No metabolic effects of mustard allyl-isothiocyanate compared with placebo in men.

Background: Induction of nonshivering thermogenesis can be used to influence energy balance to prevent or even treat obesity. The pungent component of mustard, allyl-isothiocyanate (AITC), activates the extreme cold receptor transient receptor potential channel, subfamily A, member 1 and may thus induce energy expenditure and metabolic changes.Objective: The objective of our study was to evaluate the potential of mustard AITC to induce thermogenesis (primary outcome) and alter body temperature, cold and hunger sensations, plasma metabolic parameters, and energy intake (secondary outcomes).Design: Energy expenditure in mice was measured after subcutaneous injection with vehicle, 1 mg norepinephrine/kg, or 5 mg AITC/kg. In our human crossover study, 11 healthy subjects were studied under temperature-controlled conditions after an overnight fast. After ingestion of 10 g of capsulated mustard or uncapsulated mustard or a capsulated placebo mixture, measurements of energy expenditure, substrate oxidation, core temperature, cold and hunger scores, and plasma parameters were repeated every 30 min during a 150-min period. Subjects were randomly selected for the placebo and capsulated mustard intervention; 9 of 11 subjects received the uncapsulated mustard as the final intervention because this could not be blinded. After the experiments, energy intake was measured with the universal eating monitor in a test meal.Results: In mice, AITC administration induced a 32% increase in energy expenditure compared with vehicle (17.5 ± 4.9 J · min-1 · mouse-1 compared with 12.5 ± 1.2 J · min-1 · mouse-1, P = 0.03). Of the 11 randomly selected participants, 1 was excluded because of intercurrent illness after the first visit and 1 withdrew after the second visit. Energy expenditure did not increase after ingestion of capsulated or uncapsulated mustard compared with placebo. No differences in substrate oxidation, core temperature, cold and hunger scores, or plasma parameters were found, nor was the energy intake at the end of the experiment different between the 3 conditions.Conclusion: The highest tolerable dose of mustard we were able to use did not elicit a relevant thermogenic response in humans. This trial was registered at www.controlled-trials.com as ISRCTN19147515

Association of insulin resistance with the accumulation of saturated intramyocellular lipid: A comparison with other fat stores.

It has been shown using proton magnetic resonance spectroscopy (1 H MRS) that, in a group of females, whole-body insulin resistance was more closely related to accumulation of saturated intramyocellular lipid (IMCL) than to IMCL concentration alone. This has not been investigated in males. We investigated whether age- and body mass index-matched healthy males differ from the previously reported females in IMCL composition (measured as CH2 :CH3 ) and IMCL concentration (measured as CH3 ), and in their associations with insulin resistance. We ask whether saturated IMCL accumulation is more strongly associated with insulin resistance than other ectopic and adipose tissue lipid pools and remains a significant predictor when these other pools are taken into account. In this group of males, who had similar overall insulin sensitivity to the females, IMCL was similar between sexes. The males demonstrated similar and even stronger associations of IMCL with insulin resistance, supporting the idea that a marker reflecting the accumulation of saturated IMCL is more strongly associated with whole-body insulin resistance than IMCL concentration alone. However, this marker ceased to be a significant predictor of whole-body insulin resistance after consideration of other lipid pools, which implies that this measure carries no more information in practice than the other predictors we found, such as intrahepatic lipid and visceral adipose tissue. As the marker of saturated IMCL accumulation appears to be related to these two predictors and has a much smaller dynamic range, this finding does not rule out a role for it in the pathogenesis of insulin resistance

Sleep deficits but no metabolic deficits in premanifest Huntington's disease.

OBJECTIVE: Huntington disease (HD) is a fatal autosomal dominant, neurodegenerative condition characterized by progressively worsening motor and nonmotor problems including cognitive and neuropsychiatric disturbances, along with sleep abnormalities and weight loss. However, it is not known whether sleep disturbances and metabolic abnormalities underlying the weight loss are present at a premanifest stage. METHODS: We performed a comprehensive sleep and metabolic study in 38 premanifest gene carrier individuals and 36 age- and sex-matched controls. The study consisted of 2 weeks of actigraphy at home, 2 nights of polysomnography and multiple sleep latency tests in the laboratory, and body composition assessment using dual energy x-ray absorptiometry scanning with energy expenditure measured over 10 days at home by doubly labeled water and for 36 hours in the laboratory by indirect calorimetry along with detailed cognitive and clinical assessments. We performed a principal component analyses across all measures within each studied domain. RESULTS: Compared to controls, premanifest gene carriers had more disrupted sleep, which was best characterized by a fragmented sleep profile. These abnormalities, as well as a theta power (4-7Hz) decrease in rapid eye movement sleep, were associated with disease burden score. Objectively measured sleep problems coincided with the development of cognitive, affective, and subtle motor deficits and were not associated with any metabolic alterations. INTERPRETATION: The results show that among the earliest abnormalities in premanifest HD is sleep disturbances. This raises questions as to where the pathology in HD begins and also whether it could drive some of the early features and even possibly the pathology.The study was funded from a grant from CHDI Foundation, Inc.CHDI-RG50786. RAB received grants from NIHR BRC-RG64473. PS is funded by an MRC Programme grant (Physiological Modelling and Metabolic Risk: MC_UP_A090_1005).This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/ana.2449

The Importance of Hydration in Body Composition Assessment in Children Aged 6-16 Years.

Body composition is associated with many noncommunicable diseases. The accuracy of many simple techniques used for the assessment of body composition is influenced by the fact that they do not take into account tissue hydration and this can be particularly problematic in paediatric populations. The aims of this study were: (1) to assess the agreement of two dual energy X-ray absorptiometry (DXA) systems for determining total and regional (arms, legs, trunk) fat, lean, and bone mass and (2) to compare lean soft tissue (LST) hydration correction methods in children. One hundred and twenty four healthy children aged between 6 and 16 years old underwent DXA scans using 2 GE healthcare Lunar systems (iDXA and Prodigy). Tissue hydration was either calculated by dividing total body water (TBW), by 4-component model derived fat free mass (HFFMTBW) or by using the age and sex specific coefficients of Lohman, 1986 (HFFMLohman) and used to correct LST. Regression analysis was performed to develop cross-calibration equations between DXA systems and a paired samples t-test was conducted to assess the difference between LST hydration correction methods. iDXA resulted in significantly lower estimates of total and regional fat and lean mass, compared to Prodigy. HFFMTBW showed a much larger age/sex related variability than HFFMLohman. A 2.0 % difference in LST was observed in the boys (34.5 kg vs 33.8 kg respectively, p < 0.05) and a 2.5% difference in the girls (28.2 kg vs 27.5 kg respectively, p < 0.05) when corrected using either HFFMTBW or HFFMLohman. Care needs to be exercised when combining data from iDXA and Prodigy, as total and regional estimates of body composition can differ significantly. Furthermore, tissue hydration should be taken into account when assessing body composition as it can vary considerably within a healthy paediatric population even within specific age and/or sex groups

Prediction of total and regional body composition from 3D body shape.

Acknowledgements: We thank all study volunteers who participated in the Fenland and validation studies as well as the research teams at MRC Epidemiology Unit and NIHR Cambridge Clinical Research Facility responsible for data collection and management. We also acknowledge support from the Medical Research Council (Unit programmes MC_UU_00006/1, MC_UU_00006/3, MC_UU_00006/4, MC_UU_00006/6). The Fenland Study was funded by the Medical Research Council and the Wellcome Trust. E.D.L.R., R.P., N.W. and S.B. are supported by the NIHR Biomedical Research Centre Cambridge [IS-BRC-1215-20014]. The NIHR Cambridge Biomedical Research Centre (BRC) is a partnership between Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, funded by the National Institute for Health Research (NIHR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. L.P.E.W. is supported by the NIHR Cambridge Clinical Research Facility.Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/501100000265Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/501100000272Accurate assessment of body composition is essential for evaluating the risk of chronic disease. 3D body shape, obtainable using smartphones, correlates strongly with body composition. We present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette (emulating a single photograph) paired with anthropometric traits, and apply it to the multi-phase Fenland study comprising 12,435 adults. Using baseline data, we derive models predicting total and regional body composition metrics from these meshes. In Fenland follow-up data, all metrics were predicted with high correlations (r > 0.86). We also evaluate a smartphone app which reconstructs a 3D mesh from phone images to predict body composition metrics; this analysis also showed strong correlations (r > 0.84) for all metrics. The 3D body shape approach is a valid alternative to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programmes