Impaired cognitive flexibility during sleep deprivation among carriers of the Brain Derived Neurotrophic Factor (BDNF) Val66Met allele

Accumulating evidence points to a genetic contribution to explain inter-individual vulnerability to sleep deprivation. A functional polymorphism in the BDNF gene, which causes a valine (Val) to methionine (Met) amino acid substitution at Codon 66, has been associated with cognitive impairment, particularly in populations with impaired frontal functioning. We hypothesised that sleep deprivation, which affects frontal function, may lead to cognitive dysfunction in Met allele carriers. To examine this, we investigated, in different BDNF genotypes, the effects of sleep deprivation on cognitive flexibility, as measured by response inhibition using the Stroop Color Naming Task. Thirty healthy, adults of European ancestry, including 12 heterozygous Met allele carriers and 18 Val/Val homozygotes, underwent 30-h of extended wakefulness under constant routine conditions. A computerised Stroop task was administered every 2 h. Error rate and reaction times increased with time awake for all individuals. Participants with the Val/Met genotype made more errors on incongruent trials after 20 h awake. While Val/Met participants also took significantly longer to respond when inhibiting a prepotent response irrespective of time awake, this was particularly evident during the biological night. Our study shows that carriers of the BDNF Met allele are more vulnerable to the impact of prolonged wakefulness and the biological night on a critical component of executive function, as measured by response inhibition on the Stroop task.</p

Circadian and wake-dependent changes in human plasma polar metabolites during prolonged wakefulness:A preliminary analysis

Abstract Establishing circadian and wake-dependent changes in the human metabolome are critical for understanding and treating human diseases due to circadian misalignment or extended wake. Here, we assessed endogenous circadian rhythms and wake-dependent changes in plasma metabolites in 13 participants (4 females) studied during 40-hours of wakefulness. Four-hourly plasma samples were analyzed by hydrophilic interaction liquid chromatography (HILIC)-LC-MS for 1,740 metabolite signals. Group-averaged (relative to DLMO) and individual participant metabolite profiles were fitted with a combined cosinor and linear regression model. In group-level analyses, 22% of metabolites were rhythmic and 8% were linear, whereas in individual-level analyses, 14% of profiles were rhythmic and 4% were linear. We observed metabolites that were significant at the group-level but not significant in a single individual, and metabolites that were significant in approximately half of individuals but not group-significant. Of the group-rhythmic and group-linear metabolites, only 7% and 12% were also significantly rhythmic or linear, respectively, in ≥50% of participants. Owing to large inter-individual variation in rhythm timing and the magnitude and direction of linear change, acrophase and slope estimates also differed between group- and individual-level analyses. These preliminary findings have important implications for biomarker development and understanding of sleep and circadian regulation of metabolism

Accurate detection of acute sleep deprivation using a metabolomic biomarker—A machine learning approach

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography–mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through “fitness for duty” or “post-accident analysis” assessments