1Program in Nutritional Metabolism and Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114; 2Pharmacology Department, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908; and 3Amgen, Inc., Thousand Oaks, California 91320
Submitted 5 March 2003 ; accepted in final form 23 April 2003
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ABSTRACT |
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subcutaneous fat; abdominal fat; synchronicity; pulsatility; cross-approximate entropy
In this study, we investigated leptin pulse dynamics in relationship to detailed assessment of body fat in healthy control subjects to assess whether leptin secretion and pulsatility per se were a function of fat mass. Using simultaneous frequent sampling, we also investigated whether fluctuations in plasma leptin concentrations during overnight fasting exhibited pattern coupling and synchronicity to GH, cortisol, and insulin by use of cross-correlation analysis and cross-approximate entropy (X-ApEn) independently. Our data demonstrate that leptin secretion, in contrast to pulsatility, is highly related to fat mass. These data suggest that fat mass may act to amplify leptin secretion from an extrinsic pulse generator during fasting. Furthermore, we demonstrate coupling and synchronicity among leptin, GH, cortisol, and insulin. Our data demonstrate that changes in leptin precede GH and cortisol but follow insulin by X-ApEn analysis during overnight fasting. These data extend our understanding of the physiology and regulation of leptin by fat mass and the potential relationship of leptin to neuroendocrine function during fasting.
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METHODS |
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Twenty healthy men with an average age of 42.8 ± 1.8 yr and average body mass index (BMI) of 24.6 ± 0.5 kg/m2 participated in the study (Table 1). The subjects were in good health, with a waist-to-hip ratio <0.95. Subjects receiving testosterone, GH, anabolic hormones, glucocorticoid, antidiabetic agents, or any other hormone or medication known to affect neuroendocrine function were excluded. Subjects with known diabetes mellitus, hemoglobin level <9.0 g/dl, and age <18 and >60 yr were also excluded. Subjects were not permitted to eat after 1800 on the day of sampling. Activity, usual diet, and composition of the last meal before testing were determined, as was sleep time during frequent sampling. Written informed consent was obtained from each subject before testing, in accordance with the Committee on the Use of Humans as Experimental Subjects of the Massachusetts Institute of Technology and the Subcommittee on Human Studies at the Massachusetts General Hospital. Body composition and GH data from this healthy control group were previously published (27), but neither cross-correlation nor X-ApEn data have been previously published.
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Frequent Sampling
Leptin, GH, cortisol, and insulin were sampled overnight every 20 min from 2000 to 0800 during an overnight fast.
Nutritional Assessment and Body Composition Analysis
Weight was determined on the 1st day of each visit after an overnight fast. Fat and fat-free mass were determined by dual-energy X-ray absorptiometry using a Hologic 4500 densitometer (Hologic, Waltham, MA). The technique has a precision error of 3% for fat and 1.5% for fat-free mass (24). Cross-sectional abdominal computed tomography scanning was performed to assess the distribution of subcutaneous (SAT) and visceral abdominal fat (VAT). A lateral scout image was obtained to identify the level of the L4 pedicle, which served as a landmark for the single-slice image. Scan parameters for each image were standardized (144-cm table height, 80 kV, 70 mA, 2 s, 1-cm slice thickness). Fat attenuation coefficients were at -50 HU, as described by Borkan et al. (7). VAT and SAT were determined. Food intake was assessed by diet history. Total caloric, protein, carbohydrate, and fat intakes were determined (Minnesota Data Nutrition Systems, Minneapolis, MN). The last meal was served to all patients at 1700 and completed by 1800 before frequent sampling. Caloric, protein, fat, and carbohydrate contents of the final meal were analyzed. Physical activity was assessed by the Modifiable Activity Questionnaire (19). Sleep status was noted at the time of each blood draw throughout the night, and the number of hours of sleep was determined.
Laboratory Methods
Hormonal assays. Leptin was measured by RIA (Linco, St. Charles, MO). Test plasma (50 µl) was incubated for 24 h at 4°C with assay buffer (containing phosphate-buffered saline and 0.05% Triton X-100), 125I-labeled leptin and leptin antiserum and reagents were incubated for an additional 24 h. Antibody-bound leptin was precipitated by addition of precipitating reagent. Tubes were centrifuged for 20 min at 3,000 g, after which supernatants were decanted and pellets counted in a gamma counter. To further validate the assay, high- and low-leptin controls were included in every assay and were required to fall within the manufacturer's established range for the assay to be accepted. Varying concentrations of leptin (4.925.6 ng/ml) were added to five human serum leptin samples, and the resulting final leptin concentration was measured by their RIA. Leptin levels (means ± SD) from the five separate assays were measured, and percent recovery was calculated as the ratio of observed over expected level. Recovery (±SD) was always between 103 and 105 ± 5%. For this leptin assay, the limit of detection is 0.5 ng/ml. The intra-assay coefficient of variation (CV) was <5% and the interassay CV was <4.5%.
GH was measured by two-site radioimmunometric assay with an intra-assay CV of 2.84.2% (Corning, Nichols Institute Diagnostics, San Juan Capistrano, CA). The assay coefficients were calculated as a quadratic function using the intra-assay SD for this assay: 4.2, 2.9, and 2.8% for GH concentrations of 1.4, 6.0, and 12.2 µg/l, respectively. The sensitivity of the assay, defined as the concentration 2 SD above the mean count of the zero standard, was determined to be 0.01 µg/l on the basis of multiple dilutions with a standard sample, and linearity of the assay was confirmed to a GH concentration of 0.05 µg/l.
Cortisol was measured by a GammaCoat [125] RIA, with an intra-assay CV of 6.67.7% (DiaSorin, Stillwater, MN). The calculated sensitivity for cortisol was 0.21 µg/dl. Insulin was assessed by RIA (Diagnostics Products, Los Angeles, CA) with an intra-assay CV of 59.3%, and calculated sensitivity for insulin was 1.3 µIU/ml.
Pulse analysis: pulse and Cluster programs. To assess leptin pulsatility, we used Cluster, a largely model-free, computerized pulse analysis algorithm, to identify statistically significant pulses in relation to dose-dependent measurement error in each hormone time series. Cluster analysis uses a sliding pooled t-test to identify data points within the hormone time series that correspond to statistically significant increases and decreases in the hormone concentrations. The occurrence of a peak is defined as a significant increase to be associated with nadirs at both sides. A nadir is assumed to be a significant decrease followed by a significant increase, with all else representing a peak. All regions that are not within a nadir are considered to be part of a hormone pulse. In performing the analysis, we specified individual test cluster sizes for the nadir and peak width of 2 (2 x 2), a minimum and maximum intraseries CV, a t-statistic to identify significant increase, and a t-statistic to define a significant decrease (35).
Cluster was used to identify pulses in the leptin time series in relation to measurement error within the samples from each subject. For this purpose, a CV of 5%, the intra-assay CV in our assay, was used in the settings of the program. The program detects only statistically significant pulses after both the limit of detection of the assay and the CV of the assay are taken into account. We identified the following properties of pulsatile leptin concentrations: pulse frequency (number of significant peaks/12 h), mean interpeak interval (time separating consecutive peak maximal), mean pulse duration in minutes, mean pulse height (maximal leptin concentration in a peak), mean incremental peak amplitude (difference between peak maximum and preceding nadir), pulse height as the percent increase over preceding baseline (100% corresponds to preceding baseline), and interpulse valley mean (the region embracing nadirs without intervening peaks).
The Cluster program does not provide information about the secretion of the hormone into the serum and the elimination of the hormone from the serum, and this information was obtained from a deconvolution analysis. The algorithm has been described in detail by Johnson et al. (34, 35). PULSE 2 and PULSE 4 are deconvolution and pulse detection algorithms that were used in the study. The program initially generates an expanding series of presumptive peak locations and then successfully removes those that do not meet statistical significance criteria. The details of the protocol for analysis have been previously described (34, 35).
Cross Correlation
Cross correlation was calculated after the concentration time series of one hormone was lagged relative to the concentration time series of another hormone. Cross correlation was carried out at variable lags, which are the times in minutes separating consecutive samples in the paired-hormone series of interest. Significant cross-correlation values for the group of 20 subjects at any particular lag were tested against the null hypothesis of purely random associations via the one-sample Kolmogorov-Smirnov statistic applied to the one-tail z-score transformed z-values, on the assumption that noncorrelated data show a unit-normal z-score distribution with zero mean. The correlation analysis was performed with the International Mathematics and Statistical Library routine DCCF (8).
X-ApEn
To evaluate relative joint asynchrony of GH and leptin and cortisol and leptin, we used X-ApEn statistics. This measure quantifies the conditional regularity of synchrony of point-by-point variations across two times series. It is distinct from cross-correlation analysis, because X-ApEn is independent of lag. This issue is discussed in detail by Pincus et al. (26). In particular, X-ApEn measures the relative pattern orderliness of two data series, with lower absolute X-ApEn values denoting greater conditional regularity or synchronicity. X-ApEn is calculated after z-score transformation of both data series. For this study, we calculated ApEn values for each hormone profile with window length m = 1 and tolerance parameter r = 20% of the average SD of the individual subject's hormone time series. In choosing the r input parameter (tolerance) in ApEn as a fixed percentage of the SD of each data set, we normalized ApEn for each profile. This so-called normalized ApEn is both translation and scale invariant. This point is important when different absolute hormone levels are expected, as they were here.
Statistical Analysis
Age, BMI, caloric intake, usual activity, sleep time, total body fat, SAT,
and VAT were compared with individual leptin pulse parameters in univariate
regression analysis. Cross-correlation and X-ApEn analyses between leptin and
GH, cortisol, and insulin were performed as described above. X-ApEn values
between leptin and GH, leptin and cortisol, and leptin and insulin were tested
for normality by the Shapiro-Wilk test and determined to have a normal
distribution. Kurtosis (-0.93, -0.57, and -1.54), variance (0.02, 0.01, and
0.01), and skewness (-0.09, -0.47, and 0.05) were determined for the X-ApEn
analyses between leptin and GH, leptin and cortisol, and leptin and insulin,
respectively. With 20 patients, the study was powered at 80% to detect a
correlation between two variables at r = 0.55. Statistical analyses
were made using JMP Statistical Database Software (SAS Institute, Cary, NC).
Statistical significance was defined as P 0.05. Results are
means ± SE.
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RESULTS |
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Leptin secretion correlated highly with total fat (r = 0.78, P < 0.001) and subcutaneous fat area (r = 0.67, P = 0.002) and not significantly with visceral fat (r = 0.43, P = 0.077; Table 2 and Fig. 1). The average valley mean leptin level and the nadir correlated significantly with total fat (r = 0.78, P < 0.001) and subcutaneous fat (r = 0.76, P < 0.001), as shown in Table 2. In contrast, leptin pulsatility did not correlate with total body fat (r = 0.07, P = 0.785), BMI, SAT, or VAT (Table 2 and Fig. 1). The fasting morning leptin level was highly correlated with the mean overnight level determined from frequent sampling (r = 0.95, P < 0.0001). Neither leptin secretion nor pulsatility correlated with age, activity, caloric, protein, carbohydrate, or fat intake, or composition of the last meal (36.7% fat, 49.3% carbohydrate, and 15.0% protein).
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Synchronicity among leptin, GH, insulin, and cortisol time series was assessed independently by cross-correlation analysis and by X-ApEn. In cross-correlation analysis, there was synchronicity between GH and leptin at a lag of -240 to +60 min (mean -39 min); e.g., a decrease in leptin was followed by an increase in GH on average 39 min later (Fig. 2). Similarly, there was synchronicity between cortisol and leptin at a lag of -460 to +460 min (mean -211 min); e.g., a decrease in leptin was followed by an increase in cortisol on average 211 min later. There was also synchronicity between insulin and leptin, but leptin changes followed those of insulin at a lag of 120 to 420 min (mean 275 min).
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Pattern synchronicity for GH-leptin, cortisol-leptin, and insulin-leptin was independently assessed by XApEn (Fig. 3). Normalized X-ApEn between GH and leptin was 0.854 ± 0.031, a global random X-ApEn indicating that the hormone pairs are related by way of patterned consistency or synchrony. The normalization of X-ApEn is accomplished with the Monte Carlo technique, which shuffles the order of the time sequence to evaluate the expected X-ApEn values for random sequences. A normalized X-ApEn of 1 corresponds to a lack of synchrony, and a value of 0 means that there is total synchrony. The mean normalized X-ApEn between cortisol and leptin was 0.891 ± 0.024, and between insulin and leptin it was 0.868 ± 0.034, also indicating relatedness by way of patterned consistency or synchrony. In addition, the fasting morning insulin level correlated highly with the leptin mean secretion (r = 0.58, P = 0.012).
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DISCUSSION |
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These data suggest differential regulation of leptin secretion and pulsatility and a schema for leptin regulation whereby fat mass acts as an amplifier, determining leptin secretion and amplitude oscillation (more fat = greater amplitude, less fat = smaller amplitude) to a given pulsatile signal of leptin secretion. In this model, only leptin pulse amplitude and mean secretion are affected by the actual fat mass, not pulsatility. These data suggest that the stimuli for leptin pulsatility may be extrinsic and unrelated to actual fat mass (Fig. 4). Alternatively, leptin pulsatility might be regulated in an autocrine/paracrine fashion by hormones or cytokines released in a pulsatile fashion from fat. However, the lack of any correlation between pulsatility and fat mass argues against this scenario as well.
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We used an every 20-min sampling frequency for leptin after Saad et al. (28) and their well-established methodology to determine relatedness, synchronicity, X-ApEn, and pulsatility. Leptin secretion, but not pulsatility, was related to fat mass at this sampling frequency. Previous studies suggest that detected leptin pulsatility increases as a function of sampling frequency (26) and that leptin pulsatility is not related to mean leptin level (22), and further studies are necessary to determine the relationship between pulsatility and fat mass at different sampling frequencies. In addition, healthy men were chosen to avoid the known effects of gender on leptin (22). Subjects between 18 and 60 yr old were chosen to avoid the effects of adolescence and old age, and age was not related either to secretion or to pulsatility in this study.
Previous studies have demonstrated relatedness of leptin to luteinizing hormone, cortisol, and GH (3, 20, 21), suggesting that leptin may be an important regulator of neuroendocrine function. In this regard, studies of Chehab et al. (10) and Ahima et al. (1) have shown that leptin acts as a hormonal signal that conveys biological information from fat tissue to the reproductive axis and that leptin treatment accelerates the onset of puberty. In our study, we sampled leptin frequently over 12 h to determine leptin release profiles and used two independent methods to assess temporal linkages, cross-correlation analysis, and determination of X-ApEn. Our data demonstrate synchronicity and pattern coupling between GH and leptin and cortisol and leptin. In this regard, we showed additionally, by X-ApEn, the lag- and scale-independent pattern coupling of leptin and GH, leptin and cortisol, and leptin and insulin.
Our data suggest that changes in GH follow changes in leptin to a significant, synchronous, and nonrandom degree. This relationship fits with the known changes of leptin and GH in undernutrition (e.g., decreased leptin, increased GH) (13) and obesity (increased leptin, decreased GH) (17, 23). These data are further supported by evidence from several groups reporting a regulatory role of leptin in GH secretion in rodents (9), sheep (32), and pigs (4). In contrast, Ghizzoni et al. (15) reported in 12 prepubertal children with idiopathic short stature a strong positive correlation between GH and leptin concentration with GH leading leptin; however, these studies were performed in children in whom the physiological relationships may differ from those of adults. Furthermore, our results pertain to immunoreactive GH, as recognized by the standard assay used.
A close association between GH and leptin may be of importance for human physiology and provide an additional level of communication between nutritional status and neuroendocrine function. In this regard, leptin receptors have been demonstrated on animal (38) and human (30, 38) fetal pituitary and in adult human hypothalamic (11), suggesting a role for leptin in regulating neuroendocrine function. Furthermore, it is possible that GH and leptin are both regulated by a common hypothalamic pulse generator. Although there was heterogeneity in the individual lag times, most of the individual associations were significant and tended to cluster fairly tightly around the estimated mean. Furthermore, the X-ApEn clearly indicates synchronicity independent of the actual directionality, demonstrating that that there is clearly relatedness and synchronicity between these two important hormones.
We also studied the specific directionality of the relationship between cortisol and leptin, and our data also demonstrate that there is relatedness and synchronicity between these two hormones. Changes in leptin precede changes in cortisol with a lag of 211 min. Cortisol is generally considered a counterregulatory hormone, and its secretion increases during fasting and stress. Our data suggest, for example, that a decrease in leptin with undernutrition may contribute, in part, to increased cortisol in such patients. Available data indicate that glucocorticoids potently stimulate leptin expression in vitro (37). In addition, several studies carried out in humans have reported that high-dose exogenous glucocorticoids increase circulating leptin concentration in normal and obese subjects (12, 25). Ghizzoni et al. (15) found synchronicity between cortisol and leptin in prepubertal children. Our data using deconvolution methodology in a large number of adult patients also demonstrate significant synchronicity between leptin and cortisol. Furthermore, we were able to demonstrate a forward directionality with leptin leading cortisol.
What are the specific signals to pulsatile leptin secretion? Our data demonstrate that the changes in GH and cortisol lagged behind leptin, suggesting that other factors are likely to regulate leptin in vivo. Previous studies have demonstrated that leptin decreases and the circadian pattern is altered with fasting, before any decrease in fat mass (16). Such data suggest that insulin or other nutritionally mediated signals may act on adipocytes as a tonic or pulsatile signal to leptin secretion. Our study demonstrates a high degree of synchronicity and cross correlation, such that changes in leptin follow those in insulin by a mean lag of 275 min. Utriainen et al. (33) demonstrated that insulin infusion increases plasma leptin concentrations over 4 h in normal-weight subjects. Boden et al. (5) also demonstrated that leptin increases in response to hyperinsulinemia, with a significant lag over a few hours, independently of changes in glucose. Additional studies suggest that leptin falls rapidly within a few hours after fasting, in association with decreased insulin, independently of changes in glucose (6).
In contrast to previous studies investigating the dynamic relationship between energy intake and leptin (18), we chose to study our patients in the fasting condition, because the primary purpose of the study was to determine the relationship of leptin pulse dynamics to body composition. Nonetheless, our data, in a large group of healthy subjects, provide additional evidence supporting the potential importance of insulin as a nutritional signal to leptin. Metabolic regulation of leptin by insulin and/or other nutritionally mediated hormones or cytokines may be important to stimulate and modify leptin secretion by fat cells. Additional studies with increased sampling frequency are needed to further define the relationship of insulin to leptin in acute and chronic nutritional regulation.
Taken together, our data demonstrate synchronicity and cross correlation between leptin and GH, cortisol, and insulin. The specific directionality of these analyses suggests that changes in leptin precede those of GH and cortisol during overnight fasting, whereas changes in insulin appear to precede those in leptin. These data suggest a construct whereby leptin is regulated by insulin, and perhaps other nutritional signals, and in turn regulates GH and cortisol. Our data extend and support the hypothesis that leptin may be an important second messenger relaying critical nutritional information to higher neuroendocrine centers. In this regard, a complex loop of neuroendocrine feedback may be operating, whereby nutrition signals leptin and leptin signals neuroendocrine hormones, e.g., cortisol and GH as well as sex steroids, which differentially affect fat differentiation and accumulation in various fat depots. Other potential candidates that may regulate leptin include gastrointestinal regulated hormones such as ghrelin, which increases acutely during fasting and falls rapidly after nutrient ingestion (29), but causality cannot be determined in this cross-sectional study.
The biological complexity of the interplay between nutrient signals and neuroendocrine function is significant; therefore, our construct is just one of a number of potential models and is based on our data and specific to the population of healthy men, tested overnight, and using the specific testing paradigm of every 20-min sampling. Different results might be obtained investigating the dynamic regulation during feeding or by using a different sampling paradigm that included sampling throughout the day. Nonetheless, these results extend our knowledge of the interrelatedness of leptin, insulin, cortisol, and GH during the overnight period of maximal leptin secretion.
Data from this study using detailed body composition methods and pulse detection algorithms advance our understanding of leptin regulation, demonstrating that leptin pulsatility and amplitude parameters are differentially regulated. Leptin pulsatility is not related to fat mass. In contrast, pulse amplitude and other leptin pulse characteristics are highly related, strongly suggesting that fat mass amplifies the response to an exogenous pulsatile signal. Furthermore, we demonstrate that leptin is highly synchronous with insulin, GH, and cortisol. The directionality of the cross-correlation analyses suggests that leptin may function as a second messenger, integrating nutritional signals, e.g., from insulin, to regulate neuroendocrine function. Our study demonstrates that there are important physiological links between these hormones, of likely importance in the regulation of energy homeostasis.
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DISCLOSURES |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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REFERENCES |
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