Ezzati, Ali

Abstract Objective To identify and disseminate research priorities for the headache field that should be areas of research focus during the next 10 years. Background Establishing research priorities helps focus and synergize the work of headache investigators, allowing them to reach the most important research goals more efficiently and completely. Methods The Headache Research Priorities organizing and executive committees and working group chairs led a multistakeholder and international group of experts to develop headache research priorities. The research priorities were developed and reviewed by clinicians, scientists, people with headache, representatives from headache organizations, health‐care industry representatives, and the public. Priorities were revised and finalized after receiving feedback from members of the research priorities working groups and after a public comment period. Results Twenty‐five research priorities across eight categories were identified: human models, animal models, pathophysiology, diagnosis and management, treatment, inequities and disparities, research workforce development, and quality of life. The priorities address research models and methods, development and optimization of outcome measures and endpoints, pain and non‐pain symptoms of primary and secondary headaches, investigations into mechanisms underlying headache attacks and chronification of headache disorders, treatment optimization, research workforce recruitment, development, expansion, and support, and inequities and disparities in the headache field. The priorities are focused enough that they help to guide headache research and broad enough that they are widely applicable to multiple headache types and various research methods. Conclusions These research priorities serve as guidance for headache investigators when planning their research studies and as benchmarks by which the headache field can measure its progress over time. These priorities will need updating as research goals are met and new priorities arise.

AbstractWe aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge‐based cut‐offs for each component of ATN and compared it to two data‐driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data‐driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia‐like and normal groups. Both data‐driven classification methods performed better than the empirical cut‐offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data‐driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

AbstractBackgroundThe NIA‐AA Research Framework on Alzheimer’s disease (AD) represents an important advance in the biological characterization of the AD spectrum. While this framework predicts disease progression, definitions of amyloid, tau, and neurodegeneration positivity based on cut‐scores may limit the performance of predictive models.Method252 cognitively normal (CN), 424 amnestic mild cognitive impairment (aMCI) and 128 AD participants were included in this study. aMCI participants at baseline were classified using 2 different methods: 1) Using conventional AT[N] criteria based on proposed cut‐offs in CSF or brain imaging measures: participants were classified on the AD continuum (A+T‐N‐, A+T+N‐, A+T‐N+, A+T+N+) and non‐AD continuum (all other subgroups); and 2) using a machine learning (ML) based method (ensemble linear discriminant model built using CN and AD participants) to classify MCI participants as CN‐like or AD‐like based on continuous CSF biomarkers, regional amyloid PET, FDG‐PET, and volumetric MRI (variables contributing to AT[N] classification; Table 1). Predictive performance of AT[N]‐based classification and ML‐based classification were assessed using clinical outcomes at 0.5, 1, 2, 3, 4, and 5 years of follow‐up.ResultA total of 414 aMCI participants had at least one wave of follow‐up (N of 397, 400, 346, 297, 238, and 93 at 0.5, 1, 2, 3, 4, and 5 years, respectively). The progression rate from aMCI to AD was 5.8% at 6m, 9.5% at 1y, 17.9% at 2y, 19.9% at 3y, 20.2% at 4y, and 15% at 5y of follow‐up. Presence of AD pathology at baseline (AT[N]‐based AD continuum) predicted progression to AD with high sensitivity but modest specificity during follow up (Table 2). ML‐based classification was less sensitive but substantially more specific. ML‐based classification models were significantly more accurate than AT[N]‐based classification at all follow‐up periods (McNemar test, p‐value<0.001 for all).ConclusionA single cut‐point approach used in AT[N] classification lacks accuracy when research questions require high diagnostic or prognostic certainty. A multivariate approach using ML methods can improve accuracy of prediction of disease progression. PPVs were higher at all follow‐up time points for ML‐based classification, indicating that progression to AD occurs at a higher rate in persons identified by ML.

Objectives To examine the association between vascular pathology and rate of cognitive decline in older adults independent of Alzheimer's disease ( AD ) pathology. Design Prospective cohort study. Setting Community sample. Participants Individuals from the Einstein Aging Study autopsy series (N = 62). Measurements The Blessed Information‐Memory‐Concentration ( BIMC ) test was used to assess global cognitive status. AD pathology was quantified according to Braak stage (<3 vs ≥ 3). Vascular pathology was quantified using a previously reported macrovascular lesion ( MVL ) score. The association between vascular pathology and antemortem rates of cognitive decline adjusted for level of AD pathology was assessed using linear mixed‐effects models. Results Mean age was 81.8 at enrollment and 89.0 at death. Participants with more than two MVL s had faster cognitive decline than those with no MVL s (difference in annual rate of change in BIMC 0.74 points/yr, P = .03). Braak stage was also associated with cognitive decline (difference 0.57 points/yr, P = .03). The difference in rate of cognitive decline between those with more than two MVL s and those free of vascular lesions persisted after adjustment for AD pathology (difference in rate of change in BIMC 0.68 points/yr, P = .04). The effect of vascular pathology on cognitive decline was not significantly different according to AD pathology. Conclusion Vascular brain pathology is associated with rate of cognitive decline after adjusting for level of AD pathology.