Mild cold effects on hunger, food intake, satiety and skin temperature in humans.

BACKGROUND: Mild cold exposure increases energy expenditure and can influence energy balance, but at the same time it does not increase appetite and energy intake. OBJECTIVE: To quantify dermal insulative cold response, we assessed thermal comfort and skin temperatures changes by infrared thermography. METHODS: We exposed healthy volunteers to either a single episode of environmental mild cold or thermoneutrality. We measured hunger sensation and actual free food intake. After a thermoneutral overnight stay, five males and five females were exposed to either 18°C (mild cold) or 24°C (thermoneutrality) for 2.5 h. Metabolic rate, vital signs, skin temperature, blood biochemistry, cold and hunger scores were measured at baseline and for every 30 min during the temperature intervention. This was followed by an ad libitum meal to obtain the actual desired energy intake after cold exposure. RESULTS: We could replicate the cold-induced increase in REE. But no differences were detected in hunger, food intake, or satiety after mild cold exposure compared with thermoneutrality. After long-term cold exposure, high cold sensation scores were reported, which were negatively correlated with thermogenesis. Skin temperature in the sternal area was tightly correlated with the increase in energy expenditure. CONCLUSIONS: It is concluded that short-term mild cold exposure increases energy expenditure without changes in food intake. Mild cold exposure resulted in significant thermal discomfort, which was negatively correlated with the increase in energy expenditure. Moreover, there is a great between-subject variability in cold response. These data provide further insights on cold exposure as an anti-obesity measure.The study was funded by NIHR, BRC Seed Fund, individual grants: ML and MS: Marie Curie Fellowship, CYT: Welcome Trust Fellowship, SV: MRC, BHF and BBSRC, AVP: BBSRC.This is the final version of the article. It first appeared from Bioscientifica via https://doi.org/ 10.1530/EC-16-000

An Investigation Into the Differences in Bone Density and Body Composition Measurements Between 2 GE Lunar Densitometers and Their Comparison to a 4-Component Model

We describe a study to assess the precision of the GE Lunar iDXA and the agreement between the iDXA and GE Lunar Prodigy densitometers for the measurement of regional- and total-body bone and body composition in normal to obese healthy adults. We compare the whole-body fat mass by dual-energy X-ray absorptiometry (DXA) to measurements by a 4-component (4-C) model. Sixty-nine participants, aged 37 ± 12 yr, with a body mass index of 26.2 ± 5.1 kg/cm2, were measured once on the Prodigy and twice on the iDXA. The 4-C model estimated fat mass from body mass, total body water by deuterium dilution, body volume by air displacement plethysmography, and bone mass by DXA. Agreements between measurements made on the 2 instruments and by the 4-C model were analyzed by Bland-Altman and linear regression analyses. Where appropriate, translational cross-calibration equations were derived. Differences between DXA software versions were investigated. iDXA precision was less than 2% of the measured value for all regional- and whole-body bone and body composition measurements with the exception of arm fat mass (2.28%). We found significant differences between iDXA and Prodigy (p < 0.05) whole-body and regional bone, fat mass (FM), and lean mass, with the exception of hip bone mass, area and density, and spine area. Compared to iDXA, Prodigy overestimated FM and underestimated lean mass. However, compared to 4-C, iDXA showed a smaller bias and narrower limits of agreement than Prodigy. No significant differences between software versions in FM estimations existed. Our results demonstrate excellent iDXA precision. However, significant differences exist between the 2 GE Lunar instruments, Prodigy and iDXA measurement values. A divergence from the reference 4-C observations remains in FM estimations made by DXA even following the recent advances in technology. Further studies are particularly warranted in individuals with large FM contents.LPEW and PRM are supported by the NIHR/Wellcome Trust Clinical Research Facility Cambridge. MCV is supported by the MRC Elsie Widdowson Laboratory (Programme numbers: Physiological Modelling of Metabolic Risk, MC_UP_A090_1005, and Nutrition, Surveys and Studies, MC_U105960384)

SMIM1 absence is associated with reduced energy expenditure and excess weight

This is the author accepted manuscript.Data and code availability; Participants’ phenotypes and SMIM1 locus genotypes are accessible via the relevant cohort environments: UK Biobank (https://www.ukbiobank.ac.uk/) and MVP (https://www.mvp.va.gov/pwa/discover-mvp-data). Access to these cohorts requires an active project application. All the data generated for this study are available in an anonymized version in supplementary tables or in the Zenodo repository at: https://zenodo.org/records/10685501. The code used to analyze the cohorts is available at https://github.com/stefanucci-luca/vel_ko_analysis. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.Background Obesity rates have nearly tripled in the past 50 years, and by 2030, more than one billion individuals worldwide are projected to be obese. This creates a significant economic strain due to the associated non-communicable diseases. The root cause is an energy expenditure imbalance, owing to an interplay of lifestyle, environmental, and genetic factors. Obesity has a polygenic genetic architecture; however, single genetic variants with large effect size are aetiological in a minority of cases. These variants allowed the discovery of novel genes and biology relevant to weight regulation and ultimately led to the development of novel specific treatments. Methods We used a case-control approach to determine metabolic differences between individuals homozygous for a loss-of-function genetic variant in SMIM1 and the general population, leveraging data from 5 cohorts. Metabolic characterization of SMIM1-/- individuals was performed using plasma biochemistry, calorimetric chamber and DEXA scan. Findings We found that individuals homozygous for a loss-of-function genetic variant in the Small Integral Membrane Protein 1 (SMIM1) gene, underlying the blood group Vel, display excess body weight, dyslipidemia, altered leptin-adiponectin ratio, increased liver enzymes, and lower thyroid hormone levels. This was accompanied by a reduction in resting energy expenditure. Conclusion This research identified a novel genetic predisposition to being overweight or obese. It highlights the need to investigate the genetic causes of obesity to select the most appropriate treatment given the large cost disparity between them.National Institute for Health and Care Research (NIHR)British Heart FoundationNHS Blood and Transplan