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Microglia become progressively activated and seemingly dysfunctional with age, and genetic studies have linked these cells to the pathogenesis of a growing number of neurodegenerative diseases. Here we report a striking buildup of lipid droplets in microglia with aging in mouse and human brains. These cells, which we call ‘lipid-droplet-accumulating microglia’ (LDAM), are defective in phagocytosis, produce high levels of reactive oxygen species and secrete proinflammatory cytokines. RNA-sequencing analysis of LDAM revealed a transcriptional profile driven by innate inflammation that is distinct from previously reported microglial states. An unbiased CRISPR–Cas9 screen identified genetic modifiers of lipid droplet formation; surprisingly, variants of several of these genes, including progranulin (GRN), are causes of autosomal-dominant forms of human neurodegenerative diseases. We therefore propose that LDAM contribute to age-related and genetic forms of neurodegeneration.Microglia in the aging hippocampus accumulate lipid droplets, and are functionally impaired and inflamed. Lipid droplet formation in microglia is regulated by genes linked to neurodegeneration such as progranulin.

The UK Biobank combines detailed phenotyping and genotyping with tracking of long-term health outcomes in a large cohort. This study describes the recently launched brain-imaging component that will ultimately scan 100,000 individuals. Results from the first 5,000 subjects are reported, including thousands of associations, population modes and hypothesis-driven results. Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.

Executive functions demonstrate variable developmental and aging profiles, with protracted development into early adulthood and declines in older age. However, relatively few studies have specifically included middle-aged adults in investigations of age-related differences in executive functions. This study explored the age-related differences in executive function from late childhood through to old age, allowing a more informed understanding of executive functions across the lifespan. Three hundred and fifty participants aged 10 to 86 years-old completed a battery of tasks assessing the specific roles of inhibitory control, working memory, cognitive flexibility, and planning. Results highlighted continued improvement in working memory capacity across adolescence and into young adulthood, followed by declines in both working memory and inhibitory control, beginning from as early as 30–40 years old and continuing into older age. Analyses of planning abilities showed continued improvement across adolescence and into young adulthood, followed by a decline in abilities across adulthood, with a small (positive) change in older age. Interestingly, a dissociation was found for cognitive flexibility; switch costs decreased, yet mixing costs increased across the lifespan. The results provide a description of the developmental differences in inhibitory control, working memory, cognitive flexibility and planning, above any effects of IQ or SES, and highlight the importance of including middle-aged adults in studies seeking to establish a more comprehensive picture of age-related differences in executive function.

For the past three decades, apolipoprotein E (APOE) has been known as the single greatest genetic modulator of sporadic Alzheimer disease (AD) risk, influencing both the average age of onset and the lifetime risk of developing AD. The APOEε4 allele significantly increases AD risk, whereas the ε2 allele is protective relative to the most common ε3 allele. However, large differences in effect size exist across ethnoracial groups that are likely to depend on both global genetic ancestry and local genetic ancestry, as well as gene–environment interactions. Although early studies linked APOE to amyloid-β — one of the two culprit aggregation-prone proteins that define AD — in the past decade, mounting work has associated APOE with other neurodegenerative proteinopathies and broader ageing-related brain changes, such as neuroinflammation, energy metabolism failure, loss of myelin integrity and increased blood–brain barrier permeability, with potential implications for longevity and resilience to pathological protein aggregates. Novel mouse models and other technological advances have also enabled a number of therapeutic approaches aimed at either attenuating the APOEε4-linked increased AD risk or enhancing the APOEε2-linked AD protection. This Review summarizes this progress and highlights areas for future research towards the development of APOE-directed therapeutics.Apolipoprotein E (APOE) is the greatest genetic modulator of sporadic Alzheimer disease risk. This Review provides a comprehensive update on our current knowledge of the genetics of APOE and its role in Alzheimer and other neurodegenerative diseases, and summarizes emerging APOE-targeted therapies designed to prevent or slow down Alzheimer disease.

Cognitive ageing research examines the cognitive abilities that are preserved and/or those that decline with advanced age. There is great individual variability in cognitive ageing trajectories. Some older adults show little decline in cognitive ability compared with young adults and are thus termed ‘optimally ageing’. By contrast, others exhibit substantial cognitive decline and may develop dementia. Human neuroimaging research has led to a number of important advances in our understanding of the neural mechanisms underlying these two outcomes. However, interpreting the age-related changes and differences in brain structure, activation and functional connectivity that this research reveals is an ongoing challenge. Ambiguous terminology is a major source of difficulty in this venture. Three terms in particular — compensation, maintenance and reserve — have been used in a number of different ways, and researchers continue to disagree about the kinds of evidence or patterns of results that are required to interpret findings related to these concepts. As such inconsistencies can impede progress in both theoretical and empirical research, here, we aim to clarify and propose consensual definitions of these terms.

Aging induces molecular, cellular and functional changes in the adult brain that drive cognitive decline and increase vulnerability to dementia-related neurodegenerative diseases. Leveraging systemic and lifestyle interventions, such as heterochronic parabiosis, administration of ‘young blood’, exercise and caloric restriction, has challenged prevalent views of brain aging as a rigid process and has demonstrated that aging-associated cognitive and cellular impairments can be restored to more youthful levels. Technological advances in proteomic and transcriptomic analyses have further facilitated investigations into the functional impact of intertissue communication on brain aging and have led to the identification of a growing number of pro-aging and pro-youthful factors in blood. In this review, we discuss blood-to-brain communication from a systems physiology perspective with an emphasis on blood-derived signals as potent drivers of both age-related brain dysfunction and brain rejuvenation. In this Review, Villeda and colleagues describe blood-to-brain communication from a systems physiology perspective, with an emphasis on blood-derived signals as potent drivers of both age-related brain dysfunction and brain rejuvenation.

Anti-amyloid treatments for early symptomatic Alzheimer disease have recently become clinically available in some countries, which has greatly increased the need for biomarker confirmation of amyloid pathology. Blood biomarker (BBM) tests for amyloid pathology are more acceptable, accessible and scalable than amyloid PET or cerebrospinal fluid (CSF) tests, but have highly variable levels of performance. The Global CEO Initiative on Alzheimer’s Disease convened a BBM Workgroup to consider the minimum acceptable performance of BBM tests for clinical use. Amyloid PET status was identified as the reference standard. For use as a triaging test before subsequent confirmatory tests such as amyloid PET or CSF tests, the BBM Workgroup recommends that a BBM test has a sensitivity of ≥90% with a specificity of ≥85% in primary care and ≥75–85% in secondary care depending on the availability of follow-up testing. For use as a confirmatory test without follow-up tests, a BBM test should have performance equivalent to that of CSF tests — a sensitivity and specificity of ~90%. Importantly, the predictive values of all biomarker tests vary according to the pre-test probability of amyloid pathology and must be interpreted in the complete clinical context. Use of BBM tests that meet these performance standards could enable more people to receive an accurate and timely Alzheimer disease diagnosis and potentially benefit from new treatments.Anti-amyloid treatments for early symptomatic Alzheimer disease have greatly increased the need for biomarker confirmation of amyloid pathology and blood biomarker tests offer an accessible and scalable biomarker test. This Consensus Statement provides recommendations for the minimum acceptable performance of blood biomarker tests for clinical use.

Alzheimer’s disease (AD) predominantly occurs as a late onset (LOAD) form involving neurodegeneration and cognitive decline with progressive memory loss. Risk factors that include aging promote accumulation of AD pathologies, such as amyloid-beta and tau aggregates, as well as inflammation and oxidative stress. Homeostatic glial states regulate and suppress pathology buildup; inflammatory states exacerbate pathology by releasing pro-inflammatory cytokines. Multiple stresses likely induce glial senescence, which could decrease supportive functions and reinforce inflammation. In this perspective, we hypothesize that aging first drives AD pathology burden, whereafter AD pathology putatively induces glial senescence in LOAD. We hypothesize that increasing glial senescence, particularly local senescent microglia accumulation, sustains and drives perpetuating buildup and spread of AD pathologies, glial aging, and further senescence. We predict that increasing glial senescence, particularly local senescent microglia accumulation, also transitions individuals from healthy cognition into mild cognitive impairment and LOAD diagnosis. These pathophysiological underpinnings may centrally contribute to LOAD onset, but require further mechanistic investigation. In this perspective, the authors hypothesise that glial senescence, requiring senescent microglia burden, perpetuates further aging, Alzheimer’s pathologies, and senescence. Increasing glial senescence is proposed as necessary to drive individuals from healthy cognition into cognitive decline and dementia.

Early diagnosis of Alzheimer’s disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer’s disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.

Alzheimer’s disease can be treated by targeting amyloid-β plaques and diagnosed in vivo by biomarkers, prompting the revision of criteria for the diagnosis and staging of this disease.