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High Respiratory Quotient Is Associated with Increases in Body Weight and Fat Mass in Young Adults

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High Respiratory Quotient Is Associated with Increases in Body Weight and Fat Mass in Young Adults European Journal of Clinical Nutrition (2016) 70, 1197–1202 © 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 0954-3007/16 www.nature.com/ejcn ORIGINAL ARTICLE High respiratory quotient is associated with increases in body weight and fat mass in young adults RP Shook1, GA Hand2, AE Paluch3, X Wang3, R Moran4, JR Hébert4,5,6, JM Jakicic7 and SN Blair3,4 BACKGROUND/OBJECTIVES: Metabolic disturbances, such as reduced rates of fat oxidation (high respiratory quotient (RQ)) or low energy expenditure (low resting metabolic rate (RMR)), may contribute to obesity. The objective was to determine the association between a high RQ or a low RMR and changes in body weight and body composition over 1 year. SUBJECTS/METHODS: We measured RQ and RMR in 341 adults using indirect calorimetry, along with body weight/body composition using dual-energy X-ray absorptiometery, energy expenditure using an arm-based activity monitor and energy intake using dietary recalls. Participants were classified into low, moderate or high RQ and RMR (adjusted for age, sex, race and body composition) groups according to tertiles by sex. Follow-up measurements were completed every 3 months. RESULTS: Individuals with a high RQ had larger gains in body weight and fat mass compared with individuals with a low/moderate RQ at month 3, and increases in fat mass were more than double among individuals with a high RQ at 12 months (1.3 ± 3.0 vs 0.6 ± 3.7 kg, P = 0.03). Individuals with a low RMR did not gain more body weight nor fat mass compared with individuals with a moderate/high RMR. CONCLUSION: The primary finding is a high RQ is predictive of gains in body weight and fat mass over a 12-month period among young adults, with changes occurring as soon as 3 months. In addition, a low RMR was not associated with gains in body weight or fat mass over the same period. European Journal of Clinical Nutrition (2016) 70, 1197–1202; doi:10.1038/ejcn.2015.198; published online 25 November 2015 INTRODUCTION studies suggest that low RMR is predictive of subsequent weight 3,7–10 1,2,5 At the most basic level, obesity is the result of a chronic imbalance gain, whereas others do not. between energy intake and energy expenditure. However, the Thus, the purpose of the present study was to explore the exact etiology is considerably more complex and may involve a longitudinal associations of RQ and RMR on changes in body variety of physiological and behavioral factors. Metabolic dis- weight and body composition over 12 months in a group of healthy young adults. We also aimed to document temporal turbances, including reduced fat oxidation and reduced resting changes in body composition, RQ and RMR through rigorous and metabolic rate (RMR), have been identified as possible predictors repeated measures of each over 1 year. of changes in body weight and body composition. Respiratory quotient (RQ) reflects the ratio of carbohydrate to fat oxidation; when measured in a fasting state stored fat is the MATERIALS AND METHODS primary fuel source. If an individual has a low RQ, she/he oxidizes This manuscript reports findings from The Energy Balance Study, which has more stored fat at rest compared with an individual with a high been described in detail previously.11 Briefly, participants were healthy RQ and theoretically is protected against future fat accumulation. young adults aged ⩾ 21 and ⩽ 35 years and with a BMI ⩾ 20 and ⩽ 35 kg/ A relatively small body of research exists on the prospective m2. All study protocols were approved by the University of South Carolina relationship between RQ and weight gain, with early studies from Institutional Review Board, and informed consent was obtained from each two decades ago suggesting a positive association,1–3 but results participant before data collection. 4,5 A dual energy X-ray absorptiometer was used to measure bone mineral from more recent studies mixed. density, fat mass (FM) and fat-free mass (FFM). The scan was completed RMR, the amount of calories burned performing normal with a Lunar DPX system (version 3.6; Lunar Radiation Corp, Madison, WI). physiological functions (for example, respiration, brain activity), All anthropometric measurements were completed once every 3 months represents the largest contributor (60–80%) of total energy for the duration of the study. In addition, self-reported body weight expenditure in humans. Given the intricate balance of energy 12 months before baseline and self-reported weight gain 44.5 kg (10 intake and expenditure in the regulation of body weight, it is pounds) over the 3 months before baseline also were recorded. RQ and RMR were measured at baseline via indirect calorimetry using a hypothesized that small changes in RMR could result in a large 6 ventilated hood and an open-circuit system, TrueOne 2400 Metabolic reduction in the number of calories burned over time; however, Measurement Cart (ParvoMedics, Salt Lake City, UT, USA), following a this relationship is uncertain. For example, some prospective standardized protocol.12,13 Briefly, a 15-min resting period preceded 1Department of Kinesiology, Iowa State University, Ames, IA, USA; 2School of Public Health, University of West Virginia, Morgantown, WV, USA; 3Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; 4Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; 5Department of Family and Preventive Medicine, University of South Carolina, Columbia, SC, USA; 6South Carolina Statewide Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA and 7Department of Health and Physical Activity, Physical Activity and Weight Management Research Center, University of Pittsburgh, Pittsburgh, PA, USA. Correspondence: Dr RP Shook, Department of Kinesiology, Iowa State University, 241 Forker Building, Ames, IA 50011, USA. E-mail: [email protected] Received 3 January 2015; revised 1 October 2015; accepted 8 October 2015; published online 25 November 2015 Fat oxidation, metabolic rate, weight gain RP Shook et al 1198 a 30-min data-collection period; the metabolic cart was calibrated before (46 years using NDSR) registered dietitians employing a multi-pass each test using known gas concentrations and volumes as recommended approach, which utilizes prompting to reduce food omissions and by the manufacturer; all measurements occurred in the morning standardizes the interview methodology across interviewers.23 Interviews (o0900 hours) following a 12-h dietary fasting state and at least 24 h occurred on randomly selected non-consecutive days over 14 days to after the last bout of any structured exercise; participants remained quiet minimize preparation that could bias recall.24 As described above, and still in the supine position throughout the entire procedure and were measurement of energy intake was completed once every 3 months. kept awake with continuous monitoring; and the room was maintained in Participant characteristics were based on demographic and physiologi- low light, noise was kept at a minimum and the temperature was maintained cal measurements using means and s.d.’s for continuous variables and at a constant 26–30 °C. RMR was determined as the average value of 10 percentages for categorical variables. Statistical significance for compar- consecutive minutes with the lowest coefficient of variation. RMR was ison between groups was tested using t-tests for continuous variables and calculated from O2 consumption and CO2 production as measured chi-square tests for categorical variables. Pearson’s correlation coefficients continuously during the testing period with a constant airflow rate into were calculated between the continuous variables and respiratory 12,14,15 the hood; RQ was calculated as VCO2/VO2. Given no widely accepted quotient. A linear mixed models regression random-intercept growth criteria exist to categorize RQ levels, participants were classified as ‘high’ if model was used to analyze the longitudinal data25 for changes in first total they were in the upper tertile for RQ among the entire cohort by sex (Males, body weight, followed by FM. Statistical adjustments were made for RQ ⩾ 0.807; Females, RQ ⩾ 0.805) or ‘Low/Moderate’ if they were in the potential confounders (sex, age, race and change in MVPA, energy intake bottom two tertiles at baseline. This process was repeated for RMR (adjusted and energy expenditure). All computations were performed using SAS 9.3 for age, sex, race, FFM and FM), with participants classified as ‘low’ if they (Cary, NC, USA). Because of differences in sample size between the groups, were in the bottom tertile for RMR among the entire cohort by sex (Males, a post hoc power analysis was performed using G*Power 3 (Germany), RMR ⩽ 1618.4; Females, RMR ⩽ 1275.7) or ‘moderate/high’ if they were in the which yielded a power of 0.88 to detect F-test differences between groups upper two tertiles at baseline. In addition, we estimated RMR based on two based on an effect size of 0.50. widely cited prediction equations: Harris–Benedict16 and Mifflin–St. Jeor.17 We repeated the primary analysis using other classification options (groups based on tertiles, quintiles and so on), and the results were similar to the two- RESULTS fi group analysis. Cardiorespiratory tness (CRF) testing was conducted at Selected demographic and anthropometric variables are reported baseline on a treadmill (Trackmaster 425, Carefusion, Newton, KS, USA) with respiratory gases sampled using a TrueOne 2400 Metabolic Measurement overall and for each RQ group in Table 1 (by the RMR group in Cart (ParvoMedics, Salt Lake City, UT, USA) using a Modified Bruce protocol, Supplementary Table 1). Overall, our participants (n = 419) were and all participants exercised to volitional fatigue. young adults (27.6 ± 3.8 years) with nearly equal numbers of males Energy expenditure was estimated using an arm-based monitor (48.7%) and females (51.3%), with no difference in body weight or (SenseWear Mini Armband, BodyMedia Inc.
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