10. doi: 10.1016/j.jalz.2018.03.004

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NIA‐AA Alzheimer's Disease Framework: Clinical Characterization of Stages

Ronald c. petersen.

1 Department of Neurology, Mayo Clinic, Rochester MN

Heather J. Wiste

2 Department of Health Sciences Research, Mayo Clinic, Rochester MN

Stephen D. Weigand

Julie a. fields.

3 Department of Psychiatry and Psychology, Mayo Clinic, Rochester MN

Yonas E. Geda

4 Department of Neurology, Barrow Neurological Institute, Phoenix AZ

Jonathan Graff‐Radford

David s. knopman, walter k. kremers.

5 Department of Radiology, Mayo Clinic, Rochester MN

Mary M. Machulda

Michelle m. mielke, phd, nikki h. stricker, terry m. therneau, prashanthi vemuri.

6 Department of Radiology, Mayo Clinic, Rochester MN

Clifford R. Jack, Jr

Associated data.

Data from the MCSA, including data from this study, are available upon request.

To operationalize the National Institute on Aging – Alzheimer's Association (NIA‐AA) Research Framework for Alzheimer's Disease 6‐stage continuum of clinical progression for persons with abnormal amyloid.

The Mayo Clinic Study of Aging is a population‐based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross‐sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months.

Among 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44–59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short‐term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1).

Interpretation

The frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145–1156

The recent publication of the National Institute on Aging – Alzheimer's Association (NIA‐AA) workgroup proposes 2 clinical staging schemes for the Alzheimer's disease (AD) research framework: (1) the commonly used clinical syndromes of cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia, and (2) a numeric clinical staging scheme of 1 to 6 for individuals who are on the AD spectrum (abnormal amyloid, A+), in which stages 1 to 3 characterize pre‐dementia and stages 4 to 6 characterize dementia. 1 The numeric clinical staging scheme incorporates both current cognitive and functional performance as well as cognitive or neurobehavioral decline. The numeric clinical staging scheme was intended to aid in the design of randomized controlled trials, avoid the syndromic labels that can be imprecise when evaluating inclusion criteria and outcomes, and put some structure on the very early stages of subtle clinical changes in CU individuals who did not meet criteria for MCI (ie, stage 2).

Although these recommendations are appealing, they were proposed as a research framework needing evaluation. We operationalized criteria derived from the Mayo Clinic Study of Aging (MCSA) and evaluated the frequency and stability of the pre‐dementia stages (ie, stages 1–3).

Ascertainment, Enrollment, and Characterization

The MCSA is a longitudinal population‐based study of cognitive aging among a stratified random sample of a geographically defined population in Olmsted County, Minnesota 2 , 3 that began in 2004. Residents aged 30 to 89 years old are enumerated using the medical records‐linkage system of the Rochester Epidemiology Project 4 and individuals are randomly selected by 10‐year age and sex strata, such that men and women are equally represented. Sampling procedures are repeated to maintain approximately 3,000 active participants who are evaluated every 15 months. Because the numeric clinical staging scheme evaluates current performance as well as decline, our analysis used both cross‐sectional and longitudinal measures. We included 1,755 participants without dementia age 50 years or older with complete data at 3 consecutive MCSA visits for measures used to define the NIA‐AA numeric staging. Visit 3 was considered the index visit for staging individuals and data from this visit were used for cross‐sectional measures. Data from visits 1, 2, and 3 were used for longitudinal measures of recent decline.

A clinical diagnosis of CU, MCI, or dementia was determined independently of biomarkers and previous clinical data and diagnoses. Information for each participant was reviewed by a consensus committee composed of physicians, neuropsychologists, and study coordinators. 2 , 3 Participants who did not meet established criteria for MCI 5 or dementia 6 were deemed CU. Individuals with a diagnosis of dementia were excluded from this analysis.

All MCSA participants underwent a battery of 9 neuropsychological tests 7 at each visit. In the current analyses, we focused on memory and attention/executive function domains because they are often the earliest domains to decline in aging and typical AD. 8 , 9 , 10 , 11 Memory tests included the Wechsler Memory Scale‐Revised (WMS‐R) Logical Memory‐II (delayed recall), WMS‐R Visual Reproduction‐II (delayed recall), and Auditory Verbal Learning Test (delayed recall). Attention/executive function tests included Trail Making Test Part B and the Wechsler Adult Intelligent Scale‐Revised (WAIS‐R) Digit Symbol Substitution Test. Each test was z‐scored among CU participants aged 50 years and older who were newly enrolled in the MCSA between 2004 and 2012. 12 Domain z‐scores were created by averaging across the 2 or 3 component z‐scores and these domain scores were themselves z‐scored by calculating a weighted mean and a weighted SD where the weights were based on the age and sex distribution of the Olmsted County population. 3

Patient Consent

The MCSA was approved by the Mayo Clinic and Olmsted Medical Center Institutional Review Boards. Written informed consent was obtained from all participants before they joined the study.

Definitions of Clinical Staging Dimensions

Operationalizing the NIA‐AA numeric clinical staging scheme involves developing an explicit decision rule to assign individuals to a numeric stage based on data obtained from a battery of clinical and neuropsychological measurements. In a typical research setting, a large number of tests are used and therefore there are essentially an innumerable number of ways to operationalize the staging scheme. For our approach, we grouped the features of the numeric clinical stages along 4 dimensions: objective cognition (OBJ), subjective cognitive decline (SCD), neurobehavioral symptoms (NBS), and functional impact on daily life (FXN). We specified cutoff points for abnormality based on prior work and extensive experience. The definitions of OBJ, SCD, NBS, and FXN are described below and summarized in Table  1 .

Measurements and Cutoff Points Defining Dimensions Used for NIA‐AA Numeric Clinical Staging

Operationalization of the clinical staging uses 4 dimensions: objective cognition (OBJ), functional assessment (FXN), subjective cognitive decline (SCD), and neurobehavioral symptoms (NBS). OBJ and FXN are cross‐sectional measures and SCD, ∆OBJ, and ∆NBS are measures of recent decline. Sensitivity to cutoff points was evaluated for the OBJ dimension and the alternative cutoff points used are labeled as (a) and (b).

BAI = Beck Anxiety Inventory; BDI = Beck Depression Inventory‐II; ECog = Everyday Cognition (ECog); FAQ = Functional Activities Questionnaire; NIA‐AA = National Institute on Aging – Alzheimer's Association.

Objective Cognition

The numeric staging includes 2 components of objective cognition: current cognitive performance (which we define at MCSA visit 3) and decline in cognition (which we define using MCSA visits 1, 2, and 3). We evaluated 2 cutoff points for each component to assess the sensitivity of the staging to variations in the cutoff points.

For current cognitive performance (OBJ), we defined abnormal as a memory or attention/executive z‐score of −1.5 z or lower or of −2.0 z or lower. The cutoff point of 1.5 SDs below the mean is commonly used for defining MCI. 13 , 14 The cutoff points we used correspond to the seventh and second percentiles of z‐scores in the Olmsted County, Minnesota, CU population. The cutoff point of ≤ −2.0 z indicated more severe impairment than ≤ −1.5 z and resulted in fewer individuals being classified as abnormal on OBJ.

For a decline in cognition (ΔOBJ) measure, we calculated annual decline in memory or attention/executive z‐score over three visits (approximately 30 months) by fitting a linear regression within each participant. We used cutoff points of ≤ −0.1 z units/year and ≤ −0.2 z units/year to define abnormal ∆OBJ. These cutoff points were supported by data from the Harvard Aging Brain Study, the Australian Imaging, Biomarker, and Lifestyle Study, and the Alzheimer's Disease Neuroimaging Initiative, in which individuals with abnormal annualized cognitive z‐score change — using cutoff points ranging from −0.14 to −0.26 — had an increased risk of MCI. 15 They were also supported by an analysis among 1,913 MCSA CU individuals where any degree of annual decline in memory or attention (ie, ≤ 0 z units/year) was associated with increasing odds of progressing to MCI, with higher odds seen for greater rates of decline (data not shown). The cutoff point of ≤ −0.1 z units/year corresponds to the rate of memory decline in CU at age 85 previously reported 16 and ≤ −0.2 z units/year represents a greater decline in cognition (ie, more impairment) resulting in fewer individuals being classified as abnormal on ΔOBJ.

Subjective Cognitive Decline

For SCD, we used the Everyday Cognition (ECog) 12‐Item Assessment, a self‐report measure of level of independence in performing cognition‐based daily tasks. 17 In a recent MCSA study, van Harten et al demonstrated that individuals who had a consistent SCD (ie, those who had any score on the 12 items ≥3) and those with a self‐reported concern more rapidly progressed from CU to MCI. 18 Therefore, we defined SCD as a score of ≥3 on any of the items on the ECog 12‐item test plus a concern. The participant's ECog assessment was used for stages 1 to 2, whereas the participant's and study partner's ECog assessments were used for stages 3 to 6. Although SCD is a decline measure, the ECog questionnaire is designed to assess recent changes over time. Therefore, we used the ECog measures ascertained at visit 3 to define SCD in this study.

Neurobehavioral Symptoms

We used the Beck Depression Inventory‐II (BDI) and Beck Anxiety Inventory (BAI) for the neurobehavioral symptom (∆NBS) dimension. 19 , 20 We used the standard cutoff points of ≥13 on BDI and ≥8 on BAI, which indicate clinical depression or anxiety (ie, mild to severe depression or anxiety). Using these cutoff points, depression and anxiety have been shown to differentiate CU individuals who are likely to progress to MCI in previous work from the MCSA. 21 , 22 Because this dimension is defined as new onset of NBS, we only considered individuals to have abnormal ∆NBS if the person had both BDI <13 and BAI <8 at the beginning of the MCSA (visit 1) and the person had either BDI ≥13 or BAI ≥8 at the current visit (visit 3).

Functional Impact on Daily Living

The Functional Activities Questionnaire (FAQ) was used as the primary measure of functional impacts on daily life (FXN). 23 The FAQ includes 10 questions, which score an individual's ability to perform activities from 0 (normal) to 3 (dependent) resulting in a total score of 0 to 30 points. Based on prior MCSA data, an FAQ total score >0 occurs in only 13% of non‐demented participants, 24 although the mean FAQ among CU may be greater than 0. 25 Therefore, we used FAQ 0 to 1 to indicate no functional impairment in this study. A score of 6 or greater has been found to separate MCI and dementia, and was used here to indicate significant functional impairment. Scores of 2 to 5 were considered mild impairment. 26 The FAQ measure at visit 3 was used for staging.

The decision tree in Figure  1 depicts how the 6 numeric stages were operationalized based on the various cross‐sectional and decline dimensions described above. The stages were defined as follows:

Decision tree for determining National Institute on Aging – Alzheimer's Association (NIA‐AA) numeric stage. Flow chart detailing how participants are classified into the 6 numeric stages (or are indeterminate) based on the dimensions defined in Table  1 : objective cognition (OBJ), functional assessment (FXN), subjective cognitive decline (SCD), and neurobehavioral symptoms (NBS). OBJ and FXN are cross‐sectional measures and SCD, ∆OBJ, and ∆NBS are measures of recent decline.

Stage 1: Normal OBJ, FXN, SCD (participant), ΔOBJ, and ∆NBS. Stage 2: Normal OBJ and FXN with at least one of the following abnormal: SCD (participant), ΔOBJ, or ∆NBS. Stage 3: Abnormal OBJ with no/mild FXN and at least one of the following abnormal: SCD (participant or study partner), ΔOBJ, or ∆NBS. Stages 4 to 6: Abnormal OBJ and FXN with at least one of the following abnormal: SCD (participant or study partner), ΔOBJ, or ∆NBS.

Individuals with data that do not fit into any of the defined stages are labeled as “indeterminate.”

Amyloid Positron Emission Tomography Imaging

Amyloid positron emission tomography (PET) imaging was used to identify individuals on the Alzheimer spectrum. It was performed with Pittsburgh Compound B. 16 , 27 , 28 , 29 Standardized uptake value ratios (SUVRs) were calculated as the median uptake in a composite region of interest (prefrontal, orbitofrontal, parietal, temporal, anterior and posterior cingulate, and the precuneus regions) 27 normalized by the median uptake in the cerebellar crus gray matter. 16 Since an amyloid PET was not available at each visit, participants were categorized as having abnormal amyloid PET (A+) if they had SUVR ≥1.48 at MCSA visit 1, 2, or 3. Participants were classified as A− if they had SUVR <1.48 at visit 3 or later.

Estimating Frequencies of Stages

Multinomial regression models were fit with the numeric stage as the outcome and continuous age and sex as predictors. From these models, the estimated frequencies (percentages) in each stage at each age and sex were summarized. Likelihood ratio tests were used to test if the staging frequencies differed systematically by sex.

Role of the Funding Source

The funding sources did not influence the design, collection, analysis, interpretation of the data, writing of the report, or the decision to submit for publication.

Because the NIA‐AA numeric clinical staging is characterized for individuals on the Alzheimer continuum, our main analysis included 243 A+ participants. Additional analyses were done among 449 A− participants and among 1,755 combined A−, A+, and unknown A status participants. As shown in Table  2 , the median age of A+ participants was 74 years (range = 53–92), 52% were women, and the median education was 14 years. The A− participants were younger (median age = 68 years, range = 53–88 years) and 43% were women. Among the overall sample, the median age was 71 (range = 52–92) and 50% were women.

Participant Characteristics

Characteristics of MCSA participants with abnormal amyloid (A+), normal amyloid (A−), and all participants regardless of amyloid status.

CU = cognitively unimpaired; IQR = interquartile range; MCI = mild cognitive impairment; MCSA = Mayo Clinic Study of Aging.

Figure  2 shows the estimated percentage of A+ participants in each stage at different ages by sex for 4 staging definition variations. The 4 variations arise from evaluating combinations of the different OBJ and ∆OBJ cutoff points. The overall patterns of the curves look similar but there are some quantitative differences. Across all definitions, most 50‐year‐old participants were in stage 1. However, 89 to 90% were in stage 1 using staging definitions with −0.2 z units/year for the ∆OBJ cutoff point (see Fig 2B, D) compared with only 66 to 76% using the cutoff point of −0.1 z units/year (see Fig 2A, C). A larger percentage of 50‐year‐old participants were in stage 2 using −0.1 z units/year compared to −0.2 z units/year (24–33% [see Fig 2A, C] vs 9–10% [see Fig 2B, D]). Among the 80‐year‐old participants, they were more distributed throughout the stages: 24 to 36% stage 1, 32 to 47% stage 2, 18 to 27% stage 3, 1 to 3% stages 4 to 6, and 3 to 9% indeterminate for all definitions. The current study only included persons without dementia, hence the low percentage for stages 4 to 6. There were no significant differences in the percent of individuals within each stage by sex.

National Institute on Aging – Alzheimer's Association (NIA‐AA) numeric stage frequencies by age and sex among A+ participants. Estimated percentage in each NIA‐AA numeric stage at each age and by sex for 4 different staging definitions where the cutoff points for the cross‐sectional objective criterion (OBJ) and the longitudinal objective criterion (∆OBJ) are varied. Estimates are from cross‐sectional multinomial regression models with stage as the outcome and continuous age and sex as predictors. Solid lines represent the estimates for women and dotted lines represent the estimates for men.

Stage 2 was the most challenging to define as participants were required to have normal OBJ and FXN at visit 3 but be abnormal on at least 1 of 3 decline measures (SCD, ∆OBJ, or ∆NBS). Most stage 2 A+ participants had only 1 abnormal decline measure across the 4 staging definition variations (78–80%), whereas 14 to 18% had 2, and 5 to 6% were abnormal on all 3 (Fig  3 ). The most common reason for inclusion in stage 2 was having abnormal ∆OBJ only (44–59%), whereas 11 to 21% had SCD only, and 9 to 13% had ∆NBS only. Because the ∆OBJ cutoff point of −0.1 z units/year required less of a decline in cognition for classification as abnormal ∆OBJ, more participants were classified as stage 2 by ΔOBJ only when using this cutoff point (see Fig 3A, C). In contrast, using the ΔOBJ cutoff point of −0.2 z units/year led to more participants classified by SCD only or ∆NBS only (see Fig 3B, D).

Components of the stage 2 definition. Percentage of stage 2 A+ participants with each combination of decline components (subjective cognitive decline [SCD], longitudinal objective cognition criterion [∆OBJ], and neurobehavioral symptoms [∆NBS]) for 4 different staging definitions where the cutoff points for the cross‐sectional objective criterion (OBJ) and the ∆OBJ are varied. Bars within each panel may not necessarily add to 100% due to rounding.

The number of individuals who did not fit into one of the stages (ie, indeterminate) was small (10–12 individuals or 4–5%). The majority of the indeterminate individuals had normal OBJ but mild or impaired FXN (64–82%), whereas a smaller number had abnormal OBJ but were normal on all SCD, ∆OBJ, and ∆NBS decline measures (18–36%).

Figure  4 shows the percent of A+ participants in each stage for the 4 staging definition variations for either clinically defined CU or MCI participants. Among CU, most participants were in stages 1 or 2 (88–92%) but the proportion in stage 1 versus stage 2 depended on the choice of the cutoff point for ∆OBJ. Similar numbers were in each (43–44% stage 1 vs 45–48% stage 2) using the cutoff point of −0.1 z units/year (see Fig 4A, C), whereas 56% were in stage 1 and 33 to 35% were in stage 2 using −0.2 z units/year (see Fig 4B, D). Only 3 to 8% of CU participants were in stage 3, none in stages 4 to 6, and 4 to 5% were indeterminate. Among MCI participants, most were in stage 3 (71% when OBJ ≤ −1.5 z units [see Fig 4E, F], 65% when OBJ ≤ −2.0 z units [see Fig 4G, H]), with roughly equal numbers in stage 2 (9–15%) and stages 4 to 6 (12%). A small number were in stage 1 (3–6%) or indeterminate (3–6%).

Comparison of the numeric clinical staging and clinically defined diagnosis. Percentage of clinically defined cognitively unimpaired (CU) and mild cognitive impairment (MCI) A+ participants in each numeric clinical stage for 4 different staging definitions where the cutoff points for the cross‐sectional objective criterion (OBJ) and the longitudinal objective criterion (∆OBJ) are varied. Bars within each panel may not necessarily add to 100% due to rounding.

To assess stability of the staging definitions, we compared the stage at visit 3 to the stage at visit 4 (approximately 15 months later) among 198 of 243 (81%) A+ participants with follow‐up data (Fig  5 ). Stability differed across the stages and 4 staging definition variations. Among individuals in stage 1 at visit 3 (see Fig 4, top row in each panel), at visit 4, 51 to 67% remained in stage 1 (stable), 32 to 50% moved to a higher stage (worsened), and a small percent (0–3%) were indeterminate at visit 4. Among individuals in stage 2 (see Fig 4, second row in each panel), 38 to 63% remained stable, 4 to 13% worsened, 24 to 41% improved (moved to stage 1), and 7 to 9% were indeterminate at visit 4. More improved with the ∆OBJ cutoff point of −0.2 z/year (see Fig 4B, D) compared to −0.1 z/year (see Fig 4A, C). Among stage 3 individuals (see Fig 4, third row in each panel), 56 to 60% were stable, 20 to 28% worsened, 17 to 20% improved, and 0 to 4% were indeterminate at visit 4.

Stability of staging definitions. Percentage of participants that stayed in the same stage (stable; blue), moved to a lower stage (improve; green), moved to a higher stage (worsen; red), or were indeterminate (grey) between visits 3 and 4 (approximately 15 months) among 198 A+ participants with follow‐up. Percentages are shown for 4 different staging definitions where the cutoff points for the cross‐sectional objective criterion (OBJ) and the longitudinal objective criterion (∆OBJ) are varied. Stage at visit 4 (follow‐up) was defined in the same way as stage at visit 3 but used visit 4 for the cross‐sectional measures (index visit) and visits 2, 3, and 4 for the decline measures. Row percentages may not necessarily add to 100% due to rounding.

Applying the NIA‐AA Staging Scheme to A− and all Individuals

Although the NIA‐AA staging scheme was designed for A+ participants, we also used our entire cohort and the subset of A− persons to further explore the utility of the scheme. Figure  6 shows the percent of 243 A+ participants, 449 A− participants, and all 1,755 participants that fall in each stage by age and sex using the staging definition of OBJ ≤ −1.5 z and ∆OBJ ≤ −0.1 z/year. Although the curves showed some variability, they were not dramatically different from those in the A+ participants.

National Institute on Aging – Alzheimer's Association (NIA‐AA) numeric stage frequencies by age and sex among A+ participants, A− participants, and all participants. Sensitivity analysis showing the estimated percentage in each NIA‐AA numeric stage at each age and by sex among A+ participants, A− participants, and among all participants using the staging definition where the cutoff point for the cross‐sectional objective criterion was OBJ ≤ −1.5 and the cutoff point for the longitudinal objective criterion was ΔOBJ ≤ −0.1. Estimates are from cross‐sectional multinomial regression models with stage as the outcome and continuous age and sex as predictors. Solid lines represent the estimates for women and dotted lines represent the estimates for men.

The numeric clinical staging scheme proposed by the NIA‐AA research framework for AD was designed to facilitate clinical characterization of A+ participants in randomized controlled trials for AD. When these criteria were applied to a population‐based sample of A+ individuals, focusing on stages 1 to 3, we found that the population without dementia could be classified using various implementation strategies for these criteria with some reservations.

Age constituted a major factor in determining the frequencies of the stages. As would be expected, most A+ 50‐year‐old individuals were stage 1, whereas among A+ 80‐year‐old individuals, only 24 to 36% of the population was stage 1 with corresponding increases in stages 2 and higher. This parallels the incidence of mild cognitive impairment and dementia with age. 14

The frequencies of stages 1 to 3 were clearly influenced by the implementation of the criteria used to define the stages. Changing the threshold used for defining cognitive impairment (OBJ) to classify individuals with more severe impairment (−2.0 z) as abnormal resulted in an increased percentage in stage 2 and a decreased percentage in stage 3, as would be expected. Using the −0.2 z units/year cutoff point for ∆OBJ resulted in a decreased percentage in stage 2 and a corresponding increase in stage 1 compared to −0.1 z units/year. Although these cutoff points may appear relatively lenient, our prior work has demonstrated that practice effects significantly impact performance trajectories and that trajectories vary by biomarker profile. 7 , 30 Papp et al found an increased risk of MCI among individuals with subtle cognitive decline using tertile cutoff points for annualized cognitive change raging from −0.14 to −0.26 in their data. Those results indicate that small declines in cognition, similar to declines examined in this study, can be meaningful. 15

When assessing which of the 3 criteria, SCD, ∆OBJ, or ∆NBS, contributed the most to qualifying for stage 2, ∆OBJ was the leading factor. SCD was the second qualifying measure, with ∆NBS being third. Although ∆OBJ was the leading factor in qualifying for stage 2, the percentage of individuals in stage 2 with ∆OBJ only did vary with different cutoff points. Few individuals were classified as stage 2 based on ∆NBS only. However, the implementation of the criteria for ∆NBS required a normal BDI and BAI at visit 1 and a change in either by visit 3; in this population‐based sample, incident depression and incident anxiety were not common. This may have compromised the utility of this measure. These data suggest that ΔNBS may not be a useful element by which to classify individuals on the AD spectrum and raise questions about the appropriateness of this measure in the AD framework. In addition, NBS, such as depression, may actually improve over time making it a less reliable feature of AD. 31

We compared the classifications of individuals in the stages to our clinical diagnoses of CU, MCI, and dementia. In general, our CU diagnosis corresponded to stages 1 and 2 and MCI to stage 3. Within MCI, the frequency of stage 3 varied from 65 to 71% and from 15 to 9% for stage 2 depending on the OBJ cutoff point used (≤ −2.0 z vs ≤ −1.5 z). Although we expect these 2 classifications to be similar, it is not surprising that there is not perfect agreement. The published criteria for MCI 5 do not require a certain degree of cognitive impairment but rather a change in cognition only. Therefore, MCI individuals could be classified as stage 2 because stage 3, as defined in the framework, does require a degree of cognitive impairment in addition to a change in cognition.

The proposal to develop stages for the AD continuum was intended to improve the current classification of clinical syndromes, such as CU, MCI, and dementia. The concern about the cognitive syndromes pertains to their lack of specificity and the boundaries between the conditions. 32 It is well recognized that these entities exist along a cognitive continuum, but the fractionation into clinical syndromes is useful for both clinical practice and research. A recent evidence‐based medicine review of over 11,500 publications on MCI documented that the construct is useful, its prevalence is high (15–20% of the population 70 years and older), and that progression to dementia is predictable within boundaries. 14 In addition, the clinical acceptance in the United States and Europe is high. 33 , 34 However, there are problems with the lack of precision of the diagnostic boundaries; hence alternative characterizations have been sought. 35

The numeric clinical staging scheme proposed in the NIA‐AA research framework attempts to circumvent some of these concerns by giving the stages a numerical label. However, many of the fundamental issues persist (eg, boundaries between stages 1 and 2, and between stages 2 and 3). The present study assessed some of these issues with data from a longitudinal population‐based study of aging and cognition, the MCSA. The frequencies of stages 2 and higher increase with age and there are some transitions between the stages in expected directions. However, there is uncertainty in the boundaries between the stages, which is most notably demonstrated in the most nuanced category of stage 2. Stage 2 is meant to capture individuals in the range of “unimpaired” cognition who may be transitioning toward impairment (ie, individuals with the earliest detectable clinical evidence of symptoms attributable to Alzheimer continuum pathology). However, given the numerous ways to capture stage 2, most of which depend on longitudinal data, a standardized characterization will be challenging. Choosing different cognitive measures or abnormal thresholds would affect the classification of individuals in this stage. We found most stage 1 individuals were stable or worsened (ie, moved to a higher stage) and most stage 2 individuals were stable or improved (ie, moved to stage 1) at the next visit. However, using different ∆OBJ cutoff points affected the stability; the ∆OBJ cutoff point of ≤ −0.2 z/year resulted in more stable and fewer worsening for stage 1 and fewer stable and more improving for stage 2. The nontrivial reversion plus indeterminate proportions of the A+ participants suggests that more work needs to be done in refining the variables used to define the numeric stages.

The construct of SCD is also challenging but recent research is shedding light on this issue. 36 , 37 Because many factors affect SCD, it is probably best when combined with an objective measure of performance. Finally, the ∆NBS measure may be the most challenging. Studies have shown the emergence of subtle psychiatric symptoms in evolving cognitive impairment, yet developing a reliable metric for ∆NBS can be difficult. 38 , 39 This problem is further complicated by wide use of antidepressants and anxiolytics among elderly persons with access to primary health care. The framework might consider eliminating the ΔNBS category in characterizing individuals on the AD spectrum for clinical trial purposes.

This study represents one of the first attempts to fit data into the numeric clinical staging proposed by the NIA‐AA research framework. The devil is in the details when trying to be more specific with respect to the precise definitions and some of the challenges in implementing this strategy are highlighted. Although the staging works to a degree — you can classify participants into the stages with few indeterminates — it is not easy to implement given the number of decisions underlying operationalization (ie, which assessments and cutoff points to use) and it is not clearly an improvement over the clinical syndromes given the nontrivial reversion rates in stage 2. The AD framework should be modified to account for the lack of contribution by ΔNBS and the relative instability of stages, particularly stage 2, with respect to longitudinal outcomes, and the framework will need to be evaluated in other populations to assess generalizability.

Author Contributions

R.C.P., H.J.W., S.D.W., and C.R.J. contributed to conception and design of the study. R.C.P., H.J.W., S.D.W., W.K.K., M.M.Ma., T.M.T., and C.R.J. contributed to acquisition and analysis of data. R.C.P., H.J.W., S.D.W., J.A.F., Y.E.G., J.G.‐R., D.S.K., W.K.K., V.L., M.M.Ma, M.M.Mi., N.H.S., T.M.T., P.V., and C.R.J. drafted a significant portion of the manuscript or figures.

Potential Conflicts of Interest

Nothing to report.

Data Availability

Acknowledgments.

R.C.P. had full access to all the data in the study and had final responsibility for the decision to submit for publication. Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health (NIH) under Award Numbers U01 AG006786, P30 AG062677, R01 AG011378 and a Zenith Award from the Alzheimer's Association. The funders had no role in the conception or preparation of this manuscript.

[Correction added on March 23, 2022, after first online publication: The copyright line was changed.]

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Criteria for diagnosis and staging of alzheimer's disease.

Starting in 1984, the Association has been at the forefront of the criteria to diagnose Alzheimer’s disease. An Alzheimer’s Association workgroup and National Institutes of Health workgroup (NINCDS-ADRDA) was the leading criteria for trials and diagnosis in the clinical setting, until the 2011 National Institute on Aging and Alzheimer's Association (NIA-AA) clinical guidance. Building off this, the 2018 NIA-AA research framework provided context of current scientific knowledge to outline a framing for research needs and hypotheses to be examined closer. “Revised Criteria for Diagnosis and Staging of Alzheimer's Disease” was published June 27, 2024 in Alzheimer’s & Dementia®: The Journal of the Alzheimer’s Association , with corresponding commentaries published in Nature Medicine and Nature Aging . The 2024 version incorporates the latest in science to define more fully the criteria for diagnosis and staging of Alzheimer’s. Diagnosing Alzheimer’s disease is advancing with science. By incorporating new scientific insights and technological advances, these new criteria aim to:

• Improve current diagnosis, including accuracy.
• Provide context for a biological definition that will inform the next generation of clinical trials. 
• Lay a foundation that moves us toward personalized approaches for Alzheimer’s treatment that’s rooted in biology.​

Current and future recommended use of criteria

Why update the criteria now, previous criteria, guidance and frameworks.

The 2024 criteria provides an outline for the diagnosis and staging of Alzheimer's disease. Several core principles emerged from these efforts, including:

• Alzheimer’s disease should be defined biologically, not based on a clinical syndrome(s).
• The disease is a continuum that begins with the appearance of changes in the brain associated with the disease processes in asymptomatic individuals, which progresses through stages of increasing levels of disease-related brain changes, eventually leading to the appearance and progression of clinical symptoms.
• The disease is diagnosed in individuals by abnormalities on core biomarkers.

The criteria do not support the evaluation of Alzheimer's disease-related brain changes in asymptomatic individuals for purposes of clinical care at this time — i.e. outside the context of observational or therapeutic research studies. In early 2022, the Alzheimer’s Association convened a steering committee — chaired by Clifford Jack, M.D., Mayo Clinic — to lead the translation of the 2011 diagnostic guidance and the 2018 research framework into the newly proposed diagnostic criteria. These new criteria do not constitute clinical practice guideline recommendations. Review the Criteria Under the leadership of Dr. Jack, the workgroup presented their work at several scientific conferences, including the Alzheimer’s Association International Conference® (AAIC®) 2023, at Clinical Trials in Alzheimer’s Disease (CTAD) 2023 and at AD/PD 2024.

In a new era where treatments that address the underlying biology of Alzheimer’s disease are available and will continue to emerge, the present document has progressed from a framework for research to criteria for diagnosis and staging that are intended for clinical use as well as research. Validated biomarkers in the 2018 framework were based on either cerebrospinal fluid (CSF) assays or imaging. Since then, some plasma-based biomarkers with excellent diagnostic performance have been developed and are being clinically validated. The 2024 criteria have correspondingly incorporated plasma biomarkers into updated criteria for biomarker categorization, disease diagnosis and staging. Lastly, research studies have demonstrated that imaging and fluid biomarkers within a category — although often concordant — are not interchangeable in many clinical scenarios. In the present document, the workgroup has updated biomarker classification criteria to accommodate nonequivalence between fluid and imaging biomarkers within a category.

2022-2024 workgroup members: Revised criteria for diagnosis and staging of Alzheimer's disease

Workgroup members were selected to achieve a range of scientific expertise, the broad representation of different institutions (public, academic and private) and professional organizations involved with Alzheimer's research, and geographic and gender diversity. Given the importance of the criteria to support research studies testing clinical interventions, scientific expertise included regulatory science via a representative of the U.S. Food & Drug Administration. The process of criteria development included public comments at AAIC 2023, CTAD 2023, AD/PD 2024 and other meetings, a public-facing website providing the most recent draft, and the opportunity to submit web-based feedback.

• Jeffrey Scott Andrews, PharmD, Takeda
• Thomas G. Beach, M.D., Ph.D., Banner Sun Health Research Institute
• Teresa Buracchio, M.D., U.S. Food and Drug Administration
• Maria C. Carrillo, Ph.D., Alzheimer’s Association, convener and steering committee
• Billy Dunn, M.D., Independent, steering committee
• Ana Graf, M.D., Novartis
• Oskar Hansson, M.D., Ph.D., Lund University
• Carole Ho, M.D., Denali Therapeutics
• Clifford R. Jack Jr., M.D., Mayo Clinic, chair and steering committee
• William Jagust, M.D., University of California, Berkeley
• Eliezer Masliah*, M.D., National Institutes of Health, steering committee
• Eric McDade, D.O., Washington University in St. Louis
• José Luis Molinuevo, M.D., Ph.D., Lundbeck
• Ozioma Okonkwo, Ph.D., University of Wisconsin, Madison
• Luca Pani, M.D., University of Miami, Former Italian Regulatory Agency
• Michael Rafii, M.D., Ph.D., University of Southern California
• Laurie Ryan*, Ph.D., National Institute on Aging
• Phillip Scheltens, M.D., Ph.D., Life Science Partners
• Eric Siemers, M.D., Acumen
• Heather M. Snyder, Ph.D., Alzheimer’s Association
• Reisa Sperling, M.D., Brigham and Women’s Hospital, Harvard
• Charlotte E. Teunissen, Ph.D., VU University Medical Center

* Advisory member of the workgroup

View each workgroup member's disclosures (PDF).

Each workgroup initially issued proposed recommendations that were posted for public comment. The final versions of the guidance, revised to reflect input from the professional community at large, appear as free-access papers in Alzheimer's & Dementia: The Journal of the Alzheimer's Association .

National Institute on Aging (NIA) — Alzheimer's Association Diagnostic Guidelines Focusing on the Three Stages of Alzheimer's Disease (2011)

By incorporating new scientific insights and technological advances, the 2011 guidance aimed to improve current diagnosis, strengthen autopsy reporting of Alzheimer's brain changes and establish a research agenda for future progress in earlier detection and even greater diagnostic accuracy. The workgroups developed guidance that focused on the three stages of Alzheimer's disease:

• Preclinical (presymptomatic or asymptomatic) Alzheimer's.
• Mild cognitive impairment (MCI) due to Alzheimer's.
• Dementia due to Alzheimer's.

Introduction Written by representative members from the three workgroups focusing on Alzheimer's stages, this introduction provides background on the initiative and summarizes key issues and perspectives. Clifford R. Jack Jr. et al. "Introduction to the recommendations from the National Institute on Aging – Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease." Alzheimer's & Dementia: The Journal of the Alzheimer's Association 2011;7(3):257 – 262. Preclinical (asymptomatic) Alzheimer's disease This is a newly defined stage of the disease reflecting current evidence that measurable biomarker changes in the brain may occur years before symptoms affecting memory, thinking or behavior can be detected by affected individuals or their physicians. While the proposed guidance for preclinical Alzheimer's disease identifies these preclinical changes as an Alzheimer's stage, they do not establish diagnostic criteria that doctors can use now. Rather, they propose additional research to establish which biomarkers may best confirm that Alzheimer's-related changes are underway and how best to measure them. Reisa A. Sperling 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." Alzheimer's & Dementia: The Journal of the Alzheimer's Association 2011;7(3):280 – 292. Mild cognitive impairment (MCI) due to Alzheimer's disease In this stage, mild changes in memory and thinking are noticeable and can be measured on mental status tests, but are not severe enough to disrupt a person's day-to-day life. This guidance for mild cognitive impairment (MCI) due to Alzheimer's disease details four levels of certainty for ruling out other causes of MCI and arriving at a diagnosis of MCI due to Alzheimer's. Only the first level of certainty, which relies on core clinical criteria similar to those used today, is currently recommended for widespread use in general clinical practice. Marilyn S. Albert 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." Alzheimer's & Dementia: The Journal of the Alzheimer's Association 2011;7(3):270 – 279. Dementia due to Alzheimer's disease In this stage, impairments in memory, thinking and behavior decrease a person's ability to function independently in everyday life. This guidance for dementia due to Alzheimer's disease updates and clarifies clinical criteria to diagnose dementia from all causes and specifically from Alzheimer's disease. These criteria are sufficiently broad and flexible to be used now both by community practitioners without access to neuropsychological testing, specialized brain imaging, or cerebrospinal fluid testing and by specialists engaged in research or clinical studies who have access to such tools. Guy M. McKhann 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." Alzheimer's & Dementia: The Journal of the Alzheimer's Association 2011;7(3):263 – 269.

National Institute on Aging (NIA) – Alzheimer's Association Guideline on Neuropathologic Assessment of Alzheimer's During an Autopsy (2012)

In addition to the clinical guidance on the three stages of Alzheimer's disease, the Alzheimer's Association with the National Institutes of Health (NIH) convened workgroups to develop criteria for documenting and reporting Alzheimer's-related brain changes observed during an autopsy. Key recommendations include:

• Ranking the severity of Alzheimer's pathology based on three hallmark changes.
• Reporting these rankings as "Alzheimer's disease neuropathologic changes," whether or not the person was ever diagnosed with Alzheimer's during life, with a goal of understanding the full range of brain changes that may occur in people with or without Alzheimer's symptoms.
• Including assessment of Lewy bodies, vascular abnormalities and other brain changes that commonly coexist with Alzheimer's hallmarks.

Bradley T. Hyman et al. "National Institute on Aging – Alzheimer's Association guidelines on neuropathologic assessment of Alzheimer's disease." Alzheimer's & Dementia: The Journal of the Alzheimer's Association 2012;8(1):1 – 13.

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NIA-AA Research Framework: Toward a Biological Definition of Alzheimer's Disease

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New biological research framework for Alzheimer's seeks to spur discovery

NIA, Alzheimer's Association convene effort to update disease definition, speed testing

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The research community now has a new framework toward developing a biologically-based definition of Alzheimer's disease. This proposed "biological construct" is based on measurable changes in the brain and is expected to facilitate better understanding of the disease process and the sequence of events that lead to cognitive impairment and dementia. With this construct, researchers can study Alzheimer's, from its earliest biological underpinnings to outward signs of memory loss and other clinical symptoms, which could result in a more precise and faster approach to testing drug and other interventions.

The National Institute on Aging (NIA), part of the National Institutes of Health, and the Alzheimer's Association (AA) convened the effort, which as the "NIA-AA Research Framework: Towards a Biological Definition of Alzheimer's Disease," appears in the April 10, 2018 edition of Alzheimer's & Dementia: The Journal of the Alzheimer's Association . Drafts were presented at several scientific meetings and offered online, where the committee developing the framework gathered comments and ideas which informed the final published document. The framework, as it undergoes testing and as new knowledge becomes available, will be updated in the future.

The framework will apply to clinical trials and can be used for observational and natural history studies as well, its authors noted. They envision that this common language approach will unify how different stages of the disease are measured so that studies can be easily compared and presented more clearly to the medical field and public.

"In the context of continuing evolution of Alzheimer's research and technologies, the proposed research framework is a logical next step to help the scientific community advance in the fight against Alzheimer's," said NIA Director Richard J. Hodes, M.D. "The more accurately we can characterize the specific disease process pathologically defined as Alzheimer's disease, the better our chances of intervening at any point in this continuum, from preventing Alzheimer's to delaying progression,"

Evolution in thinking

This framework reflects the latest thinking in how Alzheimer's disease begins perhaps decades before outward signs of memory loss and decline may appear in an individual. In 2011, NIA-AA began to recognize this with the creation of separate sets of diagnostic guidelines that incorporated recognition of a preclinical stage of Alzheimer's and the need to develop interventions as early in the process as possible. The research framework offered today builds from the 2011 idea of three stages—pre-clinical, mild cognitive impairment and dementia—to a biomarker-based disease continuum.

The NIA-AA research framework authors, which included 20 academic, advocacy, government and industry experts, noted that the distinction between clinical symptoms and measurable changes in the brain has blurred. The new research framework focuses on biomarkers grouped into different pathologic processes of Alzheimer's which can be measured in living people with imaging technology and analysis of cerebral spinal fluid samples. It also incorporates measures of severity using biomarkers and a grading system for cognitive impairment.

"We have to focus on biological or physical targets to zero in on potential treatments for Alzheimer's," explained Eliezer Masliah, M.D., director of the Division of Neuroscience at the NIA. "By shifting the discussion to neuropathologic changes detected in biomarkers to define Alzheimer's, as we look at symptoms and the range of influences on development of Alzheimer's, I think we have a better shot at finding therapies, and sooner."

In an accompanying editorial , Masliah and NIA colleagues, including Dr. Hodes, highlighted both the promise and limitations of the biological approach. They noted that better operational definitions of Alzheimer's are needed to help better understand its natural history and heterogeneity, including prevalence of mimicking conditions. They also emphasized that the research framework needs to be extensively tested in diverse populations and with more sensitive biomarkers.

Batching and matching biomarkers

The NIA-AA research framework proposes three general groups of biomarkers—beta-amyloid, tau and neurodegeneration or neuronal injury—and leaves room for other and future biomarkers. Beta-amyloid is a naturally occurring protein that clumps to form plaques in the brain. Tau, another protein, accumulates abnormally forming neurofibrillary tangles which block communication between neurons. Neurodegeneration or neuronal injury may result from many causes, such as aging or trauma, and not necessarily Alzheimer's disease.

Researchers can use measures from a study participant and identify beta-amyloid (A), tau (T) or neurodegeneration or neuronal injury (N) to characterize that person's combination of biomarkers in one of eight profiles. For example, if a person has a positive beta-amyloid (A+) biomarker but no tau (T-), he or she would be categorized as having "Alzheimer's pathologic change." Only those with both A and T biomarkers would be considered to have Alzheimer's disease, along a continuum. The N biomarker group provides important pathologic staging information about factors often associated with Alzheimer's development or worsening of symptoms.

Framework for certain research only

The authors emphasized that the NIA-AA research framework is neither a diagnostic criteria nor guideline for clinicians. It is intended for research purposes, requiring further testing before it could be considered for general clinical practice, they noted.

They also stressed that the biological approach to Alzheimer's is not meant to supplant other measures, such as neuropsychological tests, to study important aspects of the disease such as its cognitive outcomes. In some cases, the article pointed out, biomarkers may not be available or requiring them would be counterproductive for particular types of research.

The authors acknowledge that the research framework may seem complex, but stress that it is flexible and may be employed to answer many research questions, such as how cognitive outcomes differ among various biomarker profiles, and what the influence of age is on those relationships.

In its commentary the NIA leadership developed a table to help explain how the proposed framework might be used and where it might not apply:

References:

Jack CR et al. NIA-AA Research Framework: Towards a Biological Definition of Alzheimer's Disease . Alzheimers Dement . 2018 Apr 10. doi: 10.1016/j.jalz.2018.02.018

Silverberg N et al. NIA commentary on the NIA-AA research framework: Towards a biological definition of Alzheimer's disease . Alzheimers Dement . 2018 Apr 10. doi: 10.1016/j.jalz.2018.03.004

About the National Institute on Aging : The NIA leads the federal government effort conducting and supporting research on aging and the health and well-being of older people. The NIA is designated as the lead NIH institute for information on Alzheimer’s disease. It provides information on age-related cognitive change and neurodegenerative disease, including participation in clinical studies, specifically on its website.

About the National Institutes of Health : NIH, the nation's medical research agency, includes 27 institutes and centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases.

NIH…Turning Discovery into Health®

November 2023 Update

In recent months, the Alzheimer's Association formed a workgroup whose charge was to examine the 2018 Alzheimer’s disease research framework and the 2011 clinical guidance in the context of current scientific knowledge and, if appropriate, update the diagnostic criteria. Workgroup members were selected to represent a broad range of scientific expertise; public, academic, and private institutions involved with dementia research; and geographic and gender diversity. The workgroup includes representation by the NIH National Institute on Aging (NIA) as well as the U.S. Food and Drug Administration. The revised criteria will be a product of the Alzheimer’s Association. 

The Alzheimer’s Association will remove NIA from the formal title of the evolving new diagnostic criteria to provide clarity based on NIH policy regarding use of its name and those of its institutes, centers, and offices . 

NIA looks forward to continuing its collaborative efforts with the Alzheimer’s Association and the other organizations involved in the workgroup with the primary goal of helping people living with Alzheimer’s and their caregivers. We anticipate that this report, which reflects on the current development and application of biomarkers for Alzheimer’s disease, will be important in informing the evolving research agenda.

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Director, Clinical Translational Program

Division/Office/Lab: Center for Alzheimer's and Related Dementias

Status: Open

Job Type: Scientific

Scientific Area or Specialty: Neurodegenerative diseases, clinical research, translational research

Pay/Grade: Commensurate with experience

NIA and the National Institute of Neurological Disorders and Stroke (NINDS) are recruiting for a Director (Senior Investigator/Senior Clinician) of the Clinical Translational Program of the Center for Alzheimer’s and Related Dementias (CARD) . The incumbent will direct the clinical arm of the already established, and very productive, CARD at the National Institutes of Health (NIH) and will be located within the Clinical Center on the NIH campus in Bethesda, Maryland. CARD, which organizationally falls within the NIA/IRP, is a translational science-centered research program that seeks to initiate, stimulate, accelerate, and support research in  Alzheimer’s disease (AD) and Alzheimer’s disease-related dementias (ADRD), leading to the development of improved understanding, treatments, and cures.

The Clinical Translational Program is an exciting new program within CARD that will serve as the NIH’s central hub for the clinical translation of human-based research in AD, ADRD, and in those who are at risk for developing AD or ADRD. This unique opportunity will enable an innovative scientist to develop a research program in the space of AD/ADRD using substantial resources, and through providing leadership and clinical oversight for a diverse group of intramural investigators and clinicians (including the future recruitment of individuals) who will be actively engaged in cutting-edge research on, and development of, therapeutic approaches to AD/ADRD. 

Who Should Apply?

• The tenure-eligible position requires an M.D. or equivalent  medical degree. 
• Applicants from the United States are required to have a license to practice medicine in the United States.   Additionally, American Board of Psychiatry and Neurology (Neurology Boards) certification/eligibility is required.  
• Foreign national applicants are required to have both qualifying credentials: eligibility for a license to practice medicine in the United States, and a visa classification that authorizes full patient contact privileges.
• Applicants should have considerable clinical experience including clinical research experience in neurodegenerative diseases, especially AD/ADRD. Their track record should include and combine clinical and translational research, diagnostic development, and clinical trials of behavioral interventions or experimental therapeutics in AD/ADRD. Candidates should have a strong track record in developing clinical/translational research on AD/ADRD, a strong record of productive research in the aforementioned areas, and a publication record in the fields relevant to this position. 

The Director, Clinical Translational Program, will lead the clinical aspects of CARD and will work with the CARD Director, and NIA Scientific and Clinical Directors, to set the direction of clinical research within CARD, ensuring that the research vision is complementary to the overall program of research within CARD and the NIA IRP. 

• The preferred applicant is expected to coordinate  activities with multidisciplinary research efforts in CARD and  throughout the AD /ADRD field and to have demonstrated ability to stimulate and lead collaborative interactions amongst scientists at multiple institutions, nationally, and internationally.  
• Demonstrated ability in scientific leadership, management of personnel, and budget oversight is  required . 

Salary is commensurate with experience and accomplishments.   A full Civil Service package of benefits (including retirement, health and life insurance, Thrift Savings Plan, etc.) is available.   NIA may be able to pay relocation expenses.   All employees of the Federal Government are subject to the conflict-of-interest statutes and regulations, including the Standards of Ethical Conduct. For additional information regarding the cutting-edge research programs currently within CARD and recent news and breakthroughs, please visit the CARD website, https://card.nih.gov/ .  

How to Apply

• To apply, please send a cover letter, curriculum vitae and bibliography, a  one-to-two page mentoring and diversity impact statement, specifically detailing activities involving women and persons from other groups which are underrepresented in biomedical research , a one-to-two page statement of clinical research interests, and three letters of recommendation (sent directly from the letter writers) to: Jamie Hertzfelt, Chief of Staff, Office of the Scientific Director, NIA via email at  [email protected] , noting Vacancy # CARD-24-01-T42-JH. 
• Applications, including letters of recommendation, must reference the vacancy #CARD-24-01-T42-JH for consideration.  
• The application period opens Sept. 25, 2024, and closes at 11:59pm Eastern Time on Oct. 4, 2024 .  

DHHS and NIH are Equal Opportunity Employers. The NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.

You may also be interested in

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Scientific Area/Specialty: Neuroscience, genetics, genomics

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Pay/Grade: Commensurate with research experience and accomplishments

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Applying the NIA Health Disparities Research Framework to Identify Needs and Opportunities in Chronic Musculoskeletal Pain Research

Affiliations.

• 1 Department of Anesthesiology, Division of Pain Medicine, University of Florida Health at Jacksonville, Jacksonville, Florida.
• 2 Department of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, Florida.
• 3 Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, Florida; College of Nursing, University of Florida, Gainesville, Florida.
• 4 Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, Florida; Department of Aging and Geriatric Research, Institute on Aging, College of Medicine, University of Florida, Gainesville, Florida.
• 5 Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, Florida.
• 6 Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, Florida; Department of Aging and Geriatric Research, Institute on Aging, College of Medicine, University of Florida, Gainesville, Florida; Department of Anesthesiology, Division of Pain Medicine, College of Medicine, University of Florida, Gainesville, Florida. Electronic address: [email protected].
• PMID: 34280570
• PMCID: PMC8890583
• DOI: 10.1016/j.jpain.2021.06.015

Disparities in the experience of chronic musculoskeletal pain in the United States stem from a confluence of a broad array of factors. Organized within the National Institute on Aging Health Disparity Research Framework, a literature review was completed to evaluate what is known and what is needed to move chronic musculoskeletal pain research forward specific to disproportionately affected populations. Peer-reviewed studies published in English, on human adults, from 2000 to 2019, and conducted in the United States were extracted from PubMed and Web of Science. Articles were reviewed for key words that focused on underrepresented ethnic/race groups with chronic musculoskeletal pain applying health factor terms identified in the NIAHealth Disparity Research Framework four levels of analysis: 1) environmental, 2) sociocultural, 3) behavioral, and 4) biological. A total of 52 articles met inclusion criteria. There were limited publications specific to underrepresented ethnic/race groups with chronic musculoskeletal pain across all levels with particular research gaps under sociocultural and biological categories. Current limitations in evidence may be supplemented by a foundation of findings specific to the broader topic of "chronic pain" which provides guidance for future investigations. Study designs including a focus on protective factors and multiple levels of analyses would be particularly meritorious. PERSPECTIVE: Chronic musculoskeletal pain unequally burdens underrepresented ethnic/race groups. In order to move research forward and to systematically investigate the complex array of factors contributing toward health disparities, an organized approach is necessary. Applying the NIA Health Disparities Research Framework, an overview of the current state of evidence specific to chronic musculoskeletal pain and underrepresented ethnic/race groups is provided with future directions identified.

Keywords: Health disparities; aging; chronic musculoskeletal pain; ethnicity/race; health factors.

Copyright © 2021 United States Association for the Study of Pain, Inc. Published by Elsevier Inc. All rights reserved.

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Conflict of interest statement

Conflict of interest statement: Authors have no conflicts of interest to declare.

The Levels of Analyses and…

The Levels of Analyses and Priority Focus Areas in the NIA Health Disparities…

• Abdallah CG, Geha P: Chronic pain and chronic stress: Two sides of the same coin? Chronic Stress 1:1–10, 2017. - PMC - PubMed
• Agbemenu K: Acculturation and health behaviors of african immigrants living in the United States: An integrative review. ABNF J 27:67–73, 2016 - PubMed
• Ahn H, Weaver M, Lyon DE, Kim J, Choi E, Staud R, Fillingim RB: Differences in clinical pain and experimental pain sensitivity between Asian Americans and whites with knee osteoarthritis. Clin J Pain 33:174–180, 2017 - PMC - PubMed
• Albert SM, Musa D, Kwoh CK, Hanlon JT, Silverman M: Self-care and professionally guided care in osteoarthritis: Racial differences in a population-based sample. J Aging Health 20:198–216, 2008 - PMC - PubMed
• Allen KD, Golightly YM, Olsen MK: Pilot study of pain and coping among patients with osteoarthritis: A daily diary analysis. J Clin Rheumatol 12:118–123, 2006 - PubMed

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