Impact of COVID-19 on the Onset and Progression of Alzheimer's Disease and Related Dementias: A Roadmap for Future Research.

COVID-19 causes lasting neurological symptoms in some survivors. Like other infections, COVID-19 may increase risk of cognitive impairment. This perspective highlights four knowledge gaps about COVID-19 that need to be filled to avoid this possible health issue. The first is the need to identify the COVID-19 symptoms, genetic polymorphisms and treatment decisions associated with risk of cognitive impairment. The second is the absence of model systems in which to test hypotheses relating infection to cognition. The third is the need for consortia for studying both existing and new longitudinal cohorts in which to monitor long term consequences of COVID-19 infection. A final knowledge gap discussed is the impact of the isolation and lack of social services brought about by quarantine/lockdowns on people living with dementia and their caregivers. Research into these areas may lead to interventions that reduce the overall risk of cognitive decline for COVID-19 survivors.

Inflammation, negative affect, and amyloid burden in Alzheimer's disease: Insights from the kynurenine pathway.

BACKGROUND: Depressive symptoms in Alzheimer's disease (AD) predict worse cognitive and functional outcomes. Both AD and major depression inflammatory processes are characterized by shunted tryptophan metabolism away from serotonin (5-HT) and toward the neuroinflammatory kynurenine (Kyn) pathway. The present study assessed associations between Kyn and behavioral, neuroanatomical, neuropathological, and physiological outcomes common to both AD and negative affect across the AD continuum. METHODS: In 58 cognitively normal, 396 mild cognitive impairment, and 112 AD participants from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI1) cohort, serum markers of 5-HT, tryptophan, and Kyn were measured and their relationships investigated with immunologic markers, affect and functional outcomes, CSF markers of beta-amyloid (Aß) and tau, and regional gray matter. RESULTS: A higher Kyn/Tryptophan ratio was linked to many inflammatory markers, as well as lower functional independence and memory scores. A higher Kyn/5-HT ratio showed similar associations, but also strong relationships with negative affect and neuropsychiatric disturbance, executive dysfunction, and global cognitive decline. Further, gray matter atrophy was seen in hippocampus, anterior cingulate, and prefrontal cortices, as well as greater amyloid and total tau deposition. Finally, using moderated-mediation, several pro-inflammatory factors partially mediated Kyn/5-HT and negative affect scores in participants with subclinical Aß (i.e., Aß-), whereas such associations were fully mediated by Complement 3 in Aß+ participants. CONCLUSION: These findings suggest that inflammatory signaling cascades may occur during AD, which is associated with increased Kyn metabolism that influences the pathogenesis of negative affect. Aß and the complement system may be critical contributing factors in this process.

Aging-related changes in fluid intelligence, muscle and adipose mass, and sex-specific immunologic mediation: A longitudinal UK Biobank study.

BACKGROUND: Obesity in midlife and early late-life is associated with worse normal cognitive aging. Dual-energy X-ray absorptiometry (DEXA) suggests that visceral adipose mass (VAM) plays a predominant role, whereas non-visceral adipose mass (NVAM) and lean muscle mass (LMM) have shown conflicting relationships. It is unknown how longitudinal, cognitive changes in age-sensitive domains like fluid intelligence (FI) correspond to VAM, NVAM, and LMM in women and men. Furthermore, changes over time in blood leukocyte sub-populations may partially or fully account for sex-specific associations. METHODS: Data on 4431 late middle-aged, cognitively unimpaired adults (mean = 64.5 y) was obtained from the UK Biobank prospective cohort across 22 centers. FI scores, blood leukocyte counts, and covariates (age, social class, education) were measured at three 2-year intervals over 6 years. DEXA collection overlapped with these intervals. Sex-stratified growth curves, structural equations, and Preacher-Hayes mediation were used to estimate direct and indirect effects. ß-weights were standardized. RESULTS: More LMM predicted gains in FI scores among women (ß = 0.130, p < .001) and men (ß = 0.089, p < .001). Conversely, more VAM and NVAM independently predicted FI decline equally among sexes (e.g., NVAM: women: ß = -0.082, p < .001; men: ß = -0.076, p < .001). Among women, FI associations were fully mediated by higher eosinophil counts via VAM (λ = 30.8%, p = .028) and lower lymphocyte counts via LMM (λ = 69.2%, p = .021). Among men, FI associations were partially mediated by lower basophils counts via LMM (λ = 4.5%, p = .042) and higher counts via VAM (λ = 50%, p = .037). CONCLUSION: The proportion of LMM and VAM equally influenced male FI changes over 6 years, whereas higher LMM among women appeared to more strongly influence. FI changes. Leukocyte counts strongly mediated VAM- and LMM-related FI changes in a sex-specific manner, but not for NVAM. For clinical translation, exercise studies in older adults may benefit from assessing sex-specific values of DEXA-based tissue mass, FI, and leukocyte sub-populations to gauge potential cognitive benefits of less VAM and more LMM.

Neural, Hormonal, and Cognitive Correlates of Metabolic Dysfunction and Emotional Reactivity.

OBJECTIVE: Prediabetes and type 2 diabetes (i.e., hyperglycemia) are characterized by insulin resistance. These problems with energy metabolism may exacerbate emotional reactivity to negatively valenced stimuli and related phenomena such as predisposition toward negative affect, as well as cognitive deficits. Higher emotional reactivity is seen with hyperglycemia and insulin resistance. However, it is largely unknown how metabolic dysfunction correlates with related neural, hormonal, and cognitive outcomes. METHODS: Among 331 adults from the Midlife in the United States study, eye-blink response (EBR) we cross sectionally examined to gauge reactivity to negative, positive, or neutrally valenced pictures from international affect picture system stimuli proximal to an acoustic startle probe. Increased EBR to negative stimuli was considered an index of stress reactivity. Frontal alpha asymmetry, a biomarker of negative affect predisposition, was determined using resting electroencephalography. Baseline urinary cortisol output was collected. Cognitive performance was gauged using the Brief Test of Adult Cognition by telephone. Fasting glucose and insulin characterized hyperglycemia or the homeostatic model assessment of insulin resistance. RESULTS: Higher homeostatic model assessment of insulin resistance corresponded to an increased startle response, measured by EBR magnitude, for negative versus positive stimuli (R = 0.218, F(1,457) = 5.48, p = .020, euglycemia: M(SD) = .092(.776), hyperglycemia: M(SD) = .120(.881)). Participants with hyperglycemia versus euglycemia showed greater right frontal alpha asymmetry (F(1,307) = 6.62, p = .011, euglycemia: M(SD) = .018(.167), hyperglycemia: M(SD) = -.029(.160)), and worse Brief Test of Adult Cognition by telephone arithmetic performance (F(1,284) = 4.25, p = .040, euglycemia: M(SD) = 2.390(1.526), hyperglycemia: M(SD) = 1.920(1.462)). Baseline urinary cortisol (log10 µg/12 hours) was also dysregulated in individuals with hyperglycemia (F(1,324) = 5.09, p = .025, euglycemia: M(SD) = 1.052 ± .332, hyperglycemia: M(SD) = .961 (.362)). CONCLUSIONS: These results suggest that dysmetabolism is associated with increased emotional reactivity, predisposition toward negative affect, and specific cognitive deficits.

Family history and TOMM40 '523 interactive associations with memory in middle-aged and Alzheimer's disease cohorts.

INTRODUCTION: Family history (FH) of Alzheimer's disease (AD) affects mitochondrial function and may modulate effects of translocase of the outer mitochondrial membrane 40 kDa (TOMM40) rs10524523 ('523) poly-T length on memory decline. METHODS: For 912 nonapolipoprotein ε4 middle-aged adults and 365 aged adults across the AD spectrum, linear mixed models gauged FH and TOMM40 '523 interactions on memory and global cognition between baseline and up to 10 years later. A cerebrospinal fluid mitochondrial function biomarker was also assessed. RESULTS: For FH negative participants, gene-dose preservation of memory and global cognition was seen for "very long" versus "short" carriers. For FH positive, an opposite gene-dose decline was seen for very long versus short carriers. Maternal FH was a stronger predictor in aged, but not middle-aged, participants. Similar gene-dose effects were seen for the mitochondrial biomarker aspartate aminotransferase. DISCUSSION: These results may clarify conflicting findings on TOMM40 poly-T length and AD-related decline.

Insulin resistance predicts brain amyloid deposition in late middle-aged adults.

BACKGROUND: Insulin resistance (IR) increases Alzheimer's disease (AD) risk. IR is related to greater amyloid burden post-mortem and increased deposition within areas affected by early AD. No studies have examined if IR is associated with an in vivo index of amyloid in the human brain in late middle-aged participants at risk for AD. METHODS: Asymptomatic, late middle-aged adults (N = 186) from the Wisconsin Registry for Alzheimer's Prevention underwent [C-11]Pittsburgh compound B (PiB) positron emission tomography. The cross-sectional design tested the interaction between insulin resistance and glycemic status on PiB distribution volume ratio in three regions of interest (frontal, parietal, and temporal). RESULTS: In participants with normoglycemia but not hyperglycemia, higher insulin resistance corresponded to higher PiB uptake in frontal and temporal areas, reflecting increased amyloid deposition. CONCLUSIONS: This is the first human study to demonstrate that insulin resistance may contribute to amyloid deposition in brain regions affected by AD.

Vitamin B6, B12, and Folate's Influence on Neural Networks in the UK Biobank Cohort.

BACKGROUND: One-carbon metabolism coenzymes may influence brain aging in cognitively unimpaired adults. METHODS: Baseline data were used from the UK Biobank cohort. Estimated intake of vitamin B6, B12, and folate was regressed onto neural network functional connectivity in five resting-state neural networks. Linear mixed models tested coenzyme main effects and interactions with Alzheimer's disease (AD) risk factors. RESULTS: Increased B6 and B12 estimated intake were linked with less functional connectivity in most networks, including the posterior portion of the Default Mode Network. Conversely, higher folate was related to more connectivity in similar networks. AD family history modulated these associations: Increased estimated intake was positively associated with stronger connectivity in the Primary Visual Network and Posterior Default Mode Network in participants with an AD family history. In contrast, increased vitamin B12 estimated intake was associated with less connectivity in the Primary Visual Network and the Cerebello-Thalamo-Cortical Network in those without an AD family history. CONCLUSIONS: The differential patterns of association between B vitamins and resting-state brain activity may be important in understanding AD-related changes in the brain. Notably, AD family history appears to play a key role in modulating these relationships.

A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks.

INTRODUCTION: Aging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive-Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive-Agers and Cognitive Decliners, and (2) identify Positive-Agers using neuronal functional connectivity networks data and demographics. METHODS: In this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort. RESULTS: OLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive-Agers and cognitive decliners. DISCUSSION: OLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults. Highlights: Design an algorithm to distinguish between a Positive-Ager and a Cognitive-Decliner.Introduce a mathematical definition for cognitive classes based on cognitive tests.Accurate Positive-Ager identification using rsfMRI and demographic data (AUC = 0.88).Posterior default mode network has the highest impact on Positive-Aging odds ratio.

A calorie-restricted diet decreases brain iron accumulation and preserves motor performance in old rhesus monkeys.

Caloric restriction (CR) reduces the pathological effects of aging and extends the lifespan in many species, including nonhuman primates, although the effect on the brain is less well characterized. We used two common indicators of aging, motor performance speed and brain iron deposition measured in vivo using magnetic resonance imaging, to determine the potential effect of CR on elderly rhesus macaques eating restricted (n=24, 13 males, 11 females) and standard (n=17, 8 males, 9 females) diets. Both the CR and control monkeys showed age-related increases in iron concentrations in globus pallidus (GP) and substantia nigra (SN), although the CR group had significantly less iron deposition in the GP, SN, red nucleus, and temporal cortex. A Diet X Age interaction revealed that CR modified age-related brain changes, evidenced as attenuation in the rate of iron accumulation in basal ganglia and parietal, temporal, and perirhinal cortex. Additionally, control monkeys had significantly slower fine motor performance on the Movement Assessment Panel, which was negatively correlated with iron accumulation in left SN and parietal lobe, although CR animals did not show this relationship. Our observations suggest that the CR-induced benefit of reduced iron deposition and preserved motor function may indicate neural protection similar to effects described previously in aging rodent and primate species.

Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network.

The balance between excitatory glutamatergic projection neurons and inhibitory GABAergic interneurons determines the function of cortical microcircuits. How these neurotransmitters relate to the functional status of an entire macro-scale network remains unknown. The posteromedial cortex (PMC) is the default mode network (DMN) node with the greatest functional connectivity; therefore, we hypothesized that PMC glutamate and GABA predict intrinsic functional connectivity (iFC) across the entire DMN. In 20 healthy men, we combined J-resolved magnetic resonance spectroscopy to measure glutamate and GABA in the PMC and resting fMRI followed by group Independent Components Analysis to extract the entire DMN. We showed that, controlling for age and partial GM volume in the MRS voxel, PMC glutamate and GABA explained about half of the variance of DMN iFC (represented by the network's beta coefficient for rest). Glutamate correlated positively and GABA correlated negatively with DMN iFC; in an alternative statistical model which included the glutamate/GABA ratio, the ratio correlated positively with DMN iFC. Age had no independent association with DMN iFC. No other network was associated with PMC glutamate or GABA. We conclude that regional neurotransmitter concentrations in a network node strongly predict network but not global brain iFC.

Alzheimer's Disease Genetic Influences Impact the Associations between Diet and Resting-State Functional Connectivity: A Study from the UK Biobank.

BACKGROUND: Red wine and dairy products have been staples in human diets for a long period. However, the impact of red wine and dairy intake on brain network activity remains ambiguous and requires further investigation. METHODS: This study investigated the associations between dairy and red wine consumption and seven neural networks' connectivity with functional magnetic resonance imaging (fMRI) data from a sub-cohort of the UK Biobank database. Linear mixed models were employed to regress dairy and red wine consumption against the intrinsic functional connectivity for each neural network. Interactions with Alzheimer's disease (AD) risk factors, including apolipoprotein E4 (APOE4) genotype, TOMM40 genotype, and family history of AD, were also assessed. RESULT: More red wine consumption was associated with enhanced connectivity in the central executive function network and posterior default mode network. Greater milk intake was correlated with more left executive function network connectivity, while higher cheese consumption was linked to reduced posterior default mode network connectivity. For participants without a family history of Alzheimer's disease (AD), increased red wine consumption was positively correlated with enhanced left executive function network connectivity. In contrast, participants with a family history of AD displayed diminished network connectivity in relation to their red wine consumption. The association between cheese consumption and neural network connectivity was influenced by APOE4 status, TOMM40 status, and family history, exhibiting contrasting patterns across different subgroups. CONCLUSION: The findings of this study indicate that family history modifies the relationship between red wine consumption and network strength. The interaction effects between cheese intake and network connectivity may vary depending on the presence of different genetic factors.

Associations Between Insulin-Like Growth Factor-1 and Resting-State Functional Connectivity in Cognitively Unimpaired Midlife Adults.

BACKGROUND: Insulin-like growth factor (IGF)-1 plays an important role in Alzheimer's disease (AD) pathogenesis and increases disease risk. However, prior research examining IGF-1 levels and brain neural network activity is mixed. OBJECTIVE: The present study investigated the relationship between IGF-1 levels and 21 neural networks, as measured by functional magnetic resonance imaging (fMRI) in 13,235 UK Biobank participants. METHODS: Linear mixed models were used to regress IGF-1 against the intrinsic functional connectivity (i.e., degree of network activity) for each neural network. Interactions between IGF-1 and AD risk factors such as Apolipoprotein E4 (APOE4) genotype, sex, AD family history, and age were also tested. RESULTS: Higher IGF-1 was associated with more network activity in the right Executive Function neural network. IGF-1 interactions with APOE4 or sex implicated motor, primary/extrastriate visual, and executive function related neural networks. Neural network activity trends with increasing IGF-1 were different in different age groups. Higher IGF-1 levels relate to much more network activity in the Sensorimotor Network and Cerebellum Network in early-life participants (40-52 years old), compared with mid-life (52-59 years old) and late-life (59-70 years old) participants. CONCLUSION: These findings suggest that sex and APOE4 genotype may modify the relationship between IGF-1 and brain network activities related to visual, motor, and cognitive processing. Additionally, IGF-1 may have an age-dependent effect on neural network connectivity.

Adiposity and insulin resistance moderate the links between neuroelectrophysiology and working and episodic memory functions in young adult males but not females.

INTRODUCTION: Obesity and insulin resistance negatively influence neural activity and cognitive function, but electrophysiological mechanisms underlying these interrelationships remain unclear. This study investigated whether adiposity and insulin resistance moderated neural activity and underlying cognitive functions in young adults. METHODS: Real-time electroencephalography (EEG) was recorded in 38 lean (n = 12) and obese (n = 26) young adults with (n = 15) and without (n = 23) insulin resistance (18-38 years, 55.3% female) as participants completed three neurocognitive tasks in working memory (Operation Span), inhibitory control (Stroop), and episodic memory (Visual Association Test). Body fat percentage was quantified by a dual-energy X-ray absorptiometry scan (DEXA/DXA). Fasting serum insulin and glucose were quantified to calculate Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) values, for which a higher value indicates more insulin resistance. Hierarchical moderated regression analysis tested these interrelationships. RESULTS: In males, greater frontal negative slow wave (fNSW) and positive slow wave (PSW) amplitudes were linked to higher working memory accuracy in participants with low, but not high, body fat percentage and HOMA-IR levels. In contrast, body fat percentage and HOMA-IR did not moderate these associations in females. Furthermore, body fat percentage and HOMA-IR values moderated the relationship between greater fNSW amplitudes and better episodic memory accuracy in males, but not females. Finally, body fat percentage and insulin resistance did not moderate the link between neural activity and inhibitory control for either sex. CONCLUSION: Young adult males, but not females, with higher body adiposity and insulin resistance showed reduced neural activity and worse underlying working and episodic memory functions.

Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories-a UK Biobank Random Forest classification study.

Aging has often been characterized by progressive cognitive decline in memory and especially executive function. Yet some adults, aged 80 years or older, are "super-agers" that exhibit cognitive performance like younger adults. It is unknown if there are adults in mid-life with similar superior cognitive performance ("positive-aging") versus cognitive decline over time and if there are blood biomarkers that can distinguish between these groups. Among 1303 participants in UK Biobank, latent growth curve models classified participants into different cognitive groups based on longitudinal fluid intelligence (FI) scores over 7-9 years. Random Forest (RF) classification was then used to predict cognitive trajectory types using longitudinal predictors including demographic, vascular, bioenergetic, and immune factors. Feature ranking importance and performance metrics of the model were reported. Despite model complexity, we achieved a precision of 77% when determining who would be in the "positive-aging" group (n = 563) vs. cognitive decline group (n = 380). Among the top fifteen features, an equal number were related to either vascular health or cellular bioenergetics but not demographics like age, sex, or socioeconomic status. Sensitivity analyses showed worse model results when combining a cognitive maintainer group (n = 360) with the positive-aging or cognitive decline group. Our results suggest that optimal cognitive aging may not be related to age per se but biological factors that may be amenable to lifestyle or pharmacological changes.

Exploring the secrets of super-aging: a UK Biobank study on brain health and cognitive function.

Communities across the globe are faced with a rapidly aging society, where age is the main risk factor for cognitive decline and development of Alzheimer's and related diseases. Despite extensive research, there have been no successful treatments yet. A rare group of individuals called "super-agers" have been noted to thrive with their exceptional ability to maintain a healthy brain and normal cognitive function even in old age. Studying their traits, lifestyles, and environments may provide valuable insight. This study used a data-driven approach to identify potential super-agers among 7121 UK Biobank participants and found that these individuals have the highest total brain volume, best cognitive performance, and lowest functional connectivity. The researchers suggest a novel hypothesis that these super-agers possess enhanced neural processing efficiency that increases with age and introduce a definition of the "neural efficiency index." Furthermore, several other types of aging were identified and significant structural-functional differences were observed between them, highlighting the benefit of research efforts in personalized medicine and precision nutrition.

Beer, wine, and spirits differentially influence body composition in older white adults-a United Kingdom Biobank study.

BACKGROUND: Aging is characterized by body composition alterations, including increased visceral adiposity accumulation and bone loss. Alcohol consumption may partially drive these alterations, but findings are mixed. This study primarily aimed to investigate whether different alcohol types (beer/cider, red wine, white wine/Champagne, spirits) differentially associated with body composition. METHODS: The longitudinal UK Biobank study leveraged 1869 White participants (40-80 years; 59% male). Participants self-reported demographic, alcohol/dietary consumption, and lifestyle factors using a touchscreen questionnaire. Anthropometrics and serum for proteomics were collected. Body composition was obtained via dual-energy X-ray absorptiometry. Structural equation modeling was used to probe direct/indirect associations between alcohol types, cardiometabolic biomarkers, and body composition. RESULTS: Greater beer/spirit consumptions were associated with greater visceral adiposity (ß = 0.069, p < 0.001 and ß = 0.014, p < 0.001, respectively), which was driven by dyslipidemia and insulin resistance. In contrast, drinking more red wine was associated with less visceral adipose mass (ß = -0.023, p < 0.001), which was driven by reduced inflammation and elevated high-density lipoproteins. White wine consumption predicted greater bone density (ß = 0.051, p < 0.005). DISCUSSION: Beer/spirits may partially contribute to the "empty calorie" hypothesis related to adipogenesis, while red wine may help protect against adipogenesis due to anti-inflammatory/eulipidemic effects. Furthermore, white wine may benefit bone health in older White adults.1.

Using machine learning to predict COVID-19 infection and severity risk among 4510 aged adults: a UK Biobank cohort study.

Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization). Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4510 participants with 7539 test cases. We downloaded baseline data from 10 to 14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases in a subset of 80 participants with 124 test cases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean. Model predictions using the full cohort were marginal. The "best-fit" model for predicting COVID-19 risk was found in the subset of participants with antibody titers, which achieved excellent discrimination (AUC 0.969, 95% CI 0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC 0.803, 95% CI 0.663-0.943) and included only serology titers, again in the subset group. Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.