Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset

Causal Effects of Schooling on Memory at Older Ages in Six Low- and Middle-Income Countries: Nonparametric Evidence With Harmonized Datasets

OBJECTIVES

Higher schooling attainment is associated with better cognitive function at older ages, but it remains unclear whether the relationship is causal. We estimated causal effects of schooling on performances on the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) word-recall (memory) test at older ages in China, Ghana, India, Mexico, Russia, and South Africa.

METHODS

We used harmonized data (n = 30,896) on older adults (≥50 years) from the World Health Organization Study on Global Ageing and Adult Health. We applied an established nonparametric partial identification approach that bounds causal effects of increasing schooling attainment at different parts of the schooling distributions under relatively weak assumptions.

RESULTS

An additional year of schooling increased word-recall scores by between 0.01 and 0.13 SDs in China, 0.01 and 0.06 SDs in Ghana, 0.02 and 0.09 SDs in India, 0.02 and 0.12 SDs in Mexico, and 0 and 0.07 SDs in South Africa when increasing schooling from never attended to primary. No results were obtained for Russia at this margin due to the low proportion of older adults with primary schooling or lower. At higher parts of the schooling distributions (e.g., high school or university completion), the bounds cannot statistically reject null effects.

DISCUSSION

Our results indicate that increasing schooling from never attended to primary had long-lasting effects on memory decades later in life for older adults in 5 diverse low- and middle-income countries.

Does Schooling Improve Cognitive Abilities at Older Ages? Causal Evidence From Nonparametric Bounds

We revisit much-investigated relationships between schooling and health, focusing on schooling impacts on cognitive abilities at older ages using the Harmonized Cognition Assessment Protocol in the Health and Retirement Study (HRS) and a bounding approach that requires relatively weak assumptions. Our estimated upper bounds on the population average effects indicate potentially large causal effects of increasing schooling from primary to secondary. Yet, these upper bounds are smaller than many estimates from studies of causal schooling impacts on cognition using compulsory schooling laws. We also cannot rule out small and null effects at this margin. However, we find evidence for positive causal effects on cognition of increasing schooling from secondary to tertiary. We replicate findings from the HRS using data on older adults from the Midlife in United States Development Study Cognitive Project. We further explore possible mechanisms behind the schooling effect (e.g., health, socioeconomic status, occupation, and spousal schooling), finding suggestive evidence of effects through such mechanisms.

Association between dementia diagnosis at dialysis initiation and mortality in older patients with end-stage kidney disease in South Korea

BACKGROUND

The prevalence of dementia is 2- to 7-fold higher among patients with end-stage kidney disease (ESKD) than among the general population; however, its clinical implications in this population remain unclear. Therefore, this study aimed to determine whether comorbid dementia increases mortality among older patients with ESKD undergoing newly initiated hemodialysis.

METHODS

We analyzed data from the Korean Society of Geriatric Nephrology retrospective cohort, which included 2,736 older ESKD patients (≥70 years old) who started hemodialysis between 2010 and 2017. Kaplan-Meier survival and Cox regression analyses were used to examine all-cause mortality between the patients with and without dementia in this cohort.

RESULTS

Of the 2,406 included patients, 8.3% had dementia at the initiation of dialysis; these patients were older (79.6 ± 6.0 years) than patients without dementia (77.7 ± 5.5 years) and included more women (male:female, 89:111). Pre-ESKD diagnosis of dementia was associated with an increased risk of overall mortality (hazard ratio, 1.503; p < 0.001), and this association remained consistent after multivariate adjustment (hazard ratio, 1.268; p = 0.009). In subgroup analysis, prevalent dementia was associated with mortality following dialysis initiation in female patients, those aged <85 years, those with no history of cerebrovascular accidents or severe behavioral disorders, those not residing in nursing facilities, and those with no or short-term hospitalization.

CONCLUSION

A pre-ESKD diagnosis of dementia is associated with mortality following dialysis initiation in older Korean population. In older patients with ESKD, cognitive assessment at dialysis initiation is necessary.

Super-resolution microscopy as a drug discovery tool

At the turn of the century a fundamental resolution barrier in fluorescence microscopy known as the diffraction limit was broken, giving rise to the field of super-resolution microscopy. Subsequent nanoscopic investigation with visible light revolutionised our understanding of how previously unknown molecular features give rise to the emergent behaviour of cells. It transpires that the devil is in these fine molecular details, and essential nanoscale processes were found everywhere researchers chose to look. Now, after nearly two decades, super-resolution microscopy has begun to address previously unmet challenges in the study of human disease and is poised to become a pivotal tool in drug discovery.

Dynamic lifetime risk prediction of Alzheimer's disease with longitudinal cognitive assessment measurements

INTRODUCTION

The progressive nature of Alzheimer's disease (AD) highlights the importance of predicting lifetime risk and updating assessments as new data emerge. This study aimed to develop a dynamic model using longitudinal cognitive assessments for updated risk predictions.

METHODS

This study used data from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) to develop a dynamic risk prediction model based on five cognitive domains, updated annually over 10 years.

RESULTS

The lifetime prediction models based on 2384 participants showed improved area under the curve (AUC) over time, rising from 0.578 at baseline to 0.765 with 10 years of data. The models predicting AD onset before ages 85 and 90 showed superior performance, with AUCs increasing from 0.761 to 0.932 and 0.658 to 0.876, respectively.

DISCUSSION

Incorporating longitudinal cognitive assessments improves AD risk prediction as more data become available. Future research should integrate diverse data types to further boost predictive accuracy.

HIGHLIGHTS

Developed a dynamic lifetime risk prediction model. The area under the curve (AUC) increased from 0.578 at baseline to 0.765 with 10 years of data. The models predicting pre-85 and pre-90 risks demonstrated superior performance.

Cognitive, Behavioral, and Functional Outcomes of Suspected Mild Traumatic Brain Injury in Community-Dwelling Older Persons Without Mild Cognitive Impairment or Dementia

BACKGROUND

Traumatic brain injury is associated with greater risk and earlier onset of dementia.

OBJECTIVE

This study investigated whether later-life changes in subjective cognition and behavior - potential markers of Alzheimer disease - could be observed in cognitively unimpaired older persons with a history of suspected mild traumatic brain injury (smTBI) earlier in life and whether changes in cognition and behavior mediated the link between smTBI and daily function.

METHODS

Data for 1392 participants from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging were analyzed. A validated self-reported brain injury screening questionnaire was used to determine the history of smTBI. Outcomes were measured using the Everyday Cognition scale (for subjective cognitive decline [SCD]), Mild Behavioral Impairment (MBI) Checklist, and Standard Assessment of Global Everyday Activities (for function). Inverse probability of treatment weighted logistic and negative binomial regressions were used to model smTBI (exposure) associations with SCD and MBI statuses, and Everyday Cognition-II and MBI Checklist total scores, respectively. Mediation analyses were conducted using bootstrapping.

RESULTS

History of smTBI was linked to higher odds of SCD (odds ratio = 1.45, 95% confidence interval: [1.14-1.84]) or MBI (odds ratio = 1.75, 95% confidence interval: [1.54-1.98]), as well as 24% (95% confidence interval: [18%-31%]) higher Everyday Cognition-II and 52% (95% confidence interval: [41%-63%]) higher MBI Checklist total scores. Finally, SCD and MBI mediated approximately 45% and 56%, respectively, of the association between smTBI history and poorer function, as indicated by higher Standard Assessment of Global Everyday Activities total scores.

CONCLUSIONS

smTBI at any point in the life course is linked to poorer cognition and behavior even in community-dwelling older persons without MCI or dementia. Older persons with smTBI may benefit from early dementia risk assessment using tools that measure changes in cognition and behavior. Interventions for declining cognition and behavior may also be beneficial in this population to address functional impairment.

Network Biomarkers of Alzheimer's Disease Risk Derived from Joint Volume and Texture Covariance Patterns in Mouse Models

Alzheimer's disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Early detection and understanding of risk factors and their downstream effects are therefore crucial. Animal models provide valuable tools to study these prodromal stages. We investigated various levels of genetic risk for AD using mice expressing the three major human APOE alleles in place of mouse APOE. We leverage these mouse models utilizing high-resolution magnetic resonance diffusion imaging, due to its ability to provide multiple parameters that can be analysed jointly. We examine how APOE genotype interacts with age, sex, diet, and immunity to yield jointly discernable changes in regional brain volume and fractional anisotropy, a sensitive metric for brain water diffusion. Our results demonstrate that genotype strongly influences the caudate putamen, pons, cingulate cortex, and cerebellum, while sex affects the amygdala and piriform cortex bilaterally. Immune status impacts numerous regions, including the parietal association cortices, thalamus, auditory cortex, V1, and bilateral dentate cerebellar nuclei. Risk factor interactions particularly affect the amygdala, thalamus, and pons. APOE2 mice on a regular diet exhibited the fewest temporal changes, suggesting resilience, while APOE3 mice showed minimal effects from a high-fat diet (HFD). HFD amplified aging effects across multiple brain regions. The interaction of AD risk factors, including diet, revealed significant changes in the periaqueductal gray, pons, amygdala, inferior colliculus, M1, and ventral orbital cortex. Future studies should investigate the mechanisms underlying these coordinated changes in volume and texture, potentially by examining network similarities in gene expression and metabolism, and their relationship to structural pathways involved in neurodegenerative disease progression.

Ellagic Acid: A Green Multi-Target Weapon That Reduces Oxidative Stress and Inflammation to Prevent and Improve the Condition of Alzheimer's Disease

Oxidative stress (OS), generated by the overrun of reactive species of oxygen and nitrogen (RONS), is the key cause of several human diseases. With inflammation, OS is responsible for the onset and development of clinical signs and the pathological hallmarks of Alzheimer's disease (AD). AD is a multifactorial chronic neurodegenerative syndrome indicated by a form of progressive dementia associated with aging. While one-target drugs only soften its symptoms while generating drug resistance, multi-target polyphenols from fruits and vegetables, such as ellagitannins (ETs), ellagic acid (EA), and urolithins (UROs), having potent antioxidant and radical scavenging effects capable of counteracting OS, could be new green options to treat human degenerative diseases, thus representing hopeful alternatives and/or adjuvants to one-target drugs to ameliorate AD. Unfortunately, in vivo ETs are not absorbed, while providing mainly ellagic acid (EA), which, due to its trivial water-solubility and first-pass effect, metabolizes in the intestine to yield UROs, or irreversible binding to cellular DNA and proteins, which have very low bioavailability, thus failing as a therapeutic in vivo. Currently, only UROs have confirmed the beneficial effect demonstrated in vitro by reaching tissues to the extent necessary for therapeutic outcomes. Unfortunately, upon the administration of food rich in ETs or ETs and EA, URO formation is affected by extreme interindividual variability that renders them unreliable as novel clinically usable drugs. Significant attention has therefore been paid specifically to multitarget EA, which is incessantly investigated as such or nanotechnologically manipulated to be a potential "lead compound" with protective action toward AD. An overview of the multi-factorial and multi-target aspects that characterize AD and polyphenol activity, respectively, as well as the traditional and/or innovative clinical treatments available to treat AD, constitutes the opening of this work. Upon focus on the pathophysiology of OS and on EA's chemical features and mechanisms leading to its antioxidant activity, an all-around updated analysis of the current EA-rich foods and EA involvement in the field of AD is provided. The possible clinical usage of EA to treat AD is discussed, reporting results of its applications in vitro, in vivo, and during clinical trials. A critical view of the need for more extensive use of the most rapid diagnostic methods to detect AD from its early symptoms is also included in this work.

Female-Specific Association between the Apolipoprotein E E4 Allele and Age at Diagnosis of Glaucoma in UK Biobank

OBJECTIVE

To explore the impact of the apolipoprotein E (APOE) E4 allele in the gender-specific aging process in glaucoma by illustrating the interaction between risk factors, including the APOE E4 allele, gender, and intraocular pressure (IOP), for age at diagnosis (AAD) of glaucoma.

DESIGN

A cross-sectional study included UK Biobank participants with complete data (2006-2010) for analysis. Data were analyzed in December 2023.

PARTICIPANTS

Two thousand two hundred thirty-six glaucoma patients and 103 232 controls.

METHODS

We evaluated multivariable-adjusted associations of AAD of glaucoma, APOE E4 allele (0: absence; 1: presence), and IOP using linear mixed model (LMM) analyses across groups stratified by AAD of mean age of menopause (50 years) and gender.

MAIN OUTCOMES MEASURES

Age at diagnosis of glaucoma, APOE E4 allele, and IOP.

RESULTS

Patients with glaucoma were older and had a higher percentage of males and a higher mean IOP compared to controls (all P < 0.001). Further stratifying the patients with glaucoma by AAD of 50 and gender, lower IOP (model 1 adjusted by age, βIOP = -0.096 ± 0.041, P = 0.019), and positive APOE E4 allele (model 2 adjusted by age and IOP, βe4 = 1.093 ± 0.488, P = 0.026) were associated with an older AAD in females with an AAD <50 years under univariate LMM. In multivariate LMM adjusted by age (model 3), the effect size of both factors increased in the multivariate model as the beta-value increased (βIOP = -0.111 ± 0.040, P = 0.007; βe4 = 1.235 ± 0.485, P = 0.012) (model 1 vs. model 3: P = 0.011). In females with an AAD ≥50 years, only positive APOE E4 allele (adjusted by age and IOP, βe4 = -1.121 ± 0.412, P = 0.007) was associated with a younger AAD. In males, only higher IOP was associated with an older AAD in those with an AAD ≥50 years (βIOP = 0.088 ± 0.032, P = 0.006).

CONCLUSIONS

Apolipoprotein E E4 allele may initially delay and later accelerate the development of glaucoma in females around the transition period of 50 years, which is the mean age of menopause, and importantly, this is independent of IOP. Understanding the specific transition states and modifiable factors within each age phase is crucial for developing interventions or strategies that promote healthy aging.

FINANCIAL DISCLOSURES

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The potential impact of optimal blood pressure treatment intensity to reduce disparities in dementia between Black and White individuals

BACKGROUND

Black adults have higher dementia risk than White adults. Whether tighter population-level blood pressure (BP) control reduces this disparity is unknown.

OBJECTIVE

Estimate the impact of optimal BP treatment intensity on racial disparities in dementia.

METHODS

A microsimulation study of US adults ≥18 across a life-time policy-planning horizon. BP treatment strategies were the Systolic Blood Pressure Intervention Trial (SPRINT) protocol, the Eighth Joint National Committee (JNC-8) recommendations, and usual care (non-intervention control). Outcomes were all-cause dementia, atherosclerotic cardiovascular disease (ASCVD), stroke, myocardial infarction, non-ASCVD death, global cognitive performance, and optimal brain health (being free of dementia, cognitive impairment, or stroke). Population-level and individual-level effects stratified by race were estimated.

RESULTS

Optimal population-level implementation of a SPRINT-based BP treatment strategy, compared to usual care, would increase average annual dementia incidence in White, but not Black, adults (1% versus 0%), due to hypertensive individuals' greater survival, and reduce annual ASCVD events more in Black than White adults (13% versus 5%). Under a SPRINT-based strategy, individuals with hypertension gained more years lived without dementia, ASCVD, myocardial infarction, or stroke and more years lived in optimal brain health. A SPRINT-based strategy did not attenuate individual-level race disparities in outcomes, except stroke. Due to longer life expectancy, a SPRINT-based strategy did not substantially reduce lifetime dementia risk in either group. The JNC-8-based strategy had similar but smaller effects as the SPRINT-based strategy.

CONCLUSIONS

Our results suggest that tighter population-level BP control would not reduce population-level disparities in dementia between US Black and White adults.

Imidazo[2,1-b][1,3]thiazine Derivatives as Potential Modulators of Alpha-Synuclein Amyloid Aggregation

Insoluble amyloid fibrils accumulate in the intercellular spaces of organs and tissues, leading to various amyloidosis-related disorders in the human body. Specifically, Parkinson's disease is associated with the aggregation of alpha-synuclein. However, current treatments for Parkinson's primarily focus on managing motor symptoms and slowing disease progression. Efforts to prevent and halt the progression of these diseases involve the search for small molecular compounds. In this work, we synthesized imidazo[2,1-b][1,3]thiazines in an atom-economic way by cyclization of 2-alkynylthioimidazoles using 10% AuCl as the catalyst. We identified several compounds with specific functional groups capable of both inhibiting the aggregation of alpha-synuclein and redirecting the fibril formation pathway. The investigation into how these substances function revealed that imidazo[2,1-b][1,3]thiazine derivatives can influence alpha-synuclein aggregation in several ways. They not only inhibit the primary nucleation process and maintain a balance toward nonaggregated protein states but also stabilize smaller oligomeric species of alpha-synuclein and cause the formation of fibrils with unique structures and forms. These imidazo[2,1-b][1,3]thiazines could potentially be used in developing highly efficient, small molecular weight protein aggregation inhibitors.

New perspective on central nervous system disorders: focus on mass spectrometry imaging

An abnormally organized brain spatial network is linked to the development of various central nervous system (CNS) disorders, including neurodegenerative diseases and neuropsychiatric disorders. However, the complicated molecular mechanisms of these diseases remain unresolved, making the development of treatment strategies difficult. A novel molecular imaging technique, called mass spectrometry imaging (MSI), captures molecular information on the surface of samples in situ. With MSI, multiple compounds can be simultaneously visualized in a single experiment. The high spatial resolution enables the simultaneous visualization of the spatial distribution and relative content of various compounds. The wide application of MSI in biomedicine has facilitated extensive studies on CNS disorders in recent years. This review provides a concise overview of the processes, applications, advantages, and disadvantages, as well as mechanisms of the main types of MSI. Meanwhile, this review summarizes the main applications of MSI in studying CNS diseases, including Alzheimer's disease (AD), CNS tumors, stroke, depression, Huntington's disease (HD), and Parkinson's disease (PD). Finally, this review comprehensively discusses the synergistic application of MSI with other advanced imaging modalities, its utilization in organoid models, its integration with spatial omics techniques, and provides an outlook on its future potential in single-cell analysis.

Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability

The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic resonance imaging (fMRI) data. However, we found that the genetic factors do not notably enhance the AD prediction by imaging. Thus, we focus on building an effective imaging-only model. In particular, we utilize data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a 3D Convolutional Neural Network (CNN) to analyze fMRI scans. Despite the limitations posed by our dataset (small size and imbalanced nature), our CNN model demonstrates accuracy levels reaching 92.8% and an ROC of 0.95. Our research highlights the complexities inherent in integrating multimodal medical datasets. It also demonstrates the potential of deep learning in medical imaging for AD prediction.

Neuroendocrinology of bone

The past decade has witnessed significant advances in our understanding of skeletal homeostasis and the mechanisms that mediate the loss of bone in primary and secondary osteoporosis. Recent breakthroughs have primarily emerged from identifying disease-causing mutations and phenocopying human bone disease in rodents. Notably, using genetically-modified rodent models, disrupting the reciprocal relationship with tropic pituitary hormone and effector hormones, we have learned that pituitary hormones have independent roles in skeletal physiology, beyond their effects exerted through target endocrine glands. The rise of follicle-stimulating hormone (FSH) in the late perimenopause may account, at least in part, for the rapid bone loss when estrogen is normal, while low thyroid-stimulating hormone (TSH) levels may contribute to the bone loss in thyrotoxicosis. Admittedly speculative, suppressed levels of adrenocorticotropic hormone (ACTH) may directly exacerbate bone loss in the setting of glucocorticoid-induced osteoporosis. Furthermore, beyond their established roles in reproduction and lactation, oxytocin and prolactin may affect intergenerational calcium transfer and therefore fetal skeletal mineralization, whereas elevated vasopressin levels in chronic hyponatremic states may increase the risk of bone loss.. Here, we discuss the interaction of each pituitary hormone in relation to its role in bone physiology and pathophysiology.

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease

The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis.

Considering Biomarkers of Neurodegeneration in Alzheimer's Disease: The Potential of Circulating Cell-Free DNA in Precision Neurology

Neurodegenerative diseases, such as Alzheimer's disease (AD), are a growing public health crisis, exacerbated by an aging global population and the lack of effective early disease-modifying therapies. Early detection of neurodegenerative disorders is critical to delaying symptom onset and mitigating disease progression, but current diagnostic tools often rely on detecting pathology once clinical symptoms have emerged and significant neuronal damage has already occurred. While disease-specific biomarkers, such as amyloid-beta and tau in AD, offer precise insights, they are too limited in scope for broader neurodegeneration screening for these conditions. Conversely, general biomarkers like neurofilament light chain (NfL) provide valuable staging information but lack targeted insights. Circulating cell-free DNA (cfDNA), released during cell death, is emerging as a promising biomarker for early detection. Derived from dying cells, cfDNA can capture both general neurodegenerative signals and disease-specific insights, offering multi-layered genomic and epigenomic information. Though its clinical potential remains under investigation, advances in cfDNA detection sensitivity, standardized protocols, and reference ranges could establish cfDNA as a valuable tool for early screening. cfDNA methylation signatures, in particular, show great promise for identifying tissue-of-origin and disease-specific changes, offering a minimally invasive biomarker that could transform precision neurology. However, further research is required to address technological challenges and validate cfDNA's utility in clinical settings. Here, we review recent work assessing cfDNA as a potential early biomarker in AD. With continued advances, cfDNA could play a pivotal role in shifting care from reactive to proactive, improving diagnostic timelines and patient outcomes.

Age- and amyloid-β-dependent initiation of neurofibrillary tau tangles: an improved mouse model of Alzheimer's disease without mutations in MAPT

Introducing heterozygous humanized tau to App NL-F/NL-F knock-in mice results in the first mouse model of Alzheimer's disease in which age and amyloid-β pathology interact to initiate neurofibrillary tau tangle pathology, not dependent on mutations in MAPT. Gradual progression from amyloid-β to tau pathology in NLFTau m/h mice opens possibilities for understanding processes precipitating clinical stages of Alzheimer's disease and development of translatable therapies to prevent the onset of tau pathology.

Cortical microstructure and hemispheric specialization-A diffusion-imaging analysis in younger and older adults

Characterizing cortical plasticity becomes increasingly important for identifying compensatory mechanisms and structural reserve in the ageing population. While cortical thickness (CT) largely contributed to systems neuroscience, it incompletely informs about the underlying neuroplastic pathophysiology. In turn, microstructural characteristics may correspond to atrophy mechanisms in a more sensitive way. Fractional anisotropy, a diffusion tensor imaging (DTI) measure, is inversely related to cortical histologic complexity. Axial diffusivity and radial diffusivity are assumed to be linked to the density of structures oriented perpendicular and parallel to the cortical surface, respectively. We hypothesized (1) that cortical DTI will reveal microstructural correlates for hemispheric specialization, particularly in the language and motor systems, and (2) that lateralization of cortical DTI parameters will show an age effect, paralleling age-related changes in activation, especially in the prefrontal cortex. We analysed data from healthy younger and older adult participants (N = 91). DTI and CT data were extracted from regions of the Destrieux atlas. Diffusion measures showed lateralization in specialized motor, language, visual, auditory and inferior parietal cortices. Age-dependent increased lateralization for DTI measures was observed in the prefrontal, angular, superior temporal and lateral occipital cortex. CT did not show any age-dependent alterations in lateralization. Our observations argue that cortical DTI can capture microstructural properties associated with functional specialization, resembling findings from histology. Age effects on diffusion measures in the integrative prefrontal and parietal areas may shed novel light on the atrophy-related plasticity in healthy ageing.

Study of Insulin Aggregation and Fibril Structure under Different Environmental Conditions

Protein amyloid aggregation is linked with widespread and fatal neurodegenerative disorders as well as several amyloidoses. Insulin, a small polypeptide hormone, is associated with injection-site amyloidosis and is a popular model protein for in vitro studies of amyloid aggregation processes as well as in the search for potential anti-amyloid compounds. Despite hundreds of studies conducted with this specific protein, the procedures used have employed a vast array of different means of achieving fibril formation. These conditions include the use of different solution components, pH values, ionic strengths, and other additives. In turn, this variety of conditions results in the generation of fibrils with different structures, morphologies and stabilities, which severely limits the possibility of cross-study comparisons as well as result interpretations. In this work, we examine the condition-structure relationship of insulin amyloid aggregation under a range of commonly used pH and ionic strength conditions as well as solution components. We demonstrate the correlation between the reaction solution properties and the resulting aggregation kinetic parameters, aggregate secondary structures, morphologies, stabilities and dye-binding modes.

S100A9 inhibits and redirects prion protein 89-230 fragment amyloid aggregation

Protein aggregation in the form of amyloid fibrils has long been associated with the onset and development of various amyloidoses, including Alzheimer's, Parkinson's or prion diseases. Recent studies of their fibril formation process have revealed that amyloidogenic protein cross-interactions may impact aggregation pathways and kinetic parameters, as well as the structure of the resulting aggregates. Despite a growing number of reports exploring this type of interaction, they only cover just a small number of possible amyloidogenic protein pairings. One such pair is between two neurodegeneration-associated proteins: the pro-inflammatory S100A9 and prion protein, which are known to co-localize in vivo. In this study, we examined their cross-interaction in vitro and discovered that the fibrillar form of S100A9 modulated the aggregation pathway of mouse prion protein 89-230 fragment, while non-aggregated S100A9 also significantly inhibited its primary nucleation process. These results complement previous observations of the pro-inflammatory protein's role in amyloid aggregation and highlight its potential role against neurodegenerative disorders.

The role of olfactory dysfunction in mild cognitive impairment and Alzheimer's disease: A meta-analysis

PURPOSE

This comprehensive meta-analysis investigates the association between olfactory deficits in mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

A thorough search across databases identified articles analyzing olfactory status in MCI or AD patients. Methodological quality assessment followed PRISMA guidelines. Hedges' g effect size statistic computed standard mean differences and 95% confidence intervals. Moderator analysis was conducted.

RESULTS

Among the included studies (65 for MCI and 61 for AD), odor identification exhibited larger effect sizes compared to odor threshold and discrimination, in both MCI and AD samples. Moderate effect size is found in OI scores in MCI (k = 65, SE = 0.078, CI 95% = [-1.151, -0.844]). Furthermore, compared to MCI, AD had moderate to large heterogeneous effects in olfactory identification (k = 61, g = -2.062, SE = 0.125, CI 95% = [-2.308, -1.816]). Global cognitive status is positively related to olfactory identification impairment in both MCI (k = 57, Z = 2.74, p = 0.006) and AD (k = 53, Z = 5.03, p < 0.0001) samples.

CONCLUSION

Olfactory impairments exhibit a notable and substantial presence in MCI. Among these impairments, odor identification experiences the greatest decline in MCI, mirroring the primary sensory deficit observed in AD. Consequently, the incorporation of a straightforward odor identification test is advisable in the evaluation of individuals vulnerable to the onset of AD, offering a practical screening tool for early detection.

The Relationship between Diet, Depression, and Alzheimer's Disease: A Narrative Review

PURPOSE OF REVIEW

This narrative review evaluates the role of diet in the relationship between depression and Alzheimer's disease (AD).

RECENT FINDINGS

AD and depression are often comorbid, and depression appears to independently increase the future risk of AD. Evidence suggests diet influences the risk of both conditions directly and indirectly. Diet impacts neurochemical and biological processes that may affect the development and progression of depression and cognitive dysfunction. The dietary components offering the greatest protection against depression and AD are yet to be determined. Current evidence highlights the importance of polyphenolic compounds, folate, B vitamins, and polyunsaturated fatty acids, along with adherence to dietary patterns like the Mediterranean diet, which includes multiple beneficial dietary factors.

SUMMARY

The investigation of dietary factors in the prevention of depression and AD is a comparatively young field of research. Comprehensive highly characterised longitudinal datasets and advanced analytical approaches are required to further examine the complex relationship between diet, depression, and AD. There is a critical need for more research in this area to develop effective preventive strategies aimed at maintaining mental and physical health with advancing age.

Multi-class Prediction of Cognitively Normal / Mild Cognitive Impairment / Alzheimer's Disease Status in Dementia Based on Convolutional Neural Networks with Attention Mechanism

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset and progressive development. AD is a health issue that is attracting attention as the world's populations get older. Although there is currently no effective treatment for this disease, early diagnosis is necessary to help people prevent it. Here, we developed an MRI-based deep learning and multi-class AD classification and prediction framework. Based on the traditional Visual Geometry Group 19 (VGG19) architecture, we embedded the Convolutional Block Attention Module Attention layer to build an Alzheimer's directional prediction 3D convolution model referred to as AD_Net in this paper. We used MRI images from the ADNI open data resource to train, classify, and predict three different conditions: AD, mild cognitive impairment (MCI), and cognitively normal (CN). Experimental results showed that the prediction accuracy and system robustness of AD_Net are superior to those of basic VGG19. To improve the accuracy of the prediction, we developed a multilayer perceptron (MLP)-based model to incorporate additional factors into the model, such as age, gender, and Mini-Mental State Examination score. We presented the model performance for groups with and without directional influence factors. Accuracy for the two groups was 51.2% and 89%, respectively. The latter group had a standard deviation as low as 1.8%, reflecting predictive performance that is both good and stable. Our model can be applied to the early diagnosis of Alzheimer's disease and other similar conditions that can be diagnosed by MRI and the patient's underlying factors.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.

Effect of Population-Level Blood Pressure Treatment Strategies on Cardiovascular and Cognitive Outcomes

BACKGROUND

The large and increasing number of adults living with dementia is a pressing societal priority, which may be partially mitigated through improved population-level blood pressure (BP) control. We explored how tighter population-level BP control affects the incidence of atherosclerotic cardiovascular disease (ASCVD) events and dementia.

METHODS

Using an open-source ASCVD and dementia simulation analysis platform, the Michigan Chronic Disease Simulation Model, we evaluated how optimal implementation of 2 BP treatments based on the Eighth Joint National Committee recommendations and SPRINT (Systolic Blood Pressure Intervention Trial) protocol would influence population-level ASCVD events, global cognitive performance, and all-cause dementia. We simulated 3 populations (usual care, Eighth Joint National Committee based, SPRINT based) using nationally representative data to annually update risk factors and assign ASCVD events, global cognitive performance scores, and dementia, applying different BP treatments in each population. We tabulated total ASCVD events, global cognitive performance, all-cause dementia, optimal brain health, and years lived in each state per population.

RESULTS

Optimal implementation of SPRINT-based BP treatment strategy, compared with usual care, reduced ASCVD events in the United States by ≈77 000 per year and produced 0.4 more years of stroke- or myocardial infarction-free survival when averaged across all Americans. Population-level gains in years lived free of ASCVD events were greater for SPRINT-based than Eighth Joint National Committee-based treatment. Survival and years spent with optimal brain health improved with optimal SPRINT-based BP treatment implementation versus usual care: the average patient with hypertension lived 0.19 additional years and 0.3 additional years in optimal brain health. SPRINT-based BP treatment increased the number of years lived without dementia (by an average of 0.13 years/person with hypertension), but increased the total number of individuals with dementia, mainly through more adults surviving to advanced ages.

CONCLUSIONS

Tighter BP control likely benefits most individuals but is unlikely to reduce dementia prevalence and might even increase the number of older adults living with dementia.

Neurobehavioral Mechanisms Influencing the Association Between Generativity, the Desire to Promote Well-Being of Younger Generations, and Purpose in Life in Older Adults at Risk for Alzheimer's Disease

OBJECTIVES

Generativity, the desire and action to improve the well-being of younger generations, is associated with purpose in life among older adults. However, the neurobehavioral factors supporting the relationship between generativity and purpose in life remain unknown. This study aims to identify the functional neuroanatomy of generativity and mechanisms linking generativity with purpose in life in at-risk older adults.

METHODS

Fifty-eight older adults (mean age = 70.8, SD = 5.03, 45 females) with a family history of Alzheimer's disease (AD) were recruited from the PREVENT-AD cohort. Participants underwent brain imaging and completed questionnaires assessing generativity, social support, and purpose in life. Mediation models examined whether social support mediated the association between generativity and purpose in life. Seed-to-voxel analyses investigated the association between generativity and resting-state functional connectivity (rsFC) to the ventromedial prefrontal cortex (vmPFC) and ventral striatum (VS), and whether this rsFC moderated the relationship between generativity and purpose in life.

RESULTS

Affectionate social support mediated the association between generative desire and purpose in life. Generative desire was associated with rsFC between VS and precuneus, and, vmPFC and right dorsolateral prefrontal cortex (rdlPFC). The vmPFC-rdlPFC rsFC moderated the association between generative desire and purpose in life.

DISCUSSION

These findings provide insight into how the brain supports complex social behavior and, separately, purpose in life in at-risk aging. Affectionate social support may be a putative target process to enhance purpose in life in older adults. This knowledge contributes to future developments of personalized interventions that promote healthy aging.

Development and validation of the Michigan Chronic Disease Simulation Model (MICROSIM)

Strategies to prevent or delay Alzheimer's disease and related dementias (AD/ADRD) are urgently needed, and blood pressure (BP) management is a promising strategy. Yet the effects of different BP control strategies across the life course on AD/ADRD are unknown. Randomized trials may be infeasible due to prolonged follow-up and large sample sizes. Simulation analysis is a practical approach to estimating these effects using the best available existing data. However, existing simulation frameworks cannot estimate the effects of BP control on both dementia and cardiovascular disease. This manuscript describes the design principles, implementation details, and population-level validation of a novel population-health microsimulation framework, the MIchigan ChROnic Disease SIMulation (MICROSIM), for The Effect of Lower Blood Pressure over the Life Course on Late-life Cognition in Blacks, Hispanics, and Whites (BP-COG) study of the effect of BP levels over the life course on dementia and cardiovascular disease. MICROSIM is an agent-based Monte Carlo simulation designed using computer programming best practices. MICROSIM estimates annual vascular risk factor levels and transition probabilities in all-cause dementia, stroke, myocardial infarction, and mortality in a nationally representative sample of US adults 18+ using the National Health and Nutrition Examination Survey (NHANES). MICROSIM models changes in risk factors over time, cognition and dementia using changes from a pooled dataset of individual participant data from 6 US prospective cardiovascular cohort studies. Cardiovascular risks were estimated using a widely used risk model and BP treatment effects were derived from meta-analyses of randomized trials. MICROSIM is an extensible, open-source framework designed to estimate the population-level impact of different BP management strategies and reproduces US population-level estimates of BP and other vascular risk factors levels, their change over time, and incident all-cause dementia, stroke, myocardial infarction, and mortality.

2024 Alzheimer's disease facts and figures

This article describes the public health impact of Alzheimer's disease (AD), including prevalence and incidence, mortality and morbidity, use and costs of care and the ramifications of AD for family caregivers, the dementia workforce and society. The Special Report discusses the larger health care system for older adults with cognitive issues, focusing on the role of caregivers and non-physician health care professionals. An estimated 6.9 million Americans age 65 and older are living with Alzheimer's dementia today. This number could grow to 13.8 million by 2060, barring the development of medical breakthroughs to prevent or cure AD. Official AD death certificates recorded 119,399 deaths from AD in 2021. In 2020 and 2021, when COVID-19 entered the ranks of the top ten causes of death, Alzheimer's was the seventh-leading cause of death in the United States. Official counts for more recent years are still being compiled. Alzheimer's remains the fifth-leading cause of death among Americans age 65 and older. Between 2000 and 2021, deaths from stroke, heart disease and HIV decreased, whereas reported deaths from AD increased more than 140%. More than 11 million family members and other unpaid caregivers provided an estimated 18.4 billion hours of care to people with Alzheimer's or other dementias in 2023. These figures reflect a decline in the number of caregivers compared with a decade earlier, as well as an increase in the amount of care provided by each remaining caregiver. Unpaid dementia caregiving was valued at $346.6 billion in 2023. Its costs, however, extend to unpaid caregivers' increased risk for emotional distress and negative mental and physical health outcomes. Members of the paid health care and broader community-based workforce are involved in diagnosing, treating and caring for people with dementia. However, the United States faces growing shortages across different segments of the dementia care workforce due to a combination of factors, including the absolute increase in the number of people living with dementia. Therefore, targeted programs and care delivery models will be needed to attract, better train and effectively deploy health care and community-based workers to provide dementia care. Average per-person Medicare payments for services to beneficiaries age 65 and older with AD or other dementias are almost three times as great as payments for beneficiaries without these conditions, and Medicaid payments are more than 22 times as great. Total payments in 2024 for health care, long-term care and hospice services for people age 65 and older with dementia are estimated to be $360 billion. The Special Report investigates how caregivers of older adults with cognitive issues interact with the health care system and examines the role non-physician health care professionals play in facilitating clinical care and access to community-based services and supports. It includes surveys of caregivers and health care workers, focusing on their experiences, challenges, awareness and perceptions of dementia care navigation.

Internet-Based Conversational Engagement Randomized Controlled Clinical Trial (I-CONECT) Among Socially Isolated Adults 75+ Years Old With Normal Cognition or Mild Cognitive Impairment: Topline Results

BACKGROUND AND OBJECTIVES

Social isolation is a risk factor for cognitive decline and dementia. We conducted a randomized controlled clinical trial (RCT) of enhanced social interactions, hypothesizing that conversational interactions can stimulate brain functions among socially isolated older adults without dementia. We report topline results of this multisite RCT (Internet-based conversational engagement clinical trial [I-CONECT]; NCT02871921).

RESEARCH DESIGN AND METHODS

The experimental group received cognitively stimulating semistructured conversations with trained interviewers via internet/webcam 4 times per week for 6 months (induction) and twice per week for an additional 6 months (maintenance). The experimental and control groups both received weekly 10 minutes telephone check-ins. Protocol modifications were required due to the coronavirus disease 2019 pandemic.

RESULTS

A total of 186 participants were randomized. After the induction period, the experimental group had higher global cognitive test scores (Montreal Cognitive Assessment [primary outcome]; 1.75 points [p = .03]) compared with the control group. After induction, experimental group participants with normal cognition had higher language-based executive function (semantic fluency test [secondary outcome]; 2.56 points [p = .03]). At the end of the maintenance period, the experimental group of mild cognitive impairment subjects had higher encoding function (Craft Story immediate recall test [secondary outcome]; 2.19 points [p = .04]). Measure of emotional well-being improved in both control and experimental groups. Resting-state functional magnetic resonance imaging showed that the experimental group had increased connectivity within the dorsal attention network relative to the control group (p = .02), but the sample size was limited.

DISCUSSION AND IMPLICATIONS

Providing frequent stimulating conversational interactions via the internet could be an effective home-based dementia risk-reduction strategy against social isolation and cognitive decline.

CLINICAL TRIALS REGISTRATION NUMBER

NCT02871921.

Sarcopenic obesity is part of obesity paradox in dementia development: evidence from a population-based cohort study

BACKGROUND

Sarcopenic obesity, a clinical and functional condition characterized by the coexistence of obesity and sarcopenia, has not been investigated in relation to dementia risk and its onset.

METHODS

We included 208,867 participants from UK biobank, who aged 60 to 69 years at baseline. Dementia diagnoses were identified using hospital records and death register data. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models to evaluate the associations of obesity, sarcopenia, and sarcopenic obesity with dementia risk, stratified by sex. Stratified analyses were performed across dementia-related polygenic risk score (PRS). Restricted mean survival time models were established to estimate the difference and 95%CIs of dementia onset across different status. Additionally, linear regression models were employed to estimate associations of different status with brain imaging parameters. The mediation effects of chronic diseases were also examined.

RESULTS

Obese women with high PRS had a decreased risk (HR = 0.855 [0.761-0.961]), but obese men with low PRS had an increased risk (HR = 1.223 [1.045-1.431]). Additionally, sarcopenia was associated with elevated dementia risk (HRwomen = 1.323 [1.064-1.644]; HRmen = 2.144 [1.753-2.621]) in those with low PRS. Among those with high PRS, however, the association was only significant in early-life (HRwomen = 1.679 [1.355-2.081]; HRmen = 2.069 [1.656-2.585]). Of note, sarcopenic obesity was associated with higher dementia risk (HRwomen = 1.424 [1.227-1.653]; HRmen = 1.989 [1.702-2.323]), and results remained similar stratified by PRS. Considering dementia onset, obesity was associated with dementia by 1.114 years delayed in women, however, 0.170 years advanced in men. Sarcopenia (women: 0.080 years; men: 0.192 years) and sarcopenic obesity (women: 0.109 years; men: 0.511 years) respectively advanced dementia onset. Obesity, sarcopenia, and sarcopenic obesity were respectively related to alterations in different brain regions. Association between sarcopenic obesity and dementia was mediated by chronic diseases.

CONCLUSIONS

Sarcopenic obesity and sarcopenia were respectively associated with increased dementia risk and advanced dementia onset to vary degree. The role of obesity in dementia may differ by sex and genetic background.

Nocturnal blood pressure dipping, blood pressure variability, and cognitive function in early and middle-aged adults

Higher nighttime blood pressure (BP), less BP dipping, and higher BP variability have been linked with worse cognitive function in the elderly. The goal of this study is to explore whether this relationship already exists in early and middle adulthood. We further examined whether ethnic differences between African Americans and European Americans in BP parameters can explain ethnic differences in cognitive function. 24-h ambulatory BP monitoring and cognitive function were obtained from 390 participants (average age: 37.2 years with a range of 25-50; 54.9% African Americans; 63.6% females). We observed that higher nighttime BP, decreased dipping, and higher variability were significantly associated with lower scores on the Picture Sequence Memory Test. Significant negative associations between variability and overall composite scores were also observed. No significant associations between average 24-h or daytime BP and cognitive function were observed. Ethnic differences in nighttime diastolic pressures and dipping can explain 6.81% to 10.8% of the ethnicity difference in the score of the Picture Sequence Memory Test (ps < .05). This study suggests that the associations of nighttime BP, dipping, and variability with cognitive function already exist in young and middle-aged adults. Ethnic differences in nighttime BP and dipping can at least partially explain ethnic differences in cognitive function. The stronger association of these parameters with cognitive function than daytime or average BP in this age range raises the importance of using ambulatory BP monitoring for more precise detection of abnormal BP patterns in young adulthood.

Asian Cohort for Alzheimer's Disease (ACAD) pilot study on genetic and non-genetic risk factors for Alzheimer's disease among Asian Americans and Canadians

INTRODUCTION

Clinical research in Alzheimer's disease (AD) lacks cohort diversity despite being a global health crisis. The Asian Cohort for Alzheimer's Disease (ACAD) was formed to address underrepresentation of Asians in research, and limited understanding of how genetics and non-genetic/lifestyle factors impact this multi-ethnic population.

METHODS

The ACAD started fully recruiting in October 2021 with one central coordination site, eight recruitment sites, and two analysis sites. We developed a comprehensive study protocol for outreach and recruitment, an extensive data collection packet, and a centralized data management system, in English, Chinese, Korean, and Vietnamese.

RESULTS

ACAD has recruited 606 participants with an additional 900 expressing interest in enrollment since program inception.

DISCUSSION

ACAD's traction indicates the feasibility of recruiting Asians for clinical research to enhance understanding of AD risk factors. ACAD will recruit > 5000 participants to identify genetic and non-genetic/lifestyle AD risk factors, establish blood biomarker levels for AD diagnosis, and facilitate clinical trial readiness.

HIGHLIGHTS

The Asian Cohort for Alzheimer's Disease (ACAD) promotes awareness of under-investment in clinical research for Asians. We are recruiting Asian Americans and Canadians for novel insights into Alzheimer's disease. We describe culturally appropriate recruitment strategies and data collection protocol. ACAD addresses challenges of recruitment from heterogeneous Asian subcommunities. We aim to implement a successful recruitment program that enrolls across three Asian subcommunities.

Investigating lysozyme amyloid fibril formation and structural variability dependence on its initial folding state under different pH conditions

Protein fibril formation and accumulation are associated with dozens of amyloidoses, including the widespread and yet-incurable Alzheimer's and Parkinson's diseases. Currently, there are still several aspects of amyloid aggregation that are not fully understood, which negatively contributes to the development of disease-altering drugs and treatments. One factor which requires a more in-depth analysis is the effect of the environment on both the initial state of amyloidogenic proteins and their aggregation process and resulting fibril characteristics. In this work, we examine how lysozyme's folding state influences its amyloid formation kinetics and resulting aggregate structural characteristics under several different pH conditions, ranging from acidic to neutral. We demonstrate that both the initial state of the protein and the solution's pH value have a significant combined effect on the variability of the resulting aggregate secondary structures, as well as their stabilities, interactions with amyloid-specific dye molecules, and self-replication properties.

Dementia classification using a graph neural network on imaging of effective brain connectivity

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.

Preventive cardiology for the aging population: how can we better design clinical trials of statins?

INTRODUCTION

Older adults form a fast-increasing proportion of the world population. However, gains in increasing quantity of life have not been accompanied by similar gains in quality of life. Older people frequently experience frailty, memory problems, and chronic diseases including cardiovascular disease (CVD) and neurodegenerative diseases. Recent trials have demonstrated the efficacy of anti-hypertensive therapy in older populations but failed to show benefits for aspirin.

AREA COVERED

Statins clearly reduce CVD events in middle-aged populations. There seems to be evidence that the effect is similar in primary prevention older populations based on meta-analyses mainly from sub-groups in large trials, but this becomes less clear with increasing age. However, given differences in drug metabolism and possibly efficacy, competing co-morbidities, their effects on mortality, disability, and dementia in this age group remain to be determined.

EXPERT OPINION

Two large trials are now underway to clarify the role of statin therapy in people aged over 70 years using endpoints of mortality, disability, and neurocognitive endpoints as well as standard cardiovascular disease outcomes. They may provide also provide more evidence on how to approach the over 80 year age group.

Endocrine Dyscrasia in the Etiology and Therapy of Alzheimer's Disease

The increase in the incidence of dementia over the last century correlates strongly with the increases in post-reproductive lifespan during this time. As post-reproductive lifespan continues to increase it is likely that the incidence of dementia will also increase unless therapies are developed to prevent, slow or cure dementia. A growing body of evidence implicates age-related endocrine dyscrasia and the length of time that the brain is subjected to this endocrine dyscrasia, as a key causal event leading to the cognitive decline associated with aging and Alzheimer's disease (AD), the major form of dementia in our society. In particular, the elevations in circulating gonadotropins, resulting from the loss of gonadal sex hormone production with menopause and andropause, appear central to the development of AD neuropathology and cognitive decline. This is supported by numerous cell biology, preclinical animal, and epidemiological studies, as well as human clinical studies where suppression of circulating luteinizing hormone and/or follicle-stimulating hormone with either gonadotropin-releasing hormone analogues, or via physiological hormone replacement therapy, has been demonstrated to halt or significantly slow cognitive decline in those with AD. This review provides an overview of past and present studies demonstrating the importance of hypothalamic-pituitary-gonadal hormone balance for normal cognitive functioning, and how targeting age-related endocrine dyscrasia with hormone rebalancing strategies provides an alternative treatment route for those with AD.

Developing the Healthy Actions and Lifestyles to Avoid Dementia or Hispanos y el ALTo a la Demencia program

INTRODUCTION

With Alzheimer's disease and related dementias (ADRD) representing an enormous public health challenge, there is a need to support individuals in learning about and addressing their modifiable risk factors (e.g., diet, sleep, and physical activity) to prevent or delay dementia onset. However, there is limited availability for evidence-informed tools that deliver both quality education and support for positive behavior change such as by increasing self-efficacy and personalizing goal setting. Tools that address the needs of Latino/a, at higher risk for ADRD, are even more scarce.

METHODS

We established a multidisciplinary team to develop the Healthy Actions and Lifestyles to Avoid Dementia or Hispanos y el ALTo a la Demencia (HALT-AD) program, a bilingual online personalized platform to educate and motivate participants to modify their risk factors for dementia. Grounded in social cognitive theory and following a cultural adaptation framework with guidance from a community advisory board, we developed HALT-AD iteratively through several cycles of rapid prototype development, user-centered evaluation through pilot testing and community feedback, and refinement.

RESULTS

Using this iterative approach allowed for more than 100 improvements in the content, features, and design of HALT-AD to improve the program's usability and alignment with the interests and educational/behavior change support needs of its target audience. Illustrative examples of how pilot data and community feedback informed improvements are provided.

DISCUSSION

Developing HALT-AD iteratively required learning through trial and error and flexibility in workflows, contrary to traditional program development methods that rely on rigid, pre-set requirements. In addition to efficacy trials, studies are needed to identify mechanisms for effective behavior change, which might be culturally specific. Flexible and personalized educational offerings are likely to be important in modifying risk trajectories in ADRD.

Reduction in the activity of VTA/SNc dopaminergic neurons underlies aging-related decline in novelty seeking

Curiosity, or novelty seeking, is a fundamental mechanism motivating animals to explore and exploit environments to improve survival, and is also positively associated with cognitive, intrapersonal and interpersonal well-being in humans. However, curiosity declines as humans age, and the decline even positively predicts the extent of cognitive decline in Alzheimer's disease patients. Therefore, determining the underlying mechanism, which is currently unknown, is an urgent task for the present aging society that is growing at an unprecedented rate. This study finds that seeking behaviors for both social and inanimate novelties are compromised in aged mice, suggesting that the aging-related decline in curiosity and novelty-seeking is a biological process. This study further identifies an aging-related reduction in the activity (manifesting as a reduction in spontaneous firing) of dopaminergic neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc). Finally, this study establishes that this reduction in activity causally underlies the aging-related decline in novelty-seeking behaviors. This study potentially provides an interventional strategy for maintaining high curiosity in the aged population, i.e., compensating for the reduced activity of VTA/SNc dopaminergic neurons, enabling the aged population to cope more smoothly with the present growing aging society, physically, cognitively and socioeconomically.

An explainable machine learning based prediction model for Alzheimer's disease in China longitudinal aging study

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value.

Associations of circulating saturated long-chain fatty acids with risk of mild cognitive impairment and Alzheimer's disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort

BACKGROUND

No study has examined the associations between peripheral saturated long-chain fatty acids (LCFAs) and conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). This study aimed to examine whether circulating saturated LCFAs are associated with both risks of incident MCI from cognitively normal (CN) participants and incident AD progressed from MCI in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

METHODS

We conducted analysis of data from older adults aged 55-90 years who were recruited at 63 sites across the USA and Canada. We examined associations between circulating saturated LCFAs (i.e., C14:0, C16:0, C18:0, C20:0) and risk for incident MCI in CN participants, and incident AD progressed from MCI.

FINDINGS

829 participants who were enrolled in ADNI-1 had data on plasma saturated LCFAs, of which 618 AD-free participants were included in our analysis (226 with normal cognition and 392 with MCI; 60.2% were men). Cox proportional-hazards models were used to account for time-to-event/censor with a 48-month follow-up period for the primary analysis. Other than C20:0, saturated LCFAs were associated with an increased risk for AD among participants with MCI at baseline (Hazard ratios (HRs) = 1.3 to 2.2, P = 0.0005 to 0.003 in fully-adjusted models). No association of C20:0 with risk of AD among participants with MCI was observed. No associations were observed between saturated LCFAs and risk for MCI among participants with normal cognition.

INTERPRETATION

Saturated LCFAs are associated with increased risk of progressing from MCI to AD. This finding holds the potential to facilitate precision prevention of AD among patients with MCI.

FUNDING

National Institutes of Health.

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures

INTRODUCTION

There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD).

METHODS

Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS.

RESULTS

The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau.

DISCUSSION

The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment.

HIGHLIGHTS

A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

The influence of sex difference on behavior and adult hippocampal neurogenesis in C57BL/6 mice

Animal models have been used extensively in in vivo studies, especially within the biomedical field. Traditionally, single-sex studies, mostly males, are used to avoid any potential confounding variation caused by sex difference and the female estrous cycle. Historically, female animal subjects are believed to exhibit higher variability, and this could increase the statistical power needed to test a hypothesis. This study sets out to evaluate whether a sex difference does exist in mouse behavior, and whether female mice featured higher variability. We assessed the sensorimotor skills, anxiety-like behavior, depression-like behavior, and cognitive abilities of mice through a series of commonly used behavioral tests. Except for the stronger grip force and lower tactile sensory sensitivity detected in male mice, there was no significant difference between males and females in other tests. Furthermore, immunolabeling of neurogenesis markers suggested no significant difference between sexes in adult hippocampal neurogenesis. Within group variances were equivalent; females did not exhibit higher variability than males. However, the overall negative results could be due to the limitation of small sample size. In conclusion, our study provides evidence that sex difference in mice does not significantly influence these commonly used behavioral tests nor adult neurogenesis under basal conditions. We suggest that female mice could also be considered for test inclusion in future experiment design.

Serum Vitamin D3 Concentration, Sleep, and Cognitive Impairment among Older Adults in China

BACKGROUND

Cognitive decline in older adults has become one of the critical challenges to global health. This study aims to examine both cross-sectional and longitudinal associations of levels of serum 25-hydroxyvitamin D3 (25(OH)D3) (briefed as VD3) concentration and sleep quality/duration, especially their interactions, with risk of cognitive impairment among older adults in China.

METHODS

We utilized a special subsample of adults aged 65-105 years (individuals = 3412, observations = 4816) from eight provinces in China derived from the 2011/2012 and 2014 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive impairment was measured by the Mini-Mental State Examination scale. Sleep quality was classified as good versus fair/poor, and sleep duration was classified into short (<7 h), normal (≥7 but <9 h), and long (≥9 h). The VD3 concentration was divided into three levels: deficiency (VD3 < 25 nmol/L), insufficiency (25 nmol/L ≤ VD3 < 50 nmol/L), and sufficiency (VD3 ≥ 50 nmol/L). A wide set of covariates that include demographics, socioeconomic status, family support, health practice, and health conditions was adjusted for robust findings. Multilevel random intercept logit regression models were used to examine the cross-sectional associations between VD3, sleep, and cognitive impairment, whereas logit regression models were applied to investigate the longitudinal associations.

RESULTS

In the cross-sectional analyses, when all covariates were adjusted, VD3 sufficiency was significantly associated with a 33% lower risk of cognitive impairment compared with VD3 deficiency; good sleep quality was associated with 34% lower odds of cognitive impairment compared with fair/poor sleep quality; sleep hours were not associated with cognitive impairment, although a long sleep duration (≥9 h) was associated with 30% higher odds of being cognitively impaired when baseline health was not controlled. Interaction analyses reveal that VD3 sufficiency could help to additionally reduce the risk of cognitive impairment for good sleep quality and normal sleep hours. In the longitudinal analyses, the association of VD3 sufficiency remains significant, whereas sleep quality and sleep duration were not significant associates.

CONCLUSIONS

Good sleep quality, normal sleep hours, and VD3 sufficiency are positively associated with good cognitive function. VD3 sufficiency could enhance the associations between sleep and cognitive impairment.

The influence of generativity on purpose in life is mediated by social support and moderated by prefrontal functional connectivity in at-risk older adults

Objectives

Generativity, the desire and action to improve the well-being of younger generations, is positively associated with purpose in life among older adults. However, the neural basis of generativity and the neurobehavioral factors supporting the relationship between generativity and purpose in life remain unknown. This study aims to identify the functional neuroanatomy of generativity and mechanisms linking generativity with purpose in life in at-risk older adults.

Methods

Fifty-eight cognitively healthy older adults (mean age = 70.78, 45 females) with a family history of Alzheimer's disease were recruited from the PREVENT-AD aging cohort. Participants underwent brain imaging and completed questionnaires assessing generativity, social support, and purpose in life. Mediation models examined whether social support mediated the association between generativity and purpose in life. Seed-to-voxel analyses investigated the association between resting-state functional connectivity (rsFC) to the ventromedial prefrontal cortex (vmPFC) and ventral striatum (VS) and whether this rsFC moderated the relationship between generativity and purpose in life.

Results

Affectionate social support mediated the association between generative desire and purpose in life. Generative desire was associated with rsFC between VS and precuneus and vmPFC and right dorsolateral prefrontal cortex (rdlPFC). The vmPFC-rdlPFC connectivity moderated the association between generative desire and purpose in life.

Discussion

These findings provide insight into how the brain supports social behavior and, separately, purpose in life in at-risk aging. Affectionate social support may be a putative target process to enhance purpose and life in older adults. This knowledge contributes to future developments of personalized interventions that promote healthy aging.

From genetic correlations of Alzheimer's disease to classification with artificial neural network models

Sporadic Alzheimer's disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer's gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.

Disparity of smell tests in Alzheimer's disease and other neurodegenerative disorders: a systematic review and meta-analysis

Background

There are discrepancies of olfactory impairment between Alzheimer's disease (AD) and other neurodegenerative disorders. Olfactory deficits may be a potential marker for early and differential diagnosis of AD. We aimed to assess olfactory functions in patients with AD and other neurodegenerative disorders, to further evaluate the smell tests using subgroup analysis, and to explore moderating factors affecting olfactory performance.

Methods

Cross-sectional studies relating to olfactory assessment for both AD and other neurodegenerative disorders published before 27 July 2022 in English, were searched on PubMed, Embase and Cochrane. After literature screening and quality assessment, meta-analyses were conducted using stata14.0 software.

Results

Forty-two articles involving 12 smell tests that evaluated 2,569 AD patients were included. It was revealed that smell tests could distinguish AD from mild cognitive impairment (MCI), Lewy body disease (LBD), depression, and vascular dementia (VaD), but not from diseases such as frontotemporal dementia (FTD). Our finding indicated that in discriminating AD from MCI, the University of Pennsylvania Smell Identification Test (UPSIT) was most frequently used (95%CI: -1.12 to -0.89), while the Brief Smell Identification Test (B-SIT), was the most widely used method in AD vs. LBD group. Further subgroup analyses indicated that the methods of smell test used contributed to the heterogeneity in olfactory threshold and discrimination scores in group AD vs. MCI. While the moderating variables including age, MMSE scores, education years in AD vs. LBD, were account for heterogeneity across studies.

Conclusion

Our finding suggests smell tests have potential value in early differential diagnosis of AD. UPSIT and its simplified variant, B-SIT, are widely used methods in the analyses.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID = 357970 (PROSPERO, registration number CRD42022357970).

Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age

BACKGROUND

Composite scores of magnetic resonance imaging-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed AD signatures, have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared with thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities and whether diffusion- and thickness/volume-based signatures each capture unique AD-related phenotypic or genetic information remains unknown.

METHODS

Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and a magnetic resonance imaging-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from 3 waves of the Vietnam Era Twin Study of Aging (VETSA) (baseline/wave 1: mean age [years] = 56.1, SD = 2.6, range = 51.1-60.2). Subsequent waves occurred at approximately 5.7-year intervals.

RESULTS

MD and thickness/volume signatures were highly heritable (56%-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance.

CONCLUSIONS

Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.

Metal Nanoparticles in Alzheimer's Disease

Nanotechnology has emerged in different fields of biomedical application, including lifestyle diseases like diabetes, hypertension, and chronic kidney disease, neurodegenerative diseases like Alzheimer's disease (AD), Parkinson's disease, and different types of cancers. Metal nanoparticles are one of the most used drug delivery systems due to the benefits of their enhanced physicochemical properties as compared to bulk metals. Neurodegenerative diseases are the second most cause affecting mortality worldwide after cancer. Hence, they require the most specific and targeted drug delivery systems for maximum therapeutic benefits. Metal nanoparticles are the preferred drug delivery system, possessing greater blood-brain barrier permeability, biocompatibility, and enhanced bioavailability. But some metal nanoparticles exhibit neurotoxic activity owing to their shape, size, surface charge, or surface modification. This review article has discussed the pathophysiology of AD. The neuroprotective mechanism of gold, silver, selenium, ruthenium, cerium oxide, zinc oxide, and iron oxide nanoparticles are discussed. Again, the neurotoxic mechanisms of gold, iron oxide, titanium dioxide, and cobalt oxide are also included. The neuroprotective and neurotoxic effects of nanoparticles targeted for treating AD are discussed elaborately. The review also focusses on the biocompatibility of metal nanoparticles for targeting the brain in treating AD. The clinical trials and the requirement to develop new drug delivery systems are critically analyzed. This review can show a path for the researchers involved in the brain-targeted drug delivery for AD.