An approach to quantifying abnormalities in energy expenditure and lean mass in metabolic disease

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.

Centile reference chart for resting metabolic rate through the life course

OBJECTIVE

Reference centile charts are widely used for the assessment of growth and have progressed from describing height and weight to include body composition variables such as fat and lean mass. Here, we present centile charts for an index of resting energy expenditure (REE) or metabolic rate, adjusted for lean mass versus age, including both children and adults across the life course.

DESIGN, PARTICIPANTS AND INTERVENTION

Measurements of REE by indirect calorimetry and body composition using dual-energy X-ray absorptiometry were made in 411 healthy children and adults (age range 6-64 years) and serially in a patient with resistance to thyroid hormone α (RTHα) between age 15 and 21 years during thyroxine therapy.

SETTING

NIHR Cambridge Clinical Research Facility, UK.

RESULTS

The centile chart indicates substantial variability, with the REE index ranging between 0.41 and 0.59 units at age 6 years, and 0.28 and 0.40 units at age 25 years (2nd and 98th centile, respectively). The 50th centile of the index ranged from 0.49 units (age 6 years) to 0.34 units (age 25 years). Over 6 years, the REE index of the patient with RTHα varied from 0.35 units (25th centile) to 0.28 units (<2nd centile), depending on changes in lean mass and adherence to treatment.

CONCLUSION

We have developed a reference centile chart for an index of resting metabolic rate in childhood and adults, and shown its clinical utility in assessing response to therapy of an endocrine disorder during a patient's transition from childhood to adult.

Resistance to thyroid hormone induced tachycardia in RTHα syndrome

Mutations in thyroid hormone receptor α1 (TRα1) cause Resistance to Thyroid Hormone α (RTHα), a disorder characterized by hypothyroidism in TRα1-expressing tissues including the heart. Surprisingly, we report that treatment of RTHα patients with thyroxine to overcome tissue hormone resistance does not elevate their heart rate. Cardiac telemetry in male, TRα1 mutant, mice indicates that such persistent bradycardia is caused by an intrinsic cardiac defect and not due to altered autonomic control. Transcriptomic analyses show preserved, thyroid hormone (T3)-dependent upregulation of pacemaker channels (Hcn2, Hcn4), but irreversibly reduced expression of several ion channel genes controlling heart rate. Exposure of TRα1 mutant male mice to higher maternal T3 concentrations in utero, restores altered expression and DNA methylation of ion channels, including Ryr2. Our findings indicate that target genes other than Hcn2 and Hcn4 mediate T3-induced tachycardia and suggest that treatment of RTHα patients with thyroxine in high dosage without concomitant tachycardia, is possible.

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 (HFFM_TBW) or by using the age and sex specific coefficients of Lohman, 1986 (HFFM_Lohman) 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. HFFM_TBW showed a much larger age/sex related variability than HFFM_Lohman. 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 HFFM_TBW or HFFM_Lohman. 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.

Distinct Basal Metabolism in Three Mouse Models of Neurodevelopmental Disorders

Prevalence of metabolic disturbances is higher among individuals with neurodevelopmental disorders (NDDs), yet this association has been largely overlooked. Investigation into human disease remains challenging, as a complete pathophysiological understanding relies on accurate modeling and highly controlled variables. Genetically engineered mouse models are widely used to gain insight into the biology of human NDDs, but research focus has been on behavioral and neurophysiological abnormalities. Such models not only allow for evaluating usefulness in reproducing human features, including similarities and discrepancies with rodent phenotypes, but they also represent a unique opportunity to observe and quantify novel anomalies. Here, we present the first characterization and comparison of basal metabolism in three mouse models of NDDs, namely, Down syndrome (DS; Dp(16)Yey/+ mice), 16p11.2 deletion syndrome (16pDel; 16p11.2^df/+ mice), and fragile X syndrome [FXS; Fmr1 knock-out (KO) mice] and their wild-type (WT) counterparts. Using the Comprehensive Lab Animal Monitoring System (CLAMS) coupled to EchoMRI, as well as quantification of key plasma metabolites by liquid chromatography mass spectrometry (LC-MS), our in vivo study reveals that each mouse model expresses a unique metabolic signature that is sex-specific, independent of the amount of food consumed and minimally influenced by physical activity. In particular, we identify striking differences in body composition, respiratory exchange ratio (RER), caloric expenditure (CE), and concentrations of circulating plasma metabolites related to mitochondrial function. Providing novel insight into NDD-associated metabolic alterations is an essential prerequisite for future preclinical and clinical interventions.

Room Indirect Calorimetry Operating and Reporting Standards (RICORS 1.0): A Guide to Conducting and Reporting Human Whole-Room Calorimeter Studies

Whole-room indirect calorimeters have been used to study human metabolism for more than a century. These studies have contributed substantial knowledge to the assessment of nutritional needs and the regulation of energy expenditure and substrate oxidation in humans. However, comparing results from studies conducted at different sites is challenging because of a lack of consistency in reporting technical performance, study design, and results. In May 2019, an expert panel was convened to consider minimal requirements for conducting and reporting the results of human whole-room indirect calorimeter studies. We propose Room Indirect Calorimetry Operating and Reporting Standards, version 1.0 (RICORS 1.0) to provide guidance to ensure consistency and facilitate meaningful comparisons of human energy metabolism studies across publications, laboratories, and clinical sites.

Descriptive epidemiology of energy expenditure in the UK: findings from the National Diet and Nutrition Survey 2008-15

Background

Little is known about population levels of energy expenditure, as national surveillance systems typically employ only crude measures. The National Diet and Nutrition Survey (NDNS) in the UK measured energy expenditure in a 10% subsample by gold-standard doubly labelled water (DLW).

Methods

DLW-subsample participants from the NDNS (383 males, 387 females) aged 4–91 years were recruited between 2008 and 2015 (rolling programme). Height and weight were measured and body-fat percentage estimated by deuterium dilution.

Results

Absolute total energy expenditure (TEE) increased steadily throughout childhood, ranging from 6.2 and 7.2 MJ/day in 4- to 7-year-olds to 9.7 and 11.7 MJ/day for 14- to 16-year-old girls and boys, respectively. TEE peaked in 17- to 27-year-old women (10.7 MJ/day) and 28- to 43-year-old men (14.4 MJ/day), before decreasing gradually in old age. Physical-activity energy expenditure (PAEE) declined steadily with age from childhood (87 kJ/day/kg in 4- to 7-year-olds) through to old age (38 kJ/day/kg in 71- to 91-year-olds). No differences were observed by time, region and macronutrient composition. Body-fat percentage was strongly inversely associated with PAEE throughout life, irrespective of expressing PAEE relative to body mass or fat-free mass. Compared with females with <30% body fat, females with >40% recorded 29 kJ/day/kg body mass and 18 kJ/day/kg fat-free mass less PAEE in analyses adjusted for age, geographical region and time of assessment. Similarly, compared with males with <25% body fat, males with >35% recorded 26 kJ/day/kg body mass and 10 kJ/day/kg fat-free mass less PAEE.

Conclusions

This first nationally representative study reports levels of human-energy expenditure as measured by gold-standard methodology; values may serve as a reference for other population studies. Age, sex and body composition are the main determinants of energy expenditure.

Key Messages

This is the first nationally representative study of human energy expenditure, covering the UK in the period 2008-2015.

Total energy expenditure (MJ/day) increases steadily with age throughout childhood and adolescence, peaks in the 3^rd decade of life in women and 4^th decade of life in men, before decreasing gradually in old age.

Physical activity energy expenditure (kJ/day/kg or kJ/day/kg fat-free mass) declines steadily with age from childhood to old age, more steeply so in males.

Body-fat percentage is strongly inversely associated with physical activity energy expenditure.

We found little evidence that energy expenditure varied by geographical region, over time, or by dietary macronutrient composition.

Estimating physical activity from self-reported behaviours in large-scale population studies using network harmonisation: findings from UK Biobank and associations with disease outcomes

BACKGROUND

UK Biobank is a large prospective cohort study containing accelerometer-based physical activity data with strong validity collected from 100,000 participants approximately 5 years after baseline. In contrast, the main cohort has multiple self-reported physical behaviours from > 500,000 participants with longer follow-up time, offering several epidemiological advantages. However, questionnaire methods typically suffer from greater measurement error, and at present there is no tested method for combining these diverse self-reported data to more comprehensively assess the overall dose of physical activity. This study aimed to use the accelerometry sub-cohort to calibrate the self-reported behavioural variables to produce a harmonised estimate of physical activity energy expenditure, and subsequently examine its reliability, validity, and associations with disease outcomes.

METHODS

We calibrated 14 self-reported behavioural variables from the UK Biobank main cohort using the wrist accelerometry sub-cohort (n = 93,425), and used published equations to estimate physical activity energy expenditure (PAEESR). For comparison, we estimated physical activity based on the scoring criteria of the International Physical Activity Questionnaire, and by summing variables for occupational and leisure-time physical activity with no calibration. Test-retest reliability was assessed using data from the UK Biobank repeat assessment (n = 18,905) collected a mean of 4.3 years after baseline. Validity was assessed in an independent validation study (n = 98) with estimates based on doubly labelled water (PAEEDLW). In the main UK Biobank cohort (n = 374,352), Cox regression was used to estimate associations between PAEESR and fatal and non-fatal outcomes including all-cause, cardiovascular diseases, respiratory diseases, and cancers.

RESULTS

PAEESR explained 27% variance in gold-standard PAEEDLW estimates, with no mean bias. However, error was strongly correlated with PAEEDLW (r = -.98; p < 0.001), and PAEESR had narrower range than the criterion. Test-retest reliability (Λ = .67) and relative validity (Spearman = .52) of PAEESR outperformed two common approaches for processing self-report data with no calibration. Predictive validity was demonstrated by associations with morbidity and mortality, e.g. 14% (95%CI: 11-17%) lower mortality for individuals meeting lower physical activity guidelines.

CONCLUSIONS

The PAEESR variable has good reliability and validity for ranking individuals, with no mean bias but correlated error at individual-level. PAEESR outperformed uncalibrated estimates and showed stronger inverse associations with disease outcomes.

Short-term Change in Resting Energy Expenditure and Body Compositions in Therapeutic Process for Graves' Disease

Objective In the medical treatment of Graves' disease, we sometimes encounter patients who gain weight after the onset of the disease. To estimate the energy required during the course of treatment when hyperthyroidism ameliorates, we measured the resting energy expenditure (REE) and body composition in patients with Graves' disease before and during treatment in the short-term. Methods Twenty patients with newly diagnosed Graves' disease were enrolled, and our REE data of 19 healthy volunteers were used. The REE was measured by a metabolic analyzer, and the basal energy expenditure (BEE) was estimated by the Harris-Benedict formula. The body composition, including body weight, fat mass (FM), muscle mass (MM) and lean body mass (LBM), were measured by a multi-frequency body composition analyzer. We tailored the nutritional guidance based on the measured REE. Results Serum thyrotropin levels were significantly increased at three and six months. Serum free thyroxine, free triiodothyronine and REE values were significantly decreased at one, three and six months. The REE/BEE ratio was 1.58±0.28 at the onset and significantly declined to 1.34±0.34, 1.06±0.19 and 1.01±0.16 at 1, 3 and 6 months, respectively. Body weight, MM and LBM significantly increased at three and six months. Conclusion The REE significantly decreased during treatment of Graves' disease. The decline was evident as early as one month after treatment. The REE after treatment was lower than in healthy volunteers, which may lead to weight gain. These data suggest that appropriate nutritional guidance is necessary with short-term treatment before the body weight normalizes in order to prevent an overweight condition and the emergence of metabolic disorders.

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 β or α (β: 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 RTHβ 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.

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

BACKGROUND

Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.

METHODS

Measurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation.

RESULTS

Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day-1 and 49.8 (16.3) kJ day-1 kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day-1 kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day-1 kg-1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day-1, r ~ 0.90).

CONCLUSIONS

In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Validity of predictive equations for resting metabolic rate in healthy humans

Background: There are several predictive equations for estimating resting metabolic rate (RMR) in healthy humans. Concordance of these equations against measured RMR is variable, and often dependent on the extent of RMR. Part of the discrepancy may be due to an insufficient accuracy of metabolic carts, but this accuracy can be improved via a correction procedure. Objective: To determine the validity of predictive RMR equations by comparing them against measured and corrected (i.e. the reference) RMR. Methods: RMR was measured, in 69 healthy volunteers (29 males/40 females; 32±8 years old; BMI 25.5±3.8 kg/m2) and then corrected by simulating gas exchange through pure gases and high-precision mass-flow regulators. RMR was predicted using 13 published equations. Bland–Altman analyses compared predicted vs. reference RMRs. Results: All equations correlated well with the reference RMR (r>0.67; P<0.0001), but on average, over-predicted the reference RMR (89–312 kcal/d; P<0.05). Based on Bland–Altman analyses, 12 equations showed a constant bias across RMR, but the bias was not different from zero for nine of them. Three equations stood out because the absolute difference between predicted and reference RMR was equal or lower than 200 kcal/d for >60% of individuals (the Mifflin, Oxford and Müller equations). From them, only the Oxford equations performed better in both males and females separately. Conclusion: The Oxford equations are a valid alternative to predict RMR in healthy adult humans. Gas-exchange correction appears to be a good practice for the reliable assessment of RMR.

Normalizing resting energy expenditure across the life course in humans: challenges and hopes

Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.

Energy expenditure in mechanically ventilated patients: The weight of body weight!

BACKGROUND & AIMS

Optimal nutritional care for intensive care unit (ICU) patients requires precise determination of energy expenditure (EE) to avoid deleterious under- or overfeeding. The reference method, indirect calorimetry (IC), is rarely accessible and inconstantly feasible. Various equations for predicting EE based on body weight (BW) are available. This study aims at determining the best prediction strategy unless IC is available.

METHODS

Mechanically ventilated patients staying ≥72 h in the ICU were included, except those with contraindications for IC measurements. IC and BW measurements were routinely performed. EE was predicted by the ESPEN formula and other predictive equations using BW (i.e. anamnestic (AN), measured (MES), adjusted for cumulated water balance (ADJ), calculated for a body mass index (BMI) of 22.5). Comparisons were made using Pearson correlation and Bland & Altman plots.

RESULTS

85 patients (57 ± 19 y, 61 men, SAPS II 43 ± 16) were included. Correlations between IC and predicted EE using the ESPEN formula with different BW (BW_AN, BW_MES, BW_ADJ, and BW_BMI22.5) were 0.44, 0.40, 0.36, and 0.47, respectively. Bland & Altman plots showed wide and inconsistent variations. Predictive equations including body temperature and minute ventilation showed the best correlations, but when using various BWs, differences in predicted EE were observed.

CONCLUSION

No EE predictive equation, regardless of the BW used, gives statistically identical results to IC. If IC cannot be performed, predictive equations including minute ventilation and body temperature should be preferred. BW has a significant impact on estimated EE and the use of measured BW_MES or BW _{BMI 22.5} is associated with the best EE prediction. Clinical trial registration number on ClinicalTrial.gov: NCT02552446. Ethical committee number: CE-14-070.