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Does Physical Activity Energy Expenditure Explain the Between-Individual Variation in Plasma Leptin Concentrations after Adjusting for Differences in Body Composition?

University of Cambridge, Departments of Public Health and Primary Care (P.W.F., J.L., N.J.W.), Clinical Biochemistry (S.F., I.H., S.O.) and Medicine (S.F., S.O.), Cambridge, United Kingdom; and Department of Mathematics (M.-Y.W.), Hong Kong University of Science and Technology, Hong Kong

Address all correspondence and requests for reprints to: Dr. Paul W. Franks, Institute of Public Health, University of Cambridge, Robinson Way, Cambridge CB2 2SR, United Kingdom. E-mail: pwf23{at}medschl.cam.ac.uk.

	Abstract

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Leptin is secreted by adipose tissue and acts upon receptors located in the hypothalamus to modify energy balance. Investigations of the relationship between leptin and physical activity energy expenditure (PAEE) at population level are scarce. The majority of studies addressing this topic are limited by their measurement of PAEE (i.e. questionnaires or ecological comparisons between rural and urban ethnic groups). To our knowledge, no studies have directly examined the relationship of objectively assessed PAEE and leptin in a large free-living population-based cohort. Therefore, we measured fasting plasma leptin and insulin concentrations, cardiorespiratory fitness (O_2max.pred), PAEE, and body composition in 758 Caucasian people (aged 40–65 yr). In sex-combined multiple regression analyses, leptin was significantly associated with PAEE (ß = -0.19, P = 0.0027), but not with O_2max.pred (ß = -0.0002, p = NS). The association between PAEE and leptin was significant in men when adjusted for percentage of body fat (ß = -0.28, P = 0.004) but not women (ß = -0.12, P = 0.18) but was significant in both men and women when adjusted for body mass index (men: ß = -0.28, P = 0.005; women: ß = -0.23, P = 0.01; combined: ß = -0.26, P = 0.00008). These data suggest the existence in this population of an independent inverse association between PAEE and fasting plasma leptin level.

	Introduction

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OBESITY RESULTS FROM a combination of adverse genetic and environmental factors and is associated with a wide range of common diseases including coronary heart disease, diabetes, stroke, and cancer (1, 2). The hormone leptin is known to be involved in the maintenance of energy homeostasis through appetite and neuroendocrine adaptations. It is secreted by adipose tissue and acts upon receptors in the hypothalamus to suppress appetite and increase metabolic rate in rodents (3). In theory, this circuit acts as a self-regulatory feedback loop, whereby the development of excess adipose mass leads to an increased leptin secretion, which signals a decreased food intake and increased basal energy expenditure, thus averting the onset of obesity. However, the infusion of exogenous leptin is not as effective in obese subjects with endogenous leptin as it is in obese subjects who are leptin deficient (4, 5).

Although there is a high degree of correlation between total adiposity and leptin levels, at population level there is considerable inter-individual variability in this relationship (6, 7). Hence, many people with normal levels of leptin are overweight and obese. This indicates that some individuals are less sensitive to the moderating effects of leptin on adiposity than are others. The mechanisms underlying this variability are unclear but may involve other modifiable metabolic parameters such as maximum oxygen uptake (O_2max) and physical activity energy expenditure (PAEE).

Evidence supporting an independent association between PAEE and leptin level is equivocal. This is due to the small number of studies that have attempted to investigate this association (8, 9, 10, 11, 12, 13), and the imprecise measurement of energy expenditure that is a characteristic of most of these studies (8, 9, 10, 11, 12). The latter is likely to be the product of the difficulties faced in accurately measuring PAEE in free-living populations.

In this study, we objectively investigated the role energy expenditure and predicted O_2max play in modifying the relationship between leptin and obesity in a sub-sample of the Ely study (n = 758), an ongoing investigation into the etiology and pathogenesis of type 2 diabetes and related endpoints in more than 1122 men and women from Cambridgeshire, UK.

	Materials and Methods

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The Ely Study

Selection of the subjects and metabolic tests. The volunteers were all participants in the Medical Research Council Ely Study, a continuing population-based cohort study in Ely, Cambridgeshire, UK, the design of which has been described previously (14, 15). The original sample of 1122 individuals without known diabetes were recruited between 1990 and 1992 at random from a population-based sampling frame consisting of all people in the city of Ely aged 40–65 yr in 1990 (15). The initial response rate was 74%. Fifty-one individuals were found to have prevalent but undiagnosed diabetes (15). Between 1994 and 1997, a second examination was performed at a 4.5 yr interval in all those individuals who did not have diabetes by WHO criteria at baseline (n = 1071). Twenty subjects had died in the interim, and 937 of the remaining volunteers attended for the second examination (89% restudy rate) (16). Of these, leptin and energy expenditure data were available for a total of 758 people. These individuals constituted the sample for this particular study.

At each phase of the study, volunteers attended the clinic at 0830 h, having fasted since 2200 h the previous evening and underwent a standard 75 g oral glucose tolerance test (OGTT). Blood samples were taken at fasting, and 30 and 120 min following oral glucose. Plasma specific insulin was determined by two-site immunometric assays with either ¹²⁵I or alkaline phosphatase labels. Cross-reactivity was less than 0.2% with intact proinsulin at 400 pmol/liter and less than 1% with 32–33 split proinsulin at 400 pmol/liter. Interassay coefficients of variation were 6.6% at 28.6 pmol/liter (n = 99), 4.8% at 153.1 pmol/liter (n = 102), and 6.0% at 436.7 pmol/liter (n = 99), respectively. Body fat percentage was calculated using a standard impedance technique (Bodystat, Isle of Man). Height and weight were measured in light clothing. Body circumferences were measured in duplicate using a metal tape. Ethical permission for the study was granted by the Cambridge Local Research Ethics Committee.

Assessment of resting and exercise oxygen consumption-heart rate (HR) relationship. The protocol for undertaking the individual calibration between HR and energy expenditure has been reported previously (14, 17, 18). This method correlates to a high degree with energy expenditure assessed using the gold standard techniques of doubly labeled water (DLW) and whole body calorimetry (19, 20). The oxygen consumption-HR relationship was assessed at rest with the subject lying and then seated, using an oxygen analyzer calibrated daily using 100% nitrogen and fresh air as standard gases. Subjects bicycled on a cycle ergometer at several different workloads to provide the slope and the intercept of the line relating energy expenditure to HR. Each subject cycled at 50 revolutions per minute and the workload was progressively increased from 0 W, through 37.5 W, 75 W, and 125 W in stages each lasting 5 min. At each workload, three separate readings were made of HR, minute volume, and expired air oxygen concentration. The 125 W level was only undertaken if the HR had not reached 120 beats per minute by the end of the 5 min at 75 W. The oxygen concentration in the expired air and minute volume data were used to calculate oxygen consumption after correction for standard temperature and pressure. Energy expenditure (kilojoules/minute) was calculated at each time point as oxygen consumption (milliliters/minute) x 20.35. Mean resting energy expenditure was taken as the average of the lying and sitting values. The slope and intercept of the least squares regression line of the exercise points were calculated. Flex HR was calculated as the mean of the highest resting pulse rate and the lowest on exercise. This point was used in the analysis of HR data to discriminate between rest and exercise. Below this point, energy expenditure was assumed to be equivalent to rest. Energy expenditure above this level was predicted from the slope and intercept of the regression line calculated during the exercise test. Predicted O_2max (O_2max.pred) was measured from the linear regression as predicted oxygen consumption at maximum HR (220-age) and is expressed in the results as an absolute volume per minute. The volunteers wore the HR monitor (Polar Electro, Kempele, Finland) continuously during the waking hours over the following 4 d. HR readings were directly downloaded into a computer via a serial interface, and the individual calibration data were used to predict minute energy expenditure for each person. Sleeping energy expenditure was calculated as 95% of basal metabolic rate (BMR) where this was derived from published prediction equations (21, 22). Physical activity energy expenditure (PAEE), which is the ratio of total energy expenditure to BMR, was computed for each day and averaged over the 4-d period.

Assessment of fasting plasma leptin concentration. Plasma leptin was measured using a dissociation-enhanced lanthanide fluoroimmunoassay in-house two-site immunometric assay using commercially available antibodies (R&D Systems Ltd., Abingdon, U.K.). The analytical sensitivity was 0.1 ng/ml, intraassay coefficient of variation was 4.4% or better across the range 0.7–125 ng/ml. Between batch coefficients of variation are 7.1% at 2.7 ng/ml, 3.9% at 14.9 ng/ml, and 5.7% at 54.9 ng/ml, respectively (n = 30). Once blood was collected, it was centrifuged, aliquoted, and then placed in cold storage at -80 C.

Statistical analyses

The means and SDs of anthropometric, biochemical, and physical activity data were calculated by sex. Those variables that were not normally distributed were normalized by logarithmic transformation and are presented in the results as geometric means. Pearson correlation coefficients were computed for all variables against leptin, and between PAEE and percentage of body fat, and PAEE and maximum oxygen uptake. Two-tailed independent samples t tests were performed to detect differences between sexes for each variable. Multiple linear regression analyses were performed using SPSS, Inc. (release 10.0.5) to assess the relationship of leptin with PAEE, O_2max.pred and with resting energy expenditure. Leptin was treated as a predictor variable when examining its relationship with resting energy expenditure (REE), whereas it was treated as an outcome variable in the analyses involving PAEE and O_2max.pred. All analyses were adjusted for age and percentage of body fat [or body mass index (BMI) in separate model], and fasting insulin. Because leptin level differed markedly between men and women after adjustment for body composition, all analyses were stratified by, or adjusted for, sex.

	Results

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Table 1 shows the characteristics of the 325 men and 433 women who took part in this study. Men and women were of similar age. Women had higher fasting insulin (P < 0.05), percentage of body fat, and leptin levels (P < 0.001), whereas men had higher levels of PAEE, higher REE, and higher predicted maximum oxygen uptake (O_2max.pred) (P < 0.001). The mean anthropometric characteristics were comparable to values observed in nationally representative samples, suggesting that this cohort is not selected on the basis of degree of overweight (23). The mean values for PAEE and O_2max.pred were both significantly greater in men. Comparison with national cohorts to determine the magnitude of possible selection bias is not simple for energy expenditure as there is relatively little comparable data at a population level. The values for O_2max.pred are slightly lower than those observed in participants in the Allied Dunbar National Fitness Survey for people of this age (24).

View larger version (23K): [in this window] [in a new window]   FIG. 1. Geometric mean plasma leptin levels (upper 95% confidence limit of the mean) stratified by above (women, white solid bars; men, horizontal stripes) and below (women, black solid bars; men, diagonal stripes) the sex-specific median for physical activity energy expenditure and by quartile of body fat percentage for men, n = 325; women n = 433.

	Discussion

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Even with objective assessment, physical activity is measured with considerably more error than oxygen uptake (25). Therefore, the association of PAEE and leptin is likely to be an underestimate relative to that of maximum oxygen uptake and leptin. This study provides new information about the relationship of PAEE and predicted maximum oxygen uptake with leptin. These findings are different to some others published to date on this topic, perhaps as a reflection of the different approach to measuring PAEE in this study.

Although we included potential confounding factors such as fasting insulin, body composition, age, and sex, in our models, our assessment of obesity through bioimpedence lacks precision compared with dual energy x-ray absorptiometry or other densitometry techniques, which are sometimes employed in small-scale studies. It is possible that residual confounding by adipose mass may persist between PAEE and leptin. However, measurement error such as this is unlikely to be substantive and may be less than in studies where only BMI has been employed as a measure of obesity. The allocation of days on which volunteers attended our laboratory for the assessment of PAEE was random and involved week and weekend days. We have previously demonstrated that there is no overall difference in energy expended on week and weekend days (14). In view of these points, it is unlikely that selection bias could explain our results.

Large-scale population-based data on objectively assessed PAEE are scarce. Absolute PAEE in the present study is slightly higher than that reported in some studies using the DLW method (26, 27, 28). This may be due to the calculation of PAEE in the present study and especially the energy expenditure estimate below the flex HR (i.e. during rest). However, the important issue is the relative value of PAEE within the population. The flex HR method has been thoroughly validated against the DLW method (20, 29, 30, 31) without any significant associations between the mean of the methods and the difference between the methods when using a Bland-Altman approach. This indicates that total energy expenditure predicted from flex HR is not heteroscedastic. Thus, it seems unlikely that high PAEE values observed in this study, as compared with those observed in other DLW studies, would bias the leptin-PAEE association observed in the present study.

The use of bicycle ergometry in the calibration of HR against exercising energy expenditure may result in a different slope in obese compared with nonobese subjects when the same relationship is considered during load-bearing activities. However, it remains unclear whether full load-bearing activities (e.g. walking) or partial load-bearing activities (e.g. cycling) are better for the purpose of calibrating HR against oxygen uptake, since both have there advantages and disadvantages. Furthermore, the possibility that estimates of PAEE may differ between obese and nonobese people is unlikely to be a major issue in populations where body composition is relatively homogeneous, as in the Ely cohort. Therefore, the use of cycling in the calibration phase is unlikely to substantially bias our results.

This present study is based on a cohort of middle-aged Caucasian men and women recruited through a general practice register, which at the time of initial sampling held information relating to virtually all the 90,000 inhabitants of the city of Ely. People were selected at random from the register and invited to take part in this study. The response rate to this invitation was high (89%), and the characteristics of people who took part in this study are similar to those reported in other nationally representative samples. These points suggest that selection bias is unlikely to substantially affect the generalizability of our findings to other UK populations.

Previous studies that have examined the relationship of physical activity with leptin level have almost exclusively focused on the comparison of pre- and post-exercise intervention leptin levels. Some of these studies have reported that exercise brings about a decline in leptin level, (32, 33), whereas others have found no effect (34, 35, 36, 37). In this study we were interested in how the level of objectively measured PAEE related to leptin. The reason most studies involving exercise interventions may not adequately address the nature of this relationship is because people who adhere to exercise interventions may compensate by decreasing other forms of energy expenditure (38). Moreover, pre- and post-intervention differences in leptin levels may also be due to factors that are not related to fitness or activity, which were not necessarily satisfactorily dealt with by post hoc adjustment in the majority of existing studies. Although few exercise intervention trials have been designed to address these issues, data from some trials strongly support the view that aspects of physical activity are important in leptin modulation. For example, in an exercise intervention trial conducted in obese males where energy balanced was maintained through dietary restriction, exercise training brought about a decrease in plasma leptin levels independently of alterations in body composition (39). Nonetheless, the question of whether habitual energy expenditure is important in the modulation of leptin thus far remains unresolved.

We are only aware of a small number of studies that have attempted to examine the relationship between PAEE and leptin levels within free-living populations. Two of these were ecological comparisons of rural and urban populations in distinct ethnic groups, (8, 9), and two employed unvalidated questionnaires to access PAEE (10, 11). A third study, in which subjective assessment of PAEE was employed, used the Paffenbarger questionnaire (12). In the ecological studies, one found a positive association with urban residence and leptin levels, (9), whereas the other found a positive association with rural residence (8). Neither study adjusted for differences in energy intake and proportion of substrate consumed between the different populations. Therefore, it is unclear whether PAEE, energy balance, or nutritional factors was the primary determinant of leptin levels. In the studies where PAEE was assessed through questionnaire, one found no association with leptin level, (11), whereas the remaining two studies reported an inverse association (10, 12). In these examples, the effect of measurement error and response bias prohibit a meaningful understanding of how PAEE and leptin levels are related. The final study employed objective measurement through doubly labeled water (13) but was small in size (n = 46) and focused on older, sedentary African-Americans. Results indicate that leptin level was significantly associated with resting energy expenditure, but not with total or PAEE in women, whereas no indices of energy expenditure were associated with leptin level in men. This population was defined by the investigators as ‘sedentary’ and thus, by its very nature, is limited in its ability to examine the relationship between PAEE and leptin levels.

In this study, we clearly demonstrate that leptin levels are lowest in those who are physically active. This result is independent of other known confounding factors, including obesity. Our finding is not supported by results from murine studies, which indicate that elevated leptin level and activity level are related (3). However, this may be due to opposing orders of effects. That is, in the murine experiments, animals deficient in leptin were administered exogenous leptin that resulted in an increased physical activity level. In contrast, the relationship between leptin and PAEE in healthy free-living humans, who are not deficient in leptin, may function in the reverse direction, in that specific components of PAEE, such as sympathetic nervous system modification and increased circulating catecholamine levels, may have suppressive effects on leptin level (40, 41). This notion is supported by exercise intervention trials, where, although some demonstrate that leptin level declines following exercise and others that exercise has no effect on leptin, none have shown that exercise increases leptin level.

This study demonstrates that, independent of obesity, fasting insulin, and other confounding factors, leptin level and PAEE are inversely related. The cross-sectional nature of this study precludes the determination of the direction of causality. However, given experimental data, it seems unlikely that leptin causes a decline in physical activity level. Thus, it is plausible that physical activity modifies a number of mechanisms underlying leptin action, such as sympathetic nervous action.

To date, no controlled trials have attempted to examine the specific effects of increased PAEE on leptin levels. It is approaches such as this that will advance are understanding of how PAEE modifies leptin level, and whether there is the potential for targeted preventive intervention with physical activity for people who appear resistant to obesity regulation through the action of leptin.

	Acknowledgments

 
We are grateful to the staff of the St. Mary’s Street Surgery, Ely, and to H. Shannasy, S. Curran, and Drs. P. Murgatroyd, M. Hennings, and A. M. Prentice for their help with the fieldwork for this study.

	Footnotes

 
The Ely Study was funded by the British Diabetic Association, the Anglia and Oxford Regional Health Authority, and the Medical Research Council. P.W.F. was supported by a Medical Research Council doctoral fellowship; I.S.F., S.O., and N.J.W. were supported by the Wellcome Trust.

Abbreviations: BMI, Body mass index; DLW, doubly labeled water; HR, heart rate; PAEE, physical activity energy expenditure; REE, resting energy expenditure.

Received September 11, 2002.

Accepted March 16, 2003.

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