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.

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.

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.

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.

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.

APOE, TOMM40, and sex interactions on neural network connectivity.

The Apolipoprotein E ε4 (APOE ε4) haplotype is the strongest genetic risk factor for late-onset Alzheimer's disease (AD). The Translocase of Outer Mitochondrial Membrane-40 (TOMM40) gene maintains cellular bioenergetics, which is disrupted in AD. TOMM40 rs2075650 ('650) G versus A carriage is consistently related to neural and cognitive outcomes, but it is unclear if and how it interacts with APOE. We examined 21 orthogonal neural networks among 8,222 middle-aged to aged participants in the UK Biobank cohort. ANOVA and multiple linear regression tested main effects and interactions with APOE and TOMM40 '650 genotypes, and if age and sex acted as moderators. APOE ε4 was associated with less strength in multiple networks, while '650 G versus A carriage was related to more language comprehension network strength. In APOE ε4 carriers, '650 G-carriage led to less network strength with increasing age, while in non-G-carriers this was only seen in women but not men. TOMM40 may shift what happens to network activity in aging APOE ε4 carriers depending on sex.

Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study.

BACKGROUND: 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). METHODS: Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. 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. RESULTS: The "best-fit" model for predicting COVID-19 risk 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. CONCLUSIONS: 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.

Genetic Factors of Alzheimer's Disease Modulate How Diet is Associated with Long-Term Cognitive Trajectories: A UK Biobank Study.

BACKGROUND: Fluid intelligence (FI) involves abstract problem-solving without prior knowledge. Greater age-related FI decline increases Alzheimer's disease (AD) risk, and recent studies suggest that certain dietary regimens may influence rates of decline. However, it is uncertain how long-term food consumption affects FI among adults with or without familial history of AD (FH) or APOE4 (ɛ4). OBJECTIVE: Observe how the total diet is associated with long-term cognition among mid- to late-life populations at-risk and not-at-risk for AD. METHODS: Among 1,787 mid-to-late-aged adult UK Biobank participants, 10-year FI trajectories were modeled and regressed onto the total diet based on self-reported intake of 49 whole foods from a Food Frequency Questionnaire (FFQ). RESULTS: Daily cheese intake strongly predicted better FIT scores over time (FH-: ß= 0.207, p < 0.001; ɛ4-: ß= 0.073, p = 0.008; ɛ4+: ß= 0.162, p = 0.001). Alcohol of any type daily also appeared beneficial (ɛ4+: ß= 0.101, p = 0.022) and red wine was sometimes additionally protective (FH+: ß= 0.100, p = 0.014; ɛ4-: ß= 0.59, p = 0.039). Consuming lamb weekly was associated with improved outcomes (FH-: ß= 0.066, p = 0.008; ɛ4+: ß= 0.097, p = 0.044). Among at risk groups, added salt correlated with decreased performance (FH+: ß= -0.114, p = 0.004; ɛ4+: ß= -0.121, p = 0.009). CONCLUSION: Modifying meal plans may help minimize cognitive decline. We observed that added salt may put at-risk individuals at greater risk, but did not observe similar interactions among FH- and AD- individuals. Observations further suggest in risk status-dependent manners that adding cheese and red wine to the diet daily, and lamb on a weekly basis, may also improve long-term cognitive outcomes.

CSF glucose tracks regional tau progression based on Alzheimer's disease risk factors.

INTRODUCTION: Glucose hypometabolism and tau formation are key features of Alzheimer's disease (AD). Less is known about the relationship between fasting glucose and regional tau accumulation. METHODS: Cerebrospinal fluid (CSF) glucose was linearly regressed on regional tau (flortaucipir) among 169 Alzheimer's Disease Neuroimaging Initiative (ADNI3) participants. Flortaucipir uptake was examined by Braak stages and regions of interest (ROIs). Interactions were explored between CSF glucose and AD risk factors including regional amyloid beta (Aß), sex, Apolipoprotein E ε4 (APOEε4) status, AD parental family history (AD FH), and cognitive impairment (CI). RESULTS: Interactions found higher CSF glucose tracked less tau in ROIs or Braak stages I/II (women, APOE ε4+, regional Aß), III/IV (AD FH+, regional Aß), and V/VI (AD FH+). CI drove Braak III-VI associations. DISCUSSION: Among women and APOE ε4 carriers, higher CSF glucose tracked less early-stage tau. Higher CSF glucose may reflect compensation against tau spreading in CI, Aß+, or AD FH+.

Walking in the Light: How History of Physical Activity, Sunlight, and Vitamin D Account for Body Fat-A UK Biobank Study.

OBJECTIVE: The high prevalence of vitamin D deficiency and obesity drives the need for successful strategies that elevate vitamin D levels, prevent adipogenesis, and stimulate lipolysis. This study provides a theoretical model to evaluate how physical activity (PA) and sunlight exposure influence serum vitamin D levels and regional adiposity. This study hypothesized a posteriori that sunlight is associated with undifferentiated visceral adiposity by increasing the ratio of brown to white adipose tissue. METHODS: Using 10-year longitudinal data, accelerometry, a sun-exposure questionnaire, and regional adiposity quantified by dual-energy x-ray absorptiometry imaging, a structural-equation mediation model of growth curves was constructed with a data-driven methodology. RESULTS: Sunlight and PA conjointly increased serum vitamin D. Changes in vitamin D levels partially mediated how sunlight and PA impacted adiposity in visceral and subcutaneous regions within a subjective PA model. In an objective PA model, vitamin D was a mediator for subcutaneous regions only. Interestingly, sunlight was associated with less adiposity in subcutaneous regions but greater adiposity in visceral regions. CONCLUSIONS: Sunlight and PA may increase vitamin D levels. For the first time, this study characterizes a positive association between sunlight and visceral adiposity. Further investigation and experimentation are necessary to clarify the physiological role of sunlight exposure on adipose tissue.

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.