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The biomarker cortisol assesses the impact of biopsychosocial stressors that activate the stress response system. Hair has emerged as a valid and non-invasive means of gauging cumulative cortisol deposited over month-long periods of time. Established protocols for the extraction of hair cortisol are being validated and refined in humans, yet methodological information about hair characteristics on cortisol remains limited. In addition to external hair exposures (e.g. dye, time spent outside), we examined hair categorization or type (e.g. kinky, straight) by extending a hair typing methodology for scientific use that is currently popular among hair care professionals. We then examined the interaction between hair type and race on cortisol levels with a hair questionnaire. Three studies were pooled to investigate how sample weight, hair type, race, heat exposures, and hair treatments impacted cumulative hair cortisol concentrations. Study 1 consisted of Adult Kenyan Medical Workers (N = 44); Study 2 Mexican and Mexican Americans (N = 106); and Study 3 American Youth (N = 107). We found significantly higher cortisol in 5 mg of hair when compared to larger sample weights, and higher cortisol in those who spent more time outdoors. Cortisol concentrations differed between racial groups and varied by hair type; moreover, there were directional differences in cumulative cortisol from straighter to curlier hair types which depended on racial group. In addition to demonstrating the impact of relatively novel control factors like hair sample weight, outdoor exposure, and hair type, the present study illustrates the importance of disentangling hair type and race to understand variability in cumulative hair cortisol. These influences should be included in future studies that measure hair cortisol.

Background: Aging is often characterized by progressive cognitive decline in memory and executive function. Yet, some adults aged 80 years or older, are "Super‐Agers" that exhibit cognitive performance akin to 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. Method: 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. Result: Despite model complexity, we achieved a precision of 77% when determining who would be in the "positive‐aging" group. 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 (see figure). Conclusion: Our results suggest that optimal cognitive aging may not be driven by age per se, but biological factors that may be amenable to lifestyle or pharmacological changes. [ABSTRACT FROM AUTHOR]

Background: 58‐79% of late‐onset Alzheimer's disease (AD) is due to genetic heritability. The epsilon 4 (ε4) variant of Apolipoprotein E (APOE) is the strongest genetic risk factor for AD, yet only accounts for 20% of AD heritability. In addition, the APOE ε2 or ε3 variants appear to have either a protective or neutral effect on AD risk respectively. Translocase of Outer Mitochondrial Membrane 40 (TOMM40) is next to APOE and may modulate different APOE variant effects, such as with the Single Nucleotide Polymorphism (SNP) at rs2075650. Thus, we explored main and interaction effects of TOMM40 and APOE on longitudinal measures of executive function and memory. Method: 10,347 adults (40‐70y) in the UK Biobank were genotyped for APOE and TOMM40 '650. Executive function and memory were tested at baseline and twice over a period of 6‐10 years. Main effects within APOE haplotypes and TOMM40 SNP genotypes (A,G) were analyzed, as well as interactions between these genetic markers. Result: Having the TOMM40 '650 G risk variant was related to better baseline cognitive scores regardless of APOE haplotype (ps <.05), but a greater decline over time in APOE4 carriers (ps <.05) The effects of the APOE haplotype were strongly modulated by sex. Men vs. women had less protective or more detrimental effects based on APOE2 or APOE4 haplotype respectively. Conclusion: The data suggests that TOMM40 modulates APOE, regardless of its haplotype. Future studies should stratify APOE by haplotype to re‐test our findings. In addition, more research needs to be done on the effect of having what we term a "mixed" APOE haplotype (carrying a copy of both the protective and risk gene variants). [ABSTRACT FROM AUTHOR]

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., Competing Interests: This study uses UK Biobank data. UK Biobank has ethical approval from the North West Multi‐centre Research Ethics Committee (MREC) (https://www.ukbiobank.ac.uk/learn‐more‐about‐uk‐biobank/about‐us/ethics). Written informed consent was obtained from all participants., (© 2024 The Author(s). Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)