Inter-individual Variation in Posture Allocation: Possible Role in Human Obesity.

Transcription

1 Inter-individual Variation in Posture Allocation: Possible Role in Human Obesity. Supporting Online Material. James A. Levine*, Lorraine M. Lanningham-Foster, Shelly K. McCrady, Alisa C. Krizan, Leslie R. Olson, Paul H. Kane, Michael D. Jensen and Matthew M. Clark. Endocrine Research Unit, Mayo Clinic, Rochester, MN, USA. *To whom correspondence should be addressed. 1

2 1. Subjects Subjects for weight maintenance NEAT (Fig. 1). Twenty healthy, sedentary volunteers were recruited aged (mean + SD) years. Ten subjects (5 females and 5 males) were lean (BMI<25 kg/m 2 ) and ten subjects (5 females and 5 males) were obese (BMI kg/m 2 )(Table S1). All the subjects worked in sedentary jobs, 18 were white, 1 black and 1 Hispanic and the majority of the subjects were middle socioeconomic class and lived within 10 miles of the GCRC. None of the subjects worked in our laboratories or on the study team. Subjects were excluded if they used any medication at the time of the study or within six months of prior to the study, exercised more than twice each week, smoked, used alcohol, were pregnant, had any acute or chronic illness, failed psychological evaluation for depression, eating disorder or reported mental illness or had unsteady body weight (>2 kg fluctuation over the six months prior to study). Subjects for weight perturbation experiments (Fig. 2). To understand whether obese people would adjust their postural allocation with weight loss, we recruited 7 obese (defined as a BMI >30 kg/m 2 ), sedentary subjects (4 females; 3 males; years; BMI, kg/m 2 ). These were subjects 11, 12, 13, 14, 15, 16 and 18 in Table S1. To understand whether lean individuals would adjust their postural allocation with weight gain, we recruited 10 lean, sedentary individuals (6 females; 4 males; years, BMI kg/m 2 ). All the lean subjects from Table S1, except Subject 7, plus a 28 year old white female, BMI 24 kg/m 2 were recruited. Subject 7 was initially enrolled but was unable to follow the weight gain protocol. 2

3 2. Study Design and Methods Study design for weight maintenance NEAT (the data set shown in Fig. 1). The twenty subjects were initially studied as outpatients for three weeks. Meals were prepared in the metabolic kitchen at the Mayo Clinic General Clinical Research Center (GCRC). All foods were weighed to within 1g. Volunteers were fed so as to establish the dietary intake necessary to maintain steady-state body weight. The diet composition was 45 % carbohydrate, 35 % fat and 20 % protein. The volunteer s body weight was measured each morning under standardized conditions (with an empty bladder, without shoes and wearing consistent, light clothing); these measures were made by trained GCRC personnel using the same calibrated scale (ScaleTronix 5005; S/N , Wheaton, IL). Subjects were instructed not to adopt new exercise practices and to continue their usual daily activities and occupation. Informed written consent was obtained after the nature and possible consequences of the study were explained and the study was approved by the Mayo IRB. Measurements of body composition. Each volunteer was weighed daily as described above. Body fat and mineral mass were measured in duplicate using Dual X-ray Absorptiometry (DXA) (Lunar, Madison, WI) after the three weeks of baseline feeding. To ensure that our measures of body composition were reproducible and precise: (a) we used the same DXA scanner throughout the study, (b) we calibrated the DXA scanner before each measurement with tissue phantoms, and (c) we calibrated the DXA scanner against tissue blocks of known composition weekly. Fat-free mass was calculated from the difference between body weight and fat mass. The test-retest difference for duplicate measurements was <2 %. Data for the subjects are shown in the Table S2. 3

4 Measurements of basal metabolic rate (BMR). BMR was measured on three consecutive mornings (Days 21, 22, 23) at 06:30 in subjects who had slept uninterrupted the previous nights in the GCRC (subjects were admitted to the GCRC on the evening of Day 20). Subjects were not moved prior to measurements and had not eaten since 21:00 the night before. For each measurement the calorimeter (Columbus Instruments, Columbus, IH) was calibrated using gases of known composition. Subjects were awake, semi-recumbent (10 o head bed tilt), lightly clothed and in thermal comfort (68 to 74 o F) in a dimly lit, quiet room. Measurements were performed for 30 min during which time subjects were not allowed to talk or move. The test-retest difference for duplicate measurements was <2 %. Weekly alcohol burns for the calorimeters averaged 99%. Data for the subjects are shown in the Table S2. Measurements of thermic effect of food (TEF). Postprandial thermogenesis was measured on Day 23 after the measurement of BMR. Subjects were given a meal that provided one third of their daily intake (45 % carbohydrate, 35 % fat, 20 % protein). Energy expenditure was measured using the indirect calorimeter for 15 of every 30 minutes (to prevent agitation) for 450 minutes. The area under the curve was calculated and tripled to represent daily TEF. Data for the subjects are shown in the Table S2. Measurements of total daily energy expenditure (TDEE). TDEE was measured for Days using doubly labeled water (S1-S3). Baseline urine samples were collected and after timed administration of the isotopes, urine samples were collected at 07:00, 12:00, and 18:00 each day for 10 days. Measures of TDEE derived using doubly labeled water were in excellent agreement with the measures of baseline weight-maintenance dietary intake (r 2 =0.9, P<0.001, with an 4

5 intercept not different from zero and a slope not different than one). Total NEAT was calculated for Days using the equation; NEAT = TDEE (BMR + TEF). Data for the subjects are shown in the Table S2. Measurement of total-daily-body posture and movement. We used a physical activity monitoring system (PAMS) that we had devised and validated (S4-S6). This system is unique because it captures data on body posture and movement in duplicate continuously every half-second for 10 days. PAMS included six sets of sensors (Fig. S4), four inclinometers (each of which captures two axes of acceleration against the earth s gravitational field, CXTA02, Crossbow Technology Inc., San Jose, CA) and two triaxial accelerometers (each captures motion in x, y and z axes, CXL02LF3-R, Crossbow Technology Inc). The 14 axes of data were binned and stored every half-second on two data loggers (Ready DAQ AD2000, Crossbow Technology Inc). The inclinometers were attached to the right and left outer aspect of the trunk and right and left outer aspect of the thigh. The two accelerometers were placed over the base of the spine. Specially designed underwear was used to attach the sensors. The two data loggers were stored in a pouch worn around the waist. The PAMS weighed <1kg. Every 24 hours, study staff removed the sensors while the subject showered for 15 minutes. During this time, data from the data loggers were downloaded to a personal computer and analyzed using Excel programs (Microsoft, Seattle, WA). The time taken showering was taken to represent standing for this period of time. In this fashion body position and motion were calculated times over 10 days; representing 483,840,000 data points for the 20 subjects. All the subjects tolerated the sensor load and study protocol. Subjects continued their normal 5

6 occupations, hobbies and other day-time and night-time activities. Sensor determination of body posture using the sensor system was correct for 700/700 measurements of posture compared to written responses by two independent investigators after video tape review. We test each PAMS sensor daily using electronic testing equipment we have devised. We also test the PAMS daily on each subject for posture and for graded ambulation at 0, 1, 2, 3 mph (~10 minute protocol). There were log linear relationships between accelerometer output and velocity with r 2 >0.98 in all cases. The relationship between the paired accelerometers showed an Intraclass Correlation Coefficient (ICC) of Combining energy expenditure measurements with total-daily body posture and movement to define the energetic components of NEAT. On Days 21 and 22, subjects were admitted to the GCRC overnight (from the night of Day 20). After the measurements of BMR were completed as described above, energy expenditure was measured while subjects wore their PAMS sensors and either sat motionless, stood motionless, moved between different postures and walked at 1, 2 and 3 mph on a calibrated treadmill. The measurements of energy expenditure during the different postures allowed free-living postural energy expenditure to be calculated. The regression equations between velocity, energy expenditure and accelerometer output allowed free-living ambulatory accelerometer output to be translated into energy expenditure (S4-S6). These factorial determinations of NEAT showed a linear positive relationship with total NEAT calculated from doubly labeled water (corrected ICC=0.90, P<0.001); the slope and intercept were not significantly different from the line of identity (Fig. S3). 6

7 Study design for weight loss and weight gain experiments. For the obese subjects, having established weight maintenance energy needs, each subject underwent intensively supervised caloric restriction by 1000 kcal per day below their weight-maintenance needs of, kcal/day. All meals were provided on our GCRC and subjects were seen daily by nursing staff, study staff and/or by a psychologist. Postural allocation was measured twice per second for 10 days before (baseline) and for the last 10 days of caloric restriction. Postural data were analyzed on 24 million data points. For the lean subjects, having established weight maintenance energy needs, we overfed these individuals by 1000 kcal/day excess for eight weeks. All meals were provided by our GCRC. We measured posture allocation twice per second of every day for 10 days before (baseline) and at the end of overfeeding. We captured a total of 420 million points of data on posture for 10 subjects. Informed written consent was obtained after the nature and possible consequences of the study were explained and the study was approved by the Mayo IRB. Data analysis and statistics. Postural allocation was calculated for each subject as the number of minutes spent lying, sitting and standing/ambulating. To address our primary hypothesis that standing/ambulating posture is greater in lean compared to obese subjects a 2-tailed unpaired t-test was used assuming normal distribution. To address our primary hypothesis that sitting posture is greater in obese compared to lean subjects a 2-tailed unpaired t-test was used assuming normal distribution. To address our secondary hypothesis that lying time was greater in the obese compared to the lean subjects a 2-tailed unpaired t-test was used assuming normal 7

8 distribution. To compare NEAT from doubly labeled water and PAMS, the Intraclass Correlation Coefficient was calculated. Regression analyses were used elsewhere as indicated. Statistical significance was defined as P<0.05. The Excel macros (programs) used for data analysis are available from 8

9 3. Supporting Figures Accelerometer output (AU/day) Fat-free mass (kg) Fig. S1. Fat-free mass versus accelerometer output. Open diamonds are data from females and filled diamonds from males. Accelerometer output (AU/day) R 2 = NEAT from Doubly Labeled Water/weight (kcal/kg/day) Fig. S2. Non-exercise activity thermogenesis (NEAT) by doubly labeled water, adjusted for weight, versus accelerometer output. Open diamonds are data from females and filled diamonds are data from males NEAT instruments (Kcal/day) NEAT doubly labeled water (kcal/day) A I Fig. S3. The relationship between Non-exercise activity thermogenesis (NEAT) determined from doubly labeled water versus NEAT measured using the instruments. Open diamonds are data from females and filled diamonds from males. The uncorrected Intraclass Correlation Coefficient (ICC) is and the corrected ICC is Fig. S4. Harnesses for Accelerometers (A) and Inclinometers (I). The harnesses are worn as underwear. 9

10 4. Supporting tables Subject Gender Age (years) BMI (kg/m 2 ) 1 F F M M F F M F M M M F M M F F M F F M Table S1: Demographic details of participants. 10

11 Lean (n=10) Obese (n=10) Age (years) Weight (kg) * BMI (kg/m 2 ) * Fat mass (kg) * Fat-free mass (kg) BMR (kcal/day) * TEF (kcal/day) NEAT (kcal/day) Table S2. Body composition and energy expenditure for the lean and obese subjects. Fat mass and fat free mass were determined from Dual X-ray Absorptiometry. Basal metabolic rate (BMR) is the energy expended when an individual is laying at complete rest, in the morning, after sleep, in the postabsorptive state (S7,S8). Thermic effect of food (TEF) is the increase in energy expenditure associated with the digestion, absorption, and storage of food (S9-S12). BMR and TEF were measured using indirect calorimetry. The BMR value is the mean of three measurements. Non-exercise activity thermogenesis (NEAT) was measured using doubly labeled water. The relationship between NEAT measured using doubly labeled water and the NEAT instruments is shown in Fig. S3. Data are shown as mean + standard deviation. * Represents a significant difference between the groups, P<

12 Supporting references and notes S1. W. A. Coward, Proc. Nutr. Soc. 47, 209 (1988). S2. D. A. Schoeller, C. A. Leitch, C. Brown, Am. J. Physiol. 251, R1137 (1986). S3. J. R. Speakman, Am. J. Clin. Nutr. 68, 932S (1998). S4. J. A. Levine, P. A. Baukol, K. R. Westerterp, Med. Sci. Sports. Exerc. 33, 1593 (2001). S5. J. Levine, E. L. Melanson, K. R. Westerterp, J. O. Hill, Am. J. Physiol. 281, E670 (2001). S6. J. A. Levine, E. L. Melanson, K. R. Westerterp, J. O. Hill, Eur. J. Clin. Nutr. 57, 1176 (2003). S7. O. Deriaz, G. Fournier, A. Tremblay, J. P. Despres, C. Bouchard, Am. J. Clin. Nutr. 56, 840 (1992). S8. L. E. Ford, Am. J. Physiol. 247, H495 (1984). S9. J. O. Hill, M. DiGirolamo, S. B. Heymsfield, Am. J. Physiol. 248, E370 (1985). S10. D. A. Alessio et al., J. Clin. Invest. 81, 1781 (1988). S11. J. L. Kinabo, J. V. Durnin, Br. J. Nutr. 64, 37 (1990). S12. G. W. Reed, J. O. Hill, Am. J. Clin. Nutr. 63, 164 (1996). 12