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Background As human longevity increases and Alzheimer’s disease (AD) increasingly becomes a significant societal burden, finding pathways or protective factors that facilitate exceptional brain aging without AD pathophysiologies (ADP) will be critical. The goal of this viewpoint is two-fold: 1) to present evidence for “exceptional brain aging” without ADP; and 2) to bring together ideas and observations from the literature and present them as testable hypotheses for biomarker studies to discover protective factors for “exceptional brain aging” without ADP and AD dementia. Discovering pathways to exceptional aging There are three testable hypotheses. First, discovering and quantifying links between risk factor(s) and early ADP changes in midlife using longitudinal biomarker studies will be fundamental to understanding why the majority of individuals deviate from normal aging to the AD pathway. Second, a risk factor may have quantifiably greater impact as a trigger and/or accelerator on a specific component of the biomarker cascade (amyloid, tau, neurodegeneration). Finally, and most importantly, while each risk factor may have a different mechanism of action on AD biomarkers, “exceptional aging” and protection against AD dementia will come from “net sum” protection against all components of the biomarker cascade. The knowledge of the mechanism of action of risk factor(s) from hypotheses 1 and 2 will aid in better characterization of their effect on outcomes, identification of subpopulations that would benefit, and the timing at which the risk factor(s) would have the maximal impact. Additionally, hypothesis 3 highlights the importance of multifactorial or multi-domain approaches to “exceptional aging” as well as prevention of AD dementia. Conclusion While important strides have been made in identifying risk factors for AD dementia incidence, further efforts are needed to translate these into effective preventive strategies. Using biomarker studies for understanding the mechanism of action, effect size estimation, selection of appropriate end-points, and better subject recruitment based on subpopulation effects are fundamental for better design and success of prevention trials.

In 2010, we put forward a hypothetical model of the major biomarkers of Alzheimer's disease (AD). The model was received with interest because we described the temporal evolution of AD biomarkers in relation to each other and to the onset and progression of clinical symptoms. Since then, evidence has accumulated that supports the major assumptions of this model. Evidence has also appeared that challenges some of our assumptions, which has allowed us to modify our original model. Refinements to our model include indexing of individuals by time rather than clinical symptom severity; incorporation of interindividual variability in cognitive impairment associated with progression of AD pathophysiology; modifications of the specific temporal ordering of some biomarkers; and recognition that the two major proteinopathies underlying AD biomarker changes, amyloid β (Aβ) and tau, might be initiated independently in sporadic AD, in which we hypothesise that an incident Aβ pathophysiology can accelerate antecedent limbic and brainstem tauopathy.

Sex and gender—biological and social constructs—significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex‐ and gender‐specific drivers of resilience, including a sex/gender‐oriented review of risk factors, genetics, AD and non‐AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender‐aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender‐specific resilience throughout disease stages. Highlights Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex‐specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex‐specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

Background The \"resistance vs resilience\" to Alzheimer’s disease (AD) framework (coping vs avoiding) has gained interest in the field in the last year. In this viewpoint, our effort is (i) to provide clarity to the usage of the framework in the context of the ATN (amyloid/tau/neurodegeneration) system as well as in lifespan and cognitive aging studies and (ii) to discuss the challenges of matching these concepts to specific biological mechanisms. Main body In the context of the ATN system, the main goal of the resistance vs resilience framework is to make a fundamental distinction between risk factors that may help halt the development of AD pathologies (AT) (“resistance”) vs delay processes downstream to AT, i.e., neurodegeneration (N) and the clinical expression of the disease (“resilience”). The process of resilience in dementia and aging research should be envisioned as a process that is developed over the lifespan. Greater neurobiological capital to start with (initial brain reserve), maintaining brain structure and function (brain maintenance), or greater adaptability of cognitive strategies to perform a task (cognitive reserve) could all contribute to higher resilience to pathologies later in life. Simply put, resilience is not only a response to pathological processes (i.e. increased brain function to compensate for increasing AD pathology) but also reflects individual differences in brain structure and function that can be built over the lifespan (e.g., through education, lifetime cognitive, and physical activities). Further, the resistance vs resilience terminology can be extended to study other pathological processes such as cerebrovascular lesions, Lewy body disease, or TDP-43. However, some challenges do exist: (i) when studying multiple neuropathologies, the study design and framework will drive the usage of terminology; (ii) it is unavoidable that the measurements of resilience (brain structure and function) will reflect both the effect of pathologies and the impact of several risk and protective factors throughout the lifespan. Therefore, identifying resilience brain markers across lifespan, aging, and dementia studies, notably with longitudinal study designs, will be an important step towards understanding mechanisms of action. Conclusions While the field advances towards consensus definitions of existing concepts, the resistance vs resilience terminology may provide clarity in the communication of results in aging and dementia studies as well as provide a framework for the development of both hypotheses and study designs.

With the use of publicly available software, reconstructed facial images from deidentified cranial MRI scans were matched to photographs of individual study participants 83% of the time as the first choice from a panel of photographs. This raises the possibility of identifying anonymous research participants.

With the aging of the population, the burden of Alzheimer's disease (AD) is rapidly expanding. More than 5 million people in the US alone are affected with AD and this number is expected to triple by 2050. While men may have a higher risk of mild cognitive impairment (MCI), an intermediate stage between normal aging and dementia, women are disproportionally affected with AD. One explanation is that men may die of competing causes of death earlier in life, so that only the most resilient men may survive to older ages. However, many other factors should also be considered to explain the sex differences. In this review, we discuss the differences observed in men versus women in the incidence and prevalence of MCI and AD, in the structure and function of the brain, and in the sex-specific and gender-specific risk and protective factors for AD. In medical research, sex refers to biological differences such as chromosomal differences (eg, XX versus XY chromosomes), gonadal differences, or hormonal differences. In contrast, gender refers to psychosocial and cultural differences between men and women (eg, access to education and occupation). Both factors play an important role in the development and progression of diseases, including AD. Understanding both sex- and gender-specific risk and protective factors for AD is critical for developing individualized interventions for the prevention and treatment of AD.

A new classification for biomarkers in Alzheimer's disease and cognitive ageing research is based on grouping the markers into three categories: amyloid deposition (A), tauopathy (T), and neurodegeneration or neuronal injury (N). Dichotomising these biomarkers as normal or abnormal results in eight possible profiles. We determined the clinical characteristics and prevalence of each ATN profile in cognitively unimpaired individuals aged 50 years and older. All participants were in the Mayo Clinic Study of Aging, a population-based study that uses a medical records linkage system to enumerate all individuals aged 50–89 years in Olmsted County, MN, USA. Potential participants are randomly selected, stratified by age and sex, and invited to participate in cognitive assessments; individuals without medical contraindications are invited to participate in brain imaging studies. Participants who were judged clinically as having no cognitive impairment and underwent multimodality imaging between Oct 11, 2006, and Oct 5, 2016, were included in the current study. Participants were classified as having normal (A−) or abnormal (A+) amyloid using amyloid PET, normal (T−) or abnormal (T+) tau using tau PET, and normal (N−) or abnormal (N+) neurodegeneration or neuronal injury using cortical thickness assessed by MRI. We used the cutoff points of standard uptake value ratio (SUVR) 1·42 (centiloid 19) for amyloid PET, 1·23 SUVR for tau PET, and 2·67 mm for MRI cortical thickness. Age-specific and sex-specific prevalences of the eight groups were determined using multinomial models combining data from 435 individuals with amyloid PET, tau PET, and MRI assessments, and 1113 individuals who underwent amyloid PET and MRI, but not tau PET imaging. The numbers of participants in each profile group were 165 A−T−N−, 35 A−T+N−, 63 A−T−N+, 19 A−T+N+, 44 A+T−N−, 25 A+T+N−, 35 A+T−N+, and 49 A+T+N+. Age differed by ATN group (p<0·0001), ranging from a median 58 years (IQR 55–64) in A–T–N– and 57 years (54–64) in A–T+N– to a median 80 years (75–84) in A+T–N+ and 79 years (73–87) in A+T+N+. The number of APOE ε4 carriers differed by ATN group (p=0·04), with carriers roughly twice as frequent in each A+ group versus the corresponding A– group. White matter hyperintensity volume (p<0·0001) and cognitive performance (p<0·0001) also differed by ATN group. Tau PET and neurodegeneration biomarkers were discordant in most individuals who would be categorised as stage 2 or 3 preclinical Alzheimer's disease (A+T+N−, A+T−N+, and A+T+N+; 86% at age 65 years and 51% at age 80 years) or with suspected non-Alzheimer's pathophysiology (A−T+N−, A−T−N+, and A−T+N+; 92% at age 65 years and 78% at age 80 years). From age 50 years, A−T−N− prevalence declined and A+T+N+ and A−T+N+ prevalence increased. In both men and women, A−T−N− was the most prevalent until age late 70s. After about age 80 years, A+T+N+ was most prevalent. By age 85 years, more than 90% of men and women had one or more biomarker abnormalities. Biomarkers of fibrillar tau deposition can be included with those of β-amyloidosis and neurodegeneration or neuronal injury to more fully characterise the heterogeneous pathological profiles in the population. Both amyloid- dependent and amyloid-independent pathological profiles can be identified in the cognitively unimpaired population. The prevalence of each ATN group changed substantially with age, with progression towards more biomarker abnormalities among individuals who remained cognitively unimpaired. National Institute on Aging (part of the US National Institutes of Health), the Alexander Family Professorship of Alzheimer's Disease Research, the Mayo Clinic, and the GHR Foundation.

Altered iron metabolism has been hypothesized to be associated with Alzheimer's disease pathology, and prior work has shown associations between iron load and beta amyloid plaques. Quantitative susceptibility mapping (QSM) is a recently popularized MR technique to infer local tissue susceptibility secondary to the presence of iron as well as other minerals. Greater QSM values imply greater iron concentration in tissue. QSM has been used to study relationships between cerebral iron load and established markers of Alzheimer's disease, however relationships remain unclear. In this work we study QSM signal characteristics and associations between susceptibility measured on QSM and established clinical and imaging markers of Alzheimer's disease. The study included 421 participants (234 male, median age 70 years, range 34–97 years) from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center; 296 (70%) had a diagnosis of cognitively unimpaired, 69 (16%) mild cognitive impairment, and 56 (13%) amnestic dementia. All participants had multi-echo gradient recalled echo imaging, PiB amyloid PET, and Tauvid tau PET. Variance components analysis showed that variation in cortical susceptibility across participants was low. Linear regression models were fit to assess associations with regional susceptibility. Expected increases in susceptibility were found with older age and cognitive impairment in the deep and inferior gray nuclei (pallidum, putamen, substantia nigra, subthalamic nucleus) (betas: 0.0017 to 0.0053 ppm for a 10 year increase in age, p = 0.03 to <0.001; betas: 0.0021 to 0.0058 ppm for a 5 point decrease in Short Test of Mental Status, p = 0.003 to p<0.001). Effect sizes in cortical regions were smaller, and the age associations were generally negative. Higher susceptibility was significantly associated with higher amyloid PET SUVR in the pallidum and putamen (betas: 0.0029 and 0.0012 ppm for a 20% increase in amyloid PET, p = 0.05 and 0.02, respectively), higher tau PET in the basal ganglia with the largest effect size in the pallidum (0.0082 ppm for a 20% increase in tau PET, p<0.001), and with lower cortical gray matter volume in the medial temporal lobe (0.0006 ppm for a 20% decrease in volume, p = 0.03). Overall, these findings suggest that susceptibility in the deep and inferior gray nuclei, particularly the pallidum and putamen, may be a marker of cognitive decline, amyloid deposition, and off-target binding of the tau ligand. Although iron has been demonstrated in amyloid plaques and in association with neurodegeneration, it is of insufficient quantity to be reliably detected in the cortex using this implementation of QSM.

Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.

In a 2014 cross-sectional analysis, we showed that amyloid and neurodegeneration biomarker states in participants with no clinical impairment varied greatly with age, suggesting dynamic within-person processes. In this longitudinal study, we aimed to estimate rates of transition from a less to a more abnormal biomarker state by age in individuals without dementia, as well as to assess rates of transition to dementia from an abnormal state. Participants from the Mayo Clinic Study of Aging (Olmsted County, MN, USA) without dementia at baseline were included in this study, a subset of whom agreed to multimodality imaging. Amyloid PET (with 11C-Pittsburgh compound B) was used to classify individuals as amyloid positive (A+) or negative (A−). 18F-fluorodeoxyglucose (18F-FDG)-PET and MRI were used to classify individuals as neurodegeneration positive (N+) or negative (N−). We used all observations, including those from participants who did not have imaging results, to construct a multistate Markov model to estimate four different age-specific biomarker state transition rates: A−N− to A+N−; A−N− to A−N+ (suspected non-Alzheimer's pathology); A+N− to A+N+; and A−N+ to A+N+. We also estimated two age-specific rates to dementia: A+N+ to dementia and A−N+ to dementia. Using these state-to-state transition rates, we estimated biomarker state frequencies by age. At baseline (between Nov 29, 2004, to March 7, 2015), 4049 participants did not have dementia (3512 [87%] were clinically normal and 537 [13%] had mild cognitive impairment). 1541 individuals underwent imaging between March 28, 2006, to April 30, 2015. Transition rates were low at age 50 years and, with one exception, exponentially increased with age. At age 85 years compared with age 65 years, the rate was nearly 11-times higher (17·2 vs 1·6 per 100 person-years) for the transition from A−N− to A−N+, three-times higher (20·8 vs 6·1) for A+N− to A+N+, and five-times higher (13·2 vs 2·6) for A−N+ to A+N+. The rate of transition was also increased at age 85 years compared with age 65 years for A+N+ to dementia (7·0 vs 0·8) and for A−N+ to dementia (1·7 vs 0·6). The one exception to an exponential increase with age was the transition rate from A−N− to A+N−, which increased from 4·0 transitions per 100 person-years at age 65 years to 6·9 transitions per 100 person-years at age 75 and then plateaued beyond that age. Estimated biomarker frequencies by age from the multistate model were similar to cross-sectional biomarker frequencies. Our transition rates suggest that brain ageing is a nearly inevitable acceleration toward worse biomarker and clinical states. The one exception is the transition to amyloidosis without neurodegeneration, which is most dynamic from age 60 years to 70 years and then plateaus beyond that age. We found that simple transition rates can explain complex, highly interdependent biomarker state frequencies in our population. National Institute on Aging, Alexander Family Professorship of Alzheimer's Disease Research, the GHR Foundation.