Writing in the December 30, 2019 online issue of Neurology, researchers at University of California San Diego School of Medicine and Veterans Affairs San Diego Healthcare System report that accumulating amyloid — an abnormal protein linked to neurodegenerative conditions such as Alzheimer’s disease (AD) — occurred faster among persons deemed to have “objectively-defined subtle cognitive difficulties” (Obj-SCD) than among persons considered to be “cognitively normal.”
Classification of Obj-SCD, which has been previously shown to predict progression to mild cognitive impairment (MCI) and dementia, is determined using non-invasive but sensitive neuropsychological measures, including measures of how efficiently someone learns and retains new information or makes certain types of errors.
The new findings, say authors, suggest that Obj-SCD can be detected during the preclinical state of AD when amyloid plaques are accumulating in the brain, neurodegeneration is just starting, but symptoms of impairment on total scores on thinking and memory tests have not yet been recorded.
“The scientific community has long thought that amyloid drives the neurodegeneration and cognitive impairment associated with Alzheimer’s disease,” said senior author Mark W. Bondi, PhD, professor of psychiatry at UC San Diego School of Medicine and the VA San Diego Healthcare System.
“These findings, in addition to other work in our lab, suggest that this is likely not the case for everyone and that sensitive neuropsychological measurement strategies capture subtle cognitive changes much earlier in the disease process than previously thought possible.
“This work, led by Dr. Kelsey Thomas, has important implications for research on treatment targets for AD, as it suggests that cognitive changes may be occurring before significant levels of amyloid have accumulated.
It seems like we may need to focus on treatment targets of pathologies other than amyloid, such as tau, that are more highly associated with the thinking and memory difficulties that impact people’s lives.”
Study participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), an on-going effort (launched in 2003) to test whether regular, repeated brain imaging, combined with other biological markers and clinical assessments, can measure the progression of MCI and early AD.
Seven hundred and forty-seven persons were involved in this study: 305 deemed cognitively normal, 153 with Obj-SCD and 289 MCI. All underwent neuropsychological testing and both PET and MRI scans.
The research team found that amyloid accumulation was faster in persons classified with Obj-SCD than in the cognitively normal group.
Those classified as Obj-SCD also experienced selective thinning of the entorhinal cortex, a region of the brain impacted very early in Alzheimer’s disease and associated with memory, navigation and perception of time.
Persons with MCI had more amyloid in their brain at the start of the study, but they did not have faster accumulation of amyloid compared to those with normal cognition.
However, those with MCI had more widespread temporal lobe atrophy, including the hippocampus.
Broadly speaking, scientists believe that for most people, AD is likely caused by a combination of genetic, lifestyle and environmental factors.
Increasing age is a primary, known risk factor. The amyloid hypothesis or amyloid cascade model posits that accumulating amyloid protein plaques in the brain kill neurons and gradually impair specific cognitive functions, such as memory, resulting in AD dementia.
However, many scientists are now questioning the amyloid hypothesis given the large number of clinical trials in which drugs targeted and successfully cleared amyloid from the brain but did not impact the trajectory of cognitive decline.
The ability to identify those at risk for AD before significant impairment and before or during the phase of faster amyloid accumulation would be a clinical boon, said authors, providing both a way to monitor disease progression and a window of opportunity to apply potential preventive or treatment strategies.
A rendering of amyloid protein plaques accumulating between neurons in the brain. Image is credited to NIA.
Currently, both approaches are limited. Some risk factors for Alzheimer’s can be minimized, such as not smoking, managing vascular risk factors such as hypertension or following a healthy diet with regular exercise. There are a handful of medications approved for treating symptoms of AD, but as yet, there is no cure.
“While the emergence of biomarkers of Alzheimer’s disease has revolutionized research and our understanding of how the disease progresses, many of these biomarkers continue to be highly expensive, inaccessible for clinical use or not available to those with certain medical conditions,” said first author Thomas, PhD, assistant professor of psychiatry at UC San Diego School of Medicine and research health scientist at the VA San Diego Healthcare System.
“A method of identifying individuals at risk for progression to AD using neuropsychological measures has the potential to improve early detection in those who may otherwise not be eligible for more expensive or invasive screening.”
Co-authors of this study include Kelsey R. Thomas, Katherine J. Bangen, Emily C. Edmonds, Christina G. Wong, Shanna Cooper, Lisa Delano-Wood, UC San Diego and VA San Diego Healthcare System; and Alexandra J. Weigand, UC San Diego, VA San Diego Healthcare System and San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology.
Disclosure: Mark Bondi receives royalties from Oxford University Press and serves as a consultant for Eisai, Novartis and Roche pharmaceutical companies.
lzheimer’s disease (AD) dementia is a chronic neurodegenerative disorder that is both progressive and irreversible [1,2]. Accumulation of brain amyloid beta (Aβ) and tau pathology are defining characteristics of the AD continuum and occur decades before cognitive symptoms are present [1,[3], [4], [5]].
Early intervention to alter the underlying Aβ or tau pathology is considered a potential approach to prevent or delay AD progression, and such treatments are in development [[6], [7], [8], [9]].
Although biomarkers for Aβ and tau pathology are often used to diagnose AD in research settings, these biomarkers are not typically used to diagnose AD in routine clinical practice today, primarily owing to resource limitations and costs [1].
If a new therapy targeting AD pathology were to become available, methods to confirm the presence of AD pathology, including positron emission tomography (PET) imaging – the only US Food and Drug Administration–approved biomarker for AD – and cerebrospinal fluid (CSF) measures, are projected to remain inaccessible to many patients [1,8,9].
As such, practical methods to determine which patients are most likely to benefit from more invasive and costly confirmatory biomarker testing for the presence of AD pathology may be helpful for prioritizing potentially limited resources.
The objective of this work was to provide practical algorithms to estimate the probability that a patient exhibiting cognitive problems possibly due to AD is Aβ positive, using currently available inputs.
Prior research has identified factors that are associated with Aβ pathology, such as age, cognitive impairment, apolipoprotein E (APOE) genotype, CSF inflammatory or protein biomarkers [10], and certain lifestyle factors [[11], [12], [13], [14]]. However, risk factor models do not directly translate into clinically useful or practical algorithms.
Many models lack external validation, include inputs with small effect sizes, or include inputs that are burdensome or costly (e.g., extensive neuropsychological testing or imaging) [10,[15], [16], [17]].
Recently, more practical algorithms to estimate the likelihood of Aβ positivity among patients with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) were published [11,18,19].
For example, the Swedish BioFINDER study’s “optimal” model used data on age, APOE genotype, and delayed recall score [18].
Although performance, as measured by area under the curve (AUC), has been acceptable in these reports, the algorithms published to date have limited flexibility because they require the input of APOE genotype and a specified cognitive test.
Moreover, the data sets are composed of patients from highly specialized clinics, and it is unknown whether the performance would remain robust in a broader population of symptomatic individuals.
Our intention was to develop algorithms that would support clinical decision-making regarding future biomarker testing, while also allowing quick administration and flexible inputs, such that providers could select their preferred cognitive measures and use of genetic information.
We anticipated that algorithms using currently available inputs would not replace Aβ tests but rather allow providers to more efficiently and confidently send symptomatic patients for more invasive and costly Aβ testing, if needed for diagnosis and treatment planning.
To cast a broad net and improve power to detect predictors, we first included all nondemented participants across two data sets in the analysis to identify predictors; in the next phase of deriving probability estimates, we focused on symptomatic patients owing to the current clinical context in which symptoms are ascertained before considering pathology. Given that there are scenarios in which genetic testing is not conducted, we designed two versions: one utilizing APOE ε4 data and another without it.
To achieve robust and generalizable algorithms, we developed a multistage statistical framework, using a combination of epidemiologic and random forest decision tree modeling methods, with an independent external validation using a community population-based sample.
Discussion
We developed and validated two practical algorithms to determine the probability of Aβ positivity in patients with SCD or MCI, using a rigorous statistical framework for probability estimation in both clinical and population-based data sets.
Feature selection was guided by the principle that to increase efficiency of biomarker testing, an algorithm ideally should be based on inputs that are quickly administered and readily available while still performing with high test characteristics.
As such, algorithm 1 was developed requiring only inputs of age and an immediate recall test, which may be administered in approximately 5 minutes. Algorithm 2 also considered APOE ε4 carrier status, a quick and often easily accessible genetic test. Both algorithms were robust across clinic-based populations (ADNI, AIBL) and the population-based sample participants (MCSA).
A strength of this study was the creation of a rigorous statistical framework as a foundation for the probability estimation. By using nested cross-validation with stratified subsampling procedures, problems caused by heterogeneity among data sets were reduced and modeling for the specific target population was improved.
This framework prevents overfitting and increases reproducibility and model robustness. Indeed, the algorithms’ performance metrics were largely similar across ADNI, AIBL, and MCSA, despite the differences in study settings and designs. This statistical structure is generalizable and could easily be extended to apply to different target populations or biomarkers.
Compared with other published practical algorithms for Aβ probability in SCD or MCI, the predictive performance of the current algorithms was similar, while carrying the added advantage of flexibility for the required inputs and validation in an epidemiologic data set. Although AUC was just slightly lower—at best 0.71 in the validation data set using age, recall z-score, and APOE ε4, compared with 0.75 to 0.82 for other models [11,18,19]—other models were tested only in specialized clinical sites.
The AUCs we observed during feature selection were in the same higher range as other models (e.g., 78% for age, APOE ε4, and cognitive test, Supplementary Fig. 2), and after we applied nested cross-validation with stratified subsampling over 1000 iterations to derive probabilities, the AUCs decreased.
This observation supports the notion that the performance of the algorithms derived here is tempered to yield more stable performance in various settings. Furthermore, AUC is not necessarily the preferred performance metric when the confirmatory test (e.g., PET) is costly and has limited availability [36]. Rather, PPV and positive likelihood ratio may be most relevant because a higher PPV more directly reduces the number of Aβ tests returned as negative (reducing unnecessary cost and burden), and a higher positive likelihood ratio conveys a larger impact on the clinician’s initial judgment [36,37].
While a 0.5 probability best balances sensitivity and specificity, the probability threshold best suited for a given clinical scenario depends on numerous factors that vary across clinics, such as patient volume and availability of PET scanners or specialists. With this in mind, our analysis considered alternative probability thresholds that may be relevant in different settings based on resource availability and provider preferences.
These algorithms were developed to maintain flexible inputs for application in clinical practice. As such, unlike previously developed algorithms, the algorithms do not require the use of specific cognitive and genetic tests [11,18,19].
Although APOE ε4 status is a strong predictor of Aβ pathology, there may be scenarios in which genetic counseling is problematic or not easily attainable. Probability values were derived both with and without APOE ε4 information, resulting in different probability distributions across the two algorithms; APOE ε4 information is not simply an additive component.
Another strength is that the algorithms do not specify which recall test must be used, as a variety of recall tests are effective at detecting MCI in clinical settings [38], with episodic memory most consistently and strongly related to cognitive decline due to AD pathology [[39], [40], [41]].
Recall tests are one of the most commonly documented cognitive assessments in current primary care [42], indicating that these algorithms can fit comfortably into current clinical practice.
The algorithms are also not prescriptive for the assessment used for SCD, in line with the 2017 Gerontological Society of America and 2018 Alzheimer’s Association tool kits, which have flexible guidelines for ascertaining SCD [[43], [44], [45]].
In clinical practice, these algorithms may be useful to increase the confidence of primary care providers or specialists in their clinical decision-making and furthermore improve efficiency by reducing the number of patients sent for Aβ testing. For patients with MCI, the use of these algorithms could shift the estimated probability of Aβ positivity from a prior probability of 0.45 to 0.50 [8,9,46,47] to approximately 0.65 to 0.75 (Fig. 3).
For patients with SCD, the estimated probability may shift from approximately 0.20 to 0.30 [48] to approximately 0.60 (Fig. 3). Confidence intervals provide reassurance on the estimated probability. In light of limited resources and high costs of confirmatory testing, providers could consider a patient’s probability of Aβ positivity and send only those patients above a given probability threshold for confirmatory testing.
Patients below the threshold might be appropriate for close monitoring (i.e., “watchful waiting”) and reassessment at follow-up visits. Such targeted referrals to specialists or Aβ testing may be necessary to reduce burden and increase access to those patients who are most likely to benefit [8,9].
Although these algorithms are designed to help clinical decision-making, they are not perfect predictors of Aβ PET. That is, while decreasing the number of false positives, there will inevitably be patients with Aβ pathology who do not meet the selected probability threshold. For this reason, the algorithms best serve as an adjunct to other considerations in the decision for specialist referral, confirmation testing, or watchful waiting. Follow-up assessments to monitor cognitive decline are important for patient care.
The moderately good predictive performance of these algorithms reflects the best of what is currently achievable for practical and low-cost inputs (lacking validated blood-based biomarkers and other potentially emerging technologies). Should a new therapy become approved for AD intervention, an estimated 14.9 million patients over age 55 years may screen positively for MCI in a single year in the US, with a health care system ill equipped for confirming pathology in this large population, and similar problems in other countries [8,9].
Application of either of these algorithms to this projected population could help diagnose individuals with underlying Aβ pathology while preventing an estimated 1 to 2.8 million negative Aβ confirmation tests. By applying a practical algorithm, there is potential to minimize unnecessary costs and burdens to the patient, provider, and health care system.
Research in Context
- 1.Systematic Review: We reviewed literature on predictive models for cerebral Aβ. Numerous factors, including age, cognitive impairment, APOE genotype, CSF inflammatory, or protein biomarkers have been associated with Aβ positivity. Available predictive models are limited by lacking external validation or requiring inputs that are burdensome or not universally available.
- 2.Interpretation: We developed a multistep statistical framework to obtain robust probability estimates across clinical and nonclinical settings using two different data sources and independently validating in a third, nonclinical population-based cohort. Compared with other published practical algorithms for Aβ probability, the predictive performance of the current algorithms was similar, while carrying the advantage of flexibility regarding the selection of recall test and APOE ε4 test.
- 3.Future directions: While these algorithms may help identify patients for biomarker testing, a validated blood-based or other low-cost, low-burden biomarker that can replace CSF or PET testing would critically improve Alzheimer’s disease detection and diagnosis.
Source:
UCSD
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