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Hormonal and Physiological Correlates of Energy Expenditure and Substrate Oxidation in Middle-Aged, Premenopausal Women¹

Division of Clinical Pharmacology and Metabolic Research, Department of Medicine and Division of Reproductive Endocrinology, Department of Obstetrics and Gynecology, University of Vermont, Burlington, Vermont 05405

Address all correspondence and requests for reprints to: Eric T. Poehlman, Ph.D., Given Building C-247, University of Vermont, Burlington, Vermont 05405. E-mail: epoehlma{at}zoo.uvm.edu

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
An understanding of the hormonal and physiological correlates of energy expenditure and substrate oxidation in middle-aged women will increase our knowledge of factors that promote changes in energy balance and adiposity. We measured resting and postprandial energy expenditure and substrate oxidation in 59 middle-aged, premenopausal women (mean ± SD age, 47 ± 2 yr) to examine the hormonal and physiological correlates of energy and substrate metabolism. Energy expenditure and substrate oxidation were measured at rest using indirect calorimetry and urinary nitrogen excretion and for 180 min after the ingestion of a liquid meal (10 kcal/kg fat-free mass; 410 ± 44 Cal). Fasting hormone levels were measured by RIA, glucose tolerance was determined by a 75-g oral glucose tolerance test, body composition was measured by dual energy x-ray absorptiometry, and peak aerobic capacity was determined by a treadmill test. Using stepwise regression analysis, we found that resting energy expenditure was predicted by fat-free mass and serum leptin concentration (r² = 66%; P < 0.01), fat oxidation was predicted by resting energy expenditure (r² = 17%; P < 0.01), and carbohydrate oxidation was predicted by serum leptin and appendicular skeletal muscle mass (r² = 21%; P < 0.01). No variables were related to postprandial energy expenditure or substrate oxidation. We conclude that in middle-aged, premenopausal women, variation in resting energy expenditure and substrate oxidation is primarily explained by fat-free mass and serum leptin levels. Thus, changes in metabolically active tissue mass or leptin concentration may partially contribute to changes in resting energy expenditure or substrate oxidation in middle-aged women.

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
IN WOMEN, the middle-age years are characterized by reductions in energy expenditure and fat oxidation (1, 2). Low rates of energy expenditure and fat oxidation at rest predict body fat gain (3, 4, 5). Changes in energy expenditure and fat metabolism, therefore, may contribute to the accumulation of total and central body fat (2, 6) and increase the risk for developing cardiovascular disease and diabetes (7, 8). An understanding of the metabolic correlates of energy expenditure and substrate oxidation in middle-aged, premenopausal women will increase our knowledge of factors promoting changes in energy balance and adiposity. Thus, the primary goal of this study was to examine the hormonal and physiological correlates of resting energy expenditure and substrate oxidation in middle-aged, premenopausal women. Because our previous studies showed that changes in energy expenditure and substrate oxidation are accelerated during the menopause transition (2), a period characterized by reductions in circulating estrogen and progesterone levels, we hypothesized that serum estrogen and/or progesterone levels would explain a portion of the variation in energy and substrate metabolism among individuals.

Reduced postprandial energy expenditure and fat oxidation may also contribute to the accumulation of body fat (9, 10). To date, however, no study has examined metabolic predictors of postprandial energy expenditure and substrate oxidation in middle-aged, premenopausal women. Thus, a secondary goal of this study was to examine the hormonal and physiological correlates of postprandial energy expenditure and substrate oxidation in middle-aged, premenopausal women.

	Subjects and Methods

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Subjects

Volunteers in the present study were recruited to participate in the Vermont Longitudinal Study of the Menopause, a 5-yr study examining changes in energy expenditure and metabolic function in women as they traverse the menopause. In this study, energy expenditure, body composition, body fat distribution, and insulin sensitivity will be measured annually in each subject for 5 yr to examine the effect of estrogen deficiency on these outcome variables. This report presents baseline data from this cohort. Participants were recruited from Burlington, VT and surrounding areas through advertisements in local newspapers. The criteria for inclusion were 1) between 40–52 yr of age; 2) premenopausal, as defined by having two menses in the 3 months preceding testing, no increase in cycle irregularity in the 12 months preceding testing, and a FSH level less than 30 IU/L; 3) nonsmoking; 4) normal electrocardiogram at rest and during an exercise test; and 5) weight stability (±2 kg) during the 6 months before testing. Women were excluded if they 1) were or planned on becoming pregnant; 2) had a history or current diagnosis of diabetes, heart disease, hypertension, or other chronic disease; 3) were taking hormone replacement therapy, chronic steroid therapy, neuroleptics, or other medication that could affect energy expenditure or metabolic function; 4) had a history of alcohol or drug abuse; or 5) were glucose intolerant, defined as a 2-h glucose level greater than 140 mg/dL after a 75-g oral glucose load.

Of the 239 women who responded to our advertisements, 122 women met the inclusion criteria after an initial telephone interview. Of those 122 women eligible for study, 83 consented to attend an outpatient screening visit. After the screening visit, 78 women were eligible for study, and 59 consented to participate. The nature, purpose, and possible risks of the study were explained to each subject before she gave written consent to participate. The experimental protocol was approved by the Committee on Human Research at the University of Vermont.

Experimental protocol

Each prospective volunteer underwent a phone interview to evaluate eligibility. If the subject met the initial eligibility criteria, she was scheduled for an out-patient screening visit and was instructed on how to record her menstrual cycle. Medical history, physical examination, biochemical laboratory tests, a 75-g oral glucose tolerance test (OGTT), and an exercise stress test were performed during the screening visit. Volunteers who met the eligibility criteria after screening and consented to participate were admitted to the General Clinical Research Center for an overnight visit approximately 2 months after their screening visit. The overnight visit occurred during the follicular phase of the menstrual cycle in 45 patients and during the luteal phase in 14 patients. For 3 days before admission, subjects consumed a standardized, weight maintenance diet provided by the Metabolic Kitchen of the General Clinical Research Center (1994 ± 254 kcal/day; 60% carbohydrate, 25% fat, and 15% protein). Bioelectrical impedance absorptiometry was performed on the evening of admission to estimate fat-free mass for preparation of the liquid meal on the following morning. Urine was collected overnight for urinary nitrogen measurement. The following morning, resting and postprandial energy expenditure and substrate oxidation measurements were performed, and body composition was measured.

Indirect calorimetry

Resting and postprandial energy expenditure and substrate oxidation were determined using the ventilated hood technique (DeltaTrac, Yorba Linda, CA). The subject was gently awakened (0630 h), allowed to void if necessary, returned to bed, and placed under the hood for 30 min. After the resting measurement, subjects consumed a liquid meal (10 kcal/kg fat-free mass; Ensure Plus, Ross Laboratories, Columbus, OH; 410 ± 44 kcal; 53.3% carbohydrate, 32% fat, and 14.7% protein). Respiratory gas analysis was performed for 180 min thereafter. Resting energy expenditure was calculated using the equation of Weir (11), and resting substrate oxidation was calculated from the tables of Lusk (12), as previously described (13). The thermic effect of the liquid meal (increase in resting energy expenditure above baseline) and change in substrate oxidation (change in respiratory quotient from baseline) were calculated by measuring the area under the curve (AUC) using the trapezoid method. The thermic effect of food was expressed as a percentage of the liquid meal consumed by dividing the AUC by the caloric content of the liquid meal and multiplying by 100.

Body composition

Fat mass, fat-free mass, and bone mineral mass were measured by dual energy x-ray absorptiometry, using a DPX-L densitometer (Lunar Corp., Madison, WI). All scans were analyzed using the Lunar Corp. version 1.3y DPX-L extended analysis program for body composition. Appendicular skeletal muscle mass was obtained from regional fat-free mass measurements according to the model of Heymsfield et al. (14), as previously described (15). Bioelectrical impedance absorptiometry (RJL 101A, Detroit, MI) was used to estimate fat-free mass using the equation reported by Goran et al. (16). Fat-free mass did not differ between bioelectrical impedance absorptiometry (40 ± 3 kg) and dual energy x-ray absortiometry (41 ± 4 kg) methods.

OGTT

A 75-g OGTT was performed after an overnight fast (0800 h). Blood samples were collected at 0, 60, 90, and 120 min for analysis of glucose and insulin levels. Glucose and insulin total AUCs were determined using the trapezoid method.

Peak oxygen consumption (VO₂)

Peak VO₂ was measured during a treadmill test to volitional fatigue, as previously described (17). Peak VO₂ data were adjusted for fat-free mass before correlation analysis, as previously described (17).

Hormone and substrate measurements

Glucose was measured by the glucose oxidase method using an automated analyzer (YSI, Inc., Yellow Springs, OH). Serum insulin was determined with a double antibody RIA (Diagnostic Products, Los Angeles, CA). The intra- and interassay coefficients of variation (CVs) for insulin were 4% and 10%, respectively. Serum concentrations of 17ß-estradiol and progesterone were determined by RIA (Diagnostics Systems Laboratories, Inc., Webster, TX). The intra- and interassay CVs for estrogen were 7.6% and 8%, respectively, and those for progesterone were 5.6% and 3.3%, respectively. Serum leptin concentrations were determined by RIA (Linco Research, Inc., St. Louis, MO). The intra- and interassay CVs for leptin were 3.9% and 4.7%, respectively.

Statistics

Means and SDs were calculated for all variables. Because leptin, estrogen, and progesterone levels had skewed distributions (Lilefors test; all P < 0.05), log₁₀-transformed values were used for correlation analysis. Relationships between variables were determined by Pearson product-moment correlation coefficients. Stepwise regression analysis was used to determine which hormonal and physiological variables explained variation in each dependent variable under resting (energy expenditure, fat oxidation, and carbohydrate oxidation) and postprandial conditions (AUC for energy expenditure and respiratory quotient and the percent thermic effect of the liquid meal). Possible predictor variables were entered into the stepwise regression if a physiological basis for explaining variation in energy expenditure or substrate oxidation was supported by prior studies. Predictor variables used in stepwise regression analysis included fat mass, fat-free mass, estrogen, progesterone, leptin, peak VO₂, AUC glucose, and AUC insulin. Fat mass was not included in the stepwise regression model predicting variation in resting energy expenditure. The rationale for not including fat mass in the stepwise regression model had both a statistical or physiological basis. Statistically, fat mass explains only a small portion of the variation in resting energy expenditure in nonobese individuals after accounting for fat-free mass (18, 19). More importantly, physiological data show that the energy expenditure of adipose tissue is minimal and can only account for 4% of the resting energy expenditure (20). Therefore, even large differences in fat mass would not be expected to contribute to variability in resting energy expenditure. In support of this idea, Segal and co-workers showed that when subjects are matched for fat-free mass, but differ widely in fat mass (up to a 28-kg average difference in fat mass), no differences in resting energy expenditure are found (10, 21). Resting energy expenditure was entered into the stepwise regression model to predict resting carbohydrate and fat oxidation, given that the absolute amount of substrate oxidized (milligrams per min) may be related to resting energy expenditure.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Physical characteristics, fasting hormone and substrate levels, and OGTT results are shown in Table 1. These data show that the cohort is representative of a nonobese sample of middle-aged, premenopausal women of average physical fitness. Hormone data show a wide range of estrogen and progesterone levels and serum leptin levels similar to those observed in prior studies of women of similar age and adiposity (22). Because no differences in resting and postprandial energy expenditure or substrate oxidation were found between women tested during the luteal and follicular phases of the menstrual cycle (by unpaired Student’s t test), data from these two groups were pooled in subsequent analysis.

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

Fat-free mass explained 58% of the variation in resting energy expenditure. This finding agrees with previous work from our laboratory (1) and others (24, 25) and was not unexpected considering that fat-free mass represents the metabolically active component of body mass. Results from our laboratory showed that fat-free mass is lost at an accelerated rate during middle-age (1), in particular during the menopause transition (2). Specifically, compared to normal age-related changes, the menopause transition resulted in a loss of 2.5 kg fat-free mass (2). If we assume that a 1-kg change in fat-free mass translates into a change in resting energy expenditure of approximately 20 kcal/day (26), we would predict that a loss of 2.5 kg fat-free mass accounts for a 50 kcal/day reduction in resting energy expenditure (2.5 kg x 20 kcal/day/kg) or approximately 53% of the net reduction in resting energy expenditure observed during the menopause transition (-95 kcal/day) (2). This contribution of fat-free mass to changes in resting energy expenditure derived from our longitudinal data is concordant with data from the present study showing that 58% of the variation in resting energy expenditure is accounted for by fat-free mass. Changes in resting energy expenditure in middle-age women, therefore, are probably due largely to changes in fat-free mass.

The leptin concentration explained an additional 10% of the variation in resting energy expenditure. This result agrees with studies from our laboratory (27, 28) and others (22, 29, 30) that suggest a role for leptin in the regulation of resting energy expenditure. The physiological significance of this relationship, however, is questionable. In most studies, leptin has explained only a small portion of the variance in resting energy expenditure (e.g. 10% in the present study). Moreover, the slope of the relationship between leptin and resting energy expenditure (7 kcal/day/ng/mL) suggests that changes in leptin levels will not promote substantial changes in energy expenditure. Indeed, Rosenbaum and co-workers (31) found no relationship between changes in circulating leptin levels and energy expenditure during dynamic periods of weight loss or weight gain. Thus, although leptin may partially regulate resting energy expenditure, we suggest that its contribution to changes in resting energy expenditure noted in middle-aged women (1, 2) is minimal.

Reduced fat oxidation may contribute to weight gain in women during middle age (5). We found that resting energy expenditure was the sole predictor of absolute rates of fat oxidation. The positive association between fat oxidation and resting energy expenditure implies that fat oxidation is determined by the basal energy needs of the metabolically active tissue. Because fat-free mass is a proxy measure of the metabolically active tissue mass and is the primary determinant of resting energy expenditure, this result suggests that fat-free mass regulates fat oxidation. Previous work from our laboratory (32, 33) and others (34) support the idea that the amount and/or metabolic activity of fat-free mass are important determinants of fat oxidation. Thus, changes in fat-free mass in middle-aged women may affect fat oxidation directly or indirectly by altering resting energy expenditure.

The leptin concentration was the strongest predictor of postabsorptive carbohydrate oxidation. The positive association between leptin and carbohydrate oxidation suggests that increasing serum leptin levels are associated with increased carbohydrate oxidation. Although cause and effect cannot be determined from correlation analysis, we suggest a physiological interpretation from this relationship. Based on previous studies that showed that leptin stimulates glucose disposal (35, 36), the most logical conclusion from these data is that leptin promotes the oxidative disposal of glucose. Carbohydrate oxidation derived from indirect calorimetry is a measure of net carbohydrate oxidation and, under postabsorptive conditions, is primarily an indicator of the oxidation of glucose derived from hepatic glycogenolysis (37). Thus, leptin may regulate carbohydrate oxidation by affecting hepatic glycogenolysis, peripheral glucose utilization, or both. Studies in rodents suggest that leptin may affect both processes. Kamohara et al. (36) showed that acute leptin administration decreased liver glycogen content and increased muscle glucose uptake and whole body glycolysis. Thus, the positive relationship between leptin and carbohydrate oxidation may reflect leptin’s ability to direct hepatic glucose stores toward peripheral tissues for oxidative disposal. An equally tenable interpretation of this relationship, however, is that carbohydrate oxidation regulates serum leptin concentrations. Studies have shown that leptin production is regulated by adipose tissue glucose metabolism (38, 39). Thus, at present, the physiological significance of the relationship between postabsorptive carbohydrate oxidation and serum leptin remains unclear, but warrants further study.

Appendicular skeletal muscle mass also explained a significant portion of the variability in carbohydrate oxidation. This result is not surprising, as skeletal muscle is a proxy measure of metabolically active tissue (correlation between appendicular skeletal muscle mass and fat-free mass: r = 0.92; P < 0.01). Thus, appendicular skeletal muscle mass reflects the amount of tissue capable of oxidizing glucose and, therefore, would be expected to exhibit a positive relation to carbohydrate oxidation.

We found no significant predictors of variation in postprandial energy expenditure or substrate oxidation. This may reflect the homogeneity of our cohort with respect to age, obesity, and glucose tolerance. These factors are known to influence postprandial energy expenditure and substrate oxidation (40), but were somewhat restricted by our selection criteria.

Contrary to our original hypothesis, neither estrogen nor progesterone predicted variability in resting energy expenditure or substrate oxidation. These results imply that the menopause-related changes in energy expenditure and substrate oxidation noted in our prior study (2) are probably not due to changes in ovarian hormone levels. These findings should, however, be interpreted with caution. The effects of these hormones on energy or substrate metabolism may not be detectable within the range of their concentrations in the premenopausal state, but only when they are reduced to postmenopausal levels. In other words, the relative absence of these hormones may be necessary to invoke changes in energy expenditure and substrate oxidation. Moreover, ovarian hormones may affect energy and substrate metabolism indirectly by modulating other hormonal and physiological processes, such as fat-free mass or leptin concentrations. Further studies that examine changes in hormones, energy expenditure, and substrate oxidation through the menopause transition or the effects of hormone replacement therapy on energy and substrate metabolism in postmenopausal women are needed to clarify this issue.

In conclusion, our results suggest that measures of the metabolically active component of body mass and circulating leptin concentration are the primary determinants of variation in resting energy expenditure and substrate oxidation in middle-aged, premenopausal women. Thus, changes in these metabolic predictor variables may explain changes in energy expenditure and substrate oxidation and the accompanying increase in total and central adiposity that occur during middle age. Longitudinal studies that examine changes in hormonal and physiological predictor variables together with energy and substrate metabolism are needed to clarify the relationships observed in this study.

	Acknowledgments

 
The authors thank all the participants who volunteered their time for this study. We are grateful to Denise Defalco-McGeein and Chris Potter for their skilled assistance, and to Dr. Andre Tchnernof for his helpful comments on this manuscript.

	Footnotes

 
¹ This work was supported by grants from the NIA (AG-13978), the USDA (96–35200-3488), and the General Clinical Research Center (RR-00109).

Received March 1, 1999.

Revised April 14, 1999.

Accepted April 22, 1999.

	References

Top Abstract Introduction Subjects and Methods Results Discussion References

 

1. Poehlman ET, Goran MI, Gardner AW, et al. 1993 Determinants of decline in resting metabolic rate in aging females. Am J Physiol. E450–E455.
2. Poehlman ET, Toth MJ, Gardner AW. 1995 Changes in energy balance and body composition at menopause: a controlled longitudinal study. Ann Intern Med. 123:673–675.[Abstract/Free Full Text]
3. Froidevaux F, Schutz Y, Christin L, Jequier E. 1993 Energy expenditure in obese women before and during weight loss, after refeeding and in the weight-relapse period. Am J Clin Nutr. 57:35–42.[Abstract/Free Full Text]
4. Ravussin E, Lillioja S, Knowler WC, et al. 1988 Reduced rate of energy expenditure as a risk factor for body-weight gain. N Engl J Med. 318:467–472.[Abstract]
5. Zurlo F, Lillioja S, Esposito-Del Puente A, et al. 1990 Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24-h RQ. Am J Physiol. 259:E650–E657.
6. Tchernof A, Poehlman ET. 1998 Effects of the menopause transition on body fatness and body fat distribution. Obesity Res. 6:246–254.[Medline]
7. Kannel WB. 1987 Metabolic risk factors for coronary heart disease in women: prospective results from the Framingham Study. Am Heart J. 114:413–419.[CrossRef][Medline]
8. Wing RR, Matthews KA, Kuller LH, Meilahn EN, Plantinga PL. 1991 Weight gain at the time of menopause. Arch Intern Med. 151:97–102.[Abstract]
9. Raben A, Andersen HB, Christensen NJ, Madsen J, Holst JJ, Astrup A. 1994 Evidence for an abnormal postprandial response to a high-fat meal in women predisposed to obesity. Am J Physiol. 267:E549–E559.
10. Segal KR, Gutin B, Albu J, Pi-Suyer FX. 1987 Thermic effect of food and exercise in lean and obese men of similar lean body mass. Am J Physiol. 252:E110–E117.
11. Weir JB. 1949 New methods for calculating resting metabolic rate with special reference to protein. J Physiol. 109:1–9.
12. Lusk G. 1924 The elements of the science of nutrition. New York: Saunders.
13. Toth MJ, Arciero PJ, Gardner AW, Calles-Escandon J, Poehlman ET. 1996 Rates of free fatty acid appearance and fat oxidation in healthy younger and older men. J Appl Physiol. 80:506–511.[Abstract/Free Full Text]
14. Heymsfield SB, Smith R, Aulet M, et al. 1990 Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr. 52:214–218.[Abstract/Free Full Text]
15. Toth MJ, Gottlieb SS, Fisher ML, Poehlman ET. 1997 Skeletal muscle atrophy and peak oxygen consumption in heart failure. Am J Cardiol. 79:1267–1269.[CrossRef][Medline]
16. Goran MI, Toth MJ, Poehlman ET. 1997 Cross-validation of anthropometric and bioelectrical resistance prediction equations for body composition in older people using the 4-compartment model as a criterion method. J Am Geriatr Soc. 45:837–843.[Medline]
17. Toth MJ, Goran MI, Ades PA, Howard DB, Poehlman ET. 1993 Examination of data normalization procedures for expressing peak VO₂ data. J Appl Physiol. 75:2288–2292.[Abstract/Free Full Text]
18. Cunningham JJ. 1991 Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr. 54:963–969.[Abstract/Free Full Text]
19. Nelson KM, Weinsier RL, Long CL, Schutz Y. 1992 Prediction of resting energy expenditure from fat-free mass and fat mass. Am J Clin Nutr. 56:848–856.[Abstract/Free Full Text]
20. Hallgren P, Sjostrom L, Hedlund H, Lundell L, Olbe L. 1989 Influence of age, fat cell weight, and obesity on O₂ consumption of human adipose tissue. Am J Physiol. 256:E467–E474.
21. Segal KR, Edano A, Tomas MB. 1990 Thermic effect of a meal over 3 and 6 hours in lean and obese men. Metabolism. 39:985–992.[CrossRef][Medline]
22. Martin LJ, Jones PJH, Considine RV, Su W, Boyd NF, Caro JF. 1998 Serum leptin levels and energy expenditure in normal weight women. Can J Physiol Pharmacol. 76‘:237–241.
23. Toth MJ, Poehlman ET. 1995 Resting metabolic rate and cardiovascular disease risk in resistance- and aerobic-trained middle-aged women. Int J Obes. 19:691–698.
24. Ferraro R, Lillioja S, Fontvielle SM, Rising R, Bogardus C, Ravussin E. 1992 Lower sedentary metabolic rate in women compared to men. J Clin Invest. 90:1–5.
25. Ravussin E, Lillioja S, Anderson TE, Christin L, Bogardus C. 1986 Determinants of 24-hour energy expenditure in man: methods and results using a respiratory chamber. J Clin Invest. 78:1568–1578.
26. Arciero PJ, Goran MI, Poehlman ET. 1993 Resting metabolic rate is lower in women than in men. J Appl Physiol. 75:2514–2520.[Abstract/Free Full Text]
27. Nicklas BJ, Toth MJ, Poehlman ET. 1997 Daily energy expenditure is related to plasma leptin concentration in older African-American women but not men. Diabetes. 46:1389–1392.[Abstract]
28. Toth MJ, Gottlieb SS, Fisher ML, Ryan AS, Nicklas BJ, Poehlman ET. 1997 Plasma leptin concentrations and energy expenditure in heart failure patients. Metabolism. 46:450–453.[CrossRef][Medline]
29. Salbe AD, Nicolson A, Ravussin R. 1997 Total energy expenditure and the level of physical activity correlate with plasma leptin concentrations in five-year-old children. J Clin Invest. 99:592–595.[Medline]
30. Tuominen JA, Ebeling P, Heiman ML, Stephens T, Koivisto VA. 1997 Leptin and thermogenesis in humans. Acta Physiol Scand. 160:83–87.[CrossRef][Medline]
31. Rosenbaum M, Nicolson M, Hirsch J, Murphy E, Chu F, Leibel RL. 1997 Effects of weight change on plasma leptin concentrations and energy expenditure. J Clin Endocrinol Metab. 82:3647–3654.[Abstract/Free Full Text]
32. Calles-Escandon J, Arciero PJ, Gardner AW, Bauman C, Poehlman ET. 1995 Basal fat oxidation decreases with aging in women. J Appl Physiol. 78:266–271.[Abstract/Free Full Text]
33. Nagy TR, Goran MI, Weinsier RL, Toth MJ, Schutz Y, Poehlman ET. 1996 Determinants of basal fat oxidation in healthy Caucasians. J Appl Physiol. 80:1743–1748.[Abstract/Free Full Text]
34. Sial S, Coggan AR, Hickner RC, Klein S. 1998 Training-induced alterations in fat and carbohydrate metabolism during exercise in elderly subjects. Am J Physiol. 274:E785–E790.
35. Berti L, Kellerer M, Capp E, Haring HU. 1997 Leptin stimulates glucose transport and glycogen synthesis in C₂C₁₂ myotubes: evidence for a PI3-kinase mediated effect. Diabetologia. 40:606–609.[CrossRef][Medline]
36. Kamohara S, Burcelin R, Halaas JL, Friedman JM, Charron MJ. 1997 Acute stimulation of glucose metabolism in mice by leptin treatment. Nature. 389:374–377.[CrossRef][Medline]
37. Tappy L, Paquot N, Tounian P, Schneiter P, Jequier E. 1995 Assessment of glucose metabolism in humans with the simultaneous use of indirect calorimetry and tracer techniques. Clin Physiol. 15:1–12.[Medline]
38. Mueller WM, Gregoire MM, Stanhope KL, et al. 1998 Evidence that glucose metabolism regulates leptin secretion from cultured rat adipocytes. Endocrinology. 139:551–558.[Abstract/Free Full Text]
39. Wang J, Liu R, Hawkins M, Barzilai N, Rossetti L. 1998 A nutrient-sensing pathway regulates leptin gene expression in muscle and fat. Nature. 393:684–688.[CrossRef][Medline]
40. de Jonge L, Bray GA. 1997 The thermic effect of food and obesity: a critical review. Obes Res. 5:622–631.[Medline]

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