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 (1H 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.

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.

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.

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.

SMIM1 absence is associated with reduced energy expenditure and excess weight.

BACKGROUND: Obesity rates have nearly tripled in the past 50 years, and by 2030 more than 1 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 etiological 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 the small integral membrane protein 1 (SMIM1) and the general population, leveraging data from five cohorts. Metabolic characterization of SMIM1-/- individuals was performed using plasma biochemistry, calorimetric chamber, and DXA scan. FINDINGS: We found that individuals homozygous for a loss-of-function genetic variant in SMIM1 gene, underlying the blood group Vel, display excess body weight, dyslipidemia, altered leptin to 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. FUNDING: This work was funded by the National Institute of Health Research, British Heart Foundation, and NHS Blood and Transplant.

Catch-Up Growth in Children Born Small for Gestational Age Related to Body Composition and Metabolic Risk at Six Years of Age in the UK.

OBJECTIVES: To determine differences in body composition and glucose metabolism according to childhood growth outcomes in a population-based sample of children born small for gestational age (SGA). METHODS: A single-centre study of 259 children born SGA identified through hospital records and contacted when aged 4-7 years. Questionnaire data on pre/perinatal history and growth parameters during childhood was collected from the parents, and in a subgroup of 150 children face-to-face assessments were performed, including anthropometric parameters, lean and fat mass, blood pressure, fasting glucose, and C-peptide. RESULTS: Based on the questionnaires, few children had formal clinic follow-up of growth, but 7% of the cohort showed a height and weight of <-2SDS during childhood, and only 2 children met the criteria for growth hormone therapy. Out of the 150 children assessed at a mean age of 6.1 ± 0.8 years, 122 (81%) showed a catch-up growth in weight. Compared to those without weight catch-up, these children had a higher fat mass index (3.13 ± 1.36 vs. 2.56 ± 0.91 kg/m2, p = 0.04), trunk-to-limb fat mass ratio (0.63 ± 0.14 vs. 0.56 ± 0.08, p = 0.002), systolic blood pressure SDS (0.09 ± 0.71 vs. -0.32 ± 0.63, p = 0.008), fasting glucose (4.5 ± 0.5 vs. 4.3 ± 0.5 mmol/L, p = 0.03), and C-peptide (306 ± 116 vs. 256 ± 112 pmol/L, p = 0.08). Among children with weight catch-up growth, those with less height gain had a lower limb lean mass index (4.25 ± 0.48 vs. 4.48 ± 0.56 kg/m2, p = 0.02) and fat mass index (1.57 ± 0.59 vs. 1.83 ± 0.77 kg/m2, p = 0.04). CONCLUSIONS: Within this population-based sample of SGA children, catch-up growth in weight was associated with higher abdominal fat mass, blood pressure and glycemia; furthermore, in these children, less height gain was associated with reduced limb lean and fat mass.

Quantifying energy expenditure in childhood: utility in managing pediatric metabolic disorders.

BACKGROUND: Energy expenditure prediction equations are used to estimate energy intake based on general population measures. However, when using equations to compare with a disease cohort with known metabolic abnormalities, it is important to derive one's own equations based on measurement conditions matching the disease cohort. OBJECTIVE: We aimed to use newly developed prediction equations based on a healthy pediatric population to describe and predict resting energy expenditure (REE) in a cohort of pediatric patients with thyroid disorders. METHODS: Body composition was measured by DXA and REE was assessed by indirect calorimetry in 201 healthy participants. A prediction equation for REE was derived in 100 healthy participants using multiple linear regression and z scores were calculated. The equation was validated in 101 healthy participants. This method was applied to participants with resistance to thyroid hormone (RTH) disorders, due to mutations in either thyroid hormone receptor ß or α (ß: female n = 17, male n = 9; α: female n = 1, male n = 1), with deviation of REE in patients compared with the healthy population presented by the difference in z scores. RESULTS: The prediction equation for REE = 0.061 * Lean soft tissue (kg) - 0.138 * Sex (0 male, 1 female) + 2.41 (R2 = 0.816). The mean ± SD of the residuals is -0.02 ± 0.44 kJ/min. Mean ± SD REE z scores for RTHß patients are -0.02 ± 1.26. z Scores of -1.69 and -2.05 were recorded in male (n = 1) and female ( n = 1) RTHα patients. CONCLUSIONS: We have described methodology whereby differences in REE between patients with a metabolic disorder and healthy participants can be expressed as a z score. This approach also enables change in REE after a clinical intervention (e.g., thyroxine treatment of RTHα) to be monitored.