An important aspect of cognitive functioning is decision-making, which depends on the correct interpretation of emotional processes. High trait anxiety has been associated with increased risk taking behavior in decision-making tasks. An interesting fact is that anxiety and anxiety-related chemosignals as well as decision-making share similar regions of neuronal activation. In order to ascertain if chemosensory anxiety signals have similar effects on risk taking behavior of healthy participants as high trait anxiety we used a novel computerized decision-making task, called Haegler's Risk Game (HRG). This task measures risk taking behavior based on contingencies and can be played repeatedly without a learning effect. To obtain chemosensory signals the sweat of 21 male donors was collected in a high rope course (anxiety condition). For the chemosensory control condition sweat was collected during an ergometer workout (exercise condition). In a double-blind study, 30 healthy recipients (16 females) had to play HRG while being exposed to sweat samples or empty control samples (control condition) in three sessions of randomized order. Comparison of the risk taking behavior of the three conditions showed significantly higher risk taking behavior in participants for the most risky choices during the anxiety condition compared to the control conditions. Additionally, recipients showed significantly higher latency before making their decision in the most risky choices during the anxiety condition. This experiment gives evidence that chemosensory anxiety signals are communicated between humans thereby increasing participants' risk taking behavior.
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Cochrane Database Syst Rev
January 2025
Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.
Background: Precision nutrition-based methods develop tailored interventions and/or recommendations accounting for determinants of intra- and inter-individual variation in response to the same diet, compared to current 'one-size-fits-all' population-level approaches. Determinants may include genetics, current dietary habits and eating patterns, circadian rhythms, health status, gut microbiome, socioeconomic and psychosocial characteristics, and physical activity. In this systematic review, we examined the evidence base for the effect of interventions based on precision nutrition approaches on overweight and obesity in children and adolescents to help inform future research and global guidelines.
View Article and Find Full Text PDFSchizophr Bull Open
January 2025
NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway.
There is a pressing need for biomarkers of violent behavior risk in psychosis. Previous research indicates that electrophysiological measures of automatic defensive reactions may have potential. The purpose of this study was to investigate associations between violent behavior in individuals with and without psychosis and electromyography (EMG) and electroencephalography (EEG) responses to startling auditory stimuli.
View Article and Find Full Text PDFInnov Aging
June 2024
Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, California, USA.
Background And Objectives: Older patients diagnosed with chronic kidney disease (CKD) have a higher risk of all-cause mortality than the general population. However, there is limited information available on how CKD relates to all-cause mortality among Black adults in the United States. We aimed to investigate how CKD relates to all-cause mortality risk among older Black adults.
View Article and Find Full Text PDFFront Psychiatry
January 2025
Department of Psychiatry, University of California, Irvine, Irvine, CA, United States.
Background: We previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model that was trained on the NAPLS-3 data, however, requires further support through implementation in an independent dataset. In this report we tested for model generalization using the previous iteration of NAPLS-3, the NAPLS-2, using the identical machine learning algorithms employed in our previous study.
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