Temporal impact of sugar metabolism on the liver

ABSTRACTPurpose: Increased sugar consumption is associated with metabolic conditions that can result in poor health outcomes. To investigate the impact of sugars such as glucose and fructose in the body, this study aims to determine the timing of effects and assess the impact of these effects. This study aims to demonstrate acute effects in metabolism as a result of sugar metabolism.Methods: Six male participants were imaged on a 3T MRI scanner at a fasted state then subsequently every hour for up to eight measurements. Between scanning sessions, they consume a 13C labeled glucose or fructose shake, have breath collected, and have blood drawn; this is repeated on a separate day to satisfy the other experimental condition. The MRI exam consists of Proton Density Fat Fraction (PDFF), proton Magnetic Resonance Spectroscopy (1H MRS), and 13C MRS. The images are processed to analyze liver volume and the spectra from the MR Spectroscopy are normalized and the peaks are quantified.Results: Liver volume is significantly different from baseline measurements at 2-, 3-, and 4-hours post-feeding with p=0.032, p=0.003, and p=0.009 respectively. Fat content is significantly different from baseline measurements at 3- and 4-hours post-feeding with p=0.026 and p=0.048 respectively. Different MR measures of fat fraction in the body produce a significant positive correlation (p=0.003). The median change of lipids (CH2) positively correlates with the median change in glycerol (p=0.011). Fat fraction does not significantly correlate with the blood measures taken, but when high choline and low choline groups are separated, new formed lipids in the blood and long-term storage of fat differ significantly, p=0.021 and p=0.03 respectively.Conclusions: Choline classification of participants resulted in a difference in new lipids in the blood and long-term storage in the liver. Low choline individuals tended to export less lipids in the blood and store more in the liver. High choline individuals exported more lipids and retained less in the liver. MR exams of the liver evaluate the health of the liver and burden to abdominal organs, whereas blood collection provided a glimpse into cardiovascular burden

The relationship between workload, performance and fatigue in a short-haul airline

The aim of this study was to determine the relationship between pilot workload, performance, subjective fatigue, sleep duration, number of sectors and flight duration during short-haul operations. Ninety pilots completed a NASA Task Load Index, Psychomotor Vigilance Task and a Samn-Perelli fatigue scale on top-of-descent of each flight and wore an activity monitor throughout the study. Weak, but significant, correlations were revealed between workload and all factors. Subjective fatigue, number of sectors and lapses were significant predictors of workload. Pilots reported higher workload when fatigue increased, the number of sectors were higher, and objective performance was worse

Early starts and late finishes both reduce alertness and performance among short-haul airline pilots

Flight crews are frequently required to work irregular schedules and, as a result, can experience sleep deficiency and fatigue. This study was conducted to determine whether perceived fatigue levels and objective performance varied by time of day, time awake, and prior night\u27s sleep duration. Ninety-five pilots (86 male, 9 female) aged 33 years (±8) volunteered for the study. Participants completed a daily sleep diary, Samn-Perelli fatigue scale, and psychomotor vigilance task that were completed before and after each flight duty period and at the top-of-descent for each flight. Pilots experienced higher self-reported fatigue (EMM = 3.92, SE = 0.09, p \u3c 0.001) and worse performance (Response speed: EMM = 4.27, SE = 0.08, p = 0.004) for late-finishing duties compared with early-starting duties (Samn-Perelli: EMM = 3.74, SE = 0.08; Response speed: EMM = 4.37, SE = 0.08), but had shorter sleep before early-starting duties (early: EMM = 6.94, SE = 0.10; late: EMM = 8.47, SE = 0.14, p \u3c 0.001). However, pre-duty Samn-Perelli and response speed were worse (z = 4.18, p \u3c 0.001; z = 3.05, p = 0.03; respectively) for early starts compared with late finishes (EMM = 2.74, SE = 0.19), while post-duty Samn-Perelli was worse for late finishes (EMM = 4.74, SE = 0.19) compared with early starts (EMM = 4.05, SE = 0.12). The results confirm that duty time has a strong influence on self-reported fatigue and performance. Thus, all flights that encroach on a biological night are targets for fatigue risk management oversight

Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication

AbstractSleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking

Temporal impact of sugar metabolism on the liver

ABSTRACTPurpose: Increased sugar consumption is associated with metabolic conditions that can result in poor health outcomes. To investigate the impact of sugars such as glucose and fructose in the body, this study aims to determine the timing of effects and assess the impact of these effects. This study aims to demonstrate acute effects in metabolism as a result of sugar metabolism.Methods: Six male participants were imaged on a 3T MRI scanner at a fasted state then subsequently every hour for up to eight measurements. Between scanning sessions, they consume a 13C labeled glucose or fructose shake, have breath collected, and have blood drawn; this is repeated on a separate day to satisfy the other experimental condition. The MRI exam consists of Proton Density Fat Fraction (PDFF), proton Magnetic Resonance Spectroscopy (1H MRS), and 13C MRS. The images are processed to analyze liver volume and the spectra from the MR Spectroscopy are normalized and the peaks are quantified.Results: Liver volume is significantly different from baseline measurements at 2-, 3-, and 4-hours post-feeding with p=0.032, p=0.003, and p=0.009 respectively. Fat content is significantly different from baseline measurements at 3- and 4-hours post-feeding with p=0.026 and p=0.048 respectively. Different MR measures of fat fraction in the body produce a significant positive correlation (p=0.003). The median change of lipids (CH2) positively correlates with the median change in glycerol (p=0.011). Fat fraction does not significantly correlate with the blood measures taken, but when high choline and low choline groups are separated, new formed lipids in the blood and long-term storage of fat differ significantly, p=0.021 and p=0.03 respectively.Conclusions: Choline classification of participants resulted in a difference in new lipids in the blood and long-term storage in the liver. Low choline individuals tended to export less lipids in the blood and store more in the liver. High choline individuals exported more lipids and retained less in the liver. MR exams of the liver evaluate the health of the liver and burden to abdominal organs, whereas blood collection provided a glimpse into cardiovascular burden

The Relationship between Workload, Performance and Fatigue in a Short-Haul Airline

Short-haul flights are associated with irregular work schedules and increased workload, due to frequent takeoffs and landings. We examined the relationship between pilot workload, performance and subjective fatigue during normal short-haul operations. Ninety airline pilots (8 female), mean age 33 (8 years) completed a NASA-Task Load Index (NASA-TLX), a Psychomotor Vigilance Task (PVT; a reaction time test sensitive to sleep loss) and a Samn-Perelli (SP) fatigue scale, over a period of 20 duty days at top-of-descent on 2762 short-haul flights. The duty days included either 2 or 4 flights per day starting at different times as scheduled during normal operations. Workload was measured using the six NASA-TLX scales: mental demand, physical demand, temporal demand, effort, performance and frustration. Lapses (reaction times [RT] > 500ms) were calculated for the PVT. Spearman correlations were calculated to identify relationships between the NASA-TLX, PVT lapses, and SP. The six scales of NASA-TLX were positively correlated with the PVT lapses (p < 0.01) showing an increase in workload when lapses increased. There was a positive correlation between subjective fatigue as measured by the SP fatigue scale and each of the six scales of NASA-TLX (p < 0.001) suggesting that pilots reported higher workload when perceived levels of fatigue were higher. Of the six workload scales, mental demand and performance were rated the highest (mental: M = 40.99, SD = 20.32; performance: M = 41.61, SD = 20.71) and effort was rated the lowest (M = 15.59, SD = 8.98). Preliminary analyses suggest that high workload is associated with poorer PVT performance and increased self-reported fatigue in this population of short-haul pilots. Future studies should explore how other workload factors (i.e. flight hours, time of day) influence self-reported and objective fatigue measures

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP

The contributions of cartilage endplate composition and vertebral bone marrow fat to intervertebral disc degeneration in patients with chronic low back pain.

PurposeThe composition of the subchondral bone marrow and cartilage endplate (CEP) could affect intervertebral disc health by influencing vertebral perfusion and nutrient diffusion. However, the relative contributions of these factors to disc degeneration in patients with chronic low back pain (cLBP) have not been quantified. The goal of this study was to use compositional biomarkers derived from quantitative MRI to establish how CEP composition (surrogate for permeability) and vertebral bone marrow fat fraction (BMFF, surrogate for perfusion) relate to disc degeneration.MethodsMRI data from 60 patients with cLBP were included in this prospective observational study (28 female, 32 male; age = 40.0 ± 11.9&nbsp;years, 19-65 [mean ± SD, min-max]). Ultra-short echo-time MRI was used to calculate CEP T2* relaxation times (reflecting biochemical composition), water-fat MRI was used to calculate vertebral BMFF, and T1ρ MRI was used to calculate T1ρ relaxation times in the nucleus pulposus (NP T1ρ, reflecting proteoglycan content and degenerative grade). Univariate linear regression was used to assess the independent effects of CEP T2* and vertebral BMFF on NP T1ρ. Mixed effects multivariable linear regression accounting for age, sex, and BMI was used to assess the combined relationship between variables.ResultsCEP T2* and vertebral BMFF were independently associated with NP T1ρ (p = 0.003 and 0.0001, respectively). After adjusting for age, sex, and BMI, NP T1ρ remained significantly associated with CEP T2* (p = 0.0001) but not vertebral BMFF (p = 0.43).ConclusionPoor CEP composition plays a significant role in disc degeneration severity and can affect disc health both with and without deficits in vertebral perfusion

Paraspinal Muscle in Chronic Low Back Pain: Comparison Between Standard Parameters and Chemical Shift Encoding-Based Water-Fat MRI.

BackgroundParaspinal musculature (PSM) is increasingly recognized as a contributor to low back pain (LBP), but with conventional MRI sequences, assessment is limited. Chemical shift encoding-based water-fat MRI (CSE-MRI) enables the measurement of PSM fat fraction (FF), which may assist investigations of chronic LBP.PurposeTo investigate associations between PSM parameters from conventional MRI and CSE-MRI and between PSM parameters and pain.Study typeProspective, cross-sectional.PopulationEighty-four adults with chronic LBP (44.6&nbsp;± 13.4 years; 48 males).Field strength/sequence3-T, T1-weighted fast spin-echo and iterative decomposition of water and fat with echo asymmetry and least squares estimation sequences.AssessmentT1-weighted images for Goutallier classification (GC), muscle volume, lumbar indentation value, and muscle-fat index, CSE-MRI for FF extraction (L1/2-L5/S1). Pain was self-reported using a visual analogue scale (VAS). Intra- and/or interreader agreement was assessed for MRI-derived parameters.Statistical testsMixed-effects and linear regression models to 1) assess relationships between PSM parameters (entire cohort and subgroup with GC grades 0 and 1; statistical significance α&nbsp;= 0.0025) and 2) evaluate associations of PSM parameters with pain (α&nbsp;= 0.05). Intraclass correlation coefficients (ICCs) for intra- and/or interreader agreement.ResultsThe FF showed excellent intra- and interreader agreement (ICC range: 0.97-0.99) and was significantly associated with GC at all spinal levels. Subgroup analysis suggested that early/subtle changes in PSM are detectable with FF but not with GC, given the absence of significant associations between FF and GC (P-value range: 0.036 at L5/S1 to 0.784 at L2/L3). Averaged over all spinal levels, FF and GC were significantly associated with VAS scores.Data conclusionIn the absence of FF, GC may be the best surrogate for PSM quality. Given the ability of CSE-MRI to detect muscle alterations at early stages of PSM degeneration, this technique may have potential for further investigations of the role of PSM in chronic LBP.Level of evidence2 TECHNICAL EFFICACY STAGE: 2